2025-12-21 11:15:30
Hi all,
Today’s our final Sunday edition of 2025, open to all. From Monday, we move to our holiday schedule – no Sunday editions until 11 January and a pared‑back weekday rhythm. You’ll still hear from us.
I’ll host a member‑only 2026 briefing on January 7. If you’re a paying member, you’ll receive the invitation — please RSVP here. (Not a member yet? This is a great time to join.)
If you are based in London🇬🇧, please scroll to the bottom of today’s edition where I ask for a favor.
Enjoy the reading and happy holidays!
Azeem
The Cold War’s defining symbols, the US nuclear carrier and the silent Soviet submarine, represented a clash of industrial-scale superpower might. I explained in my book back in 2021 how the logic was inverting. That inversion is now without doubt.
Ukraine’s Sub Sea Baby UUV, costing a fraction of Russia’s $400M Kilo-class submarine, just breached a fortified harbor to strike at point-blank range. Without a Navy, Ukraine has more than “matched” Russia at sea. It changed the accounting, as the Russians need to protect every vulnerability from systems both hard to detect and cheap enough to risk repeatedly. For Moscow, it’s the worst bargain in warfare.
Meanwhile, China’s hypersonic missiles and stealth platforms are pushing US carriers so far offshore that their air wings risk irrelevance. In wargames, “we lose every time,” says one military boss. The carrier has gone from being the Queen, able to reach every corner of the board, to being the King, protect at all costs. No wonder the Pentagon refers to this as “Overmatch.” (More details here.)
Where superpowers once competed to build the most imposing platforms, smaller actors now exploit exponential tech to invert the cost-imposition calculus: Ukraine’s drone swarm renders Russia’s Black Sea Fleet obsolete, while China’s A2/AD network negates America’s carrier supremacy. The 20th century’s totems of power are becoming the 21st century’s most vulnerable liabilities.
But “drones beat ships” or “cheap beats expensive” isn’t the takeaway. The point is that exponential technologies collapse the old prestige hierarchy. The decisive edge shifts from owning the biggest tchotchkes to building the fastest adaptation loop.
See also:
Russia’s war bloggers reveal a new model of authoritarian control: by cultivating critics who question tactics but never the war itself, the Kremlin has turned authentic dissent into a tool of legitimacy.
For years, extreme ultraviolet lithography (EUV) has been treated as the West’s unassailable hardware moat. It’s often described as the most complex machine ever built, used to print the world’s most advanced chips. See this video primer to understand its importance:
EUV was the one technology Western analysts assumed China could not replicate. After all, ASML, the Dutch firm holding a global monopoly, has shipped zero EUV machines to China since 2019. That assumption is now under strain, as we learn that a Chinese lab unveiled a working EUV prototype in early 2025, with a plan to deploy domestic tools for chip production by 2028–2030. If confirmed, this upends the calendar, which was once anchored in the mid-2030s. Export controls designed to maintain a ten-year buffer may soon look like a fence built five metres behind the finish line…
Worth noting, this timeline is not a given. A single ASML EUV tool is built from roughly 100,000 parts supplied by about 5,000 suppliers. Replicating the physics is one thing. But replicating that ecosystem is another. Frontier mass production requires many machines, high uptime, stable yields, and a deep web of optics, light sources, resists, metrology, and replacement parts. Credible skeptics note that China may be salvaging components from older ASML tools or sourcing them through secondary markets.
Still, China has repeatedly beaten Western expectations when narrowing supposedly long-lasting technology gaps. EUV has been the West’s last clean chokepoint in compute. If this bottleneck loosens, the idea of chip dominance as a stable geopolitical lever becomes far less certain.
See also:
My past conversations on the US-China decoupling with and on why China innovates while the US debates with Dan Wang.
The conventional wisdom that America’s power grid problems will hand China victory in the AI race is overblown. argues that the US has multiple viable paths to meeting AI’s energy demands, including natural gas, off-grid solar and tapping spare grid capacity during off-peak hours.
When I visited Silicon Valley in 2023, I noted the following in my reflections at the time:
I heard the word ‘exponential’ a lot. Underpinning Silicon Valley is the exponential drumbeat of Moore’s Law. But that word, ‘exponential,’ wasn’t a quotidian presence in 25 years of conversations. It’s just always been there. Goldfish don’t talk about the bowl.
