The Real AI Problem Is Not the Technology. It Is That People Have Stopped Thinking.

AI's Deepest Flaw Is Human, Not Technical

The loudest complaints about artificial intelligence right now orbit familiar territory: runaway compute costs, electricity and water consumption, companies jamming AI into products nobody asked for. Those are real. They are also beside the point.

Christopher Penn, writing in his newsletter Almost Timely News, argues that all of those problems are symptoms of a single root cause: people have stopped making decisions. They have handed their executive function to AI and walked away.

His framing warrants serious attention.

What the Data Actually Showed

Penn ran a genuine experiment. He pulled 180 days of conversations from more than 40 subreddits — communities focused on marketing, business, and AI, including r/marketing and r/chatgpt — and fed the resulting corpus, over 800,000 words of user discussion, into a NotebookLM instance. He then used Claude Code to compare what that Reddit data showed people struggling with against the topics he had already covered in his own 2026 newsletters.

Claude returned ten themes. The list included AI visibility challenges, broken deployment pipelines, agentic AI oversight degrading, 40-60% of company AI budgets being wasted on the wrong models, AI sycophancy corrupting synthetic focus groups, and concerns that companies are hollowing out their junior talent pipelines because AI handles entry-level work.

The model, according to Penn, kept steering him toward writing about measurement: specifically that marketers and operators are evaluating AI by the wrong metrics entirely — "tokenmaxxing," as the Reddit discussion apparently called it — optimizing for volume of AI output rather than quality of decisions made.

The Executive Function Problem

Penn's core argument is that the measurement obsession is a downstream effect of something more corrosive. When people treat AI as a decision engine rather than a research or drafting tool, they stop exercising judgment. They accept outputs. They iterate on prompts rather than on their own thinking.

This pattern extends beyond individual productivity. Organizations are structuring workflows around AI throughput. Junior employees who would have previously built judgment through low-stakes decision-making are instead watching AI make those calls. The judgment muscle, at the organizational level, does not get trained. A company that cannot make decisions without an LLM is brittle in a way that a cost-per-token analysis will never capture.

The Strongest Counter-Argument

The fair pushback argues this: every major productivity tool in history — calculators, spreadsheets, search engines — prompted identical warnings about atrophied human capability, and those warnings largely did not pan out. People adapted. Accountants did not stop understanding numbers because Excel existed; they stopped doing arithmetic by hand and spent more time on analysis. The same pattern, this argument goes, will hold for AI: humans will offload mechanical cognition and spend more energy on genuine judgment.

This position has historical support. Where Penn's framing adds friction to it is the specific observation that AI, unlike a calculator, generates confident-sounding prose and opinions. A spreadsheet does not tell you what to think about the numbers. An LLM will. That difference in interface may produce a different behavioral response — one where the user accepts the conclusion rather than using the tool to reach their own.

Whether that distinction holds at scale is exactly what remains unresolved.

The Budget Waste Figure Deserves Scrutiny

One specific claim in the Reddit synthesis warrants a flag. The assertion that 40-60% of company AI budgets are being wasted on the wrong models originated in Reddit discussion aggregated by an LLM, not from a peer-reviewed study or an audited corporate disclosure. It may reflect genuine frustration among practitioners. It is NOT a verified industry statistic. Treat it as a directional signal from a large sample of practitioner opinion, not a measured figure.

Practical Implications

Penn did not publish a full prescription — he flagged the diagnosis and noted he might address measurement frameworks in a future issue. But the implied direction is clear: organizations need to audit not just what AI is producing, but what decisions humans have quietly stopped making since AI arrived.

The agentic AI oversight problem Penn's data flagged is the operational version of the same issue. Agentic systems — AI that takes multi-step actions autonomously — require humans to set the objectives, evaluate the outputs, and catch the errors. If the humans in that loop have already offloaded their judgment to the AI, the oversight layer is a fiction.

The concrete unresolved question is whether existing AI governance frameworks inside companies — most of which focus on data privacy, bias audits, and cost controls — are designed to catch judgment abdication at all. Most are not.