Why Do AI Companies Want You to Fear Them?

When Fear Becomes a Marketing Strategy

A fascinating phenomenon is playing out in Silicon Valley: the very companies investing billions of dollars in AI development keep warning the world that the technology they're building could be "extremely dangerous" or even "pose an existential threat to humanity."

OpenAI CEO Sam Altman has repeatedly called for strict AI regulation at congressional hearings, describing it as a "civilization-level risk." Anthropic positions itself as an "AI safety company," with founder Dario Amodei consistently emphasizing AI's potentially catastrophic consequences. A co-founder of Google DeepMind has also stated that AI could become "one of the greatest threats facing humanity."

At first glance, these statements appear to be responsible corporate behavior. But a deeper analysis reveals a sophisticated business logic lurking behind the "fear narrative."

Fear as Power: The Art of Manufacturing Technological Mythology

When a company claims its product "could destroy humanity," it's actually conveying a deeper message: "Our technology is powerful enough to change the world."

This is a masterful brand positioning strategy. Consider: if OpenAI simply said "we've built a decent chatbot," the reaction from investors and the public would be entirely different. But when it implies that the GPT series is approaching the threshold of "Artificial General Intelligence (AGI)," the entire narrative is rewritten — this is no longer a software product, but an invention that could alter the fate of humanity.

Fear and awe are often two sides of the same coin. AI companies understand this well: making people fear AI's potential is essentially making people believe in AI's potential. And that belief is the psychological foundation supporting hundred-billion-dollar valuations.

History offers plenty of parallels. The nuclear energy industry once simultaneously played the dual roles of "savior" and "destroyer." In the tech world, from social media to cryptocurrency, "disruptive narratives" have always been the most effective weapon for attracting attention and capital. AI companies have simply taken this playbook to its extreme — they claim what they're touching is not the ceiling of some industry, but the very boundaries of human intelligence itself.

The Catalyst for Capital: How Fear Converts into Funding

The essence of venture capital is betting on the possibility of exponential growth. And nothing fires up investor imagination quite like a technology that "could change everything."

The fear narrative from AI companies provides the capital market with a perfect investment thesis: if this technology is truly that powerful, that dangerous, then the company that controls it possesses unparalleled strategic value. Investing in such a company is not just about financial returns — it's about "being on the right side of history."

The data confirms this logic. Between 2023 and 2025, total global AI funding has repeatedly hit new highs. OpenAI's valuation skyrocketed from tens of billions to over one hundred billion dollars, while Anthropic secured tens of billions in investment from giants like Google and Amazon in just two years. Latecomers such as xAI and Mistral have similarly secured massive funding rounds amid the urgent narrative of the "AI race."

Notably, these fundraising efforts are often accompanied by the argument: "If we don't do it, someone else — especially China — will." Fear serves a dual purpose here: it creates awe toward the technology itself while simultaneously generating anxiety about geopolitical competition. When these two fears compound, capital flows in willingly.

Regulatory Moats: Building Competitive Barriers Through Fear

Perhaps the most strategically significant move is the AI giants' attitude toward regulation.

On the surface, proactively calling for regulation seems to go against business interests. But the reality is precisely the opposite. When leading AI companies stand before Congress and say "please regulate us," what they're actually pushing for is a regulatory framework that works in their favor — a system of rules that, in the name of "safety," effectively raises the barriers to entry.

Stringent safety compliance requirements mean high R&D and auditing costs. For OpenAI and Google, sitting on billions in funding, this is merely part of operating expenses. But for a startup team in a garage or an open-source community, it could be an insurmountable barrier.

This strategy is known in business history as "regulatory capture" — where industry giants institutionalize their market advantages by influencing the formulation of regulatory rules. Similar patterns have emerged in oil, telecommunications, finance, and other industries. The AI industry is repeating this playbook, only this time, "protecting humanity from AI threats" has become the most persuasive justification.

The EU's AI Act already reflects this trend to some extent. The Act's tiered regulatory requirements for "general-purpose AI models" objectively favor large enterprises with massive compliance teams, while placing greater pressure on small and medium-sized developers and open-source projects.

Diverting Attention: From Present Harms to Future Doomsday

The fear narrative serves yet another, more covert function: shifting public attention away from the actual harms AI is currently causing and toward distant, vague "existential risks."

While media and policymakers heatedly debate "whether AI will wipe out humanity," the following issues that are already occurring tend to be downplayed: copyright infringement and privacy violations in AI training data; discriminatory impacts of algorithmic bias on vulnerable groups; the pollution of the information ecosystem by AI-generated content; the impact of large-scale AI deployment on the labor market; and opaque decision-making by AI systems in high-stakes domains such as healthcare and criminal justice.

These problems may not be as attention-grabbing as "AI doomsday," but they are real, happening right now, and affecting millions of real individuals. Ironically, addressing these issues may require imposing more direct, specific constraints on AI companies — exactly what they don't want to see.

Discussing "whether superintelligence will go rogue" is far safer than discussing "whether your company illegally used creators' works to train models." The former is a philosophical proposition; the latter involves real monetary compensation and fundamental adjustments to business models.

The Discourse Trap of "Safetyism"

In recent years, a school of thought that critics call "safetyism" has emerged in the AI safety field. This ideology places AI's long-term existential risks at the center of all discussions and advocates making the prevention of "superintelligence going out of control" the primary objective of policymaking.

This framework is not without merit, but it has a structural problem: it inherently grants current AI developers a special status. Following this logic, since only "those who understand AI best" can judge how dangerous AI is, the formulation of regulation must heavily rely on the technical judgment of these companies. In other words, the regulated become the core architects of the regulations.

This knowledge asymmetry gives AI companies a natural advantage in the policy arena. They can selectively disclose information, exaggerate certain risks (such as AGI going rogue) while downplaying others (such as data misuse), thereby steering regulation in the direction most favorable to themselves.

Beyond Fear: What Should We Really Be Focusing On?

Pointing out the commercial motives behind AI companies' use of fear narratives does not mean that AI risks themselves are unworthy of attention. The rapid development of technology does present unprecedented challenges, and responsible R&D and deployment are crucial.

But we need to distinguish between two fundamentally different approaches:

One is evidence-based, present-focused risk governance — addressing bias, transparency, accountability mechanisms, and social impacts in the actual deployment of AI systems. This requires participation from diverse stakeholders, including independent researchers, civil society organizations, affected communities, and the public.

The other is a fear narrative led by AI companies and focused on hypothetical futures — one that serves commercial objectives more than public interest.

For policymakers, the key is not to be held hostage by fear narratives, but to build independent technical assessment capabilities and formulate rules based on public interest rather than corporate interest. For the public, when an AI company tells you to "be afraid of us," the most worthwhile question to ask may not be "how scary is AI," but rather "who benefits from the fear."

Conclusion

Fear has always been a tool of power. In the context of the AI industry, it has been cleverly packaged as technological foresight and social responsibility, but its underlying logic is no different from any other form of commercial marketing — attract attention, establish authority, eliminate competition, and influence the rules.

True AI safety should not be built on fear, but on transparency, accountability, and democratic participation. The next time we hear an AI company executive anxiously discuss "the threat of AI," perhaps we should ask one more question: Is this concern truly for the future of humanity, or for the company's valuation?