Lifelong learning is the bridge between today’s jobs and tomorrow’s opportunities, added ILO Director-General Gilbert F. Houngbo. File image used for representation only.
| Photo Credit: Getty Images/iStockphoto

Artificial intelligence (AI) models like ChatGPT, Claude, and Gemini often give the impression that there’s a mind at work within the machine. These days they “think” in response to queries, go back and correct themselves, apologise for mistakes, and mimic many tics of human communication.

There’s no direct physical evidence to this day that a machine mind exists however. In fact, there’s good reason to believe what these machines are doing when they say they’re “thinking” is actually dealing with a physical phenomenon.

Also Read | At the last frontier of thought: will AI kill creativity?

In the 1980s, a group of physicists led among others by John Hopfield and Geoffrey Hinton realised that if you have a network with millions of neurons, you can stop treating them as individual ‘particles’ and start addressing them as a system. And the behaviour and properties of these systems can be described by the rules of thermodynamics and statistical mechanics.

Hopfield and Hinton won the physics Nobel Prize in 2024 for this work. A pair of studies published in Physical Review E has doubled down on the same idea, showing that two common ‘tricks’ engineers use to make AI models better are also such physical phenomena.

Achilles heel

A neural network is a network of processors connected to each other like neurons in the human brain and which learns and uses information like the brain. They can also be stacked in multiple layers, so that one layer prepares the inputs for the next and so on. Neural networks are at the heart of machine learning applications like generative AI, self-driving cars, computer vision, and modelling.

They also have an Achilles heel called overfitting: a network becomes so obsessed with some specific examples it has seen during its training that it fails to understand the broader patterns. Engineers have developed some techniques to prevent this. For instance, the October 2025 paper by University of Oxford and Princeton University researchers Francesco Mori and Francesca Mignacco focused on a technique called dropout. During training, the neural network is made to randomly turn off a certain percentage of its neurons, forcing the remaining ones to work harder and learn the concepts independently.

Abdulkadir Canatar and SueYeon Chung, of the Flatiron Institute and New York University, turned to a constraint called tolerance in their August paper. They analysed what happens when an AI is told to ignore any error that falls within a small range. So rather than trying to correct every little discrepancy, the network treats any answer that’s ‘close enough’ to be good enough.

While dropout and tolerance look like different programming choices, the authors of the two papers insisted (separately) that they’re both governed by the same underlying physical phenomena.

Teacher-student experiment

Both duos used a tool called the teacher-student framework to explain how. Teacher is a neural network that’s already familiar with a dataset while Student is a network that’s starting completely blank. The Student’s goal is to learn the same dataset until its internal settings are aligned with those of the Teacher.

Mori and Mignacco wrote that at first, the Student was stuck in an “unspecialised phase” when its neurons were all doing the same thing. In the authors’ mathematical models, this appeared as a plateau, or a flat line, in the error graph, and it denoted that the Student wasn’t learning.

So they argued that for the Student to become smarter, it must first undergo a “specialisation transition”. Physicists are familiar with such transitions because they use the same maths to describe liquid water turning into vapour, a process called a phase transition.

Mori and Mignacco reported that by randomly turning neurons off, dropout injected a certain amount of noise into the system, which then nudged the network out of its plateau and towards specialised intelligence — a phase transition. This description also aligns with the work of Hopfield and Hinton, who proved that the energy of a neural network is a real thing, by manipulating which the network can be made to perform better.

They even reported a formula that they said could find the supposedly ideal dropout rate: relating the activation probability, which is the chance that a neuron spits out a particular output for a given set of inputs, to the learning rate, noise level, and the learning capacities of the Teacher and Student networks.

Like atoms

Canatar and Chung also found that the consequences of changing the tolerance on the network could be described using the laws of physics, which they illustrated by applying their findings to the double-descent problem. When you give a network more data, its performance sometimes gets worse before it suddenly gets better. According to Canatar and Chung, when a network learns exactly as many examples as it has internal settings for, it reaches a point where it’s looking for more information. When it doesn’t get that information, it starts to overfit what it already ‘knows’ to every problem in its way.

The machine doesn’t reach this overfitting stage because its algorithm is flawed but because its millions of parameters are like a collection of atoms trying to go through a phase transition, and failing, they added. As a result, the results of the neurons’ computations are riddled with errors.

