AI and the next decade of work

The entry-level job is the first casualty of artificial intelligence, and the payroll data now show it. Among American workers aged 22 to 25 in the occupations most exposed to AI, employment fell about 11% between November 2022 and June 2026, while employment of the same age group in the least exposed occupations grew about 10%. That leaves the young workers in exposed jobs 19% below where they would be had they kept pace, a gap that was 15% a year earlier and is widening. Experienced workers in the same occupations show no comparable gap. The decline comes through hiring, not layoffs, and it is concentrated where AI substitutes for human tasks rather than complements them.¹ Recent US graduates had an unemployment rate of about 5.6% and an underemployment rate of 42% in the second quarter of 2026, and computer science, long sold as the safe degree, now carries a 7.0% unemployment rate for recent graduates, against 2.1% for nursing.²

Seventy years ago this was a ten-man summer project. Today 88% of organizations report using AI, more than half of the world’s population has tried a generative AI tool, and official statistics offices from Washington to Riyadh count a fifth to a third of all businesses as users (Exhibit 1). This article traces that arc, sets out what AI already does in finance, marketing, design, software, and customer service, maps what the official business surveys say about small and medium-sized enterprises (SMEs) in forty countries, and then answers, without varnish, the question behind all of it: what happens to the students of today, what the job market looks like over the next three to ten years, and who survives.

From ten men to a general-purpose technology

The 1955 proposal to the Rockefeller Foundation asked for ten people and two months. Its conjecture, that every feature of intelligence can in principle be described precisely enough for a machine to simulate it, took seventy years and two funding collapses to pay off.³ What changed in 2012 was scale: a neural network trained on a million labeled images beat every hand-built system, and from then on progress came from more data and more computing rather than cleverer rules. The transformer architecture of 2017 made language the next target, and the public release of ChatGPT in November 2022 turned a research curiosity into the fastest consumer adoption ever measured, about 100 million monthly users in two months, against nine months for TikTok and two and a half years for Instagram.^{4, 5}

The count today is not ten men. The Stanford AI Index 2026 records $344.7 billion of private AI investment in 2025, 1,953 newly funded AI companies in the United States alone, organizational adoption of 88%, and four in five university students using generative AI.⁶ ChatGPT passed 800 million weekly users in October 2025.⁷ On capability, the best way to track the trend is the length of task an AI agent can finish on its own: METR measured that length doubling roughly every seven months from 2019 to 2024, and its January 2026 update put frontier models at more than ten hours of expert work at a 50% success rate, with recent doubling faster than the long-run rate.⁸ Read those two curves together, adoption and task length, and the next three to ten years need no exotic assumption: the machines will do longer and more complex pieces of office work, and more firms will let them.

What AI already does, and why most of it is unlicensed work

The work AI has absorbed first shares one property: no law requires a human to do it. Writing marketing copy, editing a video, bookkeeping entries, first-pass research, basic design, a customer chat, or a web application are all unlicensed activities, judged only by whether the output is good enough. In software, a platform called Lovable, which lets non-programmers build applications by describing them, went from $100 million of annual run-rate revenue in July 2025 to $600 million in September 2026 with 146 employees in March.⁹ In advertising, the Wall Street Journal reported in June 2025 that Meta aims to let brands automate the creation and targeting of ads fully by the end of 2026, from a product image and a budget.¹⁰ In customer service, Klarna said in February 2024 that its assistant handled 2.3 million conversations in a month, the work of 700 agents, and in May 2025 reversed course and began hiring people again after service quality fell.¹¹ Microsoft’s study of 200,000 Copilot conversations found the highest AI applicability in media and communication work, services sales, and information and record clerks, and the lowest among forestry workers, plant operators, grounds maintenance, home health aides and nursing assistants, and extraction workers.¹²

The people doing this work describe the shift plainly. A writer on r/Upwork who earned $4,000 to $5,000 a month in 2021 asked in August 2026 where the jobs went; the first reply was that clients now write with ChatGPT and are happy with it. A Dubai advertising agency wrote in October 2025 that it had shut its copywriting division of six writers in English and Arabic. A video editor working out a notice period in August 2026 was told the firm would build an editing system instead of hiring a replacement. A real estate investor who let go a $25-an-hour assistant for a $1,000-a-month set of agents wrote that the assistant had done nothing wrong; the owner had simply found something cheaper. And an owner who replaced three roles, a support agent, a data-entry clerk, and a junior marketing writer, reported $132,000 of annual savings and a 14% fall in customer satisfaction in three months.¹³ The pattern is consistent with the research on freelance platforms: after ChatGPT, postings for writing and coding work fell 21% relative to manual work within eight months, and freelancers in affected occupations saw fewer jobs and lower earnings.¹⁴

What the official surveys say about SMEs, country by country

To measure the effect on SMEs we went to the statistics offices rather than to vendor surveys, whose definitions of “using AI” vary with what they sell. Eurostat’s enterprise survey, the US Census Bureau’s Business Trends and Outlook Survey, and their counterparts in the United Kingdom, Canada, Australia, Brazil, Singapore, and Saudi Arabia all ask a version of the same question to a representative sample of businesses. Three findings hold everywhere (Exhibit 2).

