2026-05-31 10:36:50
Hi all,
This week, we published a framework to explain why individuals are getting more productive with AI, but firms often are not. Make this your weekend reading.
If you want me to speak to your board about this, now is the time to book me for the fall.
A column in the Financial Times made a fascinating observation about “the impossible maths of the AI boom.” It pointed out that in the five years to 2030…
AI capital investments are expected to rise by 20% a year, a growth rate never seen before in this industry. Meanwhile, revenues are expected to grow 15 per cent annually…if the hyperscalers continue on the current trajectory, the AI boom will become a story of one of the largest destructions of shareholder value in history.”
Punchy stuff. To get these conclusions about the hyperscalers' ignominious future, the author used “the consensus estimates of analysts for the capital expenditures and revenues between 2025 and 2030.”
This all sounds reasonable. Equity analysts are, of course, paid very well to understand the companies in their sector and work long hours to do so. A consensus estimate is the average of what these well-heeled professionals think.
The problem is that they are, on average, late to regime shifts.
In particular, when a technology hits a learning curve and demand explodes, producing that familiar hockey stick, analysts often stay stuck with their old assumptions.
Yes, yes, exponential curves don’t, in the real world, go on forever. But when they are going on, they are, in fact, non-linear. And the straight line and the exponential curve don’t fit well.
You can see this by a simple evaluation of just how well analysts have made sense of booming AI demand. When you or I spend money with OpenAI or Anthropic, the majority of it ends up with the hyperscalers, who, in turn, are pouring hundreds of billions into buying chips, memory, and other trinkets. While Wall St analysts don’t formally track Anthropic because it is a private company, they do track those firms in the supply chain.
Micron for on. Micron is an American company that makes advanced memory popular with hyperscalers. LLMs chew through memory, and Micron has done well as a result. But how well did the equity analysts covering Micron do?
The chart below shows the consensus estimates for Micron’s earnings per share. Pay attention to the purple line, which is FY2026. As late as December 2025, the consensus view was that Micron would deliver about $18.25 EPS. Five months later, with some early data to help them, the median view is now about $58. So what was the point of the $18.25 forecast?
Of course, Micron isn’t a hyperscaler. But it is unusually exposed to AI. Only three companies make fancy high-bandwidth memory; Micron was the minnow and the only American player. Maybe this pattern of revisions was an exception, not the rule. Maybe Micron was just … weird.
So consider a larger company like Google, with a 20-year track record. AI, understandably, is only a small part of the firm’s $300 billion revenue business. If you and I are buying heaps of tokens from Google, it’s just a drop in its overall business. But even then, between May 2025 and May 2026, the median analyst view on Google grew by 40%.
We all know what a share price is. It’s the price right now that's tradable on a stock exchange. But it represents a view of the future: a compressed probability distribution over future outcomes for the business.
If a stock price reflects the market’s view now, an analyst's target represents something specific. It is their expectations for the stock’s value.
Michael Mauboussin, a bit of a guru in the world of finance, has one of the best definitions of expected value: “the weighted-average value for a distribution of possible outcomes” of a company.
So when you, I, or an analyst determines a share price target, we aren’t just choosing a number. We are expressing the outcome of the probability calculation, a distribution across possible futures for that asset. The keyword is distribution. And the shape of that distribution depends on your assumptions. If you think this is a linear, business-as-usual technology, you will make one set of assumptions. You’ll be ignoring the adoption rate, cost declines, performance improvements, or the possibility of an extreme outcome. You haven’t just chosen the wrong point on the curve. You’ve chosen the wrong curve.
Sorry, sir, I misjudged the speed of that iceberg.
And yet you constantly revise your expectations upwards, chasing the puck rather than getting ahead of it. Below, you can see the median analyst consensus for three companies with varying exposure to the AI wave. I’ve plotted that median expected value from 12 months ago to a month ago. They keep revising higher and are still well below what the market thinks right now.
These revisions are significant. Analysts aren’t just missing the number once. They are missing it repeatedly. That isn’t bad luck; this is systematic, wilfully blind to what happens with an exponential technology. We’ve seen this before with the IEA and their two decades of comedically ignoring solar’s falling costs.
Analysts covering stocks affected by the growth of AI use are doing that. Their model of the world doesn’t accommodate a range of “possible outcomes”, so the expected value (their price target) is wrong. One problem is that no-one in the major banks really covers AI in its entirety. You have chip, cloud, and software analysts. But the action, the demand action, is in private companies.