The solution? “Canatar and Chung uncovered a critical value of … tolerance that separates two regimes: one in which the neural network perfectly fits the training data and another in which overadaption is avoided. In physical terms, this regime crossover corresponds to a … phase transition,” Hugo Cui, a researcher at the University of Paris-Saclay and the French National Centre for Scientific Research, wrote in a commentary for Physics.

Some limitations

Mori and Mignacco were working with a two-layer neural network, which is like a toy model compared to the large, multi-layered deep learning networks that power AI models like ChatGPT or self-driving cars. Nonetheless, they’ve written that the “mechanisms” they’ve uncovered answer “several open questions about the mechanisms driving the performance improvement induced by dropout”.

Canatar and Chung on the other hand applied their equations to ResNet, an advanced type of neural network used to solve real-world problems like computer vision. They said that even in this setup, the same geometric and thermodynamic rules they’d found in their simpler model held true.

For decades now, engineers have often treated machine learning as a kind of ‘black box’, where they just tinker with the code until it works but without understanding why it works. In the 1980s, however, a conviction prevailed that machine intelligence is a complex product, but nonetheless a product, of statistical mechanics, which physicists understand very well. By this logic, the machine’s inner workings aren’t inscrutable so much as a physical system that can be deciphered using undergraduate physics.

These studies suggest a future where scientists could use analytic theories like the ones in the papers to estimate an AI model’s performance even before they turn it on.

mukunth.v@thehindu.co.in

Published – March 02, 2026 07:30 am IST

A panel discussion on ‘Building Innovation-First GCCs: AI, R&D & Digital Transformation’ was organised as part of BioAsia 2026 in Hyderabad on Wednesday (February 18, 2025)
| Photo Credit: Siddhant Thakur

Deputy Prime Minister of the United Kingdom David Lammy at Downing Street in London
| Photo Credit: AP

Deputy Prime Minister of the United Kingdom David Lammy at Downing Street in London
| Photo Credit: AP

India is a “very successful” emerging economy with a bigger influence in global affairs, and is the “right place” to host the AI summit, UN Secretary-General Antonio Guterres has said.

In an exclusive interview with PTI at the UN Headquarters ahead of the India–AI Impact Summit 2026, Guterres underscored that artificial intelligence should benefit the entire world and not just be a privilege reserved for developed nations or two superpowers.

“I strongly congratulate India for organising this Summit. It’s absolutely essential that AI develops itself to the benefit of everybody, everywhere and that countries in the Global South are part of the benefits of AI,” he said.

The high-powered event being held from February 16 to February 20 will be the first-ever AI summit hosted in the Global South and is anchored in the three guiding principles of ‘People, Planet and Progress’.

Mr. Guterres, who will be travelling to India to attend the Summit, asserted “it would be totally unacceptable that AI would be just a privilege of the most developed countries or a division only between two superpowers”, an apparent reference to the U.S. and China.

“It is absolutely essential that AI becomes a universal instrument for the benefit of humankind,” Mr. Guterres said.

“The role of India, [which] is today a very successful emerging economy that is having a bigger and bigger role in not only the global economy but in its influence in global affairs, India is the right place to have this Summit and to make sure that AI [is] being discussed in depth, in all its enormous potential and also in all its risks, but that AI belongs to the whole world and not only to a few,” he said.

From world leaders, including French President Emmanuel Macron, Brazilian President Luiz Inacio Lula da Silva, to technology honchos, including Google and Alphabet CEO Sundar Pichai, Adobe CEO Shantanu Narayen and Anthropic CEO Dario Amodei, the Summit is bringing together leaders, policymakers and innovators from across the world for deep-dive discussions on the way forward for AI.

Guterres had met Prime Minister Narendra Modi late last year on the sidelines of the G20 Summit in Johannesburg and External Affairs Minister S. Jaishankar when he was in New York.

Underlining his strong advocacy for multipolarity, Mr. Guterres highlighted India’s contribution in a multipolar world, saying he looks forward to discussing this with the Indian leadership during his visit to New Delhi.

“There are two things we need to avoid in the world. We need to avoid the system in which there is total hegemony by only one power or a system in which the world is divided between two superpowers,” he said.

Mr. Guterres added that he is a “very strong advocate” of the need for true multipolarity in the world.

For true multipolarity, it is important for emerging economies to have a “higher and higher” role and to establish a “stronger and stronger” network of trade, technological and international cooperation relations.

