I had a front-row seat to the social media revolution in global affairs roles at Twitter and Meta. The same mistakes are happening in AI | Fortune

"For over a decade, I worked in the engine room of the social media revolution, starting in U.S. government, then at Twitter and Meta. I led teams engaging with governments worldwide as they grappled with platforms they didn't understand. At first, it was intoxicating. Technology moved faster than institutions could keep up. Then came the problems: misinformation, algorithmic bias, polarisation, political manipulation. By the time we tried to regulate it, it was too late. These platforms were too big, too embedded, too essential."

"The lesson? If you wait until a technology is ubiquitous to think about safety, governance, and trust then you've already lost control. And yet we are on the verge of repeating the same mistakes with AI. The new infrastructure of intelligence For years, AI was viewed as a tech issue. Not anymore. It's becoming the substrate for everything from energy to defence. The underlying models are getting better, deployment costs are dropping, and the stakes are rising."

"The same mantras are back: build fast, launch early, scale aggressively, win the race. Only now we're not disrupting media instead we're reinventing society's core infrastructure. AI isn't just a product. It's a public utility. It shapes how resources are allocated, how decisions are made, and how institutions function. The consequences of getting it wrong are exponentially greater than with social media."

Rapid, unregulated technological expansion produced misinformation, algorithmic bias, polarisation, and political manipulation when social platforms outpaced institutions. Waiting until a technology is ubiquitous to address safety, governance, and trust cedes control. AI is transitioning from a tech product to foundational infrastructure affecting energy, defence, resource allocation, decision-making, and institutional operation. Underlying models are improving while deployment costs fall, raising stakes. Familiar risks reappear: opaque training data without oversight, algorithms optimised for performance over safety, and closed systems making inscrutable decisions. Repeating past mantras—build fast, launch early, scale aggressively—threatens exponentially greater consequences if safety and governance are neglected.

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