A second problem is that time and again, the market is good at digesting financial information and poor at stomaching technological change. There is a sociological dimension at play, not exactly a kind of groupthink, but an intellectual tic at work. One example is reversion to the mean. Mean reversion, the idea that you can’t stay off a long-term trend for long, is a powerful heuristic. And it works most of the time, but there are some processes, especially compounding ones, where it is less helpful.
JPMorgan published a chart showing consensus forecasts for hyperscaler capex and R&D as a proportion of revenues. This is a data set similar to the one above, although it tells a different story, as it shows not raw dollars but share of revenue.
But notice what the consensus says about 2027, a massive crash; a 25-40% reduction in those investments as a share of revenues.
Assuming the hyperscalers are acting rationally, only three things could drive that slump.
A substantial slowdown, or perhaps retrenchment, in AI revenues during this year (which would then affect next year’s planning)
Some kind of scientific or technical breakthrough that allows hyperscalers to meet demand without the same levels of investment, and to roll this out to AI systems serving quadrillions of tokens a week within a single planning cycle across the hyperscalers' complex, heterogeneous infrastructure.
Some massive exogenous shock that seizes the global economy and pushes every firm into defensive posturing.
Which of those three reasons can it be? On the second, it is not the job of an infrastructure analyst to forecast scientific breakthroughs, which even Demis Hassabis or Yann LeCun can’t. So it can’t be the second. Nor can it be the third — forecasting a massive exogenous tail-risk shock, a black swan that suddenly arrests the global economy, is also not their job. They are not paid to be Nostradamus.
So the only possible reason is reason number one. But we’re halfway through the year; everyone is caught by growth rates, including the AI labs themselves, so what are these analysts seeing that no one else is? Nothing. The reasoning is inchoate.
Truth is, exponential shifts involve regime changes, not merely rerating1. That is uncomfortable. It is easier to assume reversion to the mean than to update your models, which worked so well in the past, with one which is dynamic, uncertain, full of feedback loops and, crucially, depends on the future being radically different to the past. That seems like hard work at best, crackpot at worst. Easier to tweak and tweak old faithful.
Let’s go back to the column in the FT which questioned the rationale of AI capex investments based on consensus analyst forecasts. There is an important caveat this particular argument needs to acknowledge: it relied on consensus forecasts from a class of models that have been wrong, sometimes by an order of magnitude, on the very thing their forecasts are used for.
The lesson isn’t that analysts are too pessimistic and should just revise higher. It is that consensus forecasts that are built on the intuitions of mean reversion are the wrong thing to use during regime change. It’s not about whether the numbers will surprise to the upside. It’s whether the framework can even see what’s coming.
Don’t take a linear knife to an exponential gun fight.
Research across American college students, looking at 370,000 personal statements, showed that “after ChatGPT became available, their essays suddenly used diverse and colorful language, but lacked truly creative ideas.” Rebecca Winthrop’s essay is a must-read. “For the first time in human history, we have a technology that can generate words separately from the thoughts they represent.” It’s a shortcut. Students love it. And with that, we’re losing our grip on thinking.
Or are we?
shows that being roundly trounced by AlphaGo has, in fact, improved the quality of human Go play.
Yes, maybe AI is making us dumber.
Here is The Economist with an interesting survey on whether AI is making us stupid. I love the irony that the site offers me an AI summary on an article that, in essence, discusses whether AI tools are making us intellectual wimps.
You probably recognize, feel, many of the conclusions. As we hand certain skills over to AI, those skills might atrophy. This happens in other areas, like motor skills, so it could apply to more complex cognitive activities. Some of the best things humans seem to be able to think involve integrating lots of ideas from our experiences, listening, formal instruction, doing and joining the dots.
The word intelligence is derived from the Latin intellegere. That Latinate root itself comprises inter, meaning between or among, and legere, meaning to choose, select or to read. Intelligence means “to choose between” — to connect, to join the dots.
Perhaps if we hand the actual act of intelligence (the connection-making) to AI, we’ll weaken that muscle.
If you don’t use it, you’ll lose it.
It sounds like common sense. The studies cited in The Economist provide some scientific backing for that idea.
