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If you have read a single headline about AI in the last 2 years, you already know which jobs are supposedly under threat. Programmers are being laid off, junior developers can't find work, and tech companies are freezing hiring because one AI tool can now do the work of a dozen entry-level engineers. The discourse is loud, anxious, and honestly, a fair bit of it is justified. But, according to a new study out of MIT, it's also almost entirely focused on the wrong thing. Most people, and most headlines, treat AI as something that replaces jobs, but that's not how it works. More often, AI replaces the
tasks inside them. A lawyer doesn't disappear overnight, but the hours they spend reviewing routine contracts might quietly shrink. A journalist keeps writing, but the time spent researching background, pulling quotes, and fact-checking starts to compress. And while that might sound like a softer version of the same story, it definitely isn't. Because our entire economic system, the way we measure work, track productivity, and plan for the future is built around jobs, not tasks. GDP, unemployment figures, and wage data were designed to count jobs and people, and they do that reasonably well. But, they were never built to look inside a job and ask which parts of it AI can already technically perform. So,
the change doesn't show up where we're looking for it. By the time the disruption shows up in the official numbers, it's already well underway, and every plan governments have made to prepare their workforces has been built on instruments that are pointed in the wrong direction. It's a bit like trying to navigate a new city using a 30-year-old map. The streets look familiar, but nothing is quite where you'd expect. This is exactly why a team at MIT decided to build something new. Not another prediction of which jobs will disappear, but an actual map of where AI capabilities and human skills currently overlap, weighted by the economic value of that work. They called it the Iceberg Index, and the name turns
out to be exactly right. When you measure the work AI can technically perform across the tech sector, it accounts for about 2.2% of total US labor market wage value, roughly $211 billion. That's the visible tip of the iceberg, but when you apply the same methodology to the whole economy, the number jumps to 11.7%, roughly $1.2 trillion, and five times larger. That's the part underwater, and it includes highly educated, well-paid professionals in industries and sectors that haven't generated a single anxious headline about AI. So, as always, we've got some important questions to answer. What exactly is the Iceberg Index, and why does asking about skills instead of jobs change everything? What does the
index actually reveal about what's going on below the surface, and how much of the story we've been missing? And finally, what happens to the workers and industries that AI simply cannot touch? And why is that answer a lot stranger and a lot more expensive than most people expect? Running a small business means wearing every hat. Sales, purchases, taxes, client communication, and everything in between. That often means juggling an endless array of business tools and platforms just to keep up. That's where today's sponsor, Odoo, comes in. Odoo is an all-in-one business management platform that brings all your business operations under one roof. CRM, invoicing, payroll, taxes, and more.
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risk-free with a 14-day trial, no credit card required. If you're ready to simplify your business and get back to focusing on growth, click the link below to get started today. Every major economic transition in history eventually forced economists to build new tools to measure what was actually happening, because the old ones simply weren't designed for the new reality. In the industrial era, output per hour had to be invented because nobody had a way to capture physical productivity at scale. In the internet era, GDP ran into a rather embarrassing problem, which is that it could measure the value of encyclopedias sold in
shops, but couldn't measure the value of Wikipedia, which replaced them altogether. So, the US Bureau of Economic Analysis eventually had to build an entirely separate accounting framework, the Digital Economy Satellite Account, just to capture the value of services that existed outside normal market transactions. MIT is arguing that the AI era calls for a similar overhaul. Intelligence is now a shared input between humans and machines, and the tools we inherited were simply never designed to measure that. The Iceberg Index is MIT's attempt to build something that is. So, how did they actually do it? Well, they started by creating a digital representation of 151 million American workers spread across 923 different occupations and 3,000
counties. To map the skills each occupation requires, they use something called O*NET, a database maintained by the US Department of Labor that breaks down hundreds of occupations into their actual component skills and tasks. Things like analyzing data, critical thinking, interacting with computers, programming, and coordinating with others. And in the database, each of these skills comes with an importance rating and a difficulty level built from surveys of real workers doing those jobs. So, instead of saying there are 4 million accountants in America, the database is saying, "Here are the 37 specific skills accounting work requires, here is how important each one is, and here is how difficult each one
is to perform." Which is either a very thorough way to think about employment or a very bureaucratic one. But, either way, it turns out to be extremely useful. Then, they did the same thing for AI, cataloging more than 13,000 real production-ready AI tools that exist and are being deployed inside companies right now. From coding assistants and document processors to financial analysis software and workflow tools on platforms like Zapier. And crucially, they put every single one of those tools through the same O*NET skill taxonomy. So, you end up with a profile of what each AI tool can do, expressed in exactly the same language as the human workers. Now, you have two maps using the same framework, and for the
first time, you can make a genuinely apples-to-apples comparison between what human workers actually do and what AI systems are technically capable of doing right now. The result is a single number for each occupation, a percentage that measures how much of the wage value inside that job AI can technically perform. And the word wage value is actually the most important design choice in the whole study, because rather than asking how many accountants might lose their jobs, they asked how much of the economic value that accountants produce comes from skills AI can already perform. An accountant might spend 60% of their time on document processing and data entry, and 40% on complex judgment calls and client
