The American debate on artificial intelligence is increasingly shaped by one simple but dangerous sentence: if we slow down, China will overtake us. This sentence has become a political shortcut. It allows major technology companies and their allies to describe regulation, safety testing and public accountability as threats to national survival.

This is more than geopolitical competition. It is a cold-war mentality that treats AI as a field of dominance rather than a social infrastructure that must be governed in the public interest. Every restriction is framed as a gift to Beijing. Every delay is described as a loss of strategic advantage. Every demand for transparency is treated as an obstacle to innovation. Under this pressure, even reasonable safeguards for powerful models can be dismissed before they are properly evaluated.

The result is a race in which speed becomes ideology. Advanced AI systems can support software development, education, research and public services, but they can also assist cyber operations, fraud, disinformation, manipulation and dangerous technical workflows. If the only policy command is to win, safety becomes a burden and accountability becomes optional.

There is also a second problem: the current AI race may be economically unstable. The dominant paradigm is built around massive data centers, expensive chips, huge energy demand and models that are costly to train and costly to serve. Yet the market is already moving toward price sensitivity and price wars. If many firms are building similar systems with similar data and similar techniques, there is no durable moat. A technical lead may last weeks, not decades.

Chinese open-weight models intensify this pressure. Z.ai’s GLM-5.2 and the broader DeepSeek experience show that the gap with leading closed models can narrow quickly and at much lower cost. Even more important is the shift from “larger model” to “more efficient system.” DeepSpec and DSpark are not just about making models smarter. They are about making model serving faster and cheaper, using speculative decoding techniques to reduce wasted computation.

This matters because inference is becoming the real deployment bottleneck. Standard LLM generation is sequential and memory-bound. Speculative decoding changes the economics: a smaller draft process proposes tokens, while the larger target model verifies them more efficiently. DSpark adds confidence scheduling and semi-autoregressive drafting, aiming to improve speed without changing the model’s correct output distribution. In plain terms, the same AI service can become faster and cheaper without simply adding more hardware.

If AI becomes cheaper, faster and easier to run locally, the argument for a race of ever-larger closed infrastructure becomes weaker. The future of AI may not belong only to the largest data centers. It may also belong to efficient, open and locally deployable systems.

This is the real strategic lesson. The future of AI is not only about intelligence. It is about efficiency, accessibility, energy use, privacy and control. A model that can run on local infrastructure can protect sensitive data better than a remote black-box service. A faster serving stack reduces energy costs. Open tools allow universities, municipalities, small firms and public agencies to adapt AI to their own needs.

This does not mean that openness alone solves safety. The more capable and cheaper AI becomes, the more important governance becomes. Open does not mean uncontrolled. It means inspectable, reproducible and auditable. It allows independent researchers, public institutions and civil society to test systems rather than trust corporate assurances.

The European Union should not respond to the US-China race by entering a deregulation contest. Europe’s choice is not between stagnation and reckless acceleration. Its task is to build a different model of AI sovereignty: open, secure, efficient and democratically accountable.

The EU AI Act should not be treated as a burden. It should become the basis for a mature industrial and social strategy. Europe needs open standards, public compute, open models where appropriate, local deployment, independent evaluation, model cards, dataset documentation, audit logs and retrieval systems grounded in verified sources.

For Greece and other smaller European countries, this is practical, not abstract. Public administration, education, health, culture and language technologies should not depend entirely on opaque foreign platforms. AI in the public sector should be built as a public capability: open-source software, high-quality national datasets, local low-cost models, human final responsibility and democratic oversight.

The best answer to the new AI cold war is not to run without rules. It is to build AI that is safe, efficient, accessible and useful. Europe should treat responsible innovation not as weakness, but as strategic maturity.