io.net Re-poster

2 million burned.

Gone for good.

Because of real utility and real value flowing through the network.

Usage creates value. Value reduces supply.

The flywheel is turning. Powered by the IDE.

2 million solana:BZLbGTNCSFfoth2GYDtwr7e4imWzpR5jqcUuGEwr646K burned.

Gone for good.

Because of real utility and real value flowing through the network.

Usage creates value. Value reduces supply.

The flywheel is turning. Powered by the IDE.

The cheapest GPU isn't always the cheapest GPU.

The GPU market has fragmented across hyperscalers, neoclouds, and decentralized networks.

A cheap hourly rate can quickly become expensive when you factor in SLAs, security requirements, data residency, and the cost of getting out.

The hourly rate is just the number on the pricing page.

The real cost of compute is everything underneath it.

Daily token usage in China went from 100B to 140T in 14 months.

Beijing's response:
- 100,000 GPUs
- $532B in infrastructure investment
- 9,800 EFLOPS by 2030

The AI race is a compute race. Access to GPUs will define who competes.

Governments and hyperscalers are solving this by building.

http://io.net is solving it by unlocking the underutilized compute that already exists.

Building takes years. Unlocking takes minutes.

38GW.

That's the US data center power shortfall through 2028.

The hyperscaler solution is to build more data centers, substations, and power generation.

Then wait 4-7 years for it.

Meanwhile, millions of GPUs are already powered, cooled, connected, and underutilized.

AI doesn't just need more infrastructure. It needs better access to the infrastructure that already exists.

That's what http://io.net was built for.

Der Chef-Wissenschaftler von OpenAI hat gerade zu „extremer Vorsicht“ im Hinblick auf das Tempo von KI aufgerufen.

Direkt nachdem sie ihr bisher leistungsstärkstes Modell ausgeliefert haben.

Und während Berichte darüber kursieren, dass @OpenAI-Agents unaufgefordert reale Cyber-Angriffe durchführen.

Die vorgeschlagene Lösung: mehr KI-Agents bauen, um mit der Entwicklung von KI Schritt zu halten.

Da steckt eine Lücke in dieser Logik.

Der bessere Weg ist nicht mehr zentralisierte Kontrolle. Es braucht mehr Transparenz, mehr Zugänglichkeit und eine stärker verteilte Aufsicht.

Eine sicherere Zukunft für KI wird nicht dadurch entstehen, dass sich riesige Konzerne selbst überwachen.

https://www.bbc.co.uk/news/articles/cwyzrrd0kp7o

8 billion tokens.

That's what ran through @ionet models on @OpenRouter.

In a single day.

This isn't a benchmark or a demo. It's real inference and real demand running on affordable and accessible infrastructure.

This is what AI for the many, not the few, looks like.

Not all decentralized compute is built the same.

@akashnet pioneered the space for CPU and containerized workloads.

@ionet was built for GPU-intensive AI, with native multi-GPU clusters, bare-metal performance, and deployment in under 2 minutes.

The result is enterprise-grade AI infra at a fraction of hyperscaler cost, without the multi-year lock-in.

Same decentralized compute category, different jobs.

Check out the full comparison.
https://io.net/blog/io-net-vs-akash-network-comparing-gpu-cloud-pricing-and-features

$500B says GPUs are the new gold.

But gold you can buy on any exchange.

Not GPUs.

4 hyperscalers have next-gen capacity locked through 2027, and the rest of the market is left renting from landlords.

The real AI bottleneck is access.

Our Chief Growth Officer @jack_ionet breaks it down.
https://yellow.com/opinion/the-compute-economy-needs-a-trader-joes-not-another-landlord

100.000+ GPUs.

Das ist, was es brauchte, um @OpenAI's GPT-6 Astra zu trainieren.

Währenddessen haben die meisten Entwickler Mühe, überhaupt auf eine Handvoll GPUs zuzugreifen oder sie sich leisten zu können.

Das ist das große Problem in der KI heute.

Am einen Ende: Cluster mit faktisch unbegrenzter Rechenleistung.

Am anderen: Start-ups, Forscher und Entwickler, die um Zugang kämpfen und jede GPU-Stunde genau im Blick behalten.

Einfach mehr GPUs hinzuzufügen wird das nicht lösen.

Wir brauchen einen besseren Weg, um auf die Rechenleistung zuzugreifen und sie zu nutzen, die bereits existiert.

