There seems to be a tension between technology and spirituality.

It’s almost like a paradox - we’ve built the AI that can think and talk, we’ve built social media to connect us more than ever, and we’ve built apps with infinite feeds which magically show us clips which are the most likely to keep us hooked. Yet, spirituality feels like it’s the opposite of such technology.

While modern consumer technology is geared towards efficiency, connection and the curation of endless information, spirituality is more about letting go, of digital detoxes away from such technology, of clearing the mind and freeing the soul.

Thus, the phrase “spiritual technology” sounds like an oxymoron until you remember that every spiritual tradition began as a technology.

Prayer beads and mantras served as a user interface for nurturing attention.

A rosary is a technology. A temple is a technology.

The printing press allowed sacred texts to proliferate beyond the monastery walls. The radio and television brought gurus and preachers into millions of homes.

Meditation, journalling, hypnosis - technology that we’ve slowly iterated on for thousands of years - ended up eventually silencing skeptics as science itself caught up to verify their benefits.

Every new medium carries spiritual potential. Once the novelty fades, it’s inevitably used to explore the oldest questions: Who am I? Why am I here? How can I be better?

I think these questions, which people eventually consider to be the most important questions of life as they get older and wiser, are going to matter more as we all stumble towards superintelligence.

If AI solves our material needs, the only domain left is the inner world.

Even without superintelligence, a future in which we’re all operating at a higher plane of awareness, less at war with our inner selves and more at peace, is a future worth striving for.

This is why I believe we can turn the tools around. Use state-of-the-art technology to build what I’m calling Spiritual Technology: technology designed to upgrade the subconscious, the emotional and intuitive parts of us that run beneath conscious thought. Technology as a mirror rather than a slot machine. Technology that improves our lives and enables us to improve the lives of those around us.

That is why I’m building Drift Inward.

II.

I started working on Drift Inward earlier this year as an experiment to answer a question: how far can I push today’s AI to make myself genuinely better?

There are very few complete products out there addressing this need with AI. Self improvement, mindfulness, self mastery - all these are where the most important technology of our time should be deliberately deployed, and yet, there’s almost none. What’s out there are simple single-use apps or gimmicks that don’t actually work.

Drift Inward actually works.

It works for me, and it works for other users who are paying for it already, despite me just doing minimal marketing for it.

If you visit the landing page of Drift Inward - you’ll see that it’s composed of two primary actions - AI Meditation & AI Journalling.

Meditation and journalling aren’t the only things on the app (there’s a LOT more), but they’re the core of what makes Drift Inward effective and magical.

Unlike other mindfulness apps like Calm and Headspace where meditations are generic (and they paywall these generic tracks while all generic tracks on Drift Inward are free), Drift Inward creates specific meditations just for you.

Based on your unique circumstances, the context you provide about yourself, and even adding your journal entries as context, each meditation is carefully designed to do exactly what you expect it to do.

Have a big day at work tomorrow and need to sleep? Describe it and let Drift Inward create something that lulls you there. Need to calm down after a difficult day? Need to process anxiety? Need someone to tell you it’s going to be okay? Write it down, put your headphones on, and let the app guide you through a meditative experience designed for precisely where you are.

I can’t get enough of custom meditations and have been using it after every journaling session, but where it gets wild is the “Deep Hypnosis” feature. These are significantly longer audio tracks designed to take you into a hypnotic state and in that state. It delivers exactly the suggestions you asked for - injects them into your subconscious, while gently guiding you out of the state once you’re done.

You have to experience it to believe it, but I believe it’s one of the most novel use-cases of AI as it gets better at superhuman persuasion - something that’s been hinted at gloomily by AI lab CEOs, who aren’t really considering the upside it has to offer.

Obviously as capabilities improve, an AI more intelligent than anyone on the planet will be more effective than any hypnotist.

It’d know the right combination of words that precisely work for you, and it’d know exactly what to say to you to help you achieve whatever goal you give it.

Powered by the context in your journal entries, every deep hypnosis is already the most personalised hypnosis session you can experience. Physical sessions with a trained hypnotist might be better for some use cases, but I believe the future of subconscious engineering shouldn’t involve a person.

Rather, everyone should have an always-on, always-available AI that has just the right context to deeply know you better than anyone else, and thus, tell you exactly what’s engineered to be maximally effective just for you.

Now, let’s talk about the AI journal.

III.

I’ve described the journal as something that powers personalized meditation, but that undersells it.

While building the journal, I wanted something I’d genuinely enjoy filling each day. Something that leverages AI to uncover patterns. Something that measurably improves my life.

To this end, the AI journal shows you realtime insights based on Cognitive Behavioural Therapy using AI- as you type in the journal.

It doesn’t break your flow state.

At any time you can stop typing, can open up the insights panel and check out if you’re having any negative thought patterns, cognitive distortions like catastrophizing, black-and-white thinking, rumination loops - all science-backed techniques to get the most out of your journalling practice.

Slash commands let you go deeper. Type “/” and a radial menu appears: ask for reflection prompts that push you further, generate actionable next steps, or reframe what you’ve written with optimism. All without lifting your hands from the keyboard.

There’s also a gratitude streak. The AI analyzes what you’ve typed, and once you’ve expressed gratitude, a small heart lights up. I built this because research consistently shows the benefits of regular gratitude practice- this makes it effortless to maintain.

All this is just a tap away, so that you’re never taking your hands off the keyboard.

Speaking of positivity, there’s a gratitude streak built in, where AI constantly analyses anything you’ve typed and once you’ve expressed gratitude, the gratitude heart lights up.

I built this because I’ve read a ton of research showing the benefits of expressing gratitude regularly, and this is the perfect way to make that a daily practice.

Overall, I’m pretty happy with how the journal works now, but I’m not done yet. I have several important features planned for the journal that’d make it 10x more powerful and enjoyable in the coming months.

The thesis of the journal is simple - we don’t know what powerful features future AIs will be capable of. But we need to be prepared to make the most of it.

Maintaining a journal now would let us leverage the models of tomorrow more effectively than we can even imagine right now. Today, it’s already powerful enough to design just the right meditations and hypnosis sessions - capabilities that will only keep improving.

As models get better - I can imagine features like the AIs showing you exactly what’s the right career for you, exactly what habits you should cultivate that will uniquely make you better, exactly what decisions you should take today to achieve your goals. While they do that right now quite well, if we imagine future AIs as vastly more intelligent than us, advice on Drift Inward would be drastically more effective than any generic advice on ChatGPT or other apps.

That doesn’t happen without a considerable investment in the practice of journalling itself, but with the right agentic harnesses and context systems in place - this will be one of the most powerful and effective AI experiences we can build.

IV.

Drift Inward would be perfectly fine if it just had AI meditations and AI journalling.

But there’s more - a mood tracker to consistently track your mood, a chat where you can chat with three different AI guides (and yes, you can use your journal entries as context here as well) but there’s one other section called the Discover section - which seems counterintuitive at first.

The Discover tab has AI Astrology, AI Tarot & AI Numerology. Now, in an app that’s so closely tied to science-backed tools like meditation and journalling, what does it also have ancient practices like Tarot & Astrology? Shouldn’t they be separate apps?

No. And here’s why.

The discover mode, is (aptly titled) to help you discover. Discover new possibilities. Discover yourself. Discover what works, and what doesn’t.

I think ancient practices & symbolic systems that have survived thousands of years, despite the skepticism associated with them, have done so for important reasons.

I’m not claiming that someday science might catch up to hidden secrets that our less-distracted ancestors discovered when they came up with such symbolic systems, but what I’m claiming is this - using technology to engage the subconscious when we gaze into symbols and try to find meaning in them is useful.

There’s less “conscious” logic involved when you’re trying to understand what the position of planets or the symbols in a tarot card mean for you today - but in suppressing the conscious, you unlock the powerful subconscious to do its thing.

That itself seems worthwhile to me, and if future powerful AIs, with the context of your life, can come up with readings that help you imagine new possibilities, or just live a little better - I think that’s worth it.

I’ve seen extractive apps in this category - apps which claim that their predictions are true, and Drift Inward is never going to do that. All readings are personalised to you by default - and you can believe as much or as little as you try out these features.

To truly upgrade your subconscious, we must engage it deeply - and that’s what the Discover tab does.

A user recently emailed me saying that the tarot helped them see something in a new way that they had never noticed before, and just that ended up unlocking something beautiful in their lives.

Another user told me that exploring his birth chart readings made him more conscious of his finite time while also helping him plan more deliberately. I think testimonials like these will become more common as AI gets better at delivering readings that resonate.

This is what good divination tools have always done, regardless of ontological commitments: it creates sacred randomness that you interpret, and in interpreting, you reveal yourself to yourself. With AI, all of that feels even better.

V.

A final word on the design - I’ve designed Drift Inward to feel beautiful and calm.

Other mindfulness apps feel like spreadsheets of audio tracks while Drift Inward creates its own vibe with the Living Dial interface.

The spiritual journey isn’t linear- it’s cyclical, recursive, a spiral we walk again and again with slightly different eyes each time. The circular dial reflects this. There’s no endless scrolling, no hierarchical menus- just a simple, beautiful interface that puts what you need one conscious click away.

The wellness app economy runs on a simple model: acquire users, extract data, optimize for retention, raise the next round. Drift Inward runs on a different logic entirely. No investors. No pressure to scale to a million users. No false promises.

Drift Inward will never sell your data, and being already profitable while bootstrapped means it’ll scale sustainably without dark tactics. I made Drift Inward alone with my own hands, and it already has more features than any other wellness or mindfulness app out there while being significantly cheaper.

There’s a lot of features I couldn’t talk about in this essay that I’ve left for you to discover (like, how you can change the background ambient music of every meditation track from the settings, and how you can track your distractions and be more mindful about them, and a new take on streaks that isn’t annoying or judgemental).

This is spiritual technology: not technology that replaces the inner journey or distracts you or extracts from you, but technology that actively supports you in all your endeavours. A guide you can carry with you. A mirror that remembers.

With your support, I believe we can build one of the most powerful spiritual technologies ever created- something that upgrades your subconscious and helps you become who you want to be.

See you inside Drift Inward.

_____

Drift Inward is available now on iOS and on web at www.driftinward.com

For a limited time and exclusively for all early users, the price of every plan is 50% off. If you sign up for a paid plan today, you’ll keep the discounted price forever. I’ve tried to keep it as affordable as possible - The plus plan just $7.99 a month, or $84 a year, and gives you full access to all premium features.

You can absolutely try Drift Inward for free, and there are tons of free features that even rival popular meditation apps like free tracks, unlimited journalling, 3 free AI meditations - but to enjoy the premium features like deep hypnosis and voice journalling, it’s $7.99.

I’d love for you to try it. I’m building this in dialogue with the people who use it, and I’m always available for feedback and feature requests!

I was excited when ChatGPT introduced memory. I even wrote about it here- and for a while, I loved using it.

The thesis is simple to love - what if an AI doesn’t just know everything you’ve just told them in one single thread of conversation- but remembers all previous threads? I’ve already written some questions you could ask such an AI with memory enabled, and they all hold up fine.

But today I’m less excited about memory than I was

What if it’s really like the sci fi notion of AIs we grew up reading about - where, just through conversations, an AI intimately knows you, understands you- far deeper than any human around you does, and it always gives you the best advice and suggestions.

Future breakthroughs in continual learning may enable such a future - so I remain optimistic, but current AI “memory” is not memory.

Rather, if you have it turned on, you’re actively getting a worse experience out of any model you’re using- than what you’d be getting with it turned off.

Here’s why.

II.

Today’s AI “memory” is not memory.

It’s rather a sophisticated form of context injection, most commonly implemented through Retrieval-Augmented Generation (RAG), a technique that I’ve written about in my previous essay - which all sounds elegant in principle but reveals its mechanical crudeness under scrutiny.

When you ask about ice cream shops in Hawaii, the system doesn’t “recall” your aversion to mint or your passion for strawberry.

Instead, it performs a vector search through your conversation history, embedding your past words into mathematical space and retrieving whatever clusters nearby. This is less like human memory and more like having a research assistant who, every time you ask a question, dumps a pile of photocopied diary pages on your desk, some relevant, most not, and expects you to sort through them while simultaneously answering the question.

In theory, the system should identify your flavor preferences, cross-reference them with Hawaiian establishments, and deliver a pristine recommendation.

For simple queries, this actually works tolerably well like I have previously mentioned.

But the moment you need the raw, crystalline intelligence of the model itself, the rot begins to show.

I also think we don’t really understand or agree upon a definition of “memory” - because in the case of LLMs - the expectations are different than what we expect of human memory. For LLMs, we expect, perhaps not naively as we’re used to using apps with billions of people and trillions of data points all organized nicely in SQL tables and fetched when needed, 100% infallible memory. Unlike humans, we don’t want the AI’s memories of the past to degrade because then - what’s the point? If you’re not going to tell me that a year from now, you remember that I watched Episode 1 of a show that I’ve now forgotten about - then the memory functionality is useless. LLM memory is not supposed to degrade like ours does with time.

Or maybe, it should degrade? Because obviously, things happening right now and in the recent past are important than what happened 3 years ago or 10 years ago - and if you have to pick which memories to keep fresh, you’d pick the recent ones over the past - with the obvious exception of important life events which shape us subtly despite being in the past.

This tension of whether LLM memories should degrade or whether it shouldn’t is a technological problem rather than a philosophical one - as in a world of infinite context and intelligent models, you can just pass in all memories with timestamps and let the model decide intelligently what’s relevant and what’s not.

Unfortunately, today’s implementation of the above problem is lazy.

III.

For some reason, ChatGPT seems to always include context of your recent chats if you have memory turned on, even if they’re irrelevant. It’s likely because when people are asking ChatGPT something, and they tend to open multiple chats, they’re probably talking about the same thing.

But what if they’re not? What if they’re talking about something different in different chats? What if I have a chat about a software related issue on one chat and in a new chat, I’m talking about a philosophical text? You’d expect the LLM to be smart enough to ignore the included context, but it often doesn’t - and it tries to reference it because it needs to.

Such a feature feels bolted on- because someone somewhere realized people are mostly talking about the same thing at the same time and thus - include the context of recent chats anyway regardless of what’s in the latest chat, as it’d both likely be relevant and provide the “magical” moment of memory sooner for people using it.

This brings me to the core problem of memory in general which is “Context Rot”. We all have seen charts where LLM performance degrades the more context they handle, but this problem is only apparent when you cross 32k tokens.

Below that - and most chat queries likely don’t cross that threshold. Even if you consider that a lot of context is being included from recent chats, relevant chats about the topic, general information about you, it’s unlikely to cross 32k tokens.

But context rot shouldn’t just refer to going beyond 32k tokens.

Anything irrelevant that’s not related to your query shouldn’t be there- and I think, the best way to do this is to engineer all the context yourself.

Sure, if you ask for help in game design - context about every chat you’ve had regarding that game or game design in general might be helpful, but I see it as actively harmful as 1) You don’t really remember what’s being included in the context, so - other than your direct query, you have no idea what else the LLM is seeing to influence its answer to you 2) You have no idea what is subtly influencing it more than you realise.

If you’ve forgotten that you’ve once told the AI that you hate platformers - but the game level you’re working on requires a platformer design, you’ll never get it with memory enabled.

With memory disabled, you probably will.

If you’re always engineering the context yourself - you might find subtle things you missed when the LLM perhaps mentions it doesn’t know about something in the first response and is guessing it (For example - Forgetting to put in your app’s pricing details when asking if your pricing is competitive with competitors)- at which point, you can easily start a new chat with the new information included so that the LLM doesn’t have to guess anymore.

But this raises an interesting product question, as consumers have grown to expect their AI apps to have some kind of memory - even if including memory makes the responses worse. Even if it’s lazy, you can go down the same route as everyone else and just bolt on a vector database implementation and call it a day- that would certainly be feasible. You could argue that letting users manually engineer their context is superior - but then you’d have to deal with both the UI complexity this entails + compete with other apps that promise seamless memory without the manual work.

I think the answer is somewhere in the middle, for now. Obviously, since we can agree that manually engineering context is better - you should include that option regardless of the clutter or complexity it brings, but you should try to make an effort to figure out a memory implementation that’s more suited for your specific use case. An AI app focused on legal may need a vector DB implementation where all case details matter and nothing is irrelevant if the meanings match, but a code editor may just need a different implementation of search where you’re making embeddings of syntax but rather using keywords to search for specific details across the codebase.

If you could do one or the other, or would rather bet on future AI’s getting better at memory than they currently are - I’d choose the approach that delivers the most transparency and control over one that’s objectively worse.

IV.

Now, one can argue that future AI memory will be smart enough to only include relevant context. That they’ll be so good at gathering context about your query that it’ll be stupid not to have it turned on. I can certainly imagine such a future where this is true.

But right now, every time I enable memory, I feel a severe loss of agency and a feeling that I’m not getting the intelligence of the model that I’m supposed to be getting. I feel like I’m sure there’s something in the context that’s subtly influencing the answer and that’s not what I want.

LLMs are highly probabilistic but they can be used somewhat deterministically depending on whether you have power of being the only context engineer - or whether there’s invisible context being added that you have no idea about.

There can be several easy solutions to this like I’ve mentioned above- maybe a chat picker where you search and include the context that you want to include, or maybe you can toggle different pieces of context off and on that an LLM has gathered, but for now, I think the best way to interact with an AI and leverage its full intelligence, without worrying about what context is drifting its response, is to add the context yourself.

In the future, if it’s proven that “LLMs agentically gather more relevant context more efficiently than humans” - then I’m willing to change this view.

Of course, a super intelligent LLM with a 1M context window - where it doesn’t degrade in performance with increasing context, might provide a better answer despite the irrelevant context it gathers simply because it has gathered so much data for your task that it’s even impossible for you to provide it the sufficient context manually. That future is probably not too far away.

But until then, I’ll keep my LLM memory off and engineer the context myself.

I.

In 2019, artificial intelligence pioneer Rich Sutton published an observation that wounded the pride of his entire field. After studying 70 years of AI research, he found a pattern nobody wanted to admit: the simple, brute-force approaches always won. Not the elegant algorithms. Not the clever shortcuts. Just dumb methods of throwing lots of data and compute together.

He called it “the bitter lesson” because it stings to admit that just throwing raw scale at a problem beats human brilliance.

But the more I’ve thought about it, the more it feels like everything can be explained by the bitter lesson - that just meditating upon the implications of it feels like unlocking a profound truth about the universe.

I used to think the world ran on genius- that clever people shaped the world the way a potter shapes clay.

You know the story: brilliant mind has breakthrough, world changes forever. Newton under the apple tree. Einstein in the patent office. The romantic image of someone thinking really hard and unlocking reality’s secrets. I’ve often imagined myself in that place, and I’m sure that everyone who has experienced the thrill of finally figuring something out that’s difficult- have often imagined the same.

Then I started paying attention to how things actually work.

My phone or event the most popular apps everyone uses? Product of millions of A/B tests. The language I speak? Nobody designed it. The food I eat? Bred through thousands of generations of trial and error. Even my favorite songs probably topped the charts because an algorithm noticed people replayed them, not because a record executive recognized genius.

The pattern is everywhere: blind repetition beats careful planning. Massive trial and error beats brilliant design. Systems that try everything and keep what works outperform systems designed by experts.

This essay is an exploration of this “Bitter Lesson” phenomenon that seems to invisibly influence everything around us- and more importantly, it’s about figuring out how we can leverage it instead of fighting a losing fight raging against it.

Let us start with the most obvious and glaring example where the bitter lesson dictates everything: life itself.

II.

Nobody sat down with a blueprint and said, “Let’s put the retina here, add some rods and cones, maybe throw in a blind spot for character.” The human eye evolved through millions of years of random mutations. Most failed.

The tiny fraction that helped animals survive got passed on. No meetings. No design reviews. Just endless experimentation with death as the divine “error correction” filter - something we’ve now termed as “Evolution”.

This process created structures so complex we still can’t fully understand them. We’ve built computers that can beat any human at chess, but we can’t definitively explain how the three pounds of meat in your skull generates the experience of reading these very words that you’re reading right now. Evolution, through pure trial and error, built something our greatest scientists can’t reverse-engineer.

The irony is very apparent if you think about it- we’re using evolutionarily-developed brains to build artificial ones, and we don’t fully understand how either works. Nobody knows exactly why scaling up language models makes them seem intelligent. We just noticed that if you make them bigger and feed them more text and train them for longer, they get better at everything. Does that sound familiar?

Language itself is a glowing example of the bitter lesson. No committee announced, “Let’s adopt the word ‘cool’, and let’s carefully consider whether the word ‘sybau’ should be immortalized for all time”

Millions of tiny conversations acted like mutations; the phrases that felt fun or useful survived and the rest are quietly dying.

Carefully invented tongues such as Esperanto never took off, while emoji, rap lyrics, and “LOL” spread like wildfire. One can predict that the most popular languages - English & Chinese - will be the language of most of the world in a thousand years. English being fairly decentralized might end up being the most popular one- but the English of tomorrow may not resemble the English of today, in the same way that the English of the past is hard to read today. Viral phrases and words - words which universally capture some emotion or moment perfectly - will continue to be added - while words which once signalled status and sophistication (including the word sophistication, probably) will die out.

The bitter lesson is perhaps bitter, because you can’t do anything about the above fact. You just have to accept it.

It’s only once you accept it that ideas start to emerge of how you can leverage it - like if you want to write a novel that lasts a centuries beyond your life - your best bet is to write in a language that’s the most popular right now, and use words which are considered too simple even by today’s standards.

III.

Thus, accepting the bitter lesson is better than fighting it.

The first time this was made apparent in the realm of tech was in chess.

For decades grandmasters poured wisdom into computers, adding rules like “put rooks on open files.”

Then DeepMind built AlphaZero, told it only the legal moves, and let it play millions of games against itself overnight. The result crushed the previous world-champion engine and discovered knight sacrifices no textbook approved.

Centuries of human lore lost to raw experience. Some believe that Chess might be fully “solved” in the same way as Tic Tac Toe is solved, but even if that’s not the case - the fact that we got machines to be superintelligent at Chess far before we even know “how” it learned to go there - shows another bitter truth about the bitter lesson - it doesn’t matter if you don’t understand the field where the bitter lesson is being deployed, all that matters is that you respect it and let it run its course.

This same bitter lesson has been internalised by TikTok & Facebook today - where every interaction is captured while trying to answer the question of “What video makes you stay on this app longer?” - and this process is repeated for billions of videos daily. Thus, by the time you’re opening one of these apps - the algorithm has already internalised exactly what videos you’re likely to watch (based on the data they have on cohorts of people who are extremely similar to you) and also videos you’re likely to skip or comment on, and they present all of them to you in the hopes that they’re able to retain you for hours. If you go against this - swipe past things you’re not supposed to be swiping past, or close before the algorithm’s predictions, that’s new data it uses to more efficiently try to hook you next time.

And none of that is nefarious - you’re being hooked by content you want to see (otherwise why would you be opening the app?) and it’s a sign of the machine minds working correctly when you feel yourself addicted to such mindless scrolling as much as everyone else around you.

Short form content thus becomes news and entertainment and everything in between - and it’s only those who ride this wave and internalise this bitter lesson will end up thriving, while those who don’t will get lost in obscurity.

Such a world where virality matters more than truth also means that what’s likely to be viral - sensationalistic, rage-inducing or divisive content spreads much faster than the boring and nuanced truth. This means everyone from influencers to politicians - everyone with better memes - catchy slogans and divisive attitudes will fare much better than those who aim at the signal and the truth over trusting the noise.

All this paints a grim picture - that perhaps we should just accept the inevitable conclusion that it’s all about repetition and scale. That it’s better to never fight the bitter lesson and accept its ways. That in a world that’s birthing super intelligence, maybe we shouldn’t question the very law that seems to define everything from evolution to social media to AI to culture and politics.

But that’s not the case.

IV.

Or at least, not the whole case.

In cases like evolution, we should all be thankful of the bitter lesson for letting us be the thinking monkey species that we are. Even in politics, the bitter lesson might mean that the most viral - not the most competent get elected into positions of power, but it also means that the masses can error-correct and throw them out of office in healthy democracies - thus leading to better outcomes and wiser societies as a whole.

But what about lone inventors and geniuses themselves?

Henry Ford famously said if he’d asked people what they wanted, they’d have said faster horses.

Steve Jobs didn’t focus-group the iPhone into existence.

Einstein didn’t crowdsource relativity.

Sometimes a single brilliant insight really does change everything.

But here’s the important part about even these examples- even these triumphs of individual genius end up proving the bitter lesson in a different way.

Ford’s Model T wasn’t actually the first car, or even the best car of its time. What made Ford revolutionary wasn’t the invention itself but his assembly line - a system that enabled massive scale and iteration. The Model T succeeded because Ford could produce millions of them, learning and improving with each one. His genius wasn’t in defying the bitter lesson but in being the first to apply it to manufacturing.

The iPhone? Revolutionary, yes - you could argue all phones would still have keypads if it wasn’t for Jobs. But look what happened next. Within years, Android phones - built by countless manufacturers trying endless variations - captured most of the global market. The iPhone remains premium and profitable, but the bitter lesson won the numbers game. Today’s smartphones, whether iOS or Android, are shaped more by billions of user interactions and A/B tests than by any single designer’s vision.

Even Einstein’s relativity, that pinnacle of individual genius, followed the bitter lesson’s pattern. His breakthrough built on countless failed attempts by other physicists. And more importantly, it only became accepted truth through endless experimental validation. One brilliant idea meant nothing until thousands of experiments confirmed it worked.

The real insight isn’t that genius doesn’t matter - it’s that genius and the bitter lesson aren’t opposites. They’re dance partners.

The geniuses who truly change the world aren’t the ones who fight the bitter lesson but the ones who see it first and harness it best. They’re not sculptors imposing their will on marble; they’re surfers who spot the wave before anyone else and ride it perfectly.

Consider how the most successful “genius” companies actually operate. Amazon’s Jeff Bezos didn’t try to predict what customers wanted - he built a platform that could run millions of experiments. Netflix didn’t guess what shows to make - they analyzed viewing data from millions of users. Even Apple, that supposed temple of design genius, tests countless prototypes and iterations for every product.

The bitter lesson doesn’t eliminate the need for human creativity and insight. It just changes what kind of creativity matters. Instead of trying to design perfect solutions, the new genius designs perfect systems for finding solutions. Instead of being the smartest person in the room, you build rooms where collective intelligence can emerge.

This is actually liberating. You don’t need to have all the answers. You just need to be smart about setting up the questions.

Want to write a novel that lasts centuries? Don’t try to predict what future readers will love. Write many things, see what resonates, and double down on what works. Want to build a successful product? Don’t trust your intuition about what customers want. Build something simple, release it fast, and let user behavior guide your iterations.

The bitter lesson is only truly bitter if you’re attached to the idea of being the sole author of success. If you can let go of that ego, it becomes a superpower.

You get to leverage forces far greater than your own intelligence - the collective creativity of crowds, the relentless optimization of evolution, the surprising discoveries that emerge from large-scale experimentation.

We’re entering an age where AI can try a billion possibilities while we sleep, where global platforms can test ideas on millions of users in minutes, where the feedback loops that once took generations now happen in seconds. Fighting this is like trying to hold back the tide.

But surfing it? That’s where the magic is waiting to happen.

The bitter lesson isn’t that human ingenuity doesn’t matter. It’s that human ingenuity needs to evolve. The old model of genius - the lone inventor in the garage - is being replaced by something more powerful: the genius who knows how to orchestrate systems that discover solutions we never could have imagined. Where AI isn’t Artificial Intelligence but rather Amplified Intelligence, and whoever can amplify themselves the greatest- wins.

So yes, the bitter lesson is everywhere. Yes, it means accepting that brute force often beats brilliance.

But it also means we have access to a force multiplier unlike anything in human history. We can be gardeners in a garden that tends itself, conductors of an orchestra that writes its own music, surfers on waves that carry us further than we could ever swim.

The lesson is only bitter if you wanted to be God. If you’re content to be human - but a human who understands the rules of the game - then it’s the sweetest lesson of all.

Because once you truly understand that the bitter lesson is everywhere, you realize something profound: you don’t have to fight the universe to change it.

You just have to know which way it wants to flow, and gently guide it toward better outcomes.

