Six years ago, Yiğit Ihlamur, a former senior program manager at Google, observed that AI was surpassing human capabilities in certain areas — at least by his estimation. Equipped with this perspective, he looked into various sectors with the goal of tackling a problem that he could work on for the rest of his life.
“At an abstract level, I was intrigued by the idea of accelerating innovation, because innovation creates new products, services and experiences that were previously unimaginable,” Ihlamur told TechCrunch in an email interview. “I perceived delivering capital to innovation as a math problem and started coding and hacking my way in.”
Ihlamur decided to focus on the VC space, which he saw as behind in terms of leveraging automation and AI. With the help of several co-founders, he launched Vela Partners, a VC firm that he describes as “AI-powered” and “product-led.”
Vela is an early-stage VC with $25 million under management and 32 portfolio companies, including self-checkout startup Grabango and robotics firm Bear Robotics. Like all VCs, Vela determines — partly using predictive algorithms — new investment areas as it attempts to identify trends, source the right opportunities and suss out threats to its existing investments.
To train its predictive algorithms, Vela draws on websites and social networks for data, also leveraging paid datasets like Crunchbase.
“Vela provides market intelligence and insights of innovative ideas; hence technical decision makers can decide which tools to buy or build to grow their core businesses,” Ihlamur said. “Models must be informative and explanatory. Ultimately our approach marries AI with expert heuristics.”
Inevitably, of course, algorithms amplify the biases in the data on which they’re trained — and this can have major consequences in the VC realm. In an experiment in November 2020, Harvard Business Review (HBR) found that an investment recommendation algorithm tended to pick white entrepreneurs rather than entrepreneurs of color and preferred investing in startups with male founders. Experts uncovered similar issues with CB Insights’ Mosaic tool, which uses proxies for race, socioeconomic status, gender and disability to determine a person’s likelihood of success.
Ihlamur somewhat dodged questions around bias, acknowledging that it comes with the territory — but not necessarily offering a solution.
“A model can learn the biases of other VCs or biases of the past,” he said. “First, one needs to understand the underlying reason why these behaviors occurred in the venture market. Second, every problem is unique, and a generalized approach cannot work for everything.”
Bias issues aside, Bay Area–based Vela isn’t the first to develop algorithmic tools to inform its investment decisions. VC firms, including SignalFire, EQT Ventures and Nauta Capital, are using AI-powered platforms to flag potential top picks.
The differentiator for Vela, according to Ihlamur, is its “game-like” terminal built to assist entrepreneurs, limited partners and other VCs in using its services. Entrepreneurs can analyze tendencies in developer ecosystems like Amazon Web Services and GitHub, while whitelisted VCs can spot (with any luck) promising seed-stage startups and limited partners can ask questions about why Vela invested in a particular startup.
Vela’s GitHub repo, which includes its algorithmic models, is public — both for inspection and reuse.
“While some VCs may be experimenting with AI-based sourcing, we haven’t seen any VC taking a product-led approach,” Ihlamur said. “Anyone can go to Vela’s website and use our product. We’re building relationships with entrepreneurs and limited partners in a programmatic way — our ultimate goal is for AI and automation to touch and manage all aspects of our business.”
It’s an approach that’s worked well for Vela so far. The firm claims to be running at “break-even” level, leading or co-leading $500,000 to $1.5 million check sizes.
In the near term, Vela plans to invest mainly in AI, data and developer-focused startups. Ihlamur expressed enthusiasm for generative AI specifically, a market that could be worth $51.8 billion by 2028 — depending on which sources you believe.
“The pandemic had a positive impact on our business, as was the case for many other venture capital firms,” Ihlamur said. “OpenAI’s ChatGPT’s release provided further tailwinds for us as an AI-powered VC firm … With respect to the broader slowdown in tech, we’re not concerned as we’re break-even as a company and have capital to invest. Despite the slowdown, there are significant opportunities to seize partially thanks to the rapid progress in AI.”
