Apoorv Agrawal
Apoorv Agrawal is a partner at Altimeter Capital and a Stanford adjunct lecturer in Management Science and Engineering, where he teaches MS&E 435, Economics of the AI Supercycle. His public work focuses on AI markets, generative AI economics, and technology investing.
The Cloud Is Being Rebuilt Around Agents, Tokens, and Sandboxes
Vercel chief executive Guillermo Rauch used a Stanford MS&E435 seminar to argue that coding agents are expanding the software market rather than merely making developers faster. In his view, AI is widening software creation from professional programmers to business users and autonomous agents, while shifting cloud demand from websites and applications toward deployed agents that need token routing, sandboxes, security, observability and long-running compute.
Baseten Raises $1.5 Billion as Inference Demand Shifts Toward Open Source
Baseten’s $1.5 billion financing at a $13 billion valuation rests on a bet that AI inference is becoming a larger and more operationally demanding market as companies run more open-source and post-trained models. CEO Tuhin Srivastava says the capital will help Baseten secure diversified compute and build the infrastructure layer customers need, while Altimeter partner Apoorv Agrawal argues the shift is toward capability, control, and cost advantages rather than simple access to frontier models.
AI Application Companies Are Moving Beyond Frontier APIs to Protect Margins
Baseten founder and chief executive Tuhin Srivastava used a Stanford MS&E435 seminar with instructor Apoorv Agrawal to argue that inference is becoming the cost of goods sold for AI applications. His case is that scaled AI companies will need to move beyond default frontier-model APIs toward custom or post-trained models, both to improve margins and to protect the workflows and user signals that make their products defensible. Baseten’s role, as Srivastava framed it, is to provide the production inference stack and compute access needed to run that custom intelligence at scale.
AI Demand Is Real, but Productivity Gains Remain Unproven
Bloomberg’s Tech event in San Francisco framed the AI boom as a market caught between constrained infrastructure demand and valuations that leave little tolerance for misses. Executives from Databricks, Okta and Altimeter argued that the next bottlenecks are enterprise context, secure system access, power and capital allocation, while San Francisco Fed President Mary Daly said AI investment is widespread but has not yet produced broad, measurable productivity gains.
AI Has Split Markets Into Capex Receivers and Spenders
Altimeter Capital partner Apoorv Agrawal argues that AI has become one of the largest capital formation cycles in markets, not just another technology product cycle. Speaking to Bloomberg Technology, he said investors should separate companies receiving AI capital expenditure — including compute, memory, networking and energy suppliers — from the labs and model companies spending it, while preparing for public markets to absorb a potential wave of AI IPOs.
Frontier AI Has Become a Gigawatt-Scale Industrial Infrastructure Race
In a Stanford MS&E seminar on the economics of the AI supercycle, OpenAI infrastructure executive Sachin Katti argued that frontier AI has become an industrial systems problem, not a GPU procurement problem. Katti said usable compute now depends on synchronizing chips, memory, networking, power, cooling, buildings, land, suppliers and operators at gigawatt scale. His broader case was that OpenAI’s model and revenue ambitions depend on how quickly it can turn that whole chain into reliable infrastructure for training, inference and agentic workloads.
Enterprise AI Advantage Comes From Internal Evals and Proprietary Context
Yash Patil, chief executive of Applied Compute and a guest speaker in Stanford’s MS&E435 seminar, argues that the enterprise opportunity in AI is shifting from access to general frontier models toward the ability to define and optimize company-specific tasks. General models provide a baseline, he says, but durable advantage comes from internal evals, verifiers, feedback loops, proprietary context and product constraints that teach systems what “correct” means inside a business.
Generative AI’s Revenue Stack Is Still Inverted Toward Chips
Stanford adjunct lecturer and Altimeter partner Apoorv Agrawal argues in MS&E435 that generative AI’s economics still look unlike the software and cloud cycles investors often use to value it. In his estimates, AI revenue has grown sharply, but gross profit remains concentrated in semiconductors, while applications face inference costs, thin monetization and uncertain paths to mass-market utility. The question he puts to students is not whether AI demand exists, but how long the stack’s inverted shape can persist before applications and infrastructure capture more of the value.