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The economist. Hello and welcome to the Intelligence from the Economist. I'm your host Jason Palmer. Every weekday we provide a fresh perspective on the events shaping your world. A horrific attack in western Nigeria earlier this month is is just one sign of a troubling change. Jihadist groups are splitting, leading to more violence between them and spreading ever closer to the country's urban centers, threatening violence for all. And nobody ever gave Virginia Oliver any hassle for being a woman running a lobster boat. No one dared. We look back on a career spent working Maine's waters for nearly a century. But first, Your favorite economist and mine, John Maynard Keynes, made one wild assertion back in 1930 in his essay Economic Possibilities for Our Grandchildren. He does a potted history of humanity, pointing out the incredible pace closer to his time of what he keeps calling technical inventions and technical improvements. They'd had huge impacts on workers productivity. He concludes that by 2030 we'd all be working 15 hour weeks. I don't know about you, but four years out and that still looks unlikely. People love pointing out this folly of the great man. But let's take a broader lesson. Big technical improvements like say, artificial intelligence take maybe a little longer than you might think to have big economic outcomes.
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AI capabilities are certainly improving very fast, but the effect of AI on the economy, not so much.
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Alex Domash is our economics correspondent.
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AI may well lead to a productivity boom one day, but that productivity boom is not here yet.
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And you say that because you've been digging into the economic data. What are they saying?
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Looking at America, there has been a sort of puzzle in the macroeconomic data, especially over the last year. So throughout most of 2025, you had on the one hand a booming economy. You had real GDP growing quite rapidly for most of the year. And at the same time you had a slowdown in hiring. You had pretty sluggish employment growth. And especially in the second and third quarters in America, real GDP was growing quite rap. In the fourth quarter, we did get a real GDP print that was lower than expected. It came in at 1.4%, which sort of tempered the narrative of this big GDP boost with slow employment. But still throughout 2025, you did have real GDP growing at 2.2% while you had employment growing at an average of 15,000 jobs per month, which came out to about 0.1% employment growth through the year. So the fact that there was this big gap between real GDP growth and slow employment growth, it usually would imply that productivity growth is quite high in that workers are producing more with less
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hours worked you say the gap between those numbers would normally be attributed to a growth in productivity per worker in a way that suggests that's not the explanation here.
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Correct. So 2025, of course, was a unusual year for many reasons. But looking first at the output side, of course, we had these massive expenditures in artificial intellig. Firms were investing in AI infrastructure, and studies have shown that this did largely boost real GDP growth in America. At the same time, on the employment side, there were some funny things that were going on there as well. You had tighter immigration policy with the Trump administration. You also had this phenomenon with a lot of temporary workers that were falling out of the labor market. And what this did was basically one, it's artificially constricted employment growth, but it also meant that the types of workers that were getting pulled out of the labor market tended to work in lower productivity sectors. I will also say that while a gap between output growth and employment growth sounds unusual, actually, when I looked at the data since 1950, in 1/3 of the years, the gap between those two was actually 2 percentage points. So by that metric, what we actually saw last year was not all that unusual.
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Okay, so if instead we are looking for solid measures of the contribution of AI to productivity, where should we be looking?
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It's not a straightforward question, but one intuitive estimate that you could do is you could one, look at what does research say about adoption of AI among workers? Two, among the workers that are using AI, how frequently are they using it? Are they using it every day? Are they using it sparingly? And the third thing that you'll want to look at is what the research says about the productivity gains from actually using this technology.
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Let's start with the adoption that you mentioned. We already know that's fairly high.
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Yeah. So the adoption is high, and it's been picking up over the past year. And it is sort of striking that across a number of different studies, they all find almost the same thing, and that's that 4 out of 10 working age Americans are using AI on the job. So we're pretty comfortable that in our estimate for AI, adoption is around 40%.
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Okay. And then it was the intensity. People are using it, but how much?
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So the intensity is a little bit surprising. They're not using it as intensively as you might think. So only 13% of Americans are using AI every single day on the job. And some recent research tried to quantify total hours where workers are using AI in the workplace. And this typically comes at about an average of 2 hours per week across all workers. Which is about something like 5 to 6% of total working hours. Workers are using artificial intelligence.
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And then what seems to me to be the real nub of the question, when people use it, how much it gains them?
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Basically, yeah. And so here there's been a number of studies over the past couple of years that have tried to answer this question. Some have looked at BCG consultants and gave them a lot of real world exercises and tried to measure are they doing the tasks quicker using AI, are they able to produce their PowerPoints, produce their writing, produce their business plans more efficiently. And other studies look at in a legal context, others look in a writing context. And a synthesis of this literature found that on average, workers using AI have seen efficiency gains between 15 to 30%.
