The analogy is doing something useful. It makes tokens legible as an input, not as a product. For two years the labs have been describing what they sell as “intelligence” or “AGI” or “the smartest model,” all of which are product framings that imply you should pay for the thing itself. The utility framing flips that. Tokens are an input. You consume them to produce something else, something that normally would have required some cognitive work. That is the right intuition, and once you take it seriously the economics of AI look different than the labs have been telling us.

The analogy also carries baggage that does not fit.

What makes a utility a utility

Utilities have inelastic demand. You need electricity to keep your fridge running. You need water to live. You do not shop for kilowatts on Tuesday morning. You pay whatever the bill says, and you keep paying because there is no version of your life where you stop. The product is not amazing, you simply cannot say no.

The other thing that makes utilities work is that the commodity is fungible. A kilowatt-hour is a kilowatt-hour. Solar, coal, nuclear, hydro, it makes no difference once it reaches your meter. The meter is meaningful because both sides agree on what the unit is, and the unit is the same regardless of who supplied it. That fungibility is what makes the wholesale markets work, what lets the regulators set rates, and what makes the entire utility business model legible to investors.

Tokens are not like that.

A token from GPT-5 is not a token from Claude 4.5 is not a token from DeepSeek V4. Different tokenizers literally cut the world into different units. A 1,000-token answer from one model is not the same workload as a 1,000-token answer from another. The same task can take 800 tokens in one model and 4,500 in another, at radically different quality. Ten words from Richard Feynman are not the same as ten words from a corporate lawyer. The count is identical, the value is not. There is an underlying hidden metric to the token that represents the actual value per token, and the bill does not show it. The unit, on its own, tells you nothing.

The utility framing depends on the unit being legible. The unit is not legible.

Tokens are a factor of production

The useful thing the utility framing does is name what tokens actually are. They are an input, the same way crude is an input to a refinery. You buy them, you transform them, you sell the output. The token is not the product. The token is the raw material you use to make the product.

This is the frame I have been working through for a while, including in the differentiation-then-commoditization piece a few months ago. The core dynamic is per-JTBD, not per-model-release. For any given job that AI might be able to do, there is an exploration window during which the question is whether AI can do it at all. In that window people will pay premium prices to find out. They will use the frontier model for the translation prototype, the customer-support agent prototype, the contract-review prototype. The premium is rational because the question being answered is binary. Does this work, yes or no.

Once the answer is yes, the question changes from “can it work” to “what is the cheapest input that clears the bar.” That is when the JTBD commoditizes. Capability unlocks new JTBDs, and that unlock window is real. But every specific job that crosses the works-doesn’t-work line commoditizes fast inside its own track. Nobody uses GPT-5.5 for translation in 2026. Translation works on a much smaller model that runs at a fraction of the price, and the procurement decision is made on cost.

This is what makes the utility framing partially right. In the commoditization phase a given JTBD really does behave like a commodity input. Multiple providers, real substitutability on the outputs that meet the bar, a buyer who shops on price-per-outcome. That is commodity behavior. The token in that phase is meaningfully a factor of production, not a product. The utility analogy gets the input nature right.

It misses everywhere else.

The phase the consumer market is already in

A lot of the common consumer JTBDs are already in the commoditization phase. The capability is there, the optimization question has been answered, the frontier does not earn a premium for them anymore. This is the capability overhang I have been writing about in Gap of Imagination. The frontier keeps building capability that ordinary users cannot even imagine an application for. The premium that flows to the frontier today is partly flowing because the labs keep redefining the JTBD bar, not because users are running into capability ceilings on the things they actually want done. In simple terms, you give people an advanced agent and they ask for a selfie.

Demand keeps moving to the frontier because the labs need it to. Sam saying “the demand keeps surprising me, people always want the smartest model” is a true description of the demand curve and an incomplete description of why it looks the way it does. There is a world in which most JTBDs reach the commoditization phase and tokens behave like a real commodity market, priced near marginal cost with arbitrage running through everything routine. In that world there is still a tier of work where the value per token is enormous and the bill barely registers. Curing a category of cancer is the canonical example, the kind of problem where the answer is worth so much that asking how many tokens it took is the wrong question. Pricing a derivative nobody else priced correctly, discovering a material that does not exist yet, finding the next class of drug. Those JTBDs do not commoditize the way translation did. The labs and companies that learn to direct token spend at them are not playing the same game as everyone else routing the cheap stuff through Claude Haiku.

The labs know this, which is why their biggest product bets are not at the model layer anymore.

What this means for AGI labs

If tokens are a factor of production and the JTBD-by-JTBD commoditization is real, the closest historical analog for the labs is the oil majors. Large by revenue, thin by margin, with the value compounding through volume rather than through pricing power. ExxonMobil is one of the most consequential companies of the last century and its operating margin per barrel is unremarkable.

This is not bad news for the labs. It is just a different kind of business than the utility pitch implies. I wrote a few weeks ago in Of AI Bubbles and Token Explosions that token demand in the agentic era is going to grow by several orders of magnitude. Jevons paradox plus zero human bottleneck plus collapsing per-token cost compound into a demand curve steeper than almost anyone has modeled. The more I use and deploy agents the more I think this is true. I am already burning hundreds of millions of tokens every day, and the reason I do not run even more agentic flows is cost and time, not a shortage of use cases. That will happen to the rest of us. The build-up is justified. The labs are going to be enormous companies. They will be enormous in the way oil majors are enormous.

What they will not be is high-margin at the model layer. The surplus per task completed with tokens, which is the spread between marginal cost and marginal product, accrues to whoever owns the relationship with the user. The labs build the volume side. The application layer captures the spread.

Which is exactly why every major lab is climbing up-stack as aggressively as they can ship. Operator, Codex, Atlas, Sora, the apps platform, ChatGPT for Business. None of these are model-layer products, and for sure they are not what you would expect from a token factory. All of them are bets on owning the application layer where the surplus lives. The investor pitch is utility-shaped because utility-shaped capex needs a utility-shaped story. Sam’s repeated “intelligence is a utility” line is positioning, not analysis. The actual business plan is to capture the surplus up-stack before somebody else does. Right now, that front is enterprise. The utility pitch is the financing strategy. The aggressive up-stack push is the business strategy. They are not the same thing, and watching the products instead of the press release is the only way to see which is real.

What this means for companies buying tokens

If you are on the buying side of the meter rather than the selling side, the production-input frame has direct implications.

Almost every AI subscription you pay for today is subsidized. ChatGPT Plus, Claude Pro, the Bedrock and Vertex offerings, all of them are priced below what the actual workload costs to serve (the difference shows up on someone’s balance sheet, just not yours). The labs are eating the gap to acquire user habit, lock in workflow integration, and prove a category at scale. You are paying $20/200 a month right now because someone with capital wants you to.

That subsidy ends. The path is visible. OpenAI has no ultra developer plan, they want you on the API key (the meter). When the subsidy actually ends, every company that grew used to a $20-per-seat AI bill is going to discover that the real cost of the workload is several times that, and the convenience tax was the lab eating the difference to keep them locked in. That is also the moment tokens become legibly what they always were. A production input, with a price that has to be procured for instead of subscribed to.

The value of the tokens is very high, don’t get me wrong, which means that the companies that survive that transition are the ones who built denominator discipline early. Your token bill alone tells you nothing. Tokens per resolved ticket, tokens per closed sale, tokens per generated brief, those are the units that matter when the meter starts charging the real number. The same applies on the supplier side. The lab that costs ten times more per token is not necessarily ten times more expensive for your workload, and the only way to know is to have the denominator.

Two things to be working on now, while the era is still subsidized. First, build for swappability. The provider you start with should not be the provider you end up locked into. The orchestration layer or harness between your product and the model has to be thin enough that swapping suppliers is a configuration change, not a refactor. Every prompt format, tokenizer assumption, or function-calling convention you embed deep in your code is a switching cost the supplier collects later. Second, track outcomes per dollar, not tokens per request. Build the denominator now, while the bill is small and the room to instrument is generous.

When the subsidy ends, the companies that did this work keep their AI cost lines healthy. The ones that waited discover they spent two years embedding one supplier into the foundations of their product, and the bill is now arriving at the unsubsidized price.

The labs’ incentive is to make the meter spin faster. Yours is to extract the most outcome per unit of meter spin. Those two interests are not aligned, and that is what the utility framing is hiding.

The labs are commodity producers learning to live with commodity economics. The companies buying their output are consumers of a production input who have not started procuring it like one. Sam Altman’s utility framing is half-right about the thing that matters most. Tokens really are becoming a factor of production, and that framing is the cleanest one anyone in his position has put out loud. The half he had to leave out is that factor-of-production economics are not utility economics, and the businesses that grow on each side of the meter will be very different from what the pitch deck suggests.

Tokens are not kWs, at least for most of us.

What a company is

Let’s put on the MBA hat for a sec. My favourite place to start is Ronald Coase, 1937. Why do companies exist? In a free market, every task could in theory be a contract between strangers. The answer Coase came up with, and won a Nobel for decades later, is that markets are not free. Every transaction has a cost. Finding someone, negotiating, writing the contract, monitoring it, enforcing it. A company exists because, up to a point, doing the work inside is cheaper than doing it through the market. The size of the firm is set by where those two cost curves cross.

That definition treats the company as a black box. Inputs go in (capital, signal, time). Profit comes out. The org is the function. The people inside are an implementation detail that has been remarkably stable for two thousand years.

Why the box was full of people

For most of history, the box was full of people for one reason that has nothing to do with hierarchy, politics or the reason of the company to exist. Companies needed decisions to transform the inputs into benefits. Decisions need intelligence, and until very recently, the only place we knew how to source intelligence was inside a human brain. So humans were inside the box, both deciding and acting. The hierarchies that grew around them (Dorsey and Botha trace the lineage from the Roman contubernium through the Prussian general staff to the American railroads) were the cheapest known way to coordinate brains across distance and time. What is different now is not that this work was unnecessary. People were the only substrate that could do it. What changes is that a second substrate exists, and the interesting question is what happens when the two combine.

What is changing

Some decisions can now be made by an agent, often well, sometimes badly. Some actions can now be taken by an agent, often well, sometimes badly. The “often well” side grows every model release.

That changes the question we have to ask. It is no longer “do humans need to be inside the box at all.” It is “which decisions and which actions are now better to do with AI than with people.” Every company will have to answer that item by item, for years. Dorsey’s bet, the smoke that started this post, is that eventually the answer for almost everything is AI. Maybe he is right. Maybe he is overcorrecting because he just cut his company by 40% and needs the story to land. I do not know. What I do know is that the question itself is real, and the answer for any specific decision or action is rarely “none.”

Maybe an inversion

If most decisions and most actions inside the box move to AI, the position of the human inside changes. We used to be the operator. Spreadsheets, computers, every tool we built were extensions of us. We picked them up and put them down. Now, maybe, it is the opposite. The AI is becoming the operator, and the people inside the box are increasingly what it reaches for when it needs to do something it cannot do itself. The spreadsheet did not put accountants out of work. It moved them up one level. AI may be doing the same thing to us, one more level up.

What is still lacking

For the Dorsey version to actually arrive, four things have to become true that today are not.

First, the part of human work that is not strictly about intelligence: customer trust, ethical intuition in situations that are genuinely novel, the kind of judgment that only forms after years of skin in a game. Some of that is real signal an AI cannot generate on its own. Some of it is noise the org is better off losing (politics, taste-as-bias, the loudest voice in the room winning the argument), and a more AI-shaped org is honestly an improvement on the noise side. Both halves are true.

Second, and this is the bucket that matters most. Even a smart model only makes good decisions when it has the same context a human in the same role would have. Humans pick that context up everywhere: meetings, side conversations, Slack threads, customer calls, watercooler conversations, social cues, the unspoken stuff that lives in someone’s head from five years of doing the job. The model has none of it by default. So our new job, the part of human work that does not go away even when the intelligence is automated, is to gather context from across the company and make it legible to the machine. This is the part Dorsey makes look easy because Cash App and Square hand him a continuously updated picture of his customers for free. Most companies do not have that. They have a CRM, a few dashboards, and if lucky a wiki nobody updates. Building the layer that turns the messy reality of an organization into context the model can act on is the actual unsolved problem. It is the reason we created the org brain @luzia. Anyone who has tried to wire a model into a real business knows the model is the easy part now. Context is the hard part.

Third, agency under accountability. AI does not go to jail, and it does not lose a relationship the way a name on a contract does. Somebody human still has to be on the hook when something consequential breaks, and that gates how much agency an AI can have, regardless of how capable the model is.

Fourth, the objective. If you take the people out of the middle of the org, somebody has to write down what the org is optimizing for. The honest answer is the board. Boards already set the objective for human CEOs. In an AI-shaped org they would set it more directly, with fewer layers of interpretation in between. That is a clarification of where governance lives, not a limitation.

None of these are dealbreakers, and all of them are real.

There is a fire

Dorsey is probably more right than he was a year ago, and probably less right than he claims today. The honest way to read his essay is as a question. What does a company actually look like once intelligence stops being gated by a human brain? That exercise is, if you let it be, an optimistic one. Most companies get to rethink their fundamentals once in a generation, and this is one of those times. Done carefully, it produces two lists, one of decisions and actions to start handing to the machine, and one of places where the human role should grow. The companies that prepare will build both. The companies that do not will end up with one or the other. The smoke was Dorsey’s essay. The fire is the new shape of the company, and what is in that shape is a choice.

In April 2026 Meta ran an internal leaderboard called “Claudeonomics” where 85,000 Meta employees competed on token consumption. In 30 days they burned through 60 trillion tokens, a number that means nothing without context. At Anthropic’s public Opus pricing, a 50/50 input-output blend comes out to roughly $15 per million tokens. That would imply around $900 million a month, or $10.8 billion annualized, about 5.4% of Meta’s 2025 revenue. Call it a rough order of magnitude. The Information reported it on April 6, 2026. Two days after the story broke, the dashboard went down. Meta later said the employee who built it took it down “at their discretion.” That disappearance is the story.

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A raw token leaderboard measures who burns the most tokens, which isn’t the same as who ships the most value for the company. When the number on the dashboard is tokens, the top of the leaderboard is the engineer who never thought twice before running Claude on a whim, and the bottom is the engineer who thinks too hard about cost. Sure, tokens and productivity correlate up to a point, but at some scale the correlation breaks the same way lines of code broke as a proxy for programmer output once engineering developed higher abstraction coding languages.

Every organization using AI is sitting in a version of this right now. The bill is climbing faster than anyone expected, engineering is the early adopter, the rest of the organization is a couple quarters behind. The first instinct is to measure, and the measurement makes things worse, because the measurement has no denominator.

We need a denominator

If I told you a restaurant spent $20,000 on ingredients last month, you couldn’t tell me whether it had a great month or a disaster. Paired with meals served, the same number is a cost per plate. Paired with revenue, it’s food cost as a share of sales. Tokens on their own are the ingredient bill, and without a denominator the bill tells you nothing except that you spent money.

Tokens per PR merged, tokens per bug closed, tokens per support ticket resolved, tokens per qualified lead, tokens per feature shipped, tokens per paying user. Pick the metric the team already cares about, divide by it, and the number starts to say something. At Luzia we haven’t figured this out yet. I’m personally looking at tokens per PR shipped, tokens per feature closed, and even tokens per incremental retention. Some of the ratios are still too noisy to act on while others are pointing at obvious things we should have caught quarters ago. Trying to produce the ratio is what forces the question. A ratio you can argue about is more useful than a number you can only stare at.

Cap or don’t cap

Innovation and control pull in opposite directions here, and most people pick one and lose the other.

Cap usage and you kill the productivity gain. The engineers who figured out how to get 10x out of Claude did it by burning tokens on experiments, most of which went nowhere, and the few that went somewhere paid for the rest. Cap before you know which is which and you’re cutting the top of the curve while keeping the bottom.

Run it open and you’re writing million-dollar checks to Anthropic with no idea what came out the other side. That’s the Meta situation. Finance asks the question, engineering can’t answer it, and the conversation turns into a budget fight nobody wins.

You can spot the opposite mistake in founders bragging about tokens-per-employee in funding announcements, or, stranger still, in companies adding a token allowance to comp packages as if it were a perk. The tokens are spent on company work, so the “perk” is the company buying its own productivity and calling it a benefit. Both are the same leaderboard Meta ran, dressed up as marketing or HR. The theater works for one funding round. After that, investors start asking the tokens-per-what question, and the story collapses into the same conversation engineering had six months earlier.

You don’t cap, and you don’t budget per seat. You instrument. Six to twelve weeks of real use, logged against the right denominators, and by the end of it you know which teams convert tokens into outcomes and which teams convert tokens into nothing. This is not sophisticated. Tag the call or use separate API keys, name the team, point at the artifact where one exists, and let the ratios accumulate for a couple of months. Then you budget by outcome, not by seat. An engineering org that ships features gets more tokens. An engineering org that runs experiments nobody uses gets a conversation.

This only works if you start before the bill forces you to. Start after, and the budget fight is already happening, and nobody’s in the mood to be patient for three months.

2015, again

In 2015 every company had an AWS bill nobody could explain. Engineering owned the decisions, finance saw the number, and the number kept doubling. The conversation was the same one happening today, some version of why is this so high, why is nobody managing it, do we need to cap. A whole industry spawned around the problem, AWS implementation partners, cost-visibility vendors like Cloudability and Apptio, the Cloud FinOps Foundation, and the dedicated cloud architect role inside every mid-size company. The answer is what we now call FinOps. Instrument everything, tag every workload, assign ownership, report by team and by product. Five years of painful organizational change, and by 2020 cloud gross margin was a number every SaaS board tracked alongside revenue growth.

Tokens are the same arc on a faster clock. Eighteen months, maybe less. The companies that build the muscle now will be able to answer the board question when it comes. The ones that don’t will be writing down their AI P&L in 2027, wondering how it got away from them.

The unit is settling

Within 24 months, token margin is a number every board asks for by default, the way cloud gross margin became one by 2018. The companies that can answer “tokens per what” will optimize their spend and compound. The ones that can’t will be surprised by their own P&L, and the surprise will show up in the bill long before it shows up in the product.

A few patterns show up every time I watch a company try to build the muscle. The number survives only when finance and engineering co-own it, because whichever function holds it alone pulls it in its own direction. Finance looks at tokens without grasping the meaning. Engineering looks at tokens without paying attention to the cost. And the denominator has to be a metric the team already lives by, because inventing “tokens per PR” for an org that doesn’t already care about shipped PRs is a dead ratio by quarter two.

This week Jensen Huang told Dwarkesh Patel: “The input is electrons, the output is tokens. In the middle is Nvidia.” For your company, input is tokens. Output is whatever customers pay for. You’re the middle.

Stop asking “how many tokens did we burn.” Start asking “tokens per what.” If you can’t answer that for at least one what that matters to your business, you’re not running AI, you’re just pushing Anthropic’s run rate up and to the right.

Nobody got into the music business because they wanted to work in specialized light manufacturing. But for decades, the entire industry, its revenue, its power structure, its economics, was organized around pressing plastic discs and shipping them in trucks. The music was always the point. The plastic was never the point. The plastic was where the money was, though, and the plastic was the moat. In most cases, music was not what protected the business. Light manufacturing was.

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When the internet made distribution free, the industry almost collapsed. Not because the music died. The music was fine. The industry collapsed because the business had been built around the wrong layer, and a lot of talented people lost careers while the industry figured out what came next. The revenue model, the cost structure, the competitive advantages, all of it was attached to the plastic, not to the songs. And when the plastic became irrelevant, all of that went with it.

Then streaming asked a different question. Not “how do you replace the $15 CD?” but “what if you could have all the music that exists for $15 a month?” Revenue came back, and then some. The industry hit $29.6 billion in 2024, surpassing the CD-era peak, on a completely different model. More artists reaching more listeners than at any point in history. The music, the actual fruit, didn’t shrink when the plastic disappeared. It expanded. The music was the fruit. The plastic was the shit around it. And the shit was the only thing that died.

I think this pattern is going to play out again, across more industries than people expect, because of AI. And most of the conversation about AI and jobs is asking the wrong question.

The pattern

Not every business has this structure. Some are core all the way through. A Michelin-star restaurant, for instance: the moat is the chef, the experience, the food itself. There’s no incidental layer to peel off. The product and the infrastructure are the same thing. Those businesses are fine.

But some industries have a split that people inside them can’t see. There’s a core, the thing the customer is actually paying for, wrapped in a layer of shit that was never the point but ended up being the moat. The shit survived because it was expensive, not because it was valuable.

A platform shift doesn’t attack the core. It drives the marginal cost of the shit layer toward zero. When that happens, the moat dissolves. And if there’s real fruit inside, the fruit doesn’t shrink. It expands into territory it could never reach before, because the cost of the shit around it was the only thing holding it back.

The internet drove the cost of distribution to zero. AI is driving the cost of human intelligence, applied at scale, to zero. Same dynamic, different layer.

Photography. The core was always the eye, the moment, the story you chose to tell. The shit around it was enormous: darkroom chemistry, film stock at a dollar per shot, processing labs, printing, the entire technical apparatus that made photography a professional’s medium. You needed years of training and thousands of dollars in equipment before you could even start learning to see.

Digital zeroed all of that. The cost per photo went from a dollar to effectively nothing. And instead of killing photography, it created an explosion that would have been unimaginable in the film era. We went from millions of photos per year to billions per day. Professional photographers didn’t disappear. The ones who survived are more in demand than ever, because the only thing left to pay for is taste. The shit is gone. The fruit is all that remains.

And then the fruit did something the efficiency framing completely misses. It didn’t just expand. It mutated. Instagram, visual journalism as a daily practice, content creation as a career, the entire influencer economy, an $800 billion creator market that exists because taking a photo became free and infinite. None of that was a more efficient version of what came before. It was new territory that couldn’t have existed when each photo cost a dollar to produce. The fruit didn’t just grow. It spawned an ecosystem that made the original industry look like a rounding error.

What AI zeros

The internet zeroed distribution. AI is zeroing intelligence. Specifically, the kind of routine, repeated, human intelligence that many industries depend on at scale. Then, maybe, the rest.

I wrote about this dynamic in Differentiation, Then Commoditization: once a task crosses the reliability threshold, competition shifts from capability to cost, and the economics change completely.

Software engineering is the case I know best, because I live inside it. The core of building a software company is knowing what to build. Product sense, user understanding, the search for something people actually want. The shit around it, for decades, was writing the code, debugging it, testing it, deploying it. Expensive, slow, requiring teams of people for anything non-trivial.

That cost is approaching zero. One person can now produce what took a five-person team eighteen months ago. The result isn’t fewer engineers. It’s more software. Ideas that were too expensive to try are getting tried. Internal tools that no company would ever have staffed for get built on a weekend. The cost of building a first version has collapsed, even as the cost of distributing and scaling that product hasn’t, which is why funding rounds keep getting bigger while MVPs get cheaper. The money didn’t disappear. It moved from engineering to growth, from the shit layer to the fight for distribution.

The fruit is expanding visibly. The shit is disappearing. Same pattern as music, same pattern as photography.

Netflix is the one company I can think of that recognized this pattern from the inside and acted on it. They were a DVD-by-mail business. The shit layer was physical distribution: warehouses, envelopes, postal logistics. They saw that their own shit was going to zero, killed the DVD business before anyone forced them to, and rebuilt around streaming. The core (helping people find and watch stuff they love) went from a few million DVD subscribers to 260 million streaming subscribers globally. They burned their own plastic.

Finding the split

I think software is just the first AI-era industry where the pattern is obvious, because we’re living through it and the evidence is right in front of us. The question that interests me is how many other industries have the same structure and haven’t noticed it yet.

For any industry, there are a few questions worth asking. What does the customer actually value, and what is incidental infrastructure that exists only because it used to be expensive? If you could rebuild this industry from scratch today, with AI capable of handling routine cognitive work at near-zero marginal cost, what would you keep and what would you throw away? If the shit layer disappeared tomorrow, would the core expand into new territory (like music into streaming, like photography into the creator economy), or would there be nothing left? And the hardest question: is AI already making your shit layer cheaper, and are you treating that as an efficiency gain rather than a structural shift?

