Another $10M in compute signed & delivered. Same customer is already in talks of expanding their cluster, and we have multiple significantly larger deals in the pipeline. If you have B300 inventory, reach out and we can get it sorted in days. We also run an affiliate program, so referrals come with real monetary upside, not just goodwill. We are just getting started.

Compute demand is not stopping anytime soon !!

creativity along with facts piped.video/shorts/tdPC9tY4f…

Looks good!, someone did look into data and projects.

Life of a tech entrepreneur: Yesterday, we saw 100+ new clusters spin up out of nowhere. My immediate thought? "Oh no, are we getting DDOSed or abused " Long story short... it’s just massive organic demand. Growth is real, and the team is crushing it!

The market is finally catching up to reality. As the legendary value investor Benjamin Graham famously said "In the short run, the market is a voting machine, but in the long run, it is a weighing machine"

We are already leading DePIN in revenue. In coming weeks, the gap goes exponential. 📈 Multiple massive enterprise deals just closed and are deploying right now. We arent talking incremental growth, the new pipeline is multiple Xs larger than our entire current footprint. Demand is scaling so violently I feel its time for IO to own our own data centers :) . The floodgates are open. Don't blink. 🚀 <Image attached from depin pulse.>

Just got kicked out of a crypto Discord group because I run an AI company and have historical ties to an exchange. The paranoia in this space is cinematic. People preach decentralization and transparency, yet everyone is operating behind burner accounts named "SatoshiAnonXX". Honestly? You’re better off with anonymity. I finally understand why Zcash is running the way it is. When simply existing with a CV makes you a public enemy in a group chat, privacy isnt a feature anymore its basic survival.

We talk a lot about scaling compute, but orchestration performance is often the silent bottleneck in complex data and AI pipelines. We explored the mechanics, the metrics, and what it takes to keep systems running smoothly at scale forwardfuture.com/newsletter…

Thanks for inviting @RealAllinCrypto , had a great discussion and genuinely the depth of knowledge your team had about the industry and where we are headed is insane !!

Truly enjoyed the conversation @RealMissAI . We covered everything from the reality of the GPU monopoly to the future of AI agents buying compute. Keep an eye out for the episode dropping soon! 👀👇

io.net has now surpassed 2B+ daily tokens processed on OpenRouter one of the biggest web2 inferencing platform. As AI adoption accelerates globally, the demand for compute is growing exponentially. But, traditional infrastructure alone cannot support the scale, flexibility, and efficiency the next generation of AI requires. What we’re seeing now is real-world proof that decentralized compute networks can power production AI workloads at scale. This isn't theoretical. It’s happening now at io.net. openrouter.ai/provider/io-ne…

Multicoin has been an exceptional investor and a vital partner in our journey at IO. This article is a masterclass in their thesis, there's a reason they are widely considered 'the' definitive VC fund in the industry.

Most AI teams don’t struggle with models. They struggle with GPU costs once they try to scale. This is exactly why we built this guide at @ionet It breaks down what actually matters in GPU clusters: which GPUs to use, how networking affects performance, and how total cost quietly explodes over time. If you don’t understand your GPU economics early, infrastructure will decide your limits before your product does.

This chart shows the real impact of AI on jobs, and it’s more nuanced than the headlines. In the UK, Japan, Germany, and Australia, AI is clearly disrupting roles, but new hiring is already absorbing most of that shock. Net job loss stays in the low single digits. The U.S. is different. New AI-driven roles are outpacing losses, leading to a net gain. This kills the "AI kills jobs" narrative. It reshapes work faster than institutions can adapt. The countries that retrain faster and move people into higher-value roles will see growth. The ones that don’t will feel friction. This is a transition problem, not an extinction event.

Google just killed Unreal Engine. With Project Genie, Google is moving interactive worlds away from assets, levels, and physics graphs, and toward world models. You don’t build environments first and then run players through them. The environment is generated in real time, frame by frame, based on state, actions, and prompts. That breaks the core assumption behind Unreal and Unity. Game engines assume the world is authored, compiled, and simulated. Genie treats the world as a learned system. Movement, layout, objects, and short term memory emerge from the model, not from hand written logic or asset pipelines. For developers, this changes the cost and speed curve completely. What used to take teams, tools, and months can now start from text or images and become interactive in minutes. More importantly, this is not just about games. The same approach applies to robotics, autonomous driving, and AI agents. Training agents requires massive numbers of environments and edge cases. World models can generate those on demand, instead of relying on slow, static simulators. Unreal will still power high fidelity production for years. But the center of gravity is shifting. When worlds become generated instead of built, engines stop being the starting point. That’s what just changed.

India’s biggest growth opportunity is in AI productivity. The IMF data shows something very important. One worker in India’s services sector already produces more than 4x the output of a worker in agriculture. Services are now the most productive part of our economy. That tells us where the future is going. The report also says that if India improves innovation, productivity growth can rise by almost 40%. That is like adding the economic output of a state like Karnataka to India every decade. AI can push this even faster. Nearly 60% of Indian companies already use AI, which is far above the global average. But many still struggle with skills, tools, and proper implementation. So the real opportunity is simple: More innovation. More AI adoption. Easier rules for businesses to grow. Better movement of workers into high-productivity sectors. This is how India moves from being a large economy to becoming a truly high-productivity economy

I spoke to The Fintech Times about the part of AI that most teams still underestimate: compute economics. Everyone talks about models, accuracy, and deployment speed. But long term success is decided by inference cost, not model choice. As AI usage grows, centralized cloud pricing quietly destroys margins. That’s the real bottleneck. Decentralized GPU networks change this by matching compute supply directly with demand, cutting core costs by up to 70% and making AI businesses sustainable at scale.

Builders shouldn’t have to guess which GPU to use. They should choose based on workload, latency needs, and cost efficiency. At @ionet, we focus on making that decision simple with real benchmarks, clear performance data, and direct access to the right hardware for each use case.

I was just reading the ARK Invest Big Ideas 2026 report, and this is the kind of material every builder and investor should actually spend time on. Message is clear 👇 AI is no longer a software upgrade. It is becoming economic infrastructure. Compute, energy, robotics, blockchains, and biology are now moving as one system. Progress in any one of them accelerates the others. That is how real platform shifts are created. Take compute and energy. Global data center investment is already around $500B and is projected to move toward $1.4T per year by 2030. At the same time, the cost of AI inference has dropped more than 99% in the last year. When intelligence becomes that cheap, demand doesn’t grow slowly. It scales aggressively. Power becomes the real constraint, not models or chips. On robotics, the numbers are even more interesting. A single humanoid robot operating in a household could generate roughly $62,000 per year in economic value by replacing paid services and freeing up human time. That is not job replacement. That is productivity expansion. It creates new economic output. On blockchains, the shift is deeper than payments. When money, identity, contracts, and ownership are programmable, wallets stop being storage tools and start acting like automated economic agents. AI agents could be handling over $8 trillion in online transactions by 2030, close to 25% of global e-commerce. What ties all of this together is that intelligence is becoming cheap, automation is becoming physical, and coordination is becoming digital by default. That changes everything: How companies are built How labor is used How capital flows How value compounds There is also a macro angle most people miss. Global real GDP growth could shift toward 7%+ annually by 2030, compared to the 3% most models still assume rn. That gap is enormous.