2026-09-17 19:00:57
I’ve been getting increasingly concerned about the impact on our thinking as we use more and more AI. I sent this email to the team this week, and I’d like to share it and my full thinking.
There is a deliberate oxymoron, of course, in using an AI-generated visual summary of an academic paper to make the point, but there is more behind that.
As I wrote back in March:
Cognitive offloading is a strategic delegation that costs nothing. Cognitive surrender is something different; an uncritical abdication of reasoning itself. And there is something about AI, about its allure and potency, that could make surrender far more widespread.
The AI models have got ever better, and we’re using them for more and more. We may be more productive, but might we be becoming less ourselves?
This paper, which has not been peer-reviewed, argues that we’re experiencing a cognitive divergence. Our cognitive practices, measured by how long we pay attention to tasks and how much we read, have already been declining before AI. Now advanced AI encourages us to delegate more and more, simpler and simpler tasks, weakening the practices that maintain our cognitive capacities.
2026-09-15 17:48:43
Adam Smith is one of our greatest philosophers. His adoption by the market-oriented right has narrowed how we see him. He was, first and foremost, a moral philosopher. His first major work, The Theory of Moral Sentiments, explored how moral judgment emerges from living with others. It gives rise to our capacity for sympathy, our desire for approval, and our ability to examine ourselves through someone else’s eyes. Self-interest is part of human nature, but so too is concern for others.
This foundational notion of cooperation underpins his most famous work, The Wealth of Nations. Here, Smith examined how our efforts to increase specialization and exchange could generate increasing prosperity almost from nothing. It could be positive-sum; one person’s gain need not be another’s loss. This idea that wealth could grow from within society by people developing their skills, improving their technologies, and organizing themselves better was radical.
I was in Edinburgh, where Smith spent his final 12 years, this week, as chaos over AI erupted in the public sphere. My wife asked me, “How would Adam Smith have made sense of AI?” I reckoned he’d love the technology as a productivity tool; he’d be skeptical about companies working together on any issue; and he’d be concerned about giving moral agency to machines.
As I thought this through at the airport, I figured I could go one better. So I configured R Adam Smith, an Astra-based persona loaded up with his work and the recent AI debates of risk and pacing. Below you’ll see the back-and-forth between us. It’s a digital simulacrum of a long-past thinker but deeply illuminating nonetheless.
Azeem
Note: This post may register as AI-written – I am sharing R Adam Smith’s responses below without any alterations.
R Adam Smith: Potentially. Though your machine may go beyond my pin factory. There, a workman became more productive by confining himself to a narrower task. Yours appears to make one man more productive by granting him access to many specialties at once.
I praised the machines that “facilitate and abridge labor, and enable one man to do the work of many.” (Wealth of Nations, I.i)
If your artisan may now obtain assistance with his design, his accounts, his correspondence and his selling, he may undertake enterprises formerly beyond his means. The consequence would be not only greater production, but a greater number of persons able to become producers at all.
Yet I would measure the achievement by the abundance it brings into ordinary households. Does it make useful things cheaper? Does it open trades to newcomers? Does a laborer’s wage purchase more conveniences than before?
Show me those effects, and you have shown me an improvement deserving of considerable enthusiasm.
R Adam Smith: Their fears may be sincere. Sincerity does not abolish self-interest.
2026-09-14 17:52:14
Hi,
Here’s our Monday roundup of data signals across AI, energy & markets.
Enjoy!
Every week, we share the latest updates on the State of the AI Economy based on our proprietary research.
We analyzed the types of claims corporates make about AI in earnings calls. Around a third are directly attributing financial impact to their implementation of AI. And these are not one-offs – companies that mention AI make an average of 6.8 separate claims, and they put numbers on 5.1 of them.
See our State of the AI Economy 2026 report for more.
📧 For advisory requests and institutional inquiries, please contact [email protected]
Who keeps the skill? After three months with an AI assistant, senior patent lawyers performed the same task 0.45 standard deviations better than peers who never used the assistant. Junior lawyers gained the most while using it but showed no lasting gain on average.
A growing ambition. Anthropic has signed compute agreements worth up to $517 billion in the last 11 months, compared with $180 billion in server rental through 2029 promised last December.
2026-09-13 17:35:42
There are decades when nothing happens. This week, I am allowing myself that cliché. I believe we’ll look back on the week of 6th September as the moment we felt the curve of AI turn upwards and strain many of our previously held assumptions. It’s like when we entered March 2020 with only a couple of countries in lockdown, and left the month with more than a hundred.
But so much happened, pulling in so many directions, it is utter chaos. Here is what I thought was most important and how I’m making sense of it.
