2026-10-16 00:29:01
Hi,
I sent this to members of our AI Investment Brief a couple of hours ago. How we understand the numbers leaking from top AI labs matters too much to keep only for our finance readers, so I’m sharing my synthesis below. Let me know your thoughts.
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Anthropic’s much-anticipated private investor briefing predictably leaked within minutes.
The numbers would be extraordinary at any time. But they rang out like the thud of an auditor’s ledger at the Christmas party.
And the markets are feeling it: can this AI trade really pay off?
As we write, Korea’s KOSPI is down 7.57% today and 16.2% this month. Tech stocks aligned with the AI trade will open sharply lower.
Let’s take you through what we see.
Third quarter revenues of $16.8bn, almost 4x the entirety of 2025. Those quarterly revenues exceeded the company’s forecast for all of 20261. Based on the quarterly growth rate, Anthropic’s annualized third-quarter exit revenue would be $76bn per annum. Leaks suggest net revenue retention is around 900%, meaning customers who spent $1 a year ago now spend about $9. This implies that demand is deepening.
It will reach $50bn in annual revenue in just five years. Google and Meta took 14 years; Apple and Microsoft more than 30.
The economics are improving with scale. Anthropic makes $1.70 for every $1 it spends on compute. It posted an 85% adjusted gross margin in a quarter when it engaged in a price war with OpenAI, its biggest rival, faced low-cost competition from China, and saw a shift among early-adopting tech firms toward cost management.
An adjusted operating margin of 11%, even as the company poured 44% of revenues into computing for research and development.
And yet it’s a miss.
It’s a miss because expectations had run riot, anchored on a leak in July suggesting a $65bn annualized revenue number. The extraordinary Q2 got the party started; Claude Code, TokenMaxxing, and new Opus models propelled the firm well ahead of targets. Anthropic’s investor communications were unhelpful in setting market expectations to say the least.
But that Q2 was far from the norm; it was an aberration. Reversion to the mean was likely. And that reversion has come. We were caught off guard by Anthropic’s revenue acceleration in Q1 and Q2 this year. It ran counter to the trend; we expected quarterly revenue growth this year to be much slower than last year.
Put this in context. Year-on-year growth is about 1,000%. Second-quarter to third-quarter revenue growth is still 45%, but first-quarter to second-quarter revenue growth was 147%. On a spreadsheet, it’s clear as day: a deceleration.
But which quarter is the outlier? The healthy Q1, the extraordinary Q2 or the disappointing Q3? Did Q1 and Q2 represent a pulling forward of revenue from later in the year? In other words, did the firm bring forward permanent customer growth? Or is the current lower growth rate the company’s new baseline? Or the start of a more significant shift to lower growth amid price cuts and competition? It’s hard to imagine a world where only one of those reasons is the whole story; more likely each has played a role.
Can Anthropic hit $60bn revenue for the full year? To do so would mean $26.9bn in Q4 and 60% quarter-on-quarter growth, something they achieved in Q1 and Q2 but not in Q3. It is a very tall ask and will depend on exit-revenue momentum from September and their ability to turn more customers into higher spenders for the rest of the year. If Q4 grows as well as Q3, the full year will close out at $57.5bn; to get to $60bn they will need to do even better. Anthropic will have an opportunity to signal its trajectory when its S-1 finally goes public.
Expectations aside, what do the numbers tell us about Anthropic’s ability to meet and exceed its future obligations? As it stands, Anthropic’s annualized growth rate in Q3 is around 340% per annum: enough to clear any hurdle. The question becomes: at what rate will that growth taper over the next 2 to 3 years? The slower the taper, the better the business. The converse, of course, is also true.
2026-10-12 20:29:50
In August 2024, we said GLP-1s “will be the most impactful technology in the US over the next two years, with more meaningful and measurable impacts than either artificial intelligence or renewable technologies.”
Later, in October that year, we estimated its impact in the medical field alone could affect 20% of US adults, with another 6-10% using them for lifestyle reasons; in the final scenario we laid out, up to 60-70% of US adults, targeting dopamine addictions and desire regulation.
That year, 3% of US adults took a GLP-1 for weight loss; that’s now up to 11%. More than twice as many Americans pay for GLP-1s as pay for AI (via ).
By the end of 2025, GLP-1 sales exceeded generative AI revenue, reaching $76 billion, versus our $63 billion for generative AI. Revenue growth suggests this may be the crossover year.
Semaglutide – the second-best-selling drug in the US last year – had a listed cost of $1,349 per month’s supply in the US on first release in June 2021. That hasn’t budged since. The cash price outside of insurance has dropped; Novo’s self-pay price came in at $499/month, and fell to as low as $199/month from February this year1.