The pattern I described in The Gap of Imagination applies here too: most people inside an industry can’t picture what it looks like without the shit, because the shit is all they’ve ever known. The darkroom technician couldn’t picture Instagram. The record label exec couldn’t picture Spotify. The inability to imagine the post-shift world is what makes the shift feel like destruction rather than expansion.

Who captures the value

The pattern tells you what happens to the core. It does not tell you who profits.

When music went to streaming, it was Spotify and Apple who captured the upside, not the artists and not the old labels. When photography went digital, it was Instagram and Apple (the iPhone camera) who won, not Kodak and not the photographers. Whoever builds the new layer between the liberated fruit and the people who want it captures disproportionate value. Not the people who grew the fruit. Not the people who used to run the shit layer. The ones who build what comes after. I wrote about this in E-commerce After The Prompt, and the dynamic is the same across industries: in a world where agents handle discovery and transactions, the layer between supply and demand is where the value concentrates.

Netflix is interesting because they’re one of the few who managed to do it to themselves. They recognized their own shit layer, destroyed it, and built the replacement. Most incumbents don’t have that instinct. Most incumbents add more shit on top (their own chatbot, their own AI wrapper, their own “AI-powered” version of the same bad product) and hope the moat holds.

For AI, that replacement layer barely exists yet in most industries. The fruit is being liberated, industry by industry, as the cost of intelligence drops and things cross the reliability threshold. But the infrastructure that connects the expanded supply to the people who need it is still missing. The accounting firm’s clients don’t have an agent managing their books. The uninsured farmer doesn’t have a micro-policy. The small business that could never afford a lawyer still doesn’t have access to legal judgment.

The fruit has always been there. The shit was the only thing keeping it small.

In my last post I wrote about what it feels like to build with AI agents at full intensity. This one is about the number behind that feeling, and why it convinced me this isn't a bubble.

Last Friday I burned 800 million tokens in a day, or to make it into a tangible amount, I produced the equivalent of 1,600 Don Quijotes. A year ago, that would have taken months. Two years ago, I wouldn’t have attempted it.

The Staircase

When Luzia was five people meeting in a cleaning company in Albacete (long way from there), I drew a chart for our very first all-hands that explained our mission as a company.

The idea behind it is something I’ve been thinking about a lot lately, how most people can’t picture what AI can actually do for them, so they never ask for it. This chart was the first version of that idea. Two lines: the AI frontier, which grows exponentially, and people’s adoption, which grows in steps. Our job is to stay as close to the frontier as possible so we can pull the staircase up.

Those steps map cleanly to token count. Each generation of AI technology adds a zero to your consumption.

~0 tokens. Most of humanity. Roughly 70-80% of the world. They’ve heard about AI, they have not experienced it.

~1K tokens per session. You open ChatGPT or Luzia, ask it to write an email, close the tab. About 20% of the population. 2023 technology.

~10K tokens per session. Reasoning models. For the first time the models could actually think and self-correct, and the use of tools (grounding, web search, documents) made AI genuinely useful for real work. Unfortunately, until DeepSeek broke open reasoning, most of these capabilities were locked behind paywalls, and as I’ve written before, most people don’t pay for AI. So the majority of users have never experienced what a thinking model can do. Maybe 10% of AI users are here. 2024 technology.

~100K tokens per session. Agentic coding, with capabilities that go well beyond coding. Claude Code, Codex. You stop writing prompts and start describing systems. You give the agent a goal, it plans the steps, writes the code, runs it, checks if it works, fixes what’s broken, and comes back when it’s done. You go make coffee. About 1% of people. 2025 technology.

~100M+ tokens per session. Autonomous agents running in parallel, building and testing and deploying while you sleep. You become the bottleneck, and the more you get out of the way, the more gets done. I wrote about what that feels like in my burnout post. The percentage of the population here rounds to zero.

Each step up, the number of people shrinks while consumption per person goes up by orders of magnitude.

Measuring the Gap

At Luzia we serve millions of users across Latin America. Most of them are on that first step, asking questions, translating text, getting help with homework. Real value, but our average user consumes maybe 1,000 tokens per session. I consumed 800 million in a day. That ratio, roughly 1 to 800,000, is the imagination gap expressed as a number.

Karpathy said something on the No Priors podcast recently that stuck with me. He talked about personal token throughput as the new measure of leverage, and that the question now is “what token throughput do you command?”

Your personal token consumption is a rough but real proxy for how much value you’re extracting from AI. If you’re at 0, AI doesn’t exist for you. If you’re at 1,000 tokens a day, it’s a fancy search engine. If you’re at 100 million, then we are talking. The gap between those two isn’t just quantitative, it’s a completely different relationship with the technology.

The Math

I love back-of-the-envelope calculations. They’re wildly inaccurate, but good for perspective.

You can actually estimate global token consumption from the staircase. Take the roughly 8 billion people on the planet, apply the percentages from each step, multiply by their average daily token use, and you get somewhere around 10-20 trillion tokens per day from direct human usage. Add enterprise API calls, batch processing, internal tools, and you’re in the neighborhood of 100 trillion tokens per day, which lines up with what the big providers are reporting (Google went from 9.7 trillion tokens/month to 480 trillion in a single year).

Now imagine a world where everyone moves up the staircase. Not to where I am, just to 1 million tokens per day, which is a moderate step 4. With 5 billion people (conservative), that’s 5,000 trillion tokens per day from humans alone. Add agents talking to agents, orchestration overhead, retries, tool calls, at a 10x multiplier, and you’re somewhere around 50,000 to 100,000 trillion tokens per day.

Today we do 100 trillion. That world needs 1,000 times more, and we are, if reports are true, using close of 100% of installed capacity.

All the words humanity has ever written, every book, every website, every email, every text message, adds up to roughly 130 trillion tokens. Once everyone has a real AI assistant, we’d burn through all of that in minutes.

And tokens cost energy. I went deep on this back in 2024, and the numbers have only gotten more intense. Even with 100x efficiency gains (aggressive by any measure), that level of consumption needs about 1 TW of continuous power. That’s 50-100 nuclear power plants dedicated to inference alone. It’s why every hyperscaler is buying nuclear and why Alphabet committed $175B+ in CapEx for 2026.

This Is Not a Bubble

A founder of an AI company saying “this is not a bubble” is about as credible as a hair dresser telling you that you need a haircut. I’m biased and I know it. I wrote about the bubble question back in August 2024, and my answer then was “it depends.” Eighteen months later, I have a clearer view.

The bubble argument usually rests on two things: the amount of CapEx going into AI infrastructure, and the lack of realized business value so far (MIT’s GenAI Divide report found that 95% of corporate AI pilots fail to deliver ROI). Simplified: AI needs mass adoption to justify the spend, and people are clearly not there yet.

That was a reasonable worry in 2024 or even in 2025. Agent harnesses changed the equation, starting with Claude Code and continuing with OpenClaw and Cowork. You don’t need that many people using AI effectively for demand to go through the roof. I’m one person. I burned 800 million tokens in a day, the equivalent of 800,000 users at step one of the staircase. And that's only comparing against the 20% who use AI at all. For the other 6 billion people, the number is zero. One person at the top of the staircase generates more demand than most of humanity combined1

There will be corrections along the way. There always are. But calling this a bubble misreads where the demand is coming from. It’s not hype cycles or consumer excitement. It’s agents that multiply compute by 10x or 100x per task, and companies restructuring around them because the economics are too compelling to ignore. As I wrote in my predictions post, things in AI stay hard until they don’t, and when the flip comes, it is not gradual.

My worry isn’t about whether the demand is real. It’s about who gets access. If the supply side keeps up, everyone eventually gets their million daily tokens. If demand outruns supply, tokens stay abundant for those who can pay and scarce for everyone else. That version hits emerging markets hardest, the ones Luzia serves, the ones I care about most.

Less Than 1%

I wrote in The Year to Push that the gap between where you are and where the best AI users are is still closeable. That’s still true. But the window gets shorter every month, because the top of the staircase keeps moving up.

We are at less than 1% of the progress bar. The math says we’re three orders of magnitude from stabilization. I burned 1,600 Quijotes in a day, and I’m one person.

Imagine when that’s normal.

Last Tuesday I was putting Noa to bed and I caught myself checking my phone waiting for an agent to finish a deployment. Not something urgent. A side project. A watch face for my Garmin. My daughter asked me something and I answered on autopilot, and when I realized what I was doing I felt a kind of shame that’s hard to describe because the thing making me absent wasn’t work stress or a deadline. It was excitement. I wanted to see if the thing I’d built actually worked.

I need to back up and explain how I got here.

What happened

Over the past few weeks, while doing my actual job, I also built: Spilo (with Javi), a product now used by thousands of users. Drophere.cc, a server application for agents to host websites. A Garmin watch face. A bunch of video games Luzia themed. A real-time companion app for live events (private prototype for now). And probably a few things I'm forgetting.

I did this because I wanted to learn. I wanted to see how far the tools could go, how much one person could actually ship when the constraints that used to exist suddenly didn’t. The answer turns out to be: very far. And also: too far, if you’re not careful.

Six months ago, none of this was possible for me. The coding agents weren’t reliable enough, the workflows didn’t exist, the whole thing was still in “nice demo but doesn’t really work” territory. Then something flipped, and in a matter of weeks I went from running occasional experiments to running five parallel projects with tens of agents handling development, testing, documentation, and maintenance. It felt so cool.

And then, I noticed I got tired. Not the normal tired of a long week. Tired to exhaustion. And the confusing part is that I enjoyed almost every minute of it.

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Where the exhaustion comes from

The pipeline never stops. Once you have a working setup with coding agents, you can run multiple projects at the same time. Agents are constantly producing: this PR is ready, that test failed, this deployment needs a decision. You’re context switching between projects, re-reading what you wrote two hours ago, figuring out the next step for each thread. It’s like managing a team that never sleeps, never takes a break, and ships faster than you can review. The bottleneck moved from production to attention, and attention is the one thing you can’t multiply.

One thing that makes it both incredible and exhausting: you get user feedback and you can implement it within minutes. Someone says “this button doesn’t work right” and fifteen minutes later it’s fixed and deployed. That loop is addictive. It’s also relentless, because there’s always another piece of feedback, always another thing to improve, and the cost of acting on it is so low that not acting feels like a choice.

The guilt loop. Every moment you’re not running agents, there’s a pull: I could be building something right now. And when you finish a project, you tell yourself you’ll clear the list and then stop. But software needs are infinite. There’s always another idea, another thing that would only take a couple of hours, and a couple of hours is now enough to build something real. So you start the next one. The friction that used to protect you from overcommitting (this would take weeks, I’d need a team, it’s too expensive) is gone. It turns out that friction was doing more work than I realized.

This is the part that affects my family. It’s not that I’m working 18-hour days. It’s that even when I’m not working, part of my brain is running a background process on what I could be building. That’s a different kind of tired than just long hours.

The slot machine effect. You write a prompt, kick off an agent, and wait. While it runs, you’re anticipating the result. Will it work? Will the avatar generator I just built actually look cool? (I actually wrote that one while writing the post xD) Then the notification comes, you check it, and there’s a hit of excitement mixed with “almost, but these two things need fixing.” So you go back, tweak, run again. It’s a variable-reward dopamine loop, the same mechanism that makes actual slot machines addictive, except you built the machine yourself and the rewards are real products.

Why I’m hesitant about this post

I’ve written repeatedly that the biggest risk with AI is people not using it. I wrote “The Year to Push.” I still believe that. A post about AI exhaustion feels like handing ammunition to the people looking for reasons to stay on the sidelines, and there are a lot of those people.

The number of people operating at this intensity is tiny, probably 0.0001% of the population. Most people haven’t sent their first serious prompt. The biggest risk is still people not using AI at all.

But it probably will become more people’s problem over time. Six months ago I couldn’t work like this even if I wanted to. The tools weren’t there. Now they are, and they’ll only get better and easier to use. What took me weeks of experimenting to figure out will eventually be the default workflow, and the questions I’m asking myself now (how do you disconnect, how do you protect your attention, how do you keep this from eating into the parts of your life that matter) might be worth thinking about before the answers become urgent.

What I know and what I don’t

I know it’s really fun. That sounds contradictory after everything I just wrote, but it’s true. Seeing how much you can build, how fast things move from idea to working product, how constraints you took for granted just disappeared, it’s exhilarating. I needed to see how far I could push both the technology and myself, and the answer is: further than I expected, in both directions.

What I don’t know is how to manage it. I don’t have advice. I haven’t figured out the off switch. I haven’t learned to disconnect. I’m just continuing, hoping that either I get used to the intensity or I crack, and then I’ll know where the limit is. That’s not a recommendation. It’s a confession.

This exhaustion is a luxury problem. It comes from having too much capability, not too little. If you’re reading this and you haven’t started using AI seriously, please don’t take this post as your excuse. The overwhelming majority of people need to push harder, not pull back.

This is a note from slightly ahead on the trail, where the air is thinner than I expected. The view is incredible. I just need to figure out how to breathe.

This is not a story about Javi being exceptional. He is, but that’s not the point. The point is what became possible, and how fast it changed. Twelve months ago, this would have been a three-month project and a small engineering team. What changed?

Writing software got radically cheaper and faster.

In the span of a few months, most frontier companies, Luzia included, moved to AI-written code as the default. At this point, AI writes the overwhelming majority of the code we ship. Internal benchmarks suggest productivity has nearly doubled. It happened fast, and it did not happen gradually. It flipped. One model update in November changed the game.

I don’t know whether the same thing will happen in your field. It might happen in months, in years, or never. But I do understand the mechanism: things stay hard until they don’t, and when the flip comes, it is not gradual.

Last week I was trying Claude’s Cowork, the thing that sits on your computer and handles tasks for you. My first reaction was: not there yet. Then I remembered what coding agents felt like a year ago. Same reaction: nice idea, doesn’t really work. Then November happened, and coding flipped. I’m not frustrated about Cowork anymore. I’m forcing myself to use it.

That is why I’m writing this.

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The gap is still closeable

The gap between where you are and where the best AI users are is still closeable. That will not be true forever.

Right now, the frontier is visible. You can still reach it. The tooling is accessible, the models are good, and most people still have not seriously tried. Most usage is still first-layer stuff: summarize this email, fix my grammar, rewrite this paragraph. Most people have not touched the second or third layer. I wrote recently about the gap of imagination: the distance between what AI can do and what people actually ask it to do. That gap exists at the industry level, where we keep rebuilding chatbots, but it also exists at the individual level. This post is about that second gap.

Keeping up is hard

Keeping up with AI is genuinely uncomfortable, for reasons that are worth naming.

The external ones are obvious. The rate of change is absurd, which makes it hard to keep up. There is an enormous amount of noise in the AI space, which compounds the problem. Separating signal from hype is almost a full-time job. And on top of all that: nobody actually knows what will be possible in six months. Maybe the labs know. We don’t.

The internal ones are less obvious. Keeping up requires doing things most of us naturally avoid: sustained effort, constant updating of priors, the discomfort of being bad at something before you become good at it. What didn’t work a month ago might work now, which means you have to be willing to try things you already tried and failed. That’s psychologically harder than it sounds.

And there’s a third thing. The fact that software is cheap now means that eventually more people will be able to do these things too. When a skill becomes widespread, the bar for good and good-enough rises. Competition doesn’t reward good, it rewards being ahead. That’s not an argument against pushing. It’s the argument for pushing now, before the new standard hardens.

And underneath all of it: what if I work this hard, and nothing flips in my domain? That’s the fear. I get it.

That’s what the 2x2 helps us understand1.

But what if I’m wrong?

Start with the obvious inner objection: what if this never really takes off? What if the bubble bursts, and AI turns out to be a huge wave of talk that goes nowhere? What if I work hard for the next 12 months and this scenario never materializes?

Paul Graham has a famous essay about doing what you love. One of its core ideas is that people who work on things they genuinely love have a structural advantage: what feels like painful effort to others feels almost like a hobby to them. I agree with that completely, and it has mattered a lot in my own life. But I also think the ability to enjoy learning new things is itself trainable. And right now, there are not many alternatives. We have to learn.

The worst case of working hard on AI is that you become very good at a very useful thing. For years I taught Excel and financial modeling, and I watched people waste entire days on problems that one formula would have solved. AI feels like that, multiplied by a hundred. (if only people in the world understood sumifs). Even if progress stopped today, knowing how to use these tools is worth a lot. The floor is high.

The upside case is different. When AI coding suddenly became far more usable, the people already close to the frontier benefited immediately. The people who waited to see whether it was real were left with a gap that is now harder to close, not easier.

I believe that if this moment comes, the outcome will be brutal and barbelled2: some people will do unbelievable things, and others will fall badly behind. The choice, right now, is whether you want to be close to that frontier when it happens.

The noise around AI risk

There is one more thing here that frustrates me.

Much of the public conversation about AI is focused on risk. Researchers, journalists, regulators. Some of those risks are serious, and someone does need to think about them. But for most people, the effect is not careful engagement. It is paralysis. People close off instead of leaning in.

My instinct is that the best protection against the downsides of AI is broad adoption, not avoidance. The disruption that might come will be smaller, and more manageable, if as many people as possible are using the technology and benefiting from it. The worst outcome isn’t that AI is powerful. It’s that AI is powerful and only a small group knows how to use it.

Your job is to make sure you are not left behind while others are still debating what this means.

How to start

Get mentally ready first. This is going to be uncomfortable. The rate of change is fast, the signal-to-noise ratio in the AI space is terrible, and you’ll have to constantly update things you thought you knew. That discomfort is the point. If it were easy, it wouldn’t be an opportunity.

Default to AI more often than feels natural. Make “AI first” a real heuristic. Make it a rule: AI first. Most things won’t work. Some will surprise you. What matters is that you build an intuition for where the edges are, and those edges are moving. What failed last month may be trivial today.

Pay for a real subscription and actually push it. Not the free tier. Then use it for problems that feel slightly above your level. The people who get the most out of these tools are the ones who give them hard problems, not the ones who use them only for email.

Do not be afraid to ask stupid questions. For the first time, we have a technology that is endlessly willing to help you learn. You just have to ask.

Build something. Anything. I don’t care how small. You have a problem, you have a tool, and you can probably connect them in an afternoon.

With the main barriers gone, what remains is agency and taste.

There are very few moments in a career where the asymmetry is this good, where the floor is high, the ceiling is very high, and the window is visible but closing.

The cost of this opportunity is hard work. That is not such a terrible price.

P.S. I do not usually write in this tone, but this one matters. More and more people are reaching out because they feel they are falling behind. The anxiety is real. The peer pressure is real. That is exactly why now is the time. I’ll get back to more technical topics soon.

Fidji Simo calls this “capabilities overhang.” Benedict Evans calls it lack of product-market fit. I’m not arguing with either of them, they’re both right. But I want to go one level deeper, because I have a strong hypothesis of why it happens, and I think that’s the more useful question for anyone building on AI. And it comes down to two things we, as product builders, are getting wrong.

First: we keep trying to solve everything with a chat interface. I wrote about that in my 2026 predictions - the year of boring AI I called it, where the real wins come from workflows, not chatbots.

Second, and more important: we are not closing the gap of imagination.

That’s what this post is about.

Here’s a diagram I drew in 2023 when I started Luzia. The idea was simple: make sure people don’t fall too far behind the frontier. But “not falling behind” has two requirements. People need to use AI. And people need to actually extract value from it.

We at Luzia, and the industry, got decent at the first one. The adoption curve AI achieved is unlike anything before it. But we have mostly failed at the second.

Why people are not finding the value?

To understand why, you need to understand what AI actually is today. The most bullish answer is “almost everything.” The more honest answer looks like this:

AI can be insanely good at coding and, on the same day, tell you to walk to a car wash that’s 40 minutes away. The frontier is jagged, not a smooth capability curve but a weird mix of genius and embarrassing failure.

The problem is that where AI is good and where it isn’t is not documented anywhere that matters. Not beyond evals and benchmarks that mean nothing to 99.9% of people. Which means that for someone to get value from AI, three things have to happen simultaneously: AI can actually do it, the user discovers that it can, and the user decides to try it.

The first one is the AI labs’ job. The second and third are ours.

The job of product builders

Let me make this concrete with an example not far from reality. Let’s say that today’s frontier AI can perform executive assistant (EA) tasks with a high degree of autonomy and accuracy. Especially after the recent wave of agent capabilities initiated by OpenClaw.

Great. We all get a free EA. What a wonderful world.

Except most of us have never had an EA. So we do what I’d do: ask it to reorganize my calendar. Maybe, once I build enough trust, book a flight. And I think I’m getting good value.

But a good EA is way more than that. A great EA is Donna from Harvey (if you don’t get the reference you are missing out on a great TV show). A good EA is not calendar hygiene, it goes deep into leverage: framing your decisions before you have to make them, filtering ruthlessly what actually deserves your attention, closing every loop so nothing falls through the cracks, and pressure-testing your ideas with someone who has no ego in the outcome (full disclosure: I haven't had a real EA, that's actually from a conversation with an AI about what one would do).

And AI can deliver roughly 70% of this today. Not 100%, deep intuition and strategic feel still need time and context. But 70% is a lot. Most people are getting 0%.

The gap between 0% and 70% is not a model problem. It’s an imagination problem. I can’t picture what a great EA does, so I can’t ask for it. I can’t picture what great AI product look like, so I can’t ask for it.

Responsibility #1: close the gap of imagination. Make sure people can picture what good looks like, so they can ask for it.

The second problem is different. Even when users know AI can do something, they still need to want to do it.

In most cases, what we’re offering is an improvement on the status quo. And most people don’t spend their days optimizing. That’s just a few sickos like me who can’t do anything twice without thinking about scripting it.

Fabriccio Bolsi, Prosus CEO, put it plainly at a recent meeting: “I’ve tested all your agents and none of them gets me what I want for dinner faster and more smoothly than the two taps on the iFood app.” Hopefully it wasn’t talking about Luzia, but in any case, that’s a slap and stinks because it is mostly true.

Christensen’s rule: if you want to change someone’s default, your experience needs to be 10x better. Not marginally better. Not “AI-powered” better. Actually just better, in a way people feel immediately. AI has created the room for that 10X experience, we need to deliver on it.

However, we keep shipping chatbots that look identical to each other, chasing flashy demos and one-shot Loom videos, when we should be obsessing over boring AI that just works for the 80% of people that don’t spend the day behind a screen. I wrote about this in my 2026 predictions, and the local AI angle specifically here.

Responsibility #2: stop building AI for demos. Build it for defaults.

Both of these things, imagining on behalf of users, and building for defaults instead of demos, matter for two reasons.

For consumer AI companies closing this gap is existential. It’s the difference between building something that sticks and building something people try once, and then never again.

But more importantly: if we don’t solve this, the gap between those who use AI at its full potential and those who don’t becomes too wide to close. That’s not a product problem. That’s a society problem.

It’s the reason I started Luzia.

She grabbed the Reachy Mini we put together during Holidays, this little robot you can program by talking to it. Using Claude Code (directed by me) and wisprflow.ai (a dictation app), she told what she wanted: “Make a game where you pick a color and I have to guess it.”

The AI asked her questions. How many guesses should she get? What should happen if she’s wrong? She worked through each piece, tested it on the robot, found it was too easy, made it harder. Thirty minutes later, she had a working game.

I’m not telling you this to brag. I’m telling you because of what happened next.

A few days later, a neighbor friend came over. Saw her playing the game. Asked how she made it. My daughter: “I just told the robot what I wanted.” The friend’s response? “My mom says ChatGPT is for cheating.”

That kid’s ceiling just got set. Not by AI. By her mom.

We obsess over whether AI will harm our children. Wrong question. The harm comes from us. From parents paralyzed by fear, schools banning tools instead of teaching them, politicians gaining votes by talking about AI doom-day scenarios to kids who’d otherwise adapt naturally.

