Kite, a startup development of an AI-powered coding assistant, abruptly halted last month. Despite securing tens of millions of dollars in VC backing, Kite struggled to pay the bills, says founder Adam Smith revealed in a post-mortem blog post, ran into technical headwinds that made finding a product-market fit essentially impossible.
“We failed convey our vision of AI-assisted programming because we were more than 10 years early to bring to market, that is, the technology is not ready yet,” Smith said. “Our product was not making any money, and it took too long to to figure that out.”
Kite’s failure doesn’t bode well for the many other companies that are pursuing — and trying to commercialize — generative AI for coding. Perhaps the most famous example is Copilot, a code generation tool developed by GitHub and OpenAI priced at $10 per month. But Smith notes that while Copilot promises a lot, it still has “a long way to go” — he estimates it could cost more than $100 million to build a “production-grade” tool capable of reliably synthesize code.
To get a sense of the challenges facing players in the generative code space, businessroundups.org spoke to startups developing AI systems for coding, including Tabnine and Deep Code, which Snyk acquired in 2020. Tabnine’s service predicts and suggests next lines of code based on context and syntax, like Copilot. DeepCode works a bit differently, using AI to notify developers of bugs as they code.
Tabnine CEO Dror Weiss has been transparent about what he sees as the barriers to mass adoption of code-synthesizing systems: the AI itself, user experience, and monetization.