Two years later, that implicit faith has crystallized into what Eric Schmidt calls the “San Francisco Consensus,” a set of premises that unite those leading the AI development:
[B]eneath the apparent discord lies a deeper consensus around a number of key ideas. Most of those leading the development of Al agree on at least three central premises: First, they believe in the power of so-called scaling laws, arguing that ever larger models can continue to drive rapid progress in Al. Second, they think the timeline for this revolution is much shorter than previously expected: Many Al experts now see us reaching superintelligence within two to five years. Finally, they are betting that transformative Al (TAl), or systems that can outperform humans on many tasks, will bring unprecedented benefits to humanity.
While I don’t disagree with these premises, I’d note that the Valley has always rightly believed in exponential improvements and has been repeatedly wrong about where that technology leads. The same technologists who promised the internet would democratize knowledge gave us filter bubbles and platform monopolies. The consensus describes what believers expect to build. It has little to say about what happens next. Eric, too, acknowledges as much.
This is where Europe’s skepticism can have value, even if it’s often misdirected. The bitter taste of the platform era left many European founders and policymakers doubtful of Silicon Valley’s claims about technology’s benefits. Fair enough. But equally, it would be a mistake to assume this means AI isn’t powerful – it is. The question is who shapes what it becomes. We need these perspectives to meet and work together more than ever. As Eric says,
Silicon Valley alone cannot answer the profound economic, social, and—quite frankly—existential questions that will come of this transformation.
See also:
For a different perspective: this essay argues that those who push the apocalyptic narratives around superintelligence are looking to avoid the difficult questions about algorithmic harm, labor exploitation and corporate accountability.
AI’s main scaling wall is energy, both to power chips and to cool them, as I’ve argued many times over. Data centers are no longer background infrastructure; they’re competing for land, power and political attention. As those constraints tighten on Earth, proposals to move compute off-planet are becoming more attractive. Investor Gavin Baker calls data centers in space “the most important thing in the next three to four years.” The benefits are: ~30% more usable solar flux than Earth’s surface under ideal conditions, no weather or atmospheric absorption, “free” cooling via radiators and lasers through vacuum. Of course, there will be engineering obstacles to solve.
When batteries started attracting skepticism, the debate fixated on energy density and cycle life. But the decisive factor was manufacturing scale: over three decades, learning curves drove a 97% drop in cost per kWh – this turned batteries, once exotic technology, into universal infrastructure. Orbital compute sits at a similar inflection. The physics largely works but the constraint is industrial scale. If launch costs fall toward ~$200/kg by the mid‑2030s on Starship‑class trajectories1 and hardware follows comparable learning curves, space‑based compute could go from speculative to strategically viable.
AI, technology & science:
argues that Google’s TPUs vs. Nvidia’s GPUs may be the most consequential chip architecture battle since CISC vs. RISC.
OpenAI’s competitor to Nano Banana is out. It’s good and fast, but it lacks the infographic capabilities that really made Banana shine for me.
For the first time, an AI model has matched human experts at analyzing language itself.
Speech to reality: an AI system that builds what you ask for.
When LLMs play repeated game-theory scenarios like the Prisoner’s Dilemma, their strategic behavior is shaped as much by prompt language as by model architecture. Arabic and Vietnamese prompts drive more defection, while French elicits cooperation. The paper is worth looking into.
New research shows multi-agent systems can boost performance by 80% on parallelizable tasks but cut it by up to 70% on sequential tasks. The key to scaling AI isn’t adding more agents; it’s matching the right architecture to the task.
Markets:
Nearly 20% of Meta’s 2024 revenue from China came from ads for scams, illegal gambling, pornography and other banned content.
Greg Brockman said OpenAI was sometimes “sacrificing the future for the present […] The demand will far exceed what we can think of.”
McKinsey, which built an empire advising companies to cut workers, is now cutting its own.
Alexandr Wang has reportedly told associates he finds Zuckerberg’s micromanagement “suffocating.” Ouch.
on America’s tech productivity: the gains are hard to measure meaningfully, and what real gains exist flow to oligopolists rather than ordinary Americans.
Society and culture:
The UK government wants Apple and Google to embed nudity-detection algorithms into their devices at the operating system level to combat underage access to pornography.
