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.
2026-06-07 11:21:28
American companies spending the most on AI have seen their revenue grow five times faster than the economy as a whole. Non-spenders are tracking the economy closely. This is according to Ramp, a fintech with a side hustle in excellent firm-level data across the US.
Ramp’s finding echoes this 2020 AEA paper by James Bessen and colleagues. Bessen examined broadly defined automation investment at the firm level across the Dutch non-financial economy between 2000 and 2016. Firms that automated grew sales 2% faster than those that didn’t.
But which firms automated? Other studies by Acemoglu et al. give some kind of answer: automotators were already largely more productive and had higher output than non-automators. In my book, I took this further, arguing that any type of automation is a complex undertaking and requires a better management team than not.
AI also seems to be fuelling a surge in new business formation, according to Torsten Slok. It’s much easier and cheaper to launch a company today with the help of LLM-based AI. A mediocre ChatGPT lawyer, finance director or marketing manager is, after all, better than none.
In the hysteria of the AI narrative, all of this is boringly plausible. It’s a technology which reduces the costs of certain classes of cognitive activities; so we’ll do more of them.
Anthropic released some fascinating data on how Claude has changed the way Claude itself is built. The amount of code contributed per developer is eight times higher in the current quarter than the average to the end of 2024. There is a meaningful uptick starting in 2025 as Claude 4 and Claude Code were rolled out. We discuss whether lines of code are a meaningful metric below.
I found this chart more interesting—showing how well Claude Code can complete different classes of tasks. It appears that the Mythos, Anthropic’s latest release, which is only available internally and to select organisations such as the NSA, has demonstrated a step change in capabilities.
Anthropic took the opportunity to warn against the risk of recursive self-improvement, going as far as suggesting “it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up.” To be fair to Anthropic, they’ve been reasonably consistent with that idea for a few years—as have others.
A multi-lateral process can’t hurt things much, especially in today’s fractious environment. However, I’m sceptical about this threshold for “recursive self-improvement” as a notion of a runaway technology. For two reasons. First, we are dealing with increasingly abstract layers of automation that accelerate the product roadmaps of human-directed companies (not out-of-control machines). So there may be a risk, but it isn’t recursive. It’s a strategy. The second is that commercial realities act as a natural attenuator: securing the capital, the chips, and the power will prove less tractable than pouring out reams of Python code.
2026-06-04 01:07:06
Would you go to your gym if you had to pay for every visit? Probably not. We generally prefer those bundled memberships, even if we never hit the leg press after January.
AI companies have, until recently, been a lot like gym owners, offering generous bundles. And most AI users have been like gym-goers, occasional, sometimes frequent users. But others have been voracious and incessant, churning through their Claude and ChatGPT subscriptions.
And now, the AI labs have changed their tune, introducing more usage caps and metered pricing, particularly for coding tools.
So does moving from bundled pricing to metered pricing expand or shrink markets?
It depends.
Bundles expand markets when marginal costs are low and when customers get a diverse set of benefits from the bundle. A gym membership is a good example: it doesn’t matter whether the gym-goer visits twice a month or 10 times a month; it costs the same for the fitness company to serve. At the margin, if everyone went all the time, the model might break due to congestion and increased cleaning and maintenance costs.
However, if a product has variable costs, a pricing bundle is really a decision about who bears the risk of over- or under-use. If the bundle favours the buyer, the buyer has no incentive not to max it out. Few of us can spend more than 20 or 30 hours a week in a gym, but many of us can run riot with AI coding models. And AI models do run up substantial variable costs for those peddling them.
Chatbot usage is all over the map. Sarah Friar, OpenAI’s finance chief, noted that ChatGPT Pro users hit the app 11 times more frequently than active free users. That 11x is wide enough. But consider what happens with agents. If I am hunting-and-pecking in a chatbot, I’ll struggle to consume 100,000 tokens a day. R Mini Arnold, my agent, won’t get out of its SSD for less than 100,000,000 tokens a day. I spotted one user racking up 130 billion tokens in a month.
Moving from bundles to usage-based pricing brings with it sticker shock. But it is manageable. Consider Uber. The taxi company has put an AI cap on its 5,000 developers. Today, they are limited to $1,500 per month or $18,000 dollars per year per agentic coding tool. Like Oliver Twist, developers can ask for more. If all 5,000 developers spent the full $18,000 a year, the bill would be $90m. Against Uber’s 2025 free cash flow of $9.8bn, that is less than 1%. It’s essentially immaterial; a good CFO can deal with it in a heartbeat. And Uber is likely to be a heavier spender than most of Main Street. The pricing debate isn’t really about affordability.
