Weekend reading links

1. Are people overreacting to small struggles?

When asked if they would consider someone experiencing typical fluctuations in mood (described as broad happiness but occasional moments of worry, frustration or loss of confidence) as having a mental illness, more than half of young Americans say yes, up from just a fifth 15 years ago.

2. A picture of state finances.

3. The promise of quantum computing.

Quantum computers can transcend the limitations of the traditional binary computer bit, which can exist in two states, denoted by zero and one. By contrast, quantum bits, or “qubits”, can exist in both those states at once. This allows quantum machines to survey multiple potential solutions simultaneously, rather than dealing with them one by one like a conventional computer. One analogy is a maze. Where a quantum computer can examine the whole map to find a way through, a traditional machine will keep exploring dead ends until it finds the route. Quantum computers’ superior processing power should make them better able to generalise from small amounts of data and sift through multiple complex patterns...

But many companies are already experimenting with the technology because of its promised leap in capability, predicting early uses for the machines in areas such as chemistry and materials science. The idea is that because of their own workings and structure the computers will be better able to analyse and predict chemical behaviour determined by atomic and subatomic interactions governed by quantum rules. In a sense, they will be speaking the same language rather than translating an analysis into a string of ones and zeros as a traditional computer does. As a result, a sufficiently powerful quantum machine should in theory be adept at predicting the interactions between drugs and living cells that determine whether a new pharmaceutical will work. Such possibilities have already led tech companies to pair up with industrial groups.

4. India's trade account in a nutshell.

In 2025-26, its exports of services, at $421.3 billion, was close to the export of goods worth $446.1 billion. On the other hand, imports of goods ($783.4 billion) were way above the imports of services ($204.7 billion). Thus, while India recorded a merchandise trade deficit of $337.3 billion, it had a surplus of $216.6 billion on the services account.

5. Brilliant article by Simon Kuper on how football came to be dominated by Western Europe.

Western Europeans didn’t start by asking, “How can we win the World Cup?” Instead, they pursued a different goal: making amateur football cheap and widely available. The intended outputs were happiness, community and public health. Winning World Cups was a byproduct... I began playing football aged six, in 1976, after moving from London to Leiden in the Netherlands. Most Dutch boys I met belonged to a football club... The little Leiden region had dozens of football clubs. Some fielded 20 senior teams, seven teams of under-eights and so on. Many people built their identity and social life on being the right-back or linesman of the 14th team. The Netherlands in the 1970s reached two World Cup finals. Everyone played and understood how to play. Football is geometry — about creating space when you have the ball, and shrinking it when you don’t. That knowledge is all around you in western Europe, unlike in Asia, Africa, the US or Brazil... In 2017, the average Dutch person lived 1.6km from a football field. Neighbouring Germany’s football federation is the world’s largest sports association, with more than 7.7mn members...

As a father, I raised three footballers in Paris, now the game’s deepest talent pool. Almost all Parisian suburbs, or banlieues, have well-kept sports complexes, with artificial fields, used nonstop: at half-time of any amateur game, children storm on to the field for a kickaround. So structured is the system that my son had to earn a coaching diploma to train his little club’s under-eights. His own beloved coach, Mustapha Sangaré, who only joined a football club aged 15, now plays for Bulgaria’s Levski Sofia and Mali. He is far from an anomaly: almost 100 players across all squads in the current World Cup were born in France and just under 70 in the Netherlands.

In another article Tej Parikh looks at why China and India does so badly in football.

This is a striking statistic, pointing how globalised football has become and how the leading European clubs have become the feeding grounds for national teams. 

At this World Cup, more than 72 per cent of players appear for a club outside the country of their national team, and almost one in four are foreign born. (More than half of Cape Verde’s squad was born outside the nation and ply their trade in various European leagues.)

6. In what will prove to be a dramatic decision, DP World, which operates the Jebel Ali port that has been paralysed by the closure of the Strait of Hormuz, is reportedly planning to build a new port and a container terminal on the UAE's eastern coastal area of Fujairah. 

Shifting some of the port’s capacity outside Dubai marks a seismic change for the emirate, which has established itself as a global trade and finance hub partly off the back of Jebel Ali’s growth... But DP World’s plans align with a broader UAE government initiative to attempt to bulletproof its economy against future hostilities with Iran by reducing its dependence on the strait, where shipping has been disrupted by Iranian drones and missile strikes since the US-Israeli attack. The new project would deepen DP World’s presence on the Gulf of Oman, allowing containers to enter and leave the country without having to pass through the strait, before moving them on trucks overland to Dubai, Abu Dhabi and neighbouring Gulf countries. Since the war began at the end of February, Iran has fired nearly 3,000 drones or missiles at the UAE — more than any other country... DP World’s plans underline how the Iran war has forced governments and companies in the region to reconsider infrastructure and economic corridors developed on the premise that there would be uninterrupted passage through the strait.

7. Rote memorisation in schools is celebrated in China.

The guidelines to the gaokao, an exam for 18-year-olds and the world’s largest standardised test, describe memorisation as “the most basic level of ability”, placing it first among six traits that include comprehension, analysis and synthesis, appreciation and evaluation, expression and application, and inquiry. At the simplest level, the Chinese script itself, which operates at the level of the syllable and involves thousands of individually meaningful characters, requires years of memorisation... It is hard not to draw a contrast with the English-language west, where rote memorisation has taken on a faintly pejorative meaning. More than a century and a half ago, at the height of Britain’s industrial age, Charles Dickens was skewering the “facts alone are wanted in life” approach of fictional educator Gradgrind in the novel Hard Times.

8. Trump's makeover of the US State Department. 

Abandoning the precedent of the past 60 years, Trump has brushed aside the foreign service officers who have typically run at least two-thirds of embassies. Of the 101 nominations for ambassadorships in his second term, just nine were career diplomats. All this is against the backdrop of swingeing cuts to the department, whose workforce has shrunk by more than 3,000, over 20 per cent, since Trump resumed office.

