2026-05-11 19:29:06
A couple of weeks ago, Bloomberg reported that TSMC had no plans to use ASML’s newest chipmaking machine – High-NA EUV (EUV stands for extreme ultraviolet) – through 2029, citing cost as the issue. This may be a big deal.
Moore’s Law was always two things: a physical observation about transistor density, and an economic bargain about cost. The physics has been slowing for years.
Is TSMC’s hesitation the first sign that the economics are reversing, too?
Semiconductor manufacturing experienced one of the steepest learning curves ever recorded in any industry. For five decades and 10 generations of technology, each more expensive tool delivered cheaper chips – reliably, every 18 to 24 months.
The consequences are everywhere around us. The smartphone in your pocket has more raw compute than a 1990s supercomputer and costs less. The cloud infrastructure that runs modern AI exists because each successive generation of chips was cheaper per operation than the last. Software could eat the world because the hardware kept getting cheaper.
The cost per transistor – the broadest measure of chip economics – stopped falling in 2011. The narrower measure that tracks what lithography itself delivers, transistors per wafer-dollar, kept improving for another decade, helped by the industry’s shift to extreme ultraviolet light (EUV) around 2019, a shorter wavelength that let chipmakers print finer features and restored the cost-down curve. Now it has reversed too. High-NA should be the next step in the bargain, but if the best customer won’t take it, the bargain doesn’t hold.
2026-05-10 11:05:00
One of the best to understand AI capability curves. — Mark C., a paying member
Hi all,
Back home after three weeks on the road and easing back in.
In this week’s issue:
First, AI self-improvement. thinks there is a real chance a frontier model trains its successor by 2028. If that is true, what should we already be seeing in how the labs hire, spend and build?
Then, jobs and AI. In some of the occupations most exposed to AI, postings are rising.
Finally, what will more capable AI agents mean for token budgets? Exponential View members get access to my interactive model.
Let’s jump in!
Anthropic’s has argued that there is a 60% chance that a frontier model will train its successor by 2028. It is an exciting claim, perhaps revolutionary, perhaps frightening; the prospect of a recursive intelligence explosion.
There are plenty of reasons to read Jack’s essay and conclude that the picture might not be so clean.
One objection is that frontier training now looks less like a pure research challenge and more like an industrial scaling problem. The bottlenecks are not only about optimizing CUDA kernels. They are about negotiating land leases in Wyoming, securing power infrastructure, obtaining chips, and hiring the electricians to wire it all together. Over a three-year horizon, those physical constraints may matter more than algorithmic advances.
So what can we infer from the revealed preferences of frontier labs? If automated R&D were truly likely by 2028, what would we expect their behavior to look like now?
First, hiring would change. Labs would still want elite researchers, but the profile would shift towards people who can make research agents useful. Fewer pure researchers, more research multipliers, people who can build an automated research factory.
Second, labs would overinvest in compute before the automation arrives. Because of those physical constraints, they would want more GPUs, more memory, more power, more data centers, more inference capacity, and better internal tooling.
If a lab believes the R&D loop is about to accelerate, then waiting becomes expensive. You would expect it to tolerate ugly near-term cash burn in order to secure the pre-commitments it needs.
2026-05-04 19:15:50
Hi all,
As we wrote three months ago, we are in the midst of a compute crunch – demand is running ahead of supply and our position has stayed the same:
The real risk isn’t that we’ve invested too much in AI. It’s that we haven’t invested nearly enough.
Today’s quick look at the data shows that much of the supply remains latent, waiting on enterprise funding. When firms start spending serious money, the compute crunch will get crunchier.
Nvidia B200 GPU rental prices grew 114% in six weeks. Frontier model releases pull demand toward the newest chips; the premium customers pay per hour to get a B200 instead of an H200 has grown by more than 6x.
Infrastructure provider Lightning AI says that some forty of its customers are seeking 400,000 GPUs, ten times the current fleet of 40,000. Providers are already rationing GPUs by customer size. Microsoft now requires Blackwell customers to lock in at least 1,000 chips for a year and is cutting off smaller customers whose servers sit idle.
2026-05-03 10:25:52
Hi all,
Over the past week, I have been in China with , meeting AI and robotics teams including Zhipu and MiniMax (the two publicly listed foundation model companies), as well as Kimi, Alibaba, Xiaomi, Bytedance and others.
Demand is booming. Zhipu, for example, is serving 5.5 trillion tokens per day. Developers are rushing to the platform, joining at about ten a minute. Keeping up with inference loads is a struggle in China, as it is in the US. Teams universally acknowledged compute constraints, particularly the shortage of Nvidia chips. But that isn’t stopping innovation under those constraints.
Researchers and developers are free to use whichever model they want. Anthropic’s Claude was the preferred choice for technical teams, but it was clear that “dog-fooding” is commonplace.
