9 ms·

Management as AI superpower: Thriving in a world of agentic AI

109 points by swolpers 9mo ago

Aurornis 9mo ago
> And there was something else: most early startups need to pivot, changing direction as they learn more about what the market wants and what is technically possible. By lowering the costs of pivoting, it was much easier to explore the possibilities without being locked in or even explore multiple startups at once: you just tell the AI what you want. In my experience so far, AI prototyping has been a powerful force for breaking analysis paralysis. In the last 10 years of my career, the slow execution speed at different companies wasn't due to slow code writing. It was due to management excesses trying to drive consensus and de-risk ideas before the developers were even allowed to write the code. Let's circle back and drive consensus in a weekly meeting with the stakeholders to get alignment on the KPIs for the design doc that goes through the approval and sign off process first. Developers would then read the ream and realize that perfection was expected from their output, too, so development processes grew to be long and careful to avoid accidents. I landed on a couple teams where even small changes required meetings to discuss it, multiple rounds of review, and a lot of grandstanding before we were allowed to proceed. Then AI comes along and makes it cheap to prototype something. If it breaks or it's the wrong thing, nobody feels like they're in trouble because we all agree it was a prototype and the AI wrote it. We can cycle through prototypes faster because it's happening outside of this messy human reputation-review-grandstanding loop that has become the norm. Instead of months of meetings, we can have an LLM generate a UI and a backend with fake data and say "This is what I want to build, and this is what it will do". It's a hundred times more efficient than trying to describe it to a dozen people in 1-hour timeslots in between all of their other meetings for 12 weeks in a row. The dark side of this same coin is when teams try to rely on the AI to write the real code, too, and then blame the AI when something goes wrong. You have to draw a very clear line between AI-driven prototyping and developer-driven code that developers must own. I think this article misses the mark on that by framing everything as a decision to DIY or delegate to AI. The real AI-assisted successes I see have developers driving with AI as an assistant on the side, not the other way around. I could see how an MBA class could come to believe that AI is going to do the jobs instead of developers, though, as it's easy to look at these rapid LLM prototypes and think that production ready code is just a few prompts away.
- Exoristos 9mo ago
  > In my experience so far, AI prototyping has been a powerful force for breaking analysis paralysis. So is an 8-ball.
  - cheschire 9mo ago
    ... of cocaine.
  - gyanchawdhary 9mo ago
    an 8 boll doesn’t ship code .. which might be why you like the comparison kind sir
    - layer8 9mo ago
      Neither does AI prototyping.
    - gyanchawdhary 9mo ago
      @layer8 .. yeah ... nobody claimed prototypes ship .. but i guess they just prevent teams like yours from shipping nothing.
- sbsnjsks 9mo ago
  [dead]
- alexhans 9mo ago
  I haven't had the analysis paralysis problem because I've always been quite decent at restructuring environments to avoid bureocracy (which can one of the most dangerous things for a project) but one thing I've observed is that If operations are not ZeroOps then whoever is stuck maintaining systems will suffer by not being able to deliver the "value adding cool features that drive careers". Since shipping prototypes doesn't actually create value unless they're in some form of production environment to effect change, then either they work and are ZeroOps or they break and someone needs to operate on them and is accountable for them. This means that at some point, your thesis of "The dark side of this same coin is when teams try to rely on the AI to write the real code, too, and then blame the AI when something goes wrong" won't really work that way but whoever is accountable will get the blame and the operations. The same principles for building software that we've always have apply more than ever to AI related things. Easy to change, reusable, compostable, testable. Prototypes need to be thrown away. Otherwise they're trace bullets and you don't want to have tech debt in your tracer bullets unless your approach is to throw it to someone else ans make it their problem. ----- Creating a startup or any code from scratch in a way that you don't actually have to maintain and find out the consequences of your lack of sustainable approaches (tech debt/bad design/excessive cost) is easy. You hide the hardest part. It's easy to do things that in surface look good if you can't see how they will break. The blog post is interesting but, unless I've missed something, it does gloss over the accountability aspect. If you can delegate accountability you don't worry about evals-first design, you can push harder on dates because you're not working backwards from the actual building and design and its blockers. Evals (think promtpfoo) for evals-first design will be key for any builder who is accountable for the decisions of their agents (automation). I need to turn it into a small blog post but the points of the talk https://alexhans.github.io/talks/airflow-summit/toward-a-shared-vision-of-llm-evals-in-airflow-ecosystem.html#/title-slide https://alexhans.github.io/talks/airflow-summit/toward-a-sha... - We can’t compare what we can’t measure - Can I trust this to run on its own? Are crucial to have a live system that makes critical decisions. If you don't, have this, you're just using the --yolo flag.
