Pulkit

Market Cap Per $1 of Yearly Revenue 10 DePIN networks 👇 → $ALTHEA | @AltheaNetwork : 2.4x → $IO | @ionet : 6x → $GEOD | @GEODNET : 15x → $GLW | @GlowFND : 27x → $SCP | @ScPrimeCloud : 30x RENDER sits at 324x. FIL at 595x. Same sector, 249x between the ends.

BREAKING: Binance has been hacked with wallets across the Ethereum, Bitcoin, and TRON networks seeing outflows of -$5.7 billion. The hack includes $1.45 billion worth of Bitcoin and $875 million worth of ETH. If confirmed, this would mark the biggest hack in crypto history. We expect more information shortly.

IO Intelligence at @ionet has crossed 1 trillion requests processed. Getting here also meant closing two launch ready products - BC8 and Quantum Agents, and shelving a mobile app. We had to choose where to put our focus. Last year, we were running Cyber Monday campaigns to make B2C subscriptions work. We ran hackathons across Asia and Eastern Europe and tried products from agent creation to training as a service. Several attempts did not find enough traction. We gradually focused on reaching developers inside the tools they already used. That meant partnerships with vibe coding platforms and becoming an inference provider in tools such as @goose_oss. On @OpenRouter, we went from roughly 500 million daily tokens in March to 20 billion. We also became a day zero launch partner for leading open model labs. Two engineers have carried much of the work on integrations, tool calling, serving costs and reliability while the workloads kept changing. Many others helped us get here. Some are still at io.net. Others have moved on. Their contributions are still part of what we build on, and this milestone belongs to them too. Congratulations to the team, past and present. Thank you to the partners and developers who supported us when progress was harder to see. Before io.net, I thought product market fit was the point where things would settle down. Now I understand how much doubt comes before it, and how a new model or price change can reopen questions you thought you had answered. Growing usage does not make those questions go away.

Over 20B tokens processed on OpenRouter in a single day. In July, we were serving around 3B a day. Same machines. More output. This is what optimisation and a better tech stack look like. Built on io.net.

AI compute is starting to look less like a commodity and more like something that can be priced, financed and traded. The pieces are already showing up. 1. Compute markets @akashnet, @spheron, @AethirCloud, @ionet, @hyperbolic_labs, @nosana_ai, @rendernetwork, @vast_ai, @runpod and @computeexchange are making GPU capacity easier to source. The problem is still pricing. An H100 hour isn't really the same everywhere. Location, setup, latency and reliability all change what that hour is worth. 2. Price discovery @silicondata and @OrnnExchange are working on GPU price benchmarks. This matters because you can't build a proper futures market without having a reference price. CME is preparing H100 and B200 futures based on Silicon Data indexes, while ICE is working with Ornn on cash-settled compute futures. 3. Financing @USDai_Official and @gaib_ai are taking the next step: using GPU infrastructure and its expected revenue as the basis for financing. USDai currently reports $398M TVL, $283M in collateral and 13 active GPU-backed loans. That's where compute starts becoming something that can support credit, not just workloads. 4. Usage rights @AskVenice and @0G_labs are working on transferable access to AI services. Venice's DIEM, for example, represents daily API credit rather than ownership of a GPU. The problem here is portability. A useful compute asset eventually needs to work across providers, not just inside one application. 5. Derivatives CME, ICE, @Lighter_xyz, @bitget and @tradexyz are already testing markets around future GPU prices. That creates an obvious use case: AI companies can hedge rising compute costs. GPU providers can hedge falling rental income. Traders take the other side. The question is whether real hedgers eventually provide the demand, rather than most of the volume coming from traders. 6. What is still missing Collateral and settlement are harder. You can't store an unused December GPU hour and sell it next year. The resource expires. That makes compute different from most financial assets and makes the way these contracts are collateralized important. Stablecoins and systems like x402 could eventually handle the other side: an agent buying compute by the request, job or model call and settling automatically. We're not at the finished market yet. But the pieces are starting to connect: compute → price → financing → usage rights → derivatives → settlement. That's the part I'm watching. If this develops properly, compute won't just be something AI companies pay for. It becomes something markets can price, finance and hedge.

Layers of Decentralized AI 👇 2026: ~$21B MC 2030: ~$80B (~40% CAGR est.) $NEAR → Confidential AI Intents $TRAC → Verifiable Knowledge Graph $NIL → Inference Under Encryption $SERV → Multi-Agent Orchestration $TAO → Open Market For Intelligence $QUBIC → Mining Redirected To AI $VVV → Private, Uncensored Inference $FET → The AGI Alliance $GRASS → AI's Web-Data Layer $GRT → Indexing Every Chain $IO → GPU Network For AI $FIL → Verifiable Storage Market $EIGEN → Restaking For Verification Seven layers. One stack. 21 ways to own it. The compute is real. The winners aren't decided.

500M tokens a day in March 2026. 15B tokens a day yesterday. That is daily volume across @ionet hosted models on @OpenRouter, served on an almost same mix of H100, H200 and B300 GPUs since Day 1. Along the way, we became day zero launch partners for open source reasoning models including @Zai_org, @Kimi_Moonshot, @Alibaba_Qwen , Nemotron, @MiniMax_AI and GPT OSS. Nvidia has built something useful with the recent Nemotron 3.5 Lightning. A fast, capable and efficient open model, with weights, datasets and recipes that give teams like ours room to optimise. Yesterday, the OpenRouter provider token share showed @ionet at 64% token share for Nemotron 3.5 Lightning, the largest provider share. Effective prices per million tokens: Input: $0.0335 Output: $0.179 Our volumes and price reflect both usage and affordability. We are serving this workload profitably on an inference stack built and operated end to end in house. Our small team still runs two to three experiments every week. We track tool calling changes, frontier lab updates and what communities on vLLM and Sglang are discussing, then test what matters on our own workloads. Demand changes with time zones and days of the week. Launch excitement fades. New providers arrive and price wars start. You get used to revisiting your assumptions. With GPU rental costs rising, compute efficiency and reliable tool calling matter more. A low token price only helps if the task gets done correctly. Open models are becoming capable enough to build around and economical enough to serve at scale. There is a lot of engineering behind making both true at once. Thank you @NVIDIA and the Nemotron team, @OpenRouter and @alexatallah for allowing providers like ourselves to work seamlessly, and our engineers, who know how much work sits behind a neat row in a pricing table.

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.>

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