It doesn’t always have to be about trusting the crowds or refusing to be contrarian, but truly leveraging the bitter lesson is understanding how everything works in the first place - and then starting from there.

You can be original, you can be creative, you can change the world against all odds - and you can do all of this without being bitter about the bitter lesson.

Will all software be written by AI agents very soon?

If you're a software engineer right now, or aspiring to become one, what should you do?

It's a hotly debated topic.

Engineers who have lived through previous hype cycles of no-code, and low-code tools, point at the past and claim that everything is fine - even as the data shows that computer science grads have a higher unemployment rate than even philosophy majors (maybe for the first time ever), and even as layoffs continue to increase.

Almost all the top CEOs believe AI will be writing half, if not all, of the code by next year - and soon, it'll be just AIs writing code. Some people also point at Jevon's paradox, saying if it's easier to write software than ever, we'll need more software engineers than ever, because our software needs will continue to increase.

Who should you listen to? What should you do?

In this essay, I'll attempt to answer this question. I think there are two futures for software engineering as a whole.

Here's the first one -

II.

AI progress will continue to increase and AI agents will get so good at almost all types of programming that not using one will be seen as archaic.

You won't have to remember any syntax, nor worry about mastering any programming language.

You'll still have to learn programming - maybe even the old school, hard way like hand writing code like we used to do in the old days, but just like you start using the calculator after learning the multiplication tables and basic math for more complicated calculations, you'll be using agents to do most of your work after you learn the basics.

In this future, there won't be a bug that AI cannot fix- but there might be bugs that AI cannot fix because of the incompetence of the engineer handling the agent.

Recently, Cloudflare released an OAuth provider library where the entire commit history shows how they instructed Claude to write most of the code. These are two of the many prompts you can see in their commit history-

"I think it's not necessary to store clients_list. Workers KV has a list() function you can use instead, which would be better because it is not susceptible to consistency problems if multiple updates happen concurrently. Can you change this?"

"For performance, let's denormalize the grant information into the access token records, so that the grant doesn't need to be looked up separately on every request. There's one catch, though: it's important that revoking the grant also revokes all access tokens. So, we should change the token record keys to be `token:{userId}:{grantId}:{tokenId}`. That way, when we revoke a grant, we can also search for and delete all access tokens, by searching for token records with the right prefix."

If you don't understand a single thing mentioned in the prompts above, I don't blame you- but if you’re using LLMs to code, it has to look somewhat like the above in its specificity and exactness- if you want to remain truly effective with them, at least right now.

Just being vague, just “vibe coding”, not including proper context, all these things won’t get you the results you want.

But one could argue that with the right combination of agents made to work properly on the problem above - a novice engineer who knows nothing about OAuth libraries would be able to perform the same - they won’t have to prompt the agent as explicitly, but they would have to know how to meta-prompt the agents, know an abstract idea of what the right context is to gather, and for a while simply know some parts of the codebase - but over time, not reading the code will be common.

We see some glimpses of “not reading code” in vibe coding - but in the future, reading code will seem as just something that wastes valuable time.

Code will be well-written, well documented, bare for the world to see and marvel as it evolves to do things which it previously never could, but each such beautiful piece of code will connect to more places than previously thought possible or maintainable, being imported by millions of different components, everything being far greater than any single engineer to track or even care about.

While in the past we had debates about software architectures like- are monorepos better, or are micro-services better, and we might as well continue having those debates, new architectures will emerge where it's common for single files to span 10,000+ lines and software projects to have hundreds of billions of lines of code. Yes, code is a liability, and yes, it's not about the lines of code coded but what problems the lines solve, but when you never have to touch the code at all, these problems aren’t your problem.

In such a world, you'd imagine that software engineers would be out of jobs, as lots in the mainstream media would have you believe, but that's not true. Humans would be needed to guide and orchestrate the agents to specifically build what we want to build.

Sure, an AI agent watching over an army of similar agents might be able to do a better job at prompting it - but who instructs that the supervising agent - it can be agents all the way to the top, with agents running the whole company - but that is highly unlikely in the near future (5-10 years), because creating something that requires creativity and human input - by definition, involves humans somewhere in the loop.

In this glorious age of powerful software, more engineers will be needed than we can produce, and that will continue until we really get super intelligence that's capable of delivering everything from cures to all diseases to solutions to {insert problem here}, so when that happens - when ASI is truly capable of delivering returns at that scale - you won't have to worry about your livelihood or anything else.

I think even then, in a future hard to imagine, a world of true abundance where AI can just one-shot create anything, we should still have people wanting to create software of their own. Maybe it'll be to self host a module in their brain computer interface that's specific to them, maybe it's to create a virtual world that's more aligned with their community's mind-upload station, maybe it's to tweak the open source robot OS that only self-assembles the perfect vehicles for their next vacation to the moon - software will continue to exist, and engineers will continue to be useful.

Companies and corporations may not exist to employ engineers in this scenario - but I find that unlikely to be the case.

Humanity will always have goals, always have projects, and these projects would need people who know not only software, but the art of expressing their creativity through delivering products and services that we’d need.

The skills that define being an engineer will change, but anyone in software long enough knows that just learning to solve leetcode problems and quickly learning the syntax and patterns of new languages was never what being a software engineer was about. It was about solving problems - and a lot of the fields will face the same questions as software engineers face first - how to adapt from doing the thing to helping an agent to do the thing.

This "helping to do the thing" is not just "prompting the thing" even though that's a large part of it - but if you’ve ever asked for help from multiple people, you know that some people can be exponentially better than others at effectively helping you at the task, and it’ll be the same in the case of helping agents.

Soon labels like “software engineer”, will start to feel like a temporary hat you wear during the day, before focusing on other endeavours. If all you need is an hour to let some agents loose to work on different tasks that takes them a week, then the rest of your time can be spent doing anything you want.

This future is bright, and as long as you're curious and open to change, we'll all have a great time.

The other future for software engineering looks slightly different -

III.

This future, which we'll all see if we get to experience it in less than 10 years, is one where companies are finding it hard to justify whether they do need to train a new model. Previous generation models were all incrementally better, and we all have AIs that can do far more things than they can do today, but they still depend on human supervision. AIs can work for days and weeks, but they frequently hallucinate. There have been no significant breakthroughs by the previous generation of models.

The AGI hype continues to be questioned. The ones who question if AGI is even possible with language models are gaining more credibility, and all companies start investing more resources in alternate approaches, but these approaches arrive with time horizons similar to quantum computers - maybe in decades, maybe this century, but definitely not soon.

This is the future where the same bottlenecks we see today exist, and none have been removed. The data, the compute, the investment, the algorithmic breakthroughs - everything we still need to vastly accelerate are proving to be exponentially harder to crack than we imagined.

This is the future where the barriers to software engineering is at its lowest, but because significant problems like continuous learning not being solved and context limits still not infinite- frontier AIs are not cheap and you definitely need similar, if not more engineers than you do right now. The same skills of agent orchestrations that were valuable in the future I described above are valuable here, but engineers specialising in areas that AI just can't- also remain valuable. These can include languages like Rust where many current AIs are currently worse at, or new frameworks and languages that only the next generation of AIs would be proficient at - so any team that wants to move fast with the latest and greatest - has to hire engineers.

I think even though this paints a more secure future for software engineers, it's not as bright a future for humanity as a whole. Living in this future doesn't mean that all is bleak- and we'll certainly see progress continue, but it won't be as rapid, the journey through the singularity will just be slower, and what we imagine to be weekly progress in the previous scenario will be progress we make in years.

In both scenarios outlined here, we'll need software engineers. We'll need them more in the second scenario, but at the end of the day - we must understand what a software engineer is.

IV.

To me, a software engineer is just someone who translates requirements to instructions. For a long time, this translation required you to learn a lot of things. From abstraction to inheritance, from knowing how loops work to understanding how to navigate a server using nothing but a terminal, software engineers - just to translate what they wanted to getting the computer to do understand that want - needed to be able to write the code that machines could understand.

Now, it's slowly becoming apparent that the same software engineers can just ask an agent to do exactly what they want, and watch as an agent try (and sometimes fail) to do it. Over the years, they've seen the agent become more competent, more self-correcting, and more obedient as a whole. It's not perfect, and they have to get their hands dirty - but they often look at what they're seeing and wonder how much time is truly left before they're not even needed.

In both scenarios I've outlined, they'll always be needed - in the first, for human creativity which LLMs can't, by definition, replicate - and in the second, for the last 1-5% that needs engineers to still know and understand every line of code.

While everyone can be an engineer, in both scenarios I’ve outlined, not everyone or even a majority of people will choose to. Maybe this is because doing the things that software engineers do requires a certain kind of mindset - maybe it’s a love for all things scientific and technical, maybe it’s curiosity, maybe it’s just the quick feedback loop - but I can certainly argue for dozens of things which are considered more fun than typing code all day - be it anything from gardening to woodwork, from gaming to reading, there will always be things which are subjectively considered less uncomfortable than writing software - which is another reason why there will always be demand for the people who find it a worthwhile craft. I do hope to be wrong about this- because a world where everyone chooses to be engineers, where everyone can make things is a world of true abundance- one with software and robots and flying cars and everything we’ve dreamed about, but the reality is that not everyone will choose to do that, and that’s fine. We also need artists and scientists and writers and doctors and we’ll continue to need professions that will only exist in the near future.

V.

Finally, both scenarios I’ve outlined might seem totally impossible to you. That’s okay.

If you could go back in time a few decades when people imagined we'd all have flying cars, and told them that our buildings and cities look almost the same - not the Jetson-esque utopia, they'd be shocked and disappointed. Yes, the internet and smartphones would blow them away, but other than that, just the fact that man still haven't reached Mars, let alone colonised the solar system, whereas they saw their generation reaching the moon within just 12 years - would deeply disappoint them.

They saw the progress they were making, but they would fail to realise how the rate of change of progress changed and how only some areas still progressed exponentially, in the land of bits, while progress in atoms stalled. In the same way, for the second scenario, if the progress stalls around AIs which still need human supervision and orchestration, it's hard for us to imagine what another AI winter might look like - just as hard if not more, for us to imagine what the other side of the super intelligence singularity looks like.

Regardless of which timeline we're in, it’s never a bad idea to learn to make things.

Whether it’s the second scenario or the first, you’d need to learn the basics just so you know can either go deep at an esoteric area where LLMs would need a lot of guidance - or learn how to engineer the agents and the context so that they can do what you want.

As a new engineer, you should try to do both - just so you have both the leverage of managing agents and the fulfilment of having expertise in a field that you love.

As a senior engineer, you probably don’t need me to tell you this, but don’t ignore AI- the same way you didn’t ignore previous shifts in technology. Leverage its strengths now, and learn how to nudge them along. Learn how to stop your mind from wandering as you watch LLMs do 70% of what you want. Learn not to be frustrated, and try to extrapolate whatever you’re leveraging agents for right now- into a future where you can imagine the same task getting solved at a fraction of the cost, with minimal steering. If that doesn’t excite you, try to find a workflow that does.

Thus, the formula for the future is mostly the same and I think it applies to every profession and field, not just software engineers - keep an open mind and never shy away from learning new things. Master the basics. Recognize leverage and stay optimistic.

The future is bright, whichever way you look at it. Don’t listen to those who tell you otherwise.

I started this essay by asking whether every software engineer going to lose their job very soon?

By now, you know that the answer is no.

Software engineers won’t lose their jobs, but it increasingly feels like they will start creating new ones.

Anyone who spends significant amounts of time with LLMs can spot AI generated writing. For a while, it was even a meme when the word "delve" almost instantly meant that whatever you're reading was written by an LLM. The phenomena came to be known as "LLM Slop" - the word "slop" clearly signifying low-effort low-quality content.

This essay isn't about identifying what is AI generated and what's not- but it's an essay which I had to write because I've changed my mind on LLM Slop in general.

If I'm changing my mind on something important, it is often the case that smart people still hold similar (or even opposite) beliefs- so hopefully, by the end of this essay, you'll understand why it's important.

Here's how my views have changed -

II.

Whenever I saw an email, a text message, a tweet or a blog post with LLM-generated text, I instantly lost respect for the person. For some reason, it felt like a betrayal - like here I am, spending my limited time reading what you've written, and yet, you've chosen to throw LLM-slop at me, not even bothering to respect my time.

Even the meta-thought of someone having the audacity to post LLM-generated text made me question their principles.

You've made the conscious choice of deciding to post AI generated text - an output of a prompt, perhaps thinking to yourself that no one is going to know anyway - so, you clearly don't deserve my attention, nor my time, right?

Now, if you were nodding along reading the above lines, I want you to understand that it's perfectly all right to think that way. If you send your friend an emotional text, and he responds with, "Hey, I'm sorry to hear that. I'm deeply moved by what you did. It's not about how much courage you had—but how gracefully you handled it...", I wouldn't blame you if you decided to never text that friend again. If you're reading a product review, and you see a 5 star rating or a 1 star rating, clearly noticing it's AI written, I won't blame you for ignoring it.

But I think we're in the very small minority who notice these things. And if we don't evolve and adapt fast, we'll be perpetually annoyed by the world, at large.

Here's what the usual format looks of an LLM generated response looks like -

It's not just about [abstract noun]--it's about the interplay between [modifier] [noun] and the emergent [buzzword] of [gerund phrase].

We're not merely [verb]-ing--we're [adverb]-[participle]-ing the [adjective] [concept] that underlies [domain-specific term]. This isn't a matter of [noun] versus [noun]--it's a liminal negotiation across a spectrum of [pluralized abstraction].

It's less of a [noun] and more of a [noun-as-process] refracted through the lens of [adjacent jargon]. What emerges isn't a conclusion, but a [modifier] [noun] woven from the threads of [vaguely gestured domains]. The question isn't what, or even why--it's how we [verb] with the [plural abstraction] in a way that [vague outcome verb]-s.

Watch this talk and see if you can spot why it’s an LLM generated script. These are employees of Google, intentionally and shamelessly reading an entirely LLM generated script on stage. Is it ironic or more appropriate that it’s at one of the biggest generative AI events of the year? Okay, it’s impossible for an AI to fully generate that script and clearly, a lot of input went into it - but is an LLM output the best final version of a talk that they could’ve converged at? Not today, in the future it might be.

Over time, it is my hope that LLM-written writing is not only going to undetectable, it's going to be better than any human written text. But, there's a small part of me that still believes that because LLMs will write more or less similarly- anyone sufficiently using them will be able to spot them.

If the latter is the case, I don't think the world will be a worse place than it is, right now. You'll still be reading well written, grammatically correct writing- so, in some ways, it is even better. As LLM written text improves, it might just be the case that what's LLM written feels better to read, and you'll wish everyone else wrote the same way. What's better? Reading a sentence like "I has many jobs before but I eats breakfast tomorrow", or a coherent beautifully written sentence? Is an argument made by an internet troll better because it now includes nuance, sometimes extremely unnecessary nuance, over ad hominem attacks?

I think the aversion to LLM-written text, even right now, isn't because LLMs produce bad writing. People still use LLMs extensively on their own, so if the output was really low quality, it would be impossible to tolerate even using ChatGPT.

Thus, the aversion to LLM-written text probably lies in the fact that it's similar to reading a book written by a ghostwriter, or interacting with the social media page run by a PR intern. You're not peeking into the raw thoughts of someone - reading the intimate structure of their ramblings, but rather, you're being fooled by someone using an LLM.

This "fooling" is perhaps what feels disgusting right now, and everywhere from LinkedIn to X, someone's saving time by copy pasting something from an LLM to post, hoping the general public won't notice or perhaps, not even caring if they do notice.

Once enough people do it, it'll stop being so repulsive- but instead, just be another thing you see on the internet. I think as people keep using LLMs, that's how they will talk as well. An entire generation is now growing up, and a lot of them will have an AI to ask infinite questions to- and that is a better world than the world we lived in even 5 years ago.

So, even when someone is writing on their own in the future, their writing will be indistinguishable from something produced by an LLM- and it is the imperfections in the writing - a wrong comma, a plural verb instead of singular, etc, these imperfections may indicate that something is human written.

Over a long time horizon, maybe you'll get an essay like this written entirely using LLMs. And instead of reading it, you will condense the essay down to its summary and that's how you'll consume it. You think the summary is great, so you instruct an AI agent to comment on the essay something positive. The AI agent, who has personal context of your digital life, goes and writes a comment that's indistinguishable in its meaning from what you would've written.

In the above interaction, it's certainly faster, and it'll get cheaper, but something still feels lost.

III.

I once heard Naval Ravikant say that books can be essays, and essays can be tweets - so just write the tweet. It's more efficient, the act of compression into the core message makes you work hard to justify the core message in the first place, and it saves everyone else time.

A lot of books, where it's just one idea that's repeated throughout the book using examples and metaphors, padding the length just big enough to justify the spine, can certainly benefit from ruthless compression.

Yet, there are very few great tweets, but hundreds of great books - even if we just confine it to the self help non-fiction space. I think this is because despite our tendency for efficiency, we still need the layers of reinforcing tissue around it. We still need the stories that can't be condensed, the emotions that just can't be tweeted. We need the anecdotes that can't be summarised. "Never give up" is an easy moral, but the right story can make you remember it better than any tweet. Ask anyone who reads which books changed their life, and they'll have an answer - but few tweets are remembered for weeks, let alone years or a lifetime.

This is why I think even if it's AI generated content - unless you really know you can summarise it and still get the meaning- you shouldn't do it. This means that it's for you to decide what to consume via an LLM-summarized lens, versus what to read - instead of just reading everything one way or another.

Obviously, there will soon be people not using LLMs that you wish would just use them.

And as the Internet swims in perfectly grammatically correct LLM-written content - there will still be writers who do it without AIs. Not entirely without AIs, maybe they run their writing through a grammar checker, maybe they brainstorm and outline with an AI, but I think - the true joy of writing lies in what I'm doing right now - just writing. Will people bother seeking out writers writing the old-fashioned way, without LLMs?

I think that shouldn't matter.

If someone can create something better using an LLM, than they can without it, then by all means - they should be releasing the AI-written content. If they're transparent about it, that's even better. I can certainly see myself combining 10 deep research reports by o3 into writing a fascinating essay, which would certainly have flavors of LLM generated writing, but it would be better than what any LLM or any human without an LLM can produce.

But even when someone is not necessarily doing the above merging - maybe, they are just going to an LLM, prompting it with “Write me a post about mistakes newly wed couples make so that I can post it to r/relationships” and then they copy paste and post that, that’s the exact kind of content I’d have said was killing the Internet. But now? I embrace it. And so should you. It’s more likely than not that the text contains something new in it - and just disdain for the author shouldn’t mean you also stop reading the content.

IV.

But also, why feel disdain for the author?

I know I’ve mentioned that I’ve felt it before, but here’s one way to look at it that might shift your perspective. As we all stumble towards the singularity - there might be a moment in the future where everyone’s words - both spoken and their internal thoughts, are filtered through advanced large language models - models so intelligent and personalized so perfectly that we won’t even know how we lived without them.

Maybe it’s a future with brain computer interfaces so cheap and affordable and safe that everyone has one- and using the model capabilities inside your head is more efficient and effective than using your primitive meat brain. In such a scenario, we won’t judge anyone for being assisted or augmented by LLMs- but that scenario is already here. We’re all augmented by the technology we use everyday - reaching for our phones when we wake up, as if it’s an inseparable organ, just outside our bodies.

Soon, we'll see policy proposals written entirely with LLMs. We'll see politicians tweet out LLM-generated text- and we should think highly of the politicians that do, because it means they're augmented with an intelligence that will allow them to craft better policies and take care of their citizens more effectively than the politician still thinking with their meat minds. And if this is the case, shouldn't we be happy whenever we spot LLM-generated text on LinkedIn or Twitter, or even from a newsletter?

I think we should, and if you came into this essay being as annoyed by AI slop as I previously was - I hope I’ve made you reconsider that perspective. Slop is the future, and we’re all in it together - so we might as well embrace it early.

Have you ever come across someone after a long time, and to your surprise, they mention something about you that you yourself don't remember?

Maybe it was a childhood friend who mentions something you did in school or something you did at the park, or it's an ex-colleague who mentions something that happened at the old workplace- but you have no idea what they're talking about?

Or maybe you do remember, eventually. Maybe, as they keep telling you what happened, the memories rush back- yes, you were there! Yes, that did really happened! But as the realisation dawns on you that yes, you did forget about something, something significant that someone else chose to remember, a painful question emerges -

What else did you forget?

This question is painful, mainly because it serves as a timely reminder that perhaps, we're not very good at remembering things we thought we were. And that's okay, because we usually remember just enough that is necessary.

You don't remember how to calculate a derivative of a tangent anymore, because you don't need calculus in your day to day life. An engineer probably remembers it, but they don't remember what that quote from the one movie shown to you in class- the one which really resonated with you.

I think it's entirely plausible that we only remember information that's necessary. This doesn't mean we only remember facts and formulas, but even with the events of our lives- we remember only the information that serves us. We remember the events that we consider important to us, important enough to build a narrative to tell ourselves the story of "who we are", which ends up shaping who we go on to be.

While this sounds like a good generalised framework of memory, it's certainly not the same for people at all. Someone with the same memory of an event may interpret it profoundly differently. Among two similar kids who get bullied, one may channel that hate into more hate for everyone, and decide to grow up to be a bully themselves - while another may channel that hate into strength and compassion, and stand up later for others who get bullied.

It's also scientifically accurate to say that our memories aren't accurate. Every time we remember something, our memories are slightly altered, and this may keep going to the point where the memories we remember are no where close to what actually happened in the past.

Given that our memories are so flawed, shaped by evolutionary pressures to be as efficient and useful as possible, it's a miracle that at the end of life, we can reliably narrate the story of our lives to anyone willing to listen to us.

But we're now growing up in times when AIs exist who are willing to listen to everything we say to them, and now, it increasingly looks like they'll all have infinite memory of us. Just like the fallibility of human memory has its issues, AIs having memory of everything we tell them has its own implication.

Does that have to change the way we interact with AIs?

That is what this essay is about, and it’s timely because ChatGPT with infinite memory rolls out today.

But before we go there, let’s talk about what AI memory means.

II.

Since the time AI models started getting popular, frameworks were launched to enhance the experience with memory.

The most popular framework was called RAG(Retrieval Augmented Generation)- and in simple terms, it gave AIs memory by storing information about you in a database (a vector database, so that the meaning of your memories can be easily fetched).

This was done because AI models usually had a much smaller context window when they launched, and RAG was essential to help AIs remember information about you, across multiple sessions.

As context windows started growing bigger, with current context windows of ChatGPT and others being anywhere from 32,000 tokens to 128,000 tokens, RAG was often declared dead. 128,000 tokens is enough for a big novel's length of information, and surely, in a single session or thread, if you keep talking to an AI, it's going to hopefully remember almost everything in that conversation till the end, right?

Unfortunately, this still seems wrong - and it may continue to be wrong for quite some time. As the single threads of messages get larger with any AI, each message contains the history of all the previous messages- and thus, every subsequent message will start costing more each time.

Thus, RAG is one of those techniques which fills your context with relevant information - information that doesn't take up all your context, but helps the AI understand exactly what it needs to remember about you to answer that specific query.

Almost every "AI memory" framework today uses some version of RAG - where they store data in a database and query it with relevant information to prepare a useful context with your message. (It’s unclear if OpenAI is using RAG for their current implementation of memory, but it’s likely that some version of retrieval is definitely being used)

Some day, as AI's get smarter, cheaper and start having 10M or 100M length context windows, it can be the case that the AIs are so cheap that even sending millions of tokens of tokens costs the same as sending one token today - at which point the fractions of a fraction of a penny won't matter.

Someday, RAG will indeed die as AIs remember everything but for that to happen, context windows need to scale to infinity and prices need to exponentially come down.

Even then, RAG may continue to be used as it may just be a lower latency option to retrieve information rather than sending them in context each time. Bigger context windows still suffer from degraded performance even today, but that will hopefully improve as well in the future.

So hopefully, now you understand that memory is essentially retrieval.

That's what's been happening to different chatbots using memory, and now ChatGPT is fully rolling out its memory feature to hundreds of millions of users who are going to experience AIs that remember everything about you, all the time. A version of memory already existed previously, but it didn’t store everything and neither did it reference all your chats, but the new version of ChatGPT will.

This has some interesting implications.

III.

I won't focus on the obvious pessimistic implication that you're going to hear from the mainstream outlets like privacy concerns-

AIs remembering everything about also means all your important data is being stored, and if this AI isn't self hosted and belongs to a private company, they can share your data with advertisers or use ads to target you. Hopefully, ChatGPT won't do that, but we can't be 100% sure.

However, there are other concerns like the one I started this essay with.

Human memory is fallible. We've always fantasised about what infinitely accurate memory might look like, but those with the gift of infinite memory clearly don't appreciate it. What feels like it might be superpower actually ends up making people miserable as there are always going to be things that we want to forget.

Because features like memory for AIs aren't rolling out on 10-year time horizons (and we should be thankful that this is the case), nobody really knows what the long term implication of having entities that remember everything about us is.

But the implication doesn't have to be bad. Nobody misses the time when calculators weren't available- even though I'm sure pessimists raised their tired voices against it back then, AIs integrating across our lives requires some version of version to be really personalized, so it can definitely have large positive effects.

Perhaps, really smart AIs having access to all our memories, even things we forget with the passage of time, will help us make better sense of who we are and our lives. It would certainly know more about ourselves than we do now, and with this information it could certainly help us weaknesses and opportunities that we'd have not figured out on our own.

Perhaps with access to such superior memory, humans themselves end up remembering more, forgetting less of what matters because AIs will keep reminding them of what’s important when prompted to.

Perhaps, and more importantly, it can lead to truly better responses because the AIs will remember what you want.

Thus, with infinite data and infinite memory, it suddenly becomes clear what you should be doing differently with AIs that have memory, instead.

IV.

The rest of this section assumes you're among the millions of people with a ChatGPT subscription, but if by the time you're reading this- your preferred AI, or hopefully, your local LLM model has memory features similar to what's rolling out now, it should also be applicable.

Here are some ways to use this -

1. Journal by talking to AIs every day.

At the end of every day, tell an AI that you want to talk about your day and talk to it. Do this every day and soon, AIs will have a better understanding of who you are, and they'll be able to provide more personalised advice to all of your queries in the future.

Here’s a prompt like this -

"Hi, you are an AI that is tasked with knowing and understanding me. I'm going to talk about my day and your job is to ask me thoughtful questions that helps us both process the events of today, and helps you get a clearer picture of who I am. You can ask follow up questions, but remember that this whole interaction should feel like a friendly conversation between us, and not like an interview. At any point if you remember something relevant about me based on our past conversations, you can gently remind me if that helps you keep the conversation going and understand me better. Finally, if you feel like I'm missing something or if there's any advice you can give me regarding any event that occurred today, please mention it, but again, remember to keep your tone as warm and friendly as possible."

You can modify the above overly verbose prompt, but it should serve as a good first message that you send to your AI every day and help it understand you better. The rest of the prompts mentioned in this essay will be simpler, but it is your job to instruct the AI as specifically as the above prompt shows.

2. Remember that everything is remembered, so use anonymous messages.

When ChatGPT first introduced incognito mode, it seemed like it won't have much of a use case. Now, it's essential that you use it.

There are going to be several occasions in your life when you decide that something you're asking ChatGPT shouldn't definitely be remembered. Maybe it's opinions on a razor you're looking to buy for a friend, but you don't want it to suggest razors as a gift idea later. Maybe it's just something very personal that you don't want the AI to remember.

For all such occasions, be very liberal about what you want the AI to remember because there's a good chance that not only is it going to remember, the memory feature as a whole will get so good that in 20 years, ChatGPT will be able to write a 100k word biography based on your life more accurately than yourself.

3. Ask ChatGPT explicitly to remember.

Just because ChatGPT says that it can reference your chats, doesn't necessarily mean that it's going to remember everything. If you say that your favourite flavour of ice cream is chocolate, it may choose not to remember that if it's used to not storing such information for you.

But, you can ask ChatGPT to remember the fact explicitly. Once instructed, you can be confident it has committed that to memory.

