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One argument I have heard in favour of this is that management knew this would be a side effect, but that it's more important to have people engage with AI as m

One argument I have heard in favour of this is that management knew this would be a side effect, but that it's more important to have people engage with AI as much as possible simply to explore what is actually possible. You are effectively knowingly wasting money in the expectation that you might learn something useful that will be more valuable in the long run.

the_snooze 5mo ago
My questions for that approach are: Why treat AI as a special technology that needs enterprise-scale exploration to come up with a useful application? And why not take the alternative approach of identifying the subset of people who have indeed found solid uses and spread their best practices around? The top-down approach to encouraging (mandating?) AI usage strikes me as infantilizing to the workers, who are perfectly capable of choosing which tools they use and when.
- jjk7 5mo ago
  A tool so good, the workers need to be forced to use it.
  - creative_name3 5mo ago
    Workers are good at their job using tools they know because they had years/decades to hone that craft. The new tool might make a lot of that experience obsolete. Also, some people that were good with old tools might be great with the new tool. Some may not. Overall, I don't think it's a bad idea to burn some tokens (and money) to let people experiment.
- tverbeure 5mo ago
  Human nature? In the early nineties, it was common for experienced electrical engineers to keep on using schematic entry digital design and look down on RTL and synthesis tools, despite that fact the latter was already way more productive. At some point, management had to put their foot down and force everyone to switch to using synthesis. It's not unreasonable to assume that many people are set in their ways and unwilling to change their behavior without a bit of a push.
  - nophunphil 5mo ago
    I guess the only difference between this and your example is the concrete efficiency gain from RTL and synthesis tools versus dubious applications of AI. I do agree with the second point about pushing people to explore new ways of doing things though.
    - tverbeure 5mo ago
      > dubious applications of AI Leaving aside the ethical aspects of using AI (not because they're not valid, because they're off topic for this discussion), in my line of work, the capabilities and productivity improvement of AI are staggering. Most of it is not writing the new code, which is but a small part of chip design, but everything else. I can't give a concrete work example, but here is an experiment that I ran a month ago. https://tomverbeure.github.io/2026/04/12/AMIQ-License-Key-Generation.html#using-codex https://tomverbeure.github.io/2026/04/12/AMIQ-License-Key-Ge.... If it can do that, it's not hard to imaging similar use cases related to root causing complex simulation failures. It is frighteningly good at that.
      - ua709 5mo ago
        > use cases related to root causing complex simulation failures. That's a pretty interesting use case. I assume this is for RTL simulation given the thread, but how do you connect the output of the simulator to the AI?
        tverbeure 5mo ago
        For a small case, a colleague took a screenshot of waves in the waveform viewer and pasted it into the AI tool. It worked. But for large cases, use tools to extract all interfaces from the waveform file and save it as a text file, or add $display statements in the Verilog itself to dump the transactions. A SOTA LLM will eat it up. You point it to the RTL, a log file with hundreds of thousands of lines, and give it a few lines to explain how it is supposed to behave. Just tell it "My simulation is hanging. Figure why." Wait 15 minutes and it will tell you why it hangs and which line to change in your code to fix it. I've done the experiment after the fact: I had spent ~3 days to fix complicated 3 bugs. I then rolled back the code and told it "Here is the spec. Find all the bugs in this code". It found all 3 bugs in around 30 min. That's when I realized that things won't be the same anymore. (And don't get me wrong: I love debugging simulations.)
- blitzar 5mo ago
  googles 20% project time was a good thing, sadly they dont even seem to do it anymore. for the bulk of corporate workers this brief period of time where they get to play an ai token game is the only break from generating TPS reports all day long.
themafia 5mo ago
> engage with AI as much as possible simply to explore what is actually possible "Research" isn't part of my job title. If you don't know what's possible then why are you deploying it? You should be telling _me_ what's possible. I mean, you _paid_ for it, how can you possibly not know what you were getting? > in the expectation that you might learn something useful that will be more valuable in the long run. "I'll take `what even are profits?' for $200, Alex."
- datsci_est_2015 5mo ago
  Hear hear. An overly generous steelman in my opinion as well. Have 10% of your employees focus on finding ways to properly leverage the new technology - don’t pressure 100% of your employees with bull shit metrics.
- my-next-account 5mo ago
  >"Research" isn't part of my job title. If you don't know what's possible then why are you deploying it? You should be telling _me_ what's possible. I mean, you _paid_ for it, how can you possibly not know what you were getting? Extremely weird take for a knowledge worker.
aerodexis 5mo ago
in this instance - it seems like Amazon employees are wasting money exploring ways to waste money.
otabdeveloper4 5mo ago
No, it's literally because some dumb manager read a blog where an influencer said that you ain't a real AI native and ain't worth shit unless your developers are spending $XXXX on tokens each day. It's that simple. (Never mind that these bloggers are just writing ad copy for cloud providers.)
red_admiral 5mo ago
Are the people engaging though, or are they telling the AI "go do some busywork" and then minimizing that window and getting on with their job?
newswangerd 5mo ago
All so that they can lose this accumulated knowledge during the next round of layoffs.
watwut 5mo ago
That still sounds like a dumb strategy. Or, more likely, post hoc rationalization. You reward me for wasting tokens and punish me for not wasting them, I will maximally waste them and wont "explore hownto make them useful". The latter wastes less tokens and that is punished.
asdfman123 5mo ago
Exactly. That's the problem ICs don't want to admit. Managing a lot of people at scale is messy and you have to use crude solutions. It's impossible to know everything that's going on. If you were a manager you wouldn't do any better. Out of the crooked timber of humanity, no straight thing was ever made.
- judahmeek 5mo ago
  Your argument that bad processes can't possibly be improved is contradicted by all of recorded history.
  - asdfman123 5mo ago
    That's because you've misconstrued my argument. My argument is that everything is a tradeoff and while management can be MORE conscious, there's a certain level of bullshit that's inevitable. But more specifically ICs tend to want to say "if you just let me do what I know is right it would be fine." That's a trade-off, too, though. That solution means a lot of people will be messing around due to no accountability.
    - croon 5mo ago
      If the only accountability here is token input/output, after automating that employees would be messing around with no accountability either way. If setting the actual goal you want to achieve as a manager, and then trusting employees to allocate their focus accordingly, you will absolutely have people faffing off (as likely can't be avoided), but at least those who don't will optimize towards what works according to their actual expertise at least.
- duxup 5mo ago
  I think that's a convenient excuse for managers at the top to not have to deal with their own sub par middle and lower managers...
oytis 5mo ago
If companies are suddenly willing to spend money on letting their staff experiment, why not let them experiment with what they want to? They probably know more about technology than you do, otherwise you wouldn't need them.
simulator5g 5mo ago
This is induced demand for AI to justify building more datacenters, which will bring AI costs down, and the idea is that will eventually bring demand up organically.
red-iron-pine 5mo ago
in short: dogfood the tool extra hard, and figure out what it can do. this means wasting a lot of metaphorical dog food, but now everyone will be 100% how it tastes. this then allows you to shift client expectations and alter offerings. ... or it's just dumb mgmt. but let's be charitable.

