Zhang says his company makes money on each individual conversation after excluding certain overhead costs, but he declined to comment on the startup’s overall profitability. With $100 million raised from venture capitalists including Andreessen Horowitz and Accel, Decagon has the flexibility to prioritize growth over profitability. “Whether we could be pricing more, it’s always like a ‘what if?’” he says. “But in general we’re pretty happy right now.”
“So Cheap”
Erica Brescia, a managing director at the investment firm Redpoint Ventures, had an epiphany about AI agent pricing last month. The $250 price tag on Google’s new AI Ultra plan astounded her. “All this is so cheap,” she recalls thinking. “It’s disproportionate to the value people are getting.” She felt a price of at least double would make more sense. (That same week, Nvidia CEO Jensen Huang told Stratechery that he would hire an AI agent for $100,000 per year “in a heartbeat.” )
Previously, Brescia worked as the chief operating officer of GitHub, which helped set the bar for AI pricing. GitHub’s Copilot coding assistant started at $10 a month in 2022, months before ChatGPT’s debut. Brescia says GitHub went with a price that would attract a critical mass of users. The goal was gathering data to improve the service, and GitHub’s parent company, Microsoft, didn’t mind taking a loss on the new tool to make that happen. In reality, a price 100 times higher would now better reflect the value Copilot provides to software developers, Brescia estimates. (GitHub chief operating officer Kyle Daigle tells WIRED that the company’s goal is to support, not replace, developers and that “pricing reflects a commitment to democratizing access to powerful tools.”)
Today, Copilot tops out at $21 a month. And similar tools have followed its lead, including Zed, which has received $12.5 million in funding from Redpoint and others. In May, the company started charging a minimum of $20 a month for an AI-assisted code editor it built from the ground up.
Zed CEO Nathan Sobo expects AI companies to charge more over time because the current pricing models aren’t sustainable. But relative to humans, he wants to keep AI agents affordable so anyone can use them to augment their work, develop better software, and create new jobs. “I want as much intelligence at my disposal at as low a cost as possible,” he says. “But to me, included in that is potentially a junior engineer using this technology, ideally at as low a cost as possible.”
Decagon’s Zhang feels the same way about AI coding tools. “Would we pay more? Marginally? Yeah,” he says. But “$2,000? Probably not.” He adds “the hunger for good engineers is infinite.”
AI entrepreneurs suggest that agents could command higher prices if they were easier to set up and more reliable to use. For instance, Nandita Giri, a senior software engineer who has worked at Amazon, Meta, and Microsoft, says she would pay thousands of dollars annually for an AI personal assistant. “But strict conditions apply—you can’t get frustrated by using it,” she says.
Unfortunately, that day feels far away. As a personal project, Giri tried developing an AI agent that could prevent psychological burnout. “It just canceled all my meetings,” she says. Certainly a solution, but not the ideal one.
Now, some companies are hiring “AI architects” to help oversee agentic systems and cut down on gaffes. The question is who will occupy those roles in the future if early-career workers are cut off from opportunities today. Simon Johnson, an economist at the Massachusetts Institute of Technology, doesn’t expect companies to take into account the social cost of career disruption in making their pricing decisions. He suggests governments lower payroll taxes for entry-level roles to encourage hiring. “The right lever to pull is one that reduces costs to employers,” Johnson says.
Arrigoni is choosing a third path. At Loti AI, he has prioritized steadily hiring junior engineers and hasn’t employed AI coding tools. If the job apocalypse comes, “I don’t want to be at fault,” he says.
