Most AI programs put too much weight on one large initiative. This talk makes the case for a portfolio of fast experiments, direct contact with users, and prototypes that are allowed to die.
What people take home
- Why one large AI bet is usually the wrong unit of work.
- How gravel-road prototypes create evidence before commitment.
- The organizational conditions that let useful experiments survive.