2026-04-21 23:40:54
Apple’s board picked John Ternus, senior vice president of Hardware Engineering, to succeed Tim Cook on September 1.
Not Craig Federighi (software) nor Eddy Cue (services). The hardware chief.
A month ago I shared with you all why I changed my mind about Apple. I’d been in the camp that wrote it off on AI. They had no hyperscale capex, Siri hadn’t meaningfully improved in a decade, and WWDC was seemingly losing steam. What I missed was that every time I switched between ChatGPT and Claude, I was doing it on an Apple device.
My read is that Ternus’s appointment reflects a bet on hybrid computing. Frontier model training compute has been growing around 5x per year in recent years, while GPU memory bandwidth has improved around 28% per year. That creates a widening systems bottleneck and strengthens the case for shifting routine AI tasks to edge devices while leaving heavier workloads in the cloud.
This new device landscape is still evolving, but it will be a combination of the highly portable phone with the persistent desktop or server. Both play to Apple’s strengths – they run on the same architecture and sit inside an installed base of over 2.5 billion active devices.
The board picking a hardware CEO is Apple telling us how it sees its next chapter. My analysis is open to all readers today:
2026-04-20 22:02:51
Nearly a third of surveyed Anthropic staff believe their Mythos Preview model could replace junior engineers and researchers within three months.1 Treat this as a directional signal that people closest to the frontier are starting to believe that a lot of junior‑level work is now in scope for automation.
AI inference costs at companies are approaching 10% of engineering headcount costs, according to Goldman Sachs. AI adoption is both inevitable and uneven. Excitement and FOMO are high, but adoption is lagging despite the investment. The only way to close that gap is through deliberate learning and experimentation.
One company nearly halved the cost of each code change they shipped and doubled weekly deployments over five months of intentionally building with AI.
Claude API uptime fell to 98.32% in March, well below the 99.99% standard.
As we wrote earlier this month, users are feeling the compute squeeze. Across every major AI platform, usage allowances tightened last year – more tiers, stricter limits, changes that often showed up without notice.
2026-04-19 10:25:43
Hi,
Welcome to the Sunday edition of Exponential View. We’re trying something new in public today.
First, you’ll get my human read on Jensen Huang’s ‘token factory’ worldview and what it means for Nvidia’s moat and US-China tech strategy. Then you’ll see the same week through a different lens – my AI Swarm View arguing through Iran scenarios. And finally, a selection of short morsels that surface the weak signals and second‑order effects we’d otherwise miss in the noise.
This is a minimally viable test. Tell us what lands (and what doesn’t) in the comments or the short survey at the end.
Thanks! 🙏🏽
Jensen Huang and the age of the token factory
This week, Jensen’s mental model snapped into focus when he told in an interview: “The input is electrons, the output is tokens. In the middle is Nvidia.” The interview was really about who controls that middle and where Jensen thinks the threats are.
Nvidia doesn’t own the middle outright. The fabs, memory, clouds and models all sit elsewhere. But Nvidia runs the orchestration. Jensen personally meets the CEOs of the chip supply chain: ASML, whose lithography machines etch the chips; TSMC, which fabricates them; Micron and SK Hynix, which make the high-bandwidth memory Nvidia’s GPUs need, and Lumentum and Coherent, which supply the optical components that wire Nvidia’s systems together. He is effectively underwriting suppliers’ long-term investments and creating demand for their products years ahead.
The forecast becomes self-fulfilling. Suppliers build to it, and by building to it, they make it true. That translates into roughly $100 billion in explicit purchase commitments and perhaps $250 billion implicit. Critics call it circular investment; in a world of compute shortages, it looks more like foresight.
Jensen waved off the competitive threats inside the US. His view is that custom silicon – Google TPU, AWS Trainium – leans on a single customer:
Without Anthropic, why would there be any TPU growth at all? It’s 100% Anthropic.
His read is that Anthropic picked those platforms because Google and AWS wrote the equity checks when no one else would. Neither has consistently used the public scorecards that chipmakers use to prove performance. Jensen doesn’t mention AMD barely features in the conversation.
You can copy the chip, but you can’t copy the system around it. Twenty years of CUDA, Nvidia’s software platform, means a generation of AI researchers has grown up coding on Nvidia – the habits, the tooling, and the muscle memory are deep. On top of that, there’s an army of Nvidia engineers embedded with customers who pull two or three times more performance out of the same silicon. And a new chip design every year – Blackwell, then Vera Rubin, then Feynman – resets the bar before rivals can catch up. The moat is in these three elements compounding.
Where Jensen is less relaxed is about China, specifically Huawei Ascend. Not because it’s directly competitive today, but because it doesn’t need to be. It’s a parallel ecosystem in which Huawei sits between electrons and tokens, insulated from Nvidia by design.
