Peter Maddison and Dave Sharrock look at the AI software delivery lifecycle through clock speed. Code generation is getting close to machine speed. Customer behavior, product discovery and the approvals needed before anything gets built still run at social speed. They come back to the theory of constraints: the whole system moves at the pace of its slowest part, and coding has rarely been that part. Along the way they talk about what it means to build a hundred variations of a feature, why token and validation costs add up fast, and why moats shrink when competitors can copy a product in weeks.
This week’s takeaways:
Speeding up code generation does little for delivery when the real constraint sits in discovery, approvals and waiting to see how customers respond.
Building many versions of an idea is now cheap, but every thrown-away version costs tokens, and validating what customers actually want is still slow.
Social speed is part of your moat, so be careful about replacing human interactions with machine ones in places where customers value the human part.
Chapters:
0:00 Welcome And Catching Up
0:40 AI And The Future SDLC
1:10 Machine Speed Versus Social Speed
5:45 Approvals And People Systems Stall Flow
10:45 Experiments At Scale And Token Economics
13:55 Moats, Stickiness, And Faster Competitors
17:00 Takeaways And Theory Of Constraints
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