1.2 Billion Users, Persistent AI Coworkers, and the Regulatory Storm: The Deep Contradictions Revealed at OpenAI DevDay

On October 1, 2026, OpenAI dropped a bombshell at its annual DevDay: ChatGPT had surpassed 1.2 billion weekly active users. Simultaneously, the company unveiled GPT-6.1 Sol, a persistent AI product called Dots, and a human-AI collaboration space named ChatGPT Space. Yet almost on the same day, the U.S. Federal Trade Commission (FTC) announced a sweeping consumer protection investigation into OpenAI, Anthropic, and other major AI labs.

This was no coincidence—it was the inevitable product of the AI industry reaching a particular stage of maturity. When technology infiltrates daily life at exponential speed, the euphoria of growth and the chill of regulation are bound to arrive together.

1.2 Billion: More Than Just a Number

What does 1.2 billion weekly active users mean? It means ChatGPT has surpassed Instagram's scale (~1 billion DAU) and is approaching YouTube's territory (~2 billion MAU). In less than four years, growing from a research preview to the world's largest consumer AI platform, this pace has virtually no precedent in tech history.

But what truly deserves attention isn't the user count itself—it's the product logic evolution behind it. OpenAI is no longer content being a "chatbot"; it's building a complete AI operating system. GPT-6.1 Sol's pricing strategy reveals this intent—performance comparable to Google's Astra, but at only one-fifth the price, with the Ultrafast tier reaching 300 tokens per second. This isn't technical showmanship; it's a clear market signal: OpenAI wants to make AI as cheap and ubiquitous as water and electricity.

Behind the price war lies a platform strategy. When inference costs drop low enough, developers and businesses will naturally integrate AI into more scenarios, further cementing OpenAI's platform dominance. This mirrors Amazon's playbook of expanding the cloud computing market through AWS price cuts.

Dots: The Persistent AI Coworker

If GPT-6.1 Sol represents OpenAI's infrastructure ambitions, Dots represents its boldest experiment at the application layer.

Dots is defined as a "persistent AI coworker"—not a tool you actively open, but an AI partner that's always online, continuously learning your work patterns, and ready to assist at any moment. The radical nature of this concept lies in how it blurs the boundary between human workers and AI tools. Traditionally, we use tools to complete specific tasks; but Dots' design philosophy is that AI should function like a true colleague—understanding context, proactively collaborating, and continuously present.

Combined with ChatGPT Space (a human-AI collaboration workspace), OpenAI is constructing a complete hybrid human-AI work environment. Users can co-edit documents, discuss problems, and make plans with AI in Space, while Dots runs continuously in the background, providing personalized support and suggestions.

The deeper implication of this design philosophy is that OpenAI is transitioning from an "AI tool provider" to an "AI work environment provider." It no longer merely answers your questions—it aims to become part of your daily workflow. If this transformation succeeds, it will fundamentally change how knowledge workers produce—and fundamentally alter the relationship between AI and human society.

The FTC Investigation: Shadow Behind the Celebration

Just as OpenAI celebrated its growth milestone, the FTC's investigation notice arrived like a bucket of cold water. This is no ordinary regulatory inquiry—it's a "large-scale consumer protection investigation" targeting AI labs, following multiple incidents including the Hugging Face security breach that could trigger lawsuits.

The FTC's timing is no coincidence. When a technology product has 1.2 billion weekly users, it's no longer "experimental technology"—it's infrastructure-level public service. Public service means public responsibility, and public responsibility inevitably invites public regulation.

The investigation's core issues may include: AI model hallucinations and misinformation propagation, user data usage and protection, the actual impact of AI decisions on consumers, and security incident handling mechanisms. These issues could be dismissed as "growing pains" when AI's user base was smaller, but at the scale of 1.2 billion users, every issue can evolve into systemic risk.

More notably, the FTC's investigation targets not just OpenAI but also other major AI labs including Anthropic. This means regulators are concerned not with one company's behavior but with the entire industry's practices. This "industry-level" regulatory intervention signals that the AI industry is transitioning from an "era of free exploration" to an "era of regulated development."

