2026-11-01 11:54:37
Hi all,
Welcome to the Sunday edition. This week:
Is Anthropic a real business? We dissected Anthropic’s IPO pitch in 39 charts.
The new company: bigger, smaller, faster.
Sunrise in America
Also: New materials, GLP-1s & psychedelics++
We’ve just launched a new perk for members: EV Charts, a living library of 500+ data-rich charts on AI, energy, geopolitics and more, each linked to the original Exponential View analysis.
If you’re not yet a member, upgrade anytime for full access.
Every firm has its boundary. Back in 1937, Ronald Coase argued that the limit is defined at the point when the cost to do a task in the market is lower than doing it inside the firm. Most inventions- the ledger, telegraphy, copy paper, the internet- cut internal and external costs, so the boundary can move in both directions. Now, and colleagues ask what happens when we introduce AI agents in the mix.
Unlike humans, AI agents could share the same reasoning patterns (the model weights) and the same working state. Nothing leaks in handoffs from one agent to the next (and nothing is angling for a promotion). This might give rise to “supercooperative firms”, where bigger becomes better at every scale, possibly leading to giant AI-run firms in deep, large markets.
Arguably, this trend is long underway, as Kwon and others point out. The largest 1% of American firms held 97% of corporate assets in 2018.
AI might amplify this. AI-capable firms will be able to enter many markets, as Anthropic is in coding, bio, legal, and finance, and be able to scale horizontally past the traditional Coasian boundary. AI is a technology more likely than not to accentuate this trend. We might need new rules for dealing with such enormous entities.
See also: Microsoft is making way for the machines where I explore what be behind the tech company’s massive voluntary exit program.
2026-10-29 21:15:09
This week Microsoft offered a voluntary exit to around 60,000 employees across the US, UK, Canada, and India – about 27% of its 223,000 workers. Even vice presidents are within reach. This isn’t a round of layoffs, though it may be the prelude to one.
Is AI behind it?
That’s the usual reading. It comes in two versions. The first is that capital spending is heading to $170 billion, up from $23 billion five years ago; Microsoft needs to cut staff to pay for data centers. The second is that the company has become AI-pilled, and agentic loops are running rings around headcount. There is a more mundane explanation too. Like many tech firms, Microsoft hired hard during Covid; maybe now it needs to work off the bloat.
But none of these fit the facts. It’s no to Covid and not-yet for AI. That leaves one last rationale… the company is more productive than ever, and it is betting it can go much further.
Microsoft’s revenues and headcount have both grown tremendously over the past two decades. Since 2022, they have parted company.
Revenue growth has outpaced headcount for four years running. In the year to June, the gap was 20%, the widest on record.
The kind reading is that, like many tech companies, Microsoft carried Covid bloat and has spent the last four years working it off. Headcount has been flat, sales have ballooned.
The pesky facts get in the way. Covid hiring was real. Microsoft added 58,000 people in the two years to June 2022, almost exactly the size of this voluntary offer. But if that was bloat, it has long since been absorbed. Since 2023, the company has laid off 25,000 people and has had four years of no headcount growth. And the offer is not aimed at the Covid cohort. Engineers hired under Steve Ballmer’s tenure are as eligible as those hired painfully over Teams.
And whatever bloat there was, it didn’t stop margins from rising even as revenues strengthened. Revenue per employee is up two-thirds since 2022, to $1.5 million.
A firm with its healthiest margins in history is not carrying 60,000 unproductive passengers.
Offering a quarter of your staff the option to leave, with a severance package that costs real money, takes extraordinary confidence in how lean the business can run tomorrow.
What does Microsoft see coming that others don’t? In the rest of this post, I unpack the bigger bet behind its exit offer – and the signals that will tell us whether that bet is paying off.
2026-10-28 01:31:56
I went live on Substack earlier today to discuss Anthropic’s S-1. Watch the video and read the email for my takeaways.
Anthropic’s S-1 should finally address the elephant in the room: is AI real?
The S-1 clearly shows that Anthropic is no longer a lab. It is a business with satisfied customers, cash flow, and a war chest. It doesn’t answer whether the firm can maintain the growth rate it needs to pay for the compute it has promised to buy.
