AI’s brand crisis is not just a public relations issue, but a sign that the technology is entering the social stage

The AI industry is undergoing a “legitimacy test”

If the main theme of AI over the past two years was capability leaps, then the more important variable going forward may be social acceptance.

Business Insider reported on a discussion centered on the “AI branding problem”: marketers, brand consultants, and communications experts are thinking about how to stop the public from seeing AI as a dangerous, abstract, even threatening technology. This kind of discussion may look like a PR fix, but it actually touches the most fundamental reality of the AI industry: when a technology begins to penetrate society at scale, it no longer just needs to prove what it can do; it must also explain why it should be trusted.

This matters because AI is no longer just a topic for developers, researchers, and capital markets. It is entering high-frequency scenarios such as search, office work, customer service, advertising, content creation, programming, education, and healthcare. Its externalities are therefore rapidly amplifying: it affects job structures, content ecosystems, data boundaries, platform power, and infrastructure investment. Branding disputes are not incidental; they are the inevitable result of technology expanding to the edge of society.

From “model capability” to “social interpretive power”

What AI companies are best at talking about today are three kinds of stories: stronger models, faster product iteration, and more compute investment. But what the public receives is another narrative: will automation replace jobs? Was data used for training without authorization? Will generated content amplify misinformation? Is massive infrastructure spending just a race among a few tech giants?

The gap between these two narratives is the root of the AI branding crisis.

In the early stage of technological expansion, capability alone was enough to create appeal; but once AI enters the real economic system, branding begins to take on a heavier function: it has to help users judge whether the technology is safe, controllable, and worth embedding into their workflows and way of life. In other words, in the AI era, branding is no longer just an “identification tool,” but a “risk translator.”

That is also why controversies around AI so easily slide from the product level to the institutional level. What consumers worry about is not just whether a certain feature is easy to use, but whether this technology will reshape the value of their labor, personal data sovereignty, and information environment.

Public concerns are rewriting the commercial path of AI

Several signals mentioned in the report are worth paying attention to: unemployment, data usage, misleading content, and training data authorization have already become some of the most concentrated sources of anxiety among U.S. consumers. This shows that the AI industry’s biggest challenge is not whether its capabilities are strong enough, but whether society is willing to accept the way it expands.

For tech companies, this will bring changes on several levels.

First, AI commercialization will depend more on specific scenarios than on abstract visions

Past AI marketing often revolved around “general intelligence,” “a productivity revolution,” and “the future of work,” but this narrative is becoming increasingly unable to support large-scale trust.In the past, AI marketing often revolved around “general intelligence,” “the productivity revolution,” and “the future of work,” but this narrative is becoming increasingly hard to sustain at scale in terms of trust. What the public is more willing to accept are visible applications: medical assistance, educational tools, enterprise automation, coding efficiency gains, and enhanced search.

This means AI commercialization will become more “de-grandized,” shifting toward scenarios that are explainable, verifiable, and able to be implemented locally.

Second, the communication strategies of tech giants will become more like those of consumer goods companies, rather than laboratories

The report mentions drawing on Procter & Gamble’s marketing approach, which actually reveals a deeper shift: AI companies are moving from “technology brands” to “consumer brands.”

The core of the P&G-style approach is not to talk about concepts, but to talk about before-and-after comparisons, concrete benefits, and improvements in daily life. Applied to AI, this means moving from “we have the most powerful model” to “where exactly will you save time, reduce costs, and get help.” This shift shows that AI competition is no longer just about parameters and rankings, but about user perception, user experience, and scenario efficiency.

Third, AI companies must begin to address the distributional consequences of technology

If a technology brings productivity gains while simultaneously compressing some jobs, then the social resistance to it will rise rapidly. Mechanisms such as retraining, income compensation, or support for affected groups around AI may not become the mainstream business model, but they increasingly look like a necessary supporting arrangement.

This is not a moral stance, but industrial governance. Technological expansion without a distribution mechanism will ultimately, in turn, weaken the legitimacy of the technology.

The next stage of the AI industry: from a growth race to a governance race

Many people still understand AI competition today as a contest of model capability: whose reasoning is stronger, whose multimodal abilities are better, whose agent is more practical. But from the perspective of industrial structure, what will truly determine long-term success or failure is becoming another set of capabilities.

1. Who can explain computing power investment as a public benefit

NVIDIA’s market capitalization reaching a historic high shows that the market still believes computing power is one of the scarcest foundational resources in the AI era. But the more concentrated computing power becomes, the greater the outside world’s questioning of “why such enormous infrastructure investment is needed.” For companies, data centers, energy, chips, and cloud resources are not just cost issues, but also issues of social communication.

When AI infrastructure shifts from R&D spending to a public issue, companies must answer: what kind of productivity, employment, and service capabilities will this investment ultimately become?

2. Who can build a trust framework before regulation tightens

AI regulation is accelerating globally. Whether it is data governance, copyright disputes, or the authenticity of content and the management of high-risk applications, regulation will make the “expand first, explain later” model increasingly difficult to sustain.

This means branding is no longer just the responsibility of the marketing department, but part of the company’s overall governance capability. Product design, compliance systems, security teams, legal affairs, and communications departments will all be forced to become more tightly integrated.### 3. Who Can Find a New Balance Between Open Source and Closed Source

The open-source ecosystem in the AI industry provides visibility and speed of diffusion, but it also amplifies the risk of misuse; closed-source models are more conducive to control and commercialization, yet they can easily trigger distrust over “black-boxing.” Behind the brand issue lies, in fact, this structural contradiction: users want powerful capabilities, but they also want to know how it works, who is in control, and how responsibility can be traced.

In such an environment, transparency itself becomes a competitive variable.

Why the Difference in Trust Between China and the United States Is Worth Watching

The survey cited in the report shows that Chinese respondents have significantly higher trust in AI than Americans do. This difference should not simply be understood as a “cultural preference,” but rather as a difference in how the technology is embedded.

If a technology mainly appears through practical applications, efficiency gains, and specific business scenarios, society is more likely to accept it; if it is mainly discussed in terms of “future shocks,” “labor replacement,” and “geopolitical competition,” the public is more likely to see it as a destabilizing force.

The implication for global AI competition is straightforward: What ultimately determines the pace of AI adoption is not just the model itself, but also the way it is narrated socially, the regulatory structure, and the path to commercial implementation.

AI is not merely a software industry; it is simultaneously an infrastructure industry, a media industry, a labor-restructuring industry, and a political-economic industry. Any perspective that understands AI only through technical parameters is already no longer sufficient.

Behind the Brand Issue, It Is Actually a “Maturity Signal” of a Technological Revolution

When a technology truly enters deep waters, it is often not because everyone praises it, but because everyone starts talking about it.

This was true of the early internet, social media, cloud computing, and it is now true of AI. Criticism, skepticism, scrutiny, anxiety, governance, and redistribution—these words may seem like obstacles to technology, but in reality they mark the transition of technology from the experimental stage to the social stage.

The AI brand crisis reminds us that this revolution is no longer just about algorithmic upgrades or capital frenzy; it is now touching labor, trust, platform power, and public order. What will determine the future landscape of the AI industry is not necessarily who tells the best future story, but who can most effectively turn technology into a real infrastructure that society can bear, institutions can explain, and users can understand.

And that is the harder part of the AI era.