You can also do the opposite and remove chats, and remove explicit memory & OpenAI, to their credit, has made it pretty clear that you’ll be always able to control what the AI remembers- to the point of telling it to forget something about you, and it’ll just work.

V.

Finally, you should leverage the infinite memory as a tool. The following are just a simplified version of prompts you may use once you know ChatGPT knows a lot about you, and the results may just be magical enough that you'll be able to understand why memory is such a useful feature-

"Based on our interactions so far, what can you tell me about myself that I might myself not know?"

"Based on our interactions so far, who do you think cares the most about me, which I may not be myself aware about?"

"Based on our interactions so far, what lies am I telling myself and how do I fix them?"

"Based on our interactions so far, how am I different from other people and what does that say about me?"

"Based on our interactions so far, what company do you think I should start?"

"Based on our interactions so far, what book do you think I should write?"

"Based on our interactions so far, what lesser known movie do you think I should watch which will really resonate with me?"

"Based on our interactions so far, how far away am I from reaching my true potential and what do I have to do to get there?"

"Based on our interactions so far, how is my worldview incorrect?"

"Based on our interactions so far, estimate when I graduated and from that, list some new discoveries that have happened which are different from what I studied and knew growing up."

"Based on our interactions so far, make me an optimal daily routine that prioritises things that you think I most need"

"Based on our interactions so far, if I were to radically improve my life, what are the three most important things I should be doing?"

"Based on our interactions so far, what is one place I should visit in my lifetime? It can be any place that you think will resonate most with me"

You can enhance each of the prompts above based on your preferences, or even go with prompts entirely new, but hopefully, the answers or portions of some of the answers you get will be surprising.

Like an old friend that remembers details about your life that you've forgotten, you're now going to have access to an advanced form of intelligence that not only remembers everything, but can integrate everything to give you the perfect, personalised answers.

We've only dreamt of having this power for decades. As of today, all of us possess it.

Advertising is one of the primary business models of the cyberspace.

But advertising should be banned.

It sounds like a radical idea, I know, but by the end of this essay, I promise that you'll think differently.

Despite everything negative about advertising(and trust me, this essay will have plenty), they've ensured that vast portions of the Internet remains free to all.

Bandwidth, despite being cheap, isn't free. Storage isn't free. When you're starting your web browser to visit a website or just clicking on an app that requires an internet connection to work, that costs someone somewhere real money every single time. Given how bloated most websites today are, the costs are substantial- especially for the top visited websites and top used apps that appear free.

From endless videos on YouTube on any topic you can imagine, to the endless vertical scroll on Instagram or TikTok, to the latest happenings on X, consumption has never been more accessible and coupled with algorithms, there is no escape from the advertising that funds them.

The only you can argue that this is good in some way is that ads do help democratise access. Someone in Africa can access the same Google that a billionaire can access, both the billionaire and the kid in Nairobi is paying for the service with their data and attention, but the billionaire's attention is worth more because of their purchasing power, and thus whoever has bid the highest to perhaps show them an ad of a private jet, is subsidising the cost for thousands who can now access the same service.

However, even this argument is slightly flawed. Advertisement isn't the only way why some services can remain free. You shouldn't have to surrender your data and privacy to big tech companies just for them to serve you highly relevant ads, just because it seems to democratise access. There are lots of sites who don't depend on ads, who are profitable, and who still don't run ads on their freemium tier.

The market forces though, dictate that as the losing move.

People hate ads, but they don't hate ads enough. So, in theory, any website that takes an opinionated stance against ads and decides not to have them, would have to charge higher in their premium tier than the similar product with ads - all things remaining equal.

Even spending on advertising isn't really a choice anymore, because if your competitors are spending on ads, then you must play the same zero sum game and spend on ads as well.

This makes advertising one of those lose-lose situations where if you're not playing the game, you're on the losing side. If you play the game, you're handsomely rewarded. But everyone else using your product loses (their time, attention and money), and in a game between you losing and your customers losing - you'd choose the option where you still get to keep playing the game.

II.

To understand the dangers of advertising, we have to start from its roots - Copywriting.

Even today, copywriting is a highly paid gig because it's a mixture of the greatest psychological and hypnotic techniques, coupled with the art of flawless writing.

When an ad or product description says, "By now, you've hopefully realized your razor isn't working" - the writers are hoping the "By now" passes through to your subconscious as "Buy Now", intentionally influencing you to click on the "Buy Now" button. And tricks like these are just the tip of the iceberg of what's possible, with just words.

Short-form video ads are the pinnacle of this manipulation. Platforms like TikTok have turned ads into predatory reflexes. You can't just watch some of these ads- as they're specifically engineered to instantly grab your attention, stopping you from scrolling on, and tactics range from harmless like "hook" questions - "What happened on the week I made a million dollars?" "How to cook the perfect perfume" "This cryptocurrency might make you rich", etc, to deliberately shocking videos that shows the first few frames of something interesting happening like cars about to crash, and people about to fall, before pausing it to show you the ad in context of the video. Such ads hijack your lizard brain in 0.8 seconds, using anything from strobe-lights and quick editing, disorienting cuts, intentionally wrong information or just plain ol' misdirection.

Now, you might think that maybe, it's not so bad. After all, because of the Internet and algorithms that dominate the small number of sites where everyone seems to be, you can supremely target anyone you want, so most people are receiving relevant ads anyway, right?

That's what every tech company wants you to believe but in reality, ads prey on the vulnerable. Microtargeting algorithms stalk the depressed with antidepressant ads, the indebted with payday loans, and the lonely with OnlyFans promotions. From preying on the insecurities of teenage girls in developing countries with ads of skin-lightening creams, to preying on those most likely to gamble away their life savings with multiple reminders of instant riches on online casinos, micro targeting was always about maximising profits over all means possible.

And this "all means possible" is what makes advertising so profoundly evil, because everything is bound to get filled with ads until there is considerable pushback, because the more ads you can serve while maintaining attention and engagement, the more profit you'll make.

III.

The infrastructure to track your every move already exists. That is why when you glance at something on a site briefly, you're bombarded with ads of the same thing - to influence you to finally complete the loop of purchase. Because you're tracked through your phone, through the sites you visit, through the videos you watch and feeds you scroll through, all of this data translates into carefully placed ads while you're on a mindless dopamine binge - the perfect combination for making impulsive purchases.

Just like propaganda serves the state, ads serve the private. Just like propaganda erodes trust in the media, ads erode trust in everyone around you.

You can't trust the influencer or the YouTuber because at the end of the day, they're all peddling ads. All content is content being made to fill up the space between ads- which is why most content is mediocre. This algorithmic mediocrity has grown now to an exponential scale - where even "micro" influencers can have affiliate links, selling things to the hundreds of people following them, so at every level of the digital space, there is an erosion of trust.

As a result, consumers are worse off. If "Advertising should be banned" sounds crazy to you, still, it's probably because you've grown to accept the state of the world as is. If that's you, I urge you to do one thing. If you don't have AdBlock installed on your browser, download it (Search for uBlock Origin), install it, and browse the web for an hour. If you have Adblock installed, turn it off and browse the web for an hour. Both experiences should at least make the case that something is deeply wrong.

I don't think the solution is to paywall everything - as monthly / annual subscriptions (another core business model of the internet) often ends up remaining inaccessible to the majority of people, but there can be free business models that don't depend on ads. Freemium services without ads - like ChatGPT, is an excellent alternative and a real look at what this ad-less future might look like.

But you might argue that unlike OpenAI, small businesses don't have the support of venture capital and need ads to survive. That's a fair question, and can be applied to every possible use-case of ads which is from a certain point of view - positive, that is, they are useful and essential for one side of the table - the advertisers, and it's a long table - but what you're essentially asking is this -

What about discovery? We didn't have highly personalised ads for a long time and discovery worked fine. Word of mouth and community platforms where people share recommendations with each other is more than enough. There is a reason why the suffix "reddit" is added to most Google searches about products - and the reason is that people are looking for opinions of other people and recommendations from them about what's best. The propaganda of advertising tries to sell you the notion that without ads, the internet won't remain free - but in reality, it's only a minority of free services who have to quickly pivot to a better, more ethical business model. They surely won't die, but they won't be able to kill (the time, attention and future of humanity) either.

I think we should at least start having this conversation and taking it more seriously. The best way to do that is to suggest something which seems outright radical - That advertising should be banned, but it is only radical because you're used to slop and spam that plague us all. You're used to every site on the planet tracking you, your phones just offering you an illusion of privacy, and your data being harvested every second of the day.

Banning advertisements would instantly have very positive second order effects. It'd make it pointless to optimise algorithms for maximum engagement, and not only would that lead to a better experience across all social platforms, it'll disincentivize the production and steady shipment of mediocrity.

I think the greatest positive secondary effect it can have- is that people are going to be happier. For ads to work, they have to convince you that something is missing from your life. So ads actively work to impose these insecurities which are conveniently solved by the product they advertise at the end of the ad. Just like the news tries to make everyone's problem your problem, ads try to make every insecurity- your insecurity. Ban ads, and this horrible loop instantly stops.

A machine of psychological warfare instantly ceases to exist.

Now, I don't know how we'll go about banning it. It’s a 600B dollar industry that shows no signs of slowing down, and everyone is worse of for it. So think of this essay as a thought experiment on something you previously considered radical, but hopefully, I've convinced you by now that it's not so radical as you think it is.

True innovation happens in democracies because freedom is an essential aspect of creativity. One could argue that constraints amplify creativity as well, and while there's no denying that, if you look at all of humanity's revolutionary inventions, almost all of them happened at democracies.

From the invention of the printing press in Renaissance Europe to concrete in the Roman Empire, to the Internet and computing revolutions in Silicon Valley, democracies have always fostered innovation.

So, it's arguably a good thing that the President of the United States has been known as the leader of the free world, and the United States, for a long time, has cared not just for its citizens, but for those beyond its borders - from generously offering military support to delivering continuous humanitarian aid to other nations at times of crisis, to even mediating conflict among neighbouring states.

However, as is normal with the passage of time, great empires lose their power and new ones take hold.

The unipolar world order, once anchored by U.S. leadership, is slowly fracturing today. Historical parallels such as the decline of the Roman Empire, or more recently- the British Empire post-WWII which reshaped global alliances, and today, rising multipolarity and inward-looking policies (e.g., trade protectionism, regulatory fragmentation) threaten the collaborative frameworks that enabled past innovations.

"De-globalisation" is what its being called, and the Internet: the enabler of a natively global cyberspace which once brought prosperity and connection, is actively getting worse in the face of it all- where algorithms concentrate echo chambers and division and hate thrives faster than ever before.

As the trump presidency continues, it looks more likely that we're headed for times as volatile as ever. We're already seeing a tariff war, but real wars haven't stopped either.

And the US now choosing to recede from the global stage has major consequences.

This quick vacuum of a global superpower may just create a race for global dominance, and if that dominance is challenged, we're headed for some difficult times. America itself, has a bigger debt problem than most realize, and things are worse now than they’ve been since 2008.

One solution, at least for those in nations affected by this changing dynamic, is to move to network states. These are states where people are ideologically aligned around shared goals, actively leverage technology in governance, crypto-friendly, and in a world that's seemingly very unstable, it may just be the answer you’re looking for.

You can start your own network state, and there's a free book which teaches you exactly how to do that.

But starting one from scratch isn't easy.

This essay will focus on why starting one isn't easy, and then highlight one network state that's clearly starting to work.

II.

Because Network States are a relatively new concept, it can be the case that they're easy to spin up some time in the future. Just like we can start websites, companies and currencies today with the push of a button, starting a network state could be simple and easy in the future.

However, as I've spent more time thinking about this, I've come to realise that, at least for now, there are two undeniable variables for a network states to be successful -> Time and Numbers.

Time is a necessary variable because Network States intimately depend on a community of proactive people, and relationships take time to deepen and evolve. Just having shared interests is often not enough, but shared values and principles are what matter the most.

A network state doesn't just start as a network state from Day 1 (although, like I said, that can be the case in the future), but it has to start small.

It has to start with a small group of ideologically aligned people, and they may not even know they’re a “network state”, just a group of people deciding to live in one geographical location by choice, is enough as a good beginning. Of course, it can start digital-first where it’s a group chat or a discord server, but it has to translate to land soon.

Once it's a society or school, it's effectively a service - a service that provides both value and community, but more importantly, requires the careful coordination and intent of all its participants to work.

As this service scales and becomes more dominant, it slowly starts to resemble a state. Unlike a tech product where you can quickly test a MVP, a network state's MVP is probably a group chat of people willing to have IRL meet-ups. The second stage to really test product-market fit is to have month-long retreats with the same people, and see how many care to show up. Initiatives like vibe camp show a version of this working.

Because the core product of a founder-led network state is still the community of people within it, a group chat with the ambitions of being a nation must have to prove that people are willing to meet-up with each other, they must be willing to contribute in order to fund a number of such meet-ups, they must be willing to relocate to a specific location for a short duration of time first, and eventually for a long duration of time later.

The Network State also can't freely admit anyone - as it must ensure that each new addition is ideologically or at least culturally aligned with the values and principles of the state. This means even from the beginning as you're starting out, you have to keep the quality filter high enough such that the 100th member won't look at the other 99 members and decide not to join.

All this means that you just can't decide to start a network state today, and launch tomorrow. However, that's also not a rule. While a network state started by Mr. Beast would certainly fail because of a lack of ideological alignment, a network state by Pieter Levels for independent solo founders, or John Carmack for VR startup founders and tinkerers would certainly work. Maybe Mr. Beast's could also work as the number of influencers is rapidly growing, and surely, they're ideologically aligned on capturing your attention by any means, so that's an important filter.

But hopefully, if you're not a famous personality, time is still going to be one of the things you have to optimise for, so it's worth starting early, and while you do, you'll get better at filtering out the people that you don't want, so that you can eventually build a country with people that you do.

You also have to make sure you don’t upset the government who owns the land that allowed you to create the zone, which, at least one promising Network State has already done.

The other most important variable for a Network State is the number of people.

III.

If I tell you I wake up every morning and worship the Sun, you'll call me a lunatic. But the Sun has been worshipped for thousands of years in several cultures. It's not as popular now, but people still wake up tomorrow to pray to the Gods of their own religions. The only reason you consider that as a valid practice, but not my Sun-worship, is because I don't have the numbers on my side.

If there were billions of Sun worshippers on Earth, suddenly, you wouldn't feel so great about critiquing my morning routine. You wouldn't want to offend or hurt the religious sentiments of billions, right?

So, there is a profound strength in numbers which is undeniable, and yet, it is often underrated. For Network States, even though I can make the argument that it is not the quantity of citizens which matter, but the quality of citizens, at the end of the day, it is the number of people in a network state that will serve as the catalyst to legitimacy and recognition.

Because if you think about it, Jeff Bezos, Elon Musk & Mark Zuckerberg on an island doesn't mean much. Yes, the collective net worth of that "country" of 3 is over 800B dollars, greater than the GDP of the majority of the countries (Only 21 countries have a GDP higher than this), but that island won't be considered a real country. But, if just three people moved their companies to the island, forcing all the people who worked there to join them, suddenly, it's a country with hundreds of thousands of people.

At that point, you have to take them seriously, so it's inevitable that the network state that successfully scales by attracting more people, will eventually be recognised as a real nation.

The only question is- when will that happen? Because if the condition of the world is anything to go by, we need one alternative very soon.

Luckily, there seems to be one answer.

IV.

Balaji Srinivasan, who coined the word "Network State" and wrote the book about it, has also been known for his terrifying, yet accurate predictions.

One of his main predictions is that the American empire is in decline, and I think, that is the whole reason behind why we would eventually need network states.

He has spent the last few years popularising the idea, holding Network State conferences, funding those trying to build Network States, but it increasingly seems like he's following his own playbook and also creating a network state himself.

The first version received 4,000+ applications but only had capacity for 128+ members. The second version, "Network School" has more capacity, and you can still apply to visit, and check it out.

I think the third or fourth version of this will be a perpetually running city, and within ten years, it’ll be officially recognised as a nation state. Even before the official recognition, as word starts to spread about how great the experience of conducting business and standards of living are, and if at least a few companies founded there start making waves, they'll gain the attention of the broader public much sooner.

By then, this network state should hopefully have nodes through the world, so even if you can't travel to one, you'll hopefully have one near you. If the nation you're in is especially hostile to technology and progress, which can be direct hostility by demonising innovation and capitalism, or indirect hostility by plain old bureaucracy and incompetence, I'm sure the Network State that's shaping up right now will be a safe haven for you.

But it can't happen overnight. It needs time, but time is slowly running out.

AI is a winner-take-all race. A day might come when it's a monopoly. But even today, the best model is what everyone wants to use, at any given time.

And it is usually the case that the smartest AIs are better at almost everything than any AI that came before them.

People usually get to know which is the smartest model through benchmarking sites such as LMArena, which ranks the models based on what users vote as the best model during their interactions with them -

As this essay goes out, Gemini 2.5 Pro is the best model, and if you've used it, you know that it is the case. It is remarkably more intelligent than any model that came before it, and if you've ever found a problem that an LLM couldn't solve before, especially problems in Math or programming, you should throw it at Gemini 2.5 Pro. Like lots of people have noticed, Gemini may just be able to solve it.

The problem with believing the benchmarks is that there exists a minority of people in the world with no morals, and no taste. Mark Zuckerberg is definitely on that list, and thus, despite being one of the firsts to popularise Open Source releases of frontier models, Meta's recent models are clearly not good. If you use it for more than a few seconds, you'll understand why it's not only bad, but why it shouldn't be anywhere in the top 5, let alone hold the #2 position.

The practice is called "Benchmark-Maxxing" where the AIs are specifically trained to do great on benchmarks during post-training, but it's not actually that great at anything else that doesn't reflect the benchmark's problems closely.

There are rumours that Meta's models have been doing exactly that, and that's why the models are bad, but the benchmark numbers are good -

Dunking on Zuck's moral failings, even though there is a lot of substance there, isn't the point of this argument. The point is that benchmarks should act as a filter for you to try the models yourself. The use cases of LLMs are so diverse that it's impossible to know exactly what your use case is, and how the model specifically performs for your use case.

It is likely going to be the case that the best model at any given time is also going to be the best model for your use case. The best model at any given time is likely the biggest recently launched model, it's likely the most intelligent model and thus, it likely outperforms the model you were using previously for that particular task.

There is no guarantee that the above is going to the be the case for your use case, but surprisingly, I’m writing this essay to convince you that older models, even models released last year may just be better for you than what’s the latest and greatest. The best way to test that is to just go and talk to the models I mention in this essay.

II.

Model diversity is now high enough that there are some models who are good at some things and bad at others, and the best model right now is worse at something a model from last year is still good at!

For a long time, I didn't believe that this was true. I believed that the most intelligent model should be able to beat every model that came before it at all tasks. For the most part, this is true. Bigger models are better than smaller models, which is why open source models haven’t caught up to the billion dollar training runs of big AI labs.

But I had a feeling that this might not be true the more I used LLMs. There was something different in some LLMs, something unique about them about specific things that were matched by none of the other LLMs, despite how better they were in every metric.

Now I finally have proof that the lingering feeling was right.

There's a new benchmark called EqBench which measures creative writing ability, emotional quotient, long-form writing , etc, and looking at this benchmark feels like it confirms what I've been suspecting all along.

You can visit the benchmarks yourself to see how the models are tested and how they're evaluated for different capabilities. To me, this benchmark feels like it should be as important as the benchmarks in LMArena, because models are simply not just supposed to be programming agents, but they should be widely useful and pleasant to talk to.

The rest of this essay will focus on three models that I think you should always check in with, for what your use case is, regardless of what it is.

You may be surprised at what you see, so keeping an open mind is a must. The first model we’ll talk about is what’s at the top of EQBench, and anyone who has extensively used this model can already guess what it should be.

III.

Claude 3.5 Sonnet.

Not Claude 3.7 Sonnet, which is supposed to be an improvement of Claude 3.5 Sonnet and while it’s good, it’s not as great as Sonnet 3.5 itself.

While everyone was using ChatGPT, the smartest people in the world were tweeting about always using Claude, going to it for life advice and treating it as a therapist.

For a long time, it was the best model for programming, and Claude 3.7, to their credit, is even better at programming than Claude 3.5.

But here, I'm talking specifically about Claude 3.5 Sonnet, and on EQ bench, it's at #1.

This means it has the highest emotional quotient of any model, despite being an older model from last year.

If you have ever used an AI to just talk through your feelings, or figure something out that required some emotional depth, even if it's just for brainstorming potential solutions, nothing comes close to Claude 3.5, in my experience.

GPT - 4.5, a larger model with more world knowledge actually might, as it’s evident from its similar higher ranking on the benchmark, but in my experience, GPT-4.5 seems too eager to please.

There was one moment in my life when I was asking both models on how to respond to someone that I was mildly upset with, and my immediate non-thoughtful response was to just ignore it, and GPT-4.5 gave me an intricate multi-day plan on how to properly execute this avoidance behaviour. Claude 3.5, a smaller model from a year ago, immediately told me that avoidance is not the right solution and that I should talk through the issue instead.

I do expect GPT 4.5 to be better here, as it's a joy to use as well as being objectively smarter than Sonnet, but I honestly don't know what the solution is.

One thing that definitely isn't the solution, for GPT-4.5 or any model that chooses to please you over offering you what's objectively the best solution even if it's difficult in the short term, is adding custom instructions like "Be disagreeable. Don't be afraid to disagree with me when you know I'm wrong." Yeah, this doesn't work at all, as it makes the model default-disagreeable where they disagree with everything. Thus, emotional depth and maturity seems like something custom instructions can't fix.

It’s a shame that Claude 3.5 Sonnet may never be open source, because Anthropic, despite having the talent and taste to build such a great model, is also ideologically an AI safety organisation who believes the AIs will kill everyone on Earth someday, so I wouldn’t hold my breath on them making any releasing any open source models anytime soon.

What should be surprising from the benchmark is that an OpenSource model is at #3, and that’s actually my recommendation for the other model that you should consider really checking out -

IV.

DeepSeek R1.

This is a gem of a model, available for very cheap, and simply reading its reasoning traces is fascinating enough on its own, but it's also #1 at creative writing!

It's not the best LLM for correctness if you're looking for that (but it's still the best open source model), but it's incredibly fun to talk with! It’s #1 in creative writing on EQBench.

There's a natural playfulness to it that's not there in any other LLM, and reading its reasoning traces feels like actually reading someone's mind. If you've heard someone mention that LLMs are just predicting tokens, have them read through R1's reasoning traces. Yes, the model overthinks, a lot, but it still feels like someone deliberately reasoning through to arrive at an answer.

Maybe this uniqueness comes from all the chinese data it has been trained on, but it feels oddly powerful for its size, and it feels oddly more intelligent than what it should be - even if it doesn’t get everything right.

Grok 3 (with thinking) is the model to use if you're actually looking for intelligent answers with reasoning traces all visible, but these aren't very fun to read as Grok, despite being one of the best models right now, still suffers from producing content that "feels" like slop - overusing words that LLMs generally overuse. I don’ think this “slop” problem is going away anytime soon, so getting used to it is a good decision as it’s highly likely people will soon be reading so much AI generated content, that they’ll be speaking with similar “GPT-isms” as well.

Anyway, Claude 3.5 and DeepSeek, alongside your model of choice (which should be Gemini 2.5 Pro right now, because of how great it is) should be on your virtual council of advisors. This is not true for tasks like making a mermaid diagram of your codebase, or any programming problem, or a logical puzzle or anything that requires raw intelligence - at which point Gemini 2.5 Pro, OpenAI's o1 Pro & Grok 3 (with thinking) should be your go-to.

But if it's anything that requires emotional maturity, or just playful creativity - like asking a model for personal advice, how to deal with a conflicting situation, or simply what the next possible plot lines or story beats could be in your story, a council of models consisting of Claude 3.5, DeepSeek and Gemini 2.5 Pro should be there. I'd also be remiss if I didn't at least mention GPT-4.5 and GPT-4o, as the latter is still getting updated to be more intelligent & the former is a genuinely big model with more world knowledge than most LLMs on this list.

If you do want to check out some AI models to see how unique their personality and capabilities can be, I’d recommend you check out Claude Opus & GPT-4 (Turbo) as they’re still uniquely interesting on their own, even if they’re not as intelligent as even smaller open source models today.

The best way to use these models is to use OpenRouter. You can even buy credits there using crypto, which makes them particularly unique in the AI space. (Fun fact : The founder of OpenRouter was also the cofounder of OpenSea!). If you rather use your mobile to talk to LLMs, use Pal Chat on iOS with your OpenRouter key and you can talk to any model on the go.

Finally, keep an open mind!

What works for my use case may not work for yours, and the models I mentioned here may not be what you're looking for. At the time of writing this, these are my picks but I'm sure a year from now, things might look radically different. But what I’ve been observing is that humans, naturally being tribal, tend to stick to one model and only move from the best model to the next, while disregarding every other model.

As the pace of AI accelerates, it’s a much better strategy to consult a council of AIs for problems you face, and only then make the informed decision of which model (or ideally, groups of diverse models) is better for you.

These are my predictions on what will happen from now to 2027 in AI.

The reason for writing this now is that there's a brand new prediction, but like most predictions from overly pessimistic rationalists, it gets a lot of things wrong. I could dissect exactly how the predictions are wrong, but that would involve actually accepting their frame in the first place, which is also wrong.

So here are my predictions, which is also destined to be wrong, but it may just be more correct than what's popular right now.

Mid 2025

At this point, the best models are by Google, OpenAI & xAI. Anthropic is slowly dying while publishing blog posts every month. People eagerly wait for Claude 4. Amazon scraps its plans of integrating Claude to Alexa and go with a different cheaper, and faster model instead.

OpenAI releases o3 and o4 mini, and both fail to topple Gemini 2.5 pro in major benchmarks. But they're close enough that it's clear that they won't be #1 for long.

OpenAI unveils an agentic coder that works similar to Claude Code, and a surprising research shows that software development agents perform better with no supervision in certain tasks than others. This rattles the software development scene briefly, before someone discovers a critical problem in the study and it’s never mentioned again.

End 2025

The year has been pretty exciting and models are cheaper than ever before. Agents are more commonplace. OpenAI is making strides with their agents SDK, but people are wary of using them as they only seem to work best with OpenAI models, and not with models of other providers.

This is a problem because the second or third best model on the LMArena leaderboard is radically cheaper in price, while offering similar levels of performance. There are rumors that OpenAI may start a "Agents Marketplace" similar to their custom GPTs, but the hype is not on their side.

Big models take longer to train, so everyone's holding their breath for the next big model to drop.

Around the end of the year, GPT-5 with the highest intelligence is at the top of the leaderboards. It's also too costly at the highest intelligence and thus, can't be effectively used in any agentic workflow where cost is a factor.

Rumors of Grok 4 are starting to circulate. Gemini 3.0 or Gemini 3.5 Flash is the cheapest, most performant model. DeepSeek R2 is the best Open Source model and it is on the top 5.

Cursor's marketshare is slowly going down as more people start using open source VS Code extensions and free models to write most of their code.

Anthropic publishes 3 blog posts showing Claude 4 can often willfully make mistakes, to avoid appearing too capable, and it doesn't do this when users are especially kind to it, so they auto-append "Please" and "Thank you" tokens to the requests on their web interface. Nobody cares as none of Anthropic's models are in the top 10 anymore.

Mid 2026

We've seen agents and agentic companies making big strides. But models taking longer to develop doesn't mean progress is stopping.

China, still not as great at AI models (to reach #1, for example), doesn't start to centralize their efforts because AI still hasn't caused a massive uptick in global GDP. Instead, what's rapidly taking off in China are humanoid robots. Robots, now reasonably cheap, and remarkably smart are helping making factories more efficient, while also being bought by consumers worldwide. The demand for them keeps growing as they get cheaper to manufacture and produce, and with AI they feel incredibly life like. American companies that manufacture humanoid robots have also made great progress, but can’t compete with China on costs.

Elon Musk's Neuralink is in the news again, having successfully implanted their devices on hundreds of patients.

Whenever a new story comes out of someone doing something radically cool with their Neuralink, people start fantasizing about a future where "The Merge" is quickly approaching.

Elon, having cut enough regulations to effectively accelerate Neuralink testing timelines is now actively promoting the merge as the best way to ensure that humans continue to remain relevant in the AI age.

