Tianqiang Liu

Monday morning briefing: The stories you missed while scaling last week. Jensen Huang spent the week predicting $1 trillion in AI chip orders. The rest of the industry spent it figuring out what that means for their workforce, their products, and who gets a seat at the table. 🟢 The $1 trillion call NVIDIA's annual GTC conference ran in San Jose. Huang took the stage and announced $1 trillion in confirmed purchase orders for Blackwell and Vera Rubin systems through 2027. Uber announced a robotaxi fleet across 28 cities on four continents by 2028. BYD, Hyundai, Nissan, and Geely all confirmed they are building level 4 autonomous vehicles on Nvidia's platform. Huang: "I believe computing demand has increased by 1 million times in the last two years." 💰 The new currency of Silicon Valley On the same stage, Huang proposed that every Nvidia engineer receive an annual token budget on top of their base salary. "They're going to make a few hundred thousand dollars a year. I'm going to give them probably half of that on top as tokens so that they could be amplified 10 times." He added that Nvidia's 42,000 human employees will one day work alongside hundreds of thousands of AI agents. Token access, he said, is already becoming a standard question in Silicon Valley job negotiations. 📱 OpenAI hits refocus OpenAI announced it is merging ChatGPT, its Codex coding platform, and its Atlas browser into a single desktop superapp. Context: Anthropic now captures 73% of first-time enterprise AI spending. Claude overtook ChatGPT as the most downloaded app in the US this month. 🏗️ The infrastructure beneath the names you know Berlin-based Upvest raised $125M led by Sapphire Ventures and Tencent , with Bessemer and BlackRock also backing. Valuation: €640M, up from €360M twelve months ago. Upvest builds the API infrastructure that powers investment products for Revolut, N26, DKB, and Santander's Openbank. It processed over 100 million orders in 2025 across 30+ institutions. CEO Martin Kassing: "The $125M round, just 12 months after our Series C, underscores our momentum to be the top choice for financial institutions launching and scaling best-in-class investment experiences at lightspeed in Europe."

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Founders often ask us: what does it really take to build a durable AI company? In enterprise AI, we’re seeing two models emerge again and again: Oil wells (systems of record) & Pipelines (automation layers). 1️⃣ Oil wells become the source of truth for data & associated workflows. We see two entry points: Rip + Replace: When legacy systems are weighed down by tech debt, buyers are motivated to take the risk to replace with new AI-native systems. hyperexponential rebuilt insurance pricing & underwriting from ingestion to submission; Valon collapsed 25+ mortgage servicing systems into one; Vesta redesigned loan origination so tasks could run in parallel. Greenfield: When no system exists, startups capture customers early and grow with them. Rillet started as the first ERP for SMB finance teams, automating manual workflows, and has since expanded into replacing incumbents like NetSuite. Oil wells take longer to drill, but once established, they create deep, durable moats. Owning the system of record unlocks workflows no one else can build and builds switching costs. 2️⃣ Pipelines (automation/orchestration layers) -- These sit on top of existing systems and automate the “glue work” humans do between them. We’ve seen two main patterns: Fragmented Systems: When many entrenched systems coexist, pipelines unify workflows without requiring rip-and-replace. FurtherAI provides agentic workflows for insurance, automating cumbersome processes (submissions, loss runs, compliance) across multiple systems. Human Middleware: When humans are the bridge between systems, pipelines digitize that work. Concourse builds AI agents for finance teams, connecting into several financial systems so teams can query and analyze without manual effort. Sola lets customers record a workflow once and turns it into a live AI agent for tasks like invoice reconciliation. Customers don’t have to choose. Enterprises will often buy both: a new system of record in one area, lightweight automations in another. But for founders, the strategies are distinct but both can be massive. What matters is not trying to do both at once, but knowing which game you’re playing to win. New post by me & Joe Schmidt IV in comments!

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Why Waymo will never catch up with Tesla? The answer is simple: scalability Waymo uses LiDAR (Light Detection and Ranging), a sensor that shoots laser pulses to map the 3D world. Waymo uses this along with cameras and extra hardware to make the car “more accurate”. Elon Musk calls this "stupid" Why? Every car needs multiple LiDAR units worth thousands. Every city needs HD maps. This model creates high fixed costs and requires massive capital investment for every new city or expansion area How Tesla is planning to achieve scalable Full-Self Driving (FSD)? First cut off LiDar Humans drive with eyes and a brain = Eight cameras on every car + AI Tesla uses a Vision-Only, Deep Learning approach, relying on eight relatively cheap cameras already built into every mass-market car. This makes the FSD software a zero-marginal-cost asset that can be deployed globally with a simple over-the-air update. Tesla has millions of customer vehicles on the road, operating in every corner of the world. This creates a Data-Flywheel -> More cars = More Training data But Waymo is limited to Geofencing which is designed to work flawlessly only within its pre-mapped areas I believe Tesla will disrupt the entire automobile industry The only other player who is competing in this space is BYD For rest players who are not changing fast, a slow death is imminent #Tesla #FSD #Waymo #AutonomousDriving #VisionOnlyAI #LiDAR #ElonMusk #AIRevolution #FutureOfMobility #DataFlywheel #SelfDrivingCars #DeepLearning #TechDisruption #Toyota #Volkswagen

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Voice is a super interesting modality right now - maybe the first modality we're seeing move to open source models across a number of scale ups / enterprises. Reliability concerns, high costs, and open source model performance are pushing engineers to do their own fine tuning vs. relying on third-party vendors of proprietary models. Many of these orgs have already been collecting their own first-party data and now with third-party vendors like Extrian, David AI, etc they can train really high quality models. RL has been insanely hyped, but it's been unclear how long it will take scale ups and enterprises to actually lean in. Voice AI might be hitting that inflection point faster than expected.

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