Anthropic's long "fabled" Mythos 5 got genomics tools and about a week, mostly on its own. It assembled single-cell data for millions of cells across 138 species, trained its own machine learning model that beat one published in Science. It was one hundred times smaller.
Read that again. Largely autonomous. One week. Smaller and better than the published state of the art.
For most of the history of science, the binding constraint was us. Human hands, human hours, the years to learn a technique, the months to run the experiment. We built our entire research economy around that scarcity: the grant cycle, the slow accretion of papers. I spent years inside that machine, moving hundreds of millions of euros across borders, and I can tell you it assumes labour is the bottleneck.
That assumption is quietly dissolving.
The same family, Anthropic says, compressed a two-month code migration into one day for Stripe, produced drug-design candidates for nine of fourteen protein targets with no human guiding the work, and generated molecular biology hypotheses its own scientists preferred to the human alternatives four times out of five. One was later confirmed, independently, by a lab working on the same protein.
I want to be careful, because wonder makes a poor analyst. These are Anthropic's own results, and the world is more stubborn than a launch post. But even discounted heavily, something real has moved.
You can read that movement most clearly in what they chose not to release. The strongest version, Mythos 5 with its safeguards lifted, goes only to vetted cyber defenders and, for now, the US government. The model the rest of us can use quietly reroutes questions on cybersecurity and biology to a weaker one. The reason is plain: the same intelligence that designs a gene therapy can design a virus.
We have reached the point where access to capability is itself a question of governance. Not a hypothetical for a future ethics panel. A product decision, shipped today.
So here is what I keep turning over. If labour is no longer the scarce thing in science, then judgment becomes everything. Knowing which question matters, which experiment is worth running, which door to leave closed. The bottleneck shifts from the bench to the mind, and from the individual to the institution. Can our funding systems and our creaking research culture metabolise tools that no longer wait for us?
I build in this space, so I am not a neutral observer. But I did not get into it because I love software. I got into it because somewhere tonight a researcher is staring at a deadline with an idea that might change something, and for the first time in human history they may have a collaborator that never sleeps. What we do with that is still ours to decide. It always was. That is the frabjous, terrifying gift of this moment: the tools are finally ready. The question is whether we are.
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