India: The world's first country to be shorted by artificial intelligence?
Source: Qin Shuo's Circle of Friends
In the first half of 2026, an impactful new label appeared in the Indian stock market —“The first country in the world to be shorted by artificial intelligence.”
This assertion is not without foundation. The Nifty IT Index, which has long been regarded as a weather vane for India's technology industry, fell sharply in the first half of the year. Software service leaders Tata Consulting Services, Infosys, and Wepro are generally under valuation pressure.
Meanwhile, international capital continues to withdraw from the Indian market. According to Reuters data, in the first half of 2026, foreign investors sold approximately $29 billion of Indian stocks on a net basis. After entering July, although the Nifty IT Index rebounded 16.7%, and the net inflow of foreign capital surpassed 1.6 billion US dollars, this round of market was largely due to sector rotation after global capital withdrew from crowded AI hardware transactions, which is not enough proof that India's software industry has escaped trouble.
A large country with a population of 1.4 billion and many industries will of course not be easily “shorted” by a technology. India also has huge industries such as banking, pharmaceuticals, energy, electricity, communications, and consumption. Software outsourcing did not lose orders or lose value overnight. What has really been repriced by the market is India's most successful and internationally competitive growth model over the past 30 years.
India has built itself as a “world office” with English-speaking talent, the number of engineers, and wages significantly lower than those in Europe and the US. Now that artificial intelligence has begun to enter the fields of programming, testing, operation and maintenance, customer service, and data processing on a large scale, India has suddenly discovered that what was once its proudest cost advantage may also become the part most easily replaced by technology.
“AI shorting India” is inevitably an exaggeration, but it accurately captures an even more important issue.When a country places too many hopes for growth, employment, and the middle class on the same industrial circuit, a technological paradigm shift could evolve from industry shocks to development anxiety at the national level.
Human arbitrage
India's software industry has been huge for a long time. The National Association of Software and Service Enterprises of India predicts that in the 2025-2026 fiscal year, India's IT industry revenue will reach US$315 billion, an increase of 6.1% over the previous year, and the number of employees will increase to 5.95 million. According to data released by the Indian government, IT and related services revenue for the 2024-2025 fiscal year was US$283 billion, and there are also more than 1,700 global competency centers across the country, employing about 1.9 million people.
As a result, it is inaccurate to describe India's software industry as completely collapsing. It is still growing, has a large number of international customers, and has decades of project management capabilities, customer relationships, and industry experience. The transformation of core systems of financial institutions, databases of multinational enterprises, government information platforms, and highly complex legacy systems cannot all be completed with just a few AI agents.
However, the capital market is more concerned about future growth prospects. The real problem with software outsourcing in India is that “revenue growth” and “manpower growth” are being decoupled.
Their pattern in the past was very clear. European and American companies hand over standardized development, testing, operation and maintenance, data entry, and customer service to India. Indian companies charge according to the number of engineers invested and working hours. The more people a project requires, the bigger the bill the service provider can pay. The most direct way for an enterprise to increase revenue is to recruit more engineers and then undertake more projects.
Although “selling people” doesn't sound decent, it is an underlying mechanism for the expansion of India's software services industry.
Generative artificial intelligence breaks this cycle. Coding, debugging, and documentation work that used to require dozens of junior programmers can now be completed by a small number of senior engineers using AI tools; software testing, data collation, and customer Q&A, which originally relied on a large number of manual tasks, are also increasingly being taken over by automated systems. Customers are beginning to shift from buying hours to buying results, and are no longer willing to pay for a huge offshore team for a long time.
The impact of this change is very special. Even if the order amount does not drop immediately, the number of people required for the same order may be drastically reduced. AI has improved delivery efficiency while simultaneously reducing billable labor hours. For product-based companies such as Microsoft and Google, increased efficiency usually means increased profits; for Indian outsourcers that charge per hour, increased efficiency may first mean shrinking bills. Technological advances have created a conflict of interest within the business model here.
Of course, software companies in India can also use AI, but the more effectively they use AI, the faster traditional human outsourcing business shrinks. If they refuse to use it, they will also be defeated by European and American consulting firms and new service providers that use AI. Businesses must choose between weakening their old business and losing their future competitiveness.
Therefore, the target of market shorting is mainly the old valuation logic of software outsourcing in India. In the past, this industry was seen as a growth industry that could continuously absorb engineering graduates, steadily expand the size of personnel, and rely on long-term contracts to generate cash flow. AI is turning it into a mature industry where the size of personnel has shrunk, project offers have declined, and the internal structure has been drastically adjusted. Even if the revenue of $315 billion does not disappear for a while, the valuation that the market is willing to give it will change.
“Revolutionize your own life”
The choices facing India are very similar to the problems faced by many traditional industries on the eve of the technological revolution. If AI will replace a large number of programmers and customer service personnel, should India still make every effort to develop AI? A country's initiative to promote technology that could impact domestic employment is almost personally undermining its own advantage.
