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

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

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

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

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

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

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.