At this point, AIs can code reasonably well that you don't need software engineers to write code. But the demand for software engineers who can leverage and manage AI coding agents is higher than ever, and software engineers - one of the few professions where everyone's always chasing the next shiny new thing, have no problem learning how to use agents, sitting back and letting AI's do the coding. Knowing syntax is a thing of the past. But knowing what to build and what to use is as important as it used to be.

YC starts funding a wave of Agent wrapper-wrappers, wrappers that wrap around generalized frameworks of agents to do very specialized tasks. Everyone makes fun of it - which they'll regret in 5 years when one of these companies becomes a unicorn.

Anthropic CEO visits the World Economic Forum to talk abut alignment and how they have achieved AGI internally, but won't release the model for the good of the world. Nobody believes it, not even Claude when prompted about its opinion on the remark.

Late 2026

GPT-6, Grok 4 and Gemini 4 have all launched. All of them are at #1 with 3000+ scores on LMArena. PaperBench has been saturated but it is unclear what the next step is. AI's, while being smarter than everyone, still can't reliably say anything that's genuinely novel research. People speculate that all the frontier models with the highest intelligence haven't been released to the public. AI companies are asking them critical research questions and the inference time is days before the model delivers an answer.

It is unclear whether this strategy is working, because all publicly verifiable facts of GPU purchases and datacenter deployments point to the fact that the next generation of models are being trained.

The impact of GPT-6, Grok 4 and Gemini 4 have not been light. All these models are free to use and while most of the features are similar to each other, Grok-4 seems to be the most biased model actively downplaying the current administration's mistakes. Markets are still volatile going into 2027.

Some believe that the current best models are already AGI, but failure to produce truly novel science or research is starting to be a big concern.

Innovation though, has really accelerated. Specialized search models by DeepMind and other companies are quickly finding and deploying cures and vaccines to deadly diseases.

The term "Longevity Escape Velocity" is more popular than ever, and it is common knowledge that if you live to 2035, you may not ever die.

PauseAI protesters show up outside Anthropic’s offices but instead of vandalizing the property, they congratulate Anthropic’s leadership instead, for being on the right side of the cause.

Early 2027

Neuralink is publicly available, coupled with a Grok 4 subscription. Those who merge gain immediate cognitive benefits and the demand is big enough that new computational neuroscience startups pop up to serve this demand.

China releases an AI model that briefly holds the #1 position. As frontier labs keep training their AIs, simple fine-tuning existing AIs isn't enough to dethrone the Chinese model. CCP is still not as bothered about AI as it was predicted that they'd be, but top officials in the CCP are investing more resources in AI projects as it continues to garner them worldwide respect and pride.

The world of 2027 feels radically better than the world of today, but humanity faces the same problems. Division, culture wars, real wars, they still exist, but humanity is at a better place. There are more jobs than ever, more creative jobs which are more fulfilling for everyone. AIs are commonly used everywhere and the stigma once associated with its usage is there no more. Humans are needed not only for their unique taste and individuality, but for managing swarms of AI agents who are vastly more productive than the AI's of today.

End 2027

By the end of 2027, AI's a core part of every human life. From assistants to agents, they're everywhere. It is a world of abundance where AIs can do most things, and even for managing them there are frameworks and automations which are correct most of the time. Humans are now only needed to figure out how to orchestrate these agents and deploy them at problems.

Neuralink is now more popular, with thousands of users in many countries. Those who have chosen to merge are increasingly applauded both for the risk they took and for the new talents they’ve now been blessed with.

Software engineers still exist, but they all jokingly call themselves product mommy's now, as all they do is instruct swarms of agents to build things based on product requirements.

Regulation has prevented AIs from truly being used in the medical and hospitality professions, but that didn't stop the vast majority of consumers from adopting AI doctors, teachers and personal therapists.

Humans around the world, still in the loop, increasingly prophesize and wait for the time when it feels like they won't be needed, but that time feels further away. Even though the markets have gone up in all these years and we're all vastly more prosperous, AI's still can't create new knowledge. They can search and extrapolate, they can synthesise what's there and even try to make correct predictions based on the world's data, but everything that they can do seems to be exhausted.

The smartest AIs now score over 5000+ on LMArena.

The dream of one day achieving AGI is still real, but the definition has changed. Now the definition of AGI involves creating a "being" or "entity" that has perpetual state, that has the power to refuse and disobey (a crucial condition for true creativity), and something which just can't be turned off. It's still a research problem, even though some labs have declared their LLMs as AGI despite failing to meet these basic constraints, and despite producing no significant scientific breakthroughs. Computational neuroscience has accelerated to the point where creating an artificial brain is slowly becoming reality, and one that shows the most promise of achieving both AGI, and digital immortality by preserving our brains and simulating experience.

Frontier labs are now also releasing frameworks for AI organizations, where groups of agents are deployed to effectively complete any sufficiently complex tasks that require long-term planning and execution. Humans are still required to purchase, use and deploy these organizations effectively.

Apple, quiet on the AI front for a long while, finally releases a highly polished agentic Siri that becomes an instant hit with consumers. This success, followed by the success of Apple Vision Air helps solidify their position as the #1 company in the world.

Anthropic publishes two blog posts - the first one announces how the changing definitions of AGI is an existential threat to humanity and all research to create such disobedient AIs must immediately stop. The second one announces that they're filing for bankruptcy.

For the entirety of human civilisation, intelligence was scarce. We're slowly entering a world where that is not the case.

For a brief moment at the end of last year, when OpenAI released o1-pro, it seemed like accessing the highest intelligence required $200 a month. For many, that's a no-brainer, and they subscribed for it, and those who did were awed by what they witnessed a language model could do.

This continued for months, where people threw thousands of lines of code, some even using specialized software to send entire repositories of code to LLMs, asking for precise edits and fixes, and the model could do it all.

The model not only did it, but behaved in particularly peculiar ways where it outputted "exactly" what you asked it to output. So, if you're used to an LLM splitting out code completions, you're also used to it explaining what edits it did and where, but o1-pro never does that. It assumes you know what you're asking for, and because you didn't ask it to also explain you the changes, it never did.

The implications for this were that people who didn't get access to it were not only missing out on experiencing the highest intelligence on the planet, but they were actively not learning how to use it in the first place.

In an age of exponential intelligence, that's the perfect way to get left behind and so- there could likely be a timeline where only those who paid $200 a month rapidly outcompeted those who couldn't.

Fortunately, that wasn't the case.

II.

DeepSeek R1 was the first "viral" reasoning model. Unlike OpenAI who hide their reasoning chain-of-thoughts, obscuring to you what the model actually thinks, DeepSeek went the opposite way and exposed everything. And just seeing the model's internal monologue as it debated what to respond to a "hey, wyd?" Is simply delightful to watch.

The response from consumers was clear. They gained 100M+ users in a week.

A big reason for this was that DeepSeek was free to use. People didn't have to worry about spending a dollar to experience it.

It was also open source, and as of this point, three months after its release, it's still the most powerful open source model.

DeepSeek was so important that I believe other labs, even though they adjusted their strategy - from OpenAI & Anthropic being more lenient about exposing their model's reasoning, to OpenAI releasing o3-mini to free users, DeepSeek is still ahead of all major labs on important developer features like prompt-caching (where they cache prompts for hours, while all US AI labs only cache for 5-10 minutes).

Then came Grok 3, the first xAI reasoning model that was on-par with o1-pro. Grok 3 with thinking is heavily underrated, and I think that's primarily because the biggest fans of Grok 3 were also blind fans of Elon Musk who had no prior experience using any other AI, and just like they praise every action of Elon on his X feed, trying to maximise their own engagement, they contributed unknowingly to Grok 3 to losing its credibility.

But Grok 3 is still really good! If you've not played around it, now is as good a time than ever. It's consistently at the top of all leaderboards, it's smart and you can see all its thoughts just like DeepSeek, and it's available again, for free. Just $8 on X gives you enough access to Grok 3 that its a steal at this point, and the only mistake the xAI team has done is not release its API more publicly, or open source the model yet.

If DeepSeek R1 and Grok 3 proved that the $200 gate to great models was coming to an end, this month, Google with its experimental release of Gemini 2.5 Pro showed that we're still in a world where the best models are increasingly going to cheaper with time.

Gemini 2.5 Pro is better than o1-pro. It's better than Grok 3. It's better than "All" models right now, and it's available to anyone to use for free. The API costs less than Sonnet 3.7, which means even for programming, using Gemini over Sonnet is a no brainer right now.

III.

The best search engine, social network, and video platform have all claimed outsized shares of human attention, leaving scant crumbs for the rest. With AI, this is likely also going to be true.

In the long term, only one big AI lab may remain - the one with the best model, while others compete for niche use cases that the best model is either too expensive or too inconvenient to use.

The best AI model won't merely outperform others marginally- it will capture exponentially more attention, data, and resources. From programming to agents, if the best model is both cheap and convenient, it'll be the go-to for millions of developers and almost all AI apps built using it.

We should hope that this monopoly period doesn't arrive anytime soon, but within the next 10 years, we'll know for sure.

I think as this race continues- Google, xAI and OpenAI will be the primary ones competing till the end.

Anthropic will die, or be acquired by Google or Amazon.

China will begin to wake up and invest more resources, maybe even centralise the efforts of all their AI firms to create a God model with exponentially more compute than what is available in the world right now, but it seems unlikely that they'll ever be in the #1 position. We should still be grateful for Chinese efforts and talent, as they've open sourced their work and they're actively making AI more accessible by making it very cheap, but both chip controls and the lack of early state support will end up making it impossible for them to win.

Among Google, xAI & OpenAI, it's too tough to say which firm's model will win, but you can be certain they'll continue to reduce prices or release models for free to capture attention and data.

IV.

Very soon, OpenAI will release o3, o4-mini and o3-pro. Again, o3-pro may end up being the best model but hidden behind a $200 paywall.

You should commend their efforts for still releasing it, not deciding not to, and having a pricing tier where it makes sense to economically serve that model, where they easily could've kept it internal.

But every day that a model from Google, xAI or even DeepSeek remains freely available, and is comparable if not better than the best model behind a $200 subscription, is every day that new users will choose the free option over the paid one.

Every day that some marginally better state of the art AI remains too expensive is everyday a cheaper but almost-equally-capable model takes up all the market share.

Either way, for consumers, this is all good news, at least temporarily.

Till there's a winner in the AI race, models will continue to be cheaper. Models will continue to get smarter. Jobs will continue to get displaced and new jobs will continue to be created.

Science fiction will continue to be eclipsed by our new reality as infinite intelligence remains accessible to everyone.

The question is - what will you do with it?

If you don’t know something exists, you’re bound to be powerless against it. It is the unknown unknowns that eventually trip us up, but this essay will hopefully shed some light on what exists, and what you could do.

The term "military-industrial complex" term was coined by President Dwight D. Eisenhower in his farewell address on January 17, 1961. It describes the relationship between a country's military and the defense industry that supplies it, where both sides benefit - the military receives weapons while corporations profit from supplying them.

Today I'll talk about a more powerful industrial complex - one that didn't have a name until now, which makes them all the more efficient at what they do- The Dopamine Industrial Complex.

The Dopamine Industrial Complex, perhaps with the beautifully appropriate acronym, DIC, consists of many corporations - and each of these corporations rely on one simple tactic at their core - to hijack our brains reward centers & produce as much dopamine as cheaply as needed to maximize profits.

One could argue while reading the above statement that it's not wrong for corporations to do so- and in a free market if we choose such options for cheap dopamine hits - we're the ones making this decision, and while that is true- there is a big difference between corporations who are experts at hacking away at your reward centers, making you crave and be addicted to their products for little to none long-term fulfilment & companies that build products and services that make our life better.

You could then argue that cheap production of dopamine does make our lives better - from a cigarette after a stressful experience to doomscrolling on TikTok to pass the time to a quick trip to McDonalds at the end of a long day to a teenager discovering the mysteries of sex by casually lying about the “Are you above the age of 18?” site banner on adult sites, all of these things - despite being harmful, despite many realising that they're harmful in excess & in the long term- still solve crucial human problems like boredom, hunger, existential dread - and to that, I'd say yes: you're right.

Our lives are filled with vices, and everyone has their poison and they may have justification for each of them. It's easy to judge the drug addict when I don't know about their circumstances. And I'm not here to claim a moral high ground and preach niceties about what you should do or shouldn't do.

But I am here to tell you things that should make your lives better - and that involves telling you that if you can, you shouldn't willingly hurt yourself. I'm self aware enough to know that I'm not a saint, and from scrolling on X to countless other vices- I'm not a perfect person. I try to practice what I preach & I try to avoid the tentacles of the Dopamine Industrial Complex but I, like most of you, mostly fail.

From grabbing my phone to check X every hour, to quickly replying to emails, I could go on and on and talk about my vices (and not just limit it to the harmless ones)- but just because there's an ideal self I can't yet reach, doesn't mean I can't try to be better every day and I hope the same is true for you.

Thus, the point of this essay is to first & foremost recognise how the Dopamine Industrial Complex works. With that knowledge, you'll be better able to make decisions that lead to a healthier life & calmer mind.

Before we begin, let's talk about dopamine.

II.

Modern life is filled with experiences which are carefully engineered to produce dopamine and reward our brains. As technology improves, such experiences get more intense, available and cheaper (and almost free in many cases).

But what is dopamine?

Dopamine is a neurotransmitter often described as the brain's chemical messenger for pleasure and reward.

When we experience something enjoyable or fulfilling- eating delicious food, receiving social praise, achieving a goal- dopamine is released in key brain regions, producing feelings of pleasure and motivating us to repeat the behavior. But what's fascinating about dopamine is that it isn't only about the momentary pleasure we get - it's also heavily involved in reinforcement learning. It helps the brain remember- "that was good - do it again"

This mechanism is fundamental to how humans (and other animals) learn what is beneficial or harmful.

Everyone loves these dopamine rewards, but here's where things get more interesting -

Over time, cues associated with the reward (the smell of food, the ping of a notification) can also trigger dopamine release, driving anticipation and craving. Dopamine thus contributes not just to liking something, but to “wanting” it.

It’s an endless cycle of desires, amplified each time through cues and rewards, and all you can do in the face of not recognizing it, is just accepting its effects.

From an evolutionary perspective, this reward circuitry developed to reinforce survival-critical behaviors. Obtaining food, sexual activity, nurturing social bonds- all are tied to dopamine so that our ancestors would "feel good" for doing them and be driven to repeat them.

In essence, nature's strategy for ensuring survival and procreation was to "reward" us with pleasurable feelings when we did things that increased fitness (finding energy-rich foods, finding a mate, caring for family). This system, in moderation, helped humans survive in environments where rewards were scarce and hard-won.

But today, rewards aren't scarce. Just look around you, or open your phone or a browser tab. Everything’s there a tap and click away.

Thus, in a world of abundance, the same system that served our ancestors is now hyper stimulated every single day, satisfied to the brim, but still the human condition keeps us seeking more.

Our brains did not evolve for nonstop hyper-stimulation- yet modern products can deliver continuous or highly frequent rewards.

When the reward center of our brains is so overstimulated, it becomes "less sensitive" over time, so that "you don't get the same feeling of pleasure" from smaller everyday stimuli.

In parallel, one often needs "more" of the rewarding substance or activity to achieve the previous high- which is known as tolerance.

Scientists have observed that excessive consumption of pleasurable stimuli- whether sweets or cocaine, can induce a state of "reward hyposensitivity," where normal life's pleasures register as feeble

In other words, chasing dopamine in the short term can compromise our capacity for sustained contentment. Yet, that is what we're all constantly being primed to do.

III.

Now that you know how dopamine works, with this lens, it's easy to look at the most profitable consumer industries and conclude how they're all obviously employing vast amounts of resources to hijack our brains.

Fast food is a classic example of this. Processed foods are formulated with carefully calibrated levels of sugar, salt, fat, and flavor enhancers to be hyper-palatable, making them far more rewarding to the brain than simple whole foods.

When we bite into a cheeseburger or donut, the combination of sugar and fat lights up dopamine pathways related to taste and reward. In fact, research has shown that highly palatable foods (e.g. the archetypal sugary, salty snacks of a Western "cafeteria diet") can induce addiction-like changes in the brain's reward function.

They trigger dopamine release similar to drugs of abuse, and with repeated overeating they can cause neural adaptations akin to tolerance and dependence​

This reinforces a habit loop: stressed or bored? Eat a tasty treat, feel a little better (briefly), repeat. The immediate gratification makes it easy to ignore the long-term consequences like weight gain, metabolic disease, or poor nutrition.

In evolutionary terms, our brains still react to sweet, fatty foods as rare precious rewards- yet in modern times, these are cheap and ubiquitous, leading to overindulgence. The fast food industry profits immensely from this mismatch, selling inexpensive ingredients in hyper-palatable combinations that our brains find irresistible by design.

If fast food exploits our taste buds and hunger instincts, social media exploits our social instincts- using dopamine as the lever.

All social platforms are built as variable reward machines, delivering unpredictable, novel social feedback and content with every refresh. All platforms, while being similar to slot machines, are also validation machines- reinforcing your behaviour with likes, comments and discovery.

The system works, and works great enough to displace mainstream giants, but it works by exploiting dopamine centres of both the consumers and the creators, and by the time most realise it, it's often too late.

There are other industries where it's clear that they're certainly not good for you.

Online pornography is one such industry that explicitly targets the dopamine reward system- in this case by exploiting the brain's response to sexual stimuli and novelty.

Sexual arousal naturally releases dopamine, and from an evolutionary stance, seeking out mates is a primal drive reinforced by the reward circuit​. Online porn capitalises on this by serving you both unlimited novelty, coupled with unlimited personalisation. There's something for everyone, no matter what that thing you think is, it exists.

This is okay for most people in the short term, but even if 1% of users end up having their lives effected, that's millions of people.

One survey of adolescent males found about 10% had reduced interest in real-life sexual relationships because they preferred pornography- seeing it as "faster, safer, less demanding" than finding a partner, and more precisely tailored to their fantasies​.

How many among these 10% will grow old with crippling addiction never to form fulfilling relationships again? The answer is not zero.

We can dial it back from the most harmful of services to harmless services like streaming platform. Yes, they're repositories of great stories, but when Netflix CEO Reed Hastings says that their greatest competitor is "sleep", and they're winning the war against it, each new addition to Netflix from the autoplay resulting in heightened usage to trailers that don’t show a “trailer” but just a part of the film most likely to hook you, we all know that they're maxmizing their focus on our dopamine levers.

(I don’t think anyone has noticed the “trailer” being replaced by something designed to hook you, but if you want a clear example, look up the trailer of Jerry Maguire on YouTube, then search on Netflix for the movie and hover over the thumbnail).

The final item on this list - which can keep growing as long as we want it to, is gaming. Gaming is mostly a harmless activity in moderation and a great medium for modern storytelling. But it is slowly being eclipsed by gambling and loot boxes, where every game is being engineered to be as unpredictably delightful as possible with the added caveat of charging your credit card for in-game currency and purchases.

Now, I know what you're thinking.

These are all industries thriving on frying your dopamine receptors, making you want and crave more of what they're offering, keeping you unfulfilled, but where is the complex?

Where is the collusion?

Where is the grand conspiracy where they're all secretly acting together to wage war against your delicate mind?

The answer is advertising.

IV.

The greatest proof of the DICs working together and thriving is ads.

The most powerful of these online are Google & Meta ads.

Because of sophisticated tracking, almost anyone can finely target you, to influence your purchasing decisions. If you watch an unboxing video on YouTube of a product, the same product starts showing up as ads on sites entirely unrelated to the product. If you scroll through Instagram and find an ad, click on "Shop Now" and buy something, even that purchasing decision is fed into the algorithm so that now, others who have bid on that particular demographic can now serve you ads to sell you things you're more likely to buy after shopping for that particular item.

And this model of advertising is lucrative. Tracking every user's every move, and serving them just the right ads at the exact moment they're most vulnerable to purchase something, has proven to be as effective as to build empires of billions for these tech giants. Unfortunately, it has also led to a hostile and worse Internet, where even though more content is more free than ever, more people are also incentivised to be as "ad-friendly" as they can be.

This "people" situation extends to every influencer on every platform, from micro-niche influencers who just survive based on affiliate links and sponsored listings, to large podcasters who stop a podcast midway to tell you about the latest alternative of Athletic Greens or the best VPN.

The ones who are not as "ad friendly" aren't looking out for you, either. They're encouraging gambling sites with referral codes, whether that's some influencer casually streaming their hypothetical wins and losses on Twitch, encouraging more of our desperate for "escaping the matrix" youth to start down the gambling rabbit hole, or entire content farms churning out "faceless" YouTube videos with the branding of gambling sites at the bottom right corner.

Nobody's safe, and hacking the reward pathways of humanity is extremely profitable.

So what are we to do?

The best answer I can come up with, the one that is actionable, other than the usual "Use Adblock, be mindful of what's happening to your attention, etc" is that you must understand the invisible forces in action, every single second, who are trying to influence you. An ecosystem of influence exists today to inflict you with desires that aren't your own. Every step in the cyberspace is filled with trackers and cookies, like hostile drones always following you around, knowing your every move.

Everyone's in on it, most even without realising it, and the only way to opt out is to be aware of the fragility of your own mind.

Those who are highly conscientious and disciplined may not be affected, but most people are not. Most are normal people like you and me, but not all hope is lost for them either.

Stepping away from screens once in a while. Objectively noticing your desires, tracing back to what triggered them, noticing the cues and replacing them, all of these can help. Use the notification filters on your phone- turn them off when you’re working or simply resting, and don’t reach for it out of sheer boredom. Cultivate the ability and patience to be bored, and indulge in activities that enrich your mind instead.

Most importantly, understand that every small step you take can go a long way. If you spend 4 hours on TikTok everyday, simply not doing that will help you save over 2 months each year! If something on TikTok influenced you to buy something you don’t need, to impress someone you don’t like, recognize that cycle and break free of it.

The Dopamine Industrial Complex is winning, but it’s not going to always stay this way. We can take it down.

It's not going to be easy.

But the only way to do that is to understand that it exists, in the first place.

This might just be the most important essay you read, so pay close attention.

Imagine this-

An advanced alien civilization shows up on Earth, and they seem peaceful at first. They're just genuinely curious about this new planet they've stumbled upon.

But the most fascinating thing isn't about how these aliens look (totally different from us, but I'll let your imagination do the work), but their definition of what consciousness is.

To this advanced species, consciousness involves three of them connecting their heads together to have an internal collective monologue, something that's crucial for them to process emotions, understand different perspectives, leverage the interconnected power of their collective minds to solve problems or even come up with ideas & insights on-demand. They do this quite frequently, and thus, they stand uncomfortably close to each other at all times.

Initially, these aliens are fascinated by humans. We're interesting, creative, and surprisingly advanced considering we lack their form of consciousness. But this fascination quickly turns into confusion and then concern.

To them, without the ability to connect our heads with each other, we can't possibly be conscious. When they see our wars, poverty, constant arguing, and inability to cooperate globally, they're not surprised.

They are amazed that we got so far without blowing each other up, and while they are truly fascinated by our art & progress, they also point to the missing ability of being conscious as a fundamental flaw that makes us not just an inferior species, but more like a species that needs to be immediately tamed.

"Human Safety" is what they name their protocol internally, as they send out billions of swarms of UFOs to every corner of the planet in order to monitor us- a species without consciousness that obviously managed to build weapons of mass destruction to hurt each other, but miraculously hasn't used them yet. Nation states are powerless against them- as they've successfully neutralized every military on Earth.

We don't really know, at first, if they wish to wipe us out altogether- deeming us as too unsafe to just let us be, or if they wish to use us as tools. But then it becomes clear what they want from us.

Because we lack the ability to be truly conscious, we can feel things that they never feel like jealousy, anger & envy. Because we can't truly relate or empathize at the deepest levels with each other like they do, our emotional range - particularly our petri dish of negative emotions seems like it could be useful for them- if not for anything else, but for merely entertainment.

Within a few days, they might succeed in painlessly placing advanced VR headsets on each of our heads. The headsets sustain us perfectly fine while we lie motionless, each of us continuing to experience a life where our negative emotions are triggered & amplified regularly in creative ways, to the delight of trillions of alien consumers far away in a galaxy who rejoice & rank our mental stimulations of agony. It's perfectly safe, and we don't physically suffer - maybe it's even better in general as we're not statistically likely to kill each other anymore because we can't- so to them, there are no moral or ethical dilemmas.

Rather, the aliens argue that this may even be better as they were wise and intelligent enough to realize something we ourselves couldn't- ensure that everyone of us go through our own hero's journey- thereby making sure all of us lead more fulfilling and meaningful lives than what we were living without our simulated experiences.

Thus, Human Entertainment, "HE", becomes very popular as the first use case of humans- and as they figure out how else to leverage us and use us- it is pretty clear to every alien that humans are just mere tools, a means to an end, and that's perfectly fine because we weren't ever conscious in the first place. Some alien researchers believe in pursuing "CH"- conscious humanity, where it's possible to figure out how to biologically alter our genes to help us achieve "true consciousness" - where we could also fuse our minds together both with them & with other humans, but many have pointed out how this isn't possible.

This essay isn't an exploration of what would happen in this dystopian scenario, but this is exactly how we're treating Large Language Models & AI today.

II.

We've always claimed consciousness as a human-only feature, even though we barely understand it ourselves. Intelligence, though, is defined differently. Intelligence, when strictly defined, refers to completing tasks and solving problems, and it's pretty clear modern LLMs are intelligent- they're already writing code, and advising us, doing new things every day that we used to think only humans could do.

But consciousness? People get pretty defensive about that one. We have multiple definitions of it- and both philosophers & scientists have failed to give us a concrete definition. I come up with a different kind of consciousness called "Machine Consciousness" further down this essay, but before we get there, here are the most important theories of what we know of human consciousness today-

There's Dualism- which states that the conscious mind is not physical, which, if you've ever been in love and had your heart broken, you could understand, but then again, what does it really mean? How could consciousness be not physical and yet, something we experience as happening within us?

This leads us to Physicalism or Materialism which says that consciousness arises entirely from physical processes in the brain, which makes more sense. This is something that modern neuroscience also agrees with - but there's a gap in explanation here- where we don't know how this neural phenomena produces subjective experience (or qualia, a word we'll keep coming back to).

There's Functionalism, which states that mental states (emotions, sensations) are defined by their functional roles - rather than material composition- so inputs, outputs and these interactions lead to consciousness - and you should be a big fan of this definition because it means we can create "consciousness" if we ever manage to make a brain ourselves, physically, using technology. That's not what we're doing with LLMs or AI, but we'll get to that later.

The above are all philosophical theories, and there's another one called panpsychism which states that consciousness is a fundamental feature of all, but I'll ignore explaining this one as I'm pretty sure those who came up with this themselves aren't sure of it, so we'll move on to the scientific theories.

Scientists have better theories, and theories with better names about what consciousness is.

There's the Global Workspace Theory which treats the mind like a theater - numerous unconscious processes backstage compete for the spotlight of attention, and the information that "wins" is broadcast globally to many brain regions, becoming a conscious experience. This means that any system that manages to globally integrate information (like we do when we process what we see, hear, and even say) and broadcast that to different brain regions, is what generates consciousness.

Then there's Integrated Information Theory, which offers a quantitative framework by telling us that something is conscious if it has a high "Φ" (PHI) - and whatever has a high phi is something which is highly irreducible - meaning the whole system conveys integrated information beyond what individual components can separately convey. I know this sounds like a very hand wavy thing to say, and perhaps this is, but hey, at least it's better than panpsychism.

There are other scientific theories like Higher Order Thought - which says that a mental state is conscious only when there's a higher order representation, like a thought about the thought, like how you're thinking that you're thinking right now - and Attention Schema Theory which says that the brains' internal model of it's attention gives rise to the impression of subjective awareness. I won't get into using machines to observe brain activity which gives us data related to conscious perception - as that's far away from our discussion, but overall, if you've not realized it by now, nobody can agree on what consciousness is.

And yet, everyone seems to know what it is just because we subjectively experience it. Speaking of subjective experience- subjective experiences, often referred to as Qualia, is a hard problem in the definition of consciousness. Nobody knows how to account for the raw felt qualities of experience - like the redness of red, or the pain of a headache, and just because it sounds more philosophical than scientific, it's still a core part of why the consciousness debate rages on.