the_snooze 5mo ago

My questions for that approach are: Why treat AI as a special technology that needs enterprise-scale exploration to come up with a useful application? And why not take the alternative approach of identifying the subset of people who have indeed found solid uses and spread their best practices around? The top-down approach to encouraging (mandating?) AI usage strikes me as infantilizing to the workers, who are perfectly capable of choosing which tools they use and when.

jjk7 5mo ago
A tool so good, the workers need to be forced to use it.
- creative_name3 5mo ago
  Workers are good at their job using tools they know because they had years/decades to hone that craft. The new tool might make a lot of that experience obsolete. Also, some people that were good with old tools might be great with the new tool. Some may not. Overall, I don't think it's a bad idea to burn some tokens (and money) to let people experiment.
tverbeure 5mo ago
Human nature? In the early nineties, it was common for experienced electrical engineers to keep on using schematic entry digital design and look down on RTL and synthesis tools, despite that fact the latter was already way more productive. At some point, management had to put their foot down and force everyone to switch to using synthesis. It's not unreasonable to assume that many people are set in their ways and unwilling to change their behavior without a bit of a push.
- nophunphil 5mo ago
  I guess the only difference between this and your example is the concrete efficiency gain from RTL and synthesis tools versus dubious applications of AI. I do agree with the second point about pushing people to explore new ways of doing things though.
  - tverbeure 5mo ago
    > dubious applications of AI Leaving aside the ethical aspects of using AI (not because they're not valid, because they're off topic for this discussion), in my line of work, the capabilities and productivity improvement of AI are staggering. Most of it is not writing the new code, which is but a small part of chip design, but everything else. I can't give a concrete work example, but here is an experiment that I ran a month ago. https://tomverbeure.github.io/2026/04/12/AMIQ-License-Key-Generation.html#using-codex https://tomverbeure.github.io/2026/04/12/AMIQ-License-Key-Ge.... If it can do that, it's not hard to imaging similar use cases related to root causing complex simulation failures. It is frighteningly good at that.
    - ua709 5mo ago
      > use cases related to root causing complex simulation failures. That's a pretty interesting use case. I assume this is for RTL simulation given the thread, but how do you connect the output of the simulator to the AI?
      - tverbeure 5mo ago
        For a small case, a colleague took a screenshot of waves in the waveform viewer and pasted it into the AI tool. It worked. But for large cases, use tools to extract all interfaces from the waveform file and save it as a text file, or add $display statements in the Verilog itself to dump the transactions. A SOTA LLM will eat it up. You point it to the RTL, a log file with hundreds of thousands of lines, and give it a few lines to explain how it is supposed to behave. Just tell it "My simulation is hanging. Figure why." Wait 15 minutes and it will tell you why it hangs and which line to change in your code to fix it. I've done the experiment after the fact: I had spent ~3 days to fix complicated 3 bugs. I then rolled back the code and told it "Here is the spec. Find all the bugs in this code". It found all 3 bugs in around 30 min. That's when I realized that things won't be the same anymore. (And don't get me wrong: I love debugging simulations.)
blitzar 5mo ago
googles 20% project time was a good thing, sadly they dont even seem to do it anymore. for the bulk of corporate workers this brief period of time where they get to play an ai token game is the only break from generating TPS reports all day long.

