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This Meeting Could Have Been A Podcast - and now it is. This week we transformed our Oct 21 AI team meeting into an algorithmically distilled episode of USV Radio. We cover multi-agent systems, decentralized training, and how we're thinking about investing in the space: https://soundcloud.com/thelibrarian-46645069/oh-usv-how-to-invest-in-ai-multi-agent-learning-decentralized-training-video-games

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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.

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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.

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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.

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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.

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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.

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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)

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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.

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Incredibly excited to announce our investment in Daylight and to work with @kyle @morley and team! Starting with a personalized transaction recommendation API, Daylight is working to grow onchain GDP. More thoughts here: https://blog.usv.com/daylight

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5. Emergent Properties: The Future of Health Metrics // We were fascinated by the idea that health can be measured through emergent properties like variability in biological systems. This approach could lead to more accurate and holistic health assessments, moving beyond traditional metrics like blood pressure or cholesterol levels.

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4. The Rise Of Non-Invasive Monitoring // Non-invasive monitoring technologies are becoming increasingly sophisticated and ubiquitous, allowing for real-time health tracking without the need for intrusive procedures. This trend is likely to revolutionize preventive medicine and chronic disease management, presenting some very significant opportunities for increasing agency in personal health.

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3. Machine Learning Meets Muscle Aging // One presenter discussed using machine learning to analyze skeletal muscle aging. This approach has the potential to uncover novel biomarkers and therapeutic targets that were previously imperceptible, making it a promising area for exploration.

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2. Targeting Inflammation: The Key to Longevity // We learned that in addition to being a critical factor in many aging processes, Inflammation functions as a communication network for disease in the body. By understanding and targeting these pathways, we can develop interventions that significantly improve healthspan and lifespan.

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1. Gene Expression: Mimicking Dietary Effects // One speaker revealed that certain drugs can produce the same gene expression changes as dietary restrictions. This means we could potentially harness the benefits of diets like keto or fasting through pharmacological means, opening up new avenues for metabolic health treatments.

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Last week’s OH @ @usv eavesdropped on the future of Health. With Compound, we hosted founders & academics for a day of Biohacking. Below are just a couple highlights of the talks from researchers at Columbia, NYU, Mount Sinai, and The Alliance for Longevity Initiatives…

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5/ Knowing Who We Are: Many of the opportunities discussed, while important, felt out of the purview of a smaller fund like ours. We discussed finding opportunities that better aligned with our core thesis. Using micro-mobility as just an example, we thought battery analytics or e-bike marketplaces might make more sense than investing directly into an e-bike manufacturer.

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4/ Climate Adaptation Technologies: Technologies that help communities adapt to climate change, such as wildfire monitoring systems and flood prediction tools, are becoming increasingly essential. Investing in these solutions can address immediate climate risks and build long-term resilience. It’s also an underinvested area in our portfolio.

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3/ Climate Fintech: The growing need for financial solutions to bridge funding gaps in climate tech presents a pretty big opportunity. Exploring climate fintech can provide the necessary capital for innovative solutions to scale, driving significant impact in carbon drawdown.

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2/ Microgrids for Decentralized Energy: Microgrids present a significant investment opportunity, particularly in regions without legacy infrastructure. They offer flexibility and resilience by functioning independently or connecting to larger grids, making them a key player in the future of energy.

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1/ Edge Devices for Energy Efficiency: Edge devices that can be easily installed by consumers to manage energy use more efficiently, such as smart thermostats and plug-in solar panels, offer a promising market. These technologies can bypass traditional utility barriers and directly appeal to eco-conscious consumers. They also can have data network effects.

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