Comment by sdeframond
5 days ago
Groups resist to change - the bigger the group, the most resistance there is.
As a leader, pushing for rapid change cannot really be nuanced lest the push dissipates into the organization's entropy.
5 days ago
Groups resist to change - the bigger the group, the most resistance there is.
As a leader, pushing for rapid change cannot really be nuanced lest the push dissipates into the organization's entropy.
Perhaps, but the change you get (if any) is most likely to be what you push for and reward/punish.
It's irrational to push for tokenmaxxing (literally "please increase our AI spending") and not expect that this is the result you are going to get. You won't get productivity increase, since that is not what you are pushing for - you will get token usage maximization (engineers running inane agentic tasks against your code base to increase usage, using company paid AI for their side projects, etc, etc).
The evidence suggests that many tech leaders do not realize that an immediate result of heavy handed uninformed top down decision making is transforming the “work together, succeed together, giving quality” ethos into a cynical game theory minimax effort to game whatever stupid arbitrary metrics are used to implement the top down fad of the quarter; do it consistently and you get a work force that can be given a metric and immediately, instinctively, tell you how the work flow will be adjusted for the new metric, and where the difficult problems will be shunted to.
I'm not sure the leaders would disagree with what you're saying. They tokenmaxxed to understand what it looks like when AI gets into every corner of the business; now they feel they've gotten enough info (or at least that more info wouldn't be worth the cost), so they're adding in cost controls. As the article says, this is not great for AI model providers trying to predict what their future revenue is going to be, but it's not obvious that there's any mistake here for AI users.
> They tokenmaxxed to understand what it looks like when AI gets into every corner of the business
Perhaps that is what they were trying to do, but the reality is that all they will have got is a large token bill. The decision makers may have hoped that tokens would be used in most productive fashion possible so they could evaluate if the cost was worth it, but what they will have actually got is what they asked for and measured, high token usage (applied to whatever people needed to do to get their usage stats up, regardless of productivity).
The other business-as-usual factor is that there will be false reporting up the chain, so if the company understands the CEO want to see high AI usage and productivity gains, then s/he will see high AI usage (a large token bill) and will be fed success reports of corresponding productivity gains.
In a typical corporate environment, if all your peers are reporting success, achieving what the CEO wants, do you want to be the only one reporting failure? So - everyone reports high AI usage (easy for the employees to make happen), and most everyone also reports productivity gains if they understand this is the expectation.
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It’s not clear companies were measuring anything but token usage. What information could leadership have collected to determine what worked, what didn’t, and what needs more data? Other than the balance sheet and revenue, do companies actually have sufficient information to understand the results?
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