Where should AI inference run? Three things decide

Ask a team where their AI runs and you'll usually get a shrug and a cloud provider's name. Ask why it runs there and the shrug gets bigger.

Where AI runs is about to matter as much as which model you pick. On the Money Moves podcast, our CEO Paweł Czech told hosts Samantha Lewis and Ian Epstein what decides it: "The main three things are latency, data privacy, and ownership or sovereignty of the compute that you're using."

Three words, three questions. Here they are one at a time.

First, a pattern worth noticing

The internet spreads communication to everyone. Then commerce. Then finance. Each time, something that used to sit with a few institutions ended up in everyone's hands, everywhere.

Intelligence is next. So far, though, it's spreading in reverse: into a handful of hyperscale sites, owned by a handful of companies. Paweł's argument on Money Moves is that this can't last if AI is going to be part of daily life. In his words: "If we want to benefit from these improvements in our day-to-day lives, it means that compute will have to go decentralized."

The three questions below explain why.

1. Latency: how far is the answer?

Every AI request travels. It goes from a phone, a camera, a car or a machine to wherever the model runs, and back. When the model runs far away, the trip takes time.

For a chatbot, a short wait is fine. For a robot arm, a traffic system or a hospital monitor, the wait is the product. You can't ask a machine on a factory floor to pause while its request crosses a continent.

As AI moves from screens into the physical world, more of it becomes this kind of work: constant, real-time and local. That work wants compute close to the people and machines using it.

The question to ask: where are the people and machines that use this model, and how far is the compute from them?

2. Data privacy: where does the data have to stay?

To answer a question, a model has to see the data. Sometimes that's a public web page. Often it's a patient record, a customer conversation, a factory's camera feed or a company's internal documents.

A lot of that data has rules about where it can go. Some rules come from law, some from contracts, and some from the plain fact that a company doesn't want its crown jewels leaving the building. If the only capacity available sits in a few regions, the data has to travel to it, or the project stops.

It's simpler to bring the compute to the data. Placement that respects residency stops being a workaround and becomes part of the design.

The question to ask: where is this data allowed to go, and does our setup make that easy or hard to prove?

3. Ownership: who controls the compute you depend on?

This is the one people skip. If your product depends on AI, it depends on someone's compute. Who owns it? Who decides the price next year? Who decides what runs first when capacity gets tight?

Right now, for most teams, the answer is "a few very large companies", and the terms are theirs. That's a strange position for something that's becoming as basic as electricity or bandwidth.

Early in the episode, Paweł described the bigger idea behind it: "It's actually the thought of universal basic ownership." More people and more places should be able to own and run the capacity AI depends on, not only rent it from a few landlords.

The question to ask: if our main provider changed its prices or priorities tomorrow, what could we do about it?

Put the three together

Any one of these questions can be answered with a workaround. All three at once can't. Low latency, data that stays put and real choice over who runs your compute all point the same way: toward many smaller sites, close to power and demand, instead of a few giant ones far away.

That creates its own problem. Thousands of small sites are hard to use one by one. You need them to behave like one system.

What we're building

That's the gap Starfield is built for. We deploy standardized compute close to power, connectivity and demand, then connect it through one programmable network for AI inference.

Workloads are placed by policy, on latency, data residency, model and load, so each of the three questions becomes a setting rather than a compromise. Every workload can generate an auditable record of commitment, placement and delivery, so you can show where your AI ran, not only claim it.

Distributed AI infrastructure at network scale. That's the short version.

Listen to the full episode

Paweł makes the case at length on Money Moves, "Globalized intelligence: How AI Can Be Built for Humanity, Not Just Hyperscalers", with Samantha Lewis of Mercury Fund and Ian Epstein of Profitr. The three-part answer comes at 25:36.

If latency, residency or ownership is holding up your AI plans, talk to us: Request capacity or Partner with Starfield below.

Quotes

“The main three things are latency, data privacy, and ownership or sovereignty of the compute that you're using.”