Larry Aschebrook, Founder & MP @GSquared: How We Lost Money on Uber and Made Millions on Lyft
Larry Aschebrook (guest), Harry Stebbings (host), Narrator, Harry Stebbings (host)
In this episode of The Twenty Minute VC, featuring Larry Aschebrook and Harry Stebbings, Larry Aschebrook, Founder & MP @GSquared: How We Lost Money on Uber and Made Millions on Lyft explores from Trailer Park To Tech Titan: G Squared’s Wild Ride Larry Aschebrook, founder of G Squared, recounts his unconventional path into venture and growth investing, starting from buying secondary shares in elite tech companies with his last savings and no formal VC background.
From Trailer Park To Tech Titan: G Squared’s Wild Ride
Larry Aschebrook, founder of G Squared, recounts his unconventional path into venture and growth investing, starting from buying secondary shares in elite tech companies with his last savings and no formal VC background.
He explains how a concentrated, secondary‑heavy strategy in names like Alibaba, Spotify, Coursera, Lyft, and others generated enormous DPI for LPs, but also how overconfidence and 2020–21 exuberance led to painful mistakes in Uber, 23andMe, Getir and an overextended 2020 vintage.
Larry dives into the mechanics of direct secondaries, structured primaries, heavy co‑investment, and why his firm optimizes relentlessly for cash returns and velocity of capital rather than paper multiples.
Throughout, he reflects candidly on firm building, ego, co‑invest pitfalls, AI positioning, and how his upbringing in deep poverty still shapes his risk appetite, paranoia, and desire to build an enduring investing franchise.
Key Takeaways
A concentrated, secondary-led approach can massively outperform—if you truly earn your information edge.
G Squared built very large positions in a small number of winners (Alibaba, Spotify, Coursera, Toast, Lyft, Revolut) by starting with small secondary checks, using them as a “Trojan horse” to get primary‑level data and founder access, and then scaling positions aggressively where the numbers supported it.
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DPI and velocity of capital matter more than paper multiples in illiquid venture portfolios.
Larry is explicit that his firm is hired to return cash, not show high TVPI/MOIC; they optimize for 2–2. ...
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Guardrails and process discipline are critical to surviving euphoric markets.
In 2020–21, G Squared relaxed its quantitative discipline, leaned too heavily on qualitative signals and ‘who’s in the round,’ diversified decision‑making away from its core PMs, and overpaid for growth, leading to a challenged 2020 vintage that had to be repaired through structured deals and painful triage.
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Co-investment can be a powerful scaler—but only if tightly tethered to core conviction positions.
Early on, G Squared used large co‑invest pools (sometimes 4x fund size) to be relevant in mega deals, but the 2020–21 experience showed that bespoke, one‑off co‑invests around LP preferences create misalignment and recrimination; now they limit co‑invest to their own top positions and equal‑weight portfolios.
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Listening to “spider sense” and walking away early can be as valuable as a great investment.
Larry’s near‑investment in Theranos, which he pulled back from at personal cost after bad gut feeling and his wife’s scientific skepticism, likely averted catastrophic financial and reputational damage—sharpening G Squared’s diligence checklists and conviction to walk away even late in a process.
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Overconfidence and refusal to walk away can turn one mistake into a half‑billion‑dollar problem.
With Getir, the initial Gorillas deal was process‑sound, but Larry’s decision to double down with another $100M to ‘fight and fix’ a deteriorating situation instead of accepting losses likely cost the firm and its LPs around $500M in capital and future commitments.
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In AI, it can be rational to focus on the few foundation winners and infrastructure, not spray the application layer.
G Squared is concentrating capital in OpenAI, Anthropic, Databricks, Wiz and infrastructure/picks‑and‑shovels like Lambda and Scale, arguing that new LLM entrants face prohibitive time and capital barriers, and that AI is now embedded across verticals rather than a standalone “sector bet.”
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Notable Quotes
“We made a ton of money on Lyft and we lost money on Uber.”
— Larry Aschebrook
“I went back to our LPs and said, 'Listen. We fucked up. We need to pivot. We need another 300 million bucks because I need to protect this thing.'”
— Larry Aschebrook
“The money’s there, you close. Start deploying it. Build a portfolio. Show some improvement in NAV. It is inertia that makes it easier.”
— Larry Aschebrook
“I think the main mistake we made was believing our own bullshit.”
