Newspaper Summary 231026

 

eMudhra put the Net on red alert. What happened?

By Shouvik Das

On 22 October, homegrown internet services firm eMudhra Ltd said it’ll no longer provide trusted digital certificates—the mainstay of internet safety, which it compromised a day prior. So far, eMudhra was the sole Indian firm offering this infrastructure. Mint explains the hype behind the fiasco, and how it affects India.

What is eMudhra, the firm behind the controversy?

Founded in 2008 by Venkatraman Srinivasan, Bengaluru-based eMudhra Ltd provides digital identity verification, cybersecurity, paperless workflow solutions, and enterprise security. It went public on 1 June 2022 in a ₹412.79 crore IPO, with promoters currently holding a 54.4% stake. In FY26, it reported ₹701.6 crore in operating revenue and ₹110 crore in net profit, supported by six international subsidiaries. Its key domestic offerings include digital signatures for India’s income tax and provident fund filings, as well as multi-step corporate identity verification. The other part of the business, which certifies whether a website is secure, was seemingly compromised here.

1. What brought it to the forefront?

On 17 October, a cybersecurity researcher reported on Mozilla’s incident tracker that websites using eMudhra’s digital certificates may have been compromised. eMudhra is a Ministry of Electronics and IT (MeitY)-registered authority and the only Indian issuer globally trusted by major web browsers, including Chrome, Edge, Safari, and Firefox. Websites rely on digital certificates—using complex 115-character cryptographic keys—to encrypt data through Transport Layer Security (TLS) and prevent fraud. However, eMudhra’s certificates used simplistic keys that could allow attackers using a basic laptop to easily guess, spoof, and breach secure sites, reflecting a critical lapse in the trust infrastructure company’s security protocols.

2. How big is the impact?

Globally, users could see data security alerts or isolated service disruptions, though no breaches have been reported yet. MeitY has launched a formal probe, noting that the impact is isolated to eMudhra’s global web-security division, which falls outside the ministry’s regulatory audit scope. eMudhra said it has cleared required browser security audits and estimated the direct financial impact at just 1% of revenue (around ₹7 crore), while acknowledging unquantifiable reputational risks. The incident disclosure triggered a sharp sell-off, causing eMudhra’s stock to tumble 37%.

3. What does this mean for internet in India?

It remains unclear if eMudhra’s weak security keys were directly responsible for any of the breaches. However, on the day eMudhra acknowledged the flaw, State Bank of India disclosed that 3,897 users had personal data exposed online, including mobile numbers, addresses, and loan details. While no direct link between the two events has been established, weak encryption keys lower defences—allowing cyber attackers to intercept sensitive data undetected. For now, no direct breaches stemming from eMudhra’s certificate vulnerability have been confirmed.

4. How has eMudhra responded?

In a 21 October BSE filing, eMudhra attributed the weak security keys to a technical flaw and said it is investigating the cause. It clarified that it suffered no cyber breach, and maintains full control of its infrastructure. It said its domestic digital signature and eSign services remain unaffected. Later on Thursday, it announced that it’ll exit the global certification business due to disproportionate reputational damage and lack of profits, while committing support for clients

EU’s carbon tax is inviting disaster: Piyush Goyal

By Utpal Bhaskar

BERLIN: India’s commerce and industry minister Piyush Goyal on Thursday said the European Union’s (EU) carbon border adjustment mechanism (CBAM), a tax on carbon-intensive imported goods, is “inviting disaster”.

Speaking at the Berlin Global Dialogue 2026, Goyal said, “If you have put a CBAM on yourself, you put a 50% import duty to bring steel into your country, you are inviting disaster. Your cost of living will go up. Your people will be unhappy. Your industry will be out-priced.”

For its part, New Delhi has secured commitments from the EU, including a forward-looking most-favoured-nation assurance that any flexibilities granted to third countries under CBAM regulations will also be extended to India.

Speaking separately at the inauguration of the India Centre at the German Council on Foreign Relations (DGAP), Goyal said the India-European Union (EU) free trade agreement (FTA) may become operational within four to five months. He added that the proposed FTA with the 27-member EU is expected to be signed at the end of the calendar year.

