Apoorv Agrawal
Showing 1–11 of 11 transcripts.
- Stanford Online56 min
Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | The GPU Economy
Sunny Madra, Brad Gerstner, Apoorv Agrawal
Brad Gerstner of Altimeter Capital and Grok co-founder Sonny Maduro outline a transformative shift where AI distribution costs are now compute-intensive, driving the integration of deterministic architecture with Nvidia's GPU ecosystem to accelerate inference. This strategic fusion, which led to Nvidia's $20 billion acquisition of Grok, enables a 2.5x increase in token generation while addressing critical power and memory constraints to support the transition from chat-based tools to autonomous agents. As the industry approaches Artificial General Intelligence faster than anticipated, the convergence of these hardware innovations and emerging regulatory frameworks aims to redefine global economic output and the future value of human labor.
SemiAnalysis, Altimeter, Nebius, Glean.. 12 Hot Takes From Biggest Names in AI
Dylan Patel, Qasar Younis, Apoorv Agrawal, Arvind Jain, Ariel Cohen, CJ Desai, Gil Feig, Nikhil Benesch, Barak Kaufman, Max Junestrand, Marc Boroditsky, Laura Diorio, Kasser, Mark
RAISE Paris marked a decisive industry shift from speculative hype to enterprise-grade cost reconciliation, as buyers now demand clear ROI and physical AI adoption outpaces volatile large language model growth. Key figures including Applied Intuition's Kasser and analysts from Altimeter Research warned of an impending market bust driven by unsustainable spending, while companies like Navan and TurboPuffer demonstrated new economic models focused on profitability and reduced inference costs. The event concluded with a consensus that success requires resilient multi-model strategies, robust data layers, and a global expansion mindset to navigate rising hardware prices and geopolitical energy constraints.
- Stanford Online34 min
Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Economics of Generative AI
Apoorv Agrawal, Chloe Fang, Ali Ghotzi, Jensen, Mark Andreessen, Demis
Aporv, leader of Altimeter's AI-focused investment firm, leads a nine-week course applying Chatham House rules to analyze the economic stack of the artificial intelligence sector. The curriculum dissects the current "triangle" market structure where semiconductor firms capture 75% of revenue growth while application layers struggle with thin margins due to significant inference costs. Participants develop mental models to navigate Series A investment opportunities and predict a potential decade-long shift in value distribution should hyperscalers successfully deploy specialized ASICs to alter the cost equilibrium.
- Stanford Online49 min
Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Applications, AI in Life Sciences
Eric Kauderer-Abrams, Apoorv Agrawal, Eric Abrams, Josh
Chai Discovery and Anthropic are establishing a new drug discovery paradigm by serving as tool providers that convert biological engineering from a trial-and-error art into a scalable, AI-driven discipline. By integrating large language models with wet-lab validation, these firms aim to compress the traditional ten-year development cycle into five years while democratizing research capabilities for individual scientists. The convergence of these technologies, supported by massive data generation and strategic partnerships with major pharmaceutical companies, positions AI-native infrastructure as the critical lever for the United States to compete globally in biotech.
- Stanford Online49 min
Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Applications, Coding AI
Guillermo Rauch, Apoorv Agrawal
Vercel, a $9.3 billion infrastructure firm founded by Guillermo Rauch, is pivoting its business model from standard web pages to "agentic infrastructure" to support the exponential growth of AI coding agents and token-based consumption. The company leverages a full-stack approach anchored in open source frameworks like Next.js, enabling enterprises such as Meta and Notion to deploy self-driving cloud capabilities that automate software configuration and security. This strategic shift, which has driven a threefold growth rate since October 2024, positions Vercel as the dominant platform for high-velocity, agent-generated code while redefining industry pricing and deployment standards.
