The Smart Position on AI is Between Panic and Propaganda
- Sheldon Dunn, MBA, PhD
- Mar 7
- 4 min read
Overclaiming the impact of AI is becoming a business model.
Don't get me wrong. I like large language models. I have been using them heavily for a long time, and I think they already create real value in research, writing, analysis, and certain kinds of operational work.
But using a tool is not the same as surrendering your judgment to the people selling it. Technology firms overclaim. Enthusiasts amplify. Markets reward bold stories. Meanwhile, actual adoption still has to survive the stubborn complexity of converting a "capability" into a customer service experience.
That is the tension behind the chart below. The real question is not whether AI matters.
It does.
The real question is whether the evidence being used to sell its labor-market impact is strong enough to justify the certainty being projected onto it.
The chart is not the problem. The sermon built on top of it is.
The chart itself is not what bothered me.
What bothered me was watching a theoretical exposure graphic get preached as though it had already measured the future of work.
Within hours, people were using it to announce that AI is “coming for all knowledge work,” that managers should prepare for sweeping white-collar disruption, and that the only real question is how fast the red will swallow the blue.
That is not analysis, it's hype laundering.
A vendor publishes a model. Influencers strip away the assumptions. Then the simplified version starts circulating as managerial truth. By the time it reaches LinkedIn, a speculative graphic has turned into economic scripture.
The blue shape is not destiny.
The blue area is not job loss. It is not even direct job coverage. It is a theoretical exposure estimate built by combining O*NET task data, Anthropic’s own usage data, and a 2023 framework (Eloundou et al., 2023) that scores tasks partly by whether an LLM could make them at least twice as fast. That estimate is then rolled up into very broad occupational categories.
Useful? Maybe. But it is still a model of possible task acceleration, not the labor market caught on camera (anthropic.com).
That distinction should matter a lot more than it apparently does.
People keep staring at the blue area as though it were a neutral measurement of what AI can “really do.” It is not. It is a constructed ceiling. And constructed ceilings have a funny habit of becoming destiny the moment evangelists repeat them often enough.
The red does not owe the blue anything.
The pious reading of this chart is that the red will eventually expand until it fills the blue.
Maybe it will, maybe it won't (probably not)
A capability estimate does not get to skip the hardest part of technology adoption: fitting into real work.
Real organizations are not clean task lists. They are handoffs, exceptions, trust problems, compliance demands, legacy systems, customer expectations, and local workarounds. Vendors are usually much better at imagining where their tools could fit than understanding where they actually will.
So the gap is not automatically evidence of delayed inevitability.
It may also be evidence that the ceiling was oversold.
The evidence does not support the swagger.
If people were reading the report carefully, the commentary would be much more restrained.
The report does not show broad white-collar displacement. Its main labor-market finding is that unemployment has not systematically increased for highly exposed workers since late 2022. It does find tentative evidence of slightly slower hiring for recent college graduates (22–25 years old) entering exposed occupations.
Those are signals to be aware of, not permission slips for CEOs, consultants, and AI evangelists to speak as though mass displacement is destiny.
What this chart actually revealed
The most revealing thing about Anthropic's report wasn't what it said about the future of work, but the discourse around it.
It showed how quickly people confuse “AI could speed up parts of a task” with “AI is coming for your white-collar jobs.” It showed how easily a (likely biased) vendor estimate becomes received wisdom once enough people repeat it confidently. And it showed, again, how much AI commentary is really just message amplification, instead of real insight.
That is the missing argument.
The manager’s job is not faith
Managers do not get paid to believe. They get paid to decide under uncertainty.
A serious manager does not need to choose between AI panic and AI worship. The useful stance is skepticism tied to operations.
Treat charts like this as prompts, not prophecies. Ask where the tool has already improved speed, quality, margin, or customer experience in a workflow like yours. Ask what extra verification, training, and redesign it requires. Ask which part of the claim is observed behavior and which part is vendor extrapolation.
That is the smart position on AI.
Not fear. Not faith. Judgment.
Footnote:
Anthropic’s report is a company-published research article, not a peer-reviewed journal publication. Eloundou et al. (2023) is an arXiv preprint, which also is not peer reviewed in the journal sense. That does not make either source useless, but it does mean they should be treated as provisional rather than as settled evidence.
References
Anthropic. (2026, March 5). Labor market impacts of AI: A new measure and early evidence. https://www.anthropic.com/research/labor-market-impacts
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models (arXiv preprint No. arXiv:2303.10130). arXiv. https://arxiv.org/abs/2303.10130
National Center for ONET Development. (2026). ONET database (Version 30.2) [Database]. O*NET Resource Center. https://www.onetcenter.org/database.html
The distinction between a theoretical exposure estimate and an actual measured outcome is something I ran into directly while studying CAPM and the single-index model in FIN 311. Beta is a model of expected sensitivity to market movement, not a recorded fact about how a stock will actually behave, but people quote it as if it were destiny for a specific stock's future. Dunn's point about the blue area becoming a constructed ceiling that gets treated as inevitable maps onto that almost exactly, a useful model gets repeated so often that people forget it was built on assumptions in the first place. For financial advising specifically, this feels directly relevant, because a client who read one AI headline is going…
The essential claim is that while there might be a large difference between the blue “ceiling” representing potential usage and the red “floor” representing current usage, this is not necessarily an indication that the red will inevitably fill the blue at some point but rather that the ceiling has been oversold since real organizations are complicated systems of handoffs, exceptions, trust, compliance, and legacy technology in which integrating a new capability into practice is difficult. According to Dunn, management is a matter of judgment, not belief, either fear or faith, and includes questions about where AI has already sped up the process or added value and what additional validation, training, and redesign it requires.
One interesting aspect from the article that I found quite relatable to my journey of MIS and IT/project management was that the capability of AI doesn't mean automatic replacement in the workplace. This ties directly into my area of study where AI can definitely increase the speed of writing documentation, creating summaries of the tickets, and organizing project notes, yet there is no certainty in what is important because the decision is made by people. The AI may create a summary of a problem faced by a user during remote troubleshooting, yet it cannot determine the complete context, the level of importance, or even whether or not the suggested solution would fit for the particular system. Deductively speaking, since real…
I liked the balanced perspective in this article. Recently, I feel like people talk about AI in very extreme ways. Some people think AI will replace everything, while others think it solves every problem perfectly.
As a business student, I have seen AI being useful for brainstorming, organizing ideas, and saving time, but I also think people can become too dependent on it. Sometimes the output looks professional, but it does not fully understand the real context.
This feels similar to entrepreneurship. Tools can help improve efficiency, but they cannot replace human judgment, decision-making, or understanding customer needs.
For me, AI seems most useful when it supports human work instead of replacing critical thinking.
The most interesting part of this article is how easily speculative AI charts are treated as proof that job loss is inevitable. This mirrors the common hype in marketing, where AI is often framed as an automatic replacement for creative work. In our class venture, AI could assist with tasks like caption writing and promotion brainstorming, but that does not automatically create a better customer experience or a smoother workflow. In reality, the hardest part is not generating content; it’s making sure the content fits a specific business, audience, and local context.
From group projects I’ve been part of, tools only create value when the team actually redesigns how they work around them. Otherwise, AI just produces more output without…