I told my boss this:
- Right now the AI race has a lot of similarities to the dotcom bubble. The subject is packed with risky loans based on huge debts. Those huge debts are expecting to be paid as AI becomes profitable, but AI companies are largely loosing money.
- All those loans and infrastructure create the burden of sunk costs leading to a desperate need to succeed.
- The people feeling that desperation are the same people who own the largest marketing, news, and social media networks in the world.
- As a result, there’s a lot of hype around AI. A lot of “kool-aid,” and everyone wants you to drink it. If you drink the kool-aid, that means you’re also bought into the problem. You also need it to succeed, thus making their problem into your problem.
I explained to him that mature, professional use of AI is going to wind up following a similar path to data engineering. It’ll start with bullshit standards, “prompt engineers” and the like, but eventually SE disciplines are going to define who makes best use of AI. You’re going to have niche use cases for daemon AIs, local LLMs, and remote models. You’ll have stronger frameworks around session management, context management, agent permissions, …
It’s not going to be like this forever, “dump all your shit into our web upload and let the AI figure everything out in one go.” It’s going to become more fragmented, bounded, dare I say deterministic… orchestratable.
Then I told my boss, it would be better if he could frame his excitement around these future use cases… so we can skip the kool-aid stage and get right into the good stuff.
He agreed, until about a week passed. Then it was AI hype again.