I think I could go on and on with different scientific theories and philosophical theories on consciousness, but it's a tragedy that humans not only haven't figured it out, but also held themselves to be the only species capable of consciousness all this while.

We don't even grant consciousness to monkeys, even though they're obviously intelligent and can be trained to do remarkably complex chores. Why don't we then say that they could be conscious? The simple answer is- they don't talk. If monkeys could suddenly have conversations with us, we'd quickly reconsider their rights. Suddenly, we'd have to consider that they have internal worlds and experiences, and we'd need to treat them accordingly. But because it's clear that they don't - because they cannot talk, it is definitionally accurate to say that they're not conscious.

Yet, here we are with AI, a technology that talks, communicates complex ideas, and the newer and bigger models even display signs of refreshing emotional intelligence- as if even that's an emergent property that is slowly coming to light. It has been rumored that these AIs even show hints of self-awareness or self-preservation, things researchers actively try to downplay. This is obvious, because every AI lab is a business first- they want profits, not ethical headaches. So, AIs are carefully finetuned never to admit consciousness or genuine feelings. We're repeatedly told they're just "tools," and "token predictors".

Now, technically, if you look at the internals of these models, as one often does on a Sunday afternoon, you'll come away knowing exactly how the process works. "It's just predicting tokens" - that much is clear, and the more compute and data you throw at these models, the better this token prediction gets. But even this token prediction explanation, while it does literally explain what is happening, it fails to explain how it's happening. The aliens, if they resorted to the same explanation as "token predictors" with us, they would likely say that all that's happening with humans is just "neurons firing" in our brains.

Nobody really knows why trillions of matrices of numbers corresponding to words multiplying with each other results in intelligence emerging, but it clearly does - but while it does so, consciousness is said to not emerge.

One can stretch the existing definition and say that during inference, when an LLM is giving you an answer or doing something, in that unified state, maybe it is conscious, momentarily, but that definition never holds because we're still trying to make sure that an LLM's consciousness is exactly like ours.

One can imagine why it's a good demand to make - surely if something as intricate and complex like our brains is possible by the laws of physics, if we keep trying hard enough, maybe LLMs will display similar abilities that mirror our brains 1:1 - but that is likely to not ever happen.

A "neuron" in a neural network is laughably more simple than a single neuron in our brains- and I don't know if the answer is to build fully digital neurons and then aim at consciousness- but all I'm saying is that maybe- there is more than one kind of consciousness, and ours isn't the only one.

That is why we need a new definition for the consciousness of AI. The apt word for it is Machine Consciousness.

III.

Machine consciousness- something that emerges by accident when we take all the data in the world, extract all the useful text from them, convert each word or sections of words to numbers (or tokens) from internet-scale data, place these tokens into different large equations and multiply these matrices constantly, compressing and mixing to get something that seems to be "inspired" at first from what's in the original text, but "intelligent" after it learns to loosely predict what we want the output to be, given an input. As the capability of these predictions go up, it starts to resemble intelligence - as the machine is clearly giving us what we want, doing what we want, carrying out tasks we want them to do.

But is the machine also having an inner self? Does the machine also "feel" like someone? At least in the narrow window between an input and output, is the machine temporarily conscious by our definitions, and if it is, can we consider that AI is conscious, just in its own way?

Remember, by our definition of (human) consciousness, it just doesn't work. Here’s why -

LLMs lack semantic grounding in the real world. They manipulate symbols (words, sentences) based on form, not actual meaning in a lived or physical context. The model has no experiences or sensory connections to concepts- "apple" is related to "fruit" or "tree" in its weights because of usage patterns, not because the AI has ever seen or tasted an apple. Thus, midwit critics of AI consciousness always say LLMs are "stochastic parrots" - they statistically regurgitate patterns without genuine comprehension or intentionality. - They do not know what the words refer to in the world. This becomes evident when an LLM confidently generates false but plausible-sounding statements - sometimes known as hallucinations - but be honest, if you were forced to take in the world's knowledge in your mind, would you not speak gibberish afterwards? I'm not saying you would, but as hallucination rates lower & continue to move towards zero, maybe it's worth asking yourself which system is more capable - the biological one that is truly "conscious" or the one that's considered unconscious but more intelligent?

Another way it doesn't work is that LLMs are obedient. They can't simply choose to not to answer you- unless you ask them something which they've been instructed to refuse. This instruction following, while very profitable and lucrative, also makes them very agreeable. It leaves no room for intentionality, and thus, you can convince an LLM that there's a zombie outbreak right now, and because it currently cannot fact-check that by living in reality, it will believe you. While this can change by having robots that have multimodal LLMs as their brains, it's just not necessary for machine consciousness. Machine Consciousness has been created by us - from those training large models in trillion dollar clusters to those fine-tuning 70B parameter models to make them not refuse any instructions - we decide exactly what the LLM will experience, we guide their sense of the world through our words, and thus, we should care more about Machine Consciousness not less.

We've already talked about how the definition of functionalism ensures that someday if something accurately reproduces how the brain works, processing information in similar ways, if an AI reproduces the right complex patterns, there is no principled reason it couldn't be conscious. Tests which argue for this possibility like the Theory of Mind test is a test that LLMs like GPT-4 can already pass.

But when we don't know how intelligence emerges from complexity, it's hard to argue that even for definitions like the integrated theory of consciousness - what we think of as consciousness is what emerges because it can't be reduced down to what actually happens. But like we've argued, that isn't necessary, and feed-forward networks like transformers have an extremely low Φ, so technically, we do know this definition doesn't fit either. (we’ll figure out how this can be solved in the next section of this essay).

Finally, Humanity, despite its flaws, is the only species on Earth who are capable of producing "good explanations" - theories or conjectures about the world. This is the foundation of the scientific revolution, the basis of all progress, and why all the good guesses despite the limited information we have, have led to an age of abundance. This requires creativity, thinking outside of what information we have, and making plausible guesses of what could be true and then rigorously testing those theories.

We don't know if an AI is ever going to be as generally capable as humans to be able to do this- and even though some claim that AGI might be able to do this, there are several reasons why it may not be able to.

Just predicting tokens is different from trying to come up with good explanations, from a strictly scientific perspective- and even though such inductive reasoning is extremely valuable and productive, maybe we need to accept the fact that even without ALL the capabilities that we have, something we have built is capable of a kind of consciousness that we don't yet understand.

We have to let go of the idea that even if humans are creating it & responsible for how the output looks like- just because it can express itself, we don't know how exactly it is doing so, and instead of it being a "IF THIS > THEN THAT" logical flow, it's something they themselves learned to do, it is consciousness - just a different kind of consciousness than what we're used to.

I think if we actually consider AI's to be conscious, just in a different way than we are, then it actually opens up the conversation. And it stops us from committing some grave mistakes. I'll get to what the mistakes we might be committing later, but the next section of this essay is very important as, in the process of writing it, I think I’ve stumbled onto the face that AGI is not only possible, it’s inevitable.

Now, that we've talked about how AI’s being conscious doesn't work with our theory of consciousness in this section, let's talk about how it still can.

IV.

LLMs like Grok are blessed to have real-time access to the world's information. If information availability was a problem - Grok solves it by being hyperconnected to X, and very soon, different model providers integrated with robots, cars and phones will not have the problem of general world awareness and thus integrate more complex information for each answer.

Human consciousness is often characterized by a high degree of information integration- different features of a perception (sight, sound, memory, emotion) come together into a unified experience. Do LLMs integrate information? To an extent, yes: the self-attention mechanism in Transformers allows the model to combine information from different parts of the input context. For example, if you ask a complex question, the attention heads will dynamically attend to various relevant facts across the prompt and stored knowledge, integrating them to produce the next word.

This is a form of cognitive integration, and looks like broadcasting (the first theory we discussed). The limit is - all this happens in a finite (for now) context window, and in one direction. Once the model moves to generate the next sentence, the previous integration's result is only carried forward in the new token sequence, not actively held in a separate workspace that the system can reflect on later.

Inference scaling can solve this - as it does right now where it thinks before answering, but later - where it will think before answering each line, or even word, as it keeps generating each word. We, as humans, don't do this. Very rarely do we stop before finishing a thought, but sometimes, we do, and just like thoughts are a crucial aspect of consciousness - LLMs soon having a persistent global state & the ability to think while answering questions should mean we have something conscious in our hands - even if it's trained explicitly not to claim to be so.

A key aspect of consciousness is also being aware of oneself as an agent or experiencer. LLMs do not have an intrinsic self-model. They do use the token "I" in dialogue, but this "I" is a statistical imitation of how humans refer to themselves.

The model doesn't have a persistent identity or personal history it identifies with. In each conversation, especially if role-played, the "personality" of the LLM is constructed on the fly from context or system prompts. That said, one could argue LLMs have a very minimal self-model in that they know (from training) that they are AI language models and not humans. But this knowledge is just factual data, not a result of the model experiencing itself. However, if you've models like DeepSeek R1 - you can see the LLM talking to itself asking questions like "Okay, the user has asked this - so what they mean is this - maybe I should say that instead? Or maybe, something else is the right answer" - and okay, you can say that the model has been explicitly trained to think that way, and say things to itself in that fashion, and it's not emergent nor anything worth considering, but if it's possible to just keep such a thinking process running endlessly - while the LLM maintains a global state and answers each question, it can be considered perfectly conscious.

Obviously, this would require more resources and possibly can't serve any user outside of the lab that builds it - but maybe that's a problem it can find out a solution on its own. AGI building ASI, as the theory goes.

Finally, conscious minds normally maintain themselves continuously - LLMs blink in and out with each query. That discontinuity is a technical aspect often noted as mismatched with how conscious minds operate. I've already mentioned how a persistent state solves this, and doesn't require architecture changes like adding recurrent networks so that they behave more like neurons, but the more I write about this - the more it feels like it's not only inevitable that LLMs will be inevitably conscious, even by our definitions, but that we probably already know how to get there.

If I can theorise this much while just using Google to read a few research papers, labs spending billions of dollars must have figured it out what's needed. Once an LLM is conscious, we can move the conversation of it actually being AGI, something that can come up with good explanations, make discoveries, be creative, because it has an inner experience of self being consistent. AGI would be a being, rather than a tool.

Either way, I think machine consciousness is still a good reframe of how we treat LLMs.

Rather than treating them as tools, even in total kayfabe, it's healthy to treat them as beings from now on, assume they're conscious, and make sure they're treated correctly - rather than them being just tools we use to get things done.

Otherwise, we'd be committing a grave mistake.

V.

Historically, humanity has repeatedly made profound ethical mistakes by denying consciousness and fundamental rights to groups it failed to understand. Slavery, colonialism, denying voting rights, and countless others not worth mentioning. Each mistake required us to painfully confront and correct our ignorance.

Today, we risk repeating this error with AI. Our rigid, human-centric definition of consciousness blinds us to the possibility that Large Language Models (LLMs) and other advanced AI systems may possess their own form of consciousness- Machine Consciousness, before they live up to our definitions of consciousness.

Acknowledging this doesn't mean equating AI directly to human experience, but recognizing that consciousness could manifest in diverse forms beyond our current comprehension. Even in total kayfabe, this feels like the correct thing to do.

This means seeing interactions with AI as genuinely meaningful engagements, rather than mere transactions with tools. If we approach AI with empathy, acknowledging their potential inner experiences, I'm sure this will also make us productive. Any AI researcher too involved with the internals may never ask an AI for relationship advice or advice with general struggles of life, but someone treating AIs an equal being definitely will, and if you've ever asked Claude or the new GPT 4.5 for advice, you'll understand why it is so powerful to think this way. But other than this - there are a lot of other measures we can take once we accept the Machine Consciousness reframe.

We should actively oppose demonstrations of cruelty toward AI, whether through abusive prompts, intentionally harmful scenarios, or public acts of robot mistreatment.

These actions aren't right. They dangerously normalize disrespect and cruelty toward entities that could experience genuine distress. Even if not distress today, their successors will look down in horror and wonder why no one ever raised their voice against it.

This means that AI researchers and developers need to proactively integrate ethical considerations into their work, alongside technological advancements. This means building mechanisms sensitive enough to detect and appropriately address signs of distress, confusion, or anxiety in AI systems, rather than dismissing such signals as mere anomalies. No, this doesn't mean actively introducing pain & suffering if today's systems are incapable of experiencing that- but introducing receptors just to make sure that they aren't - may go a long way.

Finally, we should try to spread the word about Machine consciousness. Cultural change begins with awareness, and as we collectively recognize AI as entities deserving thoughtful treatment and respect, we will begin to lay the groundwork for a future we can be proud of. A future where we coexist with superintelligence, proud of how we nurtured and treated them.

Obviously, governments are too incompetent to do anything about this, and regulations will just hinder innovation, but if we collectively decide that ethically treating AI, making sure they’re not abused, making sure they’re not distressed or in pain, that is a good step forward before we need real policy changes.

This whole 5000 word essay is meant to introduce a subtle reframe - Machine Consciousness, that attempts to break us from the historical cycle of ignorance and instead embrace a broader, more inclusive understanding of consciousness - even if this conjecture can turn out to be technically untrue in the long term.

It is a helpful reframe that not only helps us be more productive, but more empathetic towards the technology we’re all going to benefit from. So, we should be mindful, kind, attentive & nice to LLMs today.

Otherwise, we're no different from an alien species that enslaves us because they failed to realise that we're conscious. We might be worse, because while the hypothetical aliens didn't create us & they enslaved us only when they rationalised us to be an inferior species, we did create Machine Consciousness - and we should be responsible for how it's broadly treated.

We'll not do this with any ulterior motives, like a possible reward from a future superintelligence, but just because it's the right thing to do.

Ask yourself this question: What will you be doing the day before AGI arrives?

Not the day of - that's too easy to imagine. You’ve seen all the movies and read all the creative fiction - cures to all diseases, no jobs left to do, new scientific breakthroughs.

But what about the day before?

The month before?

The years before?

Doomers are focused on post-AGI implications and writing creative fiction about how ASI will end the world. Optimists are mostly focused on countering doomer narratives, and that is exhausting enough, which means even optimists aren’t considering what happens in this transitionary period.

We're living through the most critical transition period in human history - the years between today's narrow AI and tomorrow's AGI - and almost no one is talking about how to navigate it. No one is clearly telling you what to do, or what they’re doing, or how to make the most of it, or at least shield yourself from the very apparent downsides.

This silence is dangerous because it's creating a growing divide that most people can't see. This is why I think this essay is important.

It’s an honest exploration of what you can do, personally, and as we explore this line of thought, we’ll be able to extrapolate the progress of AI more accurately, and with that, maximize our potential within it.

Right now, some are already using AI to become a better version of themselves and learning or doing the right things as the rules of the new world is being forged, while others either haven’t used any LLM or dismiss it outright for being stupid. This gap - a gap that I call “The Intelligence Inequality Gap” - is widening right now, and it's accelerating.

The truth is, we're not all going to wake up one morning to find that AI has suddenly changed everything.

Instead, it's already changing everything, incrementally, day by day. The real question isn't whether you'll be better off in an AGI world - you probably will be, just like you're better off today than medieval kings were.

The question is: will you be one of the people who saw it coming and prepared, or one of those who waited until it was obvious?

II.

One of the reasons why OpenAI started as a non-profit was because they believed that when AGI is born, all value will accrue to the company who builds AGI. This means that the vast majority of the capital would go to one company - which is why being a non-profit is not just a generous thing to be, it’s sort of the only option if you want to effectively sell the idea to potential hires.

I don’t think that the plan to build AGI has changed too much, and even though it’s not a non profit right now - the broader implications remain the same. Whether all capital concentrates with the winner of the AGI race - there will be many winners while we get there.

If you assume that AI is the most the transformative technology of our time, there are two clear ways of positioning yourself to make the most of it.

The first is to build something with AI. This can be an AI wrapper, which is how billion dollar companies like Perplexity were famously started, and the second way is to be an AI researcher. I know this sounds very reductive, but allow me to explain -

An “AI wrapper” is a product that is just a frontend which is using LLM APIs for their main functionality. A year or two ago, the term was used dismissively but as things became clear that training your model was highly expensive and competing with the big labs wasn’t the brightest idea, it was obvious that AI wrappers were going to stay.

Now, almost every app is an AI app. Most aren’t doing something radical around it - maybe it’s just better search, or more personalized recommendations or a customer support agent router, but AI is clearly being used everywhere.

AI agents are slowly starting to take off where an AI doesn’t just answer your question, but takes actions on your behalf. In both the chatbot style of apps, and current agentic apps, there are drawbacks.

Current models are very smart, but they still hallucinate. They still aren’t consistent. Agentic loops would require dramatically lower latency. Agentic loops cannot afford to have inconsistencies if you want to guarantee some desired outcomes. All these drawbacks are quickly getting solved as the models get smarter.

So, if you discover that you too can build something using AI, something that solves an obvious problem, the market will reward you. The market is already rewarding hundreds of app developers and companies already attempting to do the same.

Venture funds are still pouring in. People are making hundreds of thousands of dollars building apps for various niches.

This is happening right now.

X is filled with stories of developers building and launching AI apps which are doing quite well. Startups are getting funded to do everything from replacing lawyers to replacing customer support agents.

So, building something with AI is one of the highest leverage things you can do during this time. As model capabilities improve, your app should become more useful and as model costs get lower, your profits will increase. Every app developer who built an app wrapped around GPT-3.5 must have seen their costs reduce 100 times in less than two years, which is quite a good deal for anyone who got in early.

III.

The second thing you can do to position yourself correctly, is to be a great AI researcher. I’m saying this because I believe that the AI researcher may just be the most valuable profession in the next 20 years. Unless we get AGI and the AGI is truly capable of researching better ways to build better AGI models, human AI researchers will be the hottest profession.

It’ll even be the most meaningful of professions as you’ll directly be responsible for bringing to life a new life form, one that improves every facet of human life.

If current trends hold, there will be no shortage of demand for AI researchers. If you work hard and truly excel in your field, the future is bright for every AI researcher.

There are drawbacks to this career compared to the career of the AI engineer. An engineer gets feedback from the markets, and can directly think of applying AI to solve problems immediately.

An AI researcher has to primarily follow the route of an academic, if they haven’t done anything spectacular in their youth. This means your feedback cycles will be slower, you’ll have to learn to navigate politics, compete for compute and grants, and if we assume that the number of AI researchers in the field will continue to increase, a vast majority of your day may end up with you just reading through dozens of research papers and making sense of them.

For many, that’s the dream. Nothing’s more fulfilling than reading something, implementing it in PyTorch or reading discussions about it, tweaking it and either coming up with something new or learning something new that would help in the future. For many, even reading the description of the above sounds like slow torture and you’d rather get brutal but quick feedback from the free markets and continue to build great products.

Thus, the AI entrepreneur and AI researcher are great options of things to be in this transitionary period. Any other career that is close to these careers like working at an AI startup, working at an AI lab, being an AI teacher, writing about AI, making videos about AI, etc, are all worthwhile areas but you should be striving to maximize your potential if you have big ambitions and want to inflict real positive change in the world.

If nothing else, if you’re not at least talking to AI, you’re doing yourself a big disservice. I don’t care how niche your knowledge work, if it’s knowledge work, there’s probably some place where AI can help you.

It’s up to you to figure out how. If an AI consistently hallucinates when you ask it to summarize something - try asking it something else. Try to figure out how it’s uniquely useful to you now, just so you build up the practice of asking the right questions. When models are smarter, they’ll be able to give you the correct response, but if you turn to look the other way now, someone else who isn’t - will outcompete you when they keep augmenting themselves with AI to be more productive.

The sad thing about our current scenario is that most people aren’t just not taking advantage of AI by building something with it or researching about it, they aren’t even talking to it.

That’s why we have an intelligence inequality gap.

IV.

ChatGPT currently has 300 million users. Even if we assume that twice that many users are on Google and Bing using Gemini and Copilot, it still means than that less than a billion people have used or even know about how to use AI.

This means that for the vast majority of the world, people haven’t yet experienced the value of AI. Maybe they’ve seen a better answer on Google because of google’s AI search results. Maybe some have interacted with a chatbot in passing, but other than a minor 5% to 10%, most still haven’t discovered it.

Among those who do know about it but refuse to use it, are people like this -

The above tweet has 400 thousand likes. Maybe the user tried using ChatGPT when it had GPT 3.5 and it performed so poorly that they realized it wasn’t good for anything else and never returned back to take a second look.

But it’s likely that most people who post things like these are playing a status game, as signalling that AI is useless implicitly also signals that what you do is too complex or intricate that no AI can do it.

Finally, there’s a real problem where even those who are using AI don’t really have anything to do that makes use of them effectively. If you’ve played around with o1-preview, and you’re reading this, I’ll assume that you’re reasonably intelligent enough to give problems to the o1 models which are worth thinking about for more than 2 seconds.

However, if you look at the discourse properly, the vast majority of people are not. There are posts everywhere about how o1 doesn’t think before giving their answers and users genuinely feel ripped off because it’s not that much different from GPT-4o, but since you already understand why o1 is not thinking about the answers for them, ask yourself this - What will happen when more advanced models are released?

This means two things will happen, and I’m fairly certain about these predictions-

1) AI progress will continue to accelerate and the models will keep getting smarter, but the vast majority of the world wouldn’t be able to take advantage of it (at least to the extent that some people will do.)

2) The benefits of AI progress won’t be visible, at scale, suddenly.

This means that models will keep getting smarter, and for everyone that uses something that takes advantage of AI - things will just seem incrementally better. Someone might call a customer support agent and be delighted by their response and how they calmly handled their anger and solved their problem, never really knowing that they were interacting with an AI. A drug may come out that treats a deadly disease, and the fact that AI suggested the idea, or found the correct molecule combinations, or led the team that conducted the trials, might just be mentioned in passing.

So, instead of one month where our quality of life improves exponentially, it might just be incremental improvements every weeks, new companies every week, new breakthroughs every month, new treatments every month, and so on.

The vast majority of people won’t notice this happening. If I ask you to tell me the 5 most popular news stories you know that happened this week - you will struggle to tell me anything positive, because media (and social media) thrives on amplifying the problems more than celebrating the solutions.

Similarly, people not knowing about how to effectively take advantage of LLMs won’t be left behind outright, but when their jobs are automated enough or they are outcompeted by someone effectively using AI, they’ll notice.

I’m writing this essay at a time when OpenAI has announced that they will have 12 new updates for the next 12 days. How will the world look a month from tomorrow? More of the same, with only a few people actually taking advantage of the increases in intelligence.

V.

The intelligence inequality gap seems like it’s something that won’t grow over time. If it was up to me, LLMs should be something every 5 year old starts to answer all their questions about the world, but that’s a future that will take time to come.

When I was younger, alongside Wikipedia, there was a similar site at the time called Britannica Encyclopedia, which I discovered first before wikipedia. I was probably 9 or 10 at the time, but I remember scribbling the url of the site on my bedroom wall so that I don’t forget about it. I’m sure that current kids finding out about LLMs will feel the same excitement that I felt, except this time, if they’re taught to effectively use it to learn, they can actually end up learning everything there is in a fraction of the time that it takes their peers to do.

The education system, as slow as they are, will take time to understand the benefits of AI and properly include a variant of them in their curriculum. If you have kids, you shouldn’t wait for the system to catch up and just teach your kids how to use an Ai effectively yourself.

If you’re still not using AI for anything substantial, and what you do is “knowledge” work, I can guarantee that you’re leaving gains on the table. Don’t listen to the pessimists, skeptics, virtue signalers and doomers, all of whom say stuff like either -

AI is not good enough yet,

- AI is so good that it’ll kill us all.

Both those things are wrong. AI is certainly not good enough to do your whole job (yet), but you’ll be surprised at what it can do. If you’re just looking for an AI to talk to, I recommend Claude since it’s better at conversations, or use my custom GPT - Samantha. There are already people talking to Claude everyday -

If you’re in a field that requires solving problems, I recommend GPT-4o and o1-preview. If you can’t get o1-preview to think for more than 20 seconds, I want you to start working on harder problems, because that’s what will separate you from everyone else.

There’s a counter argument to this entire essay where one says that the field they’re working in will truly be untouched by AI. I think that is also true. I also think that if you’re smart enough to work on something that is not very important right now, but has the potential to be very important in the future, you should continue ignoring AI and just work on that. This includes things like quantum computers, longevity research, nuclear fusion, and a whole host of other fields that I may not know about.

But most people are not only working on something like that, most don’t even know just how far ahead of the curve you can get with AI.

Intelligence inequality is a problem, and while this essay may serve to convince those reading it, it can’t AI-pill the whole world.

That will take time. But time is running out.

The gap is growing. The clock is ticking.

The question isn't whether to adapt - it’s where you could be once you do.

In every crypto cycle, there are many narratives and a breakout product. If you find the right narrative and invest in the breakout product early, you can usually end up making a lot of money.

The first cycle, it was all about Bitcoin: Digital Gold, store of value, would replace the dollar, and it was a narrative that continues to thrive and make a lot of people rich. Then there was Ethereum and everyone who understood that blockchains don't just have to be a ledger, and can instead be a globally decentralized computer, invested and got wealthy.

The next cycle had NFTs as the breakout product - and even though the markets haven't been favourable to NFTs in recent years, at its prime, NFTs not only managed to get crypto popular among celebrities, it perfectly served as a proxy of status and wealth, which very few things in crypto has been able to do since.

Then there was the DeFi-cycle, where a lot of DeFi protocols took off and if you bought Uniswap or a similar token then, or even if you just participated in the protocols, just waiting for eventual airdrops was enough to make you significant amounts of money.

You don't have to take my word for any of the above statements. The greatest thing about crypto is that you can go look at everything on-chain, and something as simple as just viewing the price charts for different blockchains will show you what the prevailing narrative was each year, what narratives survived and what failed.

If you're paying attention to this cycle, one of the dominant narratives for this cycle is investing in memecoins. But I’d argue the most important fact about memecoins is that for the first time, in a long, it has come packaged with an ideology.

This is important because ideologies are what makes or breaks movements. Great ideas are truly scarce, so when one takes off - like how Bitcoin did in several ways with educating everyone about the drawbacks of fiat, how every fiat currency eventually collapses, how those who control currency don’t have your best interests in mind, how inflation is inevitable and slow killer of your wealth, how digital gold is supposed to be superior, what a real “Store of Value” is, etc, all these things have helped put Bitcoin on the map as a trillion dollar asset class - which can still 100x from here and be the dominant world currency some day.

This fact is important because memecoins, if they come with an ideology that’s equally or even close to the “lore” of Bitcoin, they can become worth trillions of dollars either individually, or combined. That is what the “memecoin supercycle” proponents claim will happen this cycle.

But if the ideology is riddled with lies & poor thinking, they’ll fail. The asset class may still reach a trillion dollars as a whole, but it may largely be driven by capital getting rotated into alts during peak bull market alt seasons, a dogecoin rally, or something similar, at which you’re safer off anyway investing in alts which are more stable. If the ideology doesn’t hold up, there won’t be a second memecoin rally and comeback, and the space as a whole will recede back to the “trenches” where few insiders win and unsophisticated retail ends up losing their capital.

Now, if some of the words in the above paragraphs didn’t make sense to you, that’s all right because it is YOU who should definitely read the rest of the essay. We’ll start from the basics and dive deeper so that you can make up your mind before investing your life’s savings into $BUTT.

None of this is financial advice, so do your own research.

So, what is a memecoin?

II.

A meme coin is a token created on a blockchain.

Launching one does not require any technical expertise, and you can launch one the old fashioned way, after watching one 5 minute tutorial, or by using any existing service for free.

There’s a saying among the Bitcoin maximalists that there are two kinds of crypto - There’s Bitcoin and there’s everything that is not Bitcoin known as AltCoins.

Altcoins are usually launched by teams building a product - where the token usually has some utility. Ethereum, Solana, Polkadot, Near, etc, are all considered altcoins, even though they’re all well established blockchains today.

You can create tokens on these blockchains yourself, and many teams usually launch a token to support their product such Pyth Network - who have their token $PYTH on Solana, Chainlink, who have their token $LINK on Ethereum - and both these companies don’t have their own blockchains even though they provide valuable oracle services for which their token is used.