A Colorado AI company led by former Intel CEO Pat Gelsinger created a benchmark measuring how well AI models align with Christian values. In a twist, Chinese models from Alibaba and DeepSeek outranked American competitors from xAI, Google and Anthropic.
We once dismissed animal suffering as mere mechanical reflex; this essay argues that we may be making the same mistake with AI.
writes about slop as the price we pay for democratized creation.
We’re hunting for an office with 10-12 desks, easy access to meeting rooms, and a flexible, grown‑up environment. Ideally near Great Portland Street, Tottenham Court Road, Bloomsbury or Marylebone. We’re open to partnerships in exchange for space, too. If you have a lead on a spare floor, studio, or a flexible space we can grow into, get in touch.
Thanks for reading!
“Starship‑class trajectories” means launch profiles made practical by SpaceX’s Starship: very heavy payloads, full reusability, frequent flights, and much lower cost per kilogram to orbit.
2025-12-20 23:40:29
🎅🏼 We recorded this to publish after Christmas, but demand for year‑end reflections prompted an early release - so if you hear me say Christmas has passed in the video or podcast, that’s why!
Listen on Spotify or Apple Podcasts
As we approach the end of the year, I want to reflect on what made 2025 special – what we learned about AI, what surprised me, and what it all means for the road ahead. This was the year artificial intelligence stopped being a curiosity and became infrastructure. The year models got good enough to do real work. And the year we began to understand just how profoundly this technology will reshape everything.
You can listen/watch my reflections on 2025 (widely available) or read all my notes below if you’re a paying member.
I cover:
The models matured
The work shifted
Orgs are slow
Atoms still matter
Money’s real
K is the letter of the year
And my seasonal movie recommendation for you 🎁
My favourite development of 2025 has to be Nano Banana, Google’s image generation service. Beyond its fantastic name (which, as a Brit, I’ll admit Americans say much better than I do 🤭), it represents something remarkable. Pair it with Gemini 3 Pro, present a complex idea, and ask it to turn that into an explanatory diagram. The visual results can be phenomenal with some clever prompting. It’s also really fun for video generation:
But the deeper story is that in 2025, many models got good enough to do long stretches of work. Claude 4.5 from Anthropic is fantastic for working with long documents and for coding. GPT-5 excels at deep research and certain classes of problem-solving – I’ve been using version 5.2 for financial modeling. Google’s Gemini 3 Pro is markedly better than previous generations. And I’ve been turning to Manus more often.
All of these models are now capable of doing what I would describe as a few hours’ worth of high-quality work. This has meant that I, as someone who hasn’t been allowed near software code for more than a decade, have been able to build my own useful applications. What was derisively called “vibe coding” a year ago has transformed into something genuinely productive for me. I will write more on this in my EOY lessons for genAI use in the next few days.
One of the biggest changes I’ve noticed is cognitive. There’s been a shift from the effort of actually doing the work to the effort of judging the work, specifying problems well, turning those problems over to a model, examining the output, and deciding what and how to move forward. You need to maintain a high degree of mental acuity as you evaluate: are the assumptions reasonable? Are there obvious errors?
Those of us who have managed people and led teams will recognise that it’s a bit like managing and leading, except your team member is a machine that can work in parallel across many domains simultaneously. And therein lies an additional challenge: the cognitive load of selecting which model to use for which task has now landed squarely on me, the user. It’s clear for coding – that’s Claude. But for the nebulous set of other problems, the choice between GPT 5.2 and Gemini 3 is never obvious beforehand, though it usually becomes clear in retrospect.
In a way, we all start to behave like developers. Developers think constantly about their tooling and workflows. They consider what in their weekly cadence can be automated. If you’re using AI effectively, you’ll spend less time putting bricks in the wall, more time figuring out how best to organise the bricks and specify what you need. If you have a static way of working with your AI system, you’re missing out. The models are becoming more capable. More importantly, the application layer around the models – the tools they can access, the use of memory, the use of projects – is making them dramatically more useful. So you simply have to keep experimenting and trying out new things.
My experience is that about three-quarters of what I’m doing today, I wasn’t doing three or four months ago. Models have become that much more capable. If you treat AI like a simple operating system upgrade, rather than something whose capacity grows and requires regularly changing how you work, you will miss out on the benefits.
