4 ms·

On the scale of a company, augmenting is replacing. If a worker plus AI can do the work of two workers without AI (but cheaper), you go for that; and it doesn'

• skinfaxi 6mo ago
  The point is if a worker plus AI can do the work of two workers without AI, then why not keep both workers and have them both use AI to have the equivalent of four non-assisted workers?
  • apothegm 6mo ago
    Because you don’t have enough work that really needs doing, at least in that particular area. You cut engineers because the bottleneck to increased revenue isn’t software features or bugs, it’s marketing/sales; human beings’ limited attention for which there is now more competition than ever; and customers’ available funds. ETA: this is sometimes (though not always) very different for a mature company than an early stage startup.
    • otikik 6mo ago
      That is a very convenient message for marketing and sales people. The fact that their whole job is crafting messages shouldn’t raise any eyebrows.
      • mattkrause 6mo ago
        Ha! I’d never thought about it like that but…yeah. I suspect another big part of it is that marketing and sales are relatively easy to measure and to scale. You can hire one, two, or three new salespeople and expect that revenue will change more or less proportionately. Fixing (or ignoring) a handful issues doesn’t scale so smoothly—-there are jumps where the product suddenly seems much better/worse.
      • eru 5mo ago
        I took 'engineer' vs 'marketing' and 'sales' as just examples in the grandfather message. The essence was about bottlenecks to revenue.
  • thfuran 6mo ago
    Because the entire structure of the business is designed for approximately the amount of work it currently does and likely has no particular immediate use for twice as much work in most departments.
    • sumeno 6mo ago
      In 20 years I have never been on a team that didn't have twice as much work as we had people to do it. Businesses are not magically efficient
      • hilariously 6mo ago
        Your experience of how the world works is usually because of what work you have done. You can't grow twice as many crops, sell twice as many groceries, drive twice as many busses, because of AI - fundamentally there's a consumption problem as well. Many businesses are not bottlenecked by processes that are computer based.
        • deleted 6mo ago
          [deleted]
        • cootsnuck 6mo ago
          But the firms in the headlines doing layoffs after layoffs aren't growing crops, selling groceries, or driving busses... They're knowledge work roles in companies selling intangible products and services. It's large corporations doing this much more than SMBs.
          • mattkrause 6mo ago
            And I’m sure those companies also have “backlogs" due to limited labor/labor costs. There are always shelves to face, vehicles with deferred maintenance, and so on. Obviously, there are limits: I’m not sure what my local grocery store or bus line would do with 100 new workers, but I have no doubt they could put a few people to work right away.
  • asdfologist 6mo ago
    Diminishing returns on additional labor.
    • deleted 6mo ago
      [deleted]
  • yababa_y 6mo ago
    There's only so much to do and coordination costs (already burdensome) become overwhelming.
  • csoups14 6mo ago
    I think you're viewing this from the perspective of someone who has a functioning brain and plentiful concepts and ideas that aren't being built because you're labour-constrained. Companies like Meta simply don't have productive uses for all of that human + AI labour. Meta spends tens of billions a year paying people to throw shit at the wall and see what sticks. If the idea well you're going to is running dry, AI with a smaller number of humans can slop out the stuff you do want to build more efficiently is their implicit argument, especially when you don't care about quality (as is the case with Meta). Layoffs are also being used to tell a story around efficiency to investors while companies wait for the billions they're plowing in AI actually show profit.
  • avereveard 6mo ago
    The demand doesn't necessarily double.
  • LogicFailsMe 6mo ago
    Except that comforting C-suite narrative does not reflect reality. 2026 agents both increase productivity by knocking clearly specified but error-prone and tedious tasks out of the park whilst simultaneously vexing and annoying their users with hallucinations and downright lies on tasks with intrinsic ambiguity. This is made worse by the token providers with their constant tweaks to their deployments to cut costs w/o losing accuracy which flat doesn't work out well for the end user.
  • bregma 6mo ago
    If one woman can produce a baby on 9 months, why can't you get 9 women pregnant and produce a baby in one month?
    • eru 5mo ago
      That works for throughput just fine. And if you have a bit of an inventory, you can hide the latency. That's also how CPUs are made and sold: it takes a few months to make a physical CPU, but you can just go into a shop and buy one whenever you feel like it.
• trimethylpurine 6mo ago
  That's not always the case. Augmenting certainly can mean that, but it can also mean doing something that people couldn't do before. For example, looking through meta data in a SQL environment that you didn't know existed to troubleshoot an issue. And a million other things. The odds of any employee not knowing everything are very good, even when humanity as a whole had already discovered that thing.
• nutjob2 6mo ago
  When the AI models hallucinate up a catastrophe, managers will reevaluate that calculus. Humans are accountable and act accordingly, models are not.
  • disgruntledphd2 6mo ago
    Yeah, it's probably be something like: 2025: agents are the future! 2026: we don't need any new employees _stuff gets real_ 2027: wow, employees are actually pretty good value.*
  • eru 5mo ago
    Not all humans act like they are accountable.
• hmmokidk 6mo ago
  I think there are risks: - AI pricing is variable, probably the cheapest it will ever be right now - AI produces a lot more shit for humans to review, and you will always need humans. If you don’t focus on keeping things simple you will probably play yourself unless you’re good at separating out blast radiuses. - I see a lot of super low quality work that doesn’t solve the problem but it’s like look that guy solved the problem in one day! Promote him! Everyone is happy except for the end users who for whatever reason are being totally ignored (whose problem it fails to appropriately solve) and I saw this in accounting software so…hello eventual lawsuits?
  • valvar 6mo ago
    Why wouldn’t inference just keep getting better and cheaper as hardware and algorithms improve?
    • MobiusHorizons 6mo ago
      The typical playbook for a VC funded startup is to race to a monopoly where the company can have higher margins. Prices continuing to go down for the consumer over time would require competition to stay high in the long term, and even then it’s not clear if even current prices are profitable.
      • zozbot234 6mo ago
        The current level of AI has plenty of inherent competition from local models. In the long term, most of the profit will probably be from very smart models that run at something closer to datacenter scale over long inference loops - where local inference can't do much and even third-party inference/small neoclouds will be severely challenged. That is a very natural "moat" and has natural cross-efficiencies with AI model training, which requires a similar scale.
  • CuriouslyC 6mo ago
    AI is definitely not the cheapest it will ever be right now. The frontier is getting more expensive, but the same capability will get cheaper over time.