Milana AI

End of Prioritization I’ve been thinking about the tension between exploitation and exploration lately - mathematically best described by the multi arm bandit problem. You can’t do everything because trying something has a cost. Just as so many other laws of physics are changing with AI, I think this one is about to change too. For any intelligence+execution bound work you can imagine the cost of exploitation (trying something) is rapidly approaching zero (modulo inference). In that world, the value of exploration goes up dramatically — you can simply try more things. This is a broad, important concept that applies to thousands of trade-offs in companies and society that we previously took as immutable. It also tells you something about where value accrues in the future. People who can identify compelling new paths to explore will have far more value to add than people who are experts at specialized exploitation of known paths. I have a feeling this might even have implications for the multi-armed bandit problem in the formal mathematical sense, but that’s a bit beyond my expertise. Think about a growth team that A/B tests two landing pages a week because each variant costs real design and eng time — now they test fifty. Or a product team that agonizes over which feature to build next because they can only ship one — now they build all of them and let users decide. It’s like Monte Carlo simulation for everything, except you’re not simulating — you’re actually doing it. Every path gets run. Prioritization as we know it is obsolete. You don’t pick what to do — you do all of it. The only art left is knowing which bandits are worth arming.