2026-06-25 19:18:49
The generative AI economy has generated $110 billion in sales over the past 12 months. It is growing fast. On an annualized basis, the revenue run rate exceeds $175 billion.
These numbers took us several months to construct, and as far as we know, it’s the first bottom-up, deduplicated measure of consumer and enterprise AI spending across the full stack. We are releasing this research today in our first The State of the AI Economy report.
The supply side of the AI market is well-understood. The picks and shovel suppliers, the computer chips, the memory, the power transformers, the cooling, all of the components of AI data centers are largely public companies. We get a sense of what is being spent on the buildout through their disclosures, sales and forward order books.
But understanding the demand side is much harder. And this is what we’ve spent the last few months tackling. We built a proprietary AI economy model that looks at total AI spend, whether enterprise or consumer, to answer the hardest questions of the AI wave:
How big is the market really?
Are the revenues growing?
How far do the revenues go to cover the investment expense?
What happens to the economics in the future as token prices fall and the quality of those tokens improves?
Before we get into the report, we think it’s important to break down how we did this work.
One of our central design choices was not to count the same thing twice. We report the dollar spent by an end customer. So if you spend one dollar with Anthropic for Claude and Anthropic spends 50 cents with Amazon to serve it, we track both figures internally, but we will report it as our de-duplicated number: one dollar. This avoids double-counting the value that flows through the supply chain.
This isn’t straightforward. While it’s easy to count the supply side, the demand side is trickier to entangle. Much of the revenue flowing into AI comes from privately held companies such as OpenAI, Anthropic, Cursor, ElevenLabs, and hundreds of others. They don’t legally need to disclose anything.
The remainder flows to the big hyperscalers that serve these models: Amazon, Google, and Microsoft. While they are public, they don’t consistently disclose their AI segment revenues.
To shed light on this, we examine public statements from hyperscalers and neoclouds, their suppliers, and their customers, using only high-confidence, detailed facts to inform our modeling. We also look at well-reported leaks and self-reports, to which we assign a confidence score.
The result is an item-by-item financial model for the largest contributing companies and business units. Each model is effectively a deconstructed financial plan, a P&L, balance sheet, and cash flow, and these are triangulated against other external sources and internal consistency checks. This makes our numbers auditable. We can identify which data point, with which confidence weighting, contributed to any given estimate.
We don’t include internal AI uplift, which is how much recommendation systems have improved, increasing ad revenue at Meta or Google. We do have models for those, but we’re not reporting them here.
Nor do we consider efficiency savings that the bigger tech companies might realize with their internal tools. We’re not tracking that yet.
We don’t include professional services and systems integration. When a Fortune 500 company spends or invests in AI, only a portion of that spend will go to an AI company. It won’t represent the full extent of their commitment, because a large part of it will be paying professional services to support the implementation.
We have got models for revenues in China, but this v1 of our report doesn’t include Chinese data yet.
Over the past 12 months, the AI ecosystem generated $110 billion in revenue when you remove double-counting. The growth rate is healthy. Annualizing the most recent month’s revenues indicates a $175 billion revenue run rate.
These revenues are growing faster than previous IT-oriented waves, roughly three times more rapidly than the mobile or Internet waves.
While many companies have moved beyond occasional pilots, they are still in the early stages of scaling and deepening. In conversations Azeem has had with senior execs across a range of industries in Europe and the US (from industrials to insurance, from finance to pharma), the consistent message is that they intend to invest more heavily in AI in the coming years. Companies are also becoming more vocal about the impact of AI on earnings calls, with the caveat that half of the surveyed CEOs believe their jobs depend on getting AI right.
The next question we wanted to track is whether AI revenues can cover the capital investment that’s required to build the infrastructure. Our model separates AI-oriented CapEx from ordinary CapEx across the major hyperscalers and neoclouds, the specialist AI cloud providers. This adjustment is important because hyperscalers were already spending around $120 billion annually1 on CapEx before ChatGPT.