9. China's remarkable success in reducing air pollution by 60% since 2013. 

10. Interesting that the IT sector explains half the difference in productivity growth between the US and EU.

Strikingly, even though the tech sector was only 9.2 per cent of US GDP, against 5.4 per cent of the EU’s, almost half of the difference in productivity growth between the two economies was explained by differences in the relative size of this one sector. Moreover, productivity growth in the EU’s (relatively small) tech sector was also measured as being lower than in the US one. So, overall, the tech sector alone accounts for well over half of the overall difference in growth of GDP per head.

This is important

Life expectancy for US men was 76.5 in 2024, against an average of 80.5 in comparable high-income countries. For women, it was 81.4 against 84.8. That is despite spending a far higher proportion of its GDP on health. The US homicide rate was 5.9 per 100,000 in 2023, against 1.3 in France and 0.9 in Germany. Its prison population was 542 per 100,000 in 2023, against 130 in France and 69 in Germany. Thus, if one takes a wider view of human welfare, the US is very far from superior.

11. China reports the lowest quarterly growth rate in decades at 4.3% for the second quarter of 2026. Industrial production and exports are propping up growth, even as consumption declines.

Retail sales added just 1 per cent in June from a year earlier, while fixed-asset investment was down 5.7 per cent year on year for the first half of the year, compared to 4.1 per cent in the first five months. Industrial production, one sign of strength, grew 5.3 per cent last month on a year earlier... Separate data on Tuesday showed exports soared 27 per cent year on year in June, adding to signs of reliance on trade to support economic activity... Julian Evans-Pritchard, head of China economics at Capital Economics, noted that the GDP data brought it “closer in line” with the consultancy’s alternative measure, which has been around 3 per cent.

Underlining the importance of exports, there was a surge in China's EV exports in June.

China’s monthly car exports rose to a record 1mn cars in June as part of an overall surge in trade that will heighten tensions with partners such as the EU. Shipments of cars rose 71.2 per cent from a year earlier to 1.06mn, putting the country on track to export more than 10mn cars this year, up from 7.1mn last year and more than double the 4.9mn in 2023. The surge in exports comes as domestic sales slow sharply following the phaseout of EV subsidies and a decline in demand for fuel-powered cars... The wave of Chinese exports has been driven by lower-cost cars boasting superior software, further threatening carmakers from Japan, South Korea, Europe and the US... China’s exports of rare earths in June fell 34 per cent year on year and 6.4 per cent in the first half, following tight export controls on the minerals, which are essential for high-technology products... The NBS’s Wang said China’s exports of green energy-related products such as lithium batteries and wind turbines increased 37.6 per cent and 35.6 per cent, respectively, during the first half.

And this highlights how production and exports have sustained growth.

Industrial production rose by 5.4 percent in the first six months of the year, versus the same period last year. High-tech manufacturing rose by more than 13 percent over that period. But fixed asset investment — which includes infrastructure, property construction and manufacturing — fell by 5.7 percent. Real estate development dropped 18 percent. The value of China’s exports surged by more than 20 percent in the first half. But consumer spending, which a Moody’s Analytics report said “remains the economy’s weakest link,” faltered. Retail sales of consumer goods increased by 1.3 percent over the first half of the year.

12. Ruchir Sharma holds that peak China was reached in 2021, and since then it has been a story of decline, papered over by exports and AI.

Since then, China’s share of global GDP has fallen in nominal terms from 18 to 16.5 per cent, while the US share has risen to 26 per cent. China’s growth rate has dropped below the rest of the world, including the US. In real terms, independent estimates now put China’s growth in real terms closer to zero than to the official target of 4.5 to 5 per cent... China’s population also peaked in 2021. Last year, births hit a record low, and deaths hit a record high. The working-age population is on pace to shrink by 75mn every decade this century... After adding little in the 2010s, net exports now account for about a third of the country’s growth, driven mainly by AI-related goods... And though every country now hopes for an AI-driven productivity miracle, the expected boost in China is about a third of a percentage point by 2030, hardly enough to halt its decline.

13. India agriculture statistics

If one looks at the growth areas in agriculture in the 12 years between 2011-12 and 2023-24, production of paddy and wheat rose by just 27 per cent. Fruit and vegetable production rose by 52 per cent, and milk production by 85 per cent, even though they did not receive any substantial support by way of subsidies or minimum support price procurement by the government. The true growth areas in agriculture are out of direct central government support and depend largely on producer enterprise... In 2023-24, the value of output of cereals (mainly paddy and wheat) was ₹8.5 trillion, while the value of the largely cooperative-controlled milk sector was ₹12.2 trillion.

14. Outbound corporate investments from India on the rise.

Indian companies have announced overseas equity investments worth more than $14bn in the first four months of the fiscal year that began on April 1, compared with $18.7bn in the previous 12 months. The outflows come as foreign investors flee India’s markets at the fastest pace ever this year, pulling out more than $23bn as of the end of June over a lack of AI champions.

15. Simon Kuper on Lionel Messi.

On the field, Messi sees everything. All his career, he ignored the ball for the first five minutes and instead walked around, memorising the position of each opponent and the spaces between them. But now he spends almost the entire game walking and scanning. When he breaks into a run, his teammates know he has seen an opening. They play to serve him. When he moved to the right wing against Egypt, seeing space there, the team remade itself around him. Argentina, two goals down after 78 minutes, won 3-2. Scaloni said afterwards: “We were not the ones who told him to go out to the right.” Messi moved right again against England and again Argentina came back to win.

16. The AI-boom is spilling over to energy sector.  

Initial public offerings for energy firms raised $12.6bn in the first half of this year, according to data firm Dealogic. That marks the highest half-year level since the peak of the dotcom bubble in late 1999 and the highest first-half figure on record. It is well above 2025’s full-year total of $4.3bn. The surge in fundraising comes as access to the vast amounts of energy needed to run data centres emerges as a bottleneck in a multi-trillion-dollar AI investment boom... US electricity demand is projected to increase 39 per cent between 2026 and 2035, according to consultancy ICF, in large part due to ballooning demand from data centres...

