Are Chinese open-source AI models ‘winning’ by being cheap and deployable?

/ Technology / By

Summary: A growing number of US companies are experimenting with Chinese open-source AI models because they’re fast, cheap, and can be customised—especially after what some leaders call the “DeepSeek moment.” The shift isn’t about whether the US or China has the single best closed model. It’s about whether open-source ecosystems—where Chinese labs are increasingly prominent—are becoming the most practical foundation for real-world AI products.

If that’s true, “winning the AI race” won’t only be about headline model demos. It will be about adoption, cost, distribution, and developer preference.

The key claim: why US firms would use Chinese models

The BBC report gives several reasons companies are turning to Chinese open models:

  • they can be freely downloaded and customised
  • cost can be dramatically lower than proprietary models
  • they perform well enough to improve products like recommendation engines and customer support

The Pinterest example in the report is illustrative: a US consumer platform using Chinese models to improve recommendations. That’s a shift from “AI is geopolitical” to “AI is procurement.”

The “DeepSeek moment” and what it changed

The report suggests that when a high-performing model was open-sourced, it catalysed a wave:

  • more open models
  • more experimentation
  • more adoption by startups that cannot afford closed-model pricing

Open-source models reduce two barriers:

  1. price (you pay for compute, not for a vendor license)
  2. control (you can host the model yourself)

That second point matters for enterprises worried about data exposure.

Why open-source matters in practice

Open-source models create an ecosystem advantage:

  • developers can tweak and fine-tune
  • companies can build bespoke applications
  • switching costs are lower than with proprietary APIs

In many industries, open-source wins when:

  • performance is “good enough”
  • the ecosystem moves fast
  • costs matter

AI is now entering that stage.

The cost argument: why “90% cheaper” changes behaviour

The report cites claims that improved recommendations can come at much lower cost compared to proprietary models.

This matters because AI costs can scale quickly:

  • inference costs rise with usage
  • training costs rise with ambition

If a model is 80–90% cheaper and 80–90% as good, many businesses will take that trade.

In other words, “best model” is not always the winner. “Best economics” often is.

The Hugging Face signal: adoption as a scoreboard

The report points to Hugging Face trends, where Chinese models frequently occupy top download spots.

Downloads matter because they imply:

  • developer interest
  • ease of use
  • community tooling

It’s similar to how Linux became infrastructure: not always the flashy consumer story, but the practical foundation.

The strategic contradiction: open-source and geopolitics

One of the most striking quotes in the report is the irony:

  • the autocracy (China) is “democratising” technology through open models

Regardless of politics, open-sourcing models has a strategic benefit:

  • it makes the model family a default choice for developers
  • it accelerates ecosystem growth
  • it puts pressure on proprietary vendors

That can yield global influence without direct export of services.

The US incentive structure is different

The report contrasts Chinese model builders with US firms like OpenAI:

  • US companies face intense pressure to monetise quickly
  • proprietary models are easier to monetise
  • open-source models can undermine pricing power

That creates a tension:

  • open-source accelerates adoption
  • closed models capture revenue

Some US firms have experimented with limited open releases, but the main investment often goes into proprietary systems.

The “AI race” framing may be wrong

If “race” means “who has the best model,” it’s one story.

If “race” means “who becomes the default platform developers build on,” it’s another.

In many tech eras, the default platform wins by:

  • being cheap
  • being flexible
  • being widely integrated
  • having a strong ecosystem

That’s why the report’s focus on open models is important.

Risks: supply chain, trust, and compliance

Enterprises adopting Chinese models will face questions:

  • model provenance and security (is it safe? is it backdoored?)
  • licensing and compliance
  • geopolitical risk and future restrictions

In practice, companies mitigate this by:

  • hosting models on their own infrastructure
  • restricting data flows
  • running independent evaluations and red-teaming

But the risk is real: AI is increasingly a national security topic.

What to watch next

  1. Enterprise procurement behaviour: do more Fortune 500 companies shift to open models?

  2. Regulatory responses: will governments restrict model usage, distribution, or training data?

  3. Ecosystem momentum: which model families dominate developer tools and integrations?

  4. Quality convergence: if open models keep improving, proprietary pricing faces pressure.

  5. On-device AI: open models can be compressed and run locally, which could accelerate adoption.

Bottom line

Chinese open-source models are gaining traction not because every company “wants China to win,” but because open models can be fast, cheap, and controllable.

If this trend continues, the AI landscape may look less like a two-country arms race and more like a platform shift where open ecosystems drive adoption—while proprietary vendors fight to justify premium pricing.

Sources

n English