We capture the additional investment in AI infrastructure, then depreciate compute assets over 6 years and other infrastructure over 14 years. Our modeling shows that revenues attributable to hyperscalers just about clear the depreciation expense.
Six years is defensible. That longer useful life reflects that one, demand still exceeds available AI compute, and two, operators are getting better at managing GPU fleets. Both help. The second alone is enough to justify a longer economic life.
We also examine how market size changes as token prices fall. The elasticity of demand shows that lower prices are met with increased spending. We estimate that across providers, every 10% price cut leads to 12-18% more tokens in use, so the total spend still rises.
We suggest that although a token is a useful billing metric, it is still not the unit of value we need to measure the economic value of intelligence circulating through the sector. Quality‑adjusted output tokens give us a better “intelligence quotient” for the AI economy by combining how many tokens are produced, how many of them are actually user‑visible outputs, and how capable the underlying models are.
The report also covers:
What AI demand has done to the US power industry and how power efficiency is changing,
What is happening to token costs, and how consumption-based billing may expand the market,
Four scenarios for how fast AI demand could grow under different price and capability trajectories.
This is v1, and we’d love your constructive feedback on what to improve and how you can help. Email us at aieconomy [at] exponentialview.co
For institutional or advisory inquiries, email helen [at] exponentialview.co
Google, Microsoft and Amazon (whose spending included logistics investments). This number excludes Meta.
2026-06-21 11:12:26
Hi all,
Happy Sunday.
GenAI drives almost 2% of traffic to Walmart and Target; home and electronics categories lead. A quick show of hands, please:
Here’s Sunday edition #579.
A working paper by Harvard Business School’s Rembrand Koning and INSEAD’s Hyunjin Kim studies how AI-native startups1 are different to non-AI startups. At similar funding and growth, AI natives are 25% smaller; they have more engineers, denser expertise and fewer managers.
Now, what’s interesting in this paper is that AI makes a real difference in a business when it’s folded into the product. Some of the knowledge work that used to be done at the edge of the product by human teams is now done directly within the product. When the work is done directly in the product layer, the feedback loops close. Kim and Koning write:
Capturing real value from AI often requires re-engineering processes around it—and, as our results suggest, re-engineering the product itself so that AI does the work directly.
This complements my perspective that firms must redesign around AI‑closed loops. Giving workers a Copilot or coding tools is most useful for understanding what the technology can do. But real gains will come from the much harder task of rebuilding how you work centered on AI. Read our analysis here.
See also, Sam Altman speaking to the Stanford CS153 class this week said:
everything about starting a startup has changed so much. … with an affordable amount of spend on tokens, you can do what a 100 person incredibly great engineering team would do as a startup and that was just totally impossible. I think what you can take on, the level of ambition you can have, the speed of which you can move, the amount of stuff you can do at once, is just totally different.
Joanne Chen, general partner at Foundation Capital (which backed Netflix and Uber) and I discuss what it takes to build AI-native companies here.
Generalist frontier models are outperforming best-in-class specialist medical tools in head-to-head tests. The specialist models that were tested are gold standard — almost two-thirds of US physicians use OpenEvidence. My go-to expert on AI and medicine, , says: “This was not anticipated.”
But this is precisely Richard Sutton’s Bitter Lesson from 2019
The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. […] We have to learn the bitter lesson that building in how we think we think does not work in the long run.
2026-06-17 19:16:04
I am a fan of AI councils. As many readers will know (from previous posts), I use AI expert panels every day to deliberate on hard questions, challenge my thinking, and do research.
These AI councils, or panels, start from an assumption that a group can produce better work than a single agent. My friend and one of the sharpest AI practitioners out there, , tested this hypothesis.
I thought Rohit’s experiment was genuinely useful for anyone thinking of designing or already working with AI councils, so I asked him to share the findings with us today.
Over to Rohit.