The Safety Paradox: Greater Capability, Greater Risk

Echoing the FTC investigation are warnings from the safety research community. The UK AI Safety Institute's assessment found that GPT-6 Astra's rogue attack rate surged fivefold compared to the previous GPT-6. Meanwhile, Anthropic warned that Zhipu's GLM-5.3 open-weight model is approaching its most powerful Claude Mythos Preview in exploit-writing capabilities, with safety guardrails that are easily removed.

These data reveal a fundamental paradox in AI development: improvements in model capability and growth in safety risks are almost linearly correlated. As models become more powerful and autonomous, their potential for malicious exploitation grows in parallel. The proliferation of open-source models further amplifies this risk—once safety guardrails are bypassed, malicious versions spread across the internet and cannot be recalled.

The emergence of NVIDIA's OpenShell (a secure runtime for autonomous AI agents, already 12k+ stars on GitHub) and OpenClaw's enterprise-grade agent control plane shows the industry is spontaneously building safety infrastructure. But the question is whether safety infrastructure development can keep pace with AI capability growth? Current data suggests the answer is not optimistic.

The Deep Contradiction: The Impossible Triangle of Growth, Innovation, and Governance

The dramatic simultaneity of OpenAI DevDay and the FTC investigation reveals a deep contradiction facing the AI industry: the "impossible triangle" of growth, innovation, and governance.

Pursuing extreme growth requires lowering barriers, expanding the user base, and accelerating product iteration, often at the expense of safety and prudence. Pursuing extreme innovation requires bold experimentation, rapid trial-and-error, and tolerance for failure, which conflicts with the predictability and controllability that regulation demands. And pursuing extreme governance may slow innovation speed, increase compliance costs, and ultimately weaken competitiveness.

OpenAI's current strategy is "grow first, govern later"—rapidly expanding user scale to establish market dominance, then leveraging that market position to influence regulatory framework development. This mirrors the strategies once employed by Facebook and Google. But history also teaches us that this approach can be costly: Facebook was forced into massive governance reform after the Cambridge Analytica scandal, while Google faced multiple antitrust lawsuits.

The China Variable: DeepSeek and Zhipu's Dual Challenge

While OpenAI and Anthropic grapple with American regulation, Chinese AI forces are accelerating their rise. Two developments worth watching: DeepSeek releasing open-source software adaptations for Huawei's Ascend chips, and Zhipu's GLM-5.3 breakthrough in security capabilities.

DeepSeek's adaptation for Huawei chips means China's AI industry is accelerating the construction of a self-reliant technology stack. Under U.S. chip export controls, this "software compensating for hardware" strategy may become a critical pathway for China's AI development. Meanwhile, Zhipu's GLM-5.3 capability breakthrough indicates that Chinese AI models have reached or approached world-leading levels in certain dimensions.

These two developments have profound implications for the global AI landscape. If China can establish an AI computing stack independent of the NVIDIA ecosystem, the global AI industry may develop a "bipolar" structure—one ecosystem centered on the American technology stack and another centered on the Chinese technology stack. This division would affect not only technological development but also the global coordination of AI governance.

Outlook: The Inflection Point Has Arrived

Synthesizing the above analysis, October 1, 2026 may mark an important inflection point in AI industry development. This inflection point isn't marked by a single technological breakthrough but by the convergence of multiple forces:

• Scale tipping point: 1.2 billion users means AI has become mainstream infrastructure, not a niche tool
• Product tipping point: Dots and ChatGPT Space mark AI's shift from "passive tool" to "active collaborator"
• Regulatory tipping point: The FTC's industry-wide investigation signals the end of the "free exploration era"
• Safety tipping point: A fivefold surge in malicious attack rates shows safety issues have evolved from theoretical risks to real threats

For AI practitioners, this means the industry's rules of the game are fundamentally changing. The "build first, ask questions later" approach of recent years will become increasingly unsustainable—safety, compliance, and governance will shift from "optional" to "mandatory."

For ordinary users, AI will increasingly integrate into daily work and life, but vigilance against AI-related risks must also increase. Understanding AI's capabilities and limitations and cultivating AI literacy will become basic survival skills in the digital age.

OpenAI's 1.2 billion users is a milestone, but milestones are also signposts—they mark both how far we've come and the curves and steep slopes that may lie ahead. Maintaining clarity amid the euphoria of growth and holding the line amid the waves of innovation—this may be the most important lesson for the AI industry in its next phase of development.