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Yes. A few miscellaneous thoughts.
(1) First, the new bottleneck on AI is prompting and verifying. Since AI does tasks middle-to-middle, not end-to-end. So business spend migrates towards the edges of prompting and verifying, even as AI speeds up the middle.
(2) Second, AI really means amplified intelligence, not agentic intelligence. The smarter you are, the smarter the AI is. Better writers are better prompters.
(3) Third, AI doesn’t really take your job, it allows you to do any job. Because it allows you to be a passable UX designer, a decent SFX animator, and so on. But it doesn’t necessarily mean you can do that job *well*, as a specialist is often needed for polish.
(4) Fourth, AI doesn’t take your job, it takes the job of the previous AI. For example: Midjourney took Stable Diffusion’s job. GPT-4 took GPT-3’s job. Once you have a slot in your workflow for AI image gen, AI code gen, or the like, you just allocate that spend to the latest model.
(5) Fifth, killer AI is already here — and it’s called drones. And every country is pursuing it. So it’s not the image generators and chatbots one needs to worry about.
(6) Sixth, decentralized AI is already here and it’s essentially polytheistic AI (many strong models) rather than monotheistic AI (a single all-powerful model). That means balance of power between human/AI fusions rather than a single dominant AI that will turn us all into paperclips/pillars of salt.
(7) Seventh, AI is probabilistic while crypto is deterministic. So crypto can constrain AI. For example, AI can break captchas, but it can’t fake onchain balances. And it can solve some equations, but not cryptographic equations. Thus, crypto is roughly what AI can’t do.
(8) Eighth, I think AI on the whole right now is having a decentralizing effect, because there is so much more a small team can do with the right tooling, and because so many high quality open source models are coming.
All this could change if self-prompting, self-verifying, and self-replicating AI in the physical world really gets going. But there are open research questions between here and there.

28.6.2025
The view that imagines AI wiping out jobs or causing some overnight shock to the system doesn’t contemplate that companies are a made up of a series of bottlenecks. When AI accelerates work in one area, you run into a bottleneck somewhere else.
As any individual workflow gets more efficient, the ultimate productivity gain is still constrained by some other part of the system. And usually it’s the case that that part of the system will not have inherently seen the same impact of AI efficiency, which means humans are still doing the work.
Take almost any process in an enterprise and you can see how this plays out. If AI Agents generate leads for the sales team, the bottleneck will be humans to have conversations with those customers. And if the leads are good, that will mean more sales hiring. If AI Agents generate more code, you will eventually be bottlenecked by the engineers that can review and incorporate that code into production.
You can quickly see how this scales to any process in an organization. Economists and others tend to totally miss how work actually happens in a company; it’s not a series of wholly independent tasks, but instead highly interdependent tasks that all link to each other across a system.
This is of course the natural rate limiter of AI efficiency gains, but also the reason why humans will still be doing so many jobs in the future.
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