4 ms·

The incentive structure for managers (and literally everyone up the chain) is to maximize headcount. More people you managed, the more power you have within the

by itake 1y ago

The incentive structure for managers (and literally everyone up the chain) is to maximize headcount. More people you managed, the more power you have within the organization. No one wants to say on their resume, "I manage 5 people, but trust me, with AI, its like managing 20 people!" Managers also don't pay people's salaries. The Tech Tools budget is a different budget than People salaries. Also keep in mind, for any problem space, there is an unlimited number of things to do. 20 people working 20% more efficiently wont reach infinity any faster than 10 people.

skeeter2020 1y ago
Maybe 40 years ago or in some cultures, but I've always focused on $ / person. If we have a smaller team that can generate $2M in ARR per developer that's far superior to $200K. The problem is once you have 20 people doing the job nobody thinks it's possible to do it with 10. You're right that "there is an unlimited number of things to do" and there's really obvious things that must be done and must not be done, but the majority IME are should or could be done, and in every org I've experienced it's a challenge to constrain the # of parallel initiatives, which is the necessary first step to reducing active headcount.
- paulsutter 1y ago
  Exactly, it’s much easier with a new organization. In my previous company, we would speculate about where to use AI and we were never sure. In the new company we use AI for everything and produce more with substantially fewer people
  - ipython 1y ago
    Do you have any examples of the types of tasks you’ve found the most success with using ai ?
    - itake 1y ago
      we use AI (LLMs) to improve the recall and precision of our classification models for content moderation. Our human moderators can only process so many items per day, at a high cost. AI (LLMS) act as a pre-filter, auto-approving or auto-rejecting before they get to the humans for review.
  - ToucanLoucan 1y ago
    Does anyone want what you're producing though? I don't mean to be dismissive and crappy right out of the gate with that question, I'm merely drawing on my experience with AI and the broader trends I see emerging: AI is leveraged when you need knowledge products for the sake of having products, not when they're particularly for something. I've noticed a very strange phenomenon where middle managers will generate long, meandering report emails to communicate what is, frankly, not complicated or terribly deep information, and send them to other people, who then paradoxically use AI to summarize those emails, likely into something quite similar to what was prompted to be generated in the first place. I've also noticed it being leveraged heavily in spaces where a product existing, like a news release, article, social media post, etc. is in itself the point, and the quality of it is a highly secondary notion. This has led me to conclude that AI is best leveraged in such cases where nobody including the creator of a given thing really... cares much what the thing is, if it's good, or does it's job well? It exists because it should exist and it's existence performs the function far more than anything to do with the actual thing that exists. And in my organization at least, our "cultural opinion" on such things would be... well if nobody cares what it says, and nobody is actually reading it... then why the hell are we generating it and then summarizing it? Just skip the whole damn thing and send a short, list email of what needs communicating and be done.
    - silverquiet 1y ago
      The anthropologist David Graeber wrote a book called "Bullshit Jobs" that explored the subject. It shouldn't be surprising that a prodigious bullshit generator could find a use in those roles.
    - delusional 1y ago
      > Does anyone want what you're producing though? He's either lying or hard-selling. The company in his profile "neofactory.ai" says they "will build our first production line in Dallas, TX in Q3." well, we just entered Q4, so not that. Despite that it has no mentions online and the website is just a "contact us" form.
    - itake 1y ago
      I've spent hours each week on Sora 2 and ChatGPT. I clearly have been enjoying what AI has offered. ChatGPT has largely replaced my google searching.
2OEH8eoCRo0 1y ago
> for any problem space, there is an unlimited number of things to do. That's what I've wondered. We don't just run out of work, products, features, etc. We can just build more but so can the competition right?
deaux 1y ago
> The incentive structure for managers (and literally everyone up the chain) is to maximize headcount. More people you managed, the more power you have within the organization Ding ding ding! AI can absolutely reduce headcount. It already could 2 years ago, when we were just getting started. At the time I worked at a company that did just that, succesfully automating away thousands of jobs which couldn't pre-LLMs. The reason it ""worked"" was because it was outsourced headcount, so there was very limited political incentive to keep them if they were replaceable. The bigger and older the company, the more ossified the structures are that have a want to keep headcount equal, and ideally grow it. This is by far the biggest cause of all these "failed" AI projects. It's super obvious when you start noticing that for jobs that were being outsourced, or done by temp/contracted workers, those are much more rapidly being replaced. As well as the fact that tech startups are hiring much less than before. Not talking about YC-and-co startups here, those are global exceptions indeed affected a lot by ZIRP and what not. I'm talking about the 99.9% of startups that don't get big VC funds. A lot of the narrative on HN that it isn't happening and AI is all a scam is IMO out of reasonable fear. If you're still not convinced, think about it this way. Before LLMs were a thing, if I asked you what the success rate of software projects at non-tech companies was, what would you have said? 90% failure rate? To my knowledge, the numbers are indeed close. And what's the biggest reason? Almost never "this problem cannot be technically solved". You'd probably name other, more common reasons. Why would this be any different for AI? Why would those same reasons suddenly disappear? They don't. All the politics, all the enterprise salesmen, the lack of understanding of actual needs, the personal KPIs to hit - they're all still there. And the politics are even worse than with trad. enterprise software now that the premise of headcount reduction looms larger than ever.
- ckcheng 1y ago
  Yes, and it’s instructive to see how automation has reduced head count in oil and gas majors. The reduction comes when there’s a shock financially or economically and layoffs are needed for survival. Until then, head count will be stable. Trucks in the oil sands can already operate autonomously in controlled mining sites, but wide adoption is happening slowly, waiting for driver turnover and equipment replacement cycles.
- 1y ago
deleted 1y ago
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by itake 1y ago

