In 2026, three of America’s most competitive AI players—OpenAI, Anthropic, and Google—set aside rivalries to defend against model extraction and bolster ecosystem security. They joined the Frontier Model Forum to coordinate detection and blocking of Chinese labs using distillation at industrial scale, while continuing to deliver practical AI tools to customers. Distillation remains a standard technique for building smaller models, but when used by attackers with proxy networks and tens of thousands of fake accounts, it becomes a real threat to safety and profits. The backdrop is Washington’s push for formal, ecosystem security-driven policy and information sharing, and the core race is to shape the next decade of AI as attackers speed up through automated pipelines that adapt to new model releases. The defenders must coordinate across APIs, clouds, and payments.
model extraction in plain sight: why it matters
Distillation has long been a standard method for refining ideas into smaller, cheaper versions of a model. The twist now is that competitors use the same trick at scale—not to improve your own product, but to imitate a rival’s capabilities without paying for the R&D behind them. Anthropic highlighted the scale: millions of exchanges and proxy networks that run on roughly 24,000 fraudulent accounts, with MiniMax alone driving a large share of those attempts. When attackers route traffic through networks of fake accounts and mix extraction traffic with ordinary requests, they hide in plain sight, challenging any single company to shut them down quickly. OpenAI says DeepSeek used more obfuscated methods against American labs, while Google’s threat intel flagged a surge in what it calls model extraction attacks on Gemini—one campaign produced tens of thousands of prompts aimed at emulating the model’s chain-of-thought reasoning. This isn’t a cosmetic issue; it touches the core of product safety and market integrity. The message for ecosystem security is stark: once a company builds a guardrail, it has to work across the entire ecosystem, not just inside its own fences.
ecosystem security as the backbone of AI progress
Washington’s interest isn’t only about lost revenue. Leaders everywhere know that models built through unauthorized distillation tend to shed the safety guardrails that US labs painstakingly install—safety checks on bioweapon instructions, cyberattack guidance, and other high-stakes features. When such lean, potentially dangerous tools flow into military or intelligence contexts, the risk compounds quickly. Officials have floated a formal information-sharing center to coordinate responses, a sign that the appetite for structured collaboration is warming up even among rival firms. The broader concept here is ecosystem security: hardening the internet of AI across API routers, cloud providers, and payment infrastructures. Without a coordinated approach, even one weak link can tip the balance. The frontier of this space is less about who wins a single contract and more about who can keep the entire system resilient.
the frontier forum in action: cooperation over clashing egos
The Frontier Model Forum, an industry nonprofit founded with Microsoft in 2023, has quietly become a backstage where competitors practice restraint and share threat intel. The unusual nature of this alliance—rivals uniting to deter theft while still chasing the same customers—points to a pragmatic truth: safety and innovation are not mutually exclusive, and synchronized defense can coexist with healthy competition. The plan is not to police every pipeline with a single hammer but to create a distributed, smarter defense that understands multi-vendor architectures. That requires more than talk; it requires standardization, rapid incident response, and cross-vendor visibility into suspicious activity. In short, it is a rare case of collaboration that respects market dynamics while recognizing that ecosystem security is bigger than any single company.
operational speed versus safety: how defenders adapt
Attackers have shown astonishing speed: multi-stage pipelines that blend synthetic data generation with reinforcement learning, all designed to mimic a rival model on a different foundation. Anthropic caught a campaign in progress and watched a pivot within 24 hours of a Claude release, redirecting traffic to capture capabilities from the latest version. Translating that speed into defense means coordinating policy, tooling, and throttling across different clouds and payment rails, so a single misstep by one provider doesn’t cascade into a broader security flaw. OpenAI has called for an ecosystem security mindset—hardening not just a single lab but the entire API network, the cloud layers that connect them, and the payment brokers that finance use. The math is unglamorous but decisive: one weak link can undermine the strongest fortress. ecosystem security.
For practitioners, practical steps are accessible and actionable. Start with multi-layer monitoring that examines both user inputs and sequences of requests over time. Build safer distillation pipelines by validating outputs and removing unsafe data channels. Prefer architectures that complicate mass proxy use and require stronger identity verification for access to key APIs. And maintain coordinated threat intelligence with trusted partners to map evolving attacker playbooks. All of this feeds back to ecosystem security as a guiding principle.
In 2026, this isn’t a tale of doom; it is a cautious, optimistic blueprint for safer AI. The willingness of OpenAI, Anthropic, Google, and their allies to coordinate publicly signals a shift from secrecy to structured resilience. If the industry can keep pace with attackers—without stifling creativity—the next decade of AI can be more open, better guarded, and a bit more entertaining to watch. ecosystem security, model extraction.
Original article note: This synthesis draws on coverage of the Frontier Model Forum and related disclosures by Bloomberg. Thank you to Bloomberg for providing context and data that helped shape this read. Original article: Bloomberg.
Want to weigh in? Share your thoughts in the comments below and join the conversation about how we can balance rapid AI progress with strong, ecosystem security.
FAQ
- What is model extraction? A process where an attacker attempts to replicate a rival model’s capabilities by feeding inputs and observing outputs, often using automation to mimic performance.
- What is distillation in AI? It is a method to train smaller, cheaper models from larger ones. Used legitimately, it speeds deployment; used in attacks, it can copy capabilities without paying for R&D.
- How can companies defend themselves? By multi-layer monitoring, rapid incident response, and coordinated threat intelligence across providers; and by strengthening guardrails across APIs, clouds, and payment systems.
- What about policy and regulation? Officials are exploring formal information-sharing centers and ecosystem-wide security standards to keep guardrails intact while encouraging innovation. ecosystem security is central to this conversation.
References
- Times of India — article noting rivals’ collaboration to address Chinese challenges
- NIST AI Risk Management Framework
- White House OSTP — AI policy and risk management