Why I think AI take-off is relatively slow

(marginalrevolution.com)

22 points | by amrrs19 小时前

4 comments

  • baazaa14 小时前
    When people think AI is going to lead to rapid automation I genuinely don't understand what mental model of the economy they must have.

    I'm trying to pivot out of data which IMO is a scam industry and thought I'd consider automating white-collar work. After all, there's a huge amount of excel-monkey work that can be trivially automated with scripts and I've done stuff like that before. But then I realise there's not even a job title for this sort of work, nor are there any firms in my country doing stuff like this. There's simply no demand whatsoever for process automation (I'm expressly not talking about automation engineers in manufacturing etc.)

    It's not hard to see why. No-one's going to automate themselves out of a job, nor are managers going to automate all the people they manage out of a job because then they're also redundant. Often labour-saving innovations are brought-in by upstarts but business dynamism is low so there's not a lot of that happening. I can almost guarantee that a bank circa 2050 will look a lot like a bank now, short of some runaway superintelligence completely reconfiguring society.

    • Centigonal10 小时前
      > I'm trying to pivot out of data which IMO is a scam industry

      Would you be open to writing more about this? I work in data, and I find it valuable to hear criticisms of the fundamentals of the field from people with enough context to actually articulate meaningful criticism.

      I saw this video a few months ago, and I thought it was pretty sloppy, but it made a few pretty good points: https://www.youtube.com/watch?v=pOuBCk8XMC8

      • baazaa6 小时前
        My experience precisely mirrors that described in various posts here: https://ludic.mataroa.blog/blog/get-me-out-of-data-hell/

        I live in Melbourne like the author and I certainly don't speak for the entire industry. But here's it's mostly government and banks and other enterprise where data is just a cost-centre so middle-management could say they were big on data science, which has since transformed into being big on AI.

        There's no end-use for most of the work, there's no stakeholders, often the product being delivered will be a 'platform' of some sort and everyone outside the data area is afraid of sounding stupid by asking what that means (it doesn't mean anything). The result is entire services can go down for months at a time and no-one notices (and then when someone does realise management covers up the fact no-one noticed because it's embarrassing).

    • falcor8414 小时前
      I would assume that we'll see much faster change via appearance of new smaller and nimbler competitors, rather than adoption by current companies. I expect a lot of rapid trial and error, similar to what we saw in the 90s with the advent of web startups. And who knows which (if any of them) will become the next Amazon and Google.

      As for banks in particular, seeing the success of the likes of Revolut, I think we'll see more disruption in this space too.

    • Dlooooloo13 小时前
      [dead]
  • paulryanrogers15 小时前
    > Let’s say AI increases the rate of good pharma ideas by 10x. Well, until the FDA gets its act together, the relevant constraint is the rate of drug approval, not the rate of drug discovery.

    IIUC, the FDA is slow on purpose. Because proving drugs aren't worse than the problems they treat takes several phases of large trials. Even if AI could cure all cancers tomorrow, I hope we still maintain a proven scientific method to QA that before releasing it widely.

  • egberts19 小时前
    ... and disoriented.

    The machine learning does not reason thereby making today's "AI" do cognizant dissonances (or hallucinations).

    In short, GIGO: garbage in, garbage out.

  • fulafel10 小时前
    TLDR: Amdahl's law.