The valuation for @AnthropicAI is down to "just" $1 trillion.
That’s the equivalent of 100,000 startups funded with $10 million each.
The indsutry may have lost perspective when a trillion sounds disappointing.
AI’s future needs more builders who can afford to participate.
That’s what @ionet is for.
Your team is in São Paulo, Lagos, and Manila.
Your inference endpoint is in us-east-1, just because that’s where the account was created.
So now some of your team waits on every call, your data sits wherever it landed first, and your compliance work keeps growing.
That’s what we call the global team tax.
Where your compute runs is an architecture decision.
@ionet lets you choose where your GPUs run. Across 130+ countries.
$14.3 million in 30-day annualised revenue.
@ionet is #1 on the DePIN Pulse leaderboard.
Accessible, affordable AI compute, built for open models.
The demand is real. So is the revenue.
depinpulse.app/
A chip giant. Congressional scrutiny. A board investigation.
It reads like a spy thriller because AI infrastructure is concentrated enough to be one.
But the real plot is dependency.
Access to compute shouldn’t hinge on any one boardroom, capital, or CEO.
That’s what we’re solving for at @ionet.
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.
