Amir Nurani (pictured top), broker-owner at Left Coast Leaders in San Diego, said the numbers are already pointing in one direction. Originator licenses are renewed every year, which makes the headcount easy to track, and he expects to see that number decline.
“I think we are going to see a minimum of a 10% decline in renewed licenses for mortgages,” Nurani told Mortgage Professional America. “The volume is still contracted, and I think this is the normal part of the cycle. The cycle for mortgages is always the same. When rates drop, every lender in the United States starts a hiring frenzy. Then rates start going up, lenders go to layoffs, originators stop renewing their licenses, and you start to see a downtrend.”
AI-driven downsizing
Nurani said the decline is not just about demand. AI is already compressing the workload that used to justify larger originator teams, and if every originator can handle twice the volume with demand staying flat, the math points to fewer originators.
He noted that the same dynamic is playing out across financial services. Last week, Visa announced it was cutting 7% of its staff. Earlier this week, Zillow and Google announced additional layoffs of their own.
“AI is already hitting white-collar jobs,” he said. “Mortgage is not insulated from these types of shifts. AI expands the reach of the originator, but at some point it hits the demand cap. If it took 100 originators to meet the demand in a certain environment, and now every originator can do 2x the volume with demand staying static, the natural order of things is going to reduce that headcount.”
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GUARDD, founded by Sherwood Neiss who is also the co-founder of Crowdfund Capital Advisors (CCA) and one of the authors of the JOBS Act which approved online capital formation, recently met with the Securities and Exchange Commission (SEC) to discuss secondary trading of tokenized exempt securities.
Exempt securities would include securities issued under Reg A, Reg CF and Reg D. Public securities are moving in the direction of becoming digital securities or tokenized assets and private securities are heading that way as well.
Tokenized securities struggle a bit with semantics but the SEC currently defines these assets as: “a financial instrument enumerated in the definition of “security” under the federal securities laws that is formatted as or represented by a crypto asset, where the record of ownership is maintained in whole or in part on or through one or more crypto networks.”
GUARDD is a Fintech platform that helps private companies raising funds under Reg A+, Reg CF, or Reg D, to publish standardized ongoing disclosures so their securities can trade on Alternative Trading Systems (ATSs) while complying with state “Blue Sky” laws. Securities law in the US leans heavily on disclosure.
GUARDD met with the SEC Crypto Task Force to discuss how the SEC’s crypto rulemaking and secondary trading of tokenized, exempt securities through a Qualified Disclosure Publisher.
GUARDD explained that exempt securities offerings “inherit the same structural trap” even if they are tokenized. The proposal is:
“How QDP-published disclosure can anchor uniform national secondary trading — preempting inconsistent state manual-exemption requirements while preserving state anti-fraud authority — and how this interacts with the CLARITY Act’s taxonomy if enacted.”
In a letter sent to the Commission in June, GUARDD lamented the current environment where currently there is an on-ramp but no exit, regarding to exempt securities, specifically addressing difficulties for securities issued under Reg CF.
GUARDD explained:
“Regulation Crowdfunding provides a decade-long natural experiment in what happens when a primary market is built without secondary infrastructure. Per CCLEAR transaction-level data: more than $2.95 billion has been raised across 10,899 offerings by 9,300+ issuers since 2016 — yet less than 1% of issuers have achieved meaningful secondary liquidity. The largest secondary marketplace for crowdfunded securities has quoted only 25 companies, with roughly $1.4 million in total trading volume, against billions raised in the primary market. One issuer spent more than $90,000 and over a year attempting state-by-state compliance solely to enable lawful secondary trading for its investors. Meanwhile, 257 of these companies went on to attract $5.04 billion in institutional follow-on funding — success their earliest retail investors cannot sell into. Tokenized securities issued under new exemptions will inherit precisely this trap unless the proposing release builds the exit alongside the on-ramp.”