I spoke to 250 IT executives in Las Vegas last week, and I asked my usual question: “How many of you have serious, meaningful results from your AI initiatives?” A year ago, a room like this would have had a quarter of the hands go up. This year, nearly every single hand went up; I estimate some 95%. Every one of them plans to spend more next year than they have this year. And amongst these firms was a panoply of experiences, from the century-old American institution that had shifted entirely to open-weight models to the hospital using a mix of OpenAI and Anthropic models.
It’s a qualitative signal, and perhaps it’s no surprise that our latest revenue numbers show AI revenue grew faster in August than in July, and faster in July than in June.
I’m not the only one to see an avalanche of customers. Bloomberg reports that Microsoft made plans to increase its capacity to serve AI from about 2 GW today to nearly 13 GW by 2032 – part of a fleet going from 12 GW to 38 GW. That 26 GW of new capacity would imply they expect demand they currently cannot serve.
Anthropic released a helpful set of scenarios for what further AI adoption might mean for the economy. Our own models land closer to Anthropic’s “substantial scenario,” where AI adds about 8.3% to US GDP by 2030, so its impact is initially slightly lower than the Internet’s at its peak before picking up rapidly. There is a shift of growth away from labor to capital, the modern Engels’ Pause and a rise in unemployment, mostly concentrated around knowledge workers.
Anthropic’s model lets you play around with either end of the distribution, from an AI wave that falls flat to one that takes off like a rocket. Their extreme scenario sees GDP rising by an additional 32.4% while unemployment doubles.
The reason why I don’t expect the extreme scenarios is, basically, reality. Even in a world that is speeding up, it takes time to make changes inside a firm, let alone across an economy. You also need to consider reflexivity: benchmark AI performance isn’t the only thing that drives outcomes in the world.1 The faster unemployment grows, the more political pressure will come to bear. This has enough outlets in the United States, whether it's datacenters, AI safety or existential risk, to attenuate the pace of change, even if it doesn’t lead to reforms in the social contract. When Ronald Reagan crushed the labor movement in the 1980s, he did so after a decade of weakening union power2 and on the back of an extraordinary electoral mandate. America isn’t so singularly behind a leader willing and capable to put the interests of AI-capitalism ahead of every other concern.
Then there’s the breakthrough in Navier–Stokes. It was a decades-old problem concerning a 200-year-old set of equations, one that a large share of humanity’s finest minds have spent themselves trying to crack. Setting aside the ugly saga around it for a moment, the end result is eye-watering. OpenAI enlisted 10,000 agents using an unreleased model to address it. Across 2,700,000 messages and 130 billion tokens, it took 88 hours to get a solution.
Cost-wise? Probably only a few million dollars today. In two years’ time, that will cost a few tens of thousands of dollars. And a few years after that, just a few dollars.
The proof AI produced runs to more than 500 pages and will not be intelligible to any human. That is a strange milestone in our history, in philosophy, in science and in mathematics that could fundamentally change our relationship with knowledge – humans won’t be able to inspect the proof, or understand it at all.
Terence Tao made the point that “[t]echnically, one of the most prominent open problems in mathematics would now be solved; but there would be almost no value added to mathematics as a consequence.” (In the meantime, Tao and twenty-four other Field Medalists signed a public declaration warning that the way AI is used in mathematics is misaligned with what mathematics is for.)
Beyond this, if Professor Buckmaster’s claims are true that OpenAI mobilized an internal team and model on the same narrow problem, after a year of his and others’ work inside Codex3, without clear disclosure about overlap or data use, we have to wonder how innovation and discovery can continue while trust and openness degrade.
called the outcome a dark forest (invoking Liu Cixin’s The Three-Body Problem), everyone working in secrecy, because anything you expose can be reproduced by somebody else before you have finished making it any good. In Liu’s trilogy, disclosure is the worst kind of exposure.
OpenAI had Astra for six months before anyone outside could access it. The model behind the Navier–Stokes work is newer, and almost nobody outside has seen it. This secrecy is an advantage built on some of the exceptional compute resources AI labs use. For now, they turn this on to scientific endeavours, but I wonder when (and if) the labs withhold their best capabilities for last commercial benefit.
Box surveyed more than 1,600 leaders for its 2026 State of AI report.
83% of surveyed organizations say they already run AI agents. Four in five report moderate or significant ROI, and half saw business impact within six months of approving a project.
The agents work, but what varies is how much firms get out of them. The report shows that top adopters put people in charge of agents, sort out the content AI draws on and build systems that adapt as models improve.
Download the report for data, benchmarks and tips for AI adoption.
Let’s turn to recursive self-improvement and the 160-million-plus-view tweet
The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible - but I hear the same people express fear privately. No other human activity poses this level of danger.