“It will move very rapidly towards signature. And I want to thank all the countries of the European Union. Because this is one agreement and I urge you to reflect on the past: whether you have seen an agreement move with this alacrity,” Goyal said, adding, “the sense of urgency, the mutual commitment to make this happen was truly remarkable”.

The development is significant as India and the EU account for a quarter of the world’s gross domestic product and a third of its trade. India and the EU wrapped up talks for an FTA in January 2026, capping negotiations that were first launched in 2007, paused in 2013, and again revived in 2022. Under the FTA, over 99% of Indian exports by value and 97% of tariff lines will gain preferential entry into the EU, unlocking opportunities for small businesses and creating jobs for women, artisans, young people and professionals.

China’s DeepSeek raises $15 bn

By Bloomberg

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Chinese artificial intelligence (AI) pioneer DeepSeek has raised about 100 billion yuan ($15 billion) in its latest fundraising round, one of the country’s largest ever and a sign of the startup’s ambitions to compete globally with the likes of Anthropic PBC and OpenAI.

The Hangzhou-based firm boosted its financing target at least twice as it talked with investors, from 50 billion yuan to 80 billion yuan before reaching the final tally, according to people familiar with the matter. Battery pioneer Contemporary Amperex Technology Co. Ltd and WeChat operator Tencent Holdings Ltd were among the biggest investors in the round, said the people, asking not to be named as the details aren’t public.

DeepSeek’s funding sets the stage for one of the most highly anticipated Chinese market debuts in years. The AI upstart triggered a $1 trillion meltdown in global markets in 2025 when it showed the ability to compete with Silicon Valley’s leaders at a fraction of the cost of their models, but it’s never had the financial resources of the US frontier labs.

The latest fundraising shows that imbalance is beginning to moderate, although it still has far less financial firepower than Anthropic and OpenAI, which have each raised more than $100 billion. Like rival Moonshot, DeepSeek is preparing for an initial public offering that would be an important milestone for Chinese markets, Bloomberg News has reported.

DeepSeek has begun to spend more aggressively to build out AI data centres and to develop more open-source models for use in China and abroad.

Meta will split stock, create new class to fund AI spending

The company says the 2-for-1 split won’t affect Mark Zuckerberg’s voting control

By Meghan Bobrowsky

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Meta Platforms is getting creative to fund its massive investments in artificial intelligence.

The company reported record revenue for the third quarter and said it would create a new class of nonvoting stock to raise the capital it needs for developing and serving AI models. The new shares will be issued as a dividend to holders of common and supervoting shares, with an effective date of Jan. 19, 2027. The effective 2-for-1 stock split won’t affect Chief Executive Mark Zuckerberg’s voting control or the voting power of other shareholders, the company said.

In a filing, Meta said Zuckerberg would be able to immediately sell 20% of his new nonvoting shares, potentially helping him pay a new wealth tax on California’s upcoming ballot. The company said Zuckerberg faced a possible diminishment of his voting interest if the tax forced him to sell shares, and that he had discussed the possibility of relocating. Zuckerberg has committed to living in the same state as Meta’s headquarters for at least eight years barring unforeseen circumstances, the filing said.

The company said it brought in $64.6 billion in revenue, a 26% year-over-year increase, and net income of $9.8 billion. The revenue figure exceeded the expectations of analysts surveyed by FactSet. The net income total was considerably below analysts’ forecast of $17.2 billion, dragged down by a $9.9 billion legal charge related to the company’s settlement of social-media harm lawsuits.

In the current quarter, Meta expects revenue to be in the range of $73 billion to $76 billion, the high end of which is above analyst expectations. It also revised its range of expected capital expenditures for the year, raising the low end to $135 billion, from $130 billion. The high end of the range remained unchanged at $145 billion.

Meta finance chief Susan Li didn’t provide a capex estimate for 2027 but said company leaders “expect to continue investing aggressively” to support both its core business and AI bets.

Meta’s free cash flow went into negative territory for the first time since it became a public company, although analysts had expected a stronger downturn. The company reported negative free cash flow of $2.1 billion versus analyst projections of negative $9.4 billion.

Meta’s shares rose 4% in after-hours trading.

Investors have at times been hesitant to support the spending spree Zuckerberg has undertaken to transform Meta into an AI company, repeatedly pushing him to see near-term returns.

