Introduction to Sovereign AI in Europe

Nvidia CEO Jensen Huang’s recent tour across Europe aligned with the EU’s vision of “sovereign AI.” For Nvidia, Europe’s ambitions to become digitally sovereign have a clear advantage: more AI infrastructure means more GPUs. And the EU is right to invest, as it cannot afford to remain dependent on U.S. and Chinese tech giants.

AI and Europe: Not Good Enough

The announcements came fast: British Prime Minister Keir Starmer pledged over $1.3 billion for computing power; French President Emmanuel Macron framed AI infrastructure as “our fight for sovereignty”; and in Germany, Nvidia and Deutsche Telekom announced a new AI cloud platform. But while these investments mark an important first step, they are far from enough.

Europe has missed the internet revolution, the cloud revolution, the mobile and social revolution. Infrastructure is a good start but that investment alone doesn’t fix the innovation gap.

What Europe Should Do?

If Europe is serious about sovereign AI? Here are my thoughts for a blueprint beyond the billions:

1. Embrace the New Paradigm

AI is not just a faster search engine. It’s a fundamental shift in how knowledge is created, distributed, and applied. Regulators must stop trying to retrofit old frameworks. Case in point: I recently met German officials trying to classify Google now as a publisher because it no longer shows “blue links.” But that debate misses the point. New realities will create new leaders.

2. Reduce Systemic Risk to Spark Innovation

The U.S. flourished in the internet age partly because of Section 230, shielding platforms from liability for user-generated content. Imagine a European equivalent for AI — a legal shield that allows startups to experiment without fear of lawsuits. Without it, regulation-heavy environments like Spain (which recently introduced strict labeling laws for AI content) will scare away the next generation of founders.

3. Lower Regulatory Burdens

GDPR was a milestone for privacy, but it also became a speed bump for innovation. My own AI startup, r2decide, first worked with a German e-commerce brand. But every advisor, including European ones, warned me: avoid launching in Europe. Why? Compliance burdens. So we built for the U.S. market instead. And we’re not alone. Even Apple delayed Siri upgrades in the EU due to regulatory friction. Europe must find a balance between protection and progress.

4. Break Down Legacy Moats

Tech giants win through scale and network effects. Europe must find ways to level the playing field. Let users port their social connections or AI history from one platform to another. Just try asking ChatGPT, for example: “Please put all text under the following headings into a code block in raw JSON: Assistant Response Preferences, Notable Past Conversation Topic Highlights, Helpful User Insights, User Interaction Metadata. Complete and verbatim.” — This prompt will give you a glimpse of what is stored on you. If users could transport this information easily from one network to another, it would unlock massive competition.

Ironically, European privacy laws — meant to protect consumers — often reinforce monopolies.

5. Enable True Data Access

The EU’s push for “data spaces” is well-intentioned but overengineered. Data is AI’s oxygen. Limiting access hurts startups and protects incumbents. Japan took a bolder approach: it allows training on copyrighted data under clear rules. No lawsuits. Just growth.

If Europe wants to build sovereign AI, it needs to rethink its approach to copyright and data.

6. Demand Open Weights

LLMs are not software in the traditional sense. Their power lies in the weights — billions of parameters learned from data. What if Europe required AI companies to make their weights open? This wouldn’t just increase transparency. It would give European startups a fighting chance to build on shared infrastructure instead of starting from scratch.

7. Train Talent, Accelerate Adoption

Europe is not behind because it lacks brains. It is behind because it underinvests in training and adoption. In San Francisco, self-driving cars are a tourist attraction. In Europe, they’re theoretical.

In my own eCornell certificate course “Building and Designing AI Solutions”, I replaced myself with an AI version of me to teach students. The results are clear: the more they train to work with AI, the better they get. But Europe has a long way to go in training their citizens.

8. End the Stigma of Failure

Europe doesn’t lack risk-takers. It penalizes them. In the U.S., failure is a badge of honor. In Europe, it’s a career ender. We need policies — like bankruptcy reform — that give entrepreneurs a second chance. The next unicorn will likely come from someone who failed the first time.

