2 min lesson
Artifacts: the feature that made the shift believable
Explain your answer to "Why did artifacts (proof-of-work recordings), rather than a smarter model, drive adoption of async cloud agents?" Add one concrete detail from the lesson.
Step 1 of 2
Artifacts: the feature that made the shift believableproof of work
Proof of work is what made async adoption believable. Artifacts gave a cloud agent the ability to navigate its own machine and come back with evidence: for a front-end change it spins up a browser, tests the change, records a video and returns it with screenshots and test results. The human reviews an AI's work the way they'd review a junior engineer's, by the result rather than by reading every diff.
Cloud-agent adoption rose after artifacts shipped. Cursor staff quoted a share-of-PRs figure for it in a workshop, but this module cannot trace that number to a published source, so check it before you repeat it. What you can narrate is the mechanism. The constraint was reviewer trust in unwatched work, the lever was proof-of-work artifacts, and the signal to watch is the share of merged work that came from agents. Output became outcome, and adoption followed.
When the room asks for a product opinion, resist the matrix and tell this as a decision: "The third era moves attention from output to outcome, so review becomes the bottleneck. The highest-value bets build reviewer trust. Artifacts did that: proof-of-work recordings let humans review the result, and cloud-agent adoption rose after they shipped." If you quote a number, say it came from a Cursor workshop and that you would verify it, then treat it as the assumption your own bet starts from. Next, name your own bet against the same bottleneck and pre-commit its kill criterion.