Today, investors in private securities typically understand they may hold limited liquidity opportunities beyond an IPO or an acquisition. Yet markets have developed for Reg D securities that have opened up access for early shareholders to sell shares, if they choose to do so. The environment for Reg CF issuers remains underdeveloped.
GUARDD sees an avenue for boosting liquidity via rulemaking to make disclosure standardized to make it easier for secondary transactions to take place.
GUARDD states:
“Disclosure-based secondary market infrastructure is where those goals converge: it protects investors through current, standardized, verified information, and it makes U.S. venues viable by making U.S.-issued tokens tradable.”
Heightened liquidity is a characteristic of crypto markets that have boosted its popularity for investors (speculators). A tokenized asset or digital security, including those which are private, could benefit from improved options for sellers as well as buyers.
The Government Accountability Office spent nearly two years auditing how the Department of Education instructs its student loan servicers, and the headline finding is something any functioning leadership team already knows: talk to the people doing the work before you change the work.
The new GAO report found Education routinely skips early coordination with the companies servicing federal student loans, which together manage over $1.6 trillion for about 43 million borrowers.
GAO reviewed 68 change requests (the formal documents Education uses to direct the contractors it pays to service federal loans) issued between March 2020 and December 2024. All four servicers interviewed said instruction would improve if Education looped them in before, or immediately after, requesting a change.
Education acknowledged early coordination has value, then rejected GAO’s recommendation to set formal criteria for when to do it.
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Why This Matters
When Education and its servicers spend months clarifying what a change actually requires, borrowers absorb the delay. One change request triggered six rounds of questions and answers over a 2-month period. Another — a marked-up rewrite of a servicer’s original 300-plus page contract — took more than a year and a half to implement. Borrower advocates told GAO the fallout showed up as long hold times and undertrained customer service reps, exactly when borrowers needed answers most.
In one case, Education gave servicers a single business day’s notice before publishing IDR payment counts on StudentAid.gov — leaving call centers unprepared for the wave of borrower questions that followed.
The Numbers
Five student loan servicers manage the portfolio:
Nelnet – 12.07 million borrowers
Aidvantage – 9.21 million borrowers
MOHELA – 6.73 million borrowers
Edfinancia l- 6.52 million borrowers
CRI – 2.93 million borrowers
36% of change requests from fiscal years 2023 through 2025 were designated “emergency” or “quick pace”, which are rush classifications. In fiscal year 2025 alone, it was 42%.
Emergency status can cut a servicer’s response window from 10 days to 2.
How This Connects
This is the same agency GAO recently flagged for halting its servicer oversight reviews and that the Inspector General found had cut 40% of its staff. The timing is not great: servicers are in the middle on the largest repayment overhaul in decades, including the court-ordered end of the SAVE plan and the launch of the Repayment Assistance Plan under the One Big Beautiful Bill Act.
Education says formal coordination criteria would slow it down, but the GAO is keeping the recommendation open. The real test is how cleanly servicers execute the repayment changes that took effect in July 2026 and whether the 2028 deadline to move off sunsetting plans arrives with fewer surprises than the last transition.
Education did hold a multi-day summit with servicers in September 2025 on the new repayment plans, so someone is reading the memos.
Unlike previous waves of automation, machine learning and generative AI (Gen AI) technologies can perform non-routine cognitive tasks, such as those involving written or spoken language, and have the potential to affect a wider range of occupations. By augmenting or replacing workers in these tasks, these technologies promise to deliver significant productivity gains. AI adoption, while still limited, seems to be linked to productivity gains across industries in the US, although it can only explain a small fraction of the aggregate pick up in US productivity. This post examines the emerging evidence from UK industry data and finds some indication that AI is contributing to productivity growth following a similar pattern to previous key technologies.
