Urbanomics

Some thoughts on the AI trade

noreply@blogger.com (Urbanomics) — Sun, 05 Jul 2026 23:30:00 +0000

The AI trade is clearly blowing into a bubble of utterly spectacular proportions.

FT Alphaville points to the interesting reality that both equity valuations and earnings of the underlying companies are in bubble territory. They quote from the monthly market update by Joachim Klement and Francisca Reis of Panmure Liberum.

In 1929, the cyclically-adjusted P/E-ratio (CAPE) of the S&P 500 reached 32.6x according to Prof. Robert Shiller’s data. This was 1.8 standard deviations above trend at the time. In 2000, the CAPE reached 44.2x, or 3.3 standard deviations above trend – a clear sign of a bubble. However, as our chart below shows, earnings in both instances were within normal range, less than one standard deviation above trend. Today, the CAPE is at 41.0x, or 2.9 standard deviations above trend. Once again, we are clearly in bubble territory for stock market valuations.
However, unlike in previous bubbles, we are having extremely high CAPE at a time when earnings themselves are 1.8 standard deviations above trend. In other words, we are in a valuation bubble at a time when earnings are in a bubble themselves. If we correct for the earnings bubble, the current CAPE would be 67.6x or 4.6 standard deviations above trend, a bubble that surpasses anything ever seen in US history by an extreme margin. If valuations followed a normal distribution (which they don’t, so don’t take this literally), this would happen in 0.00019% of months or once every 43,432 years.

See just the Shiller CAPE ratio.

That being the case, what makes any assessment of the AI trade problematic is its unique nature.

It is the creation of a general-purpose technology that is most certain to transform several aspects of business models, work practices and trends, and life itself (and maybe even more). There are five stakeholders on the supply side of the AI story - semiconductor chips, cloud and data centre infrastructure, LLMs, financing, and support services. They encompass a vast spectrum of businesses - chip designers and makers, equipment makers, cloud infrastructure services, AI LLM developers, data centre developers, utilities, financial institutions, etc. On the demand side are all the different kinds of AI applications users, spanning every imaginable industry.

While the LLMs and the cloud services are software, the rest are all hardware. At the vanguard are the top public limited companies of our times, all with formidable moats and strong balance sheets. Till the recent surge in private credit, the AI investments were being financed from their large cash surpluses. Further, all of them make massive and growing profits, even creating an earnings bubble. Finally, given the network effects generally associated with digital technologies, the AI landscape is more likely to create winner-takes-all markets.

Also, so far, the AI trade has been running on the massive investments pouring into the development of AI LLMs and their applications. This investment binge is being driven by an intensely competitive race to outdo each other in improving the LLM algorithms and building up scale capabilities. The achievements to date on this have been truly spectacular. The applications development, though gathering pace, is lagging. No killer Apps or hardware have emerged on the landscape.

The true reckoning for the AI trade will depend on whether the AI algorithms developed will find commercially valuable use cases. More specifically, whether these use cases, which will emerge in the years ahead, will justify the massive investments already made and being committed.

Even more specifically, if the market smell tests of the AI promise endure, the bubble will continue to inflate. Such persistence of bubble inflation is, to a great extent, the perpetuation of stories. Elon Musk and SpaceX are the totemic illustrations. The two critical metrics of aggregate profits possible from the AI industry are the addressable market and margins. However, both are nearly maxed out in the valuation pricing on the upstream (supply) side. The best that can therefore be expected is that the downstream (demand) AI applications industry starts to create successful products. This would let the AI trade inflate more, at least for the immediate future.

On the contrary, if the market smell test points to the other way, at least in a few vanguard areas like autonomous driving or software development or healthcare and life sciences, then all bets are off and the wheels will start to come off. Another downward pathway is if the upstream supply-side margins start to get squeezed due to competition or disruption or rising costs. Given the asymmetric nature of such turns (bubble inflated gradually upwards, but the pop could be rapid), we could then see a rapid unravelling of the AI trade, with all its disruptive implications. The disruptions could be economy-wide, far deeper and broader than with the dotcom and other bubbles, given the wide sweep of markets that the AI industry has enveloped.

Interestingly, even as the seven largest data centre operators are planning to spend $848 bn this year, five times what they spent in 2022, the stock prices of the applications-side hyperscalers are struggling. Microsoft is down nearly 20% this year, and Meta is down 11%. The AI trade is now running purely on the chipmakers.

The critical place in the market to keep a close eye on may be the downstream side of AI applications and product development.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 04 Jul 2026 07:54:50 +0000

1. Indian universities have come to dominate the global rankings for research paper retractions, following complaints of plagiarism, fake peer reviews, and other misdemeanours. According to the Retraction Watch database, India recorded 887 research paper retractions in 2025, form 21% of all retractions (second to China with 41%) despite contributing just 5% of all publications, and occupying six of the world's top 10 universities for retractions.

2. Stunning statistic about the Algerian demographic dominance of the Les Blues, the French national soccer team.

Only two of France’s 26 players are of non-immigrant ancestry — 22 of them have African roots. Some, such as Mbappé, are deeply connected to their country of origin.

And this dynamic of reverse migration and migrant domination elsewhere.

Of England’s 26 players, eight have Caribbean forebears, 10 African, and 20 who were eligible to represent at least one other country because of their family heritage or birthplace. Belgium has players who trace their lineage to its former colonies — Congo, Burundi and Mali. The Portugal team has those of Cape Verdean, Angolan, and Guinean lineage. Those surplus to their requirements — players of African descent born and raised in Europe — in turn, populate most of the African teams. Ten of the 11 Senegalese players in the starting lineup against France in the opening game were born in France... Six of the players in the US national team are of Afro-Caribbean heritage... There are those from Nigeria, Ghana, and Jamaica. Three are from Hispanic backgrounds — Christian Roldan (Guatemala), Ricardo Pepi (Mexican-American) and Jesús Ferreira (Colombian-American). As many are from Europe, and three others have dual nationalities... For Australia's Socceroos, four players were born in refugee camps. The players represent 15 ethnic backgrounds and include a Malaysian with Sri Lankan roots. Eighteen other players have direct immigrant or refugee heritage. To contextualise, only one Black player (Sam Morris) and another of Caribbean descent (Andrew Symonds) have represented Australia in cricket.

This is a brilliant article by Sandip G about the magnificent quartet in the French team - Kylian Mbappe, Ousmane Dembele, Michael Olise and Desire Doue.

3. The US military learns from Iran.

The US has, similarly, begun to build its production of drones. It used the Lucas, a one-way attack drone reverse-engineered by start-up SpektreWorks from an Iranian Shahed-136, for the first time in combat in February. The Pentagon is looking to begin mass-producing them, and has requested to triple its spending on drones and related technologies to over $74bn next year.

4. Shenzhen, with 400,000 taxi drivers licensed to provide services in 26 platforms, will permit robotaxis from July 1. The new rules will allow the Shenzhen government to promote the “orderly development” of robotaxi tests, demonstrations and commercial pilots either in select zones or citywide.

5. Friedrich Merz's landmark pension reforms

Under proposals agreed by a bipartisan commission, a compulsory initial contribution of 0.5 per cent of employees’ pre-tax income, rising to 2 per cent by 2031, will go into a Swedish-style public pension fund managed centrally and invested in capital markets. Contributions are split 50/50 between employees and employers. The statutory minimum retirement age of 67 is set to rise in line with life expectancy; rights to early retirement for people with 45 years of contributions will be restricted.

Such measures have become vital to reduce the deficits of Germany’s unsustainable pay-as-you-go system. Some 16.5mn baby boomers will retire by 2036 with only 12.5mn new workers joining the workforce, according to some estimates. The government spent about a quarter of the total federal budget on plugging gaps in the system in 2024; economists say that could double to 50 per cent in two decades.

Linking the retirement age to life expectancy is projected to mean only a gradual increase — to 67.5 by 2041 and 70 by 2091. But economists say this is the only sound way to stabilise the system without spiralling payroll taxes or huge federal subsidies. Narrowing early retirement rights will address a drain of experienced workers amid acute skills shortages.

6. Important graphic that highlights the extent of renminbi depreciation since the beginning of 2022 and the surge in surplus.

7. China expands export restrictions on dual-use items against Japanese companies in the latest instance of weaponisation of its manufacturing dominance.

The companies added to the export control list include subsidiaries of Mitsubishi Electric and Mitsubishi Heavy Industries. The restrictions will also apply to several Japanese government research organisations including the National Institute for Defense Studies. Chinese exporters are banned from selling to the entities on the restricted list, and foreign organisations or individuals are also prohibited from selling dual-use items that were built or originated in China. China last expanded the list to 40 companies in February. In parallel with the expanded export control list, China’s commerce ministry on Monday put 20 Japanese companies and organisations on its watchlist, meaning they will get closer scrutiny in any matters relating to potential dual-use technologies. The list includes subsidiaries of Fujitsu, Mitsui E&S, Hitachi, Komatsu and Terra Drone. Beijing’s targeting of Japanese companies is the latest example of China’s weaponisation of trade in recent years. The EU Chamber of Commerce in China in April has found that Beijing has nearly tripled its use of export controls in the past five years. While some instances have been in response to western measures, the researchers noted that Beijing’s controls have also frequently targeted trade chokepoints.

8. GST balance sheet.

Though average GST collections in absolute terms are almost 90 per cent higher than those in the pre-GST period, other parameters tell the real story. The average growth rate in tax collections and the tax-GDP ratio are lower under the GST regime compared to the pre-GST era. Collections were further marred by GST cuts starting September 22, 2025.

9. India's labour market challenge.

With high-skilled IT services under pressure, a financial sector with limited capacity to create low and medium skilled jobs and manufacturing struggling to gain traction, the one area where jobs are being created is low-end services. Delivery riders for Zepto, which has just filed its papers for an IPO, have gone up from 49,278 in 2024 to 2.21 lakh in 2026 — more than a four-fold increase. Zomato and Blinkit have almost doubled to 10 lakh riders in two years. Swiggy now has 6.1 lakh riders, while Uber, at 14 lakh active drivers, outstrips Indian Railways. The gig economy is emerging as an urban employment sink.

10. The Uniqlo-Toray partnership that underpins Fast Retailing's spectacular growth.

In April 2000, Uniqlo founder Yanai paid a visit to the offices of Toray, a Tokyo-based chemicals and materials conglomerate he had read about in a magazine. It marked the start of a strategic collaboration that provided Uniqlo with arguably its most important advantage over rivals: access to high-tech, specialised fabrics. After testing 10,000 prototypes, Toray invented a material combining four types of synthetic yarn that absorbs moisture from the body and converts it to heat. Uniqlo branded it Heattech, and since 2003 has sold 1.5bn garments made from or containing it. Toray also helped develop the fabrics behind AIRism, used as a breathable base layer, and the Ultra Light Down ranges of packable jackets insulated with bird plumage... control over fibre shape and fineness, dubbed nanodesign, has helped to make highly water-repellent and durable $50 lightweight parkas, creating a far cheaper alternative to specialist outdoor brands such as Patagonia. The two sides are entering a fifth phase of collaboration that aims to combine synthetic fabrics with natural ones, such as introducing cashmere into Heattech products to make them softer. Okawa believes few other retailers have such deep relationships with their key suppliers.

11. Good FT long read about the Jamie Dimon succession struggle at JPMorgan. The things that stand out are the following:

One, the appointment of someone to the top position in any organisation, public or private, is bound to be opaque and involve considerable discretion. The only disqualification would be the egregiously ineligible. For any others, there will always be ways to spin it as a fair process. This holds with greater effect as the stakes go up.

Two, in the succession struggle, dominated as they are by a multiplicity of considerations, among those eligible or qualified, it is rare that the most professionally competent will emerge as the successor.

Three strong leaders will always delay succession, and even when they choose to retire, will seek to retain enough levers to influence the decisions of their successors. Most often, it is about decisions involving the promotion of the interests of those in the organisation closest to them, their pet projects or initiatives, broader organisational strategic shifts, etc.

More on the limits to China's growth trajectory

noreply@blogger.com (Urbanomics) — Fri, 03 Jul 2026 02:20:56 +0000

The valuations of the US AI stocks and its public debt binge are not the only bubbles waiting to pop. An equally big bubble that could pop is China’s investment-driven and debt-fuelled growth model. Of the three bubbles, the last could well be the most consequential.

I had blogged here explaining the hard limits to China’s current growth trajectory. This post highlights the unsustainability of the country’s credit-driven growth.

The Economist has a very good read on China’s economic growth strategy, with its latest focus on high-technology sectors.

This puts China’s infrastructure investments in perspective.

The old model of growth took shape on China’s coasts before spreading to the interior. Factories in the wealthy east employed poor migrant labourers from the hinterland. Those migrants in turn, unable to obtain residency in metropolises, often used their earnings to invest in property back home. Towering apartment blocks erected during the two-decade property boom have sprung up in the smallest towns, employing tens of millions of construction workers each year and hoovering up low-end manufactures. High-speed rail has penetrated the poorest counties.

All this investment was fuelled by local-government borrowing. One tally puts these debts at around 60trn yuan ($9trn), or 43% of GDP. The comparable figure in America is 12%. The poorest regions often relied the most on debt-fuelled construction of houses, roads and bridges. This has left some places, such as Guizhou province in the south-west, with dazzling infrastructure (including a bridge 626 metres high, the world’s tallest) along with insurmountable debts. Few of these costly public works have so far come close to generating the revenues needed to pay back creditors.

As the Cold War with the US and the West intensifies, President Xi has set the goal of global leadership in advanced technologies - EV, batteries, semiconductors, AI, robotics, fusion, etc. This has set the stage for a new round of competitive investments by governments across the country, with enabling credit supply measures.

A national semiconductor fund has raised roughly 687bn yuan over the past 12 years. Government-backed fund managers watched their coffers swell to nearly 400bn yuan last year, an increase of 75% from 2024. In December the state launched a 100bn-yuan national venture fund with a mandate to invest in aerospace, semiconductors, brain-linked machines and quantum technology. Many local governments, including in small cities, are creating similar vehicles using tax revenues and capital from local state companies. They are setting up “high-tech zones” and “AI parks” to lure innovative companies with tax breaks and other perks. These new tech businesses are meant to generate tax revenue and help local governments grow out of their debts, says Jean Oi of Stanford University. While officials wait for their homespun DeepSeek, the AI lab that stunned the world last year with its powerful model, the central government relaxes the rules to give them more time to repay their debts.

The massive expansion of investments and intense competition have generated spectacular failures.

In 2021 the city government of Yichun invested 2.3bn yuan to help build an EV factory in a sprawling National High-tech Development Zone. But in contrast to successful EV clusters like those in Shenzhen and Hefei, the facility was isolated from suppliers and expertise required to build cars efficiently. It has since halted production. The rest of the industrial zone looks just as lifeless… A decade ago a fund with local- and central-government money poured around 150bn yuan into Guizhou, a mountainous province in central China, mostly into data storage and cloud-computing. But these ventures could not be integrated with local industry. The companies building the data centres are based on the coasts, the server parts are made elsewhere and local demand for the data capacity is scarce… The north-western industrial city of Lanzhou has invested in commercial space flight and a “drone economy” project even as it struggled to pay its bus drivers for several years (asking them to take out personal bank loans to tide them over)…
Mr Xi’s industrial policy promotes fierce competition in which companies and their host places, sometimes down to city districts, duke it out. This competitive pressure pushes down prices and elevates quality. The best businesses which emerge from this free-for-all, like BYD in carmaking, Huawei in electronics or Xiaomi in both, are formidable and ready to take on the world. They are also rare—and concentrated in established commercial centres, with deeper talent pools and pockets. Profits are even rarer. Investment returns accrue less to individual companies and more to integrated supply chains, which lower costs and speed up product cycles and innovation, says Chi Lo of BNP Paribas, a bank.

The share of industrial firms generating losses has shot to a record high of around 32% in April, up from 10% in 2011 and above the previous peak during the Asian financial crisis in 1998. Corporate debt is also high and rising. Mark Williams of Capital Economics, a consultancy, notes that Chinese firms owe twice as much to domestic banks and bond investors today as they did in 2019. In that period, GDP has expanded by a third. Companies may move away from productive activities and instead chase subsidies that are available for centrally supported sectors, he says… a trio of IMF economists calculated last year, China’s “total factor productivity” (which captures how efficiently both capital and workers are used) was 1.2% lower than it would have been in the absence of industrial policy over the past decade or so. GDP was 2% lower, equivalent to forgoing around $400bn in value added each year. The more companies get caught up in the chase for subsidies and, by slashing their prices, for customers, the harder it will be for them to wring out profits.

China faces a confluence of headwinds - slowing economy; weakening aggregate demand and consumer sentiments (with persistent low domestic consumption); ten quarters of factory deflation (PPI −2.6%) (involution or neijuan); the property market still in crisis and recovery perhaps still a few years away; local governments deeply indebted; large excess capacity across industries; growing backlash among trade partners against surging Chinese exports; intense competition among domestic companies squeeze margins and are leaving companies running losses; and zombie firms kept alive by local and central government subsidies, cheap credit, and debt repayment rescheduling.

Each panel below is a single number with its recent track. Red is contracting or dangerous; amber is stalling; green is the part still running hot, which is exactly where the next overcapacity is building.

In an environment where consumption is weak, any squeeze on investment and reduction of exports (due to rising backlash among trade partners) will only lead to job losses and social discontent. This has increased the reliance on investments to achieve the 4.5-5% GDP growth rates.

And exports are critical to absorb the excess capacity that has been built up.

After infrastructure, real estate, and manufacturing in the first two decades, the current focus of the investment-driven growth model has been advanced technology sectors like EVs, batteries, semiconductors, robotics, AI, etc., where there is also a geopolitical imperative arising from the Cold War with the US. The macroeconomic policy mantra has been to “hold growth at ~5%, protect jobs, and roll the borrowing forward each time.”

All this points to the model of a giant economy-scale Ponzi scheme where investment is shifting from one sector to another in order to sustain a target GDP growth rate and prevent job losses, while also accumulating a growing pile of massive debts.

A debt-funded model works only while each new yuan of credit produces enough output to service it. In China, that link has snapped: credit keeps compounding while nominal growth fades - the classic signature of a system paying old debts with new ones.

The cleanest unsustainability signal isn't the debt level per se but its productivity: total social financing has passed 309% of GDP, and in 2025 credit grew +8.9% while nominal GDP grew just +4.1% — more than twice as much debt as output. When each extra point of growth costs ever more borrowing, new credit is increasingly servicing yesterday's liabilities rather than funding tomorrow's, which is the arithmetic that ends the loop. And this is also reflected in the continuously rising fiscal deficit, especially in the off-balance sheet side.

Worsening matters, these trends coincide with a period in which fiscal revenues have flatlined in nominal terms and declined relative to the economy since 2020.

As the RAND report writes, with the fiscal space disappearing, the government has relied on credit and mandates to keep the wheel spinning.

Beijing can still mobilize large-scale borrowing for industrial policy. The central government and policy banks retain substantial capacity because Beijing can expand bond issuance, and national commercial or policy banks raise trillions of yuan annually in quasi-sovereign debt to finance strategic sectors. By contrast, local governments are squeezed; bond quotas are capped, land sale revenue has fallen, and off–balance sheet LGFV borrowing is under regulatory pressure. The most important immediate trigger was Beijing’s “three red lines” policy introduced in 2020, which sharply curtailed developer borrowing and land sales. Because land sales to developers had long been a major source of revenue for local governments, the resulting property downturn led directly to a collapse in land sale proceeds and fiscal capacity at the local level. A correction was likely inevitable because of the structural exhaustion of a land finance model that relied on perpetually rising property values to fund local growth. Therefore, localities are less able to cofinance subsidies or guidance funds.
Policy is shifting as a result. Centrally directed (or funded) credit and investments remain important. Meanwhile, traditional cash subsidies and local incentives play a reduced role. Government procurement has fallen relative to GDP. Instead, Beijing is leaning on lower-cost directives and mandates (including procurement requirements, regulatory obligations, and selective credit guidance) that require less fiscal outlay but effectively spread the costs of industrial policy across firms and institutions required to comply.

There are hard limits to how far this can go.

Since around 1980, the political bargain between the Communist Party and the Chinese people was simple - rising prosperity in exchange for political acceptance. With consumption weak, any squeeze on investment, or a real loss of export markets to a rising trade backlash, flows straight into jobs. Youth unemployment is already near 18%. The flywheel is kept spinning not only for growth, but to keep that bargain intact. How long can the flywheel keep spinning?

The problems of additionality and technology sector skew in the public funding of startups and innovation

noreply@blogger.com (Urbanomics) — Wed, 01 Jul 2026 01:36:25 +0000

Public risk capital funding of innovation and startups in India is done almost entirely through the VC-driven Fund of Funds (FoF). The RDIF is only the latest effort.

However, as I have blogged earlier, there are two important concerns with public funding of innovation through the FoF approach.

One, would it primarily expand the investable universe of startups (genuine additionality) or primarily subsidise returns on investments that would have happened anyway (returns-amplification for private investors)? Second, would it prioritise scalable digital technology innovations at the cost of manufacturing and industrial innovations?

The evidence on both suggests returns amplification for private investors and the dominance of technology innovators. This should raise concerns about whether scarce public funds are being deployed most effectively. This post points to yet more evidence in this regard.

The Ken has analysed the performance of the Self-Reliant India (SRI) Fund, a Rs 50,000 Cr fund with 20% government contribution (the rest coming from VC and PE) to finance MSMEs, and found both concerns being validated.

The SRI fund model is described here.

SRI Fund is being implemented by NSIC Venture Capital Fund Limited (NVCFL), which is an Alternative Investment Fund (AIF) of Category II registered with SEBI. SRI fund is oriented to provide the funding support through NVCFL to the Daughter Funds for onward provision to MSMEs as growth capital, in the form of equity or quasi- equity, for the following:

Since the start of the fund in 2020, it has backed around 750 companies and catalysed more than Rs 16,000 crore of investment.

Nearly seven out of every 10 companies it has backed are tech-heavy businesses. Only three are in traditional manufacturing… In fact, several of these companies had already been backed by VC firms before the SRI Fund got anywhere near them. Truemeds, for instance, is backed by Accel, Peak XV, and Westbridge, while Chai Point is backed by Eight Roads Ventures and Saama Capital. Understandably, this dims, if not outright flouts, the proposition of the SRI Fund, which was supposed to scope out companies “ignored” by venture capitalists…

“Fund managers get empanelled under SRI with the intention of backing underserved MSMEs,” said Ishaan Ajay, a development-and-sustainable-finance consultant. “But when faced with the choice between a profitable but slow-growing industrial supplier and a software platform capable of scaling rapidly, they tend to gravitate towards the latter. It’s what they understand the best.” Just consider the numbers. Suppose there’s a precision components manufacturer growing 15% annually with, say, 18% Ebitda margins. That may be a great business, but if you invest Rs 10 crore today, you’ll get only 2–3X your money over seven years. Contrast this with a spacetech company, which, if it succeeds, could be worth hundreds of crores.
“Venture funds are built around the second outcome,” said an analyst at a VC firm. This explains why SRI-funded companies tend to be tech-heavy. For every Bellatrix Aerospace in the portfolio, there’s an invisible machine-parts manufacturer outside it that was passed up… startups are generally more innovation- and tech-heavy, whereas MSMEs tend to be traditional, manufacturing-heavy businesses…
The government, for its part, tries its best to redirect funds towards their intended purpose. If it doesn’t agree with a fund’s investment choices, it expresses its objections, according to a VC with knowledge of the matter. But at the end of the day, it’s one LP among many. And when it can’t sway others, it ends up “recusing” itself from investing in that particular portfolio company, added the VC… “There is no prescription that a certain percentage of the fund must necessarily go towards traditional manufacturing or non-tech MSMEs,” a VC said. Which is the one thing that might have prevented all the confusion.

The point about returns amplification for private investors has also been highlighted in a study of IFC’s blended finance deals in the 2000-20 period, which found comparable financial returns to non-blended projects but “no statistically significant excess private mobilisation beyond what IFC’s standard lending would attract.” In other words, blending did not increase the quantum of private investment — it redistributed risk between IFC and private co-investors.

This finding is echoed in a 2023 study of SIDBI’s Fund of Funds for Startups (FFS) 1.0 by the Impact and Policy Research Institute (IMPRI), India’s Startup Engine: A Policy Review of the Fund of Funds Initiative. It finds that most FFS 1.0 capital went to established VC funds that would have raised capital independently. It also found that “crowding-in effect was primarily reputation/signal, not financial additionality”; the government’s involvement functioned more as a validation of fund managers to private investors than as a necessary injection of capital. While FFS 1.0 delivered a 2x mobilisation ratio, it was mostly in already-functioning VC markets.

