LLMs don’t need more power. They need fewer FLOPs.

A recent note from Jim Cramer framed the AI debate in familiar terms: power, electricity, and physical limits. One line stands out because it quietly carries the entire thesis:

“If someone were to come up with a less energy-intensive way to produce compute, I would be very nervous. They haven’t and they won’t.”

That statement assumes something critical — and wrong.

It assumes that every AI query deserves full inference.

It doesn’t.

The debate is framed around physics, but the problem is systems

Most discussion about AI economics focuses on:

• Chip efficiency
• Power generation
• Data-center scale

That framing treats compute cost as a hardware problem. It isn’t.

The dominant source of waste in LLMs today is not inefficient silicon.
It’s unnecessary execution.

Modern AI systems behave like this:

• Every prompt triggers full model inference
• Every inference runs at peak compute
• No judgment occurs before the expensive work begins

That is not how mature compute systems evolve.

The missing step: pre-execution provisioning

There is a decision layer missing from today’s LLM architecture.

Before inference starts, the system should ask:

• How complex is this request?
• How much reasoning is actually required?
• Is full inference justified here?

That step does not exist today.

As a result, trivial, repetitive, low-stakes, or already-answerable queries are treated the same as genuinely complex ones.

That is where the energy goes.

This does not change physics

This matters.

The solution is not:

• Cheaper transistors
• Faster clocks
• New power sources

It does not reduce energy per FLOP.

It reduces FLOPs per question.

That distinction is everything.

Compression didn’t change bandwidth physics.
Indexes didn’t change disk speed.
Caching didn’t make CPUs faster.

They reduced waste.

Why power constraints make this inevitable

As power becomes constrained, there are only three options:

1. Spend more on electricity
2. Slow growth
3. Stop doing unnecessary work

The first two compress margins.
The third improves them.

No amount of chip innovation offsets running full inference on queries that don’t require it.

That is why selective execution isn’t speculative. It’s forced by arithmetic.

What this will look like in practice

This won’t be announced as a breakthrough.

It will arrive quietly, under names like:

• Inference tiering
• Execution gating
• Workload qualification
• Dynamic reasoning depth

And when it does, LLM systems will suddenly appear less energy-intensive — without violating a single law of physics.

Not because compute got cheaper.

Because waste got removed.