relationships. Those are very different things, and treating them as equivalent would tell you almost nothing useful. Because automating 60% of someone's time doesn't mean automating 60% of their value. Weighting by wage value, on the other hand, means the index reflects where the actual economic exposure sits. Now, there are a few things the Iceberg Index deliberately does not do, and it's worth being clear about them. It doesn't account for physical robotics. For now, this is purely about digital and cognitive AI tools. And it measures technical capability only, not outcomes, which means it doesn't predict job losses or forecast when any of this will actually happen. What companies do with
this information, when they do it, and whether regulators allow it, none of that is in the model. Think of it less like a weather forecast and more like an earthquake risk map. It tells you exactly which buildings are sitting on a fault line, not if or when the tremor will hit. So, what does the map actually show? Before we get into the findings, let me tell you about the Economics Explained newsletter. This channel covers big stories worth 15 minutes of your time, but there's a lot of important economic stuff happening every week that doesn't quite make the cut for a full video. So, we write about it instead, covering things like the businesses selling fake academic research that's corrupting
trillions in government spending, why three companies are quietly profiting from pharmacies closing, and how a water shortage in Mexico sent cilantro prices up 400%. It goes out once a week, it's completely free, and it won't waste your time. The link is in the description below, or scan the QR code on screen. Now, back to what the Iceberg Index actually found. It starts with the part everyone already knows about. In 2025 alone, more than 100,000 job losses were linked to AI restructuring. AI is already writing more code every day than every human developer on the planet combined. And we already know that when you measure the wage value of the work inside those tech jobs that AI can
technically perform, it comes to about 2.2% of the entire US labor market, roughly $211 billion across 1.9 million workers. This is where almost every headline and every policy paper and every anxious conversation about AI and jobs has been focused. But, 2.2% is a misleading place to stop. 2.2% is just the visible tip of the iceberg, but the capabilities driving that number, skills like document processing, routine analysis, and data handling, don't belong just to tech. They show up across hundreds of occupations that would never appear in a headline about AI layoffs.
The same capabilities that make a coding assistant useful to a software engineer overlap heavily with the work of a financial analyst, an HR coordinator, an insurance claims processor, and a legal secretary. So, when you run the index across all the skills, that's where you see the number jump from 2.2% to 11.7% of the total US labor market, or roughly $1.2 trillion in wage value. This means the anxiety about AI that's been dominating headlines for 2 years has been aimed at roughly 1/5 of the actual problem. The other 4/5 have been sitting on a fault line that no government, no company, and no individual worker has been preparing
for. And the people sitting on it aren't who you'd expect. According to a separate Anthropic study tracking actual AI usage in professional settings, the most exposed group earns 47% more on average than the least exposed, is nearly four times as likely to hold a graduate degree, and is 16 percentage points more likely to be female. In plain terms, anyone whose working day is built primarily around reading, writing, analyzing, and summarizing information. People who, by any reasonable measure, did everything society told them to do, and did it well. Now, the gap between what AI can technically do and what it's actually doing in practice is still enormous. And for the moment, that
gap is what's keeping much of this exposure theoretical rather than real. For computer and math workers, AI is theoretically capable of handling around 94% of their tasks, but in observed professional use, it's currently doing about 33%. And a similar pattern shows up in legal work, in architecture and engineering, and across many other professions. The technical capability is already there, but right now it's being held back by regulation, integration challenges, and the simple fact that most organizations still require a human to check AI's work. These are all friction points that may resolve themselves as technology matures. And while the full exposure hasn't landed yet, the leading edge of it is already
visible in the hiring data. IBM replaced a chunk of its HR department with AI tools, while Salesforce stopped hiring engineers and lawyers because, as its CEO put it, AI can do the work. Entry-level employment in AI-exposed occupations has already dropped 14% compared to the pre-ChatGPT era. And that's probably just the beginning because job postings fall before employment. And entry-level job postings across the US have dropped 35% since January 2023. Now, if you had to guess which states are sitting on the biggest fault lines, you'd probably say California, Washington, and New York. But, you'd be wrong. According to the Iceberg Index, South Dakota, North Carolina, and Utah
show higher exposure values than California or Virginia. And yes, I know those aren't exactly the states generating anxious op-eds in the New York Times. The problem is, their economies happen to be heavily concentrated in administrative and financial work, which are the exact skills sitting in that gap between what AI can technically do and what it's currently doing in practice. California's workforce is diversified enough that the exposure spreads thin, but in states built around finance and back-office services, the vulnerability concentrates. Tennessee makes this point most starkly. Its tech sector exposure
is 1.3%. Nothing that would trigger alarm bells in any standard workforce planning model, but its Iceberg Index is 11.6%, which means the white-collar workforce keeping Tennessee's factories running is 10 times more exposed than the tech sector everyone's been watching. Ohio and Michigan follow the same pattern. These states have spent years preparing for robots to take over the factory floors, but the white-collar disruption is arriving first. Which brings us to the most important finding. If the exposure is this widespread and this geographically distributed, why aren't the people responsible for preparing the workforce seeing it? A big part of it is simple.
