That said, if you can't figure out how to use AI in a software job you should look into it. Not using AI at this point is a lot like not using CAD as an architect.
They also use a bunch of dumb metrics like, total PRs submitted, total comments made on PRs, etc. To the point that, there are multiple heavily used internal tools to game these metrics. Eg, auto-comment LGTM on any approved PR. Thus, making the metrics even worse than they would have been prior.
> Managers are discouraged from using token use to measure performance, according to a person familiar with the matter.
Like CAD and architects, if you're not using LLM's while coding it's an issue, but Amazon is very clear that this isn't an official metric. I would believe managers know how many tokens you're using, but it sounds like they just interviewed a disgruntled employee who didn't like AI and published it.
AI is genuinely useful for many tasks. But 2x or greater business value from engineering orgs isn’t it. And even if it was business are terrible at measuring value added on an individual basis.
What they can measure though is token use. I’ve heard the same thing from other large companies my friends work for.
It’s bad enough that I’ve moved a significant amount of money out of US large-cap stocks.
When LLMs are capable of actually doing a good job, then it might be like that. We are not there yet, and we may never be.
No thanks I’ll just watch y’all slip down the slope.
Does CAD software regularly generate an incorrect design that results in a catastrophic failure of the building?
Heh. No need to be ashamed, I used to believe them when they lied to me like this too!
"Wow, look at how fast employee # 2 is setting money on fire! Let's promote him!"
You should have asked AI to come up with a better analogy.
Hell, throw a Tarot reading in the middle of the loop so the agent has non-deterministic behavior too.
https://github.com/trailofbits/skills/tree/main/plugins/let-...
Amazon management wants to play five-dimensional chess? Play Balatro instead.
However I see tons of people on LinkedIn with ways of backing up context, not wanting to lose context, etc.
This seems like another way the system is being misused. Higher context usage also uses more tokens. I suspect you get worse (and slower) output too than a dense detailed context.
a) you find a particular context that executes well and want to preserve parts of it or not have to repeat explanations
b) you want to continue a session so you don't have to rebuild the context from scratch
I think A is something where it's totally reasonable to preserve pieces as part of like a prompt library or equivalent, or directory-specific agent files, that kind of thing.
I think B is much more likely to lead to problems if you do it over a long time, but it can be pretty useful for getting the last drop of juice out of the metaphorical orange.
I think the antipattern (that I've done myself, admittedly) is swapping between different restored contexts for different tasks or roles - at that point you should be either converting it to more durable documentation if warranted, or curating it more specifically than "restore the entire context" even if it's just one-off.
Ideally that replaces the back and forth cycle of it's this, no it's that, it's that for reasons XYZ with a single ingestible blob that gets the agent up to speed.
If every exchange is treated as an independent query/response then it's much easier to see how cutting out the fluff using a combination of its summaries and your own helps stay focused.
Is that in the contract to use AI tools? If not, then what are they on about.
Very very few jobs in the US give you a contract.
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).
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.
People use AI differently and they can be equally productive with a variety of token usage quantities.
Also, different kinds of work are differently amenable to using AI.
Using it to grade people is, err, rather unwise.
Everyone I talk to has nowadays KPIs tied to AI usage on their performance evaluation.
It's astonishing how society forgets.
Senior management let go our localisation staff. Now they want us to use AI to translate. They still want manual review.
We use Github Copilot at work, we get a measly 300 requests with the budget to go over if necessary. Opus 4.7 or GPT 5.5 would eat all of those up in a day. Are we supposed to be using more than the allotted amount, do management see that as a good thing. Or is it best to stick within the allocated amount. Who knows? Management are playing games everywhere it seems.
One of the weirder things about all this is how arbitrary and non objective the billing structure seems. One of the reasons I'm happy to use it at work, but won't ever personally subscribe. It's so opaque.
It does not get any better than that
Jensen, Sam, Dario: https://i.imgur.com/AI7rtCY.jpeg
― Charlie Munger
People churning out slop is slowing me down and the full effects of it won't be felt for a while.
In my view you should 1) use AI as a tool to help you learn and 2) write boilerplate you could have easily written yourself. Getting it to think for you is counterproductive (at least until it replaces us entirely).
Codex was pretty sure something was wrong with the response object being returned by the endpoint in question. It turned out there was a conversion method applied to the endpoint response, which mutated its input. This method had been running w/o problems for a while, until the dev put it in a useEffect. At this point, React dev mode's policy of rendering everything twice kicked in, which caused the second pass through the conversion method to fail on the now-mutated input object.
Codex never even hinted that the conversion method mutating the input could be a problem, nor anything about React dev mode rendering everything twice (specifically to catch problems like this). Apparently, neither of those came up much in its training data.