skeeter2020 1y ago

Maybe 40 years ago or in some cultures, but I've always focused on $ / person. If we have a smaller team that can generate $2M in ARR per developer that's far superior to $200K. The problem is once you have 20 people doing the job nobody thinks it's possible to do it with 10. You're right that "there is an unlimited number of things to do" and there's really obvious things that must be done and must not be done, but the majority IME are should or could be done, and in every org I've experienced it's a challenge to constrain the # of parallel initiatives, which is the necessary first step to reducing active headcount.

paulsutter 1y ago
Exactly, it’s much easier with a new organization. In my previous company, we would speculate about where to use AI and we were never sure. In the new company we use AI for everything and produce more with substantially fewer people
- ipython 1y ago
  Do you have any examples of the types of tasks you’ve found the most success with using ai ?
  - itake 1y ago
    we use AI (LLMs) to improve the recall and precision of our classification models for content moderation. Our human moderators can only process so many items per day, at a high cost. AI (LLMS) act as a pre-filter, auto-approving or auto-rejecting before they get to the humans for review.
- ToucanLoucan 1y ago
  Does anyone want what you're producing though? I don't mean to be dismissive and crappy right out of the gate with that question, I'm merely drawing on my experience with AI and the broader trends I see emerging: AI is leveraged when you need knowledge products for the sake of having products, not when they're particularly for something. I've noticed a very strange phenomenon where middle managers will generate long, meandering report emails to communicate what is, frankly, not complicated or terribly deep information, and send them to other people, who then paradoxically use AI to summarize those emails, likely into something quite similar to what was prompted to be generated in the first place. I've also noticed it being leveraged heavily in spaces where a product existing, like a news release, article, social media post, etc. is in itself the point, and the quality of it is a highly secondary notion. This has led me to conclude that AI is best leveraged in such cases where nobody including the creator of a given thing really... cares much what the thing is, if it's good, or does it's job well? It exists because it should exist and it's existence performs the function far more than anything to do with the actual thing that exists. And in my organization at least, our "cultural opinion" on such things would be... well if nobody cares what it says, and nobody is actually reading it... then why the hell are we generating it and then summarizing it? Just skip the whole damn thing and send a short, list email of what needs communicating and be done.
  - silverquiet 1y ago
    The anthropologist David Graeber wrote a book called "Bullshit Jobs" that explored the subject. It shouldn't be surprising that a prodigious bullshit generator could find a use in those roles.
  - delusional 1y ago
    > Does anyone want what you're producing though? He's either lying or hard-selling. The company in his profile "neofactory.ai" says they "will build our first production line in Dallas, TX in Q3." well, we just entered Q4, so not that. Despite that it has no mentions online and the website is just a "contact us" form.
  - itake 1y ago
    I've spent hours each week on Sora 2 and ChatGPT. I clearly have been enjoying what AI has offered. ChatGPT has largely replaced my google searching.

2OEH8eoCRo0 1y ago

> for any problem space, there is an unlimited number of things to do. That's what I've wondered. We don't just run out of work, products, features, etc. We can just build more but so can the competition right?

deaux 1y ago

> The incentive structure for managers (and literally everyone up the chain) is to maximize headcount. More people you managed, the more power you have within the organization Ding ding ding! AI can absolutely reduce headcount. It already could 2 years ago, when we were just getting started. At the time I worked at a company that did just that, succesfully automating away thousands of jobs which couldn't pre-LLMs. The reason it ""worked"" was because it was outsourced headcount, so there was very limited political incentive to keep them if they were replaceable. The bigger and older the company, the more ossified the structures are that have a want to keep headcount equal, and ideally grow it. This is by far the biggest cause of all these "failed" AI projects. It's super obvious when you start noticing that for jobs that were being outsourced, or done by temp/contracted workers, those are much more rapidly being replaced. As well as the fact that tech startups are hiring much less than before. Not talking about YC-and-co startups here, those are global exceptions indeed affected a lot by ZIRP and what not. I'm talking about the 99.9% of startups that don't get big VC funds. A lot of the narrative on HN that it isn't happening and AI is all a scam is IMO out of reasonable fear. If you're still not convinced, think about it this way. Before LLMs were a thing, if I asked you what the success rate of software projects at non-tech companies was, what would you have said? 90% failure rate? To my knowledge, the numbers are indeed close. And what's the biggest reason? Almost never "this problem cannot be technically solved". You'd probably name other, more common reasons. Why would this be any different for AI? Why would those same reasons suddenly disappear? They don't. All the politics, all the enterprise salesmen, the lack of understanding of actual needs, the personal KPIs to hit - they're all still there. And the politics are even worse than with trad. enterprise software now that the premise of headcount reduction looms larger than ever.

ckcheng 1y ago
Yes, and it’s instructive to see how automation has reduced head count in oil and gas majors. The reduction comes when there’s a shock financially or economically and layoffs are needed for survival. Until then, head count will be stable. Trucks in the oil sands can already operate autonomously in controlled mining sites, but wide adoption is happening slowly, waiting for driver turnover and equipment replacement cycles.
1y ago

deleted 1y ago

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