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Okay, so taking it all together then, I mean, those all sound like fairly positive numbers. These are all pointing towards productivity gains. When does the massive boom that people are talking about actually start?
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Yeah, so if you do a back of the envelope calculation using these three numbers, so 40% adoption at an average of two hours per week, at an average of 15 to 30% efficiency gains from using the technology. Technology, what you find is that productivity over the past year would have increased between 0.25 and 0.5 percentage points, which sounds very small, but it's actually not insignificant. However, that's almost certainly an overestimation. One, that assumes that every time a worker is using AI, they are using it to its full productive capacity and using it to increase their efficiency. And two, it assumes that all of the time saved from using AI is then getting redeployed to other productive tasks. And I think anybody thinking about their own experiences in the workplace knows that this is hardly a realistic.
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If something makes my my job easier and take less time, I'm going to knock off early.
B
And fascinatingly, there was actually a study that came out over the past couple of months that looked at tech workers using AI. And maybe this isn't that surprising if you've been following the news, but tech workers are working more than ever, so I might be increasing their productivity. And those increases in productivity have actually gone into working more hours, experimenting more with the newest models with Claude code and Vibe coding and all of that stuff.
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So what's the take home message here? Is it the issue that adoption is still on the rise and people are still figuring out how to use it, and that we're too early in the story to say what the ultimate productivity tale is?
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So the real message is that when you look at history and you look at when an economy actually sees a productivity boom. The lesson is quite clear that productivity gains actually occur when firms reorganize their production around the technology technology and when they start adopting new business models rather than workers just using the technology more. For example, when electricity first arrived, you didn't see a big productivity boost from simply replacing steam engines with electric motors. Where you actually saw the productivity gains was when floor plans in factories were redesigned to capitalize on electric power. The same occurred during the computer age as well. Of course, many will be familiar with Robert Solow, a Nobel Prize winning economist who had a quote that basically the computer age could be seen everywhere except in the productivity statistics. And the reason was that simply using computers was not what was actually going to lead to the big efficiency gains. It was when companies actually restructured and reorganized and retooled their operations around this technology. And so organizing businesses around artificial intelligence is when the real productivity gains will occur.
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Alex, thanks very much for joining us.
B
Thank you for having me, Jason.
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On February 2, as every evening the residents of Kayama in western Nigeria follow the call to prayer. Many never made it to the mosque. Armed men brutally attacked two villages in the region. Residents were shot at close range or their throats slit. Some were burned alive.
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They set them ablaze, you can see for yourself.
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But we did them no wrong.
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Nigeria's president has since sent troops to the region, including Brigadier General Nicholas Roum.
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Our aim is to stabilize the area
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and then expand our operations outwards to
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ensure that we track and possibly strike
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the people who did it. America announced it's sending military support too. But the bigger problem is not just the violence in Kiama, it's that it reached Kiama at all.
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For years, Nigeria has faced several interlocking security crises, especially in the northeast and the northwest.
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Ore Ogunbi is an Africa correspondent for the Economist.
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I think the mood in the country right now is quite bleak. This is one of the most deadly attacks that we have seen in history and we're seeing a sharp rise in violence as things are moving more south. And that's worrying because that's much closer to where the urban centres are.
A
And so who is actually behind this attack?
D
Well, in the government's first statements, they pointed to Boko Haram. And Boko Haram is often used as a general term to refer to lots of different armed groups. And actually the group that is usually referred to as Boko Haram splintered years ago into a few different ones. So there is Iswap, which has stayed mostly in the northeast. And then there's another group called Jas, and they've been spreading a lot further beyond that zone in a bid to make more money and to expand their influence. And they've come as far down as central Nigeria, so it seems like they are the most likely culprits. However, there are other jihadist groups that also operate in that area. So there's jnim, which is a group that's been linked to Al Qaeda. There are others too as well that are operating along that border with the Benin Republic. And to make matters even more complicated, there are also non ideological groups who are simply referred to as banned. They're criminal groups that normally deal with cattle and guns as well, and a lot of kidnapping. They move from the northwest downwards. And so there's also a possibility that some of the violence that we're seeing more of, not just this attack, but could also have been there.
A
So as you say, it is a massively complicated situation with a whole bunch of different groups that are vying for territory or rule of the people or the cattle. What's the relationship here?
D
Well, that's the question. It's quite complicated. And I think they all have different aims, but they all speak with one language, which is violence, unfortunately. So you've got some jihadist groups which are trying to capitalize on where the bandit groups are weaker. So they win over the locals by convincing them that they will protect them from the bandits before kind of pushing forward with their own motives. You have some areas where the bandit groups are so strong that actually if jihadist groups do try and enter them, they end up in these really violent clashes and civilians get caught in a crossfire. And then you have some groups who are coming in from kind of outside the country or more from those border territories like I mentioned, along the Benin Republic, who are also getting involved in these spats. So you've got a few different groups competing for people, power and territory and it looks different in different places. And in Kwara, which I think is quite worrying, there seems to be an overlap of quite a few.