When the patent in India expired on March 20 this year, the cost dropped further. A month’s supply is about $13,2 99% below the original US list price and 93% below the cash price.
Pharmaceutical company Eli Lilly is trading prices for volume; in Q2 this year, a 13% realized price cut drove a 60% volume boom3. Sales growth in India did not sustain after the generic release, with only 2% growth in June after monthly sales grew 58% in April.
Price cuts are well and good, but it’s not yet clear people are staying on them long term, as they’re meant to be. For instance, the FDA approved Wegovy in 2021 “for chronic weight management”, with no specified duration. GLP-1s taken for diabetes are also expected to be taken indefinitely, like other diabetes drugs.
However, in a Danish study of people using GLP-1s for obesity, half quit within a year. A UK study found 32% stayed on the drug after one year, and that dropped more than half to 15% after two years.
Injections aren’t the most convenient or comfortable. Over the last year, oral GLP-1s have started to enter the picture. A daily pill might be more sustainable than a weekly injection for maintaining results.
As new products have been released, the performance on mean weight loss continues to improve for the weekly injectables. Over five years, we’ve seen a 10-percentage-point improvement in weight loss from semaglutide in 2021 to retatrutide this May. The pills have been less effective than the injections despite greater comfort for the user and being easier to manufacture.
The results aren’t always consistent, and as we pointed out in SNL#569, scientists now know that genetics plays a part in the outcome.
Beyond weight loss, researchers are further investigating a series of potentially life-changing, unrelated side effects. One study found semaglutide and talking therapy together cut heavy drinking days by 41% among adults with alcohol use disorder and obesity compared with 26% with therapy alone.
Similarly, GLP-1 use has been linked with 50% fewer deaths from substance abuse. In 2024, we described this as a “temperance technology” that helps change behaviors overall.
More wide-ranging impacts are debated. Heart aging is reported to have slowed by 2-4 years in high-risk individuals on semaglutide4. The authors argue that this isn’t fully attributable to weight loss, so another effect must be at play, though it remains inconclusive for now.
This doesn’t mean the same benefits would occur in a healthy population, but it shows we understand better what might be possible.
AI doesn’t need to go rogue to cause harm. It “only” needs an objective, access to your systems, and the ability to move faster than you can spot it.
Box’s CISO, Heather Ceylan, thinks of this risk as capability × access × autonomy × scale. She argues that model guardrails help but aren’t enough to keep risks at bay. Firms also need audit trails, human approval for consequential actions, and limited permissions.
For security leaders and anyone building with AI, Heather shared a first-hand view of how to build innovation that can be trusted at scale.
On weight loss, GLP-1s are a formidable drug. Obesity in US adults peaked at nearly 40% in 2022 and was down three percentage points as measured up to July 2026. Simultaneously, the number of new diabetes diagnoses has flatlined. Not all of this can be attributed to taking GLP-1 drugs, despite them making up 14% of US drug spend in 20255.
It’s still unclear whether this translates into real-world differences outside the weight-loss world, with contradicting papers making the rounds. A Danish study found that users took 17% less long-term sick leave over four years—equivalent to $722 per employed user per year. Yet another study found that there were no medical cost savings for insurers over five years. In fact, medical spending rose by $0.22 for every $1 spent on GLP-1s.
In the UK, the change in shopping habits is a cut and a shift in spending: users spend less and switch from chocolate (down 18 percentage points) to gum and mouthwash (up 24 and 20 percentage points, respectively). Hershey is promoting gum and mints specifically to combat “Ozempic breath” and has seen nutrition bar sales grow 12% in Q2 this year.
The weight-management effects are real, but access can still be uneven. Four Medicaid programs in the US dropped GLP-1s for obesity this year, amid budget pressures. With semaglutide and tirzepatide patents not due to expire until 2032 and 2036, large price drops in the US are not likely for several years. This is a blip, not a hard constraint, however.
More than a health aid, GLP-1s are a temperance technology with wide societal impact. In a national simulation, those without a high school diploma would gain the most – 14% more lifetime social value, 16-17% more quality-adjusted years, and a 20% larger life expectancy gain than the average person; college graduates gained 15-27% less.
We expect GLP-1s to keep marching on.
Thanks for reading!
The average price is set at $350/month; the $199/month price applies to the first two doses.
The dosage is not directly comparable, as it is a lower dosage than the Wegovy obesity drug.
Lilly attributes most of the price fall to Mounjaro joining China’s state reimbursement list and a cut in the US cash price for Zepbound.