They responded warmly when Meta launched Muse, its new personal-assistant AI agent, in September. The app, which offers subscriptions at $20 and $100 a month, has been downloaded more than 10 million times, according to SensorTower, a market intelligence firm. Meta’s stock gained nearly 20% in the month after its debut.

Investors pointed to Meta’s potential ability to monetize the app, with one estimate from Truist Securities projecting Muse could add $28.5 billion of incremental revenue by 2030. On a call with investors and analysts, Zuckerberg said about 25% of people who have set up Muse use it “on any given day” and that shopping was the most popular use case.

But to become Meta’s next large business, Muse will have to clear a number of hurdles, beginning with widespread unpopularity of AI as expressed in polls. Muse has also been blocked by dozens of websites, including Amazon.com, limiting its utility in carrying out shopping and other tasks.

Still, Meta sees Muse as the centerpiece of its whole operation and has restructured the company around the division that houses it, called Meta Superintelligence Labs. It also announced a new business line focused on selling AI tools to enterprise customers.

The goal, Zuckerberg said, is to create “a large-scale Muse network” where billions of Muse agents work together to get more done for people and businesses.

Another weight on the company’s bottom line has been legal costs related to the many social media lawsuits it is facing. In August, Meta settled with 48 states for up to $18 billion and agreed to make a number of changes to the way its platforms operate accounts for teens.

On Wednesday, ahead of its earnings report, a judge in New Mexico ordered Meta to pay $25 billion after a jury found the social-media company misled users about its data-privacy practices. Meta didn’t immediately respond to a request for comment about the judgment.

UBS turns warrants to 4.8% stake in 360 One

By Agnidev Bhattacharya

agnidev.bhattacharya@livemint.com

MUMBAI:

Swiss investment bank UBS AG acquired a 4.8% stake in Karan Bhagat’s wealth manager 360 One Wam Ltd after converting 20.5 million warrants into equity shares.

When the warrants were initially allotted on 21 July 2025, UBS paid 25% of the exercise price. It paid the remaining 75% exercise price of ₹1,584 crore ($190 million) on Thursday, 360 One said in a statement. The latest tranche of equity shares was issued at ₹1,030 per share, bringing the total consideration for the warrant transaction to ₹2,112 crore.

Shares of 360 One opened at ₹1,030.10 on the National Stock Exchange on Thursday.

In April 2025, UBS agreed to transfer its onshore wealth management business in India to 360 One for a net consideration of ₹307 crore, while 360 One clients booked in Singapore would be served by UBS. The agreement also established a revenue-sharing model tied to client onboarding.

As part of the deal, UBS was to acquire warrants representing close to a 5% stake in the Indian wealth management company.

The warrant exercise “marks the latest milestone in the institutions' strategic collaboration,” 360 One said.

Mumbai-based 360 One offers investment and financial advice to over 9,000 families in the ultra-high-net-worth individual and high-net-worth individual segments. The company manages assets of more than ₹9 trillion.

HOW SOARING AI BILLS ARE VEXING WALL STREET EXECUTIVES

After one-too-many surprise bills, finding a way to control costs has become an imperative for many CFOs

By Bloomberg

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No one ever wants to give a technology team a blank check. Yet for many chief financial officers (CFOs), that’s exactly what they were asked to do in the early rush to embrace artificial intelligence (AI). Now, after one-too-many surprise bills, finding a way to control costs has become an imperative.

It’s a simmering issue in banks, where technology budgets are already under pressure from competing demands ranging from legacy information technology (IT) modernization to cybersecurity. Some of the greatest hopes for productivity improvements are pinned on agentic AI, which can tackle complex tasks autonomously, but are inherently difficult to budget for as the amount of computing power each task consumes can be unpredictable.

“I actually don’t think anybody in the organization has a good handle on it—if I am going to do XYZ, what is going to be my cost,” said Bhavi Mehta, a partner at Bain & Co. and its global lead for advanced analytics in financial services. “As AI moves toward more sophisticated reasoning and agentic workflows, the tokens required for a given task can vary significantly. That makes understanding cost alongside business value increasingly important as banks scale AI,” he added.