In this regard, he said the recent trade agreement between India and the European Union is a “good example”. “India is one of the most relevant emerging economies,” he said.

“We are seeing across the world, more and more, the creation of a network of, I would say, all developed countries but also, and very importantly, emerging economies creating a true multipolarity without any hegemony, and allowing, then, multilateral organisations to be effective,” he said. Emphasising the importance of multipolarity, Mr. Guterres spoke about his “frustration” over the failure of the UN Security Council to address conflicts and maintain international peace and security.

“When one looks at the UN, you can imagine my frustration when I see the Security Council unable to take decisions, and it is clear that we need a fundamental reform of the Security Council, first of all, to represent the world as it is today and not after the Second World War. And second, to be able to take effective decisions for peace and security around the world,” he said.

Mr. Guterres added that for a “fair” multilateral system, both in the UN and international financial institutions, emerging economies need to have a stronger voice and be at the centre of this networking.

“I see India in the centre of those emerging economies, and this is something I would be delighted to discuss with Prime Minister Modi because I have a lot of hope for the role that India can play in shaping this multipolar world,” he said.

At the Summit, Mr. Guterres will be joined by senior UN leaders, including UN High Commissioner for Human Rights Volker Turk and Under-Secretary-General and UN Secretary-General’s Envoy on Technology Amandeep Singh Gill.

Mr. Guterres further underlined that “it is always fantastic” to go to India, “a democracy with an enormous diversity and extraordinary civilisation and culture.” The UN chief elaborated that he is currently reading about how India, for centuries, has been the main factor of transformation of the world, “starting hundreds of years before Christ and going on for centuries, with an enormous influence of Indian culture, of Indian civilisation that we can see in China, Southeast Asia, the Indian Ocean.”

“Even in the time of the Roman Empire, there were very strong connections with India and a very important influence of Indian culture in what is today the Mediterranean area. “So it is always an enormous pleasure to visit India,” Mr. Guterres said.

Published – February 15, 2026 10:02 am IST

Anthropic’s AI model Claude was used in the U.S. military’s operation to capture ‌former Venezuelan President Nicolas Maduro, with Claude deployed via ​Anthropic’s partnership with data firm Palantir. File.
| Photo Credit: Reuters

1. What is it about Claude’s latest release that has spooked markets and hurt Indian IT stocks?

On January 30, Anthropic released 11 open-source plugins for Claude Cowork, its AI workplace suite. Unlike conventional chatbots, Cowork functions as an autonomous digital colleague: it reads files, drafts documents, reviews contracts, and executes multi-step workflows across legal, finance, sales, and marketing—with minimal human instruction. Days later, Anthropic followed with Claude Opus 4.6, a model capable of coordinating teams of AI agents for financial research and due diligence.

The market reaction was swift and brutal. A Goldman Sachs basket of US software stocks fell 6% on Tuesday, February 3. Thomson Reuters plunged 15.8% (a record), LegalZoom sank 19.7%, and RELX dropped 14%. Nearly $285 billion in market capitalisation was erased globally. In India, the Nifty IT index fell 5.87%—its steepest fall since March 2020—wiping out nearly ₹2 lakh crore. TCS and Infosys each fell over 7% on the day; Tech Mahindra lost over 5%. For the week, the index declined 6.4%, with Infosys down 8.2% and Tech Mahindra 7.1%. The core fear: if one AI agent can do the work of teams, India’s headcount-based outsourcing model faces existential repricing.

2. What is the ‘SaaSpocalypse’ and why are SaaS companies threatened?

The term—coined by Jefferies—captures the fear that AI is replacing software, not just enhancing it. Traditional SaaS charges per user seat; when AI agents execute workflows autonomously, fewer humans need the software. As CNN reported, “Why do I need to pay for software if internal development now takes developers less time with AI?” asked Thomas Shipp of LPL Financial. Salesforce is down 26% year-to-date; the S&P 500 Software & Services Index has fallen roughly 20%.

Also Read | How SaaS platforms can help career counsellors guide students

Bank of America called this an “indiscriminate selloff” resembling the DeepSeek moment of January 2025, when China’s DeepSeek shook the assumption that AI required massive capital and Nvidia lost $589 billion in a day. That panic proved overblown. BofA argues this selloff rests on contradictory premises: AI capex collapsing while AI adoption becomes so pervasive it makes software obsolete. Yet the structural shift is real. The question is whether markets are pricing a decade of disruption into a single week.

