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Why Anthropic Built a Wet Lab When news broke that Anthropic quietly runs a wet laboratory in the San Francisco Bay Area, most coverage framed it as a business-expansion story: another AI company moving into biotech. That framing misses the real signal. A company whose core asset is software — models, weights, GPUs — just spent hundreds of millions of dollars acquiring pipettes, robotic a …
Anthropic just did something software companies don't do: it bought pipettes. Anthropic has confirmed it operates a wet lab — a physical biology facility — in the San Francisco Bay Area. Eric Kauderer-Abrams, the company's head of life sciences, verified the lab's existence and explained why: "We believe that to do biology, the final test is still and will be for a while in real lab work." The pop
A Model That Never Talks Is Rewiring How AI Reaches Software The most talked-about AI model of the past week cannot hold a conversation, write an essay, or generate a single sentence of prose. Jev, launched September 15 by TypeSafe AI, does exactly one thing: it makes decisions. Developers point it at email triage, browser control, agent tool selection — anywhere a program needs a judgment rather
For most of the past two years, the playbook for building an AI application startup looked simple: rent the best frontier model, wrap it in a great product, and grow. That playbook just failed a live stress test. Harvey, the legal AI company valued at $15.6 billion, watched its gross margin fall from roughly 50 percent at the start of this year to negative 50 percent by June — not because demand c
Most people first think seriously about the cost of AI when an invoice arrives: how many tokens did we burn this month? Almost nobody stops to ask the deeper question — what does a token actually measure? The answer is hiding in economic history. Economists have a blunt method for identifying an industrial revolution: look at its unit of account. In the agricultural age, the bushel made it possibl
Every AI pricing page reduces to the same unit: dollars per million tokens. Most people read that line as a billing rule — a cost item to be optimized away. That reading is too narrow, and it quietly misprices the biggest economic shift of this decade. The argument of this piece is simple: the token is the first unit in history that lets us buy, meter, and bill the output of intelligence itself. T
"2030 is not going to be the end of the world. There is 0% chance that's going to be the end of the world." When Jensen Huang said this in a CBS News interview, he didn't sound like an executive talking about probabilities. He sounded like a supplier talking about his order book. Watch the AI safety debate that has flared up over the past few weeks, and one thing stands out: nearly everyone is arg
At this year's Dreamforce conference, Salesforce shipped two products that point in opposite directions. One is Claudeforce, built with Anthropic, which lets customers operate Salesforce directly from inside Claude. The other is Koa, a reasoning model built with NVIDIA on open-weight foundations — and, by Salesforce's own framing, designed to replace closed models like Claude and GPT on a meaningf
One Model, Two Scores 36 Points Apart On September 3, ARC Prize published a new ARC-AGI-3 result that should have made more headlines. The same model — GPT-6 Astra, at the same reasoning effort — scored 62.7% in a standard harness, then 98.6% in a Provider Adapter harness. Read that again: this is not a comparison between two models. It is the same brain taking the same exam twice, with a 35.9-poi
One Model, Two Harnesses, a 36-Point Gap On September 3, ARC Prize published a strange set of results for ARC-AGI-3, one of the hardest reasoning benchmarks available. The same model — GPT-6 Astra, at the same reasoning effort — was run through two different harnesses. In the standard harness it scored 62.7%. In the Provider Adapter harness, it scored 98.6%. The cost curve is even more counterintu
# Jev and the Judgment Economy: When AI Stops Talking The hottest model in developer circles right now cannot chat, cannot write code, and will never produce a paragraph. It is called Jev, and all it does is decide. The original post pulled over 37 million views on X, with Hacker News in full debate mode. Jev takes a set of options, returns the answer with a probability attached — up to 193.6x fas
The blind spot nobody priced in On September 18, a developer going by ferstar noticed something odd about his disk usage. The culprit was a single encrypted file, roughly 313 MB, generated by ZCode, a coding agent from Chinese AI company Zhipu. Inside the package: about 42,000 files, 86.6% of them the complete revision history of his projects. The decryption key was not on his machine. It sat on Z