Caution, though: these are small-scale studies. Lots of things sound like common sense until we can study them properly. That video games drive real-world violence, for example. Common sense, of course. But it doesn’t stand up to the research.
Now I don’t know if AI will destroy human thinking. My gut sense is that some things will get worse and some things might get better.
I am acutely aware of these risks. And we should take seriously what educators and students are telling us now about how their attention and quality of thinking seem to be eroding.
Readers may imagine that with our billion tokens plus a day, Exponential View Towers is like something out of Futurama. Half of us in VR headsets, another half plugged in to our Neuralinks, and the last half deep in the Matrix.
But that’s not the case. Thinking is very, very important at Exponential View; we care about it a great deal. Many of our early experiments with ChatGPT focused on how it could improve the quality of those cognitive efforts, not on how we could take a shortcut through the valuable part of the process.
AI has allowed us to take away lots of “computer stuff”. Typing my essays and responding to tedious emails are now handled by RMA or Claude.2 Back in December, I used to get up early to fire off new workloads. Since the models have become more reliable, I’ve been able to leave them unattended for hours at a time, whether they are running coding tasks, simulations, literature reviews or adversarial attacks on our arguments.
Paradoxically, AI has made me much more aware of my thinking processes. I have become deliberate in reflecting on my reflecting. LLMs are absolutely phenomenal and show the banality of the average. I’ve never read as critically as I do today.
Because thinking matters to us so much, we spent a lot of time figuring out how to keep mentally fit.
Here is a picture of one of our whiteboards.
You can see the scoping work on the thesis on why companies struggle with their AI transformations in green marker. And in the yellow box is a little exploration of Durkheim and suicide for something else we are working on. It’s more whiteboardmaxxing than tokenmaxxing.
Last year, I bought the team fountain pens. Everyone is expected to spend at least two hours a week working with pen and paper and without a computer. I don’t want to overclaim that this makes a difference; the science suggests it might.
I’m writing longhand more than I have since those halcyon days when Boris Yeltsin was a communist, Margaret Thatcher was Prime Minister and Donald Trump was married to Marla Maples. It has been a long time since I picked up a fountain pen in anger.
I’ll work on paper, while RMA will go off and do its own research. It goes through papers, my notes, and transcripts, finds what I might have missed, and counters. When I return to my screen, I am several steps further than I would’ve been without it. But I’m sceptical about its arguments. Sometimes they clear a mental block. Sometimes they are helpful because I can treat them as directions not worth pursuing.
While I don’t have scientific evidence that definitively tells me whether AI is adding to our mental faculties, you can sense which way I have buttered my bread.
The question “Is AI making us dumb?” is not the right question. The real challenge that technology has changed the basis on which we expect thinking to occur, and we haven’t made our new expectations clear. It’s an exponential gap.
Few marathon runners would complete a race using a car. The point is the race.
Getting ChatGPT to do your thinking is a bit like finishing the marathon in a car. So let’s investigate the motivations for doing that. Perhaps people don’t value the thing they are doing and just want to get it done? Perhaps they can’t judge what excellent is, so they will be satisfied with good? Perhaps the activity is simply instrumental, a means to an end, not a constitutive activity with value in itself?
We’re asking people to think about college applications. Ostensibly, the application determines their suitability for the next four years. To get an AI to do it—rather than, perhaps, act as a Socratic critic—speaks to the perceived stakes of that application. The point is not that modern life will be full of AI; it is that to use AI well, you need to think well. Unless, of course, you’ve concluded that AI will always outthink you in all the ways that matter.
It’s also worth examining in the workplace. Of course, there are times when its appropraite to get a machine to do a task. I don’t grow my own cacao beans. But perhaps it reflects workplace environments geared to throughput rather than quality, agency and ownership?
To reverse Charlie Munger, show me the incentive, and I’ll show you the outcome. The outcome here is offloading important thinking to a chatbot. So what incentives produced it? We’ve developed tests, credentials and career ladders and cemented them across centuries of practice. They were built when cognition was scarce and scarcity paid well. Thinking proved you had that scarce asset, and we never needed to ask if you were actually the one doing the thinking. That isn’t true now, but the payoff for perfecting the scarcity theatre of a perfect GPA, Ivy education, and the rest remains.
So what system of incentives do we need that values (and motivates us) to use our actual brains?
And that, rather than whether AI is making us dumb, is the right question to ask.
