Further reading

The “since 2024” part can be checked in the underlying data, and it holds. The BLS publishes the labour share for the nonfarm business sector (private businesses outside farming) as an index. That index barely moved in the two years to the end of 2024: 96.900 in the last quarter of 2022 against 96.940 in the last quarter of 2024, a difference of four hundredths of a point. It has fallen in five of the six quarters since, to 93.446. Converted to the percentage the release publishes, that is roughly 54.8% at the end of 2024, falling to 52.8% now. About two percentage points of output has moved from workers to the owners of capital in eighteen months, after two years in which nothing moved.

Quarterly readings, 318 of them, from the first quarter of 1947 to the second quarter of 2026, taken from the BLS labour-share index for the nonfarm business sector and rescaled so the last point equals the 52.8% the release publishes. Both panels start their vertical axis at 52%, not at zero: the whole 80-year range is thirteen points wide, and an axis starting at zero would flatten it into a straight line. Read the panels for direction and turning points, not for how steep the drop looks. The lower panel enlarges the tinted window in the upper one. Nothing here feeds a number on the front page.

Two other figures in the same release point the same way. Real hourly compensation, which is pay after allowing for consumer prices, fell 3.3% in the quarter and is down 0.1% on a year earlier: pay is standing still while output grows. Profits per unit of output at nonfinancial companies rose in the quarter at a pace that works out to 43.0% a year, the fastest since the second quarter of 2021. Over four quarters they rose 17.8%, the fastest since the last quarter of 2021. Output per hour rose 1.4% in the quarter and 2.2% over the year. The productivity gain is real; it is not reaching pay.

What the release does not show is a productivity boom running away from wages. Output per hour has grown at 2.1% a year since the end of 2019. That is above the 1.5% a year of the previous business cycle, but it is exactly the average since 1947. On the BLS’s own numbers, output per hour is growing at its normal historical pace while the share of that output going to workers is the lowest ever recorded.

What it tests: the starting assumption behind the §6 dial. Until this release, the front page had no measured series for the labour share at all. The dial was arithmetic with nothing to check it against; the two labour-share rows in the watch list on the tracking page now give it that check. The §6 dial rests on a specific premise, taken from the BIS: in the scenarios where AI does not replace workers, labour stays an essential input and its share of income does not move at all, whatever the growth rate. The dial only starts turning once the bet pays off and capital’s larger claim on income is locked in. This release is the awkward case for that premise. By the front page’s own reading in §4, AI is not yet replacing workers. Today’s AI revenue comes from tools and from the loop (firms in the boom buying from and investing in each other), and the jobs data show no one being displaced. Yet the labour share is falling anyway, by about 1.3 points a year.

Three things could be causing the fall, and more than one can be true at once. It may have nothing to do with AI. The US labour share moves with profit margins, with how much machinery is used per worker, with energy prices and with the business cycle. It was falling for twenty years before anyone bought a GPU, the kind of chip AI runs on. It could be AI already replacing workers, which is what the bet needs, and which §4 says is not visible, since the jobs data show no displacement. Or AI could be changing the wage bargain without replacing anyone yet. What sets your pay is not whether your employer has replaced you but what your employer’s alternative to paying you is worth, and a credible replacement changes that the moment it becomes credible. On this third route the share falls in anticipation, and nobody has to lose a job for it to work.

That third route is the one the front page had not allowed for, and it is the one that breaks the dial’s starting assumption. The dial assumes the share stays put until AI actually replaces workers, so the front page has been watching the jobs data for displacement as the thing that starts the clock. If the bargaining route is real, the clock starts earlier and runs silently: the transfer from workers to capital begins with the threat, and would never show up in the jobs figures. §6 already argues the reverse direction: a shrinking share weakens workers’ bargaining power. Plainly the cause runs both ways, which makes it a circle rather than a sequence. That is a stronger objection to the dial’s premise than “the share moves for other reasons”. The honest position is that this release cannot tell the three routes apart. It rules none of them in.