Only 12 of the 50 largest global banks tracked by AI benchmarking firm Evident have publicly attributed projected or realized returns to their adoption of AI, according to its latest report. Yet it’s a rare CEO who doesn’t talk up the potential benefits. Rising investment in the technology came up during third-quarter earnings calls at several US banks from Citigroup Inc. to Goldman Sachs Group Inc., but only a few executives had precise figures for their AI expenditures or the financial benefits they are generating.

So as the industry moves from the experimentation to the deployment stage, Bloomberg News spoke with more than a dozen engineers, executives and advisers about their experiences and strategies.

Match the model to the job

While tokenmaxxing—the practice of engineers using AI as much as possible—has become notorious—that’s not always the cause of token usage blowout. When Deutsche Bank AG’s monitoring flagged individual employees consuming unusually large volumes of tokens, many turned out to be doing useful work, said Christoph Rabenseifner, the bank’s chief strategy officer for technology, data and innovation. Instead of cutting them off, the bank steered that usage in a more productive direction. It also tested one task on a smaller, specially trained model instead of a large frontier model, cutting costs several times over, Rabenseifner added.

Banco Santander SA runs large frontier models only to check whether an application meets its quality bar, then shifts proven workloads to lighter, cheaper ones. The approach helped cut execution costs from about €5 ($5.61) to €0.03 per loan approval process, said Ignacio Bernal, the bank’s chief artificial intelligence officer.

“When you move AI from pilots to production across a global bank, inference cost stops being a rounding error and becomes a real operational cost line,” Bernal said, noting some processes run millions of times a year, driving up computing expenses significantly.

Canadian Imperial Bank of Commerce (CIBC) has found that its most powerful models, including Anthropic PBC’s Opus, can cost C$3,000 ($2,130) per employee a month, a bill that could add up quickly across thousands of workers. As a cost-control measure, the bank restricted access to a few hundred employees, while capping its latest in-built workspace CAI 2.0, still in testing, at 250 users who can each spend between C$4 and C$250 a month. “It’s too powerful to have 50,000 people just using it without control,” Jaime Tatis, CIBC chief data and artificial intelligence officer, said in a July interview.

Agentic AI is emerging as a primary driver of rising costs. Big banks announced 19 agentic AI use cases in the first quarter of 2026, up from eight the previous quarter, according to Evident. In the second quarter, the number halved – not because banks are cooling on agents, but because the firms are asking them to take on more complex work. Wells Fargo & Co., for instance, wants agentic AI to master 1,100 steps in the mortgage process before deploying it across workflows, the Evident report said. Unlike conventional AI tools, agentic systems autonomously execute complex, multistep tasks, which include analysing financial statements, tracking borrowers, verifying documents and detecting money laundering activity. This processing demands significant computational resources, translating into heavier token consumption, and in turn, drives up monthly costs.

At one Asian lender after some teams burned through their allocation of tokens in the first week, the bank decided to pool tokens and build an agent that trims prompts before they reach a model, according to a senior data executive who asked not to be identified talking about internal matters.

Consider Alternatives

The cost question has brought Chinese models into the conversation. A July JPMorgan Private Bank note said a GLM model from Beijing’s Z.AI performed on par with the best model available in March at more than 80% less than the priciest frontier model at the time.

Singapore’s biggest lenders are experimenting with a variety of models. United Overseas Bank Ltd’s neutral AI framework is open to Chinese providers, and DBS Group Holdings Ltd says it is not overly reliant on any one provider, whether it is US or Chinese ones.

Regulators have flagged some concerns. Canada’s banking watchdog recently asked staff to draft minimum standards for how lenders use frontier AI models, including on security, while the US Federal Reserve vice chair for supervision Michelle Bowman has warned banks to shore up their systems against AI-driven threats.

That makes less-vetted models, particularly foreign ones, harder to justify to boards. Risk chiefs also worry that AI agents, once deployed, can behave unpredictably inside a bank’s own systems, a concern that has gained traction in recent months.

National Australia Bank Ltd cited security and governance concerns when considering alternatives to US-built AI models, including Chinese ones, Bloomberg News reported in August.

Oversight Strategy

During the early rush to cloud services more than a decade ago, banks took at least five years to control costs that rose with usage and bring them under a dedicated budget line, according to J.R. Storment, executive director of the Tokenomics Foundation. What’s different now is that financial chiefs are reacting to AI cost spikes within 6 to 18 months of adoption, making it difficult to put a final price tag on premium AI models, he added.

