This podcast is the single most important podcast to know what is going on in tech every week. On the agenda this week: - NVIDIA Crushes Quarter and Buys Hugging Face - OpenAI Cuts Off Cursor - Instinct Hits $2.5BN Valuation and The Race for AI Assistants - Cognition Raises at $46BN, Linear $2.5BN and Clay $7BN My notes with @rodriscoll and @jasonlk below: 1. How Instinct Could Follow the Same Path as Replit and Lovable In the early days, cloning lightweight AI tools is trivial. Defensibility emerges by rapidly adding complex workflows like security automation and multi-agent orchestration. Products that start without a moat can build formidable ones over time by solving dozens of evolving customer requirements faster than anyone else. 2. Hugging Face Acquisition Explained As the maker of compute, NVIDIA benefits when AI token traffic flows through 30% gross-margin open-source models rather than 70% gross-margin closed models where platforms capture more of the economics. Driving down software margins allows a greater share of total ecosystem spend to flow directly into GPUs. 3. The Bull Case for Clay Being a $100 Billion Company Autonomous agents executing go-to-market strategies around the clock could consume 10x to 100x more tokens and software usage than human sales teams ever could. As a leader in agentic GTM, Clay is positioned to capture an enormous wave of automated outreach, campaign analysis, and global prospect engagement. 4. The Bull Case for Linear When software teams build 100x more features at 50x the speed using AI, legacy project management tools and manual Kanban boards begin to break down. Linear can become the agent-friendly system of record for coordinating, tracking, and managing thousands of issues generated simultaneously by human-agent development teams. 5. The Three Ways the Wheels Come Off the Bus for NVIDIA NVIDIA’s record-breaking momentum faces one fundamental existential threat: a sudden collapse in end-user demand for AI intelligence. Hyperscaler CapEx buildouts and complex vendor financing arrangements work only as long as customers continue aggressively buying frontier-model tokens throughout the supply chain. 6. We Are All Building Compound Startups Today AI development tools have accelerated code production dramatically, making narrow point solutions increasingly vulnerable. To survive rapid competitive convergence, software startups must embrace becoming compound companies that ship expansive, multi-module product suites covering the entire customer workflow. 7. We Did Not End Up Doing More With Less. We Did More With More, and That’s Why European Startups Fail The belief that AI would allow companies to shrink headcount and simply do more with less has not played out as expected. Winners are compounding capital and talent to do vastly more with more, putting underfunded point solutions, particularly across Europe, at risk of being overwhelmed by aggressively scaling U.S. competitors. (links in comments)

This podcast is the single most important podcast to know what is going on in tech every week. On the agenda this week: - NVIDIA Crushes Quarter and Buys Hugging Face - OpenAI Cuts Off Cursor - Instinct Hits $2.5BN Valuation and The Race for AI Assistants - Cognition Raises at $46BN, Linear $2.5BN and Clay $7BN My notes with @rodriscoll and @jasonlk below: 1. How Instinct Could Follow the Same Path as Replit and Lovable In the early days, cloning lightweight AI tools is trivial. Defensibility emerges by rapidly adding complex workflows like security automation and multi-agent orchestration. Products that start without a moat can build formidable ones over time by solving dozens of evolving customer requirements faster than anyone else. 2. Hugging Face Acquisition Explained As the maker of compute, NVIDIA benefits when AI token traffic flows through 30% gross-margin open-source models rather than 70% gross-margin closed models where platforms capture more of the economics. Driving down software margins allows a greater share of total ecosystem spend to flow directly into GPUs. 3. The Bull Case for Clay Being a $100 Billion Company Autonomous agents executing go-to-market strategies around the clock could consume 10x to 100x more tokens and software usage than human sales teams ever could. As a leader in agentic GTM, Clay is positioned to capture an enormous wave of automated outreach, campaign analysis, and global prospect engagement. 4. The Bull Case for Linear When software teams build 100x more features at 50x the speed using AI, legacy project management tools and manual Kanban boards begin to break down. Linear can become the agent-friendly system of record for coordinating, tracking, and managing thousands of issues generated simultaneously by human-agent development teams. 