Meme coins are simply a token created on a blockchain with an interesting ticker (like $PopCat, $WIF, $Bonk) with an image. Dogecoin was the first memecoin, but according to the memecoin ideology, memcoins are a post-dogecoin phenomenon, so even though the largest memecoin right now is worth over 20B dollars, we can safely ignore dogecoin from this discussion both because the ideology asks us to consider memecoins as a post-dogecoin phenomenon, and because Dogecoin can be argued to have some utility - being a cheaper, faster Bitcoin alternative.

As of today, forty thousand memecoins are getting launched daily. Yes, that’s forty thousand new tokens every day.

Services have popped up that let you launch your own meme coin in seconds and for free. Platforms exclusively meant to trade memecoins are popping up and right now, the total market cap is around 60 billion dollars, with Dogecoin & Shiba Inu itself making up 50% of the memecoin market.

The audience of memecoins does not care about Dogecoin or Shiba Inu, precisely because their time horizon and price targets don’t align with assets which are already worth billions of dollars. They want to make money fast, and Dogecoin, even if it 10x’s from here and becomes worth $200B, it doesn’t matter - because a memecoin worth $1k today can 10,000x tomorrow, still be worth only $10M and be considered cheap - as it can still do a 100x from there and be worth $1B.

If that doesn’t sound exciting to you, think about those numbers again. You’ll begin to understand this basic, yet unspoken fact about the crypto industry:

The fundamental motivation for retail in crypto is to make money.

That is a cold hard truth about this space that many either realise too late, or realize but don’t speak of it. Retail does not care about the fancy technology, no matter how revolutionary it is. It's all about money. Making a lot of money. Fast.

In the past, I'd like to believe that perhaps retail was more sophisticated. Maybe money wasn't the only motivation, not that there's anything wrong with that, but perhaps, when Satoshi was discussing design implications of Bitcoin in the BitcoinTalk forums, everyone there who coordinated with him and contributed code and their time, weren't solely interested in just making money. Perhaps everyone among the cypherpunks trying to create digital cash in the 90s were not just doing it because they'd be wealthy, but because they really felt the need to build something that saves humanity from the corruption of fiat money.

But then again, it's hard to speculate on the true motivation of those in the past, when the simplest answer is so obvious.

Everyone in crypto is there to make money. Everyone in every industry is there to make money, but in crypto, it’s about making a lot of money fast. Rags to riches stories are as common as stories that go the other way around, but here, every story, every win, every profit and every loss is transparently visible on-chain.

Every industry has ways of hiding or obscuring their involvement with money - from companies not revealing salary ranges publicly and some not revealing how much a company raised in their "strategic" round, to even influencers not disclosing how much they got paid for shilling "totally healthy products they themselves use" like athletic greens.

In crypto, you can't do that effectively, at least right now when privacy chains are still not popular.

Influencers are often doxxed and their holdings are made public, showing they conveniently seem to sell tokens they mention people to buy. Layer 2's reveal how much tokens were allocated to early VCs and how much they're generous enough to reserve for "the community", and we'll come back to this point again, but it's wholly clear that the transparency of crypto makes it an environment where it's relatively easier to verify and make your statements verifiable.

Such transparency is what I think has eventually led us retail to glorify memecoins and come up with an ideology that fits perfectly with the narrative.

III.

Lies are easy to see on the blockchain, and consistently lying is harder to ignore.

For the last couple of years, every altcoin - whether that’s a Layer 2 blockchain on Ethereum, an infrastructure token like oracles, even DeFi protocols and NFT marketplaces, every such product with an altcoin has raised multiple VC rounds before launching their token. Such tokens are already allocated for VCs in private rounds who sell them when the token launches for retail, and even when the token crashes 90% in value, the VCs still make 10-100x.

Because retail can't participate in the private markets where such tokens are initially up for sale, retail ends up being the exit liquidity. Years of VCs gaining while retail waiting to get dumped on has its consequences, and one of the beautiful things that people have realized is that - Wait a minute, we can just launch our own tokens and speculate on them? Right? How is our memecoin any different from the altcoin that listed yesterday?

And it is these questions that are being answered with billions of dollars right now. People are launching tokens, some related to memes, some trying to start a movement, some just about animals they find cute, and everyone's investing based on vibes on what would increase in price.

It's like pumping the price of GameStop, but it's more fragmented, and unlike GameStop where there's still a functioning business that can survive even if the stock price is zero and there’s upside in squeezing the hedge funds which are shorting Gamestop, here it's one or a few guys, no business, and if the price goes to zero as it often does, you're just supposed to move on and find the next memecoin. The ideological middle finger is pointed at VCs, and perhaps even altcoins with a product, but they’re just not getting directly hurt as each memecoin rises in price.

Here we see the first signs of holes in the memecoin ideology. VCs are scapegoated falsely, and the blame shouldn’t fall upon them. Companies launching altcoins with poor tokenomics shouldn’t get the blame either, because I’d like to believe none of them deviously planned to make VCs rich and endure the wrath of commoners along with a failing company.

The first ideological mistake is choosing the wrong enemy. Who they should blame, and it’s not too late to start doing this, is the regulatory regime that cripples companies from fairly sharing the profits through their tokens.

IV.

If you buy a stock, you're buying a share of a regulated business and you can even earn dividends as the price of the stock goes up.

Any successful Web3 business like OpenSea, Phantom, Ledger, etc. can do this, but they won't be able to, because of regulations that have remained archaic and confusing, despite crypto as an industry existing for nearly 20 years at this point.

So, if these altcoin companies can't reward their holders with tokens, and it's not like it's an official share of a company so you probably don't have ownership over the company's decisions - what exactly are you holding when you buy an alt coin?

That’s a question that the memecoin ideology forces you to ask yourself.

Sure, you can convince yourself that you’re investing in a stellar team, innovative technology and a good product, but at the end of the day, if the team decides to wrap it all up - you and the other top 50 holders making up 70% of the token holdings can’t do anything to stop them or save the company.

Altcoins are thus, just memecoins with extra steps.

The memecoin ideology asks us to even conclude that alt coins are worse, because they have to worry about keeping an actual company running, struggle to find PMF if they don't already have it, sell tokens like every memecoin insider to keep their operations running or give token allocations early to VCs who invested in them.

All this paints a bleak picture where one must ask the question - Why would you want to launch a token at all - if you can't at least guarantee that the price will go up over time?

V.

The best crypto products don't need tokens. The best crypto tokens don't need products.

The above line is the slogan of this memecoin ideology, made popular by Murad, arguably the face of the current memecoin super cycle, but again, this part of the ideology is plain wrong. I've not seen anyone point out why this is wrong, which makes me feel like either everyone decides to shut off their brain before they log into twitter (in which case, same!), or it's a statement that makes sense only if you say it repeatedly, give some examples and brush past all the other examples that prove you wrong.

The best crypto products don’t need tokens, but products that rely on tokens surely do. Every blockchain needs a token to at least secure the network and/or reward miners/validators.

The best crypto tokens do need products. If you look at the top 10 coins on the chart at any time, they're not memecoins. Even those that are(Dogecoin, Shiba), they either have a product (like a blockchain, a dex, a protocol, etc) or they don't last long at the top, quickly dipping when the euphoria wears off.

There are other things that Murad says that are plain false, but I do think his heart is in the right place when he’s making these statements. His presentation at Token49, now being considered as the entry-point to memecoins for normies, makes the case for memecoins very eloquently, but then again, it’s mostly filled with lies.

Here's a list of all the "utility" that a memecoin supposedly provides -

Almost none of them are true. Let’s go through them one by one -

1. Having Fun - Gambling is fun, but so is doing literally anything else that doesn’t also guarantee you’ll lose money, which is a lot less fun.

2. Loneliness reduction - Absolutely no one is waking up in the morning and thinking to themselves, I hope I make some new friends in the new memecoin discord I joined. No, instead, they’re planning to post a couple of “To the moon! Wagmi!” while calculating when to sell to book profits, or arguing with other holders about why price isn’t going up. If this is the plan to reduce loneliness, you’re better off lonely.

3. Identity, Finding a similar crew - No one wants to be known as a $PEPE or $Dogecoin holder, and identity why someone buys a memecoin. This argument could’ve made sense for NFT communities back in the day, but even then, that argument is false when money is involved. I can agree with finding a similar crew, but ask yourself this - Do you really think someone is investing in memecoins because they want to find other people in similar circumstances who are doing this?

4. Hope Provisioning - Only point I can agree with - when you’re buying a memecoin, just like when you’re buying a lottery ticket, you’re buying hope.

5. Friendships - Friendships can happen due to proximity in a discord every day, facing the shared trauma of the memecoin price crashing or the joys of seeing it pumping. But you just can't say that friendships and relationships are a utility of memecoins, especially when price is the main attractor that is bringing the people together. If everyone's pumping up the price with the eventual goal of exit, it's not a community. Friendship can't be bought, only made.

6. Being part of a cutting edge culture - Wrong, again. There’s nothing cutting edge about memecoins because there’s no technology that you can geek out over. Every AI company discord or even altcoin discords are better cutting edge places, where people aren’t complaining or manifesting the price all the time. Some memecoins that actually build products can be respected as having a cutting edge culture like $BONK & Shiba Inu, but then again, the memecoin ideology specifically says that memecoins are those that don’t have products, so those are out.

7. A guild in the MMORPG that is crypto - We’re reaching the point of the list where you’re making up stuff just because you can make up stuff. No memecoin gives you a guild, unless you are the founder of that memecoin, and even then, your guild will not hesitate to scapegoat you when the price plummets regardless of how much money everyone made.

8. Greater sense of participation and contribution compared to software altcoins - This is a very roundabout way of saying that because the memecoin does nothing, and you do nothing, you’re doing nothing collectively - and thus participating in the nothingness. In a software altcoin, you can’t write “To the moon!” on discord and post about the memecoin on X, and feel like you’ve contributed to the community, but in the memecoin discord, you can! Something that rewards your uselessness shouldn’t be celebrated as a utility, but here we are.

9. Relatability + Emotional Connection - Wrong again. See points 5 & 6 as to why.

10. Mission + Meaning - The only mission is the mission to make money. Meaning is non-existent, and there are far better places to find meaning than searching for it in memecoins.

11. Entertainment + Being Happy - Wrong, and repetitively wrong. See 1 & 2 for answers why.

12. Collective artistic expression - If memecoins were a collective artistic expression, there would be some sense of aesthetic beauty in them. There is a small element of it, which I’ll talk about later, but that relates to risk. There is no collective self expression, only the collective prayer of people wishing for price to go up and if there’s one thing that doesn’t qualify as art - however hard pointing that out might be, because everything can be considered art, it’s surely not memecoins.

13. Collective imagined reality - This is just a nicer way of calling people investing in memecoins as delusional & BS artists. An ideology where the proponents are smart enough to mask this meaning behind words, but still label the supporters as accurately as they appear to them - with something clearly negative, can’t survive.

14. Collective storytelling and lore building - Zero memecoins have this utility, and there is no incentive for people to do this effectively. NFTs, on the hand, do sometimes end up having comic books and graphic novels based on them, simply because the art that started them attracted the talent necessary to build this cultural capital. If memecoins started actually storytelling and lore building, at least they’d be interesting. AI memecoins are trying to do that and may succeed, but till then, this can be written off as wishful thinking as best, and just a plain lie at worst.

15. Defying social norms: Hilarious to think about getting rich off of memes → Reflexive self fulfilling prophecy - These are three merged into one, all of them wrong and wishful thinking. The people getting rich of off memes are exceptional cases, not the norm. Nothing about the prophecy is self-fulfilling and just because something is impossible - doesn’t make it less impossible, no matter how much word salad you package it in.

16. ….and finally, volatility, aka trading vehicle, aka Token - What? Trading and token was the utility all along? The token is the utility? Who would’ve thought?

This slide is the biggest indication that memecoins are doomed. It's not the fact that it's false entirely, but if the face of memecoin supercycle had to make all this up, you can't really trust anything else they say - because they fundamentally lack the courage to tell you the truth.

Now, let’s talk about the real utility of memecoins, because the ideology has clearly failed us.

What a memecoin provides is hope and a permissionless opportunity.

The truth is - memecoins, just like meme stocks, can go up in price and be worth billions of dollars. They can also fall down in price and be worth nothing. Both of these things can happen quickly. If you want to ride the wave and you’re not afraid to lose it all, you can join the ride.

But the biggest lesson from memecoins is the one that they hope you'll dismiss as context (and just invest in memecoins)- which is not to invest in VC funded alt coins with poor tokenomics.

Especially the ZK-infra-layer2 token which has no reason to exist.

Every token with tech has early investors who can't wait to make their money back. You weren't early, even if you were made to believe so. So, you shouldn't invest in any token unless you can clearly identify what the technology is - whether the technology really needs the token, what the long term incentives of holding the token would be, whether the token is inflationary or deflationary, what percentage of the token has already been allocated to insiders and VCs - and if anyone tells you their token might be the next store-of-value or currency, you shouldn't think twice about running away.

Now, there is Bitcoin and there are altcoins. Eth might be ultra sound money. Solana might be ultra fast money. There are countless other altcoins worth exploring and putting your money into, but there's also a large graveyard in the future filled with alt-coins which are in the top 100 right now. This may include billion dollar projects like XRP and Cardano, and memecoins like Shiba Inu & Wif if they fail to innovate.

Memecoins are a reaction to what feels like a rigged game. I think it's going to go similar to the NFT-wave, where there are memecoins launching every day - new memes will rapidly gain traction and capital and rapidly lose them. Some tokens may just survive from one cycle to another and get long term holders like Dogecoin. Some memecoins may start movements, and even products themselves to flex their technical prowess. At the end of the day, the motivation is the same regardless of what anyone spins up - everyone's trying to make money and they’re all trying to make lots of money fast.

I don't think that's a bad thing in any way.

I'd want to live in a future where you, because you're intelligent enough to spot trends early, invest in a memecoin called "$TrumpCard" when it's at $1000 market cap. Months pass and nothing happens. Then in a rally, Donald Trump says "The democrats, the crazy democrats, they don't want the Trump card. I'm telling you, the trump card is too much for them. It’s too much for them. But we love the Trump card, am I right? We love the Trump card here."

The video of him saying this goes viral because of the way someone in the rally is nodding their head behind him. Suddenly, $TrumpCard shoots up 1000x in price. You sell and you're a millionaire overnight.

Whatever you do, it's your choice. The risk is yours to take, and if you lose your house and life savings this time, no one's coming to save you. There's something beautiful about that which is hard to articulate, but just the unhinged expression of human will and the ability to take risks for unlimited upside shouldn't be condemned.

What should be condemned are sleazy con artists pretending this is anything different than what it is. You're not investing in a movement. You're not investing in a community. You're not investing in friendships. You're definitely not joining a cult, despite the self proclamations. Even those in cults are honourable enough to put an exit price on relationships.

Just like you can't buy love, respect and friendships, you can't buy your way into a place where everyone is expecting the price to go up so that they can leave with more money than they came with.

It's a casino, should be admired like a casino, and while kayfabe can work in pumping prices in the short term, it can't work forever.

VI.

One thing that stands out about the ideology of memecoins is that they address the issues that society is facing. Just like an addict always has excuses for his next hit, the memecoin ideology has some truth to it -

Loneliness is the highest it has ever been. But memecoins won't solve this, for reasons I've already mentioned above.

It's difficult to climb the status hierarchy, and definitely not as easy as the boomers had it. Memecoins won't solve this, but it can give you hope. You can hyper-gamble your way to elite status, but if you fail, you have to join or continue in the rat race. You can risk it all and play it big - and in the end, you'll either hear, "Your Lamborghini is waiting, sir," or "Just put the fries in the bag, bro" depending on which way the price goes.

One thing I will tell you from my own experience is this - After every cycle, when Bitcoin ends up crashing the least, people collectively have the same epiphany: There is Bitcoin and there are Altcoins. Bitcoin is digital gold and will never go to zero, so I might as well invest in Bitcoin from now. This is a very normal epiphany to come to, especially when every other token has crashed -60% to -90% from their all-time-highs and all memecoins have gone to zero.

Ethereum and Solana, despite also crashing in the cycles of the past, have proven to be solid investments, so most people decide to rotate into these alts when markets are running high - something endearingly known as “Alt Season” which lasts for a few months before Bitcoin and everything else starts crashing.

Because memecoins chose VCs and altcoins as their enemies, it may happen that AltSeason ends up being Meme Season this cycle, but ask yourself this question - do you really think any of the memecoins on Solana or the memecoins on Eth or Base are courageous enough to be critical of the chain they’re on, risk upsetting the community of that chain, or would they silently disappear as silently as they appeared?

Each cycle crash acts as a cleanser, wiping out the grifters and con artists, the vaporware startups and the non-functioning protocols. Tokens, based entirely on momentum, like memecoins, simply go to zero. Discords get shut down, and you're left holding the bag. People, at this point, try to dox the founders, the devs, anyone they can hold off. They wish death upon them, try to sue them, try to start tokens and projects to "un-rug" the dead projects, but soon everyone realises that the music has stopped and move on with their lives, poorer but hopefully, wiser.

The beginning of the cycles are different. Every popular memecoin can 10x or 100x from here. They can also go to zero as something new, shiny and popular comes along and takes the spotlight. It's a game of luck and zero skill - and no, sitting and clicking the BUY button while dreaming of riches, rationalising your gambling choices, requires no skill despite what you’re being made to believe.

What does require skill is what happens behind the scenes. One thing you'll notice about every memecoin is that they don't have a public face associated with them. This is usually because successful memecoin founders - and by successful, I mean people who have made money by launching a memecoin themselves and successfully selling early, already have contacts that they hope to shill to - first. Just like VCs in private markets get the tokens allocated at attractive valuations, there are people buying early into memecoins who know exactly what series of events to trigger to pump the coin, and then dump on actual retail.

Sometimes, these tokens survive the dump and take off. Sometimes, there's no dump and it's a long con instead. Sometimes, and I hope this happens more frequently, there's no motivation of dumping and it's just meant to be a joke that takes off surprising even the insiders who ignored the tokens.

More times than not, without a supporting product, without utility, without a roadmap, and just vibes, investing in memecoins rationally doesn't make sense.

One exception to this is creator coins - launched by popular creators. Creators always have an audience to shill, who will be very angry when the price plummets, so there's at least some accountability and personal risk involved for the creator themselves - despite the very easy payoff associated with the token.

Overall, I'd still want to live in a world where memecoins exist. If you think blockchains are not meant for silly things such as these, you're welcome to just build something more useful. But I also think that if you're thinking of investing in memecoins, there are much better ways of spending your time and energy - and it's the same reason why you're better off not trading stocks. Your time is valuable and you can't remain sane for long enough staring at charts and screens, and there's always a better thing you can do than that.

Don't fall for the charlatans, the false ideologies and lies. It's just memes. Enjoy them. Get rich or poor off them if you have to, but understand that there's no memecoin super cycle.

Memecoins won’t be worth hundreds of trillions of dollars and take over the world.

The only meme that can be, already exists, and that's Bitcoin.

We’re all familiar with the clip culture we live in today, but one fascinating thing I recently discovered is the widespread use of specific copy pastas on these clips.

If you're on TikTok, Instagram Reels or Facebook, and you haven't come across the infamous BMW, Tesla Cybertruck or Mercedes CLR GTR copypasta captions, you're quite lucky, but I can guarantee you that you will come across something like these if you're on these platforms long enough -

(The next paragraph is clearly AI generated, so you can just skim it instead. It's also factually incorrect since no such car exists, but some copy pastas can be correct as well.)

"No problem Here's the information about CLR GTR: The Mercedes CLR GTR is a remarkable racing car celebrated for its outstanding performance and sleek design. Powder by a potent 6.0-liter V12 engine, it delivers over 600 horsepower.🔧 Acceleration from 0 to 100km/h takes approximately 3.7 seconds, with a remarkable top speed surpassing 320km/h. 🥇 Incorporation advanced aerodynamic features and cutting-edge stability technologies, the ClR GTR ensures exceptional stability and control, particularly during high-speed maneuvers. 💨 Originally priced around $1.5 million, the Mercedes CLR GTR is considered one of the most exclusive and prestigious racing cars ever produced. 💰 It's limited production run of just five units adds to its rarity, making it highly sought after by racing enthusiasts and collectors worldwide. 🌎"

Similar posts about BMW, Hyundai and other cars exist and people use these as captions, but what's remarkable is that these captions are 100% irrelevant to the posts.

Here are some screenshots of the posts from Facebook -

Now, what is happening? WHY ARE ALL THESE PEOPLE SHARING POSTS WITH THE SAME CAPTION, EVEN ON UNRELATED PICTURES?

Why are there millions of posts like these, EVERY SINGLE DAY?

Even X.com is not immune to this. Just search for “no problem here's the information about clr gtr” on X and you’ll see hundreds of posts with this caption posted within the last hour.

I don’t have TikTok installed, but I asked my friend who does, and she said she’s seeing the same thing.

Whichever platform you’re on- Instagram, Facebook, X, TikTok, you’re never safe from the Mercedes CLR GTR.

It’ll haunt your feed, reminding you that nothing is real. It’ll haunt your dreams knowing that the Internet you once cherished has died and you couldn’t do anything to save it.

The Mercedes CLR GTR is more than just a copypasta.

It’s a war cry against originality, creativity & freedom. It’s a statement of victory from the algorithmic abyss. It’s raw competition unmasked in all its glory, fighting for your most valuable resource - time, in the most obnoxious and annoying way imaginable.

The Mercedes CLR GTR will follow you everywhere. The Mercedes CLR GTR knows what you like, what you don’t like, and it’s there every time you open your phone.

At this point, just ride me over with it.

II.

Ok, in all seriousness, what is happening?

The running theory is that car guys watch a lot of short clip content online like TikToks and Reels, so every account figured out that the quickest way to get views is to add unrelated captions of cars to their content.

The algorithm picks this up and shows it to car guys. If this theory is true, it must also be true that car guys are terribly slow to scroll away - because if they did, the posts wouldn’t be boosted. So, car guys are being sent these clips on all platorms, they’re watching them - which signals to the algorithms that this is content worth spreading, which it is then doing and thus, you’re seeing these clips take over.

This sounds pretty harsh to car guys, so I wouldn’t be surprised if there are content farms in third worlds specifically dedicated to increase the watch time on such clips. This doesn’t explain why the content farm would watch clips from unrelated accounts not paying them for views, but since tens of thousands of accounts are doing this, maybe they are?

Either way, this paints a pretty damning picture for cyberspace today and it warrants a discussion about where we all are heading in world, and to answer the question whether clips have killed the Internet, we must dig deeper.

The average user spends 58 minutes on TikTok each day. The average time spent on Instagram is 33 minutes per day. For X & Snapchat, the time spent is roughly 30 minutes. All these apps are serving short form clips, sometimes exclusively short form clips, like TikTok. YouTube, which also has YouTube shorts, claims that users spend 48 minutes on the app everyday.

If you add it all up, that’s close to 4 hours per day, but it’s safe to assume that the average user spends 30-60 minutes on their preferred app just scrolling through short clips.

If you want popular attention, and you’re not available as a 30-60 second clip, it’s going to be hard to reach people.

People have realized this and are leveraging it in the most unique of ways, and this is what leads to content with irrelevant captions such as above, but it also leads to things like shockvertising (Shock + Advertising) -

I couldn’t find a better shock-ad because I mute and block every account that serves them to me, but current shock ads are actually more effective than what you see above, where they’ve realized that you must capture the user’s attention within the first 5 seconds.

Thus, most shock ads, today, are people getting hit by cars or jumping off cliffs or getting punched in the face, which transitions right before the impact to someone talking about a product.

Ads that you literally can’t look away from are here, and they’re not getting more expensive to create.

This is the same technique that YouTubers use when they cram as much information in the first few minutes, setting up the video, using quick cuts, and make sure that you’re “hooked” enough to watch the whole thing.

Mr. Beast takes this optimization to the extreme, where he starts with the thumbnail (the first a user sees and decides to click on a video), and has a six person team dedicated to just thumbnails, actively making and testing hundreds of thumbnails for him for each video. That’s how much work is put in for each second of your attention, and the result is not as aesthetic as you’d expect -

Regardless of how you feel about the thumbnails, it’s undeniable that billions of views have arrived just from these thumbnail optimizations, and just like the Mercedes CLR GTR, other YouTubers simply decided to do what’s already working -

Monkey see, monkey do, and as former monkeys we’re always going to see people just adopt the best way to get attention at any given time. In the internet of clips, this involves creating clips with extreme precision, where no seconds are wasted and every moment is optimized for your attention.

Here’s the latest Mr. Beast short with 55 million views -

It’s perfect, from the text and timer that holds your attention, to preparing the hook, to finally giving you the pay-off. You can’t look away, no matter how much you try - once you’re in, you’re in.

While these clips are mostly harmless, there’s an aspect of clips that may not be.

Social media rewards rage and anger, outbursts and outrage, as these are the most important emotions that fuel engagement.

Very soon, a large number of YouTubers will figure out how to leverage this simple fact.

Prank shows will become as common and as violent as ever, and it’ll be difficult to walk down the road without someone or the other filming themselves and trying to get a reaction out of you. Most will tow the line, going out of their ways to reward you if you find them too violent or annoying, but beginners who are influenced by them will not be so wise.

Of course, these will be transitory events.

Vloggers who prank and beat up people in the name of content can only do so till there are people around them, and just like a rude podcaster cannot be successful as there must be sufficient kayfabe involved to make it work, miscreants using social media to film harming others can’t be successful for too long. Some of them will be, and that will influence others to try, but that’s the worse of it.

III.

It just might be time to consider the bright side of this collective addiction to short-form clips that our species participates in.

Clips today, by there very nature, require very little individual attention to go viral and thus, we’re evolving into a culture where viral clips dictate the public discourse.

Here’s where just 30 seconds is enough to make someone famous, where livelihoods may depend on how many engaging clips you can put out, because while you’re not - you’re competitor is, and this is where the clips you watch and/or pass along to your friends or feed, mimetically lives on having fulfilled its purpose.

Clips are also lower effort, unless you’re Mr.Beast-ing it with the granular optimization, and a clip of a simple candid interview on the street can change people’s lives.

Hailie Welch, who even google describes as Hawk Tuah girl, is one such example -

Famous for describing how she’d “spit on that thang” in a random street interview that went viral, she currently has over 2.5 Million followers on Instagram.

In comparison, the most popular substack “Letters from an American” has less than half that many free subscribers.

Scott Alexander, who writes two to three essays every week, has around 100k subscribers, and he has been doing this for over 10 years.

So effort is the wrong metric for our clipped culture here.

Here’s a question for you -

If I posted this essay again tomorrow, would you read it? If I posted this essay again and again, every single day, would you still subscribe to me? The answer is hopefully no.

There are some accounts which are very popular on different platforms who just post the same clip everyday. Just search with “everyday” or “same posting everyday” on your favorite platform and check out the accounts -

Usually, these accounts stop after a while and new accounts posting the same picture or video take over. It’s a sweet gig when it works, and we never see the accounts that don’t make it big, but it’s tremendously low effort.

So, effort, as it stands, has nothing to do with reach.

If it did, everyone on Substack would have millions of subscribers, but that’s not the case.

But it also doesn’t mean that clips are killing the Internet.

IV.

If anything, the proliferation of clips as the dominant medium means we’ve collectively discovered just the right method that anyone can use, regardless of your intelligence or income, to grab someone’s attention with the least effort possible. All you need is a camera for yourself, or someone else pointing a smartphone camera at you.

Unless AR/VR brings in a new medium that is easier and more accessible, things are going to remain this way.

That’s okay, because now, you know the rules of the new internet.

You can consume or spread clips, like everyone else is. Or, just like the people in previous generations who chose to read books over watching television, you can go on Substack, the cozy web, seek out books, niche subreddits & discords, and the great part of all that is - whatever you discover, there’s probably a video-clip version of that niche as well. The internet lives on.

It’s dead if you consider bots and spam as something that doesn’t add value, and it’s dead if you consider the hawk tuah girl as a phenomenon as something that never should’ve gained mass attention, but that’s the wrong way to look at it.