Nitin Madan, formerly CFO at Barclays Plc’s India operations, remembers the pressure well. When he asked his colleagues on the technology side which AI models they planned to deploy and what they would cost, he was told such questions were “making everybody uncomfortable,” Madan said.

As new AI tools and upgrades hit the market in rapid succession, Madan said AI spending across the industry can’t be spread over several years the way most software costs are, so it hits profits right away. No one can say with confidence whether today’s models will still be in use three years from now, he added. At JPMorgan Chase & Co., some employees were “spending more on tokens than their salary” and yet the company wasn’t rationing AI usage at that time, Zachery Anderson, chief data and analytics officer for the bank’s payments division, told Semafor in June. JPMorgan has since set monthly spending limits of $2,000 on Anthropic’s Claude Code for certain engineers, Business Insider reported this month. The bank’s CFO Jeremy Barnum said on the lender’s earnings call this month that its annual AI expense was about $700 million, though a precise figure is hard to pin down because the technology is now spread across the entire bank.

At many other banks, leadership has also shifted from treating AI spending as virtually limitless to finding ways to contain it.

Alex, a senior machine-learning engineer at a large European bank, was asked to cap his team’s AI spending in February this year. The trigger was an €80,000 bill the team had piled up after building an advanced chatbot and deploying it across thousands of workflows. The sum, though hardly outrageous for a large bank, signalled to the finance chief that it might snowball into millions if left unchecked. For Alex, who asked not to be identified by his full name to avoid professional repercussions, the question quickly shifted from whether the chatbot worked to whether the bank could afford to run it at scale. To cut costs, he reworked the chatbot so it cached answers to frequently asked questions and resurfaced them whenever a user asked again. Simpler requests went to open-source models. Only token-intensive prompts, the ones requiring deeper searches, went to premium models from platforms like ChatGPT or Claude. That brought the AI bill down by roughly a third.

Some banks are tackling the forecasting problem earlier. French lender BNP Paribas SA approves every AI project after its running cost is calculated. The group’s internal platform sets a token price, multiplies it by expected volumes and weighs the total against the value the tool should deliver, said Hugues Even, the bank’s group chief data officer. BNP covers most of its computing needs with its own chips, bought as multi-year investments, and supplements them with cloud providers. BNP spreads oversight of AI across committees chaired by the heads of technology, risk, finance and human resources, plus a group AI committee led by the chief executive officer (CEO). “Each one is taking his share of the cake,” Even said. The finance chief controls the cost of deploying AI across the group, he added.

Accountability Focus

Many lenders are contending with the speed of change, as newer and pricier models arrive before the last integration is complete. And to help them measure and rein in the potential cost surges, a cottage industry is emerging.

Ramp, which helps companies track and control token spending, says one in three businesses using its tool found out where they could shift from costlier frontier models to cheaper ones without compromising quality.

Rogo Technologies Inc., an AI startup that works with bankers, says it pools demand across clients to negotiate discounts with AI labs and lets banks set token budgets down to individual users.

Tokenomics Foundation, whose members include JPMorgan, Bank of New York Mellon Corp., KPMG LLP, and Google Cloud, has been created to set shared industry standards for tracking AI costs, which are often opaque. It seeks to measure what companies get back in return.

Access has become a hiring issue, too. At the Asian lender, developer candidates ask in job interviews about their token budget alongside their salary. Engineers ask which models they will get and what limits apply, Madan the former CFO said.

David Hardoon, who led AI enablement at Standard Chartered Plc and is now head of advanced AI for Southeast Asia at Accenture Plc, argues every large organization needs a chief AI officer with real authority.

While several banks already have one, few have given the role enough power to impose discipline on forecasting and deployment without slowing innovation. A chief AI officer could own that question but only with the authority to act across the whole organization, Hardoon said.

For now, keeping a tab on the cost equation during the AI gold rush can cause internal tensions, especially when bank executives feel they cannot risk falling behind on adoption.

The thinking is: “If we’re not doing it, our competitors are doing it,” said Vitaly Gordon, CEO of Faros AI, a software firm that helps banks and tech companies tie AI spending to results.