- Stanford Online50 min
Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Building AI Factories
Chase Lochmiller, Apoorv Agrawal
Hyperscalers are pouring capital into AI infrastructure that rivals historic U.S. projects, driven by a shift in bottlenecks from chip availability to securing powered shells and skilled labor. In Abilene, Texas, Crusoe is deploying a 2.1-gigawatt campus hosting tenants like Oracle and OpenAI, where rapid construction faces significant wage inflation due to a scarcity of tradespeople and tripling costs for power equipment. While traditional hardware risks obsolescence, the economic model shows accelerated returns as managed services can halve the payback period to two years, even as the sector grapples with future challenges in labor supply and open-source competition.
- Stanford Online49 min
Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Applications, Applied AI
Tuhin Srivastava, Apoorv Agrawal, Doohan
Base10, led by CEO Toohin, provides a managed inference infrastructure that powers over 30 trillion tokens daily by aggregating 18 clouds to optimize costs for custom open-source AI models. The company differentiates itself from hyperscalers by abstracting complex hardware management and enabling customers to post-train proprietary models, a strategy driven by a thesis that GPU scarcity will remain permanent as agentic demand grows exponentially. With a projected capital expenditure of $7 billion to secure 150,000 B200 equivalents, Base10 aims to industrialize AI deployment through modular data centers and a transition from compute markup to token-based pricing.
- Stanford Online46 min
Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Infrastructure, Capstone Case
Former Intel CTO Kati Katti, now at OpenAI, details the organization's aggressive pursuit of 30 gigawatts of compute capacity to support a future dominated by complex agentic workloads and massive inference demands. This strategy involves overcoming severe supply chain bottlenecks and grid constraints through specialized infrastructure like nuclear power and Cerebras accelerators while prioritizing gigawatt-scale deployment over fragmented edge solutions. Katti predicts the AI value chain will eventually shift from hardware foundations to application-layer outcomes, as the industry races to solve the critical shortage of logic and memory fabrication capacity.
- Stanford Online48 min
Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Enterprise Internal Knowledge
Stanford graduate and Applied Compute CEO Yash Patil explains how the AI industry is shifting from general pre-training to specialized post-training on proprietary data to solve enterprise bottlenecks. He argues that while frontier models like OpenAI's O1 leverage test-time compute, future progress depends on continual learning from sparse, real-world rewards and deterministic environments like software coding. Patil concludes with a bullish outlook on compute hardware while warning that pure data-selling businesses will fail as synthetic generation and robotics become the new differentiators.
- RAISE Summit23 min
Oege De Moor (XBOW) & Apoorv Agrawal (Altimeter): Hackers with GPUs Offensive Security in the AI Era
Oege De Moor, Apoorv Agrawal, Uhey Dhimur
Altimeter Capital-backed startup Expo has deployed an autonomous AI agent that ranks as the world's top ethical hacker, identifying over 1,200 vulnerabilities including critical zero-days in enterprise giants like Palo Alto Networks and Salesforce within a single week. This superhuman capability forces a strategic industry shift toward AI-driven defense to combat a "skill shortage" in malicious actors that has enabled nation-states to automate attacks with unprecedented speed. While the market anticipates a major cyber incident within twelve months due to rising AI-generated code risks, human security roles are expected to evolve from technical execution to the strategic orchestration of these autonomous systems.
Understanding OpenAI’s $500B Valuation
Altimeter Capital identifies OpenAI as the definitive leader in the consumer AI super cycle, citing its $10 billion revenue run rate, 700 million weekly active users, and strategic GPT-5 rollout as drivers for a projected $200 billion revenue potential. While the firm acknowledges intense competition from Meta's capital reserves and Anthropic's enterprise dominance, it argues that OpenAI's unmatched speed of iteration and deep user retention create a formidable moat. The analysis further highlights Altimeter's parallel investments in AI-driven cybersecurity platforms like Expo and Palantir's forward-deployed engineering model, signaling a broader shift toward automated agent infrastructure and high-impact talent allocation as the primary bottlenecks of the era.