Key technologies, industrial revolutions and productivity growth: a historical perspective
New technologies differ in their economic impact: some ‘General Purpose Technologies’ (GPTs) have a central role in economics because they tend to have a large and long-lasting impact on productivity growth. They are characterised by (a) Pervasiveness: they are widely used and spread to most sectors; (b) Improvement: they are capable of ongoing technical improvement; and (c) Innovation spawning: they make it easier to invent and produce new products or processes. Each past Industrial Revolution was associated with a new GPT: the First Industrial Revolution (Britain, 1760s–1830s) with the creation of steam engine, the Second (US, 1870–1914) with the invention of electricity and the Third (US, 1960s–2000s) with the introduction of Information and Communication Technologies (ICTs).
In addition to a new GPT, each Industrial Revolution was also accompanied by the ‘Invention of a Method of Invention’ (IMI), a significant change in the way new ideas are generated, which raises the productivity of the technological process itself. In the First Industrial Revolution, the new method was based on systematic empiricism and experimentation. In the Second, the IMI was the creation of the industrial R&D laboratory. In the Third, rapid developments in computers (ICT) provided a new method for innovating. Importantly, GPTs and IMIs are different concepts, and ICT is the only example of a previous technology that was both a GPT and an IMI.
Artificial intelligence as a ‘revolutionary’ technology and its impact on productivity
Many authors agree that AI, more specifically Gen AI, has the characteristics of a GPT: it can be used for a very broad set of tasks and therefore has the potential to spread widely across the economy; it generates innovation, for example in user interfaces or product design; and its capabilities have been expanding dramatically since its introduction. It also enhances the research process, confirming it is also an IMI.
Gen AI seems to be advancing at an unprecedented pace, with potentially large impacts not only on productivity but also on human labour. For these reasons, it is crucial to monitor its diffusion and impact on the economy. This post introduces a possible framework to think about the impacts of AI as a supply-side shock in the first instance. It then examines the evidence of AI impacts on UK labour productivity, measured as output per hour worked. A change in labour productivity can come either through higher output or through fewer hours worked, possibly due to lower employment. The current evidence on AI impact on labour demand is presented in a companion post.
Figure 1 depicts two broad transmission channels of the impact of AI on the macroeconomy.
Figure 1: Transmission channels for AI macroeconomic impacts
Sources: Author’s figure based on Acemoglu and Restrepo (2019), Aghion et al (2018), Bresnahan and Trajtenberg (1995), Crafts (2021), Brynjolfsson et al (2025), Baily et al (2025) and Haskel et al (2025).
The efficiency channels increase workers’ or capital’s productivity, as automation expands the set of tasks that can be executed by capital. Through this channel, AI predominantly affects the labour market, by either substituting labour (extensive margin automation), complementing it (task complementarity) or creating new tasks. AI can also make already automated tasks more productive (deepening of automation).
The innovation channels boost productivity growth by changing the nature of the scientific process, are related to the role of AI as a GPT and an IMI and affect the technology parameter (A) in the production function.
These two channels represent the initial impact of AI on the (real) components of the supply side of the economy: the overall effect of AI on the macroeconomy will ultimately depend on general equilibrium effects through the demand side of the economy. For example, changes in employment or wages could affect household consumption and changes in the desired level of capital stock could boost business investment. Expectations about future returns or income from AI could affect both consumption and business investment.
Is Gen AI improving UK productivity?
The experience of previous GPT eras – particularly the ICT revolution in the US – can help answer this question. In the first phase of the ICT revolution, the ICT-producing sector – semiconductors, hardware, software and communication equipment – contributed strongly to labour productivity growth, whereas in the second phase labour productivity was driven by industries outside of the production of information technology, particularly the highest ICT-using industries.
Chart 1 compares industries’ contributions to UK labour productivity growth for two different time periods: the ‘post Gen AI’ period (2023–25) and the ‘Pre-Covid 19’ period (2010–19). Chart 1 shows that the major contributor over the most recent period has been the ICT sector, the ‘AI-producing’ sector in the UK. While the ICT contribution was higher in 2010–19, this was driven mainly by technology improvements in the telecommunication industry in that decade, for example increased coverage and broadband speeds, reflected in a quality adjustment in published statistics.