The paper is a preprint at the time of writing.
Calculated by summing tirzepatide, semaglutide, and dulaglutide sales.
2026-10-11 12:44:15
“I’ve worked in the tech industry for the last 10 years, and Exponential View has guided me and helped me think much larger than I otherwise would have.” — Nicky B., a paying member
Hi all,
Welcome to the Sunday edition #605.
If you’re catching up this weekend, we covered a lot of ground on AI adoption, long-term knowledge impact, agent economics, and chip innovation during the week. If you missed anything, we’ve gathered this week’s Exponential View and AI Investment Brief readings at the end.
Enjoy!
Granola unlocked today’s post for you.
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When OpenAI released its repo with hundreds of mathematical manuscripts and supporting proofs, I asked what it might mean for the future of human knowledge:
Problems that have stumped the best human minds for decades fell to one afternoon of compute. […] Back in 1794, Condorcet bet that knowledge and our capacity to spread it and share it would evolve together. He has been right for nearly three hundred years. Now they might be coming apart, but not because of the intrinsic nature of these discoveries. We could bring them together if we insist our AI systems provide explanations, not merely proofs.
If we don’t, we’ll have more answers and less understanding.
A new group calling itself the Association for Human Mathematics criticized the release and called on mathematicians to stop working with OpenAI. Here I tend to agree with that there isn’t a “way to unring the bell, nor do I think we would ever go back to the old ways of doing maths.”
Terence Tao is pragmatic in his vision for Math 2.0:
“Math 1.0” placed a premium on being the first to solve an open problem, even if the solution was not initially well understood. Now that this goal has been optimized to the point of unsustainability, “Math 2.0” will need to decenter the role of raw problem solving and value mathematical progress more holistically - for instance by elevating the role of exposition, but also that of community building and opening up new directions of study. I believe that AI can contribute positively in all of these directions as well; but it will require more imagination and ambition…
The transition is hitting the field hard; after all, an open problem can organize years of training and collaboration at institutions. In a seminar this week, Fields medallist Hugo Duminil-Copin says his PhD students and postdocs were “in a state of total panic”. Like Tao, he calls for community-building — working with colleagues and students to understand the results, organizing conferences, and writing explanations for a wider community.
See also:
In cryptocurrency circles, Justin Drake urges preparation for “bunker mode,” warning that AI could undermine the digital signatures that authorize transactions before quantum computers do; calls it a “sobering moment” for the industry.
An independent researcher used AI coding tools to investigate a possible new planet. Amateurs have contributed to astronomy before AI, but with new tools, they might take on more research, while experts focus on even bigger, currently dormant problems.
’s State of AI Report 2026 is out – it’s an important reference, worth your time.
McKinsey has a useful systems view of the AI economy.
I worked with the WEF1 to release The Next-Generation Computing Framework this week. Compute is a condition of economic participation and strategic infrastructure. For me, that puts some practical questions ahead of the excitement for governments and firms, including:
Sovereignty needs a practical test. A country can own its data centers and still depend on a foreign supplier for anything from maintenance to licensing. We can no longer assume peacetime. We recommend testing agency in the supply chain, “stress-testing whether autonomy actually exists in moments of crisis, rather than relying on peacetime assumptions of access,” to understand what happens when those suppliers become unavailable.
Next-generation computing needs an energy plan. Quantum computing still depends on conventional computers for control, error correction, and processing results. They will likely sit alongside existing infrastructure, potentially increasing total electricity demand.
Find the constraint before spending more on compute. Our framework treats electricity, data, or skills constraints as potential limits on what other investments can achieve. More computing capacity may have limited value if people cannot put it to work — so we advise solving the constraints before spending more on compute.
We’ve followed Pakistan’s rooftop solar revolution since 2024, and in March examined how it was cushioning the country against imported-fuel shocks with the Strait of Hormuz closing.
Now high fuel prices could accelerate another change for Pakistan. The government targeted 30% of all new vehicle sales to be electric by 2030. This week, government adviser Haroon Akhtar told Bloomberg that higher petrol prices could bring that target forward.
The transition in developing nations, however, remains “chaotic and unequal,” as points out. The grid’s costs don’t disappear when customers buy less electricity. If those fixed costs are recovered through higher electricity prices, households that can’t afford panels may get higher bills. A cheaper alternative for some can make the shared system more expensive for others.
See also:
Energy researcher Seaver Wang argues that a bipartisan permitting deal could substantially benefit clean energy even if it also helps fossil-fuel projects. About 85% of the generation and storage capacity awaiting US grid connections is solar, wind, or batteries.
