Can code generative AI succeed after Kite sinks? • Zoo House News

Kite, a startup developing an AI-powered coding assistant, was abruptly shut down last month. Despite securing tens of millions of dollars in VC backing, Kite struggled to pay the bills, founder Adam Smith revealed in a post-mortem blog post, and encountered technical headwinds that made it essentially impossible to come up with a suitable product to find for the market.

“We couldn’t achieve our vision of AI-powered programming because we came to market more than 10 years early, meaning the technology isn’t ready,” said Smith. “Our product hasn’t been monetized and it took too long to figure that out.”

Kite’s failure does not bode well for the many other companies pursuing generative AI for coding — and trying to commercialize it. Perhaps the best known example is Copilot, a code generation tool developed by GitHub and OpenAI that costs $10 per month. However, Smith notes that while Copilot shows promise, it still “has a long way to go” — and estimates that it could cost over $100 million to develop a “production-quality” tool that can reliably synthesize code can.

To get a sense of the challenges players face in the generative code space, Zoo House News spoke to startups developing AI systems for coding, including Tabnine and DeepCode, which Snyk acquired in 2020. Tabnine’s service predicts and suggests the next lines of code based on context and syntax, like Copilot. DeepCode works a little differently, using AI to notify developers of bugs as they code.

Tabnine CEO Dror Weiss spoke transparently about what he believes are the barriers standing in the way of mass adoption of code synthesis systems: the AI ​​itself, the user experience, and monetization.