Companies that have been able to raise money on public markets include those involved in complex, capital-heavy projects such as nuclear and geothermal power plants, while investors have also been willing to back businesses trying to develop new technologies... “This is a moment in which speculative projects are being funded and underwritten,” said Julien Dumoulin-Smith, a Jefferies research analyst covering power, utilities and clean energy. “They’re not just limited to venture capital or private equity.”... Nearly two-thirds of the energy companies that floated this year and last are now trading below their offer price, according to Dealogic. That compares with less than 40 per cent of IPOs across all sectors that are underwater.

And Wall Street Banks are already AI trades. 

Four of the five big Wall Street banks reported yesterday: JPMorgan Chase, Bank of America, Citigroup and Goldman Sachs (Morgan Stanley chimes in today). The numbers were outstanding, as one would expect in a quarter when markets whipped around and big deals were done. In aggregate, equity and debt trading revenue at the four hit $38bn, up more than a third from a year ago and 60 per cent higher than two years ago. Investment banking fees, at $10bn on the quarter, have grown almost as much... It is AI that has markets churning and drives capital-raising. The banks are another example of the false “broadening” of the stock market that has also driven up industrial and utility stocks in the past few years. All these sectors have lived, and could die, with AI.

And their profits are not confined to the US, with even Asia becoming a major source.

Equities trading in Asia is helping power a record-breaking run from Wall Street’s banks, with the region on course to surpass Europe as the industry’s second-largest source of revenue behind the US. In the past 12 months, clients of large investment banks have ploughed into companies in Asia that provide critical infrastructure to the AI semiconductor industry, including South Korean SK Hynix, Taiwan’s TSMC and China’s Cambricon Technologies... In the most recent quarter, the largest investment banks collectively reported an unprecedented $25.7bn in earnings from equities trading and called out Asia as a crucial factor in the growth.

17. Even by the low standards of Trump 2.0, this is surely an outrageous example of private profiteering from public office. 

Donald Trump’s social media company has discussed charging traders and investors as much as $100,000 a month for faster access to the US president’s posts on his Truth Social platform. Trump Media & Technology Group (TMTG) has quoted the six-figure monthly sum in talks with prospective buyers of the “Truth API” data service, according to people familiar with the matter. Proprietary trading firms and hedge funds pay huge sums for ultrafast data feeds because every millisecond counts when reacting to market-moving news. Trump often makes major announcements on Truth Social that trigger huge fluctuations across global markets... TMTG, which is majority owned by the Trump family, controls Truth Social...

A pitch sheet circulated by TMTG to promote Truth API, seen by the FT, lists 10 “documented market-moving posts” from the president’s Truth Social account. On April 9 2025, for example, the document says Trump’s “THIS IS A GREAT TIME TO BUY!!!” post “restored” $4tn to the market capitalisation of the S&P 500. Trump’s post in early June that the US would hit Iran “very hard tonight” caused a 6 per cent intraday jump in oil prices, the document says. “When @realDonaldTrump Truths, the world reacts,” the document continues. “No comparable signal exists. No official API has ever been offered. Until now.” Trump has also touted specific stocks, complimenting companies such as Nvidia and Apple and fuelling rallies in their share prices. More recently after the outbreak of the war with Iran, Trump posted on March 23 that there had been “very good and productive conversations with Iran”, sending oil prices falling sharply.

The probation problem in India's public sector

Public recruitments come with a one- or (mostly) two-year probation period. The idea is to correct a Type I error (a false positive recruit) in the recruitment process. 

Underlining its importance, the DoPT Master Circular on Probation/Confirmation of 2019 has this to say about probation:

‘Probation should not be treated as a mere formality. The existing powers to discharge probationers should be systematically and vigorously used so that the necessity of dispensing with the services of employees at later stages may arise only rarely.’ 

Unfortunately, while it is part of all recruitment rules, it is widely believed that there are few instances of discharge during probation in any local, state, or central government recruitments.

Before we analyse this, it must be clearly stated that data on this is very patchy in the absence of disclosures by either the DoPT, UPSC, state PSCs, or the central or state government cadre controlling agencies. Empirical evidence, or whatever can be gathered, shows that across every service - All-India Services, central Group A civil, central Group A technical, state civil services, state police officers, state medical services, the great mass of teachers, constables and healthcare workers - the discharge rate on performance grounds is effectively zero. 

This is what Claude gathered about central government cadres.

And this about state government cadres.

It must be disclosed that both the data are not validated, but conforms to the widely known anecdotal knowledge. 

The rare discharges that do occur are almost invariably for fraud (fake caste, disability, TET or degree certificates) discovered post-selection, or for medical/physical failure during pre-probation training. Fraud detection and physical fitness testing are legitimate filters, but they are not the probation function. This has been confirmed by every major civil-service review (Hota 2004, Yugandhar 2003, Second ARC, Baswan 2016). 

There are three examples of probation discharges outside of fraud and medical or physical ineligibility. An estimated 2-5% of the scientists and engineers recruited by the Department of Atomic Energy are discharged for non-completion of the academic curriculum. An estimated 3-15% of the police recruits by state and central government paramilitary forces are discharged for physical and disciplinary reasons (not deficiencies in the acquisition of policing capabilities). Thanks to supervision by the High Courts, an estimated 0.5-1% of every batch of state civil judges and munsiffs are discharged for failure to meet defined output and quality metrics during probation.

The absence of any discharge deterrent increases the stakes associated with the recruitment process itself. This most likely contributes to the fraudulent practices that are pervasive across recruitments at all levels. 

This is a comparative assessment of probation discharges globally and from India’s own private sector. 

The deterrent effect of even a few discharges can be significant. 

So what can be done about this?

The probation instrument has not served its purpose because the framework it operates within provides neither the assessment infrastructure (objective performance criteria linked to a role profile), disclosure requirement (not even DoPT publishes data on probations), nor the political-economy incentive (senior officers who discharge a subordinate invite litigation and administrative-tribunal action). In the circumstances, reforming the probation clause without reforming both the criteria and the incentive structure will change nothing. 

A low hanging fruit is to shine light on the problem and make it mandatory for all departments to disclose the status of probation confirmation, extensions of probation, and affirm that all the probationers met the requisite benchmarks for the same. 

