Investing Data is Evolving: AI, The Degenerate Economy & More | Matt Ober | Social Leverage
Timestamps are as accurate as they can be but may be slightly off. We encourage you to listen to the full context.
Podcast Summary
In this episode of Other People's Money, Matt Ober, General Partner at Social Leverage, delves into the evolution of the data economy and its impact on investors, vendors, and AI companies. (02:23) He argues that data is becoming commoditized as AI transforms pricing models from fixed contracts to consumption-based marketplaces where users will pay less per data point but consume exponentially more. (03:51) Matt also discusses how Social Leverage uses data for seed-stage investments, his career journey from quantitative hedge funds to venture capital, and where the firm is currently focused in the fintech and wealth management sectors.
- Main themes: The commoditization of alternative data, AI's impact on data business models, transitioning from hedge funds to venture capital, and emerging opportunities in prediction markets and the "degenerate economy."
Speakers
Matt Ober
Matt Ober is General Partner at Social Leverage, a veteran seed-stage venture capital firm founded in 2009. Before joining Social Leverage, Matt served as Chief Data Scientist at Dan Loeb's Third Point, where he built the firm's data analytics and technology platform supporting investments across equities, structured credit, venture capital, and cryptocurrency. He previously held the position of Head of Data Strategy at WorldQuant and was a founding member of WorldQuant Ventures, focusing on private investments in fintech, data, and technology.
Key Takeaways
Embrace Consumption-Based Data Pricing Models
Matt explains that the data industry is shifting from fixed pricing to consumption-based models where users pay significantly less per data point but consume exponentially more data. (03:51) He predicts that what costs a dollar today might cost a penny in the future, but the volume will increase by 1000x. This shift is being driven by AI's need for massive data consumption, and data vendors who adapt to this model will see their valuations increase despite lower per-unit pricing. Companies should prepare for this transition by building flexible pricing structures and focusing on volume-based revenue streams rather than fixed contracts.
Focus on Becoming "Beta" Rather Than "Alpha" as a Data Provider
As Matt notes, "if you're on the vendor side, I always like to say to them, you may be the alternative data or you may be alpha, but you really wanna be beta because then everybody needs you." (39:05) This means data companies should aim to become essential infrastructure rather than niche competitive advantages. Consumer transaction data and app usage data have successfully made this transition - they were once alternative data sources providing alpha but are now considered essential beta that no serious investor can ignore when analyzing companies like Starbucks or Netflix.
Leverage AI to Disrupt Legacy Data Infrastructure
Matt argues that AI-native companies can now replicate the work of thousands of offshore employees with just eight AI agents, building global fundamental datasets faster, more accurately, and cheaper than incumbents. (05:52) This presents opportunities for startups to challenge established players like FactSet and S&P by offering the same data with better terms, faster delivery, and lower costs. Companies should focus on building AI-driven data collection and processing workflows from the ground up rather than trying to retrofit legacy systems.
Build Data-Driven Investment Processes Across All Strategies
Drawing from his experience at Third Point, Matt emphasizes that modern investment firms cannot ignore data integration. (33:54) As he puts it, "you can't say you're investing in Starbucks but not looking at consumer transaction data." This applies beyond just consumer companies - every investment decision should be supported by relevant data streams, whether fundamental, alternative, or real-time operational data. Investment teams need to develop capabilities to analyze data or risk being outcompeted by firms that do.
Target the Right Exit Scale for Your Fund Size
Matt explains Social Leverage's approach to exits, where a $300-400 million acquisition can return their entire fund if they invest $1 million at a $5 million valuation. (18:18) This contrasts with mega funds that need billion-dollar exits due to their large check sizes and high valuations. Seed-stage investors should educate founders about these different expectations and ensure cap table construction allows for profitable exits at lower valuations, providing optionality that mega-fund-backed companies often lose.
Statistics & Facts
- WorldQuant grew from managing $500 million to $1 billion in assets to $30 billion during Matt's six-and-a-half-year tenure, with the firm spending hundreds of millions of dollars on data and operating 600 people across 26 offices globally. (32:08)
- Social Leverage invests $1-2 million checks in companies with sub-$10 million valuations, with a sweet spot of $4-8 million post-money valuations, allowing a $300-400 million exit to return almost their entire fund. (18:25)
- Consumer transaction data, which was once alternative alpha-generating data, has become essential beta that all serious investors now use when analyzing consumer companies like Starbucks and McDonald's, demonstrating how alternative data becomes commoditized over time. (39:26)
