Can data revolutionize early-stage investing? Traditional venture capital relies heavily on networks, intuition, and subjective analysis. But what if there was a way to systematize early-stage investing — eliminating inefficiencies and bias? Dave Lambert, Co-Founder and Managing Director at Right Side Capital Management, has done just that. By applying a data-driven, ultra-diversified approach, his firm has built one of the most active pre-VC investment portfolios in the U.S.
Dave shares how Right Side Capital has invested in over 2,000 pre-VC startups using a systematic, quantitative investment model. He explains why diversification is critical for early-stage investing, how AI is making startups more capital efficient, and why traditional ownership percentage targets don’t make sense. This conversation dives into the evolution of venture capital and the role of data in optimizing portfolio construction.
Dave Lambert brings decades of experience as both an entrepreneur and investor. Before co-founding Right Side Capital, he launched and scaled two startups, giving him firsthand insight into the inefficiencies of early-stage fundraising. His firm is pioneering a model that removes friction from the fundraising process — providing high-quality founders with quick capital and investors with optimized returns.
Transcription:
Sam Silvershein: Welcome to Driving Alpha. I’m Sam Silvershein, vice president here at Alpha Partners. Today’s guest is Dave Lambert, co-founder and managing director at Right Side Capital Management, arguably the most active pre-VC stage investment firm in the United States.
Dave and his team have a unique data-driven approach to investing in early-stage pre-VC startups. They started investing in 2012 and now have a portfolio of over 2,000 companies. Dave has a long and varied career as an entrepreneur and investor. Prior to co-founding Right Side Capital Management, he founded and ran two successful startup companies—Acorn Computer, a computer company that he bootstrapped right out of college, and WorkMetro, an online recruitment marketplace.
Dave is a graduate of Stanford University, where he specialized in values, technology, science, and society, with a focus on AI. Dave, welcome to the podcast.
Dave Lambert: Thanks. It’s great to be here.
Sam Silvershein: Glad to have you. Let’s start with your background as an entrepreneur. You have a deep understanding of the challenges and opportunities that startups face. Coming into venture, what are some of the key learnings that you brought from your time as an operator?
Dave Lambert: The biggest thing for me is that, in the ‘90s, I founded and ran a computer hardware company, and in the 2000s, I built a software company. My first one was bootstrapped, and the second one took venture capital from two prominent Bay Area VC firms. I was also an active angel investor from the mid-’90s to the mid-2000s.
The biggest perspective I brought in was that the earliest stages of raising capital were dysfunctional—both from the entrepreneur’s and the investor’s perspectives. It was incredibly inefficient, with a lot of room for improvement. My partner came up with the genesis of our idea, and when he shared it with me, I immediately recognized the pain points he was addressing, because I had experienced them firsthand.
Sam Silvershein: Tell me a bit about those dysfunctional aspects.
Dave Lambert: It was very different back then. In the 2000s, it wasn’t easy to find out that VC firms even existed. There wasn’t a website where you could download a list of all VC firms and their contact information. There was no AngelList or anything similar. Just finding investors—even in the Bay Area—was difficult.
From an investor’s perspective, it was equally challenging to find startups. There were no online tracking systems or marketplaces. It was all about personal networks. And once you engaged on either side, the decision-making process felt completely bespoke, subjective, and inefficient. It was clear to me that investors were making decisions in a way that had to be mathematically wrong.
Ultimately, we wanted to create more transparency, reduce friction, and make it easier for high-quality founders to raise capital—something I wished had existed when I was raising funds as an entrepreneur.
Sam Silvershein: One of the things that sets Right Side Capital apart is your data-driven approach at the earliest stages. What types of data are you looking at when these companies are still in their infancy?
Dave Lambert: When we started in 2008 and 2009, our initial thesis was based on the idea that the entire startup world operates on a power-law distribution. At the time, the concept of power-law investing wasn’t well known.
We saw successful tech startups as “positive black swan” events—unexpectedly large outcomes that seemed unlikely in foresight but obvious in hindsight. Our initial research focused on understanding how much diversification was necessary to capture those positive outliers.
Through our data analysis, we realized that an investor would need well over 100 investments to begin smoothing out the risk curve and capturing consistent returns. We also recognized that portfolio diversification should occur annually to account for vintage risk. This insight was a breakthrough for us because it showed that most angel investors and VC firms weren’t adequately diversified. That realization led us to believe that many best practices in venture capital weren’t actually optimal for returns.
Sam Silvershein: So you’re still finding pre-VC companies that are generating revenue and bootstrapping before entering the venture world?
Dave Lambert: The biggest misconception about us is that “pre-VC” means we invest at an earlier stage than pre-seed funds. That’s not actually true. We invest in companies that are raising round sizes that are too small for professional venture capital—even at the pre-seed stage.
These are often companies that already have revenue, typically $5K–$30K in monthly recurring revenue (MRR), but they only need $200K–$500K in funding. That round size is too small for most pre-seed VC firms, so we step in.