themafia 5mo ago

> engage with AI as much as possible simply to explore what is actually possible "Research" isn't part of my job title. If you don't know what's possible then why are you deploying it? You should be telling _me_ what's possible. I mean, you _paid_ for it, how can you possibly not know what you were getting? > in the expectation that you might learn something useful that will be more valuable in the long run. "I'll take `what even are profits?' for $200, Alex."

datsci_est_2015 5mo ago
Hear hear. An overly generous steelman in my opinion as well. Have 10% of your employees focus on finding ways to properly leverage the new technology - don’t pressure 100% of your employees with bull shit metrics.
my-next-account 5mo ago
>"Research" isn't part of my job title. If you don't know what's possible then why are you deploying it? You should be telling _me_ what's possible. I mean, you _paid_ for it, how can you possibly not know what you were getting? Extremely weird take for a knowledge worker.

aerodexis 5mo ago

in this instance - it seems like Amazon employees are wasting money exploring ways to waste money.

otabdeveloper4 5mo ago

No, it's literally because some dumb manager read a blog where an influencer said that you ain't a real AI native and ain't worth shit unless your developers are spending $XXXX on tokens each day. It's that simple. (Never mind that these bloggers are just writing ad copy for cloud providers.)

red_admiral 5mo ago

Are the people engaging though, or are they telling the AI "go do some busywork" and then minimizing that window and getting on with their job?

newswangerd 5mo ago

All so that they can lose this accumulated knowledge during the next round of layoffs.

watwut 5mo ago

That still sounds like a dumb strategy. Or, more likely, post hoc rationalization. You reward me for wasting tokens and punish me for not wasting them, I will maximally waste them and wont "explore hownto make them useful". The latter wastes less tokens and that is punished.

asdfman123 5mo ago

Exactly. That's the problem ICs don't want to admit. Managing a lot of people at scale is messy and you have to use crude solutions. It's impossible to know everything that's going on. If you were a manager you wouldn't do any better. Out of the crooked timber of humanity, no straight thing was ever made.

judahmeek 5mo ago
Your argument that bad processes can't possibly be improved is contradicted by all of recorded history.
- asdfman123 5mo ago
  That's because you've misconstrued my argument. My argument is that everything is a tradeoff and while management can be MORE conscious, there's a certain level of bullshit that's inevitable. But more specifically ICs tend to want to say "if you just let me do what I know is right it would be fine." That's a trade-off, too, though. That solution means a lot of people will be messing around due to no accountability.
  - croon 5mo ago
    If the only accountability here is token input/output, after automating that employees would be messing around with no accountability either way. If setting the actual goal you want to achieve as a manager, and then trusting employees to allocate their focus accordingly, you will absolutely have people faffing off (as likely can't be avoided), but at least those who don't will optimize towards what works according to their actual expertise at least.
duxup 5mo ago
I think that's a convenient excuse for managers at the top to not have to deal with their own sub par middle and lower managers...

oytis 5mo ago

If companies are suddenly willing to spend money on letting their staff experiment, why not let them experiment with what they want to? They probably know more about technology than you do, otherwise you wouldn't need them.

simulator5g 5mo ago

This is induced demand for AI to justify building more datacenters, which will bring AI costs down, and the idea is that will eventually bring demand up organically.

red-iron-pine 5mo ago

in short: dogfood the tool extra hard, and figure out what it can do. this means wasting a lot of metaphorical dog food, but now everyone will be 100% how it tastes. this then allows you to shift client expectations and alter offerings. ... or it's just dumb mgmt. but let's be charitable.