5. The Three Ways the Wheels Come Off the Bus for NVIDIA NVIDIA’s record-breaking momentum faces one fundamental existential threat: a sudden collapse in end-user demand for AI intelligence. Hyperscaler CapEx buildouts and complex vendor financing arrangements work only as long as customers continue aggressively buying frontier-model tokens throughout the supply chain. 6. We Are All Building Compound Startups Today AI development tools have accelerated code production dramatically, making narrow point solutions increasingly vulnerable. To survive rapid competitive convergence, software startups must embrace becoming compound companies that ship expansive, multi-module product suites covering the entire customer workflow. 7. We Did Not End Up Doing More With Less. We Did More With More, and That’s Why European Startups Fail The belief that AI would allow companies to shrink headcount and simply do more with less has not played out as expected. Winners are compounding capital and talent to do vastly more with more, putting underfunded point solutions, particularly across Europe, at risk of being overwhelmed by aggressively scaling U.S. competitors. (links in comments)

This podcast is the single most important podcast to know what is going on in tech every week. On the agenda this week: - NVIDIA Crushes Quarter and Buys Hugging Face - OpenAI Cuts Off Cursor - Instinct Hits $2.5BN Valuation and The Race for AI Assistants - Cognition Raises at $46BN, Linear $2.5BN and Clay $7BN My notes with @rodriscoll and @jasonlk below: 1. How Instinct Could Follow the Same Path as Replit and Lovable In the early days, cloning lightweight AI tools is trivial. Defensibility emerges by rapidly adding complex workflows like security automation and multi-agent orchestration. Products that start without a moat can build formidable ones over time by solving dozens of evolving customer requirements faster than anyone else. 2. Hugging Face Acquisition Explained As the maker of compute, NVIDIA benefits when AI token traffic flows through 30% gross-margin open-source models rather than 70% gross-margin closed models where platforms capture more of the economics. Driving down software margins allows a greater share of total ecosystem spend to flow directly into GPUs. 3. The Bull Case for Clay Being a $100 Billion Company Autonomous agents executing go-to-market strategies around the clock could consume 10x to 100x more tokens and software usage than human sales teams ever could. As a leader in agentic GTM, Clay is positioned to capture an enormous wave of automated outreach, campaign analysis, and global prospect engagement. 4. The Bull Case for Linear When software teams build 100x more features at 50x the speed using AI, legacy project management tools and manual Kanban boards begin to break down. Linear can become the agent-friendly system of record for coordinating, tracking, and managing thousands of issues generated simultaneously by human-agent development teams. 5. The Three Ways the Wheels Come Off the Bus for NVIDIA NVIDIA’s record-breaking momentum faces one fundamental existential threat: a sudden collapse in end-user demand for AI intelligence. Hyperscaler CapEx buildouts and complex vendor financing arrangements work only as long as customers continue aggressively buying frontier-model tokens throughout the supply chain. 6. We Are All Building Compound Startups Today AI development tools have accelerated code production dramatically, making narrow point solutions increasingly vulnerable. To survive rapid competitive convergence, software startups must embrace becoming compound companies that ship expansive, multi-module product suites covering the entire customer workflow. 7. We Did Not End Up Doing More With Less. We Did More With More, and That’s Why European Startups Fail The belief that AI would allow companies to shrink headcount and simply do more with less has not played out as expected. Winners are compounding capital and talent to do vastly more with more, putting underfunded point solutions, particularly across Europe, at risk of being overwhelmed by aggressively scaling U.S. competitors. (links in comments)

The story of ClickHouse is truly insane. Started as an open-source project; scaled into the fastest-growing database product ever. Year 1: $0 Year 2: $12M Year 3: $50M Year 4: $200M Year 5 (not complete): My bet is $450M. My notes from our discussion with @ceo_clickhouse below 👇 1. Are Large U.S. Enterprises Scared to Work With Frontier Model Providers? Large enterprises remain skeptical of “zero data retention” claims and wary of sending proprietary source code to frontier labs due to IP indemnification and data leakage concerns. Rather than exposing production code, companies may limit frontier model usage to less sensitive workflows like code review while turning to open-weight alternatives for critical data. 2. How Do You Assess Defensibility and Moat in Companies That Scale Faster Than Ever Before? When an application scales from zero to $100M in ARR in a single year, investors must rigorously question its underlying moat. Hypergrowth without high switching costs leaves companies vulnerable to rapid churn as customers move effortlessly to the next model or tool that leapfrogs the incumbent. 