Of the AI-using sectors, expected positive contributions come from Administrative and support activities and Manufacturing. Financial and insurance activities, instead, show a negative contribution, despite being large and a strong AI adopter. Possible explanations include output mismeasurement and other negative factors offsetting positive AI impacts.
Chart 1: Industry contributions to labour productivity growth
Note: Average contribution to year-on-year output per hour growth in percentage points.
Sources: ONS output per hour and author’s calculations.
Productivity in the AI-producing industry
AI-producing activities are not explicitly identified in the current Industrial Classification (SIC 2007). Of the five layers of the AI supply chain – hardware, cloud computing (AI infrastructure), training data, foundation models and AI applications – UK AI activity is concentrated in AI infrastructure and AI applications.
Cloud computing services are included in ‘Data processing, hosting and related activities’(SIC code 63110). AI applications include both AI products – the development, customisation, and licensing of AI-powered software (‘Business and domestic software development’, SIC code 62012) and AI services – the provision of expert advice and technical services (‘Computer consultancy activities’, SIC code 62020).
Chart 2 shows that the contribution of both AI-producing industries stands out: ‘Computer programming, consultancy and related activities’ (SIC 62) increased its contribution to annual productivity growth tenfold, from 0.01 percentage points to 0.10 percentage points between the two periods, and ‘Information services activities’ (SIC 63), which was dragging on productivity pre-Covid, switched to a positive contribution of 0.06 percentage points.
Chart 2: Contribution of the ICT sector to aggregate productivity growth, by section
Notes: Average contribution to year-on-year output per hour growth in percentage points. The telecommunications sector is excluded, since its quality improvement in the pre-pandemic decade greatly affects the contribution of the ICT sector summarised in Chart 1.
Sources: ONS output per hour and author’s calculations.
The increase in the contribution of these two industries suggests that AI might be already boosting the productivity in the GPT-producing sectors, similarly to what happened during the ICT boom.
Productivity in the AI-using industries and across sectors
Is there any indication that AI-using industries are also becoming more productive? The second highest contributor to recent labour productivity growth shown in Chart 1 is ‘Administrative and support services’. Within these, ‘Office administrative and business services activities’ have contributed the most to aggregate productivity growth most recently, while dragging on growth in the past (Chart 3). Business service activities seem to be highly exposed to AI automation, and therefore these findings suggest that AI might have started to improve productivity in these (AI-using) areas.
Chart 3: Contribution of the administrative and business services sector to aggregate productivity growth, by section
Note: Average contribution to year-on-year output per hour growth in percentage points.
Sources: ONS and author’s calculations.
What can be said across a wider cross section of industries? Chart 4 shows the change in the contribution to aggregate productivity growth between the two periods across industries, relative to their AI adoption rate.
The regression line has a positive slope, and, although adoption only explains a small part of the variance, it is suggestive of an association between higher AI adoption and an increase in the industry’s contribution to aggregate productivity.
Chart 4: Industry contribution to productivity growth and AI adoption
Notes: Change in the average quarterly industry contribution to annual labour productivity growth over the period 2023–25 relative to 2010–19 by AI adoption. AI adoption is the percentage of companies in the industry that used any form of AI up to December 2025. Fitted line y = 0.105x – 1.3918 (slope coefficient p-value=0.065; R-squared= 0.1028).
Sources: ONS output per hour by industry; ONS Business Insights and Conditions Survey and author’s calculations. This work was undertaken in the Office for National Statistics Secure Research Service using data from ONS and other owners and does not imply the endorsement of the ONS or other data owners.
Conclusion
The previous General purpose technology era – the ICT era – was characterised by an improvement in productivity of the GPT-producing sectors, followed by faster growth in the GPT-using sectors, following widespread adoption of the technology. Similarly, with AI as the new GPT, we highlighted some evidence of an increasing contribution of AI-producing industries to aggregate productivity growth. We also showed a tentative association between AI adoption and an increase in the contribution to aggregate productivity across industries. This is of course only suggestive of a correlation, not causation. To help us understand whether AI is indeed causing productivity improvements it will be important to continue monitoring these productivity indicators, as well as developments in labour markets and in the measurement of AI adoption, together with additional and more granular analysis.