The first step in any systematic effort would be to define a few objective and easily captured metrics of probation performance that are proximate to their roles. This would usher in transparency and shape expectations among probationers about their roles. 

Second, the probation performance evaluation should be made a mandatory exercise, by a committee consisting of the Departmental head (or representative), officer responsible for training within the Department, and the Director of the Training Academy. This would mitigate the political economy deterrent to discharges. 

Third, there could be a mid-way evaluation of the probationers by the same committee, which discusses any laggards and inform them about where they are falling behind. The same should be documented. This could shape expectations and ensure that the probationers are forewarned before any discharge. 

Fourth, the evaluation reports should be submitted every year by the Departments to DoPT (and its state government equivalents) and the UPSC/SPSC, failing which no recruitments by the Department should be allowed. This would bring departmental accountability to the process of probation confirmation. 

The aforesaid measures would constitute a simple and realistic start to addressing one of the most farcical features of the recruitment process. 

Higher FARs, but very few plots can avail them

Many state governments in India have issued executive directions increasing the permissible Floor Area Ratios (FARs) in their cities. However, these upzoning reforms are likely to struggle to meet the objective of densification due to restrictive conditions to avail the increased FAR. Specifically, three gate-keeping elements - minimums on road width and plot size, and a maximum on height - leave the upzoning reforms largely stillborn. 

To understand why, we need to keep in mind the street layout of the typical Indian city. The colony street widths are typically 9 m (30 ft) or less, and at best 12 m (40 ft). Even the connecting roads are no more than 12 m. In any city, a very small proportion of properties, and an even smaller proportion of residential land use, will have road widths greater than 9 m. Only the arterial roads, which are in any case mostly commercial and higher-valued, are above 12 m.

A comparison of upzoning reforms across the ten biggest states reveals some interesting insights. For a start, the upzoning itself is generally marginal, and even where significant, the higher FARs can be realised only on wider roads. In simple terms, the upzoned FAR apply to a tiny minority of parcels - greenfield layouts and edge plots on arterial roads. Every state except Gujarat, UP (individual) and Haryana (small plot) sets the FAR uplift threshold above 12 m - typically 18 m, 24 m or 30 m. But even for the three, the uplift is marginal and only for a few categories of properties. Also, none of the three touches group housing or vertical redevelopment on narrow-road plots.

Further, plot size and setbacks compound the problem of a minimum road-width gate. Even where a 12 m road technically qualifies, high-rise / group-housing rules require minimum plot sizes of 750–2000 sqm and setbacks of 6–12 m. In existing settlements, individual plots average 60–150 sqm, and assembling five to twenty of them is legally and commercially nearly impossible without a TDR/land-pooling instrument. Even then, practical challenges are daunting.

This also means that even the TOD zones cannot benefit from the upzoning. In existing town cores, which are where TOD catchments actually sit, FAR reform delivers almost nothing until the road-width and plot-size gates are lifted or bypassed. 

In other words, the upzoning reforms largely bypass the built-up city and are relevant only to the greenfield areas. In these areas, the uplifts linked to higher road widths end up benefiting only the large developers. They, in turn, build high-rise gated communities of higher-end housing, mostly unconnected to mass transit and with multiple car-users in each household. Ironically, this also ends up expanding the sprawl, flooding the roads with cars, thereby worsening traffic and increasing pollution.

On the other hand, it does nothing for the smaller developers who are likely to develop affordable mid-rises (say, 6-12 floors) inside the existing colonies. Instead, they end up constructing low-rises (up to 4-5 floors). Further, the unit economics given high land prices mean that even these low-rises gravitate towards the suburbs. 

It is these mid-rises that are likely to contribute meaningfully to expanding supply and addressing the affordable housing problem. Unless the upzoning covers the 9 m road width and smaller plots (which make up the vast majority of the potential developable properties in any city), there cannot be any significant impact on housing supply and the affordable housing problem. 

Further, to realise the full potential of such upzoning, it must be complemented with sharply increased mass transit services, especially buses, that cover these colonies. The quality, frequency, and connectivity of the bus network must be high enough to induce people to shift from car usage.

Workers and startups are helping train AI to replace them

Data annotation work is increasingly moving up the value chain, from tagging and labelling data to replicating the work of semi-skilled (on the factory floor) and skilled (consultants, analysts, lawyers, engineers, and doctors) workers. 

Startups sell data to AI labs, which use it to train and refine their AI algorithms and develop software products/solutions that replicate the work of these workers. In other words, the semi-skilled and skilled workers, or at least some among them, are feeding their time and skills into the AI algorithms that seek to replace them and their kind. Both the training startups and those workers offering their services to them are basically helping make themselves redundant. 

On this, the FT has a very good film about how Indian startups are paying factory floor workers and gig workers (and even people in their homes doing regular household chores) to use cameras and record their work. Data annotation is becoming the new BPO for India’s IT industry. 

The Ken has an article that raises the possibility that for all the attention and hype around robotics, Indian startups might remain stuck at the lowest end of the robotics value chain - data collection. 

India was the back office for the IT boom. It became the annotation and reinforcement-learning labour pool for the generative AI boom. It is now emerging as the behavioural data factory for the physical AI boom... Building the robot is only half the problem. Building the intelligence behind it is much harder. That requires data. Vast amounts of it. Unlike large language models, which were trained on the equivalent of hundreds of years of human reading scraped from the internet, robotics companies are working with barely a fraction of that in video... What they need is meticulous, first-person recordings of humans interacting with the physical world, carefully collected, annotated, and painstakingly structured. 

So the industry turned to India. Across the country, workers are recording themselves doing everyday chores for data-collection firms, which then sell that footage to companies such as Tesla, Figure AI, and Agility Robotics to train their humanoids. Indian startups see this as a moment to claim a seat in the global AI value chain. The country has over 260 robotics startups, and investors are beginning to pay attention... The footage being recorded by Indian workers becomes proprietary once it leaves the country. The datasets assembled from it are accumulating on foreign servers. The foundation models trained on them are owned by foreign companies.

