The AI Ceiling Is Lower Than Anyone Is Saying | by Addo Zhang | Jun, 2026 | MediumSitemapOpen in appSign up<br>Sign in

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The AI Ceiling Is Lower Than Anyone Is Saying

Addo Zhang

7 min read·<br>1 day ago

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TL;DR<br>“AI is short-term overestimated, long-term underestimated” is a half-thesis propped up by financial narrative. LLM utility is locked within the symbolic domain, and software engineering productivity has never been measurable — for the “long-term underestimated” half to hold, we’d need a fundamentally different paradigm from statistical learning.<br>AI’s Real Boundary: From Financial Narrative to Epistemological Ceiling<br>I. The Starting Point: A Financialized Tech Thesis<br>“AI is overestimated in the short term, underestimated in the long term” — this is Amara’s Law applied to AI. It sounds reasonable, but it bundles two fundamentally different claims:<br>The tech narrative : Technology adoption curves have inertia. Early expectations run too high, but the eventual value exceeds them.<br>The financial narrative : Capital always invests in the future, buying expectations. When capital floods an industry, bubbles form. On the supply side, those holding positions have a structural incentive to hype the asset — not through coordination, but as a product of market mechanics itself.<br>These two narratives stacked together produce the current shape of the AI market. But they need to be examined separately, otherwise the second half of the thesis — “long-term underestimated” — quietly borrows its logic from the first, when in fact they’re independent bets.<br>The tech narrative half isn’t wrong. Technology does get underestimated — steam engines, electrification, the internet: each followed the path of “bubble bursts → real diffusion → exceeds early expectations.” Amara’s Law holds historically because these technologies eventually broke through their early application boundaries and penetrated a broader physical domain.<br>The problem is: that penetration came with conditions. Electrification spread because the physical domain welcomed it — it reduced friction without introducing new uncertainty. LLM’s spread encounters the opposite: the deeper it goes into the physical world, the higher the uncertainty, not lower.<br>So the second half of the tech narrative, “long-term underestimated,” requires a premise: that the technology can diffuse. That premise needs to be proven independently for LLMs — you can’t borrow it from historical patterns.<br>A clarification: this article is about the current mainstream AI paradigm — the statistical learning path represented by LLMs. Not a verdict on all possible AI paths, but an assessment of the utility boundary of this specific path.<br>II. What’s Being Sold Isn’t a Product — It’s a “Win Probability”<br>Let’s be clear about one thing: a bubble is itself proof of value. Capital doesn’t inflate bubbles around zero-value assets — AI has real value, which is precisely why a bubble can form. Industries with no future and no value don’t even get to have bubbles.<br>The dispute isn’t about “is there value?” It’s about “how much value, in which domain?” Pricing has been set at “AI can penetrate the entire physical domain,” but the real utility domain may be only the symbolic space — that gap in magnitude is the actual problem.<br>The ROI on AI investment remains unresolved. Hundreds of billions of dollars have already been deployed. If the returns were real, the math should have been provable by now. That gap is itself a signal.<br>The honest framing is: a large portion of current AI investment is essentially an option, not an investment in current efficiency — a bet that “if general AI ever arrives, early investors have bought an entry ticket.” Options can legitimately show no traditional returns for a long time. That’s fine in itself.<br>The problem is that vendors are packaging options as guaranteed efficiency gains . There’s a structural parallel to the 2008 subprime mortgage crisis:<br>What happened in 2008 was this: the probability that “a borrower might repay” was layered, repackaged, and sold to buyers who didn’t understand the underlying risk, with professional institutions backing the packaging. It wasn’t fabrication of returns — it was relabeling uncertainty as “safe.”<br>The AI narrative does the same thing — “AI might transform productivity,” a probability, gets packaged through media, analysts, and influencers into a certainty of efficiency revolution. The ones left holding the bag are enterprises (large subscriptions), governments (compute subsidies), and retail investors who made decisions based on that certainty.<br>III. How the Bubble Bursts: Slow Bleed, Not Guillotine<br>The AI bubble won’t have a clean detonation moment the way 2008 did.<br>The underlying “win probability” of the subprime crisis was actually calculable — borrower income and housing price data existed, they were just deliberately obscured....