behnamoh 9mo ago
5 years ago: ML-auto-complete → You had to learn coding in depth Last Year: AI-generated suggestions → You had to be an expert to ask the right questions Now: AI-generated code → You should learn how to be a PM Future: AI-generated companies → You must learn how to be a CEO Meta-future: AI-generated conglomerates → ? Recently I realized that instead of just learning technical skills, I need to learn management skills. Specifically, project management, time management, writing specifications, setting expectations, writing tests, and in general, handling and orchestrating an entire workflow. And I think this will only shift to the higher levels of the management hierarchy in the future. For example, in the future we will have AI models that can one-shot an entire platform like Twitter. Then the question is less about how to handle a database and more about how to handle several AI generated companies! While we're at the project manager level now, in the future we'll be at the CEO level. It's an interesting thing to think about.
- tintor 9mo ago
  one-shot doesn't mean what you think it means. one-shot means you provide one full question/answer example (from the same distribution) in the context to LLM.
- philipwhiuk 9mo ago
  > more about how to handle several AI generated companies! The cost of a model capable of running an entire company will be multiples of the market cap of the company it is capable of running.
  - behnamoh 9mo ago
    "AI-generated company" as in the AI writes the A-Z of the code required to have a working platform like Twitter. Currently it can build some of the frontend or some of the backend, but not all. It's conceivable that in the future AI can handle the entire chain. Also you're forgetting the decreasing cost of AI, as well as the fact that you can buy a $10k Mac Studio NOW and have it run 24/7 with some of the best models out there. Only costs would be the initial fixed cost and electric (250W at peak GPU usage).
pglevy 9mo ago
> I think many people have the skills they need, or can learn them, in order to work with AI agents - they are management 101 skills. I like his thinking but many professional managers are not good at management. So I'm not sure about the assumption that "many people" can easily pick this up.
philipwhiuk 9mo ago
> I find it interesting to watch as some of the most well-known software developers at the major AI labs note how their jobs are changing from mostly programming to mostly management of AI agents. "AI labs" Can we stop this misleading language. They're doing product development. It's not a "laboratory" doing scientific research. There's no attempt at the scientific method. It's a software firm and these are software developers/project managers. Which brings me to point 2. These guys are selling AI tooling. Obviously there's a huge desire to dogfood the tooling. Plus, by joining the company, you are buying into the hype and the vision. It would be more surprising if they weren't using their own tools the whole time. If you can't even sell to yourself...
- ajzushzb 9mo ago
  AI works best when you’re selling it (author fits in this category as well).
  - gyanchawdhary 9mo ago
    ... and perhaps AI sucks when you’re fearful it collapses years of “experience” into a prompt
    - Ronsenshi 9mo ago
      If only there were no conflict of interest - since you're directly invested in AI hype.
      - gyanchawdhary 9mo ago
        Invested lol
    - deleted 9mo ago
      [deleted]
  - wordpad 9mo ago
    Reminds me of blockchains and blockchain advocates.
ajzushzb 9mo ago
It’s hard to take this author seriously given there’s no way they reviewed the work their students did.
- pkilgore 9mo ago
  It's vibes all the way down now.
augusteo 9mo ago
The limiting factor at work isn't writing code anymore. It's deciding what to build and catching when things go sideways. We've been running agent workflows for a while now. The pattern that works: treat agents like junior team members. Clear scope, explicit success criteria, checkpoints to review output. The skills that matter are the same ones that make someone a good manager of people. pglevy is right that many managers aren't good at this. But that's always been true. The difference now is that the feedback loop is faster. Bad delegation to an agent fails in minutes, not weeks. You learn quickly whether your instructions were clear. The uncomfortable part: if your value was being the person who could grind through tedious work, that's no longer a moat. Orchestration and judgment are what's left.
- auguste0 9mo ago
  Translation: Assimilate or die. Patently shocked to find this on profile: > I lead AI & Engineering at Boon AI (Startup building AI for Construction).
- lukev 9mo ago
  > The skills that matter are the same ones that make someone a good manager of people. I disagree. Part of being a good manager of (junior) people is teaching them soft skills in addition to technical skills -- how to ask for help and do their own research, and how to build their own skills autonomously, how to think about requirements creatively, etc. Clear specifications and validating output is only a part of good people management, but is 100% of good agent management.
  - layer8 9mo ago
