What if... we stop for a moment, and then, after thinking for a moment, we stop hammering nails with a microscope, and stop using token usage as a metric of productivity?
I know it's sounds stupid, but what if
I know it's sounds stupid, but what if
True visionaries think outside the box, but most tech executives are forcing their employees into black boxes, out of fear of not doing exactly what their competitors are doing.
We have lemmings for leaders, and that means that—much like the LLMs that are being shoehorned into everything—there isn’t room for original thinking. Everyone’s strategy looks exactly the same.
If one is a CxO who's looking out for one's job security, herd-like behavior is the safest option, due to the (near universal) structure of "performance"-based executive remuneration.
First is that despite a lot of waste, some innovation will arise from an enterprising employee finding some interesting use case. A lot of the tokenmaxxing is just waste, but out of that waste may arise a small number of genuinely powerful use cases.
Second is that many workers will be entrenched in their ways. If your executive goal is to achieve the above (find innovative ways of using AI), then you need to move everyone to use it. Most will just waste tokens, but someone may find a novel and useful way of using it that benefits the organization. It is difficult to achieve these without forcing people to act since their default is to follow the well-worn grooves.
So mandates like these are a top-down forcing function like a slime mold feeling out different paths to find resources.
Some devs in my org have fully embraced AI; some would not even use AI if not for leadership mandates and linking usage to performance reviews (I know, I think this is stupid, too). I can see why mandates could be useful since some folks definitely won't be inclined to use AI.
Absolutely, but most management are not leaders, the moment someone pushes the idea to stack rank based on token usage, it gets approved and some genuine people will be impacted.
Post-ZIRP era proved there are very few strong leaders, before that everyone was behaving like they're most amazing leader because they read some books and raised $10M
Imagine you employ me as a hotel manager, and I come to you and say: "sure I spent all our food budget internationally in three months, and sure I have nothing really to show for it, but for those three months, we had a lot of food fights"
Your manager then goes on to explain they not only need more money to cover the food budget, but also they need to quituple the cleaning budget too.
Oh and the service level has dropped, because not all clients liked being in the middle of a food fight.
However "we might have some innovation in the food delivery system of our hotel chain"
> we might have some innovation in the food delivery system of our hotel chain
> Imagine you employ me as a hotel manager, and I come to you and say: "sure I spent all our food budget internationally in three months, and sure I have nothing really to show for it, but for those three months, we had a lot of food fights"
It's not how it should work, because food fights are stupid and have no upside.
Even if everyone else is having them.
It's not a fair analogy because AI isn't completely stupid, and there are situations where it does provide a benefit.
But a rational business would ask if the upside is worth the cost, if the pipeline can be restructured to concentrate and amplify the benefits, if some elements are better being done the old way, if there are strategic threats if tokens become much more expensive, and so on.
Instead we're getting a wave of "Cut workers, cut costs, derp" and that's as far as the "thinking" goes.
The worst thing about AI is that it shows how shallow and stupid current C-suites are.
The US used to have real tech visionaries. Now it has tech cargo cultists, all chasing an IPO cash out and hoping the music doesn't stop before they get their bag.
Thats the problem here. The idea is that we can build more stuff, quickly.
However in uber's case, they just burnt loads of money to push a metric that wasn't really related to productivity.
A lot of monkeys will also eventually type up Shakespeare?
So if the people who embrace AI areore successful then the others will follow. Just like every other new tech. Why does AI have to be forced? What's the hurry? Especially when there's no clear example of a company jumping ahead because of their use of it.
It's idiots being driven by FUD. That's the reason.
> What's the hurry?
There's also a need to quickly adopt and understand the technology; take the Internet for example. If we were talking about the Internet, forcing teams to build and publish web pages would be one valid way to get teams comfortable with the tech, the workflow, the shift in how to propagate and convey information to an audience.
Without a mandate, many teams won't adopt the Internet as a medium of information exchange because their processes work just fine and have worked for the last 20 years.
I think it's fair to put AI in a similar light. Unless teams adopt it and use it, it's hard for an org to understand how to get value out of this technology and how it affects existing processes and assumptions.
The web took off all by itself because it had a clear value proposition for some use cases.
> The web took off all by itself because it had a clear value proposition for some use cases.
Sears was the OG Amazon. Imagine if Sears had seen it as the new digital catalog.
Blockbuster missed on streaming until it was too late.
