HomeNeoCognition secures $40M seed to develop human-like learning agents
Investors are actively seeking out AI researchers to launch startups focused on making AI more reliable and efficient.
Yu Su, an Ohio State professor who leads an AI agent lab, said he initially resisted pressure from venture capitalists to commercialize his research. He finally took the leap last year and spun out his work into a startup after realizing that advances in foundational models could enable truly personalized agents.
NeoCognition, a startup that Su describes as a research lab developing self-learning AI agents, has just emerged from stealth mode 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 angel investors, including Intel CEO Lip-Bu Tan and Databricks co-founder Ion Stoica.
“Today’s agents are generalists,” Su (pictured left) told TechCrunch. “Every time you ask them to perform a task, you’re taking a leap of faith.”
According to Su, the core issue is a lack of consistency. He said that current agents—whether from Claude Code, OpenClaw, or Perplexity’s computer tools—successfully complete tasks as intended only about 50% of the time.
Because agents are still so unreliable, they aren’t ready to be trusted as independent workers, Su told TechCrunch. NeoCognition aims 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 true power lies in our ability to specialize. When we enter a new environment or profession, we can quickly master its unique rules, relationships, and consequences.
NeoCognition is building agents to mirror this exact process.
“For humans, our continuous learning process is essentially the process of building a world model for any profession or environment,” Su said. “We believe that for agents to become experts, they need to learn autonomously to build a model of any given micro-world.”
Su sees this capacity for rapid specialization as the critical missing piece to making AI work reliably on its own.
While it is possible to train agents for autonomous tasks, they must be custom-engineered for a specific vertical. NeoCognition is different because it builds agents that are generalists, capable of self-learning and specializing in any domain.
NeoCognition plans to sell its agent systems to enterprises, including established SaaS companies, which can use them to build agent-workers or enhance their existing product offerings.
Su highlighted that the investment from Vista Equity Partners is especially valuable for this reason. As one of the largest private equity firms in the software space, Vista can give NeoCognition direct access to a vast portfolio of companies looking to modernize their products with AI.
NeoCognition currently employs about 15 people, most of whom hold PhDs.
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Investors are actively seeking out AI researchers to launch startups focused on making AI more reliable and efficient.
Yu Su, an Ohio State professor who leads an AI agent lab, said he initially resisted pressure from venture capitalists to commercialize his research. He finally took the leap last year and spun out his work into a startup after realizing that advances in foundational models could enable truly personalized agents.
NeoCognition, a startup that Su describes as a research lab developing self-learning AI agents, has just emerged from stealth mode 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 angel investors, including Intel CEO Lip-Bu Tan and Databricks co-founder Ion Stoica.
“Today’s agents are generalists,” Su (pictured left) told TechCrunch. “Every time you ask them to perform a task, you’re taking a leap of faith.”
According to Su, the core issue is a lack of consistency. He said that current agents—whether from Claude Code, OpenClaw, or Perplexity’s computer tools—successfully complete tasks as intended only about 50% of the time.
Because agents are still so unreliable, they aren’t ready to be trusted as independent workers, Su told TechCrunch. NeoCognition aims 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 true power lies in our ability to specialize. When we enter a new environment or profession, we can quickly master its unique rules, relationships, and consequences.
NeoCognition is building agents to mirror this exact process.
“For humans, our continuous learning process is essentially the process of building a world model for any profession or environment,” Su said. “We believe that for agents to become experts, they need to learn autonomously to build a model of any given micro-world.”
Su sees this capacity for rapid specialization as the critical missing piece to making AI work reliably on its own.
While it is possible to train agents for autonomous tasks, they must be custom-engineered for a specific vertical. NeoCognition is different because it builds agents that are generalists, capable of self-learning and specializing in any domain.
NeoCognition plans to sell its agent systems to enterprises, including established SaaS companies, which can use them to build agent-workers or enhance their existing product offerings.
Su highlighted that the investment from Vista Equity Partners is especially valuable for this reason. As one of the largest private equity firms in the software space, Vista can give NeoCognition direct access to a vast portfolio of companies looking to modernize their products with AI.
NeoCognition currently employs about 15 people, most of whom hold PhDs.
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