Our relationship with AI determines our child’s relationship with AI. We set the ceiling.

The Harvard Report Effect

One of those silly theories I invent to help me understand the world: pick any topic, there’s always a “research study” that supports your point of view. From time to time, that research study makes it into the media. People who haven’t read a paper in their lives get it out of context. The rest of us see the headlines, and a vague correlation becomes the topic of all the TV experts. And all of a sudden, a new “the expert says you need to XXX” is the new general belief.

AI is no different. Every few months, a study emerges from a prestigious university. Headlines follow: “AI Makes Kids Dumber,” “Screen Time Destroys Attention Spans,” “Technology Ruins Critical Thinking.” The media amplifies it. Parents panic. Schools respond with bans.

Most people can’t distinguish correlation from causation. Eight out of ten, according to a statistic from Harvard (pun intended). So when a study shows kids who use AI score lower on some metric, we assume AI caused it. We ignore alternative explanations: maybe struggling students seek out AI more because they’re already behind, or maybe the test itself measures skills that matter less in an AI world, or maybe we’re testing skills that don’t matter—like memorizing kings.

The pattern repeats every technological shift. I’m old enough (first time that I write that sentence!) to remember a few of these. Television would rot brains. Calculators would destroy math skills. WordPerfect (not a typo, that was a thing) would make you a dumb writer. Google would eliminate memory. Each time, the panic was overblown. Each time, we adapted.

Want to go further back? The Pessimists Archive documents centuries of technology panic. People feared bicycles would cause insanity. The telephone would destroy written communication. Even the printing press had critics who worried it would weaken memory and spread dangerous ideas.

But this time is different, isn’t it?

The Uncertainty Problem

I have two kids. Noa turns sixteen in 2035. Telmo will be thirteen. I spend unhealthy amounts of time thinking about what their world will look like. I can’t predict it. Nobody can.

I’m biased, I’m techno-optimistic. I believe technology makes us better, not worse. But even if you’re skeptical, the forecasting problem remains the same.

Forecasting is hardest when the rate of change accelerates. When I was eight and when I was twenty, the gap was enormous, but at least the direction was predictable: faster, cheaper, smaller technology. For my kids of today, the world might be unrecognizable by the time they graduate.

This uncertainty produces two failure modes. First, we try to predict specifics and optimize for scenarios that won’t materialize. Second, we do nothing because we can’t see clearly enough.

Both are mistakes.

The things that will always matter are slim and summarized in two: agency, and taste But you can’t directly teach these things. You can only model them. And kids are more likely to develop them if the adults around them treat uncertainty as normal, not terrifying.

We can’t teach our kid to be adaptable to a future we’re terrified of. Fear is contagious. So is confidence.

Why We’re Probably Overreacting

The evidence for catastrophic harm isn’t there yet. Some studies show concerns. AI might reduce critical thinking in specific contexts. Children trust AI outputs too readily without verification. Social development could suffer if kids replace human interaction with chatbots.

These are real concerns. They’re also manageable.

Compare them to the documented benefits: personalized tutoring at scale, accessibility for learning disabilities, instant feedback that accelerates learning, democratized access to education globally. AI can already do things we couldn’t afford to do for most children before. At Luzia, we’ve seen millions of users in Latin America get access to quality education tools they never had before.

The question isn’t “Is AI risky?” Of course it’s risky. Everything is risky. Car accidents kill more kids than all illnesses combined, and we still drive them to school every day; we just make them wear seat belts and child restraints.

Avoidance means your kid falls behind while others build fluency in something I’m certain is here to stay. Engagement means you guide them through the risks. Easy choice.

Technology Is Here to Stay

Even if I’m wrong about AI being net positive, it doesn’t matter for most of us. AI isn’t going away. You can’t protect your kids from it by pretending it doesn’t exist. You can only protect them by teaching them to use it well.

If we’re constantly on our phone complaining about technology, guess what our kid learns? That technology is both irresistible and shameful. Not a great combination.

Arguing that AI is bad is the easy path. It requires no learning, no adaptation, no discomfort. Just moral certainty and fear. Learning how to make the best of AI is harder. It requires curiosity, experimentation, tolerance for failure. We almost always take the path of least resistance. But the path of least resistance today creates the most resistance tomorrow.

Think about calculators. Schools initially banned them. The fear was rational: kids would never learn arithmetic. But once calculators became ubiquitous, the ban became absurd. Today, nobody argues we should remove calculators. Instead, we teach when to use them and when to do math by hand.

AI will follow the same arc. The schools banning ChatGPT today will integrate it tomorrow. The parents terrified now will normalize it soon. The lag between panic and acceptance creates opportunity for some kids and disadvantage for others.

The kids whose parents figured it out early get a head start. Ours can be one of them.

When IQ Becomes a Commodity

This time feels different because we’re not automating a narrow task. We’re automating what made us feel different for generations: intelligence itself.

But we’ve been here before.

Long ago, we taught hunting and fighting. Raw power mattered. Then machines made strength a commodity, and we shifted to dexterity: farming, crafting, building. The industrial revolution automated dexterity, so we pivoted to IQ. For the past century, education meant developing intellectual capability: reading, reasoning, problem-solving.

Now IQ is available like water from a tap.

Andrej Karpathy, one of the best programmers alive, recently wrote:

If Karpathy feels behind, what chance does everyone else have?

The same transformation is coming for every knowledge worker. When raw intelligence becomes a commodity, what’s left?

Agency and taste.

Agency is the sense that your actions matter and the habit of starting things anyway. Taste is knowing good from mediocre when infinite options exist.

A kid asks AI to help write a story. Agency is deciding to write it in the first place. Taste is reading what AI generates and saying “this is boring, make it funnier”—then iterating until it’s actually good.

These skills aren’t taught in schools. They’re modeled at home.

Our AI Literacy Sets Their Ceiling

If you don’t understand AI, you can’t teach your kid to use it responsibly. You can’t model good judgment about when to use it and when not to. You can’t help them debug when it fails. You can’t show them how to verify outputs or recognize hallucinations.

Our ignorance becomes their limitation.

This isn’t about learning to code or becoming an AI expert. It’s about basic fluency: knowing what AI can and can’t do, understanding its failure modes, developing intuition for when it’s useful.

Most importantly, it’s about your relationship with the technology. If you treat AI as magical or terrifying, your kid absorbs that. If you treat it as a tool, with all the goods and the bads, they learn to use it pragmatically.

Schools will eventually catch up. Eventually. But they’re slow. Universities are banning AI in assignments while the job market demands AI fluency. High schools are fighting plagiarism battles instead of teaching citation and verification. Elementary schools are doing nothing because nobody knows what to do.

Meanwhile, children whose parents are AI-literate are already building things, learning faster, and developing skills the educational system hasn’t figured out how to test.

The gap will widen before it narrows.

Schools Will Need to Rework Curricula

Remember memorizing kings? Spanish history taught through rote memorization of Gothic kings? Completely useless knowledge, but schools taught it for decades because the system was slow to change.

We’re doing the same thing now. Giving kids tablets and calling it “digital literacy” when they’re already digital natives.

The current educational model optimizes for memorization and standardized testing. AI demolishes both. Why memorize facts when you have instant access to all knowledge? Why practice arithmetic when computers calculate faster?

Yes, struggle builds character. As Cleo Abram discussed with Mark Zuckerberg, doing hard things matters. But there’s productive struggle—learning to think—and pointless struggle—memorizing facts Google knows.

We still teach kids arithmetic even though calculators exist. Why? Because understanding basic math helps you know when the calculator is wrong. The same logic applies to AI. Kids still need writing skills to know when ChatGPT produces garbage. They still need critical thinking to evaluate AI outputs.

The curriculum needs to shift focus:

Critical evaluation: How to verify AI outputs, recognize hallucinations, cross-check sources. Research shows children as young as preschool can learn AI literacy.

Problem framing: AI executes well but needs clear direction. Asking good questions matters more than knowing answers.

Creative judgment: When a thousand options exist, taste determines value.

Human skills: Empathy, collaboration, leadership, communication. The harder these are to automate, the more valuable they become. As one education leader notes, “the biggest lesson we’ve learned is, it’s not about the content.”

Schools will resist this shift. They always do. The institutions most invested in the old system are slowest to change. This creates a window where parents can substitute: teach your kids these skills at home while waiting for schools to catch up.

Or don’t wait, work on it at home.

The Gap

We’ll adapt. We always do. And that unrecognizable world of 2035 will be simple 2035. But how well we adapt depends on how much we make of AI, not whether we avoid it.

The gap between what institutions teach and what matters is ridiculous, and widening.

Schools optimize for plagiarism detection while the job market demands AI fluency and agency. They ban ChatGPT while employers expect AI-augmented work. They test memorization when memory is free.

But it’s not just schools.

I recently spoke at a large tech company about AI. I started my talk with a simple question: “How many of you think this company needs to invest more in AI training courses?” Everyone raised their hands.

Everyone, at a tech company, full of what I am pretty sure was high IQ people.

“You all work here. AI is new for everyone. Get the agency you need to get up to speed. Don’t wait for the company to teach you.” Those who know me will know I used slightly non-post-friendly wording. ;)

The silence was uncomfortable.

This is the problem. Adults lack agency too. We wait for permission, for training programs, for someone to tell us it’s okay to learn. Then we pass that learned helplessness to our kids.

The way I see it, two groups of children are emerging: Group A, kids making things happen. Group B, kids told AI is cheating, banned from experimentation, learning that trying new things requires permission

The gap between these groups will be enormous. Not because Group A has better AI, but because Group A has agency and Group B doesn’t.

What to Do

Stop waiting for perfect information or politicians’ guidelines. You won’t get it. The studies are contradictory, the experts disagree (for God’s sake, there are no experts—this is new!), and the technology changes faster than research can track.

Instead, model what you want them to learn. Use AI yourself. Let them see you experiment, fail, adjust.Show them that “I don’t know how” could be the beginning of something cool. Let them try things under supervision—prohibition builds naivety, experimentation builds judgment. When they create something with AI, don’t just praise it. Ask: “Is this good? How could it be better?” That’s how taste develops. And balance AI with what AI can’t replace: human connection, physical activity, unstructured play.

Push schools to adapt. Not through angry emails, but through questions: How are you teaching kids to use AI? How are you using AI? What’s your plan for when every student has access? Are you preparing them for the world as it will be, or as it was?

Most importantly, relax. Our kids will be fine. They’re more adaptable than we think and there will be a new Harvard Study to prove it soon.

AI systems want to learn. They’re designed to improve through interaction, to get better with feedback, to adapt to new patterns. Humans are the same. The models learn through trial and error. So do your kids. The difference is: the models don’t need permission to try, our kids do. And we’re the one who grants it.

We adapted before. We’ll adapt again.

You set the ceiling.

Not the “two-party, I’m chatting with a bot and I know it” version. The original version: a three-party imitation game, where you chat with a human and a machine at the same time and you have to guess who is who. In the UCSD study (March 31, 2025), GPT-4.5 was judged to be the human 73% of the time when prompted with a human persona. That’s not “it sounds good.” That is “it beats actual humans at seeming human.”

2025 was also the year the new models saturated most of the hardest benchmarks (industry jargon for “we ran out of signal”), including ARC-AGI (the #2, because the #1 was supposed to be AGI, but we already ruined that word).

Leaving for 2026, we still have hope in the humbly named “Humanity’s Last Exam”.

So why am I talking about 2025 in a 2026 prediction post?

Because, as I did in my 2024 and 2025 posts, I’ve decided to be 100% correct. Meaning: 10/10 predictions. I’ll do it using two advanced techniques:

1. I’ll go to fundamentals so we talk about trends, not headlines.

2. I’ll attach probabilities to everything. If I say “90% chance” and it doesn’t happen, I was still right. I just taught you how to always be right on TV.

What are those fundamentals that will continue?

Jokes aside, 2025 was the year in which, as I argued in my Differentiation, Then Commoditization post, AI started to just work for many more use cases. A different question is what people actually use it for, but the “does it work?” debate stopped being the bottleneck.

We also pretended we forgot about model numbers (GPT-5.2?, Claude…?), which is obviously a lie because we are still terminally online. But something real did change: we moved from “wow, it can talk” to “shit it can do (parts) of my job”.

This simple illustration finally helped me visualize what’s going on. For the last 3 years I tried to picture AI as a concentric circle, my team has suffered countless versions of that slide in all-hands, but it never fully explained the weirdness. This does. The frontier is jagged. It can be insanely good at one thing and embarrassingly dumb at another, on the same day. And that jaggedness is basically the story of 2025.

The big unlock was not just raw intelligence. It was the combination of reasoning + tools + research workflows. Models got better at taking time, checking themselves, calling tools, reading sources, and producing something closer to an actual work product. Not just an answer.

Agents also went mainstream, mostly because models got reliable enough in verifiable domains, especially math and coding, where you can check outputs and run loops. This is also where reasoning actually cashes out. If you can verify, you can reinforce. If you can reinforce, you can build systems that feel like “agents” instead of demo magic.

Even Andrej Karpathy basically said “I feel behind” as a programmer

And we got the first real glimpses of “AI move 37” outside of games. Not “it wrote a poem,” but “it found something new.” DeepMind’s AlphaEvolve is a clean example: a Gemini-powered coding agent that searches for algorithmic improvements, including things that matter in real infrastructure, not just toy problems. You can call it early, you can call it narrow, but the direction is obvious: once the loop is there, discovery compounds.

Finally, 2025 was the year where I, and a lot of other people, ran out of ideas on how to test new releases. That is the key insight to understand 2026.

Because if you can’t even tell what got better anymore, it usually means progress is moving to places that are harder to observe from the outside.

2026: AI research and Application Layer take different paths

AI has theoretically unlocked extreme value in many areas. The paradox is that we are yet to capture most of it, and that is my main prediction for 2026.

While we will keep hearing about AI progress, there will be a split between AI research progress and application layer progress. Or said differently, two frontiers:

The public frontier will optimize for headlines. New benchmark records, new “state of the art” claims, new demos that look insane on X, and just enough novelty for money to keep flowing and consumers to keep trying the releases. Benchmarks have become marketing, which is fine, it is how the game is played.

The private frontier will be labs going back to fundamentals. Not because they became humble, but because pushing the frontier now requires more than “scale harder.” It is reasoning, training loops, verifiers, tools, data, and automating pieces of research itself. The stuff that compounds, but is harder to show on a leaderboard.

In this landscape, open source is the forcing function that keeps both worlds connected. It compresses the gap and makes “just wait for the next closed model” a weak strategy. It also erodes margins and pricing power, because once open weights are close enough, the enterprise conversation becomes “why are we paying for this.”

So yes, we will keep seeing progress everywhere. But it will feel weirdly bifurcated: the loud progress you can screenshot, and the quiet progress that actually moves the frontier.

Let’s see this thesis in detail.

AI Research Progress

1. Going back to lab work

While scaling laws have held in 2025 (more compute still tends to mean better models), improvements are harder to spot, and the capex to reach the next level is getting astronomical. The reasoning paradigm drove a big chunk of the gains, partly because it gave models something underappreciated: the ability to use tools in a reliable way. If you can plan, call tools, verify, and loop, you can turn “smart” into “useful”.

Compute-efficient companies (aka Google with TPUs) will keep brute forcing their way into better consumer models. The Gemini 3 launch is basically that thesis in product form: bigger is better.

The rest have a big bet to make: do we bet on ASI/AGI being achieved via generalization or via specialization?

• Generalization means brute force the way to AI. Bigger generalist models that can do everything.

• Specialization means automating the machinery of AI progress itself. Research loops. Verifiers. Search. Synthetic data. Systems that improve systems.

My 2026 prediction: all the AI labs will focus on specialization. Why?

1. There is enough room for value capture in the application layer that even if the frontier slows, we still have years of gains to ship.

2. Pure brute force is seeing decreasing performance per dollar, and the dollars are getting silly.

3. Specialization gives the best headlines for fundraising. Not “we trained a bigger thing.” More like “we discovered something new,” “we automated a chunk of research,” “we built a loop that compounds.”

And if I am right, that is bad news for the observability of progress. There will be two AI performance frontiers:

• the one we see, focused on benchmarks, product demos, and application layer wins

• the one we won’t see, focused on automating AI research and building training loops

Conveniently, this prediction is hard to falsify. Which means I’m probably right.

2. The gigawatt age

Even if research is focused on automating AI research, bigger and more powerful data centers will be necessary to serve the growing demand for training, research, and inference. Given data center timelines (2–3 years), this is not much of a prediction for 2026. The prediction is the shape of the conversation: we will keep hearing crazier buildouts, and 2026 will be the year 1GW stops being a meme and becomes a real unit people use with a straight face.

Industry has stopped talking about data centers as a function of “how many H100s,” and started talking about them as a function of energy provided, because energy is the bottleneck now.

Power becomes allocation: do you spend MW on discovery (research), scaling (training), or distribution (inference)?

My prediction is that this allocation problem becomes painfully explicit. Not in a philosophical way. In a budgeting way. In a “this site has X MW, we have to choose what it’s for” way. Altman’s point is the same: if compute is limited, you are effectively choosing what to prioritize.

My 2026 prediction: we will see at least one flagship data center openly framed as gigawatt-scale infrastructure, and the primary brag metric will shift toward efficiency, not just size.

Why? Because energy buildout has lag. You can’t spin up power capacity on a quarterly timeline. So the knob you can actually turn in 2026 is efficiency. That means the GPU story shifts from raw FLOPs to FLOPs per watt. Same capability, fewer megawatts. Or more capability inside the same power envelope.

This is also where the “two frontiers” idea shows up in physical form: the public frontier wants cheap, reliable inference. The private frontier wants enormous contiguous training runs. They both want the same megawatts. And in 2026, that competition becomes the main plot.

3. Reinforcement Learning from Verifiable Tasks

A bit of history here. ChatGPT was “only” a fine-tuned version of GPT-3, where via reinforcement learning from human feedback the model learned to talk like a product, not like a weird autocomplete. That small change started this revolution.

The next step (and most of 2025) was labs realizing something simple: if you can train against automatically verifiable rewards (like humans learning, did I do it correctly or not?), models start to develop strategies that look like “reasoning” to humans. They break problems into steps, they backtrack, they try different approaches, they learn to “work it out.” Karpathy explains it better than anyone, and he also makes the underrated point: RLVR ended up being high capability per dollar, so it gobbled up compute that otherwise would have gone to pretraining.

That also explains why math and coding jumped first. Not because those domains are magical, but because you can score them. You can run tests. You can check answers. You can reward correctness instead of confidence.

My 2026 prediction: verifiable loops expand beyond math and coding into messy real-world workflows—but mostly by turning the world into something scorable via tools.

This is the bitter lesson: you don’t need the model to become wise. You need the workflow to become checkable. Search turns “knowing” into “finding.” Code turns “thinking” into “running.” APIs turn “guessing” into “querying.” Logging turns “it worked once” into “we can measure it.” Once a task has a scoreboard, you can reinforce. Once you can reinforce, reliability stops being a vibes debate and becomes an engineering problem.

Now, you’ll see a clear bottleneck, if every new domain requires bespoke training environments (or fleets of robots1 grinding experience), bespoke tool sandboxes, bespoke reward functions, bespoke evals… then we’re not building “AI employees.” We’re building an industrial pipeline for teaching very capable systems a million narrow skills.

That’s not nothing. In fact, it’s probably the main way capability becomes usefulness in 2026.

If you buy the “loops-not-learners” framing, the obvious question becomes: where do we get cheap interaction data and cheap feedback? Because the real world is expensive. Slow. Risky. Full of edge cases you only discover by breaking production.

So if you can’t afford reality as your training loop, you simulate it.

Which brings us to world models.

4. World models for training loops and new data

In 2025 we saw the first really impressive world models. Some people saw them as a toy (cool games, cool videos). Others saw them as one of the missing pieces of the training pipeline.

In a few words, world models are AI systems that build internal, dynamic simulations of the world so they can predict how a world evolves and how actions change it. The key word is ‘closed loop’: you act, the world responds, it stays coherent, and you can keep going.

This is where the “verifiers” idea connects. Agents need environments. Environments create interaction. Interaction creates data. Data creates rewards. Rewards create reliability. And if real-world interaction is expensive or dangerous, a simulator gives you the loop without the cost.

If we were to get world models right, we could create useful environments to spin for training agents, other models, or even cooler, embodied AI (robots). These world models could be general (simulate a broad world, like Genie) or specific (simulate the physics of a narrow domain). We’re still far off, but to make it concrete: imagine we get a good enough driving simulator. The advantage of “collecting real driving data” shrinks, and the bottleneck shifts to “how good is your simulator.”

So far there have been two main research lines:

• video generators (Veo, Sora, etc.)

• actual world models (Genie-style closed loop)

Both are brutally expensive to train and run, which is exactly why this matters for 2026. If a lab can make a playable simulator cheap enough to run, it becomes a forcing function: suddenly you can train loops at scale, and suddenly “agent reliability” is not just a prompt problem, it is a data + loop problem.

My 2026 prediction: we will see the release of a playable, general world-model simulator by an AI lab other than DeepMind. Playable meaning: not just a video, but real-time interaction for minutes, where actions have consistent consequences.

My second prediction (more gambling): at least one frontier lab will publicly hint that synthetic data coming from world models is in the training pipeline. Not a full technical write-up, just the usual vague “we use simulators for training” line that makes everyone on X lose their mind.

And yes, this will matter for consumers too. Once models can “simulate” better, you get better physics intuition, better planning, better multimodal accuracy. It won’t just be prettier videos. It will be more useful systems. Next: downstream effects.

5. AI downstream effects

The most exciting and probably most bullish prediction is related to real world impact. I am preparing a longer write-up on this idea, but let’s start shaping it here.

The coolest thing about AI is not that it writes your email as a professional copy editor, or that you can ask for a website and get it in 10 min for less than $1 in cost. It’s that you have available intelligence from a tab. And once intelligence is cheap, the question stops being “what can the model say” and becomes “what can the model do.”

So here is my bullish 2026 take: attention shifts from “new model dropped” to “AI did something real.” Think a new AlphaFold moment.

I am intentionally picking biology here. Not because biology is easy (it is not), but because it has three unfair advantages for AI:

First, biology has a lot of structure. Under the mess, there are rules. Second, biology has bottlenecks that look like bottlenecks AI is uniquely good at attacking: search, pattern recognition, hypothesis generation, and synthesis across huge literatures. Third, and most important, biology has feedback loops. You can test things. You can run experiments. You can score outcomes. You can get closer to verifiable rewards than in most human domains.

So the prediction is: we will see at least one widely-visible, hard-to-dismiss moment where AI materially accelerates a biological pipeline. Drug discovery, protein design, wet lab automation, or a new class of biological insight that becomes a reference point.

Why do I think this happens in 2026?

Because, cynically, incentives. As Charlie Munger said, show me the incentives. We are building gigawatt-scale infrastructure. We are asking society to tolerate massive buildouts. And at the same time, most consumers can barely feel the difference between “model A” and “model B.” How do you justify the buildout when the average person thinks the main use case is rewriting emails?

You show a win that feels undeniable. A result that is legible to normal people. Something that makes the infrastructure story feel less like hype and more like a down payment on actual progress.

Application layer

If the private frontier is about back to labs, insane investments and verifier loops, the public frontier is better products that ship value under real constraints.
The models will keep improving, but the differentiator in 2026 is who turns that capability into a workflow that sticks. The app layer is the monetization layer of the split.

In B2B, the moat is workflow + data loops. In B2C, the moat is interface + distribution.

Enterprise workflows

OpenRouter’s State of AI report (worth reading, though a bit depressing to see the use case distribution) coined the “Cinderella / Glass Slipper” effect, which is a fancy way to name something very simple: once a model becomes good enough for a specific task, people stop switching. Retention locks in. The model disappears, the workflow stays.

