Latest Interviews
Showing 1–6 of 6 transcripts.
Clear all filters- 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 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 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.