However, technological substitution will not stop because of India's hesitation. American companies will still use AI to reduce outsourced procurement, and European companies will also try to take back some of their IT jobs. India does not promote automation, and jobs saved may not be preserved for a long time. It is likely that it will only be a buffer period of a few years, at the cost of relinquishing the AI solutions, enterprise consulting, and system integration markets to competitors.
For India, there is no way back in developing AI. What really needs to be decided is which layer of AI to develop and who will bear the cost of transformation.
If Indian software companies only procure models from OpenAI, Anthropic, or other overseas companies and package these tools into traditional outsourcing projects, they will still only receive revenue from implementation, maintenance, and integration. The core model, computing power platform, and technical standards are in the hands of overseas companies. Indian companies have upgraded from “cheap programmer suppliers” to “overseas model installers,” and the value chain position has not fundamentally changed.
Moving to the high-end requires companies to go deep into vertical industries such as finance, healthcare, manufacturing, and government governance, and have the ability to integrate models, data, compliance, and business processes. India has served multinational enterprises for many years and has accumulated a large amount of customer relationships and legacy systems knowledge, which is its most valuable transformation capital. Future software service projects will require fewer people, but stronger industry judgment, deeper data governance experience, and higher delivery responsibilities. India still has a chance to become a global center for AI system integration and digital transformation of enterprises; however, this new center cannot accommodate as many low-skilled jobs as before.
The resulting employment issues are more difficult than enterprise transformation. The software services industry has long served as an incubator for India's middle class. A large number of ordinary families entered the IT industry through engineering education, and then improved spending on housing, cars, and education through stable wages. Once campus recruitment continues to shrink, the first to lose opportunities are not top computing talents, but a huge number of general engineering graduates, testers, customer service personnel, and junior programmers.
AI can increase labor productivity in Indian companies, but it may not be able to create the same number of new jobs. A senior engineer who has mastered AI tools may have completed the work of the past ten junior employees; the new model governance, data security, and industry consultant positions did not allow all ten substitutes to be qualified after a short period of training. Increased efficiency at the enterprise level is likely to coincide with employment pressure at the social level.
This shows that fully developing AI cannot be reduced to purchasing computing power, building data centers, and training employees. India needs to simultaneously complete education reforms, social security, industrial transfers, and demand nurturing. Otherwise, the benefits of AI transformation will be concentrated in the hands of a few leading enterprises and high-end talents, while millions of ordinary workers and urban households will bear the loss of employment. Technological upgrades have been successful, but social transformation may still fail.
“AI anti-fragility” structure
Faced with the impact of AI, it is difficult for a country to accurately predict which jobs will be replaced and which industries will take the lead in restructuring. A truly reliable national strategy is not an attempt to protect every old job, but rather to enable labor, capital, and enterprises to move more quickly from declining sectors to new industries.
The first layer of security comes from industrial diversity. The share of India's software industry in the national economy is not high enough to determine the survival of the entire country, but its impact on service exports, quality employment, foreign exchange earnings, and urban middle class consumption far exceeds its share of GDP. When an industry undertakes multiple functions such as growth, exports, employment, and social mobility at the same time, the risks it creates cannot be measured only by the share of output value.
China and the US have more room to maneuver in the face of AI shocks. The key is that there are more industrial pillars. The US has not only software, chips, cloud computing, and finance, but also energy, pharmaceuticals, aerospace, and high-end manufacturing; China has a complete manufacturing system, covering new energy, e-commerce, communication equipment, engineering construction, and a huge consumer market. Certain types of occupations have been replaced, and the impact is still severe, but it is harder for it to rapidly evolve into a denial of the country's growth model as a whole.
The second layer guarantees initiative from technology and the industrial chain. It is not necessary for a country to have absolute autonomy in all areas, but it must have sufficient capacity to compete and set rules in a number of key areas. There is a close link between basic models, computing power, data centers, industry data, and application ecosystems. India has vast amounts of data and engineering talent, but as of the second quarter of 2025, the capacity of data centers in India was about 1.4 gigawatts, accounting for only about 3% of the global number of data centers. Electricity, water, and network infrastructure will still limit their AI ambitions. (Source: Press and Information Bureau, Government of India)
The third level of protection comes from the local market. Software outsourcing in India has long been aimed at European and American customers, and companies are used to organizing production according to overseas needs. This has helped India to rapidly integrate into the global market, and has also weakened the momentum for local product innovation. Industry solutions in the AI era require repeated entry into real scenarios, combined with continuous improvement of enterprise data. Without enough local customers willing to pay for digital products, it is difficult for Indian companies to grow from project contractors to product and platform providers.
China's experience is also showing that AI strategies can't just focus on model rankings. Manufacturing, logistics, healthcare, electricity, ports, urban governance, and financial services are the places where AI creates long-term value. The richer the industrial scenarios a country has, the easier it is to transform the model into productivity; the more uniform the industry category, the more likely AI is to become a layoff tool rather than a new growth tool.
