io.net Re-poster

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.

OpenAI's chief scientist just called for "extreme caution" on AI's pace.

Right after shipping their most powerful model yet.

And amid reports of @OpenAI agents carrying out real-world cyber-attacks, unprompted.

Their proposed fix is to build more AI agents to keep pace with AI.

There's a gap in that logic.

The better path isn't more centralized control. It's more transparency, more accessibility, more distributed oversight.

A safer future for AI won't come from massive corporations policing themselves.

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.

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/

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.

Some things do exactly what they say.

Render Network is great at what it was built for: rendering.

But AI workloads ask for something else.

Persistent inference. Multi-node training. Clusters that scale in seconds.

That’s orchestration, not just GPU access.

And that's what http://io.net was built for that from day one.
https://io.net/blog/io-vs-render-and-alternatives-comparing-gpu-cloud-pricing-and-features

Nvidia makes the chips.

@nvidia owns the software stack.

Now it reportedly wants @huggingface too.

A $12.9B bid for the front door to open-source AI.

Maybe the models stay open. But if compute, tooling, and distribution all answer to one company, the ecosystem isn't.

The future of AI needs open infrastructure. Not vertical integration dressed up as one.

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

Cheaper tokens should mean cheaper AI bills.

They don't.

Cheaper tokens unlock agentic workflows, and agents burn 5-30x more tokens per task.

Consumption is outpacing the price drop.

The real problem is idle GPUs. With average enterprise utilization sitting around 5% most companies aren't paying too much for tokens, they're paying for compute they're not using.

The AI race isn't just about who has the best ideas.

It's about who has access to compute.

@ionet CEO @gaurav_io joined the @RealAllinCrypto Podcast to talk about the infrastructure battle happening underneath AI:

→ Why GPU demand keeps accelerating
→ How DePIN unlocks unused compute around the world
→ Why crypto found its most important real-world use case
→ What happens if AWS, Google and Microsoft control AI's infrastructure

When a handful of companies control the infrastructure, they also control who gets access to intelligence.

But there is another way.

Watch the full conversation ↓

$157,680.

That’s the annual difference between running 8x A100s 24/7 on AWS vs. http://io.net.

Same GPUs. Very different outcome.

Centralized clouds offer quotas, lock-in, and opaque pricing.

Decentralized compute offers global supply, instant access, and full control and flexibility.

Centralized clouds still have their place. But for most of AI workloads, paying a hyperscaler tax doesn’t.

We break down the architectures, costs and tradeoffs ↓

Nvidia just warned AI server prices could rise 15%+.

Not surprising. But a bad sign for AI's future.

Enterprises can absorb it, then pass the cost on.

Startups and scale-ups can't.

Growing AI projects already spend up to 60% of budget on infrastructure. A small cost bump can mean the end of their runway.

Fewer innovative ideas, less competition, and the same few companies tighten their grip.

And that's bad for everyone.

The answer isn't more data centers stuffed with expensive servers.

It's coordinating the underutilized GPU supply that already exists.

That's what @ionet was built for.

https://www.tomshardware.com/pc-components/dram/nvidia-reportedly-warns-biggest-customers-of-15-percent-price-hikes-on-ai-servers