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.
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 medalist in CFA Institute's AI Investment Challenge introduces EarningsIQ–an AI app that analyzes earnings calls and management confidence using voice patterns.
Perhaps this is just a psychological loss, but mortgage rates are now higher than they were a year ago.
If you look at rates last August, they were below current levels, per the daily rates tracked by Mortgage News Daily.
At last glance, a 30-year fixed averaged 6.75%, while its year-ago average was closer to 6.50%.
This is yet another blow to the housing market, which has struggled with affordability woes for several years now.
But there are a couple things that could get rates back below 2025 levels…
Mortgage Rates Are Now Above 2025 Levels
If you look at this chart from MND, you’ll see that mortgage rates are now above their year-over-year levels.
Back in August 2025, the 30-year fixed began dropping fairly precipitously and sizably.
It was around 6.60% in early August, then fell all the way to around 6.125% by mid-September.
The driver back then was a really weak jobs report, which threw the whole “resilient labor market” narrative into question.
There were actually a series of really bad jobs reports and massive downward revisions that pushed bond yields and mortgage rates lower.
While it was good news for rates, it wasn’t so wonderful for the wider economy.
But then labor somehow got better and we started getting some beats on the numbers.
In addition, the Iranian conflict broke out at the end of February and that’s really pushed mortgage rates higher.
They finally fell below 6% right before the war broke out, per MND, the first time they had done so since late 2022.
Since then, it’s been a very rocky road with the 30-year fixed up anywhere from 0.75% to 0.875%.
And importantly, rates now sit above their YoY levels and it the gap could widen as time goes on.
If you look at the chart, rates kept falling as the year went on so we could be up a half-point or more by next month compared to 2025 levels.
That would look pretty ugly. Especially for the Trump administration, which campaigned on lower mortgage rates and promised even better than we saw in the past.
Two Things Can Push Mortgage Rates Lower Again
I got to thinking and there are two main levers that can push mortgage rates lower, perhaps getting them back below 2025 levels.
It’s not going to be easy though since rates spent much of the second half of 2025 at 6.375% or lower.
The first one, which I’ve carried on about many times, is a resolution with Iran. That conflict explains most of the run up in mortgage rates over the past year.
Problem is even if it gets resolved, I assume some premium will remain entrenched in rates. They won’t go all the way back to where they were pre-war.
Some defensiveness will be baked into rates and it’ll be hard to fully remove it.
The second piece is labor, which complements the inflation tied to the war and higher oil prices.
Inflation and jobs are the dual mandate of the Fed and also what drive mortgage rates.
If we get more weak labor data again, mortgage rates can ease that way as well.
Some poor labor market data could be enough to sway the Fed to hold off on any expected rate hikes.
The Fed doesn’t set mortgage rates, but Fed rate expectations can play a role. And weak economic data is mortgage rate-friendly.
So those are basically the two things that can get mortgage rates back on track.
Of course, you don’t really want to root for jobs losses and higher unemployment.
That means the thing you should be rooting for if you want lower mortgage rates is an end to the conflict in Iran, an opening of the Strait of Hormuz, and dropping oil/gas prices.
Before creating this site, I worked as an account executive for a wholesale mortgage lender in Los Angeles. My hands-on experience in the early 2000s inspired me to begin writing about mortgages 20 years ago to help prospective (and existing) home buyers better navigate the home loan process. Follow me on X for hot takes.
9 out of 10 agency clients say their agency helps them succeed. 4 out of 10 also plan to shrink that relationship within a year. Brian Gerstner has the research to explain how both are true, and it’s less dire than it sounds.
Gerstner co-founded Agency Core, which surveyed 579 agency leaders and 400 clients in 2026 on how AI is changing agency work. He and I talk through why agencies are landing in different camps: some have built real authority and charge more for it, some are still figuring out where AI fits, and plenty have room to move from routine deliverables toward strategy work.
This one’s for agency owners and marketing consultants feeling the ground shift under AI. They cover niching down without shrinking your whole business, why pricing power still exists for the right positioning, and the marketing leadership gap AI has exposed.
Guest Bio
Brian Gerstner is co-founder of Agency Core, an independent research initiative studying how agencies are adapting their business models in the AI era. He’s also president of White Label IQ, a 90-person team that works exclusively with agencies on outsourced production and development work. Gerstner has spent more than 20 years in the agency business and built Agency Core to surface the attitudes and behaviors driving agency success, in addition to the tactics.
