The AI Industry Doesn't Lack Marketing—It Lacks Good Products

When the Market Votes with Its Feet, Marketing Can't Explain the Predicament

Recent market signals are jarring: election results, data center bans, and a full-scale selloff of the AI trade. In the face of this, the industry's first reaction is often "the PR wasn't done well" or "the narrative was misunderstood." But there's a more direct explanation that deserves serious consideration—the problem isn't marketing, it's the product itself.

This judgment comes from a widely circulated video commentary, and its core point is quite sharp: AI companies go around claiming they're going to change the world, even joking that they're going to "kill everyone," yet when users actually open the product, the experience doesn't live up to these promises. The market decline isn't because everyone misunderstood AI, but because the product failed to convince anyone.

The value of this perspective is that it pulls the discussion back from "how to tell a good story" to "how to build something good." For any technology industry, when external skepticism erupts in concentrated fashion, the most dangerous response is to define criticism as a communication problem.

"Voting with your feet" is a classic concept in economics and public choice theory, referring to the way individuals express preferences through actual actions—leaving, exiting, or not participating—when they cannot express them verbally. In the context of capital markets, it manifests as investors directly selling stocks and withdrawing funds rather than publishing commentary or making suggestions. As opposed to "voting with your hands" (influencing decisions through voting or speaking out), voting with your feet is often faster and harder to reverse through PR spin. The article borrows this phrase to emphasize that the market's selloff of the AI sector is a collective statement of action, reflecting doubt about the product's actual value rather than a misunderstanding of the narrative.

The Most Widely Used Products Are Precisely the Most Mediocre

The commentary points out an awkward fact: Google's AI Overviews is one of the most widely used AI products in the world, but its experience isn't good; free ChatGPT is likewise directly described as "bad."

This reveals an industry-wide misalignment. The most widely used products aren't necessarily the best experiences—they're simply bundled into entry points users can't live without. The summaries that pop up automatically in the search box, the free but limited models in the chat window—users didn't actively choose them, so it's hard to say they were persuaded by them.

What truly determines an industry's reputation isn't daily active user numbers, but how users feel in the moment of use. When a product is seen by hundreds of millions of people yet considered by a considerable portion of them to be "useless or even worse," the damage to the industry's reputation is far greater than a single marketing misstep.

Google AI Overviews is a generative AI summary feature launched by Google Search in 2024, automatically generating an AI-synthesized answer at the top of search results so users can get an overview without clicking links. Built on the Gemini model, it covers billions of search users, hence its enormous usage. But since launch, the feature has repeatedly produced factual errors and absurd suggestions (such as recommending adding glue to pizza), triggering widespread criticism of its reliability. This case typically illustrates: a strong distribution channel can push a product to a massive user base, but it cannot replace the product's own persuasiveness.

The "First Good Product" That Held High Hopes Still Isn't Enough

The commentator gives Meta's Muse a rather high designation—"the first great consumer AI product." But even this promising product still can't do everything Meta promised.

This detail is crucial. It shows the problem isn't "there are no good products," but that "good products are still less complete than the marketing claims." The difficulty of consumer AI isn't one or two stunning moments in a demo video, but whether it can reliably deliver on its promises in daily use. When there's an obvious gap between product capability and marketing, users' disappointment gets amplified, eventually turning into distrust of the entire category.

In other words, the industry's current state is: there are highlights, but not enough products that can turn those highlights into reliable everyday experiences.

"Consumer AI products" refers to AI applications aimed at ordinary consumers rather than enterprises or developers, typically characterized by ease of use, embedding in daily scenarios, and low barriers to use—such as chat assistants, intelligent search, and image generation tools. Unlike enterprise-facing APIs or professional tools, consumer products directly undergo the experience test of mass users, and word-of-mouth spreads extremely fast. The difficulty lies in this: demo scenarios can be carefully designed, but stability, accuracy, and response quality in daily use cannot be sustained through packaging. Therefore, the consumer market is often the most rigorous battlefield for testing the gap between AI promises and experience.

The Only Path to Changing Perceptions Is Shipping Better Products

The commentary's conclusion is plain to the point of being brutal: if this industry wants to change how the outside world sees it, it must ship better products—it's that simple.

This sentence deserves to be chewed over repeatedly by everyone in AI. When capital markets tighten their patience, regulatory attention rises, and public sentiment turns skeptical, any PR spin looks pale. The only thing that can reverse the narrative is making users feel "this thing is actually useful" the moment they open the product.

From an industry perspective, this also means the center of competition will shift: from competing over model parameters and launch-event volume to competing over product reliability, completeness, and real-world value delivery. Only companies that can first close the "promise-experience" gap will have a chance to win trust in the next cycle.

For users, this judgment is equally instructive: to evaluate an AI product, don't look at what it claims it will do, but at what it can reliably do well right now.

One-line Summary

The root cause of the market selloff of AI isn't a marketing failure, but products that simply aren't good enough.

Paragraph Summary

In response to recent market signals such as the AI trade selloff and data center bans, the article rebuts the industry's habitual "the PR wasn't done well" explanation, pointing out that the problem is that the products themselves don't live up to the hype. Citing a video commentary, it notes that the most widely used AI products—such as Google AI Overviews and free ChatGPT—don't offer good experiences, and exposure doesn't equal persuasiveness; even Meta Muse, touted as "the first great consumer AI product," hasn't fully delivered on its promises. The conclusion is that the only path for the industry to change perceptions is to ship better products, and the focus of competition will shift from parameters and volume to reliability and real value delivery.