Why Your CEO Thinks Gen AI Is Magic, And What To Do About It
It isn’t just because they are buying Altman’s latest hype or because they are clueless
Classical software is deterministic. Given the same inputs and state, you get the same outputs every time. Generative AI is probabilistic. Given the same inputs and state, you get different outputs every time.
To get results from a traditional software language, it takes a lot of time to setup conditions properly, write the code and test before you get a single useful result. But once you did, you could rapidly crank out more and more results at scale given Moore’s law, modern internet / networks, and cloud computing.
But generative AI is different because it can give you a near-instant “answer” that is completely hallucinated and then give you another “answer” that is 180 degrees different but correct, a moment later.
That feels like magic to leaders who grew up in a world where getting a good answer took time but meant you could repeatably get more good answers. This leads to thinking like “I can write a prompt to do that in 3 minutes, so in 3 days you should be able to ship it.” But that kind of thinking ignores the inherent probabilistic models that make LLMs possible.
If CEOs treat Gen AI as magic that will instantly 10x their progress, they not only are going to get bad results, they will unrealistically stress their teams who are trying hard to get good ones.
Why smart CEOs misread this
Mental model mismatch. Years of deterministic systems train executives to expect exactness. GenAI returns likelihoods, not certainties.
Anthropomorphic pull. Natural language invites people to project understanding and intent where there is only pattern prediction.
False confidence from speed. The model answers instantly, so people assume it understands fully.
What the probabilistic core actually means
To use GenAI well you need to think in probabilities. Treat each output as one plausible answer from a distribution, then judge performance across many trials, not a single run.
That means that reliability comes from the system around the model, not from a clever prompt. Builders need to embed prompts in workflows with retrieval, tools, tests, logging, and review so the product behaves predictably and ships real value.
You might also want to consider Bayesian approaches as a recent paper showed that it could detect hallucinations and drastically improve the quality of results. This is just fancy language for doing Gen AI things many times in a structured way to reduce errors / hallucinations.
What you should do about it
1) Educate and train, then keep doing it
Give leaders a clear mental model: LLMs predict tokens, not truths. Show repeat prompts with different outcomes, then show how guardrails reduce variance. Gen AI is a tool not magic or a brain. It isn’t human, it just pretends it is. You need to trust systems, not single answers.
2) Define sharp problems and harshly prioritize
AI creates infinite ideas and infinite half work. If you don’t define the problem well and don’t define sharp, important problems, you will get the results you deserve. Precision turns probabilistic output into useful work.
3) Use shipyards
Create mixed crews that own a problem from input to impact. Each shipyard brings product, engineering, data, design, and other stakeholders. They ship small increments on a fixed cadence, measure actual outcomes, and fold learning into the next release. No “AI magic dust.” Concrete targets, stable interfaces, and visible checkpoints.
The bottom line
Gen AI feels like magic because probabilistic outputs wear a human voice. Treat it like an instrument, not an oracle. Train leaders on how the instrument behaves, choose precise problems, prioritize hard, and run shipyards that turn variance into value.
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Wow, didn't expect this take on the mental model mismatch. Could you maybe expand on the 'anthropomorphic pull' and its affect on CEO expectations?