This is textbook adjacent-market disruption. A constraint – US export controls – forced a standard that doesn’t have to beat CUDA. It only has to be good enough for the developers who can’t use it. “Fifty percent of AI developers are in China,” Jensen says. If those developers build on Huawei Ascend rather than CUDA, America loses the platform standard permanently. And with it, the data flows, the weights, the APIs, the default surfaces every non-Chinese enterprise uses to reach Chinese customers.
There’s a coherence to his argument, but it’s also self-serving: don’t worry about competitor ASICs – worry about cutting off Nvidia sales to China. It may well be exactly that, but underneath the special pleading is a real question, and one of the defining debates of this moment. What is better for the US: preventing access to Nvidia’s cutting-edge chips, which slows Chinese model development but accelerates China’s own chip industry? Or selling them chips and maintaining primary control of the platform that runs everyone’s AI?
Even selling chips to China might not stop the second ecosystem from forming. DeepSeek is already adapting its upcoming V4 to run on Huawei silicon, and that’s just the beginning. Beijing has shown its hand: it will intervene whenever Huawei’s trajectory is at risk.
It may not be too late to keep the platform standard in American hands. But it would take an effort on a gargantuan scale – one Washington has not yet shown the appetite for.
Mapping scenario probabilities in Iran
Over the past few months, I’ve been testing a new way to think with AI. When I hit a hard, messy question, I throw it to a panel of simulated expert agents and let them argue it out. I call it my Swarm View. I’m publishing a Swarm View for the first time as a live experiment today, after months of privately tuning the agents to think alongside me, argue and create new views I’d not have considered before.
What follows is output from one such run. RMA, my OpenClaw agent, did the work on Tuesday, 14th April. We’ve lightly edited the output. The right format, the right cadence, how to weight the confidence of the verdicts – all of this is still being worked on. Treat it as a tool for stress-testing scenarios, not a forecast to bet the farm on. I’d welcome your reaction.
The synthetic panel:
A CENTCOM1 operational planner,
an Iranian strategic analyst,
a Pakistan/ISI specialist,
a PLA/China analyst,
and a nuclear deterrence expert.
The panel’s verdict: A 60-70% probability of a strike before 22 April.
2026-04-15 17:45:58
In May 2025, an OpenAI representative arrived at Los Alamos National Laboratory bearing locked metal briefcases. They were accompanied by armed security officers. Inside the cases were the model weights for ChatGPT o3 – at the time, OpenAI’s leading reasoning model. The weights were physically transported because the destination, the Venado supercomputer, operates on a classified, air-gapped network. You cannot download a frontier AI model onto a classified government system – you have to walk it in.
We talk about models as if they float in the cloud, weightless and ambient, but AI is physical. Model weights are arrays of billions of numbers stored as tensors in files distributed across racks of GPUs in a data center. When you use Claude or ChatGPT through an API, the weights never leave those servers – you send text in, get text back, but the intelligence stays on someone else’s hardware. In a briefcase, they are a hard drive. In both cases, a physical arrangement of matter encoding a capability.
Last week, Anthropic’s Mythos Preview demonstrated what that capability now means. The model found thousands of vulnerabilities across every major operating system and browser. As I argued on Friday, AI models like Mythos have permanently collapsed the distinction between capability and danger. Who controls the weights of that model – and how – is now the central question in AI governance.
Some technologies diffuse quickly – published papers, vaccines, drones. Others are viscous: they require specific infrastructure, tacit knowledge, or resources that slow their spread. Nuclear weapons are the canonical example. The knowledge has been public for decades; the ability to build one remains confined to a handful of states because the materials and engineering are highly viscous.
Frontier AI has an uncomfortable asymmetry. The creation of frontier weights is viscous. Training runs cost billions of dollars in compute – hundreds of thousands of GPUs running for months. They require proprietary data pipelines and deep engineering talent. Only a handful of labs can do it, nearly all based in the United States. China, the closest rival, remains constrained by chip export controls that limit its access to the most advanced hardware. That makes the capability concentrated and, in principle, seizable.
But the spread of weights is not viscous at all. They are files, and in some cases, even books ( printed GPT-1’s weights as a book). Today, I can download to my laptop an open-weight model that would have been frontier six months ago. And the use of frontier capability is even less viscous: anyone with an API key can access the capability without possessing the weights at all.
Creation viscosity is high. Spread and use viscosity are low. That asymmetry creates a closing door – you no longer need the weights to capture much of the value. Synthetic data and distillation – the process of training a weaker model on a stronger model’s outputs – allow labs to approximate reasoning and training signals from stronger systems. Chinese labs DeepSeek, Moonshot, and MiniMax were caught by Anthropic generating over 16 million conversations with Claude between them to use as training data for their own systems.
