Big bet engineering
When building is cheap, you can try things that used to be too expensive to attempt.
In a world where building a feature costs $200K and takes 6 months, you plan carefully. You debate extensively. You try to get it right the first time because iteration is expensive.
In a world where an AI-augmented team can prototype in days and build production quality in weeks, the calculus changes. The cost of trying something that doesn't work is low. The cost of NOT trying is high — because your competitors who are experimenting will find the winners before you do.
Big bet engineering means
More experiments, less planning. Try three lightweight versions, measure which one resonates, double down on the winner. The cost of three prototypes is less than the cost of six months of planning for one.
Smaller bets, more frequently. Instead of one massive release, continuous small releases, each a data point. Each release teaches you something. The learning compounds.
Technical exploration as standard practice. Spikes take hours with AI assistance. Make exploration continuous, not a special event that requires justification.
The organizational implication
You need leaders comfortable with ambiguity and rapid iteration. Leaders who need a complete plan before authorizing work will bottleneck this model. Leaders who can say "try it for two days and show me what you learn" will unlock it.
This is a cultural shift, not just a technical one. The organization needs to be comfortable with:
- Starting work before the plan is complete
- Killing experiments that aren't working without blame
- Celebrating learning, not just shipping
- Multiple competing approaches running simultaneously