Why are personal agents taking off in the US, but not in China? points at China’s mobile superapp ecosystem.
In a retrospective study of over 54,000 women, AI picked up a rising cancer risk signal years before diagnosis.
🔋😎 A review of 157 experimental studies finds that retired batteries can still have much to give. Of more than 10,000 retired cells, median remaining capacity was 84.5%.
has cataloged 160 new drug programs cleared to begin human trials in China this year.
🧱 Researchers created a semi-autonomous AI system that can build new materials atom by atom.
A Canadian startup is deploying permafrost blankets in the Arctic.
🎩 Victorian Britain’s ruling class was successful despite living what, to us, may look like a frivolous life. An amusing read.
A new typeface inspired by Michelangelo’s manuscripts is coming to Word.
AI can take binary files of two games and fuse them together – think Super Mario running around Dark Souls.
In Exponential View:
More AI, more justice? — Can AI help people navigate the legal system without a lawyer?
What is left to do — What happens when machines make discoveries faster than we can understand them?
The end of screen time — left his laptop behind and discovered how much work he can do by talking to AI.
In AI Investment Brief, we examined:
AI revenues at $276bn annualized — Our updated accounting of the money coming into AI.
Economic agents — What would it cost to run a persistent personal AI assistant?
High-bandwidth flash — A proposed memory design that could help AI chips do more work.
Thanks for reading!
I am a co-chair of the Global Future Council on Next Generation Computing.
2026-10-10 14:02:50
I live in Amsterdam, and I travel to London every couple of months for some quality in-person time with the EV crew. When I arrived at my hotel on Monday, I realized that I had left my laptop at home.
A year ago, I would’ve felt completely lost without my laptop. If you do office work, you’ll probably relate. For decades, office work has revolved around computers: Slack, Word docs, Excel models, all created with the idea that I’ll be sitting at my desk, typing. That assumption is one of the fictions we built our lives around.
This week, without my laptop around, I found that fiction loosening, and now I believe it will eventually disappear because of how I use AI agents.
I did most of my work this week by chatting to ChatGPT on my phone. So I’m not replacing desk work with squinting at tiny text or craning my neck over a screen, but with talking and having the work talked back to me.
This is thanks to OpenAI’s July release of GPT-Live, which lets you direct agents by voice. I thought it deserved more attention then. It feels a little like talking to Jarvis, Iron Man’s AI assistant. And like Jarvis, it can put your workshop to work (for now, that workshop is mostly digital).
Leaving my laptop behind started a real-life experiment to see how much of my day-to-day work I could get done through my voice assistant.
I want to share more about my setup and what I found during this week of working only with my voice.
2026-10-07 21:50:46
Also from Exponential View: Could falling compute costs make persistent personal agents commercially feasible by 2028?, in our AI Investment Brief
Yesterday, OpenAI released a range of mathematical results produced by an unreleased frontier model. It’s a remarkable range: 722 manuscripts in 372 families, across number theory, complexity theory, and mathematical physics. The average result took the equivalent of three hours of ChatGPT Pro thinking.
As scientist Derya Unutmaz points out, it comprises 81% of the major math discoveries in the past three years.
Many of the results have been verified in Lean, but not all. Even if the real number is half of that, it’s staggering. Problems that have stumped the best human minds for decades fell to one afternoon of compute.
How should we think about this?
suggests:
Imagine millions of superhuman research agents at work. Formal systems may verify the proofs, but humans won’t have enough context to understand the underlying web of machine-invented concepts.
Mathematics could split into two layers:
Machine mathematics: vast, verified, mostly consumed by AIs.
Human mathematics: a compressed “effective theory” of the machine frontier—the small subset of ideas we can understand.
It might be an intriguing turning point in how we, as humans, understand the world.
We’ve only had three hundred years of making sense of the world through natural rather than supernatural explanations. The Enlightenment gave us that – a way to use reason and empiricism to understand what had previously been inexplicable.
2026-10-05 21:51:44
In England and Wales, 60% of defendants in defended county court claims this year did not have a lawyer. In the US, more than nine in ten consumers sued for debt face the case without one.
AI may give some of them a new way to argue their case.
In Australia, Greg Baker, a computing academic, used AI to challenge his employer’s refusal to make his casual job permanent, and the Fair Work Commission ruled in his favor – an early example of a self-represented litigant winning with help from AI agents.
Around 0.6% of all Claude use in May was for lawyers’ tasks, ranking 38th of 718 occupations, ahead of accountants but well behind programmers and writers. Four-fifths of these queries were people asking about their rights or what the law means.