One check bears on the two AI routes, and it is geographic. The measure that can be compared across regions is the whole-economy wage share from the European Commission’s AMECO database, carried in the tracking page’s watch-list row next to the US one. On that measure the euro area’s labour share has been rising since 2022 while the US share falls. If AI were replacing thinking work, or shifting the wage bargain, across the developed world, the European half of that world is showing the opposite. That narrows the claim. The record low is real, and the break in the series is real, but on present evidence it is a US development rather than a developed-world one, and the front page’s argument is about the developed economies as a group. It does not close off the two AI routes, and the reason is worth stating: AI is used more widely in the US than in Europe, so a real AI effect arriving in the US first would produce exactly this shape. At this stage both readings predict a gap between the two, which is why it settles neither. What would tell them apart is Europe’s series turning down later without a European capex boom of its own. That is a thing to watch, not a thing this release shows.

So the release is not evidence that the bet is working. A record-low labour share is exactly what the replacement story eventually predicts, which makes it the easiest number on this page to over-claim. Nothing in the release puts any of the fall down to AI, and this page does not either. What the release does is remove the reason for assuming the share holds still while the question is being decided. It also explains why §4’s count in jobs keeps finding nothing: the same loss of wages can arrive as a smaller pay rise rather than as anyone losing a job.

One thing it is not is a correction to a number in the model. The 60% labour share used in §4 and §6 is the BIS’s figure for whole economies across the developed world. The 52.8% here is for the US nonfarm business sector, which leaves out government, households, non-profits and farms, and has always read lower. They measure different things, and neither updates the other. What the US series adds is direction, and the direction is down, faster than before. The comparable whole-economy series is the tracking page’s row beside it, and on that measure no developed region reads 60%. Whether the front page’s 60% should move is recorded as an open question in the ledger, not quietly changed.

US Bureau of Labor Statistics, “Productivity and Costs, Second Quarter 2026, Revised”, USDL 26-1434, released 8:30 a.m. ET, 3 September 2026, read at source. Flagged by Mike Konczal, Bluesky, 3 September 2026, whose post quotes the release verbatim and adds the post-2024 reading. The quarterly path is this page’s own arithmetic on the BLS labour-share index for the nonfarm business sector (series PRS85006173, 2017 = 100), retrieved from the BLS public API and rescaled so that the Q2 2026 index of 93.446 equals the 52.8% the release publishes; the ledger records the conversion. The other figures — real hourly compensation, unit profits, productivity by cycle — are quoted from the release text. Whole-economy comparison from European Commission AMECO, series ALCD0 (adjusted wage share, total economy), spring 2026 vintage. Sourcing note: Konczal’s two posts are a thread — the second is his own reply to the first — and the labour-share figure was confirmed against the BLS release rather than taken from the posts. As always here, every figure is the cited author’s claim, feeds no model above, and the ledger records it that way.

On jobs he is blunt: the displacement is not in the data. The two occupations you would have picked to go first, he says, are long-haul truckers and junior coders, and hiring in both is still growing. He gives two reasons. One is Erik Brynjolfsson’s J-curve: for a period, probably under ten years, firms have the technology but have not yet reorganised themselves around it, and during that period few jobs are lost. The other is Luis Garicano’s messy jobs argument, which Posen calls compelling. Almost every job involves more specific knowledge and more working relationships than its description suggests, so the lists of “most exposed occupations” published by consulting firms and international bodies are probably misleading. His example from history is the handloom weaver, who really was replaced, while the wider pattern of job losses in the Industrial Revolution looked nothing like what a study of “exposure” written beforehand would have predicted. Where he does allow that AI may already be visible is in reduced hiring of younger workers, though he warns against blaming AI for that, since it is tangled up with the reshuffling of jobs after Covid.

On productivity he calls the debate legitimate and open, and puts himself just on the pessimistic side. He does not think AI gave growth any measurable productivity boost until the last year and a half. As for the largest claims made for it: we should be so lucky, but we are not there yet.