Sandra Batten works in the Bank’s Structural Economics Division.
If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.
Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.
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A new survey reveals the hypocrisy driving summer productivity slumps—and why the boss suspecting your “sick day” might be using the exact same excuse.
Sarah Miller, a software engineer from the liberal stronghold of Madison, has heard all of the chatter about a civil war in the Democratic Party between progressives and moderates. But all she cares about is winning.
That’s why Miller said she is voting in Tuesday’s gubernatorial primary for David Crowley, the Milwaukee County executive endorsed by two-term Gov. Tony Evers. He faces democratic socialist Francesca Hong in a contest that will be the latest test nationally of how far Democrats are willing to swing to the political left.
Wisconsin’s primary comes a week after progressive Abdul El-Sayed narrowly defeated Rep. Haley Stevens in Michigan’s U.S. Senate primary, setting up a crucial test for progressives in November in a key battleground state. Also on Tuesday, voters in Minnesota will decide another U.S. Senate contest featuring a centrist and a progressive.
In a year where voters upset with the Democratic Party mainstream have propelled outsider candidates to victory across the country, Crowley is hoping to buck the trend in Wisconsin. He’s banking on the support of Evers, one of the state’s most popular elected officials, to make up ground in the waning days of the chaotic primary.
Crowley said that El-Sayed’s slim margin of victory “does give me a little bit of hope.”
“We’re going to focus on those undecided voters,” Crowley said.
Wisconsin Democratic voters grapple with electability, chaotic primary
“The divide is real,” Miller, 49, said of the struggle between progressives and more mainstream Democrats. But her focus remains on November, when the winner of the primary will face Republican U.S. Rep. Tom Tiffany, a staunch supporter of President Donald Trump and one of the most conservative members of Congress.
Miller, who described herself as a progressive, called Hong a “very, very weak candidate” and described her popularity as “a little bit of a mirage.”
“I don’t think she can win,” Miller said at a Crowley campaign stop a week before election day at a winery in the rolling hills of New Glarus.
Crowley is banking on more voters feeling that way. A Marquette University Law School poll of Wisconsin Democratic primary voters conducted in late July, before former Lt. Gov. Mandela Barnes dropped out, showed Hong with a significant lead over the other candidates. More than 112,000 absentee ballots had already been returned by the day the poll was released.
Complicating the path for Crowley is the fact that he withdrew from the race on July 8 and only got back in 10 days later, with the backing of Evers, after Lt. Gov. Sara Rodriguez ended her candidacy. Then, 12 days later, Barnes also ended his campaign amid allegations of inappropriate behavior.
Barnes and Rodriguez, who were both seen as potential front-runners, remain on the ballot and votes already cast for them can’t be undone.
“We’ve created a mess,” said Michael Gebben, a 64-year-old Democrat who voted absentee for Rodriguez before she dropped out. “Hopefully we can get out of it.”
Hong bats down controversies as Crowley makes a final sprint
As Crowley sprinted across the state to capture undecided voters, Hong was batting down controversies.
Hong, a single mom and former restaurant cook, faced questions about past social media posts where she called for canceling Thanksgiving because “we should stop celebrating colonialism,” disparaged Valentine’s Day as “another day capitalism tells you how to show love” and repeatedly called for defunding the police.
She has backtracked or clarified many of those past statements in recent days, now saying that she does not support abolishing the police.
Tiffany pointed to Hong’s comments on Thanksgiving and said she wants “to destroy the traditions and culture that unite us.” Tiffany, who had 10-times more cash on hand than either Hong or Crowley did entering August, has portrayed the race as a contest between chaos and common sense.
