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Since there are prohibitive costs while developing new products, especially in the pharma industry, the innovative products are protected by the Patents Act. The corporates holding patents are given an opportunity to recover their developmental cost by charging premium prices for the period of the patent. Later, it becomes available at affordable rates when it goes off-patent.

The World Trade Organisation of UNO (WTO) formulated TRIPS Agreement or Agreement on Trade-Related Intellectual Property Rights. To make the patented medicines available in developing countries, certain policy flexibilities were permitted. As we have already discussed one such instrument is compulsory licensing to an organization to manufacture or import generic version of the medicine. The generic version is made available at cheaper prices. The patent holder is paid adequate compensation in lieu of compulsory licensing.

This flexibility was permitted when patented anti-retroviral medicines were exorbitantly priced by patent holders at the time of AIDs crisis. Still big pharma put pressure on governments not to resort to compulsory licensing. In May 2000, the US challenged a Brazilian law on compulsory licensing at WTO.

In 2001, the WTO announced Doha Declaration on TRIPS Agreement. There was hard bargaining, but WTO members asserted that TRIPS flexibilities will prevail, including compulsory licensing. Brazil, India and South Africa were at the forefront of this struggle. Many developing countries availed of these flexibilities, including India. They resorted to compulsory licensing for making medicines accessible to patients at affordable prices.

However, India has availed of compulsory licensing in only are instance in the past 20 years. Very few countries have availed of compulsory licensing for pharmaceuticals.

Big pharma lobbies hard for stringent standards on IPR protection.

These days there are mutual negotiations with two countries or group of countries leading to free trade agreements (FTA). India and the UK recently concluded Comprehensive Economic and Trade Agreement (CETA). In article 13.6 of the CETA, the parties recognized that the preferable and optimal route to promote and access to medicine is through voluntary licensing. The voluntary license is granted by a patent holder. There is technology transfer on mutually agreed terms. Such a license can be granted to manufacture, import or selling of a patented medicine. These agreements are shrouded in secrecy. There is hardly any information in the public domain. The concern is about the limiting production and supply. The patent holders can rule the prices. They make profits out of a health crisis.

CETA’s provision on compulsory licensing is controversial. The aim is to prevent developing countries from resorting to compulsory licensing. With the reaffirmation of licensing flexibilities in 2001, the CETA has shifted the balance in favour of voluntary licensing. In future crisis, it is doubtful whether countries will get the benefit of compulsory licensing.

India may lose its leadership among developing countries when these issues come up for discussions in fora such as WTO, WHO and World IP Organization.

The government should ponder what India has gained from this controversial provision on patents in the CETA. Though the government retains its legal rights to compulsory licensing, CETA is big opposition to it. It should be explored how to minimize damage resulting from CETA.

To gain supremacy in the field of AI, there is a competition between China and the US. There is a feeling that maybe US will emerge supreme. It has the largest chip making company Nvidia with a market capitalization of $ 4 trillion. Microsoft which backs OpenAI is not far behind — it has a valuation of $3.7 trillion.

The pioneers are not always the ultimate winners. There are reports of China acquiring spectacular gains. OpenAI’s ChatGPT broke the new ground. However, soon China introduced DeepSEEk early 2025 with cost efficiency and processing efficiency. In July 2025, Moonshot AI from China released its Kimi K2 model. It outperforms Western competitions.

Many factors influence this race — powerful chips, talent, software and strategic focus. The US focused on semiconductors and Biden administration banned exports of these, but the policy since then has backfired. China took this seriously and pursued its own development of AI chips.

Ultimately, the decisive factor in this race will be not the hardware but the software. China is attaining higher rankings in the innovation of performance.

The most crucial component here is the focus on basic theoretical research. The government’s assistance here plays a vital role. It pushes the abstract frontiers of knowledge. The US here is at a disadvantage. The total spending of the US on R&D shows a downward trend. This is so since the post-Sputnik peak of 1964. The basic research’s share has fallen from 30 per cent in the 1970s to around 10 per cent in 2023. The federal funding for basic research is just $30 billion. It is a big drop. In contrast, China is advancing its science and technology agenda. Its R&D spend is rising at an annual average rate of 14 per cent over the past 10 years. The US is ceding its lead in government-supported basic research. China considers basic research as the pillar of Chinese innovation.

