Europe’s AI wake-up call: Cybersecurity threats, sovereignty fears, and a growing demand for ROI dominated VivaTech

As 180,000 attendees descended on Paris, conversations revolved around cybersecurity risks, European dependence on U.S. AI, and whether the returns on AI investment are actually materializing.

Welcome to Eye on AI. Beatrice Nolan here. In today’s issue:

• All the news from Paris’ VivaTech conference.
• The stream of researchers leaving Google DeepMind continues.
• The defeat of New York Congressional candidate Alex Bores—and what it means.
• OpenAI and Broadcom unveil new AI chip.

I spent last week in Paris for VivaTech, Europe’s largest startup and tech conference, which managed to draw 180,000 attendees, despite a record heatwave in France.

VivaTech’s biggest headliner, Jeff Bezos, was in techno-optimist mode, arguing that AI will create a labor shortage rather than mass redundancy. He paired that with his longer-term vision of moving heavy industry off Earth and building a permanent industrial presence on the Moon.

Executive Chairman of AMI Labs and former head of Meta’s AI efforts, Yann LeCun, was happy to provide a counterpoint, taking the time to tell CNBC that the AI industry could be heading for a “big bubble explosion” if companies fail to bring costs down fast enough.

The winner of the most memorable moment, however, goes to two humanoid robots. While attempting to perform a choreographed demo at a booth, the pair slowly drifted backward into a row of televisions and sent two of the screens crashing to the floor—a counter to some of the hype on display in the exhibition hall.

The more grounded conversations elsewhere at the conference revolved around immediate realities: who controls AI, how to counter some of the current risks, and what Europe, still reeling from the shock of being cut off from Anthropic’s Mythos model, needs to do to control its access to AI.

Mind the gap

Perhaps unsurprisingly, the event was preoccupied by one of the more imminent risks posed by AI: cybersecurity. The debut of Anthropic’s Mythos and OpenAI’s GPT 5.5 Cyber has fueled anxiety among both business and government leaders due to their advanced hacking abilities.

These models can now, at speed and scale, do what once required time and specialized human knowledge: find unknown vulnerabilities in critical software and generate working exploits. The worry is that the same capability lands in the hands of criminal groups and nation-state actors before most organizations have had time to shore up their defenses.

Anthropic and OpenAI have both staggered the release of recent cyber products and models with the aim of giving defenders a head start—granting initial access only to vetted security firms and critical infrastructure operators so they can patch their systems before the capabilities spread more widely.

But there is some concern about what happens if that window—the period when defenders have access to better AI models than attackers—closes too quickly

“I think long term it favors defenders,” Peter DeSantis, SVP at Amazon, said in an interview. “But the concern is short term.”

At the moment, he warned, security teams are still scrambling to understand the new kinds of attacks these models enable and to update their practices and tooling, potentially creating a gap between how quickly AI can be used offensively and how quickly defenses are adapting.

Earlier this week, OpenAI expanded its Daybreak initiative, pairing GPT-5.5-Cyber with a new open-source patching effort called Patch the Planet to work with firms including Cloudflare, Cisco, and CrowdStrike with the aim of helping organizations find and fix vulnerabilities.

A few days prior, Thibault Sottiaux, OpenAI’s head of core platforms, told me the idea behind the staggered rollouts was to “be responsible and help accelerate cyber defense.”

“We have invested a lot in our safety stack, to ensure that generally accessible models are responsible for broad use,” he said. “But then we have more specific models that advance different tiers in terms of cyber capabilities, those we restrict behind different levels of our trusted access program.”

Europe reckons with AI sovereignty

The U.S. decision to abruptly cut off access to Anthropic’s frontier models last week gave the conversation around “sovereign AI” a new urgency. While it was clear the risk of dependence felt much more real for European companies and policymakers, it was also clear that sovereignty means different things to different people.

Cohere CEO Aidan Gomez told Fortune that sovereignty needed to start with domestically controlled infrastructure—chips, power, data centers, and private deployment all under national control. Failing that, he said, countries should form strategic alliances to counter U.S. and China’s grip on the infrastructure needed to power AI. 

Big Tech executives took a less hardline approach. Amazon’s DeSantis said that no nation—including the U.S.—really has a truly sovereign infrastructure, and suggested that trying to achieve such a high bar was unrealistic. 

Instead, he argued for a more pragmatic approach: keep sensitive data in-country and give governments or companies clear control over how AI is governed, rather than attempting to recreate the entire hardware and supply chain stack within each nation. 

He added that, in his view, using large shared data centers in the cloud is the most practical way to let countries keep data local and control how AI is run, while still keeping capacity, costs, and power use manageable. (Of course, given that DeSantis is from Amazon, the world’s largest cloud provider, he would say that.)

Companies get practical about AI

At VivaTech, executives were less enthused by some of the loftier promises of AI, and keen to chat about more practical topics like training, workflow automation, and AI spend.

Philippe Rambach, Schneider Electric’s chief AI officer, told me he had recently made AI training mandatory for the company’s entire 160,000 staff. Rather than chasing shiny demos, he described being picky about AI pilots and only backing projects with a defined business case and a path to scale. According to Rambach, it’s all part of an effort to treat AI as an operational tool rather than an innovation toy.

From the product side, OpenAI was seeing a similar shift. Enterprise customers still want the best models, but they also want to know whether they are actually worth the money.

“We receive a lot of inquiries about: Is the ROI there? I’m running all these agents, are they actually providing value for us?” Sottiaux said. One strategy for the company, he said, is to focus on tighter cost control—making the models more efficient so companies can do more with less.

With that, here’s more AI news.

Beatrice Nolan
beatrice.nolan@fortune.com
@beafreyanolan

This story was originally featured on Fortune.com