First, adoption has roughly tripled in two years. In the European Union, 20% of enterprises with ten or more employees used at least one AI technology in 2025, up from 8% in 2023; among small firms of 10 to 49 staff the share went from 6 to 17%.¹⁵ In Canada the share tripled from 6.1% in the second quarter of 2024 to 19.2% in the second quarter of 2026.¹⁶ In the United Kingdom it rose from about 12% in late 2023 to about 35% of businesses with ten or more employees in June 2026.¹⁷ Across the OECD, firm-level use grew 42% in 2025 alone, from 14.2 to 20.2%.¹⁸ Second, the gap between large and small firms is wide and persistent: 55% of large EU firms against 17% of small ones, 37% of US firms with 250 or more staff against under 20% of those with four or fewer, 70% of large Singapore enterprises against 23% of SMEs, 50% of large Brazilian companies against 15% of small ones.^{15, 19, 20, 21} Third, use is still shallow: only 10% of adopting UK businesses describe it as extensive, and the most common technology is a large language model.¹⁷

Put on a map, the official figures show where SMEs are in the race (Exhibit 3). Denmark leads at 42% of enterprises, Finland at 38, the United Kingdom and Sweden at 35, and Saudi Arabia, whose statistics authority counted 33.1% of establishments using AI in 2025, up from 27.6% in 2024, sits among the leaders.²² The United States and Canada are at about a fifth, Brazil at 17%, Australia at 12%, and Romania at 5%.^{15, 16, 19, 21, 23} We found no published Qatari business survey of AI use; the nearest official evidence is the AI Index’s population measure, which puts the UAE among the highest in the world at 54% of people having used generative AI.⁶

For an SME owner the useful number is the size gap, because it shows how much is still to come. In every country with a size breakdown, large firms use AI two to eight times as often as small ones (Exhibit 4). Large firms adopt first because they have the data, the IT staff, and the legal cover; small firms adopt when the tool arrives inside software they already pay for. That second wave is now under way, and it is why the Reddit threads from small business owners in 2026 are about agents handling intake, scheduling, invoicing, and social posts rather than about pilots.¹³

What it means for the students of today

Here is the honest reading of the evidence (Exhibit 5). The economy as a whole has not shed jobs to AI: the Stanford team finds no widespread displacement, the Yale Budget Lab finds the occupational mix shifting only modestly, and employers surveyed by the National Association of Colleges and Employers in spring 2026 planned to hire 5.6% more new graduates than the year before.^{1, 24, 25} But the entry rung of the ladder in exposed fields is being removed. US entry-level postings fell 7.5% in the year to May 2026 while senior postings rose 14.7%.²⁶ New-graduate hiring at the twelve largest US technology companies is down about 65% from 2019, with design down 48% and even engineering down 11%.²⁷ In the United Kingdom, 8,383 graduate vacancies were advertised in July 2026, down 45.6% in a year and the lowest since the count began in 2016.²⁸ The Dallas Fed finds the mechanism is fewer young people moving from outside the labor force into a first job, not layoffs.²⁹ And the AI Index records that employment of software developers aged 22 to 25 has fallen nearly 20% since 2024.⁶

Why the entry level? Because the junior job was made of exactly the tasks AI does best: checkable, codified, screen-based work that senior staff used to hand down. The Stanford authors put it precisely: AI substitutes for the digitized and codified knowledge that is the core of formal education, and complements the tacit knowledge that experienced staff hold.¹ That is the trap for a graduate. A degree certifies codified knowledge. The thing employers now pay for, judgment built on experience, was acquired by doing the junior work that is disappearing. The CPAs asking on r/Accounting how a firm trains the next generation when AI does the workpapers and reconciliations are describing a broken apprenticeship, not a productivity gain.¹³ A graduate on r/resumes with a 3.5 grade average, a ten-month internship, three deployed applications, and 1,300 applications had no full-time offer in September 2026.¹³

A degree certifies codified knowledge. The thing employers now pay for, judgment built on experience, was acquired by doing the junior work that is disappearing.