A
And so as this ultimately becomes a wider spreading clash over territory, the government surely has had some thoughts on how to stop this happening?
D
Well, yes, exactly. And normally when there's a big attack like this, just as the president did, in this case, he sent in the troops. I think the challenge is just how effective these response efforts actually are, because they often lead to reprisal attacks. And by the time the fighters, whether that is the jihadists or the bandits, come back, most times the military presence has gone and the people aren't protected anymore. So I think there's always a fear that when troops are sent in, especially after the fact that that you actually end up putting the communities at more risk in the long term. And unfortunately, the army is actually spread quite thin across the country. They're trying to put out multiple fires at once. So there are still these ongoing conflicts in the Northeast. You still have the focus of banditry being in the Northwest, and now they're trying to put out fires in the southwest, also central communities as well. So the military spread quite thin. And even though they're trying to respond, I think it's proven quite tough for them to keep on top of things and also to protect communities from further retaliation attacks.
A
Well, putting out fires is a good way to put it. This all sounds quite reactive. Is there something proactive to be done, something more systemic to be done to stop the spread?
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I think you have state governments that are trying to take the security of their own states into their own hands, so through the police, but also through local paramilitary forces, so forest guards, for example. And that's quite important because there's a lot of forest cover, especially that runs down the border of Nigeria with the Benin Republic, which is where a lot of jihadists and bandits are now taking cover. And so I think the hope is that the states can harness their internal resources and do a bit more to support the army. But I think what people are hoping will make a real difference at the moment is that America is also sending in troops. They're saying that their troops won't be getting involved in combat and that this has more to do with weapon sharing and training and intelligence support. But if that does signal to jihadists and bandits alike that Nigeria is better supported and that Nigeria is prepared to fight back, then maybe that will finally make a difference.
A
Orey, thanks very much for your time.
D
Jason, thanks so much for having me.
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The knocking of a diesel engine coming to life, followed by steady, low chugging and a roar that grows quieter in the distance. That is the sound of early mornings in coastal Maine.
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John Fasman is our senior culture correspondent and is standing in for our obituaries editor this week.
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Once on a time. It's a very long time. It's a year or it may. Virginia Oliver first went out on the water with her father, who sold lobsters and had a general store in the Muscle Ridge Islands when she was 8 years old. Back then, traps were rickety crates of wooden slats that needed heavy ballasts to sink them and strong arms or a sturdy winch to raise them. Not like today's wire mesh traps that sink easily and get pulled up by mechanical haulers. This was before the Great Depression and the Second World War and not too long after those sea cockroaches were considered fit only for prisoners, servants and farm animals. Female lobstermen are rare today, as the profession's common name suggests, but they were unheard of when Ginny was growing up. Back then, women could knit nets for the wooden traps their husbands made, but their place was unsure. As much as she enjoyed being on the water with her father, Ginny went to school, living on the mainland with her aunts and grandfather in Rockland during the week. Then she married and had four children, and when the youngest was 9, she went back to paid work. She spent 19 years at a printing press in Rockland, lugging heavy equipment around, but got tired of it. She told an interviewer when she was 101 years old, with lobstering, I wouldn't have to work half as hard and I could be my own boss. So one day when her husband came home, she told him, I just quit. I'm going with you. It's not hard to see why. The intricate waterways wending among tiny pine forested islands in the Pedobscot Bay traverse a rugged, dramatic, intimate seascape which tourists empty their pockets every summer to see for a fleeting week or two what she got to see every day. Rocky outcroppings were sealed the throughout hoot at passing boats, the sun rising over Vinalhaven and riots of reds and golds and setting behind the mainland as the sky goes from periwinkle to cornflower blue to an endless canopy of stars. From the time she left the printing press, she reminded her husband Bill, her fishing partner for 60 years, and then her son Max, who took Bill's place after he died, that she was indeed her own boss and theirs too. She cut an unusual figure on the water, always going out in earrings and lipstick. As she explained, you never know who you're going to see. Lobstering can be tough. If a skipper sets traps in waters that everyone knows belong to lobstermen from another harbor, she might find her traps emptied or buoys missing. If things get really bad, tires can get slashed or guns drawn. But Ginny was tough, too. Nobody ever gave her grief for being a woman running a boat. I'd have told them off if they did, she said. She had a mouth like a sailor. Three days a week, Ginny would wake up at 2:45am for the 15 minute drive south to Spruce Head, where she and her son Max kept their boat, the Virginia, named by her late husband. They would row their little skiff from the shore to where their boat was moored for the night. After sating Virginia's appetite for diesel and hauling her bait on board, usually a box of reeking menhaden, which bottom feeding lobsters love, she and Max would leave the harbor at daybreak. She skippered her own boat until a fall when she was 103, confined her to the mainland 95 years after she first put to sea with her father. She did this in relative obscurity until a local filmmaker persuaded her to appear in a documentary called Conversations with the Lobster lady. When she was 99. Viewers learned that she went to the supermarket every day just to get out of the house and see people, and that her children, then aged 74, 76, 78 and 81, still came for supper every Saturday night. Soon enough, television networks and feature writers found her. A local poet and author wrote a children's book about her life. The only thing that seemed to discomfort her were threats to her independence. After a doctor asked her why she was still lobstering in her late 90s, she said, well, it's because I wanted to go. And she confided to an interviewer, he really made me mad. She liked keeping busy and modest. Manor to her bones, took the attention in her stride. There's always something to do, she would say in her musical down east accent. I don't think I'm anything too special. But others disagreed.
