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Sundar Pichai, Google and Ruchir Sharma, investment-writer have expressed reservations about the AI hype that could lead to an AI bubble. There is a distinction between the rea l transformational nature of AI and the market euphoria that surrounds it. Google’s reservations are relevant since it is from a company whose rise coincides with the progress of AI. Google does not foresee the collapse of AI but expects us to have realistic expectations. AI has to be integrated into business and society. Ruchir Sharma is concerned about the rising stock prices of tech firms not justified by their earnings. VCs rushing to fund the startups’ AI layer reminds us of the bubbles in the past.

It is true AI has the potential to change the world but that does not make every AI company worthy of sky-high valuation. There are triple-digit price-to-sales ratios, optimistic revenue projections, and business models which rely on subsidised computations. There is heavy capital expenditure commitment.

AI firms are treated as high-growth software firms. The cost structures resemble those of public utilities. There are heavy infrastructure investments (cloud providers, GPU investment). It is to be seen how fast these investments could be monetized. Can there be as fast AI adoption? AI could be a low-margin commodity rather than a growth engine. This does not mean AI story is illusory. It only means that the hype cycle must be separated from the underlying value. Policy makers and businesses can avail of the AI Opportunity, but they must prevent the unnecessary hype not supported by fundamentals.

There should be balance between ambition and pragmatism. AI investments must be prudent. The expectations are to be managed in the midst of a dynamic innovative environment. AI’s lasting impact does not depend on short term market excitement. AI has to be integrated to social and economic systems. The integration must generate real value. A transformative technology should not be overshadowed by a financial bubble.

Nvidia, the American chip making company, claims that its technology is a generation ahead of the industry. There is speculation that Google is inching towards a big place in AI space by using TPUs or tensor processing units.

What is central to the development of AI are the semiconductors or chips which enable machines to process huge amounts of data. Nvidia occupies the leadership position on this frontier. Its chips run every AI model and its influences every place where computing is done.

Of late, it is reported that Meta, the parent company of Facebook and WhatsApp, could possibly strike a deal with Google to use its TPUs for the data centers. Traditionally, it has been using Nvidia chips. Nvidia has secured a $ 5 trillion valuation in late October, 2025. It is the first company to do so. Alphabet, the the parent company of Google, has also acquired the status of crossing the $ 4 trillion mark in November 2025. These two developments highlight the rivalry between Nvidia and Google. In fact, there is a recent slide in Nvidia stock.

In the early stages of LLM training, Nvidia’s graphics chips played a vital role in number crunching. It led to a surge of demand for Nvidia’s GPU chips such as Hopper or recent Blackwell chips. These are more flexible and more powerful than Google’s TPUs.

The TPUs are an altogether a different chip category called application-specific integrated circuits or ASICs. These are designed to run AI-based compute tasks. These are more specialized than CPUs and GPUs. It is too early to compare TPUs and GPUs in terms of cost and performance. It is always a welcome proposition to have more suppliers of accelerated compute. Still Nvidia has a 70 per cent margin.

TSMC, the Taiwan-based chip maker, is cautious about the enhancing the supply like crazy. It is possible that we AI bubble may burst, and if that happens, there will be no orders and lots of idle capacity. The new entrants such as Google will have to consider this factor.

TPUs have been developed for the last one decade, and these have been sold for the cloud business for the last five years.

Still Nvidia retains an edge by providing software to complete the whole ecosystem along with chip hardware. The software of API or Application. Programming Interface consists of a set of defined instructions that enable different apps to communicate with each other. This is called CUDA. It facilitates parallel programmes using GPUs. Thus, GPUs are deployed in all supercomputing sites around the world. In mobile computing, Tegra mobile processors are used. These are also used in vehicle navigation and entertainment systems.

TSMC from Taiwan is a backend player in semi-conductors. Nvidia, Intel, AMD, Samsung, and Qualcomm are the front-end players.

In computers, the most important component is the CPU. Here Intel and AMD are the market leaders. GPUs are the new addition to computer hardware. Initially, these were sold as cards that can be plugged into a PC’s motherboard to add computing power to an AMD or Intel CPU.

Nvidia chips powered the compute surge needed in high-end graphics for gaming and animation apps. AI apps later adopted GPUs by relying on their tremendous computing power. Computers are thus getting GPU-heavy in the backend hardware.

Advanced systems used for training generative AI tools now deploy half a dozen GPUs for every CPU. GPUs are no longer just add-ons to CPUs.

Google has to sneak into this market with its specialized chip. There are manufacturing constraints. And it is a matter of being a part of the ecosystem created by Nvidia.

