A scientific paper is no longer a document you read — it turns itself into an agent you can put to work. Out of 100 computational biology papers, 74 made the jump with no human intervention at all. Hong Kong is building a brain for the entire city. And Singapore, tired of waiting for companies, just gave the whole country six months of ChatGPT Plus.
Today from Bregenz, quieter than usual — it's past midnight and my son is asleep. A world tour: Stanford, Hong Kong, Singapore, Argentina and Japan. The thread underneath: governments have stopped waiting for companies to roll out AI, and started doing it themselves.
In this episode:
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The paper that becomes an agent (
01:34) — the best story of the day, and I forgot to put it in the intro. The framework is
Paper-to-Agent, from Zhang Cheng Miao and colleagues at Stanford, published in
Nature. It reads the manuscript, pulls in the datasets, the supplements and the code, and builds an MCP server with tools. Across
100 computational biology papers,
74% were fully agentified without a human touching them, producing nearly
600 proposed tools. The 26 that failed did so because the paper never properly specified its data, code or models. The AlphaGenome agent took about
45 minutes and roughly
$14. I just spent two nights at the Radisson Blu at Zurich airport — $14 doesn't buy you one cocktail there. Now think about your own process documentation for the monthly close. Or a service manual wired straight into the machine, reading its own fault codes.
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Hong Kong is building a second brain for the city (
06:37) — Chief Executive John Lee announced an integrated
AI City Brain on 16 September. First phase: a task force, and connecting the digital infrastructure of emergency services so real-time information flows between agencies. It's a plan, not a platform. But the logic is hard to argue with — when a typhoon hits, you don't fire thirty separate signals at transport, public works and emergency response.
Hangzhou was Alibaba's first big city brain: higher traffic speeds, less congestion, fewer accidents, fewer people in hospital. Then ask whether your ERP can see the machine, the late truck and the angry customer at the same time.
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Singapore stopped waiting for companies (
11:02) — Prime Minister Lawrence Wong announced the
SkillsFuture portal programme on 16 September: six months of free
ChatGPT Plus and Google AI Pro, plus more than
200 AI courses. And remember what came before — roughly
$3,000 a month, for three years, for people over 45 to go and learn AI. When I say that number to a room of industrialists they nod, until I say "per month". No company on earth does that. Singapore's reasoning is blunt: AI transformation inside existing companies is mostly failing, so subsidise the population directly and take the productivity. Whether six months of dependency on American tools is a wise choice, nobody knows. They're still doing more for an entire population than almost anyone else.
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A neural network for grains of rice (
14:05) — Argentina's seed institute
INASE signed a technical agreement with
MachVision: a neural network trained on thousands of clean images to identify rice varieties. Why it matters — variety determines price, and it determines legal position. Someone declares an expensive variety and it turns out to be another. This already works in Argentina for wheat and malting barley; rice is the hard case. And the same optical-marker principle covers wood, fruit, the supply chain of a single mango, or sorting recycled metals. This is the other half of AI that isn't an LLM — the half I'll be talking about in Graz this week.
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The pickle-flavoured lemon tart (
17:00) — from
29 September, Lawson is selling three generative-AI-inspired products in Japan: a lemon tart with pickle flavour at
¥270, a sake red bean butter and yoghurt bread at
¥180, and a Japanese-style parfait salad at
¥475. Not a pop-up —
4,700 stores (the salad only gets 3,800; even Lawson has limits). The prompt was essentially "give me something we would never invent ourselves", so they deliberately withheld the market and customer data that would have regressed it to the mean. Hundreds of strange combinations, then cost and production data, then actual humans smelling and tasting. Lawson is explicit that humans prototyped and refined it. Is it a marketing gag? A hundred billion trillion percent. It's still a better process than yours.
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Malcolm out (
20:25) — who actually decides, and why the answer is increasingly "not the companies".
Stay chaotic. Stay human.
What is AI Neanderthal by Malcolm Werchota?
AI news for humans, not robots.
Every weekday, Malcolm Werchota comes out of the cave and explains what actually happened in AI — in plain language, with the numbers checked and the hype taken out. Around ten stories a day: three explained slowly, the rest fast.
No jargon. No breathless futurism. Just what happened, what it costs, and who gets handed the bill — explained the way you would explain it to a smart friend who does not work in tech.
If you run a company, sit on a board, or simply want to understand the technology reshaping your industry, this is your daily 25 minutes. Keep the stories that touch your world.
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