Coders Love AI — Can Law, Finance, Customer Service Catch Up?

AI coding tools have changed daily routines for software engineers at young Silicon Valley firms. The end product remains unchanged: vast quantities of code. Yet rather than perching and keying it in, they murmur into microphones. Those spoken directions are then transcribed through artificial intelligence and passed to AI agents — software capable of acting inside programs and, here, composing it.

Most other office staff have yet to reach “in this world”, concedes Colin Jarvis of OpenAI, maker of ChatGPT. His company’s prospects — and the wider AI surge — depend on that shifting. To warrant massive spending on data centres, American cloud majors plus AI laboratories like Anthropic and OpenAI require uptake of the technology to keep expanding swiftly. A rough calculation finds that paying for the data-centre expansion will demand total yearly income from AI use hitting $2.5trn per year by the end of the decade, up from around $150bn now.

Why developers adopted AI coding tools first

Adoption to date has been strongest by far among software developers. Four-fifths of programmers say they use an AI coding tool, according to Stack Overflow, an online forum. SemiAnalysis, a research firm, reckons that for Anthropic and OpenAI coding now accounts for over half their combined annual recurring revenue — a trendy measure of sales that extrapolates from the most recent month.

In June 2025 the combined annual recurring revenue of Cognition, Cursor, Lovable and Replit, four AI coding startups, was roughly $800m. Today it stands at $6bn.

Programmers’ zeal helps account for AI spreading faster across sectors where they represent a bigger portion of jobs (see chart). Bulls might interpret that pattern as proof the AI boom is only beginning, with expenditure on the technology climbing far higher in coming years as additional uses mature. Yet structural factors could also make coding an exception.

Law, finance and customer service are growing fast

Three contenders frequently promoted as AI’s next breakout uses are lawyering, finance and customer service. Expansion in each has been striking. Harvey, Clio and Legora, three developers of AI legal tools, have roughly doubled their combined annual recurring revenue in the past year to $1bn. Harvey says the amount of time lawyers spend on its platform is doubling each month.

Bankers and other finance types are also starting to embrace the technology. Rogo, an AI startup building tools for the financial-services industry, added 100 enterprise customers in the past quarter and increased annual recurring revenue by 50%.

AI is also taking off in customer service. Sierra, which builds tools for the purpose, reached an annual recurring revenue of $200m in June, double the figure in November last year. Venture capitalists are bullish. So far this year they have poured $3bn into AI startups in this area, more than for any other category of AI application. Investment in AI coding startups has stalled, in part because AI labs’ own coding tools have grown so quickly.

Those three uses share certain traits with coding. They address large addressable markets, and assignments are frequently routine. Still, four elements distinguish coding: access to training data; ease of checking model output; limited human contact required by the job; and software engineers themselves. AI firms are attempting to bridge those gaps elsewhere, though success will not be straightforward.

Four advantages that make coding different

One reason AI has worked so well as a programming companion is the enormous corpus of open-source code posted online. It has therefore entered models, representing almost a fifth of all training data in some cases. An equivalent mass of public material just does not exist for customer complaints or contract negotiations.

Machine-produced code is additionally comparatively simple to verify. Software testing is standard engineering practice: one script examines another. That raises the odds that flaws in AI-written code get spotted before causing damage. What is more, testing aids model improvement by signalling whenever an error occurs. Judging if an angry client felt soothed or a valuation model properly captured subtlety in corporate accounts is trickier.

Coding assistants enjoy a further edge because much context needed for an assignment already lives inside a program’s code-base, or is accessible via digital conduits linking software, known as application programming interfaces. In numerous other settings vital knowledge is absent from digital records, dwelling instead in a call-centre worker’s mind or a rival counsel’s facial expression.

Can AI close the gap in other office work?

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. Startups such as Mercor are connecting AI companies with professionals in various fields who can transfer their expertise into models.

Ping Wu, boss of Cresta, an AI customer-service startup, says that by analysing thousands of call-centre transcripts his firm 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.

Yet the final driver of AI’s triumph in coding might prove tougher to copy: software engineers themselves. Being both nerdy and accustomed to change, given that programming languages are updated every few years, they adopt novel technology instinctively. Much uptake of AI coding has therefore occurred bottom-up, with engineers finding and trying tools independently, 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, for their part, must 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. ■

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