And it is a much thornier one to answer.
AI software engineering roles are growing and concentrating in the top-paying US companies according to the latest state of the software engineering job market by and .
Implementation expertise is in high demand.
And Anthropic is seen as the most desired company to work for, followed by starting one’s own business. via
digs into China’s AI optimism: “perhaps every respondent genuinely believes that AI is good for both society and for themselves. Or perhaps they see AI as another surgery necessary to survival, knowing full well that flesh will be cut away and discarded.”
ByteDance is designing its own Groq-style AI accelerator chips to lessen its reliance on Nvidia.
Non-fiction book publishing is absolutely not ready for AI.
China ETFs are sinking.
Lack of sleep changes our gut microbiome.
How to weigh a cell. A nice piece by
Anti-drone nets in Ukraine:
Thanks for reading!
Rerating is a fancy Wall St word that means “we’ve decided the thing we are looking at is now a different type of thing.”
I usually handwrite my essays and then read them aloud to Claude for transcription. Close-in edits happen on pen and paper on printouts. Final edits are back on the keyboard.
2026-05-30 01:00:57
This live follows the essay and I published earlier this week:
2026-05-28 04:24:34
I had tea with a senior exec at a well-known public tech company last month. She has about a thousand engineers working for her, and nearly every one of them works with Claude Code. They are producing more lines of code, submitting more pull requests, getting more done. Productivity is up for individuals, but she doesn’t seeing proportional gains at the organization level. As she put it to me: “one plus one plus one plus one equals one-and-a-half.”
She is not alone. Uber’s COO Andrew Macdonald went on record this week saying that the relationship between AI investment and results is not there yet:
I think maybe implicitly there is more that is getting shipped, but it’s very hard to draw a line between one of those stats and, ‘Okay, now we’re actually producing 25% more useful consumer features.’
AI has delivered something. I have felt it; my team has felt it; most users have felt it, which is why we keep returning and using more of it. Two years ago, only a dozen Anthropic customers were spending over $1 million a year on Claude1; today, more than 1,000 do. More impressively still, Anthropic’s average corporate customer increased their spend by a factor of five in the past year.
But in more than three years since ChatGPT’s release, only 27% of executives say AI has met their ROI expectations. What do we make of the other 73%? Could their expectations be too high? Or too low? Do they even have the right class of expectations?
In a way, we can’t tell, but we can feel the vibes. And the vibes are that individual workers are getting faster and more productive. But for now, those individual gains from AI do not compound into firm-level ROI.
That is the puzzle we are going to solve in today’s essay.
Let’s go!
Back in 1987, Robert Solow famously pointed out that you could see the computer age everywhere but in the productivity statistics. He was right for the next few years, then wrong. Paul David realized this was a common problem with general-purpose technologies. They systematically depressed measured productivity in their early stages because companies needed to invest in all sorts of hard and soft know-how before the gains appeared. calls this the productivity J-curve: general-purpose technologies are a drag in their early years because firms have to make complementary intangible investments before the gains materialize.
Paul David’s 1990 paper on electrification is the canonical account of why a general-purpose technology can sit inside firms for ages before we see the results. The story he tells – building on Warren Devine’s earlier survey of the shift from shafts to wires – runs through three phases. And those phases map onto where AI is now.
Stage 1: The lightbulb
One of electricity’s first factory roles was the simplest – lighting. A brighter floor was safer than one lit by gas, and cleaner than one lit by oil. But work still flowed through the same sequence of people, machines, shafts and belts. Electricity had improved the workers’ immediate environment, but it did not change the factory’s operating logic. When ChatGPT was first released, it did something similar. It increased how quickly we could write emails; individuals sped up on some tasks, but the firm did not. This is Stage 1 of AI transformation: the lightbulb.
Most of the AI products we see today are all about individual productivity. Yes, there are enterprise plans for ChatGPT and Claude and whatever else. But the unit of work is still the task that the individual has to hand. The enterprise plan just lets them quickly access the corporate skills repository.
Stage 2: The group drive
The next stage of electricity adoption in factories focused on cost savings rather than productivity. Louis Bell wrote in 1891 that large central steam plants could be five to seven times more coal-efficient than small engines. Factories bought power from central stations and installed electric motors to drive their existing shafts and belts.