    It’s teaching them in the first place. You can’t teach an LLM. Writing a heap of AGENTS.md is not teaching. LLMs take it as information, but they don’t learn from it in any non-superficial sense.
    - joe_mamba 9mo ago
      >You can’t teach an LLM. Actually you can. Training data, then the way you describe the task, goals, checkpoints, etc is still training.
deleted 9mo ago
[deleted]
- deleted 9mo ago
  [deleted]
Bukhmanizer 9mo ago
When thinking about automation people overindex on their current class biases. For 20 years we heard that robots were going to take over the “burger flipper” jobs. Why was it so easy to think that robots could replace fast food workers? Because they were the lowest rung on the career ladder, so it felt natural that they would be the first ones to get replaced. Similarly, it’s easy to think that the lowly peons in the engineering world are going to get replaced and we’ll all be doing the job of directors and CEOs in the future, but that doesn’t really make sense to me. Being able to whip your army of AI employees 3% better than your competitor doesn’t (usually) give any lasting advantage. What does give an advantage is: specialized deep knowledge, building relationships and trust with users and customers, and having a good sense of design/ux/etc. Like maybe that’s some of the job of a manager/director/CEO, but not anyone that I’ve worked with.
- edmundsauto 9mo ago
  > Being able to whip your army of AI employees 3% better than your competitor doesn’t (usually) give any lasting advantage. What do you mean by “better”? The advantage is speed. Shipping a feature in 1 week instead of 1 month is a tremendous advantage
  - TheWas7ed 9mo ago
    How is it an advantage is everyone has access to the same tool? Maybe 1 week is just the new baseline.
    - edmundsauto 9mo ago
      I think the logic still holds due to the red queen effect. If everyone else is getting 3% better and you’re not, it could spell trouble. Medium term, I expect ai adoption to compound. So if you can be 3.5% better, it could become a massive advantage over a few years compared to the competition.
afro88 9mo ago
I thought I had a great startup idea. It was niche, but a solid global market. It was unique. There was a genuine pain point being solved. My MVP solved it. The pricing worked, the tiers were sound. At least ChatGPT, Gemini and Claude told me it was. I did so many rounds of each one evaluating the other, trying to poke holes etc. Reviewing the idea and the "research", the reasoning. Plugging the gaps. Then I started talking to real people about their problems in this space to see if this was one of them. Nope, not really. It kinda was, but not often enough to pay for a dedicated service, and not enough of a pain to move on from free workarounds. Beware of AI reviewing AI. Always talk to real people to validate.
- Bukhmanizer 9mo ago
  It’s weird when the failure modes of AI are similar. I once solved a Leetcode problem kind of unorthodox and both ChatGPT and Gemini both said it was wrong in the same way. Then I asked both of them to give me a counter example and only Gemini was able to realize the counter example would have actually worked.
amadeuswoo 9mo ago
The "management as superpower" framing assumes people thoughtfully evaluate AI output. In practice, most users either review everything (slow, defeats the speed benefit) or review almost nothing (fast, but you're trusting the AI entirely). The MBAs who did well probably had domain expertise to spot wrong answers quickly, that's the actual superpower, not generic "management skill
1313ed01 9mo ago
Is there any hope of turning this around so I can still do fun work and AI can take over the management roles instead?
deleted 9mo ago
[deleted]
fogzen 9mo ago
The linked tweets seem like exaggerations or hyperbole. "I shipped 22 PRs yesterday, and 27 the day before" – huh? Are these PRs just one-liner copy changes or something? Even if you're not coding, you have to at least be verifying the changes right? At 15min to run CI and deploy, that's 5.5 hours spent just for changes to go into production. Spending just 15minutes to review and verify and 15minutes to merge and deploy 22 changes is 11 hours, no breaks, no meals. Smells like bullshit to me.
ykurtov 9mo ago
That's his revenge at BigTech for dumping on MBA holders for decades.
moomoo11 9mo ago
Ugh. Speaking of management.. Fire MBAs and other “management” types. If they’re not technical and you’re building something technical, they need to go. Anyone who says otherwise gets fired too. Keep the engineers who consistently get Exceeds Expectations. Fire everyone else. No Pip just go please. Keep a few EE product managers. Fire the rest. Hire a few QAs who can work with AI and work with product to ensure the stuff actually works. You don’t need that many people anymore and a couple of quality people can’t hurt. I don’t trust engineers enough, sorry. You need discerning eyes. Fire everyone else. Give the best people AI and they will be able to put out more good work. If someone doesn’t get this, fire them too because they’re clearly not EE level. Scale this to the whole org.
- _se 9mo ago
  You could've just said "I don't understand how organizations of people work" and saved yourself a bunch of typing.
  - moomoo11 9mo ago
    You're fired
drsalt 9mo ago
this is going to be the ultimate rug pull, for lack of a better expression. without actually gaining real skills, how are people relying on AI going to cope when it is unavailable or taken away or becomes too expensive? there's people advocating for UBI but why would you have incentive to donate income to incompetent unproductive people.

