Cognition Seeks $2 Billion Valuation for AI Code-Writing Tool

Cognition Labs is reportedly aiming to become the next multibillion-dollar artificial intelligence (AI) startup.

The company, which is developing an AI tool for writing code, is in discussions with investors to raise money at a valuation of up to $2 billion, The Wall Street Journal (WSJ) reported Sunday (March 31).

The report said that, if successful, the round would increase Cognition’s valuation to almost six times what it was weeks ago. Sources tell the news outlet that Silicon Valley venture firms including Founders Fund, already a shareholder, are in talks to invest in the round.

According to WSJ, Cognition began as a cryptocurrency company but pivoted to AI amid the technology’s rising popularity. Earlier this month, it debuted its AI coding tool, Devin, which can reportedly autonomously complete complex coding tasks like creating custom websites. 

The WSJ added that some investors believe Devin marks a significant leap in AI intelligence and could lead to the widespread automation of software development. 

The round is happening as a number of other AI companies are seeking significant jumps in valuation. For example, this month saw reports that the Canadian AI startup Cohere was in advanced discussions to raise $500 million, bringing its valuation to $5 billion. The company was valued at $2.2 billion last June after raising $220 million.

And French AI model developer Mistral was valued at $2 billion late last year, jumping roughly seven times in valuation from the previous year.

At the same time, tech giants aren’t “sitting idly by,” as PYMNTS wrote last week, with companies like Google, Microsoft and Meta aggressively constructing their own AI large language models (LLMs).

Although leading tech companies have an edge, the race for supremacy in AI involves more than just the sector’s biggest players, that report said, though advancing the frontiers of AI often requires substantial investments in computational power and research talent.

“The hurdle for building a broad foundational model is that training on increasingly large data sets is extraordinarily expensive,” Gil Luria, a senior software analyst at D.A. Davidson & Co., said in an interview with PYMNTS. 

“The only reason OpenAI can afford to do so is the backing of Microsoft and the Azure resources it makes available to OpenAI. The broad models, such as the ones leveraged by ChatGPT, have ingested huge portions of human knowledge and continue to train on new content, which is what makes them so versatile in many domains of expertise.”