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Before we get into the column, please be sure to read my scoop with Cory about AI chip developer Groq’s latest fundraising talks, which includes details on revenue projections and chip sales.
When former Bain Capital Ventures partner Sarah Smith raised a $16 million fund to back early-stage startups earlier this year, she decided to use artificial intelligence to answer a question that venture capitalists say usually comes down to human instinct: Which founders are worth meeting?
The Menlo Park, Calif.–based investor developed a screening system using Anthropic’s Claude, which she says she trusts with sensitive information over other models. The system helps her decide whether she should take a first meeting with a founding team, as well as analyze those she’s invested in.
“I hear a lot of [general partners] brag about only investing in 1% of the companies [they] meet,” said Smith, who previously worked as an executive in human resources at Quora and operations at Meta Platforms.
“In my mind, that’s so much wasted time on your part,” she said. “I would rather be investing in one to two of every 20 or 25 companies I meet,” versus one of every 100 companies.
Her efforts show the expanding ways venture capitalists are experimenting with AI. As I wrote last month, startups like Juniper Square are building agents—or AI that can work without humans to continuously direct it—to help venture capitalists manage their funds and deal flow. And Smith wrote her own software, which saved money.
Her screening system analyzes a founder’s pitch deck, cold e-mail and LinkedIn profile and then spits out a number between 1 and 100. If a startup scores between 85 and 100, they rank as “exceptional,” and the tool will highly recommend taking a meeting with that founder.
She bases 40 of the 100 points on the team and their track record in the sector they're pursuing. Smith weighs the importance of co-founders so highly that a solo founder likely would not get an introductory meeting with her. The system subtracts 5 points for an unrealistic valuation, 10 points for lacking a technical co-founder, and subtracts more points if they’re in certain sectors, like consumer companies, that she finds less attractive as investment opportunities.
Her 100-point system is based on her personal experience as an investor and operator, as well as on research, such as a Stanford University study on startups worth more than $1 billion.
The screening tool has also helped identify founders outside her network of professional connections who have reached out to her. She invested in her first company after it made such a cold pitch recently. The startup, Anyreach, scored high on her tool.
So far, Smith’s self-named fund has backed 20 startups and is looking to make a total of 50 investments, writing an average check of $250,000 in each company. She has reserved no cash for follow-on funding, a move she says helps increase her chances of getting an early check into a new company. “Every dollar I would put into reserves is an opportunity cost of another shot on goal,” she said.
She also used AI to pull together metrics such as revenue or monthly active users across her various portfolio companies, rather than typing numbers into a spreadsheet. And during her fundraising process, she used AI to respond to some questions from limited partners and fed Claude her deck so it would have the answers.
“I’ve built a ton of my firm on AI, and I intend to be a solo GP forever,” she said. She says she “will probably never have full-time staff outside of” her virtual executive assistant.
AI doesn’t solve the biggest pain of venture capital, of course, which is spotting founders before anyone else does. Smith teaches a class about entrepreneurship at Stanford, which has helped her stay close to students who could launch startups.
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