It's because the average organization has lots of people who don't care about their own productivity and won't adopt new tools or processes unless forced to. This is true of most new tech - lots of workers had to be forced into using computers - but AI also has some other bumps to cross like lots of people who tried early models and then wrote them off, not realizing how fast they'd improve. And most orgs have no infrastructure or processes for allocating individuals token budgets, and most employees have no experience of properly deploying budgets.

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.

> what leads people to this type of thinking once they get in a group setting

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.

> what leads people to this type of thinking once they get in a group setting? It just... seems endemic at this point.

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)

we are going through our second AI transformation, the first one didn't work that well because the tools were shit.

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.

Won’t be canned for going with the herd. I think it’s that simple, even if the herd is running off a cliff.

This is a consequence of elements of monopoly power existing in your organization. When you don't have to compete for income you honestly forget how. Then the company becomes a cargo cult of bad ideas driven by managers struggling to differentiate themselves.