As I have blogged several times, the startup and innovation sector is going through the same journey that the infrastructure sector has undergone over the last three decades. Multiple policy experiments to catalyse DFIs - IDFC, IIFCL, NIIF, and now NaBFID - have struggled to crowd in private capital into the riskier infrastructure segments like water supply and sewerage, mass transit, solid waste management, street lighting, energy-saving companies, electricity distribution, etc. Instead, these institutions and instruments may have ended up competing and crowding out private capital in the entirely derisked segments of the infrastructure sector.

In this context, it is also useful to ask the question whether the VC model is the right instrument for the funding of non-technology startups and innovations.

Livemint has two long reads that describe the emergence of consumer food brands catering to the niche category of health-conscious people. Sample this

The past few years have seen a spurt in new food brands specializing in regional staples and cooking ingredients. From rural Bengal winter specialty date palm jaggery (nolen gur) to ancient emmer wheat flour (Khapli atta) from Maharashtra, regional staples are finding customers beyond their states of origin. Some brands like Gurugram-based Anveshan and Two Brothers Organic Farms from Pune have crossed a critical mass, with annual sales close to ₹200 crore in 2025-26… Anveshan sources ingredients from key growing regions—like Pollachi in Tamil Nadu known for its high-quality coconuts and aromatic varieties of groundnut from elsewhere in Tamil Nadu and Karnataka—suited to make cold-pressed oils. The ghee, made from the milk of native breeds like the Gir from Gujarat is made using the traditional bilona process where milk is first set to curd and later churned to separate the butter (this has resulted in a new category called ‘cultured ghee’)…
Venture capital funds are betting on these brands: in September last year, Two Brothers raised a $15 million round taking its total fundraise to $25 million (and its post-money valuation to $85 million or ₹781 crore as per data from market intelligence platform Tracxn)… In April this year, KisaanSay, which markets single-origin grocery items like cardamom from Idukki and black raisins from Nashik, raised ₹34 crore ($3.6 million), taking the total funding at the Gurugram-based startup to $5.6 million since it was set up in mid-2022.
In a way, these brands have also helped promote traditional farming practices with more farmers returning to heirloom grain varieties such as the fragrant, short-grain Kala Namak rice grown in eastern Uttar Pradesh and emmer wheat in Maharashtra and Karnataka. These grains have a low glycemic index (a measure of the spike in blood sugar levels from carbohydrate intake) making them suitable for diabetics and often have higher protein and fiber content compared to conventional hybrid varieties… Three distinct factors—growing consumer willingness to pay for clean food driven by a surge in lifestyle diseases, the rise of quick commerce (allowing brands to quickly test consumer response), and influence of social media platforms are reshaping the premium staples market.

All these businesses are distinct from the rapidly scalable technology startups. Take the story of the Two Brothers Organic Farms.

They use organic farming and traditional methods of primary processing to make ghee, jaggery, Khapli flour, and cold-pressed oils. The jaggery is made using native sugarcane with lady finger extract used as a coagulant. “In Khapli, we have created a revolution. We own a seed bank and pay farmers 2.5-times the price of regular wheat. More than 800 farmers grow this wheat for us in 3,000+ acres,” Satyajit said. “It took us ten years to build this brand. At the back-end, our farms are open to everyone (to visit). Consumers trust us and we have a 70% retention rate. But a proliferation of brands (offering traditional, hand-processed staples) also carries the risk of a dilution in quality standards,” he added with a note of caution.

Much the same can be said about most businesses outside of technology - food, textiles, footwear, manufacturing, recycling, etc. Building these businesses requires painstaking efforts for several years, and there are natural limits to their scalability. They are unsuitable for the VC model of funding, as the second Livemint story about the plant-based nutrition startup Oziva shows.

Venture funding, Aarti Gill (co-founder of Oziva) points out, comes with built-in expectations around exits within five to eight years. Unlike venture-backed software companies, consumer health brands compound slowly through trust, habit and repeat behaviour. “If you have investors willing to stay for 10 or 15 years, that changes the equation completely,” she adds. Gill broadly sees three paths for consumer brands: staying profitable and scaling independently over a long period, going public after reaching meaningful scale, or partnering strategically with a larger company. For Oziva, the third route eventually felt like the most practical one. And, that’s where HUL came in… For Oziva, the partnership offered distribution scale, capital and the ability to build beyond a digitally native audience.

Therefore, at a conceptual level and as a framework, it may be a prudent choice for public policy on the funding of startups to distinguish between technology and non-technology sectors. While VCs are appropriate for technology, with their smaller and rapid growth phase, the same may not be appropriate for non-technology startups, which require longer incubation and growth periods. However, in India, most public risk capital funding happens through the VC-driven FoF strategy, which raises concerns of returns amplification and a preference towards technology startups and innovations.

This is also a concern since technology innovations, far from creating jobs, often also tend to destroy jobs, whereas the non-technology innovations, especially in manufacturing, create good jobs. The graphic below captures the problem.

Note: The percentage breakup is probably even more skewed in favour of technology startups.

It also raises the question of effective strategies - instruments and institutions - for funding such non-technology innovations. How are such innovations funded globally? Are there institutional structures in India (state or central governments) that offer promise and can be adopted with tweaks? What are the lessons from the likes of the Maharashtra Aerospace and Defence Fund? What are actionable recommendations on funding of non-technology startups? If it also requires the government to directly fund them, what institutional structures are most practical and realistic? I’ll explore these in another post.

Indian economy's cost competitiveness constraints - a graphical summary

noreply@blogger.com (Urbanomics) — Mon, 29 Jun 2026 01:43:01 +0000

I had blogged here, arguing that the Indian economy faces a cost-competitiveness constraint. I had written that on many inputs, domestic firms face the cost structure of a developed country. This post will examine the empirical evidence in this regard.

This cost structure reflects in the global competitiveness of the country’s manufacturers. India suffers from a persistent and high price disability compared to peers across manufacturing sectors.

What are the contributors to this competitiveness wedge?

Indian factory wages (~$2.1/hour) are the lowest in Asia, about two-thirds of Vietnam's and a third of China's. Its effective corporate tax rate at 17.2% is among the lowest, its GST rates are comparable, and its logistics costs (while contested) are at least not much higher.

So if India loses on net cost, the disability is entirely in non-wage factors: land, capital, power, fuel, scale, tariffs on inputs, and labour productivity.

Start with land, the largest cost wedge. Urban land is priced like that of a rich country, and, most problematically, hoards the nation's savings. Mumbai ranks among the world's 20 most expensive prime markets, and Indians park ~77% of household wealth in real estate. The high land valuations result in capital misallocation and squeeze out the capital that would otherwise have resulted in investment.

In fact, high land valuations, even in the smaller cities, appear to be a big entry barrier for businesses. An Indian manufacturer pays roughly 5–15 times more for industrial land than a Chinese counterpart in a comparable tier-2 city, and 1–4 times a Vietnamese peer. That gap is policy-made. China's local governments subsidise industrial land to attract production, whereas India's restrict supply (FSI, fragmented titles, slow acquisition) and treat land as a fiscal asset to maximise revenues.

Now, come to power tariffs. Indian industrial users pay ~₹7.5–9/kWh ($0.09–0.11), materially above what comparable Asian peers' large industrial users effectively pay. Indian electricity is among the world's most expensive on a PPP basis, with one of the world's widest spreads across consumer types.

The comparisons of electricity prices based on averages are misleading. For example, India and China have similar average electricity tariffs at $0.08/kWh. It conceals that India has one of the world's widest cross-subsidy spreads (farms near zero, industry penalised). The rate that matters for competitiveness is the industrial slab, which is materially higher. Industry overpays to cross-subsidise farms and households, again, another policy choice. To this, we must add another 15–25% for diesel backup against unreliable supply.

The same applies to gasoline prices. Kept outside GST as a revenue mainstay for state governments, petrol and diesel carry stacked central excise plus state VAT. Roughly half the pump price is tax. In raw dollars, India's fuel is dearer than China's, Vietnam's or America's, despite far lower incomes.

The cost of capital adds to the wedge. The credit-to-GDP ratio measures the availability of capital. The other half of the problem is the price. Indian firms pay materially more than peers - across banks, bonds and the SME segment. In fact, after stripping out inflation, the gap stays large, which is the more realistic measure of whether real investment can clear its hurdle rate.

The capital markets are no different. An Indian mid-cap pays roughly 2–3 times the bond yield a Chinese mid-cap pays and ~50% more than a US one, in nominal terms. After inflation, the gap narrows but doesn't vanish. India's real lending rate (~5%) is ~2 percentage points above China's, and the real MSME rate is among the world's highest. Combined with credit availability stuck at 55% of GDP, this means Indian firms, and especially the missing middle, face the worst combination of dear and scarce capital among large economies.

Let’s dig a bit deeper into the capital cost wedge.

Econ 101 informed that the high cost of capital is a reflection of the demand-supply mismatch. On the demand side, as we have seen, the high land valuations are encouraging resource misallocation. What about the supply side?

The country’s reasonable gross domestic savings rate (~30%) conceals a disproportionately high share of illiquid assets. When 77% of wealth sits in physical assets (land and gold), and only ₹40 for every ₹100 of household financial savings reaches the financial system, the banks have less to lend, the bond market stays thin, and the price of credit rises.

In other words, there’s a link between the land and capital problems facing the economy. The causal chain goes something like this - physical-asset preference (land + gold) → low household financial savings (5.3% of GDP) → shallow bank deposits & bond market → low credit-to-GDP (55%) → dear capital (~9% vs ~3.5% in China). The same wealth-allocation pattern that raises land prices also raises the cost of capital. It is one mechanism, not two.

Worsening matters, the net household financial savings fell from ~11.5% of GDP in FY21 to 5.3% in FY24, a 50-year low. The table below breaks down how the cost wedge feeds itself. Households are borrowing more (against assets), and routing more new savings back into physical assets.

The cost constraints impact the economy in two ways. One, it restrains private investment. Second, it weakens global competitiveness and hurts exports.

Take the first. Overall investment slid from a 35.8% peak in 2007-08. Public capex has since surged, but private corporate investment never recovered, flat near 11-12% of GDP. Flush with cash, firms deleveraged and bought financial assets instead of building capacity. I blogged here on this.

The East Asian economies found exports to rich countries as the outlet to overcome thin domestic demand and drive economic growth. Unfortunately, in India’s case, this is exactly the outlet that the cost wedge closes: dear capital, costly logistics, unreliable power and the world's highest tariff walls all erode competitiveness. The result is that India is losing its share of labour-intensive exports to Vietnam and Bangladesh , even as its overall merchandise share stalls near 1.8%.

In fact, the Economic Survey 2016-17 chapter Clothes & Shoes: Can India Reclaim Low-Skill Manufacturing? warned that the space China vacated was being taken by Bangladesh and Vietnam in apparel, and Vietnam and Indonesia in leather and footwear. A decade later, the warning could not have been more prophetic.

In the net, India wins on wages (~$2.1/hour) but loses on net cost. So the disability is entirely from non-wage factors: capital, power, logistics, scale, tariffs on inputs, and productivity. India has a cost structure that is comparable to that of a developed country. That's the central analytical point.

India enters every labour-intensive sector with the world's cheapest workers and exits with a 10–20% cost disability. This is proof that wages aren't the binding constraint. The wedge comes from the rest - scale, input tariffs, capital, power, logistics and productivity, in a market structure that punishes the firms that should be growing. This is why India hasn't replaced China, where Vietnam and Bangladesh have, despite the structural opportunity being identical for all three.

Indian businesses (and especially manufacturers) face a structural cost constraint. It does not have to do with taxation or wages, but with factors like land, power, fuel, cost of capital, input tariffs, labour productivity, and business size/scale. A disproportionate attention and effort go into taxation reforms instead of these more important structural limiting factors.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 27 Jun 2026 02:52:58 +0000

1. New research by Emma Harrington, Natalia Emanuel, and Amanda Pallais shows that remote work is adversely impacting mental health. They paraphrase Robert Putnam to argue that Americans "typing alone" brings serious social consequences.

In 2024, nearly 80 percent of workers said they would be happiest if they could work remotely... Surveys of over half a million Americans from the last decade and a half revealed an uncomfortable truth: Despite its advantages, remote work has significantly deepened Americans’ isolation and distress. Our estimates indicate that remote work explains a third of the deterioration in mental health between 2011 and 2024... Our study compares workers in jobs that could be done remotely, such as finance and software engineering, with workers in jobs that must be done in person. People in remote-capable jobs worked from home three times as often in 2024 as in 2019. As they did, their days became far more solitary. Eighty-four percent of remote workers spend their workday entirely alone. Over half report feeling less connected to their colleagues. Even when communicating online, people working from home receive less feedback from their co-workers and contact fewer people outside their immediate teams.

These workers did not compensate by socializing more outside work. More days passed with no social contact of any kind... In one study, when commuters were instructed to connect with a stranger near them, they reported being happier than those who continued in silence as usual, much to their own surprise. With fewer social encounters, workers in jobs that can be remote saw steeper increases in distress, mental health visits and prescriptions for antidepressants than other workers did... The pain was not evenly shared. People who lived with their spouse and kids saw their mental health hold fairly steady, while those who lived alone experienced a 20 percent decrease in mental well-being. Overall, we found that the rise of remote work increased distress by 7 percent, which accounts for a third of the total increase over the 13-year period we measured.

They argue that face-to-face time with colleagues has no substitute.

2. Katie Martin points to the different ways in which bonds and equities are reacting to Trump policies.

US government bonds, or Treasuries, have never recovered from the drop in price they suffered around the start of the war. Investors in this market, who broadly consider themselves a more cerebral bunch than those in stocks, never bought the hints of a ceasefire with Iran. Bond prices have still not returned to square one, leaving borrowing costs markedly higher. With the prospect of interest rate rises ahead to douse inflation pressures exacerbated by the Iran war, and relentless more borrowing, this is likely to remain the case for some time.

3. As AI threatens to bring down India's tech sector, this is a good article.

On the whole, Indian IT companies spent around 3.7 per cent of their total revenue on R&D in the year that the report covered. This is minuscule compared to around 15 to 25 per cent that Silicon Valley companies spend on R&D. The top IT companies are laggards of first order. For example, in 2022-23, Infosys spent just 0.9 per cent of its total revenue on R&D. The figure for TCS was 1.30 per cent. For Wipro it was 0.5 per cent while for HCL it was 1.60 per cent. The other big companies don’t fare all too well. Reliance, a giant in every way, spent only 0.53 per cent of its total turnover on R&D in 2022-23. Tata Steel is at 0.67 per cent. Maruti Suzuki spent 0.65 per cent on R&D.

4. Indian markets have more to fall before they become competitive.

The FPI outflows have tracked the decline of rupee, feeding a self-fulfilling cycle.

5. The costs of RBI's FCNR (B) deposits and foreign currency borrowing schemes.

If the scheme were to attract $50 billion of FCNR (B) deposits and $20 billion of foreign borrowing by banks and public-sector enterprises, the mark-to-market loss on the RBI’s swap position could approach ₹64,000 crore at current market prices, besides increasing the RBI’s balance-sheet risk. This is not merely an accounting cost. The subsidy is real and will be monetised by participating non-resident Indians (NRIs), banks and borrowers.

Large Indian banks are raising five-year FCNR (B) deposits in dollars at 6 per cent. Their attractiveness is evident from the willingness of overseas banks to lend against the same deposits at around 5 per cent. This, in turn, will allow wealthy NRIs to achieve double-digit leveraged dollar returns against India cross-border risk. Indian banks can further transform the FCNR (B) deposits into clean five-year rupee funding at around 6.4 per cent, below comparable government bond yields.

Will the RBI regret having offered the FCNR (B) deposits to NRIs?

Banks are being permitted to offer leverage to NRIs. The currency risk on such deposits will be borne by the RBI. As reported by this newspaper, State Bank of India is offering leverage of up to nine times on deposits of more than $1 million. Calculations indicate that this could translate into returns of over 14 per cent. Other banks are likely to come up with similar schemes for NRIs.

Banks are competing aggressively for FCNR (B), and are also offering leverage to increase returns.

6. A new large-scale survey experiment of EU companies shows that firms substantially underestimate competitors' current AI investment, and when updated about their competitors' future AI investment plans they increase their own AI investment plans in a statistically significant manner. But this effect, while strong for domestic peers, is weak for information on foreign peers.

We documented large underestimation of competitor AI investment, substantial belief updating in response to information, and a clear asymmetry in how firms react to domestic versus foreign competition... A 1 pp increase in the expected share of domestic peers investing in AI raises a firm's own expected AI investment rate by 0.570 pp. These complementarities are absent across borders: the effect of an increase in the expected share of foreign peers investing in AI on a firm's own expected AI investment rate is statistically insignificant... Firms update both domestic and foreign beliefs when informed, but their own expected AI investment rate responds primarily to domestic posterior beliefs. These findings suggest that strategic complementarities in innovation weaken with distance, broadly understood to include not only geography but also informational, cultural, and market frictions... This asymmetry helps explain why AI diffusion may remain geographically uneven, even within an integrated economic area like Europe. While firms may observe and learn from foreign competitors, their behavioral response to such foreign signals is much weaker compared to domestic competitors.

7. Aswath Damodaran makes a great point about hedge funds, private equity, and private credit - all niche businesses which had a role, but have vastly overextended themselves and set themselves up for failure.

Each one began as a genuinely good niche business solving a real problem. Hedge funds 30 years ago produced positive alpha, beating passive investing by 3 to 5 percent annually. Today they look like expensive mutual funds, underperforming passive by roughly 1.5 percent. Private equity started as a focused, disciplined strategy for a small set of operators and has grown into a sprawling category that now struggles to deliver the returns that justified its emergence. Private credit had a legitimate original purpose, which was lending to borrowers that banks structurally could not serve. What killed each of these businesses was the same disease. Overreach. A $200 billion niche business gets sold as a $20 trillion opportunity. When that scaling happens, sloppiness follows, bad actors enter the space, and the average quality of every participant deteriorates. The original alpha disappears not because the strategy stopped working, but because too much money chased too few good deals. The danger with private credit is far more severe than the parallel problems in private equity and hedge funds. Equity investors take their losses and move on. Lending businesses, when they overreach, take others down with them. Banks. Pensions. Insurance companies. Sovereign wealth funds. The systemic linkages run far deeper than most participants understand, and the social costs of a real default cycle in private credit would extend well beyond the funds themselves.

8. Friedrich Merz initiates measures to address Germany's rising pension burden, which took up 41% of all federal government welfare spending in 2024. The proposals came from a bipartisan committee of MPs who were appointed to examine and make suggestions.

Germany’s pay-as-you-go system is facing widening deficits, with 16.5mn baby boomers retiring by 2036 and only 12.5mn new workers joining the workforce, according to the Cologne Institute for Economic Research. The government in 2024 paid €118bn to plug holes in the system, or about a quarter of the total federal budget. That share could double to 50 per cent within the next two decades, according to economists... Under the proposal, a compulsory individual contribution of 2 per cent of salaries would “be managed centrally and invested in capital markets”... The move would be a novelty for risk-averse and cash-loving Germans, who have been more reluctant than European peers to embrace capital markets to invest their large savings... Other recommendations include linking the statutory retirement age — currently 67 — to the country’s life expectancy and withdrawing early-retirement incentives. For every year gained, people should work eight months longer, the commission proposed. The experts also suggested raising the age — currently 64 — at which people who have made contributions for 45 years are able to go into retirement with their full pensions. Unions are likely to oppose the measure.

9. India has been a laggard in attracting FDI.

10. Ten years on, Brexit has turned to 'Bregret'!

The Brexiteers persuaded a small majority — the vote was 52 percent to 48 percent — that Britain could throw out the austerity that had followed the 2008 global financial crash, reverse the hollowing out of well-paid manufacturing jobs and trade freely and profitably on international markets. Immigrants who had flocked to Britain from Eastern and Central Europe would be sent home. Europe merely held Britain back, and to choose to leave was to believe, as Britons had before, that the nation was meant for more...

It was, of course, a fantasy... The economy has stalled and trade has shrunk. Britain is poorer than it might have been. Its gross domestic product is at least 4 percent — but could be as much as 8 percent — lower, according to independent calculations, while business investment is more than 10 percent lower. It added new frictions to the lives of Britons: new border checks when traveling to E.U. countries, stricter residency rules for living there, fewer opportunities for students to study abroad. Even just using a cellphone while “roaming” often costs more than it used to. There have been other costs, one of them a weakening of the glue between the nations of the United Kingdom itself. The referendum result was more a statement of English than of British nationalism — majorities in Scotland and Northern Ireland voted to remain. Forced to leave, Scottish nationalists claimed stronger cause to promote their case for full independence from England, and the complex political arrangements for Northern Ireland needed to protect the Good Friday peace agreement between Irish nationalists and British unionists in the province have weakened the cause of the unionists.

Rather than a newly independent Britain cutting a swath on the international stage, economic realities forced cuts in spending on foreign aid and diplomacy. The hopes among Brexiteers for a new Anglosphere, adding the English-speaking Commonwealth nations of Canada, Australia and New Zealand to Britain’s “special relationship” with the United States, turned to dust, and Britain’s privileged place in Washington was lost to Mr. Trump’s disdain for traditional alliances.

11. Fascinating graphic that maps the values of AI models.

The models’ answers, in English, on topics ranging from political petitions to God, suggest values that are different from those of most people. In fact, the models are often more extreme than the average respondent in every country included in the polling. On the survey’s “cultural map”, " AI models fall overwhelmingly into the quadrant populated by rich countries. The worldview of GPT models, created by OpenAI, is more secular than any country on earth (see chart 1). Gemini models, made by Google, place more weight on individual freedom (for example, “homosexuality is justifiable”) than people do anywhere. No model reflects the worldviews of most African or Muslim countries.

12. The Economist looks at the issue of popular backlash against AI. This scenario in particular is important.

Scenarios in which some countries give in to popular rage but others forge ahead are also worrying. If America succumbs, it could cede the global ai frontier, and the attendant cyber and military capabilities, to authoritarian China. Europe and Canada are more risk-averse than America. If they choked off ai while the rest of the world kept pushing forward, their losses could be unrecoverable. More than two centuries after the Industrial Revolution, few countries have managed to catch up with the first movers.

13. Rolex SA is a profit-making company with $12-13 bn in revenues and $3-4 bn in profits whose ultimate owner is a spiritual holding company (SHC), a charitable trust called the Hans Wildorf Foundation. Rolex SA has no public shareholders, investors, or owning family, and has been so since 1960. A similar example is Robert Bosch GmbH, the German engineering giant, which has 94% ownership by the Robert Bosch Foundation (SHC) holds 94 per cent and the Bosch family the rest. In both cases, the management and ownership have been clearly separated.

Why it's hard to see beyond the dollar?

noreply@blogger.com (Urbanomics) — Thu, 25 Jun 2026 02:07:52 +0000

A recurrent theme in international economics is the debate on the role of the dollar. As the global reserve currency, the dollar confers on the US an exorbitant privilege to borrow in its own currency, thereby allowing it not only to run large current account deficits but also to finance its fiscal deficit. The exorbitant privilege means that the normal rules of fiscal and monetary policies and capital flows management do not apply to the US in running its twin deficits.

As the US-China tensions intensify, there have been debates on whether the renminbi can emerge as an alternative to the US dollar. China has taken several measures to internationalise its currency. Today, some estimates suggest that 40% of its trade is settled in renminbi.

But there's a fundamental problem with China's efforts to prop up the renminbi as a global reserve currency. A currency can be a global reserve not only if it becomes used in cross-border trade transactions, but also, perhaps more importantly, if it has a deep, liquid, and stable market for investing the surpluses generated. In other words, countries should have confidence not only in transacting in this currency but also in retaining and investing their surpluses in it. What use is a foreign currency surplus when there are limited options to deploy it?

Such investments can be in the debt and equity markets in the host country of the reserve currency. This brings into play a third and fourth dimension, which are an accounting reality: the host country must be a net borrower (or run a current account deficit) and must be able to finance it with a financial account surplus. Both in turn require an open capital market (into which capital can be brought in and taken out with ease), the fifth dimension. In the absence of the last three dimensions, the host country will be unable to absorb and deploy the surpluses, and foreigners will not have the confidence to plough their surpluses into this currency.

China is very far from being in a position to offer the renminbi as a credible reserve currency. In fact, the Euro stands a far better chance of being an alternative.

This graphic captures what can be called a five-gate test of a reserve currency.

While the exorbitant privilege may have allowed the US to overcome the Triffin paradox (the only way to be a reserve currency is to run a trade deficit, which will, in turn, erode its status as a haven currency), the burgeoning US public debt and persistently high budget deficits (both of which debase the dollar) threaten this ability.

