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$1BN annualized runrate 200 employees 3.5 years Such fun to sit down with Harry and discuss the future of open/closed and specialised vs generalised intelligence. The future isn’t in duopoly. It’s in millions of companies building special products. They must own their intelligence.
Everyone gets angry with me for saying triple, triple, double, double is dead. Fine, I do not really care. Venture is about investing in unbelievable outliers. Anomalies that own markets with generational founders. @FireworksAI_HQ is an example of this. They scaled to $1BN in ARR in just 3.5 years. They also have just 200 employees making it an insane $5M revenue per head. @lqiao just raised a whopping $1.5BN at a $17BN valuation and I sat down with her. Added my notes and the episode below: 1. The Challenges That Come From Such a Fast Development Cycle for Chips Hardware innovation is moving so quickly that rapid SKU cycles now outpace traditional depreciation timelines, changing the financial calculus of building versus renting infrastructure. Founders should prioritize growth and market agility over immediate gross margins, avoiding premature optimization until customer workloads stabilize. 2. Why National Sovereignty Is Real in AI and Every Company Should Have Its Own Model Frontier models function like a society’s core electricity grid. Relying entirely on a third-party API creates the existential risk of sudden disconnection, making model ownership and infrastructure independence critical for both sovereign nations and enterprise businesses. 3. Why the Future Is Millions of Specialized Models Frontier providers bake their own design tastes and values into models, which inevitably misaligns with enterprise business logic. The future belongs to millions of specialized, “one-size-fits-one” models tailored to proprietary data, consistently outperforming generalized AGI on accuracy, speed, and unit economics. 4. The Transition From the Year of Coding to the Year of Co-Work AI adoption has rapidly evolved from engineering-centric coding tools to a diversified ecosystem of B2B co-work agents. Founders and VCs must look past crowded developer environments to capture massive value in specialized workflow automation across legal, finance, healthcare, and other enterprise functions. 5. How a 10x Cost Reduction Will Drive a 100x Explosion in Usage Temporary supply chain backlogs will eventually ease, compressing infrastructure and model-tuning costs by 10x over the next three years. This deflation in token unit economics will turn intelligence into a near-frictionless commodity, driving a massive surge in enterprise production usage. 6. Why Avoiding the Application Layer Is Essential for Platform Focus Platform defensibility requires strict focus on multi-chip agility without creating vertical hardware or software dependencies. By refusing to move up into the application layer, infrastructure platforms avoid competing with their own ecosystem and maximize their value in specialized model orchestration. 7. Biggest Lesson From Working With Jensen Huang on Leadership Leadership in hyper-velocity markets is defined by rapid judgment, not executive privilege. Because critical information degrades as it moves through layers of corporate hierarchy, leaders must stay close to ground-level technical details to maintain execution speed and avoid flawed strategic calls.