I’m on the flight back to London right now. Hannah is continuing on to Shenzhen to check out more hardware firms. We’ll write something in more depth in the next couple of weeks. For now, here’s a short video from one of the many demos we saw at Unitree:
Huge thanks to our local hosts who were generous with their time, hospitality and swag. And a shout-out to Rintoul, who pulled together this amazing trip with expertise and great humor.
Azeem
gets to grips with that odd Silicon Valley paradox, that many engineers think the very thing they are creating, AI, will mean that the “median person is screwed”. What’s more, those same builders profess to having no clue how to stop this deracination.
Jasmine was in China with me this week. She “gets” the people in American AI labs and the culture surrounding them better than anyone. Her stellar essay in The New York Times is revealing:
In general, tech industry sources expressed more extreme concern about the labor market impacts of A.I. in private conversation — but suddenly became optimists once I turned on the mic.
I don’t agree with the depth of fatalism coming from the labs. Diffusion of the technology will be slower than flicking a light switch, even if it proves faster than the rollout of electricity.
But that fatalism is itself a fact worth taking seriously. The beliefs might become behaviors. An example: junior hiring might slow down, not because AI can do juniors’ jobs well, but because the labs believe it can and persuade employers that it can. A hiring freeze is an almost inevitable consequence of the patterns of belief.
The danger is that the theory of displacement becomes the logic of our responses. The point should be that we live in a state of uncertainty, as argued later in this issue. That uncertainty means we should be skeptical of preordained outcomes, however smart and well-paid those who make them are.
See also:
Another way to frame the moment is Dostoevskian: intelligent people convince themselves they have discovered a truth so important that ordinary ethical constraints no longer apply to them. See, Palantir.
This week, Chinese courts have ruled that replacing someone with AI is not, by itself, a lawful reason to fire that person. It is one of the first such decisions worldwide.
The US government has classified the grid supply chain as a national defense bottleneck.
A new paper out this week tested seven frontier models on their ability to role-play professional philosophers and simulate expert judgment. Some models looked passable on average, especially on questions where philosophers already agreed. But real philosophers agree far, far less than the AI. The variance of views was two to four times higher in humans’ favor.
2026-04-26 10:40:51
When, a year ago, I spoke with Ukrainian veteran drone pilot Jack de Santis, he said that soldiers who leave the front for rehabiliation and return after eight or nine months, need full retraining because drone warfare changes that fast. Since then, Ukraine accelerated the pace of iteration to seven days. I was so struck by this number, that I asked the team to dig into it and prepare a detailed briefing for members:
When GPT-5.5 rolled out this week, OpenAI’s reasoning research lead Noam Brown, tweeted
with today’s AI models, intelligence is a function of inference compute. Comparing models by a single number hasn’t made sense since 2024. What matters is intelligence per token or per $.
With the compute crunch, doing more with less compute could be a winning strategy.
And it runs counter to the culture American AI labs enjoyed for the past few years. The mantra was “moarrr compute, better benchmarks.” Chinese labs, with less capital and minimal access to cutting-edge compute, didn’t have that luxury. So they adapted. In a nutshell, how much real-world capability can they afford to deploy per token, per user?
DeepSeek’s new V4 model is marginally worse than GPT-5.4 ( has an excellent technical breakdown of it), but it is 4x cheaper, reflecting, in part, lower compute costs. As inference costs approach 10% of total engineering headcount spend, that line item begins to matter.
argues further:
In China, compute is more than an expense line. It is a strategic constraint shaped by export controls, chip supply, cloud capacity, domestic hardware readiness, and inference economics. […] DeepSeek is turning compute scarcity into a set of design specifications.
I am on my way to Beijing and will be looking to understand exactly how this plays out on the ground.
Agentic AI is reshaping how software gets built, and Agentforce is out in front.
Catch the TDX sessions on Salesforce+ to see what this looks like in practice: deep dives on Agentforce, Data 360, the core Salesforce platform, vibe coding, Slack, and more – all available to stream for free.
By watching TDX on Salesforce+ you can:
See what’s coming next: Hear directly from the leaders defining the roadmap for AI-native development.
Go beyond the main stage: Access broadcast‑only segments and candid interviews you won’t find elsewhere.
Start with the main keynote to experience how software is being reinvented, then explore the full programme at your own pace.
In 2018, I argued that “automated perfection is going to be common… What is going to be scarce is human imperfection.” I returned to it a few months ago:
When I first wrote about artisanal cheese, I imagined this shift unfolding more slowly, alongside the automation of routine office work and putting more robots on assembly lines. I didn’t anticipate that, nine years later, I could build custom software in an hour or produce work that once required entire teams while walking through customs.