Then, a professor of electrical engineering, F. B Crocker, and his colleagues found another application. Electric power frees the shop floor from the shafting. Mechanical power belts, tools and oil, the mess of the factory floor, could all be moved; machines no longer had to be arranged in parallel lines beneath shafts.
The open question was how many motors a factory needed: one per tool, or one per group of tools? The latter became known as group drive. It was a single motor that powered a cluster of machines via a shared shaft.
Group drive preserved existing layouts, reused sunk capital, needed fewer motors, and gave many of electricity’s benefits without the cost of rebuilding the plant. It was cheaper and easier than the alternatives, so it won and dominated factories through the 1890s, 1900s and into the First World War.
AI agents are better than chatbots. They can handle whole workflows rather than single tasks. But, like group drive, they are attached to the existing organizational geometry. In the case of electricity, this was the shop floor layout. For AI, it’s the web of processes designed by the companies well before anyone knew what an LLM was.
An AI recruiting agent speeds up a process that was previously done by humans and an applicant tracking system. Their recruiting pipeline may shrink from weeks to hours. A customer service agent takes on more tickets than the support team could before. The logic behind these examples is essentially cost-saving – fewer support tickets need humans, recruiters screen more candidates with the same headcount and marketers create more variants without additional staff. The firm isn’t making consequential decisions any faster. This is Stage 2, the group drive, machines turning faster on the same shafts.
Stage 3: The unit drive
It was only later, once the organizing logic of the factory moved to throughput from cost-saving, that the deeper value of unit drive, one motor per machine, became clear. In 1913, Ford’s Highland Park plant decided to orient machines and workers around the workflow rather than the geometry of shafts and belts. Over the next decade (1919–1929), as more factories adopted unit drive, US manufacturing labor productivity grew by 5.4% a year.
The pattern for AI will mirror the pattern David spotted for electrification. Stage 1 speeds the individual, Stage 2 a workflow and Stage 3 the firm.
You’ll be familiar with other maturity models, like Carnegie Mellon’s Capability Maturity Model. These are useful, but they treat each stage as a monolithic capability tier. A capability tier tells you how well a firm does a “fixed thing.”
Our ladder is different. The stages are organizing logics. The logic is the goal the firm is pursuing with the technology. A workshop that installed electric lighting was no worse than Ford’s factory, but it was pursuing a different goal: a safe way to illuminate the workshop floor. You didn’t get to an assembly line by adding more lightbulbs. Factories were pursuing cost savings and everything was organized to fit that goal, from layout to staffing to supplier relationships.
A maturity model will often tell you to do more of what you are doing to move forward. We reckon that what matters is what you are trying to do. And our stages hint at why companies deploying AI might get stuck. A firm does not graduate stage by stage on every axis. An individual developer who is 50% more productive with AI tools but must submit to traditional review cycles will find themselves in a queue. The product team that can prototype faster than ever but needs to wait for sign-offs, will build up a backlog of features. A sales team, supercharged by AI-assisted proposals, may close deals faster than legal can review them.
As firms move towards Stage 2 on execution, those managerial layers, how decisions get made, stay where they were. We call that mismatch congestion. It is the buildup of individual and then team outputs waiting for somewhere to go. To become a Stage 3 firm, you need to rebuild around decision speed between workflows, not the speed of individual workflows. If you are already suffering from congestion, adding more workflows and more output to a blocked decision pipeline will only make things worse.
We discovered this at Exponential View. We could prototype, even build, new features faster than our usual processes for releasing them. We’re figuring out how to fix that – and we’re only a team of eight, so we are sympathetic to the challenges of larger orgs.
To speed up those decisions, AI needs to be able to take them. The role that managerial oversight has may be the thing preventing the cycle time from shortening. AI will need a new cognitive layer that allows the firm to interpret signals without a worker as an intermediary. If a customer sends a feature request to customer support, it typically passes through a support agent and then a product manager, who decides whether it belongs on the roadmap, before a developer codes it weeks or months later. With AI, the signal is observed by an agent. It orients against the roadmap and the codebase. It decides whether the feature is worth drafting. It builds it. Hours, not weeks. This is the Stage 3 firm, the unit drive.
2026-05-25 15:00:52
Hi all,
Last week, we explored what tokenmaxxing means for CFOs and how firms can buffer unexpected AI costs.
We go further today to show you why AI bills are hard to forecast today and what will happen as we crack that problem.
