Dell CTO: Stop Regulating Specific AI Technologies, Focus on Long-Term Vision Instead
John Roese, global chief technology officer and chief AI officer at Dell Technologies, warned governments against regulating specific artificial intelligence models because the technology changes faster than rules can be written.
"AI moves fast, government doesn't," Roese said in Las Vegas at Dell's annual event. "No government really moves at the speed of the technology industry."
By the time a regulation is finalized and implemented, the underlying technology has already shifted, rendering the rules obsolete. Roese pointed to early European AI regulations introduced before Retrieval-Augmented Generation (RAG) existed. Those rules assumed companies would need to hand over sensitive data directly to AI providers-a requirement that became unnecessary once RAG technology allowed generic AI models to securely connect to private enterprise data without exposing it.
"Some of them tried to define privacy and control policies on the assumption that you just had a single model with no external data and that everything changed," Roese said. "Stay away from the actual specific technology. It changes too fast."
What Government Should Regulate Instead
Rather than targeting specific technologies, governments should establish long-term frameworks around capabilities and outcomes. Roese cited the United Kingdom's approach as an example worth following.
The UK estimated it would need 20 times more computing capacity to serve the country during the AI era than it currently has. This goal-based strategy avoids specifying which technologies or vendors to use, instead defining what the country needs to achieve.
"That may sound abstract, but it is actually a very good goal because it essentially says, 'I don't know how I will do it, but I know I must have a lot more compute available within this timeframe,'" Roese said.
The Compliance Burden
Even with improved policy quality over the past two years, global companies face a compliance nightmare. Dell operates in over a thousand jurisdictions with different AI policies that don't communicate with each other.
"They don't talk to each other, and they don't rationalise," Roese said.
He urged policymakers to ask statisticians and mathematicians to calculate the global cost of implementing proposed regulations before finalizing them. "That way, policymakers can decide: Is the cost too high? Could it be done in a more efficient way?" he said.
Sovereign AI and Hybrid Infrastructure
Roese supports national AI strategies, particularly for countries like India and France pursuing sovereign AI capabilities. Governments have access to the same hardware and technology as major cloud providers, he said.
"If a government wants to build AI at the hardware level, there is absolutely no difference in capability that they could deploy themselves versus what the most advanced hyperscaler model company in the world is using. They are the same products. We sell them to anybody," Roese said.
For large enterprises and governments, hybrid AI architecture-combining on-premises systems with cloud services-is the realistic long-term approach. No major company has successfully moved everything to a single cloud provider, despite the "cloud-first" strategy being promoted years ago.
"Enterprises are complex, costs are complex, and people inevitably like the idea of choice and a distributed architecture," Roese said. "With AI, it has to be hybrid because you need to be able to reach out into the real world rather than just aggregate everything."
India's Position
India's government demonstrates technical literacy on AI policy, according to Roese. Senior officials understand the technology and aren't following blindly.
"I think there are all the normal challenges that they have to work through, such as energy and job displacement. These are all complex issues," Roese said.
India has established AI hubs in Bengaluru, Hyderabad, and Mumbai, attracted major infrastructure investments from Google, Nvidia, and Amazon, and built a large tech workforce capable of supporting AI development.
For government officials navigating AI policy, understanding the difference between regulating technology versus outcomes is critical. AI Learning Path for Policy Makers covers the governance and policy frameworks that shape how governments approach AI strategy.
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