“Wisconsinites can’t afford for November to come down to the most extreme things Tom Tiffany and Francesca Hong have said online,” Evers’ spokesperson Britt Cudaback said this week in a social media post explaining why the governor endorsed Crowley.
Crowley’s mention of the Evers endorsement drew a round of applause at both of his campaign stops in New Glarus and Madison this week, but similar votes of confidence haven’t always been decisive in other primaries. In Michigan, Democratic Gov. Gretchen Whitmer endorsed the moderate Stevens who came up short.
Even with Evers’ backing, Crowley insists he is not an establishment Democrat despite having served more than three years in the Legislature and as the top elected official in Milwaukee County, the state’s largest, since 2020.
“When people try to call me the establishment, I look at that as a slur, particularly as an African American candidate,” said Crowley, who is looking to become Wisconsin’s first Black governor. “I have never run for office to protect the establishment. I’ve always been here to protect people.”
Voters say message from progressive candidates resonates
Hong supporter Chris Vestin has heard the concerns about electability before.
To him, the fears about Hong’s chances against Tiffany in November sound like a repeat of those who pushed Hillary Clinton over Bernie Sanders in the 2016 presidential race. Sanders defeated Clinton in the Wisconsin primary by 13 points that year, before losing the presidency to Trump. Trump carried Wisconsin in 2016, lost it in 2020 and then won it again in 2024.
“It frustrates me when they say that person is unelectable,” Vestin said of Hong, who is vying to become Wisconsin’s first woman governor and first Asian American governor. “A lot of times it’s racist, it’s sexist.”
Vestin, 54, called Evers’ endorsement of Crowley “counterproductive.”
“Establishment Democrats want to take the choice away,” Vestin said.
On Tuesday in Minnesota, voters will choose between moderate U.S. Rep. Angie Craig and progressive Lt. Gov. Peggy Flanagan in the Democratic primary for U.S. Senate that’s playing out in the aftermath of Trump’s aggressive immigration enforcement surge in the state.
But the stakes are even higher in Wisconsin.
Democrats are trying to hold onto the governor’s office and flip both chambers of the Legislature to have full control for the first time since 2010.
Republicans transformed Wisconsin by enacting a wide array of conservative priorities, including effectively doing away with public worker collective bargaining, when they had full control from 2011 to 2018.
“We need a trifecta,” Crowley told his supporters at the New Glarus rally. “And who is at the top of the ticket matters.”
Crowley supporter Lindsey Lee, 61, of Madison said before the rally there that he believed previously undecided Democrats were coalescing around Crowley.
“It’s like the last 50 yards of a horse race,” Lee said. “Hong is obviously in the lead but Crowley is coming up on a side lane. I think it will be a photo finish.”
Bilt and Qatar Expand Collaboration with 3X Avios on Rent
Bilt has expanded its partnership with Qatar Airways Privilege Club. Bilt Members who hold an eligible Qatar Airways Privilege Club Credit Card can now earn a total of 3 Avios per $1 spent on rent payments up to $50,000 per calendar year, processed through Bilt.
This expansion extends the existing 1:1 Bilt Points to Avios transfer relationship with Qatar Airways Privilege Club into an accelerated Avios earning opportunity on rent payments processed through Bilt for Qatar Airways Privilege Club Credit Cardholders in the U.S. For cardholders, this new offering further elevates an already rewarding proposition that includes additional Avios on dining and Qatar Airways spending, tier fast-track, and Qpoints earning.
Eligible cards include the Qatar Airways Privilege Club Visa Signature Credit Card and the Qatar Airways Privilege Club Visa Infinite Credit Card, powered by Cardless.
Bilt Members who pay rent with either card will earn a total of 3 Avios per $1 spent on rent through Bilt. Members can also continue to transfer any additional Bilt Points earned through non-rent spend to Avios with Qatar Airways Privilege Club at the standard 1:1 ratio. But those are not free points. These payments come with a 3% fee, which means that this opportunity only makes sense if you’re looking to put more spend on your card, or you’re willing to buy Avios for 1 cent each.