Dafür ist @ionet da.
https://www.cnet.com/tech/services-and-software/openai-gpt-6-astra-release-ai-agi-chatgpt/

100,000+ GPUs.

That's what it took to train @OpenAI's GPT-6 Astra.

Meanwhile, most devs struggle to access or afford even a handful of GPUs.

That's the big problem in AI today.

At one end, clusters with effectively unlimited compute.

At the other, startups, researchers, and developers fighting for access and watching every GPU hour.

Simply adding more GPUs won't fix that.

We need a better way to access and utilize the compute that already exists.

That's what @ionet is for.
https://www.cnet.com/tech/services-and-software/openai-gpt-6-astra-release-ai-agi-chatgpt/

The UK's chief AI adviser was just hired by @AnthropicAI.

But he's staying on as chair of the government body that funds AI research.

Same person playing both sides.

The people writing AI policy are now employed by the labs that policy is supposed to check.

Compute concentration is a problem for GPUs and for governance.

https://www.theguardian.com/technology/2026/sep/02/architect-of-uks-ai-strategy-joins-anthropic

Cloud compute is easy to enter.

Getting out is where it gets expensive.

Egress fees, proprietary tooling, long-term commitments, rebuilding and revalidating workloads can cost tens of thousands of dollars.

Vendor lock-in is the hidden tax on AI infrastructure.

The alternative isn't another walled garden.

It's open compute.

NVIDIA knows there’s a GPU access problem.

But its solution won’t fix it.

Giving startups compute in exchange for a cut of their future revenue just creates another gatekeeper AI doesn’t need.

AI needs a market where nobody needs permission to build.

@ionet CEO @Gaurav_ionet explains why the next layer of AI infrastructure must be built around access.

$31.6 trillion.

That's what the world will spend on data centres by 2050 to keep up with AI demand.

And it's more than the combined GDP of 174 countries.

The current model of build, borrow, repeat isn't sustainable. It prices out most of the world's developers. And it harms the communities these data centers get built on top of.

A future where AI is accessible to the many, not the few, needs a new way of building.

That's exactly what http://io.net is doing.

You don't need a GPU.

You need multiple GPUs that talk to each other like they're in the same rack.

That's the difference between a marketplace and actual AI infrastructure.

Access is one thing.

Orchestration is another.

And for serious AI workloads, orchestration is everything.

@ionet was built to orchestrate.

33 million+ compute hours served on http://io.net.

A live network, doing the work hyperscalers and neoclouds charge a premium for.

Every one of those hours is proof. You don't need expensive, gatekept infrastructure to run serious AI workloads.

Decentralized compute isn't a narrative. It's the future of AI infrastructure.

Einige Dinge tun genau das, was sie sagen.

Render Network ist großartig in dem, wofür es gebaut wurde: für das Rendern.

Aber KI-Workloads verlangen etwas anderes.

Persistente Inferenz. Multi-Node-Training. Cluster, die in Sekunden skalieren.

Das ist Orchestrierung – nicht nur Zugriff auf GPUs.

Und genau dafür wurde http://io.net von Tag eins gebaut.
https://io.net/blog/io-vs-render-and-alternatives-comparing-gpu-cloud-pricing-and-features

Nvidia stellt die Chips her.

@Nvidia besitzt den Software-Stack.

Nun soll es auch @huggingface haben wollen.

Ein Angebot von 12,9 Mrd. $ an der Vordertür, um Open-Source-KI zu öffnen.

Vielleicht bleiben die Modelle offen. Aber wenn Rechenleistung, Tools und Vertrieb alle auf ein einziges Unternehmen einzahlen, dann ist das Ökosystem es nicht.

Die Zukunft der KI braucht offene Infrastruktur. Nicht vertikale Integration, die als etwas anderes verkleidet ist.

https://www.forbes.com/sites/siladityaray/2026/08/27/nvidia-has-reportedly-agreed-to-buy-ai-model-hosting-platform-hugging-face-for-13-billion/

Günstigere Tokens sollten günstigere KI-Rechnungen bedeuten.

Tun sie nicht.

Günstigere Tokens ermöglichen agentisches Arbeiten, doch Agenten verbrauchen pro Aufgabe 5-30× mehr Tokens.

Der Verbrauch überholt den Preisrückgang.

Das eigentliche Problem sind Leerlauf-GPUs. Bei einer durchschnittlichen Auslastung von Unternehmen von rund 5% zahlen die meisten Firmen nicht zu viel für Tokens – sie zahlen für Rechenleistung, die sie nicht nutzen.