Many, many legacy companies did not understand the web and did not understand the impact of the Internet to their business model.
> And you think forcing blockbuster's software teams to use the Web would have changed that?
And even more importantly, the companies who went all in early and spent too much money on it too early without good reason went boom. You had to have actual business reason for it to be success.
Those were always there, and will always be there. The type of time frames people are getting anxious about now rarely work in the real world, though, where potential customers don’t just switch products/service provides unless they’re facing catastrophic outcomes if they don’t.
And AI is not making the difference there that people think. I worked on a product that entered the market as a newcomer, wooed plenty of customers, and even though everyone _wanted_ it, only customers _urgently_ looking for a solution got on board quick (within <6 months).
Ironically enough, the product pivoting to Agentic AI hard killed a ton of momentum and interest from customers, despite exciting investors.
> I would prefer to search for new usages in a more strategic way
Looking back at the Internet, who would have thought that it would eventually create a Netflix, Amazon, Shopify, Spotify, Google Maps, etc. Just wild the things that ended up coming out of pushing bits over a wire with few simple protocols.
In an ideal world, you make strategic bets, but I can also see the case for the opposite this early in the lifecycle of a technology. You just don't know until you try.
Mid/late 2023, it wasn't at all obvious that it would take over coding that fast.
I definitely get the impression that many people thought it would eventually create shopping, streaming, and mapping sites.
I think people were less likely to have predicted things like social media or YouTube, though those weren't ideas sprung from a vacuum either.
None of these shifts were obviously the right bet and many organizations lost because they missed the opportunity. Now orgs are on the same horizon and I can see why they don't want to miss this window.
Sears was a different story, in that they were a real estate company with a store front and retail real estate took a nosedive due to ecommerce. But that's a different discussion.
They have a whole management team and can’t seem to find a way to judge or god forbid encourage developers…
For example, everyone talks about strategy, but when you ask them what's our strategy answer is usually something like:
* let's figure out together
* industry changing is so fast, we should revisit plans every quarter
...
"Welcome to the new contractor who will be the artitect our new infrastructure. What is your dream IT setup?"
"Yeah, we can't afford that. Lets revisit once you wrangle those 2003 Dell R620's running Windows 2008 with no patching."
And that is why after eight months i'm terminating my contract on Friday and swimming back to shore.
I mean that's more of an ex-post statement.
Ex-ante they look at things as objects and visualise/simulate what one ought to do independently, as opposed to being a lemming.
https://en.wikipedia.org/wiki/Elinor_Ostrom#%22Design_princi...
What if the goal of an economic system was to support everyone instead of maximizing the upside for winners? Perhaps that's the sort of change necessary for improvement. Perhaps having billionaires is the failure state.
A goal fails - who sets a goal? The keyword is system.
An economic system needs something like a Nash equilibrium where defectors are sufficiently discouraged (and cooperators are rewarded as you imply). https://en.wikipedia.org/wiki/Nash_equilibrium
Lets talk my bonus, I will open the bidding at $1 per token.
I participate in some management-focused online communities. It’s crazy how many threads there are from frustrated managers trying to get their teams to stop thinking that their token use will be used as a proxy for their performance.
I think a few dumb companies did this and then it spread across social media, triggering a mass panic from engineers afraid their companies will be doing the same thing.
It’s getting so bad that the conversation is shifting to how to identify and coach the token-maxxers to stop wasting the team’s budget every week.
Because it is going to happen. Do you think metrics are tracked for fun?
Even if current leaders don't do it, next people might do it, how do you tell new leaders that we don't look at this metric? Metric exists to take action based on it
Nobody wants 90% of their token budget spend going to the 10% of people wasting them for number-go-up purposes.
Inefficient token use is going to become a metric.
I know - slightly off topic - but would you be willing to share this list?
I'd be curious to hear from people well versed in group psychology/dynamics and/or just a lot of leadership/people experience: what leads people to this type of thinking once they get in a group setting? It just... seems endemic at this point.
Obviously nobody here is going to know what I do or don't know, but I'm just increasingly curious what I am not understanding about this type of thing. It seems so obvious, yet that makes me ever more suspect that I'm oversimplifying it, or just totally ignorant about the problem in general.
