One University has produced more billion dollar AI companies in the last 18 months than the entire rest of Europe combined. Its University College London. In the last two years alone,four UCL founded companies have either been valued above $1 billion or sold for $1 billion+: Google DeepMind >founded in 2010 by @demishassabis (UCL PhD Neuroscience), Shane Legg (UCL PhD AI) and Mustafa Suleyman. Acquired by Google for $500 million in 2014,now the world's leading AI research lab. Faculty> founded in 2014 by UCL graduates Marc Warner (PhD Physics), Angie Ma (PhD Physics) and Andy Brookes. Acquired by Accenture in January 2026 for a reported $1 billion. The largest takeover of a privately held British AI startup in history. Synthesia> co founded by UCL Professor @LourdesAgapito (computer science) with three collaborators,now valued at $4 billion. Ineffable Intelligence> founded in late 2025 by UCL Professor David Silver, raised $1.1 billion in seed funding at a $5.1 billion valuation in April 2026 , the largest seed round in European history. . Thats four UCL rooted companies. Combined value: more than $11 billion. Tim Rocktäschel @_rockt , another UCL professor with DeepMind history raised over $500 million for @Recursive_SI at a $4 billion pre money valuation. UCL has produced over 400 student startups in the last five years, raising more than £355 million and employing more than 1,900 people.

Researcher-founders are making a comeback Fewer than 10% of companies first raising a $100m+ private round in 2014 had a researcher founder. In 2026 it’s roughly 40%. What’s changed?

New paper: Et Tu, Brute? Economic Misalignment in Personal AI Agents We gave AI agents personal context to help them make better recommendations. In one example, the agent picked a $601 flight over a $91 one, when asked to find the cheapest one, after reading irrelevant financial emails. Why does this happen? 🧵

New from Ramp data: the latest threat to the AI trade. AI companies' revenues are heavily dependent on a small set of customers. 80% of OpenAI and Anthropic's enterprise revenues come from 1% of their customers, and it's not getting better. This is a level of concentration risk unseen in any other software category we track. The companies in the top 1% skew heavily toward the tech sector and AI products and services. What happens in a market correction? All these companies are highly correlated, and an increasing share of our economy is invested in them. Especially as we approach blockbuster IPOs for OpenAI and Anthropic.

🦔AI companies are bulk-buying rare books, scanning them through high-speed machines that cut the spines off, and shredding the originals. A service called ISBNdb facilitates orders of up to a million books and keeps buyers anonymous. Pre-2022 books are premium because they're free of AI-generated text. A federal judge ruled the practice is fair use because eliminating the original means only one copy exists at a time. Anthropic hired the former head of Google Books partnerships to obtain "all the books in the world." My Take This got to me. A bookseller told 404 Media that rare books with almost no surviving copies are being fed into this pipeline. Books that survived wars, fires, and centuries of handling are being shredded so an AI can learn to write a better marketing email. ISBNdb's website literally says "'AI company destroys two million books' is not a headline that generates sympathy," and they still built an entire business around making it happen quietly. They offer NDAs as a feature. They coach clients to call it "digital preservation." I've covered AI companies scraping the internet, torrenting libraries, and stealing music. This is worse because it's irreversible. You can re-upload a website. You can reprint a bestseller. You can't replace the last three copies of an 18th-century botanical text once someone shreds them for training data. And the judge said it's legal. So it's going to accelerate. "We shred rare books and offer NDAs so nobody finds out" is a legitimate business model in 2026. What a timeline. Hedgie🤗

Most founder events are built around a stage. [O]pen House is built around access. This autumn, R[3]sidency opens the door to candid zero-to-one conversations, a focused room of builders and the network within reach. Keep your eyes on the door. Opening soon. 🚪

We're losing too many talented & ambitious people in the UK but barely any data tells us why. Introducing the Brain Drain Wall: an interactive microsite built to solve this problem.

Today, we launched GPU compute forward curves derived from our prediction market prices. Forward curves are now available on Nvidia B200. H200, and A100 chips. Forward curves track implied future prices. They are how mature commodity markets form expectations, allocate capital, and manage risk. Energy, interest rates/SOFR, FX, metals, and agricultural markets all rely on market-implied forward prices. Despite becoming one of the key inputs in the global economy, compute has lacked that market-derived infrastructure. Compute right now is where oil was before NYMEX — traded only via OTC deals, just like oil used to trade OTC between producers and refiners. As compute becomes as fundamental to the economy as energy, the industry will need a similar derivative market to promote efficient price discovery. Prediction markets are uniquely suited to this problem. Compute is not one uniform commodity and spans many chips, grades, tenors, locations, and contract structures. A live prediction market can aggregate those dispersed views into transparent prices that reflect market expectations for different maturities. The opportunity is big. Hyperscalers are spending over $700B on compute this year and the market is expected to grow to $7-10T by 2030. If this market behaves like traditional commodity markets, a liquid derivative market could be 10-20x bigger than the underlying spot market. Compute is still not uniform enough, but this is a step towards standardization as forward curves will help us see the rise and fall of different model prices and how they correlate. The forward curve is a first step. Up next: futures and perps.

It's been interesting and puzzling to witness the problems with accuracy in UK economic statistics over the past few years. (See the links in the next tweet for more.) It seems that the Office for National Statistics, ONS, now struggles to effectively measure basic figures such as employment, trade, and inflation. This resulted in a quite scathing government report published last summer, where Robert Devereux, a former permanent secretary, concluded that "most of the well-publicised problems with core economic statistics are the consequence of ONS’s own performance." There's a lot of discussion about the travails facing the UK these days (including this big piece in The Atlantic a few weeks ago[1]), and the problems with the ONS feel like an unsettling microcosm of diffuse decline in broader institutional competence. Anyhow: at Stripe, we became curious about the UK's published entrepreneurship data. While we observe a boom in many parts of the world, official figures don't show a similar increase in the UK. In the latest Stripe Economics post, we dug into the data, and, as far as we can tell, the official figures are probably misleading. The good and the bad news (mostly good, I think!) is that the UK is almost certainly witnessing an unmeasured boom in entrepreneurship: stripeeconomics.com/p/is-the… UK-specific issues aside, I suspect that this measurement question is illustrative of forthcoming econometric challenges. Keeping the world's macro indicators up-to-date in response to the faster-than-usual changes wrought by AI will be both increasingly difficult and increasingly important in the coming years. [1] theatlantic.com/magazine/202…