If this were genuinely a consultant's writing, they would need to present a modeling hypothesis for why failure occurs in organizations and support it with metrics. But claiming their own observation of 0% is not a hypothesis.

Of course, they can write it on a personal blog. That's not inherently bad.

But when they're rejecting client requests while offering no opinion on how to model the metrics that would define success—that's the problem. This type of writing is just an illusionary piece that claims to offer insight. It's a bad form of human writing. I like to call this style the 'Rhonda Byrne Secret type.'

At the very least, a consultant should provide modeling of their hypothesis and actionable advice.

Explaining everything with a single reason—that people are afraid of being laid off if they don't adopt AI—is just a cop-out. There's no hypothesis, no reasoning behind why that claim holds. It's trying to explain everything with just that one assertion. I'm not even sure if that holds up organizationally

The OP offers no logical reasoning about how decisions are made or why they break down.

A proper piece of writing would have looked like this:

Why did the client want to adopt LLMs?

What problems arose during the client's process of adopting LLMs?

How does this connect to the organization's internal issues and disrupt its pipeline?

You need at least that level of description. This is just an emotional post that seems to say 'I wish LLMs would fail.

If they had really explained organizational dynamics, they would have talked about the asymmetry of expected costs.

Professional managers and mid-level managers prioritize career defense above all else, so they're extremely risk-averse. They would have explained the costs of adopting AI versus the costs of failing to adopt AI.

If they reject AI adoption, shareholders or the board would say they're 'failing to read the trend.' If they adopt AI and fail, they can package it as 'early market entry costs'—a defensible risk.

Providing that kind of defensive logic to decision-makers is exactly what consultants do.

It's described as being held at gunpoint, but it's really just a prisoner's dilemma. In a market where competitors and vendors are all lying and saying 'we've achieved 100x productivity with AI,' the moment someone tells the truth and says 'we don't need AI for our business,' they become a political target both inside and outside the organization. In the end, everyone knows it's useless, but they approve budgets for 'fake AI projects' anyway. I can offer that kind of cheap insight too—it's a Nash equilibrium where the system has been driven to a race to the bottom.

And I'm not even a consultant

Honestly, it's just shouting into an echo chamber. They call it organizational failure, but it's really just 'come on, my side.' A consultant who does that is untrustworthy. It means they have no metrics.

Same goes for their 0% claim. It's overgeneralization, isn't it?

So why did they fail, what went wrong, and how should these AI projects be analyzed—and by what metrics? They skip all that and just throw out a single KPI. What insight does that offer?

Honestly, there are a lot of people on HN who just shout that AI is useless because their own jobs are at risk. That's an undeniable fact. In my country, Korea, we say 'like a pheasant hiding its head in the ground.' It means denying reality and lacking self-awareness.

I've also seen many failed AI projects due to a lack of proper evaluation of AI capabilities. I don't want to deny that premise.

But if you're going to make that argument, you need to describe why they failed. There's none of that here, just this:

'We're going to opt out because it looks like AI projects will fail.'

They package it as being cautious, but when you peel back the wrapper, it really means:

'We can't keep up with the AI trend. And we can't tell what works and what doesn't.'

Sure, AI has a bubble. No one can deny that. But separate from that, it is useful. Do you really think millions of consumers would stick around for something useless?

Social problems are arising, and people are losing jobs because of it. Let's think the other way around. It doesn't make sense for people to lose jobs over something that's truly useless and wrong.

People are losing jobs because it's useful and valuable. So wouldn't it be more accurate to say that the problem is 'over-application' rather than the thing itself being bad? In other words, we could say that it's being valued more than it's worth. But this argument just says it's bad, period, with no nuance. I can't trust someone who talks like that.

AI writing, if you give it a red-team prompt, can at least refine the surface of counterarguments to your claim. But this—from overgeneralization to extremes—is a textbook example of bad human writing