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John Fasman on Virginia Oliver, who's died aged 105. That's all for this episode of the Intelligence. The show's editors are Chris Imperial and Jack Gill, our deputy editor is Sarah Lornjuk, and our sound designer is Will Rowe, with help this week from Mark Burrows. Our senior producers are Rory Galloway and Henrietta McFarlane, and our senior creative producer is William Warren. Our producers are Jonathan Day and Anne Hanna, and our assistant producer is Kunal Patel. We'll all see you back here tomorrow for the weekend. Intelligence this week we pay a visit to Lithuania, one of the NATO member Baltic states whose security stance has changed considerably since the war in Ukraine began. There's a national push for preparedness in case Vladimir Putin sets his sights on neighbors to the north. We embed with one of the territorial defense outfits that's training up. Not soldiers, just normal folk who hope never to use the skills they're now honing.
Date: February 27, 2026
Host: Jason Palmer, The Economist
Main Guest: Alex Domash, Economics Correspondent
This episode explores the paradox between the rapid advances in artificial intelligence (AI) and its seemingly modest impact on economic productivity data. Host Jason Palmer and economics correspondent Alex Domash delve into the reasons why, despite significant technological change, the productivity boom many expect is not yet visible in the numbers. The discussion addresses how AI is adopted in the workplace, the current scale of its economic effect, historical parallels with past technologies, and what it will take for AI to truly transform productivity.
Introduction (00:03–02:02):
Why Aren’t We Seeing the Boom? (02:02–03:38):
How to Measure AI’s Effect? (04:46–06:55):
Domash outlines a three-part measurement:
Memorable quote:
“Workers using AI have seen efficiency gains between 15 to 30%.”
— Alex Domash (06:22)
Net Effect So Far: (07:07–07:59):
When combined, these factors might suggest a 0.25 to 0.5 percentage point boost to productivity, “which sounds very small, but it’s actually not insignificant.”
Domash cautions this may overstate reality, as it assumes perfect redeployment of time saved and maximal use every time AI is applied—a “hardly realistic” scenario.
Funny aside:
“If something makes my job easier and take less time, I’m going to knock off early.”
— Jason Palmer (07:59)
Domash adds: Tech workers are working more hours, not less, with productivity increases leading to more experimentation and overtime (08:04).
“Big technical improvements like, say, artificial intelligence take maybe a little longer than you might think to have big economic outcomes.”
— Jason Palmer (01:35)
“AI may well lead to a productivity boom one day, but that productivity boom is not here yet.”
— Alex Domash (02:14)
“Four out of ten working age Americans are using AI on the job.”
— Alex Domash (05:24)
“Workers using AI have seen efficiency gains between 15 to 30%.”
— Alex Domash (06:22)
“If something makes my job easier and take less time, I’m going to knock off early.”
— Jason Palmer (07:59)
“The real message is that productivity gains actually occur when firms reorganize their production around the technology and when they start adopting new business models, rather than workers just using the technology more.”
— Alex Domash (08:38)
The tone of the discussion is analytical yet accessible, blending data-driven insights with historical anecdotes and a touch of humor. Both Palmer and Domash explain complex economic phenomena in clear terms, highlighting both promise and caution regarding AI’s transformative potential.
This episode offers a nuanced look at the much-hyped but as-yet-unrealized economic impact of AI, distinguishing between adoption, usage, and the deeper organizational changes required for true productivity gains. The key message: the AI revolution’s effects are still in progress, and history suggests the real leap will come when workplaces fully retool around these new capabilities—not simply when they plug in new tech.
For readers: This summary distills the major economic conversation in the episode. Advertisement, intro/outro, and non-AI content (such as segments on Nigeria and Virginia Oliver) have been omitted per your instructions.