In the early days of electricity, the smart money believed power would be local.

Factories built their own generators. Hotels installed private dynamos. Industrialists bragged about self-sufficiency.

Then came this thing we now call “the grid.”

Centralized generation. Massive plants. Gigantic turbines.

Scale won. Efficiency won. The small generators were scrapped.

So when AI arrived, the prevailing assumption was that this story would rhyme.

“Build bigger plants.”

“Aggregate the power.”

“Sell it by the kilowatt—or the token.”

And so we have data centers the size of cathedrals. GPU clusters humming like modern monasteries. Capital expenditures large enough to boggle the minds of Roman emperors.

The hidden assumption beneath all of it? Future AI value equals centralized inference.

But that assumption is set to crack.

Sounds complex, but it’s actually simple.

Let me unpack why it’s happening, what it means for AI’s “Phase Two”—and why the next monstrous wave of AI gains won’t accrue to the same names that dominated Phase One.

The Gazillion-Dollar Bet

Inference is when an AI model uses what it has already learned to produce an answer.

Simple.

The massive AI data center buildout only makes sense if inference dwarfs training.

By a lot.

Training, as you know, is spectacularly expensive—hundreds of millions of dollars—but it happens episodically.

Inference, on the other hand, happens every time someone prompts a model, every Copilot suggestion, every API call.

Training is the capital expense. Inference is the recurring revenue.

If usage scales to trillions of daily interactions, and the data suggests it will, inference becomes the annuity that justifies tens of billions in annual GPU capex.

So the gazillion-dollar bet the hyperscalers are making: high-value inference stays predominately centralized.

Meaning, the most valuable workloads—the ones people pay real money for—remain within their house.

The bet, in short:

→ Intelligence lives in the cloud.

→ Rent it by the API call.

→ Collect the toll.

It’s a very clean story.

Investors adore clean stories.

But history has a peculiar habit of punishing them.

Atoms Don’t Autoscale

Here’s the issue…

Most AI demonstrations so far involve:

• Writing emails
• Generating code
• Producing images
• Summarizing documents

All perfectly suited to centralized inference.

In this arena, latency is tolerable. Bandwidth is modest. And the environment is almost entirely digital.

But the global economy is not primarily made of emails.

Roughly 70% of GDP still involves physical activity, tied to physical locations:

• Warehouses
• Factories
• Retail stores
• Construction sites
• Hospitals
• Airports

Atoms, not tokens.

And atoms have rules.

A robot can’t wait 80 milliseconds for permission from a distant server before deciding whether to step left or right. (You don’t want a welding robot waiting on a round-trip to a Virginia data center.)

An augmented reality headset can’t tolerate cloud-induced hesitation without turning the wearer pukey green.

Sensor streams are not polite little text prompts. They are continuous, high-bandwidth firehoses measured in tens or hundreds of megabits per second per device.

Before the cloud ever sees anything useful, that torrent must be filtered and interpreted locally—because physics is an impatient beast and doesn’t wait for round trips.

In short…

You can centralize information. It’s a lot harder to centralize reflexes.

That’s why this next wave is set to be a lot different from the last.

A Most Probable Scenario

If inference meaningfully shifts to edge devices or specialized models, centralized growth risks slowing even as capex stays high or climbs in response to competition.

Compute fragments.

It moves into:

• Retail back rooms
• Factory floors
• Hospital wings
• Distribution centers

Small servers. On-prem systems.

If smaller models end up handling 60-70% of tasks, then that’s absolutely catastrophic for the hyperscaler thesis.

BUT the most probable scenario is something in the middle.

A hybrid.

In this model—edge handles reflexive/perception tasks while cloud handles heavy reasoning—hyperscalers don’t collapse.

They just don’t compound like they did. And markets don’t pay 30-40x multiples for “steady.”

So we arrive at the fastest ARR lane for physical AI.

Prepping For Phase Two

The hyperscaler trade was Phase One.

The market repriced around one assumption: centralized inference would scale exponentially and capture the majority of AI value.

That trade has largely been expressed.

Phase Two—the distributed, physical AI layer—is less winner-take-all than hyperscale cloud.

That alone creates oxygen for disruption.

If AI moves into the 60–70% of GDP tied to physical environments—retail, logistics, manufacturing, construction, healthcare—inference fragments.

That shifts capital toward:

→ embedded AI chips (automotive/industrial-grade chips in the $30–$300 range)

→ industrial vision systems (depth, LiDAR, radar, machine vision)

→ ruggedized edge servers, private low-latency networking, spatial middleware (indoor positioning, shared coordinate systems)

→ digital twins, and robotics fleet management software.

Some of these layers—like private low-latency networking—are defensible by incumbents.

Everything else on that list has more room to rotate.

What does this mean? Inference volume in physical workflows scales with deployed devices—not API calls.

Again, this is a massive shift even if it’s modest.

If even a humble percentage of AI compute migrates into on-prem, real-time perception and coordination, they will be the next compounding layer.

Phase One built the brains. Phase Two wires them into the world.

As always, we’re on the lookout for new opportunities in this space. And our Paradigm Mastermind Group members are already ahead of this curve.