This is the enterprise dynamic of 2026: the battle is not “who has the smartest model.” The battle is who owns the workflow. Who has the integration. Who has the permissions. Who sits inside the system of record. And who gets the data loop that makes everything better over time.

Because once you’re embedded, the good old SaaS retention dynamics kick in.

So here is Prediction 6.

6. Moats are the loops (enterprise edition)

In 2024–2025, a lot of enterprise AI was read-only: summarize, search, draft, suggest. It was useful (RAG systems finally started to work), but it still left the annoying part to the human: actually doing the thing.

My 2026 prediction: approved write access becomes mainstream. AI stops suggesting and starts committing changes, with guardrails and audit trails.

That shift matters because write access is where the data loop becomes real:

• when AI reads, you get productivity

• when AI writes, you get automation

• when you automate, you get feedback

• and feedback is how systems get reliable

The gating factors are not model IQ. They are permissions, logs, rollbacks (as we spoke Claude Code just killed one Twitter demo repo I was working on!), and verification. If you solve that, agents stop being demos and start being boring, reliable automation.

7. Agents beyond coding and research

If 2025 was the year everyone discovered the word “agent,” 2026 will be the year we collectively admit the obvious: agents still fail too often.

The problem is simple math, reliability compounds in the wrong direction. If a system is “95% accurate per step,” it feels great in a demo… until you ask it to do 10 steps in a row. Then your success rate collapses. Which is why most real products today either stay read-only, or they hide the agent behind approvals and guardrails.

So my 2026 prediction is not “autonomous agents everywhere.” My prediction is more boring and more real:

My 2026 prediction: we will see one general-purpose agent workflow outside coding/research become mainstream, but it will look like approvals + retries + audit logs, not autonomy.

The winning shape will be an “ops agent,” not a “CEO agent.” In other words, agents expand, but they expand where the work is verifiable and where failure is cheap. The pattern is the same as the rest of the post: loops win. Verifiers win. Guardrails win.

And this is also where the pricing story gets really weird. Models will keep getting cheaper per token (open source, routing, competition), but agents are token-hungry: more steps, more tool calls, more context, more retries (orders of magnitude not factors). So 2026 will feel like a paradox for builders:

Prices per token go down, but cost per successful task stays stubborn unless you engineer the loop.

That is why the enterprise winners won’t be the companies with the flashiest agent demos. They will be the ones with the most boring system: evals, monitoring, permissioning, and a narrow set of workflows that run reliably 100 times a day.

A Bonus Prediction: The last 4 months of coding agents have been probably the craziest (and quietest) stretch of the last three years. Not because models suddenly got “smarter,” - which they did - but because they got reliable at the full loop. That is the difference between “autocomplete” and “junior engineer that never sleeps.”

2026 is the year the world realizes this. Not via a flashy demo, but the change of the default workflow for shipping software. Inside Luzia, my most productive backend engineers are already doing something close to the extreme version of that future. On some weeks, ~99% of the code that lands is AI-written.

Coding is the first domain where “agents” stop being a story and start being a baseline. Will that be the future for everything else?

8. AI Roll-ups

Why are we not realizing more gains from AI? Acemoglu’s main point: organizational bottlenecks - basically, we all struggle to change our defaults and ways of doing things. Then..

What’s the shortcut to see these productivity gains?

The shortcut is simpler: don’t sell AI to the incumbents. Buy the incumbents.

My 2026 prediction: roll-ups become a mainstream distribution strategy for AI in boring industries. People stop pitching “we built an AI for X” and start pitching “we bought X and made it 2x more profitable.”

A simple mechanism:

• fragmented industries (a thousand small players, same playbook everywhere)

• lots of repetitive work (admin, compliance, scheduling, quoting, claims, back office)

• low tech adoption + low talent density (they cannot build this themselves)

• AI can do a significant amount of the task (30-50%?) but not all (otherwise just sell the process!)

And here comes the loop again: buy distribution, create the training loop and see again prediction 6.

Cautionary tale: roll-ups show up in late-cycle moments, when cheap capital makes “buy growth” look like genius. Historically, acquisition waves can create real value, but they also create the same failure mode: integration gets postponed until the market stops forgiving you, culture hits you in the face, churn kills your client list.

Consumer habits

I have a strong conviction on the horizontal nature of AI, well beyond chatbots. As a horizontal technology, it will leak into almost all areas of our life. That will take time, and a ton of experimentation, and with winner-takes-most dynamics, the winners won’t be “best model.” They’ll be whoever nails the interface + distribution.

If B2B moats are workflow + data loops, B2C moats are habit + UI + quality perceived. And in 2026, UI becomes the battleground because models keep commoditizing.

9. AI will meet the real world in transactions

So far AI has mostly lived in the online world: write, search, generate, summarize. The online world meets the real world in one place: transactions. We already saw “agentic commerce” hype explode in late 2025, but my 2026 prediction: “agentic commerce” becomes boring. The breakout UX is AI + one-tap approval, not autonomy.

I wrote about the commerce side here: AI + e-commerce. And the local angle here: Local AI.

10. Image generation moves from aesthetics to application

2023 was “looks like a dragon.”
2024 was “look, I made a dragon.”
2025 was “look, I made a REALLY cool dragon, Ghibli style.”
2026 is “look, I made a useful infographic explaining the physics of dragon wings in low-density liquids, with labels that actually spell correctly.”

In short: from vibes → to work, with realism as table stakes. The edge in 2026 is realism + consistency + instruction-following + editing (the most underrated feature of nano-banana pro is “reasoning and the use of tools”).

My 2026 prediction: the breakout image workflows are boring (and useful) business workflows.
Product photos, menus, real estate listings, ads, catalog variation, customer-support visuals, compliance-friendly marketing and a lot of AI-slop in feeds.

The winning shape won’t be “blank canvas + prompt.” It will be first-step usability: templates, guided prompts, and one-click starting points that get you to a usable draft in 10 seconds. The generator becomes invisible; the workflow becomes the product.

And importantly: models stop being one-shot. The loop becomes: generate → edit → constrain → re-edit → keep character/brand consistent. That means:

• Consistency becomes a killer feature: same character, same product, same brand style across 20 variations (aka the storyboarding / catalog loop).

• Multi-step instruction-following actually matters: not “apply one style,” but “do three edits in order, keep X fixed, change Y, and don’t break Z.” Benchmarks like GenEval and T2I-CompBench++ are basically measuring this “can you follow constraints without breaking the scene” problem.

• Text rendering + structured graphics go mainstream: the shift from “it spelled a word right” to “it produced an infographic / market map that carries dense information.” (This is where image becomes a document format, not just a picture.)

  

• Editing is the primitive: image-in → image-out, not just text-to-image. If you can’t reliably edit an existing asset, you’re not in the enterprise budget. Datasets like MagicBrush exist for exactly that: instruction-guided edits you can evaluate. GitHub

This also ties back to world models. Better simulation and better “physics intuition” shows up as fewer impossible hands, fewer nonsense shadows, fewer objects melting into each other. Not because the model is artistic—because it’s less wrong about how the world works.

11. We will continue seeing attempts to crack social + AI

We will keep seeing “AI + social” launches, and they will keep looking obvious in hindsight: create content faster, remix faster, reply faster, flirt faster, meme faster. The supply side is solved.

The problem is the demand side. Social is not “content.” Social is status + identity + taste + inside jokes + timing. AI can generate infinite posts, but it still struggles to generate credibility. And without credibility, you don’t get network effects. You get a feed of perfectly fine slop.

So my 2026 prediction: we’ll see a lot of tries, some viral moments, and no durable winner. The products that “work” will be small, niche, and format-specific (one loop, one community, one vibe) and more importantly, obvious in retrospect.

The exception I can imagine winning is not a new social network, but an AI layer inside existing networks: better creation tools, better editing, better inbox triage, better “help me reply,” better community management or why not, more entertaining AI-slop.

12. AI that you don’t know is there.

My 2026 prediction: the best AI product launches next year won’t feel like “AI products” at all.

They won’t start with a chat box. They won’t ask you to prompt-engineer your life (and yet, now all companies are “teaching” prompting to their workers, again, organizational bottlenecks"). They will just quietly show up inside the workflow and remove friction: auto-fill the form, triage the inbox, summarize the call, draft the follow-up, update the CRM, route the ticket, flag the weird edge case, generate the asset variant, and move on.

This is the consumer version of the same thesis: as models commoditize, interface becomes the product. That has been my core thesis since Luzia started. Not “we have a model.” Everyone has a model. The question is: what UI shape turns capability into habit?

And 2026 is when we see the interface split into two winning shapes:

1) AI embedded inside existing products (invisible AI).
The AI layer disappears into buttons and defaults. “Smart reply” becomes “smart do.” Most users won’t even know which model is running. They will only notice that the thing got easier.

2) AI with a purpose-built GUI (visible AI).
The opposite of “one chat to rule them all.” Think NotebookLM or Luzia Tools style: a dedicated interface that constrains the problem, keeps context tight, and what’s even more important, makes clear what you can do with AI.

Don’t get me wrong, chats are here to stay - we drive most of our lives speaking! - but there is a lot more to AI than a chatbot.

Where both worlds collide: Open Source

A final take for 2026: open source is the forcing function that keeps the two-frontier world from breaking apart.

On one hand, labs want to go private: longer training loops, more verifiers, more tool use, more “automation of research,” more secrecy. On the other hand, they need revenue now: enterprise deals, consumer retention, distribution. Open weights compress that tension, because first to solve a use case wins retention (Cinderella is back”)

• Open models make “wait for the next closed model” a weak strategy. Capabilities diffuse fast enough that the enterprise question becomes “why are we paying for this?”

• That turns the model into a passthrough cost. The defensibility moves up the stack: workflow + integration + permissioning + data loops.

• And it forces productization. You can’t retreat fully into private research if the “good-enough” option is closing behind you.

Open source is also the diffusion engine of the private frontier. Closed labs do expensive discovery, open ecosystems industrialize it, and the time window to extract monopoly rents shrinks.

And without entering geopolitics, pressure is not evenly distributed: China is the accelerant here. As I wrote in the past:

This looks like the Made in China 2025 playbook: pull as many workloads as possible into their stacks, learn from usage, starve U.S. counterparts of revenue, and, meanwhile, build the capabilities (chips, supply, know-how) to run the AGI race.

So here’s my open source prediction for 2026:

By the end of the year, the performance gap between open-weight models and closed frontier models stays around ~3–6 months for most enterprise-relevant workloads. Not because open source “wins,” but because the gap is now small enough that pricing power collapses unless you’re bundling a workflow people can’t leave.

Closing

Models stop being the story. Loops become the story.

We crossed the Turing Test without blinking, not because it didn’t matter, but because it didn’t change your Monday. The next wave does. Not as a “new model dropped” headline, but as a slow, irreversible takeover of workflows:

• AI gets write access (with guardrails) and suddenly it’s not “helping,” it’s operating.

• Agents don’t become autonomous, they become boring: approvals, retries, logs, and a few workflows that run 100 times a day without drama.

• Discovery compounds where feedback is real (biology, code, simulators), and that’s where the first undeniable “AlphaFold-like” moments show up.

• Open weights compress the gap enough that pricing power collapses unless you own the workflow people can’t leave.

So my summary bet: 2026 will feel less magical in demos, and more magical in outcomes. If 2023 was “talking,” and 2024 was “seeing,” and 2025 was “thinking,” then 2026 is the year AI starts doing.

See you in 2027. I’ll be 100% correct again.

The other day, I noticed something surprising. Two years after GPT-4’s debut, I’m running OpenAI’s GPT-OSS-20B on my two-year-old laptop. It uses about 3.6B active parameters, fits neatly in 16 GB, and scores around 85.3% on MMLU — basically early GPT-4 territory — but my only cost is electricity. No cloud bill, no GPU farm, no latency.

When an open-source model is “good enough” and runs on consumer hardware, the economics shift. In AI, that’s the moment the game changes: the control point moves from scarce, expensive capability to distribution and delivery.

This isn’t new. Econ 101 says there are two ways to make money: differentiate, or be the low-cost provider. In AI, those map neatly onto two phases of the race. Phase 1 is about unlocking new jobs no one else can do. Phase 2, once those jobs are unlocked, is about serving them faster, cheaper, and more conveniently than anyone else.

The rest of this post is about what happens when you cross that line — and why every advantage you hold in Phase 1 has an expiration date.

From Econ 101 to AI: two ways to make money

There are two sources of profit: differentiation and cost leadership. If you’re unique, you can charge a premium; if not, the low‑cost player captures most of the value. PCs in the 1990s moved from IBM’s high‑margin differentiation to commodity clones; smartphones show the split today (Apple integration and brand vs. Android OEM scale). I’ve argued before that models commoditize once a capability is widely available — the laptop moment suggests useful nuance.

If we think about technology and AI as an enhancement tool- a bicycle for the mind in Job’s words - automation is the end game. For a task to be automated, three conditions need to be met: (1) enough intelligence; (2) enough reliability; (3) delivery at a lower cost than the alternative. Many everyday tasks — email drafting, summarization, boilerplate coding — sit just below this line. We can now do them reliably and cheaply with the existing models. Many other (sometimes not that complex) tasks are now within reach with frontier models (i.e., long-term memory, narrow domain agents…), while there is still a large group far from reach (i.e., AI research, autonomous reasoning for long periods of time…).

While seemingly not that relevant, thinking about what "good enough" means, and what happens when you clear the bar, has massive implications for how the AI market is shaping.

Reframing the AI race as a two-phase race

The automation threshold reframes the AI race into two distinct phases: phase 1 unlock the job (differentiation), phase 2 win on delivery (cost + distribution).

Phase 1 — Differentiation (unlocking jobs)

Phase 1 is simple: clear the IQ × reliability bar for a specific job. When you do, you get paid because you’re the only one who can deliver it.

Before clearing the bar, you are in the experimentation phase, playing to see how much you can extract from the model. Clear the bar, and it stops being a demo—it’s an automation. Phase 1 is the fancy market position that breaks headlines, brings astronomical valuations, and burns billions of dollars.

Phase 2 — Commodity (serving jobs efficiently; the “good‑enough” period)

Once a job is unlocked, the advantage flips to cost and convenience. We have all been there. It’s nice to automate, it’s nicer if you do it as cheap as possible.

Since ChatGPT started the frenzy, no competitor has been able to really maintain a long-term technical differentiation lead. Phase 2 always comes. We have seen it with models—you can count in the hundreds the number of GPT-4 level models out there—and in the application layer where JTBD were quickly commoditized — copywriting, narrow domain assistants—

In short: a model launches, a category unlocks, then the crowd piles in and prices fall and then Christensen’s rule applies. Once the model is “good enough,” it modularizes and profits migrate to the new bottleneck. In Phase 2 the bottleneck isn’t the model; it’s distribution and workflow—owning where the work happens and the data + process + UX that wrap the model. The model becomes a replaceable part1.

Cloud (2006 → ). EC2 and S3 (AWS services) let startups run workloads they couldn’t on-prem. Containers and IaC then made those workloads portable. From there the fight shifted to price/perf—silicon, commitments, scale—and value moved from “who hosts” to “who wires hosting into the developer workflow.”

Windows (1990s → 2010s). Windows was the control point until the web—and later the cloud—pulled apps and data off the PC. Profits moved up the stack. AI will rhyme: once models are good enough, leverage sits in workflow and distribution, not in the model.

Lab‑by‑lab (through the two‑phase lens)

No-one can skip Phase 2. AI performance converges; the strategy is to monetize differentiation while building the lowest‑cost delivery machine for when capability spreads. Let’s see each lab where they are:

OpenAI — playing both games

OpenAI is a strange mix: a category-defining consumer product (ChatGPT, probably worth a trillion on its own) and a frontier lab releasing some of the most powerful models. That forces a dual plan—keep unlocking new jobs (Phase 1) while industrializing delivery (Phase 2). The catch is financing: consumer ARPU and a commoditizing API won’t pay for city-sized training runs, so the AGI story is part of the business model. You need visible leaps to keep belief—and capital—flowing, even as you push routing and serving costs down. If AGI story fades, OpenAI is still a once-in-a-generation company, but not probably the one that everyone expects.

A Not Very Brief Note on GPT-5 release

OpenAI finally shipped GPT-5. Not a thunderclap, but a frikin good release—and cheaper to run. It’s a system, not a single model: a small classifier sends easy stuff to small paths and only escalates (with or without “think”) when needed. That’s pure Phase 2—routing for cost, latency, and reliability after Phase 1 unlocked the jobs. We’ve been doing this classifier→router setup for ~2.5 years; nice to see it at platform scale. Net: it just works, which is exactly what the market needs now.

Anthropic — the delicate one

Anthropic is the delicate one. They started on the OpenAI path (consumer + models), lost the broad consumer race2, and found a Phase 1 beachhead in coding—Claude 3.5 was genuinely great and really kickstarted the vibe-coding wave (Cursor/Lovable/etc.). The problem is that Phase 1 differentiation in coding seems even more ephemeral than that of other labs, so it works, until it doesn’t.

Open Source and other AI labs are aiming at coding, and competition keeps closing in, likely sliding Anthropic into Phase 2, but with big training bills and a thinner user base to finance the AGI plans (Amazon buys?).

Does anyone remember Inflection AI? Inflection AI was one of the OG AGI labs, trying to find differentiation through Emotional Intelligence. A valid hypothesis that did not find PMF, and as a result, the lack of consumer traction together with extremely high training costs resulted in the death of the company

In Anthropic world, the AGI hype - and the safety speech (we are the saviors of the world) isn’t optional; it’s the story that keeps capital flowing. Without it, they need to own more of the workflow (agents, repos, tests, policy) or the edge evaporates.

Google — distribution + cost + frontier (pick two? pick three.)

Holy moly, Google resurrected from the dead. After the Bard disaster, faking some benchmarks and some racial stereotypes problems, Google made the most spectacular turnaround in the race.

Google’s position is a hedge across both phases: frontier research (Gemini/Imagen/etc.), default distribution (Search/Workspace/Android), and low-cost serving (TPUs + data-center integration). Even without AGI, Phase 2 rewards the infra advantage and distribution. With AGI, Google has the compute, data, and channels to be a competitor in the race. Both routes being self-funded by the largest cash cow machine of history, Google Ads. The strategic challenge is product coherence: turning platform-wide distribution into habit-forming jobs while exploiting TPU cost curves and maneuvering a giant company with hyper-optimized margins.

Apple — the on‑device endgame

Apple’s advantage is distribution on the device layer. From the outside, the strategy seems solid and aligned with the two-phase framework. Sitting out the hype while models shrink and quality improves to a point where on-device inference gives them an advantage — If only it was because that was the OG strategy and not the place where they found themselves after the disaster of Apple Intelligence. On-device inference pushes marginal cost to the user, reduces latency, and fits Apple’s privacy brand. The key is product framing: ship small models where they’re undeniably useful and capable (writing, summarization, personalization), and use private cloud only when necessary.

The biggest risk for Apple is that they continue making terrible bets, and even when the time comes that on-device inference is possible at scale, they miss the boat and either deliver an Image Playground v2, or worse, they are completely caught off guard.

Meta — accelerating Phase 2

Meta’s play was to collapse differentiation across the industry by open-sourcing fast (leaking LLaMA, and releasing LLaMA 2 and 3). It worked: the market moved into Phase 2 sooner, where models are swappable and cost/distribution decide share. Chinese model companies were born. That conveniently aligns with Meta’s incentives: AI isn’t the destination, it’s the amplifier—more relevant feeds, more performant ad inventory, more ways to keep you in-app. The “AI Manifesto” reads like goodwill; the strategy is colder: make the model a commodity so nobody else owns the choke point.

And Meta can afford to wait. Recent AI stumbles aside, the cash machine keeps running ($18.3B of net income) and Zuckerberg is in founder mode, creating the AI version of “Real Madrid” so that if AGI arrives, they’ve kept a seat at the table; if it doesn’t, AI still lifts the core business. Either way, Phase-2 economics favor Meta’s strengths: ship fast, open-weight where it helps, and drive inference cost down so every surface quietly gets better—no need to chase a ChatGPT-level product.

AWS — the commoditization bet

AWS was born to be the cheap, reliable place to run your jobs—swap out a hundred flaky in-house systems for one bill and decent uptime. Andy Jassy built that DNA - and a ton of technology, and it’s still the plan. They’re betting on Phase 2 - commoditization: everyone will need inference, not bespoke frontier workloads, and AWS already sits on most enterprise cloud spend. If Christensen is right, their advantage will be their position, many cloud loads are already in AWS. The play is obvious—soak up inference as it commoditizes—backed by tons of infra and their own chips (Inferentia and Trainium) to make serving really cheap (even if they trail Google on raw performance in their chips)

It’s a coherent strategy with one big risk: there are two other clouds, and AWS’s lock-in was a first-mover story. Do customers experiment with OpenAI/closed APIs and… just stay there? Or do they boomerang back once models are commodities? In case people stayed in OpenAI and the AGI race stays hot, AWS hedged a bit with an Anthropic right-to-match, meaning, AWS could put - a likely expensive - offer on the table to buy their ticket to the AGI table, but hey, same as Meta or Apple, they are sitting in a pile of cash.

Microsoft - Almost forgot about them

Microsoft’s AI strategy is straightforward: rent the frontier from OpenAI and sell it through the distribution it already owns—Windows, Office, GitHub, Azure — via a very thin product layer (and copied features)

The unusual OpenAI–Microsoft partnership—Microsoft put up the capital and gets product and Azure upside—let Redmond “buy” Phase 1 while it wired Copilot into the stack. The bet is Phase 2: when models commoditize, price/performance on Azure and default placement in enterprise workflows matter more than model deltas. The biggest risk for them is similar to that of Apple AI. Thus far, Microsoft has not demonstrated the capability to release good AI products, with few to no hits. There is a chance that Phase 2 arrives, and they still get disrupted because of their inability to ship good products.

China — scale and price pressure

After the DeepSeek January moment, China has been consistently shipping top-performing models across coding, images, text, and math. The strategy is clear: erode differentiation fast, the way Meta did, but without the red tape that slows U.S. firms. I’m also hearing from EU companies about inbound from Chinese clouds with very aggressive inference pricing. This looks like the Made in China 2025 playbook: pull as many workloads as possible into their stacks, learn from usage, starve U.S. counterparts of revenue, and, meanwhile, build the capabilities (chips, supply, know-how) to run the AGI race.

Is there space for small model providers and other competitors in the AI space? If so, what?

My bet is there is room for small labs and startups, but only where the model is a part, not the product: pick a narrow job-to-be-done, ideally local to your user (start where things don’t scale), integrate the base model with proprietary data and a tight workflow (ton of UI work here), and price the outcome, not the token.

Treat the model as swappable infrastructure, and build the moat around data, workflow, and where the user already lives.

Training costs and the long game

Frontier AI is a capital-allocation race: you either self-fund (Meta, Google), charge consumers (OpenAI?), or raise (OpenAI, Anthropic)—while unlocking new jobs faster than training costs rise. Training costs are outpacing unit revenues in many categories; that leaves two viable paths for the leadership of companies to justify crazy amounts of CAPEX expenditures: (1) Pascal’s-wager AGI—pay for a shot at outsized returns; (2) Compounding utility—unlock new automations fast enough that revenue growth beats CAPEX.

AI agents that work—firstly in narrow domains and later for general purposes—will unlock massive economic gains

From: Trial, Error and Move 37

Firms with durable cash engines or patient platform partners outlast pure-play labs, unless labs manage to keep the AGI hype up. Make Phase-1 money, build Phase-2 moats, and assume the control point keeps sliding to distribution—ultimately to the device.

In summary

The AI market is two races at once. Phase 1 is about unlocking new jobs by crossing the IQ × reliability bar; if you’re first, you earn rents. Phase 2 arrives fast: the capability diffuses, unit costs collapse (routing, distillation, better serving), and value shifts to whoever sits closest to the workflow—distribution, data, and UX—often ending up on-device for stable tasks.