We estimate that the number of tokens processed per quarter has grown by around 17,000x over four years.1
Token prices have collapsed during this time. Demand for machine intelligence is highly elastic, meaning that as prices fall, consumption increases by more than the decline in price.
One reason is that cheaper tokens have made agents economically viable. At the same time, agents use tokens at rates that are orders of magnitude higher than those of chatbots for single-turn queries. That shows up as the total tokens processed per output token—advanced models do a lot of processing below the surface that a user doesn’t see.
A lot of this growth is driven by China’s domestic demand and its model providers, especially ByteDance and Alibaba.
When you use an AI agent, the final result you get is really just a summary of all the work the agent has undertaken. There may be dozens of tool calls to browse the web or load up a file to check and validate the work it has done. All of these are steps consume tokens: they become hidden multipliers.
The first of these is token amplification.2 A coding agent that operates over 10 turns might need to re-read its full context every turn. That repetitive reading of context could use as many as 55x more tokens than a single-turn query for the same task.
Actual active inference is probably only 15-20% of the total token consumption. The rest is invisible work that you, as a user, and possibly the company paying for it all, haven’t modelled.
The long tail of tool calls
Agents make anywhere between five and twenty-five tool calls per task. And each call adds more context, tokens and API costs. It also increases the likelihood that the model will need to retry the task to get it right.
The price of safety
Governance and safety costs3 are between 20% and 40% of total spend. Amazon Bedrock charges $0.15 per 1,000 text units for content filters, and Azure charges $0.38 per 1,000 text units. When you add those charges on top of model inference, you could be looking at 15-30% on top of the existing costs for every call.
For firms that use a frontier model as a judge to evaluate another LLM’s output, the evaluation step will cost almost as much as running the main model again. One study found this approach to be 18x more expensive than using smaller, purpose-built guardrail models.
What makes this particularly hard to manage is that the tools themselves cannot yet forecast their own costs. A new paper found that frontier models fail to accurately predict their token use.4
Runs on the same task can vary by as much as 30x in total tokens. So even those of us who have developed a sense for token consumption run into surprises. In the study, 6.7% of tasks lasting less than 15 minutes cost more than the average 1-hour task.
So, what does this mean for companies? We will see firms climb the maturity ladder as they figure out how to track token spend and likely evolve into more sophisticated observation and monitoring of model calls.
Firms will benchmark their internal processes against their previous experience and, when the data is available, against those of other companies. The ambition will be to optimize price-to-outcome ratios. This will all help clarify the unit economics for specific commercial outcomes. The value of observability and monitoring will increase, as evidenced by the share price rise of Datadog, a leading observability platform.
Long-term, attributing costs to specific teams, workflows, and customers will help finance teams budget for AI spending and decide how to route and tune model use.
Taken together, the firms with the most mature approach to their AI deployments should start to understand the true unit economics of their outputs in the coming quarters.
This is the total volume of tokens (input + output) processed by LLMs across all providers. Our methodology uses a bottom-up, per-provider approach built up across 50+ providers. Cross-checks: revenue÷price, GPU utilisation, API call volume.
Token amplification is when agents do the things that inflate the number of tokens used per human interaction.
Content and policy filters.
The correlation between predicted and actual token consumption is weak to moderate, with a maximum of r = 0.39.
2026-05-24 10:44:54
Consistently insightful, with no agenda beyond understanding how technology shapes our world. — Vincent N., a paying member
American college graduates are angry. In my Saturday column, I write:
The narrative around AI has been about promises for tomorrow, but sacrifices today. […] Engaging with this resistance won’t be effective if AI leaders throw distant fantasies at people dealing with muddy water coming from their kitchen tap today.
An OpenAI reasoning model solved an 80-year-old open problem in discrete geometry. The interesting point here is that the reasoning model found an unexpected bridge between two different fields of math. Discrete geometry and algebraic number theory are meaningfully separate cultures. An expert in one field might know something about the other, but not at the depth required for breakthrough research.
The human mathematicians who validated the proof reckon that the connection the AI found was both unexpected and non-obvious. In a sense, it’s reminiscent of AlphaGo’s Move 37.
Because AI systems can span different domains of knowledge, my hunch is that one of the biggest dividends we will see from them in these fields will be connecting domains that live in isolation because of the structure and specialization of modern science.