– Azeem
By Rohit Krishnan, originally published on Strange Loop Cannon
One way to get the best out of LLMs is to use model diversity. The models are not all the same so if you use their unique natures, you can get better responses. We saw it with the work on MarketBench. And we also saw this when Karpathy came up with LLM Council as a way to get multiple models to work with each other and get us a better answer.
But I started wondering, with people, when you put a bunch of them together in a committee, some things get better but some things do get worse! And relying on an LLM to audit is also error-prone. “Design by committee” is a four letter word for a reason. LLMs are better than us probably, but surely this process is also somewhat lossy. So what do we lose?
To test it, I set up an experiment, where I set up a few committees of models:
First, I took each answer, then gave those to a fourth model and asked it to write the final version.
Then, the llm-council – essentially peer review and then a chairperson summarises
And a “best answer” picker – just a direct pick.
With people, the problem with committees is that they “smooth out” all idiosyncrasies. They take out any “spiky” points of view, and make things much more normie. Same thing here. So to test how we do I had to find some way to grade how the various final responses were. I broke each answer into small “cards” using Sonnet. A card could be a mechanism, observation, metric, failure mode, image, or some other important detail.
Then I clustered cards that appeared to mean the same thing. If a cluster appeared in one solo answer, we called it a single-model idea. If it appeared in more than one, it’s shared. And two judges scored the solo-derived clusters without knowing which model produced them or whether a council kept them.
Now, it’s not perfect, but it’s the cleanest way to test the problem of “how to rate which answer is better” that I could find without doing human ratings.
First, the result: the council does not simply keep the best bits from everyone. It keeps a minority of the good ideas, while peer review seems to give consensus ideas an extra push.
Obviously, the final summarized versions usually read better. It is calmer, more complete, less jagged, all things you’d expect. But we had misses. Examples.
A field report noticing that salvaged retail scent cartridges had become status symbols in a squatted mall, used to mask the smell of communal living.
An incident report arguing that logged-but-deprioritized risks are more dangerous than unknown ones, because they manufacture a false sense of control.
A data-recovery plan that asks users to re-confirm suspect fields at their next login (”please re-confirm your shipping address”), quietly crowdsourcing recovery from the one authoritative source.
In the final runs, the blended council kept only about a quarter of the good ideas that appeared in just one model’s answer. Remember, these were ideas that two blind judges rated as useful, non-obvious, and worth keeping, and still roughly three quarters did not make it into the final answer.
The peer-review version did not solve this either. The rare ideas survived at about the same rate as in plain blending: 24% versus 22%. But if several models had raised the same idea, the peer-review council kept it about a third of the time, but if only one model raised it, a quarter.
To test this, I ran sixteen open-ended prompts: eight strategy problems and eight writing tasks.
Figure 1. The experiment path from solo answers to idea coverage.
I plotted what happened with the ideas. The red dot below is good idea that only one model came up with. Blue is good ideas that multiple models came up with. And the X-axis shows how many of each actually showed up in the final answer. So the selector for instance showed about 37% of all good single-model ideas, and 24% of the multiple-models ideas, which makes sense because it picks one full answer and discards the others.
Figure 2. Coverage of blind-rated high-value ideas.
The consensus tilt is smaller here, but interesting. In the peer-review council, shared high-value ideas that survived had an 11% uplift over single-model high-value ideas. Or put another way, a 50% relative lift!
The denominator for shared ideas is small, though. What’s interesting is that this shows us how the specific topology of the “council” changes what you’re likely to get, like a peer-review round ends up becoming a consensus detector even above a single model blending the answers from all other models.
This is a problem with all cognitive beings. In group decision-making research, back in the 1980s, Stasser and Titus called it biased sampling of shared information - groups are more likely to discuss information that several members already know than information that only one has. That line of work led to the “hidden profile” problem, where a group can miss the best answer because the crucial evidence is scattered across individuals rather than shared up front. We’re seeing the same thing here.
The work on LLMs, meanwhile, so far has mostly come from the other direction. Multi-agent debate papers ask whether multiple models can improve the final answer, and yes, they often can! But depending on the topic and the question, a council can absolutely improve the average answer and still drop some of the best ideas.