Why national sovereignty is real in AI and every company should have their own model “Think about a general intelligence model as a power line. Every country should own their own power line. It's a scary moment when your power line is cut off and your day-to-day stops working. It's not just countries; every single company should have their independence too. You don't want any single person to cut you off.” @lqiao What does no one think about that everyone should think about with regards to sovereignty @bgurley @matthewclifford @BillAckman @soundboy
Everyone gets angry with me for saying triple, triple, double, double is dead. Fine, I do not really care. Venture is about investing in unbelievable outliers. Anomalies that own markets with generational founders. @FireworksAI_HQ is an example of this. They scaled to $1BN in ARR in just 3.5 years. They also have just 200 employees making it an insane $5M revenue per head. @lqiao just raised a whopping $1.5BN at a $17BN valuation and I sat down with her. Added my notes and the episode below: 1. The Challenges That Come From Such a Fast Development Cycle for Chips Hardware innovation is moving so quickly that rapid SKU cycles now outpace traditional depreciation timelines, changing the financial calculus of building versus renting infrastructure. Founders should prioritize growth and market agility over immediate gross margins, avoiding premature optimization until customer workloads stabilize. 2. Why National Sovereignty Is Real in AI and Every Company Should Have Its Own Model Frontier models function like a society’s core electricity grid. Relying entirely on a third-party API creates the existential risk of sudden disconnection, making model ownership and infrastructure independence critical for both sovereign nations and enterprise businesses. 3. Why the Future Is Millions of Specialized Models Frontier providers bake their own design tastes and values into models, which inevitably misaligns with enterprise business logic. The future belongs to millions of specialized, “one-size-fits-one” models tailored to proprietary data, consistently outperforming generalized AGI on accuracy, speed, and unit economics. 4. The Transition From the Year of Coding to the Year of Co-Work AI adoption has rapidly evolved from engineering-centric coding tools to a diversified ecosystem of B2B co-work agents. Founders and VCs must look past crowded developer environments to capture massive value in specialized workflow automation across legal, finance, healthcare, and other enterprise functions. 5. How a 10x Cost Reduction Will Drive a 100x Explosion in Usage Temporary supply chain backlogs will eventually ease, compressing infrastructure and model-tuning costs by 10x over the next three years. This deflation in token unit economics will turn intelligence into a near-frictionless commodity, driving a massive surge in enterprise production usage. 6. Why Avoiding the Application Layer Is Essential for Platform Focus Platform defensibility requires strict focus on multi-chip agility without creating vertical hardware or software dependencies. By refusing to move up into the application layer, infrastructure platforms avoid competing with their own ecosystem and maximize their value in specialized model orchestration. 7. Biggest Lesson From Working With Jensen Huang on Leadership Leadership in hyper-velocity markets is defined by rapid judgment, not executive privilege. Because critical information degrades as it moves through layers of corporate hierarchy, leaders must stay close to ground-level technical details to maintain execution speed and avoid flawed strategic calls.
Why the future is millions of specialized models “A model provider infuses their own judgment and taste into the training process, and you cannot guarantee it matches yours. There's no specialized general company. Every company is special, with its own design principle, taste, and target audience. Because of that, one company's judgment will misalign with yours. That's why you need to tune those models to match yours. The future will not be a few AGI models dominating the world. There will be millions of specialized models, one per use case.” @lqiao Walk me through the decision-making process to build your own models @Avishai_ab @MaorShlomo @winstonweinberg @eisokant @BrendanFoody
Everyone gets angry with me for saying triple, triple, double, double is dead. Fine, I do not really care. Venture is about investing in unbelievable outliers. Anomalies that own markets with generational founders. @FireworksAI_HQ is an example of this. They scaled to $1BN in ARR in just 3.5 years. They also have just 200 employees making it an insane $5M revenue per head. @lqiao just raised a whopping $1.5BN at a $17BN valuation and I sat down with her. Added my notes and the episode below: 1. The Challenges That Come From Such a Fast Development Cycle for Chips Hardware innovation is moving so quickly that rapid SKU cycles now outpace traditional depreciation timelines, changing the financial calculus of building versus renting infrastructure. Founders should prioritize growth and market agility over immediate gross margins, avoiding premature optimization until customer workloads stabilize. 2. Why National Sovereignty Is Real in AI and Every Company Should Have Its Own Model Frontier models function like a society’s core electricity grid. Relying entirely on a third-party API creates the existential risk of sudden disconnection, making model ownership and infrastructure independence critical for both sovereign nations and enterprise businesses. 3. Why the Future Is Millions of Specialized Models Frontier providers bake their own design tastes and values into models, which inevitably misaligns with enterprise business logic. The future belongs to millions of specialized, “one-size-fits-one” models tailored to proprietary data, consistently outperforming generalized AGI on accuracy, speed, and unit economics. 4. The Transition From the Year of Coding to the Year of Co-Work AI adoption has rapidly evolved from engineering-centric coding tools to a diversified ecosystem of B2B co-work agents. Founders and VCs must look past crowded developer environments to capture massive value in specialized workflow automation across legal, finance, healthcare, and other enterprise functions. 5. How a 10x Cost Reduction Will Drive a 100x Explosion in Usage Temporary supply chain backlogs will eventually ease, compressing infrastructure and model-tuning costs by 10x over the next three years. This deflation in token unit economics will turn intelligence into a near-frictionless commodity, driving a massive surge in enterprise production usage. 6. Why Avoiding the Application Layer Is Essential for Platform Focus Platform defensibility requires strict focus on multi-chip agility without creating vertical hardware or software dependencies. By refusing to move up into the application layer, infrastructure platforms avoid competing with their own ecosystem and maximize their value in specialized model orchestration. 7. Biggest Lesson From Working With Jensen Huang on Leadership Leadership in hyper-velocity markets is defined by rapid judgment, not executive privilege. Because critical information degrades as it moves through layers of corporate hierarchy, leaders must stay close to ground-level technical details to maintain execution speed and avoid flawed strategic calls.
