These three investors think founders need some TLC (Collective funds)

Venture capital is a networks business — from networks of founders to the web of investors and angels and gossamer threads of potential customers, talent, and service providers. The density of those networks determines success: find just the right person to fit a role or a slot on a cap table, and a startup might just get a bit more lift.

It’s a topic that Casey Caruso has thought a lot about. In a research project at Stanford, she looked at a slightly different form of network density: using convolution neural network (CNN) models to evaluate investment decisions, working with a group of three other authors to analyze how to optimize VC using algorithms. It’s a cross-over point she’s familiar with, building upon a technical background and an engineering role at Google while also part-time investing with Bessemer.

While at a dinner at San Francisco’s northern Italian restaurant SPQR in Lower Pac Heights, she talked about investing with friends Lauren Stephanian, now a principal at blockchain-focused Pantera Capital, and Terri Burns, a partner at GV. They realized that much like how all roads lead to Rome, all three were on paths heading for the same direction: using technology to improve venture decision-making. “We are all computer scientists by training,” Caruso said. “Because of that fundamental training, we all approach problems pretty pragmatically.”

The three began collaborating outside of their day jobs on how to integrate AI better into the earliest stages of venture, identifying features from models while also being open to the qualitative nature of the business. Then, they decided to more formally build a compact around investing in 2019, creating TLC Collective (their combined initials) as a base to invest from.

Investing using their own combined capital, TLC writes angel and pre-seed checks into companies built by technical founders. So far, the group has invested in 11 companies, including data discovery platform Select Star (which I profiled a couple of weeks ago), audio breakout app Clubhouse, biology data platform Watershed, remote work manager Friday, cryptocurrency risk compliance platform TRM and a variety of others.

While their investments span sectors, the thread holding them all together is the technical chops of the founders. “We invest in very technical teams because we are very technical and that is our first qualifier,” Caruso said. Stephanian meanwhile emphasized that while technical talent is a key benchmark, the trio can diverge on areas of focus. “Despite having a similar background, we all have different interests and skillsets,” she said. They noted that Burns focuses on consumer, Stephanian on fintech, enterprise and crypto, and Caruso on frontier tech.

So far, the group remains a “side gig” for the three, and they are continuing to iterate on their underlying algorithm. “We go back and forth between using the actual algorithm versus just using it as a framework or reference,” Caruso explained. “We are finding a balance between the art and the science by applying our programming background.”

The collective’s pace has been roughly an investment per quarter, a bandwidth that the group said they are likely to continue for the time being. They continue to invest their own capital, and they don’t feel pressure to expand into new models like rolling funds or crowdfunding — at least, not yet. “We haven’t even considered doing a rolling fund,” Caruso said, although noted that the group is part of On Deck Angels. Stephanian said that the competition today in that space is keen. “I have gotten so many messages from people who are raising their own syndicates,” she said.

The firm’s checks range from the tens of thousands to the hundreds of thousands of dollars per investment.

Like the networks powering their AI models and the networks they are building among their founders, TLC Collective has built a triangle of connections amidst its investors. As those connections expand out, the hope for the group is that they are able to expand the data data to optimize their models while also investing in the best technical founders growing new businesses.

Source: TechCrunch

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