The third strand is about measurement. Weisenthal raised it, and the sources below trace it to its origin. US GDP misses most of the value Nvidia adds by designing chips that are made and sold abroad. No goods leave the US, so no export is recorded; no foreign buyer pays separately for the chip design, so no export of intellectual property is recorded either. Posen’s answer has two halves. The accounting question (whether you measure output or income, and what counts as an import or an export) does not change the underlying path. Nor does it change Nvidia’s profits: the money arrives regardless and is split between shareholders, reinvestment and workers. But if 0.3% a year really was missed, and really belongs to the AI sector, he grants that it tells a different story about productivity.

What it tests: the jobs arithmetic in §4, and the growth dial in §6. On jobs, Posen agrees with the front page’s current reading, and makes its future harder to observe. §4 says today’s AI revenue comes from tools and from the loop, not from replacing workers. The watch list read July’s first-reported jobs figure of −23,000 as a shrinking workforce rather than displacement. The BLS revised it to +21,000 on 4 September. That removes the fall without changing the reading, since August’s +162,000 is still no displacement on the scale the bet needs. Posen reaches the same place from the labour side. But the messy-jobs argument goes further than the front page does. It says the lists of exposed occupations that any estimate of the wage pool leans on are the wrong shape, so the pool of wages the bet must reach may not be reachable job by job in the way the arithmetic assumes. That is a caution about the target, not about the bill.

On the measurement point, note what it does and does not touch. It does not add a dollar of revenue to the bet. Nvidia’s contribution is already on the front page, on the bill side: what Nvidia earns is what the five biggest AI builders spend on chips, seen from the other side of the invoice. And the gap chart is built from the five’s own reported revenue, which no national-accounting rule changes. What it touches is §6, where the dial asks what growth in the developed economies the build-out is being measured against. If US growth is understated by roughly the amount claimed, the dial’s recent readings are low by that much. And the understatement is itself a product of the boom, so it grows as the boom grows.

Bloomberg Odd Lots, “Adam Posen Thinks Things Could Get Very ‘Messy’ for the Fed”, 1 September 2026. Sourcing note: Bloomberg’s own transcript is behind its bot protection, so Posen’s remarks here are read from a third-party automatic transcript and are paraphrased rather than quoted; that transcript misspells several of the names it reports, so no wording is presented as verbatim. The two substantive claims are cited to their own sources instead. The messy-jobs argument is Luis Garicano, Jin Li and Yanhui Wu, “Messy Jobs: The Work That AI Cannot Reach” (June 2026). The 0.3% figure is not Posen’s and not the Peterson Institute’s: it comes from Isabel Juniewicz, Daniel Carey, Phil Trammell and Anson Ho, “The Nvidia-sized hole in US GDP statistics”, Epoch AI, 24 August 2026, which puts the understatement at about 0.3 percentage points of US GDP growth over the past year — a gap averaging some $30bn a quarter through 2025 — and projects it toward two percentage points by the end of 2028 if Nvidia keeps growing at its current pace. Epoch says it confirmed the absence with the Bureau of Economic Analysis.

Has any of it shown up yet? A qualified yes. Bick and his co-authors compared how far each industry had adopted AI with how much its productivity growth had picked up against its own pre-2020 trend. They did this for the US from the fourth quarter of 2022 to the third quarter of 2025, and for Europe over 2022 to 2024. Where the share of an industry’s workers using AI rose by 10 percentage points, output per hour grew by an extra 2.9 percentage points in total over the period. “We find this strong evidence for AI having positive effects,” Bick says, while noting that the estimates do not prove cause and effect, and that other spending on automation could explain some of the result. The same exercise run on employment found no clear job gains or losses at industry level either way.