3. What are real-world examples of AI disruption in legal, financial, and health services?

None of this should have been a surprise. The trajectory was clearly visible. In March 2023, Bloomberg released BloombergGPT, a 50-billion parameter LLM trained on 363 billion tokens of proprietary financial data—the largest domain-specific financial dataset ever assembled. Bloomberg’s CTO Shawn Edwards said it would “enable us to tackle many new types of applications” with “much higher performance out-of-the-box.” BloombergGPT proved that domain-specific AI could outperform general models on financial tasks by significant margins: sentiment analysis, entity recognition, news classification, and query automation. It was the proof of concept. Claude Cowork’s finance and legal plugins are the logical extension—taking what BloombergGPT demonstrated within one platform and making it available as an autonomous agent across any enterprise.

Also Read | Is there an AI bubble? Financial institutions sound a warning

Legal: Claude’s legal plugin—automating NDA triage, contract review, and compliance tracking—triggered the sharpest market reaction. Thomson Reuters recorded its largest single-day drop ever. LegalZoom fell nearly 20%. RELX (LexisNexis’s parent) and Wolters Kluwer each lost over 13%.

Financial: If BloombergGPT was the industry building AI for itself, Goldman Sachs embedding Anthropic is the industry letting AI run itself. Goldman disclosed a six-month partnership with Anthropic to build autonomous agents for trade accounting, compliance, and client onboarding. CIO Marco Argenti said the bank was “surprised” at Claude’s capability beyond coding—particularly in parsing regulatory documents and applying rule-based judgment. The shift from BloombergGPT (a domain model assisting analysts) to Goldman-Anthropic (autonomous agents replacing back-office processes) is the leap that spooked investors. FactSet fell 10%; S&P Global and Moody’s declined sharply.

Healthcare: Cognizant’s partnership with Palantir embeds agentic AI within its TriZetto healthcare platform—which processes over half of US medical claims—handling patient routing, claims adjudication, and supply chain tasks, with human oversight for exceptions.

Anthropic CEO Dario Amodei has warned AI could displace half of entry-level white-collar jobs within 1–5 years. Salesforce’s Marc Benioff has said the company will not hire additional engineers or lawyers because of AI.

4. How are Indian companies addressing this disruption, and how should they?

Indian IT firms are investing, but incrementally. TCS-TPG has committed $2 Billion to Hypervault AI data centres; Wipro earmarked $1 Billion for AI360; Infosys has partnerships with NVIDIA and Intel. Cognizant’s Palantir-TriZetto integration is the most forward-looking—combining domain expertise with a leading agentic platform.

The challenge is pace. As Rest of World noted, Cowork’s plugins automate precisely the high-volume, repetitive work that is Indian IT’s bread and butter. The “slower enterprise adoption” defence rings hollow when Goldman Sachs is embedding Anthropic engineers in its back office to co-design autonomous agents, and the Pentagon has consolidated 75 data/AI systems under Palantir’s $10 billion Army contract. The required pivot is from labour arbitrage to AI deployment partnerships. Indian firms possess unmatched domain expertise in banking, insurance, and healthcare—the Cognizant-Palantir model, where domain knowledge meets platform capability, is the template.

5. Does this hurt Indian IT employment or create a new kind of opportunity?

The immediate signal is concerning. TCS has reduced headcount by approximately 11,000 recently; multiple CTOs have stopped hiring freshers entirely. Entry-level testing, maintenance, and compliance roles are most at risk. One fintech firm told The Ken fresher hiring on certain teams has gone from 80% to zero.

Yet new demand is emerging. Every AI agent that performs autonomous work in a regulated environment — healthcare claims, financial audits, defence logistics — requires what the industry calls HITL (Human-in-the-Loop) processes: oversight, validation, exception handling, governance, and ethical review. These roles demand domain knowledge and judgment, not just coding ability. Palantir itself, despite its autonomous capabilities, emphasises that its ontology-driven approach requires humans to define the business logic and maintain governance frameworks.

Goldman’s Argenti stressed agents will be “digital co-workers,” not replacements, because compliance requires human judgment for edge cases. Three opportunities exist: deployment partnerships that embed and govern agentic platforms inside enterprises; HITL operations centres for regulated industries; and massive reskilling to train engineers to architect and supervise AI systems rather than write boilerplate code.