109 points by swolpers 9mo ago

> And there was something else: most early startups need to pivot, changing direction as they learn more about what the market wants and what is technically possible. By lowering the costs of pivoting, it was much easier to explore the possibilities without being locked in or even explore multiple startups at once: you just tell the AI what you want. In my experience so far, AI prototyping has been a powerful force for breaking analysis paralysis. In the last 10 years of my career, the slow execution speed at different companies wasn't due to slow code writing. It was due to management excesses trying to drive consensus and de-risk ideas before the developers were even allowed to write the code. Let's circle back and drive consensus in a weekly meeting with the stakeholders to get alignment on the KPIs for the design doc that goes through the approval and sign off process first. Developers would then read the ream and realize that perfection was expected from their output, too, so development processes grew to be long and careful to avoid accidents. I landed on a couple teams where even small changes required meetings to discuss it, multiple rounds of review, and a lot of grandstanding before we were allowed to proceed. Then AI comes along and makes it cheap to prototype something. If it breaks or it's the wrong thing, nobody feels like they're in trouble because we all agree it was a prototype and the AI wrote it. We can cycle through prototypes faster because it's happening outside of this messy human reputation-review-grandstanding loop that has become the norm. Instead of months of meetings, we can have an LLM generate a UI and a backend with fake data and say "This is what I want to build, and this is what it will do". It's a hundred times more efficient than trying to describe it to a dozen people in 1-hour timeslots in between all of their other meetings for 12 weeks in a row. The dark side of this same coin is when teams try to rely on the AI to write the real code, too, and then blame the AI when something goes wrong. You have to draw a very clear line between AI-driven prototyping and developer-driven code that developers must own. I think this article misses the mark on that by framing everything as a decision to DIY or delegate to AI. The real AI-assisted successes I see have developers driving with AI as an assistant on the side, not the other way around. I could see how an MBA class could come to believe that AI is going to do the jobs instead of developers, though, as it's easy to look at these rapid LLM prototypes and think that production ready code is just a few prompts away.