Key Takeaways
Only 13 to 16% of agency owners fully execute on the strategic priorities they name as most important.
Niching down doesn’t require picking an industry. A region, attitude, or strategic approach can build the same “confident differentiator” status.
Client demand for agencies hasn’t dropped. The questions clients ask have changed, from “can you build this” to “should we do this.”
Commoditized deliverables (brochures, basic content, routine reports) are losing pricing power fast. Strategy, judgment, and direction are not.
29% of clients expect fee reductions tied to AI, while clients working with a differentiated, authoritative agency are willing to pay more, not less.
Chasing AI as a standalone offer is a shrinking window. It’s already table stakes, and clients want it used intentionally rather than pitched as the product.
Great Moments
[02:43] – What separates a “confident differentiator” agency from the rest
[05:49] – Only 13 to 16% of agency owners fully execute their own top priorities
[07:56] – Gerstner reconciles the two seemingly contradictory client statistics
[14:07] – What the pricing data shows about fees, AI, and expertise
[17:49] – Niching down doesn’t mean picking one industry, it means having a focus and sticking to it
[20:17] – The hidden challenge: retraining an existing team that isn’t built for the work agencies need now
Memorable Quotes
“There’s a reason the compass was invented before the clock. It’s because it’s more important to know where you’re going.” — Brian Gerstner
“There is still probably more opportunity than ever before if you can take the time to see it.” — Brian Gerstner
“If you focus down, if you niche in, if you lean into an area, it is an investment. It’s hard. Growth is painful.” — Brian Gerstner
“Coming in the strategy door is a far better relationship than coming in the vendor door.” — John Jantsch
“The moment other people start saying these people have a great reputation in this area, that’s when you’re truly establishing that confident differentiating position.” — Brian Gerstner
“You’re gonna have to hire a strategic thinker who can become a leader, because the doers, we can outsource.” — John Jantsch
Resources
Agency Core, agency pricing, agency strategy, AI marketing, Brian Gerstner, niche marketing, White Label IQ
For most of the last decade, I looked at Amazon(AMZN -1.72%) and thought, “Amazing business, but too messy for me as a shareholder.”
I saw razor-thin retail margins, huge capital spending, and a company that seemed to reinvent itself every other year.
Now I find myself changing my mind, not because the stock has gone up, but because the underlying business looks very different than the one I kept passing on.
Image source: Getty Images.
The profit engine I underestimated
For years, I treated Amazon as just a retailer. That was my first mistake. Today, its own filings make it clear that the profit center is Amazon Web Services (AWS), not cardboard boxes. In 2025, AWS generated roughly mid-double-digit billions in operating income, far more than the retail segments, and did so with margins that look more like a software company than a store.
What changed my thinking was seeing that AWS is not a side hustle layered on top of e-commerce. It is the backbone of a huge part of the internet, with long contracts, deep integration, and economics that can fund a lot of experimentation elsewhere.
When I used to worry about Amazon’s spending, I did not fully appreciate that a high-margin engine was quietly paying the bills.
Today’s Change
(-1.72%) $-4.77
Current Price
$272.65
Key Data Points
Market Cap
$2.9TMarket cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded shares. Implied market cap may vary.
Day’s Range
$270.73 – $282.79
52wk Range
$196.00 – $287.20
Volume
45.1M
Avg Vol
50.5M
Gross Margin
50.77%
A quiet advertising giant
The other blind spot for me was advertising. I always thought of ads as something that mattered for Alphabet and Meta, not Amazon. Then I started reading Amazon’s own numbers and language around “advertising services” and “retail media.” Earlier this year, Amazon disclosed more than $60 billion in annual ad revenue, growing at roughly 20% year over year and outpacing the core business.
That is not just banner clutter. It is brands that are paying for a front-row seat at the moment of purchase. Amazon sits where intent is strongest. The more I thought about that, the more I realized this might be one of the most durable profit streams for the company, built on shopper data and closed-loop measurement that is hard to replicate.
The logistics moat I used to see as a cost problem
My last hang-up was the warehouses, planes, and vans. I saw a cost monster. Over time, Amazon’s history of its fulfillment and delivery network started to read differently. This is not just a way to move packages. It is an infrastructure grid that gets products closer to customers than most rivals can, and now supports third-party sellers, same-day delivery, groceries, and more.
