Peter Diamandis, founder of the XPRIZE Foundation, has recently argued that people behave better when they know they are being watched. Oracle co-founder Larry Ellison made the same argument roughly two years earlier, at a public investor meeting. Both men believe that AI-powered surveillance systems could improve public behavior — a position that represents a recognizable strand of tech-industry optimism about what monitoring technology can accomplish.

Ellison laid out his case at Oracle's 2024 Financial Analyst Meeting, describing a future where citizens and police alike would be under continuous, shared surveillance. He said such a system would ensure people "will be on their best behavior" — a phrase Diamandis has now echoed in his own recent comments.

The core idea — that observation changes how people act — is not new. It traces back to Jeremy Bentham's panopticon (a prison design where inmates could always be watched), and more recently to criminology research on how the threat of being caught affects behavior. What is new is the technical foundation. Today's large-scale AI systems can process video feeds, biometric data, and behavioral patterns in near real time, at a cost that makes city-scale deployment realistic. Ellison and Diamandis are essentially saying that modern AI finally makes this old theory workable.

That argument is worth examining closely. The claim rests on a specific model of human behavior — that people rationally weigh the cost of being caught and adjust accordingly — which does work in some situations but not others. More importantly, it sidesteps a harder question: whose definition of "best behavior" gets written into the system, and who controls the output. Ellison's remarks did not engage with those questions, and if Diamandis follows the same pattern, there remains a substantial gap between the behavioral claim and the safeguards needed to put such a system to work fairly.

Worth flagging: neither of these men sits outside the interests at stake here. Oracle builds cloud and database infrastructure that many governments and enterprises rely on to store and analyze large-scale data collections; Ellison has a direct financial stake in normalizing AI-integrated surveillance. Diamandis, through his various ventures, has consistently promoted a technology-forward view of solving human problems. Both are genuine believers in technology's capacity to improve society, and their track records show it. But that same belief shapes which concerns they emphasize and which they downplay.

The civil liberties risks are already well documented. Surveillance systems tend to expand over time — what starts as airport security can end up monitoring entire cities. They chill people's willingness to speak up or dissent. AI-based systems that identify faces or behavior often make different errors depending on the race or gender of the person being analyzed. And there is no guarantee of fair legal process when an algorithm flags someone before a human being has even reviewed the case. These are not theoretical problems. They have appeared in real deployments, from London's extensive CCTV network to facial recognition pilots in U.S. and Chinese cities.

What Diamandis and Ellison have done is give coherent, well-funded voice to a particular tech-world viewpoint: that unmonitored human behavior is a problem to be engineered away. That worldview is worth paying attention to, regardless of whether the behavioral science behind it actually holds up.

The real pivot point is likely to be regulatory. The European Union's AI Act puts real-time biometric surveillance in public spaces in a high-risk category and bans most uses of it. The United States has no equivalent federal rule. That difference means the debate Ellison started and Diamandis is continuing will unfold in very different ways depending on where you live — and the behavioral optimism of both men may collide directly with legal structures built on different assumptions about how much surveillance power a state should have.

Technology has a pattern of making observation cheaper and faster, eventually turning theoretical ideas into working systems. The question now is not whether AI surveillance is technically possible — it clearly is. The question is what rules will be in place before it becomes normal — and whether the people most confident in its benefits have genuinely reckoned with who gets to set the boundaries.