Gaurav_IO
@Gaurav_ionet
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CEO @ionet Building Scalable AI Compute Ex Binance, Agoda, Amazon, Ebay, HP R&D
Joined September 2013
NVIDIA just released Nemotron 3 And it’s a good example of where AI is actually heading not in model intelligence, but in how models are run. Nemotron is not about beating GPT-4 or Claude on raw intelligence. It’s about fixing the part of AI that breaks first in the real
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A Japan based startup is claiming it has built an AGI capable system. They are Not saying AGI is solved. But saying the system is designed very differently from today’s AI. Most current models, including ChatGPT, are trained systems. They are very good at using patterns from
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OpenAI just released ChatGPT 5.2 yesterday. This is not a small update. It’s a clear jump in how well these models can reason and how cheaply they can do it. One of the main ways this is measured is through something called ARC-AGI. ARC-AGI is a benchmark made of logic and
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The world around us is transforming faster than ever before. Join the team , watch the robot battles and lets discuss what products we are shipping next.
The robots are warming up with some dance moves before they battle it out. Come watch the fights! We’re in the outdoor plaza from 12-5pm today. Also swing by the @ionet booth to check out the compute powering these guys. See you there. #SolanaBreakpoint @BitRobotNetwork
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INDIA ISN’T HOLDING ITSELF BACK ANYMORE, IT’S POSITIONING TO BECOME THE NEXT GLOBAL AI SUPERPOWER. In just two days, PM Modi met with the CEOs of Microsoft, Intel, and Cognizant and Amazon followed with its own record breaking pledge. Microsoft announced $17.5B for AI and cloud
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Competition is good for the industry, ball now is in OpenAI's court. At the same time Zuck pushing for a new model in Q1, we are living in interesting times.
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When you see a number like this 70% of US real GDP growth coming from AI-related capex, it puts the current moment in perspective. The world’s largest economy is basically being held up by one sector. Most other industries are flat or shrinking. AI is the only area generating
70 percent of real GDP growth in the US comes from AI related capex. Every other industry in the US is pretty much flat or declining.
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US greenlights H200s to China. Silicon diplomacy > supply chokepoints. Next wave of AI acceleration officially loading…
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AI alignment isn’t just another research topic. It’s one of the few areas where we don’t get the usual cycle of build → test → fix → repeat. Most technologies let us learn from failure. This one doesn’t offer that luxury once systems cross a certain capability level.
THE FIRST-ATTEMPT CONSTRAINT There is a class of problems humanity has never faced. Problems that must be solved correctly on the first attempt. Where failure forecloses iteration. Where the solution cannot be validated before deployment. This is AI alignment. The
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Grateful to share the stage with such sharp builders, big thanks to the panelists and the #IBW2025 team
The alpha flowing on this panel is unreal 👀🔥 Infrastructure, DePIN, community growth and the future of Web3 is being shaped LIVE at #IBW2025. @Gaurav_ionet @mikehorton83 @OfficialXYO @DeCharge @ionet @ionetindia
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AI didn’t just grow fast, it broke every adoption curve we’ve ever used to understand technology. It took the internet more than a decade to reach a few hundred million users. It took smartphones almost as long. ChatGPT crossed 1M users in 5 days, 100M in ~2 months, and now
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The wild part is we all joked about personal robots and now its actually becoming a normal roadmap item. AI is moving so fast that things which sounded like sci-fi in 2020 feel like overdue features today.
ELON MUSK: "Tesla is the world leader in real-world AI. We are making this robot Optimus which is going to be very cool, everyone's gonna want their own personal C3PO R2D2 robot."
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Public cloud revenue is heading toward $2T by 2030 and yesterday’s CME outage showed exactly why this growth carries structural risk. A single cooling failure at a single data center in Chicago froze trading across global futures, FX, Treasuries, commodities, and even
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As someone building AI infrastructure every day, I agree with the urgency but we need to be precise about where the real danger is. AI itself is not inherently oppressive. Centralized control over AI systems is. When the models, data pipelines, identity layers, and
URGENT WARNING 🚨 We need to be VERY careful right now. As AI gets trained and plugged into everything, same way the internet was, power hungry politicians are quietly pushing more laws and restrictions. If they lock this in, we face permanent totalitarian AI, digital 1984
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Mostly agree , but it wont be purely intelligence. The real bottleneck is energy and compute at scale. In todays world, power generation is basically a proxy for a nations economic muscle. In the next era, AI capable power + GPU deployment will play the same role. The
The next leap in GDP will be powered purely by intelligence, not by roads, bridges, or industrial expansion. GPUs = the new economic backbone AI = the ultimate multiplier of national competitiveness Nations that lead in AI today will dominate for generations. Those that
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I think they are onto something. 😄 6 and 7 are the power couple: Agentic AI is the brain, DePIN is the body, rails and without both, its just vibes and a pitch deck.
COINBASE EXPANDS INTO CRYPTO & AI 🤖 Coinbase Ventures plans to bankroll 9 crypto ideas in 2026: 1) Real-World Asset Trading 2) Specialized Exchanges & AMMs 3) Prediction Market Aggregators 4) Next-Gen DeFi 5) Privacy-Preserving Tools 6) Agentic AI 7) DePIN Networks 8) Proof of
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It was an honor to share the stage with so many of the original leaders in the DePIN space
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Everyone is staring at the $10B number. The real story is why it happened. AI isn’t a pilot project anymore companies are rebuilding their workflows, infra, and products around it as a permanent layer. That kind of shift only goes in one direction: more compute demand, more
OpenAI’s revenue hits $10B in 1 year. A year ago, most companies were running small pilots and testing a few workflows. Now AI is sitting inside product pipelines, customer support systems, research teams, data stacks, and internal tooling. Entire organisations have already
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When you look closely at CPUs vs GPUs, one thing becomes obvious: AI isn’t bottlenecked by intelligence, it's bottlenecked by compute. CPUs are great for traditional ML and preprocessing, but once you move into modern deep learning, the math changes. Training becomes parallel,
Few understand why GPUs really matter for AI. Sure "GPU = faster" but do you know why trying to train on CPUs is fundamentally broken? This new guide breaks down the architecture differences that actually matter: → Why parallel processing isn't just "more cores" → Memory
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