Democracies are increasingly anxious about China’s AI rise, but not always clear about what exactly is at stake. The sudden emergence earlier this year of DeepSeek, China’s most popular open-weight model to date, brought the issue to the surface. Western commentary has since swung between alarm over Beijing’s rapid progress and a lingering belief that it still lags behind. For example, OpenAI indicates that China is rapidly narrowing the gap, even if it does not yet lead. Jensen Huang, CEO of NVIDIA, stated plainly: China is “not behind.” His remark did more than challenge a technical narrative. It sharpened a mounting unease that few countries have the strategy or readiness for what China’s expanding AI footprint might mean.
This is where much of the current alarm goes astray. The disruption DeepSeek represents lies not necessarily in surpassing Western frontier models, but in showing that China can sustain a commercially viable AI ecosystem, capable of producing services that are globally competitive in both performance and price. Yet public anxiety continues to fixate on the fear of falling behind in a model race, while missing a more immediate blind spot: the lack of a coherent strategy to confront the systemic risks posed by China’s accelerating deployment of AI services within democratic societies.
Taiwan’s Chinese AI Moment
Nowhere is this fear of falling behind more palpable than in Taiwan. DeepSeek was a wake-up call for Taiwanese policymakers. It forced a national debate not just about the scale of China’s AI progress, but also about Taiwan’s own slow, fragmented efforts to develop a homegrown language model of competitive scale or visibility. The question “Why can’t Taiwan have a DeepSeek of its own?” quickly became a source of political pressure and institutional soul-searching.
The main reason this anxiety has taken hold so quickly is that it found a domestic target. TAIDE, Taiwan’s state-backed initiative to build a language model reflecting its linguistic and cultural specificity, became an unflattering point of comparison. DeepSeek’s scale and openness turned it into a mirror, exposing TAIDE’s limitations and pushing it into the spotlight of public scrutiny.
In February 2025, just weeks after DeepSeek’s R1 made headlines, TAIDE released an updated model, Llama 3.1-TAIDE-LX-8B-Chat, with improved decoding for Traditional Chinese and an expanded context length of 131K tokens. But these upgrades did little to shift the narrative. Against DeepSeek R1’s 671 billion parameters, full open weights under an MIT License, and instant global recognition, TAIDE looked less like a sovereign breakthrough and more like a belated prototype: earnest in ambition, modest in delivery, and quietly eclipsed before it could step into the ring.
Scrutiny deepened with a formal investigation by Taiwan’s Control Yuan, the constitutional body responsible for government oversight. In April 2025, it concluded that TAIDE suffered from poor coordination, inadequate data access, and systemic governance failures. Of the 58 government datasets gathered for training, only two were fully accessible through the national open data platform. The Control Yuan faulted the Ministry of Digital Affairs for failing in its data governance mandate and criticized the Executive Yuan (Taiwan’s cabinet) for neglecting inter-agency coordination.
What was meant to signal AI sovereignty had instead revealed a state apparatus unprepared to supply the very data its national model depended on. TAIDE became a case study in disjointed planning and symbolic ambition, trapped in a maze of legal ambiguities, fragmented processes, and underdeveloped institutional capacity.
Tech Nationalism Misplaced
The dynamics that played out around DeepSeek and TAIDE exemplify a now-routine political ritual: the anxiety of falling behind is repackaged as a patriotic quest for “sovereign AI.” But this impulse often substitutes rhetoric for strategy, leaving more fundamental policy questions conspicuously untouched.
This posture has already shaped institutional responses. Taiwan’s claims of “sovereign” AI development often mask a more modest reality: fine-tuning advanced foreign models like LLaMA rather than building truly indigenous systems. When DeepSeek made headlines, the government’s first move was not to invest in core infrastructure or long-term capability, but simply to ban the model’s use across public agencies. The appeal is obvious. It is easier to rally around an existential slogan than to commit to a politically unglamorous reform agenda. But clarity at the level of rhetoric tends to conceal confusion at the level of strategy. It also silences harder policy questions, the kind that require more than slogans.
Specifically, the problem with this nationalist narrative is that it commits Taiwan to a path that is both too ambitious and too narrow.
Too ambitious, because it defines success in terms used by superpowers. Building a sovereign language model is treated as a national imperative, with implicit comparisons to the United States and China. There appears to be a quiet belief that Taiwan’s chipmaking prowess entitles it to model-building success – as if fabs could substitute for foundation models. Yet Taiwan lacks the commitment to the scale required to match these benchmarks, whether in training data or global deployment channels. It continues to operate with limited public funding and a fragmented policy environment. Neither is well suited to support long-term, high-stakes AI development.