3. How Does This AI Cycle Compare to Prior Technology Shifts and Transitions? Unlike the gradual adoption curves of the internet and mobile eras, the current AI wave is accelerating at an unprecedented pace. Agentic experiences are maturing rapidly, driving explosive revenue growth and placing historically unique performance demands on underlying data infrastructure. 4. What Job Does Not Exist Today That Will Be Very Prevalent in Five Years? A critical new corporate role could be an AI finance function dedicated entirely to managing token consumption and resource allocation across the enterprise. But the role may ultimately be short-lived as autonomous AI agents increasingly manage their own infrastructure spend and budget execution. 5. What Should Investors Be Worried About Today That They Are Not? The biggest overlooked risk in AI today is revenue durability. While infrastructure software benefits from high switching costs, agentic applications can have exceptionally low barriers to switching, raising questions about long-term retention as models and products continually leapfrog one another. 6. Why Revenue Concentration Is a Real Concern Operators and investors should treat revenue concentration as a critical risk, with any single customer or vertical accounting for more than 10% of revenue representing significant exposure. Sustainable enterprise value requires a diversified customer base so losing one account never threatens the company’s overall growth trajectory. (links in comments)

I first met @matanSF following a kind intro from @MattEvantic.   We went for a walk in Hyde Park. I was in my trusty short shorts and it was kinda awkward as a walk.  It was awkward because after 5 mins, he was clearly a genius and for the next 55 mins, I just had to pretend like I had more questions to ask before saying with intense eagerness, “can I invest”.  Thank the lord he let me. And through that I got to spend time with his co-founder, @EnoReyes.  Eno is this insane combination of a truly brilliant technologist with an intense awareness of what it takes to build an insanely high-margin, efficient business in AI.  I sat down with Eno when he was in London recently (episode in comments) and have added my handwritten notes below.  Special thanks to @rabois @shaunmmaguire @byersblake @Sabina_Smith_ @laurenmhreeder for some amazing question suggestions. -- 1. The Frontier in AI Right Now The true frontier of AI is building verification systems where no benchmarks exist. Creating systematic frameworks for what “good” looks like allows AI to reliably execute complex, high-friction human tasks that were previously difficult to automate. Is this the true beauty of Instinct @noahrshinn @saranormous 2. The Two Ways That Large Language Models Will Win Frontier model providers face two paths: dominate infrastructure through high-volume inference or move up the stack into high-margin applications. But model-locked applications can conflict with what enterprises actually want: the best possible outcome across multiple models. Love to hear your thoughts on this specifically @AnjneyMidha @mmurph 3. What Is Required to Get the Most Out of Models? Simple gateway routing outside the execution layer delivers only basic cost savings. Maximizing agentic performance requires operating statefully inside the workflow itself, dynamically understanding context, execution history, and what needs to happen next. How do you think about this @alexatallah @shensi @ThibaultJaigu @rauchg @koblovinamerica 4. Why 80% of Neo Labs Will Die and What Separates the Winners From the Losers The winners will anchor themselves to durable enterprise workflows that do not disappear as underlying frontier models improve. Single biggest advice to VCs on investing in neolabs today @LiamFedus 5. Why the Harness Is So Valuable and Who Ultimately Is the Sovereign of Your Intelligence Continuous learning and workflow optimization happen at the harness layer, not inside closed model APIs. True enterprise sovereignty requires owning your harnesses and learning loops so critical intelligence remains proprietary rather than being ceded to third-party labs. 6. Why We Should Not Be Scared to Use Chinese Open-Source Models Labeling open-source weights as dangerous “Chinese models” can obscure the distinction between model provenance and actual security risk. Open models can reflect creator biases, but their risks should be evaluated technically rather than by origin alone. As open weights improve, they could power a growing share of standard enterprise workflows.

