Former Google senior program manager Yiğit Ihlamur noted six years ago that AI was outperforming humans in several areas. With this mindset, he explored numerous areas to find a lifelong problem.

Ihlamur told TechCrunch that accelerated innovation produces new goods, services, and experiences that were previously impossible. “I saw funding innovation as a math problem and started coding and hacking.”

Ihlamur focused on the VC industry, which he perceived as lagging in AI and automation. He founded Vela Partners, an AI-powered, product-led VC firm, with numerous co-founders.

Vela manages $25 million and 32 portfolio businesses, including Grabango and Bear Robotics. Vela, like other VCs, uses predictive analytics to find trends, opportunities, and dangers to its investments.

Vela uses Crunchbase and websites and social networks to train its prediction algorithms.

“Vela provides market intelligence and insights of innovative ideas, so technical decision makers can decide which tools to buy or build to grow their core businesses,” Ihlamur added. “Models must explain. We combine AI with expert heuristics.”

Algorithms exacerbate biases in the data they’re trained on, which may have big VC implications. Harvard Business Review (HBR) concluded in a November 2020 experiment that an investment recommendation algorithm favoured white entrepreneurs over entrepreneurs of color and firms with male founders.

Experts found similar concerns with CB Insights’ Mosaic tool, which combines race, socioeconomic status, gender, and handicap to predict performance.

Ihlamur acknowledged prejudice but did not propose a remedy.

“A model can learn other VCs or past biases,” he noted. First, one must understand why these venture market behaviors developed. Second, every situation is unique, thus a generic strategy cannot work.”

Vela’s automated investment tools aren’t the first. SignalFire, EQT Ventures, and Nauta Capital use AI to identify excellent selections.

Ihlamur says Vela’s “game-like” interface for entrepreneurs, limited partners, and other VCs sets it apart. Entrepreneurs may evaluate developer ecosystems like Amazon Web Services and GitHub, whitelisted VCs can hopefully find interesting seed-stage firms, and limited partners can ask Vela why they invested in a startup.

Vela’s algorithmic models may be viewed and reused on GitHub.

Ihlamur added that while some VCs may be experimenting with AI-based sourcing, none have taken a product-led strategy. “Anyone can use Vela’s product. We’re programmatically creating relationships with entrepreneurs and limited partners—our objective is for AI and automation to touch and handle all elements of our business.”

Vela has succeeded with it. Leading or co-leading $500,000 to $1.5 million checks, the business claims “break-even” status.

Vela will focus on AI, data, and developer businesses in the near future. Ihlamur liked generative AI, which might be valued $51.8 billion by 2028, according to some sources.

Ihlamur claimed the epidemic helped his business and many others. As an AI-powered VC business, OpenAI’s ChatGPT release boosted us. We’re break-even and have capital, so the tech downturn doesn’t worry us. Despite the delay, AI’s rapid advancement has created several opportunities.