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.
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.
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.
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-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.
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?
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.)
>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?
A bottom-up approach has a far better chance of finding those particularly good use cases, and if you lean on the people how found those fits, they're more persuasive than top-down edicts. They actually know what they're talking about. If the point is to leverage AI for better work outcomes, someone with your experience is far more valuable than "here's a dashboard, make the number go up," which seems to be what's going on at Amazon.
I read that BSV source code is about three times shorter than similar design in Verilog and also has three times smaller defect density (defects per significant line of code). So just by changing the HDL from Verilog to BSV one can have nine (9) times less defects in the design.
If LLMs truly are as good as their proponents say, engineers will use them even if management outright forbade it. The fact that people aren't using them, and have to be forced, is extremely strong evidence that they are not in fact that useful.
See my other reply in this subthread. For my line of work, they are in fact ridiculously useful.
Surely you can't argue in good faith today that LLMs aren't useful? It was a valid argument a year ago, but the latest models are absolutely useful at solving whole classes of problems.
They're not perfect, need to be carefully monitored, can cause weird gambling like dopamine rushes and can cause lazy development habits to creep in. But none of those things negate the fact that, in many situations, they are useful.
Short-term, sure in some contexts. Long-term? Nobody knows yet.
Useful = able to be used for practical purposes.
As an extreme example, most effective weapons are useful to the person using them, but aren't necessarily a net benefit.
Is AI going to make life meaningfully better for most people? That's uncertain. Is it useful for the tasks in front of me today? Yes, definitely.
You include those only in second round along with guidelines and recommendations on how to use it effectively.
Also, we are talking about large companies here. There will be plenty of more suitable seniors.
[1] https://en.wikipedia.org/wiki/Domino_logic
There was no synthesis algorithms that would map VHDL or Verilog designs into domino logic elements at the time. I believe that the most work in the synthesis-to-domino-logic area was done at the beginning of current century.
So, DEC's engineers and, I think, Intel's engineers were doing work using schematics well into 21-st century.
"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."
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.
Extremely weird take for a knowledge worker.
It's that simple.
(Never mind that these bloggers are just writing ad copy for cloud providers.)
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.
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.
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.
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.
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.
If you as a “leader” refuse to go along with the crowd and you’re right, then after the dust settles you look like someone who guessed right. Oh and now we’re in a recession so you are probably having a bad time regardless. You maybe get one promotion, congratulations.
If you refuse to go along with the crowd and you’re wrong, you look like a Luddite, you probably got fired at some point along the way and your judgement reputation is hurt.
If you do go along with the crowd and the crowd is wrong, you are just in the same boat as everyone else. You are probably about the same as if you went against the crowd and you were right, possibly even better because it can take awhile to be proven right and you could be hurt in the middle.
So, I think, once something like this picks up enough steam, it’s just logical on a per individual basis for everyone to go along with it, regardless of how they feel about it internally.
But doing so properly requires expending a serious amount of cognitive effort & agile methodology, which is the exact opposite of what Amazon's management has demonstrated here.
It's quite possible they aren't trying to measure performance but are literally just trying to increase token consumption to feed the bubble and hype.
Plus pressure employees may find new unique use cases for AI.
It's like if your goal is inflation, you give out tons of money and as long as its spent, you achieve your goal.
Absurdly wasteful but Goodhart's Law almost never fails.
It makes for pretty charts, extrapolations, and projections.
It doesn’t matter if the numbers are not particularly correct. As long as the data gathering step can be justified it’ll do. Though bonus points if making the number bigger is a good thing (v.s. tracking something like number of sev 1 issues).
> The first step is to measure whatever can be easily measured. This is okay as far as it goes.
> The second step is to disregard that which can't be easily measured or give it an arbitrary quantitative value. This is artificial and misleading.
> The third step is to presume that what can't be measured easily really isn't very important. This is blindness.
> The fourth step is to say that what can't be easily measured really doesn't exist. This is suicide.
— Daniel Yankelovich, "The New Odds"
This why AWS is bleeding good engineers for years. What is left is starting to look like Boeing post McDonnell merger...
They took out a quarter of their documentation page limited real estate, with AI doc shorts nobody asked for, nobody needs, and cant disable.
At Amazon, something like this is likely a closely watched experiment. They knew it would incentivise waste. But they don't know what the other effects will end up being. Nobody knows -- this thread is full of loose speculation. So Amazon runs the experiment and collects the data.
----
The annoying thing about goals and incentives is that they can either be phrased in terms of input metrics (behaviours within our control) of output metrics (the outcomes we want). Input metrics are bad because they lead to skewed incentives and gaming the metrics. Output metrics are bad because they're largely affected by chance and external circumstances. (This indeed means a goal cannot be SMART on its own, because A and R are typically in tension.)
Amazon knows this. Their WBR structure is essentially about trying to set goals and targets for input metrics, and then carefully observing how input metrics correlate with output metrics. They're using a semi-scientific process to tease out the causal structure of their business. I would assume this token target is followed very closely to learn exactly what its effects are on output metrics that drive revenue and cost.
For more on this, I thnk the best public writing is Carr's Working Backwards and Chin has written about it on Commoncog too.
Simpler explanation management has no ideas and goals and this is a replacement strategy. Because they too are affected by "experimental metrics" to a degree, but that doesn't excuse this trite "science".
Any "answer" this would provide wouldn't be of higher quality than this speculation.
My favorite hilarious metric is measuring the amount of work done by counting lines of code written per day
Or by hours spent in the office
https://nordicapis.com/the-bezos-api-mandate-amazons-manifes...
If a manager or a manager's workforce under it just sat around and ignored AI just because it's stupid and irrelevant and useless, they lose one tool to justify their existence amongst their peers who do not express such views. If they sat around and did their jobs as-before WHILE "investing" on tokenmaxxing, they gain a double dip-able vanity metric like "we spent 12.34 quadrillion tokens last quarter" plus "our new method helped us reduce token count by 10^24 this quarter".
You may call it a fraudulent behavior from a hypothetical shareholder's perspective in this hypothetical scenario, which it is, and call it Goodhart's law scenario too, which it also is, but it's a completely normalized behavior in relative terms. Project Hail Mary is a lighthearted work of fiction.
I worked for a healthcare tech startup that made everyone wear fitbits and you got cheaper health insurance premiums if you averaged a higher # of steps every day. People were putting their fitbits on drillbits and whirring them around to log like 20,000 steps a day.
The moment they made it a metric they failed to do anything useful.