How software engineers in Silicon Valley spend their days has changed dramatically over the past few years. The output is still the same: reams of code. But instead of sitting and typing, they whisper into microphones. Their instructions are then transcribed using artificial intelligence and fed to AI agents — bots that can interact with software and, in this case, write it.

Other white-collar workers are not yet “in this world”, admits Colin Jarvis of OpenAI, maker of ChatGPT. The future of his employer — and the AI boom at large — hinges on that changing. To justify their colossal outlays on data centres, America’s cloud giants, and AI labs such as Anthropic and OpenAI, need adoption of the technology to continue spreading rapidly. A back-of-the-envelope calculation by The Economist finds that covering the cost of the data-centre build-out will require total annual revenue from the use of AI to reach $US2.5 trillion ($3.5t) per year by the end of the decade, up from around $US150b currently.

So far uptake of the technology has been strongest by far among software developers. Four-fifths of them say they use an AI coding tool, according to Stack Overflow, an online forum. In June 2025 the combined annual recurring revenue of Cognition, Cursor, Lovable and Replit, four AI-coding start-ups, was roughly $US800 million. Today it stands at $US6b. SemiAnalysis, a research firm, reckons that coding accounted for over half the combined annual recurring revenue of Anthropic and OpenAI as of the second quarter of this year.

That explains why AI has diffused more quickly in industries where software engineers account for a larger share of employment. Optimists may see this as evidence that the AI boom is just getting started, with spending on the technology set to reach far greater heights in the years ahead as other applications mature. Yet there may also be structural reasons why coding is an outlier.

Three candidates often touted as the next runaway application for AI are lawyering, finance and customer service. All are experiencing significant growth. Harvey, Clio and Legora, three developers of AI legal tools, have roughly doubled their combined annual recurring revenue in the past year to $US1bn. Harvey says the amount of time lawyers spend on its platform is doubling each month.

Bankers and other finance types are also coming to embrace AI. Rogo, an AI start-up building tools for the financial-services industry, added 100 enterprise customers in the past quarter and increased annual recurring revenue by 50 per cent.

AI is also taking off in customer service. Sierra, which builds tools for the purpose, reached an annual recurring revenue of $US200m in June, double the figure in November last year. Venture capitalists are bullish. So far this year they have poured $US3b into AI start-ups in this area, more than for any other category of AI application. (Investment in AI-coding start-ups has stalled, in part because AI labs’ own coding tools are growing so dominant.)

These three applications bear some similarities to coding. They are big potential markets, and the work is often formulaic. But four factors set coding apart: the availability of training data; how easy it is to test a model’s output; the amount of human interaction involved in the work; and software engineers themselves. AI companies are trying to make their other markets more coding-like, but doing so will not be straightforward.

Part of why AI has proved so successful as a programming buddy is that huge amounts of open-source code are published on the internet. It has thus made its way into models, accounting for almost a fifth of all training data in some cases. The same volume of publicly available content simply does not exist for customer complaints or contract negotiations.

Code generated by AI is also relatively easy to validate. Software tests are a routine part of engineering: one piece of code checks another. That increases the likelihood that any errors in AI-generated code are caught before they wreak havoc. Moreover, testing helps to improve the performance of models by alerting them when a mistake is made. Assessing whether an irate customer was appeased or a valuation model appropriately reflected the nuance in a company’s accounts is trickier.

Software-writing tools have the added advantage that much of the information required to complete a task already exists in a program’s code-base, or can be reached through the digital pipelines that connect software, known as application programming interfaces. In many other contexts, however, crucial information is not available in digital form, instead residing in the brain of a contact-centre worker or the facial expression of a counterparty’s lawyer.

AI companies are working on all these challenges. Winston Weinberg, the boss of Harvey, says that advances in AI mean his firm is now able to train its tools in part by relying on “synthetic” data, which are generated by AI systems themselves.

Start-ups such as Mercor are connecting AI companies with professionals in various fields who can transfer their expertise into models. Ping Wu, boss of Cresta, says that by analysing thousands of call-centre transcripts his AI customer-service startup can infer some of the knowledge lodged in the heads of staff, such as how refunds are handled. Legions of so-called forward-deployed engineers are now being dispatched by AI companies to their customers’ offices to troubleshoot problems and improve their tools.

The last factor behind AI’s success in coding may be hardest to replicate: software engineers themselves. Being both nerdy and accustomed to change (given that programming languages are updated every few years), they are natural adopters of new technology. Much of the adoption of AI coding has thus happened in a bottom-up way, with individual engineers discovering and experimenting with the tools of their own accord, notes Anish Acharya of Andreessen Horowitz, a venture-capital firm.

Call-centre agents, by contrast, typically have neither the incentive nor the freedom to experiment on the job, and tend to wait for technologies to be deployed top-down. Lawyers and finance workers must also tread carefully to avoid falling foul of the many regulations that govern their occupations.

Other applications of AI will certainly continue to grow, and may well accelerate as models improve and companies become more comfortable with handing over tasks to AI agents. But it could be a while before the majority of office drones spend their days whispering into microphones.