Sam Silvershein: Do you have ownership mandates for your first check?
Dave Lambert: Unlike most VCs, we don’t focus on ownership percentage. The only thing that matters to us is our expected return multiple. Some VCs insist on owning a specific percentage of a company, but we’ve never understood why.
Our typical check sizes range from $200K–$400K, and valuations tend to be between $2M and $4M. But if a company has exceptional traction, we’re willing to make exceptions—investing $100K into a $5M valuation, for example. Ownership percentage is irrelevant to us; we just focus on return potential.
Sam Silvershein: And how does your analysis change as companies start to scale and raise future rounds?
Dave Lambert: When we started in 2012, most M&A activity in tech occurred below $100 million, and secondary markets weren’t robust. We initially optimized for capital-efficient paths forward and focused on sub-$100 million exits.
However, by the mid-2010s, private equity exploded, tech companies were being acquired at higher valuations, and there was more demand for stock than supply. This allowed us to participate in secondaries at Series C rounds or beyond, even at valuations in the hundreds of millions.
As for selling, if one of our positions grows disproportionately within a fund’s NAV, we look to sell part of it into the next round. In some cases, we manufacture liquidity ourselves through secondary transactions.
Sam Silvershein: You mentioned AI earlier. How do you see AI changing startup investing?
Dave Lambert: AI is doing something that hasn’t happened before in startup economics.
For decades, the cost to build a product has been going down. But until recently, the cost to scale a company—hiring, marketing, operations—hadn’t changed much.
What we’re seeing now is that AI is reducing operational costs, allowing startups to scale faster and more efficiently than ever.
Sam Silvershein: Are you seeing companies that could reach massive valuations with very small teams?
Dave Lambert: Yes. It wouldn’t surprise me if, between now and 2030, we see billion-dollar companies with fewer than 40 employees.
That would have been unheard of even a few years ago. But with AI, it’s now possible for startups to automate entire functions that used to require large teams. That changes the entire venture landscape.
Sam Silvershein: Does that mean companies will need less VC funding?
Dave Lambert: Potentially, yes. If AI allows startups to scale without hiring dozens or hundreds of employees, they won’t need to raise as much capital—or they might be able to bootstrap for much longer.
That would completely change how VCs approach ownership, deal structure, and follow-on investments.
Sam Silvershein: That’s fascinating. Have you built a team internally to help identify the right time to sell secondaries?
Dave Lambert: Yes, we have a structured process for this. If a company’s valuation grows too large relative to our fund’s NAV, we consider selling part of our position. If a company is profitable and not raising another round, we may manufacture liquidity through secondary sales. During the 2021 bubble, we actively sold positions at peak valuations to lock in gains.
Sam Silvershein: Smart move. A lot of investors held on for too long.
Dave Lambert: Yeah. We’ve been through multiple boom-and-bust cycles, so we knew the bubble wouldn’t last forever.
Sam Silvershein: You’ve seen multiple market cycles. Given your experience, how do you think today’s AI boom compares to past trends?
Dave Lambert: AI is fundamentally different. In the past, new technologies mainly reduced the cost of building a product. AI is reducing the cost of running and scaling a company.
From the 1980s to the early 2000s, it took millions of dollars to build software. By 2010, that number dropped to hundreds of thousands. Today, with AI, you can build an MVP in weeks with just one or two people.
Sam Silvershein: How does that affect investing at later stages?
Dave Lambert: If startups can grow without needing massive teams, we could see a major shift in how capital is deployed.
In the past decade, companies needed to raise big rounds for scaling. But if AI automates large portions of sales, marketing, and operations, founders might not need as much funding—or they might be able to bootstrap for much longer.
That would completely change how VCs approach ownership, deal structure, and follow-on investments.
Sam Silvershein: So, theoretically, a billion-dollar company with 30-40 employees could become the norm?
Dave Lambert: Exactly. It was rare before—companies like Instagram were anomalies. But now, this could become commonplace.
Sam Silvershein: Given these shifts, how does Right Side Capital plan to evolve?
Dave Lambert: Our core strategy won’t change—we’ll continue making ultra-diversified, data-driven investments.
However, we’re monitoring AI-driven companies closely. If the cost to scale keeps dropping, it might allow us to deploy even more capital across a larger volume of companies while maintaining strong returns.
Sam Silvershein: Last question: What advice do you wish you had received when you first entered the private markets?
Dave Lambert: Patience. Venture investing is a long game—it takes years to see results.
When we started, we knew our model made sense, but we still had to wait nearly a decade to fully prove it. Unlike startups, where you can pivot and iterate quickly, investing requires conviction and long-term thinking.
Also, if you’re getting into this business, make sure you love it—because if you don’t, the long time horizon will burn you out.
Sam Silvershein: That’s a great perspective. Dave, I really appreciate you joining the Driving Alpha podcast. Excited to see what’s next for Right Side Capital!
Dave Lambert: Thanks, Sam. Great conversation!