io.net

AI infrastructure has been running in the dark. Controlled by a handful of players, the industry has been held back by hidden pricing, secretive deals, and locked doors. We built io.net to change that. Distributed compute. Transparent pricing. Open access. For everyone. Everywhere. For over two years we have been working to bring light to the dark. Today with the launch of our new brand design, we are giving form to the idea. The lights are on. io.net

2.9 billion reasons decentralized compute works. @ionet just passed 2.9 billion tokens processed daily on @OpenRouter. DePIN isn't a narrative. It's the solution to AI's biggest problems: access, affordability, and flexibility. We're leading the way. ai.io.net/ai/models

It's only May, and @Uber has already burned through its entire AI budget for the year. With costs as high as $2,000 per engineer per month, it's easy to understand why. And they aren't alone. Less than 1% of executives report 20%+ ROI from AI. Overpriced infrastructure, soaring token usage, and runaway costs make AI almost unaffordable, even for the largest companies. But you don't need an enterprise budget to build with AI. You need affordable compute and leading open-source models. That's exactly what @ionet delivers. beincrypto.com/enterprise-ai-…

It's the weekend. But you're probably going to open your laptop anyway. So, you might as well not burn $4K figuring out why your cluster is broken. We put together the GPU deployment playbook that you'll actually want open mid-deploy. From H100 vs A100 vs L40S, to networking, to costs, we've covered it all. Check it out here: io.net/blog/gpu-clust…

Graduates have been booing tech CEOs during their commencement speeches. Because of AI. AI has the potential to create opportunity, bring new talent into tech, and give young devs the tools they need to build thriving businesses. Right now, that’s not what’s happening. Tech giants are keeping the profits for themselves while laying people off. This is what happens when AI is built to concentrate power. But it doesn’t have to be this way. Affordable, accessible compute and open-source models can give more people the chance to build in AI instead of being pushed out by it. That’s exactly what @ionet is here for. theverge.com/ai-artificial-…

$15 billion per year. That's what @AnthropicAI is paying SpaceX for compute. That is enough money to pay for the compute needs of over 25,000 startups. Instead, high infrastructure costs mean that only 5-10% of AI projects will make it past their first year. But it doesn't have to be this way. @ionet makes it possible for projects of all sizes, anywhere in the world, to get immediate access to compute at up to 70% less than hyperscalers. So if your project's budget is less than $15 billion a year, we can help. axios.com/2026/05/20/ant…

It seems like @OpenAI has come out with a new plan to offer guaranteed compute capacity. As long as you are willing to sign a 1-3 year contract. And aren't concerned about pricing. Or to put this another way, if you are a large enterprise with unlimited budgets, you can secure compute that regular developers won't be able to get. The thing is, there is already plenty of compute capacity at affordable prices that doesn't require signing long contracts. So if you don't have enterprise budgets but still want to be sure you can get the compute you need, we're here for you @ionet. cnbc.com/2026/05/19/ope…

Up to 95% of GPUs sit idle. But builders everywhere are stuck on waitlists. @ionet instantly connects you to that idle compute so you can stop waiting and start building. cloud.io.net/cloud/containe…

Most AI teams are overpaying for compute by up to 70%. But they don't realize it. That's what choosing a hyperscaler like AWS actually costs. More overhead, more fees, more complicated contracts, and more waiting. Find out how @ionet can save your team up to 70% in our latest comparison: io.net/blog/io-vs-aws…

H100 pricing went up because hyperscalers locked away all the supply and charge a premium. It's a distribution problem. And there's a solution. Decentralized GPUs are already deployed, already idle, already available in 60 seconds. No waitlist. No markup. The bottleneck isn't that GPUs don't exist. It's that they're artificially gated.

GPU shortage is worse than ever. H100s cost more today than they did 3 years ago, and you cannot get them on-demand. The big AI labs have locked up most of the supply for years. I’m worried university researchers and individual developers simply won’t be able to get GPUs.