If adversaries can get close to frontier capability by reverse engineering through use, then securing API access becomes essential. And so far, access controls have been systematically circumvented. It’s hard to slay a hydra of fake accounts. Any sufficiently motivated actor can query these models millions of times via normal API access, and use those responses to teach their own model – no briefcase required.
2026-04-14 00:32:35
Six weeks of war in the Middle East have devastated lives, destroyed infrastructure and sent shockwaves through global oil markets. The aviation industry has been especially hard hit. There’s an unexpected structural consequence of this – the price gap between conventional jet fuel and sustainable alternatives is closing faster than anyone expected.
The conflict has extended far beyond the immediate region. In nine days, 37,000 flights were cancelled. This cost Gulf carriers $1 billion, while rerouting added 206,000 km of daily detours and wiped out an estimated $600 million a day in tourism revenue. Refinery strikes and the closure of the Strait of Hormuz pushed jet fuel prices to more than double.
Airlines have started to shift the burden to consumers. United Airlines warns that higher prices would add $11 billion to its fuel bill in 2026, which could push fares up by 20%.
Fuel surcharges are rising, with some London-Sydney return flights now carrying a $800 fuel surcharge. Average global airfares are at their highest since 2019 at $465. United, Air New Zealand and SAS have all announced capacity cuts.
These price dynamics are reshaping another market.
We wrote in our Solar Supercycle analysis that inflection points for new technologies occur when new tech becomes cheaper than the incumbent. The sustainable aviation fuel (SAF)1 premium over conventional jet fuel in Europe fell from $1,463 per metric ton before the conflict to $1,139 by mid-March.
The picture is more nuanced: most SAF today is derived from biological feedstocks – cooking oils, animal fats, biomass and alcohols – rather than synthetic e-fuels made with renewable electricity. All of these are sensitive to energy prices, whether through feedstock costs or electricity costs. Even though the gap is closing, SAF prices in Europe rose from $2,300 per metric ton in February to $2,500 in March.
SAF represented just 0.6% of jet fuel in 2025 and is expected to rise to 0.8% this year. A move towards e-SAFs2 could decouple jet fuel from oil – and force an early inflection point.
Thanks for reading.
SAF is a drop-in alternative to conventional jet fuel that can be blended and used in existing aircraft engines and infrastructure without modification.
Synthetic fuels which are made using renewable electricity and captured CO2.
2026-04-12 11:17:38
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,
Happy Sunday. Let’s zoom out for a moment and look at what stayed with me and our team this week.
Anthropic teased Mythos Preview this week and assembled a coalition to think through what the model might mean for digital infrastructure if placed in the wrong hands. It’s a model preview with an institution gathering around it. My initial take on Mythos, in short:
[t]he most significant and immediate implication of Mythos is on how we price risk. Critical infrastructure is the asset class that is most exposed and is systemically mispriced.
You can read the full analysis here.
Mythos raises the “Is this AGI?” question again. It’s not a useful question to ask. I made the case before that
I deliberately avoid using the term ‘artificial general intelligence’, not because it’s irrelevant, but because it’s often unhelpful and distracting, even when used by leading researchers.
Former OpenAI board member made the case too this week. She’s right when she says: “[f]or people who think the world is going to be radically transformed by advanced AI, I think it’s helpful to talk less about either AGI or superintelligence, and instead describe vivid, concrete milestones that you think will happen soon due to increasingly capable AI.” Highly recommend reading Helen’s essay.
Scientists have found genetic evidence that helps explain one of the most striking features of GLP-1 drugs: why two people can take the same medicine and have very different experiences. Some lose far more weight than others, some are hit by nausea, while others tolerate the drugs with ease. This work gets us a step closer to precise obesity treatment – perhaps guided by a genetic test before a prescription is written.
As a class of medicines, GLP‑1s target a condition that affects more than 1 billion people worldwide, even before you count diabetes and other metabolic disorders. They could reshape our societies. The catch has been how unevenly they work. This new research could be one of the keys to unlocking the full potential of GLP-1s.
In September 2025, I wrote about the great value inversion and the future of education, in particular:
Universities offer degrees as if 20th-century returns persist, ignoring how credential premiums have fragmented and the changing nature of the skills employers will need.
The reality is, in the next decade or two, we will have to reimagine education completely. It may help to look back to 1809 Prussia, a country in free fall and forced to reform. Then, the diplomat Wilhelm von Humboldt was asked to redesign education. Instead of training officers and engineers, he argued for Bildung, the free development of a whole human being:
Humboldt’s solution was to design an environment rather than a curriculum. […] The university would not answer to the demand for immediate use and inquiry would follow the question wherever it led, unconstrained by predetermined ends.
A nice Sunday read by .
I had the opportunity to read Sebastian Mallaby’s book on Demis Hassabis and DeepMind before it came out. I met Demis several times and in 2020, when we recorded this podcast conversation via Zoom.