I Started Studying AI and Somehow Ended Up Studying the Power Grid

I didn’t start studying artificial intelligence because I wanted to understand the electrical grid.

Yet somehow, that’s where the trail led.

What began as an effort to better understand AI models gradually expanded into compute infrastructure, data centers, electricity generation, transmission constraints, capital investment and eventually governance.

Looking back, the progression makes perfect sense.

Models → Compute → Data Centers → Electricity → Transmission → Capital → Governance

Each subject exposed a dependency underneath the previous one.

And that has changed the way I think about AI.

Follow the Dependency

When most conversations about artificial intelligence begin, they start with the model.

How capable is it?

How quickly is performance improving?

What tasks can it automate?

What happens when agents become more autonomous?

Those are important questions. But eventually I started asking another one:

What does this depend on?

AI models depend on compute.

Compute depends on chips, servers, cooling systems and data centers.

Data centers depend on enormous amounts of reliable electricity.

Electricity depends on generation capacity and the infrastructure required to move that power where it is needed.

That means transmission capacity, substations, interconnection queues, permitting and infrastructure investment suddenly become part of the AI conversation.

Keep following the chain and another layer appears.

Capital.

Someone has to finance all of this infrastructure.

Companies have to decide how much capacity to build, where to build it, what assumptions justify the investment and whether expected AI demand will generate sufficient returns.

Now the conversation isn’t simply technological.

It is operational, financial and strategic.

And eventually it becomes a governance problem.

Capability Is Only One Layer of the System

One of the most important lessons I’ve taken from this progression is that technological capability doesn’t guarantee system-level capability.

A model might become significantly more powerful while the infrastructure supporting its deployment remains constrained.

More demand for compute can create pressure on data-center capacity.

Additional data centers increase electricity demand.

New generation capacity doesn’t necessarily solve the problem if transmission infrastructure cannot deliver that electricity where demand is growing.

And even if every physical constraint could be removed, organizations still have to determine whether the resulting investments make economic sense and whether the systems being deployed can be governed responsibly.

This creates an interesting possibility:

The biggest constraint on the next stage of AI might not always be AI.

It could appear somewhere else in the dependency chain.

Systems Don’t Respect Industry Categories

We tend to organize knowledge into categories.

Artificial intelligence.

Energy.

Finance.

Infrastructure.

Risk.

Governance.

But real systems don’t care about those categories.

Their dependencies cross them constantly.

An AI strategy can become an infrastructure strategy.

An infrastructure strategy can become an energy strategy.

An energy constraint can become a capital-allocation problem.

A capital decision can create new operational risks.

And those risks eventually require governance.

What looked like several unrelated subjects was actually one interconnected system viewed from different layers.

That’s what systems thinking started changing for me.

Instead of asking only:

What can this technology do?

I became increasingly interested in another question:

What has to remain true for this entire system to work?

That question exposes a very different landscape.

The Bottleneck Moves

Another realization followed.

Bottlenecks don’t disappear simply because technology improves.

They move.

If models become more efficient, another constraint may become more important.

If compute capacity expands, electricity availability may become the limiting factor.

If generation expands, transmission may become the constraint.

If infrastructure expands rapidly, capital discipline or governance may become the limiting factor.

This means leaders evaluating AI cannot look exclusively at technological capability.

They also have to understand the dependency chain supporting that capability.

Otherwise, it becomes easy to mistake progress in one layer for readiness across the entire system.

The Infrastructure Underneath the Intelligence

I still study AI.

But now I find myself paying much more attention to what surrounds it.

Energy infrastructure.

Data-center development.

Transmission.

Capital allocation.

Operational risk.

Governance.

Not because I abandoned the original subject, but because following it seriously kept expanding the boundaries of the system.

That’s probably the biggest lesson this journey has taught me:

You don’t really understand a system until you understand what it depends on.

AI may appear to be a software revolution when viewed from the surface.

Follow the dependency chain downward, however, and you eventually reach physical infrastructure, energy, economics and governance.

And sometimes the most important part of a technology isn’t the technology itself.

It’s everything that has to work underneath it.