Seen through funding: self-funded giants (Meta, Google, Microsoft, Apple) can wait out hype cycles and lean into Phase 2 economics—cheap, integrated delivery where users already work. Externally funded frontier labs (OpenAI, Anthropic) must keep the AGI story alive to finance ever-larger training runs while racing to industrialize serving so API price pressure doesn’t crush margins.

Strategically: make Phase-1 money while you can, but build the Phase-2 machine—routing, evals, reliability SLOs, and deep workflow hooks—because models become swappable parts. That’s where the control point moves, and that’s where durable profit will live.

Appendix. How AI labs differentiate and how to compete in cost?

Sources of differentiation

• IQ — We have gone in two years from talking to models as smart as toddlers (GPT-2) to having unlimited PhD-level intelligence. That’s an increase in raw power and IQ, the type of increase that is (was?) predictable with the scaling laws (which I touched on here and here). This is what we mostly show with benchmarks. Improvements in raw power can be general, or can be domain-specific (law, healthcare, tool usage...)

• Reliability means correct results with consistency: minimal hallucinations, controlled failure modes, and predictable latency. In agentic flows — where a JTBD spans many LLM calls—steerability and low variance prevent error cascades. That’s the difference between a cool demo and a production service.

• Other (short-lived) sources of differentiation. We’ve also seen attempts to stand out via ultra-long context windows (million-token+, even 10M-token demos), new architectures (MoE, SSM-style variants), and whole new “reasoning” modes and tool use. They create daylight for a release or two, but so far none has held as a moat: the edge diffuses via knowledge sharing and model distillation.

How to compete on cost

How to compete on cost in AI could be the topic of several books, but I’ll try to group it into three routes: y

Architecture. There is a line of research on architecture that prioritizes efficient inference, enabling smarter token generation per model parameter. One of the most common is Mixture of Experts (MoE), which in short divides the model in sub-models specialized in different topics that are activated as needed. The other line of work for architectural improvements in efficient inference focuses more on improving cache usage.

Distillation & specialization. This is the process of distilling the capability of large models (IQ + dexterity) into small (sometimes specialized) models that can run very efficiently. Classic distillation still works; modern variants push reasoning into smaller models (see Let Them Think). This, at its core, is the technique that allows OpenAI and other providers to continuously improve the free plan.

For a given number of parameters—weights—there is a trade-off between model capability (IQ + dexterity) and model knowledge. If you want to create very intelligent yet cheap-to-run models, the model's core knowledge will need to decrease; they will be like smart kids who haven’t studied much, and the reverse is also true. This strategy—clearly followed by the OSS models from OpenAI—makes inference dependent on grounding, such as RAG, either from online searches or other sources, which, at the end of the day, passes the cost of inference to the user.

Hardware & systems. DeepSeek proved that you can always squeeze a bit more from a given TPU. Hardware optimization can come from two fronts: integrated systems, where models are custom-made for specific hardware architectures (think about the Google Cloud TPUs or AWS Inferentia 2); or optimizing the workload for specific machines (DeepSeek R0 for the Nvidia-H2O) or creating chips specialized in inference (groq).

On‑Device Is Phase‑2’s Endgame. When a task stabilizes, it migrates to the endpoint. Local inference collapses marginal cost to electricity and amortized hardware, improves privacy, and slashes latency.

No search pages, no comparison grids, no banner ads-just a conversation that smoothly transitions from intent to purchase via a trustworthy recommendation. At that moment, the traditional storefront disappears, replaced entirely by the prompt.

The core thesis of this article is straightforward: GenAI will fundamentally reshape e-commerce, whether merchants like it or not, because those who have the demand hold the power—but for merchants ready to adapt, significant opportunities await.

One topic I’m parking for a future post is how performance-marketing budgets will likely migrate from keywords and social feeds to paid placement—my thinking there isn’t fully baked yet, and it deserves its own deep dive.

It's All About Demand...

Power on the internet belongs to those who aggregate demand. Amazon capitalized on Prime and its one‑click checkout to capture purchase intent; Facebook and Instagram did the same for discovery through targeted ads. GenAI assistants eliminate the last remaining friction: the cognitive load of translating desire/problems into search keywords, and if properly executed, all the other parts of the funnel. As these interfaces internalize catalogs and simplify checkout processes, shopping becomes an interaction with a conversational agent. In this new reality, the conversational layer-not the merchant-owns the customer relationship.

Why are Amazon ‘brands’ called QDYZPP and GHKWXUE? Because inside the Buy Box, any random string will do—watch the breakdown.

For merchants, this presents challenging trade-offs. Historically, brands that chose direct‑to‑consumer strategies sacrificed marketplace visibility in exchange for full control over customer relationships, even as they became increasingly dependent on platforms like Facebook for customer acquisition. On the flip side, selling through someone else’s platform, i.e., Amazon, provided immediate distribution but eroded margins and data sovereignty. An agent-driven, GenAI aggregator further tightens these constraints: if customers never visit your site, does your carefully‑crafted brand experience still matter? I think the article that best captures this idea is Ben Thompson’s take on Nike returning to Amazon.

...And Demand Follows Great Experiences

The obvious follow-up is … but will customers adopt GenAI? Customers follow great experiences.

Customers will adopt GenAI shopping because the value proposition is obvious: clearly describing a "job-to-be-done" to an AI is faster and more intuitive than endless scrolling or search optimization. “I need a new pair of shoes to play tennis”. AI can combine reviews, specifications, social proof, and price data into clear, personalized recommendations. The agent’s memory further streamlines the shopping process by retaining user preferences, previous purchases, and shopping habits.

Making Lemonade

If life gives you lemons, make lemonade. Merchants must work on two dimensions. The first is the obvious: embrace AI and start exploring different channels and opportunities (Luzia, ahem, ahem). The second is to reorient their strategies around three familiar yet crucial dimensions: selection, brand, and leverage over fixed costs.

First, selection. AI assistants can only recommend products they can access and understand. Merchants should expose their catalog through modern commerce APIs (such as MCP standards), inventory endpoints, and rich metadata. But merely being eligible is not enough. Real competitive advantage will come from unique inventory -exclusive SKUs, limited‑run collaborations, or special bundles. The more differentiated the inventory, the greater the likelihood it surfaces as the AI’s recommendation, anything other than that will inevitably lead to commoditization (the TEMU effect)

Second, brand. Aggregators commoditize suppliers unless consumers specifically request them by name. Develop compelling narratives, build community around your brand, and maintain physical or unique digital experiences that keep your brand embedded in consumers’ memories. Brand recall ensures the customer requests ”Nike Pegasus 43,” rather than a generic substitute, “shoes to run my first 10k”.

Third, fixed‑cost leverage. Every conversational AI transaction will implicitly carry a toll, whether as revenue share, sponsored placements, or the opportunity cost of lost organic traffic. Merchants must build models where gross margins scale faster than these imposed costs. Adopting subscriptions, membership perks, and owned fulfillment capabilities convert customer‑acquisition costs into amortized assets. Amazon’s strength was always in making its warehouse network incrementally more efficient with each additional order-merchants must apply this principle at a micro-level.

What’s in it for us, agents?

This is an easy question. For agents of the world, such as Luzia, this is an opportunity to deepen the relationship with the user by unlocking new areas of value, making the vision of a full-blown assistant a reality. This ultimately has two main benefits:

As I explained in my post on AI monetization, the more value is unlocked, the more opportunities there are to capture it. With valuable transactions happening through our platform, opportunities for monetization arise in obvious forms-transaction fees, product placements, rebates, and many others that will need to be invented. Once an agent owns demand, it can levy a toll on every downstream supplier.

The second benefit is the data flywheel. The more engagement and use cases are unlocked, the better we get to know our users, the more local we can be, and thus the more we can help them.

Monetization and data matter only while users trust the agent’s incentives. Push paid placement past relevance and the flywheel stalls.

Aggregator Overreach: A Cautionary Tale
When Amazon treated every brand as a fungible line in a search-results table, Shopify and Facebook stepped in: merchants could buy demand, own the customer file, and keep their margin and information. The same cycle hit restaurants: high take-rates and zero data on Uber Eats or Rappi pushed many to multi-home (selling in more than one platform). Lesson learned: squeeze too hard and the supply you aggregate funds its own escape route. For GenAI agents the guardrail is clear—stay additive. Charge a toll for convenience, yes, but leave room for brands to differentiate and for merchants to build equity, or the next Shopify-plus-Meta combination will rise from the margins you compress.

The Hard Part: Technical Challenges That Require Thinking

Many Unsolved Challenges = Opportunities

If the goal is frictionless commerce triggered by a conversational prompt, today’s reality still presents several technical hurdles at each step of the funnel. Let’s unpack them, starting from the moment intent is expressed:

Discovery and Matching. Agents must interpret subtle cues and context. While modern LLMs excel at natural-language understanding, they still need structured, continuously updated data to return accurate matches. Merchants have to expose rich product metadata via modern commerce APIs-think MCP standards or product-JSON schemas—so agents can spot nuances, like distinguishing a “giftable” watch from one built purely for performance. Just as Shopify became the picks-and-shovels vendor for merchants squeezed by Amazon’s Buy Box, a new class of ‘MCP enablers’ will surface catalogs and inventory to GenAI agents—Shopify-for-APIs instead of Shopify-for-web pages. Scalability matters: manually re-indexing every merchant catalog is neither scalable nor cost-effective. As adoption of a common standard reaches critical mass, laggards will be sidelined-much like banning Google from indexing your site.

UI and UX. How you shop depends on what you are shopping, whether is a staple item or a one off purchase and when you are shopping it. Today’s familiar—if clunky—grids at least set expectations. Replacing them will require fast, messy iteration. Our “book-a-restaurant” beta proved it: a chat reply naming the venue fell flat; users also demanded photos, ratings, even table snapshots. The lesson is clear: GenAI must learn to assemble the right blend of text, cards, and visuals for each scenario, and that will take relentless A/B testing before the interface feels both novel and trustworthy.

Recommendation and Memory. Personalization depends on robust (and compliant) memory. Technology in this area is improving rapidly-our own memory layer has increased engagement by approximately 300 basis points-but memory comes with marginal costs beyond electricity. The more context you want to add, the more expensive each query becomes. Introducing memory without smart optimizations could quickly increase costs by 2-3X. Balancing performance, privacy, and unit economics will be an active area of experimentation.

Checkout and Payments. To deliver a true end-to-end, “job-to-be-done” experience, payments must be cracked-and this is where things get thorny. On one side: legitimate security concerns; on the other: fragmented regulation. Both are justifiable, yet both demand heavy innovation. Questions pile up:

• Are users comfortable authorizing an agent to transact on their behalf-and under what limits?

• Can the agent commit them to installments?

• How do we sandbox hallucinations?

• How do we navigate regulatory fragmentation?

• And my personal favorite: How do we make any of this work within the iron grip of PSD2? (Forgive the sarcasm-my last two years at Amazon were spent ensuring PSD2 didn’t sink the subscription economy.) How do we make AI that uses Pix in Brazil? Local matters

The good news: these challenges are solvable. Forward-looking merchants who engage with emerging standards (MCP and MCP-UI) and shore up their technical foundations won’t just clear the hurdles-they’ll define the track for everyone else. Partnering early with regulators will be equally critical: help shape the rules or live by rules someone else shapes for you.

From Global Model to Local Moment

GenAI supplies infinite IQ, but only local texture—Pix in São Paulo, cash-on-delivery in Buenos Aires, contra-entrega riders in Bogotá—turns that intelligence into sales. Agents and merchants that ignore such rails are restaurants that forgot to cook. The flip side is favorable path-dependency: markets that skipped credit cards and desktop web have no habits to unlearn, letting super-apps in emerging economies sprint past incumbents still chained to legacy infrastructure.

That’s why local AI is the real moat. Merchants and assistants that bind global models to on-the-ground data—local SKUs, regional pricing, real-time FX through providers like iFood, Rappi, or small mom-and-pop stores—will spin a faster flywheel: more trust → more usage → richer context → better recommendations.

Where Does This End?

Ultimately, this transition will result in a handful of dominant AI assistants integrated deeply into operating systems, messaging apps, and smart devices. We’ll see fierce competition around schema standards, product feeds, and promotional formats. As the competitive landscape evolves, value will increasingly shift towards the point of intent, replicating the historic shift from shelf space in physical stores to search results online. Storefronts will persist, but increasingly as APIs rather than as consumer destinations.

This transformation, however, is not a threat but an opportunity. GenAI commerce represents an additional distribution layer rewarding uniqueness, memorability, and operational efficiency. The way I think about it is as Uber and the car-riding business, Uber TAM wasn’t limited to taking market share from taxis, which they did, but expanding the mobility to many more rides.

Merchants should approach this prompt-driven landscape strategically, just as they approached the browser two decades ago-with clarity about what only they can own. Agents might capture customer queries, but merchants will always own the differentiated answers that keep customers returning.

In short, the path from chat prompt to checkout isn’t a straight API call; it winds through regional regulations, vernacular, and trust. Merchants and agents that embed those local realities into their data flywheels will turn one-off transactions into durable, high-frequency relationships.

Outside our circle, most people aren’t chatting whether openAI or Google will rule the world. In fact, about 80% of the global workforce doesn’t sit behind a desk—they’re driving buses, serving customers, working construction sites, or farming fields. For them, AI isn't about chatbots or multimodal embeddings—it's about whether technology can truly make their daily tasks simpler, quicker, or less frustrating.

The gap between people like us—early adopters—and everyone else isn't just about design. It's a huge opportunity to create tech that genuinely helps people in their everyday lives, and it's also the reason we started Luzia: to help everyone avoid falling behind.

This essay makes the case for what I call Local AI—AI built around cultural, linguistic, skill-level, and regulatory realities unique to each market.

Give me electricity, I will put a light bulb

AI is a general-purpose technology—a fundamental shift that, piece by piece, will quietly reshape our lives until we barely notice it's there. AI is not the first, and as such there are powerful lessons from previous ones. Let’s look to electricity:

In the early 1900s, factories installed electric lights and motors but stuck with their old steam-powered workflows. It helped, but productivity stayed flat. Only when Henry Ford rebuilt his entire factory around a moving assembly line did electricity transform the world. Suddenly, assembling a Model T dropped from over 12 hours to just 93 minutes, slashing costs and creating an entirely new economy. Electricity wasn't about the bulb—it was about reorganizing everything around it.

AI today is in the "light bulb" phase: it can write emails, tutor kids, or summarize meetings, which is extremely useful, but the reality is that for most people, it's still just a chatbot. Chatbots are helpful, but they're nowhere close to AI’s true potential.

Why? Because most AI experiences aren't instantly useful or intuitive enough to justify changing habits. Trust alone doesn't drive adoption; immediate, frictionless value does. Just look at iFood or Pix: adoption exploded because they delivered instant, obvious benefits. Chatbots today demand too much thinking, guessing, and effort from users. They require users to update their mental models of what AI is, from the useless Siri to the useful Luzia. Mass adoption won't come until AI becomes effortless from the very first interaction, whatever that interaction or use case may be.

We started Luzia with a chatbot because it was easy to integrate quickly into people's lives through platforms like WhatsApp, and f** yeah it worked - we reached millions within the first months. But we quickly realized that people don’t want to learn how to talk to an AI—they want immediate solutions. A quick, simple real product example: Luzia users can solve math problems through chat or a dedicated tool—and three times as many prefer the tool. Similarly, ten times more users create summaries with the summary tool rather than the main chat, and five times more have educational conversations with Luzia Teacher rather than Luzia. Why? Because tools and custom characters require no effort, no guesswork—just results.

This shift to practical, intuitive, "in-your-face" AI isn't just better design—it's essential for mass adoption. With 80% of workers worldwide not at desks but driving trucks, farming, or caring for patients, AI must blend seamlessly into everyday tasks. This is Local AI: practical, immediate, and built around real-world workflows, empowering everyone—not just tech-savvy users—to benefit from AI's true potential.

AI for Everyone, Effortlessly

Our early success with WhatsApp taught us two things: people quickly adopt easy solutions; people prefer human connection with their assistant, even when they are virtual. The next challenge was clear: owning the user experience entirely to deliver simpler, even more intuitive and connected products.

We’ve learned that truly great AI products don’t need explanations—they just work. Guided by that insight, we’re moving beyond the chat box to one-tap tools woven into everyday Latin-American routines: closing an iFood or Rappi hyper customized orders (we know the user even before they are craving some food), getting on-budget product suggestions the moment you open the app, flipping forty pages of lecture notes into a two-minute summary, or surfacing the cheapest empanada stand within five blocks. From uni to corner tienda, each touchpoint is local, instant, and effortless—AI exactly where you need it, the moment you need it.

Localization is subtle—just enough to feel relevant, never distracting or forced. But our true differentiator is distribution. We grow organically within tight networks by consistently delivering high-quality, frictionless experiences, and we leverage strategic local partnerships to integrate seamlessly into daily routines, appearing exactly when users need us. Our on-the-ground presence is deliberate and authentic—Luzia is fundamentally a LATAM company. This means deep local insights, genuine trust, and direct user connection.

Luzia isn't about complex interactions; it’s about effortless, immediate results, empowering every user, everywhere.

The moment is now

AI is the electricity of our era. But just as electricity only changed the world when people redesigned factories around it, AI will only transform lives when we redesign workflows and respect local contexts. Latin America offers fertile ground: adoption is growing and trust is high, yet no one has defined what “AI” feels like for ordinary people.

Luzia will define what AI actually feels like for everyday Latin Americans. By combining deep localization, native integrations, AI-first workflows, and distribution embedded in the apps people already trust, we can create that “of-course” moment when even your parents see AI as a utility, not a gimmick. We’re not stringing light bulbs in a steam-era factory; we’re laying the assembly line for the AI age. The ingredients are in place—millions of users, committed capital, and on-the-ground partners—so the only thing left is execution.

I launched Luzia to ensure people like my mom—literally our first user—can directly benefit from this revolution. AI is, at the risk of repetition, intelligence as a service: an opportunity for everyone to better understand the world around them. With understanding comes empowerment. That's our DNA at Luzia—giving people the knowledge and skills to advocate for themselves. To deliver on that promise, we must stay close to people's daily lives, deeply understand their needs, and ensure this generation's electricity adapts seamlessly to the way they actually live.

In February 2025, we witnessed a watershed moment in the AI-search relationship when education platform Chegg filed a landmark lawsuit against Google. The company alleged that Google's AI Overviews had transformed the search giant from a 'search engine' into an 'answer engine,' keeping users on Google's platform rather than directing them to original sources. Chegg reported their non-subscriber traffic plummeted by 49% in January 2025, compared to just an 8% decline in mid-2024. This legal battle highlights the existential threat some businesses face as AI reshapes discovery patterns, with Chegg even exploring going private as a direct result of this traffic diversion. The case represents the first major antitrust lawsuit specifically targeting AI-generated summaries and could set precedents for how content creators and platforms negotiate the new invisible search landscape.

I have been asked several times to talk about SEO in the genAI era, and this post is my attempt to compile my thoughts on how online businesses should adapt to a new reality where the traditional commercial funnel is being reshaped—from browsing tens of pages to receiving customized answers. Additionally, I'll discuss my thesis that synthetic data—generated by other models—will increasingly dominate AI training, traditional SEO positioning and brand discovery could temporarily become more challenging. Warning, this post will be less technical than what I normally write.

Hard Data On Generative AI as a New Channel

While generative AI-driven traffic is still small compared to traditional channels, it's rapidly becoming a significant force in online shopping.

Adobe Analytics reports that between July 2024 and February 2025, traffic from generative AI sources to U.S. retail websites surged by an astounding 1,200%. This influx is reshaping the entire consumer journey, with 55% of users leveraging AI for research, 47% seeking product recommendations, and 43% hunting for deals. Although the growth might seem outsized, it aligns with a rapidly accelerating trend—traffic from generative sources has doubled every two months since September 2024, with nearly 40% of consumers already adopting generative tools. One notable caveat is a 9% lower conversion rate, likely driven by selection bias and increased friction during the purchase process.

Interestingly, as we saw when we experimented with these ideas in Luzia, these shoppers are highly engaged (more time spent, lower bounce rates), which is for sure partly a result of the novelty effect, but I would argue that there are also many advantages to this new experience.

The Good, The Bad, and The Invisible Search

AI search is convenient, personalized, and educational—it saves users from endless tab-hopping. Remember that colleague with so many Chrome tabs you could barely see the logos (ehem, Javi)? GenAI kills that.

We, users, no longer explore—we receive direct answers, bypassing multiple discovery points. I call this "invisible search" (I probably didn't coin the term but couldn't find an original reference). It's an experience where relevant products and responses come straight to you.

The impact of this 'invisible search' paradigm is already measurable. Recent studies reveal that pages featured in Google's AI Overviews can experience traffic spikes up to 3.6x their normal clicks, creating new winners in the digital landscape. Conversely, high-ranking pages (positions 1-3) excluded from these AI Overviews suffer dramatically, seeing up to 50% fewer clicks compared to searches without AI summaries. This represents a fundamental shift in the SEO equation: ranking #1 organically is no longer enough if you're not selected for the AI Overview. The effect varies by intent as well—informational searches see traffic diverted from top positions but increased for positions 3-10, while transactional searches benefit featured pages regardless of their original position. This data confirms we're witnessing not just an evolution but a revolution in how visibility translates to traffic.

Yet, along with these advantages, users might notice a few trade-offs: information isn't always the most current, new content can be slower to surface, and occasional AI errors or hallucinations do occur. Nothing we can’t fix, but something to keep in mind.

This shift means businesses must rethink their strategies. While you might experience fewer direct website visits, adapting effectively ensures your overall online presence and sales remain robust.

How to Stay Relevant: Optimizing Across the AI Lifecycle

I find it useful to structure thinking around the different stages of the AI lifecycle, considering what we can do at each stage to stay relevant. A helpful analogy is imagining your company’s optimization through the lens of not an old-fashioned mechanistic algorithm, but rather a knowledgeable product or industry expert. Think of Google’s PageRank as the old algorithm, while Luzia represents the modern expert. When Luzia recommends a product or explains a topic, she incorporates more nuance and context into the decision.

This lifecycle has three main phases: pre-training, fine-tuning, and inference. Pre-training is when the AI model is initially trained on vast amounts of data, building its foundational knowledge—think of this as stocking the shelves of a library. Fine-tuning happens next and is about refining and specializing the model's knowledge based on additional, targeted training—similar to organizing that library into clearly marked, trusted sections based on reputation and accuracy. Finally, inference is when users directly interact with the AI model. At this stage, the model uses its existing knowledge -all the way up to its cuttoff date2- to provide real-time answers, but it can also incorporate additional, current information from external sources within a "context ”. The context window allows the AI model to temporarily include recent or dynamic information, such as current product prices, real-time availability, or breaking news. Practically, this means the model can enhance its responses by accessing scraped web data provided at the moment of our question. This technique makes the cutoff date less relevant, and what’s more important for the topic here is what makes it possible to use genAI for commercial purposes.

How Online Search Works in GenAI: When the model identifies it requires up-to-date information, it translates the user's query into one or more online searches—imagine it performing a quick Google search. The model then scrapes relevant websites, extracting timely information. Because it's impractical to share every detail, only the most pertinent parts are included in the model's context window (normally through a vector search). Finally, combining this fresh context with its existing knowledge, the model crafts an informed and accurate response. This process is fundamental in platforms like Google, Perplexity, OpenAI and Luzia.

Pre-Training (Get Noticed)

Pre-training is the phase where the AI model acquires its foundational knowledge from almost the entirety of the internet data. Essentially, it's the model's first impression of the world—and first impressions matter. What the model learns about your brand is fundamental.

Why does this matter? Research [1][2] consistently shows AI models perform significantly better with concepts they've encountered frequently during their initial training phase. Models struggle with less common or "long-tail" topics absent from their training data. Retrieval methods applied in inference—more on this later—but it can't fully replicate the depth of understanding gained during initial learning. Simply put, you're at a disadvantage if you're not visible during pre-training.