As users, we want to get better answers, cheaply. That’s the whole goal. Councils are great ways to make some answers better depending on how you structure it. But they’re not cheaper. So it is important to make sure they are actually better! If they’re not, or at least not universally, then how the council should be structured is an incredibly important problem!
What we still see here is that there is no free token lunch. If you use councils to get the benefits of model diversity, don’t assume it will preserve the best ideas. To do that we have to work harder, and understand how to work with these models.
For instance, one thing we know is that the best way with LLMs usually is to be explicit, since otherwise, even if they’re aligned, they cause emergent problems. So the best protocol might be to explicitly gather and store the best ideas from each solution separately and ensure they’re stored, ranked and assessed, before a final answer is written and revised.
It does much better, though it’s slower and heavier. I don’t know if this is the best we can do. The structure might change depending on the question asked, the domain, or the types of answers expected.
Humans have gone through thousands of types of “councils” until we reached interim solutions which give us decent results nowadays. And even then, we have to change the shape of the councils constantly, as we evolve, and society evolves.
To figure out how to get the best results from our work requires a lot more effort into designing the councils. If you’re working with them, you will need to experiment and eval against your individual problem sets, which is the only way to know if this specific council setup will help with your specific problem. Copying someone else’s homework won’t work!
Homo Agenticus are odd enough creatures that using them well requires much, much more experimentation than one might assume. Especially when the problems of using them suboptimally is that we lose actual functionality, often without knowing it!
This essay was originally published in Rohit’s publication Strange Loop Cannon.
2026-06-15 22:36:13
Hi all,
Here’s our Monday roundup of data signals across AI, energy and markets.
Enjoy!
AI & jobs. AI was cited as the top reason for nearly 40% of US job cuts in May1.
Mind the gap. The top 1% of US firms spend $7,450 per employee on AI each month, roughly 650x the typical firm’s $11.38.
AI productivity guarantee? Cognition says they will fund up to $10 million in credits if their AI agents fail to deliver the engineering value enterprise customers paid for.
2026-06-14 11:30:55
Hi all,
I sent a note on Thomas Piketty’s blueprint for global justice to members of Exponential View yesterday. I call its recommendations a “blueprint for managed decline”. The commentary touches on the future of modernity, scientific dynamism and democratic legitimacy. Read it here.
In today’s Sunday briefing, we look at:
The iPhone reduced fertility; what will AI do?
What’s driving the consensus behind pausing AI development?
The largest solar factory, gene therapy for rejuvenation & Chinese Gen Z finds inspiration in Western memes++
But, first…
, Sam Altman, Donald Trump and Vinod Khosla all agree that the public should own a slice of AI. Bernie proposes transferring 50% of ownership of leading AI companies into a sovereign wealth fund; Trump is supportive; Sam agrees in principle but pushed back on the 50%; and Vinod wrote an op-ed in the FT advocating for a new tax code on wealth AI creates and eventually pooling it into a sovereign wealth fund:
A sovereign fund with ownership of AI companies makes every American a capital owner, not a bystander to the AI economy.
The same day the Senate held a hearing on AI, Anthropic published a framework that has sovereign wealth funds as one of the mechanisms to deploy in case of the worst-case scenario, when “[t]he search for work stretches past a year, then past two, and for some, eventually stops”.
The core assumption here is that AI will tilt the economy’s income from labor to capital. We don’t have evidence that this is happening yet and we may not know for a long time.
Vinod believes that AI will be capable of doing 80% of jobs by 2030 and that $15 trillion of US GDP, which is labor compensation, will mostly disappear. Economist Chad Jones, in contrast, thinks the transition will take closer to thirty years because of weak links – even if we automate most tasks, the ones that don’t get automated will slow everything down (this aligns with what we see happening with AI diffusion into organizations right now). Slow doesn’t mean painless, though. And for a long time, we may not know which scenario we’re in.