For more information about the Qatar Airways Privilege Club Credit Cards benefit and how to get started, visit bilt.com/p/qatar-card.
From Silicon Valley executives promising to automate white-collar work to headlines claiming AI is foreclosing the graduate entry market, the strained ‘low fire, low hire’ environment has increasingly been ascribed to technological transformation. UK vacancies nearly halved since their 2022 peak – a contraction so sustained it has reshaped the British hiring market for the better part of three years. This post examines how evidence of AI-driven transformation at the hiring margin is proving considerably more tenuous than the headlines suggest.
Brynjolfsson et al (2025) at Stanford University champion vacancy compression in AI-exposed occupations as a canary, the labour market’s early warning system and a leading indicator of technological displacement in adolescence. Lambert and Schindler (2026) counter with a discomfiting reframe. Rather than AI, the hiring market is shifting due to the structural upheaval that remote working visited upon firms’ internal labour dynamics, breaking the lower rungs of the career ladder.
The UK makes for an exigent test case. The vacancy retrenchment here has been severe even compared to peer economies, compounded by the previous post-pandemic over-hiring, an energy shock from a land war in Europe, cyclical deterioration, and additional pressures on labour demand. This post builds off other monitoring efforts and this broader framework for tracking how AI may diffuse through the economy. This article focuses on the labour-demand channel within that framework, seeking to disentangle AI’s contribution from the surrounding storms to reveal whether the hiring market is beginning to display the kinds of patterns we might expect during the early stages of a general-purpose technology transition. What looks like weather damage to the labour market may, beneath the surface, already be a shifted shoreline.
The vacancy retreat
The fall in vacancies was not spawned from a single event. The labour market, which was exceptionally tight when vacancies peaked in 2022, gradually loosened in the following years as the cycle unwound. Pandemic over-hire met its correction as firms confronted the scale of labour they had banked against demand that never fully materialised. Remote working may have played a role too, raising the cost of training junior staff and prompting firms to withhold intake. National Living Wage upratings and changes to employer National Insurance contributions formed part of the wider labour-demand environment. Simultaneously, businesses weighing AI investment may have become more reluctant to refill headcount.
Occupational signals
Identifying technology’s footprint in the UK labour market first requires a credible measure of where AI capabilities augment labour. To address divergence between individual measures, this analysis coalesces five leading indices from the academic and industry literature, each capturing a different dimension of occupational exposure. Sourcing the original works’ task-level ‘AI susceptibility’ assessments, we reconstruct the AI exposure scores using UK occupational and industry employment data aggregated with pre-treatment employment weights. As such, the exposure scores are constructed to reflect the structure of the British labour market rather than a US-derived benchmark. By benchmarking multiple exposure frameworks and validating the resulting scores against reported AI adoption in the Bank’s Decision Maker Panel and ONS Business Insights and Conditions Survey, the measure aims to provide a more robust signal of technological exposure than any single index alone.
Chart 1 shows the strongest signal of technology disruption to hiring appears at the occupational level where occupation-indexed AI exposure exhibits a strong, monotonic correlation with online-vacancy contraction across UK occupations.
Chart 1: Growth in advertised vacancies falls as occupational AI exposure rises
Notes: Spearman p = -0.70, p <0.001, n = 26. The composite score is validated against surveyed businesses’ reported Al adoption from the Bank of England’s DMP (p = +0.68, p = 0.006, n = 15) and ONS BICS (p = +0.85, p < 0.001, n = 14).
Sources: ONS Online Job Adverts via Textkernel; internal calculations. Composite Al exposure score is a weighted average of percentile ranks across four measures (Felten et al (2021) and (2023), Henseke et al (2026), Anthropic Economic Index (March 2026 release) and Eloundou et al (2023) included as a robustness measure.
Sorted into terciles by exposure percentile in Chart 2, high-exposure groups lost 15% of online adverts, mid-exposure 10%, low-exposure 6%. The sharpest declines land where the task-based account predicts; customer service down 23%, administrative occupations down 22%. These are the task bundles – scheduling, correspondence, routine information processing – that generative systems can now credibly substitute.
