Thirty seconds during the Super Bowl now commands roughly $8 million, with prime placements flirting with $10 million.

In 1967, that same half-minute cost $37,500.

Attention has appreciated more reliably than most blue-chip stocks.

Meanwhile, Ring—your friendly neighborhood doorbell camera company—paid Super Bowl prices to bring its most controversial “fine print” feature into prime time.

In case you haven’t heard…

“Search Party” is a feature inside the Ring ecosystem that turns millions of privately owned doorbell cameras into a distributed object-detection AI network.

It was pitched as a lost-dog rescue tool. The broader implications—as is evident by the backlash—landed immediately.

The system uses computer vision models to identify “pets.”

When a “pet” is reported missing in a given area, participating Ring devices in that geographic radius automatically begin scanning recent and live footage for visual matches.

If a likely match appears, the device owner receives a notification and can choose whether to share the clip.

The massive reaction on X made the skepticism impossible to miss.

And connections began to surface.

But this wasn’t the first time Amazon—the owner of Ring—revealed its cards.

Remember the Roomba

When Amazon tried to buy the company behind the Roomba, most thought it was about vacuums.

It wasn’t. It was about floor plans.

A Roomba maps your house in microscopic detail. Square footage. Room usage. Furniture placement. Traffic patterns.

Amazon saw something more valuable than a cleaning device. It saw a living, breathing interior map of millions of American homes.

After all, a mapped home removes a lot of guesswork.

Layout, room usage, furniture placement, and traffic flow reveal income, life stage, habits, upgrade timing, even future purchases.

It also exposes behavioral patterns.

It reveals when rooms go dark, where people linger, how routines flow, when a nursery appears, when an office expands, when movement slows.

At scale, that data becomes a living behavioral dossier—not just what you own, but how you live inside it, what you’re likely to need next, and when you’re most vulnerable to suggestion.

And regulators saw it too.

The deal collapsed under scrutiny.

That moment—like the Ring backlash—is a tell.

Data is the New Asbestos

For fifteen years, “data” was treated like oil.

Collect it. Store it. Monetize it later. Venture capital rewarded accumulation. Engineers built pipelines wide enough to swallow the world.

The assumption was simple and perfectly logical: more data equals more value.

But, increasingly, the assumption is breaking. For both political and technological reasons.

Starting with politics, the posture has changed since 2010.

Stored archives are discoverable. Biometric traces trigger statutes. Cross-border transfers require legal choreography. The longer information lives, the more regimes it passes through—and the more ways it can be weaponized in court or regulation.

Meanwhile, with the rise of AI, data governance is far more politically charged than it was in 2010.

Data still holds value. It just no longer arrives without embedded risk. Companies once boasted about filling the lake. Now they budget for the liability it creates.

Firms like Meta, Amazon, Google, and major banks now shorten retention windows, limit internal access, segment AI systems, and actively delete historical data. For example, Ring was forced to move toward configurable and default deletion periods rather than indefinite storage.

That doesn’t mean privacy is coming back full force.

It means the right to accumulate behavioral exhaust is consolidating into institutions with sovereign mandate, huge regulatory insulation, or more explicit public authorization.

BUT, even so…

At the same time, technological pressures are mounting that change the logic, too.

And that part is definitely investable.

The Investment Angle

The “collect everything now, monetize it later” logic makes sense if more data inherently means more bucks and cheap storage means no one cares.

But right now, the cost side of the ledger is growing teeth.

For frontier AI models, dumping more and more behavioral exhaust into the hopper no longer guarantees breakthrough gains. Quality now matters more than volume.

Meanwhile, synthetic data can increasingly simulate what once required real human traces.

And federated systems train models without centralizing raw information, raising efficiency.

Even more crucial…

“Edge computing”—running models on the device locally, bypassing the cloud—reshapes the equation entirely.

It cuts latency (delay), lowers bandwidth strain, improving the economics of robots, autonomous vehicles, drones, wearables, smart factories, and any system that has to sense and act in real time.

These systems can’t depend on constant cloud round-trips. Local processing favors smaller, optimized models. Intelligence travels outward.

When the cost of keeping everything exceeds the marginal value of keeping it, shrinking the archives becomes the rational move.

The incentive flips.

Collecting everything used to win. Now using less, better, wins.

Tomorrow, I’ll share the most underlooked—but most powerful—sector innovating on that front.