Predicting how fast artificial intelligence will improve usually falls to a familiar group: professors at leading universities, heavily cited AI researchers and seasoned economists. They are the people policymakers and businesses turn to when they want a sense of what comes next.
According to a detailed interim report from the Forecasting Research Institute (FRI), that group has often been too cautious. On benchmarks and on some adoption indicators, progress over the past few years has run well ahead of what experts expected.
Who was asked: a panel built from the top of the field
The FRI has been gathering forecasts on AI progress since mid-2022 through several studies and projects. The first round of its Longitudinal Expert AI Panel, or LEAP, included 339 experts: 76 computer scientists, 76 industry specialists, 68 economists and 119 AI policy professionals. Among the computer scientists were 30 professors at top-20 institutions and 10 of the 200 most-cited AI authors.
Much of the attention in AI drug discovery goes to models that design molecules from scratch. Enveda starts somewhere else: with the chemistry that plants and microbes have already produced.
Investors have now backed that approach with more money. The biotech startup has raised a $311 million Series E at a $2 billion valuation. Catalio Capital Management led the round, and Iconiq was among the other participants. The new valuation is double the one Enveda reached 12 months ago.
Nature as the starting library
Conventional drug discovery often means building a compound in the lab and then testing whether it does anything useful. Enveda's premise is that many powerful medicines may already exist in plants and microbes, waiting to be found and put to use.
Robotics has long run into the same wall. A model can describe a kitchen, a warehouse shelf or a workbench in detail, but that does not mean it knows what a robot arm should do next. Describing a scene is one skill. Acting in it is another.
Black Forest Labs, often shortened to BFL, is going after the second skill with FLUX 3 Action, a new open model built for robots. The company describes it as a world-action model, and its pitch rests less on size than on doing more with fewer parameters and less time.
From watching to acting: what a world-action model does
Most AI announcements are about the model itself: a new release, a better benchmark score, a lower price. Anthropic's latest news is different. It is about what the model found in biological data, and about who did the physical lab work that followed.
Only a week after confirming that it runs a wet biology lab in the Bay Area, Anthropic said the lab had already produced what the company considers a significant result. It describes the find as a new enzyme "system" with "properties reminiscent of CRISPR."
What was found: an enzyme system hidden in viral DNA
CRISPR began as a natural immune system in bacteria, which use it to defend themselves against viruses. Researchers later turned it into a gene-editing technology, and it is now widely used in labs.
Progress in AI agents has mostly been measured in capability: better reasoning, more fluid conversation and access to more tools. That is good news for a company that wants to automate customer service or place an agent inside internal workflows. It is less comforting for the security team that has to work out what the agent will do when someone tries to trick it.
A recent special edition of the AI Weekly newsletter looked at that tension. It paired a sponsored perspective from testing company Spec27 with six pieces of research on agent security. The common thread is that a smarter model does not automatically make a safer agent. As agents become more flexible and more deeply connected to data and tools, their attack surface grows with them.
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