The White House's outgoing artificial intelligence adviser, Sriram Krishnan, has declared that the Trump administration will never create a formal, centralized licensing regime for AI software, ruling out what critics and some lawmakers have called an "FDA for AI." In a wide-ranging interview, he also placed the blame for growing public backlash against the technology squarely on the industry's own leaders, accusing them of doing a "terrible job" of explaining AI's benefits. For ongoing reporting on how governments are shaping the future of AI, follow our latest AI developments.
Krishnan, 42, a former venture capitalist who served as President Donald Trump's senior AI adviser, made the remarks in an in-depth interview with the Financial Times, reported by the Times of India and PYMNTS on July 5, 2026. His comments offer one of the clearest statements yet of the administration's deregulatory posture toward artificial intelligence as he prepares to leave his post.
"There Will Not Be an FDA for AI"
At the center of Krishnan's message was an emphatic rejection of a centralized regulatory body modeled on the Food and Drug Administration, which oversees food and prescription drugs. Proponents of an "FDA for AI" have argued that powerful AI models should undergo a licensing and review process before they can be deployed, much as new pharmaceuticals must clear safety trials before reaching the public.
Krishnan dismissed the idea outright. "There will not be an FDA for AI," he said. "Setting up a centralised agency requiring a team of lawyers before you can get a model out would put sand in the gears of the AI revolution. That is never, never going to happen under President Trump."
The stance positions the United States in sharp contrast to the European Union, whose AI Act established a tiered regulatory framework with obligations tied to the perceived risk level of AI systems. By rejecting a licensing model, the administration is betting that lighter-touch oversight will accelerate American innovation and preserve the country's lead in the global AI race.
Blaming the Industry's "Doomer Messaging"
Perhaps the most striking portion of the interview was Krishnan's blunt criticism of the very industry he helped advise. He argued that the leaders of elite American AI labs have actively undermined public trust by relentlessly emphasizing catastrophic risks.
"The AI sector has done a terrible job of explaining the benefits of the technology," Krishnan stated, noting that genuine breakthroughs such as advanced medical diagnostics have been completely overshadowed by corporate fear-mongering.
He was unsparing in diagnosing the cause. "Leaders of American AI labs have focused so much on the dystopian narrative and scenarios, whether it is job loss, whether it is existential risk, that a lot of people are going: well, I don't know if I want this," he explained. By constantly warning the public about apocalyptic possibilities, tech executives have, in his view, turned voters against their own products.
A Public Stake in AI's Wealth
Krishnan connected the public's unease to a deeper sense of exclusion from the enormous wealth the AI boom is generating. "Voters want to feel they have a seat at the table and this is not just two or three companies becoming incredibly powerful and wealthy," he said.
To address that distrust, he threw his support behind a White House proposal to require top AI companies to transfer equity stakes to the American public. President Trump reportedly discussed the idea with OpenAI chief executive Sam Altman. While Silicon Valley executives have pushed back fiercely, branding the concept a form of "backdoor nationalization," Krishnan insisted that giving ordinary citizens a tangible financial stake is essential to winning broad acceptance.
"I do think some way for regular people to feel like, OK, when I use this model, or when I see the graphs going up and I watch on CNBC, I am benefiting. I think that is good," he said. "Americans need to feel that AI is something which is empowering them."
A Central Architect of Hands-Off AI Policy
Before joining the administration, Krishnan worked closely with tech billionaire Elon Musk and with David Sacks, whom Trump appointed as the administration's AI and cryptocurrency czar. During his tenure, Krishnan became a central figure in the White House's efforts to block state-level AI regulations, advocating instead for a fragmented, innovation-first approach that leaves most oversight to the market and existing agencies.
His departure comes at a pivotal moment. With state legislatures and foreign governments pushing ahead with their own AI rules, the federal government's reluctance to establish a dedicated regulator leaves a patchwork of standards that companies must navigate. Supporters argue this flexibility fuels competition; critics warn it leaves gaps that could allow harm before any authority steps in.
Competing Visions for AI Governance
Krishnan's interview crystallizes a fundamental fault line in the global debate over AI oversight. On one side are those who see mandatory licensing, audits, and pre-deployment review as indispensable safeguards for a technology that can generate convincing misinformation, automate cyberattacks, and displace workers. On the other are voices like Krishnan's, who contend that heavy regulation would hand an advantage to foreign rivals and stifle the very innovation that could deliver AI's benefits.
What is unusual is Krishnan's willingness to fault the industry itself, not just overzealous regulators, for the public's anxiety. His argument suggests that even under a famously business-friendly administration, AI companies may face pressure to change how they talk about their own technology, trading apocalyptic warnings for a more measured case about productivity, scientific progress, and economic opportunity.
As the United States continues to chart its deregulatory course, the tension between rapid AI development and public confidence will only intensify. Whether a public equity stake, voluntary commitments, or industry self-restraint can bridge that gap remains an open question, one that will help define the next chapter of AI policy.
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