GoKawiilHow much control are you willing to give an LLM over your digital life?
Getting the most value from a model means giving it the keys. For a control freak or the AI hesitant, it seems like a lot. For Andrew Ambrosino, the lead engineer for OpenAI’s desktop app, it’s the only way to test the future, which is why that app now has access to, and control over, his inbox, his Slack account, his phone, apps like Notion and Figma, and more.
“If I’m asking it to write a document, is there a possibility that it’s going to pull from a private DM on that subject and not know that it’s not supposed to share some info? Yes,” Ambrosino told TechCrunch. “I’ll do it for the job. I will take the personal hit here and there if I have to. And I haven’t had to.”
Ambrosino works on OpenAI’s biggest bet, ChatGPT Work, which was released last month and is available on the company’s lowest subscription tier, for $20 a month. The product is intended to allow white collar workers to field AI agents – hooking LLMs up to the digital workflows used by accountants, investors, doctors and everyone else whose day-to-day is dominated by their computer.
OpenAI’s marketing copy puts the goal succinctly: A world where “where [artificial] intelligence goes beyond answering questions to helping everyone turn their biggest ideas into reality.”
For software developers, that shift is already happening, but it’s been slow to spread to other departments. ChatGPT Work is a modified version of the company’s Codex coding tool. It’s meant to give non-engineers a version of the same functionality that software engineers already get from agents: an AI tool that doesn’t just answer questions, but completes multistep projects on its own.
“In this new factor, ChatGPT can actually do entire, very complicated tasks for you all autonomously in a way that is delightful and safe,” Tibault Sottiaux, who leads OpenAI’s core product work, including Work, told TechCrunch. “It’s the very mission of OpenAI—to bring everyone along.”
Commercially, that matters a lot. Agents that work for longer stretches burn through more tokens, which makes them more lucrative for OpenAI on a per-user basis. Reaching new professions is crucial – not just for OpenAI, but for the industry at large. If coding has proven lucrative territory for AI labs, it’s still a tiny subset of the professional work AI tools need to enable if these companies are to justify their massive investment in training and computation. While labs have been focused on software engineers, vertical-specific competitors like Harvey (for law) and Clay (for sales) have been chasing those customers with a model-agnostic approach, meaning they’ll plug in whatever AI works best at the time.
Industry analysts see this as one of the major challenges facing OpenAI and its competitors. “If the labs cannot rapidly get ahold of the key complementary assets needed to scale AI in the market, value will accrue elsewhere,” Christian Catalini wrote on a16z’s Time to Build blog.
Making the AI apps work for people who aren’t software engineers requires more hand-holding. OpenAI’s non-engineering workforce, like the communications and finance teams, started using Codex “at a time that it was actively hostile to them—asking them about code and showing them, ‘oh, you have an empty diff for this thing,’” Ambrosino said, referring to a technical readout meant for software changes. “So, we started to make it more general purpose between February and now.”
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