Sunday, July 19, 2026

Today's technological signals are very interesting: AI impacts the Indian IT industry, agents come into reality, the "memory" of AI products and the surge in Token calls. To put it bluntly, the wave of AI is no longer as simple as "tool upgrades", but directly renovates the industry, social structure, and business logic. Let's first talk about the fact that India's IT has been shorted by AI-this is a real impact on the industry. India has relied on outsourcing from people to people for 20 years and has been relying on "head-based arbitrage" to work for the West. But after AI came, code was written faster, cheaper, and without mistakes. Every time a new AI is released in Silicon Valley, the Indian stock market collapses. This is not a joke, but a reality. A large number of programmers 'jobs were directly taken away by AI, and outsourcing giants paid a large share of profits instead of R & D. As a result, there was no moat. AI kicked this old business model of "pay-per-head", foreign investment ran away, and stock prices were halved. India skipped industrialization and missed the flywheel of Internet products. At this time, even IT was suppressed by AI and basically locked in the old era of "labor dividends". This is a warning to developers: If your workload still stays at the "labor price difference" rather than creating new products and values, you will be ruthlessly crushed by AI at any time in the future.

Let's take a look at the new developments in agents and AI models. At the WAIC Shanghai Conference, AI mobile phones, robots, Agents, and agents appeared one after another. Manufacturers are desperately trying to push AI into the real world. They are no longer just toys on the screen, but small partners who can do things, remember things, and work with you. The highlight here is actually AI's "memory"-Agent Memory. Now that the models are all rolled to almost the same level, whether they can remember the user's history, preferences, and habits determines the stickiness and experience. Claude can remember you, Character.AI always loses memory. This difference is the winner and winner of the next generation of AI products. The engineering behind it is actually quite difficult: how to make Agents remember details while ensuring security, privacy, and performance? It could be a data leak or a slow response, and the user would run away after using it once. For developers, Agent Memory is not a nice-to-have, but the key to the success or failure of a product. Now that this track has just started, whoever makes the memory function fast, accurately and safely can seize the next wave of traffic entrance.

Finally, the skyrocketing number of Token calls is also very interesting. The average daily Token call volume in China has increased by four orders of magnitude in two years, and it has become basic trading units such as "electricity and water" in the AI era. In the past, developers thought that Token was a cold knowledge in technical documents, but now it is a core indicator that cannot be avoided in the commercialization of AI products. How Tokens are priced, how to optimize calls, and how to reduce costs directly determines whether your product can survive. In the past, making SAAS counted CPU and memory, but now making AI products requires Token. For ordinary developers, this wave is a forced upgrade: you don't understand Token scheduling and cost control, and you can't make sustainable AI applications. Engineering is also becoming more and more difficult-the model is bigger, the reasoning is more expensive, and users are pursuing real-time feedback. How can we find a balance between performance, experience and cost? This is not something that can be solved by writing a Demo. It is a real test of engineering system capabilities.

To sum up: AI is not as simple as "helping you write some code", it is truly rewriting the industrial division of labor, product logic, and engineering system. The collapse of India's IT industry, the evolution of Agent capabilities, and the outbreak of the Token economy are all alarm bells. Ordinary developers and entrepreneurs should no longer imagine that they can get along by "saving some effort with AI" and must embrace the new engineering challenges and product opportunities brought by AI head-on. Whoever can master the three things of AI tools, Agent memory, and Token scheduling will have the opportunity to stand firm in the new generation of industries. Otherwise, wait to be shorted.

Today's most interesting sense of tearing comes from India. On one hand, there is a hard-core breakthrough in the successful entry of private rocket Vikram-1 into orbit; on the other hand, the Indian stock market has been shorted by AI and IT outsourcing giants have been cut in half. This contrast just reveals the drastic reassessment that the global labor value chain is undergoing. In the past two decades, India has relied on labor price differences to play IT outsourcing, but now AI has directly overturned the table. This is not only a crisis in India, but also the same "brain-piecework" trap in China's Internet industry. StackOverflow's traffic cliff and Shopify CEO's sentence that "anti-AI programming people overestimate the quality of human code" are both declaring that the moat of executive white-collar workers has dried up. Product managers no longer roll PRD because AI can do it in two hours, and the competitive dimension of the industry is completely shifting from hand speed to judgment and architectural capabilities.

