At Meta, Microsoft, Salesforce and other large companies, devs are purposefully burning tokens (and money!) to inflate their AI usage and hit AI usage metrics which they treat as targets.
  • limdaepl@feddit.orgEnglish
    31·
    1 month ago

    Most people have learned by now, that “lines of code“ is a terrible metric for evaluating productivity. Why are we doing the exact same thing with AI tokens now?

    • halcyoncmdr@piefed.socialEnglish
      23·
      1 month ago

      Because middle manglement has a constant compulsive need to justify their existence by finding new ways and metrics to “manage”.

    • mabeledo@lemmy.worldEnglish
      3·
      30 days ago

      You would be surprised to know how many managers still rely on this metric, even if it’s not part of their KPIs.

    • AA5B@lemmy.worldEnglish
      1·
      29 days ago

      Before ai, my company’s misguided kpi was the number of merge requests

      At least that one worked well for me since I’m generally making many small changes to an existing code base

    • Djehngo@lemmy.worldEnglish
      1·
      30 days ago

      Because companies have been talking up how their adoption of AI is going to make them faster and more able to capitalise on opportunities in order to prop up their valuations for a while now and it seems to work as far as share price goes.

      Being able back up this talk with metrics showing that their employees are all in on AI reinforces this, since the share price is the metric the business optimises for over product development employee reviews will index on this over cost effectiveness, and at most big tech companies engineers are very much making every decision with an eye to performance review optimisation (i.e. how it will affect their next review rather than the product they are building)

      There is also some lesser incentives in that meta employees care directly about the meta share price since a lot of their compensation is in the form of RSUs.

      I’m not condonig this as a desirable state of affairs, just explaining the incentive curve that the actors are following.

  • CoconutLove@lemmy.todayEnglish
    12·
    30 days ago

    I’ve heard meta engineers get judged on metrics like how many comments they made on PRs. So, engineers set up their agents to comment on every new PR. Now every PR in the company has thousands of repetitive comments on it.

    Also, to meet this tokenmaxxing quota, engineers are setting up their agents to run all night long while they sleep.

  • AA5B@lemmy.worldEnglish
    81·
    1 month ago

    Yeah I can see the temptation. Ai usage is part of my kpis and is the one place I got dinged on my review. I thought i was insulated as a “partly coding” position, but they put me against full time coders so I look even worse

    I guess they’re trying to force change, make us figure out how to make it work. The skeptic in me thinks it rewards people who have time to screw around, but when I set aside a week to see what I can do, I did increase my ai use.

    I did find some useful tasks that also increase my “agentic” score! But my “quality” score (ai generated lines I accept) is stick at zero. We currently pay a flat fee but that’s changing next months to pay per token

      • AA5B@lemmy.worldEnglish
        2·
        1 month ago

        Hopefully not. The kpi just states usage.

        Looking at the report provided by the ai vendor, there are several measures that might apply. There is also a “quality” measure that seems to be the number of lines accepted. I don’t have any indication that they card about that, but it is there

  • CheeseNoodle@lemmy.worldEnglish
    5·
    1 month ago

    AI going from ‘It’ll make everything so much more efficient!’ to deliberately doing everything as inneficiently as possible is just… idk beyond even satire at this point.

  • uenticx@lemmy.worldEnglish
    4·
    29 days ago

    If you use “maxxing” as a suffix, I instantly hate you. You’re already vibe-coding, which probably means you couldn’t pour piss out of your boot if directions were slapped on the heel.

  • uuj8za@piefed.socialEnglish
    4·
    1 month ago

    I need to start doing this… I think I recently got flagged for not using AI enough…

    • percent@infosec.pubEnglish
      2·
      1 month ago

      Do you not use it enough because yet get bad results? I discovered that, no matter how smart the LLM might be, its first attempt is never its best work. Tell it to review its work (or its plan, if using planning mode). If it makes any changes, tell it to review its work again. Repeat until there are no more changes.

      (You don’t actually have to do this repetition manually; just tell the AI to do it in a loop. I recommend making it into a SKILL.md so you don’t have to explain the loop every time.)

      With these loops, I get better results AND burn lots of tokens. (Yes, it feels strange that excessive token consumption is actually considered a good thing)