There are still real risks: massive capital spending, regulatory scrutiny, and a business culture that demands constant reinvention. But after 10 years of standing on the sidelines, I now see a company whose profit mix has shifted toward AWS and advertising, with a logistics network that looks more like a moat than a burden. For me, that combination finally justifies owning Amazon as a long-term core holding, sized reasonably, instead of treating it as a great business I never quite trust enough to buy.
People become more suspicious of a humanoid robot that makes errors, especially when the robot is an expressive conversation partner.
In our new study published in the journal Science Robotics, we had 50 people hold conversations and make joint decisions with the commercial humanoid robot Pepper, which is designed to be expressive and recognize emotions. Sometimes we had the robot give sound advice. Sometimes we had it make conversational mistakes, interrupting people or pushing illogical suggestions.
For some participants, the robot was animated, using gestures, eye contact and nods. For others, it stayed motionless.
We measured four things: brain activity, levels of the hormone oxytocin, self-reported trust and our observations of the robot’s influence on participants’ decisions.
We found that when people interacted with an expressive robot that violated interaction norms, their oxytocin levels increased. Oxytocin is popularly known as the “love hormone” for its role in social bonding, so the straightforward prediction is that it declines when a partner disappoints you.
Instead, the higher a person’s oxytocin during an expressive robot’s errors, the less they trusted the robot and the less often they took its advice. It turns out that the hormone was tracking with suspicion, not affection.
Errors damaged trust and diminished influence whether or not the robot was expressive. What expressiveness in the robot changed in participants was how their brains handled the moment.
Reading someone’s brain during a real conversation is hard because the conventional method requires lying motionless inside an MRI scanner. Instead, we used functional near-infrared spectroscopy, a portable sensor worn on the forehead that tracks oxygen levels in the brain while people move and talk normally.
The two brain regions we closely watched were the dorsolateral prefrontal cortex and the medial prefrontal cortex. The dorsolateral prefrontal cortex monitors uncertainty and flags when expectations or norms get broken. The medial prefrontal cortex supports “mentalizing,” the everyday work of inferring what another party intends.
When an animated robot erred, people seemed caught off guard and had to work harder to make sense of an awkward social situation. Activity rose in the two brain regions, and the two started working together more closely. That closer teamwork predicted the rise in oxytocin levels, which itself predicted falling trust and less influence on participants’ behavior. In contrast, this coordinated brain activity was absent in participants who interacted with expressionless robots.
With expressive humanoid robots entering homes, nursing homes and work places, how people react to them is increasingly important. Boris Roessler/picture alliance via Getty Images
Why it matters
Robots are moving into homes, hospitals and workplaces, where trust in robots determines whether people use them at all. A common design assumption has been that lifelike, socially expressive robots earn more trust, which protects a robot’s “reputation” even when it makes mistakes.
However, research is beginning to show that that assumption is faulty. Our work shows that expressive cues appear to shift how people perceive a mistake out of the category of technical malfunction and into the category of social violation, like those that happen between people.
A motionless robot’s error looks mechanical, while the same error from an animated robot engages the machinery you use to judge people.
What other research is being done
Researchers increasingly treat trust as a multilevel phenomenon – spanning individuals, relationships, networks of people and societies – rather than a single attitude.
Much research on oxytocin involves humans interacting with humans, where the hormone is tied to bonding, though a growing body of work shows that those effects depend on the context, uncertainty and perceived threat.
Others are using wearable brain imaging systems to study social cognition in natural encounters between people, which isn’t possible when subjects are in scanners like MRI machines.
What’s next
The participants in this study were all young men, and we used one robot design. A key next step is testing whether the same oxytocin-linked vigilance appears in women, mixed groups, other cultures and other robot designs. Our brain sensor also reached only the front of the brain, leaving deeper regions involved in social processing unmeasured.
We also want to examine whether robots can repair trust after a mistake by acknowledging the error, apologizing or signaling good intent, the way that people do after awkward or uncomfortable interactions.
The Research Brief is a short take about interesting academic work.
Hasan Ayaz, Professor of Biomedical Engineering, Science and Health Systems, Drexel University; Ewart J. de Visser, Technical Director, Warfighter Effectiveness Research Center, United States Air Force Academy; Frank Krueger, Professor of Systems Social Neuroscience, George Mason University, and Yigit Topoglu, Research Scientist, Warfighter Effectiveness Research Center, United States Air Force Academy
This article is republished from The Conversation under a Creative Commons license. Read the original article.
