Six years ago, Yiğit Ihlamur, a former senior program manager at Google, noted that AI was surpassing human capabilities in certain areas, at least by his estimation. Equipped with this perspective, he delved into various industries with the goal of tackling a problem he could spend the rest of his life working on.
“On 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 saw providing capital for innovation as a math problem and started coding and hacking my way in.”
Ihlamur decided to focus on the VC space, which he said was lagging behind in leveraging automation and AI. With the help of several co-founders, he launched Vela partnersa VC firm he describes as “AI-powered” and “product-driven”.
Vela is an early-stage VC with $25 million under management and 32 portfolio companies, including self-checkout startup Grabango and robotics company Bear Robotics. Like all VCs, Vela—partly using predictive algorithms—determines new areas of investment as it seeks to identify trends, find the right opportunities, and resolve threats to its existing investments.
To train its predictive algorithms, Vela pulls from websites and social networks for data, also using paid datasets like Crunchbase.
“Vela provides market intelligence and insights into innovative ideas; hence, technical decision-makers can decide which tools to buy or build to grow their core business,” said Ihlamur. “Models should be informative and explanatory. Ultimately, our approach combines AI with expert heuristics.”
Of course, it’s inevitable that algorithms reinforce biases in the data they’ve been trained on – and this could have major implications in the VC world. In a experiment in November 2020, Harvard Business Review (HBR) found that an investment recommendation algorithm tended to choose white entrepreneurs over entrepreneurs of color and preferred to invest 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 about bias, acknowledging that it comes with the territory — but didn’t necessarily offer a solution.
“A model can learn other VCs’ biases or biases from the past,” he said. “First, one must understand the underlying reason why this behavior occurred in the venture market. Second, every problem is unique and a one-size-fits-all 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 highlight potential top picks.
The differentiator for Vela, according to Ihlamur, is the “game-like” terminal built to help entrepreneurs, limited partners and other VCs use its services. Entrepreneurs can analyze trends in developer ecosystems like Amazon Web Services and GitHub, while whitelisted VCs (with a bit of luck) can spot promising early-stage startups and ask limited partners questions about why Vela invested in a particular startup.
Vela’s GitHub repository, which contains its algorithmic models, is public – both for inspection and reuse.
“While some VCs are experimenting with AI-based sourcing, we have yet to see a VC take a product-driven approach,” said Ihlamur. “Anyone can go to Vela’s website and use our product. We build relationships with entrepreneurs and limited partners in a programmatic way – our ultimate goal is for AI and automation to touch and control all aspects of our business.
It’s an approach that has worked well for Vela so far. The company claims to be operating at a break-even level and leads or co-lead $500,000 to $1.5 million in check sizes.
In the near term, Vela plans to invest primarily in AI, data, and developer-focused startups. Ihlamur was particularly excited about generative AI, a market that could be worth $51.8 billion by 2028 — dependent what sources you believe.
“The pandemic had a positive impact on our business, as it has for many other venture capital firms,” said Ihlamur. “The release of OpenAI’s ChatGPT provided further tailwinds for us as an AI-powered VC firm… With regard to the wider slowdown in technology, we are not concerned as we are breakeven as a company and capital have to invest. Despite the delay, there are significant opportunities to seize, thanks in part to the rapid advancements in AI.”