However, this must be seen against the reality of a world with surpluses that must primarily be parked in some credible currency asset and the absence of any alternative, there are three possibilities: the dollar continuing to be the reserve currency, the euro emerging as an alternative, and the global imbalances getting corrected.

The second option is more feasible than renminbi because Europe already has deep, liquid, and stable financial markets and an open capital account. However, it would also require Europe to run deficits. While Europe has traditionally run surpluses, there are signs that it might be declining. Besides, the European governments, especially Germany, have shown greater willingness in recent times to borrow and undertake fiscal expansion to invest in infrastructure and defence.

The third option is perhaps the most desirable and sustainable insofar as it addresses the distortions in international economic relations. But it would require the US to rebalance towards fiscal consolidation and reduce consumption, China to abandon its beggar-thy-neighbour trade policy and embrace domestic consumption, and Europe to shift towards public investment. The first two are deeply problematic political economy challenges and are unlikely without crises and convulsions.

This leaves us with the conclusion about the dollar’s continued dominance, not because Americans are clever or the Federal Reserve is wise, but because of an unusual coincidence of architecture: a large enough economy, a deep enough financial system, a current account deficit large enough to absorb the world’s surpluses, an open capital account that lets capital exit, and political institutions stable enough to make the open vault credible. No other country currently has all five, and China is structurally prevented from acquiring three of them without abandoning its development model.

Whether you like it or not, for the foreseeable future, the dollar is likely to remain the Hobson’s choice.

Comparing the R&D expenditures by Indian firms and their global peers

noreply@blogger.com (Urbanomics) — Wed, 24 Jun 2026 02:49:21 +0000

This blog has been a consistent critic of corporate India’s reluctance to invest in R&D. With the emergence of AI applications that are disrupting software development, the big Indian IT companies have been criticised for their low R&D expenditures. There has been a slew of commentary in recent days bemoaning India’s deficient private sector R&D spending and urging corporate India to embrace innovation. See this, this, this, and this.

This blog has argued that the nature of India’s market, with its price-sensitive customers and small premium market segments, may not allow Indian firms the cash flow cushion required to invest in R&D. Others have pointed to cultural and other factors as being responsible. There are problems with each of these lines of reasoning.

I used Claude and analysed the annual accounts and statements for the last five years of the 3-4 top Indian companies and their like-to-like peers in Europe, Northeast Asia, and the US on revenues, profits, margins, and R&D expenditures across eight industries. Specifically, how do the sizes (by turnover) of the median Indian companies and their peers compare? How do they compare on PAT margins and R&D as a share of revenues?

The headline takeaway is that Indian companies tend to be more profitable than mature Western peers (services, autos, pharma, telecom) but smaller in scale and far less R&D-intensive than the global leaders in product/innovation-driven sectors (consumer electronics, software products, EMS modules, speciality chemicals).

While it confirms the low R&D spending of Indian companies, it also points to a more nuanced narrative. While Indian companies are much smaller than their global peers, they are either the leaders or are at the top in profitability.

Let’s examine the headline findings.

The big IT consulting/services firms are the main targets of the growing chorus of criticism in the mainstream media on the lack of dynamism and low R&D spending. It is worth noting that while Indian software service firms have margins that are significantly higher than their peers (arising not from any innovation or efficiencies but from the labour-cost arbitrage), their R&D spending lags. The R&D as a percentage of revenue runs 0.3-0.5% at Indian firms vs 0.8-1.0% at say, Accenture, a 2-3 multiple gap. Further, while the R&D spending shares of the likes of Accenture have been rising, those of the Indian firms have been stagnant or even declining (Infosys) in recent years.

Also, the R&D expenditures of the big software service firms in the US do not include the significant amounts spent on acquisitions each year. For example, Accenture deploys $2-5bn annually in 30-40 acquisitions per year, many in AI specialities (data engineering, vertical-domain AI, ML platforms), whereas the largest Indian firms do 2-5 acquisitions per year, usually smaller and more conservatively-priced. Accenture treats acquisition as a substitute for internal R&D, while Indian firms treat it as a supplement to organic build-out. The cumulative effect is that Accenture has accumulated dozens of niche AI consulting practices acquired pre-2024, whereas Indian firms have built mostly organically and more slowly.

However, it must also be said that some of the criticism also reflects a tendency to conflate what is an inherently low R&D industry with the R&D-intensive product-focused Big Tech and AI firms. IT services have never been an innovation-focused industry. Further, compared to several other industries (as we shall see), the R&D spending of Indian IT services firms is not that far behind their Western peers.

While Indian software product firms hold up on margins, they too lag on R&D. The 5-6 percentage-point gap between US and Indian product company R&D spending is the closest real number to “the innovation gap” people often talk about. On size, if you take out OFSS, which is a subsidiary of Oracle, there is no Indian company with even $300 million in revenues. The four Indian product companies combined generate ~$1.5 bn in revenue. Salesforce alone does $38 bn. Adobe does $21 bn. They are dwarfs to their global peers. This is the real software industry gap.

In the automobile industry, Indian OEMs outperform their global peers on profitability. The R&D intensity at 3.6% is half of Europe's but comparable to Japan and the US. This conceals the fact that, despite being in the business for decades, the big Indian OEMs continue to depend on foreign designers and engines. They have been comfortable doing business by licensing technology and importing engines. Further, where India really lags is in the frontier technologies like batteries and electric vehicles. All these point to an ambition or aspiration gap.

Indian EMS profitability is again slightly better than that of Chinese and Taiwanese, but the R&D gap is the starkest in the entire analysis. Indian EMS spends 0.5% of revenue on R&D vs Chinese 4.1% vs Taiwanese 2.2%. Indian players are doing pure box-build assembly, whereas the Chinese players are designing modules. This is the value-capture gap. This is an area where the market is at the cusp of a massive expansion, and it is disappointing that Indian EMS’s have not sought to move up the value chain despite the promising opportunities that they face.

Indian pharma companies fare better than their software counterparts in both margins and R&D spending compared to their Western peers. Here, however, the Chinese are far ahead. Chinese pharma R&D intensity (17.5%) is more than double Indian (7.5%), with Hengrui, Sino Biopharm aggressively pivoting to innovator drugs. India's generics-and-biosimilars model is very profitable today but less R&D-intensive, raising the question of where margins go in 5 years.

The consumer electronics industry must count as one of the biggest disappointments. There is essentially no Indian consumer electronics industry comparable to its global peers. Even the biggest Indian firms are tiny when compared to their global peers. Indian companies (Havells, Voltas, Whirlpool India, Crompton) are appliance brands relying on outsourced electronics, explaining the 0.6% R&D figure compared to Korea's 7.8% or Japan's 5.4%. No Indian brand has any comparable R&D capability. Most of what's made in India is for foreign brands (Apple via Foxconn, Samsung via Dixon).

Chemicals is one of India's better stories. While the top Indian firms are large and their margins are second only to Chinese firms, their R&D expenditures again lag.

On the telecom side, Indian service providers are now the most profitable in the world, driven entirely by Jio and Airtel post-Indus-Towers consolidation. Verizon and AT&T look mediocre by comparison. The R&D number for telecoms is essentially zero everywhere except China Mobile and NTT since telecom is considered a capex/spectrum business, not an R&D business.

So what do all these mean?

The failure to produce even a mid-sized IT product firm, even after five decades of being a leader in the software services industry, is more an indictment of India’s entrepreneurship than of the IT services firms themselves. The IT product industry has had several favourable factors confluencing - the IT services industry produced enough talent and experienced professionals to supply both entrepreneurs and team leaders, there is an abundant low-wage workforce, it does not suffer regulatory failures like an inverted duty structure, high input costs or taxes, and there’s a large global market to serve. But even this combination was not enough to make even a one-billion-dollar IT product firm.

More than the IT services industry, it is perhaps the Indian pharma industry that is emblematic of the lack of business dynamism and entrepreneurship. The country has had a serious pharma industry, with several large generics manufacturers, for over six decades. Many of the leading firms of today were established by entrepreneurs who worked in the public sector entities. They had the opportunity to move up the value chain by building massive integrated industrial facilities. Even in contract manufacturing, they have remained stuck at the small-molecule synthesis and have struggled to move up the value chain to complex therapeutics and contract research.

Alongside the software product industry, consumer electronics should perhaps count as corporate India’s biggest failure. India has had a consumer electronics industry for several decades. With a very large market, Indian firms had the opportunity to ride the economic liberalisation, expansion of the middle class, and the global export market.

Interestingly, the Korean and Japanese OEMs (LG, Samsung, Daikin, Hitachi) have deeper Indian manufacturing (in terms of value addition in products like refrigerators, air conditioners, and washing machines) than most Indian brands because they invested in component plants in the 2000s-2010s when Indian brands were happy to rebrand imports.

One structural reason for this difference also points to the lack of ambition and reluctance to pursue the export markets. The Korean and Japanese OEMs treat India as a manufacturing base for both the domestic market and exports (LG exports refrigerators to the Middle East from India; Daikin to Southeast Asia). That export-anchored manufacturing economics justifies deeper component investment. Indian brands have historically been domestic-market-only and saw little case for backward integration when components were cheap to import from China.

It is therefore an unfortunate reality that the vast majority of AC and refrigerator compressors, and higher-end motors (for front-load washing machines) are imported.

The above analysis points to a corporate world that is stuck in a comfort zone, reluctant to assume risks by trying to move up the value chain or push aggressively into the next generation of products or technologies. There is a strong preference to stay on the sidelines and wait for technologies and products to emerge elsewhere. I’m not sure whether there is even one industry where Indian firms have been pioneers in showing the way with the next generation of products. Across industries, they always follow the trends in developed markets by copying and imitating.

The large captive market (domestic and foreign) is considered a safe enough moat (thanks to a combination of price-sensitive customers and import protections) that would allow these firms to grow for a long time to come. There is little incentive (apart from inclination) to explore and expand beyond this comfort zone.

In a globalised market, across industries, competitiveness is critically dependent on continuously moving up the value chain. It is a treadmill where, like the Red Queen, firms must run hard to retain their global competitiveness. And Indian firms, across industries, have shown consistent reluctance on this. It points to a problem of what I have described earlier as an entrepreneurship deficit.

With this entrepreneurship deficit comes a low risk appetite. This reflects in the reluctance to deploy capital. Moving up the value chain and expanding to foreign markets, essential requirements to becoming globally competitive, demand assuming significant risks by making large capital investments with long-term bets. These investments would also include cultivating supplier ecosystems, funding and nurturing startups, and long-term partnerships in general. Indian firms, especially the largest ones, have shown great reluctance to assume the risks and make these investments.

The reluctance to invest despite the consistently high margins across industries may be a symptom of the entrepreneurship deficit. As we have observed, firms are satisfied with their domestic markets and have limited or no appetite to expand into export markets. Further, the tepid growth of the domestic market (a reflection of the low aggregate demand growth, in turn a reflection of the narrow base of the consumption class) also discourages significant investments. All this manifests in a preference for short-term gains and avoidance of long-term competitiveness.

This is a nice summary of the motivations driving Indian firms.

India has a large domestic market, and the economy is growing at 6–7%. So, you can bring what has worked elsewhere, deploy it in the market, and make a lot of money. It’s less risky. That’s what corporates have been doing. It’s the cycle of development. But when you want to compete internationally, you need to think about your own ideas.

Another reason to invest more and pursue export markets is to increase size. As the analysis shows, even the largest Indian firms across the eight sectors are small compared to their global peers. Despite being more profitable than their global peers, their much smaller size is an important obstacle to global competitiveness.

The argument that government policies have been a binding constraint is not convincing. For one, the software industry, despite largely serving the global market and not being significantly constrained by public policy, did not produce any product firm or product of note despite several technological trends sweeping the industry in the last three decades. Second, even among the leaders in different industries, there has been little appetite to move up the value chain, expand into global markets or pursue new generation technologies and products. Third, even in localising manufacturing by nurturing local supply chains and partners, storied Indian OEMs have been behind foreign OEMs who have entered the market much later.

Fourth, the argument that import restrictions have prevented Indian firms from becoming competitive flies against the reality that the Northeast Asian economies built their manufacturing successes in highly restricted markets. Instead, as Joe Studwell has written, they gained competitiveness by competing in the export markets.

Now the government has thrown caution to the wind and, through the Rs 1 lakh Cr Research Development and Innovation Fund (RDIF), is funding even large corporates on their R&D endeavours. This may be the most that governments can do to push their industries towards innovating. It remains to be seen whether even this is sufficient.

In conclusion, it appears that Indian firms suffer from an entrepreneurship deficit, risk aversion, and a lack of intrinsic desire to think big (by moving up the value chain, pursuing next-generation technologies, and expanding into export markets). They seem satisfied with serving their captive local markets, continuing their existing product lines and business models, and following global leaders in technology and product trends. This is a reality, borne out strikingly by evidence. Whether it is culture or something else can be a matter of debate.

PS: On the issue of entrepreneurship, Claude had this comment on the prospects for India’s software services industry.

Accenture sells outcomes and prices its services on the value of the transformation. Indian firms sell capacity and price on the cost of the underlying labor. Generative AI threatens the capacity-pricing model directly because it compresses the labor hours needed. It enhances the outcome-pricing model because AI-enabled transformations are higher-stakes and command premium fees.
This is why the next 2-3 years will be the real test: not whether Indian firms have AI capabilities (they clearly do), but whether they can shift their pricing and packaging model fast enough before Generative AI deflation hits their core managed-services contracts. Accenture has already crossed that bridge; TCS, Infosys and Wipro are mid-bridge with the macro tailwind weakening.

This is a test of entrepreneurship and reinvention of business models for the Indian IT services firms.

A framework for the application of AI in public systems

noreply@blogger.com (Urbanomics) — Mon, 22 Jun 2026 02:14:52 +0000

I have not blogged about AI in development. One reason is that, notwithstanding several claims, I have not come across promising examples that have been successfully applied at a reasonable scale within public systems in the Indian context.

I have blogged here, urging caution on the expectations of the impact of AI on development in countries like India. The central argument is that the binding constraint on AI impact in the public sector is rarely technical capability. It is the gap between what AI can do and what institutions are prepared to validate, adopt, and integrate, a gap shaped by the validation cost, regulatory frameworks, incumbent system stickiness, and the political economy of transition.

As an analytical framework for the application of AI in development, I can think of three lenses: the layer where the AI intervention is proposed, the nature of the problem to be addressed, and the constraints on deployment and adoption.

In the first lens, AI can provide structured information to feed into decision-making; or directly enable decision-making by synthesising information into ranked options or risk scores, thereby augmenting human judgment; or deliver specified outputs in the form of drafts, transaction alerts, assessments, etc.

The second lens can distinguish between complicated/complex and wicked problems. The former has many variables but is tractable with better data and modelling (crop yield prediction, supply chain optimisation, credit risk scoring, traffic signal timing, etc.), whereas the latter involves contested goals, adaptive actors, and systemic feedback (improve learning outcomes or skills, institutional reform, manage urban growth, etc.). While there are limits to AI, especially in the latter, complicated sub-problems within the former are amenable to AI.

The third lens covers the gap between technical feasibility and deployed impact. This gap is shaped by validation cost (regulatory standards must be met), the incumbent system stickiness (when the AI solution demonstrates value that helps overcome the systemic inertia), trust and legitimacy (system or society must accept the efficacy and reliability), and political economy (overcome the entrenched vested interests).

The three lenses are analytical dimensions, or orthogonal axes on which any individual intervention can be located. Then we have the horizontal or vertical use cases, which are a typological cut across the universe of interventions. It sorts use cases by their organisational footprint (does this sit in every department or only one) rather than analysing the nature of any single use case.

The graphic below applies the three lenses to each item in an illustrative catalogue of horizontal/vertical use cases.

As a prudent strategy, it may be useful to move first in low-friction domains - where there are no regulatory incumbents, where information asymmetry is high, and where the beneficiary is a motivated adopter - and use the evidence and trust built there to lower the political cost of adoption in high-friction domains. In this reading, as is the trend in the private sector, it will be some time, if ever, before vertical sectoral use cases of AI become efficacious and reliable enough to be adopted at scale.

Based on the above, what are the most valuable and widest spanning productivity and efficiency increasing uses of LLMs by governments in developing countries (with low state capabilities)?

I can think of two in particular - application in the generation of quasi-judicial and adjudication orders of all kinds, and in the recording of M-Book in engineering works. The breadth of their use, the stakes involved, the low current baseline, and the manageability of their deployment gaps make them areas with potentially transformative impacts.

The first one ranges from charge sheets to orders in service matters and disciplinary cases, to regulatory and licensing decisions, assessment and adjudication by tax, land, and other authorities exercising statutory powers, including court and tribunals.

Each of these has a standard structure - factual matrix, applicable legal provisions, consideration of arguments, findings, and operative order - that AI can populate with high-quality given structured inputs. The official provides the facts and the decision, and AI generates the legally coherent reasoning and formal language.

The value of this comes from the low baseline of generally poor quality of orders issued that suffer both from basic procedural/hygiene deficiencies and more substantive application of judgment. These deficiencies and lapses, especially of the former kind, immediately invite disputes. In fact, such poorly crafted orders become the starting point for a long series of administrative processes that manifest in disputes and harassment, appeals and litigations, that clog administrative bandwidth, lock up scarce capital, impair balance sheets, and generally waste effort and resources.

These kinds of orders also contribute a very large share of the litigation against the government that clogs the judiciary. The major reason why governments lose such cases is the quality of orders in terms of poor drafting and adherence to basic administrative compliance and procedures. Poorly drafted orders are often set aside, not because the underlying decision was wrong, but because the reasoning was inadequately articulated. AI can have a significant impact in this area in both improving the quality of orders and even forcing systems to comply with procedural requirements.

The volume of such orders is enormous. A mid-level income tax officer may need to issue hundreds of assessment orders annually; a district collector may handle thousands of revenue proceedings. The drafting burden is a critical bottleneck in disposal rates. Further, AI-generated drafts can be trained to flag inconsistencies, missing procedural steps (e.g., failure to give opportunity to be heard), and citation of superseded legal provisions, thereby acting as a compliance check before the order is issued.

An AI application that generates the first draft of the order, for the adjudicating officer to revise and issue, can have a transformative cascading impact down the chain. The first draft can be hard-coded to serve as a forcing function to ensure compliance with a checklist of processes; the order itself could be validated, and its logic could also help with tightening the substantive exercise of judgment itself.

On the face of it, this is a low-hanging fruit. All it requires is a library of orders of all kinds bundled together, and a template for different kinds of orders. An enterprise or team subscription to Claude or OpenAI, that has a Zero Data Retention (ZDR) configuration for their API (thereby ensuring customer data is not retained and used for training the algorithm), and which allows access to Claude through Amazon Bedrock or Azure OpenAI within a private Virtual Private Cloud, can address any privacy and security concerns on sharing internal data.

This would be a quick deployment and superior to any indigenous or sovereign LLM model development (which should continue and could perhaps be deployed in parallel to learn and get refined).

The latter work can be taken up in a mission mode by the National Informatics Centre or a public think tank (say, the National Centre for Good Governance, NCGG) by deploying a team of competent experts. It can consolidate the library of orders and develop standardised LLMs for a few high-volume and value use cases - disciplinary cases, GST and income tax adjudication orders, and orders on a few categories of land claims settlements. They could work with a few state governments, the CBIC (state indirect tax departments), and the CBDT.

The other high-value and promising application can be in the recording of the Measurement Book (or M-Book) for engineering works - roads, buildings, canals, drains, electricity lines, etc. It records the quantity of work done, certified by a junior engineer, against which payment is authorised. It is also one of the most fraud-prone documents in public works administration, susceptible to over-measurement, fictitious entries, and post-facto alteration. Besides, it is also a procedurally burdensome activity. Its importance arises from the fact that it is the basis on which contract payments are made.

For certain categories of works, AI-assisted quantity and quality measurement from photographs and drone-based progress tracking and volume estimation can be very reliable. Smartphones equipped with computer vision applications can estimate dimensions and quantities from site photographs (lengths of pipes laid, areas of surface plastered, volumes of earthwork excavated). Computer vision models can be trained to assess the quality of construction work from photographs by detecting visible defects in concrete, checking alignment of masonry, and identifying sub-standard finishing. For large civil works (earthwork, embankments, reservoirs, large buildings), drone imagery processed through photogrammetry software can generate accurate volumetric estimates and three-dimensional progress models.

At the very least, AI-assisted photographs and drone outputs can be used to validate M-Book entries, surface quality concerns and other discrepancies. Once M-Book entries are digitised, AI can generate contractor bills from certified measurements, cross-check against contract rates, identify arithmetic errors, flag unusual patterns (e.g., a sharp increase in claimed quantities near bill submission deadlines), and route completed bills for authorisation. Apart from improving the quality and increasing accuracy of payments, the time savings and reductions in delays in payments will be significant.

The examples of order-drafting and M-book opportunities are likely to be particularly appealing for officials because they also significantly reduce their workload and drudgery. They are deployable at medium friction, directly legible to senior officials, and capable of generating rapid, measurable impact that builds institutional confidence in AI tools.

Another area of promise is the categorisation or triaging of cases in several public domains.

The Ken has an article on the application of AI by courts in India. A promising application of AI is in the categorisation and triaging of cases.

Half of India’s backlog—25 million cases—could disappear quickly if AI were used not to decide cases, but to triage them. The opportunity lies in the huge volume of matters that are effectively already dead on arrival: cases filed beyond limitation, petitions missing mandatory disclosures, matters rendered moot by changes in law, or disputes where precedent has already settled the question, leaving only a narrow technical point to close. Courts identify these only in occasional manual clean-ups—slow, inconsistent, and impossible to scale.
An AI system, by contrast, could scan filings in bulk, flag statutory defects, detect outdated or defective pleadings, classify time-barred matters, and surface cases appropriate for summary disposal. These could then be bundled and placed before judges for quick orders, clearing the undergrowth so courts can focus on disputes that actually require judicial time.

Like with court cases, triaging of outpatient (OP) cases in primary health centres (PHC), community health centres (CHC), district hospitals, and medical colleges is an area where AI can play a potentially significant role. The daily OP load in these hospitals (at least the better ones among them) is multiples of what a doctor can manage, leaving them overburdened and stressed. The result is inefficient use of the doctor’s time, inadequate diagnosis time, incorrect diagnosis, wrong OP referrals, and so on.

In all these hospitals, OP cases come to doctors with limited or no triaging. An AI-based triaging application where the symptoms are entered at the OP-registration, nurse and doctor-level, can significantly improve work conditions, increase hospital productivity, and enhance the quality of treatment.

Similarly, citizen grievances or consumer complaints received in any office, especially public-facing ones, can be triaged for routing them to the right desks/officials, escalating to senior officials, analysing repeat complaints, and so on.

Triaging is already one of the early emerging successes of AI, with examples like Bank of America’s digital assistant “Erica”, which handles billions of client interactions and has reduced call centre volumes by 40 per cent.

In general, the judiciary’s case load management is an area where, in theory, AI applications can have a transformative impact, especially in categorising cases, transcription of witness statements, and the preparation of orders. The Ken article has a good description of an AI application in a courtroom in Kerala.

A witness spoke; an AI-powered tool listened. A clean, searchable transcript appeared in real time—punctuated, structured, permanent. Stenographers were not clambering to catch up, litigants were not begging for readable copies. This split-screen view—one courtroom running on memory, the other on machine comprehension—is not a metaphor. It is India’s judiciary in 2025: a system where 19th-century workflows and modern AI systems operate side by side, neither quite replacing the other. Earlier in 2025, the Kerala High Court issued an office memorandum, making AI-assisted live transcription mandatory across all district courts…
“Today, the AI-transcribed witness testimony is uploaded as soon as the proceedings are completed. Earlier, the witness testimony would be handwritten by the judge, and a party’s lawyer would apply for a readable copy and seek adjournment on that basis,” said Joseph Rajesh, the IT Registrar of the Kerala High Court. “All this time delay is now cut down to nil.”… A court order that once took four or five hours to type can now be generated in under an hour. A witness deposition that once required handwritten dictation, shorthand transcription, and final review can be captured in a single digital stream.

The article also has this graphical exploration of the various possible use cases for AI within the judiciary.

There can also be daunting practical challenges to its adoption

Consider defect detection—the clerical step where filings are checked for completeness. In Delhi’s Tis Hazari courts, the rules are so complex that Nyaay’s engineers joked: If we can crack defect detection here, we can crack it anywhere. The joke holds true. AI had to be customised court by court. And transcription? In many urban courts, there are stenographers, but in district courts, they are scarce. AI fills a vacuum, but only if the court has electricity, microphones, and a judge willing to trust the output.

So how can these AI applications be deployed?

