AI tools can make a working software prototype quicker to produce, but Bookipi chief executive Tim Lee argues that speed is no longer the scarce resource: judgement, verification and integration are. His prescription is to iterate quickly in private, then release only after stronger quality checks.
Watch Desk analysis
What happened
In a Forbes Councils essay, Lee argues that AI-assisted coding shifts agile software development’s emphasis away from ever-shorter release cycles and towards the quality and value of what ships. He calls his approach “move fast, launch slow”.
In practice, that means testing more versions and scenarios before release, setting documented checks for performance, security, usability and consistency, and judging work by outcomes such as reliability and task completion rather than lines of code or tickets closed. Lee also argues that product managers, designers and engineers can collaborate earlier around working prototypes, with expert review still in place.
Why it matters
AI can make producing a first version easier without making that version useful, safe or pleasant to use. Teams that measure output by code volume or release frequency risk mistaking activity for progress. Lee’s proposal puts the hard question where customers would put it anyway: did the product work better for them?
His essay cites a 2025 METR trial in which experienced developers using AI tools took longer on their tasks, despite believing the tools had made them faster. That is Lee’s account of the study, not a result established by the essay itself. The broader point is practical: time saved on drafting can reappear as time spent checking and integrating.
Our read
“Move fast, launch slow” is a useful corrective, not a command to bring back heavyweight sign-off for every button. AI-assisted teams still need iteration; they also need to show that their faster drafts survive contact with testing and users. If your team uses AI to build software, define the checks and customer outcomes before the next sprint turns into a race to produce more.
What to watch
- Whether teams measure reliability, task completion and customer outcomes alongside delivery speed.
- Whether AI-generated code receives documented checks for security, performance and usability.
- Whether faster prototyping reduces hand-offs without weakening expert review.
Discussion spark: When AI makes prototypes cheap, should software teams release less often after stronger checks, or keep shipping quickly and let users expose the flaws?
Sources and evidence
- Redefining Agile In The Age Of AI – Forbes (6 October 2026, 10:00 UTC)
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