AI is bigger than the companies building it

The further AI moves into the real world—into infrastructure, security, work, and politics—the more complicated the story becomes

I’ve been covering AI as my daily beat for four years — first at VentureBeat, and now at Fortune. For most of us covering the space (shout-out to y’all), AI beat coverage has orbited around a small number of companies building frontier models, including OpenAI, Google, Anthropic and Meta.

The drama has been undeniable: The model launches! The product features! The benchmarks! The leadership soap opera! The talent wars!

Of course, I have always made it a point to publish about other issues, including AI policy and regulation, the chips powering the AI boom, and how top companies are adopting AI. Still, it always seems like the latest tit-for-tat between Sam Altman and Dario Amodei has sucked up the oxygen. Every investment round, every embargo, every hyped-up beef on X has been at the center of the 24/7 news cycle.

Part of the reason, I think, is because this style of reporting presents a simple narrative of believing, building and battling for attention and growth. But I think the complex messiness of AI is becoming harder to ignore as AI gets out into the wild, into the real world. The daily AI news cycle is drama for Silicon Valley. But the story has expanded far beyond the Bay Area—into the workplace, infrastructure, supply chains, communities, politics, security, and people’s daily lives.

None of this is to say that the frontier model companies don’t matter—they absolutely do. They’re still setting the pace of the technology and shaping what’s possible. But the story is getting far more complicated and consequential.

That’s where my reporter ears really perk up. Once AI is out in the wild, we’re not just talking about model capabilities, or product features, or even what a frontier AI billionaire thinks. We’re talking about control, risk, cost, consequences.

“Why'd ya have to go and make things so complicated?” sang Avril Lavigne back in the early-aughts. I say bring it on, because once you start looking at AI through that lens—control, risk, cost, consequences—you start to see the same story showing up everywhere.

In the AI data center boom and its impact on local communities. In the new challenges facing cybersecurity teams as AI-powered attacks ramp up. In how companies are navigating backlash, how elections could be shaped, and the explosion of products trying to operationalize AI across every industry.

That story is what happens as AI moves from contained systems—labs, models, companies—into complex, real-world systems, and all the consequences that follow. And none of it will play out exactly as even the best experts expect.

I sometimes think about this when I’m sitting forlornly on a New Jersey Transit train, trying to get from my central New Jersey home to Fortune’s offices in downtown Manhattan.

It’s often a total shit-show: decades-old trains, aging tracks and wiring—and rail tunnels under the Hudson River that are more than a century old, were seriously damaged during Hurricane Sandy, and still need major, long-term repairs.

Everyone knows they have to be fixed. But they can’t just be taken offline, so a new tunnel has to be built first—and that process is expensive, slow, and politically fraught, with years of funding fights and delays. And meanwhile, I sit with hundreds of fellow commuters, frustrated, staring at the marshes of the Meadowlands, wondering how my life turned into this when Japanese commuters have enjoyed bullet trains since 1964.

Yup. It’s never just about the technology.