The next three to ten years, and who survives

The forecasts split into three camps, and we think readers should see all three. The employer camp, in the World Economic Forum survey, expects 170 million jobs created and 92 million displaced by 2030, with 41% of employers planning to reduce their workforce where AI can replicate the work; the roles expected to decline fastest are postal clerks, bank tellers, data-entry clerks, cashiers, and administrative assistants, and the roles growing fastest in number are farmworkers, delivery drivers, construction workers, salespeople, nurses, teachers, and care workers.³⁰ The engineering camp, McKinsey Global Institute, estimates that today’s technology could already do 57% of US work hours, 44% through software agents and 13% through robots, and stresses that this is technical potential, not a forecast.³¹ The alarm camp, led by Anthropic’s chief executive, expects half of entry-level white-collar jobs to go within one to five years; the skeptics, including the AI pioneer Yann LeCun and the Yale Budget Lab, answer that the aggregate data do not yet show it.^{32, 24} The IMF’s own estimate is that 40% of jobs worldwide and 60% in advanced economies are exposed, with roughly half of those exposed likely to benefit and half to lose.³³

Our reading is that the alarm camp is right about the entry level and the skeptics are right about the aggregate, for now. If firm adoption across the OECD kept growing at 2025’s rate of 42% a year, more than half of firms would be users within three years, and the task length agents can handle is doubling in well under a year. Together those two trends mean the shortage of junior roles in exposed fields is a structural change, not a cycle. Who survives it is already visible in the data and the threads. The work that holds is work where a license requires a human signature, where the body has to be present, where a client relationship or an accountability is the product, or where tacit judgment is needed to decide what the machine should do and to check what it did. Audit, law, medicine, engineering sign-off, nursing, the trades, construction, logistics, and frontline sales sit there; so do the senior roles whose postings are rising. What does not hold is the unlicensed, codified, screen-based task done for a fee, and the junior job that consisted of it. Licensing slows substitution inside a profession, but it does not stop a firm doing the same work with fewer juniors per partner.

Qatar and the Gulf start from a different place, and the difference cuts both ways. Of 127,477 employed Qataris in 2024, 66,701 worked in public administration and a further 25,041 in education and health, together 72%, so for a Qatari graduate the entry rung is set by government hiring and the state’s own digitization choices rather than by the market.³⁴ The 2.1 million non-Qatari workers, including almost all of the private-sector finance, marketing, administration, and technology staff, have the direct exposure, and for them a lost job is also a lost residency. Saudi Arabia’s 33% business adoption and the UAE’s 54% population adoption show how fast the region’s employers are moving.^{6, 22}

For students, graduates, and the SMEs that will or will not hire them, the evidence supports a short list. It is also the basis of the workforce and operating-model work we do in strategy consulting in Qatar.

• Choose fields where the license, the body, or the relationship is part of the product. Nursing at 2.1% unemployment and computer science at 7.0% are the same labor market read correctly. Health, education, engineering with professional sign-off, the trades, and client-facing sales are where the official growth lists point.
• Treat the degree as the floor and the portfolio as the proof. Employers are hiring judgment, and judgment is shown by finished work: a business run, a product shipped to real users, a problem solved for a real client. One commenter on r/jobsearchhacks noted freshmen shipping products to 50,000 users with coding agents and getting the internships that applications alone did not.¹³
• Learn to direct and verify the machine in your own field. The accountant who maps a year of bank transactions with an agent and then checks it is more valuable than either the agent or the clerk it replaced. Every discipline now has this layer, and it is where the junior role is being rebuilt.
• Take the first job for the tacit knowledge, not the title. Seek employers that still put juniors next to seniors. A firm that has automated its workpapers and kept no one to learn from them is a worse first employer than one that pays less and trains.
• If you own an SME, cut tasks, not the pipeline. The owners who report savings also report falling satisfaction scores, escalations that humans used to absorb, and no one left who understands the process. Automate the intake, the invoicing, and the first draft, and keep one person who can judge the output and grow into the next.
• Do not wait for the data to settle. By the time economy-wide statistics show displacement, the students now choosing majors will have graduated. The entry-level signal is already clear.

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Seventy years after ten men met at Dartmouth, the machine does the junior work, and the record shows it in payroll data, graduate vacancies, and the threads where owners count their savings. The economy will keep most of its jobs for some years yet. The question for a student is narrower and harder: whether the job they are training for still has a first rung. For many of the unlicensed, screen-based professions it does not, and the honest advice is to choose work the machine cannot sign, touch, or be blamed for, and to build the judgment that employers will pay for before anyone hands it to you.