On wages, the claim is that pay follows the gain: “typically, wage increases reflect productivity and inflation”. Bick cites a speech by Fed Governor Christopher Waller from October 2025, in which productivity growth held above 2% “will tend to support rising real incomes and living standards without inflation pressure”. On jobs he accepts that the adjustment has costs and calls the net effect “incredibly hard to forecast”, reaching for farming as the example: about 41% of US workers in 1900, under 2% today.

He also names the short-term force pulling the other way, which is this site’s subject. Building the data centres is itself pushing prices up: the parts are not arriving fast enough, and electricity supply cannot keep up with demand in the short run. Before the productivity gain lowers prices, the spending needed to buy it raises them.

What it tests: the two pools of money in §3, and the capture rate in §4. Read carefully, the Fed’s mechanism is not the bet’s mechanism. In the Fed’s account the productivity gain leaves the seller: it shows up as lower prices for buyers and higher pay for workers. The gain is passed on, not kept. The bet needs the opposite: a quarter of the wages AI displaces arriving as revenue for the AI companies. That share is the capture rate, which §4 turns into a count of jobs. Both can hold at once only if the gain is large enough to split. So this piece does not soften the arithmetic; it says what the arithmetic has to compete with for the same dollars.

The measured effect is worth sizing carefully, because it is easy to get wrong. The 2.9 points is a total over the whole period, not a yearly rate. An industry with 10 points more AI adoption grew output per hour about 2.9% more in total over the eleven quarters to the third quarter of 2025. That is roughly one point a year. Set against the 2% a year that Waller treats as the threshold for rising real incomes, that is not a small number. If AI is the cause, which the authors are careful to say they have not shown, it is a large one.

What it is not is an answer to the question this site asks. Productivity growth and AI companies’ revenue are different things. One is output per hour across an industry; the other is money arriving in five companies’ accounts. The Fed’s own mechanism is that the gain leaves the seller, so the finding can be large and still say nothing about whether the bill gets paid. What limits the bet is not the size of the productivity gain but how much of it the sellers keep, and this study does not measure that.

The employment finding matches the front page’s jobs row from the other side. No displacement is visible at industry level, just as the monthly jobs figures read as slow hiring rather than workers being replaced on the scale the bet needs (July’s first-reported −23,000 was revised to +21,000 on 4 September, and August added 162,000). Neither is evidence that the displacement is not coming. Both are evidence that it has not started.

It also lengthens the patience clock on the tracking page (how long investors and policymakers will wait for the revenue), in the same way the IMF entry does. If a productivity boom is the expected outcome, and the Fed treats productivity growth above 2% as something that holds prices down, then policy stays friendly to the build-out while the revenue question is still open.

St. Louis Fed, “How Does Productivity Affect Inflation, Jobs and Pay?”, Open Vault, 26 August 2026, public. The adoption-and-productivity figure comes from Bick, Blandin, Deming, Fuchs-Schündeln and Jessen, “Mind the Gap: AI Adoption in Europe and the U.S.” (30 March 2026), drawn from a paper prepared for the Brookings Papers on Economic Activity, Spring 2026. The Waller quotation is from his speech of 15 October 2025.

The heart of the piece is where the risk ends up. Banks are moving the risk on their data-centre loans off their own books through deals called synthetic risk transfers, in which another investor agrees to absorb the losses. Separate companies set up for the purpose (special-purpose vehicles) keep projects off the tech companies’ own accounts. Broadcom is borrowing against chips it designs with Google. Morgan Stanley expects pension funds and insurers to buy the deals that are sold privately rather than on the open market. And the price of this debt is rising. A QTS bond backing a data centre leased to Microsoft sold in April at an interest rate of 5.7%, with three times more demand than supply. In August the same company’s new debt cost over 7.2%, the rate a company with a junk credit rating pays. Insurance has not kept up either. A single data centre now routinely costs over $10bn to build, while the largest single natural-disaster exposure at Munich Re, the world’s largest reinsurer (an insurer of insurers), was €8.5bn. Only a fraction of Meta’s $14bn Texas project is insured.