Recommendations to stay relevant

• High-Quality: Ensure your content is referenced by reputable, authoritative websites, enhancing visibility during web crawls like Common Crawl, a key source for AI training.

Forget outdated SEO tactics, including all those senseless SEO blog posts that all companies do. As we said before, AI is not just a mechanistic algorithm, it has some level of intelligence, and this intelligence is able to discern fluff from real value. Low-quality training data—all those crappy internet SEO articles—is very often removed from the training dataset because it impacts the end model performance, and increases training cost without adding any value [3].

Focus instead on genuinely valuable content like in-depth guides that comprehensively address topics from multiple angles, original research offering unique data or insights that position your brand as a trusted authority, detailed case studies providing real-world examples of your ideas in action, and clear visual content—such as graphics, videos, and interactive elements—that significantly enhance recognition and encoding by AI models.

• Proper Indexing: Confirm your website is fully indexed and accessible. Tools like Google Search Console help ensure visibility to crawlers.

• Clear Content Timestamps: Dates help AI accurately contextualize your content, making it easier for models to assign reliability and relevance.

Fine-Tuning (Build Brand)

If pre-training is about being visible, fine-tuning is about being trusted.

This is the phase where models are refined using smaller, curated datasets—often with human feedback—to improve usefulness, tone, and safety. It’s less about scale and more about precision. Can the model handle nuance? Does it respond with expertise? Does it reflect helpfulness?

And because humans—and their preferences—are involved, how your brand is discussed online can have an indirect impact. Fine-tuning doesn’t target individual brands, but it does learn from examples of good answers, helpful explanations, and reputable sources. That’s where reputation becomes structure: the more your brand appears in high-signal, trusted content, the more likely it is to shape future outputs.

Unlike pre-training, which scrapes everything, fine-tuning is curated and intentional. The teams behind OpenAI, Perplexity, Luzia and others are selecting data that improves answer quality—often drawing on: high-quality Q&A pairs, product decision flows, topic-specific knowledge benchmarks, summaries of public sentiment, and internal corpora from reliable sources.

So, no—models aren’t hardcoding brand preferences. But models do learn patterns from how helpful answers are phrased, who’s referenced, and what tone is trusted.

Recommendations to stay relevant

Your goal isn’t to “get picked” in fine-tuning—but to be part of the answer patterns that fine-tuning reinforces. Here’s how:

• Become a Referenced Authority: Be the kind of source smart people cite—research, tools, explainer content, etc.

• Drive Positive and Consistent Sentiment: Ensure you show up reliably across trusted sources—app stores, Reddit, review sites.

• Publish Structured, Domain-Specific Content: Particularly in high-trust categories like finance, health, and education.

• Design for Human and Machine Readability: Clean formatting, semantic markup, and clarity matter—both for users and models.

TL;DR: Fine-tuning won’t make you famous, but it will reward brands that behave like experts. When someone asks “best [your product category],” your goal isn’t to be found—it’s to be expected. That happens when your brand becomes a common ingredient in high-quality answers.

Inference (Real-Time Relevance)

Inference is the stage where users directly interact with the AI model. At this point, the model leverages its existing knowledge (up to the cutoff date) and dynamically incorporates real-time data gathered via online searches. As explained earlier, when the AI identifies a need for current information—such as today's product pricing or breaking news—it translates the user's query into targeted online searches. It then selects relevant results and places them into its "context window," essentially a temporary memory space allowing the model to generate timely and accurate responses.

Here's how you can influence your relevance during inference:

1. Be Indexed and Accessible:

If your content isn't part of the model's existing knowledge or returned in search results, it effectively doesn't exist for the model. Ensuring your content is discoverable is crucial:

• Structured Data: Clearly structure your content using schema.org metadata (products, FAQs, reviews). Structured data significantly boosts your visibility to AI-driven searches.

• Technical Performance: Prioritize speed, reliability, and overall site performance. AI tends to skip slow-loading or unreliable sites.

2. Get Selected into the Context Window:

Once returned in search results, the model will scrape your website (ensure it is allowed!) and decide which content chunks—if any—are relevant. Typically, only the top results are included, similar to appearing on the first page of Google results. Fresh, relevant, and clear content is essential:

• Semantic Clarity: Organize your content clearly around defined semantic clusters or topics, helping AI match your content precisely to user queries.

• Content Freshness: Timestamp your content clearly and update it frequently. AI explicitly values recency, improving your chances of being selected.

• Conciseness and Clarity: Avoid intrusive pop-ups, cluttered layouts, and excessive advertisements. AI favors easily extractable, direct answers.

3. Adapt Your Online Presence and Services for Machine Use:

I have an overdue task to research and write about MCP (Model Context Protocol), an emerging standard that allows LLMs to directly interact with online services. In the medium term, while indexing and context window selection remain relevant, direct MCP integrations will likely dominate the decision and purchase stages of user funnels. Imagine asking Luzia to buy Nike shoes for a marathon and Luzia completing the purchase seamlessly.

TL;DR. Optimizing for inference isn't just about visibility—it's about becoming the AI’s go-to source for timely answers, enabling agents to effectively utilize your site.

Embrace the new reality.

It is not only me, and all the other geeks that don’t visit Google every day. The way we discover and act online is changing fast—from browsing to getting things done, from explicit search, to invisible search. In this new reality, users don’t visit ten sites; they get one smart answer. The funnel collapses. But that doesn’t mean brands are powerless—far from it.

The main thesis in this post is brands can influence this set up. The earlier in the AI lifecycle you show up—pre-training, fine-tuning, inference—the more context models have about your brand. That context shapes how often you're surfaced and how you're represented. And while we’re still early in the game, now is the best moment to get involved. Why? Because the future may get trickier: as AI relies more on synthetic data—models trained on model outputs—it may become harder for your original content to stand out or influence meaningfully. That’s a working hypothesis, but one worth acting on.

In the era of intelligence as a service, quality wins. In a world where AI is the interface, models don’t just rank—they reason. They favor content that’s clear, valuable, and trustworthy. Old-school SEO hacks won’t cut it anymore. The models are smarter, and your content has to be too.

Last, the metrics we track need to evolve. Maybe it’s not just about traffic anymore. Maybe it’s how often you're cited by LLMs, how well agents can navigate your services, or how easily users can take action through AI. That’s the new game.

The web isn’t dying—it’s becoming intelligent.

These images come from a viral trend that started with the release of GPT-4o image generation on March 25th, a release that, I would argue, is bringing AI-generated photos back to the zeitgeist1.

I delayed writing this post over a week for two reasons, one I have been busy, two, and more importantly, I wanted to not focus on the hype, but focus on how the technology works and the implications going forward.

My main argument here is that GPT-4o's breakthrough isn't higher-quality images - they are good but not the best-, but its control and flexibility. This combination will unlock entirely new practical ways of using AI imagery.

Creating images, one token at a time.

From a technical standpoint, the key difference between GPT-4o image generation and other models such as Stable Diffusion (SD for short) or Flux (the one we use at Luzia) is that GPT generates images one token at a time, always considering previously generated tokens, in the same way GPT generates text.

To grasp the implications and advantages, let’s first take a step back and understand the current state-of-the-art in image generation.

Diffusion models

Most image generation models that had some relevance in the last 2.5 years were diffusion models. Diffusion models start from random noise—imagine static on an old TV—and gradually turn that noise into a clear, detailed image, step by step. It’s like watching a picture slowly emerge from chaos. Think of diffusion models as sculptors starting with a rough block of marble. They slowly chip away stone, refining the image bit by bit until a recognizable picture finally appears.

The process is reversed in training—you start by giving the model a perfectly defined image, then add a bit of random noise each time and ask the model to reconstruct it. Repeat this millions of times, and your model learns to create images. The key here is that the model works in the whole image the whole time2.

Over time, the quality of images generated by diffusion models has improved dramatically. Today, state-of-the-art diffusion models like Stable Diffusion XL or Imagen can produce images that feel strikingly realistic, not only visually, but also capturing subtleties such as natural lighting, textures, and even implicit physics.

Diffusion models have notable advantages—they’re cheaper to train and run than autoregressive alternatives, explaining why new ones pop up constantly. Yet, their critical drawback remains a lack of predictable control. This unpredictability prevents wider adoption, causing many casual users to abandon image generation after a few disappointing attempts. We've experienced this firsthand in Luzia: curious users see impressive images, experiment briefly, fail to recreate that magic, and rarely return. Chatbots avoided this fate by reliably meeting user needs with predictable, consistent interactions, highlighting the importance of control in AI adoption.

Autoregressive models

OpenAI’s approach to releasing its new image generation capabilities fundamentally differs from diffusion models—it relies on an autoregressive architecture. While that may sound technical, it simply means the model generates an image one small piece, or “token,” at a time, sequentially. Imagine typing out a paragraph letter-by-letter rather than printing the entire text simultaneously (like using a typewriter vs. a printing press). Each new token depends directly on the previous ones, much like predicting the next word in a sentence.

There have been rumors suggesting that GPT-4o might combine an initial autoregressive pass with a subsequent diffusion process, OpenAI’s official documentation indicates that GPT-4o’s image generation is purely autoregressive and does not incorporate diffusion models.

To visualize this, imagine building an image pixel by pixel, row by row, from top-left to bottom-right. Although OpenAI hasn't officially published all the details of GPT-4o’s architecture (OpenCloseAI), interestingly this approach mirrors what they initially explored with DALL-E 1 back in 2021, before diffusion models took the spotlight due to their lower computational costs and scalability.

But why bring autoregressive models back now? Two critical differences explain this:

1. Massive scaling: As always, more data, increased computing power, and smarter architectures dramatically improve performance. Autoregressive models, once considered too costly and slow, have become feasible at scale. Scaling up autoregressive models enhances their performance across various tasks, including text-to-image generation and multimodal understanding.

2. Multimodal integration: GPT-4o is part of an “omni” or multimodal family, meaning it uses a unified “embedding space” where text, images, and even audio coexist as numerical vectors. Imagine a massive three-dimensional space where every concept—words, images, sounds—is represented as a point, grouped by similarity. Because GPT-4o understands this shared space, it seamlessly bridges modalities, enabling richer interactions and greater coherence.

Let’s explore this idea of embedding space. Although it’s a technical concept, it’s also one of the most beautiful ones (at least in my view).

Embedding space

We’ve briefly touched on embedding spaces in previous posts, but let’s deepen our intuition a bit further. At its core, an embedding space is simply a mathematical way to represent information—words, images, or sounds—as points (vectors) in a continuous space. The closer two points are, the more similar their meanings or concepts.

Imagine plotting every word you know onto a huge map. Words like “cat” and “kitten” would cluster closely together, while “cat” and “spaceship” would sit far apart.

Now, consider multimodal embedding spaces—those that handle text, images, and audio simultaneously. Instead of just mapping words, imagine also adding visual and auditory concepts into this rich, multidimensional map. With more dimensions, the representation becomes even more precise.

But the cool thing is that with multimodality nuances matter (a lot!) The way you say something when working with multimodal embedding spaces affects where the token sits in this space. For instance, "hello" whispered softly won't land in the same spot as "HELLO!!!" shouted excitedly. This happens because multimodal embedding spaces don't just capture basic meaning; they also capture subtle differences like tone, emotion, and intensity. This allows the model to distinguish between subtle shades of meaning or emotion across modalities—like differentiating between the calm spoken word "hello," an excited wave in a video, and the written "HELLO!!!" with emojis.

Another powerful aspect of multimodality is scale. As we discussed in earlier posts, multimodal embeddings effectively expand the potential training dataset by bringing together diverse information sources—text, images, audio, and more. More data fuels better performance thanks to scaling laws, enabling models like GPT-4o to become smarter, more nuanced, and ultimately more useful.

What Makes This Approach Special?

Autoregressive image generation offers clear advantages compared to diffusion models, particularly predictability and control.

Since images are built token-by-token—much like how text is generated—the model has an easier time understanding context and adhering to specific instructions. This results in users experiencing more reliable, predictable outcomes, reducing the frustration of constantly refining prompts.

This connects nicely with another huge advantage: simplified prompting. Remember when the internet was convinced “Prompt Engineer” would become the hottest job title (I am so sorry for the 1000s of creators launching prompt engineering courses), and LinkedIn flooded with self-proclaimed “Prompt Ninjas”? Then GPT-4o (and even GPT-4) arrived, got smarter, and suddenly my typo-filled, barely coherent prompts still gave me exactly what you wanted, and no one talks about prompting anymore. We’re now seeing something similar with images: no more novel-length descriptions needed, no more tedious finger-counting. Just dump your thoughts—or better yet, a non-creative description—and it simply works.

And as you can see with the example above, this gives users flexibility. The model can generate properly written text (and five-finger hands), infographics, and many other very useful illustrations.

The new multimodal models also shine in their ability to incrementally edit and generate images, meaning users can progressively refine or stylize them. You can take your kids’ pictures and easily transform them into Disney characters or Ghibli-style selfies simply by asking. This capability, previously cumbersome or unavailable with diffusion-based tools, is now seamless.

A final, subtler detail that I think has contributed to this model going viral is that, upon release, OpenAI significantly relaxed its moderation guidelines. Users can now generate images of known people or specific styles more freely. Why does this matter? It’s two-fold: first, x.com quickly filled with fun memes featuring Trump and other famous figures; second—and more importantly—fewer limitations mean greater user control. Few things are more annoying than advanced voice modes responding, “My guidelines don’t allow me to do this,” to even the simplest requests.3

While this increased flexibility enhances usability, it inevitably brings ethical challenges, such as misinformation, copyright infringement, or impersonation. I'm optimistic we—as users—will navigate these responsibly, but it's definitely a conversation we need.

A side note. This trend of reducing model restrictions and giving users more control - started with the release of Grok3, and publicly acknowledged by Sam during the interview with Ben Thompson - about what topics to engage or not is in my view a good thing. It’s treating users as responsible people rather than imposing one specific organization views, and once again, it’s born out of competition - Grok felt more natural to use > people start using more Grok > rest of labs respond. In my view, allowing users rather than organizations to decide what topics to engage with is a positive development.

The technology isn’t perfect yet. Despite its strengths, GPT-4o still has notable limitations. OpenAI acknowledges issues such as occasional inaccuracies in highly detailed or intricate images, difficulty accurately representing complex text elements in visuals, and challenges in consistently producing exact replications of subtle artistic styles or precise visual details —but hey, this is just V1.

In practice, these advancements translate into lower user frustration and improved accessibility. Within hours of release, everyone was already creating their own Ghibli-style avatars (myself included).

A new way to create content

My main argument in this post is that the key to the success of GPT-4o isn't necessarily superior to state-of-the-art diffusion models in image quality—it's that it combines sufficient quality (it’s really good, let’s be honest) with unmatched control and flexibility. Previously, using AI image generation meant randomness, typing prompts, and hoping for usable results—all my images were always like those "what you buy on Aliexpress vs. what you receive." GPT-4o changes this by generating images through predictable, step-by-step logic, similar to crafting coherent text. While diffusion might still have the edge for entirely original compositions, and thus will still have their audience, GPT-4o's autoregressive approach excels precisely where real-world applications thrive: iterative image editing and practical creativity, which is what the masses need.

Imagine effortlessly transforming a hand-drawn sketch into a polished ad campaign, or quickly visualizing a new interior design idea. As Javier Andrés recently illustrated on LinkedIn, you could snap a picture of an outdated bedroom, quickly sketch your new vision, and let GPT-4o seamlessly turn that rough drawing into a stunning visual representation of your dream space.

Consider also the impact on advertising—imagine creating diverse, high-quality ads at scale, precisely tailored to represent your brand consistently. Similarly, envision crafting a rough, handwritten UI sketch and instantly refining it to a polished, high-definition prototype, drastically accelerating product development cycles.

You can also imagine hundreds of educational opportunities. Teachers can now transform standard lessons into visually engaging, customized infographics, making complex ideas easier to grasp. Imagine K12 educators easily creating interactive materials that not only explain topics clearly but also encourage students to explore concepts deeper than traditional textbooks ever could.

Or maybe… I'm overly optimistic, and we're just destined to flood our social feeds with charming Ghibli-style portraits.

Guilty!

P.s. OpenAI has not opened up these capabilities to the rest of the world, but we will continue working to bring them ASAP to Luzia users. Stay tuned

I finally grokked the consequences of the new wave of post-training optimization after the DeepSeek developments, and I was going to write about it just to organize my thinking. But as it almost always happens, the writing took a turn, and pieces started falling into place. Although I may be completely wrong, especially because I'm keeping each of the individual pieces at 10,000 feet, I think the pattern that emerges from all the recent developments makes total sense, explains the public statements, justifies the investments, and points to that potential path.

The key argument I will make in these lines is that through our creation of intelligent machines, we’ve reached a point where they can accelerate human progress. However, for them to truly help us, they must do more than just mimic our knowledge. They must be able to contribute to the generation of novel insights by gaining the time to think (reasoning) and the ability to explore beyond text-only domains.

For now, let’s start by understanding the purest way of AI learning: reinforcement learning.

Introduction: The Path to AGI - Decoding the AI Labs’ Roadmap

To understand what reinforcement learning is (RL for short), I will start with a couple of videos that blew my mind a few years ago.

Or even an earlier example…

The videos are totally worth it, but in case you don’t want to watch them, what you see is that through playing thousands of games, a bunch of AI agents (those blue and red little people) can develop innovative strategies to beat their opponents. From knowing nothing to being a genius, by trial and error.

The key idea behind RL is that if you let the machines learn, and you have a clear, verifiable function - winning or losing a game - the machine will learn how to play subject to the game's rules. And if you give them enough time, they will learn to play better than a human and develop creativity. What everyone now refers to as the Move 37 in the Lee Sedol vs DeepMind's AlphaGo.

The rest of the post is about how we turn the world into a verifiable function. Bear with me.

The Leap to Large Language Models: From Prediction to Intelligence

LLMs are in some ways no different than those agents in the videos above; LLMs are deep neural networks that, through millions of iterations, learn to optimize their winning-or-losing function, which in this case is predicting the next word (token1) correctly. The beauty and surprise is that with enough data and compute, what begins as a simple word predictor (GPT-2) starts to show emerging capabilities. Now, the model can predict the next word correctly in a text that has been seen before (information compression), but more importantly, solve problems that have not been seen before (information creation).

Since GPT-2 demonstrated the power of scaling laws, most AGI Labs efforts were directed towards the pre-training phase. Teaching more and more of the available text to the LLMs, pushing farther the scaling law and going from thousands of dollars to hundreds of millions for each new generation.

All this progress left us at a fascinating point where top AI models have essentially devoured all the text data we can throw at them. Sure, expanding into video and audio will push the boundaries further, but here's the thing – even these new data types alone won't be enough to crack the AGI puzzle.

The question then becomes: how or even what we should continue teaching the models?

We need to create high-quality data for the models to continue learning. We need to let them learn. To do so two paths opened ahead of us: Inference-Time-Compute and agentic interactions, and both of them require something very important, context, so before understanding each of these on its own let’s understand why context matters.

The Context Window

LLMs have no memory; the only way an LLM communicates with the external world is via the context window. Think of the context window as the model's short-term chalkboard. Everything you type—your questions, follow-ups, even little hints—gets written on this board, and that's all the model sees when it crafts its reply. Unlike humans, LLMs don't have long-term memory; they rely entirely on what’s visible in the context window to understand and continue the conversation. In short, it's the only bridge the model has to your world, and its size directly impacts how much of the conversation it can keep track of.

Why is this important? Imagine waking up with every discovery and invention of the past at your fingertips—only to have it all erased by nightfall. You’d tap into endless ideas during the day, but each evening, your hard-won wisdom would vanish. That’s the current state of LLMs: they draw on all our knowledge yet remain shackled by a fleeting context window. The next leap in AI was when we found a way to bridge that daily reset, transforming transient insights into enduring innovation.

This limitation hit hard in practical terms. When we tried to let these models really think or tackle complex tasks independently, they kept bumping into their memory ceiling. Imagine trying to write a symphony but only being able to see one measure at a time – that's what it's like for an LLM trying to reason deeply or learn to navigate the web with a limited context window. By the time they're getting somewhere interesting, their mental whiteboard is already full.

And as with most AI problems, this is a problem of the past. We are currently enjoying 1M token windows, which to put it in context, you could drop the entire Harry Potter series and would still have space to ask for a bunch of new magic spells. It’s not infinite, but give us far more wiggle room than the initial GPT’s 500 tokens.

So now that we don’t forget things at midnight, what do we do with the smartest things we have ever created? Put them to work on creating data for their training data.

A new scaling law. Inference-time-compute.

Let's talk about a breakthrough that's reshaping AI: inference-time-compute, which is essentially teaching machines to reason. While this might sound like sci-fi, it all started with a simple observation about letting models take their time to think. The story of how we got here started in January 2022, well before we all started using chatGPT.

Think Step by Step. Chain of Thought

In 2022 DeepMind researchers started talking about how giving the model time to think would produce significantly better outcomes, especially in tasks that require reasoning. They called this Chain of Thought (CoT).

The impressive performance improvement caught everyone’s attention for mainly two reasons: it was very easy to apply - tell the model to take time to think (literally), and it was a performance improvement that came in inference time, as opposed to the expensive pre-training, which means the only cost was increased inference (OOM cheaper than pre-training).

Take Your Time.

This is where things get wild. Thanks to these massive context windows, our models no longer suffer from digital amnesia – they can actually hold onto thoughts for hundreds of thousands of tokens. But the real game-changer dropped in September 2024, when OpenAI unveiled their O1 family (after one year of speculations about Q* and Strawberry). These weren't just another batch of smart models; they were the first explicitly trained to think before they speak. Taking the CoT concept to its logical conclusion, OpenAI essentially taught GPT-4o to use every inch of that expanded context window, creating models that don't just respond but actually reason their way to better, more creative answers

Let’s take the classical Lex Friedman question and compare how a model trained to think and a crude model respond.

Now compare the beauty and nuance of the above response with the raw thinking of an intelligent base model, in this case, DeepSeek V3:

A New Scaling Law

When reasoning started to work, a whole new scaling law was unlocked. I talked about it in a couple of posts (1,2), so I won’t enter into detail, but in short, now we can push the performance of AI by both raw power (expensive and more complicated in each new generation) and using the LLMs to produce high-quality data via reasoning we can continue pushing what is possible with AI.

Reasoning was, in this regard, a paradigm shift. Instead of spending tons of compute in pre-training (which we continue to), a lot more compute was devoted to post-training, hoping that what comes out of reasoning allows us to continue improving our pre-training runs. Similar to what happened from GPT-1 to GPT-2 and GPT-2 to GPT-3, the first improvements were quick wins and low-hanging fruits. We went from O1-preview to O3 within 3 months, with massive performance improvements (I wrote at length about this here)

Reinforcement Learning as a Way to Teach Models to Think

This brings us to perhaps the most mind-bending discovery yet. DeepSeek's R0 team proved something revolutionary: models could learn to think using pure reinforcement learning without human intervention. Just set up the right environment and let the model learn. It was the Bitter Lesson playing out in real time – the simplest approach, allowing the model to learn through trial and error, turned out to be surprisingly powerful. The significance of this breakthrough was best captured by Andrej Karpathy, whose tweet perfectly crystallized why this matters...

The true power of AI emerges in its “aha moments” when new information is created. And what these insights mean is that it is fed back into the AI, helping it refine itself in a continuous loop of improvement.

But for reinforcement learning (RL) to drive this cycle effectively, we must establish a verifiable way to measure success. What is a novel insight vs a well-crafted BS? What should be fed to the model, and what should we throw to the trash?

This is where the challenge arises: RL thrives in domains where correctness is objective and measurable—such as solving mathematical conjectures or playing a game—but struggles in areas where truth is subjective or ambiguous, like philosophical or artistic reasoning.

This constraint creates a bottleneck. We can only push AI so far within strictly verifiable domains before its learning potential starts to plateau. To break past this limitation, we need to find mechanisms that allow AI to explore and experiment beyond rigidly structured domains. Only then can we unlock the next frontier of machine-generated knowledge.