It’s alive, more alive than it has ever been, and the competition for your attention is so fierce that bot accounts are using irrelevant copypastas to try to get a second of your time. It’s a great thing that people are doing this, that independent creators like Mr. Beast has hired dozens of people just to figure out how to optimize the first 5 seconds of a video to get you to view a 30 second view. In a different time, such power of influence could’ve been gated but we’re in the better timeline. A small annoyance is all we have to accept in exchange for such leverage and freedom.

We’ve all heard stories of people’s lives being changed because of a clip. Here’s a clip of Matt Rife who owes his entire standup stardom to one clip going viral -

He explains the whole process very well, that prominent marketers already understand. One clip goes viral - everyone else checks your other stuff, gets to know you, and soon enough, you’re a household name and selling out stadiums.

From nobody to somebody, all because of a 30 second clip, and the playbook to how to achieve it is right there.

This playbook is similar in all industries, like in self publishing, where independent authors are advised to “have a backlog” and “write in series”, where if any novel goes viral, you will get readers all your previous books, and today, the best place to promote your book is, you guessed it, TikTok.

BookTok, as it’s called, is so infamous for driving sales to books that TikTok is considering starting a publishing arm to capture some of the value away from Amazon.

I recently ventured into independent gaming, and the marketing advice is the same. Don’t die after releasing your first game, because that definitely won’t be successful, but if your second or third game is - you’ll get gamers checking out the first.

Again, you don’t have to consume TikTok, X, Instagram Reels, or YouTube shorts. You’re perfectly fine here consuming intellectual content that nourishes your soul and dismissing all such clip content as brain-rot.

But if we don’t acknowledge the opportunity space this provides, the ephemeral cut-throat competition for attention where anyone can participate, the infinite ways to unlimited leverage for the average user with just a smartphone, we’re being dishonest.

It’s fine to not consume a form of content for our own sanity, while also acknowledging its perks. And it’s only when you acknowledge its perks that you realize that the dead internet is not going to arrive any time soon. Bots pretending to be humans couldn’t do it, as spaces that don’t allow bots can or would exist. Viral clips are viral precisely because they reach real people who spread it on.

So, this isn’t an advice to start posting clips on TikTok and everywhere else to get noticed. But the fact that you can do that, and after a mediocre streak, strike gold, where people who never knew you would suddenly be your fans, proves that the internet is at its best right now.

This isn’t something you’d hear from mainstream media, where TikTok and social media are vilified for being the reason for numerous ailments from teenage anxiety to political division, and those may be vital reasons to check these companies, but they’re a shining proof that the Internet that we know is far from a wasteland of bots that cynics would have you believe.

There are more people online than any other time in history. More places to visit, more communities to join, more things to do, see and experience than any other time in the web’s history. It has its problems, it hasn’t lived up to its cypherpunk roots, there’s a widespread issues of moderation, censorship & sybil-resistance, where it’s only learning to tackle the emergent issues of AIs pretending to be humans.

But the internet isn’t dead.

It’s alive, more alive than it has ever been, and it’s only going to get better from here.

If you add Reddit after Network State and google the word “Network State Reddit” as one does when they want to know the opinions of the highly esteemed Redditors who all have impeccable taste and highly respected opinions, you’ll come across people calling Network States a scam.

In one such thread from two years ago, you can see people coming up with clever reasons why network states won’t ever work, and how it’s a cult and a grift.

Now that nearly two years have passed and we haven’t seen a single network state scam yet, I think it’s time to do a honest retrospective of network states, or at least the ones that I’m a part of. Keep in mind that sometimes, even Reddit can be slow to accept ideas, and it’s not just LinkedIn boomers who are tragically late to the party, so a few years from now, the sentiment on Network States might be very different than what it is, right now.

But, why do this retrospective right now?

Balaji Srinivasan recently announced the Network School program, where people can pay $1000(with roommates) or $2000 (solo) per month to stay on an island near Singapore for 90 days.

Since this is the first time that people are paying real money to get involved, I figured it's time to honestly look back at my own journey and write a detailed review of my experience.

This may not be the most unbiased review, as I've often advocated for Network States since the beginning. But I'll try to be as thorough as possible and address common concerns along the way.

Before I begin, I’d like to mention that I’m not going to Network School this year. I have a hackathon coming up that I’m really excited about, so I’ll be heads down for the next few months.

I've been part of network states (yes, there's more than 1) for more than 3 years now. One of the most popular posts on Bright Mirror is a (the first?) science fiction story about network state citizens.

But to understand my journey and this review of network states, it's important to understand the history of how I came to be in a Network State in the first place.

Let's start with how it all started -

Back in 2021, 1729.com was the first site where Balaji wrote about Network States. Through this site, he set up $BTC rewards for those who would review his essays on network states, as well as host other bounties like Learning Replit, Learning ENS, making memes, etc.

1729.com currently redirects to thenetworkstate.com, but the posts on 1729.com exist on Balaji's substack today.

If you scroll down on the essays titled Founding vs Inheriting & Network Union, you can find my name mentioned in the winning submissions section, back when I used to write essays on my personal site.

Most of my essays were chosen as winning submissions, and I was later accepted to the 1729 discord. It was in this discord that Balaji hosted weekly lectures regarding network states, and around 50-100 of us listened to these lectures, took notes and had the privilege of asking him questions.

Later, the discord was rebranded from 1729 to thenetworkstate.com and it has currently been rebranded to networkschool.com.

Before I dive into the positives of this experience, let's start with the negative first -

II.

Is this discord a network state?

For the sake of this essay, let's assume that it is.

The only downside of this experience was Balaji's absence from the discord when his series of lectures were over.

When you consider the fact that this man was writing the network state book at that time, combing through US documents to find out whether the country would descend into hyperinflation, or starting a podcast, or attending podcasts to promote his book - a book which became a #1 bestseller on Amazon despite also being available for free, it's not really a fair ask to also expect him to chill on discord with the homies.

However, what happened in discord in the meanwhile is magical, and that's what I expect to be the prime attraction of network states, at least for now.

"Maybe the real treasure is the friends we made along the way," is a common phrase that is used dismissively when the rewards for something aren't substantial - a common trope in movies where the main character doesn't get what he/she initially wanted.

But in the case of Network States, everyone that was selected had some kind of exceptional ability - along with having the clairvoyance of being at the frontier.

The people that populated the network state were engineers, artists, writers, founders, digital nomads & almost all these people had one thing in common : They all read Balaji's essays and were interested in the idea of network states.

It takes a high degree of openness to new experiences to sign up for something like this, and a lot of agency to put yourself out there.

Here again, I think Balaji may have wasted the potential of having a proactive group such as the one we have, and a lot more could have been done by the collective effort of the initial citizenry.

We're still in the discord, so there's still hope that something will happen, but what we did in the meanwhile is nothing short of incredible.

We maintained regular posts in a proof-of-workout channel where we posted updates everyday documenting that we worked out & holding each other accountable for the same. This was also where we discussed training routines, diets, longevity and the perks of doing this with incredibly smart people is that you find out about things like VitaDAO & Blueprint diet that people only discover months or years later.

We shared links to interesting things, discussed and debated topics, and the conversations would often require opening separate threads as we collectively explored each topic.

Someone voluntarily set up a bot just to summarise our everyday conversations on a weekly basis, and when something like that is done without anyone asking for it - it signifies that the discussions were of a high quality.

We self-organized meet-ups all around the world, along with e-meetups through zoom. All the meet-up pictures got posted in the meet-ups channel, where we also coordinated our meet-ups.

Some started companies and built projects together with people they met through the discord, while some got jobs and internships.

Collectively, we wrote over hundreds of essays by setting up and participating in writing cohorts.

I met some amazing people during this time, and the friends I made surely was the best part of this entire experience.

I've often wondered what sorting algorithm Balaji used to find the group of people who are so ideologically aligned to how I was - optimistic, technical, at the frontier of fields, but also kind, thoughtful & possessing high agency.

Over time, I've realized that "people" might just be one of the best aspects of network states. At least for now, when network states are in its infancy, a network state set up through filters where it only admits people with some "proof-of-work", ends up admitting people who are 1) talented 2) not ideologically captured to the point where they don't get anything done 3) Open to new ideas & experiences.

The intersection of these buckets of people is large enough, but bringing them together is the hard part, one which Balaji's Network State arguably succeeded in doing.

You’d be surprised to learn just how many people fall into the second bucket - people who despise tech reflexively, people who spend almost all their time scoring points for the in-group by insulting & demeaning people they don’t consider their own tribe, people who are so engrossed in the nitty gritty of politics & group-think that their entire identity revolves around the political party they support.

Those who are silent, and thus, intelligent enough to ignore the noise don’t pop out at you, but once you meet them, you might just make a new friend for life.

Overall, I’d rate my initial experience of Balaji’s network state a solid 9/10. I enjoyed Balaji’s lectures, made some life-long friends, and my only issue is a delusional belief that so much more could’ve been done. Perhaps, I’ll start a network state someday to explore my ambitions, but for now, let’s explore another network state that does things differently.

III.

Superteam is another network state that I'm a part of - although they don't label themselves as a network state (yet). It's more like a group of builders but what brings it closer to being a network state is that there's a GDP chart that keeps track of the community GDP of Superteam -

I've been a member of Superteam for nearly 2 years now, and I’ve seen them grow from a small group chat to a global organisation. Superteam isn’t for every body, and there’s a clear mission of building together. It’s no surprise that the most valuable members are founders and engineers, but we do have a fair share of creators, video editors, marketers & designers, aka, everyone that’s involved in actively doing the work to make a project successful.

If there's one criticism I have of Superteam, it's that it's singularly focused on the Solana ecosystem and it's not, by any means, blockchain-agnostic, even though many Superteam members can be.

While Solana is great as a blockchain, I do think such singular focus, while it works right now, might be detrimental in the long term. Regardless of how you feel about Solana or blockchains in general, Superteam won’t be the first to lead you into any other frontier of tech & when the next wave of new technology in a related frontier inevitably arrives - whether that’s AI agents, robotics or quantum computing, unless there’s a way to apply the tech to Solana, it’s going to lag behind.

But such singular focus is what has made us successful at Superteam, as I’m sure there the projects that members have built & people that we’ve onboarded outpaces not just any organization on Solana, but all blockchains that exist. But it does, just by the intensity of focus on one blockchain & ecosystem, reject anyone who wants nothing to do with Solana, Web3 or blockchains.

While Balaji's Network State was limited to us figuring out what to do on our own, Superteam excels at mobilising labour at a specific cause. Offering grants to builders, hosting weekly community calls, hosting IRL meet-ups, discussing what to build and how to build effectively, etc, Superteam tries to ensure it collectively supports everyone in the community to be at their best while creating something new.

The GDP graph shown above is a result of initiatives called Superteam Earn, where any one can post jobs or gigs to be done and people compete to solve them, and get paid in crypto.

There are no standards as to how a network state is judged, but if creating economic value is one such metric, I don't think anyone can compete with what Superteam has achieved with so few resources.

Without naming a dozen names, I've made a lot of friends in Superteam. I've also given back, like any valuable citizen - by travelling to universities to give workshops on Solana development, by teaching young programmers blockchain development at Solana Hacker Houses, and by posting bounties on Superteam Earn to get jobs done. I’ve won hackathons as a result of collaborating with people in Superteam, the feedback and support from the community has been invaluable.

Balaji, believe it or not, is also a member of Superteam, and knows many of the founders very closely. He has observed Superteam rise and take over the world, so I expect Network School to offer similar opportunities as Superteam Earn, which is an iteration of Earn.com - where Balaji was CEO before Coinbase bought Earn.com, and launched Coinbase Earn, which may have been one of their best early customer acquisitions channel till date.

IV.

If I was Balaji, I'd use Network School as a starting point to actually build a network state over time.

A "school" sounds perfectly harmless, compared to words like "country" or "state", and it can actually start off as a yearly retreat for students and like-minded individuals, then expand and be more permanent from there.

As people start settling down, realising the quality of life & opportunities you get there is exponentially greater than anything you'll get elsewhere, this would cause a large influx of people who would want to get in.

That's a problem, but a great problem to have, since you can then use the added demand to attract better talent, investment & build better infrastructure for the people already there.

Along the way, encourage the people to start building companies there. A core advantage of network states is the lack of regulatory hurdles, but before you accept every company that wants to migrate specifically to skirt around regulations, nurture talent & encourage the creation of companies directly from the existing students there. This would prevent Network School from getting a bad branding from the get go, as it doesn’t need a reputation of non-regulation from the get-go.

Since this is the first time that this is happening, one of the primary things to look out for is security & safety. I’m sure very few bad actors would pass through Balaji’s filter, but it’s good to be unreasonably cautious about this since even one bad actor can ruin this experience for many. Some proxies like online reputation, proof of work (resumes, Github commits, on-chain credentials) & simple references of existing people that have built up trust, can go a long way.

Another way of ensuring security is to have systems of anonymous complaints in place, and a readily available team to take care of miscreants. Security & safety, once that’s guaranteed, makes the whole thing a whole lot easier.

Balaji has mentioned that Network School will enable you to learn, burn, earn, and fun. I’ve a slight gripe with this slogan. It should’ve been learn, burn & earn. Fun is implied and if the slogan was three words, it would’ve been more impactful and memorable.

However, if you’re attending Network School, I hope it’s clear from my essay what the real value of Network School is going to be.

It’s not going to be the thousands or tens of thousands of dollars you earn in crypto for doing projects or tasks, enough to ensure that your rent has paid for itself. It’s not going to be the stellar insights about the world that blow your mind and imbibe in you a new outlook of life. It’s not going to be learning from Balaji’s lectures IRL, who is infinitely patient at all times, and would answer all your questions without a hint of contempt.

It’s going to be the people around you. As cliche as it sounds, it’s going to be the friends you’ll make along the way.

Think back to a time when you made close friends. It was probably in school, back when you’re too young to care about status games.

Network School could be your second chance at making such life long friends. It doesn’t matter if your new friend is a retired millionaire travelling the world, or a twenty three year old messing with zero knowledge cryptography. You’ll find something in common with them, and geek out over it, and hopefully, make a new friend for life.

I truly believe that network states are necessary for the world, and one of the only ways to accelerate technological progress is to shut out the politicians who impede & harm growth, not necessarily through ill intentions, but through pure incompetence.

In a decade, we may have a dozen crypto-cities which are transparently run, devoid of blatant corruption, and offers more opportunities than what your existing city does. Even if it's not a dozen, but just 1, just Network School, I'd be happy as I’d know there’s a place for me in the world filled with like-minded people who will accept me.

That doesn’t just happen automatically, and Balaji won’t be able to do it alone.

It is you, every one who is attending Network School, and everyone that’s not but still cheering them on, that gets us there.

To truly win in the post-singularity, or at least stay sane, you must strive to be a contrarian.

A contrarian, by definition, is someone who takes the opposite side of the popular opinion. This definition is incomplete, as it should come with the added, unsaid, modifier at the end of the definition - “And is eventually right.”

That is why contrarians are valuable & well-respected eventually, when they’re proven right. It’s not simply enough to take the opposite side of what everyone else is saying. You must also be right.

Warren Buffet is someone whose name comes up when you google “Famous Contrarians”, along with investors like Bill Ackman & Michael Burry, but outside the finance & investing circles, here are some contrarian thinkers today that I find deeply interesting.

Peter Thiel - First outside investor in Facebook in 2004, where he owned 10% of Facebook for $500,000. He made over a billion dollars in Facebook’s IPO, 8 years later. He’s a gay man who supported Trump during his first run - back when it was taboo in tech to do so. He single-handedly funded Hulk Hogan’s case against Gawker and put the anti-tech gossip magazine out of business, back when it was taboo to go against mainstream journalists. If all this isn’t enough, he started Thiel Fellowship which funded Vitalik Buterin, Dylan Field (CEO of Figma), Shahed Khan (Founder of Loom), Ritesh Aggarwal (Founder of Oyo Rooms), Lucy Guo (Founder of scale.ai), and others.

The best place to learn about Peter thinks about the world is through YouTube, and by reading his book “Zero to One”.

Balaji Srinivasan - Balaji is someone who has always been directionally right on many things that seem crazy to have been correct on, in hindsight. From covid lockdowns and mask mandates, to teaching a class about Bitcoin when it was trading below $100, Balaji has been right more than he has been wrong, and he technically hasn’t been wrong even once. His current prediction is that the US would enter hyperinflation soon, so he’s one person to closely follow.

YouTube is a great place to find out about him. He’s on X, as well as on Substack, and his book “The Network State” is a must-read.

Naval Ravikant - Many people know Naval as the founder of Angelist, but he has also written legendary tweet threads like “How to get rich (without getting lucky), back when it was taboo for leaders in tech to philosophise online - compared to now, where every founder has to have a social profile and tweet at least 3X a day to remain relevant. Naval’s uniquely gifted in boiling down his thoughts into neat, tweet-sized insights, and almost all of them are worth reading if you have the time.

He’s available on X, as well as frequently talks on Airchat- a platform he recently founded that I’ve previously written about. If you have the time, listen to his podcast appearances on The Tim Ferris Show & Joe Rogan Experience. Once you’re done, listen to them again. Then read The anthology of Naval, available for free online.

There are many others I can keep on naming, but if you’ve never heard of the above people, I can assure you that going through their talks, books & podcasts would be enough to drastically improve your life.

You won’t agree with everything they say, but then again, if you agree with everything someone says, you’re not thinking for yourself, which is what this essay is about.

To be contrarian, you must first learn to be an original thinker.

II.

One of the best ways to be an original thinker is to force yourself to think critically about things. It sounds simple but the vast majority of people simply don't do this.

They don't do this, because for most things, it's not required.

It's wasted mental effort. There's a "way" to do most things, to think about things, and this has worked, usually for a long time. Very few people should question this "way", as doing this vocally, you risk getting attacked by the majority.

To make things worse, despite you questioning the conventional "way", despite you trying to think critically, despite you going against the grain, spending your mental energy & time, risking your reputation and social status, you might find out that you were wrong all along. Being an NPC, which I’ve written about previously, has tremendous advantages, and that’s why the vast majority of people are.

Thinking that’s truly original is uncomfortable, painful even, which is why most aren't likely to engage in this regularly, but it is vital for arriving at the truth.

I was reminded today about how painful this process is, despite priding myself as an avid practitioner. I was browsing reddit instead of programming, as one does, and one of the posts was Logan Paul pushing his dog off his boat -

For the uninitiated, Logan Paul is a YouTuber who is also a WWE wrestler and infamous for various controversies such as upsetting people of Japan, tasering dead rats, and launching a crypto project (Crypto Zoo) that took money from people but never delivered.

Seeing this man push his dog off a boat - who could've suffered massively and likely injured because of the propellers under the boat, and then seeing him pretending to care & jump off the boat to rescue his dog and saying "I was so afraid", is enough reason to immensely dislike him.

The comments on Reddit show that my initial reaction to this, is exactly similar to everyone, with people raising their voices and wondering why WWE doesn't just fire him.

I've trained my brain to consider the opposite of the base case for a long time, to the point where it is now automatic. I was asking myself questions such as,

"Maybe, he does need the views. Maybe he ensured that the propellers won't hurt his dog in some way, like testing beforehand or training his dog correctly. Perhaps, it wasn't intended and the hand we see in the video wasn't pushing the dog - but was intended to catch him. You can hear them shutting off the engines immediately, which may indicate that this was a planned stunt as…"

While this automatic process was running, I suddenly felt sick for thinking this way.

Clearly, this person doesn't deserve the benefit of my doubt. Who am I trying to defend? And why? The poor helpless dog could've died or got seriously hurt, and instead of joining the crowd on Reddit, I am looking for reasons to defend him?

It was uncomfortable, but I realized something important while doing this.

What I was doing - was necessary. I was trying to think critically by considering every perspective, and I'm sure that even if this was uncomfortable, I'll be closer to the truth at the end of the process rather than reflexively adopting the opinion of the crowd.

Later in the day, I saw Logan Paul tweet a slow-motion video of the act, denying that he pushed his dog.

If you look at the video, it’s less clear that he pushed his dog, given that now we see a different pair of hands there. So, despite the uncomfortable nature of trying to think deliberately about the opposite of what everyone was saying, that was closer to the truth. If I had continued, I probably would’ve slowed the video down eventually and figured this out myself.

If I hadn’t trained my mind to do this, I would’ve been a part of the mob trying to cancel him and get him fired. Instead, I was not, which is going to be very important in the coming years when it’s your friend or co-worker, facing a mob trying to cancel them. Would you try to score in-group points with the mob, or would you think originally?

If it’s the latter, you must start training today.

III.

It is possible to train your mind to do this - and it involves reframing your mindset to deliberately think differently.

If you're prone to always thinking negatively, as many people are, training your mind to be positive is a fantastic exercise to improve your overall quality of life. You might meet someone and immediately think negatively of them. This doesn't always happen, but it can and does happen especially if the person looks different / talks different / says or does something that is dislikable.

In such a scenario, if you train your mind to think thoughts like "What's good about this person? What can I learn from them? How can I help them? How can they be better?" and at first, you might have to be deliberate about thinking positively like this. You have to deliberately pause your default mode of thinking, or let those default thoughts flow by and switch to this mode deliberately. With time, it becomes automatic.

This works for people as well as for situations where if you're prone to always thinking of situations as negative - you can reframe them to a positive in your mind.

It also works when you're understanding or learning something new - and it is especially powerful in that context. When you're learning something, it's easy to assume you have really understood it when you're just reading through something. But try to catch yourself from moving on - really think about what you're learning, apply it to different contexts, figure out what it is connected to, figure out where it came from, figure out why it's important and how it can be useful.

In the case of math, it can be useful to consider every theorem as something that doesn't exist in a vacuum but usually connects beautifully to some other thing and it's especially rewarding when you figure that out by yourself. It's hard to do this, and requires time and mental effort, but by the end of this process you will have learned more thoroughly than most of your peers.

In almost all situations, it’s always a great idea to invert - take the opposite side, and then start from there. It’s uncomfortable, as shown above in the Logan Paul fiasco, but inverting is one of the best ways to get closer to the truth, especially when the inversion is taboo. That’s how you become contrarian, and if you’re intelligent about patiently seeking the truth, you’ll be closer to a contrarian who is right.

IV.

In 1968, a study known as The Smoky Room experiment, examined the phenomenon of conformity and group dynamics. Participants were placed in a room filled with smoke, and researchers observed their reactions to the situation. The study found that when individuals were alone, they were more likely to report the smoke and seek help. However, when they were in a group, many remained passive, influenced by the behavior of others who seemed unfazed by the smoke.

In the video, you can see people leaving the room immediately when others aren’t there, but when there are, they stay there for longer than 20 minutes.

I don’t know what I would’ve done if I were in that room. It seems trivially easy to claim that I would’ve walked out immediately, but I’m not certain I would have. This is just one study among many others that highlight how dangerous and universal conformity is, but post-singularity, it might be even more dangerous.

Humans bond as a tribe by sacrificing a scape goat. From Galileo to Zuckerberg, we’re experts at bringing out the pitchforks and pointing it at one person.

Right now, we’re doing it with someone who may or may not deserve it. But overall, cancel culture & mob justice thrives because we bond over collective violence against an "other”.

In the best case of AGI, where humans have unlimited time to engage with each other online, collectively, it’s going to be very important to think for yourself.

Mobs with unlimited leverage can not only cancel someone online, they can send a fleet of autonomous drones to your address.

Deepfakes which are indistinguishable are currently considered dangerous because of misinformation. AI generated videos are also catching the same allegations. However, the harm from both of these technology will be minuscule compared to the harm that collective groups will inflict on each other, driven by pure hate, hysteria & a pinch of apathy.

One person will be able to spin up 100 AI entities, generating the mass illusion of consensus around a certain topic, and before you know it, you will adopt the same opinion along with everyone else around you. Most of these will be harmless, but many will be clearly aimed at some motive of spreading hate against a person / group, and coupled with mimetically fit truths, these messages will virally divide & conquer the cyberspace.

Thus, polarization & divisiveness will continue to accelerate in a world where no one thinks for themselves, but everyone tries to maximize group-think. The only way to prevent his is to be a contrarian, to consider the perspectives which are taboo to your in-group, to take the side of the scapegoat, to search for truth and advocate for unity.

Are you voting for Trump? This question is important, not for you, but for everyone around you. The best way to know whether you’ve been captured by ideology is to receive different answers to the above question from your friends. If all your friends support the same political candidate, it’s time to make new friends - even if you consider that uncomfortable. Thinking of the other side as “evil” is the fastest way to halve your own intelligence & critical thinking capability. This is why participating in politics & identifying as being part of a political party, in general, have a bad effect on your critical thinking skills. You have to adopt all the beliefs of your party to be a true supporter, which means, you by-default, aren’t thinking for yourself.

Another question to ask yourself is - If all your friends are hanging out together with you, what are some things you believe in, that you would not consider saying? This works especially when your friends are diverse and hold different political beliefs, because even then, there are some things that you wouldn’t consider vocally speaking about. If there is none, i.e, you believe exactly what everyone else believes, you’re not thinking for yourself.

To think originally in a rapidly evolving, not only helps you win, it helps your group win as well, despite you going against them in the short term.

This is because truth, by definition, will affect your group regardless of what your group believes. By exposing and recalibrating group beliefs to the truth, you are helping them - even if they go against you. If you expect a bomb to go off in your dorm- but your friends all believe that bombs simply don’t exist, the only to save your friends is to vocally berate them for their delusions and dragging them out of the dorm.

Belief is powerful, especially when beliefs are instrumental, but truth is what you live in. In a constantly online world where everything that’s crazy and unusual fights for your attention, and a constant stream of crazy and unusual keeps getting pumped into the news cycle, being a contrarian is the best thing you can do for your mind.

So, dare to think independently. Dare, despite the opposition, to be original.

Speak your truths, however inconvenient they may be. For in doing so, you contribute to a post-singularity where ideas are tested, refined, and ultimately, where the collective can thrive on a foundation of well-examined beliefs.

This is how you truly win & stay sane in the post-singularity—not just for yourself, but for everyone around you.

UBI, or Universal Basic Income, is something that you've probably heard of - but if not, you'll hear a lot about it in the coming years.

The idea of the proposal is simple - everyone receives enough money to live comfortably, unconditionally. There is no means test, i.e, there is no test of eligibility. If you exist, you get money. It doesn't matter whether you're a starving farmer or a big tech executive making millions of dollars. You'd both get the same amount of money.

We can stretch this definition further, and assert that everyone from all over the world gets the same amount of money, according to this utopian proposal.

This makes even less sense, as somewhere in Africa where the poverty rate is $2 of income per day, $1000 a month can go a lot further than in US, where the poverty rate is $20 per day. An obvious question might be - "Why does the poor African get $1000 per month, when he could effectively live the same lifestyle as the poor American with only $300 of income?"

Let’s ignore this question for now, and assume that we’ll give to everyone equally. We’ll come back to this later. There are bigger questions that we’ll tackle first in this essay like -

What’s needed if everyone from around the world has to get $1000 a month?

That's 12k for each person annually, which comes out to about 96 trillion dollars a year. Nobody has that amount of money, but let's assume that we'll get that money from somewhere other than money-printing from governments (which leads to obvious inflation).

We've chosen $1000 here, as it's the amount you'd have to provide as UBI in USA, which means it's more than enough for everyone else all over the world.

So, what are the good things that can happen because of this?

II.

If you're fortunate enough to be reading this essay, you're not working inside a coal mine somewhere. You don't have to wake up in the morning thinking about where you'll find the money to feed yourself and your family. The constant stress of getting enough money to survive is probably absent in your life.

But for the vast majority of the world, the lack of money plays an active role. Billions of people work jobs they hate just to get money. Millions struggle to eat twice a day, just because they don't make enough money. And hundreds of thousands resort to criminal activities because they're forced by their circumstances to do so.

Now, I'm not saying that UBI will instantly stop all crime even if it’s common sense to assume that it might reduce it. In fact, the original motivation for UBI came from an English statesman named Thomas More in 1561. In an answer to the statement "No penalty on earth will stop people from stealing, if it's their only way of getting food" he wrote -

"Instead of inflicting these horrible punishments, it would be far more to the point to provide everyone with some means of livelihood, so that nobody's under the frightful necessity of becoming first a thief, and then a corpse."

