When VC met AI
AI is coming for the venture capital industry: Meet Nuno Gonçalves Pedro, Founder, Managing Partner, Chamaeleon, one of the early-adopters already using it
As a product manager turned founder in venture capital, one of my top priorities has been to make sure that our firm - Chamaeleon - leads the way among AI-driven early stage investing, but my journey started 14 years ago with Strive Capital, which I co-founded and was the first quant-driven VC firm in the Bay area, probably in the world.
While OpenAI sparked all the recent hype for artificial intelligence, this technology is far from new - we have just started reaping its benefits. We were fortunate to have seen it coming and invested years of hard work, which culminated in the launch of Mantis, our in-house platform at Chamaeleon.
Mantis leverages artificial intelligence, multi-factor analysis and our own technology stack to save time and maximize fund returns, by automatically finding the best companies in each industry
Mantis leverages artificial intelligence, multi-factor analysis and our own technology stack to save time and maximize fund returns, by automatically finding the best companies in each industry, as well as augmenting us across all the core processes of Venture Capital: deal sourcing, due diligence, portfolio management, liquidation and fundraising.
For example, Mantis automatically scans and scores companies on their fit with our thesis, using essentially zero manual labor, at the outset.
This allows our deal team to assess an amount of deals that is 28-56 times higher than what other tier-1 venture capital firms see in a year!
We also have due diligence and portfolio management engines, a robust automation infrastructure and portals for all our stakeholders. It is a comprehensive technology stack never before seen in the venture capital industry.
Our portfolio picks initially sourced by Mantis are already beating industry benchmarks, dramatically.
Does this mean artificial intelligence will replace human investors?
No. Mantis is a core augmentation layer, a co-pilot, not an autopilot. Humans are at the center of every investment and need to be involved throughout the process, especially in areas where AI and multi-factor analysis can’t help us much, like assessing critical psychological and emotional traits in founders.
It allows us to minimize time spent on sub-par deals and it greatly improves our deal flow, both in quantity, but also in quality. Just to name one example, it allowed us to get in touch with David Watkins and invest in Dirty Labs, who are now category leaders in home cleaning products.
In an increasingly data-rich environment, proactive investors will use public and select private data to analyze a deal, downgrading the pitch deck to one source of company metrics, among several.
Picture two early stage funds deploying capital in the same geographies. Fund #1 is investing in startups using the old school process that its associates, principals and managing partners love and grew to excel at.
Fund #2 invests in the same arena, but its team is augmented by an AI powered engine, like Mantis.
Both teams work exceptionally hard to maintain their reputation. Thanks to AI/ML, Fund #2 has a clear competitive edge: while it’s three in the morning and both teams are sleeping, the AI co-pilot is adding new start-ups to Fund #2’s database, scoring those start-ups, organizing meetings and informing the due diligence engine. The compounding effects are unstoppable.
This disruption should come as no surprise to industry veterans. The way startup founders and investors exchange data is incredibly outdated. Pitch decks are bold sales presentations, not unbiased sources of truth. Data rooms have cherry-picked traction data or occasionally downright erroneous information, designed to bait naive investors.
In an increasingly data-rich environment, proactive investors will use public and select private data to analyze a deal, downgrading the pitch deck to one source of company metrics, among several. When that data analysis is performed automatically, the gap between traditional funds and AI-driven ones will grow even wider.
When word gets around that firms like Chamaeleon have built their own (unfair) advantage, the entire industry will be forced to adapt or risk getting left behind. It’s a matter of time until most venture capital funds become AI-driven funds. Innovators will get there by building in-house tech, while others with less resources or capabilities may need to buy those capabilities from third parties or, otherwise, partner.
