Weekend reading links

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?

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

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

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.