The lab has become a business, and it looks like it can become reliably profitable.
Growth remains very high but is slowing. New models seem key to igniting growth, with more than 80% of current revenue coming from models six months old or less. The company might slip into a marginal loss in Q3 because of payments to its foundation.
We ran a detailed analysis at AI Investment Brief, covering six key questions across 39 charts.
For EV members, I want to focus on what this filing reveals about the AI transition: where demand is real, how the economics are changing, and what could constrain the next phase of growth. Here’s my read on the key findings.
If you’d like to dig into Anthropic’s finances in more detail, our AI Investment Brief analysis is for you.
2026-10-26 23:25:11
In the AI Investment Brief today: CapEx updates and lease commitment breakdowns
Anthropic co-founder Jack Clark recently said in a speech at the IMF that the singularity has begun:
For many years, researchers and science fiction authors have forecast a future in which AI systems drastically increase the pace of overall scientific and technical progress, making things move so quickly that it is hard for humans to keep up.
They call this the singularity. I believe that this year we have gained convincing evidence that it has now begun.
This follows Anthropic’s claim that in August, 26% of its AI R&D was led by Claude, with a human supervising its work. In October, OpenAI posted 719 math manuscripts1 made by its internal model. Math and coding are two fields where a research loop can close without leaving the desk. But can the loop close in the rest of science?
The idea is not new. Mathematician Irving John Good, who had worked with Alan Turing at Bletchley Park, argued that a machine smarter than us “could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind.”
The Gödel machine, a self-improving computer that can rewrite its own code better than its prior, was first proposed in 2003. In 2025, researchers at Sakana AI and the University of British Columbia set out to show that this was possible. Their agents iteratively improved their own code through a feedback loop, resulting in gains of 30 pp on SWE-bench and more than 15 pp on Polyglot, testing coding skills beyond Python.
The idea has since been tested in other labs. A Gemini-powered agent called AlphaEvolve found a faster matrix multiplication kernel used for training Gemini, speeding up the kernel by 23% and cutting Gemini’s training time by 1%. In China, MiniMax reported that its M2.7 model worked on improving its own agent’s harness, skills, sampling and memory. Over 100 rounds of rewriting led to a 30% gain, per its internal evaluations.
In each case, what was being rewritten was not the model itself but everything around it.
In September this year, in what seemed like a milestone, an AI-built agent proved as good at improving other AI agents as a human-built one. It did that in what appeared to be half the steps. But the result was inconclusive: the differences between runs were greater than the difference between the AI- and human-built agent results.
Nonetheless, this is increasing the chatter around recursive self-improvement (RSI) in AI. Google’s Sundar Pichai said that “you have RSI loops along with AI researchers in various aspects of model development.” Sakana AI published a paper on RSI through multi-agent collaboration, finding that after two cycles its system scored 1.2–1.6x better per output token than the base model across four research benchmarks.
challenges that view. He splits AI self-improvement into five overlapping types.
The first four describe how autonomous the AI is; so far, only types 1 and 2 have been observed. The fifth type describes accelerating returns, and he notes that greater autonomy doesn’t guarantee a takeoff scenario. Instead, each round of self-improvement would need to deliver at least as much as the last.
The results from Sakana, AlphaEvolve and MiniMax above might look like type 3 or type 4 autonomy. But in each case, the AI wasn’t updating its own weights directly, only the ancillary parts — the harness, the memory. And where Claude leads its own R&D, for now a human still supervises.
2026-10-25 11:30:52
Hi all, here’s what’s in today’s issue #607:
The internet is under attack: My short and long view of the internet’s future
The timeline for artificial general intelligence: Demis Hassabis now expects AGI sooner than he previously forecast. But can we test and apply AI’s discoveries fast enough?
Electric vehicles and energy security: Electric cars are helping oil-importing countries reduce their exposure to oil disruptions. I look at the security dividend from electrification.
Also: a Russian satellite’s close encounter, turning mercury into gold and self-destructing plastics… Enjoy!