The NYT has an article about how startups like Handshake, Mercor, and Surge in the US are paying skilled workers to collect data on their work. 

Mercor and a handful of similar start-ups are the primary middlemen in a supply chain of “human data” that may power the next generation of A.I. As OpenAI, Anthropic and other major ventures compete to become the industry’s dominant platform, the market for premium data that has been vetted by experts is exploding…They need mathematicians to annotate proofs, lawyers to mark up briefs and professors to grade essays… To use the parlance of the industry, data labeling has moved up the “value chain,” and the start-ups that offer this service have become some of the fastest growing in Silicon Valley… The data-training start-ups see a lucrative opportunity in recreating workplaces in miniature: controlled environments in which their gig workers can evaluate and reproduce emails, memos and slide presentations in context. The information emerging from such a setup, the companies boast, will help shrink the gap between what A.I. models can accomplish and what office workers actually do from one minute to the next, as ideas and instructions flow between meetings, documents and applications…

To keep improving their models — to make them more useful, more sophisticated, less prone to hallucination and mistakes — A.I. companies heavily refine what goes into them. That’s post-training, and it includes buying data from vendors like Handshake and its competitors… Deeptune, a start-up that makes “training environments” with simulations of the software programs, like Slack and Salesforce, that many workers toggle between all day long to get their work done. The idea is to painstakingly create a mirror image of, say, an investment bank so that A.I. can observe every interaction…

It may turn out that once OpenAI, Anthropic and others have taught their models to perform a certain job, their need for more training data in that area could sharply decline. In this way, Mercor, Scale, Handshake and their peers are much like the elite freelancers they employ: making money today, but in danger of being dropped tomorrow… People sign up for data-training gigs for a variety of reasons. The main one is, of course, money… Though the labor is unpredictable and rates vary… the workers who cobble together enough shifts can generate meaningful income. People might sign up because they have been laid off, or because they can’t find enough work in their field. They might do it because they’re eager to get “A.I.” on their résumé, or because they need extra cash in retirement… Many people who contract for these companies understand that this is a short-term opportunity, a brief chance to train the models to automate jobs before they themselves are automated out of the job of training models.

I asked Claude to generate a visualisation of this market landscape, including an assessment of the Indian landscape. The numbers are clearly estimates and must be validated (though at a ballpark they appear alright). 

The unit economics of the data chain shown below for a garment worker in India is instructive. She gets roughly ₹400 a day to wear the camera (or $0.60 per hour); the startup pays the factory ₹450–500 per hour; US-based startups like Human Archive price data at $1–10 per hour; and once annotated and packaged, it sells to global robotics labs at $15–50 per hour. That is a 25–85 times markup, and every rung above the worker is owned outside India.

The graphic also shows that the vast majority of AI workers are doing the BPO equivalent, whereas the vast majority of funding is going to those building the data centres. India has 170-odd AI startups that have raised $2.6 billion in total and over 260 robotics startups, but the genuine model/product builders are a tiny set, and the majority have rebranded annotation as an AI line of business (iMerit, Objectways, Awign, Karya, Deccan AI, Human Archive, Egolab, Neo Cambrian, Humyn Labs, RoBoEra, etc.). 

It must also be highlighted that in the majority of cases in India, the data goes from the garment worker to an Indian data aggregator to a robot-brain lab in San Francisco, and comes back as a robot/humanoid. The frontier LLM labs are not in that loop. This also means that none of the emerging governance conversations about frontier models - safety frameworks, export controls, model-access negotiations - touches the mainstream data collection work being done in India. India is negotiating hard for access to frontier language models while simultaneously handing over, for ₹400 a day, the training substrate for the physical models that will actually displace its manufacturing workforce. Those are two different conversations, and only one of them is being had.

Further, as the Times article highlights, while these annotation startups are flourishing now, they may not be sustainable ventures. Once experts teach the models to do something, their services are no longer needed in the same way, and the vendors themselves need the models to keep improving to show they add value, while needing them to remain imperfect so clients keep coming back. 

I asked Claude for historical precedents and got this:

Frederick Winslow Taylor’s explicit programme, from the 1890s, was for management to “gather in all of the great mass of traditional knowledge which in the past has been in the heads of the workmen.” Skilled machinists were stopwatched; the Gilbreths filmed them with chronocyclegraphs — a literal 1910s head-camera. Workers cooperated because they were paid piece-rate bonuses to do so. The tacit craft was decomposed into instruction cards and handed to cheaper, unskilled labour. Outcome: enormous productivity gains, the collapse of the craft wage premium, a machinists’ revolt, congressional hearings in 1911–12, and Taylorism banned in US government arsenals by 1915. It took roughly fifty years and the postwar labour accord before the gains were broadly shared… 

In the 1990s American hospitals routed physician dictations to transcriptionists in Bengaluru and Chennai; it was unglamorous work, but India was good at it. That corpus is precisely what trained speech recognition. The industry peaked and then largely evaporated. Compensation to the transcriptionists: zero… most startups in this space risk meeting the same fate as the transcription companies of the 1990s.

In this context, I am reminded of the claim made by Daron Acemoglu and Simon Johnson in their book Power and Progress that the trajectory of technological progress is a political choice made by society and should not be left to corporations and technocrats. Their central claim is that the direction of technology is a social choice, not a technical destiny, and that redirecting it requires countervailing power rather than better-intentioned technocrats. 

The problem, though, is that globally, and especially due to the Trump 2.0 regime, the rule makers have surrendered agenda-setting to Big Tech and AI Labs. Closer home, India has almost no leverage over the direction of frontier AI. Instead, its leverage is confined to the terms on which its labour and data enter the supply chain, and not to bending the technology’s arc. 

In the circumstances, what can a country like India do?

Here are some thoughts for consideration. One, a statutory floor rate for training-data contribution and an industry-led collective licensing body for data work are both administratively feasible and could increase value capture (from the worker’s current share of 1-2% of the value created) without banning anything. A comparator is the model of SoundExchange (US) or PRS (UK) in the music industry, which acts as a government-designated clearinghouse that collectively licenses music, collects usage fees, and distributes royalties to creators, effectively removing the burden of individual licensing. This model would also subtly frame the market in terms of treating data as labour, and not as mere raw material. 

