Thread

1. 2001zhaozhao · 1790979207 · focus · HN ↗

   This couldn't be more true. Companies will be driven by AI harnesses (as defined by this article) that automate decision-making and prioritization. In the medium term, may the company with the best harness win.

   In the longer term, the downstream impact is massive commoditization of software and invalidation of most existing moats. Data moats are gone if you can simulate the data with AI. Even platform effects can be sidestepped if AI replaces one side of the platform.

   In addition, while right now agile startups have the advantage, at some point the balance will start tilting towards whoever has the most tokens (OR perhaps durable moats will trump even near-infinite tokens; we will have to see). Startups have a limited time window to have whatever impact in the world they are hoping to have, or to build a moat that won't be disrupted by AI, but there are few of them left in the world.

   The upside is that when there is a lot of commoditization, then the consumer benefits.

   1. ForHackernews · 1790979680 · focus · HN ↗

      > Data moats are gone if you can simulate the data with AI.

      This is a hilarious premise if you work in a domain where it matters even a little bit whether the data is correct or not.

      1. epistasis · 1790980101 · focus · HN ↗

         I work in science, the data is becoming far bigger of a moat than it ever was before because of this.

         Every company can now apply the latest and greatest analysis. Data generation is where the cost is. It's where the time was spent, time that can never ever be retrieved at any cost.

         AI won't solve biology, make a pathogenic virus, etc, without tons and tons of data, of both types we know and types we have not yet figured out how to generate.

         Perhaps the area where AI has the most to help bio is in figuring out novel measurement technology. But it's not going to be able to reason or deep-net its way to figuring out systems for which we can't even measure the parts.

         1. 2001zhaozhao · 1790980373 · focus · HN ↗

            Yep private scientific knowledge is a massive new moat that you can pull if you simply invest in scientific discovery methods in general and throw enough resources and tokens at the problem. I think there are already startups specifically trying to do this. The main obstacle is whether you're actually able to pull significantly ahead of (AI-enabled) public science to make a difference, but I guess the math works out if you're sufficiently AGI-pilled.

            I agree this is a kind of data moat, but it's also arguably distinct enough to be its own thing.

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