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Introducing Honen Turn your company context into interactive courses that self-improve over time We partnered with NVIDIA to bring AI literacy to 250,000 learners We're ensuring no person or company gets left behind Get started at

Esan

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4,843,260 次观看 • 1 个月前 •via X (Twitter)

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Introducing Honen The world's fastest way to train skills needed to succeed in the Al era Simply drop in your org's docs Get interactive courses that self-improve over time To prove out our vision, we partnered with NVIDIA to bring AI literacy to 250,000 learners By 2030, 78M jobs will need reskilling due to Al, and we're ensuring no person or company gets left behind Learn more

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Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.

Ricardo

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Marc Andreessen's (Marc Andreessen 🇺🇸) mentor Jim Clark predicted NVIDIA and the internet before either existed: "Everything we sell today for $50,000 is going on a chip, on a card, on a PC, and it's going to cost $300. Either we're the company that makes that, or we get destroyed." "The function of the computer is no longer going to be mainly what just the computer does. It's going to be the fact that it can talk to all the other computers." "Both predictions proved exactly right. The CEO said we'll deal with it in the future. Jim said by the time the future arrives, it's going to be too late. So he left and built the next company."

Marc Andreessen's (Marc Andreessen 🇺🇸) mentor Jim Clark predicted NVIDIA and the internet before either existed: "Everything we sell today for $50,000 is going on a chip, on a card, on a PC, and it's going to cost $300. Either we're the company that makes that, or we get destroyed." "The function of the computer is no longer going to be mainly what just the computer does. It's going to be the fact that it can talk to all the other computers." "Both predictions proved exactly right. The CEO said we'll deal with it in the future. Jim said by the time the future arrives, it's going to be too late. So he left and built the next company."

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Mark Cuban says AI will split every company into 2 categories within 3 years "When it's all said and done over the next 3 years, there's going to be two types of companies. Those who are great at AI and those who went out of business" "The biggest challenge for a company is going to be for the CEO to make that decision that we're going to have to blow up a lot of what we do to recreate our company" "We hear a lot of stories about huge public companies that spend a lot of money to try to implement AI and it doesn't really get them a return on investment." "Because you already are running your business the way you've always run it." "The things you've considered outsourcing, your customer call centers, AI agents are perfect for. The stuff you would give to an intern, AI agents are perfect for" "But to really take advantage of AI you have to reformulate your business completely to build it on AI"

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Today we’re launching Decawork (YC S26) backed by Y Combinator : one place for IT to deploy, govern, and maintain the internal AI agents running across a company. Every team is building AI tools/agents now. They go live with real access, no standard way to ship, nobody watching. With Decawork, every agent - built in Claude Code, Codex, or anywhere else - ships with its own identity and scoped credentials. IT signs off before it's live, sensitive actions wait for approval, and every run is watched from first to last. I built AI at NVIDIA, deployed at OpenAI and Meta. Aman (aman) built Barclays' AI compliance platform used by internal teams, then shipped agents to 100K+ monthly users. We lived this and quit to solve it. Internal AI agents/tools piling up at your company? Chat with us founders[at]decawork[dot]ai or

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