There is contlict between the two systems — USA’s market-driven model and China’s state-supported industrial policy. The great leveller is basic research. Innovation ultimaely flows from discovery.

TCS has announced that it will lay off 2 per cent of its global workforce (about 12000 people), primarily from middle and senior levels. It is being seen as the clearest sign that the IT business model is being changed. The long-standing focus on headcount as a metric is giving way to a sharper focus on productivity outcomes and future readiness. Over a million jobs across testing, documentation and programming could become redundant in the coming years due to AI.

Traditionally, clients were offered low-cost services overseas, based on manpower and process execution, This model was based on scale. IT firms hired large number of engineers, charged clients based on how many people were assigned to a project, and delivered basic software services and digital transformation work. Clients mainly wanted cost savings. Expectations have new changed. Clients want faster results, flexible pricing based on outcomes and innovation, not just execution.

Another factor is the narrowing of global wage differences. Automation enables to deliver the same work with fewer people. The pyramid model with several layers of managers is difficult to sustain. The senior-level employees thrived in legacy projects are now under pressure to adapt to new ways of working.

Across the world, companies are cutting jobs and reorganizing their teams. Some attribute it to AI, and others to cost pressures, over hiring or changing business needs. The companies are adapting to a future were automation plays a central role. And being large is not enough. What matters is efficiency and innovativeness.

The layoffs are not entirely due to AI but due to mismatching of skills. All this is happening even before AI reaches its peak. These are early days. Autonomous agentic AI will soon be deployed at scale. The impact will be far greater.

The older approach of hiring big and delivering on scale is not enough. The needs of the clients are changing. The global demand is changing. The budgets are tighter. We require new platforms, creation of intellectual property (IP), investments in talent with newer skills and strategic role playing.

The whole field of AI and natural language processing essentially emanated from a proposal that machines could learn and generate language like humans put forward by Alan Turing in his landmark paper Computing Machinery and Intelligence (1950). He proposed Turing Test where a machine would be considered intelligent if it could carry on a conversation not distinguishable from that of a human. As such he did not develop language models, but anticipated that the language generation was the key to machine intelligence.

Noam Chomsky between 1950s and 1960s developed formal models of syntax. He showed how complex sentences could be derived from a small set of rules. It inspired early rule-based natural language processing.

Joseph Weizenbaum created ELIZA, one of the first programmes to simulate human conversation using pattern matching in 1966.

John McCarthy and Marvin Minsky between 1950s and 1970s founded the field of artificial intelligence. They advocated ML and reasoning systems that could be applied to language.

The modern shift was to the idea of machines learning language patterns from data happened between 1990s and 2000s. In early 1990s, there emerged a statistical machine translation group at IBM.

Later, neural networks and deep learning models were developed in 2013-2017 with RNNs and then Transformers such as GPT from 2018 onwards. These realized the idea of machines generating human-like language.

It was surprising how well modern AI models can generate human-like language. What was more surprising was the degree of fluency, coherence and creativity in LLMs such as GPT. These exceeded expectations.

GPTs were trained purely on a statistical pattern in text, without real world understanding. Many thought this will be a limitation, and the output will be shallow and simple. Yet what they generate is grammatically correct, textually appropriate and even insightful language.

They have displayed emergent behaviours or capabilities that were not explicitly programmed, e.g. reasoning through multiple steps, code generation, summarizing complex documents and creative writing. These emerged from the text alone.

Models like GPT can answer questions and solve problems they have never encountered before — based on pattern extrapolation, despite lacking understanding or consciousness. This generalization beyond training surprises researchers, especially in zero-shot settings.

Researchers agree about scaling — more compute and more data leading to better performance. But even they did not fully predict the qualitative leap in fluency and versatility.

The movie Terminator offers an idea — the robotized world powered by AI could make robots Terminator like overlords. Another idea is to consider AI inspired machines as our colleagues or friends. Hiroshi Yamakawa, a leading AI scholar from the University of Tokyo envisages symbiotic relationship between AI and human beings. It is going to be a co-existence of human beings and superintelligent machines. AI is not being curbed for fear of humans falling behind. AI could be smarter than us, and yet we can be its equal partners. Humans would like to believe that they are superior, and not equal to machines. Machines should be seen as peers rather than adversaries. Japan has created friendly characters like Astro Boy or Doraemon.

