Answer extracted from the Invest Like The Best podcast — listen to the full episode below.
Can the AI frontier stay competitive, or will one or two labs dominate everything?
Consolidation is not inevitable. High-agency entrepreneurs and researchers with proper capital, support, and networks can prevent a handful of labs from consuming the entire AI economy, even in a violently competitive landscape. Individual decision-makers matter more than structural forces.
The anxiety around AI consolidation runs deep. Many investors and observers assume that recursive self-improvement and the capital demands of frontier model development will naturally funnel power into one or two dominant labs. Sarah rejects this fatalism outright.
Her reasoning rests on what she calls a belief in the "great man and great woman theories of history." As Sarah explains in the episode, the outcome of AI development hinges not on inexorable market forces but on the people involved and their environment. Two researchers with the same code but different networks, capital sources, and support systems will end up in entirely different places.
Why the ecosystem diversity question matters now
The critical juncture is not whether open-source models exist—they already do. The question is whether a competitive Western open-source model will emerge at the frontier, one capable of competing alongside proprietary efforts from OpenAI, DeepMind, and others. This matters because it determines whether the economic and strategic benefits of AI development remain broadly distributed or collapse into a handful of organizations.
Sarah's position is that this outcome is not predetermined. Whether the ecosystem remains diverse or becomes monolithic depends on specific investments, hiring decisions, and strategic choices made by individuals right now. A researcher who gets hired by a scrappy frontier lab instead of OpenAI could change the trajectory of that lab. A founder who chooses to open-source their work instead of licensing it to a hyperscaler shapes the competitive landscape. As discussed in the episode, Sarah is tracking approximately 250 such high-agency people at the frontier of AI innovation, betting that their choices and capabilities will shape whether consolidation happens.
"If you think about the opportunity for the ecosystem in the future, is there going to be a competitive Western open source model?"
Sarah — Investor at Greylock. Sarah is an early-stage investor focused on AI, biology, defense, and robotics who co-founded or leads a venture fund with partners including Mike, Pranav, and Bella. She previously considered becoming a software entrepreneur but chose investing after realizing she is deeply curious, values understanding things deeply, and is motivated by working with extraordinary people and making them more successful through her skills and networks.
Her conviction is grounded in observed precedent. In previous technology shifts—from personal computing to the internet to mobile—periods that looked destined for monopoly often fragmented into multiple competitive ecosystems. The difference with AI is the capital intensity and the speed, but not necessarily the inevitability of concentration.
What gives Sarah confidence is not wishful thinking but operational detail. She knows the founders, the investors backing them, and the technical problems they're solving. The episode explores how these 250 people across frontier research labs, startups, and policy roles are actively shaping outcomes that most observers assume are already fixed.
Key takeaways
Key takeaways
- Consolidation in AI is not inevitable—individual agency and decision-making by entrepreneurs and researchers can prevent it.
- The real question is not whether open-source models exist, but whether a competitive Western open-source model can emerge at the frontier.
- Capital allocation, hiring, and network effects controlled by high-agency people matter more than structural market forces in determining ecosystem diversity.
- Approximately 250 entrepreneurs and researchers at the frontier of AI can materially influence whether the AI economy remains distributed or collapses into a few labs.
See also
How should venture investors evaluate whether they have sufficient domain expertise before committing to research-intensive technology bets?
Sarah argues that if an investor doesn't have grounded intuition on the bet itself, they cannot confidently rate it as 8 or 9 on conviction. She cites her own internal conviction scale to evaluate opportunities before deeper due diligence.
What makes Tony Zhao and Cheng Chi's approach to robotics generalization and data collection particularly innovative?
Sarah credits Tony Zhao and Cheng Chi at Sunday Robotics with contributing more interesting ideas in robotics over the last four years than most traditional robotics teams, particularly in their approach to data collection and model generalization.
What regulatory and infrastructure barriers are preventing rapid scaling of compute capacity for AI development?
Sarah identifies compute availability and energy supply as critical constraints over the next five to ten years. She spoke with a hyperscaler infrastructure leader who stated that nothing would move the needle for compute capacity at sufficient scale before 2030.