In search of a better understanding of our current moment, I recently went looking for A.I. adopters outside the tech industry. I asked the C.E.O. of a journalism nonprofit how he’s using A.I., and he showed me a web-based tool that he vibe-coded using Claude Code, Anthropic’s programming agent. Each morning, the tool automatically summarizes articles related to higher education, suggesting potential trends and angles that could warrant further investigation. It then e-mails a brief to him and his managing editors. A recent brief highlighted a Los Angeles Times story about a data breach of a popular learning management system called Canvas. It suggested that the editors consider sending a Freedom of Information Act request to state school systems that were impacted, asking for correspondence with the system’s parent company. “Did anyone raise red flags?” it asked. The tool is hardly revelatory, and the C.E.O. said that he “would never try to turn it into a public-facing product,” but it highlights useful information and sparks ideas. “It’s like a student or an intern,” he said.
Lately, the C.E.O. has been thinking about another inefficiency that A.I. could address. His reporters fill out regular forms to summarize the impact of their work to send to the organization’s funders. Because he has access to Claude Code, he began imagining a bot that would solicit this information in a more informal way. Perhaps reporters could type their updates directly into Slack, the messaging platform that they already use, and the bot could fill out the form on their behalf. “It won’t be hard,” he told me. When I followed up with him a couple of weeks later, he confirmed that he had indeed created a tool to help reporters draft communications with funders. (It wasn’t yet integrated with Slack.)
Another A.I. enthusiast, the co-owner and chairman of a shipping-logistics company, told me about a “big headache” that was vexing his C.F.O. “We get payments from thousands of clients,” he said. “Often, they don’t note what they’re paying us for.” The company had given up on changing clients’ behavior; instead, four staffers were assigned to match mystery checks to corresponding invoices. (I couldn’t help but think of a Sisyphean number-filing task in “Severance,” the dystopian Apple TV+ series set in a workplace.) But, earlier this year, the company gave the I.T. team access to A.I.-powered coding agents. Staffers quickly built a custom tool that automated “essentially eighty per cent” of the matching issues on a recent project, the co-owner said. He was now in the process of reassigning three-quarters of his human payment-matchers to more fruitful tasks. These examples were not the digital equivalent of a power loom, making large numbers of human jobs superfluous. Turns out, A.I. was assisting these small businesses in roughly the same way that my teen-age self had helped that consulting company—by hacking together whatever was useful.
The ability to vibe-code custom software using A.I. might be new, but it actually echoes a much older vision for personal computers. In the seventies, when these machines were first introduced, easy-to-learn programming languages such as BASIC—meaning “beginner’s all-purpose symbolic instruction code”—were meant to empower any user to write their own programs. A nineteen-year-old Bill Gates, alongside his friend Paul Allen, developed a version of BASIC to run on the Altair 8800, the very first commercially viable personal computer; they soon formed a company together that they called Micro-Soft. Steve Wozniak created his own version of BASIC for the Apple I, which he and Steve Jobs released a year later. The Apple II shipped with a copy of BASIC hard-coded into its memory chips. “That means you can begin writing your own programs the first evening, even if you’ve had no previous computer experience,” an ad declared.
This idea of bespoke computer programs made sense. Altair and Apple couldn’t anticipate every potential use for their machines, so why not let individuals decide whether they wanted to, say, analyze business data, store recipes, or simulate space battles? In practice, however, even an “easy” programming language like BASIC proved hard for most normal people to master. A minor mistake could crash an entire program. In the end, personal computing followed a different path. In 1979, a newly formed company called Software Arts developed VisiCalc, the first electronic spreadsheet program, which cost a hundred dollars and arrived on a floppy disk. The program was a profound improvement on paper ledgers, and it became the first “killer app,” selling more than seven hundred thousand copies in less than six years. VisiCalc was more powerful than anything an average user could program in BASIC, and it prompted a pivot away from D.I.Y. coding in favor of professional programs. A vast and lucrative software industry emerged, and the idea of the average person dreaming up their own custom programs was all but forgotten—that is, until generative A.I. came along. Arguably, the nonprofit C.E.O. and the shipping executive were returning to the original vision of custom computation.