Second, on the regulatory side, it may be useful to revisit the DPDP Act provision that permits employers to process worker data without explicit consent under “employment purposes”. Instead, there should be purpose limitation, or restrictions on repurposing training data for other activities, and consent requirements of all involved. 

Third, public spending on AI innovation and procurement preference could be made conditional on the recipient retaining licensing rights to datasets collected from Indian workers rather than doing work-for-hire. This would frame the collection of data as an input and not a product, and industrial policy could price it appropriately. Fourth, there is the argument about extending statutory instruments like the gig worker welfare boards or the Code on Social Security present in some states (Rajasthan, Karnataka, etc.) to cover data work. It could help build countervailing power. 

But pursuing these agendas can be costly. This being a global market, prohibiting or putting too onerous terms on value capture and the entry of data into the supply chain will backfire by moving the work to Vietnam, Ethiopia, or the Philippines. Besides, for the Indian workers, already facing an acute scarcity of jobs, the choice isn’t really on offer, and ₹400 a day is ₹400 a day. There is a collective action problem here which calls for multilateral engagement through a forum like the ILO. 

But this should not mean that we sit back helplessly and allow the market dynamics to play out. Instead, before enacting any of them, there must be a public debate on the merits or otherwise of these proposed measures. What are their respective costs, and what can be done to mitigate them? What versions, if any, of these measures should be enacted? Such debates are essential to make informed and collective social and political choices.

The public debate is important since the agenda-setting process here, like with any technology change, pushes certain considerations to the forefront while also marginalising certain others. Almost always, the former represents the interests of the corporations and elite beneficiaries of the change, and the latter represents those of the vulnerable and voiceless. Therefore, such agenda-setting debates are a purely political activity, with profound social implications. 

It is also important since there is the distinct likelihood that India could spend the next five years as the world’s back office for the third time, and when the juice has been sucked out and value captured, there could be nothing left standing that India owns. 

PS: In this context of collective action problems, it is worth taking inspiration from one very impressive and encouraging breakout (which has not received the level of attention it deserves) from South Korea. It is a tribute to the maturity and wisdom of the country’s corporate and political system and the robustness of its democracy that Samsung and SK Hynix agreed to share 10% of their windfall profits from memory chip sales, with no ceiling on payouts, with their employees for the next ten years. Sample this.

Samsung Electronics... agreed last month for employees to share the chipmaker’s blockbuster profits from an AI-led boom... SK Hynix... handed employees a similar profit-sharing deal last year... Samsung is also going to give Won500mn loans at low rates to employees... Samsung and SK Hynix together control much of the market for the advanced memory chips used in AI servers. Employees at both companies are in line for average annual bonus payouts of Won600mn, which compares with a national average salary of about Won50mn... district of Hwaseong... expected to gain corporate income tax receipts of Won1tn to Won1.3tn from Samsung alone this year, an extraordinary sum for a city authority whose annual budget is about Won3.5tn.

Weekend reading links

1. FT long read on how senator Deborah O'Neill, as chair of the Parliamentary joint committee on corporations and financial services, has single-mindedly exposed and brought the knees the Big Four auditing and consulting firms in Australia. 

Deborah O’Neill has led the charge against KPMG in Australia over a client confidentiality scandal that prompted the departure of the firm’s chair, chief executive, chief operating officer, audit leader and a senior partner over the past month... comes on the heels of a similar implosion at PwC. The rival Big Four firm came unstuck when a data leak led to the exit of senior management... An EY employee was charged with accessing the bank details of Prime Minister Anthony Albanese while working on contract at Australia’s biggest bank. Meanwhile, Deloitte partially refunded the Australian government after admitting that it used AI to compile a report...

In 2023, the Labor senator forced the publication of emails that implicated PwC partners in the tax leaks scandal. PwC partners were caught sharing secret government tax plans that one of them had obtained from his work on an advisory board in Canberra, in the hopes of winning business in the US. This year O’Neill used parliamentary privilege to air allegations made by a KPMG whistleblower that had been inadequately investigated by the firm. KPMG has now been exposed as having used confidential information from existing audit clients to try to win new business from rivals — some from PwC as its audit customers looked to switch in the wake of that firm’s woes...

She entered parliament in 2010 having spent her career in education. O’Neill soon discovered that some Big Four consultants acted like some of her former pupils — copying the answers from the back of the book and then marking their own work, as she puts it — and used her role to put the leaders of the firms under pressure.

2. Soumaya Keynes points to a fascinating study by Rebecca Diamond of Harvard University of the use of GLP drugs that appears to show increased confidence and employment rates among women in the US. The study finds, using data gathered between 2021 and 2023, that the poorest third of women in the US suffered an obesity rate 14 percentage points higher than the richest third, whereas the gap was negligible for men. 

The study's main findings:

After 18 months, women using GLP-1 drugs who start off without a job enjoy employment rates 27 percentage points higher than otherwise similar non-users. Women who start off with a job see their employment rate fall slightly, and although the data is too noisy to pick out effects on their earnings, it looks like their household income rises by 10 per cent. That second effect is a bit surprising, and possibly explained by parallel developments in these women’s love lives. Diamond estimates that GLP-1 drugs give single women a dramatic 29 percentage point increase in their chances of coupling up. On average, their new partners are richer than them, giving their household income a bump. Which could explain why a few of the women losing weight then feel able to drop out of work.

The study also points to a more disturbing consequence.

So far at least, GLP-1 drugs are disproportionately used by the rich. In Diamond’s study two-fifths of the women paid for the drugs out of pocket, at a median cost of $275 a month. Research based on Voy prescriptions shows how, adjusting for relative obesity rates, uptake is skewed towards more affluent areas. If obesity becomes an even stronger signal of economic disadvantage, the stigma attached could grow.

