Startups are burning runway waiting for compute.
Meanwhile, GPUs sit underused.
There’s a solution.
Connect that capacity to the builders who need it, at prices they can afford.
At @ionet, we want your next funding round to pay for progress. Not for coffee while you wait on a GPU.
io.net/blog/solving-the-ai-c…
Enough electricity to power roughly 300 million homes for a year.
That’s the 945 TWh data centres are projected to consume annually by 2030.
No wonder the pushback against the buildout keeps growing.
But the GPUs that already exist have been built, powered, and connected. @ionet puts the underused ones to work so there’s less need to build new capacity first.
That’s how you make AI for the many, not the few.
forbes.com/advisor/business/…
$2.5 trillion wiped off tech stocks in one day.
Set off by one country’s election.
That’s the danger of concentrated infrastructure. When AI compute is built in a few places, one country’s politics can decide everyone’s access.
There’s a better way.
Open compute, distributed across regions, and built from the hardware that already exists. That’s io.net.
32 countries host specialised AI data centres.
US and Chinese firms run 90%+ of them.
Whole nations are queuing alongside startups, renting compute on someone else’s terms.
@ionet's AI and Financial Inclusion Strategist, David Sherman, breaks down who gets left out in @techradar.
Access should be for the many, not the few.
techradar.com/pro/the-uks-ai…
“Trust us,” say the companies racing to own AI.
Geoffrey Hinton, the godfather of AI, isn’t convinced.
Neither are we.
AI could create extraordinary abundance. But when a handful of companies control access and put profit first, they decide who gets to take part.
What we need are open models, open compute, and more people deciding what AI is for.
The technology has potential.
Concentrated power has a track record.
Most GPU guides tell you to pick the cheapest H100.
Wrong question.
The right one is which compute setup fits your workload, your runway and your team, and won’t trap you in six months.
We built a framework for it.
Five decision variables. Three stages. Pre-seed to Series A.
Compute that grows with you. That's io.net.
You came to rent a GPU.
You ended up funding the whole cloud.
Infrastructure overhead. Managed service premiums. Fees to move your own data.
Every extra dollar is budget you can’t spend building.
Rent the GPU and skip the rest with @ionet.
io.net/blog/why-does-gpu-clo…
AI lets you build more than you should ship.
@ionet’s Head of Product @Raj_TheBUIDLer joins the @theaisho to talk open vs frontier models, and when an agent is actually worth its cost.
And there's coffee analogy that explains everything.
What should you stop building when AI lets you build more?
This week, we had @Raj_TheBUIDLer on the show. He leads product at @ionet, where the team serves about 20 billion tokens a day through OpenRouter.
We talked about the choices behind an AI product. When is an open model good enough? What makes a frontier model worth paying for? And does adding another agent actually help the user enough to justify its cost?
That led to a conversation about how the PM role is changing, especially when a team can build far more than it should ship.
We also covered confidential computing for banks and hospitals, whether compute futures make sense for startups, and what might come after LLMs.
And yes, coffee came up. Raj uses it to explain why compute is harder to standardise than it looks. The machine matters, but so does where and how you run it.
Full episode out now👇 w/ @Diksha_ds and @sofiaunchained