For all these use cases, the development of robust AI applications that can be deployed across institutional levels nationwide is an industrial engineering endeavour. It requires diligent, long-drawn, and high-quality problem-solving, including pilots (or beta testing), iterative adaptation and refinement. It cannot be achieved by an experiment undertaken by a district or state-level entity (or even as ad-hoc officer-driven initiatives within central government departments), as is often the case today.

The Ken article cited above points out the problems of not having one entity in charge of the creation of these national public goods.

The downside is that every state is effectively running its own lab experiment: non-standard, improvised, and dependent on the priorities (or disinterest) of whoever happens to be leading the High Court. Some innovate aggressively. Some slow-walk. Some judges independently approach AI companies, but without formal approval, nothing can enter the courtroom workflow… High Courts and district courts continue testing tools piecemeal. Language models multiply while vendors proliferate and workflows diverge… India could end up with 25 distinct judicial AI ecosystems, none interoperable, standardised, or scalable nationally…
India’s AI push is happening largely outside the Supreme Court’s flagship e-Courts project—the initiative meant to modernise the judiciary. Phase III of the programme still focuses on the basics: creating digitally readable records, enabling e-filing, and automating service of summons. A February Press Information Bureau note pegs the Phase III budget at Rs 7,200 crore, but only a little over Rs 50 crore is reserved for “integration of AI and blockchain technologies across High Courts”. In effect, the national blueprint is still laying the plumbing while the states are already experimenting with smart faucets…
If India manages to fuse Kerala’s mandate with Karnataka’s case clustering, Delhi’s defect detection, the Supreme Court’s governance frameworks, and the entrepreneurial urgency of tools like Adalat and Nyaay, the judiciary could undergo a systemic leap.

It would require a dedicated team at the national level (government department, Supreme Court, CBIC/CBDT, etc.) engaging single-mindedly on the endeavour - formulating the problem, collecting and cleaning data, developing models for different levels of government, undertaking pilots with close oversight, iterating with tight feedback loops, documenting processes, and scaling solutions gradually.

The models required for lower courts, High Courts, and the Supreme Court would vary and must therefore be customised, just as those required for hospitals of different kinds, tax and land adjudicating and appellate officials at different levels, and disciplinary authorities for different kinds of functional entities. In each case, there would be a need to do rigorous pilots to be able to refine and finalise robust enough solutions that can be deployed at scale.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 20 Jun 2026 02:33:29 +0000

1. PE firms sitting on $4 trillion of unsold assets, on investments largely made between 2020 and 2022 when rates were slashed to zero, are finding creative ways to offload them. Sample this

Blackstone is marketing a so-called collateralised fund obligation that will bundle more than $2bn of stakes in leveraged buyout funds into bonds to sell to investors and insurers, according to people familiar with the matter. The deal would provide an infusion of cash to investors in a Blackstone Strategic Partners fund, the firm’s unit that invests in other private equity groups’ funds. It is unclear if Blackstone will ultimately go ahead with the securitisation or seek to sell the stakes in a secondary transaction, one person briefed on the matter said... The vehicles, which are sliced and diced to give investors exposure to different levels of risk and return, have boomed. Issuance of CFOs soared to a record of $25.9bn last year from a modest $4.8bn in 2021, according to credit rating agency KBRA.

2. This is a brilliant articulation of the problem with articulating something purely in terms of absolute numbers and aggregates.

In his novel Hard Times, Charles Dickens described a girl called Sissy, who was having a terrible time in her lessons. Her schoolmaster told her to imagine that her schoolroom was a nation in possession of “fifty millions of money”. Wouldn’t that mean it was a prosperous and thriving state? “I said I didn’t know,” she relayed afterwards to a friend. “I thought I couldn’t know whether it was a prosperous nation or not, and whether I was in a thriving state or not, unless I knew who had got the money, and whether any of it was mine. But that had nothing to do with it. It was not in the figures at all.”

3. Interesting story about how old companies are reinventing themselves to profit from the AI-boom.

AI servers must be more tightly linked together, increasing the need for advanced cabling and optics. Shares in Corning, the 175-year-old inventor of Pyrex glass that also supplies screens for Apple’s iPhones, have increased by more than 270 per cent in the past year after it signed deals with Meta and Nvidia to supply optical fibre cabling to AI data centres. The vast amounts of electricity needed for AI training are also fuelling demand for specialised power management, high-voltage electronics and cooling technologies. This has led to big interest in traditional suppliers of electrical equipment, typically deployed in residential and industrial projects. Eaton, an Ohio-based power management company, received 240 per cent more data centre orders in Q1 this year...

French electrical equipment maker Legrand has doubled its revenues this decade with half of the growth coming from data centres, which now make up more than a quarter of its turnover. Air conditioning and liquid cooling — using water to stop chips from overheating — are in demand too. Shares in AC maker Comfort Systems USA have shot up 260 per cent over the past year, while Schneider Electric bought a stake in data centre liquid cooling specialist Motivair for $850mn last year. Utilities are rushing to supply AI companies with power — including Spain’s Iberdrola, a leading supplier of power contracts to tech groups in Europe, according to Pexapark data, and Entergy in the US, whose share price hit a record high after a $10bn deal with Meta... Several generator and engine companies have also pivoted to supplying data centres, including Caterpillar, Boeing supplier Howmet Aerospace, Finnish ship engine maker Wärtsilä and Baker Hughes, which formerly focused on oilfield services.

4. China demographics facts of the week.

This year’s cohort of gaokao-takers were mostly born in 2008, a year of 16.1m births. By 2025 births had more than halved, to just 7.9m. The demographic cliff is already visible in nurseries, which saw pupil numbers plummet from 46m to 32m between 2022 and 2025. Numbers in primary schools have also started to thin. Inevitably, over time, secondary schools and then colleges will follow.

And technology adoption

A survey of 322,000 students last year by the China National Academy of Educational Sciences, a state-affiliated think-tank, found that 85.6% of them had already tried using AI to complete their homework. On popular apps such as Zuoyebang (“Homework Help”) and Yuanfudao (“Ape Tutoring”), pupils snap photos of questions and ai walks them through the solutions. (Teachers are using similar technologies to help mark homework.)

5. Public moods on the role of government in the UK - 70% support nationalising energy and 82% water.

Rail subsidies have been rising, £12bn in operational support in 2024-25, up in real terms from £2bn in 2000-01.

6. Indian economy facts of the week.

The World Trade Organization data show that for non-agricultural goods, the share of tariff lines in the category 10-15 per cent increased sharply from 1.4 per cent to 33.5 per cent between 2014 and 2024 and for the tariff category 15-25 per cent from 1.7 per cent to 14.9 per cent, while the share of tariff lines in the category 0-10 per cent fell steeply from 90.5 per cent to 42.5 per cent over the same period.

7. From Thomas Astbridge's book on the Black Death

In its most intense phase, from 1347 to 1353, the Black Death killed more than 100mn people, or about half the population in the areas infected, Asbridge estimates. This makes it more lethal than two other great pandemics — the 6th-century Plague of Justinian in the Mediterranean and the pestilence that swept across Asia from the mid-19th century until the aftermath of the second world war — and far worse than Covid-19 in our times... Asbridge demonstrates that the Black Death was probably more devastating in cities such as Cairo and Damascus than in, say, Constantinople or Florence. In Cairo, a metropolis of 500,000 people, almost 10 times larger than London’s population, perhaps 250,000 died, Asbridge suggests.

8. On the role of luck in football tournaments.

According to one study of historical matches, the chances of the team with the worse record winning was 45 per cent in football, compared with just 36 per cent in America’s National Football League. (Yes, this pep talk has statistics. Bite me.) The knockout structure raises the role of chance, as just one dodgy penalty can crash a team out of the competition. According to numbers crunched by James Tozer of Prospect, a sports analytics company, betting odds gave the top four teams in the most recent Premier League a combined 89 per cent chance of winning (after adjusting for bookies’ ability to take advantage of fans’ optimism that their team would win). In the World Cup an upset is more likely, as that figure is only 48 per cent.

9. Aldi effect, as the discount grocery retailer seeks to expand aggressively, as it envisages 4000 stores at an investment of $9 billion in a US market where consumers are facing higher prices due to persistent inflation.

Credit card data analysed by the bank found that when an Aldi store opened, it shaved an average of one percentage point off annual sales from competitors within a 10-mile radius... Aldi prospered in postwar Germany under brothers Karl and Theo Albrecht before a disagreement led to a split in the 1960s. One offshoot, Aldi Süd, oversees Aldi’s US business after opening the first store in Iowa in 1976. The other, Aldi Nord, owns the quirky US grocer Trader Joe’s. The discounter’s stores are austere places with only about six staff on duty. They are designed for maximum efficiency: groceries are shelved without leaving their cardboard shipping trays and oversized bar codes are printed on packing so checkout operators can scan at pace. Customers must deposit a coin to obtain a shopping trolley, which is refunded if they return it. Operating cost savings fund the chain’s low prices... Aldi’s compact stores, which stock only about 2,000 product lines, are often located near competitors such as Walmart, whose large-format stores carry about 120,000 products, including low-priced groceries.

10. Andhra Pradesh shrimp production facts.

India exports approximately 8 lakh tonnes of shrimp a year, with Andhra Pradesh accounting for over 60 percent of production. The state accounts for 80 percent of the country’s shrimp exports and 34 percent of marine exports, valued at around Rs 21,246 crore annually. The state has 2.5 lakh aqua farmer families, of which 2 lakh are small and medium farmers. Another 30 lakh people depend on allied sectors. According to the Union Ministry of Commerce and Industry, India exported a record 17,81,602 MT of seafood worth US$ 7.38 billion (Rs 60,523.89 crore) in 2023-24, of which frozen shrimp alone accounted for 92 percent — a significant share from Andhra Pradesh.

11. India's PPP pioneers

GVK’s 216-megawatt (Mw) Jegurupadu plant became an early proof-of-concept under a power purchase agreement. IL&FS built a 12-km toll road between Rau and Pithampur in Madhya Pradesh, marking India’s first private toll concession.

12. This is a true success story for the Indian economy.

Between 2020 and 2026 the number of Indian retail investors rose from around 40m to 130m.

The quartet of global imbalances

noreply@blogger.com (Urbanomics) — Fri, 19 Jun 2026 02:05:13 +0000

President Donald Trump’s single-minded pursuit of rebalancing the US economy using tariffs is unlikely to yield much without addressing fundamental distortions that have crept into the US economy and financial markets. Trade surpluses are a mere symptom of deeper malaises. I had blogged about the global twin structural imbalances, arguing that an American economy skewed towards consumption and a Chinese one skewed away from consumption are two sides of the same coin.

Helene Rey (see also this report to G7) has a very nice summary of global imbalances, where she locates it within the dynamics of saving and investment, and questions the focus on tariffs.

A country that saves more than it invests lends abroad and runs a current account surplus; one that invests more than it saves borrows and runs a current account deficit. It is the collective saving and investment decisions of a country’s households, companies and government that drive imbalances. There is now some agreement — crystallising in the G7 discussions — that the sources of imbalances are linked to unbalanced growth models and mostly made at home: chronically weak consumption in China, feeble productive investment in Europe and outsized fiscal deficits in the US. Tariffs are not an effective mechanism to change any of those and issuing an international currency is no justification to run current account deficits.

She also proposes the solutions in terms of complementary actions.

China rebalancing towards consumption, Europe lifting productive investment and America repairing its public finances are not three grudging favours. They are parts of a single, mutually reinforcing policy. One country’s exports are another’s imports; one country’s capital outflow is another’s inflow. When all three move at once, each adjustment cushions the others: the deficit country that consolidates finds external demand waiting as surplus countries spend more at home, and the surplus country that stimulates demand finds a market at home rather than a protectionist wall… The IMF’s scenario of simultaneous rebalancing raises global output by around 0.8 per cent and narrows medium-term imbalances by half a percentage point of world GDP.

I think this analysis misses a critical fourth leg of the imbalance, financialisation. It cannot be seen as merely a symptom of US borrowing. As evidence, there are two natural experiments. The last two episodes of US fiscal deficit reduction (in the late nineties and mid-2000s) were accompanied by financial market bubbles (the dot-com bubble and housing and mortgage market bubble).

Underlining this, Ricardo Caballero, Emmanuel Farhi, and Pierre Olivier Gourinchas have shown that if the financial markets do not work well, the economy might accommodate investments that deliver a rate of return that is below the growth rate of the economy. In this situation, both stock market bubbles and government debt can play the useful role of displacing inefficient investments.

Foreign savings flowing into the US must find a US asset to absorb them - either Treasury debt (the fiscal deficit channel) or private financial assets (the financialisation channel). These two are substitutes, not complements. Squeezing fiscal deficits without restraining private credit pushes the same global savings glut into asset bubbles. So US fiscal consolidation alone is not sufficient, and must be paired with financial-sector reform that prevents the likes of private credit from filling the void.

I asked Claude to generate a graphic that describes the global imbalances quartet.

So, how to address these imbalances?

The rebalancing would require global diplomacy and coordination to mobilise support from all key stakeholders. This looks onerous in a deeply polarised world, of rising tensions between the West and China and the unpredictable and whimsical nature of the Trump Presidency.

In fact, among the four adjustments required, interventions pertaining to the much-derided Euro area appear to be the most promising and likely to materialise. In fact, on investments, the train has already started.

It is possible that a deep crisis on the economic front, a very likely near to medium-term possibility in either case, may force both China and the US to rebalance towards consumption and consolidation, respectively. However, there are daunting political economy challenges to be overcome in both countries, especially the US.

It is the fourth leg that might prove the most challenging. The financial markets are where the power of entrenched interests is so strong that it might require a counter-revolution to upend the order and regulate financial markets more tightly. This will also require global coordination and collective action, and not mere reforms at the US front.

Countering China's weaponisation of its manufacturing dominance

noreply@blogger.com (Urbanomics) — Thu, 18 Jun 2026 04:04:01 +0000

China’s weaponisation of its manufacturing dominance, most famously through its control of rare earth magnets production, is generally accompanied by a narrative of resignation that its trading partners must live with this reality till they develop alternative supply chains. It is widely perceived that China has a definitive upper hand, and all other countries, including the US, must play catch-up.

This begs a few questions. Isn’t China susceptible to imported products and services that are essential to its economy? Aren’t there rare earth equivalents that the US and Europe can restrict access to China, thereby bringing a bargaining equivalence between the two sides? What are those rare-earth equivalent dependencies for China today? What is the economic leverage that the West has over China that can be exercised in response to the rare earths restrictions?

This post will examine this question in greater detail.

In a 2025 G-7 summit, the European Commission President Ursula von der Leyen aptly described China’s industrial policies as creating a pattern of “dominance, dependency, and blackmail”.

Having built up dominance and dependency, the blackmail is now intensifying. The hide your strength and bide your timephase is past. In the recent past, China has come up with several measures to leverage its dominance.

China’s widely known weaponisation of its manufacturing dominance has been underpinned by a series of regulations that impose restrictions and penalties. Pre-empting efforts by Western multinationals to diversify away from China, in April this year, to “prevent security risks in industrial and supply chains”, the State Council issued Regulations on Industrial and Supply Chain Security to investigate and punish foreign firms that stop using Chinese suppliers in response to political pressure from their governments. This is a summary.

Under the new rules, regulators can question employees and examine corporate records during investigations. The regulations also allow the authorities to bar companies and individuals from leaving China if they are suspected of moving supply chains elsewhere under foreign pressure… The State Council, China’s cabinet, justified the measures as necessary to protect the country’s economic stability and national security… China’s global network of ports and port-management software gave Chinese officials detailed insight into multinationals’ supply chains, allowing them to detect when companies shift to suppliers elsewhere.

In February, it amended the state secrets law by broadening the scope of the type of information that would be considered a national security risk. It includes a new legal concept called “work secrets”, defined as information that is not an official state secret, but that “will cause certain adverse effects if leaked”. This broad sweep allows for interpretation as convenient for the government, and makes foreign companies and their employees further vulnerable.

The restrictions are not confined to foreign companies. In early June, the State Council announced rules requiring national security screening for Chinese companies seeking to invest overseas.

The rules also give the authorities new powers to scrutinize Chinese companies seeking opportunities abroad, subjecting them to national security reviews that place investments into one of three categories: encouraged, restricted or prohibited. Part of the motivation for this, lawyers say, is to keep money, talent and intellectual property in fields where China has a competitive edge from leaving the country… The measures restrict the movement of certain talent in sectors deemed sensitive, though Beijing has not defined which sectors qualify. They also give officials broader authority to review the movement of capital, including the power to force investors to sell shares or halt investments if national security concerns arise. The rules also lay the legal groundwork for regulators to bar foreign entities from investing or operating in China, including expelling them from the country, in retaliation for actions taken by their governments against Chinese investments.

In this context, it is surprising that even as China increases its bellicosity both in trade and in its foreign policy, especially the breadth and frequency of military actions in the Taiwan Straits, the response from the West has been remarkably muted. Doubtless, the dysfunctional nature of the Trump Presidency has been a major contributor. But the lack of proportionality of response from the US holds, even including the Biden Presidency.

So what are the chokepoints and vulnerabilities that China faces from the West?

Ironically, China’s biggest vulnerability is in the very industry that it utterly dominates. China dominates the downstreamof electronics (assembly, packaging, volume manufacturing) and the upstream of raw materials (rare earths, gallium, refining), but it is deeply dependent on imports for the midstream - the tools and ultra-pure materials that actually make advanced chips. Underlining this reality, China imported $385 billion of integrated circuits in 2024, more than the $325 billion it spent on crude oil, making chips its biggest single import.

China's vulnerabilities cluster at the highest-precision, most knowledge-intensive nodes, the "tools that make the tools." Photoresist is the cleanest analogue to rare earths: a narrow, chemically exotic input where Japan holds a near-monopoly and a cut-off would, in one analyst's framing, leave Chinese manufacturing with "no rice to cook with". China could meet only about 5% of its own demand for the KrF resists used in 110–180nm chips, and high-end localization was under 5% in 2022. In simple terms, semiconductor equipment is the broadest lever by value, and machine tools are the most pervasive across general manufacturing. More than 60% of the indigenous passenger jet C919’s components, including engines and flight controls, are imported.

The graphic below plots the levers (or chokepoints) that the West have over China in terms of how hard it bites and how long it would take China to replace them. The colour represents the usability of the lever, without unacceptable self-harm; the dashed arrows show the levers China is actively closing.

The red bubbles (EUV, photoresist, EDA) are near-monopolies a single country can switch off. The amber ones (equipment, foundry, CNC tools, bearings, jet engines, instruments) are oligopolies that only work as leverage if allies coordinate, which is exactly why the US has spent two years building the Netherlands-Japan-Germany-Taiwan coalition rather than acting alone.

The West’s strongest cards (AI chips, EDA software, jet engines) are precisely the ones with a shelf life, because Beijing is pouring state money into domestic substitutes. The genuine bargaining equivalence lives in the top-right quadrant - frontier technology that bites hard and takes a decade-plus to replace (EUV lithography, leading-edge foundry).

It is to be noted that the arrows point left because it points to China travelling in the direction of self-sufficiency. It highlights that while real and binding today, there is a declining shelf-life or option value with these chokepoints.

However, having said this, any escalation risks retaliation. In 2025, the US suspended engine and EDA exports, China tightened rare earths, and within weeks, both sides walked it back into a one-year truce because each could hurt the other badly. It also points to the value of a bargaining strategy where the West gradually introduce restrictions on multiple such products where China depends on imports. The restrictions should be phased in carefully and subtly by plugging the procedural and process links that China exploits to its advantage.

In this backdrop, Ely Ratner and Nick Danby have a very good essay in Foreign Affairs that outlines the broad contours of a plan to identify and squeeze China’s vulnerabilities and do unto it what it is clinically doing by weaponising its strengths. The most obvious one is, as discussed above, to tighten the restrictions on access to the semiconductor chip supply chain.

China continues to leverage chip-smuggling networks, overseas data centers, and model distillation, a technique that exploits access to frontier AI models to replicate their capabilities. New policy measures should target the channels China uses to acquire restricted chips and supporting architecture, including shell companies and unlisted subsidiaries, as well as cloud-based access to U.S. computing power and servicing arrangements that keep older semiconductor manufacturing equipment operational. Equally urgent is synchronizing U.S. export restrictions with those of the Netherlands and Japan, whose companies—ASML and Tokyo Electron—control critical chokepoints in the advanced semiconductor supply chain. Although both governments began strengthening their own policies in 2023, their controls on equipment sales, servicing, and subcomponent exports to Chinese fabrication plants and toolmakers fall short of U.S. restrictions. Washington should press The Hague and Tokyo to close these gaps. If diplomacy fails, it should consider invoking the Foreign Direct Product Rule, which extends the extraterritorial reach of U.S. export controls to restrict products made with U.S. software or technology.

The authors also write that China’s huge export volume and $1.2 trillion trade surplus can be as much a liability as it is a strength, especially at a time when the economy is weakening and struggling for anchors of growth.

It should push back against China’s export surge by bringing advanced economies facing deindustrialization together with developing countries whose own manufacturing aspirations are being displaced. This coalition could then coordinate trade measures to protect their industries, including steel, shipbuilding, batteries, and drones. Alongside tariffs, the United States could pursue high-standard trade agreements that institutionalize requirements for subsidies, state-owned enterprises, and forced technology transfers that China cannot meet. Like-minded partners could also create an anticircumvention regime by strengthening rules of origin, sharing customs data, and imposing penalties on goods routed through third countries to avoid trade restrictions. They could further impose outbound investment screening to prevent companies or individuals in the United States and allied countries from financing Chinese capabilities that the controls seek to limit.

Notwithstanding its large reserves, for a country which imports three-fourths of its crude oil with 90% delivered through vulnerable sea routes, China is extremely vulnerable to energy security.

Below the threshold of a full blockade, the U.S. Treasury Department, through the Office of Foreign Assets Control, can use maritime sanctions to dissuade shipping companies, insurers, brokers, and banks from supporting prohibited shipments. Pressure on insurance, port access, and flag registration would raise costs and create uncertainty for China-bound tankers without requiring direct military action. The Pentagon should nevertheless demonstrate its ability to disrupt or interdict China’s seaborne energy imports by exercising U.S. naval control over key chokepoints along energy trade routes.

Commodity imports are another chokepoint

China imports roughly 80 percent of its iron ore, a foundation of its steel industry, predominantly from Australia. And most of its copper and lithium inputs, which are critical to battery and defense manufacturing, come from Australia, Chile, the Democratic Republic of the Congo, and Peru. As with oil, these dependencies offer additional pressure points that can be leveraged to strengthen deterrence and compound China’s challenges across multiple sectors simultaneously. If Australia were prepared to restrict exports of iron and lithium ore, and the United States and its partners had a plan to tighten access to copper and cobalt, they would send a message to China that its industrial base could be easily disrupted and its defense production capacity degraded if circumstances warranted.

Finally, the US dollar’s dominance is the nuclear option available.

Were Washington to restrict China’s dollar access—moving from sanctions on banks supporting PLA activities to broad limits on dollar transactions in advanced technology and military manufacturing—it could impose severe costs on Beijing, disrupting Chinese financial markets and potentially triggering wider economic instability… Washington must prepare for this scenario by first communicating unambiguously that only severely destabilizing acts would trigger consequences of this magnitude: for example, large-scale cyberattacks on critical U.S. infrastructure, Chinese export restrictions that seriously imperil the U.S. economy, or an armed attack against U.S. allies and partners.

The Cold War and trade tensions between the West and China are here to stay for the foreseeable future and are most likely to be ratcheted up over time. It is therefore important that others can mobilise sufficient bargaining levers with China. All the aforesaid are likely to be very effective in restricting the Chinese economy if deployed in a coordinated manner. This would require the mobilisation of a global alliance, something the US-led West did with great effectiveness during the Cold War with the Soviet Union.

Now, with the hostility and dysfunctionality of the Trump administration, any such cohesive and credible global alliance looks very unlikely. In its absence, whatever restrictions are imposed by the US and EU independently are merely band-aid solutions, and likely to get circumvented in various ways against an antagonist who is disciplined, plays the long game, and does painstaking groundwork to accumulate its strengths and overcome restrictions. Trump 2.0 is, therefore, perhaps the best thing that an embattled Chinese government could have been gifted by its opponents.

Examining the gravitational pull of index investing

noreply@blogger.com (Urbanomics) — Sun, 14 Jun 2026 23:30:00 +0000

The blockbuster IPO of SpaceX has drawn attention to the role of the index investing market segment, which originated fifty years ago. The scrutiny will only intensify in the days ahead as both Anthropic and OpenAI are listed.