3. The rise of London's King's Cross area as perhaps Europe's AI capital.

Two decades ago, King’s Cross was central London’s most neglected district. Today, it is home to the main foreign outposts for several of the world’s wealthiest companies, from Big Tech giants Google and Meta to their richly funded AI challengers including OpenAI, Anthropic and Jeff Bezos’ Prometheus. AI researchers and entrepreneurs are packing out the area’s canal-side cafés so densely that venture capitalists prowling for their next deal are struggling to prevent their coffee meetings from being overheard by rivals.

For many, this resurgence can be traced back to one individual: Sir Demis Hassabis, the DeepMind co-founder and Nobel laureate who stayed in London to build his AI lab following its sale to Google in 2014 for £400mn. “Demis keeping DeepMind in London and resisting the gravitational pull of [America’s] West Coast is the most important thing that has ever happened to the London tech ecosystem,” says Tom Hulme, a tech investor at Alphabet’s GV venture capital unit... Just as PayPal helped launch the careers of a generation of Silicon Valley founders and investors including Elon Musk and Peter Thiel, a “DeepMind mafia” in London is pulling in billions of dollars to AI start-ups founded by Hassabis’s former lieutenants, including David Silver’s Ineffable Intelligence and Tim Rocktäschel at Recursive Superintelligence...

It was Hassabis’s pursuit of an “artificial general intelligence” capable of scientific research and a wide range of human tasks that in many ways kick-started the current AI boom. DeepMind’s sale to Google prompted Elon Musk to set up a research lab to counterbalance the internet group’s dominance of AI; that lab was OpenAI. DeepMind went on to make a series of AI breakthroughs including AlphaGo, which in 2016 beat the board game Go’s world champion Lee Sedol, and AlphaFold, which used deep learning to predict protein structures with superhuman speed.

King's Cross has emerged as the Canary Wharf of tech in UK, and this description is apt and underlines the continuing importance of personal interactions and connections. 

The density of AI talent in King’s Cross was why the government’s scientific research agency Aria took a “very conscious decision” to base itself there rather than Whitehall, says Pippy James, its deputy chief executive. “We were definitely inspired by Kendall Square in Boston,” she says, referring to the area surrounding MIT where Google, Microsoft and Amazon, as well as biotech companies Moderna and Novartis, have offices. “Value creation comes from those serendipitous collisions.” In recent weeks there has been a steady stream of American AI companies announcing moves into the area. OpenAI and Anthropic have signed leases for tens of thousands of square feet in King’s Cross. Others moving in include Bezos’s “physical AI” company Prometheus, Cursor, the AI coding company that recently agreed a $60bn sale to SpaceX, AI agent group Perplexity and open model developer Reflection. Google, which already has many researchers and engineers in the area, plans to start moving staff into its vast new “Platform 37” office this summer after almost a decade in development.

4. This is a striking factoid about the importance of chips now. 

With SpaceX going public, the list of the 10 most valuable US public companies is entirely made up of tech companies for the first time. Of those, three are semiconductor specialists. But with chips a key ingredient in AI, the other seven are also now all designing their own chips.

Some stats about the global chip squeeze.

... Elon Musk’s xAI to rent out spare capacity in its data centres. In recent weeks, Anthropic, Google and start-up Reflection AI have agreed to pay a total of around $2.3bn a month — or $28bn a year. This looks like a big return on Musk’s data centre investments. As of March this year, xAI’s total capital spending over its lifetime totalled $26.5bn... this surge in demand has already prompted a huge increase in supply, both of planned chipmaking capacity and newly minted chip stocks. One sign is the $600bn that memory chipmakers Samsung and SK Hynix said this week they plan to invest in Korea. Another is the $29bn that SK Hynix hopes to raise when its American depositary receipts begin trading in the US next week... TSMC has said it will boost its capital spending by as much as 37 per cent this year, as it did in 2025. But those increases follow two years of retrenchment and would leave 2026 capex only around 50 per cent higher than 2022. Contrast that with the biggest buyers of AI chips. Seven of the largest data centre operators are planning to spend an astounding $848bn this year, at least five times what they spent in 2022, according to a calculation by the newsletter Exponential View.

5. On the new bonds issued by SpaceX.

The bonds enjoyed very robust demand at the point of issuance, but some see that as a problem in itself. Allianz’s chief investment officer has described the market’s willingness to hand money over to Musk as a clear sign that we have moved from “a healthy boom, a stretched boom . . . into bubble territory”. Ominously, the bonds have weakened since they launched.

6. The AI LLMs scorecard. 

Where will Sarvam stand?

Also national scorecard.

7. The low-margin business of mobile phone assembly. Amber Industries which makes air conditioners for eight of the top 10 brands and more than a quarter of all ACs made in the country, now proposes to assemble smartphones for Oppo. 

Now, the company plans to sub-lease a part of Oppo India’s factory in Noida, set up SMT lines, and start assembling phones there... But smartphone assembly is one of the toughest businesses in electronics manufacturing... The target is to eventually assemble about a fifth of Oppo India’s volumes, scaling from roughly 8 million handsets in the first year to nearly 15 million in the second... Amber expects Ebitda margins of 1.5–2%, excluding benefits from the government’s production-linked incentive (PLI) scheme. That’s well below the 8.8% Ebitda margin generated by its broader electronics business in FY26, and the 7.1% operating margin recorded by its AC-heavy consumer-durables segment... Take Dixon, for instance. The company already accounts for nearly one-fifth of India’s smartphone output. Even at that scale, smartphone assembly, aided by PLI incentives, generates Ebitda margins of only about 3%.

8. AI boom compared with historical episodes.

9. On the rise of non-compete clauses in OECD countries and their adverse impact on productivity.

About 30 per cent of employers surveyed by the OECD said they had increased their use of the clauses in the past five years... It estimates that a 10 percentage-point increase in the prevalence of non-compete clauses in an industry was associated with a 1.9 per cent decline in the level of labour productivity, with workers stuck in sub-optimal jobs and firms less able to gain new skills. In many countries, non-competes have spread into parts of the labour market where the original justification of protecting sensitive information and firm-specific information is “weak or absent”, the research found, pointing to their use among entry-level fast-food staff in the US, manual workers in Italy and childcare workers or yoga instructors in Australia.