This arms race mentality across the industry is translating into cash burn and causing a situation where he said “in most cases, it’s just a CFO getting a bill and screaming.”

The MPC minutes hint at a course correction

The stark change in the stance of members from the prior policy meeting suggests that the inflationary impact of FCNR deposits was underestimated. Such missteps can prove costly

OUR VIEW

The minutes of the Reserve Bank of India’s (RBI) Monetary Policy Committee (MPC) meeting held earlier this month were eagerly awaited for two reasons. One, for the first time in RBI governor Sanjay Malhotra’s term, the MPC voted for a hike in policy rates. Two, after a long time, the opinions of members differed on the policy stance.

While the reasons for the change in stance—from neutral to calibrated tightening—are not entirely clear, most MPC members seem to have realized that they misread the inflation outlook and the liquidity position at the last meeting in early August. Both the governor and the deputy governor were far more sanguine about the inflation trajectory in August. They seem to have underestimated the impact of the enormous liquidity boost because of the deposits received under the foreign currency non-resident (bank), or FCNR-B, scheme.

In August, the governor argued that there were “limited signs of generalisation of inflation with core inflation remaining modest.” Inflation, he said, was “expected to taper from its peak in Q3.” There is no need for a “monetary response to curtail demand as of now,” he noted. Two months down the line, his assessment seems to have changed completely. In the October meeting, he noted the “risks of demand side pressures in view of the growth in monetary and credit aggregates.”

The RBI deputy governor in charge of monetary policy also offered very different assessments. In the August meeting, Poonam Gupta argued that risks to inflation and growth are evenly poised. Growth could turn out better than expected while “inflation could be marginally lower,” she said. In October, however, she argued that monetary tightening would be required to contain rising inflationary pressures, and maintain the credibility of India’s inflation-targeting framework.

Both officials blamed external factors for the worsening of the inflation outlook. However, the geopolitical factors impacting commodity prices haven’t seen much change between August and October. The uncertainty on the oil front due to the closure of the Strait of Hormuz continued, as before, as did the likelihood of El Niño hitting our crop output adversely. The only big change involved the vast inflows of foreign funds (on account of FCNR-B deposits) pushing up domestic liquidity. This could have been, and should have been, foreseen. The October rate hike seems partly an attempt to correct course and anchor inflationary expectations at a time when both inflation and bond yields are rising globally.

The narrowing spread between Indian and global bond yields may have contributed to the change in stance although just one MPC member referred to it explicitly. “Maintaining a certain gap between India and G7 interest rates is important for attracting debt capital flows,” Saugata Bhattacharya, who is an external member, noted. He also warned that the “broad monetary policy environment” in India may have become “too accommodative.”

The MPC minutes underline that the prospects of a rate cut are quite dim at this stage. The majority in the committee seem to have realized that they need to reaffirm their commitment to fighting inflation, and the change in stance seems to signal that intent. Both domestic and global conditions warrant a tight monetary policy. Policymakers can’t afford slip-ups.

Weak corporate capex: India isn’t alone in this global phenomenon

Corporate capex has been weak in much of the world but it won’t yet rise for India despite hopes being pinned on a revival

THEIR VIEW

Nikhil Gupta

(Nikhil Gupta is an India economist, executive director at CLSA India, and author of ‘The Eight Per Cent Solution’)

In the previous two parts of this series, I established that:

1. India’s corporate profits have been stable at 9-10% of gross domestic product (GDP), though its composition has changed in favour of the listed sector.

2. Lower household savings, higher fiscal deficit and narrower current account deficit (CAD) have supported profits, despite lower corporate capex.

3. India’s unlisted sector is huge, accounting for about two-thirds of the aggregate corporate sector in terms of capex, debt and gross value added (GVA), though its financial position has worsened in the post-pandemic period, hurting aggregate corporate capex.

In this final part, my key conclusions are two-fold: first, weak corporate capex is not a unique phenomenon in India. Second, considering various cyclical and structural factors, we don’t expect corporate capex to pick-up anytime soon.