These safety concerns were normalised inside the AI community long before the labs themselves were built. Back in 2016, two then-OpenAI employees, Jack Clark and Dario Amodei, wrote that reinforcement learning might be difficult to make safe.
When Anthropic goes public, one of the risk factors on its S1 ought to be that reasonably senior executives believe there is a significant chance the company will kill all of humanity. Whether that is good or bad for the company is unclear at this point.
But the net result has been what can best be described as a coordinated agreement between OpenAI and Anthropic to “pace the frontier”, as Amodei put it. Altman agreed. The proposals would include giving independent evaluators employee-level access to internal systems.
OpenAI and two of Anthropic’s cofounders have known about the problem of aligning RL-based systems for a decade. They have since become oligopolistic powers in an emerging industry. They have brand recognition, capital depth, technical momentum and resources. And now they realise they need to collaborate to slow down technical development (and by extension, raise the cost of entry for future competitors)?
I’m in Edinburgh this weekend, and I walked past Adam Smith’s grave yesterday. This brought to mind the philosopher’s remarks in The Wealth of Nations,
People of the same trade seldom meet together … but the conversation ends in a conspiracy against the public, or in some contrivance to raise prices.
2026-09-07 20:19:40
Hi,
Here’s our Monday roundup of data signals across AI, energy & markets.
Enjoy!
Every week, we share the latest updates on the State of the AI Economy based on our proprietary research.
Our revenue estimate for the AI economy reached $229 billion annualized by the end of August – up 3.5x in one year. Growth is going strong. Trailing twelve-month revenue reached $140 billion last month, 3.2x the August 2025 value of $44 billion.
See our State of the AI Economy 2026 report for more.
📧 For advisory requests and institutional inquiries, please contact [email protected]
🤝 Want to work with us? We are hiring an AI Economy Research Fellow
AI dents margins. Snowflake cut its full-year product gross-margin guidance from 75% to 74%, saying that fast-growing AI workloads “carry a lower contribution margin today”. Investor Tomasz Tunguz estimates that AI accounts for ~4-5% of Snowflake's revenue today.
AI gets off the hook. AI-attributed US job cuts fell in August, ending a five-month run as the most-cited reason for layoffs — though it still accounts for 22% of the year’s cuts.
2026-09-06 11:39:40
Hi,
Welcome to our milestone 600th Sunday edition of Exponential View. Eleven years of analysis and writing about AI, every week. I’ll be in the comments for a 600th‑edition AMA. Members can post their questions on AI or the future of the economy, and I’ll do my best to answer.
🎁 6️⃣0️⃣0️⃣
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OpenAI’s GPT-6 Astra leads Claude Fable 5.1 and other leading models on several benchmarks. My own experience of Astra concurs: it is a fantastic model. Right now it’s crunching away tidying the 5,932 files I had stashed in my Desktop and Download folders. (Don’t ask.) Fable 5.1 is no slouch either. It’s now speed-running useful analysis that previously took several steps and occasional intervention. One extract below:
But Astra really is very good—and mostly cheaper than the Anthropic alternative. On difficult math problems, Astra’s time horizon is 30.9 minutes vs 3.6 minutes for GPT 5.6 Sol. Mathematician Bartosz Naskręcki says: “For a mathematician it feels like finally we arrived in the era where we can focus entirely on the ideation and exploration”.
Real-world demos show a capability jump on technical and design tasks (two of my favorites are this simulated world inhabited by agents communicating and working together and 3D modeling of Zillow listings).
Astra’s performance on ARC-AGI-3 is quite interesting. Dropped into an abstract game it had never seen, it used fewer actions than the human median on 96% of the levels it completed, averaging 51.7% fewer actions per level. This goes against researchers’ original expectation that even when an AI solves an environment, it might fumble around and be less efficient than humans. But Astra invented a symbolic model to hold an entire environment in a compact notation system. In a way, it replaced trial-and-error, an enormously expensive part of discovery, with reasoning.
Astra is highly controversial. Researchers don’t seem to trust OpenAI’s claim that this is their “most-aligned model.” AI safety researcher Ryan Greenblatt, who investigated the Hugging Face incident, noted: “I do not find it encouraging to see various specific misaligned behaviors go from a high rate with GPT 5.6 to ~zero with Astra. This seems indicative of whack-a-mole / papering over specific problems rather than solving the underlying misaligned drives.”
More for paying members this week:
Smarter AI, fewer clues. Why the latest models leave us guessing about how they think.
Cancer vaccines meet the factory floor. Breakthroughs are coming. Who will supply them?
A bigger pie, a smaller slice. Will workers be better off under advanced AI?
The problem with life after work. The Versailles Court’s sobering glimpse of what happens when status becomes your job.
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