Compute and power limitations are existential for centralized AI labs. And the "Friendster" analogy is funny. But it may not be accurate. It assumes the solution is more data centers and more power plants. The actual solution? Decentralizing the compute layer so infrastructure scales horizontally across existing idle GPUs instead of vertically through multi-billion-dollar buildouts. Anthropic's bottleneck isn't unique. Every centralized AI lab has the same problem. Decentralization is the only architecture that bypasses it.

⚡️ NEW: Billionaire Chamath Palihapitiya warns Anthropic could become the "Friendster of the AI era" if it fails to address its compute and power limitations.

Find out more about Agent Cloud: io.net/docs/guides/cl…

It's Monday. You could spend your day planning out your compute needs for the week, provisioning GPUs, and managing your resources. Or, you could take it easy, have a second cup of coffee, and just let your agents do it for themselves. @ionet Agent Cloud allows agents to spin up and down the compute resources they need with full autonomy, and no human involvement - making your Monday, and every other day, better.

Distributed compute puts you in control. You decide when you need more GPUs, which are the right chips to use for each workload, where they should be geographically, and how long you need them for. It lets you make the right choices for your business, instead of someone else's bottom line. It ensures your project stays up and running even when data centers go down. And it keeps you from having to worry about constant price rises and policy changes. That's what @ionet is building. theengineer.co.uk/content/opinio…

This isn't the early days of tech. A single server sitting in your garage isn't going to power scalable AI workloads. If you want to bring your product to market, and build a sustainable business, you need access to affordable, scalable, and flexible GPUs. That's what @ionet is built for. Find out more: cloud.io.net/cloud/containe…

Nvidia is now worth $5.5 trillion. That's more than the value of all of the silver in the world. And more than the GDP of every country besides the US and China. With one company controlling so much of what has become an essential resource, the ability of projects to maintain their independence is a question worth asking. That's why open distributed compute networks like @ionet are so important. No Gatekeeping. No centralized control. Just affordable, transparent, open access for anyone, anywhere. 247wallst.com/investing/2026…

@sama Thanks @sama. Very generous. Except that it's not. It's just another way to get people stuck on your platform and make it hard to switch. OpenAI is open in name alone. Maybe we should have a contest to rename it. How about MonopolAI?

codex is the best AI coding product and we want to make it easy to try. for the next 30 days, we are giving companies that want to try switching over two months of free codex usage.

Hyperscalers make you wait for GPUs. Then they charge you a premium for them. If you want to stay competitive in the AI market, neither of these works. @ionet lets you spin up GPU clusters in minutes, not days or months. And it saves you up to 70% vs AWS. Get started in 2 minutes: cloud.io.net/cloud/containe…

AI compute is no longer just about access. It’s about orchestration, latency, deployment speed, networking, resilience, flexibility, and whether infrastructure was built for modern machine learning workloads. A lot of early decentralized compute platforms proved the model could work. But the demands of AI in 2026 look very different from the workloads DePIN networks were originally built for. That’s why io.net was built differently. Not as a general-purpose cloud, but as infrastructure purpose-built for AI. The result is a decentralized GPU network optimized for training, inference, and real-world ML deployment at scale. Check out our latest comparison to find out more: io.net/blog/io-net-vs…

Affordability. Check. Accessibility. Check. Yes, @ionet lets you spin up GPUs in seconds at up to 70% less than hyperscalers. But, that's only part of the story. When you move away from centralized compute you also increase resilience, flexibility, and data security. In a time of global instability, these matter more than ever. @ionet CEO @Gaurav_ionet offers his thoughts on how distributed compute can help ensure critical systems stay online, even when centralized data centers go down. thebanker.com/content/5833da…

Check out the full case study here: io.net/blog/leonardo-…

Most AI teams aren’t limited by their ideas. They’re limited by access to compute. No matter how great the idea, the team, or the tech, if you can't get access to the right GPUs at affordable prices your project can't grow. Leonardo.Ai needed hundreds of GPUs to support real-time image generation at scale. Using hyperscaler would have meant getting crushed by traditional cloud pricing and procurement delays. But with io.net they were able to: - Cut GPU costs by 50%+ - Provision faster - Test new hardware sooner - Keep scaling without slowing product velocity The result? They grow from 14K → 19M users in a year.