Despite only about 4 per cent of SpaceX’s shares being listed, a high level of demand was built in, also because some index investors will soon be required to add the company to their portfolios. Nasdaq amended its rules (as also CRSP and FTSE Russell) to allow SpaceX to enter the Nasdaq-100 index via fast-track, thereby providing it free liquidity through a captive market. Morningstar follows 6,006 US-registered mutual funds and 5,100 ETFs, covered by 3,203 separate benchmark indices against which $41.1tn of assets are managed.

The current entry requirements for indices are very liberal for large companies.

However, what distinguishes SpaceX is its very low float (just 4.25% compared to above 90% for the other big companies) and the fact that it does not yet make any profit. Toby Nangle and Co at FT Alphaville tabulated that active fund managers (who want to ignore Musk) and passive portfolio managers (who have no choice) must purchase $14.2bn of SpaceX stock in the first three weeks of trading to avoid going short.

We think that managers who have absolutely no interest in going either long or short Elon will need to collectively buy $8.5bn of SpaceX stock on the 19th June, a further $1bn on 26th June and a final $4.7bn on July 3. And so we’re talking a cumulative, de facto mandated $14.2bn of mutual fund and ETF orders by July 4 to avoid having to take a view on Elon. That number would’ve been around $11bn higher if the S&P 500 index committee had leaned a different way. But it’s $13.2bn bigger than the $1bn it would’ve been if index committees had sat tight on their existing fast-track methodologies. And this is before we count institutional assets like pension funds, insurers and foundations, as well as every single foreign owner whose benchmarking habits we currently lack information.

So this is a big deal. Clearly, the market has been brazenly manipulated by changing the rules to create the stage for SpaceX (and the other two mega IPOs).

I took the help of Claude to generate a few graphics to understand the scale of index investing, its dynamics and distortions, and possible reforms.

The conventional measures dramatically understate index investing because they count only labelled index mutual funds and ETFs. However, using the indexation definition, passive ownership of global equity mutual funds and ETFs is 50% and 60% for US equities. In fact, a stunning three-fourths of the equity market exposure of the big three US asset managers is through ETFs.

SpaceX’s shares could have an index exposure of 43% in a year when it joins the S&P 500, and that too on a volume which would be a fraction of that for the Mag7 firms. It is estimated that S&P 500 funds would need to absorb 19% of SpaceX's public float upon inclusion, with the Russell 1000 and Nasdaq-100 funds absorbing another 24%. A tiny supply of $22-27 bn would meet a mandatory demand of 43%.

The research on the impact of index inclusion on stock price offers striking findings. Gabaix and Koijen find investing $1 in the stock market increases the market’s aggregate value by about $5. Since marginal holders of equity (index funds, pension funds, insurance companies) operate under mandates that fix their equity allocations within narrow bands, when aggregate demand shifts, few participants can absorb the change, and prices must move substantially to clear the market.

Haddad-Huebner-Loualiche point to a Mathew Effect in indiex investing. They find that a $3.6trn market cap company is only about five times as liquid as a $100bn company, despite being 36 times the size. The largest stocks are disproportionately impacted by each dollar flowing into passive funds, causing the largest stocks to outperform and stock market concentration to rise. Passive investing has reduced market efficiency by over one-third.

Index investing brings the benefits of access to a low fee, diversified, and tax efficient asset pool to the retail investors. In the words of the legendary investor Jack Bogle, index investing is the equivalent of not looking for the needle but instead buying the haystack.

However, it distorts price discovery, creates inelastic demand, amplifies concentration in stocks and asset managers, and erodes governance. All of these distortions are greater for the mega-cap stocks.

Further, index-investing comes from asymmetric flow elasticity. While passive funds are mechanically symmetric (the 1:5 multiplication), the rest of the system (margin lenders, derivatives dealers, momentum funds, retail behavioural agents) is not. When flows reverse, the multiplier still applies, but additional positive feedback channels switch on, creating an overshoot below fundamental value before mean-reversion can bring prices back. The resultant instability can tip markets into prolonged and deeper downturns. This is an illustrative example

Suppose 2 years post-IPO, growth disappointments and rate-policy reversal trigger a ~20% net outflow from Nasdaq-100 trackers ($280 bn × 20% ≈ $56 bn). Applying the firm-level multiplier (~2 for stock-specific flows, higher for concentrated names): mechanical impact on SpaceX share price could be ~30–45%, on top of any fundamental revaluation. The same flow proportionally affects all Mag 7. Index investing does not just amplify upside — it removes the price-discoverers who normally cushion downside.

Below is a listing of some channels that amplify the downside.

All things taken together, index investing must reconcile the conflicting requirements of democratising investing (through passive funds) and limiting market instability. The answer lies in the empirical fact - the more capital becomes index-tracking, the less price discovery the market performs, and the more the system is exposed to flow-driven valuation and procyclical unwinding. Clearly what is optimal for individual investor is not optimal for market function. In the circumstances, this can be an illustrative framework to reconcile the conflicting requirements.

The problem with index investing is that of internalisation of its negative externalities. Passive investors capture the upside of cap-weighted indexation (low fees, diversification, momentum) without bearing the price-discovery cost, whereas active investors bear the cost of analysis but cannot compete on fees.

This free-riding can be resolved only if passive investors internalise the marginal cost they impose on the system without eliminating the substantial benefits they deliver to retail investors. Like with carbon emissions, this can be done through fees, governance obligations, or structural caps.

To dive a bit deeper, there are perhaps four levers for reforming index investing - index methodology (rules of index construction), voting and governance obligations, fund-level liquidity thresholds, and limiting structural concentration in the passive investment ecosystem. A combination of all of them is a formidable regulatory toolkit.

Each lever will have its set of reforms. It may be required to prioritise a limited set reforms that have the highest-impact-per-cost. The simplest and highest value reform could be mandatory free float minimums (say 10-15%), a twelve-month public trading history before index inclusion, and a 4-5% cap on any single stock in an index. Second, bring index providers under SEC regulation as “investment advisers” subject to fiduciary duty and disclosure requirements.

Third, for passive funds with more than 1% holding, return governance to the ETF or index fund holders by allowing them to vote their proportionate share directly via the fund, thereby reducing the influence of the Big Three asset managers. Fourth, cap the passive fund ownership of US banks at 10% (the regulatory "controlling interest" threshold) and impose mandatory liquidity stress tests for funds with more than $5 bn AUM in cases of extreme outflows and market dislocation.

Fifth, the Department of Labour should tighten defined-contribution retirement-plan portfolio diversity rules, thereby providing a structural counterweight to cap-weighted default portfolios. Such plans must offer either equal-weight or capped-weight default or cap cap-weighted defaults at e.g. 80% of the plan portfolio. This would reduce forced concentration in 401(k) default portfolios. Finally, there could be regulatory roadmap for orderly winding-down of overweight positions during the life-cycle or retirement transition. A coordinated guidance, including some rule-based gradual decumulation, reduces fire-sale risk.

The proposals above seek to reconcile the diversification and fee benefits of indexation while also internalising its costs. Once index providers face fiduciary duty, once Big Three votes are partially passed through, once retirement defaults include non-cap-weighted options, once liquidity is stress-tested, and there is some transition guidance, the same products can continue to serve retail savers while delivering far less of the distortion that has been documented.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 13 Jun 2026 05:32:29 +0000

1. Ruchir Sharma goes behind the record corporate profits share of GDP in the US (11% of GDP, up from 7% in the 1990s) and finds two problems. One, the share is boosted by the lower corporate tax rates which translate into higher fiscal deficits.

Lately the US deficit has risen to more than 6 per cent of GDP and a deficit that high reflects a large transfer of income to households and corporations. Under a well-established accounting formula, the Kalecki-Levy Equation, corporate profits are in part a mirror image of the government’s deficit. Based on this framework, deficits were the single largest contributor to the increase in earnings as a share of GDP since the late 1990s. And in this decade, deficits have accounted for more than half of corporate profits, twice the level of the dotcom era. Strip away government support, and US profits look less extraordinary.

Second, the share falls sharply when we include the universe of private companies.

Since the dotcom bust in 2000, the number of publicly listed businesses has fallen by half, with many new companies remaining private for longer, funded by private equity and venture capital. This is the new home of excess and weak earnings. As a result, the profit growth of the average business listed in standard indices provides a misleading picture of the overall economy. Profit growth has been less impressive once the private companies are included in the data. Further, private firms planning to go public are much larger and less profitable today than in the 1990s. The biggest names in the IPO pipeline, including SpaceX, Anthropic and OpenAI, have little to no profits.

2. Britain's much-delayed HS2 railway project's cost has increased by another £20bn to £102.7bn (a range of estimates ( £87.7bn to £102.7bn in 2025 prices) and will be completed only by the 2040s, with the first trains not expected till 2036.

3. Why has oil not hit $150 or $200 despite about 12-15 mbpd being taken out?

One of the biggest surprises for the oil market has been China, the world’s largest importer. It slashed inbound shipments by almost 40 per cent in May compared to last year’s average, according to Vortexa Ltd. The reduction is enough to offset anywhere between a third and a fifth of the barrels lost to the war, depending on the estimates used. At the same time, the US has emerged as the world’s most important swing supplier since launching strikes on Iran in late February. American crude and fuel exports in May were more than 2 million barrels a day higher than the average for all of last year. Other emergency measures have also eased the strain. Governments around the globe coordinated a historic release of strategic reserves, while Gulf producers rerouted shipments through alternative export routes. Some tankers continued moving cargoes via the strait despite the risks, using increasingly opaque methods to avoid military threats... Saudi Arabia’s East-West pipeline shipped millions of barrels a day to the Red Sea, while the United Arab Emirates has been piping barrels to the port of Fujairah outside the gulf.

And this is important.

Another factor keeping a lid on prices has been Trump’s relentless jawboning, making it hard for even the most bullish traders to hold long positions for prolonged periods of time. Open interest in Brent crude futures is the lowest since August as elevated market volatility forces traders to roll back risk exposure. Steep price drops on the prospect of peace have pushed many oil bulls to the sidelines, leaving them to hold small positions for very limited periods of time, several traders said. The lack of risk-taking has helped keep a lid on financial flows, while supply levers have averted the worst hit to the market. The question now, is whether that can last without a peace deal.

4. The evidence to date points to widespread AI use not translating into anything proportionate in terms of business value creation. Sample this from the software industry pointed out by John Burn-Murdoch.

5. EU energy subsidies during the Ukraine invasion and spike in gas prices.

Between late 2021 and mid 2023, EU countries spent about €540bn on subsidising energy prices to protect consumers from price shocks in the aftermath of Russia’s full-scale invasion of Ukraine and to cushion energy-intensive industries from soaring costs. Of that total, €158bn was accounted for by Germany, whose industry heavily depended on Russian gas supplies. In the wake of the Ukraine energy shock, Brussels also relaxed state aid rules for the rest of the decade to allow governments to subsidise clean technologies and industrial decarbonisation.

6. Emerging AI-related roles in the software industry.

Agent engineers who build and fine-tune agents; architects who determine how humans and agents divide tasks; AI governance specialists who keep agents compliant and accountable; AI transformation advisors who help enterprises navigate workflow reimagination; solution designers who translate business problems into agentic solution architectures; and AI assurance partners who help clients govern their own AI deployments.

7. Rana Faroohar makes the point that we may be at the cusp of a new investment super-cycle driven by the combination of AI, clean energy, defence, and manufacturing.

In a recent issue of his TPW Advisory Monthly report, investor Jay Pelosky did just that, collating data on AI, clean energy and defence spending around the world from sources including Gartner, BloombergNEF (on energy), the Stockholm International Peace Research Institute and the International Institute for Strategic Studies (on defence) and others. So far, $6.9tn was spent globally in 2025 in the three areas, and the number will probably reach $10tn by the end of this year and $16tn by 2030.

What’s more, says Pelosky, these three areas reinforce one another, amplifying potential investment. AI requires more energy. The move towards tech sovereignty in the US, China and even Europe (in a nascent way) adds to the need for investment in AI and energy, while the move towards a more 19th-century “spheres of influence” geopolitics calls for greater defence spending globally. Add to this the desire of policymakers in all three regions to increase resilience in critical sectors affected by concentration or globally dispersed supply chains: products such as advanced semiconductors, active pharmaceuticals and lithium batteries, for example.

8. Rupee trajectory over the last two years

The rupee depreciating by nearly 6 per cent against the dollar in the first five months of 2026 alone, exceeding the combined full-year declines recorded in 2025 (5 per cent) and 2024 (2.8 per cent). The rupee touched a record intraday low of 96.57 per dollar on May 19 and has lost roughly 14 per cent of its value against the greenback over the past two years. Against the euro, the erosion has been equally stark, with the Indian currency declining by more than 7 per cent over the past 12 months and nearly 16 per cent in the past two years, he added.

The problem with a depreciating rupee is that foreign investors now must earn an extra 14% over the last two years to offset the depreciation.

9. India's FTAs may be creating a perverse incentive for even domestic manufacturers due to the inversion of duty structures.

Many finished goods now enter India at low or zero duty from partners such as ASEAN, Japan, South Korea, the UAE and Australia. As a result, Indian manufacturers often pay high duties on imported inputs, especially those sourced from non-FTA countries, while competing against finished products imported duty-free under FTAs. For example, steel and aluminium attract MFN duties of 7.5-10 per cent, but machinery, industrial equipment and engineering products made from these materials can enter India duty-free under several FTAs. Indian manufacturers, therefore, face higher input costs when competing with tariff-free imported machinery produced with globally priced inputs.

Similar distortions exist in chemicals, plastics, rubber and textiles. Duties on inputs such as caustic soda, soda ash, polypropylene, PVC and SBR raise production costs. At the same time, many finished products in these sectors can be imported at low or zero duty... the growing incentive for firms to manufacture outside India rather than within it. When raw materials and components attract duties in India, but finished products can be imported duty-free from FTA partners, companies may find it more profitable to locate production abroad and export back to the Indian market. In such cases, FTAs effectively encourage offshore manufacturing at the expense of domestic value addition. ASEAN countries are increasingly becoming manufacturing hubs for supplying the Indian market.

10. The Gulf War has resulted in a surge in Chinese exports of solar panels, especially to South East Asian and African countries. This came at a time when many Chinese firms laden with excess capacity, low margins, and large indebtedness were facing an existential crisis.

11. The revival of manufacturing in the US hits the wall of labour scarcity.

Perhaps the largest problem for would-be reshorers is a lack of labour. Despite widespread nostalgia for manufacturing jobs, new US factories often struggle to find reliable workers. When Japanese group Panasonic started producing electric vehicle batteries near Reno, Nevada in 2017, the company suffered more than 100 per cent annual turnover in its early years. Not only were employees reluctant to take jobs in an access-controlled, sterile environment, but every November, the group would lose workers to seasonal logistics jobs at nearby Amazon facilities that paid a similar wage... Despite political enthusiasm for reviving manufacturing, jobs posted on LinkedIn receive fewer applications than other sectors it competes with, such as technology.

12. More on the SpaceX bubble.

Goldman Sachs, the investment bank leading the IPO, projects that SpaceX’s AI revenues need to increase 100-fold by 2030, reaching $322bn from $3.2bn today. But its AI lab remains far behind Anthropic, Google and OpenAI at the frontier, and shows little sign of catching up. Morgan Stanley also estimates that overall revenue needs to increase 180-fold to $3.4tn by 2040, up from $18.7bn last year. Earnings must flip from a $4.9bn loss in 2025 to $2.7tn.

SpaceX ends the first day of trading at 20% premium on its $135 listing price, which raised $75 bn and left it with a valuation of $2.1 trillion. The listing prospectus claimed at $28.5 trillion addressable market.

SpaceX plans to use the IPO proceeds for a range of ambitious projects, from its skyscraper-sized reusable Starship rocket, founding a 1mn-strong colony on Mars, starting a lunar economy to building a network of orbital AI data centres capable of delivering vast amounts of computing capacity... A portion must also go towards repaying a $20bn bridge loan the group took out in March to back its merger with Musk’s lossmaking AI start-up, xAI, and social media platform X.

This is staggering.

It also hands Musk a vast financial windfall, with his 42 per cent stake in SpaceX valued at more than $800bn. Combined with his $280bn holding in electric vehicle maker Tesla, his wealth has now surpassed $1tn, equivalent to about a third of the market value of the UK’s FTSE 100 index.

Richard Waters explains the rationale driving the astronomical valuation.

It is hard to find changes in SpaceX’s business prospects that account for a fourfold valuation jump within the space of a year. Rather, it is testament to Musk’s unrivalled grip on the public imagination, transmuted into Wall Street gold. When the history of SpaceX’s record-breaking IPO is written, it will go down as a textbook case of Musk’s ability to conjure a compelling vision of the tech future for both Silicon Valley and Wall Street, ably backed by an army of promoters in the financial world. (With fees estimated at about $500mn, it’s probably not surprising that there were few warnings from the Wall Street establishment that the shares might be overvalued.)...

Musk has been busy in recent months spinning up new visions capable of embellishing his company’s prospects... What starts out sounding like science fiction fantasy doesn’t take long to seem not only plausible but even downright likely. Wall Street has been more than willing to translate Musk’s tech dreams into financial projections capable of supporting the sky-high valuation, implausible as those numbers may seem. Goldman Sachs, the lead bank on the IPO, predicted that revenue would soar to $474bn by 2030, most of it from AI...

The IPO involved only about 4 per cent of SpaceX’s shares, but a high level of demand was built in, partly because some index investors will soon be required to add the company to their portfolios. Even without the index funds, SpaceX’s sheer size — at about 2.5 per cent of US stock market capitalisation — has made it too big for many investment managers to avoid.

13. South Korean capitalism is spreading wealth around

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.

14. Germany amps up spending on its ailing railways sector, where on-time arrivals of long-distance trains have fallen from 84% two decades back to 60%.

By the end of 2026, Deutsche Bahn will have rebuilt 900 kilometres of train lines since 2024, close to a quarter of its 2036 target. That is equivalent to half of the roughly 1,900 kilometres of new lines built after 1945. Flix, which started as a coach operator but also runs long-distance trains, has earmarked €2.4bn for up to 65 new high-speed trains that it plans to roll out from 2028. Italian high-speed train operator Italo also has ambitions for Germany, promising up to €3.6bn of investment in new trains if it gets multiyear access to the network.

Some thoughts on the likely impact of AI in countries like India

noreply@blogger.com (Urbanomics) — Mon, 08 Jun 2026 01:56:17 +0000

This post will argue that the long-term impact of AI innovations on the typical household’s daily life in a developing country like India will be far smaller than the discourse suggests. Much the same could be said for basic human development and public services delivery in general.

While there will be some overlap, I make the distinction between the macroeconomic impact in terms of automation and resultant job losses, and the material impact on the lives of people. Also, while much has been written about the former, this post will focus on the latter.

As a note of caution, as is the case with any such debates, evidence and data to substantiate claims are hard to come by. So there is a lot of judgment in the argument below. And I’ll only be too happy if the judgment is proved wrong.

The argument rests on four observations.

First, the bulk of the economy and daily life in these countries lies in domains where AI can barely reduce costs. The two main production modes, agriculture and manufacturing, are unlikely to be significantly affected (see the third point). The main products that we buy - homes, vehicles, jewellery, and consumer durables - and the everyday services we buy - haircuts, repairs, domestic help, transport, retail, eating out, etc. - are dominated by physical inputs of materials, energy, land, labor, and transport.

AI can reduce logistics and coordination overhead costs, which form a marginal share of the total production cost of these goods and services. The share of any of these prices that AI can potentially influence is likely a fraction. Of India’s roughly 470–565 million workforce, around 85% is informal, ~45% is in agriculture, and the formal IT/BPM/GCC sector employs only about 5.8 million people. The set of workers directly exposed to AI displacement is small - plausibly 5–8% over a fifteen-year horizon - and the consumption basket of the median Indian is composed almost entirely of goods and services whose prices AI is unlikely to meaningfully alter.

Second, the obvious rebuttal, that AI’s impact will come not through cost reduction but through dramatically expanded access to healthcare, education, finance, and government services for India’s 800-million-plus internet users, runs against some hard evidence. The last twenty years of EdTech, Medtech, SkillTech, and AgTech across India and comparable low-income contexts amount to a rich natural experiment. The result is essentially zero examples of even significant, much less transformational, district-scale impact in any of these domains.

Despite massive amounts and efforts on EdTech, aggregate learning outcomes, as measured in the likes of ASER scores, have hardly moved. In fact, the share of Class V children in rural India who can read a Class II text has actually declinedover much of the EdTech era. Like Diksha for school education, eSanjeevani logs vast consultation volumes with no measurable population health effect. It is hard to find meaningful signatures of MedTech in primary or secondary healthcare. In skill development, a succession of schemes has trained tens of millions with dismal placement outcomes, including using technology extensively. Digital Green and dozens of AgTech pilots have generated good papers, but no district has measurably transformed agricultural productivity because of them. Outside India, the picture is similar - Kenya's M-Health pilots, Brazil's rural EdTech, Indonesian AgTech apps. The hit rate on "scaled, measurable, transformational" is essentially zero.

The same could be said about most areas of public services delivery - primary health care and school education; municipal government services like tax assessment, building permissions, utility service connections; and the services of regulatory agencies. While there are pilots and small slivers of some success, aggregate impacts across all these realms attributable to digital technologies, notwithstanding numerous and repeated initiatives, have been minimal.

Third, the reason this pattern matters for AI is that it points to perhaps a wrong diagnosis being made about why previous efforts, including using technology, failed. The standard story that “the tech wasn’t good enough yet” implies AI will finally break through because it is qualitatively better. However, I’m inclined to argue that the diagnosis is wrong.

The binding constraint in these domains has never been information quality or delivery. The child in the village school does not fail to read only because she lacks access to a good pedagogical sequence; she fails also because she has accumulated large antecedent learning lags, the teacher is over-burdened, indifferent, or absent, the system has no consequence for non-learning, she is hungry and has chores in the evening, her parents cannot reinforce at home, and so on. A perfect AI tutor in Hindi and Math does not change those facts.

The villager seeking treatment doesn't suffer only because no one can diagnose her condition; she suffers also because the doctor is indifferent or isn't there, the PHC has deficient diagnostics or medicines, transport to the CHC costs a day's wage, and the prescribed drug regimen is incompatible with her work and food situation. It is not only the lack of plumbing knowledge that holds back the aspiring plumber. Instead, he also lacks an apprenticeship network, a credential the contractor trusts, and tools. The farmer is also constrained by the inertia to change long-standing practices, water, fertiliser subsidies, and the price he gets from the mill, not only by ignorance of best agronomic practice or market information.

In all four cases, the recipient is operating in a bound system where information is, at best, the fifth-binding constraint. Solving the fifth-binding constraint produces no visible improvement because the first four still hold. Even with this constraint, the ability of AI to make significant improvements at scale in these difficult contexts is questionable. More than two decades of the internet and digital technologies have made little or no impact on actual outcomes, except for a few oft-repeated pilots.

This is exactly what the vast majority of development economics literature has been telling us for two decades, and it’s why “information-delivery” technologies have a flat impact curve regardless of which generation of tech is doing the delivery. AI is, fundamentally, a much better information-delivery technology. By the logic above, it should be expected to have roughly the same impact profile - better demos and pilots, but similar real outcomes - unless something about AI breaks the pattern.

Having said this, it is also logical to argue that AI can address and relax all these constraints just enough to enable outcomes that, while not the best, are far better than those achieved now. While appealing and comforting, I am not inclined to agree.

Fourth, the genuine exceptions exist but are narrower than the transformational claims that mainstream discourse suggests. AI is plausibly different in three specific places: supply-side augmentation that flows through existing institutions (Qure.ai’s tuberculosis screening integrated into state programs is the cleanest example); voice and vernacular interfaces that break the literacy ceiling text-based apps could never cross; and AI built on top of India Stack to alter citizen-state interactions. These are real but bounded effects, not transformations.

So what’s the final assessment?

AI radiates a wide beam of capability (advice, diagnosis, tutoring, prediction); the beam hits a wall of thick structural constraints, each labelled with the human reality it represents and the domain it blocks (say, education, health, livelihood, farming). Only a thin sliver of information makes it past the wall to reach the villager with the phone below. AI delivers information, but not transformation.

The mainstream discourse on AI is built on the worldview that assigns outsized importance to knowledge-based services over the production of goods. This is a real blind spot that obscures the reality of the vast majority of non-AI-influenced interfaces and interactions in the daily lives of most Indians.

In conclusion, I’ll stick out my neck and argue that AI’s footprint on median Indian life will look much more like the mobile phone’s did - ubiquitous, individually useful, but hardly transformational on people’s daily lives. The mobile phone did not move India’s Human Development Index; it moved convenience and communication. AI is on track to do something similar: meaningful at the margin, but layered on top of structural conditions it cannot itself relax.

The global discourse, calibrated on knowledge-work economies where AI strikes the dominant production input, badly overstates the implications for a country where physical and institutional constraints set the floor.

