What Is AI Compute? Training vs Inference Explained
A simple breakdown of training vs inference, and why compute constraints now shape how AI products scale.
“AI compute” is one of the most used and least explained terms in tech. Sam Altman has even called it “the currency of the future.” Here’s what AI compute actually means, and why it’s becoming the main bottleneck shaping AI scale and costs.
At its core, AI compute describes the resources required to train and run machine-learning models at scale. But not all compute is created equal, and the difference matters.
Training Compute
Training compute is used to build models.
- Large, upfront workloads
- Happens periodically
- Drives record-breaking GPU clusters
- Makes headlines
Training is expensive, but temporary.
Inference Compute
Inference compute is used every time a model produces an output.
- Always on
- Scales with users
- Drives latency and operating costs
- Determines unit economics
Inference is where AI meets reality.
Why Inference Is the Real Bottleneck
As AI products reach millions of users:
- Costs compound
- Latency matters
- Reliability becomes non-negotiable
This is why we’re seeing increased funding into:
- Inference optimization
- Specialized hardware
- Edge and hybrid deployments
Why This Matters
Understanding AI compute helps explain:
- Why some startups scale faster than others
- Why infrastructure funding is accelerating
- Why energy and AI are converging
In the AI gold rush, compute is the shovel, energy is the handle, data is the dirt, and models are the gold bars. The real moat is how tightly the stack is integrated.
