The Two Stories You’re Hearing About AI Are Both True

This month, Time magazine made an unusual choice.

Instead of naming a politician or a celebrity as its annual Person of the Year, it crowned the architects of artificial intelligence.

The publication didn’t focus on a single person for this award. It included the chip designers, model builders and executives who have turned abstract research into the working AI systems that now sit inside office software, call centers and defense networks.

So it’s reasonable to consider this a cultural milestone. After all, Time named “The Computer” its Machine of the Year back in 1982, and its impact over the following four decades has been profound.

But here’s the thing.

At the exact same moment AI’s builders were being celebrated, regulators were moving in the opposite direction. The European Commission opened new probes into how AI models are trained. U.S. agencies are escalating their push to define accountability for AI-driven decisions. And courts continue to weigh whether today’s training practices cross legal lines.

If that sounds contradictory to you, I get it. But it’s what happens when a new technology starts becoming embedded in our daily lives.

And that’s why, as we head into 2026, the real story of AI isn’t just about innovation.

It’s about what happens when a technology becomes infrastructure.

AI’s Emergence

For most of the past decade, artificial intelligence has lived in a kind of gray zone.

Prior to 2023, AI models were impressive, but for the vast majority of humans the stakes of AI were very low. If something went wrong, it was usually the inconvenience of a glitchy chatbot giving you a wrong answer.

But AI crossed a threshold when large models became both general and embedded.

By general, I mean the same systems could write, code, reason, analyze images and operate tools. By embedded, I’m talking about systems that are no longer stand-alone apps. They’re being fused directly into search, productivity software, customer support, logistics and industrial workflows.

This is why I recommended Palantir Technologies Inc (Nasdaq: PLTR) in early 2024 to members of my flagship research service, Strategic Fortunes.

I realized we had crossed this threshold, so I urged them to consider scooping up shares of PLTR just as the company embarked on a 10X run to all-time highs.

But once a technology reaches this point, governments need to start asking who is responsible when it fails.

And that’s why regulatory scrutiny is accelerating today, as AI becomes part of our daily lives.

In Europe, the focus of this regulation is about control and competition. Regulators there are examining whether companies like Google and Meta used copyrighted or proprietary content to train models without consent.

That’s because training modern AI requires staggering amounts of data. Text, images, video, code and speech are all being scraped from across the internet and private sources. And that data advantage has become a moat for the companies that got there first and now control the largest and most capable systems.

European regulators want to ensure these companies haven’t gained an unfair advantage by how that data was collected.

They’re also pressing for transparency into how models are built and how outputs are generated under the EU’s AI Act framework.

Image: European Commission

In the United States, the emphasis is different, but just as serious.

Here, agencies are more focused on accountability. If an AI system denies a mortgage, flags a job applicant, diagnoses a patient, or controls a vehicle, someone has to answer for that decision.

“The algorithm did it” isn’t a defense that will hold water with regulators or judges.

And we’re seeing this play out in the courts right now.

Copyright lawsuits against OpenAI and Anthropic are already moving through the legal system. Federal agencies are issuing guidance that treats AI systems as part of critical infrastructure. And lawmakers are debating whether responsibility should fall on the companies that build AI, the ones that use it, or both.

But don’t assume that all this scrutiny means we’re in for an AI slowdown. Because history says just the opposite will happen.

Electricity didn’t stall because safety codes were introduced.

Image: Wikimedia Commons

The aviation industry didn’t collapse when standards were imposed. And financial markets didn’t stop evolving because disclosure rules appeared.

Instead, they became safer and more reliable.

AI is entering that same phase now.

Here’s My Take

There’s a dual narrative playing out with AI today.

On one side, a celebration of rapid innovation and the people driving it. On the other, a growing demand for oversight and guardrails.

And neither side is wrong.

The people building AI deserve recognition. After all, they’ve delivered one of the most powerful productivity tools in human history. AI is already saving companies billions of dollars, accelerating research and expanding what small teams can accomplish.

But regulators are also right to step in now, before AI failures can scale into systemic ones. Because that’s exactly what happened with the internet.

In the 1990s, the internet was celebrated as a force for freedom and growth. But regulation lagged for decades, and in many ways it still hasn’t caught up.

AI is moving faster than the internet ever did.

That means the window for getting governance right is much smaller.

As we move into 2026, we should continue to celebrate innovation. But we also need to embrace more regulation around artificial intelligence.

After all, regulation isn’t a threat to AI.

It’s proof that it has arrived.

Regards,

Ian KingChief Strategist, Banyan Hill Publishing

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