Unfortunately, like with the internet and digital technologies, this is likely to be a costly distraction for development. Instead of working to get the plumbing right, it is likely to displace resources and efforts towards getting AI solutions to address these problems. I have blogged here, here, and here on this.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 06 Jun 2026 02:41:15 +0000

1. Potato glut hits Europe, and in particular Belgium.

Europe faces a surplus of five million metric tons of the type of potato used for fries. For months, the price of a metric ton of potatoes on the spot market in Belgium, the world’s biggest exporter of frozen fries, has languished at precisely zero. It was nearly 600 euros ($690) three years ago.

2. The Murugappa Group (through Axiro Semiconductors and CG Semi), the Tata Group (through Tata Electronics), and Crystal Matrix are leading India's semiconductor chip design and manufacturing push.

3. Very good assessment of a decade of the IBC law.

The idea was to enable timely exit of non-viable firms, preserve viable businesses, restore credit discipline, and unclog credit channels... Till March 2026, 1,419 companies had emerged from insolvency with approved resolution plans, with the proportion of companies achieving such outcomes improving steadily... Creditors have realised ₹4.32 trillion through resolution plans. The oft-cited haircut of around two-thirds, measured against admitted claims, can be misleading because claims are frequently inflated while asset values are deeply eroded by the time firms enter insolvency. A more meaningful benchmark is liquidation value: Resolution plans have, on average, yielded 167 per cent of the liquidation value. Importantly, firms resolved under the IBC demonstrated operational revival post-resolution. Within five years, sales and capital expenditure nearly doubled, asset utilisation improved sharply, and the aggregate market capitalisation of resolved firms rose from about ₹2.8 trillion to ₹9 trillion...

In all, 3,003 companies have entered liquidation under the IBC, but most had little realistic prospect of revival. Their assets averaged barely 5 per cent of admitted claims, and four-fifths were already sick or defunct before entering insolvency. The IBC merely provided an orderly exit for firms that had failed long before the process began. Yet, the incidence of liquidations in India is comparable to that in the United States and significantly lower than in the United Kingdom and Australia... Resolution plans rescued 78 per cent of distressed assets, while liquidations accounted for 22 per cent. When all pathways to revival are considered — resolution plans, withdrawals, settlements, appeals, and rescues during liquidation — the number of revived companies substantially exceeds those liquidated.

4. John Burn-Murdoch points to evidence that remote working and NOT AI is responsible for the ongoing declines in entry-level hirings.

Peter John Lambert and Yannick Schindler have a fascinating counter-proposal: the take-off of remote work. Early-career workers require more supervision than experienced hires, and build important skills, knowledge and social capital by observing and working alongside senior colleagues. Working from home adds friction to these processes, making entry-level workers more costly to bring on board in terms of time and resources and slowing their prospects for promotion. As such, the rise of remote work has worsened the trade-off for hiring entry-level workers, while leaving the calculus for senior hires unchanged. The evidence fits the theory. Lambert and Schindler analysed hundreds of millions of new hires and job postings and found that although both occupational exposure to AI and remote working rates line up with the outsized pullback in junior hiring, the link with AI evaporates once you account for whether a role is remote. In other words, it only looks like AI is behind the hiring crunch for junior software developers because coding jobs are also disproportionately done remotely. Jobs less exposed to AI but amenable to remote work (eg lawyers) have also seen weak junior hiring; roles with high AI exposure but an emphasis on in-person work (eg receptionists) have held up better.

5. Is Steve Jobs leaving the greatest corporate legacy ever?

Apple now rakes in sales of over $1bn a day. Its services business alone, driven by the App Store and Apple Pay, generates more revenue than Netflix, Spotify and Adobe combined, with a margin of around 75 per cent. Under Cook, the company has returned around $1tn to shareholders through dividends and buybacks… Nearly two decades since the product launched, Apple shipped well over 200mn iPhones in 2025 and the device still accounts for about half of Apple’s $400bn of annual sales, with high product margins underpinned by the highly efficient, Asia-based supply chain also created by Cook. Apple’s astonishing profitability is sustained by an annual cadence of new iPhones, each iteration featuring largely incremental improvements on the one before. Research and development spending as a proportion of revenue went from 8 per cent at its height in 2001 to a 2 per cent low in 2012 as the iPhone boom began, meaning that for a while Apple was spending proportionally far less than its Big Tech peers.

6. Good primer on why oil prices have remained less elevated than expected - decline in Chinese oil imports (almost 4 m bpd) and rise in US exports (almost 4.5 mbpd).

7. The US economy is increasingly resembling a one-trick pony of AI.

The US corporate profit share has climbed to a record 13.8 per cent of GDP, while net income margins across the broad US equity market have recovered to about 9.7 per cent, close to earlier highs. At the same time, market leadership has become unusually concentrated: a handful of AI‑linked stocks now account for roughly 40 per cent of the S&P 500’s market capitalisation, according to Bank of America data. Headline profitability is being flattered by a small slice of the economy earning extraordinary returns from the scramble to build AI capacity...

Spending strength is increasingly coming from upper-income households where wealth and income are more tied to equities than wages. The stock market has, in effect, become part of the growth model: rising AI profits lift share prices; higher share prices support the spending power of wealthier households; and that spending helps keep demand alive. Lower-income households, by contrast, are more exposed to squeezed real incomes and softer labour-market momentum... Large technology groups have produced surging revenues and margins with only limited growth in headcount... So long as investors believe AI will earn very high long-term returns, the loop can remain self-sustaining: capital expenditure stays firm, equities stay buoyant and affluent consumers keep spending.

8. The spectacular surge in Google's capex.

Five years ago, its capital expenditure on servers, network equipment and such was $25bn, which it funded out of operating cash flows of $92bn. In 2027, analysts expect $250bn of capital expenditure, versus cash flows of $260bn — a tighter fit. In its second quarter next year, Visible Alpha estimates suggest Google will spend more than it makes, for the first time in its listed history.

9. A good illustration of deficient national security discipline comes from how the US is allowing the exports of tungsten scrap to China even as it spends money abroad buying tungsten mines.

Since early 2025, Chinese scrap traders have been seeking tungsten throughout the US, prompted by a shortage outside China caused by declining supply and intense demand from the aerospace, weapons and tools industries. The effort has set off a bidding war with American buyers and calls to ban sales of a critical national security resource to overseas buyers... Sellers said they were fielding calls from Chinese buyers looking for the material, while American buyers said they were being outbid by Chinese rivals willing to pay as much as five times the usual price...

Tungsten scrap commonly comes from worn-out industrial tools such as drill bits and mining equipment. It can be crushed and chemically processed back into tungsten powder or carbide for use in new machinery and tools. The shortage has been triggered by Beijing imposing export restrictions on it and an array of other critical minerals in early 2025 and the country cutting mining quotas. China accounts for more than half of global mined and refined tungsten supply and about half of demand...

Tungsten is broadly used in military applications, including in bullets and missiles. Traders said stocks were already low before the Iran war and that companies do not typically hold large stores of the metal. There was “no availability” outside China of the so-called “intermediate” products that manufacturers need — mined ore that has been processed... The price of tungsten has risen by more than 200 per cent since May 2025, while tungsten scrap has risen 350 per cent, according to Argus Media.

10. The AI wave is lifting all stocks, including those legacy IT firms like HP and Dell.

11. India's non-tax revenue fact of week.

In the last three years, the share of RBI surplus in the government’s non-tax revenue has stayed between 42 and 52 per cent.

12. SpaceX IPO in a graphic.

13. Real Madrid at the top of European football club valuations.

14. The universe of PSUs in India has been rising.

15. Rama Bijapurkar's categorisation of India's consumption class.

The important thing is that 93% of households have annual consumption less than $5700.

16. Anthropic facts.

Last month, Anthropic crossed $47bn in run-rate revenue, a metric used by start-ups which estimates annual revenues based on short-term performance. This is a more than fivefold increase since the start of the year... Anthropic’s valuation has soared from $350bn to $900bn in 12 weeks. It is now one of the fastest-growing companies in history.

17. The biggest threat to the US superpower status now appears to be its surging public debt, which is now $36 trillion held by the public and federal agencies

The exorbitant privilege has ensured the dollar's status as the world's pre-eminent reserve currency and the Treasury market's role as the world's safest haven asset, thereby allowing the US access to unlimited global capital at a low cost. While there are no competitors to the dollar on the horizon, the Treasury's safe haven status is facing competition.

18. The most important difference between the dotcom bubble and the AI bubble.

19. Japan is losing people.

Japan’s population peaked in 2008 at 128 million, and it is projected to fall to 87 million by 2070. The country is now roughly the same size it was in 1989... All but two of the country’s 47 prefectures reported population decreases in 2025, and the rate of decline is accelerating.

20. Finally, this says as much about the Indian stock markets as about the Korean and Taiwanese markets.

India’s stock market capitalisation was overtaken in the past week first by Taiwan and then by South Korea, as the value of Indian equities held by foreign investors slumped to a 10-year low of 7.3tn rupees ($76bn) on June 1. The value of Indian stocks was more than double that of Taiwanese stocks and roughly 3.5 times that of South Korean stocks 18 months ago, analysts at Bernstein said this week. “Fast forward just five months into 2026, and that lead has evaporated,” they added.

The challenges with TOD implementation in India

noreply@blogger.com (Urbanomics) — Fri, 05 Jun 2026 00:22:09 +0000

Transit-Oriented Development (TOD) remains an elusive urban planning goal for India despite nearly two decades of efforts. I blogged on TOD implementation in India here.

In simple terms, TOD is about minimising commute times, ideally by locating both homes and workplaces within walking distance of mass transit stations, or at least one location within walking distance.

This would entail crowding in high-density residential, office, commercial, and institutional developments around the station. This requires making it attractive for developers to prefer building within the TOD influence zone, despite its much higher land costs.

There are perhaps four big failings with the TOD policies over the years.

1. The biggest problem is that metro and commuter railway projects are primarily seen as infrastructure projects and only distantly as opportunities for urban development and economic growth. This is manifest in the limited involvement of the municipal corporations in their design. TOD is planned and executed mainly by the transportation entities without adequate ownership by the local governments.

The railways, Road Transport Corporations (RTCs), mass transit entities, state governments, and Urban Development Authorities (UDAs) view TOD primarily as station-based real estate development opportunities that lead to monetisation and revenue mobilisation. A second consideration is to increase housing supply. The UDAs tend to view TOD as an instrument to develop or monetise their own lands, mostly to increase housing supply.

The idea of minimising commute times is rarely explicitly considered as an important factor.

2. Policymakers have internalised a belief that the attractions of connectivity and higher (this too is mostly marginal) FAR are sufficient for developers to choose TOD influence zones.

But, as I blogged here, this assumption is seriously flawed. The global experience on TOD clearly points to the need for fiscal concessions to make it attractive enough for real estate development in the pricier TOD influence zone.

Given the higher land costs in TOD zones, the case for fiscal incentives to offset them and attract developers should be obvious. A TOD policy can therefore be effective only if the conflict between the objectives of minimising commute times (or densified development around stations) and maximising revenues is resolved in favour of the former, at least in the initial years

Planning officials are averse to dense high-rises. Bureaucrats are averse to foregoing revenues. Taken together, we have a TOD gridlock. The common ground is mid-rise residential property development where government lands are available around transit stations. This is the reality of TOD in India.

3. By its very nature, there cannot be a uniform TOD policy with tightly prescribed norms on use categories. However, governments (especially UDAs), struggling with the problem of housing, tend to view TOD primarily as an instrument to address housing problems in cities. This creates a pronounced bias towards housing in TOD zones. This, too, is a seriously flawed assumption.

The proportion of the different use categories varies across stations. So, for example, the vicinity of a large metropolitan downtown station would be more suitable for predominantly institutional and office uses, whereas that around a smaller suburban node would be suitable primarily for residential and commercial uses. This would allow the satellite nodes to grow by feeding off the metropolitan node, and the latter to decongest and grow sustainably. A prudent policy choice, therefore, may be to leave it to the market to decide the proportions of different uses within the TOD zone depending on the strengths and opportunities of each node.

4. TOD demands a high level of coordination among different government agencies. But it is a daunting challenge to coordinate across the universe of stakeholders involved in TOD - the municipal corporations, the UDAs, the RTCs, the metro or commuter rail SPVs, and Indian Railways.

This is best illustrated with modal integration - the seamless integration across the different modes of transport at a station, including ticketing - an essential requirement for TOD. This has proved elusive despite numerous serious efforts. Further, because each view focuses on real estate development and revenue mobilisation (or housing), and overlooks the primary objective of minimising commute times, their incentives pull in different directions.

Even something as apparently simple as the integration of the facilities of distinct metro, commuter rail, IR, and RTC in one station becomes a challenging exercise. Their physical integration as one unit to share and optimise space and services can be a bureaucratic nightmare. Instead of planning and developing the area as an integrated unit, each entity pursues its priorities with limited synergies.

There are no easy answers here. When a problem has proved elusive for a long time, a good starting point would be recognition of its complexity and piloting it. It may therefore be prudent to focus on implementing TOD at 1-2 stations in a bespoke manner.

What would be the nature of a TOD (the mix of categories of developments) appropriate for the station? What package of interventions and incentives would be sufficient to attract private developers to invest in the TOD influence zone? What governance arrangement can get all stakeholders on board at a high-enough level to ensure effective coordination?

Deploying public finance to derisk private capital in innovation and infrastructure

noreply@blogger.com (Urbanomics) — Wed, 03 Jun 2026 02:16:53 +0000

As the heading suggests, this post points to a few thoughts on the challenge of deploying public funds to derisk and crowd-in private capital into those areas of innovation and infrastructure that are not attractive enough for commercial capital.

I have blogged about public funding of innovation here and here, and this working paper is about public funding of infrastructure projects. The additionality with public finance arises from its risk-tolerant, patient, and concessional nature.

An urban water supply or sewerage, or an industrial bulk water supply, or an electricity distribution, or a solid waste management, or a streetlight energy saving project, or a mass transit project, will not attract commercial capital on its own. Similarly, a fledgling startup making transceivers, or display and camera modules, or high precision resistors/inductors/capacitors, or compressors, or brushless DC motors, or anode materials, or aluminium extrusions, or designing a narrow band IoT chip or some other mid-value chip, will generally struggle to attract risk capital.

But they can be derisked by blending with a layer of public finance. The challenge is how to do this derisking effectively. Specifically, the challenge is how public funds can be channelled to derisk these projects or sectors.

This is less of a problem with grant funding involving smaller amounts, which is simple enough to be done through public entities. In infrastructure, such grants come in the form of viability gap financing (VGF), and in innovation, they are given to those in TRL 1-5/6 stages. The problem lies in the deployment of risk capital (debt and, especially, equity and structured instruments) by public entities.

Given its administrative inflexibility and constraints, direct deployment of funds by the government itself is not only inefficient but also creates incentive distortions.

In the circumstances, the commonly suggested option is arms-length financing through Development Finance Institutions (DFIs). But this approach is seriously hampered by unreasonable expectations (about returns or at the least capital preservation) arising from a deficient understanding and acknowledgement that de-risking, by its very nature, entails a strong likelihood of losing money. It would involve investing in projects that would not attract commercial investors, by insuring for the additional risk borne by them.

Further, even with an arm's-length institutional structure, a fully government-owned entity is subject to constraints that prevent efficient deployment of funds.

The response to this problem has been to build institutional structures by partnering with private investors, even having majority private investors. This, it has been argued, will free them from the fetters and requirements faced by public entities.

However, India’s disappointing experience with infrastructure DFIs starting from IDFC, IIFCL, and NIIF, as documented in detail here, raises questions about this response. In all these cases, instead of complementing private capital, the DFI has ended up competing with private investors in their choice of investments. Instead of funding those risky sectors, the DFIs chase the derisked sectors like power generation, renewables, transmission, highways, ports, and airports.

In this backdrop, a commonly cited option, especially in the context of innovation financing, is to transfer public funds to commercial investment vehicles (or the Fund of Funds, FoF, strategy) and let the latter manage those investments. This looks great in theory, insofar as it aligns incentives and brings in private sector efficiencies.

But it has one problem. The private investors will be primed to invest, at best, in those marginally risky projects rather than in genuinely risky projects (or sectors) that sorely need public finance to derisk them. So, instead of deriskingprojects or sectors, public funding will do returns amplification for private capital.

Here, a big problem, a market friction, is the absence of a pipeline of such risky projects and sectors that investors can draw from. Their search costs are a significant enough deterrent for investors. In contrast, commercial investors have access to a widely known pipeline of investible projects or innovations.

It is also the case that the envelope of such risk capital available to fund infrastructure and innovation is much smaller than the envelope of investible projects. Therefore, there is little incentive to go beyond the confines of the mainstream and search out and fund the riskier projects.

In the circumstances, I can think of three options for the deployment of public funds such that we are able to realise its additionality, and not compete with and crowd-out private capital or end up being leveraged primarily for returns amplification.

1. Invest in FoFs, but with sharply defined funding mandates, almost prescribing the specific nature of projects to invest in, at least a part of their portfolio. However, this can be unsettling for the commercial investors and may turn away the GPs who sponsor the fund from accessing public funds.

2. The DFI could announce its offering as a set of financing instruments that meet the derisking objective. They could include credit guarantees (in the form of first-loss buffers), longer tenor, lower interest rate or hurdle rate, lower liquidation preference and a lower charge on the waterfall, subordinate debt, and so on. The DFI should market these instruments and possible investment projects to commercial investors.

3. The DFI could co-invest with private investors. This would entail the public entity scouting the project or entrepreneur, doing due diligence on it/them, and then shopping it to commercial investors with an offer of an attractive enough derisking financing layer. This would also require an acknowledgement of the fact that the role of public finance is to derisk and not maximise returns. This is perhaps the most ideal approach, one which mature entities like NIIF in infrastructure finance ought to be mandated to do.

The second and third options require highly capable and incentive-aligned institutions. Given weak state capability, that’s a demanding requirement. It is for this reason that even in developed countries, risk capital funding in infrastructure and later-stage innovations is largely deployed through FoFs, notwithstanding its aforesaid failings.

But this reality should not be a reason to ignore the failings of the FoF strategy and step away from pursuing the second or third options.

Implementing TOD in India

noreply@blogger.com (Urbanomics) — Sun, 31 May 2026 23:30:00 +0000

I have blogged here outlining nine low-hanging fruits in urban planning, here on the use of land value capture instruments, and here on TDR trading platforms. This one examines why transit-oriented development (TOD) has not worked in India and what could be done.

TOD is about densifying a defined neighbourhood around a transit station to minimise commute times. It would encourage people to use the mass transit system to commute between their offices and homes. TOD is especially relevant in the context of railway stations.

Globally, many cities owe their expansion to TOD. London and Mumbai are good examples. I used Claude to extract the schematic maps conveying TOD developments in commuter railway stations around London and Mumbai over the 1991-2011 period.

This is the London map for a longer 1991-2021 period.

The Mumbai map draws from the Marron Institute’s Atlas of Urban Expansion, and the London map uses the ONS census visualisations.

Despite several efforts for nearly two decades, Indian cities have struggled with TOD. The only genuine integrated mixed-use station-centric development in India is Gurgaon’s DLF Cyber City + Cyber Hub + Rapid Metro (125 acres, ~15 million sq ft of office, 400,000 sq ft of F&B). The Gurgaon Rapid Metro is itself a private TOD example (built by IL&FS/DLF), covering 12.85 km and 11 stations. Both were built privately by DLF and IL&FS, predating the 2016 TOD policy by years, and the Rapid Metro became financially unviable and had to be taken over by HMRTC in 2019. The MGF malls at MG Road station, Sector 29 at HUDA City Centre, and the Golf Course Road density are all pre-existing development that happens to lie near a metro — co-location, not coordinated TOD.

A fundamental problem with TOD Policy formulation is the assumption that higher FAR, coupled with the benefits of living or working adjacent to a station, will by themselves translate into utilisation. In other words, it is assumed to be sufficient enough incentive for builders and home buyers to choose the TOD zone over its neighbourhood and elsewhere. Unfortunately, this is rarely the case.

Worse still, governments tend to view TOD as also a large revenue generation opportunity from the flush of property developments. The combination of registration fees (in case of purchases), layout development fees, purchaseable FAR rate, building permission fees, etc., adds prohibitive cost layers for developers and buyers. They prefer to develop and buy elsewhere. The normalised expectation of high revenues also prevents governments from considering providing concessions. A gridlock ensues.

So how do other countries promote TOD?

The Tokyo region offers double or even triple FAR in TOD zones, and allows FAR transfer from adjacent lower-value land parcels. Most importantly, the zones have no development fees, with local governments recovering value through land appreciation and property tax growth, not upfront levies. MTR in Hong Kong also offers much higher FAR and does not levy any development charge, and even gives the land to the developer at pre-rail land values. Singapore has URA White sites where land use is left flexible, apart from a much higher FAR. The purchasable FAR rates are waived or significantly reduced. Additional FAR and no parking minimums in the US allow developers to build significantly more in the zones, especially for affordable housing. Australia allows FAR several multiples higher, fast-tracks permissions, waives off infrastructure levies for affordable housing, and even has lower property tax assessment.

The common thread across all of these is that governments provide a package of benefits in the TOD zone. It always combines higher FAR (making more revenue possible per unit of land cost) with lower upfront charges (reducing the cost stack) and faster approvals (reducing holding cost and uncertainty). The Japanese and Hong Kong models go furthest by making the TOD zone not just more permissive but more profitable than any suburban alternative.

So what can cities in countries like India do?

Fundamentally, the objective of any TOD Policy should be to ensure that it makes building in the TOD zone more attractive than outside or elsewhere.

Apart from higher land values, the TOD zone suffers from several other disadvantages - less likely to have larger vacant plots, more likely to be congested, noisier, and so on - that make them less attractive compared to their surroundings or the suburban areas. Besides, the surroundings of stations are not perceived as desirable residential locations.

In the circumstances, there must be a significant incentive to crowd-in development into the TOD zone. Such incentives come from both urban planning instruments and fiscal concessions. Apart from additional FAR, the former could include a higher base FAR, a lower price for purchasable FAR, higher TDR loading, and a more generous TDR conversion rate. Fiscal concessions could include lower stamp duty, layout development fees, building permission fees, and property taxes. If there is a betterment levy or impact fees, the same may also be discounted for those desirable developments (like high-rise buildings). These benefits could be limited to developments initiated within a certain period.

This would also require a shift in the way governments view TOD. They should prioritise the development of the zone over the maximisation of revenues. In fact, foregoing current revenues to harvest future revenues should be the mantra. The foregone revenues would be offset within a few years by those from the economic activities triggered by the developments.

In other words, the strategy should be to first crowd-in a critical mass of developments that is sufficient to sustain future development. Till this happens, revenue realisation should be strictly subordinate. This is especially required in the large Indian cities, which have poor urban planning and a culture of sprawling suburbs.

The timing of TOD adoption is also important. The best time to initiate TOD is just before the project work starts, when land values are lower, and the market is being catalysed. The marginal response to incentives is likely to be much greater in the initial stages when property prices too have not gone over the roof.

It may be useful to experiment in a few bigger cities by picking one or two locations (those with sufficient developable land) and providing a package of incentives for a period of 3-5 years. At the risk of repeating, the package should be designed keeping in mind the need to make the TOD zone significantly more attractive than its surroundings.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 30 May 2026 15:26:14 +0000

1. This captures the big problems with Chinese exports to Europe.

In the early 1970s workers at Dongfeng, or “East Wind”, imported American trucks to inform their early attempts at making off-road vehicles destined for the People’s Liberation Army. Nearly 60 years later Stellantis, the European owner of the Jeep brand, is partnering with Dongfeng to produce a new battery-powered version of the iconic American light utility vehicle for consumers in China, the Middle East and south-east Asia... International carmakers, struggling for survival amid an expensive transition to electric vehicles, are turning to China’s technologically advanced and cost-efficient factories as manufacturing bases for their global businesses. Foreign companies already account for around two-fifths of China’s car exports to Europe, when joint ventures with local groups are included, according to the Rhodium Group, a US consultancy... Indeed, Volkswagen, BMW, Nissan, Hyundai and others are increasing exports from Chinese factories with spare capacity to markets other than Europe and the US.

2. A new approach to making clean hydrogen.

Most of the hydrogen the world uses today — mainly for fertilizer and refining — is produced using natural gas in a process that creates lots of emissions. In recent years, the United States and other countries have invested billions of dollars trying to make “green” hydrogen with wind and solar power, but it has proved difficult and expensive. Now a growing number of companies think a better answer could lie underground. Dozens of start-ups are trying to find large reservoirs of natural hydrogen thought to exist below the surface. Others, like Vema, are trying to stimulate the processes that generate that hydrogen, without any emissions. It’s a field often referred to as “geologic hydrogen.”...