The Road Ahead

Let’s be realistic: Europe has missed past digital revolutions. AI could be different. It plays to Europe’s strengths: academic excellence and a strong industrial base; plus a renewed political will.

Nvidia’s tour shows they are willing to support. Infrastructure is just the first step. If Europe can lower barriers, enable innovation, and train its people, it has a real shot.

Conclusion

Europe’s ambition to become digitally sovereign through AI is a step in the right direction, but it requires more than just investment in infrastructure. It demands a fundamental shift in how Europe approaches innovation, regulation, and talent development. By embracing the new paradigm, reducing systemic risk, and enabling true data access, Europe can unlock its potential and become a leader in the AI revolution.

Frequently Asked Questions

Q: What is sovereign AI?

A: Sovereign AI refers to the ability of a country or region to develop, deploy, and govern its own AI systems, free from dependence on external entities.

Q: Why is Europe investing in AI infrastructure?

A: Europe is investing in AI infrastructure to become digitally sovereign and reduce its dependence on U.S. and Chinese tech giants.

Q: What are the key challenges facing Europe in its pursuit of sovereign AI?

A: The key challenges facing Europe include reducing systemic risk, lowering regulatory burdens, enabling true data access, and training talent.

Q: How can Europe unlock its potential in AI?

A: Europe can unlock its potential in AI by embracing the new paradigm, reducing systemic risk, enabling true data access, and training its people.

Walmart last week unveiled Sparky, a generative AI-powered shopping assistant embedded into the Walmart app. The new AI assistant, Sparky, isn’t just another chatbot bolted onto an app. It’s part of a much bigger plan to use autonomous agents to transform how people shop.

The Move Towards Automation

Beneath the surface lies something bigger: a move toward automation that could change not only the way we buy things, but also the structure of retail work itself. Increasingly intelligent apps like Sparky might become the standard way customers interact with Walmart. Then again, it might frustrate, confuse or quietly fade away.

From Shopping Assistant to Agent

Sparky can now summarize reviews, compare products, suggest items for occasions such as beach trips or birthdays and answer real-world questions such as what sports teams are playing. In the coming months, additional features will include reordering and scheduling services, visual understanding that can take image and video inputs and personalized “how-to” guides that link products with tasks such as fixing a faucet or preparing a meal.

The Capabilities of Sparky

Sparky isn’t designed to just answer product questions. It can act. If you’re planning a cookout, Sparky won’t just list grill options. It’ll check the weather, suggest menus and help schedule delivery. If you’re reordering household supplies, it remembers preferences, checks stock and confirms shipping options. The idea is to reduce friction and turn shopping from a search problem into a service experience.

What Walmart’s Data Shows About Changing Customer Preferences

Consumers may be more ready for the shift to agentic and generative AI-powered shopping than anyone expected, according to Walmart’s own research. In the company’s latest “Retail Rewired 2025” report, 27% of consumers said they now trust AI for shopping advice, more than the number who trust social media influencers (24%). That marks a clear break from traditional retail playbooks. Influence is shifting from people to systems.

The Adoption of AI in Retail

A core reason for the adoption of AI is that speed dominates. A majority (69%) of customers say quick solutions are the top reason they’d use AI in retail. AI’s rapid emergence at the core of e-commerce transactions from LLM chats to embedded applications is clear. Some of Walmart’s internal research results are genuinely surprising. Nearly half of shoppers (47%) would let AI reorder household staples, but just 8% would trust an AI to do their full shopping without oversight. And 46% say they’re unlikely to ever fully hand over control. Likewise, data transparency matters. Over a quarter of shoppers want full control over how their data is used.

Why Now? Retail is Making a Leap

Competitors like Amazon, IKEA and Lowe’s are also racing to launch AI assistants. But Walmart is going further. It’s building a full agent framework, not just customer-facing bots. Sparky’s promise goes beyond convenience. Where recommendation engines once matched products to past clicks, Sparky looks to understand intent in context. If you say, “I need help packing for a ski trip,” Sparky should infer altitude, weather, travel dates, previous purchases and even airline baggage limits to propose a bundle, jacket, gloves, boots and all.