My point is that this dev seems to have lost, in a few short months of writing everything with Codex, the ability to trace an error from its source (the error trace was being swallowed in a Codex-written catch block that spit out a generic error message). He was completely stuck and just kept doubling down on trying to get Codex to solve the problem, even checking with Copilot as a backup. I'm not optimistic about where this is headed.
Where? What industry, what kind of projects? The only one where I can imagine it to be true is vulnerability research, and I imagine all the low-hanging fruit to be picked soon
It will spin up a boilerplate uboot or BSP config no problem. I still go in and manually check and add peripherals, but opus 4.7 is terrifyingly smart.
Need to modify or add a new peripheral, it's there no problem. Or in a bare metal project, I can point it at an STM32 cubemx starter repo and ask for a feature (set up the ADC on pins 4 and 7, ask me for parameters) and it's just done. I do in a day what would probably take me 2.
It doesn't help me with reviewing others' work, or planning (I maintain that these are manual tasks). So yeah, I agree with the 40-60%. The parts of my job it helps, it really helps.
"You spent $23, over the $20 food limit. Be more careful next time. You spent $600 on tokens, $200 more than the average. Congratulations!"
> whoever spent $600 on Anthropic last night, great job leveraging Al! But to the person who spent $23 on Uber Eats please remember our limit for food is $20 per meal
This measuring of tokenmaxxing as a proxy for something beneficial to the company has got to be the single dumbest thing I have ever heard of in my entire software career.
It would be like some company in the dot com era measuring employee's internet download traffic as a proxy for productivity or internet-pilledness.
Why not just reward employees based on who's submit the largest expenses claims? That might have some correlation to work too, right ?!
Hell, I'm in the bowels of Google as an IC and it's hard to understand what adjacent teams are doing. Even harder for management that never gets their hands on anything.
So while you know engineers are probably bullshitting you with fake work, you can at least turn around and tell your supervisor the numbers. It's all a game of plausible deniability.
That said, I’m kind of having a blast using CC in corporate with all the connectors available at our disposal, and I baffled how little some of my coworkers know about what’s available and what the capabilities are. So it’s clear that perhaps some encouragement is prudent for those who are slower to embrace new technologies, but I’m not sure tokencounting and tokenmaxing are the answer.
I have an FT subscription and they keep moving toward this kind of narrative first reporting to get clicks. It’s no longer a believable paper.
I can't say that this isn't happening, but at least the parts of the company I get visibility into, what the article describes isn't my experience. There is a lot of interest in using GenAI, but people are mostly getting kudos around creative uses for GenAI, not just for raw amount of tokens. For most scaled GenAI efforts, there is a lot of focus on output metrics (metrics like accuracy, number of findings, number of things fixed, and so on).
LOL, I'd imagine even Amazon HR would be little restraint in showering such praise.
I'm surprised how few comments are written with the prior that Amazon managers aren't stupid or uninformed about how incentives work.
My guess would be that someone created the leaderboard without a lot of consultation with managers, and that some employees feel a competitive urge to try to "win" the leaderboard by burning tokens.
One of my favorite heuristics/quotes applies here: "no matter how good the strategy, occasionally consider the result."
Want to know if AI is working for your org? Ask yourself/employees to "show me the result." That requires judgment and taste (is the result something of value, or just the appearance of work having been done), but it will also save you a ton of stress and disappointment later.
If I do all of this, do I get a promotion?
Most people look at sea changes come and go. They all have a story of how they "could have bought Bitcoin when it was $100" or whatever. In an org, you don't want to have the story of "we could have done that when nobody else had", so you incentivize adoption of the tool as hard as possible and hope that dipping feet in the water makes people want to swim. If you don't already have a culture of early adoption (and no large company can) then you have to use blunt incentives. I don't think anyone has demonstrated otherwise.
Filing JIRA tickets, updates. Opening PRs, having AI review PRs. This will all use tokens.
No need to tokenmaxx, you will end up burning tokens with just regular AI usage
x187463•1h ago
...except each keystroke has an associated cost, the sum of which may equal or exceed my salary.
Weryj•1h ago
Analemma_•56m ago
Imustaskforhelp•46m ago
mass hysteria perhaps?
There used to be a time where people used to die from dancing too much (from my understanding in which hey I can be wrong, I usually am): https://en.wikipedia.org/wiki/Dancing_plague_of_1518
I think that although we wish to consider ourselves as smart and really intelligent but we run on biological machines and clocks which evolutionary have not much of a difference since 1518 or even the times when we used to hunt and forage for that matter.
HPsquared•45m ago
greesil•44m ago