Anthropic dropped a new research paper today outlining two possible futures for global AI leadership by 2028, and it reads more like a geopolitical briefing than a typical AI safety paper.
The core argument: The US currently has a meaningful lead over China in frontier AI, primarily because of compute (chips). American and allied companies (NVIDIA, TSMC, ASML, etc.) built technology China simply can't replicate yet. Export controls have made that gap real.
But China's labs have stayed surprisingly close through two workarounds:
- Chip smuggling + overseas data center access - PRC labs are apparently training on export-controlled US chips they shouldn't have. A Supermicro co-founder was recently charged for diverting $2.5B worth of servers to China.
- Distillation attacks - creating thousands of fake accounts on US AI platforms, harvesting model outputs at scale, and using that to train their own models. Essentially free-riding on billions in US R&D.
The two scenarios for 2028:
- Scenario 1 (good): US closes the loopholes, enforces export controls properly, the compute gap widens to 11x, and US models stay 12-24 months ahead. Democracies set the norms for how AI is governed globally.
- Scenario 2 (bad): US doesn't act, China reaches near-parity, floods global markets with cheaper models, and the CCP ends up shaping global AI norms, including potentially exporting AI-enabled surveillance tools to other authoritarian governments.
What makes this interesting beyond the politics:
Their new model, Mythos Preview (released to select partners in April), apparently let Firefox fix more security bugs in one month than in all of 2025. That's the kind of capability jump they're warning China shouldn't be the first to achieve, specifically around autonomous vulnerability discovery.
The framing worth discussing: Anthropic is explicitly calling distillation attacks "industrial espionage" and pushing for legislation to criminalize them. This positions them as political actors, not just AI researchers. Whether that's appropriate for an AI lab is a conversation worth having.
What do you think - is the compute gap as decisive as they claim, or is algorithmic innovation enough to close it?
[link] [comments]