Economist wrote an excellent essay, a vivid case study of how automation reshapes where humans matter most. When the internet entered the economy, 60% of travel agents lost their jobs. The routine tasks of searching, comparing, reserving etc. moved from the agents’ computers to ours. What happened to the remaining 40%? It was hardly fighting over scraps; what we wanted from travel changed in tandem. And for the surviving two-fifths, it was more upmarket, higher-end, curated. As a result, travel agent salaries grew from 87% of the private‑sector wage average in 2000 to 99% by 2025.
I like ’s word for this destination – “the relational sector”. It’s work whose value is inseparable from the human providing it.
In 2025, the world’s solar panels generated nearly as much electricity as the world’s nuclear reactors. So far in 2026, Ember’s new data shows that solar is starting to overtake nuclear on a 12-month rolling basis.1 Nuclear is still vital, of course. Last year was its best year ever in absolute terms, but its share of global electricity has fallen from a 1996 peak of 17.5% to under 9%.
The main way we explain this is that energy is becoming a technology, not a commodity. Commodities get scarcer and pricier as you extract them. Technologies are on a learning curve, getting cheaper as you make more. Solar module prices have fallen over 90% since 2010, and in 2025, solar met three-quarters of all new electricity demand on the planet. As solar rides learning curves, new markets open and make possible what was previously impossible. We call this the solar supercycle.
2026-04-25 01:11:12
Along Ukraine’s eastern front, a drone stutters mid-flight. On the operator’s screen a few kilometers away, the grainy view of splintered treelines and trenches, fades into static. The control link drops. He removes his headset, he knows what’s happened. Russia has found a way to jam the frequency. Again.
It’s 2024, near Bakmut, a few kilometers from the then-front line. The commander of the Terra drone unit of the 3rd Separate Assault Brigade Mykola Volokhov, has watched the slow degradation of his command links. A system that had worked at eight kilometers out, started to fail at four, then at two. Russian electronic warfare teams were getting better and better with their jamming. Later, speaking on camera, Mykola described the reversal with a soldier’s understatement. “But this problem was overcome… and now it is not with us. No problems!”
The repair did not come through a ministry, a tender or a requisition form. It came through a phone call. In the words of Dimko Zhluktenko, a soldier in Ukraine’s Unmanned Systems Forces:
We called the manufacturer and said, ‘Guys, we had this kind of issue...’ It is very direct, and they are very open to help us. They don’t need a shitload of documents or anything.
This is the operating model. A device is designed, fielded, disabled by the enemy, diagnosed through a conversation between the operator who lost it and the engineer who built it, then redesigned and redeployed in roughly seven days.
Industrial tempo in the grueling conditions of war. Continuity of signal, of keeping systems operational outweighs a lot else. In one unit along the Dnipro, an operator refused to take shelter during 120mm mortar fire because moving would break the radio link to his drone mid-mission.
This is how military innovation happens on parts of the Ukrainian front.
In the US and Europe, weapons systems move from concept to deployment over five, ten, sometimes fifteen years. A major capability iteration of the flagship F-35 Lightning II fighter jet can take close to a decade. That gap – one week versus seven years – is not just down to the exceptional engineering talent in Ukraine. It is the product of how work is organized, how quickly information reaches the people who can act on it and how much authority those people hold.
It is a real-time demonstration of what becomes possible when you remove the layers between the person who sees the problem and the person who can fix it. Under fire, alacrity is the difference between life and death.
While the stakes are as new as they are high, the lesson carries well beyond the muddy, blood-soaked, adrenaline-drenched trenches of Ukraine. It’s a lesson that will determine which nations will succeed, which will falter, which will invite attack, and which will stand firm as this century slowly fills with conflict.
In early 2024, Oleksii Kamyshin, then Ukraine’s Minister of Strategic Industries, formalized a new metric, “cost per kill”, as unit cost divided by probability of success multiplied by expected effect. However gruesome it may sound to those of us who never experienced war, for the Ukrainian military, it was clean, easy-to-understand, and something they could optimize.
It’s “the formula,” he said. You can’t improve what you don’t measure.
The formula directs production decisions across the industrial defense ecosystem. Weapons are not evaluated on whether they meet a specification written before the war. That is ancient history. The evaluation is the following: in the field, did the weapons work at a price the system can afford? If not, the runts are discarded, no matter the sunk cost. The high performers are scaled. The cost per kill today is $1,000, down from around $60,000 in 2022.
Much of this is familiar to Western firms, defense or otherwise. Performance is a criterion. Someone decides the drone should cost $800 and hit the target 70% of the time. The org spends three years building toward those numbers. By the time the product ships, the specification is an artefact of a long-forgotten planning meeting.