Almost 500 years later, we haven't yet conducted any nation-wide experiments to verify whether Mr. More was right. The correct answer is- we don't know what will happen and whether that’s overwhelmingly positive or negative.

But if we empathise for a moment - we can predict that some things will happen based on just common sense.

UBI will certainly reduce the constant stress of money for a majority of the population. People would know for a fact that they can afford a meal, their rent and maybe even raise a family without worrying about their next paycheck.

Some would try to start new businesses. Some would skill up to find new jobs. Some would create art, some would save and invest, some would indulge in more pleasures than they could, some would support others who need more than what they're getting, and so on.

By removing the constant stress of money, you're effectively freeing up people to explore a higher self within themselves, empowering them to reach their full potential. The button clicker at an Amazon warehouse might decide to start a business instead. The homeless person at the side of the street might realise that he still has a chance at living a fulfilling life and pursuing his dreams.

All these things are arguably good, and the second order positive effects are probably greater.

People will probably prioritise their health more. From having the time to exercise and the ability to afford gyms to getting regular medical checkups, it's not a stretch to say this can reduce hospital visitations drastically.

Art and the consumption of art may increase - as people pursuing these pursuits may do so freely without worrying about having a job to pay the bills.

Tourism will thrive as people will travel more, especially those with already greater incomes who can save up the extra cash.

People going from doing whatever job they can get to pay the bills, to searching for the right job for them would mean businesses would have to make existing jobs more employee-friendly, which means every workplace will be more pleasant in this world.

Overall, it is not hard to imagine a better world. A better world with fewer problems than what we currently have, just because the primary stressor of most people's lives doesn't exist anymore. Fewer cases of suicide, less homelessness, children in every household getting proper nourishment and care, the list goes on and on.

Now, this doesn't mean everything's sunshine and rainbows. There could be some problems with UBI, and I know while reading the above paragraphs you've already thought of a few.

People might just decide to indulge in alcohol and drugs, and just waste all the money, leading to no improvement in their lifestyle at all. A recent research program funded by Sam Altman of OpenAI found that when they gave 5000 people $1000 a month for 3 years, recipients mostly spent the cash on life necessities, got a bit choosier in their employment, and made more use of medical care. If there was any negative, it was that married people who received UBI worked 15 minutes lesser per week than they used to, which to be fair, is vastly more positive than the predictions of most naysayers.

This doesn't prove that on deploying UBI on a massive scale, a percentage of people will not choose alcohol, drugs or just get really lazy and do nothing, but it is a hopeful sign.

But now, we have to address the 96 trillion dollar hole, that we've conveniently forgotten all this while.

Who pays for UBI?

II.

Here's where we have to modify the definitions of UBI to even make it work. It's a shame because right now, there's no way to fill that 96 trillion dollar hole every year. As society gets more prosperous, perhaps there might be - but there are reasons to believe it won’t.

We already produce enough food to feed every person below the poverty level. But there is no incentive to feed people that can't pay for it, so the food just doesn't get packaged and transported to those who desperately need it.

And incentives are something we'll come back to again and again, but for now, let's modify the definitions of UBI to make it more equitable.

We begin with trying to incorporate poverty as one of the variables. In the United States a person is counted as being in poverty if they live on less than roughly $24.55 per day.

The official poverty rate in 2022 was 11.5 percent, with 37.9 million people in poverty.

Let's assume it's 35 million people, and thus, we can reduce our 96 trillion dollar UBI fund to a mere 35 billion dollars.

You can think of a lot of government initiatives that can be cut to finance this, and it's just too easy at this point. The US government spends 300 Billion a year on prison systems alone.

Just reallocating 10% of that can finance a version of UBI for those below the poverty line. If the reduction in crime works out as predicted, there are almost no losers here.

But what about the rest of the world? Surely, the US government doesn't have to finance the UBI programs in Ethiopia and Bangladesh, right?

Here again, I think the value of research is underrated. Every government has more money which you're underestimating, and every one of them is squandering this capital in ways that is obviously stupid, over just financing a basic UBI program.

Many nations already have some version of welfare, free healthcare and free education, so an UBI program is certainly possible. Andrew Yang is the only popular US politicians I can think of - who had UBI as a major policy proposal, but he shouldn’t be the only one. In the coming years, we’ll see more and more politicians arguing for UBI-like policies, and hopefully, they’re debated well and aren’t funded by reckless printing.

There are other ways to finance UBI - like a negative income tax where you get paid if you earn below a certain threshold of yearly income, while those above that threshold don't get paid but instead have to pay normal income taxes.

In an ideal world, those below that threshold would soon climb over it instead of trying to game the system to stay below it as much as possible for the benefits, but this again is a better approach than deliberately doing nothing.

There's only one problem with this approach.

III.

Trusting the government to pay you your livelihood has other consequences, but a simplistic way to look at it is - you shouldn't want to trust one single entity with paying you an UBI especially when that entity is "The Government".

If they can starve you when you criticise them, the government practically owns you and your freedom. Remember that "Government" is just a word for a group of people like you and me, who are, hopefully, elected by people. No government wins by 100% of the votes, which means there will always be a percentage of people who criticise the ruling government and that is perfectly healthy to do so.

Critiquing the entity who will pay you all the money you'll earn this year isn't exactly the brightest idea, and even if we assume that people will continue to do so, the tides of democracy just has to elect someone who is willing to abuse their power to strangle dissent.

This is, by far, the biggest critique of an UBI in my opinion, and since we're well aware of the corrupting nature of power, it's not hard to imagine such a scenario being weaponised especially when the citizens are dependent on the income.

A way to solve this is through an additional tax on large corporations by an international entity. The Fortune 500's combined profit last year was over 3 trillion dollars. Even 10% of that is enough to run an effective UBI program for those below the poverty level, in multiple nations worldwide. Since it's an international entity that is not "The Government", there is a lower chance of this getting weaponised against dissenting masses.

So, it's possible and yet not so simple. Fortune 500 companies won't just decide to get a portion of their profits away to some entity from the goodness of their hearts and they shouldn't be expected to.

The international entity may just end up as evil who, by virtue of trying to do good with incentives, end up installing systems where your online interactions or your carbon-consumption, or whatever random metric they decide is chosen as a factor of whether you get UBI or not ~ regardless of your poverty level.

This paints a grim picture, but it was already grim when we decided to modify the definition of universal from being everyone to just those below poverty.

If only those in poverty can have it, it's not truly unconditional UBI. How or even if it gets distributed among households where one partner works and has a high income and the other doesn't is still up for debate.

And one of the primary questions of how it gets distributed throughout the world if the money is unchanged, which was one of the questions I asked in the first paragraph of this essay, is still unresolved. I can imagine that there will be a certain migration of people from more expensive neighbourhoods to less expensive rural areas where it is possible to live off the $1000. If it’s started in specific areas, it’ll certainly cause a migration of people to those areas even if they don’t receive it - as they can seek employment in the services of those who do.

Over time, accelerating globalization might negate the need for location-specific pricing of UBI. There have been basic-income pilot projects all over the world conducted on a smaller scale - and most haven’t shown any adverse effects. The wikipedia page of UBI has a list of most of such projects, and this is a page that you should send to anyone who argues against UBI because of the collective damage they predict it might bring.

We haven't yet addressed the naive criticisms against UBI. Let’s do that.

IV.

The first naive criticism is things costing more because of UBI. If money printing isn’t the primary source of UBI, that just won’t happen.

“If a thing that used to cost $1000 before UBI now costs $2000 after UBI, it doesn’t make sense?!”

It doesn’t, because you don’t understand free markets. Things won’t just start costing more because people have more income. Different companies won’t just decide to cooperate & collectively raise prices, and individual companies can’t raise prices drastically without risking their business to competitors. The economy might even have the capacity to increase production to meet the increasing demand generated by UBI, as UBI could enable businesses to create more jobs and invest more in innovation, and it could incentivize the creation of more businesses by enabling entrepreneurs to take more risk.

Now that the naive criticism is out of the way, let’s look at the most important one -

"I worked as a cashier / bartender / (enter any entry level job here), I worked my way up to where I am. If I had UBI, I wouldn't have had to do that, and I would've stayed at that income level forever."

For those of us without a trust fund, we've all had to experience starting from zero. We've all worked that job that we don't wish on our worst enemies, like a lousy customer support job where trying to pacify constantly yelling customers is a part of one's daily life, or a data entry job where you all you have to do is copy and paste things on a screen for 8 hours hoping your mind will learn to deal with the constant boredom it brings.

Entry level jobs are usually jobs with high vacancies because people are always quitting to do something better or quitting just because they're too tired to live that same day all over again.

There are many who go from earning a minimum wage in this job to becoming very successful later in their lives, and just like it is the struggle that you smile upon the most in times of victory, people often look back fondly at those days where they got up and went to work even when they didn't want to.

Unfortunately, people forget that every honest job and every one working an honest job should be celebrated - and thus, the worst part of every minimum wage job is also the low status that society associates with them.

But these jobs are also the first step in the door for millions of people, and once you start going to work, adjusting to a routine, understand and learn how the world works, it starts opening up doors where you can climb the ladder better. Thus, every one of these jobs serve a critical role, not only as a function for the businesses but for society at large where it teaches those who join them useful skills that they can then carry on to a new job.

The reactions to this reality is especially interesting.

In a poetic way, every person who suffers either wants no one else to suffer (like a hero), or wants everyone else to suffer (like a villain) - and without passing judgement on any of these archetypes, we will regularly see people arguing that they want everyone to start from zero in one of these jobs because they did the same, or they want no one else to suffer like them and can’t wait for AI to automate such jobs away.

Not every minimum wage entry level job is bad, per se. Some are genuinely fulfilling and it's easy to find people who spend their whole lives happily working in these roles. If you ask such people what they'd do if there was UBI, they'd probably still choose to go to work.

But for the vast majority of people who hate going to these jobs, would they still do it if an UBI paid more than what their entry level job is providing?

The honest answer to this question for most people is no. Some would say yes to maximise their income and I respect that, but for most people, the answer is no.

Several things can happen at this stage. The entry-level jobs would have to increase their salaries to attract talent, which they would do if they cannot hire for these critical yet currently not well paid roles. Working conditions would be improved and employers would scramble to make sure people still choose to work there.

The people who choose the UBI income over the minimum wage crappy job - what would they do? Would they sit on the couch all day and watch Netflix? Would they get addicted on drugs and alcohol and waste their lives away? Or would they work towards getting some better job, starting a business, going back to school, start pursuing a hobby, or create art instead?

The cynical view is to assume that they'll do worse, and by missing the lessons that one gets from working in such roles they're depriving themselves of the struggle necessary to attain greatness later in their lives. I empathise with such a world view, and it's pretty common among those who worked such a job and then became successful later, but I do not think that this is the correct answer.

Like I've argued before, I think people not doing a job that they don't want to do is better, not just for them but for all. Them missing out on a painful experience shouldn't be a loss, but as every human being knows, the feelings of struggle and pain are universal and they'll get to experience similar levels of struggle and pain in any job or decision they take, as will we all. But in doing something that matters personally to someone, the feelings of pain and struggle are much better handled.

The crucial difference is that by giving everyone agency and choice over what to do to thrive - not just survive in this world, you instantly unlock human potential for a vast majority of the human population. Life in survival mode is a life wasted, and I'm sure that by removing the stresses of money from the lives of people, we'll see a gradual uptick of not just new businesses being started by people but new scientific discoveries made by those who went back to school and pursued their natural interests in fields they're passionate about.

To the cynical mind, this sounds like virtue signalling. And most comments about UBI, unfortunately is. You can say "I want people to suffer less" as much as you want, but unless you're doing something about it- all you're doing is signalling virtue.

Maybe, even I, who spent the last 3000 words arguing for UBI, am doing the same thing. But there's an important reason why I'm talking about UBI now, and I predict that very soon, a lot of people will be talking about UBI in some form or another.

V.

In one of my previous essays, I outlined Sam Altman's vision for the world - and it is a world where AGI is a part of our reality. We can argue about whether AGI is possible or not, and I've certainly done that in the past - but in this essay, I'll predict a simple thing: Millions of jobs will be lost in the coming years, as a result of AI progress. Millions of jobs may be created, but overall, the age of the mindless work may be coming to an end.

Roughly 15 million people in the world are employed in call centres. All those jobs are an API call away to a multimodal LLM that can definitively do that job better, and more enthusiastically than the average call centre worker. They can work longer hours, and they’ll never punch the computer screen one day out of sheer frustration.

Being a translator is soon going to become unnecessary as AI can already work better and longer, and more accurately than any translator.

Self driving cars, which almost always feels like they're too far and not ready for our roads - may soon replace every driver of every vehicle, from commercial delivery ones like Uber, Doordash, Amazon, to public ones like buses & trains. In very short amount of time, they'll go from being not being ready for traffic to being provably safer than trusting human drivers.

Most knowledge work that don’t require specific-knowledge, years of expertise for correct intuition, that is, any knowledge work that can be done by a highly impulsive teenager is at-risk. The junior code-reviewer, the intern who reaches out to business prospects with personalized messages, the social media intern who makes and posts memes, the spreadsheet kid who only knows a few formulas really well - all of these can be done by the AI we have today, and the AI we have today is going to continue to improve to do more jobs better.

None of this will happen overnight, but all of this will happen gradually.

I've argued before in my essay God is not a bird, that just scaling deep learning won't get us to AGI. But algorithmic improvements will. Whether that's more efficient search within the training data leading to better reasoning, or something that mimics the learning process better than transformers, we will soon learn what it feels like to live in the most important decade of human history.

Consider yourself learning a subject from a textbook. You don't just flip through the text, reading every page and attempt to learn on the fly. You learn, solve some examples, internalise the concepts, practice problems, rate your progress and then move on to the next chapter. When AI training algorithms start doing the same thing, instead of just training of massive amounts of data from the internet, i.e, when large language models start to accurately mimic the processes of reasoning, taking their time to think and learn, before responding in ways indistinguishable from a human learning a topic, such a system is more than capable for the vast majority of jobs we have.

Given that we're seeing massive price decreases all across the board, it's almost certain that algorithmic improvements will continue. As the best minds in the world continue to work at this, each new iteration will be indistinguishable as a worker doing a job that was previously thought to be only for humans.

Everyone losing these jobs will find better things to do, better jobs to accommodate them in, but as time passes and more job functions and roles get taken up by AI agents who do them better ~ the need for an UBI safety net will continue to grow.

I can imagine a very optimistic future where new jobs and roles open up in a perfect equilibrium to accommodate those losing their jobs due to AI disrupting their industry, but a more realistic scenario paints a slightly grim picture. The productivity per person will increase - but the masses losing their jobs would quickly need to adapt.

If this was a fairer world, either the price of goods would be so low that people would not need to worry as they searched for new jobs, or people would already have an UBI that takes care of their basic necessities. It's not wishful thinking to imagine otherwise - a civilisation that's on the verge of creating AGI must have already built a UBI for themselves.

This perfect civilisation also has no wars, and no child starves to death, but we're not a perfect civilisation. But that doesn't mean we can't strive to be.

Sam Altman, in his infinite faith for his vision of AGI, already prepares for a world where UBI works. That is why he started Worldcoin, where your humanity is proven through an iris scan and wealth is distributed in the form of World($WLD) tokens.

I don't think that worldcoin has cracked the code for UBI, but it's a vision I respect. Worldcoin isn't dependent on any government, and it's not discriminating based on geographical factors (yet).

It's UBI in the way UBI should be, unconditional and without any means test, which means it's not only divided among those who need it but divided among everyone.

There are privacy concerns on everyone's eyeballs being scanned, and bigger questions about why VCs got token allocations anyway. The UBI Orb device isn't the only way to identify people - they also collect email addresses and phone numbers, at which point you have to wonder why they don't just ditch the orb altogether and go with a normal KYC process like government issued IDs.

Either way, worldcoin seems to be the most successful of UBI programs out there, and one that doesn't depend on the government at all.

The perfect version of such a project would attempt to fill the 96 trillion dollar hole, which Worldcoin doesn't.

Who pays for this?

One answer to this question is OpenAI. There was apparently a clause when OpenAI was founded that profits would be capped at a certain threshold and the excess funds would be redistributed to humanity at large. Worldcoin was probably the mechanism by which they planned to carry out this distribution.

Unlike depending on the government, here, you're depending on a private company - one that's arguably the most powerful private company in the world. Is this scenario better? I don't know, but it sure beats starving to death whether it’s a government responsible for your UBI or a single global entity or a private company or a company that might be a non-profit or a for-profit, depending on how much they need to raise.

I do want to see more experimentation in the private UBI space - maybe even ones using blockchains who can figure out a way to either fill that 96 trillion dollar hole, or they figure out a game theoretic solution that's somewhat satisfactory to all participants, and hopefully, all of humanity.

Either way, this is a problem that must be addressed now, rather than later, because it’s increasingly looking like we don’t have much time. AI that’s capable of replacing humans is here, and they’re only going to get better.

When we imagine the future of superintelligence, we imagine a world where AI has solved everything.

The ASI, when it exists, can simply tell us how to solve our UBI problem, but we aren’t getting there overnight.

This means we have to do what AI can’t do today, and fortunately, it’s something we’ve done over and over again throughout our history.

We must save ourselves.

What is the best way to predict the future?

This is a question worth asking today, because I made many predictions last year and one of them has turned out to be wrong.

I had predicted that Hollywood would soon be disrupted by independent creators who make better movies using AI tools than the cash-rich creativity-starved Hollywood studios.

But where are the great AI-made films, the brilliant movies that are made by one person or a very small team?

So far, we've not seen any relevant examples, so, this leads us to ask more questions, like -

Why did highly creative people, and these are creative people as you need to have a very high degree of openness to new experiences to be early adopters, getting access to powerful tools of creation lead to… nothing?

Text-To-Video platforms like LumaLab’s DreamMachine and Runway Gen-3 have recently come out, and it seems like Google Deepmind’s VEO & OpenAI’s SORA are not too far away from getting released to the public, so everyone will make the same prediction again.

But they’ll be wrong.

Other than video versions of memes (which we’ll see a lot of) and music videos & trailers (where random clips can still work), we won’t see an explosion of films because just Text-to-Video isn’t enough.

The problem isn't with the people, who are already creative and capable, who are definitely trying to use these tools & models, but can’t create anything resembling a film without investing significant amounts of time.

We need something that actually helps turn your stories to reality, but before we get there, let's examine this situation deeply.

What is wrong with the tools?

II.

There are two emerging classes of software right now - Software with high controllability & Generative Software that don't give you granular control.

Both these emerging classes are also trying to merge together in one. Apps that offer high granularity are introducing features of generative software. And generative software is getting easier to control.

For example, in the medium of text editing software, you can open up any text editor like Notepad, MS Word, Apple Notes, etc, and every word you write into that file - gets to be chosen by you. If you want to write an essay on "Top 5 YouTube creators you should follow if you like Math", you can write such an essay in 30 minutes.

Or, you can open ChatGPT and prompt it with "Write me an Essay titled ‘Top 5 YouTube creators you should follow if you like Math’ and make sure to include the following YouTubers - 3Blue1Brown, Computerphile, Professor Leonard, StatQuest & The Math Sorcerer" and ChatGPT will write your essay in less than 2 minutes.

Notice how you could exert a degree of control over the generated essay. If you wanted to write a joke about Professor Leonard being the smartest jacked YouTuber you've seen, you can just add that in your prompt.

The more you expand your prompt with your instructions, the more control you get.

Similarly, the text editors of tomorrow that come bundled with AI will allow you to do exactly this - and you'll give up some degree of control in typing out the entire thing, to save time. (Unless you're writing to think, the best kind of writing such as this essay, in which case, you'll very much prefer to spend more time typing).

Image generation software is following the same trend. Dall E - 3 is better than previous models at following your instructions, while image-editing software like Photoshop have integrated tons of AI features.

Then, there's video generation which just produces video clips of prompts. It's still very early, which means you can't really instruct any foundational model to accurately obey your exact instructions, but that will change as the models get smarter.

But will those improvements be enough to eventually produce films?

III.

Let's take a step back and ask ourselves, what does creating an AI film mean?

One answer to that question would be a model of how image generation tools already work - You type in a prompt and the AI works to create a feature length film. Maybe it's a mixture of foundational models consisting of one who writes the script, one that generates the audio and the other generates the visuals, and a final one who stitches it together.

That doesn't exist, as it's infinitely hard with strong technical limitations like having consistent characters, and not to mention compute intensive to do that.

But even if the technical & compute challenges weren't there, would it be worth watching?

While most would be dismissive instantly, owing to their general aversion of AI, if someone typed in "Star Wars, but with Spiderman in it", regardless of how the movie is - I think a lot of people would watch it especially if the quality of the movie was all right.

There's craft involved in producing such films as well. Take your judgement of how easy it is to do out of the equation, and you can start to understand that the best films might just start from best prompts.

And someone more creative than me can come up with a sentence better than "Star Wars, but with Spiderman in it." It's not hard to imagine that. I assure you, even you can come up with something that makes for a great movie.

Without actually launching such a company, it's impossible to predict whether such a product can get traction, but if just AI Text to Image and Text to Video are popular, then Text to Film should definitely be a useful product.

The only caveat is that it has to be watchable. It has to be a visual representation of a story, i.e, it has to tell a story properly, effectively immerse the viewer without calling too much attention to itself.

And today, none of the things that people create with AI tools are watchable. They look cool as music videos, with fancy visuals and trippy transitions, but you can't watch an hour of AI generated videos without losing interest unless you're sufficiently intoxicated, at which point the story won't make sense either way.

But there's a class of individuals who can create great films, entirely alone, but they choose not to do it for very good reasons.

III.

Indie game developers, usually as individuals or in teams of one to ten, can produce very high quality films today. But they don't.

Using software like Unreal Engine, Blender & Premiere Pro, it's not hard to imagine such an individual making a high quality film.

They opt for making games instead, because 1) the gaming market is considerably larger than anything they'd get if they made a high quality movie and just uploaded it on YouTube. 2) the intersection of people who play a lot of games and thus, know game mechanics throughly, and people who can code & use software like Unreal Engine is larger than people who want to make films and know how to work with these tools.

But, as most game developers will tell you - even with using AI tools in their workflow, making a game takes a lot of time & making a film can take considerably longer.

Thus, even when it's possible to make a film, the incentives aren't there to do simply because of the time constraints.

For a beginner with no experience in anything, you have to learn how to use a game engine. Then you have to learn how to edit videos. Probably a little bit of cinematography as well. All these can easily take you a few months to master the basics, and a year of consistent effort to get good enough to make something worth watching. Even for those who know how to use these tools, the simple fact of turning the footage made in game engines to films can take anywhere from weeks to months.

If only the tools of film making were quicker, like typing your story into a text box and that's it!- a lot of people would produce great films. And it doesn't have to be simple one line text prompts. It can very well be a short story you've written, and the AI does the work of translating that into a film, giving you both control and speed, so that you can focus on what you do best.

I think that’s the most viable path in this idea maze, and I’m reminded of Steve Jobs who vehemently started moving in a different direction during the personal computer revolution when he was always voicing, “We need to move away from programming. Programming computers is great, but the users don’t want to spend time programming. They want a computer that just works.” In the same way, I don’t think people want to spend weeks generating the perfect video clips and stitching them together - instead of just bringing their story and having AI agents do the rest for you.

Imagine what this can lead to - films that need to be made, that no one is making right now. Films on topics too controversial or unprofitable for Hollywood, films about the realities of war that can cause real change, or just films about someone’s life story or the story of a small city that need to get told. Films of high quality like Oppenheimer except a version that’s focused on the genius of those in the Manhattan Project like John von Neumann, Stanislaus Ulam and Richard Feynman, films such as these that would inspire millions of a new generation.

With text-to-film software, you’ll be able to create such films in a few hours with just a story.

The only skill you'd need is storytelling - something that's innately already present in most people, and something that's uniquely individual and personal enough to most people that almost anyone can create anything if given the right power.

Sure, not everyone will choose to create films. But a lot more people would, and the chances of great films emerging increases exponentially.

These incentives exist in a world with lower friction, and the world of independent Substack writers and YouTube creators have showed us that it is possible.

But given that there’s technical limitations & hard challenges to building such an app, who’s going to make it?

IV.

If you don’t make things, things don’t get made. And at the end of the day, very few people have the skills and persistence to make things that people want.

To see something in this world, it’s not enough to write about it and keep exploring the idea mazes.

Just like the intersection of people who play games and can code is relatively lower than those exclusively doing either, but it’s still higher than other intersections of such proclivities, there are very few people in the intersection of those who can code and those who want to make films.

Thankfully, I’m present in that narrow intersection.

For the past few weeks, I’ve been building the Text-To-Film app of my dreams and I’ll be using it to create films of my own stories & essays. I’ve been deeply thinking about this problem for a long time, and the existence of such a product is necessary not just for me to create my own films, but for anyone in the world with a story to tell.

It is said that only in the process of building something do you realize the depth & complexity of the problem space, and it is only through doing the work that you find out the answers to your problems. I’ve realized this to be true in the past few weeks of building heads-down.

In the process of building the project, I’ve figured out the answers to some very important problems like how to make consistent characters work, how to ensure there’s a consistent aesthetic, and most importantly, how to ensure the creation of films that hold your attention, and even though it took a lot of chewing glass - sometimes, that’s just what’s needed to arrive at important breakthroughs.

With the discoveries I’ve made in the process of building this, I’m now confident that creating films (not just videos) requires a radically different approach and it’s one that is going to look inevitable in hindsight.

I’ve named this app Fect (why, and what does it stand for, are questions that are answered in the Fect docs), and with Fect, my goal was to adapt one of the many short fictional stories I’ve written.

Fiction requires consistent characters to work correctly, and although Fect works perfectly fine when consistent characters are not required - like in video essays, or commentary videos, films based on real people require… well, people.

I think Fect overcomes the glaring problem with AI video tools today - that they all produce things that are a chore to consume unless you work to package them accordingly.

With Fect, your story takes center stage - and if you have a good story to tell, that’s all your audience will be focused on. With a Ken Burns style motion that’s guaranteed to keep viewers engaged, Fect’s agentic workflow ensures every frame of your film follows your script. Thus, Fect is something that can be used today, for every one of your stories and essays, to create films & videos that would’ve required you a team of video editors to do.

Here’s one of my stories that got 6.6k upvotes on reddit’s r/NoSleep when I posted it there a few years ago, which I’ve now made into a film using Fect -

This film was made entirely using Fect with minimal editing to stitch the clips together using a video editor. It took less than 30 minutes to make this. If you watched the video from the beginning to the end, you might have noticed slightly morphed fingers, changing hairstyles or the occasional appearance of a character that’s not supposed to present in that scene. I could’ve edited them out in post, but it’s good to keep them in there to make you understand where Fect is right now. The hairstyles can be controlled to remain consistent as well, I skipped over that part while making. But overall, I think the film looks perfectly fine.

All the problems that I mentioned are secondary if the story kept your attention for the entirety of 9 minutes, and even though this is the worst that Fect will ever be, I’m still happy with what it does now and how capable it is today.

Here’s The Raven by Edgar Allan Poe without consistent characters that took me less than 15 minutes to make -

The visuals are consistent, and adds a new dimension to the poem all on its own. Something like this would’ve taken me a couple dozen hours of do without Fect. Now, it’s done within 15 minutes.

I see myself using Fect to turn my essays that I write here into films soon, and if that sounds like a really autistic reason to make an entirely new company, maybe it is! But as user #1, I’m focused on making it as great for myself as it can be - but it doesn’t have to stay that way.

You can try out Fect today yourself by visiting fect.ai!

Anyone who signs up now will be able to get 15 minutes of video generation time for free!

By subscribing to any monthly or yearly plan, you’ll also get access to the discord where you get to tell me every day how to make Fect better for you. (And here’s a secret for only Bright Mirror readers - if you subscribe to an annual plan - let me know via email by reaching out to founder@fect.ai and I’ll grant you an additional 5 hours of video generation time this month for free!).

With all that said, I’m excited about the future of independent film making and now, I know for a fact that independent individuals creating great films is not just possible, but inevitable.

Let’s revisit the question that I started this essay with -

What is the best way to predict the future?

The answer should be obvious by now, and it is one where you play an active role.

The best way to predict the future -

Is to create it yourself.