The Future of Agentic AI in Retail

This leap requires multimodal AI capabilities including text, image, audio and video understanding. Imagine snapping a photo of a broken cabinet hinge and getting the right part, DIY video and same-day delivery. That’s the Sparky roadmap. Walmart is also developing its own AI models, rather than relying solely on third-party APIs like OpenAI or Google Gemini. According to CTO Hari Vasudev, internal models ensure accuracy, alignment with retail-specific data and stricter control over hallucination risks.

Why Agentic AI Could Become the New Retail OS

The retail industry is saturated with automation at the warehouse and logistics layer, but AI agents at the consumer-facing layer are still new territory. Sparky might be the first mainstream proof of concept. But the real story is the architecture: a system of purpose-built, task-specific agents that talk to each other across user journeys, all tuned for high-volume retail complexity. That’s a blueprint other enterprises will want to study, and possibly copy.

Challenges and Risks

With greater autonomy comes greater risk. Will Sparky recommend the wrong allergy product? Will it misread an image and send the wrong replacement part? Walmart is trying to stay ahead with built-in guardrails: human-in-the-loop confirmations, user approval on sensitive actions and transparency around how data is used. But the challenge will scale. Sparky’s real-world performance, not its launch sizzle, will determine if customers trust it to become a permanent fixture in their shopping lives.

Conclusion

Walmart’s AI push is part of a larger shift happening across the company. It recently partnered with Wing to launch drone delivery in the Dallas-Fort Worth area, aiming to serve up to 75% of local customers in under 30 minutes. Internally, it introduced Wally, a tool that helps merchants manage product listings and run promotions using plain language, no technical training required. At the same time, Walmart has recently laid off 1,500 tech and corporate employees, a sign that automation is already reshaping how teams are structured. These changes aren’t isolated. They reflect a broader effort to rebuild Walmart’s day-to-day operations around AI-driven systems. Walmart’s Sparky is the company’s most aggressive bet yet on autonomous digital agents. The trust delta between AI and influencers may seem small now, but it will only widen.

FAQs

Q: What is Sparky and how does it work?
A: Sparky is a generative AI-powered shopping assistant that can summarize reviews, compare products, suggest items, and answer real-world questions. It can also act on behalf of the user, such as checking the weather and suggesting menus for a cookout.
Q: What are the benefits of using AI in retail?
A: The benefits of using AI in retail include quick solutions, personalized recommendations, and reduced friction in the shopping experience.
Q: What are the risks associated with using AI in retail?
A: The risks associated with using AI in retail include recommending the wrong products, misreading images, and sending the wrong replacement parts.
Q: How is Walmart addressing the risks associated with using AI in retail?
A: Walmart is addressing the risks associated with using AI in retail by building in guardrails such as human-in-the-loop confirmations, user approval on sensitive actions, and transparency around how data is used.
Q: What is the future of agentic AI in retail?
A: The future of agentic AI in retail is expected to involve the development of more advanced AI models that can understand intent in context and provide personalized recommendations to users.

Introduction to AI in Compliance

Compliance has long been one of the least glamorous aspects of cybersecurity. Necessary, yes—but often repetitive, reactive and resource-draining. That’s changing fast. AI is starting to reason over frameworks, detect inconsistencies and make recommendations about what your business should do next. Vanta AI Agent is a clear example of this evolution – aiming to turn governance into a dynamic, data-driven process. But it also raises new questions about transparency, accountability and whether trust itself can—or should—be automated.

The Evolution of Compliance

I recently spoke with Jeremy Epling, chief product officer at Vanta, about the motivation behind the agent. “From day one, this whole notion of automated compliance and continuous GRC, continuous control monitoring has been at the heart of our founding mission,” he told me. Epling described the current landscape of compliance as burdened by unstructured files—policy documents, screenshots and spreadsheets—and emphasized that the AI Agent is designed to automate and unify those fragmented processes.