Hydrogen is the most abundant element in the universe, and it gets made naturally in the Earth’s crust when certain iron-rich minerals react with water and rust. This process, known as serpentinization, often leaves behind rocks with a mottled green color. For a long time, many geologists believed that any natural hydrogen produced this way was unlikely to accumulate in large underground deposits because the tiny molecules would slip away through cracks in rocks. Lately, that conventional wisdom has been upended... By the 2020s, scientists were publishing papers estimating that natural hydrogen deposits underground could supply the world’s needs for hundreds of years. One promising location was North America’s Midcontinent Rift, an enormous formation of iron-rich basalt that stretches 1,200 miles from Kansas to Michigan... The Energy Department has estimated that geologic hydrogen could be produced for less than $1 per kilogram. That would be cheaper than hydrogen made from fossil fuels and one-sixth the current cost of making hydrogen from wind and solar power.

It has started attracting private capital.

Companies are racing to find the fuel. One of the best-funded start-ups, Koloma, has raised $400 million from investors including Amazon and United Airlines and has drilled exploratory wells in Iowa. HyTerra, an Australian firm, is searching for hydrogen and helium in Kansas and Nebraska. Not everyone thinks the best strategy is to search for natural deposits underground. A better idea, some say, is to create them. In Quebec, a startup called Vema Hydrogen plans to spend the rest of the year injecting water into its underground test wells to see if it can speed up the process of serpentinization that creates natural hydrogen underground... Vema has already raised $15 million and is working to raise more. There are ophiolites all over the Earth, including a ridge stretching from Costa Rica to Alaska, and the company is looking at sites in Oregon and California as well. Other start-ups, including one out of M.I.T. called GeoRedox, are developing their own approaches.

3. Semiconductor chips are one area where China lags badly.

Chinese companies will most likely make just 2 percent as many A.I. chips as foreign firms do this year, said Tim Fist, a director at the Institute for Progress, a think tank in Washington. The production gap between Chinese and foreign manufacturers is especially big for memory chips, which are essential for the large calculations done by A.I. Companies outside China will make 70 times as much memory storage capacity this year as Chinese chip makers will, Mr. Fist said...The inability to get essential tools from ASML has been a major chokehold for Chinese chip makers. Since U.S. officials led an effort to lobby the Dutch government to block shipments to China, no Chinese company has been able to buy ASML’s most advanced tools. Instead, Chinese chip makers have recruited engineers with experience using those machines at TSMC, the world’s top chip maker. And now, Chinese start-ups are trying to make their own chip manufacturing equipment... China’s A.I. companies are trying to get the computing power they need by strapping together numerous less powerful chips. Huawei has taken such an approach... The chips Huawei does produce are prone to defects and use more electricity than cutting-edge foreign ones.

4. This is one of the greatest messages from a student to a teacher, Albert Camus to his elementary school teacher Louis Germain after he won the Nobel Prize.

5. Japanification in demographics.

And this impact of smartphones is striking.

6. Soumaya Keynes has a good read on the history of export restrictions and their impact, and why trade wars will endure.

7. Southeast Asian economies struggle on the face of rising inflation from the War.

Their currencies have weakened.

The Philippines and Indonesia have already raised interest rates.

8. Good FT visual story on maritime chokepoints.

This is a good illustration of the extent of damage from the Strait of Hormuz closure.

In a sign of Bab el-Mandeb strait’s strategic importance, Djibouti – whose coastline runs along the waterway – is home to military bases of several major countries, including the US, Italy, France, Japan, and the sole People’s Liberation Army base outside China. The Bab el-Mandeb strait is among several trade chokepoints that, when blocked, require vessels to travel more than 8,000 miles. These also include the Strait of Gibraltar and the Suez and Panama Canals...

The knock-on effects of a blockage can be much more significant where there is no alternative route to fall back on, as with the Strait of Hormuz, the Øresund between Denmark and Sweden, and the Turkish straits, comprising the Dardanelles and the Bosphorus, which act as the gateway between the Black Sea and Mediterranean. With the Hormuz strait, says Jasper Verschuur, co-author of a study into the risks of the world’s 24 narrow straits, “there is no alternative for 80 per cent of the trade”.

This is India's exposure to various maritime routes

9. Sajjid Chinoy writes that India's economic problem is less of a current account and more a capital account problem, arising from the sharp decline in FDI and FPI inflows. In the circumstances, he argues that demand compression can be counterproductive by slowing growth. He suggests a combination of depreciation and augmentation measures for foreign capital inflows.

The objective must be to attract a large-enough quantum of near-term capital inflows across multiple avenues — even if it involves a subsidised swap — to change exporter, importer and investor behaviour, and prevent a destabilising overshooting of the Rupee.

I am not sure how this is at all possible precisely when capital is flowing the other direction.

10. For all talk of private participations and efficiencies, the long-distance railway networks in continental Europe is largely state-owned - Deutsche Bahn (Germany), Ferrovie dello Stato (Italy), Renfe (Spain), SNCF (France), and SBB (Switzerland). The Economist writes about how Italo, the private high-speed rail operator co-founded by Luca Cordero di Montezemolo, is trying to disrupt the German network.

11. Securitisation and deepening of financial intermediation in Europe.

The securitisation market in Europe remains moribund, comprising around 0.3 per cent of GDP compared with 4 per cent in the US.

12. SpaceX's IPO prospectus takes the widest liberties with US securities law.

13. K-shape in US economy.

And now in wage decline

14. Ukraine's drones are inflicting massive damage and casualties on Russia as the country forces its way into its most favourable situation since the war began.

Some intelligence reports indicate that a staggering 1.2mn Russian soldiers have been killed or wounded since February 2022, a casualty figure no major power has suffered in a single conflict since the second world war... Backed by some €90bn in EU loans, Kyiv is pouring resources into domestic arms production in a bid to reduce dependence on western weapons and the political constraints that often accompany them. It has moved at breakneck speed to scale up the manufacture of land, sea and air drones, artillery systems, electronic warfare equipment, and even ballistic and cruise missiles.

15. The consulting industry is threatened by AI.

Few industries are debating AI’s implications more intensely than consulting, whose core work of research, summarising data and producing neatly designed PowerPoint presentations is highly automatable. Richard Susskind, co-author of The Future of the Professions, says consultants are more vulnerable than other mainstream professions in part because the work of junior staff “can now be taken on, with mild supervision, by increasingly capable AI systems”. The sector now has two new competitors, he adds: “the AI-empowered client and disruptive start-ups. Both challenge the conventional model.”... AI also threatens one of professional services’ foundational economic models: billing by time. When a bot can review thousands of contracts in minutes and draft complex documents in seconds, the relationship between hours worked and value delivered begins to break down. Increasingly, clients are demanding pricing linked to outcomes rather than labour inputs.

16.

The missing link in India's FAR market - a trading platform

noreply@blogger.com (Urbanomics) — Tue, 26 May 2026 02:30:11 +0000

I blogged here on nine low-hanging fruits in urban planning in India, and here on the actual use of land value capture instruments in India. This post will discuss the Floor Area Ratio (FAR) and Transferable Development Rights (TDR) transactions in Indian cities.

Fundamentally, FAR, beyond a certain limit that comes with property rights, can be purchased at a defined rate (up to the master plan limit). Further, when land is given up for a public purpose, instead of receiving financial compensation, the landowners can get additional FAR that can be traded (as TDR) and availed in certain predefined locations. Finally, the TDRs can be transacted through an institutionalised platform.

The table below shows the status of FAR and TDR transactions across Indian states.

Clearly, while all states have the requisite policy and legal frameworks in place, the actual implementation (in terms of FAR sold and revenues realised, and TDRs issued and transacted) has been limited, except in Maharashtra, and to some extent in Hyderabad.

Mumbai is the most complete illustration of FAR and TDR. It allows for purchaseable FAR (or premium FSI) of 0.5 to 0.84 over the base FSI, depending on the road width abutting the plot (nothing below a road width of 9 m). It is sold at 35-50% of the Ready Reckoner Rate (RRR), varying for residential, industrial, and commercial uses. The revenues are shared between the Brihanmumbai Municipal Corporation (BMC), the state government, and the State Road Development Corporation (MSRDC). BMC itself has so far earned over Rs 14,000 Cr from premium FSI sales.

TDRs are issued (in the form of a Development Rights Certificate, DRC) by BMC for defined instances where land is surrendered for public purposes at the rate of 2.5X for the island city and 2X for suburbs against the surrendered land. It can be used across the municipal corporation limits (with some specific exclusions) in the range of 0.17 to 0.83, depending on the road width (cannot be used below 9 m roads). The TDR generated is indexed at the RRR, and an equivalent floor space is allotted at the receiving plot. Since April 2026, Mumbai has moved into a fully electronic trading platform for TDRs, and manual record keeping has been dispensed with.

An electronic trading platform is essential for unlocking the full potential of TDRs. It is particularly important since it significantly enhances TDR liquidity, enables efficient market clearing, brings transparency and certitude for landowners, and also prevents fraud and abuse. Mumbai is the only place with a blockchain-secured, workflow-automated e-trading platform that allows for buyer-seller bidding and matching.

A major reason for TDR trading failing to operate across states is the manual and opaque nature of the records and the problems with trading them. It forces cities to impose restrictions like limiting TDR to the same locality or zone, thereby sharply diminishing liquidity and deterring land owners from accepting TDRs. Until a state moves to a properly dematerialised exchange, secondary-market price discovery stays opaque, and TDR rates trade at significant discounts to face value. This is the main reason why so many landowners say, “We want money, not land.”

An illustration of its value comes from the experience of Hyderabad’s TDR Bank, which is currently only a depositary and coordination platform, though integrated with its building approval system. In the absence of a trading platform and the associated transparency and liquidity (and also the misguided unlimited FAR provision), in order to trigger TDR transactions, the state government was forced to come up with a government order (GO Ms No 95, March 2026) to mandate TDR utilisation for high-rise buildings.

All told, even highly urbanised states like Tamil Nadu, Gujarat, AP, Telangana, Karnataka, and Haryana have struggled to realise the potential of TDRs due to thin secondary markets that deter efficient market matching and price discovery.

An electronic TDR trading platform can significantly address these constraints and simplify TDR issuance by bringing complete transparency and allowing its trading across the city, thereby increasing liquidity and raising its attractiveness and credibility for land owners (both sellers/owners and buyers).

How do other countries and cities undertake TDR trading?

The table below captures how TDR trading happens across a few cities/countries.

Brazil's CEPAC is the gold standard for an exchange. It is the only system globally where development rights are explicitly structured as securities. As an aside, this also makes Mumbai’s success especially remarkable. CEPACs are auctioned by the Federal Bank of Brazil and can only be used in designated urban operation areas; they must be authorised by the CVM (the Brazilian equivalent of the U.S. SEC) to be traded on the B3 stock market, and have raised over USD 2.7 billion across 15 years from Faria Lima and Água Espraiada Urban Operations, with funds reinvested in transit, roads and parks. In Rio's Porto Maravilha, all CEPACs were wholesale-auctioned to Caixa Económica Federal, which financed all infrastructure improvement and 20 years of service costs without further public outlay.

In this context, I have a co-authored working paper here, where we propose the establishment of a similar exchange for trading FARs with mechanisms similar to those in Mumbai.

So what should be the design for a trading platform for TDRs?

The wide diversity across states and cities, coupled with the weak state capabilities, necessitates a qualified approach in designing TDR trading platforms for Indian cities. Besides, the TDR is a local commodity, whose trading is inherently local. A three-level design may therefore be prudent.

At the first level, as discussed here, there’s a need for a standardised national protocol layer, authorised by SEBI and MoHUA, and operated through a SEBI-approved entity. This layer could do four things: (a) define a Development Rights Certificate (DRC) standard with a unique national ID and QR code; (b) provide a centralised dematerialised depository with its eKYC, escrow, etc.; (c) set eligibility, disclosure, and dispute-resolution norms; and (d) supervise a national audit trail. This layer would enhance the credibility and efficiency of the platform.

Each municipal authority (BMC, GHMC, BBMP, CMDA, APCRDA, GMDA, DDA) could operate a city-bounded marketplace on the common rails, and trading within a master-planned area only. It could borrow from the Mumbai e-TDR, which has a single nodal bank (SBI) and one blockchain-secured ledger. This would be supported with a municipal TDR bank inside each metro exchange. It could be designed as a public buyer-and-lender of last resort that holds inventory, sets a price floor, and even guarantees loans collateralised by DRCs. This solves the landowner objection of “we want money, not land” by giving every certificate-holder a guaranteed cash exit at a reserve price.

This three-layer structure also matches India’s existing regulatory plumbing in trading platforms. Mutual funds, government securities, and electricity markets are organised with a national legal and depository spine, with state-specific trading layers on top.

The smaller cities in each state could have a pooled market, administered by an appropriate state government entity. Or it may be useful to start with the larger cities, and expansion to be done based on the emerging feedback.

Applying land value capture to public investments

noreply@blogger.com (Urbanomics) — Sun, 24 May 2026 23:30:00 +0000

Donald Shoup, the famous urban planner, posed this great question that goes to the heart of infrastructure finance: “How do you finance a project that has a return much higher than its cost, but nobody wants to pay the upfront cost?”

The answer lies in land value capture (LVC), a topic that I have blogged on numerous occasions (see here and here). Specifically, two instruments, betterment levy on existing developed properties, and impact fee on those which are undergoing development, have the potential to raise significant revenues.

In theory, LVC spans the spectrum from appropriating directly through ownership of a part of the developed land (through the likes of land pooling) and indirectly through one-time and/or recurring charge (betterment levy or impact fees).

The former, the best example is Gujarat’s historical town planning schemes (TPS), is perhaps the most ideal approach, insofar as it cleanly appropriates a share of the land itself (typically 40-60% of the total land). The problem is that it is an engagement-intensive activity requiring both high credibility and state capability, both deficient across state and local governments. The best illustration of this is Andhra Pradesh’s struggling experiment to develop its capital at Amaravati by pooling land through a TPS.

TPS entails that the state takes over the lands, develops infrastructure, and leaves developed plots for the landowners, all this within some defined period of time. The combination of the political economy of real estate and weak state capability creates a time inconsistency problem that erodes the credibility associated with such schemes.

It is also for this reason that, despite numerous efforts over the last three decades, there are still very few instances of genuinely successful slum housing redevelopment schemes in Indian cities. And there are numerous examples of badly delayed and poorly executed such projects.

In the circumstances, the best bet with LVC in the context of countries like India is to collect a share of the incremental value as a charge on some urban planning instrument. It is the instrument with the fewest implementation difficulties.

Consider the numerous ongoing infrastructure projects across the country, ranging from national highways and expressways to metro railway projects, airports and ports, data centres and industrial clusters, and generally any large investment that leads to the development of an area. In all these cases, there’s an immediate spike in property prices, which is almost completely appropriated privately. If some share of the property valuation increase can be appropriated through the LVC instruments, then it can be securitised to fund the projects themselves, at least a significant share of the cost.

Illustrative examples will include the nodes in the Delhi-Mumbai industrial corridor, stations in the Mumbai-Ahmedabad high-speed rail and the Regional Rapid Transit System(RRTS), vicinity of Navi Mumbai Airport, the corridor abutting Bharatmala pariyojana, the vicinity of several new airports like Jewar and Bhogapuram, and so on. None of these has any specific betterment levy or impact fee charged on properties in their influence zones. These are all massive missed opportunities and large revenues foregone.

The formally reported claims of LVC revenues from all these are either in the form of unlocking value through sales or lease of government properties, or, in a few cases, they include the sale of lands acquired through TPS.

In every one of these projects, the intent of capturing land-value gain from the project announcement onwards exists somewhere on paper. In practice, though, the only meaningful capture has come from in-kind land pooling, lease revenue/premia from sale of publicly-owned land, and a few development/impact-fee tweaks. The second one (lease revenue or premiums from sales of public lands) is pure monetisation and should not be confused with LVC.

A true betterment levy or impact fee, in the form of a charge on existing private property in the influence zone, payable from the first transaction after project announcement, has effectively not been deployed at scale in any of these projects. Therefore, the aggregated, publicly disclosed revenue from "pure" LVC instruments (betterment levy + impact fees, excluding land sales/leases/concession fees/toll/UDF) is very small, even negligible, relative to project cost in every case here. The genuine “capture” that has occurred is overwhelmingly through public-land monetisation or in-kind retention of pooled land, where the former leaves the increment on third-party private property untouched.

Perhaps the only true significant examples in India are the impact fees in a corridor of 1 km abutting the Hyderabad Outer Ring Road (ORR) (though its success in terms of realisation is a matter of debate), and the 1% metro cess on property purchases in Mumbai to fund the metro railway project. A promising effort with the Navi Mumbai Airport Notified Area (NAINA) was that of the 50% betterment levy on the increased property value notified in 2013, which was immediately reduced to a notional 0.05% following political opposition.

The political economy is an important consideration. Every such big public investment immediately invites large speculative purchases, especially by politically and bureaucratically connected individuals. They are an important sink for the large volumes of black money that slosh around the local economy. Vast sums are invested in the name of third parties (benamis) who front the local connected and influential. Throwing sand in the wheels of this speculative activity, much less appropriating a part of the windfall increments as public revenues, will be resisted to the hilt by the aggrieved interests.

It is therefore important to be careful while designing the LVC instrument. It should depend on the nature of the infrastructure project. The objective should be to capture the increment in land value that the incumbent land owner enjoys from the public investment by charging the first transaction after the project announcement (which triggers the spike). Ideally, it should also try to avoid penalising the buyer, who has internalised and paid for the land increment. However, if the increment is ongoing, like with a corridor that undergoes continuous development, there’s a case for also capturing the increment flows over time through an appropriate instrument.

It is also important to differentiate between brownfield (betterment levy) and greenfield (impact fees) areas.

Accordingly, the most appropriate betterment levy on existing (developed or brownfield) property owners (who are not likely to transact) comes from a cess on their property taxes. In theory, if the property taxes are indexed to the market value, there would be no need for a separate cess. However, this is never the case since the property prices are generally linked to the guidance or ready reckoner rates fixed by the local authorities, which are always lower than the market value, and the wedge widens after local property booms.

For properties undergoing development, the ideal option would be to charge from those selling the property. However, this is complicated and can be captured, and that too only partially (given the aforesaid wedge between market value and guidance value) from capital gains taxes. Administering this is infeasible (imagine two kinds of capital gains tax for land, where there’s land value increment from public investment and the rest). In the circumstances, for such properties, the options are to levy a charge on either the registration fees, the change of land use fees, the layout development fees or the building permission fees. The assumption would be that the charge shapes market expectations and cascades across the property market and is internalised by everyone in the property transactions chain.

It may be useful to keep in mind some principles while designing the LVC instrument. In theory, it can be charged on the first transaction for a property in the influence zone after the project announcement as a significant windfall fee, or on all transactions for a period (that reflects the time for the project to realise its benefits). A simple and practical strategy would be to impose a one-time cess (on the property value) on the first transaction on the property. A flat levy/fee, while administratively appealing, does not address the diversity in the types of development projects and their widely varying land value impacts.

An analysis of the projects mentioned above points to a few problems, even where some LVC framework exists. For one, the first transaction after the project announcement rule is not legally embedded. Most Indian betterment-levy provisions trigger at project completion (e.g., Section 37 of the DD Act 1957; Section 66 of the Bombay/Gujarat TP Act 1954/1976). By then, the original landowner has typically already transacted, and the increment has been captured privately. This is not only economically inefficient but also invites public discontent and political opposition.

Policy makers create confusion and skirt accountability by conflating land monetisation with value capture, as is the case with CIDCO, YEIDA, or DSIRDA. Alternatively, user-side instruments like User Development Fees (UDF) in airports, and tolls and concession fees on highways, also get lumped as LVC.

As the aforesaid discussion shows, designing an efficient (in terms of capturing value from the incumbent land owner at the time of project announcement) and least distortionary (in terms of not adding unreasonably to property development costs) LVC instrument requires careful thought and design.

As an illustration, I asked Claude to estimate the likely LVC revenues from the development of the newly announced data centre projects in the Visakhapatnam area in Andhra Pradesh, and other investments in the state.

It estimates a realisation of Rs 700-1500 Cr annually from the application of LVC instruments to the Google AI hub catchment, Bhogapuram airport zone, defence clusters, and metro corridors. Even half of this would be greater than the combined property tax revenue collection of all the state’s 117 municipalities.

As an implementation strategy, it may be useful to align incentives and design a revenue-sharing mechanism among all public stakeholders, especially the local government. So, for example, instead of all the LVC revenues from a national highway or other central government project going to the project development entity, a part should be shared with the local government. This creates the required local incentives to enable effective adoption of any LVC scheme.

In terms of policy mandates, it is time to mandate that all large public investment projects be accompanied by the adoption of LVC instruments by the local governments. This would entail the following: (a) notification of a LVC influence zone around the project, (b) notification of the appropriate LVC instrument and the LVC rate, (c) enabling a statutory framework and a physical mechanism for its collection, and (d) a transparent and public accounting of the realisations.

As a first step, all states should be encouraged to adopt an LVC policy, borrowing from MoHUA’s LVC policy. It may be useful for the Government of India to build on this policy and formulate a model document on both betterment levy and impact fees that can be issued as administrative orders by the state governments. To incentivise this, the Ministry of Finance should consider making this a mandatory requirement to access the 50-year interest-free capex loans being given to state governments under the Special Assistance Scheme for Capital Investment (SASCI).

The model documents and guidance on each of the betterment levy and impact fee instrument options may be useful to ensure that LVC instruments are adopted in letter and spirit. The documents should contain the detailed implementation design of the instrument, and the same should be incorporated into the financial closure, state support, and all other agreements associated with the project, and should be institutionally coded into the system along with the project announcement. All project appraisals should have tightly defined and enforceable requirements on LVC.

Finally, the central government's support for any project should be made contingent on the realisation of the LVC proceeds in both letter and spirit. This should become an integral part of the financing culture of all large projects in India. This is one of the very few big bang low-hanging fruits in the mobilisation of public revenues, especially for fiscally strapped urban local bodies.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 23 May 2026 03:38:55 +0000

1. Samsung's spectacular turnaround, from being in the doldrums as late as in 2024.

This month its market value, which has soared by 400% in the past year, hit $1trn for the first time, propelled by furious spending on artificial-intelligence infrastructure. In the first quarter of 2026 its operating profit rose to 57trn won ($38bn), more than eight times as much as a year before. Analysts expect profits to keep rising at a blistering pace, thanks in particular to the seemingly insatiable demand for its advanced memory chips... Semiconductors accounted for 61% of sales and 94% of operating profits in the first quarter. It is one of just three firms capable of making at scale the memory chips needed for ai, alongside SK Hynix, a South Korean rival, and Micron, an American one. The number of memory chips Samsung sold in the first quarter was up by about 20% on the preceding three months, but the average selling price rose by 90%.

2. The Economist argues that the calm in global oil markets despite a supply shock of some 14 mb per day can be traced to the 4 mbpd of increased exports by the US and the 4.5 mbpd of reduction in Chinese imports, coupled with rationing across countries.

3. As the rise of AI threatens white-collar jobs and increases the returns to capital, The Economist proposes some measures to redistribute the gains.

If employment falls, income that once went to workers is likely to show up as high profits in AI firms, chipmakers, data centres or elsewhere in the supply chain. Clever tax reforms, such as levies on corporate profits that are above a normal return on capital, on land and on natural resources, could capture these rents. The case for inheritance taxes to prevent the entrenchment of a capital-owning elite looks even stronger than before. At the same time governments could help workers adjust. Public wage-insurance, which smooths out falls in income after job losses, can help workers find better opportunities (and so can eventually pay for itself). Denmark’s active labour-market policies, in which the state helps people find and train for new occupations, have been proved to cut spells in unemployment...

A last set of radical ideas, such as the partial nationalisation of ai firms. This week a South Korean presidential adviser floated a citizens’ “dividend” from AI businesses, sending the local stockmarket down by 5%, before backtracking. In America politicians murmur about giving citizens shares in AI companies via “Trump accounts”. In economic terms there is little difference between a well-designed tax system and a government stake in the private sector—and countries without AI giants will have to rely on taxes rather than seizing shares in foreign companies. But America may find that some public ownership is the best way to make the social upside from the technology transparent.

4. The changing face of Reliance Industries.

5. America's remarkable productivity growth miracle since the pandemic (it predates the AI boom).

Now with AI coming of age, the productivity spurt is likely to continue.