10. The market concentration in DRAM chips.

A market in which a monopolist owns 100 per cent gets an HHI of 10,000, a duopoly scores above 5,000, and a perfectly competitive market approaches zero... The US DoJ considers anything between 1,000 and 1,800 points to be “moderately concentrated”. Per Counterpoint Research, as of the first quarter of 2026 the memory market is 38 per cent Samsung (South Korea), 29 per cent SK Hynix (South Korea), 22 per cent Micron (US), 8 per cent CXMT (China), 2 per cent Nanya (Taiwan), and 1 per cent everyone else. This gives us an HHI of 2,838... Both Samsung and SK are two of Korea’s largest conglomerates (the so-called chaebols) which benefit from cosy relations with the state, so viewing them as fierce competitors in the same memory market might be wrong-minded in this case... And if the two companies function as a single economic entity in the global DRAM market, we should probably count them together for HHI purposes. And doing this we get a much higher HHI reading of 5,042. That’s above 5,000 —the HHI of a perfect duopoly.

11. China’s excess capacity in manufacturing requires something similar to what was done by the former Prime Minister Zhu Rongji. 

In the late 1990s and early 2000s… under Zhu’s slogan of “zhua da, fang xiao” or “grasp the large, let go of the small”, Beijing retained its grip on key strategic industries while relinquishing control of a vast sea of smaller companies and factories…Thousands of mines, steel mills and other industrial sites were shut for good. An estimated 30mn to 40mn workers lost their jobs. The process was deemed painful but necessary: not only in setting up China’s accession to the World Trade Organization in 2001, but in freeing Beijing from supporting uneconomic industries… 

Over the past 15 years, as China’s share of global manufacturing surged to around one-third, the share of lossmaking industrial businesses jumped from about 10 per cent in 2010 to nearly 25 per cent last year, according to the MERICS China Overcapacities Monitor. This dynamic exists across everything from steel and cement to cars, computer chips and robots. Take the automotive sector for example. Domestic car sales last year totalled 23.9mn against estimated production capacity of 45mn to 50mn. Sales are highly concentrated among a clutch of leading companies. According to HSBC, more than 70 per cent of EV sales — including plug-in hybrids — are being soaked up by 10 brands, leaving 47 others jostling for the remainder. In the shrinking market for petrol and diesel cars, 10 brands have about 70 per cent of sales and 73 others compete for the rest.

12. South Korean capitalism and windfall profits sharing - SK Hynix and Samsung edition.

Soaring global demand for high-bandwidth memory chips used in AI systems has propelled SK Hynix and Samsung Electronics to record earnings. This week Samsung announced quarterly operating profit of Won89.4tn ($59.7bn). The windfall is being shared with employees. Last September, SK Hynix agreed to pay workers 10 per cent of annual operating profits for a period of 10 years. Samsung followed with a similar arrangement in May after its union threatened strike action. With both firms expected to earn hundreds of billions of dollars this year, average bonus payouts per memory chip worker could reach about Won600mn ($400,000) at Samsung and even more at SK Hynix. Such amounts are staggering in a country where the average worker earns Won50.6mn per year, according to Korea Enterprises Federation data. 

The Bank of Korea has warned of potential inflationary pressure as a result, and towns where many semiconductor workers live are undergoing property price jumps. Competition is intensifying for places at universities offering semiconductor “contract” programmes that guarantee jobs at Samsung Electronics or SK Hynix upon graduation. Admission scores required for some such courses now exceed the average for natural sciences at Seoul National University, the country’s top-ranked university, and are just below those needed for medicine... The boom is also reshaping Korea’s marriage market. Matchmaking agencies, which are known for using meticulously harsh metrics to rank clients, are now giving higher points to chip workers.

13. Data centres are consuming massive amounts of power and water.

Data hubs already devour more electricity globally than all but 10 countries. About 448 terawatt hours last year if you’re interested. The AI boom means that amount is on track to roughly double within four years... By 2030, they could be using enough water to meet the basic needs of all 1.3bn sub-Saharan Africans for a year, UN researchers estimate.

And it is provoking backlashes. Sample this from the US.

An unprecedented 75 US data centre projects worth around $130bn were blocked or delayed in the first three months of this year, nearly as many as in the whole of 2025, says the Data Center Watch research group. It reckons active opposition group numbers have grown from 396 at the end of 2025 to 833 by the end of March.

14. The Strait of Hormuz squeeze was not as bad as earlier episodes. 

15. Transformers are at the heart of power transmission, distribution, and use. Thanks to the AI and data centre boom, transformer prices have gone over the roof.

Specialist electrical steel — essential for transformer cores — is produced by only a handful of global suppliers, many of whom are struggling to keep up with the surge in demand. Market growth, price volatility and limited mining capacity have also strained supplies of copper, crucial to the conductivity of windingsaround a device’s core. But one of the most acute bottlenecks is the shortage of skilled workers needed to carry out complex, labour-intensive manufacturing tasks...

With up to 80,000 different designs, most transformers still have to be built largely to order, taking three to six months to make. The most demanding stage is the windings, when copper wire is applied around a transformer’s core — a “beautiful” process, according to Bruno Melles, an engineer now leading Hitachi’s global transformer business. Each winding is unique and is “still a human manual activity that we’re very proud of”, he says. The number and pattern of these windings dictate voltage — fewer turns lead to lower voltage while higher-voltage devices can have multiple windings stretching hundreds of kilometres. Once complete, the assembly is placed in a protective outer metal shell, where oil acts to insulate and cool the device...

The world’s biggest transformer manufacturers have reported tens of billions of dollars in backlogs in the first quarter of 2026... US developers are turning to imports. The EU, Mexico, South Korea and Brazil are the biggest suppliers of power transformers to the US, together accounting for more than three-quarters of imports by value last year.

Into this mix come innovations in the form of modular solid-state transformers that use modern power electronics and respond dynamically to changing power needs, enabling real-time monitoring and control (which legacy transformers with their steel and copper cannot do).