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Not a single day passes without the apocalyptical voices, warning about AI replacing jobs, dominating the media headlines. The prophets of doom have a field day, virtually every single day. While the disruptive power of artificial intelligence is well known, the fear-mongering surrounding the technology is largely misplaced.

 

The latest round of panic was centred around a new set of AI tools released by Anthropic, a US-based start-up and creator of Claude chatbot. They triggered shock waves as the IT stocks around the world suffered a sharp fall, wiping billions of dollars from the markets. The panic reaction was based on fears that AI may replace traditional software and services. Anthropic’s plugins for its ‘Claude Cowork’ agent are designed to automate tasks across legal, sales, marketing and data analysis, professional services long seen as major beneficiaries of the AI era.

 

The worry is that these automation tools, which can handle a wide range of customer service tasks, could replace many traditional software and IT outsourcing services. The scale of the meltdown was so massive that some analysts described the market reaction as a “SaaSpocalypse,” meaning a severe downturn for software-as-a-service (SaaS) companies.

 

The new plugins will lead to automation of tasks such as legal document review, compliance, sales workflows, marketing support and data analysis.

 

Exaggerated fears

 

A key takeaway for the Indian IT sector from Anthropic’s disruption is that it should move from traditional outsourcing to becoming AI implementation partners.

 

As Indian enterprises integrate Claude for critical coding workflows, dependency on large vendor teams may decline, squeezing billable hours and margins. Anthropic’s advanced AI systems also threaten the entry‑level talent pool at Indian IT firms by replacing routine development and testing tasks. However, the doomsday predictions are highly exaggerated. A closer look reveals that the scale of legacy software embedded in global enterprises is of the order of $20-25 trillion across the US and Europe—with annual technology spending of $1.2-1.75 trillion.

 

Such installed bases cannot be replaced or automated away overnight by plugging in a new AI tool. Most global enterprises continue to use AI selectively, particularly in research, discovery and limited productivity use cases. Comprehensive AI-led transformation of core operations remains at an early stage.

 

Tools like Claude may directly apply to roughly 25-30 per cent of total work in application development and related services. While application services form a significant share of revenues for large IT firms, the nature of work within that bucket varies widely. Large-scale system integration, legacy modernisation, database consolidation, testing, governance and compliance layers are not easily automated. The AI represents a powerful productivity accelerator, but not an overnight structural wipe-out.

 

The mix of skills will shift upward, with greater emphasis on AI fluency, architecture, governance and domain expertise. Fixed-price, output-based contracts—already accounting for 45-50 per cent of revenues at many firms—could rise further, reducing the centrality of pure time-and-material billing.

 

The fears that the rise of AI will cause a structural slowdown in the IT industry are largely overblown. While AI may reduce demand for some traditional services and tasks, there will be new opportunities in building new capabilities and offerings around AI platforms, tools and models.

 

At present, a significant lag exists between the exponential growth of AI capabilities and their actual adoption within enterprises. This lag provides a buffer and a window of opportunity for service providers.

 

Time for structural shift

 

Indian IT stocks too came under sustained pressure with their shares plunging by up to 8 per cent and wiping out roughly Rs 2 lakh crore in market value amid fears that automating coding, legal review, sales workflows and other enterprise functions could erode the high-margin application services revenues that account for 40-70 per cent of Indian IT companies’ business.

 

Analysts estimate that 9-12 per cent of industry revenues could be at risk over the next four years as clients pivot to AI-led efficiency over traditional service providers.

 

Also read: AI to replace most white-collar jobs in a yr: Microsoft AI chief

 

For decades, Indian IT thrived on predictable scaling: more contracts meant more hiring; more hiring meant higher billing. AI threatens to compress that equation. However, to say that they are now caught in an existential crisis is to misread the situation.

 

Many have invested aggressively in AI partnerships, cloud platforms and data engineering capabilities. TCS and Infosys have rolled out proprietary AI frameworks. HCLTech and Wipro are embedding AI into managed services offerings. AI may ultimately boost demand for integration, governance, and cybersecurity services—areas where Indian IT excels. But in the near term, cost-optimization pressures could reduce billable hours.

 

It must be pointed out that the internet industry was volatile in the beginning and was uneven in payoff, but ultimately proved to be transformative. It did not collapse; it ballooned steadily, reshaping commerce, media and governance over decades. AI too could follow a similar trajectory.

 

For Indian IT, that may mean a careful recalibration rather than extinction. The sector could transition from manpower arbitrage to intellectual property, consulting and AI governance. Margins might compress before stabilising. Workforce structures may evolve, emphasising higher-skilled engineers over sheer numbers.

 

But adaptation has been a strong point of India’s technology story. From Y2K challenges to digital transformation, the industry has repeatedly reinvented itself.

 

Move up the value chain

 

India must move up the AI value chain. This will require massive push for investments in research, fostering semiconductor ecosystems, and building proprietary platforms—not merely servicing foreign ones.

 

The history of technological evolution is replete with instances of India consistently missing the innovation bus and later struggling with the catching-up game. With such a track-record, one wonders whether the country is going to miss the AI bus too.

 

Though India is stepping up its artificial intelligence game through ‘IndiaAI Mission’ and plans to build a scalable GPU ecosystem to support AI innovation, the key question is whether we are moving fast enough in a world where AI is advancing at a lightning speed.

 

So far, the hindering factors have been the low R&D spending, limited private sector involvement, shortage of skilled researchers, poor infrastructure for research, focus on adaptation rather than creation and lack of entrepreneurial culture.

 

As part of a catching up game, India unveiled plans to build a domestic version of the Large Language Model (LLM).  However, the road ahead is full of hurdles. The challenge for India is to not just play catch-up, but also to grab the opportunity that the production of advanced AI models at low costs presents.

 

The other question is whether the funding size and scale of ambition are sufficient, given the domination of America and China in the global AI arms race. The creation of a GPU (Graphics Processing Units) infrastructure, a key component of ‘IndiaAI Mission’, is virtually a race against time. At present, the United States and China are AI superpowers, locked in a fierce race for faster development and adoption of AI.  If India is to realise its ambition of becoming a global leader in AI, it needs to plug the critical gaps in its strategy.

 

According to a research paper published in the Carnegie Endowment for International Peace, the three missing pieces in India’s AI puzzle are: talent, data, and research. Unless these issues are addressed properly, the country cannot expect to stay in the global AI race. The country needs to attract, nurture, and retain cutting-edge, top-tier AI research talent to ensure that AI innovations for the world emerge from India. Similarly, it must build up digital public data to provide inputs for India-specific AI models and research.