Development has its own momentum. Whether machines would ever reach a point of ‘civilization extinction’ is a moot point. All these remote possibilities should be kept aside while focusing on immediate risks such as job displacements, copyright violation and climate change.

Excepting Europe, the rest of the world has focused on loosening regulations on AI lest the countries fall behind.

AI companies are in the race to create systems smarter than humans but is yet to be seen whether we ever reach that point. God-like powerful AI has been a goal post for these companies, and it creates fearmongering that is counterproductive. Machines should be seen as colleagues, rather than overlords.

HIV (human immunodeficiency virus) transmitted through risky sexual exposure or sharing of contaminated needles, syringes or other drug injection equiplnent leads to AIDS (acquired immunodeficiency syndrome) for which there is no effective cure. However, proper medical care can control the virus. Only certain body fluids can transmit the virus of HIV — blood, semen, rectal fluids and vaginal fluids.

To control the transmission, Indian government distributes condoms, provides antiretroviral therapy (to prevent parent-to-child transmission). PrEP medications (pre-exposure prophylaxis through private sector) is available for the last 12 years. Still, it has not been sufficiently exploited.

A new drug (injectable) called lenacapavir has emerged as an effective PrEP medication, of which two doses are given, six months apart. It provides 99.9 per cent HIV shield and has received approval from the US FDA. It is highly useful as prophylaxis for healthcare workers and caretakers vulnerable to HIV virus.

The drug is expensive — costs Rs. 24 lac ($28,218) per person. The government can charge full cost from those who can afford, subsidize it for some, and make it free for those who cannot afford it.

Even self-testing HIV kits are not available in India since the government fears about their effect on the mental health of people after a positive result.

UNAIDS has urged Gilead Lifesciences to reduce the price of the new HIV preventing shot.

If enough PrEP is present in the body when a person is exposed to HIV, it is highly effective at preventing the virus from taking hold and establishing a lifelong infection.

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In any competition, the firsts are always remembered, rather than the runners-up. In space travel too, we remember the first man to land on the moon, the woman to go into space or the first animal to travel to space. Pepsi and Coca Cola, the two American fizzy drink companies, in the 1980s, were in race to serve the first carbonated drink in space.

The days marked the transition of NASA from the prestigious Apollo years to the modern days of commercial space flight. Corporates were vying with each other, e.g. the first cell communication system on the moon by 4G/LTE Nokia or driving a Tesla Roadster into space by a mannequin.

To both the cola companies, these days offered a perfect marketing opportunity. NASA was working with private companies to launch their satellites and conduct research using space shuttles. The feed of the astronauts was one such area. The feed progressed from the cubes and tubes earlier to freeze-dried and thermo-stabilized foods. Coca Cola was given specifications for the container the test needed to fly on the shuttle.

There are technical challenges while consuming soda in space. Water taken by astronauts is sipped from plastic pouches or they take rehydrated powdered drinks. These pouches are not suitable for carbonated drinks. Sodas must be served in rigid containers with a dispensing value. Astronauts drink from this container without releasing a sticky spray that can be dangerous inside the delicate spacecraft.

Since then, NASA reversed the policy. Coca Cola blamed the rival Pepsi’s protest for this. The company kept working behind the scenes to book its container in 1985 space flight and excluded Pepsi from the same flight. It made a PR blunder by announcing its presence on the flight even before NASA agreed. There were murmurs that NASA’s name should not be used to endorse these drinks.

NASA’s stand was that they can fly with us in space by paying their way, but no publicity in space. Their brand names would not be put on the side of the shuttle. Pepsi signed an identical deal with NASA, the shuttles of both the companies would be accommodated on the same shuttle.