Chart 2: Growth of online job adverts fell most in high-exposure occupations
Notes: Pre-period uses valid 2019 year-on-year observations because OJA starts in January 2018. Post-period covers available observations in 2023–26 Q1; March 2026 occupation cells are suppressed in the source. Terciles reflect SOC two-digit sub-major groups grouped by composite Al exposure score.
Sources: ONS Online Job Adverts via Textkernel and internal calculations.
In Chart 3’s right panel, the correlation is not clearly visible at the Industry level. This is an industrial aggregation artefact. For example, Professional services employ accountants and building services staff under the same SIC code, blurring the key occupational signal.
Unpack industries into their occupational composition in Chart 3’s left panel and the pattern re-emerges. Finance, ICT and Professional services, where high-exposure occupations account for 84%, 81% and 63% of employment respectively, show deep vacancy reversals. The compositional channel, rather than the aggregate sector, indicates where industries may be pinching hiring even when headline sectoral vacancy data obscure the adjustment.
Chart 3: Industry exposure and vacancy growth
Sources: ONS VACS02; Annual Population Survey 2022 SIC-SOC employment weights; internal calculations. Composite Al exposure score is a weighted average of percentile ranks across four measures (Felten et al (2021) and (2023), Henseke et al (2026), Anthropic Economic Index (March 2026 release) and Eloundou et al (2023) included as a robustness measure.
(a) Shows the share of each sector’s workforce employed in occupations in the top exposure tercile, calculated using APS SIC-SOC employment weights (2022). (b) Plots each SIC section’s employment-weighted mean composite Al exposure against its mean VACS02 vacancy-rate YoY growth over 2023–26 Q1 (Spearman p = +0.08, showing that aggregate sector data blur occupational composition).
Broken ladders and hollowing pyramids
Occupational signals alone cannot tell us whether the weakening in hiring can be partially explained by AI, remote working, or both. As Lambert and Schindler (2026) evidence, the industries carrying the sharpest occupational signal – Finance, ICT and Professional services – are also those that reorganised most radically around remote working during 2020–22. Both AI and WFH adoption are concentrated in highly digital, capital-intensive occupations, making the exposures difficult to differentiate. But while the infrastructural transition to support hybrid work has largely been completed, AI is still in its nascent stages and may have more persistent labour market implications.
Friebel et al (2026) provide a compelling framework for how labour markets may be shifting. The traditional pyramid structure, built on large cohorts of junior workers, may be oscillating toward a diamond, hollow at the base and centred on experienced staff. Remote working may have jumpstarted the transition by raising the cost of on-the-job training and AI strengthens the economic incentives to cement it. As generative systems increasingly absorb routine information-processing tasks, senior workers can perform more of the work that once formed the bedrock of junior hiring.
A shifted shoreline
Once the weather clears, the shoreline may look different. The UK vacancy puzzle is a picture of overlapping shocks: pandemic over-hire followed by cyclical loosening, but also remote-working adjustment, labour-cost pressures, and AI arriving close enough together that confident attribution remains premature.
What the evidence does support is that UK firms are adjusting hiring mainly through the occupational channel, with an indicative signal that this adjustment is happening prevalently in roles with a strong technology exposure. The series to watch are therefore narrow: whether employment follows vacancies down, entry-level hiring continues to weaken, and measured AI exposure translates into realised adoption. The same logic applies to productivity. As discussed in a companion article, industries reporting higher AI adoption are also showing tentative signs of improving productivity performance. If AI is genuinely behaving like a general-purpose technology, these productivity and labour-market signals should ultimately be interpreted in coordination rather than in isolation. Thus, disentangling AI’s role from these concurrent influences remains challenging, but crucial to appraising these evolving dynamics in the UK labour market.
Haley Schlicht works in the Bank’s Data and Statistics Division.
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