“It is becoming ever more common for bosses to talk up their artificial-intelligence efforts while wielding the axe. Last month Enrique Lores, chief executive of hP, said that the computer manufacturer would cut around 5,000 jobs within three years as it embeds “AI in everything we do”. The same day Marguerite Bérard, boss of ABN Amro, a Dutch bank, unveiled sweeping lay-offs of her own, declaring that her company was “embracing AI to improve client services and reduce costs”. According to Challenger, Gray & Christmas, an employment firm, AI was cited as a cause in a fifth of the lay-offs announced by American companies in October.
Much of this is posturing. A company looks better if it attributes staff cuts to its technological prowess rather than pandemic-era over-hiring. So far, the evidence that AI is changing the labour market in a big way remains weak.
Yet that could change once companies adopt the technology more widely. Over the past few years plenty of researchers have sought to identify which jobs are most at risk by speculating about the types of tasks ChatGPT-like AI will be able to perform best, and determining where those tasks are most prevalent.
A different approach is to look at the jobs where adoption of AI is already gaining pace and consider what links them. Two stand out.
First is computer programming. Some two-thirds of coders say that they use an AI tool at least once a week, according to data from Stack Overflow, an online forum. Microsoft’s GitHub Copilot, one coding tool, has 26m users worldwide. Venture-capital (VC) spending is pouring into rivals, such as Windsurf and Cursor (see chart). According to Anthropic, a model-maker, a third of queries sent to its chatbot relate to computer programming.
Second is customer service. A survey by Gartner, a research firm, found that 85% of customer-service managers planned to experiment with ai this year. Companies from IBM, an IT giant, to Lufthansa, a German airline, are injecting the technology into their customer-service operations. VC investors are also backing AI startups targeting the occupation, such as Cresta and Sierra, though they have focused on it less than coding. The share price of Teleperformance, a French customer-service outsourcer, has slid by three-quarters since the launch of ChatGPT in 2022 amid expectations of looming upheaval.
What links these jobs? Consider first the nature of the work. Both involve plenty of repetitive tasks, but so do many others. In addition, however, the tasks performed by coders and call-centre agents tend to be “context-light”, meaning that those who do them don’t need a deep understanding of the company, notes Kabeh Vaziri, of Gartner. They are also “easily verifiable”: programmers can run tests on chunks of code to ensure that they work; call-centre supervisors can look at whether a customer’s problem was resolved and how happy they were after the interaction.
A second factor that makes these two occupations particularly fruitful terrain for AI is the abundance of available data that can be used to train models. Github Copilot has an enormous repository of code to learn from; customer-service units often have years of transcripts. Other information, such as “upvotes” in coding forums, can help the ai system judge an answer and improve the model.
A third commonality is that both occupations are big prizes for AI firms to target, encouraging investment in tailored software. In America 3m people work in customer service, typically in call centres, and another 2m are software developers. Cut out manual jobs and both occupations are among the country’s five most common.
The links between coding and customer service offer clues as to where AI adoption may take off next. Junior bankers and lawyers, who are less numerous but handsomely paid, are already in AI startups’ sights.
What is more, the cost of using AI is plummeting as models and hardware become more efficient, which may lead to a wider range of fields being targeted. At the same time, big businesses are busily sorting out their siloed, disorganised data, which should help with developing custom tools for white-collar workers. The AI of tomorrow will probably be both more specialised and more widespread. When that happens, bosses who blame the technology for lay-offs may no longer strain credulity.” [1]
"Lawyers, who are less numerous but handsomely paid, are already in AI startups’ sights." Does this mean that less lawyers will arrive to run our politics, so we will have more other professions at the top, like engineers in China with more permits for building something new and exciting like in China?
AI is indeed targeting the legal profession through a wave of startups (e.g., Harvey, Legora, DeepJudge, and others) that automate tasks like legal research, document review, contract analysis, and drafting. These tools are already reshaping workflows, making lawyers more productive and reducing the time (and billable hours) spent on routine work. Early predictions, such as Goldman Sachs' 2023 estimate that 44% of legal tasks could be automated, fueled concerns about job losses. However, more recent analyses (as of 2025) suggest a tempered impact: an updated Goldman Sachs figure points to around 17% of legal jobs at risk, while experts like MIT economists argue that complex judgment, low risk tolerance in law, and AI's current limitations (e.g., hallucinations, lack of true reasoning) will prevent major displacement in the near term.
Historically, technology has augmented rather than eliminated legal roles—similar to how spreadsheets expanded accounting rather than shrinking it.
AI is likely to follow suit: it may reduce demand for entry-level or routine-focused positions (e.g., junior associates doing discovery), potentially leading to fewer new lawyers entering the field or slower growth in the profession overall.
But it could also create new roles in AI oversight, ethical governance, and specialized advisory work, while lowering barriers to legal services and increasing overall demand.
In the US, lawyers remain heavily overrepresented in politics: in recent Congresses (e.g., 118th and 119th), public service/politics, business, and law are the dominant prior professions, with roughly 30-50% of members holding law degrees (higher in the Senate). Many politicians start as practicing attorneys, leveraging skills in negotiation, advocacy, and navigating regulations.
If AI meaningfully shrinks the legal profession long-term—by making it less lucrative for mid-tier roles or reducing the "training ground" of billable-hour drudgery—it could indirectly reduce the pipeline of lawyers transitioning to politics.
That said, the shift wouldn't necessarily be dramatic or rapid. Political paths often involve state legislatures, local office, or networking, not just Big Law experience. Business leaders (already the second-most common background) could fill more seats, or other professions (e.g., educators, military veterans) might rise.
A move toward more engineers or STEM professionals at the top, akin to China's technocratic model—where Politburo members historically (and recently) often have engineering backgrounds, especially in aerospace/military-industrial fields—would require broader cultural and educational changes in the US, not just AI disruption in law.
China's leadership has prioritized engineers for decades, fostering a "build-first" mentality that's enabled rapid infrastructure and tech development (e.g., high-speed rail, semiconductors). Observers contrast this with the US's "lawyerly" system, which excels at checks, balances, and litigation but can slow permitting and innovation through environmental reviews, lawsuits, and regulatory hurdles. More non-lawyer leaders (e.g., engineers) in US politics could indeed streamline approvals for "new and exciting" projects like clean energy grids, advanced manufacturing, or housing—issues often bogged down today.
However, the US system deliberately favors deliberation over speed to protect rights and prevent overreach, lessons from history that China's engineering-driven approach has sometimes ignored (e.g., demographic policies).
AI alone won't flip this overnight; it might contribute to a gradual diversification of political backgrounds if the legal profession contracts, but systemic change would need voter preferences, education shifts, and policy reforms to prioritize building.
The ruling elites in the West didn’t have demographic policies Chinese style, their population is dying out anyway though.
In short, possible but far from guaranteed—and it could bring trade-offs worth debating.
1, Jobs for the bots. The Economist; London Vol. 457, Iss. 9477, (Dec 6, 2025): 63.
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