One of the toughest lessons of 2025 for many will be how slow and painful organizational rewiring is compared to the progress we’re making with the AI systems themselves. Let me share Exponential View’s story.
2025-12-15 19:26:12
Hi all,
Here’s your Monday round-up of data driving conversations this week in less than 250 words.
Let’s go!
AI at work ↑ Adoption grew from 40% to 45% between Q2 and Q3 this year. But the real growth is in light, frequent use throughout the week.
Enterprise sales ↑ OpenAI reported 8x year-on-year growth in its ChatGPT enterprise use, led by technology, manufacturing and healthcare.
Age of construction ↓ Construction productivity has been flat or falling across most rich countries since the 1990s, despite decades of automation and evolving manufacturing practices.
The cost of letting chips go ↑ Without any exports or smuggling, the US is projected to hold a 21x-49x lead in AI compute over China in 2026. Allowing unrestricted Hopper exports shrinks that lead to as little as ~1.2x-6.7x. (See EV#554)
2025-12-14 11:17:27
You help me keep my finger on the pulse of what is happening and what may be to come. – Joanna P., a paying member
Hi all,
Welcome to the Sunday edition.
Inside:
GPT 5.2 shows how rapidly the cost of intelligence is collapsing.
AI adoption has hit its first plateau – why breadth is flattening and where intensity will drive the next wave.
China’s H200 trap: why limited US chip access could shrink America’s compute lead and still fail to deliver leverage.
Plus: AI for teen support, a jet engine turned 42MW data center turbine, and hair‑thin brain chips streaming at ultra‑high bandwidth.
Listen on Apple Podcasts or Spotify
Open AI’s GPT 5.2 shows how rapidly the cost of intelligence is collapsing. GDPval is a benchmark that tests professional tasks in fields like finance and healthcare. GPT 5.2 beats industry experts 70% of matchups. It does so at more than 11x the speed and at less than 1% of the cost of human experts. To get a sense of model progress, that is double the wind rate of GPT 5.1 which launched a month ago.
This latest model scores 90.5% of the Arc-AGI benchmark for $11.54, more than 390x cheaper than the previous high score set by o3 one year ago. On Arc-AGI 2, a harder test, GPT 5.2 is substantially more capable than four-month-old GPT-5.
Both GPTval and Arc-AGI’s benchmarks point to a continued acceleration in model progress.
GPT 5.2 reinforces my argument that we’re going to see plenty of model variety. Claude Opus 4.5 remains a better coding model. GPT 5.2 is especially powerful when you run it in Pro Mode, but this can take 15-20 minutes. So Gemini 3 Pro still has an important role, especially for tasks that don’t need that depth. I’ve not yet had a chance to pit Gemini 3 Pro in thinking mode against GPT 5.2 Pro. Let me know in the comments if you’ve done it and what you found!
The Ramp AI Index, which tracks AI adoption through AI subscription spend, flattened in November and has effectively plateaued since July. That can look like a slowdown, but I see a transition.
Here’s what’s likely going on. Enterprise AI is already a $37 billion market growing 3.2x per year – what Menlo Ventures calls the fastest-scaling software category on record.1 But that growth is uneven and the market’s character is shifting.
2025-12-12 00:42:23
Listen on Apple Podcasts or Spotify
I recently set out my macro view on the next 24 months of AI. The response was strong and many of you wrote in with questions. In this episode, I build on that analysis and answer your questions.
Some highlights:
(03:36) The biggest AI constraint right now
(10:43) Why mid-2026 is a crucial turning point
(18:41) The market’s reaction to OpenAI’s code red
(20:51) The best strategy for middling powers?
🔔 Subscribe on YouTube for every video – including the ones we don’t publish here.
2025-12-08 23:36:24
Hi all,
Here’s your Monday round-up of data driving conversations this week in less than 250 words.
Let’s go!
Agentic AI ↑ Orchestration systems (like Poetiq) significantly boost frontier-model performance on ARC-AGI-21.
VC screening ↑ LLMs cut venture capital deal screening time from 2 hours to ~13 seconds.
Underestimating competitors ↑ 93% of companies misjudge how quickly rivals are adopting AI and robotics.
Sourcing from China ↓ A third of the members of the European Chamber in China2 are looking to shift sourcing away from China due to tight export controls.