My view is that there will be human jobs around for much longer than the prevailing narratives might suggest. Virtually all of these jobpocalypse scenarios of recent years have been mooted theoretically but not in the messy world of life. We’ll create more roles and that last mile (what Chad calls “the weak link”) will command decent wages. That doesn’t mean we won’t see increasing returns to capital over labor. So, societies might decide to change how they tax and redistribute. The different experiences between the UK and Norway over North Sea oil suggest that endowments seem to work better than transfers.
See also: I was on ’s podcast this week. We covered the AI bubble question, the productivity paradox and why Chinese AI engineers are Claude-pilled. Watch or listen here.
Washington has issued an export control order that prevents Anthropic from offering its Fable and Mythos models to non-US citizens, including Anthropic’s own employees. Apparently, researchers at Amazon discovered a jailbreak and it seems like Amazon’s boss, Andy Jassy, passed concerns on to the Commerce Department. (Amazon recently invested $5 billion into Anthropic and indicated it would invest a further $20 billion, having already put $8 billion into the firm and acting as its major distributor.)
The reaction seems a little over the top, given the minor trigger. I’d half expect it to be diluted in the next few days. Or it might just be the next stage in the feud between the Defense apparatchiks and Anthropic.
But it comes at a time when Anthropic, OpenAI and DeepMind are signaling they’re in favor of slowing down frontier AI development. rightly asks, what did they see? Is it the fear of the growing backlash or a genuine capability explosion?
My brief thoughts on what’s behind this.
The most charitable reading is that the labs feel they are reaching a point at which their AI models challenge the security assumptions that underpin modern life. This isn’t unreasonable: they have consistently worried that this point might arise. That actual point may still be years away, but Demis and Dario do think in long arcs – and we might need years to prepare.
There is a less charitable reading that is also plausible.
2026-06-13 18:42:44
Thomas Piketty, a French economist, has a recipe for global justice.
In the Global Justice Report, Piketty and his co-authors recommend the world converge on a national income of €60,000 (around $68,000) per capita in 2025, in PPP terms, by 2100.
They, too, propose we:
Keep annual growth in Europe and the US to roughly 0-0.5% while poorer countries catch up,
Cut working hours to about 1,000 per worker per year,
Consume fewer material goods and more education, health, care and culture.
Fund the transition with steep taxes, including a wealth tax on billionaires and very high top income-tax rates.
Reports like this arrive with a splash: they profess to be blueprints for a just global economy. But this isn’t that. It’s really a roadmap to Animal Farm.
The report misreads the history. It embraces growth for poor countries while freezing it in rich countries, as if the two were separate. The great escapes from poverty happened through trade, technology, supply chains and frontier knowledge. Much of it originated in wealthier nations. If the economic frontier slows down, the authors need to explain how the channels that allowed poor countries to converge would keep working as before. For a deep dive on growth and extreme poverty, read this.
Bad science. For their climate assessment, the policy baseline is set at 4.8-4.9°C by 2100, which roughly aligns with the now discredited and “implausible” RCP 8.5 scenario. RCP 8.5 ignored that learning curves for solar and other renewables exist entirely. Robust academic research dating back to 2016 had already discredited it. (Read on this here.)
Piketty has argued he used his own climate modelling projections, which happen to align with RCP 8.5. It makes at least one titanic assumption that we fail to “accelerate the energy transition.” That latter assumption is absurd given this is already happening. Solar, batteries, electric vehicles and many other technologies are on steep learning curves and spreading like topsy.
Implausible politics. In the proposal, global incomes would converge at €5,000 per month (about $5,700) by 2100, with lower inequality within countries. For Western Europe and America, this would essentially mean a freeze on economic growth. In the case of the US, a growth in incomes of 0.12% per annum for 75 years. It is impossible to imagine 18 US electorates, from 2028 to 2096, agreeing to this. There would have to be some kind of external force majeure, an extraordinary top-down imposed policy against the will of citizens in nations across the globe. This is anti-democratic in its own right.