We’ve been running an AI-first dev loop in production for ~2 years (disclaimer: I help build Ze1 and Sandscape, they are both Ai driven products). A few things we’ve learned:
Instead of cranking boilerplate, they spend Day 1 reviewing AI diffs. We pair them with a senior for a “why did the agent choose this?” teardown.
What matters is mean-time-to-rollback. If the agent + test harnesses catch breakage faster than a human pair can, 2 × better is already good economics. Reliability engineering beats perfection thresholds.
Syntax, style and most unit-level bugs are now linted or auto-fixed. Humans zoom out to architecture, data contracts, threat models, perf budgets, and “does this change make sense for the product?”. So even juniors are now a lot more involved on the sujective elements of development
So I think that as the easy bugs vanish, new failure modes show up: latency cliffs, subtle privacy leaks, energy use, fairness. The goal posts moves
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We’ve been running an AI-first dev loop in production for ~2 years (disclaimer: I help build Ze1 and Sandscape, they are both Ai driven products). A few things we’ve learned:
Instead of cranking boilerplate, they spend Day 1 reviewing AI diffs. We pair them with a senior for a “why did the agent choose this?” teardown.
What matters is mean-time-to-rollback. If the agent + test harnesses catch breakage faster than a human pair can, 2 × better is already good economics. Reliability engineering beats perfection thresholds.
Syntax, style and most unit-level bugs are now linted or auto-fixed. Humans zoom out to architecture, data contracts, threat models, perf budgets, and “does this change make sense for the product?”. So even juniors are now a lot more involved on the sujective elements of development
So I think that as the easy bugs vanish, new failure modes show up: latency cliffs, subtle privacy leaks, energy use, fairness. The goal posts moves