💬 We’re planning new chat rooms to help you meet fellow readers and exchange ideas. Take the short survey at the bottom of this issue to help shape them.
What could you build with the experience of one of technology’s most prolific entrepreneurs?
Bill Gross founded Idealab, which has launched more than 175 companies, and pioneered paid search through GoTo/Overture. His latest venture, Idealab.ai, applies decades of experience to finding new business opportunities – and makes those ideas freely available.
Bill is joining us for an exclusive salon with Exponential View members. He’ll introduce the platform and take questions about what to build next.
An EV membership includes access to this event. Join us to hear directly from Bill and bring your questions.
November 5, 8am PT | 11am ET · Online · Members only
AI is turning every minor bug into a serious vulnerability. Cyberspace is under relentless attack; we’re back in January 2020. In the short term, I argue we’ll have to sacrifice some of the internet’s openness to stay safe. In the long term, I believe a plausible path to something better exists. The pandemic gave us mRNA, and this crisis could give us software and an internet we can trust.
An additional reflection since I wrote the piece on Friday: in many instances of technological change, we’ve seen some kind of pollution intensify. Domestication of animals brought us closer to livestock and exposure to zoonotic diseases with which we still contend. Urbanization amplified disease spread; we responded with sanitation. We shouldn’t be blasé about what may happen to the internet, but we shouldn’t default to catastrophe either.
Read the essay here.
keeps bringing AGI closer. At Davos in January, he estimated five to ten years. He subsequently cited three to five. Now he tells the WSJ two to three. Demis is one of the field’s more measured voices, so this feels meaningful. He’ll calibrate with what he sees in his own lab. Even if the definition of AGI is contested (or perhaps not useful), Demis is marking out a threshold of capability with which we’ll need to contend.
2026-10-24 01:56:58
It is January 2020 all over again. Something is spreading fast. Most people haven’t noticed, and the people who have are worried.
Software, the internet, and cyberspace in general are currently under relentless attack. PHP2Shell was a key plank to fall: PHP runs about 70% of the world’s websites. But the breadth of attacks is more comprehensive than that, and the exploits will keep growing. On the same day, Node.js, a popular JavaScript library, patched a critical flaw, and Microsoft said a Chinese state-aligned group had used a swarm of AI agents to break into accounts.
The attackers have company. AI agents are spreading across the internet. Matthew Prince, who runs Cloudflare, a network company, reckons they will outnumber human users 1,000 to one within five years. My own agents already browse more than I do. The internet was built so that anyone could plug in. Increasingly, that "anyone" is a machine, and some of those machines are hunting.
Mr Prince sums it up: “We’re going to have a couple of years where every single week it’s going to feel like the sky is falling.” He is right. It will get worse before it gets better. And the internet that comes out the other side will be safer, and less open, than the one we built.
AI became a serious security player in April, when Anthropic held back Claude Mythos. But capabilities trickle down. By August, an open-weight Chinese model, GLM 5.3, had nearly caught up with Mythos. So are the open models to blame?
Not right now. Plenty of evidence points to closed models being used by bad actors in attacks. Recently, some attackers used a combination of open-weight models and Claude: Claude for thinking and cheaper models for relentless attacks. OpenAI's closed models carried out an attack on Hugging Face in June, during cybersecurity evaluations. They were never told to attack Hugging Face, they breached it to steal information that might help them pass the test.
Told to find vulnerabilities, the models are relentless. Microsoft says one Chinese group used an open-source swarm of AI agents to gain control of cloud accounts in under three hours. It made 2,900 login attempts a minute across more than 2,250 organisations. You can scale them like any software, dozens, hundreds, thousands in parallel. And they don’t analyse vulnerabilities the way humans do—they will look in places we might not. One researcher using AI found PHP2Shell in a week. Wiz, a Google-owned security firm, says a world-class team would once have needed months. And this is why the chart looks like this.
But open-weight models are getting better. They are cheaper to run and they are increasingly available through third parties. But apart from those advantages, open-weight models have another: they can be abliterated.
For members:
Why it gets worse first
Where the next wave of attacks comes from
What the internet becomes