GoKawiilInvestors are aggressively courting AI researchers to build startups that can make AI more reliable and efficient.
Yu Su, an Ohio State professor leading an AI agent lab, said he initially resisted the pressure from VCs to commercialize his work. He finally took the leap last year and spun out his work into a startup when he saw that foundational model advances could make agents truly personalized.
NeoCognition, a startup Su describes as a research lab developing self-learning AI agents, has just emerged from stealth with $40 million in seed funding. The round was co-led by Cambium Capital and Walden Catalyst Ventures, with participation from Vista Equity Partners and angels, including Intel CEO Lip-Bu Tan and Databricks co-founder Ion Stoica.
“Today’s agents are generalists,” Su (pictured right) told TechCrunch. “Every time you ask them to do a task, you take a leap of faith.”
According to Su, the issue lies in a lack of consistency. Current agents, whether from Claude Code, OpenClaw or Perplexity’s computer tools, successfully complete tasks as intended only about 50% of the time, he said.
Since agents are still so unreliable, they are not ready to be trusted, independent workers, Su told TechCrunch. NeoCognition intends to change that by developing an agent system that can self-learn to become an expert in any domain, similar to how humans learn.
Su argues that while human intelligence is broad, its real power is our ability to specialize. When we enter a new environment or profession, we can rapidly master its unique rules, relationships, and consequences.
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