It’s really about whether the customer can connect spend to value.
Fortunately, we have a good precedent that can help us think this through. In the early days of Internet advertising, going back to AT&T’s banner advert on Hotwired in 1994, advertising was sold on a cost-per-mille basis. The advertiser paid for a bundle of impressions. Perhaps users clicked, perhaps they didn’t.
Today, Internet advertising has moved to a metered model. Customers don’t buy a bundle of impressions; they buy a metered outcome.
The question is: what happened to ad pricing as we moved to the metered models? And what happened to the size and profitability of that market?
Of course, you know the answer, but it is worth seeing the pattern. When pay-per-view was introduced, it helped grow that market and is now vastly preferred.
2026-06-01 21:06:36
The verdict hasn’t changed. This is an AI boom, not a bubble. What has changed is the sheer scale.
Since we first ran this analysis in September last year, a lot has changed. Some 170 AI models have been released. The best now handle tasks four times as long as last year’s top performer. Usage has responded; quarterly token consumption has tripled.
The money has followed. The NASDAQ is up 20%; quarterly capex commitments have jumped 43% to $158 billion; and AI sector revenues have nearly doubled to $25 billion in Q1 of this year.
We’ve reviewed the data against our five empirical indicators, derived from analyzing 300 years of investment booms and busts. In our model, two indicators turning red indicate bubble conditions. Only one of these indicators is in the red; the rest range from green to amber.
Here is what these biomarkers show—and what it would take to change our minds.
For economic strain, we look at the total capex into AI, which is predominantly in building out the data centers and power connectors, the chips and cooling, and other things that go in them. Here, we consider the US portion of that divided by US GDP.
Capex has risen dramatically since September 2025, from $110 billion to $157.7 billion per quarter. Neoclouds are now contributing meaningfully to this number, with their share of total capex rising from 12% to 18%.
This rise in capex has led to economic strain exceeding 1% of US GDP, marking the first time it has entered the amber zone. AI capex now matches the scale of the late-1990s telecom build-out at its peak. Goldman Sachs estimates that aggregate AI capex will approach $1 trillion dollars in 2027. If we assume about 70% of that is in the US, this would be the indicator into the red zone towards the end of 2027.
An important note on our methodology: we used trailing twelve-month numbers for both Capex and GDP to measure economic strain. When capex rises so quickly, this may attenuate the indicator. On a quarterly basis, it actually doesn’t change the prognosis much. Economic strain remains in the amber, closer to green for this quarter.
Industry strain assesses whether the industry can afford the investments it’s making. Investments always come ahead of revenue, but speculative build-outs that predate real revenues, as seen in the telecoms and dotcom bubbles, are deeply problematic.
Quarterly revenues have roughly quintupled year on year: OpenAI from $1.7 billion to $6 billion, and Anthropic from $400 million to $4.8 billion. Based on our own bottom-up tracking of deduplicated sector revenue, we now put the figure at $25 billion a quarter, up from $13 billion in September.
One note on that number. Since the first piece, we’ve refined how we calculate sector revenue, moving to a more nuanced value-added approach that reduces double-counting across the cloud, model-lab, and application layers. Our original September figure for Q3 2025 was $25 billion; under the new method, that same quarter comes out at $13 billion.
This keeps industry strain in the red for now. But if revenue growth holds on trend – and capex forecasts don’t revise upward – it should exit the red by the end of the year.
Growing revenues address industry strain. Ultimately, if real customer revenues accelerate fast enough, they will pay for the investments being made. We measure this through doubling time: how long it takes revenue to double.
As a market matures, you’d expect revenue growth to slow, and that was our assumption in both the original September work and the early-2026 update. We forecast a deceleration. That isn’t what happened.
Revenue growth has actually accelerated, particularly by Anthropic at the turn of the year, surprising us, analysts, and even Dario Amodei.
“We tried to plan very well for a world of 10x growth per year. And yet we saw 80x. And so that is the reason we have had difficulties with compute.”
Rather than worsening as we expected, the indicator improved and is now well into the green, with revenue doubling every 0.73 years. Even if revenue growth for the rest of the year drops by 75 percentage points, this indicator would still sit in the green going into 2027.