Us China Ai Competition And Diffusion
Sources: 1 • Confidence: Medium • Updated: 2026-02-09 16:44
Key takeaways
- There is vigorous debate about whether the U.S. and China are in a new Cold War, with deep trade and supply-chain interdependence as a key complication.
- State-level AI regulation is a major risk vector, with roughly 1,200 bills being tracked across all 50 states, creating fragmentation pressure.
- Public stated opinions about AI are more negative than revealed preferences, with many people reporting panic while continuing to use AI products.
- The outcome of open-source versus closed-source AI is unresolved, and both may coexist at scale.
- Current shortages in AI infrastructure inputs (e.g., GPUs, data center capacity) are likely to trigger replication/buildout that reduces per-unit costs over time.
Sections
Us China Ai Competition And Diffusion
- There is vigorous debate about whether the U.S. and China are in a new Cold War, with deep trade and supply-chain interdependence as a key complication.
- In Washington, bipartisan sentiment over roughly the past decade has shifted toward treating China as a more serious geopolitical foe, including in AI.
- AI frontier development is framed as primarily a U.S.-versus-China race, with a strategic focus on which country’s AI proliferates globally.
- DeepSeek’s release surprised Washington observers due to perceived quality, ability to run on smaller local hardware, and being open-source.
- Since the ‘DeepSeek moment’ less than a year ago, multiple Chinese AI companies have effectively caught up to the frontier, increasing competitive pressure on U.S. players.
- China is actively competing on AI software with several major model efforts, including DeepSeek, Qwen, and Kimi, plus additional large tech players.
Regulatory Fragmentation And Preemption Tensions
- State-level AI regulation is a major risk vector, with roughly 1,200 bills being tracked across all 50 states, creating fragmentation pressure.
- California’s SB 1047 passed both legislative houses but was vetoed by the governor.
- There is a critique that the proposed state-level moratorium was politically infeasible and potentially overreaching in restricting states’ ability to regulate.
- The EU AI Act is asserted to have significantly hampered AI development in Europe and contributed to major companies not launching leading-edge AI features there.
- SB 1047 would have imposed downstream legal liability on open-source AI developers for future third-party misuse of their models years after release.
- A federal legislative attempt to impose a moratorium on state-level AI regulation was pursued but failed when a last-minute deal collapsed.
Adoption And Pricing Shifts
- Public stated opinions about AI are more negative than revealed preferences, with many people reporting panic while continuing to use AI products.
- Leading AI companies with compelling products are experiencing an unprecedented revenue takeoff rate driven by real customer demand.
- AI adoption can scale faster than past general-purpose technologies because the internet and smartphones already provide global distribution.
- Enterprise AI monetization is trending toward usage-based pricing where customers buy tokens as metered units.
- Hyperscaler cloud competition is enabling usage-based AI pricing by making advanced model access broadly available with low fixed costs for startups.
- AI startups are experimenting with value-based pricing, including charging a share of labor replacement value or marginal productivity uplift from human-AI collaboration.
Model Capability Diffusion And Open Source
- The outcome of open-source versus closed-source AI is unresolved, and both may coexist at scale.
- Leading AI application companies are evolving beyond 'GPT wrappers' by orchestrating many models, training domain-specific models, and switching to open-source when cloud token economics are unattractive.
- Smaller models tend to catch up to frontier-model capabilities within roughly six to twelve months.
- State-of-the-art open-source models accelerate the spread of AI know-how by making systems easier to study, teach, and replicate.
- A Chinese open-source model (Kimi) is claimed to replicate GPT-5-level reasoning on benchmarks while being small enough to run locally on one or two MacBooks.
- The AI industry is expected to segment into a small number of frontier ‘supercomputer’ models and a high-volume cascade of smaller embedded models.
Cost Curve And Compute Supply Dynamics
- Current shortages in AI infrastructure inputs (e.g., GPUs, data center capacity) are likely to trigger replication/buildout that reduces per-unit costs over time.
- Hyperscalers are already building their own AI chips, and multiple big tech companies are pursuing internal chip programs.
- AI chips are expected to become cheap and plentiful within about five years due to competition from NVIDIA rivals, hyperscalers, and Chinese suppliers.
- AI running on GPUs is partly path-dependent: GPUs were designed for graphics rather than AI-specific computation.
- Purpose-built AI accelerators designed from scratch could be more economically efficient than full GPUs.
- The price of AI is expected to fall rapidly, driven by collapsing per-unit input costs and high demand elasticity.
Watchlist
- State-level AI regulation is a major risk vector, with roughly 1,200 bills being tracked across all 50 states, creating fragmentation pressure.
- AI-focused chip startups are building new architectures, but outcomes may range from independent success to acquisition by large companies that can scale manufacturing and distribution.
- China is working hard to catch up on AI chips, and a reported U.S. understanding is that DeepSeek’s next version is being required to train only on Chinese chips to stimulate the domestic ecosystem (with Huawei highlighted).
- There are active discussions in Washington, D.C. about a next attempt to land a federal approach to AI regulation and federal leadership over a 50-state issue.
Unknowns
- What are the audited and comparable revenue, retention, and margin metrics behind the claimed “unprecedented revenue takeoff” for leading AI companies?
- How prevalent and stable are $200–$300/month consumer AI tiers (conversion, churn, and long-run ARPU distribution)?
- What is the real-world and independently verified performance/cost parity timeline between frontier models and smaller/open models?
- Is the claim that Chinese models have “effectively caught up to the frontier” since the DeepSeek moment supported by consistent benchmark and deployment evidence?
- Will chip supply and pricing actually become “cheap and plentiful” within five years, and what portion of AI workloads will move to hyperscaler in-house silicon versus merchant GPUs?