G-PEP explained via a deliberately absurd car analogy
The easiest way to understand Governed, Pre-Execution Provisioning (G-PEP) is to think about your car, its gas tank and a simple trip to the store.
Imagine you’re baking a cake and you realize you need a cup of sugar. You look in the pantry, find none, start your car, drive to the store, buy sugar, drive home, turn off the engine and finish baking.
Simple, right?
That’s how cars work.
Unfortunately, that is not how large language models (LLMs) work.
Now imagine what that same trip would look like if your car behaved like an LLM.
The absurdity
If your car were an LLM, it would refuse to start unless the gas tank was completely full.
Why?
Because the car does not know whether you’re driving one mile to the store or driving cross-country. To avoid running out of gas mid-trip, it assumes every trip is the longest possible trip.
This is the core pathology of today’s LLMs: worst-case provisioning by ignorance.
So the engine won’t start unless the tank is full.
You drive to the store, turn off the engine — and here’s where it gets even worse.
Because the car is an LLM, once you turn the engine off, the remaining gas in the tank becomes unusable. It doesn’t matter how little gas you actually used. The reserved capacity is locked, discarded and cannot be reused. The next trip once again requires a full tank.
The result?
Every trip — no matter how short — requires provisioning for a cross-country journey.
You would never buy a car like this.
But this is exactly how LLMs operate today.
Why this wastes enormous amounts of money
Most trips are short.
Most queries are simple.
But because the system must assume worst case before execution, most queries are massively over-provisioned. Compute is reserved whether it is needed or not, and unused capacity cannot be recovered mid-execution or post-execution.
That wasted gas is wasted compute.
And wasted compute is wasted money.
What Pre-Execution Provisioning (PEP) fixes
Now imagine a better car.
Before starting the engine, the car estimates how far you’re actually going. If you’re just going to the store, it provisions only the gas required for that trip — not a full tank.
That’s Pre-Execution Provisioning (PEP).
PEP determines how much compute a query actually needs before execution and provisions only that amount. No more worst-case assumptions. No more cross-country fuel for a one-mile trip.
The result: dramatically lower compute usage and dramatically lower cost.
What the “G” in G-PEP does
Now we add governance.
Go back to the kitchen.
You needed sugar, which triggered the trip to the store. But what if there were alternatives?
What if:
- you already had sugar but didn’t see it
- you could substitute honey
- you could borrow a cup of sugar from a neighbor
In any of those cases, you wouldn’t start the car at all.
That’s what the “G” (Governed) in G-PEP does.
Before authorizing high-cost execution, the system asks:
- Is there a lower-cost path that satisfies the user?
- Is there a zero-compute alternative?
- Is starting the engine necessary at all?
If a viable alternative exists, the engine never starts.
This is not denial of service.
It is denial of unnecessary execution.
And when execution is avoided entirely, compute savings approach 100 percent.
The Bottom Line
- PEP saves money by provisioning only the compute a query actually needs.
- G-PEP saves even more money by preventing expensive execution when it isn’t necessary in the first place.
You would never tolerate a car that wastes fuel this way.
Yet today’s AI systems do exactly that — at planetary scale.
G-PEP exists to stop the waste before it starts.
– Published Wednesday, January 21, 2026