I did so many interesting experiments with MapReduces that would run overnight.
For a while, I would even build internal services that were basically "free" because I'd just run them all at priority 0.
Over time those services got less and less reliable as overall usage started to increase, so I was forced to either justify the resources or scale back - but that was a good thing.
I feel like something similar would be a good model for AI token use: big tech companies ought to have their own self-hosted LLM data centers to power their own needs, then let employees use off-hours capacity to experiment.
Outside of experimentation, we should be encouraging token efficiency for everyday tasks. Rather than having a certain number of tokens, engineers should be evaluated based on how much they actually get done.
Using a lot of tokens to automate a process that used to require hours of human labor every week? Good use of tokens, should be encouraged.
Using a lot of tokens to debug an easy frontend bug that could have been fixed by hand, and still took you 4 hours to complete? Waste of tokens, should be discouraged.
No company with good engineering leadership should act like this is remotely a good idea.
There was a cost to it though. Codes greatly reduced redundancy, and caused large miscommunications from very small errors. As Glieck explains it, this was the opposite of the African drumming practice of adding redundancy to strengthen the relationship between the rhythm and the language that the drums mimic.
When will Uber (or your favourite company) be 'done'? They've been writing software for 16 years.
They match drivers to passengers. More software isn't going to increase the chance that I seek them out instead of taking a bus or train.
Will their software be finished in 20 years? 80?
This always happens when the metric becomes the goal, companies should nurture and foster an environment where AI is used in the most efficient way possible, first asking "do we really need an agent for this" and if so, what kind of agent is needed, what model, reasoning level, etc.
They should also promote projects that aim at saving tokens, increasing cache hits, codifying the information in ways such they use as less context as possible (graphs of knowledge are pretty good for this!)
I do not believe that engineers who are tokenmaxxing are truely productive and I have not seen any evidence whatsoever (perhaps the opposite).
I've personally found that with the right flow and codebase knowledge, that's achievable with sustainable levels of effort.
Why nobody talks about those points, which are actually the only interesting points of all this AI craze?
A more nuanced view would be something like:
* AI lets you achieve your roadmap somewhat faster, but:
* You incur tech debt that's similar to if you hired a dev temporarily for the features. You don't necessarily have someone on the team that understands the new code.
* Similarly, you aren't upskilling your junior team members. So you aren't getting skill/wage arbitrage as much as before.
* You will complicate the product. P2 features are P2 for a reason, but AI can cause them to be included and complicate the product for lower marginal gain.AI maximalists like to compare the technology to electricity. Imagine if in the early days of electrification, a CEO had rewarded staff for increasing the amount of electricity they consumed rather than finding ways to use it for business impact. Institutionalizing people who showed signs of mental illness was popular in those days, and I suspect that would have been the outcome.
Imagine if engineers were ranked based on their AWS spend. People allocate VMs and fill databases with terabytes of random bits, to get to the top of the AWS leaderboard. If you don't do this, you're ranked at the bottom, and good luck at the next review cycle. Who could have expected that this is not the road to success?
When something is abundant, people tend to waste it.
I’m perfectly happy with my base subscriptions. I have Claude Code and Codex monthly subs, plus a yearly Google AI Pro account because it was a logical upgrade from the cloud storage plan I already had. I think it worked out to something like an extra $10/month for the AI features.
I constantly rotate between them during the week, managing tokens carefully, cleaning sessions and contexts as soon as possible, and being intentional about usage.
I honestly don’t understand the appeal of these ultra-expensive max subscriptions.
It reminds me of that flying orb toy I bought for the kids a few years ago. The battery only lasted about 10 minutes, and the kids would go ape shit crazy while it worked. Then it needed a 30-minute recharge, which created a natural cooldown period.
I actually considered that a good feature. I would never want the thing running nonstop.
He's saying that like it's some grand epiphany and not the most self-evident, obvious thing I've heard this month. Some of the literal dumbest people on earth are in charge of these major companies.
I can see how Uber could burn unbelievable amounts of tokens if they start running internal features that run a bunch of prompts against every completed ride, or every customer profile, for example.
Or maybe this is about employee usage, but they introduced some stupid "you get evaluated on how many tokens you used" thing a couple of months ago when that was trendy and are just beginning to notice how much that cost?
Smart engineers are figuring out how to best use their tokens - as tokenmaxing is just as silly as gasmaxing your car.
For me that's insanity for so many reasons...
Wrote about this and the impact of to jobs here: https://x.com/deepwhitman/status/2058324179506831372
Goodhart's law strikes again at someone with enough power to be both ignorant of it and make others suffer their ignorance. You cannot simply measure productivity by tokens spent just like you can't measure it by hours spent in a chair at a desk.
I know it's sounds stupid, but what if
Classic Goodhart’s Law: when a measure becomes a target, it ceases to be a good measure.
The way these corporations are going about it is completely insane though. They're essentially ordering their employees to set money on fire or be fired themselves. The more money you burn on tokens at insane API rates, the better an employee you are. Absolutely mind boggling.
I also want to call out the false productivity opportunities AI offers. There are whole teams building their own "gas town" and not shipping features.