Sequoia estimates a 10 Trillion AI Revolution
How Sequoia Capital envisions the future of AI unfolding, and the investment opportunities they have identified.
Sequoia Capital, a leading Silicon Valley venture capital firm known for investing in early and growth-stage technology companies, recently released a presentation on AI, describing it as a $10 trillion “cognitive revolution.” Their core thesis positions this transformation as being as significant—if not more so—than the Industrial Revolution.
Konstantine Buhler from Sequoia draws a compelling parallel between milestones of the industrial era—the steam engine, the first factory system, and the assembly line—and key developments in the AI era, such as the introduction of the first GPU (the GeForce 256 in 1999) and the establishment of the first “AI factory” in 2016. Just as it took 144 years to perfect the factory assembly line, AI is now entering a crucial phase of specialization.
For a complex system like AI to mature, it must integrate general-purpose components (like foundational AI models) with highly specialized subsystems and labor. Sequoia views today’s startups as the primary drivers of this specialization, building targeted applications atop general AI technologies.
How the AI Future will unfold
Sequoia expects AI to catalyze a major economic shift by automating and expanding the market for knowledge work and services. They identify the $10 trillion US services market as the primary opportunity. Comparing it with the evolution of software-as-a-service (SaaS)—which expanded the on-premise software market—AI is anticipated not only to grow the market share but also to enlarge the services industry itself. This expansion could give rise to large, standalone public companies centered on AI, akin to the industrial giants of past eras.
Investment Trends Sequoia is watching
Leverage Over Uncertainty: Work is shifting from tasks with low leverage and high certainty to tasks where AI delivers massive leverage (100%+), albeit with less predictable outcomes. For example, a salesperson might deploy hundreds of AI agents to monitor accounts and intervene only as needed.
Real-World Measurement: The benchmark for AI performance has moved beyond academic datasets like ImageNet. Sequoia points to real-world validation, such as AI hackers competing live on platforms like HackerOne, as a more meaningful measure.
Reinforcement Learning: Previously limited to research labs, reinforcement learning is now employed by startups to train open-source models, especially in coding and software development.
AI in the Physical World: AI is expanding beyond software, powering robotics, manufacturing processes, and quality assurance systems.
Compute as the New Production Function: The emerging key metric is “flops per knowledge worker.” Sequoia’s portfolio companies forecast a 10x to 10,000x increase in compute consumption as workers begin leveraging hundreds or thousands of AI agents.
Investment Themes for the next 12–18 Months
Persistent Memory: A significant unsolved challenge in AI is long-term memory—both in retaining conversational context and preserving an agent’s identity. Current approaches, including vector databases and extended context windows, remain insufficient & have to be addressed.
Seamless Communication Protocols: Just as TCP/IP enabled the internet, new protocols are needed for AI agents to communicate and collaborate effectively. This advancement would allow agents to perform complex tasks, such as researching, comparing prices, and completing purchases autonomously.
AI Voice: Advances in fidelity and reduced latency now make AI voice suitable for real-time conversations. Applications include consumer-facing virtual companions and enterprise solutions like logistics coordination and trading desks.
AI Security: There is a substantial opportunity to embed security at every layer of the AI stack—from model development to end-user protection. Sequoia envisions a future with hundreds of AI security agents safeguarding each human and AI agent.
Open Source: Despite its precarious position, Sequoia considers open-source AI essential for a free and equitable future. They aim to support models that remain accessible and competitive, counterbalancing proprietary dominance.
Sequoia’s view is that AI is entering a phase of deep specialization, with startups leading the development of the next wave of infrastructure and applications. This moment represents an economically transformative era with long-term opportunities across services, compute, security, and open-source ecosystems.