This substitution is not destruction, but a reconstruction into new infrastructure. The average daily Token call volume of 140 trillion yuan and the ARR growth of 15 times in smart spectrum in half a year indicate that AI has moved from the Demo stage into the era of "hydropower and coal". When GPT-5.6 can use prompt words to fill a 30-year gap in the field of convex optimization, and when Step Star puts agents into mobile phones and robots and starts to work, AI is moving from a purely digital world of "chatting" to the physical world and science. Deep water areas are fully advancing.

In the next year, pure code handlers and document writers will face a more severe reshuffle, while composite commanders who can control the AI agent architecture and understand the laws of the physical world will be worth hundreds of times. The collapse of Indian outsourcing is only the first domino, and the real storm will sweep across all industries that rely on standardized mental labor. Those companies that are still using the old-era headcount bonus thinking to settle accounts will be eliminated, while companies that master the bottom-level Token factories and vertical scene implementation capabilities will define the rules of the next era.

Among today's hot signals, there are two themes that are particularly worth digging into: one is the successful first flight of Indian private rockets, and the other is AI's impact on India's IT outsourcing industry. These two events may seem independent, but in fact they reveal a larger story-the dividend distribution of technological progress and the awkward position of late-developing countries in the global industrial chain.

The successful introduction of India's private rocket Vikram-1 into orbit has been hailed by the media as "the third country in the world with private orbital launch capabilities." On the surface, this is a milestone in India's aerospace industry, but in fact, this achievement is more like a carefully packaged marketing campaign. First of all, Vikram-1's carrying capacity is only about 300 kilograms, which is two orders of magnitude worse than SpaceX's Falcon 9 (22.8 tons). Secondly, India's private aerospace industry started late and its technology accumulation is weak. This launch relies more on government resources and policy preferences than on real market-oriented competition. The most important thing is that the commercialization prospects of India's aerospace industry are worrisome-the global satellite launch market has long been divided up by giants such as SpaceX and Blue Origin, and the demand for low-orbit launches is saturated. Whether India's low-cost advantage can be sustained remains a big question mark. Historically, Brazil, South Korea and other countries have made high-profile announcements of aerospace breakthroughs, but eventually became a flash in the pan due to lack of sustained investment and market demand. India's success this time is more like a pie drawn to its own people and international investors, and the risks behind it have been deliberately ignored.

In sharp contrast, AI's impact on India's IT outsourcing industry. This article "The first country to be shorted by AI has emerged" reveals a fatal wound in the Indian economy: over-reliance on low-end outsourcing services and lack of core technology and product capabilities. India's IT industry has relied on "labor arbitrage" for the past 20 years-replacing high-cost developers in developed countries with cheap human resources. However, the emergence of AI directly overturned this table. Big models can generate code, automate testing, and even design system architecture in an instant, and India's outsourcing companies that charge per head suddenly find their core competencies gone. What is even more terrifying is that Indian IT giants have long used profits for dividends rather than R & D investment, resulting in no ability to fight back in the AI wave. The Nifty IT index has been halved in 18 months, the market value of 19 trillion rupees has evaporated, and foreign investment has fled US$23 billion. Behind these figures is the systematic collapse of a country's industrial structure. India's lesson deserves to be warned by all late-developing countries: the distribution of technological dividends is cruel. If you are just a low-end link in the industrial chain, once technological change comes, you may not even have the chance to cry.

Taken together, these two incidents are very similar to the two extremes of India's technological development: one is a high-profile "breakthrough" and the other is a silent "collapse." The former is a bubble, the latter is a reality. The success of India's aerospace industry may just tell a good story to the capital market, while the impact of AI on the IT outsourcing industry is a real industrial disaster. This reminds me of China 20 years ago, when we also cheered for the manned flight of the Shenzhou V, but what really changed China's destiny was the rise of manufacturing and the explosion of the Internet industry. Technological breakthroughs have real value only if they are transformed into industrial competitiveness. India's dilemma today is that it missed industrialization and failed to seize the opportunity of Internet products. Now that AI is here, it wants to use "high-end" projects such as aerospace to cover up the reality of industrial hollowing out. How long this trick lasts depends on the patience of capital markets and the cycle of the global economy. But what is certain is that technological progress will not stop because of a country's "ambitions."