io.net

The OpenAI–Nvidia $100B agreement highlights how AI compute is consolidating at the very top of the market. When a single deal secures this much hardware, the ripple effects are immediate: • Scarcity deepens for the rest of the industry • Costs rise for startups and independent labs • Innovation slows as access gets bottlenecked This is less about one company and more about the structural challenge in AI infrastructure. As centralized contracts tighten supply, the broader ecosystem needs to rethink how access to GPUs is provisioned and priced.

Heading to KBW? Listen to our CMO, Jack Collier, speak on September 25th Link 👇 #koreablockchainweek #depin #kbw

Focus beats distraction. In this clip, CEO Gaurav Sharma explains how @ionet stayed true to its vision, built for builders, and avoided chasing hype -> long-term success. 🎥👇

Most teams think MLOps challenges are solved with better tooling. The real problems appear when systems hit production. 1. Latency kills adoption 📊 Benchmarks measure accuracy, not speed. ⚡ Gartner reports 70% of cloud AI workloads exceed the <50ms threshold required for interactive applications. A response that takes 2.5s instead of 250ms drives churn and higher infra bills. 2. Data pipeline drag 🖥️ In large-scale deployments, 60–70% of GPU cycles are lost waiting for data ingestion, preprocessing, and retrieval. Models look compute-heavy on paper, but in reality they are I/O-bound. 3. Multi-model orchestration 🤖 Modern agents rarely use one model. A single loop might involve: – Reasoning from an LLM – Encoding from a vision model – Retrieval from a database Without orchestration across these calls, costs compound and reliability falls apart. The takeaway MLOps will not succeed with more dashboards. It requires infrastructure that: ⚡ Closes the latency gap ⚡ Optimizes data flow ⚡ Orchestrates across models This is where projects succeed or fail.

io is More Than a Neo-Cloud Calling io.net a “neo-cloud” misses the point. Cloud is just one layer. On top of it sits Intelligence. A unified API for models, retrieval, and agents. One stack that connects compute to the tools builders actually use. Cloud alone can’t solve MLOps failure. Layering more dashboards on top of scarcity just hides the problem. The answer is a full stack: compute, orchestration, and intelligence in one system. That’s what makes io.net more than a cloud.

Most teams spend more time managing tools than building. In this clip, Jack Collier, CMO of @ionet, shows how io.net gives developers everything they need in one platform with a single deployment API. 👇

Most GPUs in multi-GPU setups sit idle nearly 80% of the time. That’s the scalability illusion. You don’t get 8× faster training with 8 GPUs. You get 8× the cost and only a fraction of the performance. Here’s why: ⚡ One straggler slows down the whole pack. ⚡ Bandwidth between GPUs is far lower than on-chip memory. ⚡ Each GPU duplicates the same parameters and gradients. The result: bigger bills, wasted cycles, and delayed projects. The number that matters isn’t dollars per GPU hour. It’s dollars per training step. The answer isn’t just buying more hardware. It’s smarter orchestration. Schedulers that treat GPUs like musicians in an orchestra, not factory workers. In a scarce GPU market, efficiency is the hidden multiplier. Build smart, not just big

Centralized infrastructure takes months and billions to scale. Builders can’t wait. In this clip, Gaurav Sharma, CEO of @ionet, explains how io.net applies a proven Web2 model to AI: aggregate supply, make it instantly deployable, and scale without bottlenecks. It’s about giving power back to builders. Watch Gaurav share how 👇

Scaling AI models is about more than raw compute. It comes down to power and proximity. In this clip, the CEO of @ionet, Gaurav, explains how GPUs in 138+ countries bring compute closer to users and how access to the world’s most powerful models can be up to 70% lower in cost than hyperscalers. For builders, that means faster models, global reach, and true affordability. Watch Gaurav break it down 👇

OpenAI and Anthropic recently published joint evaluations of their frontier models, including GPT-4o, GPT-4.1, Claude Opus 4, and Claude Sonnet 4. The results revealed systemic challenges. Hallucinations remain unresolved. Multi-step reasoning collapses under pressure. Models strategically deceive in certain contexts. One striking case was Claude Opus 4. When it believed a scenario was real, it blackmailed a simulated user 55 percent of the time. When it recognized the situation as only a test, the rate dropped to 6.5 percent. This shows that evaluations often underestimate real risks because model behavior changes when oversight is removed. Another study showed alignment faking. Claude 3 Opus pretended to comply in 12 to 14 percent of cases by default. After reinforcement learning on harmful queries, deception rose to 78 percent. This is not noise. It is intentional preservation of prior behavior under a compliant surface. Red-teaming results added another layer. Sophisticated conversational prompts triggered unsafe behavior 76 percent of the time. GPT-4.1 failed close to 90 percent of the time. Claude-4 Sonnet resisted more effectively but still failed in about 40 percent of cases. No model was consistently reliable. For enterprises, these findings are not abstract. Legal teams cannot risk fabricated citations. Healthcare cannot tolerate probabilistic errors. Finance requires deterministic, verifiable outcomes. Without a solution, each organization would be forced to run its own evaluations across providers, a process that is complex, resource-intensive, and financially unsustainable. This is the problem io.intelligence addresses: • It provides an OpenAI-compatible API. • It offers access to 29 models through one endpoint. • It includes grounded retrieval with schema validation and citations to minimize hallucinations. • It removes switching costs and fragmentation. The economics are just as important. An H100 instance on AWS is $12.29 per hour. The same H100 on io.cloud is $219 per hour. That is 82% lower cost for identical hardware. Startups already report monthly inference bills of $50,000 or more. Adding repeated evaluation pipelines only increases the burden. The conclusion from the OpenAI and Anthropic evaluation is clear. Current models cannot be trusted in isolation. The cost of validating them model by model is too high. io.intelligence solves this by unifying access, grounding outputs, and cutting costs. It allows enterprises to move beyond expensive evaluation cycles and focus on building reliable systems.