6. Egypt may well be the leader in land monetisation to promote economic growth in any substantial form. (HT: Adam Tooze)

Since 2015, Egypt has increasingly contributed public land as equity, while foreign investors provide capital, development expertise, and project execution. Once a project is completed, revenues are shared according to pre-agreed division... in 2023, Egypt appointed the bank’s International Finance Corporation as its advisor for the asset monetization program, leveraging its experience supporting emerging markets. While land monetization has been tried elsewhere, Egypt’s projects are among the largest... For the Ras el-Hekma development on the country’s North Coast, Egypt contributed approximately 40,600 acres of state-owned land along the Mediterranean. The UAE (via its ADQ sovereign wealth fund) committed roughly $35 billion, the largest foreign direct investment in Egyptian history. Egypt received immediate foreign currency inflowsfor the land, a 35 percent stake in the project, and long-term profit participation... A similar project, also on the North Coast, is Alam el-Aroum/Samla near Marsa Matrouh. The Qatar Investment Authority-linked Qatari Diar is investing almost $30 billion, which includes a $3.5 billion upfront land payment for some 20 million square meters and $26 billion in development investments. A revenue share for Egypt (15 percent after cost recovery) is part of the deal.

Another arrangement is in place for Egypt’s New Administrative Capital (NAC). About thirty miles east of Cairo, the NAC is designed as the government seat and a commercial hub; reports estimate total development costs of up to $58 billion, including infrastructure and governmental, commercial, and residential districts. Foreign direct investment plays a role in specific sub‑components like the Central Business District (CBD) and future free-trade‑zone ventures. Chinese banks led by the Industrial and Commercial Bank of China provided 85 percent of funding for twenty towers in the CBD. The China State Construction Engineering Corporation developed the CBD; Gulf investors (such as the United Arab Emirates’ DP World) developed commercial parcels. The state monetized land incrementally for the NAC, and parcel sales financed development, without increasing Egypt’s debt.

All this appears very impressive. While the article paints a picture of success, it would be interesting to peel layers and scrutinise this.

7. This is anecdotal, but tells a lot about why India lags in manufacturing.

8. Spain is undertaking an ambitious experiment in immigration.

Since 2022, Spain’s foreign-born population has surged by an annual average of 665,000, the equivalent of adding a city the size of Málaga each year. Last year the country accounted for roughly one-third of the total increase in the EU’s immigrant population, according to the Rockwool Foundation, a Berlin think-tank. Supporters say the influx has given Spain’s ageing society a much-needed burst of economic vigour. Critics call it a poorly planned strategy that is straining the country’s infrastructure and creating new social tensions... In less than a quarter of a century, Spain’s foreign-born population has gone from one in 20 residents to almost one in five, a higher proportion than even the US... Last month the Spanish government’s most contentious immigration move to date took effect — a sweeping amnesty giving at least half a million people the chance to gain residency and work permits and move out of the shadow economy... applicants must prove he was in Spain before January 1 this year and has been there for five consecutive months.

The country is already experiencing an acute housing shortage, has among the highest youth unemployment rates at over 10%, and the anti-immigrant Vox party is running third. The final outcome on the rapid rise in immigration is yet to be known.

9. Ruchir Sharma points to an area where China trails badly behind the US, the negligible role of the renminbi as an international currency.

With a 17 per cent share of global GDP, but only 2 per cent of central bank reserves, China is trailing 30 to 40 years behind previous superpowers at a similar stage of their ascents... Britain at its peak accounted for 40 per cent of trade, but 60 per cent of trade payments were in sterling. China by contrast has a leading 15 per cent share of global trade, but only 2 per cent of trade bills are invoiced in renminbi...

China will remain an incomplete superpower until it can match this financial firepower. For decades, it has kept its financial system more tightly sealed than any other major nation. It now ranks in the bottom fifth of nations by international investment position, which captures the level of foreign ownership in the domestic market. Foreigners own less than 5 per cent of the stocks and bonds in China, one-fifth the level in the US. Its home market is something of a local prison. Beijing has generated economic growth with heavy infusions of government money, corralled at home by capital controls. Its money supply has multiplied sixfold since 1980 to 230 per cent of GDP, among the highest in the world. This liquidity sloshes around inside the walled economy, much of it in the domestic debt market, battered lately by a property bust. Beijing is wary of easing controls, lest it unleash capital flight.

10. The US has been the biggest oil export beneficiary of the Iran war.

11. The booming market for whey protein.

Prices for whey protein isolate have soared fivefold to €28,000 a tonne since 2023, outstripping cheese and butter prices by more than four times as producers struggle to keep pace with a booming protein market... Thirty years ago whey was primarily used in animal feed or spread on farmland as a fertiliser. Whey is the liquid separated from curds during the cheesemaking process. Now, dairy groups upgrade the liquid by filtration to produce whey protein isolate, a valuable ingredient for use in sports supplements as well as groceries such as yoghurt, bread and fizzy drinks, as weight-loss drugs and the protein megatrend propel demand. “This is reshaping the economics of dairy,” said Jose Saiz, analyst at Expana, a commodity market information service. “It used to be a product with no value . . . now cheese could become the byproduct of whey production.”

12. The UK's experience of small altnets driving broadband expansion and lowering prices has come with excesses.

Over the past decade, more than £31bn has been raised to roll out full fibre broadband across the UK, with private equity giants, including Macquarie and KKR, backing upstart challengers — or “altnets” — in the sector. Yet despite the initial investor optimism that the “altnets” could snatch customers away from industry leaders, the firms have been dogged by high build costs, lower-than-expected customer uptake and a sharp response from the country’s largest provider — BT’s Openreach — to deploy its own fibre infrastructure. The destruction of shareholder value has been brutal. At the last count, the “altnets” posted losses of more than £1.5bn in 2024, according to Enders Analysis. After accumulating some £9bn of net debt as of 2025, some companies have already been placed into administration, while others have fallen into the hands of lenders...

Prior to the “altnet boom”, which accelerated from 2021 onwards, only 24 per cent of Britons had access to full fibre internet, according to Ofcom. Fast forward five years, that number is now more than 78 per cent, with altnets serving almost 20mn homes with full fibre, according to Assembly Research. By next year, Ofcom estimates 95 per cent of UK homes will have full fibre, putting it in line with Europe’s leaders, including Spain and Luxembourg, where regulators encouraged full fibre rollout earlier than the UK. The “altnets” have also forced BT’s telecoms infrastructure provider Openreach and Virgin Media O2 to expand their own fibre networks, giving consumers a choice between providers who are now forced to think faster and harder about how to up their game. This added competition has meant prices have fallen, with the average monthly real-terms list price of UK full fibre broadband falling from £62.38 in September 2021 to £43.46 in September 2025, a drop of 30 per cent... altnets are taking close to 1mn [customer] lines from BT annually.

13. Alan Beattie argues that EU has initiatied several trade measures against China, though their implementation has been weak, primarily due to internal opposition within the bloc.

One of the recent instruments with a bit more bite is the Foreign Subsidies Regulation (FSR), launched in 2023 and designed to level the playing field against state-backed Chinese companies bidding for contracts or producing and selling in the EU. It’s notable that it gives a lot of investigatory and decision-making powers to the Commission, specifically to the internal market and competition directorates, which are used to having autonomous powers. By starting investigations into their operations, the FSR has managed to get some Chinese companies to pull out of public procurement bids. But when the EU tries to use its internal market powers to investigate supposedly subsidised Chinese businesses trading in the single market, it becomes clear just how strongly Beijing is willing to resist. Last week, in an investigation dating to 2024 into the Chinese cargo scanner company Nuctech, Beijing cited new supply chain security laws to forbid Chinese companies to comply with requests for information, saying Brussels’ extraterritorial reach was illegitimate.

14. Arvind Subramanian and Devesh Kapur on the contrasting tales of India and China in monetisation of lands by urban local bodies.

China’s land revenues increased from less than 1 per cent of GDP to more than 10 per cent at its peak. In contrast, India’s revenues have stagnated at about 1 per cent of GDP through the entire growth phase. Put differently, the Chinese government’s collections from land revenue for every urban resident that was available for spending were about 15 times more than India’s in 1999; at its peak in 2020, this multiple increased to 225.

15. As AI capex surges, there are growing doubts about whether it will generate the returns required to justify it.

For each of these hyperscalers, I collected the consensus estimates of analysts for the capital expenditures and revenues between 2025 and 2030. In these five years, capital investments are expected to rise by 20 per cent a year, a growth rate never seen before in this industry. Meanwhile, revenues are expected to grow 15 per cent annually. If we make the heroic assumption that there are no costs, then the additional revenue is the profit these companies are expected to make from their additional investments in AI data centres. Yet, even under these extremely optimistic assumptions, I calculate the implied return on investment is highly negative for all of them except Amazon. These numbers show that if the hyperscalers continue on the current trajectory, the AI boom will become a story of one of the largest destructions of shareholder value in history... If the hyperscalers want to generate, say, a 10 per cent return on investment, they would have to find an additional $2tn to $5tn in revenue a year. A tall order for a group of companies that currently generates revenues of just $1.5tn per year.

See this comparison with the technology, media and telecom (TMT) bubbles of the late nineties.

In 2025, US businesses invested almost $1.5tn in IT equipment and software. At the peak of the TMT bubble, it was $466bn or $829bn when adjusted for inflation. Indeed, the US economy is growing solely because of the tech boom. I calculate that over the past four quarters, 93 per cent of US GDP growth was explained by tech investments. Even at the peak of the TMT bubble, it barely reached 60 per cent.

16. Tim Harford points to evidence that retailers jack up prices in response to shocks much faster than they bring prices down, and that retailers make their money not so much during the upcycle than in the downward phase.

Johannes Brinkmann and Nikhil Datta of the University of Warwick recently published an analysis of the impact on petrol and diesel prices of the oil price shock in 2022, following Russia’s onslaught in Ukraine. They found that in the UK, retailer margins compressed: the wholesale price of diesel rose by 39 pence per litre, while retail prices only rose 16 pence. This is the opposite behaviour to that predicted by the greedflation hypothesis. A natural explanation of this price compression is that retailers feel under more intense scrutiny when prices are rising. Brinkmann and Datta show that searches on the petrolprices.com website increased dramatically when prices did — and that areas where such searches were more common were also areas where the price compression was more intense.

Brinkmann and Datta’s analysis is merely the latest in a long tradition of research describing “rocket and feather” pricing at the pump — capturing the idea that pump prices neither faithfully track the ups and downs of the crude oil market, nor exaggerate them — instead, they shoot up like a rocket but drift down again like a feather. What is more, the quick surge upward reaches prices less lofty than one would expect; it is during the slow descent that retailers make their money. Fifteen years ago, Matthew S Lewis and Howard Marvel noted that customers spent more effort searching when prices were rising, even though there was little benefit to that search, since most retailers were charging similar prices. When pump prices were falling, there was more variability from forecourt to forecourt and a higher return to shopping around, but most customers did not bother, feeling content that prices were moving in the right direction.

17. Preference shares are a complicated instrument.

The “preference” investors receive — usually a slightly higher dividend — comes at the expense of voting rights. Preference shares therefore arguably resemble the worst of both worlds of debt and equity. Like bonds, they do not offer any influence over the company’s decision-making. Like ordinary shares, they do not come with a contractual claim to annual payouts as dividends are subject to management discretion. Such non-voting shares have been a prominent feature in corporate Germany. Four of the 40 blue-chips in the Dax have used them to establish substantive two-tier share structures for years: Volkswagen, Porsche and BMW, the three auto giants, and glue and detergent maker Henkel. All are dominated by controlling families who hold a tight grip over the firm.

Two further Dax companies — Merck and Fresenius — are listed as “partnerships limited by shares”, a German legal structure known by its acronym KGaA, that makes some shareholders more equal than others through other means. Deutsche Bank listed its asset management arm DWS as a KGaA in 2018, too, warning at the time that this structure could dent its valuation. Yet the club of German blue-chips with differential voting rights will soon become smaller after BMW shareholders voted to abolish preference shares last week at its annual meeting. The group’s 54.7mn preference shares, representing 10 per cent of BMW’s equity, will soon be swapped into ordinary shares with voting rights... In the US, dual-class share structures have become increasingly popular as fast-growing tech groups want to tap public markets while keeping outsized voting power for insiders. Elon Musk’s SpaceX even wants to grant its CEO 10 times as much voting power as external investors.

Sample this about SpaceX

Elon Musk's special class of shares currently gives him control of 85 per cent of the voting power at SpaceX.

Some thoughts on sustaining high growth rates in India

noreply@blogger.com (Urbanomics) — Thu, 21 May 2026 04:46:07 +0000

The flight of foreign portfolio investors since October 2024, coupled with the declining net FDI, has sparked a debate on what should be done to attract and retain foreign capital to India. This has assumed greater significance given the stagnation in domestic savings at about 30% of GDP.

It is an accounting reality that if the economy has to sustain high growth rates, it must have the capital to support the high investment rates required. Domestic savings must therefore be supplemented with foreign capital. But how much can foreign capital contribute?

A recent op-ed argued that to achieve the Vikasit Bharat 2047 goal, which necessitates a 9% annual growth over the next two decades, India must strive to attract foreign capital in the range of 15% of GDP.

This may be an opportunity to step back and assess the realistic envelope of foreign capital that India can target. As an analytical framing, this would entail examining both the demand and supply sides for the different types of foreign capital available, India’s track record in attracting them, and then making a judgment.

What are good comparators of countries having successfully attracted significant foreign capital? What is the envelope of foreign capital that is looking to invest in markets like India? What’s the level of capital that India can absorb without engendering too many distortions? Based on all these, what is the realistic foreign capital target for India?

India’s net FDI inflows from foreign investors as a % of GDP have shown a continuous decline since 2020.

Taken together, net FPI and FDI as a share of GDP have never exceeded 4%, have been declining since the pandemic, and have been negative over the last two years. (All graphics below generated using ClaudeAI).

The highest FDI share of GDP was 3.6% in 2008, which has been more of an outlier since it has struggled to cross 2% in recent years.

One important thing to keep in mind is that none of the bigger economies have managed to sustain FDI beyond 3% of GDP for long periods. India’s sustained 10-year average has been 1.5% of GDP. Even sustaining it at 2% will be a challenge, leave aside 3% and above.

It is to be noted that the northeast Asian economies did not grow by importing foreign capital. South Korea grew at 8-9% for three decades with sustained FDI of about 0.7% of GDP; Taiwan, with 0.8%; Japan, with 0.2%; China, in its 1990-2010 peak-growth period, averaged ~3%. All four built investment rates of 35-50% of GDP through extraordinarily high domestic savings. The important takeaway is that, for sustained growth in a large economy, foreign capital has historically been a marginal supplement, and high growth has been delivered by domestic savings. Another important factor, which we shall discuss in detail later, has been their high investment efficiency.

Bringing together all types of foreign capital, the graphic below shows a realistic envelope of about 4.2% of GDP. Even this may be bordering on the optimistic, given India’s track record and global economic headwinds.

In fact, reversing the recent trend of declining net FDI and FPI, and reaching even a 2% of GDP target for foreign capital would also be challenging. In the circumstances, a 6.2% to 6.5% target for GDP growth rate on a sustainable basis would be an achievement. Anything beyond that will require substantial breakthroughs.

As a final word on foreign capital, it can be said that if India is to sustain high growth rates, it will surely have to attract foreign capital. But that will remain a marginal contributor. Instead, the heavy lifting will have to come by way of domestic capital, and especially by improving the efficiency of its utilisation. India must significantly increase domestic savings and also improve its capital allocation efficiency.

Since increasing domestic savings is itself a measure of broadbased economic growth and therefore endogenous (a point I have raised on several blog posts), capital allocation efficiency for any given investment rate becomes an important lever for sustaining high economic growth. The Incremental Capital Output Ratio (ICOR) measures the investment required to generate an additional unit of output, with a lower number indicating higher capital allocation efficiency.

India's ICOR has been in the 4 to 5 range since the late nineties, and has never gone below 4 on a sustained five-year period. It briefly fell below 4 during the high-growth phase of mid-2000s. Further, in recent years, it has risen to 5-5.3.

How does this compare with the East Asian economies in their high growth phases?

Each of these countries went through three to four distinct ICOR phases as it transitioned from labour-intensive take-off, through capital deepening, to maturity (or crisis). The lowest ICORs occurred during the take-off phase in Taiwan, Korea and Japan — and during China's pre-2007 reform years. The northeast Asian growth miracles (Korea, Taiwan, and Japan in the 1960s-80s) sustained ICORs of 2.6 to 3.7 through their fastest decades. China repeated the feat in the 1980s and 1990s with ICORs of 3.6-4.1, but its capital efficiency has deteriorated sharply since 2007, with ICOR roughly tripling from 3 to nearly 8. Investment efficiency deteriorated in every country either in the run-up to the 1997 Asian Financial Crisis or, in China's case, after the 2008 global stimulus.

The scatter plot below maps every economy-period in the dataset onto a single chart. The the dashed green diagonals are ICOR isoclines - every point on a given diagonal has the same ICOR. The main finding is important - the Northeast Asian growth miracles operated in a narrow corridor of moderate investment rates and very high efficiency. India operates in a corridor of similar investment rates but lower efficiency. China sits at the extreme right - very high investment rates, with efficiency that was once strong but has badly deteriorated.

India has never matched the East Asian miracle benchmark of ~3, and capital efficiency may well be the single largest constraint on its high growth ambitions. To put this in perspective, at India’s current ICOR of ~5, achieving 8% growth requires a 40% investment rate, well above India’s current ~33%, whereas at ICOR 3.5, the same growth would need only 28%.

In other words, lowering the ICOR by one full point reduces the investment requirement for any growth target by 7-10 percentage points of GDP, a larger lever than raising domestic savings, and far larger than attracting foreign capital. Therefore, the path to faster Indian growth runs through pulling the cluster of Indian dots upward and leftward, i.e., raising growth without raising the investment rate. Unfortunately, it appears to be going in the opposite direction for now.

So what drives lower ICOR?

In simple terms, it is about growth, which comes with lower investment and higher efficiency of capital conversion.

For a start, on the inputs side, services and labour-intensive manufacturing have a lower ICOR than capital-intensive manufacturing and real estate-driven development. Also, higher human resource quality brings greater bang for buck from an incremental unit of investment. On the efficiency side, higher capacity utilisation, expeditious project completion, and factor market reforms - land acquisition, labour mobility, and credit allocation - contribute to lowering ICOR. Finally, institutional quality, involving predictable regulation, contract enforcement, and competitive product markets, all raise the marginal productivity of capital.

These are also the main areas of reform that have been discussed ad infinitum.

An important observation here is that we cannot overlook the reality that while it is essential to move up the value chain to high value manufacturing, it is virtually impossible to generate high growth from it without far higher investment rates. It underscores the criticality of labour intensive sectors like textiles and footwear in the sustainable economic growth of a country, especially a large one like India. This is also underscored by the importance of labour-intensive manufacturing in the high growth periods of the northeast Asian economies.

It is also important to note that China’s ICOR tripled in 15 years (from 3 to 9) as it shifted from labour-intensive manufacturing to capital-intensive infrastructure, property, and state-directed lending. Growth fell from 10% to 5% during the same period, exactly what the ICOR deterioration predicts.

The relevance of capital allocation efficiency also highlights the importance of human resource quality. This is even more so of if the services sector is to increase its role in sustaining high growth rates.

Update on the AI spending boom

noreply@blogger.com (Urbanomics) — Tue, 19 May 2026 03:47:27 +0000

I blogged here on the emerging AI economy. This is an update on the trends in AI spending.

As the hyperscalers - Alphabet, Amazon, Meta, and Microsoft - ramp up their data centre investments to $720 bn in 2026, their age of bountiful cash flows is over. Three of them are expected to have at least a quarter of negative cash flows, and Alphabet will only just scrape above.

At around 40% of their revenues this year, the cloud giants’ capital expenditures will surpass those of the oil industry during the shale boom in the 2010s and the telecoms industry during the dotcom bubble in the 1990s… Nowadays investors judge the success of these firms on the basis of concentrated revenue contracts stretching far into the future, rather than dispersed sales received today. Mostly these contracts involve selling computing capacity to model-makers like OpenAI and Anthropic, which are themselves incinerating vast piles of cash. Total future revenue agreements have risen to $2trn, from $730bn last year, at Amazon, Google, Microsoft and Oracle (Meta is a buyer, rather than a seller, of computing capacity)…
Since the start of last year the big five have raised $260bn from bond markets, a quarter of all such borrowing by listed American non-financial firms… Nearly a third of the haul from selling bonds this year is in currencies other than the dollar… Much larger obligations lurk off-balance-sheet. The biggest are $820bn of future payments to lease data centres yet to be built, up from $270bn a year ago. Commitments to spend money on other things, like packing their data centres with chips, have risen as fast. Amazon, Google, Meta and Oracle now disclose $680bn of such obligations. Other bills are tied to special-purpose vehicles: separate entities with their own balance-sheets. Last year one assembled to build Meta’s new data-centre in Louisiana issued the biggest single corporate bond in history.

This is an interesting summary of the dynamic driving this investment boom

The five firms (hyperscalers plus Oracle) have assumed the role of central planners, attempting to make the complex chain of returns on investment work across the AI economy: data centres are useless if businesses don’t find models worth paying for, which only happens if model-makers can raise enough capital to make them. In the process, the hyperscalers have sacrificed their own returns… Big tech has also liberally lent its creditworthiness across capital markets. Many firms that contract with the giants can take those contracts to the bank (literally) and raise more debt.

There are perhaps three overlapping risks - circular financing, vendor financing, and off-balance-sheet leverage - which are not a set of isolated deals but a single integrated capital structure spanning seven participant types.

Claude generated this comprehensive diagram that captures the linkages across these seven layers.

A chipmaker (and hyperscaler) writes an equity cheque to an AI lab (purple, up the left side), the lab converts it into a purchase commitment back to that same chipmaker and the cloud landlords (orange, down the right side), the commitment becomes backlog that supports the vendors' valuations and their bond issuance (blue), and financiers fund the whole build through SPVs and private credit (amber). Money does not exit the system; it rotates.

Three properties convert a set of bilateral deals into a self-reinforcing system. First, circularity inflates apparent demand - taking equity from vendors and using it to support further borrowing has made the boom dependent on convoluted financial engineering (echoing the 1990s, when telecom-equipment makers like Lucent and Nortel advanced money to customers to buy equipment, only to face write-offs when bankruptcies hit). Second, a single weak node is load-bearing - if OpenAI cannot meet commitments, Oracle’s revenue, CoreWeave’s backlog, Nvidia’s and AMD’s sales, and the SPV lenders’ collateral all reprice together. Third, the risk has been pushed to where it is least visible (into private credit and SPVs) while equity markets have already capitalised the optimistic case. If demand does not materialise, no amount of financing ingenuity can address it, and given the amounts involved it is certain to have very large financial-market and economy-wide impacts, cascading through the balance sheets of corporates, financiers and households.

The risks are concentrated and largely contingent — they crystallise only if AI demand disappoints, but if it does, they crystallise simultaneously across all seven categories because they share the same underlying bet. Everything rests on AI demand growing fast enough to justify >$1tn in lab commitments and ~$725bn in annual hyperscaler capex. If the revenue curve stays ahead of the cost curve, the circularity is just efficient capital allocation. If it doesn't, the same circularity becomes the risk transmission mechanism.

All this circularity is also generating its set of accounting distortions. Robin Wigglesworth points to the curious spike in the “other income” line of the hyperscaler's quarterly account statements, attributable to the changes in valuations of their investments in AI companies.

Alphabet, for example, booked $37.7bn of “other income” in just the first three months of the year, accounting for over half the company’s net income over the period. Amazon reported “other income” of nearly $16bn in the first quarter, up from $2.7bn in the same period last year. That was nearly half its overall net income for the three months. Microsoft reported “only” $942mn of other income in the first three months of the year, but this line item has now made $7.2bn over the past nine months… what constitutes “other income” in this case: the ebb and (mostly) flow in the valuations of their sizeable private investments in companies like OpenAI and Anthropic…
Not only have private investments and increasingly engorged funding rounds become a meaningful driver of the hyperscalers’ aggregate earnings, but the money the hyperscalers have pumped into the likes of Anthropic and OpenAI has allowed the AI companies to sign huge computing deals with Alphabet’s Google Cloud, Microsoft’s Azure and Amazon Web Services… OpenAI and Anthropic now make up about half of the entire cloud computing order books at Oracle, Alphabet, Amazon and Microsoft.

A vendor invests equity in its own customer; the customer uses that equity to pre-commit purchases from the vendor; the vendor books the purchase commitment as backlog; the backlog supports the vendor’s own valuation and borrowing.

Even though AI expenditures are building the plumbing for the next-generation economy, something is unsettling about the scale and pace of spending. More worrying is the concentration and circularity of the transactions. Is AI spending going to be the mother of all circular trade bubbles?