The tools they're using cannot see this kind of risk. GDP, per capita income, and unemployment, the standard metrics, explain less than 5% of the variation in Iceberg Index scores across states. In some cases, the relationship even flips, meaning states that look safest by conventional measures aren't necessarily the least exposed. At the same time, the states that look most vulnerable aren't necessarily the most at risk. That means the billions being spent right now on workforce preparation may be systematically aimed at the wrong places entirely. But, that's only the picture for workers inside that 11.7%, the ones exposed to AI. What about the workers who aren't? Because according to Anthropic's research, about 30% of the workforce has
essentially zero AI exposure. This includes cooks, mechanics, nurses, plumbers, bartenders, childcare workers, people doing physical, relational, hands-on work that no language model can replicate. Surely those workers are fine, no? Well, sort of. You see, there's an economic pattern that's been quietly working against these workers for decades, long before anyone had heard of a large language model. To understand the pattern, you need to go back to 1965, when a Princeton economist named William Baumol and his colleague William Bowen noticed something odd about the performing arts.
A string quartet performing Beethoven in the 19th century required four musicians and about 25 minutes. A string quartet performing the same piece one century later required the exact same number of musicians and lasted about the same time. Nothing about the performance had gotten more efficient, no technology had come along to speed it up, and yet the cost of putting on that concert had risen dramatically, dragged upward by wages rising everywhere else as manufacturing, agriculture, and industry got more and more productive. As factories and other industries became dramatically cheaper and faster, they could afford to pay their workers more, which pushed wages up across the
whole economy, and musicians had to be paid competitively to attract talented people into the profession, even though the output, four people playing for 25 minutes, never changed. So, the cost per performance kept climbing. Economists call this Baumol's cost disease, and the clearest way to see it is to look at what happened to prices over the last 50 years. The things that got more productive, like electronics and computers, got cheaper, while the things that couldn't get more productive, like childcare, education, haircuts, healthcare, and legal services, kept getting more expensive. These are all industries where the work is stubbornly human. You can't make a nurse see patients faster or a plumber
fix a pipe remotely. So, the only way to cover the rising wage bill is to raise prices, and AI is about to make this much worse. If the Iceberg Index is right and cognitive and administrative work is about to get dramatically more productive, then the Baumol effect will accelerate. Over the coming years, a financial analyst processing with AI assistants might shrink a day's work into an hour, and a software engineer with the right tools might do the work of three people. But, the nurse will still need the same amount of time per patient, and the plumber will still need to physically be there. Their output doesn't scale with AI, so the relative cost of their work keeps rising, pulled
upward by a productivity surge happening everywhere around them. This matters because most of the work AI can't touch is essential. People don't pull their kids out of school because costs went up or skip the plumber when the pipes burst. Healthcare, education, elder care, and skilled trades aren't things people can just stop using when prices rise. And most of them are either funded or subsidized by governments that are already stretched thin. So, workers who are safe from AI disruption may find themselves in industries that governments will increasingly struggle to fund. Now, I want to be clear about something. The Baumol argument is a reasonable extrapolation from what the
study found, not a finding in the study itself. [snorts] Because the Iceberg Index tells you where the skill overlap sits right now, not what firms will do about it, how fast governments will respond, or which workers will successfully retrain. Those outcomes depend on decisions that haven't been made yet. And look, transitions like this have historically created as many new roles as they have disrupted. The transitions that went best were the ones where people could see what was coming clearly enough to prepare. Today, governments and companies are making billion-dollar decisions about AI using tools that can't see 95% of the problem they're trying to measure. The map most people are using was drawn for a
different economy. The Iceberg Index is an attempt to draw a better one. Whether anyone actually uses it is, as always, another question entirely. But, if they don't, the cost is a two-speed economy, one half dramatically more productive, the other steadily less affordable, with the fiscal pressure landing hardest on the exact services societies can't function without. Nobody can predict exactly how that plays out, least of all economists, though we did have a crack at it, exploring what happens when capitalism runs out of things for workers to do. The video should be on your screen now. Thanks for watching, mate. Bye.