— Larry Aschebrook
“TVPI and MOIC are not the stats that people should be focused on… You can’t buy food with MOIC.”
— Larry Aschebrook
Questions Answered in This Episode
How should LPs recalibrate their diligence and metrics if they want to prioritize DPI and liquidity over headline MOIC and TVPI?
Larry Aschebrook, founder of G Squared, recounts his unconventional path into venture and growth investing, starting from buying secondary shares in elite tech companies with his last savings and no formal VC background.
Get the full analysis with uListen AI
What specific quantitative guardrails would Larry now hard‑code into an investment committee to prevent another 2020‑style vintage?
He explains how a concentrated, secondary‑heavy strategy in names like Alibaba, Spotify, Coursera, Lyft, and others generated enormous DPI for LPs, but also how overconfidence and 2020–21 exuberance led to painful mistakes in Uber, 23andMe, Getir and an overextended 2020 vintage.
Get the full analysis with uListen AI
How does a manager decide when to walk away from a troubled but large position like Getir instead of ‘fighting’ with more capital and time?
Larry dives into the mechanics of direct secondaries, structured primaries, heavy co‑investment, and why his firm optimizes relentlessly for cash returns and velocity of capital rather than paper multiples.
Get the full analysis with uListen AI
Given the missed upside in names like Palantir, might G Squared ever introduce a separate, longer‑duration sleeve for exceptional winners while keeping the core DPI‑oriented model?
Throughout, he reflects candidly on firm building, ego, co‑invest pitfalls, AI positioning, and how his upbringing in deep poverty still shapes his risk appetite, paranoia, and desire to build an enduring investing franchise.
Get the full analysis with uListen AI
In an AI‑dominated landscape, what risks does Larry see for large, non‑AI‑native private companies (the ‘vampires and zombies’), and how should growth investors price those risks today?
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Transcript Preview
(upbeat music) I mean, we made ridiculous sums of money for LPs in that period. We were the s- largest shareholder of Coursera, $800 million back to LPs on Coursera. We made a ton of money on Lyft and we lost money on Uber.
What?
40% of my third fund went into Spotify. I went back to our LPs and said, "Listen. We fucked up. We need to pivot. We need another 300 million bucks because I need to protect this thing." The next three years were some of the worst of my life.
Ready to go? Larry, dude, we walked around the park and I heard your incredible story. And to be fully transparent, I didn't know the incredible story before, which is why at the end I was like, "Dude, we have to do a show together." So thank you so much for doing this with me.
Well, thank... That's, that's humbling, you know. It's, it's sometimes difficult to open up and tell the story. But, uh, I enjoyed our walk.
Listen, it's the short shorts and the great lakes that make you feel comfortable to open up. I completely understand. But I wanna start on the entry. Dialing for dollars is kind of how I was thinking about this. How did you start your way into venture and what was that entry point?
Yeah. For me, it was, it wasn't really, "Hey, I wanna be a venture capitalist and manage, you know, billions of dollars." It... For me, it was, you know, I come from nothing. Um, I was a fundraiser for academic institutions for their endowments. I was good at that. And everybody I raised money from, most of people, not everyone, most people made it investing in private companies, it would be at PE funds or venture funds, or running their own operating businesses, it didn't matter what it was, from, you know, Windows to financial management to, to PE, they created real wealth for their families. And I was like, "They're not that much different than me. I work hard. I'm smart enough. Um, maybe I can do it." So I went back to business school late in life and our business today was my thesis, and I started buying... You know, the smartphone became something that was running our lives in 2010. And I just started saying, "This is super interesting. Why don't I buy shares?" And, oh, yeah, I follow Twitter. I like Twitter. Uh, you know? What about, um, early Uber? What about early Spotify? And I just started buying shares from my classmates-
Okay.
... with my own money.
That, that sounds great, but these companies are not public at the time.
No.
And so, so how does one do that then? How did you approach it?
Well, I guess it's not knowing what you don't know is... And ignorance is bliss is kind of the same thing. I didn't realize that it was something you shouldn't or couldn't do. I just asked people. "How about w- You have shares. Can I buy some of your shares?" "Well, I never really thought about selling them 'cause I really can't sell them." "How would you buy them?" And still to this day, 15 years later, there's a form that I created at the Carey School of Business at Arizona State, one-page form that then we sent to the company to buy stock. And it still floats around. I got, I got it back from a broker not that long ago and it's, like, literally the form I created in 2010.
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