Exoristos 9mo ago
> In my experience so far, AI prototyping has been a powerful force for breaking analysis paralysis. So is an 8-ball.
- cheschire 9mo ago
  ... of cocaine.
- gyanchawdhary 9mo ago
  an 8 boll doesn’t ship code .. which might be why you like the comparison kind sir
  - layer8 9mo ago
    Neither does AI prototyping.
  - gyanchawdhary 9mo ago
    @layer8 .. yeah ... nobody claimed prototypes ship .. but i guess they just prevent teams like yours from shipping nothing.
sbsnjsks 9mo ago
[dead]
alexhans 9mo ago
I haven't had the analysis paralysis problem because I've always been quite decent at restructuring environments to avoid bureocracy (which can one of the most dangerous things for a project) but one thing I've observed is that If operations are not ZeroOps then whoever is stuck maintaining systems will suffer by not being able to deliver the "value adding cool features that drive careers". Since shipping prototypes doesn't actually create value unless they're in some form of production environment to effect change, then either they work and are ZeroOps or they break and someone needs to operate on them and is accountable for them. This means that at some point, your thesis of "The dark side of this same coin is when teams try to rely on the AI to write the real code, too, and then blame the AI when something goes wrong" won't really work that way but whoever is accountable will get the blame and the operations. The same principles for building software that we've always have apply more than ever to AI related things. Easy to change, reusable, compostable, testable. Prototypes need to be thrown away. Otherwise they're trace bullets and you don't want to have tech debt in your tracer bullets unless your approach is to throw it to someone else ans make it their problem. ----- Creating a startup or any code from scratch in a way that you don't actually have to maintain and find out the consequences of your lack of sustainable approaches (tech debt/bad design/excessive cost) is easy. You hide the hardest part. It's easy to do things that in surface look good if you can't see how they will break. The blog post is interesting but, unless I've missed something, it does gloss over the accountability aspect. If you can delegate accountability you don't worry about evals-first design, you can push harder on dates because you're not working backwards from the actual building and design and its blockers. Evals (think promtpfoo) for evals-first design will be key for any builder who is accountable for the decisions of their agents (automation). I need to turn it into a small blog post but the points of the talk https://alexhans.github.io/talks/airflow-summit/toward-a-shared-vision-of-llm-evals-in-airflow-ecosystem.html#/title-slide https://alexhans.github.io/talks/airflow-summit/toward-a-sha... - We can’t compare what we can’t measure - Can I trust this to run on its own? Are crucial to have a live system that makes critical decisions. If you don't, have this, you're just using the --yolo flag.

behnamoh 9mo ago

5 years ago: ML-auto-complete → You had to learn coding in depth Last Year: AI-generated suggestions → You had to be an expert to ask the right questions Now: AI-generated code → You should learn how to be a PM Future: AI-generated companies → You must learn how to be a CEO Meta-future: AI-generated conglomerates → ? Recently I realized that instead of just learning technical skills, I need to learn management skills. Specifically, project management, time management, writing specifications, setting expectations, writing tests, and in general, handling and orchestrating an entire workflow. And I think this will only shift to the higher levels of the management hierarchy in the future. For example, in the future we will have AI models that can one-shot an entire platform like Twitter. Then the question is less about how to handle a database and more about how to handle several AI generated companies! While we're at the project manager level now, in the future we'll be at the CEO level. It's an interesting thing to think about.

tintor 9mo ago
one-shot doesn't mean what you think it means. one-shot means you provide one full question/answer example (from the same distribution) in the context to LLM.
philipwhiuk 9mo ago
> more about how to handle several AI generated companies! The cost of a model capable of running an entire company will be multiples of the market cap of the company it is capable of running.
- behnamoh 9mo ago
  "AI-generated company" as in the AI writes the A-Z of the code required to have a working platform like Twitter. Currently it can build some of the frontend or some of the backend, but not all. It's conceivable that in the future AI can handle the entire chain. Also you're forgetting the decreasing cost of AI, as well as the fact that you can buy a $10k Mac Studio NOW and have it run 24/7 with some of the best models out there. Only costs would be the initial fixed cost and electric (250W at peak GPU usage).

