Viewpoint -Steve Atkins, Publisher & Editor TQS

After two days at GITEX AI Europe in Berlin, one conclusion stood out. The event’s most important conversations were not about building larger language models or more capable AI systems. They were about the trust infrastructure that allows artificial intelligence to operate securely, responsibly and at enterprise scale. Europe may not be trying to win the AI race on capability alone. Instead, it is quietly building the foundations upon which the next generation of AI will depend.

Walking into GITEX AI Europe in Berlin, I expected two days dominated by artificial intelligence. New models. Faster models. Bigger investments. After two years of relentless progress, it seemed inevitable that the industry would remain focused on one question above all others: who could build the smartest systems? Instead, I found something rather different.

The conversations that stayed with me were not about artificial intelligence itself, but about everything that surrounds it. Across the exhibition floor, discussions repeatedly returned to secure networking, trusted identity, governance, compliance, resilient infrastructure, sovereign cloud, digital sovereignty and the challenge of turning promising technologies into systems that organisations can actually deploy. That distinction may appear subtle, but it signals an important shift taking place across the technology industry.

The first phase of AI rewarded capability. The next phase is likely to reward credibility. As organisations move AI beyond experimentation and into everyday operations, the questions become less theoretical and considerably more practical. Can these systems be trusted with critical infrastructure? Can they operate within highly regulated industries? Can they protect sensitive information, demonstrate accountability and integrate with the governance and compliance frameworks that already exist? Increasingly, those questions determine whether AI becomes operational infrastructure or remains an impressive demonstration.

Walking the exhibition floor, one observation became difficult to ignore. Many of the companies generating the most interesting conversations were not attempting to build another foundation model. Instead, they were solving the practical challenges that emerge once organisations decide to deploy AI at scale.

Take NetBird. The company is tackling secure connectivity for increasingly distributed organisations, recognising that intelligent systems are only as trustworthy as the networks that connect them. As AI expands across cloud environments, remote teams and edge devices, networking itself becomes part of the trust architecture.

Lextrace approaches the challenge from a different perspective. As AI becomes embedded within regulated industries, governance and legal accountability move from being policy discussions to operational requirements. Organisations increasingly need to understand not only what an AI system decided, but how those decisions can be explained, evidenced and defended.

Similarly, TrustPass is building digital trust infrastructure centred on identity, credentials and verification. Knowing who, or increasingly what, is interacting with digital systems is becoming fundamental as AI agents begin communicating with people, applications and other autonomous systems.

Meanwhile, Hydrolix is helping organisations transform vast quantities of machine-generated telemetry into operational intelligence. AI systems are only as reliable as the data that informs them, making trusted operational data one of the least visible but most important components of enterprise AI.

Then there was Deepmark, whose work focuses on authenticity and provenance in an era where distinguishing genuine content from synthetic media is becoming increasingly difficult. As generative AI accelerates, establishing confidence in digital assets becomes every bit as important as generating them.

On the surface these companies operate in very different markets. Viewed together, however, they are solving variations of the same problem. Each contributes a different element of the trust infrastructure that allows artificial intelligence to move beyond demonstrations and become dependable business technology. Secure networking, governance, identity, telemetry and provenance may appear to be separate disciplines, yet collectively they determine whether AI can operate safely and confidently within enterprise environments.

Interestingly, the organisers’ own messaging pointed in exactly the same direction. Rather than concentrating solely on AI capability, much of the conference focused on cybersecurity, digital sovereignty, trusted data, cyber resilience, governance and Europe’s ability to commercialise innovation. Several briefing documents highlighted trust, resilience and infrastructure as the defining themes shaping the next phase of AI adoption.

That shift was also evident among exhibitors. Companies such as StartComply were not presenting AI as an end in itself, but as a practical means of automating regulatory compliance, governance and operational readiness. Its platform applies AI to help organisations manage frameworks such as NIS2, DORA and ISO 27001, reducing the burden of continuous compliance while strengthening organisational resilience. It was another example of AI being used to reinforce the infrastructure of trust rather than simply generate intelligence.

Perhaps that should not have been surprising; Europe has rarely attempted to compete with Silicon Valley on consumer platforms or hyper-scale internet businesses. Instead, its strengths have traditionally been engineering, industrial systems, manufacturing, cybersecurity, identity, standards and regulation. Those capabilities are now becoming increasingly relevant as AI moves into sectors where reliability matters more than novelty.

Another observation from Berlin reinforced this point.

Not every organisation making an impact was developing products. Conversations with Berlin Quantum and AI.Berlin highlighted the equally important role of collaborative ecosystems. Their purpose is not to build technology directly, but to connect startups, researchers, investors, established industry and public institutions into networks capable of accelerating innovation.

That matters because infrastructure is not built solely from software and hardware. It is equally shaped by the relationships that connect researchers with entrepreneurs, investors with innovators and emerging companies with established industry. Europe’s innovation ecosystem depends upon those connections if promising technologies are to progress from research projects into commercially successful businesses.

Europe’s challenge has never been producing outstanding research. The continent possesses world-class universities, engineering expertise and deep technical talent. The challenge has always been turning those strengths into globally competitive companies capable of scaling without leaving Europe. That theme surfaced repeatedly throughout GITEX AI Europe.

Whether discussing venture capital, startup growth, digital sovereignty or industrial competitiveness, the conversation consistently returned to one idea. Europe does not lack innovation. It needs to become better at scaling innovation. Investment, partnerships and ecosystems are becoming just as important as technology itself.

The North Star startup programme reflected exactly that ambition. Rather than acting as a conventional exhibition area, it brought together founders, investors and partners with a shared objective of accelerating the next generation of European technology companies. Many of the businesses on display had already progressed beyond proof-of-concept and were focused squarely on commercialisation, international expansion and long-term growth.

For readers of The Quantum Space, much of this will feel familiar.

Over the past year we have explored how digital identity, cybersecurity, post-quantum cryptography, trusted hardware, AI governance and regulatory frameworks are steadily converging. GITEX AI Europe reinforced that observation. Artificial intelligence is not developing in isolation. It depends upon an increasingly sophisticated trust infrastructure that determines how systems are secured, governed, monitored, verified and ultimately accepted by the organisations that rely upon them.

In many respects, that trust infrastructure may prove to be Europe’s greatest contribution to the global AI economy.

While others compete to build ever more capable models, Europe appears increasingly focused on creating the environment in which those models can operate safely, transparently and responsibly. That includes secure networking, trusted identity, resilient cloud infrastructure, governance frameworks, compliance automation, trusted data, software integrity and the collaborative ecosystems that enable all of those elements to work together.

If there was one message to emerge from GITEX AI Europe, it is that the next phase of artificial intelligence will not be determined solely by who builds the smartest models. It will be shaped just as much by those building the foundations that make intelligent systems trustworthy enough to deploy at scale. That race may attract fewer headlines than the competition to develop ever more capable models, but its outcome could prove to be every bit as significant. If Europe has identified its opportunity, it lies not in replacing Silicon Valley’s AI leaders, but in becoming the place where trusted AI is made practical.