This approach is simultaneously too narrow, because it trades performance for cultural representation. Framing the model as a vessel for national culture may be politically flattering, but it creates expectations that are difficult to meet and markets that barely exist. A system built to reflect cultural specifics might earn political capital, but that rarely translates into real-world utility. It won’t perform where it matters: commercial use, cross-border integration, or technical benchmarks.
At best, the result would be a model that achieves political coherence but offers little practical relevance. A system that cannot compete will still fail, both at home and abroad, regardless of how faithfully it attempts to encode national characteristics, assuming such traits can be operationalized at all.
The outcome is a distorted policy agenda. Political and institutional attention concentrates on the symbolic value of a flagship model, while more foundational pillars of sustainable AI development are neglected. Data governance, cross-sector collaboration, and infrastructure design receive far less attention. Ambition thus becomes substitution: the effort to claim symbolic ownership consumes the time, visibility, and resources that might otherwise support more plural and resilient AI strategies.
Confronting the Strategic Vacuum
What looked like strategic overreach in Taiwan is, in fact, part of a broader global pattern. Across regions from Europe to East Asia, participation in the AI race has come to replace coherent planning. Governments increasingly treat foundation models as strategic assets of digital legitimacy. The EU’s 2025 AI Continent Action Plan, for instance, places both frontier and open-source models at the center of its ambition to lead in trustworthy AI.
In this logic, building a national model becomes the ticket to geopolitical relevance, a signal of not falling behind. But this model-centric narrative, however tidy, confuses technological ambition with strategic judgment. For smaller and mid-sized democracies, chasing frontier AI is not only late but also misdirected. Most lack the resources to develop and sustain large-scale advanced models. Worse, they risk diverting scarce capacity into performative initiatives instead of reinforcing areas where they might lead. These include specialized applications, multilingual tools, civic deployments, or ecosystem design. The tragedy is not that they are behind. It is that they are probably running the wrong race.
What, then, should democratic countries be really worried about? Not necessarily the speed of China’s AI progress, but the strategic vacuum it exposes. Few have established frameworks for evaluating China-backed AI tools, much less for governing how those tools are adopted, adapted, or embedded in domestic systems.
Even the most developed regulatory efforts have yet to fully address this gap. For example, the EU AI Act offers a structured risk-based approach that covers functional concerns such as misuse, bias, and systemic failure, but pays limited attention to the geopolitical provenance of AI systems. Supply chain and source-of-origin risk, particularly in relation to services developed in non-democratic regimes, remain outside its current scope.
Beyond Export Controls
The urgency of the strategic vacuum becomes clearer when considering that China’s AI footprint is no longer confined to its own borders. Domestically, models like DeepSeek already underpin a wide range of industrial and governmental functions, from energy giants like Sinopec and CNOOC to financial institutions such as ICBC and Postal Savings Bank. They support knowledge management, customer service automation, policy drafting, and internal research across sectors including healthcare, education, telecom, and media. In China, foundation models now operate as essential infrastructure rather than abstract technological experiments.
The more consequential development lies in what follows. Similar services are now being exported, both directly and indirectly, through open-weight releases, enterprise integration, and third-party resellers. Chinese AI offerings appear to be entering their TikTok moment: shifting from domestic scale to global diffusion, often silently and through channels that outpace regulatory awareness. The important question now is not whether China can build competitive models, but how far and how fast those systems are spreading – and how unprepared democratic institutions remain to monitor or respond.
In Taiwan, DeepSeek’s expansion has already raised red flags. Beyond a limited directive prohibiting its use in public agencies, there is no overarching framework governing the import, application, or provenance of China-backed AI systems. Commercial and private-sector adoption remains largely unmonitored. There is scant public awareness, and even less institutional mapping, of rebranded or derivative Chinese models entering through third-party integrators or local vendors. The result is a regulatory blind spot.
Much of today’s global AI policy debate remains fixated on controlling what goes out – through export restrictions, outbound investment screening, and licensing regimes. Far less attention is paid to what comes in. Chinese models and AI-based services now circulate across borders with little oversight. This imbalance leaves democracies structurally exposed: attentive to outbound risks, yet blind to inbound vulnerabilities.
If democracies are to respond meaningfully to China’s AI rise, the answer will not be found in replicating its development strategy. What is needed is building the institutions, norms, and technical capacity to evaluate, govern, and, where necessary, reject systems that threaten democratic resilience. Without this, the model race is a distraction. The real test lies in governance, not just engineering, and in recognizing that strength comes not only from what kind of AI democracies can build, but also from what they are willing to let in.