$1BN annualized runrate 200 employees 3.5 years Such fun to sit down with @HarryStebbings and discuss the future of open/closed and specialised vs generalised intelligence. The future isn’t in duopoly. It’s in millions of companies building special products. They must own their intelligence.
Everyone gets angry with me for saying triple, triple, double, double is dead. Fine, I do not really care. Venture is about investing in unbelievable outliers. Anomalies that own markets with generational founders. @FireworksAI_HQ is an example of this. They scaled to $1BN in ARR in just 3.5 years. They also have just 200 employees making it an insane $5M revenue per head. @lqiao just raised a whopping $1.5BN at a $17BN valuation and I sat down with her. Added my notes and the episode below: 1. The Challenges That Come From Such a Fast Development Cycle for Chips Hardware innovation is moving so quickly that rapid SKU cycles now outpace traditional depreciation timelines, changing the financial calculus of building versus renting infrastructure. Founders should prioritize growth and market agility over immediate gross margins, avoiding premature optimization until customer workloads stabilize. 2. Why National Sovereignty Is Real in AI and Every Company Should Have Its Own Model Frontier models function like a society’s core electricity grid. Relying entirely on a third-party API creates the existential risk of sudden disconnection, making model ownership and infrastructure independence critical for both sovereign nations and enterprise businesses. 3. Why the Future Is Millions of Specialized Models Frontier providers bake their own design tastes and values into models, which inevitably misaligns with enterprise business logic. The future belongs to millions of specialized, “one-size-fits-one” models tailored to proprietary data, consistently outperforming generalized AGI on accuracy, speed, and unit economics. 4. The Transition From the Year of Coding to the Year of Co-Work AI adoption has rapidly evolved from engineering-centric coding tools to a diversified ecosystem of B2B co-work agents. Founders and VCs must look past crowded developer environments to capture massive value in specialized workflow automation across legal, finance, healthcare, and other enterprise functions. 5. How a 10x Cost Reduction Will Drive a 100x Explosion in Usage Temporary supply chain backlogs will eventually ease, compressing infrastructure and model-tuning costs by 10x over the next three years. This deflation in token unit economics will turn intelligence into a near-frictionless commodity, driving a massive surge in enterprise production usage. 6. Why Avoiding the Application Layer Is Essential for Platform Focus Platform defensibility requires strict focus on multi-chip agility without creating vertical hardware or software dependencies. By refusing to move up into the application layer, infrastructure platforms avoid competing with their own ecosystem and maximize their value in specialized model orchestration. 7. Biggest Lesson From Working With Jensen Huang on Leadership Leadership in hyper-velocity markets is defined by rapid judgment, not executive privilege. Because critical information degrades as it moves through layers of corporate hierarchy, leaders must stay close to ground-level technical details to maintain execution speed and avoid flawed strategic calls.
@lqiao @HarryStebbings Rockstar team @FireworksAI_HQ! 👏