The gold standard for big tech firms and startups that need compute power to run and develop AI platforms is Nvidia chips. Since quite some time Nvidia stock is facing headwinds as investors fear an AI bubble.

As we know, graphic processing units or GPUs , from Nvidia were created to accelerate the renderings of graphics — mainly in video games and other visual effects applications. However, these GPUs turned out to be well-suited to training AI models because they can handle large amounts of data and computations.

Google uses TPUs or tensor processing units which are application-specific integrated circuits or microchips. These were designed for a discrete purpose. The same tensor chips were adapted as an accelerator for AI and ML tasks in Google’s own applications.

Google and DeepMind both develop cutting edge AI models (such as Gemini) and they make available lessons so learnt to the chip designers.

Google wants to tie up with different organizations to establish tensor processing units or TPUs in data centers. Google can rival Nvidia as a leader in AI technology. Meta or Facebook plans to use Google’s TPUs. Google cloud offers both TPUs and Nvidia’s GPUs. Anthropic has already agreed to buy 1 million TPUs from Google. It shows third-party users providers of LLMs are likely to leverage Google as a secondary supplier of accelerator chips for inferencing in near future.

Growth is often resisted but is critical for prosperity. It adds to longevity and comfort and lessens the monotony of work. Still growth faces resistance as it causes upheaval and brings us come face to face with uncertainty. Previously people used to work on their own and resisted being employed by factories. The transition was slow.

Growth takes time. The nature of work and production process changed by some innovations. Some hitherto unknown innovations such as Steam Engine in the wake of Industrial Revolution were strange innovations — it was not known what to do with it. It took almost a century for these innovations to affect productivity.

Growth is unpredictable. Innovations are disruptive. They destroy jobs and create new jobs. They are transformative but it is difficult to realise their full effect.

Growth is cultural. Great Britain was the pioneer in availing of good effects of industrialization. Other countries continued to invent and had more wealth. They had more resources and better environment, Morky’s work focuses on the culture of growth.

Growth is not inevitable. Over a period of several centuries, the economics of the world did not show any growth. The last few hundred years were exceptional, where growth accelerated. Innovations lead to other innovations, and the cycle of growth gets momentum. What is required is the right conditions for growth. It is not guaranteed.

Since lower growth is always problematic, it prods the governments to participate more actively in the economy. Economic policy has certainly a role to play. Still, governments should realize that better planning does not always result into abondance. What is crucial for growth is an element of openness – — openness to risk, uncertainty, change and creativity. This is the greatest lesson we learn from Mokyr’s work.

Growth models of future will be driven by innovation. The proposition has been studied in detail by 2025 Economics Nobel laureates — Joel Mokyr, Phillipe Aghion and Peter Howitt. The winners have worked on mathematical models at the macrolevel to boost growth through innovation.

They have drawn from Joseph Schumpeter’s concept of creative destruction — it makes a product obsolete through a natural process of obsolescence. Some events such as Industrial Revolution took the world by storm in the mid-19th century. Later the process loses steam on account of imitation — no distinguished products or superior quality. Monopolies become imperfect competition. With innovation, there is a natural process of creative destruction. The ones who innovate move ahead, and others lag behind and whither. It affects subsequent growth cycles. There is disruption on account of innovation in electronics, engineering and automobiles. Innovation has driven East Asian story and the rise of China. It is true for Japan in the sixties and seventies, and for Asian tigers.

Mokyr speaks about propositional knowledge (not practically feasible) and prescriptive knowledge (which works in the real world).

The best results are obtained through the support of the financial system — availability of funds at competitive rates.

India has witnessed rapid strides by fostering startups. The technology is either indigenously developed or borrowed. There is import substitution in India, and most things are manufactured here. The government here creates a conducive environment and foreign direct investment pours in. Such innovation brings in technology too. It should be easier for companies to borrow from external sources. It fills the gap in financing innovation.

Innovation plays a great role in the growth process. In a globalized world, both ideas and funds could be borrowed. It facilitates growth faster. At the micro level, innovative firms enjoy a competitive advantage.

The economist trio has done their research pre-AI. And AI’s disruptive nature is yet to unfold. It prints to the path that is to be navigated in future in the presence of AI.

Gold’s meteoric price rise makes Wall Street dizzy. The run is semi-rational and has caught professionals off-guard. Many do not have gold holdings and are thus missing out on the bull market.

Can higher allocation for gold be justified? Gold does not generate any income. Neither there is any clear measure for its fair value. The cost of production for the metal stands at $ 1500 per ounce. It offers little guidance why the metal trades at $ 4200 — $5000. Central banks will keep interest rates low, and major currencies lose in value. Money managers adapt to this new normal. If investors increase their demand even by 14 per cent, the metal will reach $5000 by next year.

