AI Hype vs. Evidence: A Smarter Way to Invest
In an era of unprecedented AI hype, knowing when enthusiasm outruns evidence may be the most important investing skill you can develop. Markets move fast, but data accumulates slowly. And the gap between the two is where costly mistakes are made.
What Is Ethical Coherence And Why It Matters Beyond AI Hype
Most of us have heard the word ethics, and most of us have a rough sense of statistics. But very few people have encountered the idea of ethical coherence — what happens when you bring these two fields together.
The core idea is simple: drawing a conclusion before you have enough evidence isn’t just a statistical mistake. It’s an ethical one.
A Familiar Example: Book Reviews
Imagine a professional book critic who reviews a novel without reading anything else by the same author, and without comparing it to similar works in the genre. Their review might be well-written — it might even be right — but it’s built on a narrow foundation. Good criticism involves context: other books by the same author, works by comparable writers, a sense of the broader landscape.
Most of us wrote book reports in school that looked more like the first kind. They may have earned decent grades, but they weren’t ethically coherent — they were verdicts dressed up as analysis.
How Children Develop And What It Teaches Us About Timing
The same principle applies to how we assess children’s abilities. A child who can do arithmetic at age four is showing something genuinely exciting. But using that one observation to predict their entire academic future would be premature — not just inaccurate, but unfair.
The most reliable assessments tend to come later, around ages 12–14, once there’s a real body of evidence to work with. Teachers, who observe many children across the full range of outcomes, are often better positioned to make these judgments than parents, who know their own child deeply but have a smaller comparison group.
The ethically coherent approach is simple: observe, encourage, and let more data accumulate.
When AI Hype Outruns the Evidence
The current wave of AI hype accelerated with the release of ChatGPT in late 2022. That’s only a few years of observable history — a short runway from which to project decades-long adoption curves with confidence.
Some of the capital flowing into AI today reflects enormous forward expectations built on that limited history. That doesn’t mean those bets will be wrong — many may prove justified. But it does mean a meaningful portion of the AI hype is outrunning the evidence.
A Recent Parallel: The EV Cycle
We’ve seen this pattern before. In 2021, investor expectations around electric vehicles raced far ahead of actual adoption. The long-term direction turned out to be correct, but the investment cycle was far bumpier than the early narrative suggested. AI hype today rhymes closely with that moment.
The Case for Evidence-Based Thinking in an AI-Driven Market
The point isn’t that AI will fail. It’s that honest, rigorous analysis requires respecting time horizons. The sample size is still small. The long-cycle evidence isn’t in yet.
Holding intellectual humility about AI hype isn’t pessimism. It’s just good practice — and historically, it’s what separates disciplined investors from those caught in the cycle.