When long-term venture investor Matt Krna started Two Meter Capital this year, he hired only a skeleton team to manage a broad portfolio of more than 190 companies. The firm uses generative AI to do much of the day-to-day portfolio management, tracking how companies are performing and when they might raise another round. Without AI, Krna estimates he would need to hire half a dozen analysts.
“In terms of staffing efficiency, if you were to hire an analyst class of six, now it’s three,” Krna said. “I think a lot of venture funds are going to start using AI as a tool.”
Silicon Valley has been fascinated by AI for the past couple of years, with VCs racing to fund all sorts of AI startups. Now, AI is changing VC itself, making what was already a difficult field for new entrants to break into much more difficult and impacting how early-stage startups are funded.
Firms like Correlation Ventures, 645 Ventures and Fly Ventures have long used data and AI to help guide investment decisions. SignalFire, a San Francisco firm that has been a leader in the use of data, developed an expensive platform years ago that tracks more than 10 million data sources. But rapid advances this year are making the use of AI more widespread, multiple VCs told BI.
Earlier this year, Sri Chandrasekar, a partner at Point72 Ventures, noticed that a startup in his portfolio was going one week.
“Because we’re insiders, we saw, ‘Wow, the company is doing really well,'” he recalls.
He figured he’d have the data to himself, given Point72 he has access to the company’s weekly internal metrics. But he was surprised to discover a flurry of interest from other firms that he thinks can only be explained by competitors using AI models to comb through publicly available data from thousands of companies.
“This is not an accident,” Chandrasekar said. “There are signals that, if you know how to mine them online, would identify if a company was having a particularly good week, and miraculously seven funds came to them that week.”
Chandrasekar declined to name the startup or the exact metrics that were so favorable, but says they could include things like product usage or the hiring frenzy on the sales team. Firms that don’t use AI to get deals will be left behind, Chandrasekar predicts. It will be up to the general partners whether they want to use the technology to eliminate or enhance new investment roles.
“Some of them will want better coverage with the same number of people and some will just need fewer people,” Chandrasekar said. “I’d rather have better coverage so I can see more great deals.”
Bain Capital Ventures, the venture division of Bain Capital with $160 billion under management, recently built a machine learning model that helps identify inflection point companies that the firm’s partners should take a closer look at.
Christina Melas-Kyriazi, a partner at BCV who focuses on fintech and application software, says the model helped her identify a hot startup that wasn’t on her radar because it wasn’t located in a tech hub.
“We have noticed that this company has had tremendous growth and not only that, but tremendous commitment from their users,” said Melas-Kyriazi. “We immediately flew out to meet the founder and ended up making the investment. We wouldn’t have known about this company otherwise if it wasn’t for that model.”
Back offices can be reduced by up to 50%
Whatever happens to the investment teams, Chandrasekar expects the size of the back offices — which handle tasks like human resources, administration and financial reporting — to shrink by more than 50%.
“If you go to any venture firm’s website, you’ll find that half the names have nothing to do with investing,” Chandrasekar said. “I think every company’s back office is going to be significantly impacted by AI.”
Andreeseen Horowitz now employs over 500 people with its investment team growing 170% from 2017-2021. General Catalyst employs 259 people while Lightspeed has 300, according to LinkedIn data.
“If you look at the big firms, they got very, very big in the last few years,” said Andy McLoughlin, managing partner at Uncork Capital.
James Currier, general partner at NFX, wrote a much-discussed column last year explaining why he thinks the use of AI will level the playing field for venture investors in the next decade, just as stock and bond software did in The last 40 years. .
“Let’s be honest: Much of what a typical venture capitalist does—reading, summarizing, and sorting—is what the big language models already do extremely well,” Currier wrote. “We are in the last 10 years of venture capital as we know it. AI will rebuild the startup industrial complex, from its core. Venture firms will have to reinvent themselves as a combination of humans and AI.”
Still a man to man business
In general, it’s not like most VC firms are full of staff. The average firm employs just 14 people, according to Deloitte.
“They’re already so small,” said a partner at a major firm who asked not to be identified, speaking in a large conference room in their sleek and mostly empty San Francisco office. “Could you have less people? To what end? You’re making so much money as it is.”
And critical to making that money is relying on rich management fees for the mystique of venture investing, which most VCs insist is mostly about gut feeling, intuition and personal connections that AI can’t. never replace.
“It’s still a human business,” McLoughlin said. “People want to work with people.”
Uncork employs just one associate, and McLoughlin says it won’t be replacing him with AI anytime soon.
“I’m sure there are a number of AI tools that she’s using, but her value is in being very, very smart and extremely well trained,” he said.
Humans are especially hard to replace in the earliest stages, where it’s often the core of an idea with little data for AI to harvest.
“I don’t think AI is very good at analyzing things we haven’t seen before, and the best results in entrepreneurship often break the pattern,” Melas-Kyriazi said.
At its heart, entrepreneurship isn’t about finding the best deals, it’s about winning them. This comes down to a personal connection between the founder and the investor, according to Chandrasekar.
“The number one reason a founder chooses you is that they want to work with you specifically,” he said. “You spend time building a relationship with them, have dinner with them, meet their family — whatever you need to do to make them feel like they really want to work with you for what you hope is a ten-year marriage . .”
Machines are far from that.
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