6. Is this another DeepSeek moment—or something more permanent?

The comparison is instructive. In January 2025, DeepSeek shook the assumption that AI required massive capital; Nvidia lost $589 billion in a day, then rose 58% over the following year. BofA’s Brad Sills explicitly called this week’s selloff “overblown.” Gartner wrote that Cowork plugins are “potential disrupters for task-level knowledge work but not a replacement for SaaS applications managing critical business operations.” Wedbush added that enterprises “won’t completely overhaul tens of billions of dollars of prior software infrastructure.”

Also Read | What is DeepSeek, and why is it disrupting the AI sector?

The pattern will likely rhyme with DeepSeek: sharp selloff, partial recovery, then slow realisation that the underlying shift is real. DeepSeek challenged cost assumptions about building AI. Claude Cowork challenges revenue assumptions about the work AI can replace. One threatened input; the other threatens outputs. But both follow the same arc—panic, recovery, gradual structural repricing. The BloombergGPT-to-Cowork evolution shows this is not a bolt from the blue; it is a trajectory visible for three years. For Indian IT, the window to pivot from labour arbitrage to AI deployment is shorter than the market assumes.

Published – February 08, 2026 11:19 am IST

Veteran investor Raamdeo Agrawal believes artificial intelligence is not just another technology cycle-it is the defining economic force of the coming decades. In his view, the AI buildout underway globally could eclipse even China’s manufacturing-led boom of the early 2000s, and Budget 2026 marks India’s first serious step into that supercycle.

“This AI could be bigger than China,” Agrawal said, drawing a direct parallel with the 2003-2008 period that reshaped the global economy. “That was the rise of China. Now, if you listen to NVIDIA, Satya Nadella or even Elon Musk, they are saying this is humanity’s biggest capital build-out.”

Humanity’s Biggest Capex Cycle

Agrawal framed AI not as a software story but as a massive infrastructure-led transformation. 

According to Agrawal, the implications go far beyond technology companies. “So much of jobs all over-for building data centers, cables, plumbing, buildings, power,” he said, underlining that AI requires a physical ecosystem at an unprecedented scale.

For India, the significance lies in participation. “We were completely missing out. Here is the step,” he said, referring to Budget 2026’s data centre incentives. While the budget alone cannot create an AI industry, Agrawal believes it has done the crucial first task-policy alignment. “This budget has at least made us participate.”

Why AI Won’t Kill Jobs

Addressing concerns that AI could displace workers, Agrawal leaned on examples already playing out globally. Referring to medical diagnostics, he said AI now reads scans with far higher accuracy, but that has not reduced demand for professionals.

“The task is automated, not the job,” he explained. “The job of a radiologist is not to see the scan. The job is to take care of the patient.” By freeing professionals from routine tasks, AI, he argued, actually increases demand for skilled workers.

He added that fears around job losses mirror early anxieties about the internet. “Without internet there is nothing. Today India is one because of internet,” he said, noting that its impact unfolded slowly but proved transformative.

A Long-Term Bet, Not a Short-Term Trade

Agrawal cautioned against judging AI-or the budget’s AI push-through the lens of near-term market volatility. “The biggest debate is whether AI is a bubble or it’s real,” he said. His answer was unequivocal: “AI itself is real. The issue is that the investment and excitement around that could be premature.”

Drawing lessons from the internet era, he said investors tend to “overestimate the short term and underestimate the long term.” Corrections and uncertainty are inevitable, but the endgame matters more than interim noise.

Beyond AI: A Supportive Macro Backdrop

After leading with AI, Agrawal pointed to other supportive factors in the interview, including fiscal discipline and the restoration of credit growth. He described credit as “like ATF,” arguing that higher credit growth has an immediate and powerful impact on the economy.

Still, for him, AI remains the defining opportunity. “This is unfolding,” he said. “Whether we are patient enough to wait for the fruits of AI to come, or we worry about 10-20% correction-that’s the real question.”

Agrawal’s conclusion was clear: India may have arrived late to the AI race, but Budget 2026 signals intent. And if the AI supercycle plays out the way global leaders expect, the scale of opportunity could reshape India’s economic future-just as China’s rise reshaped the world two decades ago.

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