Wall Street follows the liquidity train. The rise in price commenced three years ago as China’s central bank and Chinese households increased gold holdings. This sentiment spread to the USA. The mom-and-pop traders are the market leaders. American households can see gold as an essential hedge against inflation and devaluation of the dollar. That makes the gold surge higher. The Chinese action is also a wild card. The People’s Bank of China has increased its gold reserves.

The executives are going with the flow but are risking their credibility.

The case for bullish grid is built on shifting sands.

The lead actors and actresses of romantic comedies — a genre of movies that survives by the empathy — did appeal to the audiences. Still of late, the theatre releases of Hollywood Studios have fewer romantic comedies. Netflix releases several romcoms. Yet none of the subscription-platform’s releases matched the theatre-released golden age romcoms. These days the audience is scattered across so many platforms. Theatrical releases bridge the gap by turning movies into memories.

Traditional studios had incentive to release romcoms in theatres. These movies were smart bets. They had a receptive audience. The budgets were reasonable. Rest of the mainstream movies had high production costs on account of special effects. Romcoms were bargains because the only investment was in talent — likeable stars, intelligent writers and capable directors.

A Finnish company markets these smart rings. It is a health-tracking wearable with a generative-AI-powered- advisor. The other wearables track health, but they do not guide us to make sense of the data. Oura Ring leverages generative AI to make sense of the data. There are alerts on the symptom radar. You can open AI advisor and ask it to interpret the alert.

Many C-suite executives and celebrity users have been spotted using Oura Rings. It is estimated that by the end of 2025 the rings will generate a revenue of $ 1 billion — double the revenue generated in 2024. Oura sells 5.5 million devices. It has market valuation of $ 11 billion. It has tied up with US military.

Oura tracks sleep pattern. Some users have poor sleep score — it is demoralizing for them. They can stop wearing the rings. The growth of wearables slows down. AI advisor can counsel the users and encourage them to continue its use. There could be ways to make the rings more proactive. The users, on an average, interact with the AI advisor thrice a week. The advisor is empathetic.

Rivals may not have AI advisor, e.g. Fitbit and Apple. MIT studies indicate that the addition of AI advisor makes the devices more useful.

The wearables quantify the human anatomy, but these statistics must be explained. These keep the users engaged.

At a recent event in London, celebrities expressed freely about the beauty of real diamonds since these are timeless, elegant and enduring. The event was organized by the De Beers.

It takes billions of years for real diamonds to emerge deep into the earth. They pre-date the emergence of human beings on the earth. They are purely nature’s artistry. They are unique since nature formed them. This uniqueness enhances their beauty. They are vested with individuality. They have some imperfections, and that is why they stand out.

They have originality, and that makes yoy connect to them. They are rare and real. People connect to them emotionally.

Synthetic diamonds can imitate the look but not the journey. It is the journey that shapes them. It gives brilliance to the real diamonds. In the journey, they acquire authenticity.

We value their eing scarce. There is no rush in creating them. They thus aquire a new meaning.

Synthetic diamonds being scarce. There is no rush in creating them. They thus acquire a new meaning.

Synthetic diamonds may shine bright, but they lack a story. anything that has story behind it is much more beautiful and meaningful. There is something beyond the sparkle — the history and emotions associated with real diamonds.

The US is the centre for Artificial Intelligence (AI). Europe is a laggard here. However, some prestigious companies in Europe are entering AI field, e.g. Legrand in France and Schneider Electric both founded in 1800s. They have rushed in for joining the data-center’s gold rush. There are heat-dissipating coolers and power management tools. The capital investment could reach to $ 7 trillion by 2030. Siemens from Germany and ABB from Switzerland have their own datacenters. The Magnificent Seven would like to promote the ‘Data Center Four’.

Legrand based in central France has crafted light porcelain switches to earn its revenue from kit such as rear-door heat exchangers used to cool servers. The major clients are Amazon, Google and the US Big Tech. This infrastructure is not glamourous but is critical for training LLMs. The infrastructure gobbles electricity 10 times more than the traditional data centers. European companies expect to earn more from AI.

There could be risks of diminishing returns on such heavy investments. The competition may not sustain. There could be local opposition. If there is a market melt-down of a tech company, the carnage could be heavy. However, Europe has a safety net. The industry may move from the US to Europe.

The crucial point is how far there would be adoption of AI in Europe. Very few European companies use AI — 13.5% in 2024.

There is declining birth rate here. and there is declining productivity. If more companies adopt AI, that could be a gamechanger.