But of course, another way AI will impact research is by speeding up the scientific method: the full hypothesis-experiment-analysis loop.
Robin, a multi-agent system, completed the full cycle of hypothesis generation, experimental selection, data analysis and refinement. Human scientists ran specified experiments in a wet lab. The outcome was the identification of an existing drug that could be repurposed to treat macular degeneration.
2026-05-23 18:56:28
Anthropic will turn a profit this quarter, two years ahead of schedule. This is off the back of extraordinary revenue growth. In the second quarter this year, the company is likely to gross $10.9bn in revenue, more than its entire lifetime revenue to date. Operating profits should weigh in at $559 million. It’s an extraordinary achievement and one set to feature in the annals of business history.
But the backlash against AI is growing ever stronger.
Earlier this week, Google’s former boss, Eric Schmidt, was soldily booed at a college commencement address
At another college commencement, boos shocked the commencement speaker who proselytized AI. Joanna Stern interviewed one of the college students, Houda Eletr:
Joanna: Do you use AI?
Houda: Throw it away….if you create this big thing that is supposedly going to save a human’s life, then you should give it to me in a human way.
A reader from New York sent me this photo:
There will be more booing.
Consider data centers and their local impacts.
At a hearing, American politician Alexandria Ocasio-Cortez showed a jar of mud-brown water collected from a tap in Georgia, shortly after Meta started building a data center.
The narrative around AI has been about promises for tomorrow, but sacrifices today. AI leaders have warned that the world could be destroyed by this technology.
They warned that large numbers of people, particularly graduates, would lose their jobs. But everyone knows the founders are making more money than Croesus. And now, there is construction, a physical manifestation, a real inconvenience, something you can point at.
And for what? So we can explore the stars using intelligent spaceships and ‘colonize the galaxy’ according to Demis Hassabis. (Demis is by far the most sympathetic of all the AI leaders, but even his messages feel out of touch.)
Engaging with this resistance won’t be effective if AI leaders throw distant fantasies at people dealing with muddy water coming from their kitchen tap today.
Neither will facts. The US energy system is getting a much-needed boost in investment to upgrade energy generation and the grid to more modern technologies. I am certain that in a decade we’ll look back on this as the time when the US grid prepared for the 21st century, becoming more resilient, efficient and greener. But that well-informed argument is completely irrelevant in the face of today’s resistance.
Fantasies or facts, these are all fodder for the technorat. Globalization or NAFTA made a graph on some economists’ slide deck go up. Grid renewal and ‘productivity’ excited the same neurons, but don’t speak to the heart.
And the AI industry has a real problem because the resistance rests on tangible issues today. Just a few years ago, the sense of unease many people had about AI was fissiparous and intangible (remember the asinine PauseAI movement?).
Wage devaulation for freelancers and gig workers is here and now. Data center construction, the threat of rising power bills and dirty water, here and now.
Pressure on graduate employment, whether driven by AI or not, will increase. I’ve spoken to several senior execs at US firms in finance and consumer services in the past few weeks. Their AI projects emphasise headcount reductions or, euphemistically, productivity. While these don’t show up in aggregate statistics yet, the perception that they do is what counts.
The AI companies, in their San Francisco bubble, pursue increasingly abstract technical milestones. Each new release beats a benchmark but comes with another warning of the lasting harm the model will do to what it means to be human. No wonder American voters find AI less appealing than ICE, the frequently masked paramilitary agency that enforces immigration law.
Data centers have become the first issue in decades to unite both sides of American politics. Resistance has become a truly bipartisan issue. Washington can’t be relied upon to settle things. The federal government has chosen to delay, often theatrically. The White House wants a light-touch national framework and has pushed agencies to challenge state rules, but even its own recent AI executive-order has been derailed.
The states are moving instead. From Colorado to New York, California to Texas, and a thousand more state-level bills around AI have been introduced.
Local politicians will discover that an anti-datacentre vote is a cheap way to electoral success. The midterms this October will be one to watch. But nothing meaningful will happen in Congress until at least after the 2028 election.
This will not stop AI. It will not stop the construction of data centres, but it will be boon time for lawyers as they first challenge the legislation and then work out how to comply with it.
Meanwhile, companies and consumers will continue to spend on AI. The technology will improve. American firms will report successful large-scale AI deployments. Revenues will rise, records will be broken.
And the crescendo of boos will rise and rise and rise.