Vince Hankus is a partner at Thrive Capital who joined the firm in 2019 and has worked on major investments including OpenAI, SpaceX, Databricks, and Stripe. He previously worked at Tiger Global before joining Thrive when it was a $1 billion fund with 25-26 employees.

The conversation explores Thrive's evolution from a $10 million fund founded by 26-year-old Josh Kushner in 2009 to today's $5 billion concentrated growth strategy. Hankus discusses the firm's philosophy of making large, contrarian bets on generational technology companies, with an ideal growth fund containing just 10 companies.

Key topics include Thrive's approach to building conviction through years-long relationship building, the competitive dynamics in AI investing, and emerging opportunities in life sciences and robotics. The discussion also covers specific case studies like the Carvana investment and Stripe's $2 billion round during market uncertainty.

Thrive's Evolution from Outsider to Platform

Josh Kushner started Thrive at age 26 in New York as an outsider to Silicon Valley, recruiting "misfit-y type people" who self-selected into an unobvious opportunity

Early bold bets defined the firm's DNA - Instagram received almost $20 million out of a $40 million fund, requiring additional capital raising to avoid a 50% position

The Instagram investment put Thrive "into the game" alongside Benchmark and Sequoia, establishing credibility when "no one knew Thrive Capital"

Being New York-based outsiders allowed independent conviction building without Valley echo chamber influence, exemplified by the contrarian GitHub investment

The Concentrated Growth Strategy Philosophy

Thrive's ideal growth fund contains just 10 companies with 8 investors making roughly 18 total investments annually - 2-3 per partner per year

"It's easier to catch a company that's established going to 100 billion or 200 billion than to pick the breakout company from a pack of a few thousand" - Vince on concentration strategy

75 companies reached $100+ billion value in the last decade versus only a few hundred in the $10 billion bucket, making late-stage picking more favorable odds

The strategy requires "almost dogmatic conviction" and years-long relationship building - Stripe took nearly 10 years from first investment to the $2 billion round

Building Conviction Through Deep Partnership

Investment conviction starts qualitative first, then quantitative confirmation: "We start with the people and what they're doing and the customers and the product"

Thrive spends 18+ months getting to know companies like Isomorphic, going beyond CEO meetings to understand engineering, product, and sales teams

"You really learn how clear the flow of information is in a company by meeting a lot of people on the team" - assessing organizational alignment and decision-making frameworks

The firm proactively creates investment opportunities rather than waiting for fundraising processes, approaching companies with outside-in analysis and conviction

The Carvana Contrarian Bet Case Study

Thrive invested in Carvana as a public company at $10-12 billion valuation, then doubled down when it fell 90%+ during the used car cycle downturn

"The risk-adjusted bet we were making on the data points we had were actually very good" - Vince on doubling the position at much lower prices

Conviction was built on Carvana's logistics infrastructure investment and unique talent development culture where employees stay their entire careers

The investment required firm-wide support during daily public markdowns, with LPs and team members paying a "tax" for the contrarian position

AI Investment Landscape and Value Distribution

"120% of the profit in AI is from NVIDIA" according to Stan Druckenmiller, meaning most AI companies are losing money while NVIDIA captures value

The coding AI stack flows from $20 Cursor subscriptions through frontier models to compute providers, ultimately benefiting NVIDIA as the biggest toll taker

Vertical AI adoption by lawyers and doctors is "ironic" as early adopters since they're not traditionally early technology adopters, but text-based work suits LLMs well

OpenAI faces competitive pressure as "every big tech company has a bazooka pointing at them" since none want a new big tech company to emerge

Life Sciences and Robotics as Massive AI Opportunities

Isomorphic Labs, founded by a Nobel Prize winner from Google, aims to "cure all disease" by simulating entire wet labs computationally for drug development

"It's one of the only companies in our portfolio that I think about as having the potential size as OpenAI" - Vince on Isomorphic's trillion-dollar market potential

Robotics could be "the biggest market" since consumer robots have higher utility than cars - "if everybody had one robot at $10,000 each, it makes a lot of sense"

The key question is whether robotics is in "2015 self-driving" phase or "on the precipice of cracking full-stack robots" with lower failure risk than autonomous vehicles