From AI Thinking to AI Acting. Agents and the world as a verifiable domain

The next frontier of AI learning is about action. So far, we’ve relied on verifiable domains like mathematics and programming, where AI’s outputs can be objectively evaluated. But what if AI could validate its own knowledge by interacting with the real world?

This is where agents come in. Unlike traditional LLMs, which generate answers in isolation, agents are designed to take meaningful actions—whether that’s booking a flight, researching complex topics, or even writing and executing code with minimal human intervention.

At first glance, deploying AI into the real world might seem chaotic. But in reality, the world itself acts as a verifiable domain. A well-booked flight is one that arrives at the correct destination, at the right time, within the given constraints. A $1M ARR business can be verified by looking at the accounting books. A well-coded program either runs correctly or doesn’t. Driving a car successfully means not crashing. These tasks provide natural success metrics, making them perfect training grounds for AI.

So, it’s now time to let the agents explore. This scenario has a double implication: the first is the obvious, massive economic gains, and the second, and not so obvious, an almost infinite source of novel knowledge.

AI Agents Will Unblock Massive Economic Gains.

AI agents that work - firstly in narrow domains and later for general purpose - will unblock massive economic gains2.

Narrow domain agents, the beginning

OpenAI claims that Deep Research can automate close to 10% of Expert-Level High Value Tasks. Even if this is an exaggeration, consider where we were just a year ago.

Klarna’s AI-powered customer support replaced 700 agents, demonstrating AI’s efficiency in transactional tasks.

Devin, the coding agent, automates software development, proving that AI can execute complex, multi-step processes with minimal supervision.

Broad domain agents

There are also broad domain agents. Agents that, in theory, should be capable of conducting multiple tasks autonomously. The most well-known one is OpenAI’s operator. These are agents that theoretically should be able to complete or assist on any task. These are still far from perfect and fail in many tasks, but they give us a glimpse of the future.

While agents are powerful, they’re not ready to be let loose on the world without safeguards. This is why AI labs are investing in simulations and sandboxes—controlled environments where AI can safely test its abilities. From OpenAI’s rumored internal agent simulations to models like Veo 2 and Sora, these testbeds provide the bridge between today’s reasoning-heavy AI and tomorrow’s real-world action takers.

Verifiable tasks

The second, and not that obvious consequence, is that the world becomes a verifiable domain. Tasks completed in the real world will have a direct, measurable outcome.

Now, all of a sudden, the 1M tokens of the context take a whole new dimension. If you let the agents interact with the world, the amount of new data becomes almost infinite, data that will revert back to the models.

Piecing all the parts together

Sam Altman wrote in a recent post.

I'll be the first to admit that this is a simplified view, and each piece I've touched on deserves its own deep dive. Could some of these steps fail? Absolutely. But the exciting part isn't in the details – it's that we can finally see a coherent path, one that's concrete enough for AGI labs to pursue with real conviction.

Sometimes the most complex ideas get explained in the simplest moments. As I was putting the final touches on this post, my daughter watched me fix her Lego set. “How do you know how to do that?” she asked. My response was instinctive: “I don't know, just did it a bunch of times.” Her response was brilliantly simple: “so you just try until it works.” Without knowing it, she had just summarized decades of AI research.

And that's it. We've trained LLMs to know virtually everything, but knowledge alone isn't enough. Like the turtle in the tale, human progress inches forward slowly – but now we have a chance to accelerate it. Not through mere imitation, but by letting AI generate novel insights through endless trial and error, creating more and more 'Move 37' moments that push beyond human imagination.

We may or may not be witnessing the early steps toward AGI, but Pascal's Wager3 has never been more relevant: if there's even a tiny chance we're on this path, the implications are too massive to ignore.

You can try the model yourself here, really cool to look at how the model thinks. Difficult when you read its chain of thoughts not to anthropomorphize.

DeepSeek R1 Training

LLMs to date relied on reinforcement learning with human feedback (RLHF), which means that humans (H) are in charge of defining what’s a good response (F) and what’s not a good response. R1 removes humans from the equation (almost completely3) and manages to significantly boost the performance of the model.

Our [DeepSeek] goal is to explore the potential of LLMs to develop reasoning capabilities without any supervised data, focusing on their self-evolution through a pure RL process.

What the DeepSeek team found is that as we reward a base model (in this case DeepSeek V3, a sparse model4) model for correct responses without human supervision, the model learns to allocate more time for “thinking”. Spending more time thinking doesn’t just mean generating longer responses. Instead, the model discovered how to use test-time compute more effectively—iterating on its reasoning, refining its steps, and improving accuracy without blindly increasing token count.

A particularly intriguing phenomenon observed during the training of DeepSeek-R1-Zero is the occurrence of an “aha moment”. This moment, as illustrated in Table 3, occurs in an intermediate version of the model. During this phase, DeepSeek-R1-Zero learns to allocate more thinking time to a problem by reevaluating its initial approach.

Furthermore, the research team demonstrated that by using the outcomes of the R1-Zero model (the reasoning model), they could generate a dataset that allows them to distill other open-source models and improve their performance.

This achievement suggests that small models can “inherit” reasoning abilities from larger RL-trained models

Why is this important?

We are edging closer to the 'AlphaGo moment' for LLMs

The single biggest implication is that DeepSeek’s approach provides solid evidence that the AlphaGo moment that I described in my O3 post is closer and closer.

Reasoning is a new tool at our disposal to continue pushing frontier models' limits, effectively enabling a second scaling law. In this new reality, pre-training advancements (more data, more compute, better results) play along test-time compute (more time to return a correct response). It’s a self-reinforcing loop: reasoning produces better (synthetic) data — which was a limiting factor in pre-training gains- which builds stronger base models, which in turn fuels even more refined reasoning and so on. It’s a flywheel effect — one scaling law feeds the other, creating exponential progress.

In plain word, at the end of the day, if we want to achieve superhuman intelligence, having humans in the training loop becomes a limitation. But if models are able to self-improve using RL - and it has to be proven yet that this scales beyond human level intelligence for all areas - the only limitation to performance is in the compute capacity.

Scarcity drives innovation

DeepSeek has published in the last three months two impressive models despite the aggressive China Chip Ban, proving there are technical unhobblings that can lead to frontier level performance at a fraction of the cost. While GPT-4 cost is in the hundreds of millions, DeepSeek R1 and V3 have reportedly cost $5.6M. This opens two paths, one for smaller competitors that could create capable models (otherwise how is Anthropic going to compete with Stargate or X.ai), and two those with the massive resources, could probably unleash way higher performance.

The race for reasoning has begun

Until now, there were rumors on how Strawberry (internal code name for the O-family of models) was trained. A combination of RL and A* search. Now we think we know or at least we have a proven path that works. I expect more and more labs to quickly follow up with reasoning models and using distillation, start seeing smaller models with higher reasoning abilities.

It makes sense China does Open-source5

(This is an edit after a few days of discussions on the topic)

My first reaction is that it was ironic that China was the one doing open-source. But I have updated my views.

Aliexpress commoditized online shopping. Open-source is to LLMs what Aliexpress was for online shopping. China’s strengths is cost efficiency, scale, and infrastructure dominance, potentially allowing them to commoditize AI models and undercut U.S. firms that rely on proprietary monetization. Given U.S. chip restrictions, Chinese companies are forced to optimize for lower-cost training and inference, making open-source a natural strategy to drive rapid adoption and ecosystem growth.

There is no point on training anything worse than V3 and R1

Open-source is setting the minimum performance threshold for newly trained models. If you are going to train a model from scratch, there are very few reasons to do it unless you can surpass the best open-source model. Unless you can beat these models you are better off simply taking the OS and tunning it to your needs. Rumors are indeed, that Meta had a panic moment because R1 surpassed their new Llama model, and had to stop and re-think the strategy.

Who is DeepSeek by the way?

It’s ironic that we’re learning about the inner workings of reasoning from a Chinese company rather than from one called 'Open'. But who is behind DeepSeek.

From Perplexity:

DeepSeek, a groundbreaking Chinese AI startup founded in 2023, emerged as a spin-off from High-Flyer, one of China’s leading quantitative hedge funds. High-Flyer, established in 2015 by three Zhejiang University engineers, began as a disruptor in algorithmic trading and quickly became a top-tier quant fund managing $8 billion in assets. Known for its innovative use of AI and deep learning in finance, High-Flyer built cutting-edge infrastructure like the Fire-Flyer supercomputers to support its strategies. By 2021, all its trading relied on AI, earning comparisons to Renaissance Technologies. However, High-Flyer’s ambitions extended beyond finance; driven by founder Liang Wenfeng's vision of unraveling artificial general intelligence (AGI), the firm launched DeepSeek as an independent entity to focus on LLMs and AGI research. DeepSeek has since disrupted the AI landscape with cost-effective, high-performing models like DeepSeek-V3, leveraging High-Flyer’s culture of innovation and talent-first hiring practices. This transition underscores High-Flyer’s evolution from a quant fund into a pioneer of AI research and development.

Before diving into 2025, a quick note: I've included my 2024 predictions scorecard at the end of this post (spoiler alert: I did not do bad).

The Trillion-Dollar Question: Will Scaling Laws Hold for 2025?

The Trillion-Dollar Question: Will Scaling Laws Hold for 2025?

2025 will test whether the scaling laws—more data and more compute leading to better performance—continue to hold. This is, quite literally, the trillion-dollar question that has been talked about to insanity over the past few months. With hundreds of billions of committed investment in data center infrastructure, the stakes couldn't be higher.

If the scaling laws persist, investment and progress will surge ahead. But if they plateau or break, we could face the blow-up scenario I outlined here: where we need to face a significant infrastructure write-off, which could be a good thing for the Infinity games (so that people feel they can stop throwing billions into the problem)

One of the most influential essays I read this year is "Infinity Missions". In this essay, Packy McCormick explores the concept of "Infinity Games," emphasizing the importance of pursuing ambitious, long-term projects that aim for boundless positive impact. Drawing inspiration from Blaise Pascal's philosophical ideas, McCormick argues that dedicating resources to these visionary endeavors is rational, as the potential infinite rewards far outweigh the finite risks. He encourages individuals and organizations to engage in "Infinity Missions"—ventures that, despite uncertain outcomes, have the potential to drive significant progress and innovation.

To give my take on 2025 scaling laws, let's break it into their two main components: compute and data. Progress is expected on both fronts.

Compute

We haven’t seen real scaling in computing since the Gen3 models (starting with GPT-4 in March 2023). Consequently, the last 18 months have felt like incremental progress—or even stagnation—for some (which is, btw, a big fallacy. If today you talk to GPT-3 or even the early version of 4, you would not believe how dumb they were).

What's true is that we are missing the kind of substantial scaling that fueled the leap between GPT-3 and GPT-4. In 2025, three key developments will shape the compute landscape:

1. Bigger Single-Location Clusters

   Elon Musk’s announcement of a 100k H100 data center was soon followed by plans for a 1M H100 facility. In 2025, we will see the first Gen4 models trained on clusters of at least 100k H100s. This will be the true acid test for scaling.

2. Higher GPU Performance

   Nvidia’s B100 and B200 GPUs are set to hit the market, offering roughly double the performance for the same power consumption. This means each data center will deliver significantly higher compute output.

3. Multi-Location Data Centers

   Energy remains the bottleneck for scaling single-location data centers, but multi-location setups may provide a solution. Google is believed to have used this approach for training the Gemini family, and more players are likely to follow suit.

Data

While much of the available internet text has been mined and used, we have yet to tap into the potential of multimodal data fully (think YouTube video, audio…) and synthetic data (think o3).

• Multimodal Data: Platforms like YouTube (which Google is already exploiting) hold immense untapped potential, as their video and audio content can complement traditional text-based training data.

• Synthetic Data: Synthetic data, particularly that derived from reasoning models, will increasingly feed into training pipelines. Simulation models such as Sora or Veo2 are set to become major sources of high-quality synthetic data, expanding the training horizons.

So, Will Scaling Laws Hold?

Here’s my take: While pre-training gains may become harder to achieve, we will continue to see significant model improvements driven by capacity build-up and the integration of synthetic data. Scaling isn’t just a question of adding resources but also about leveraging new forms of data and smarter compute solutions - what OpenAI called reasoning- If these advancements align, 2025 could mark another leap forward for AI.

Being Specific: Where Do I See Capabilities Progress?

What Aspects Should We Expect Foundational Models to Improve On?

1. IQ

Better models mean higher IQ. This improvement will stem from two areas:

• Pre-training Gains will be incremental gains for the new generation of frontier models like GPT-next (I have no clue about the name) or Grok 3; I suspect, however, that pre-training will be a lesser factor in 2025.

• Inference-Time Compute: Technologies like OpenAI’s reasoning (O1, O3 models), and Google’s reasoning breakthroughs are poised to steal the spotlight. These innovations will allow models to perform more complex reasoning during inference, enabling leaps in capability.

If this trend continues, AI will surpass human-level performance in economically valuable tasks such as coding, mathematics, and even some research areas. Hopefully, these advances will translate into tangible progress in fields like scientific discovery.

Why Do I Think This? Compute is already committed for the next 5 years (ingredient 1 in the scaling potion), and AGI labs have spent the last 18 months developing pipelines to extract value from synthetic data (ingredient 2). Thus, we have the two needed ingredients for scalability.

2. Reliability

While hallucinations can enhance creativity, they’re a liability in agentic flows. These flows involve models autonomously performing multiple actions to achieve a goal. For example, imagine Luzia booking your flight. You’d expect the best deal to Paris—not an accidental trip to Kathmandu.

For agentic flows to succeed, models need to become significantly more reliable. Error compounds in multi-turn interactions - accuracy of a 10-step process in a 98% accurate model goes down to 82% -. If agentic flows take hold, AI will unlock a new dimension of value creation with full automation of whole workflows.

Why Do I Think This? Agentic flows represent a massive opportunity for AI. Closing the reliability gap so that we start seeing agentic applications is critical to justifying the growing investment in foundational models. If you can disrupt a whole SaaS industry, the willingness to pay for these models will be orders of magnitude higher (a $2000 a month subscription?). Revenues for labs will continue their upward trajectory, and their CEOs will gather enough resources and support for Gen5 and Gen6 models

3. Real Multimodality

Multimodality will become mainstream in 2025. Foundational models will be capable of seamlessly integrating and understanding inputs from multiple modalities—text, image, audio, and video—to perform complex tasks. Multimodality will also reach consumers.

Why Do I Think This? Humans naturally work across multiple modalities, and there’s a heck of a lot of untapped multimodal data available (think YouTube and podcasts). Labs are incentivized to overcome the technical challenges of building true multimodal models to unlock this rich data trove.

What Will Happen to Open Source and Small Models?

4. Open Source

Open-source models will continue to evolve as long as Meta (and others) funds their training. While there might be a lag between frontier models and their open-source counterparts, the training methodologies are well-understood. It’s only a matter of time and funding before reasoning models comparable to O1 emerge in the open-source ecosystem.

Why Do I Think This? Meta has openly committed to advancing open-source AI, using it as a strategy to commoditize foundational AI technology, set de facto standards, and disrupt competitors.

5. Small/tiny Models

Small models will continue to close the gap with larger ones, albeit with a 12-18 month delay. We’ll see the rise of ultra-compact models (1B-2B parameters) designed for edge devices like smartphones and cars. Think about how powerful your car voice assistant would be even with a GPT 3.5-level model (Have you tried talking to your car lately?)

Why Do I Think This? Two accelerating trends, with no sign of slowing down, are driving the push for on-device computing:

1. Privacy: Companies like Apple emphasize user privacy, better served by processing data locally.

2. Scalability: Inference is inherently energy-intensive. The only scalable solution is to shift the cost to the user by leveraging edge devices.

Where My Assumptions Might Fail

I can see two non-zero probability scenarios that could challenge this forecast. Both are completely opposite sides of the same reality.

1. Gen4 Falls Short

If Gen4 models (GPT-next) fail to meet expectations and reasoning data isn’t enough to deliver an “AlphaGo moment,” it’ll be back to the drawing board. This could delay meaningful progress as researchers pivot to new approaches.

2. Gen4 Is Too Good

If Gen4 models are astonishingly powerful, they might not be released to the public. This could happen due to:

• Profit Motives: Companies may restrict access to maintain competitive advantages.

• Regulatory Barriers: Governments could impose restrictions to mitigate risks, slowing deployment.

And finally, any prediction for the remaining of the AGI labs?

Five major labs are pushing the frontier of AI: OpenAI, Anthropic, xAI, Google, and Meta. Here are my thoughts on each of these:

OpenAI

OpenAI will continue leading the pack in frontier models, aggressively releasing products to the public to maintain its leader perception—a critical factor for securing funding. On the product side, OpenAI is likely to double down on direct-to-consumer offerings, strengthening its brand presence. For APIs, knowing that foundational models are rapidly becoming commoditized, OpenAI will likely push API consumers toward customization as a lock-in mechanism (e.g., fine-tuning, RLHF).

Anthropic

Despite less funding (compared to OpenAI) and a narrower market adoption (vs. ChatGPT), Anthropic will remain competitive due to its high-quality product, well-positioned models—their tone is appealing for specific tasks—and its ability to attract AI talent. Anthropic’s more conservative release strategy could appeal to developers and researchers wary of OpenAI’s aggressive approach. I see a non-zero chance of Anthropic being acquired (or "acquired") by one of the hyperscalers.

xAI

If scaling laws hold, xAI stands to be one of the biggest winners, thanks to Elon Musk’s ability to achieve what often seems impossible. Grok—xAI’s model—could leverage two key opportunities: higher IQ through scaling and exclusive access to real-time information from Twitter’s feed.

Google

Google’s main advantages for the next 12 months are its access to vast amounts of data—YouTube is a goldmine—and its vertical integration, which will fuel the improvement of its models. Google will continue integrating AI across its suite of products, with the search engine being the most critical. Personally, I’ve gone from using Google Search hundreds of times a day to not opening it for weeks. While this might reflect early adopter behavior, it’s an existential threat for Google and the company is for sure reacting to it.

Meta

Meta will continue championing open-source AI and distributing its models through its own platforms, such as Instagram and WhatsApp. If scaling laws stagnate, Meta could emerge as the biggest winner as it does not depend on the profits coming from model usage to win. The company might aggressively pursue alternative AI products, such as search (is a Perplexity acquisition on the horizon?).

Other Competitors

For smaller competitors with a high density of talent, 2025 is likely to be a consolidation year. Companies like Mistral may face acquisitions or strategic pivots as the market matures and competition intensifies.

Finally, I need to confess that I am a bit lost about Amazon’s AI strategy. Yes, they released their family of models - which are decently good gen3 and tiny models-, and yes, they have invested over $8B in Anthropic, but I don’t know the exact direction they are taking. With its Capex being the highest among all hyperscalers, my best guess is that they are betting on the commoditization of the foundational layer - thus would benefit from the AI-stagnation scenario- and an increase on inference from production applications that could run in their datacenters where many clients have already their cloud expenditure.

I hope Amazon also decides to revamp Alexa, right now it is a beautiful product with the IQ of a potato.

Let’s talk application layer now

We've barely scratched the surface of what current AI can do today. Even if we stopped training any new foundational model, we have years of innovation ahead. 2025 will be a defining year for AI as we begin to witness the maturation of the application layer. The industry needs to show at last that the billions worth of investment have an economic purpose

The application layer is broad, encompassing everything from tools and user interfaces to industries like education and legal services. It’s where AI stops being just technology and starts solving real-world problems. In 2025, two themes will dominate this layer: process optimization and UI innovation.

Process Optimization: Better, Faster, Smarter

AI’s promise to enhance human productivity comes into sharp focus here. Whether through human-in-the-loop workflows (e.g., legal research and drafting documents) or fully automated processes (e.g., deploying code to production without human intervention), the goal is clear: take tasks humans do today and make them better or faster.

Low-risk applications will proliferate as businesses test and refine these systems. Mistakes in these areas are less critical thanks to built-in redundancies, making them ideal candidates for early adoption.

UI Innovation: From Invisible AI to Radical Experiments

We’re entering an era where interfaces will evolve dramatically. Some applications will make AI invisible, seamlessly integrating it into user experiences where the technology itself is secondary. Others will push boundaries, experimenting with social AI platforms and creative tools like canvas or generative art applications. This duality—practicality and bold experimentation—will define the next wave of user interaction.

In summary

2025 will test the resilience of scaling laws, as the industry grapples with whether increasing compute power and data availability can sustain the dramatic performance improvements seen in recent years. Meanwhile, the application layer will take center stage, focusing on process optimization and innovative interfaces to deliver real-world value. Low-risk automation will proliferate, and UI experiments will redefine how humans interact with AI. It’s a year where AI will move from abstract potential to practical, transformative use.

Looking Ahead

In the past twelve months, I’ve had conversations with machines that sometimes surpass human levels, witnessed building-sized rockets lifted by "chopsticks," and ridden in robo-taxis - not to speak about using satellite internet connection for the same price as fiber! Chips are being implanted into brains, and nuclear energy is resurging. In this whirlwind of progress, even water bottle lids have been improved, thanks to EU regulations aimed at reducing waste and improving sustainability. Jokes aside, AI is no longer a distant promise but a tangible force shaping our reality. The coming year promises to push these boundaries further, delivering breakthroughs we can barely imagine today. Can't wait to write the 2026 version of this post!

2024 Report Card: How Did I Do?

Time for some accountability. Last year I made some bold calls about AI's trajectory. Let's see if I earned my prediction stripes or if I need to hang up my fortune-telling hat...

Size Matters…

☝ Nailed this one. The progression in model capabilities matched expectations, with significant leaps in performance and efficiency (90% cost reduction!). Benchmarks once considered AGI-proof are now shattered. Today, we boast Ph.D.-level capabilities in physics, mathematics, coding, and more fields are falling rapidly. This evolution has also led to a shakeout in the market: “less-funded” (over a frickin billion dollars!) AGI labs are dissolving or struggling to keep pace (Inflection and Character), and the barrier to entry has skyrocketed. Dario Amodei’s remarks about the next generation requiring tens of billions underscore this reality.

Quality Over Quantity

☝ Nailed this one too. While the headlines focus on frontier model advancements, there is a quiet revolution in smaller models (under 70B parameters), achieving Gen3’s (early version of GPT4) initial performance levels (i.e., Nvidia’s 70B). This has been made possible through high-quality synthetic data (see: Phi models) and distillation techniques (big models training smaller ones). The "AlphaGo" moment I forecast—here is Karpathy’s explanation of what I mean—is not fully realized but is inching closer to OpenAI’s reasoning pipeline.

Multimodality Takes Off

➡️ Technically correct but with caveats. In 2024, we glimpsed the promise of true multimodality. Models can now speak (advanced mode) and see (e.g., screen share in Gemini or OpenAI). However, this progress often relies on modality transitions (e.g., audio transcription or image captioning). A critical next step will be UI and UX innovation to unlock multimodality's full potential—a challenge that 2025 will likely tackle.

Architecture Breakthroughs

➡️ Mixed results. Gains from unwobbling were substantial but overshadowed by size. Mamba, the architecture I predicted would dominate 2024 due to context-light management, has not gained traction. However, the trend toward increasing context length is undeniable and remains pivotal.

“If we dream into the distant future… we’ll have context length of several billion. You will feed in all of your information, all your history over time, and it will just get to know you better and better.” - Sam Altman in a Lex Fridman Interview

Post-Training Optimizations

☝ Absolutely nailed it. Let me quote myself:

"Among these optimizations, the most promising is allowing models more time to 'think.' A well-known method for this is Chain of Thought (CoT). CoT instructs the model to take its time, articulate its thought process, and solve the problem.

The results are extraordinary when combined with step-by-step verification (using another LLM to analyze each step). Leveraging current technology and applying variations of this concept, Google researchers have improved solutions for mathematical optimization problems previously unsolved (hinting at new beyond-human-level data for the model…you see the connection?)."

