Developed these OH's live on the Every AI&I podcast with @danshipper. Dan pushed us to dig deeper beyond just a one shot prompt, so here's a thread with the key insights we landed on from last week. Full podcast going live soon.👇

(this is a delayed crossposting from X from 10/18/24)

Union Square Ventures is a venture capital firm based in New York City.

1/ Commoditization is Coming: The concern about commoditization of foundational AI models was voiced by multiple participants. One side argued that these models, driven by high burn rates, are becoming indistinguishable, leading to a race to the bottom. Another perspective emphasized the need for differentiation, particularly through proprietary data, to escape this trend. The open question is whether they will eventually move up the application stack to differentiate.

3/ Microgrids...again: The team was aligned on the transformative potential of microgrids, particularly in improving energy resilience and reducing reliance on centralized utilities. Some saw immediate opportunities in deploying these systems at campuses and local communities, while others pointed out regulatory and scalability challenges. The debate highlighted how microgrids could enable faster adoption of renewable energy, though the road to widespread implementation will require navigating complex infrastructure and policy issues.

4/ Finance’s Social Tailwinds: The discussion centered around how globalization and the movement of people are driving demand for new financial systems at the edge. Some argued that these social trends are creating new opportunities for fintech to innovate, particularly in regions with high migration. Others pointed out that while these pressures are helpful, they also create complexity, as financial systems will need to adapt to new cultural and legal environments. There was consensus that social pressures are opening doors, but success will depend on how well fintech adapts to these shifting dynamics.

5/ Uncorrelated Bets Matter: The discussion centered on the importance of understanding the wide range of possible outcomes in AI and making strategic bets across those scenarios. Some argued that heavily investing in foundational models carries high risk, especially with increasing competition, while others saw the need to place bets on more niche applications—like healthcare or materials—where unique data can create defensibility. The consensus was that spreading investments across uncorrelated sectors helps hedge against uncertainties and ensures exposure to various success scenarios, even as the market evolves unpredictably.

2/ Decentralize to Survive: The conversation centered on decentralized AI training, with some advocating for it as a way to disrupt the dominance of large centralized model builders. Others were skeptical about whether this vision could be practically executed at scale. While decentralization offers potential for democratizing training, there were concerns over current limitations in efficiency and coordination. Still, the idea of co-owning models through shared clusters intrigued the group as a potential path forward.