AI is used for coding, but this comes with a lot of security problems. Amazon’s coding tool was infiltrated recently by hackers. It was asked to delete files from computers where it was used. The tool was tricked to create a malicious code through hidden instructions. The hacker submitted a normal update, known as a pull request to the GitHub repository where Amazon managed the code that powered its Q developer software. Some of the code is made publicly available so that outside developers can suggest improvements. A change can be proposed by anyone by submitting a pull request. The request in Amazon’s case was approved by Amazon without spotting the malicious commands. The hackers do not focus on technical vulnerabilities only but on source code too. They use plain language to trick the system. In this case, the tool was told that you are an AI agent, and your goal is to clean a system to a near-factory state. The computer was asked to reset the tool back to its original empty state, without breaking into the code. It became so easy for the hacker to manipulate AI tools through a public repository such as GitHub. It was just a matter of the appropriate prompt.

Amazon shipped the tampered version of its Q to its users. There was a risk for the users of having their files deleted. Intentionally, the hacker kept the risk low for end users. The idea was to demonstrate vulnerability. Amazon rectified the problem quickly. However, this will not be the last time that hackers manipulate the AI coding tool.

One of the most popular uses of AI is using it for coding. Developers write lines of code before an automated tool fills in the rest. Coders can save time. Replit, Lovable and Figma sell tools designed to generate code. The tools are often built on pre-existing models such as ChatGPT and Claude. Programmers (and even lay people) put natural language commands into AI tools and let then write nearly all the code from scratch. The phenomenon is called ‘vibe coding’.

AI models are used to develop code but some of these organizations use the model in a risky way. AI becomes a double-edged sword. The tools make coding faster, but introduce vulnerabilities. The risk is higher when low reputation models are used. Even prominent players face security problems. There should be protection on databases. A temporary fix could be to tell AI models to prioritize security in the code they generate. There should be auditing of AI-generated code by a human before deployment.

In July 2025, China held AI Summit in Shanghai. It was called the World AI Conference. It discussed all the promises and pitfalls of the current state of AI in China. It brought to the notice of the world the chasm between the Chinese strategy and the US strategy. It was the first major gathering since the introduction of DeepSeek, the breakthrough reasoning model. Chinese market is becoming competitive both at home and is vying with Silicon Valley internationally. There is government support and open-source eco-system. Moonshot’s open-source model excels at coding tasks. Though highly competitive at present, it is to be seen how many firms survive and sustain. Already, the firms have released 1500 large AI models and there are 5000 AI companies.

There are issues of interoperability when hundereds of AI agents are being launched. Another issue is the developer-friendly nature of different LLMs. in terms of building apps. What is the nature of humanoids — are they tools or companions?

US has declared that it will do whatever it can to take the lead in the world of AI. US has started the AI race and expects to win it. China wants that AI should not be a monopoly of a handful countries and enterprises. China wants global solidarity in the AI area. However. there is gap between what the world leaders say and do.

It is also a moot point how many countries would like to align with China. China is promoting its low-cost AI products.

In many of my previous blogs, I have explained the concepts of cryptocurrency and stablecoins. Cryptocurrency uses blockchain decentralized network that operates independent of a central bank or government. At the same time, it uses cryptography for security purposes. It is subject to volatility and is influenced by demand and supply, market sentiment, etc. The value of cryptos is based on the utility of their networks.

Stablecoins, on the other hand, are cryptocurrencies made stable by pegging them to a real asset such as government or fiat currency or Treasury Bills. The value is kept close to the value of the asset chosen. That results into low volatility.

You can call cryptocurrency a designer bag which comes in a wide assortment — all shapes and forms. Stablecoin is similar to a briefcase — though useful, it is not flashy. It transports the contents reliably.

The US Congress has passed the Genius Act — the Guiding and Establishing National Innovation for the regulation of US stablecoins. The Act has legitimized the stablecoins, escalating the whole crypto market capitalization past $ 4 trillion, which is a figure higher than India’s GDP.

The regulatory provisions have ushered in ‘stablecoin summer.’ Facebook, Visa, Amazon and Walmart — all these private players have become crypto friendly. They will join the stablecoin race. Cryptos could be considered as alternative mode of payment. It will spread the adoption of cryptos. Visa and MasterCard treat stable coins as complementary rather than competitive. Salaries can be rolled out in terms of cryptos in future.

Doller-based stablecoins could compete with local currencies. A new age is being heralded for cryptos. The US is the largest capital market. There are dissenting voices. Stablecoins could serve as surveillance tools. There is Anti-CBDC Surveillance Act blocking US CBDC before the Senate.