pglevy 9mo ago

> I think many people have the skills they need, or can learn them, in order to work with AI agents - they are management 101 skills. I like his thinking but many professional managers are not good at management. So I'm not sure about the assumption that "many people" can easily pick this up.

philipwhiuk 9mo ago

> I find it interesting to watch as some of the most well-known software developers at the major AI labs note how their jobs are changing from mostly programming to mostly management of AI agents. "AI labs" Can we stop this misleading language. They're doing product development. It's not a "laboratory" doing scientific research. There's no attempt at the scientific method. It's a software firm and these are software developers/project managers. Which brings me to point 2. These guys are selling AI tooling. Obviously there's a huge desire to dogfood the tooling. Plus, by joining the company, you are buying into the hype and the vision. It would be more surprising if they weren't using their own tools the whole time. If you can't even sell to yourself...

ajzushzb 9mo ago
AI works best when you’re selling it (author fits in this category as well).
- gyanchawdhary 9mo ago
  ... and perhaps AI sucks when you’re fearful it collapses years of “experience” into a prompt
  - Ronsenshi 9mo ago
    If only there were no conflict of interest - since you're directly invested in AI hype.
    - gyanchawdhary 9mo ago
      Invested lol
  - deleted 9mo ago
    [deleted]
- wordpad 9mo ago
  Reminds me of blockchains and blockchain advocates.

ajzushzb 9mo ago

It’s hard to take this author seriously given there’s no way they reviewed the work their students did.

pkilgore 9mo ago
It's vibes all the way down now.

augusteo 9mo ago

The limiting factor at work isn't writing code anymore. It's deciding what to build and catching when things go sideways. We've been running agent workflows for a while now. The pattern that works: treat agents like junior team members. Clear scope, explicit success criteria, checkpoints to review output. The skills that matter are the same ones that make someone a good manager of people. pglevy is right that many managers aren't good at this. But that's always been true. The difference now is that the feedback loop is faster. Bad delegation to an agent fails in minutes, not weeks. You learn quickly whether your instructions were clear. The uncomfortable part: if your value was being the person who could grind through tedious work, that's no longer a moat. Orchestration and judgment are what's left.

auguste0 9mo ago
Translation: Assimilate or die. Patently shocked to find this on profile: > I lead AI & Engineering at Boon AI (Startup building AI for Construction).
lukev 9mo ago
> The skills that matter are the same ones that make someone a good manager of people. I disagree. Part of being a good manager of (junior) people is teaching them soft skills in addition to technical skills -- how to ask for help and do their own research, and how to build their own skills autonomously, how to think about requirements creatively, etc. Clear specifications and validating output is only a part of good people management, but is 100% of good agent management.
- layer8 9mo ago
  It’s teaching them in the first place. You can’t teach an LLM. Writing a heap of AGENTS.md is not teaching. LLMs take it as information, but they don’t learn from it in any non-superficial sense.
  - joe_mamba 9mo ago
    >You can’t teach an LLM. Actually you can. Training data, then the way you describe the task, goals, checkpoints, etc is still training.

deleted 9mo ago

[deleted]

deleted 9mo ago
[deleted]

Bukhmanizer 9mo ago

When thinking about automation people overindex on their current class biases. For 20 years we heard that robots were going to take over the “burger flipper” jobs. Why was it so easy to think that robots could replace fast food workers? Because they were the lowest rung on the career ladder, so it felt natural that they would be the first ones to get replaced. Similarly, it’s easy to think that the lowly peons in the engineering world are going to get replaced and we’ll all be doing the job of directors and CEOs in the future, but that doesn’t really make sense to me. Being able to whip your army of AI employees 3% better than your competitor doesn’t (usually) give any lasting advantage. What does give an advantage is: specialized deep knowledge, building relationships and trust with users and customers, and having a good sense of design/ux/etc. Like maybe that’s some of the job of a manager/director/CEO, but not anyone that I’ve worked with.