Note that India’s corporate capex has been stable at 14-15% of GDP in the past decade, lower than the 17-18% in the post-2008 period and its peak of 21% in 2007-08. That makes it the lowest level in almost two decades, but it is not low compared to most other major economies. While China has had the highest corporate capex at 26% of GDP in recent years, followed by Indonesia, South Korea and Japan (20%-plus), it is between 12-14% for others such as the UK, Germany, the Philippines, US and Spain. Importantly, India’s total investment-to-GDP ratio has averaged 33% in recent years. Not only is this better than about 30% in the pre-pandemic period, but also only second to China’s 40% among the world’s major economies.

These two facts suggest that the share of the corporate sector has fallen from above 50% a decade ago to about 45% in India’s total investments, while the share of the household sector (primarily consisting of the residential property market) has risen to 42-43%, with a 12-13% share of the government sector. Since corporate capex is the most efficient, its lower share does hit the overall efficiency of India’s total investments. However, the quantity of total investments remains healthy in India owing to strong household capex. Notably, the surge in China’s total investment-to-GDP ratio from 35% at the turn of the century to its peak of 46% in the early 2010s was largely led by the household sector, whose capex more than doubled to a peak of 15% of GDP.

The corporate capex boom remains one of the most widely held expectations for India, which is yet to materialize. However, the absence of a pick-up is not unique to India. Using data on other major economies (such as China, euro-zone, Japan, South Korea, UK and US), together accounting for 70% of global GDP, we create annual data on global profits and corporate capex. The results are staggering. Global corporate capex has increased to 17% of GDP in the 2020s decade (2020-24) from 16.1% in the 2010s decade and 14.4% in the 2000s, while global profits have increased every decade from 6.1% of GDP in the 1990s to 8% in the 2000s and about 9% in the post-pandemic period.

However, the conclusion changes dramatically if we exclude China from the global aggregates. Although the rise in global profits is starker now—from 6% of GDP in the 1990s to 9.6% in the 2020s decade, global corporate capex has been actually stagnant at about 14% of GDP during the past two decades or so. China, thus, is an exception, where profit is falling with rising corporate capex. Global corporate capex excluding China has been largely stagnant in the past two decades, despite much higher profits.

Similarly, India’s prolonged corporate debt deleveraging episode in the recent decade was neither unique nor exceptional. Corporate debt fell from its peak of 70% of GDP in 2011-12 in India to a low of 48% in 2023-24, after which it has picked up towards 50%. According to data on 43 countries from the Bank for International Settlements, there were 68 episodes of corporate deleveraging during the four decades up to 2007, of which only 11 were deep and prolonged cycles, lasting seven years or more. Since 2007, however, there have been at least 19 such deleveraging episodes, majority of which are still incomplete.

Further, while corporate credit growth has picked-up in recent quarters, other drivers such as global policy uncertainty, government capex and profit growth remain unfavourable. Therefore, we don’t expect any strong pick-up in India’s corporate capex over the next few years.

Can AI-led capex change that? Notwithstanding the news of hundreds of billions of dollars invested by US tech giants, its total investment-to-GDP ratio was unchanged at 21.5% in recent years. The US’s total corporate capex has also been stable at 13.7% of GDP. Moreover, US corporate debt was at a decade low of 48.7% of GDP in the second quarter of calendar year 2026. Not only the US, but the total investment-to-GDP ratio has been lower-to-stable in China, Singapore, South Korea and the UK. Taiwan and Japan are exceptions, with increases. If so, the case for AI-driven global capex recovery needs more scrutiny.

But what if corporate capex, and thus, the total investment-to-GDP ratio picks up? In that case, policymakers will have to shift their focus towards ensuring sustainable funding of higher investment. The Indian economy has seen three episodes of a rise in investment-to-GDP ratio since the 1980s. During all these, the increase in savings lagged that in investment, leading to a wider CAD. Worryingly, the deterioration in CAD has accelerated over the decades. Therefore, if the investment-to-GDP ratio starts rising further, low household savings will become a key concern, as CAD will widen and constrain investment growth.

What else can be done to push growth higher? The quality of investment (or efficiency) must improve. Most successful economies have achieved high real GDP growth for decades with a lower investment-to-GDP ratio but better efficiency. Between 2003-04 and 2025-26, India’s incremental capital-output ratio was 4.8 times, much worse than the average of about 3.5 times for a group of 10 other economies. While impactful, such steps are challenging to implement and yield results only over the long term.