The $1,000 cost-per-kill came from a feedback loop of engineers talking directly to operators, designs changing every week, five hundred manufacturers running parallel experiments which are treated as data. Competitors can copy the number or buy the solution, but without copying the loop, they’ll just have a new specification that a slow system will fail to meet. Performance is what the system arrives at, not what it’s told to produce.
Early drones relied on standard radio control. In the Ukraine context, this worked until Russian units learned to jam or spoof their signals. So the Ukrainians initiated dozens of competing experiments across hundreds of small manufacturers, running simultaneously and without coordination.
Some engineers switched to fibre-optic guidance, a physical cable, unjammable by definition, but easily cut while in flight. Others tried an encrypted multiband radio for more flexibility, but still vulnerable to interference. What stuck was a hybrid – fibre with a live radio fallback for the moment the cable breaks.
The entire process happens within days and weeks and the speed shows up in their outcomes. Oleksiy Babenko, founder of drone manufacturer Vyriy Drone shared that the rapid iteration process – task-specific development, direct operator-to-engineer feedback, weekly redesign – increased target accuracy from around 10% to 70%, then 80% within a single upgrade cycle.
When Putin launched his invasion in 2022, there were perhaps seven domestic drone manufacturers in the whole of Ukraine. Today, Ukraine sports about five hundred manufacturers. Last year, they collectively produced 4 million units. The forecast for this year is seven million. Roughly 140x more drones than the United States produces in a year.
This scale hasn’t compromised speed. Production is distributed and it has followed the same design pattern established in the early phases of the war. There is no single dominant contractor. There is no centralized R&D pipeline. Hundreds of firms operate in parallel, each pursuing its own variations on shared problems. When conditions change, and on the battlefield there is more change than constancy, the network responds. It is simultaneous, emergent, like an immune system.
The individual firms do not have to be faster than their Western counterparts – some are, but that isn’t the point – it’s that the system as a whole is faster. The network searches a wider space for solutions, with far more simultaneous bets than any monolith can. This system is also more resilient. Firms can fail, factories can be bombed. The network persists.
More subtle but less obvious, is the variety and breadth of innovation. Curiosity is a byproduct of the network itself. Solutions win because they work. Those that succeed are stamped, repeated, scaled. Those that don’t disappear.
The Exponential View House View: Hardware iteration speed is an organizational phenomenon, not an engineering one.
The one-week cycle is not a product of exceptional engineering talent. Ukraine’s engineers are skilled, but they are not uniquely so. The cycle is a product of feedback loop architecture: how quickly failure data reaches the people who can act on it, and how many parallel experiments are running simultaneously. Any organization that treats hardware R&D as a capability problem rather than a structural problem will misdiagnose it – and find that importing the metric without importing the structure produces neither the speed nor the cost.
Ukraine has industrialized its drone capability. Underground warehouses far from the frontlines hum with the business of reengineering and manufacturing UAVs. Some of these complexes span hundreds of thousands of square feet; one facility can produce ten thousand units a month. Much of the work remains manual, but follows standardized steps. It’s light manufacturing under conditions of constant redesign. Cat Buchatskiy from Ukrainian defense analytics and innovation center Snake Island Institute told recently:
It’s comparable to what you’d see at SpaceX’s Starlink production line. This is not a garage shop industry. Our prime contractors (though we don’t have traditional primes) are producing tens of thousands of units per month and generating tens of millions of dollars in revenue.
Western manufacturing industry has always traded off scale and stability. Whether you’re making cars, semiconductors, or aircraft, a high-volume production line is a capital commitment made in advance of demand. Every lathe or robotic arm fits within a design made years before that can’t change without unravelling the investment.
According to its own estimates, if GM retools a plant rather than building a new one, the saving is $1.5 billion per facility. Honda is investing more than $1 billion to reconfigure three US plants for mixed powertrain production. Toyota’s Kentucky facility – the company’s largest plant globally – recently committed another $800 million to modernize its lines for EV production.
Every industry that has ever scaled manufacturing locks the design before the line is built.
Ukraine has broken these rules. Monthly FPV1 output grew from roughly 20,000 units in early 2024 to 200,000 by the end of the same year – a tenfold increase in twelve months, even as the designs running down those lines changed week by week.
The core mechanism is modularity (read our primer on modularity here). Most Ukrainian drone designs share standardized components, common software frameworks and interchangeable subsystems. A new guidance update or jamming-resistant radio does not require retooling the line. It requires swapping a module. Ten thousand units a month and weekly design changes don’t disrupt the process; they are the same system.2
Many of the people at the heart of this system have no background in the defense industry. They come from software, gaming, marketing, even B2B SaaS. Again, Cat Buchatskiy shared that “[o]ne of the biggest defensetech VCs now supporting the industry used to be the chief marketing officer at a workflow automation company. Many were working at Uber Ukraine or other rideshare companies when the invasion began.”