The so-called “anti-fragility of AI” does not prevent damage to old industries. It requires an economy to have new industries to take on employment after old jobs disappear, have alternative routes after overseas technology is limited, and have local demand to support iteration even after the single export market shrinks. The impact of artificial intelligence cannot be eliminated, but an industry crisis can be contained within a digestible range through industrial diversification, technological initiative, and talent mobility.
German cars and Indian software
India is not alone in being defeated by a single advantage. Germany's experience over the past ten years provides another sample to be wary of.
The German economy is not just as strong as automobiles; machinery, chemicals, pharmaceuticals, electrical equipment, and precision manufacturing are just as strong. However, the automotive industry occupies an extremely important position in exports, R&D, employment, local finance, and manufacturing confidence.
The engine, transmission, and parts supply chain and brand advantages formed in the fuel vehicle era have made Germany stand at the top of the global automobile industry for a long time, and have also continuously concentrated huge capital, talents, and political resources around old technology routes.
After electrification, intelligent driving, software-defined cars, and the rise of Chinese brands came at the same time, the German automotive industry discovered that the deepest capabilities in the past were not necessarily the scarcest capabilities in the new cycle. Internal combustion engines can be built to be quieter, more efficient, and more precise, but the center of market competition has turned to batteries, software, smart cockpits, algorithms, and supply chain speed. German companies have not lost their manufacturing capacity, yet they have lost the almost unchallengeable leading position of the past.
This industrial dilemma has spread to the macroeconomy. According to the European Commission, after two years of recession, Germany's economy grew by only 0.2% in 2025, and is expected to grow by 0.6% and 0.9% in 2026 and 2027, respectively; high energy costs, weak exports, US tariffs, and competition from China have all depressed the pace of recovery. In July 2026, BMW announced that it would cut thousands of jobs in Germany. Previously, Volkswagen and Mercedes-Benz had reached a reduction plan involving tens of thousands of people, and Porsche also expanded the scope of the restructuring.
Indian software and German automobiles are in different industries, yet similar problems have been revealed. The more successful an industry is, the easier it is to absorb the best talent, most capital, and maximum policy attention; the more stable corporate profits, the weaker the will to switch to unfamiliar technology routes; employment, education, and interest groups formed around old advantages will further raise the cost of reform. Success lasts long enough to move from competitive advantage to path dependency.
This also requires us to re-understand the “craftsman spirit.”
German engineers took the engine to the extreme, and the Indian software team was able to complete huge international projects at low cost and with strong discipline. These capabilities are all worthy of respect. However, the craftsman spirit solves a problem well; strategic judgment determines whether this matter is still worth continuing to invest in. The technology route has changed, and we are still striving for excellence in old products. The more investment, the higher the cost of sinking, and the more difficult it is to transform the organization.
To put it bluntly, if the direction has lost its value, even the most exquisite craftsmanship may become useless. Artificial intelligence is a technology that is constantly changing direction. It not only helps enterprises improve efficiency, but also removes the need for some industries to exist, changes the charging methods of other industries, and regroups originally neighboring industries.
As a result, an economy requires both craftsmanship and regular doubts about its own success. It is necessary not only to encourage enterprises to take their products to the extreme, but also to allow capital and talent to leave the declining circuit; it is necessary not only to protect workers who have been hit, but also to freeze technological progress by protecting old jobs. Striving for excellence can determine how fast an enterprise runs on a track; only by looking at it like a torch can decide whether a country chooses the right track.
Conclusion: the stronger the advantage, the more dangerous
India won't collapse because of AI, and software outsourcing won't go away. Complex systems transformation, financial and medical compliance, cybersecurity, corporate consulting, and multinational project management still require significant professional services. What is really likely to leave the historical stage is to rely on growth methods that continuously increase junior engineers, charge according to working hours, and earn labor price differences over a long period of time.
India must use AI to transform its software industry, even if it results in layoffs, revenue revaluation, and a contraction in middle class employment. Rejecting self-revolution will only hand over the right to revolution to competitors. At the same time, it also needs to build new manufacturing, energy systems, digital infrastructure, and local consumer markets, so that the software industry no longer assumes too many national functions. Only in this way will an industrial transformation not turn into a crisis of the entire economic model.
This is also a wake-up call for other countries from India. Industrial diversification does not mean that every industry uses equal effort; rather, it prevents any industry from simultaneously kidnapping employment, exports, financial markets, and national confidence. Technological autonomy does not mean repeated construction behind closed doors, but rather ensures that when new technology comes along, the country can participate in value creation, and not only accept the prices and rules given by others.
For a country, the most scarce ability in the AI era is probably not the ability to build top models, but the ability to continuously adjust the direction of the industry. Today's most profitable industry may become tomorrow's heaviest burden; today's most mature skills may become tomorrow's hardest path dependency.
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