Roll it all together and saying "just use it dammit" has some obvious advantages:
1. It's clear.
2. It's simple.
3. It eliminates all excuses employees might come up with for not using it.
The people at the top of these companies aren't stupid. They might have miscalculated how many tokens people can actually use, but that's very hard to calculate because usage is opaque and tools/processes change on a nearly weekly basis. They will eventually build out processes, tools, social conventions and performance metrics that take into account efficiency of token usage. But this is hard! Most managers aren't really assessed on the precise productivity of their teams, for instance, because productivity is often poorly defined.
Game theory! The downside of being brave vastly outweighs the upside. For the C-suite, there is no cost to herdlike-behavior, regardless of the outcome. However, there is a very high personal downside to being a maverick, and your board later discovers you made the wrong choice against the grain. The upside of being maverick and right is very limited.
Once a behavior has become mainstream, hopping on the bandwagon is no longer individually attributable to decision-makers, but is seen (and reported) as a macro-economic phenomenon: Nadella, Zuckerberg and Bezos didn't overhire - the American tech industry overhired.
Whats happening now and whos driving it is interesting. The CEO has a license for this new tool (think one of the top 4, Qwen Claude, Gemini, openAI) and really likes it. So much so that they (non coder) are making lots of little single page web apps.
The COO is bollocks deep in AI, and is saying that we cannot buy any SaaS products anymore. We must make it ourselves.
The engineering manager has seen this as an opportunity to build out a brand for engineering (its a small department in a medium sized company) by delivering quickly what the large year long efforts cant.
This has formed a slopnexus where PoCs are spun up left right and centre, but there isn't much time or thought going in to making them sustainable.
What started out as a (simple ish) asset management tool, neatly scoped into a deliverable PoC has morphed into a 5 product as one monster.
Its a mess that will either lead to burn out or disaster.
And myself being an infrastructure guy that needs to maintain all these PoCs that are now suddenly critical for production, it's the perfect nightmare.
And mind you, that dynamic always existed to a certain degree (laptop on a desk that runs some ugly Python script that does half the work of the BizOps team? Check. GCP account attached to the GSuite running a random instance for finance when the company is 101% on AWS? Check. Spreadsheet with macros that sends emails via Outlook as a mailing list manager? Check.) but at least when you discovered that you could scold them and tell them that we need to migrate this to a proper system because security. But nowadays with vibe-coded internal apps...it's a challenge.
Whats more annoying is that he changes AI provider lots, so we can never inject rules/skills to make sure he uses the right pathway from the start.
Large and fascinating topic I'm researching, very relevant for agentic AI and ML too. One way that groups can fail is that they just don't work to dampen / vote out individual errors properly (see PAC learning, Condorcet). Other kinds of errors only occur in groups, and can occur even when constituents individually aren't actually wrong. Some related stuff is:
https://en.wikipedia.org/wiki/Condorcet's_jury_theorem https://en.wikipedia.org/wiki/Group_polarization https://en.wikipedia.org/wiki/Availability_cascade https://en.wikipedia.org/wiki/Information_cascade
The last is probably the most relevant here and made worse by the negative effects of hierarchy. To quote one section:
> The negative effects of informational cascades sometimes become a legal concern and laws have been enacted to neutralize them. Ward Farnsworth, a law professor, analyzed the legal aspects of informational cascades and gave several examples in his book The Legal Analyst: in many military courts, the officers voting to decide a case vote in reverse rank order (the officer of the lowest rank votes first), and he suggested it may be done so the lower-ranked officers would not be tempted by the cascade to vote with the more senior officers, who are believed to have more accurate judgement;
For token-maxxing, our "senior officers" are just executives, and line workers aren't going to vote. Who is the senior officer for those senior officers? It's not shareholders! It's really the executives of even bigger companies, because that is the actually applicable promotion ladder. It's all kind of obvious, but also a genuinely better explanation than "monkey see monkey do". These are just the simpler things, and there's more gnarly dilemmas in https://en.wikipedia.org/wiki/Common_knowledge_(logic)
Thank you so much! This is why I love HN.
and tokenmaxxing is even worse due to https://en.wikipedia.org/wiki/Goodhart%27s_law because whatever you measure with tokens, once you start "tokenmaxxing" you have no measure to look at
"It is difficult to get a man to understand something, when his salary depends on his not understanding it." -Upton Sinclair
Trying to operate as a rational, thinking person in a lot of environments right now feels impossible. Rational thought is being treated like AI skepticism.
That VC funded gravy train is likely coming to an end. But fortunately there are also reasonably efficient models now so that the tokenmaxxers can still make the (much cheaper) tokens go brrrr.