We’re now entering an era where optimizing outputs post-training yields massive gains. The synergy between reasoning and advanced training techniques is driving unparalleled leaps across benchmarks.

My honest reaction to Willow's headlines was realizing I had no clue what they were really talking about. Yup, quantum computing sounds fancy, but what is it? Is it a more powerful laptop? Is it something completely different? As Cleo Abram puts it: "A quantum computer is not just a faster version of your laptop. It's an entirely different kind of machine designed for an entirely different set of problems."

This post summarizes the rabbit hole I have been in for the last two weeks. It is long and detailed, but as always, it forces me to clarify my thoughts.

Why is it worth entering this 🐰hole now?

Willow caught my attention, and when I started to dig, I soon understood that it was a good time to get up to speed - dummy level - into what QC means. Three key factors have aligned:

1. Technical Breakthroughs: Willow error correction (more on this below) is a giant leap to prove that scalable QC is possible, and as Sam Altman repeats, once you know something can be done, then is an engineering problem.

2. Strategic Necessity: we are in a techno-cold war, and QC is another front.

3. Commercial Potential: Early quantum applications in drug discovery and materials science are showing promise

These three things together tell me that things will move fast going forward, and I definitely don’t want to lose.

What’s a Quantum Computer?

To really unpack Willow’s news, we need to go deep into what is going on in a quantum computer. For that, let’s start by refreshing the basics of classical ones.

Understanding classic computers

At their core, computers are remarkably simple devices consisting of three main components:

• Memory to store information in binary (01001100 01110101 01111010 01101001 01100001)

• An arithmetic unit to perform calculations (2+2=?)

• A control unit to coordinate operations (if xx then)

These three operations can be done using transistors —tiny switches that can be either on (1) or off (0). By chaining these transistors together, we can create basic operations like AND and XOR and eventually build up to “complex” calculations like sums and multiplications. Once you can do multiplications, you can do anything.

For years, we have worked hard to be able to do more and more of these basic operations on our computers with a conceptually simple - technically really challenging - approach, make transistors smaller.

And we have come a long way. Modern transistors are incredibly tiny, with leading manufacturers like TSMC and Intel producing 3-nanometer chips (about 1/30,000th the width of a human hair). But we're nearing fundamental physical limits. As transistors approach atomic scales, quantum effects interfere with their operation, and we likely won’t get much more compute from existing technology.

The Quantum Difference: Not Just More Computing Power

The distinction between quantum and classical computers goes far beyond processing power. Certain problems—like factoring large numbers or simulating quantum systems—are inherently better suited for quantum computers. That does not mean that, given infinite computing power, these problems can’t be solved, but there are better ways to do it. Think of it like the difference between boats and cars—it's not that one is "better," they're just designed for fundamentally different environments and purposes.

The fundamental difference between both systems is that while classic computers can be in either 1 or 0, a quantum computer can be simultaneously in all positions. If that does not say much to you, you are not alone. All that comes below is my attempt to make sense of this idea. Let’s start with the two properties of the quantum realm that make this possible

Superposition and Measurement

Unlike classical bits that must be either 0 or 1, quantum bits (qubits) can exist in a superposition of both states. While it's tempting to think of this like a spinning coin, quantum superposition is far stranger. With a spinning coin, we don't know if it's heads or tails until it lands. However, a qubit in superposition is actually in all states simultaneously, with specific probabilities for each state that we can manipulate through quantum operations.

To visualize this, physicists use something called the Bloch sphere. Imagine a globe where the North Pole represents state |0⟩ and the South Pole represents state |1⟩. A classical bit can only be at either pole, but a qubit can exist at any point on the sphere's surface. The latitude and longitude of this point tell us the exact mixture of |0⟩ and |1⟩ states.

In reality, qubits are described by quantum wavefunctions—mathematical functions representing this complete state of possibilities that result in a probability distribution of the encoded variables. The number of states encoded by these qubits behave like classical bits 2**n

But here is the key: when we measure a qubit, it "collapses" to either 0 or 1. That means that the qubit stops being in superposition.

This might seem to negate the advantage of superposition—after all, we end up with just a classical bit. But the magic happens before measurement, in how we manipulate these superposition states. Through carefully designed quantum operations, we can make the wavefunction interfere with itself in ways that amplify the probabilities of the answers we're looking for. Introducing quantum interference.

Quantum Operations and Interference

Think about solving a maze. A classical computer would try one path at a time, systematically exploring each option. A quantum computer, through superposition, can represent all paths simultaneously. However, the real quantum advantage isn’t just "trying everything at once"—parallel classical computers can attempt that too. The unique power of quantum computing comes from interference.

Interference in quantum computing works like waves in water: waves can add together (constructive interference) or cancel each other out (destructive interference). By carefully designing quantum operations, we manipulate the qubits to interfere in such a way that the paths leading to wrong answers cancel each other out, while the paths leading to the correct answer reinforce each other. When we measure the qubits, the probability of getting the right answer has been amplified, making it much more likely to appear.

Imagine dropping pebbles into a pond to find the shortest path in a maze. Each pebble creates ripples, and as the ripples overlap, they interact. For paths that lead to dead ends (wrong answers), the ripples cancel each other out through destructive interference. But for the correct path, the ripples align perfectly, creating a strong, visible wave (constructive interference). When you measure the waves, you’re most likely to detect the strongest one—the correct solution. This is how quantum computing cleverly narrows down the possibilities.

Entanglement

One final property before we go to an algorithm.

Entanglement is the capacity for two qubits to get their states correlated. In other words, if you have two entangled qubits and measure one, you immediately know the result of the other, regardless of how far apart they are (which can be very far). Einstein called this "spooky action at a distance."

🐰 - Faster than light communication?

In quantum computing, qubits are influenced through carefully designed operations like interference and quantum gates, which manipulate their states intentionally within a controlled system. However, when it comes to entanglement, the situation is different. While two entangled qubits share a correlation such that measuring one instantly determines the state of the other, this process is inherently random, and randomness cannot be used to encode or transmit meaningful information. Unlike the deliberate manipulation of qubits in a quantum computer, you can't control the state of one entangled qubit to influence its partner in a specific way. To communicate meaningful information, a classical channel is still required to interpret or synchronize measurement results, which is constrained by the speed of light. Therefore, entanglement alone does not enable faster-than-light communication, even though it reveals a profound and instantaneous connection between particles.

Quantum Gates: The Building Blocks of Quantum Computation

Quantum gates are the fundamental building blocks of quantum computation, just as classical gates (AND, OR, NOT) are for classical computers. They are tools designed to manipulate qubits, harnessing their quantum properties like superposition and entanglement.

Single-qubit gates, such as the Hadamard (H) gate, create superposition, while Pauli (X, Y, Z) gates rotate qubits along the Bloch sphere. Phase gates (S and T) add specific phase angles to the quantum state. Multi-qubit gates unlock the true potential of quantum computing by enabling interaction between qubits. For instance, the CNOT gate (a quantum XOR) flips a target qubit based on a control qubit's state, while the SWAP gate exchanges the states of two qubits. Gates like the Toffoli (CCNOT) and CZ extend these operations to three or more qubits, allowing for intricate quantum circuits. Together, these gates form the foundation for building quantum algorithms.

Universal Quantum Computation

Here's where it gets interesting: just like how NAND gates can build any classical computation, a small set of quantum gates can approximate any quantum operation. The most common universal gate sets are:

1. Clifford + T: The Hadamard, CNOT, and T gates

2. H + CNOT + Toffoli

This means we can build general-purpose quantum computers that can, in theory, implement any quantum algorithm. However, there's a catch: unlike classical gates which are perfect, quantum gates are prone to errors. Each gate operation introduces small imperfections that can accumulate. This is why error correction (like what Willow achieves) is so crucial.

How Gates Create Algorithms

Quantum algorithms are built by carefully arranging these gates into circuits. For example:

1. Start with qubits in state |0⟩

2. Use H gates to create a superposition that become the inputs of our problem

3. Apply multi-qubit gates to create entanglement and interference

4. Measure the result

Think of it like a quantum choreography—each gate is a specific move, and the sequence of moves creates the dance that solves our problem.

The cryptography example or how QC will break the internet

The most immediate and profound impact of quantum computing will likely be in cryptography. To understand why, we need to dive into how current encryption works and why quantum computers pose such a unique threat.

How RSA Works: The Foundation of Modern Encryption

Today's internet security largely relies on RSA encryption, which is based on a beautifully simple idea: multiplying two large prime numbers is easy, but reversing that multiplication—factoring the product into its original primes—is computationally hard. This means there is no known algorithm that can solve it efficiently. For example, multiplying 17 and 23 to get 391 is straightforward. However, if you're given 391 and asked to find its prime factors, the task becomes exponentially more difficult as the numbers grow larger.

Modern RSA encryption uses numbers with hundreds or even thousands of digits. To be precise, 2048-bit RSA keys correspond to numbers with about 600 digits, while 4096-bit keys reach around 1200 digits. The difficulty of factoring these massive numbers is what keeps your private communications secure. Classical computers would need millions of years to break RSA encryption by brute force, trying one combination at a time.

Initially, I found this hard to grasp. For some reason, I thought there couldn’t be that many prime numbers within the range of RSA encryption limits. But the Prime Number Theorem tells us that while the density of prime numbers decreases as numbers grow larger (which makes sense), the total count remains astronomically high. Applying this theorem, we estimate around 10ⁱ²³⁴ primes within the range used by RSA encryption, leading to approximately 10²⁴⁶⁸ combinations of prime pairs. Clearly, not a problem for pen and paper.

Enter Shor's Algorithm

That encryption cannot be broken with classic computing, or at least there is no known algorithm that can do it efficiently, depends on how many attempts, on average, you would need to do to randomly find the two prime factors composing the encryption key.

Shor’s Algorithm, developed in 1994, fundamentally changes the game. This quantum algorithm can factor large numbers exponentially faster than classical methods (sqrt(N)), which is a much much nicer computational function.

Instead of brute-forcing each possibility, Shor's algorithm transforms the problem into finding the periodicity of a mathematical function. Using quantum superposition, the algorithm evaluates all possibilities simultaneously, leveraging quantum interference to amplify the correct periodic patterns while canceling out incorrect ones. The core step, a quantum Fourier transform, extracts the periodicity efficiently, which is then used to deduce the prime factors through classical post-processing. This process enables exponential speedup, reducing tasks that would take millions of years for classical computers into hours on a sufficiently powerful quantum computer.

To make matters worse, adversaries can capture encrypted data now and decrypt it later once quantum technology matures—a strategy known as “store now, decrypt later.” This makes quantum-resistant encryption an urgent priority.

Why This Matters: The Quantum Threat

Under this paradigm, no secret is safe, and to make matters worse, there is the “store now, decrypt later” threat. Adversaries could capture encrypted data today and store it, waiting for quantum computers capable of breaking RSA to mature (your PornHub habits are not safe no matter how much VPN you use). This makes transitioning to quantum-resistant encryption methods a pressing priority.

Qubit Overhead and Breaking RSA

Today’s qubits are not perfect, requiring significant redundancy to form a single reliable, error-corrected logical qubit. To break RSA encryption with 2048-bit keys, a quantum computer would theoretically need approximately 4000 logical qubits for the primary computation, plus an additional 2000 qubits for intermediate calculations—resulting in a total of 6000 logical qubits in a perfect system. However, due to the imperfections of current qubits, this number grows significantly.

Current quantum systems require around 1000 physical qubits to create a single logical qubit, meaning a practical system for breaking RSA would need roughly 6 million physical qubits. This staggering number highlights the challenge of scalability. Willow’s advancements in error correction reduce the overhead needed by enabling “below-threshold” error rates, where adding more qubits improves reliability rather than compounding errors, getting us closer to solving this problem.

The Willow Breakthrough

The significance of Google's Willow quantum computer lies in its ability to overcome one of quantum computing's biggest challenges: error correction. Quantum states are incredibly fragile, and maintaining them long enough to perform useful computations has been a major hurdle. This challenge has been so fundamental that many skeptics questioned whether practical quantum computers were even possible.

Previous quantum computers faced a cruel irony: while adding more qubits theoretically increased their computational power, it also made them more prone to errors. Each additional qubit increased the likelihood of the system losing its quantum properties through decoherence. It's like trying to build a house of cards in a room full of fans - the taller you build, the more likely everything is to collapse.

This is where Willow's breakthrough becomes truly revolutionary. For the first time, Google has demonstrated "below-threshold" error correction, meaning that as you add more physical qubits, the system becomes more reliable rather than less. Now, we can theoretically scale up quantum computers while simultaneously making them more stable. This doesn't just solve a technical problem - it proves that large-scale quantum computers are actually possible. As Sam Altman often says about transformative technologies: once you know something can be done, it becomes an engineering problem rather than a scientific one.

Why is below threshold so important?

While a classical computer can operate just fine in your pocket or backpack, quantum systems are disrupted by practically anything in their environment. Temperature fluctuations, electromagnetic fields, mechanical vibrations, cosmic rays, and even the simple act of observation can cause decoherence - the loss of quantum properties. This extreme sensitivity exists because quantum states like superposition and entanglement require perfect environmental isolation. It's similar to trying to balance a pencil on its tip - the slightest breeze or vibration will make it fall. This is why the images we often see of 'quantum computers' showing large octopus-like machines are somewhat misleading - those aren't actually the quantum computer itself, but rather the elaborate infrastructure needed to protect the qubits. Those massive structures contain the cooling systems (bringing temperatures close to absolute zero), magnetic shielding, vacuum chambers, and control electronics needed to isolate the actual quantum chip (which is typically smaller than a coin) from the chaotic outside world. While the quantum operations themselves are highly energy efficient, the overhead required to enable those operations makes current quantum computers massive energy consumers.

The Road Ahead

While Willow represents a significant breakthrough, the road to practical quantum computers still faces major hurdles. We need to scale up from today's hundreds of qubits to millions of reliable ones. Current coherence times - how long qubits maintain their quantum states - are measured in milliseconds, which limits the complexity of possible computations. And perhaps most importantly, we're still discovering what problems quantum computers are truly good at, with only a handful of practical quantum algorithms developed so far.

Yet the field's rapid progress is undeniable. The fact that industries are already implementing quantum-resistant cryptography shows we're not just theorizing about quantum computers - we're actively preparing for their arrival. The quantum future isn't a question of if, but when.

How else will QC change the world?

While cryptography and Shor's algorithm often dominate discussions about quantum computing (partly because of their dramatic security implications), the true potential of quantum computers extends far beyond breaking encryption. As breakthroughs like Willow's error correction bring us closer to reliable quantum systems, a whole new world of applications becomes possible. Let’s look at some nicer-than-cracking-your banking password applications

Optimization Problems

Quantum computers excel at solving certain types of optimization problems. The Quantum Approximate Optimization Algorithm (QAOA) can tackle complex logistics, portfolio management, and scheduling problems by exploring vast solution spaces simultaneously.

For example, imagine a supply chain problem with millions of possible configurations. A quantum computer could identify the optimal configuration far more efficiently than any classical method by leveraging superposition and interference.

Molecular Simulation

Simulating quantum systems (like complex molecules) is extremely difficult for classical computers - in fact, the computational requirements grow exponentially with each additional particle. Even modeling a relatively simple molecule with just 30 atoms quickly becomes intractable for our most powerful supercomputers. This is where quantum computers offer a fundamental advantage. Since they operate using the same quantum principles that govern molecules, they can naturally simulate these systems. While AI systems like AlphaFold have made remarkable progress in specific areas like protein structure prediction, quantum computers could revolutionize entire fields. In materials science, they could help design new superconductors that work at room temperature, more efficient solar panels, or better batteries for electric vehicles. In drug discovery, they could simulate how potential drug molecules interact with target proteins at the quantum level, dramatically accelerating the development of new treatments for diseases like cancer or Alzheimer's. The power of quantum simulation isn't just about speed - it's about being able to explore molecular interactions at a level of detail that's simply impossible with classical computers.

Conclusion

Let's be real - quantum computers aren't the magical 'does everything faster' machines that headlines make them out to be. They're weird, finicky beasts that need to be cooled to near absolute zero just to function, and we still barely understand what they'll be truly good at. But that's exactly what makes them fascinating.

While you won't be running Microsoft Excel on a quantum computer anytime soon (nor would you want to), they open up entirely new possibilities - from breaking the encryption that powers our digital world to simulating molecules in ways that could revolutionize drug discovery. Willow's breakthrough shows us that practical quantum computers aren't just a physicist's dream anymore - they're becoming an engineer's headache.

This post reflects my two-week descent into the quantum rabbit hole. Like most things quantum - and in life-, the more you learn, the weirder it gets. It was useful for me to get some intuition, but especially to learn how much I don’t know about this fascinating world.

Now, if you'll excuse me, I need to encrypt all my embarrassing teenage photos before these things become practical.

Further Reading

More sources

[1] The Map of Quantum Computing

[2] How Quantum Computers Break The Internet... Starting Now

[3] Quanta Magazine: Quantum Computers Cross Critical Error Threshold

[4] My Quantum Computer Rabbit hole Youtube Playlist

O3 isn’t just another AI release; it’s proof of continuing a paradigm shift centered on one key innovation: reasoning or what we fancily call test-time-compute. Since ChatGPT two years ago, AI has excelled at recognizing patterns and generating outputs, but actual reasoning — the ability to think step-by-step and verify results — has always been the elusive prize. O3 changes that, marking a monumental leap in how models approach complex, novel problems, two months after O1 — the younger brother — was announced.

In the next 12 months, expect things to get even wilder. Two parallel scaling laws — pre-training advancements and test-time compute — are accelerating like never before. As base models improve, reasoning will help to generate better synthetic data (new science discovery, simulation data, correction of errors…) creating a flywheel effect that powers exponential progress. If this pace holds, O4 and O5 could drop next year, each more transformative than the last.

So, brace yourself. The timeline for what’s possible in AI just shifted dramatically, and if O3 is any indication, 2025 might redefine everything we thought we knew about this technology. When these models' societal gains come to fruition, the world will go wild.

What is Reasoning?

Reasoning in AI is about much more than arriving at the correct answer — it’s about the steps and logic that lead there. With O3, this process has been supercharged. Here’s how it works:

You start with the base model (GPT-4o likely), and generate thousands of candidate solutions using a “chain of thought” approach. Then, a verification model — likely the same base model — evaluates these answers, checking for logical consistency, accuracy, and calculations. This verifier is fine-tuned on an enormous dataset of corrections, enabling it to detect and refine mistakes. When the reasoning is correct, those steps fine-tune the base model, creating a feedback loop that generates even better results. Think of it as synthetic data on steroids.

In more technical terms, OpenAI has trained models to explore/search the space of potential solutions (think about the classical tree search algorithm) and use automatic evaluations through LLMs to identify the best possible path. This technique breaks the implicit limitations on reasoning due to the nature of the data used for training. If you think about it, not much of the published data available for training includes reasoning, only results. With this approach, OpenAI (and others exploring this space) have turned abstract computation into actionable logic that can adapt to complex, novel problems.

The Two Scaling Curves

Reasoning is a new tool at our disposal to continue pushing frontier models' limits, effectively enabling a second scaling law. In this new reality, pre-training advancements (more data, more compute, better results) play along test-time compute (more time to return a correct response). It’s a self-reinforcing loop: reasoning produces better (synthetic) data — which was a limiting factor in pre-training gains- which builds stronger base models, which in turn fuels even more refined reasoning and so on. It’s a flywheel effect — one scaling law feeds the other, creating exponential progress.

Achievements That Change the Game

Let’s talk numbers. What O3 has achieved across multiple benchmarks (a very common way to test the models) isn’t just incremental progress; it’s a fundamental leap that forces us to rethink what AI can do. While OpenAI has scheduled the full release for early 2025 (pending safety evaluations), the preliminary results are staggering. To put these achievements in perspective, imagine a rookie athlete simultaneously breaking world records in swimming, marathon running, weightlifting, and chess. That’s essentially what O3 has done in the AI world. Here’s what the data shows:

• Math Mastery: Scored over 25% on FrontierMath — the hardest math problem set in the world- a leap from the previous best of under 2%. 25% seems like not much, but there are no more than a handful of humans in the world who can get close to that. Don’t believe me? Read the sample question below.

• Science Supremacy: Outperformed humans with 87.7% accuracy on GPQA, demonstrating a remarkable ability to handle graduate-level science questions. These aren’t easy, either.

In a parallel universe where a magnet can have an isolated North or South pole, Maxwell’s equations look different. But, specifically, which of those equations are different? — Yup, this is a real one

• Software Engineering Excellence: Dominated SWE-Bench with 71.7% and scored 99.95% in competitive coding. These are real-world coding capabilities, including debugging and verification WAY beyond the average SWE. This is bananas.

• General Adaptation: And here is the best one. Achieved 88% on ARC-AGI, breaking barriers in general reasoning tasks previously considered untouchable. This benchmark was designed to be an “AGI-resistant” test, requiring adaptive reasoning across novel tasks. The model’s success here is groundbreaking but came at a steep price tag. The ARC-AGI inference alone reportedly cost $350,000. This cost speaks about the potential of scaling computing. Give machines more time ($$) to think, and good responses will come.

But Why Does This Matter to All of Us?

“I think that most people are underestimating just how radical the upside of AI could be.” — Dario Amodei (CEO, Anthropic)

The leap from benchmarks to real-world impact is happening faster than most people realize. We’re not just talking about AI scoring well on tests — we’re seeing machines rapidly closing the gap on human capabilities and in some cases, surpassing them.

In just a few years, we’ve witnessed AI surpass human-level performance in areas once thought to be exclusively human domains: general knowledge, mathematical problem-solving, and code generation. Even more striking, we’re seeing breakthroughs in complex reasoning — long considered the final frontier of human cognitive superiority.

Let me give you a concrete example that shows why this matters. Recently, social media erupted with panic about black plastic utensils potentially containing a toxic compound called BDE-209. The story seemed credible until a researcher decided to fact-check the original paper. Here’s where it gets interesting: Ethan Mollick ran an experiment by feeding the paper to O1 (not even the latest version) through ChatGPT. Within seconds, the AI spotted a multiplication error on page 7 that had sparked the entire controversy. Think about that — instant validation of scientific research that escaped the peer review process.

This ability to quickly analyze complex documents and spot errors is just one facet of AI’s growing capabilities.

Let’s zoom in on another field that’s being revolutionized: software development. If O3 consistently outperforms the average software engineer and is deployed at scale in platforms like Devin or IDEs like Cursor, the very nature of software development will transform. English could soon become the dominant “coding language,” allowing people without traditional technical expertise to describe what they need and watch as AI builds, debugs, and optimizes complex systems in real-time.

But it’s not just about efficiency or new tools. As Dario Amodei discusses in “Machines of Loving Grace,” we are on the brink of unlocking solutions to problems that have plagued humanity for decades. Think about the possibilities: curing diseases with precision medicine, modeling climate interventions with unprecedented accuracy, or even reshaping how we generate and distribute energy globally.

Conclusion

O3 isn’t just another milestone in AI’s journey — it’s the moment that forces us to tear up our roadmaps and redraw our horizons. Two months after O1, OpenAI hasn’t simply moved the goalposts; they’ve changed the game entirely. When machines can outperform humans on graduate-level physics and solve mathematical problems that only a handful of people worldwide can tackle, we’re not just crossing thresholds — we’re shattering them.

Think about it: if this is O3, what territories will O4 and O5 explore? 2025 isn’t just going to be wild — it’s likely to be the year that rewrites the rules of what technology can achieve. The implications ripple far beyond benchmark scores and technical achievements. We’re watching the birth of systems that don’t just process information but genuinely reason through problems like a brilliant collaborator.

Here’s the kicker: even if we hit a plateau tomorrow, the capabilities we’ve unlocked with O3 will fuel a decade of innovation. But if this pace continues? We’re not just approaching a new chapter in technology — we’re opening an entirely new book. The following 12 months won’t just redefine what’s possible; they’ll redefine what we dare to imagine.

It’s here, and it’s thinking.