The article’s deepest worry is that the chips and buildings may not hold their value for as long as the debt that pays for them. “It’s like you’re financing a fax machine and then someone invented email,” says Carlos Mendez of Crayhill Capital, which has turned down debt deals backed by chips. Many lenders now want loans backed by chips repaid in full before the lease on the data centre ends. Oracle, one of the five companies in the front page’s model, is where lenders are already pushing back. It has had financing trouble at its Michigan campus, a demand from Wisconsin’s utility regulator for $7bn of collateral (assets pledged as security), and a downgrade by the rating agency S&P to triple B minus, one notch above junk.

What it tests: the strain chart on the tracking page. That chart measures each company’s capital spending (money spent on things that last for years: chips, buildings, power) against its operating cash flow (the cash its normal business brings in). This piece describes financing that moves spending off those books: special-purpose vehicles, leases, risk transfers, loans backed by chips. If more of the build-out is financed that way, the strain chart will understate the real strain: the ratio can look steady while the debt piles up where the chart cannot see it. The early warning would then come from the interest rates lenders charge, not from cash-flow statements. That is what the QTS move is: the cost of the same debt rose from 5.7% to over 7.2% in four months, before any answer on revenue.

It also adds to the patience clock. The IMF entry below says the build-out buys political patience. This piece shows lenders’ patience running out first, through higher borrowing costs. The bill stays the same size; what changes is who is stuck holding it if the revenue comes late.

One detail matters on its own: the QTS debt runs for longer than the first tenant’s lease, so the financing only works if a second tenant turns up. That is the same revenue question this site asks, in different words.

FT · The Big Read, edition of 26 August 2026, subscriber access. The FT’s data centres topic page collects its follow-ups. Figures are credited within the article to Infralogic, DC Byte, Gallup and S&P among others.

Her reading of the AI half is explicit: “What started out as a US phenomenon with AI is now becoming a growth engine for the global economy.” The Fund says the world economy has withstood the closure of the Strait of Hormuz for four reasons: countries drawing down their oil reserves, supply from outside the Gulf, lower demand and a partial return to coal. And AI investment held demand up while energy supply shrank.

What it tests: the patience clock on the tracking page. This site treats AI capital spending as a bill that AI revenue must eventually pay. The Fund treats the same spending as a boost to demand that counts as growth. Both are true, and that is the trap: spending on data centres counts as GDP while it is being spent, whatever it earns later. A build-out big enough to make up for a closed strait is also big enough that stopping it would show up as a global slowdown. That buys the build-out political patience. It does not buy it a dollar of revenue.

Reported by The National; see also the IMF media centre.

He puts this in terms of Daron Acemoglu’s idea of skill-biased technical change: the technology of the late twentieth century made already-skilled workers far more productive and left the rest behind. Anthropic’s own finding points the same way: its coding tools help most the users who already have expertise. Shaw concludes that investors have little reason to automate work where wages are low, and that only government policy changes where the money goes. He cites a 2026 study led by Erik Brynjolfsson which found that a 10% higher legal minimum wage went with 8% higher use of robots in factories, and the records of China, Singapore and South Korea in steering their industries by policy.

What it tests: the two pools of money in §3, and the jobs arithmetic in §4. Both ask whether AI revenue will come from software budgets or from wages. Shaw’s claim cuts across that question. If money keeps flowing to desk work because that is where the returns are, the pool of wages the bet must reach is not the whole white-collar total. It is the part nearest to what already-productive workers do, while low-wage physical work stays out of reach of either pool. The target narrows; the bill does not. That is the uncomfortable reading, not the reassuring one.

It also points at a lever the §5 diagram of the loop does not draw: minimum wages and industrial policy decide which kind of automation gets built, not just how fast.

FT · Free Lunch, newsletter edition of 23 August 2026, subscriber access. This edition went out email-first and carries no separate public URL. The study behind its central figure is public: Brynjolfsson, Li, Miranda, Seamans and Wang, “Minimum Wages and the Rise of the Robots”, Stanford Digital Economy Lab.