Cognition reportedly explores funding at a $40 billion valuation

Cognition, the startup behind the AI coding agent Devin, is reportedly in discussions with investors about another funding round that could value the company at at least $40 billion. Bloomberg’s sources said the valuation would be based on Cognition achieving a $1 billion annualized revenue run rate.

The reported talks come only months after Cognition raised $1 billion at a $26 billion valuation in May. At the time, chief executive Scott Wu told TechCrunch that the company had reached a $492 million annualized revenue run rate.

Revenue growth is central to the reported valuation

The potential $40 billion valuation represents a substantial increase over Cognition’s May financing valuation. The reported benchmark is not merely the size of a new round: it is tied to reaching $1 billion in annualized revenue.

Wu also said in May that enterprise customers had increased their use of Devin by 50% month over month for the preceding six months. Cognition lists Mercedes-Benz, NASA and Goldman Sachs among its customers.

Devin is positioned for software maintenance work

Wu has said Devin is not sold as a replacement for human programmers. Instead, the agent is often assigned long-tail tasks that developers may find unappealing, including updating old software and moving applications from one platform to another.

That positioning places Devin in work that can accumulate across large software estates. Organizations modernizing legacy systems or planning platform migrations may have a defined use case for an AI coding agent alongside their existing engineering teams.

Cognition’s May financing, reported as Cognition’s $1 billion AI coding funding round, highlighted the scale of investor interest in AI-assisted software development as companies evaluate how agents can be applied to enterprise engineering work.

What enterprises should assess

The reported fundraising discussions do not establish a completed deal or a final valuation. They do, however, put attention on the relationship between adoption, annualized revenue and the operational work assigned to coding agents.

For businesses, the practical implication is to assess AI coding agents against specific maintenance and migration backlogs, with clear review processes for the code and system changes they produce.