I first met @matanSF following a kind intro from @MattEvantic.   We went for a walk in Hyde Park. I was in my trusty short shorts and it was kinda awkward as a walk.  It was awkward because after 5 mins, he was clearly a genius and for the next 55 mins, I just had to pretend like I had more questions to ask before saying with intense eagerness, “can I invest”.  Thank the lord he let me. And through that I got to spend time with his co-founder, @EnoReyes.  Eno is this insane combination of a truly brilliant technologist with an intense awareness of what it takes to build an insanely high-margin, efficient business in AI.  I sat down with Eno when he was in London recently (episode in comments) and have added my handwritten notes below.  Special thanks to @rabois @shaunmmaguire @byersblake @Sabina_Smith_ @laurenmhreeder for some amazing question suggestions. -- 1. The Frontier in AI Right Now The true frontier of AI is building verification systems where no benchmarks exist. Creating systematic frameworks for what “good” looks like allows AI to reliably execute complex, high-friction human tasks that were previously difficult to automate. Is this the true beauty of Instinct @noahrshinn @saranormous 2. The Two Ways That Large Language Models Will Win Frontier model providers face two paths: dominate infrastructure through high-volume inference or move up the stack into high-margin applications. But model-locked applications can conflict with what enterprises actually want: the best possible outcome across multiple models. Love to hear your thoughts on this specifically @AnjneyMidha @mmurph 3. What Is Required to Get the Most Out of Models? Simple gateway routing outside the execution layer delivers only basic cost savings. Maximizing agentic performance requires operating statefully inside the workflow itself, dynamically understanding context, execution history, and what needs to happen next. How do you think about this @alexatallah @shensi @ThibaultJaigu @rauchg @koblovinamerica 4. Why 80% of Neo Labs Will Die and What Separates the Winners From the Losers The winners will anchor themselves to durable enterprise workflows that do not disappear as underlying frontier models improve. Single biggest advice to VCs on investing in neolabs today @LiamFedus 5. Why the Harness Is So Valuable and Who Ultimately Is the Sovereign of Your Intelligence Continuous learning and workflow optimization happen at the harness layer, not inside closed model APIs. True enterprise sovereignty requires owning your harnesses and learning loops so critical intelligence remains proprietary rather than being ceded to third-party labs. 6. Why We Should Not Be Scared to Use Chinese Open-Source Models Labeling open-source weights as dangerous “Chinese models” can obscure the distinction between model provenance and actual security risk. Open models can reflect creator biases, but their risks should be evaluated technically rather than by origin alone. As open weights improve, they could power a growing share of standard enterprise workflows.

The story of ClickHouse is truly insane. Started as an open-source project; scaled into the fastest-growing database product ever. Year 1: $0 Year 2: $12M Year 3: $50M Year 4: $200M Year 5 (not complete): My bet is $450M. My notes from our discussion with @ceo_clickhouse below 👇 1. Are Large U.S. Enterprises Scared to Work With Frontier Model Providers? Large enterprises remain skeptical of “zero data retention” claims and wary of sending proprietary source code to frontier labs due to IP indemnification and data leakage concerns. Rather than exposing production code, companies may limit frontier model usage to less sensitive workflows like code review while turning to open-weight alternatives for critical data. 2. How Do You Assess Defensibility and Moat in Companies That Scale Faster Than Ever Before? When an application scales from zero to $100M in ARR in a single year, investors must rigorously question its underlying moat. Hypergrowth without high switching costs leaves companies vulnerable to rapid churn as customers move effortlessly to the next model or tool that leapfrogs the incumbent. 3. How Does This AI Cycle Compare to Prior Technology Shifts and Transitions? Unlike the gradual adoption curves of the internet and mobile eras, the current AI wave is accelerating at an unprecedented pace. Agentic experiences are maturing rapidly, driving explosive revenue growth and placing historically unique performance demands on underlying data infrastructure. 4. What Job Does Not Exist Today That Will Be Very Prevalent in Five Years? A critical new corporate role could be an AI finance function dedicated entirely to managing token consumption and resource allocation across the enterprise. But the role may ultimately be short-lived as autonomous AI agents increasingly manage their own infrastructure spend and budget execution. 5. What Should Investors Be Worried About Today That They Are Not? The biggest overlooked risk in AI today is revenue durability. While infrastructure software benefits from high switching costs, agentic applications can have exceptionally low barriers to switching, raising questions about long-term retention as models and products continually leapfrog one another. 6. Why Revenue Concentration Is a Real Concern Operators and investors should treat revenue concentration as a critical risk, with any single customer or vertical accounting for more than 10% of revenue representing significant exposure. Sustainable enterprise value requires a diversified customer base so losing one account never threatens the company’s overall growth trajectory. (links in comments)