Speculative execution: To prevent serial bottlenecks (see next section), researchers may use two forms of speculative execution: starting lots of long experiments they’re not sure the project needs, and guessing results of experiments and feedback (see Tom Cunningham’s “Bottlenecks can be loosened with agents” section)
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Once you can use agents to automate large parts of work it feels like you’ll now be bottlenecked on the non-automated parts. But in fact the non-automated parts can often be predicted, and this loosens the bottleneck.
Imagine every report has the following:
Agent’s best-guess about what comments you’d get from Beth, Hjalmar, Ajeya.
Agent’s best-guess about survey results, if you launched the survey.
Agent’s best-guess about benchmark results.
Agent’s best-guess about how this will be received on Twitter.
In addition you could click through to see why the agent guessed each. I feel these would meaningfully loosen bottlenecks, I could iterate until the information I received from the world (human feedback, data, surveys) was maximally informative, and only then send out for review.
I pattern matched your option 2 to speculative execution, with agents you can branch off whenever the agent reach an uncertainty on the research goal.
https://metr.org/notes/2026-03-19-org-uplift-game/