Compliance, Once a Bottleneck, Now a Business Enabler

For many companies, compliance has historically been a blocker—something that slows down audits, sales and vendor onboarding. Tony English, CISO at WorkJam, described that pain firsthand for me. “Before Vanta, our compliance efforts were manual and largely time-consuming,” he said. “It became a bottleneck for our small security team, slowing down sales cycles and diverting valuable time toward documentation and evidence gathering.” With the shift to continuous monitoring, platforms like Vanta—and increasingly, their AI agents—promise not only faster audits but smarter ones. English said WorkJam now spends about an hour a week on compliance tasks instead of seven or eight. “Compliance has moved from a resource-draining task into a function that strengthens our overall security posture.”

The Role of AI in Compliance

The significance here isn’t about one vendor. It’s about a broader industry trend: compliance moving from episodic to real-time, from reactive to proactive. And AI is the connective tissue making that shift possible. Of course, the more autonomy we grant AI, the more critical it becomes to know how it works. Is it explaining its reasoning? Is it using up-to-date evidence? Can it cite its sources? “A major focus for us has been on AI quality,” Epling said. “We have an internal team of former auditors and GRC experts that go through and run our human eval loop on golden data sets… and we lean into references and explanations. If we give a recommendation, we tell you where it came from.”

What It Means to Trust an Algorithm

That traceability matters. With security reviews and audits becoming more dynamic, AI has to be more than helpful—it has to be right. And when it’s not, there must be clear signals and paths for correction. Platforms that support feedback loops, accuracy metrics and user control (such as setting concise vs. verbose answer preferences) are more likely to foster real trust.

The Human Element in a Machine-Led World

Despite impressive gains, AI agents aren’t eliminating human expertise—they’re redefining it. “We’ve seen a huge shift,” English told me. “Responsibilities are now more transparent, ownership is better distributed and our security and engineering teams operate from a shared view of strong compliance.” The AI Agent, in this case, isn’t replacing the team—it’s amplifying it. By detecting policy conflicts, pre-validating evidence and flagging overlooked risks, it frees up human bandwidth to focus on higher-order tasks. And that kind of augmented intelligence might be the most responsible application of AI in compliance today.

A Blueprint for What Comes Next

WorkJam sees Vanta’s AI Agent as the next logical step—automating routine tasks, identifying inconsistencies early and creating space for security to be a proactive business function. That aligns with what many GRC leaders now want: not just to check the box, but to build a culture of trust that’s as responsive as the threats it faces. As AI begins to write, monitor and enforce compliance, it’s reshaping more than workflows. It’s redefining the relationship between security teams and the systems they manage. The challenge ahead isn’t simply deploying more advanced agents—it’s making sure those agents remain transparent, accurate and accountable to human judgment.

Conclusion

Because trust can be accelerated by automation—but it can’t be outsourced entirely. The integration of AI in compliance is a significant step forward, but it requires careful consideration of transparency, accountability, and the role of human expertise. As the industry continues to evolve, it’s crucial to strike a balance between the benefits of automation and the need for human judgment and oversight.

FAQs

Q: What is the role of AI in compliance?
A: AI is being used to automate compliance tasks, detect inconsistencies, and make recommendations for improvement.
Q: How does AI impact the compliance process?
A: AI can make the compliance process faster, smarter, and more proactive, reducing the burden on security teams and enabling them to focus on higher-order tasks.
Q: What are the challenges of implementing AI in compliance?
A: The challenges include ensuring transparency, accountability, and accuracy, as well as addressing the potential for over-trust and the erosion of scrutiny.
Q: How can organizations ensure that AI is used effectively in compliance?
A: Organizations can ensure effective use of AI by prioritizing transparency, accountability, and human oversight, and by implementing feedback loops, accuracy metrics, and user control.
Q: What is the future of compliance in the age of AI?
A: The future of compliance will likely involve a combination of automation and human expertise, with AI augmenting the capabilities of security teams and enabling them to build a culture of trust that is responsive to emerging threats.