edmundsauto 9mo ago
> Being able to whip your army of AI employees 3% better than your competitor doesn’t (usually) give any lasting advantage. What do you mean by “better”? The advantage is speed. Shipping a feature in 1 week instead of 1 month is a tremendous advantage
- TheWas7ed 9mo ago
  How is it an advantage is everyone has access to the same tool? Maybe 1 week is just the new baseline.
  - edmundsauto 9mo ago
    I think the logic still holds due to the red queen effect. If everyone else is getting 3% better and you’re not, it could spell trouble. Medium term, I expect ai adoption to compound. So if you can be 3.5% better, it could become a massive advantage over a few years compared to the competition.

afro88 9mo ago

I thought I had a great startup idea. It was niche, but a solid global market. It was unique. There was a genuine pain point being solved. My MVP solved it. The pricing worked, the tiers were sound. At least ChatGPT, Gemini and Claude told me it was. I did so many rounds of each one evaluating the other, trying to poke holes etc. Reviewing the idea and the "research", the reasoning. Plugging the gaps. Then I started talking to real people about their problems in this space to see if this was one of them. Nope, not really. It kinda was, but not often enough to pay for a dedicated service, and not enough of a pain to move on from free workarounds. Beware of AI reviewing AI. Always talk to real people to validate.

Bukhmanizer 9mo ago
It’s weird when the failure modes of AI are similar. I once solved a Leetcode problem kind of unorthodox and both ChatGPT and Gemini both said it was wrong in the same way. Then I asked both of them to give me a counter example and only Gemini was able to realize the counter example would have actually worked.

amadeuswoo 9mo ago

The "management as superpower" framing assumes people thoughtfully evaluate AI output. In practice, most users either review everything (slow, defeats the speed benefit) or review almost nothing (fast, but you're trusting the AI entirely). The MBAs who did well probably had domain expertise to spot wrong answers quickly, that's the actual superpower, not generic "management skill

1313ed01 9mo ago

Is there any hope of turning this around so I can still do fun work and AI can take over the management roles instead?

deleted 9mo ago

[deleted]

fogzen 9mo ago

The linked tweets seem like exaggerations or hyperbole. "I shipped 22 PRs yesterday, and 27 the day before" – huh? Are these PRs just one-liner copy changes or something? Even if you're not coding, you have to at least be verifying the changes right? At 15min to run CI and deploy, that's 5.5 hours spent just for changes to go into production. Spending just 15minutes to review and verify and 15minutes to merge and deploy 22 changes is 11 hours, no breaks, no meals. Smells like bullshit to me.

ykurtov 9mo ago

That's his revenge at BigTech for dumping on MBA holders for decades.

moomoo11 9mo ago

Ugh. Speaking of management.. Fire MBAs and other “management” types. If they’re not technical and you’re building something technical, they need to go. Anyone who says otherwise gets fired too. Keep the engineers who consistently get Exceeds Expectations. Fire everyone else. No Pip just go please. Keep a few EE product managers. Fire the rest. Hire a few QAs who can work with AI and work with product to ensure the stuff actually works. You don’t need that many people anymore and a couple of quality people can’t hurt. I don’t trust engineers enough, sorry. You need discerning eyes. Fire everyone else. Give the best people AI and they will be able to put out more good work. If someone doesn’t get this, fire them too because they’re clearly not EE level. Scale this to the whole org.

_se 9mo ago
You could've just said "I don't understand how organizations of people work" and saved yourself a bunch of typing.
- moomoo11 9mo ago
  You're fired

drsalt 9mo ago

this is going to be the ultimate rug pull, for lack of a better expression. without actually gaining real skills, how are people relying on AI going to cope when it is unavailable or taken away or becomes too expensive? there's people advocating for UBI but why would you have incentive to donate income to incompetent unproductive people.