Chinese AI Models Are Reshaping the Global Landscape – What Does This Mean for Market Research?

AI image focusing on China and the worldI’ve been tracking the rise of Chinese AI systems with growing interest over the past year, and I believe there are implications for the insights and market research industry. In 2026, I think we will see an important shift. I predict that many organisations across Europe, Asia-Pacific, and beyond will turn to Chinese-developed AI as part of their solution. Note, I predict they will usually run these models locally rather than rely on cloud services.

The Quality Gap Has Closed

For those who haven’t been following the technical developments, Chinese AI models have improved dramatically. DeepSeek’s latest V3.2-Speciale model achieved 96.0% on the AIME 2025 mathematics test (a prestigious American competition), edging out OpenAI’s GPT-5 at 94.6%. The model also earned gold medals in the International Mathematical Olympiad and International Olympiad in Informatics – benchmarks that would have seemed unreachable for Chinese labs just two years ago.

What makes this particularly interesting is that DeepSeek released their model under an open-source MIT licence. This means any organisation can download it, run it locally, and fine-tune it for their specific needs, without incurring ongoing API costs or having data leave their infrastructure.

The Numbers Are Striking

According to research from OpenRouter and Andreessen Horowitz, Chinese open-source AI models grew from about 1.2% of global usage in late 2024 to nearly 30% by end of 2025. [Note, I suspect the real figure is somewhat less than 30%, there are lots of ways of measuring things, but I am sure that the Chinese models are much more widely used in January 2026 than they were in January 2025). A RAND Corporation study found that Chinese LLM market share jumped from 3% to 13% in just two months following DeepSeek’s initial release. By August 2025, Chinese providers had captured over 10% of users in 30 countries and more than 20% in 11 countries.

Alibaba’s Qwen family has spawned over 180,000 derivative models globally, making it one of the most adopted open-source LLM families. Meanwhile, MiniMax’s M2 model offers comparable performance to Anthropic’s Claude Sonnet 4.5 at roughly 8% of the price. For cost-conscious research operations, these economics are hard to ignore.

Why This Matters for Insights Professionals

I believe there are three key implications for our industry:

Cost reduction opportunities. If Chinese models can deliver comparable quality at a fraction of the cost, research agencies and insights teams could significantly reduce their AI expenditure. One startup reported 30% cost savings by fine-tuning Qwen for their specific applications.

Data sovereignty options. For organisations concerned about sending sensitive research data to US cloud providers, locally deployed open-source models offer an alternative. This is particularly relevant for European firms navigating GDPR requirements or clients in regulated industries.

Reduced vendor lock-in. The open-source nature of many Chinese models means less dependence on any single provider. As Cloudflare’s CTO noted, open protocols allow building ‘without the fear of vendor lock-in’ – a real concern when your analysis pipelines depend on a single API.

The Geopolitical Context

I feel that the growing interest in Chinese AI isn’t purely about technical merit. Concerns about US political instability under the renewed Trump administration and incidents like Microsoft suspending an International Criminal Court prosecutor’s email at the request of a US executive order have made many organisations nervous about over-reliance on American tech infrastructure.

Whether or not these concerns are justified, they’re driving diversification. A German state switched its computers from Microsoft Windows to Linux in 2025, explicitly citing digital sovereignty. The sentiment extends to AI: organisations increasingly want options.

The Concerns Are Real Too

For me, the jury is still out on several important questions. Chinese models may have been trained with different content moderation approaches, potentially affecting outputs on sensitive topics. There are legitimate security concerns about using technology from geopolitical competitors. And organisations adopting Chinese AI may face reputational questions from clients or stakeholders.

Any serious evaluation should include validation testing specific to your use cases, governance frameworks for model selection and deployment, clear documentation of which models are used for which purposes, and consideration of client and stakeholder perceptions.

What am I doing?

For over a year, I have been running models locally on my own computer to get a sense of what is possible. I host them via LM Studio and my current collection includes models from OpenAI, Mistral and Deepseek. At ResearchWiseAI, we are exploring UK-hosted solutions that do not rely on systems hosted in China, the USA, or any other cloud provider.

What Should You Do?

My practical recommendation is straightforward. I think it’s worth experimenting with one or two leading Chinese models on non-sensitive tasks. Download them to a suitable server and run them locally. Compare the outputs against your current tools. Evaluate whether the cost savings justify the considerations involved.

I’m not suggesting wholesale adoption. But I do suggest a pragmatic approach of testing these options. Ignoring this shift entirely means missing out on potential efficiency gains and being caught off guard if clients start asking questions about your AI strategy.

What’s your experience? Have you evaluated any Chinese AI models for your research workflows? I’d be interested to hear where you see the biggest opportunities and concerns for our industry.

My upcoming Webinar

This point, the rise of Chinese open-sourced models, is one of the points I will be making in my upcoming webinar on 19 February. The programme for that webinar is:

  • The NewMR State of Insights study
    I will share the results of the sixth wave of this study, looking at the main positives and negatives of the insights ecosystem, with some updates focusing on AI.
  • Predictions for 2026
    My predictions for the main AI trends in 2026. Remember, prewarned is pre-armed.
  • Prescriptions for 2026
    I will share five tips for things you should be doing with AI in 2026 to stay in the game. Walk away with five actionable steps you can implement.

You can register for the webinar by clicking here.

Sources

Note: This post was researched using AI-assisted tools and web search. All claims have been verified against the sources listed above.

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