Bitcoin reserve was established by an executive order of the US President on March 6, 2025, to avail of the first-mover advantage.

The order describes it as digital gold. Since there is a finite supply of BTC, it gives a strategic advantage to be the first among nations to create its reserve.

The US Departments of government would transfer all BTC they hold and have forfeited from criminal and civil proceedings treating them as US Digital Assets Stockpile (DAS). The additional BTC will be acquired in a way that does not cost taxpayers.

The US government by creating a reserve, treats BTC as a viable asset. By calling it digital gold, it can be used as a means to diversify assets holdings which can hedge against inflation and be the store of value (like gold reserves). BTC is treated as first among equals in all cryptos. The US holds 2 lac BTCs (17 billion) mostly obtained through criminal forfeitures.

The US actions inspire other nations to institutionalize its use. It could lead to a big shift in the global financial system. It could lead to infrastructure such as exchanges and indices. It could enhance the value of BTC.

David Sacks, the Crypto Czar, point out that BTC has $ 2 trillion market cap. It is the most secure — it has not been hacked so far. The executive order is to be converted into law. BTC is not just a technological opportunity. It will provide financial leadership to the US in 21st century. The US could reduce its deficit (without raising taxes) if the BTC appreciates in value. It may strengthen US dollar.

Cryptos are not yet regulated and are extremely volatile. SBR attempt tries to legitimize BTC. The President and his close associates may hold significant quantities of crypto portfolio personally.

India has brought digital assets under the prevention of Money Laundering Act, 2023. The service providers have to register with the Financial Intelligence Unit (FIU). Taxation policies discourage holding cryptos. India’s digital financial system consists of UPI, Aadhar-enabled payments and the digital currency. The RBI feels cryptos can undermine monetary policy, create fiscal risks and encourage capital flows.

Coinbase, the US-based crypto exchange, has been given green signal to re-enter crypto trading by the FIU in March 2025. It is to be seen how long India keeps cryptos at an arm’s length.

2024 is considered to be the second-best year for Indian cinema in terms of box-office collections — the industry earned Rs.11833 crore, slightly short of 2023’s Rs.12,226 crore (Ormax Media Report).

Despite this, cinema footfalls do remain below the pre-pandemic levels. In 2024, the drop in footfalls was 6 per cent. The decline could be attributed to digital platforms where movies get released immediately after the theatrical release or in some cases at the same time. In addition, OTT platforms are ad free, and the content can be consumed anytime.

Cinema halls do excessive advertising — in a recent case a multiplex screened a movie 25 minutes after its scheduled time, and the audience sat through the ordeal. The Consumer District Commission received a complaint and penalized the theatre.

The HC stayed the ruling. The tickets must specify when the actual movie will begin.

The issue is how to maximize the revenue without irritating the audience. The duration of advertising time could be limited, say screening time of 10 minutes if the audiences are to be kept happy.

Brands should focus on creating concise and visually compelling ads that resonate emotionally.

Indian cinema advertising grew by 10 per cent in 2024. It is expected to grow by 9 per cent in 2025. The ad revenue earned is Rs.950 crore. It is 8-10 per cent of cinema revenues.

Advertisers get the benefit of a large screen impact in cinema advertising.

With big blockbuster’s, the theatres tend to screen ads for 30-45 minutes at each screening and weekend shows.

This is a risk that they cannot afford to take .

Excessive advertising does infringe upon consumer rights. Theatres should tap other avenues such as digital displays, interactive kiosks and in-lobby promotions.

Big Tech (Google, Facebook, X, Amazon, Microsoft) not only connect us with others, but also shape how we think, consume and interact with the world.

Capitalism always favours laissez faire, since deregulation is assured to be dynamism and freedom. On the contrary, unrestrained power does not ensure freedom.

Big Tech acts as gatekeepers of information, commerce and public discourse. They have the potential to undermine competition and free enterprise. However, there are voices that regulation could stifle innovation.

We have adopted a digital lifestyle which generates voluminous data, and it is controlled by a few players.

Startups are vulnerable to the digital eco-system. This vulnerability could be weaponised.

The availability of data with Big Tech poses a challenge to state’s exclusivity and sovereignty.

Canada and Australia try to control digital news distribution. Legislation have been introduced to compensate for the content. To retaliate, Facebook blocked government ads in Canada. In Australia, Big Tech retaliated by banning the sharing of news and links.

Big Tech extends US state power globally. The US gets extraterritorial reach. Much of the world’s communication flows through the US. SWIFT’s allows US agencies access to its database. PRISM (NSA) taps user data. Huawei has been placed on US entity list.

India has to strengthen data protection (implementing Digital Personal Data Protection Act, 2023). Some critical data should be stored within the country. Innovation must flourish, but not at the cost of undermining a country’s interests.

Ilaya Sutskever, co-founder of OpenAI and now head of Safe Superintelligence in a recent conference in Canada expressed his opinion about the current AI systems based on pretraining. According to him, pretraining, as we know it, is going to end unquestionably. LLMs learn patterns from vast amounts of unlabelled data sourced from internet, books and other sources. Already, the data drawn has reached its peak, and there is no more data to be drawn. He compared this situation to fossil fuels — oil is a finite resource. Similarly, internet contains a finite amount of human-generated content.

Existing data can still take AI development farther. However, the industry is tapping out on new data to train on. There will be a shift away from the way the models are trained.

The more a system reasons, the more unpredictable it becomes. AI systems which play chess are unpredictable to the best human chess players.

Sutskever is optimistic about agentic models, which can understand things from limited data. Apart from being agentic, future systems will be able to reason. Today’s AI is mostly pattern matching based on what the model has seen. Future AI systems will be able to work out things step by step, more like the way we think.

Sutskever feels that futue models would depend not only on text but also on muli-modal data , including computer vision

The world is after attaining AI supremacy. Already a low-cost DeepSeek –R1 has caused a global sensation. India too proposes to develop indigenous AI models. India intends to support compute power by a stockpile of 18,693 GPUs. There is a proposal of 40 per cent subsidy to developers. It will reduce per hour computing costs.

The state-of-the art global models too suffer from latency and slow response time. They are less efficient than SLMs. LLMs are good performers. Distilled models (DMs) stand in-between. They are relatively less efficient than SLMs.

India cannot remain confined to one type of model. India needs foundation models and LLMs for advanced research in defence, national security and atmospheric studies- – to predict adverse national phenomena, DMS may be used. To answer a query of a farmer in his native language, a model of small and medium size having NLP capabilities could be used.

Generative AI models are capital intensive. Using GPT-4 for finding chemists selling surgical masks is a waste of resources.

LLMs such as Llama-2 and DeepSeek follow ‘open weight'(OW) system of disclosure. It permits users to fine-tune the parent model for customized requirements. OW also enables researchers to test fairness and safety features of a model.

OS models such as Mistral and Falcon not only disclose weights and codes but also information on datasets. Users can do unlimited modification to the parent model and create new models. India’s A14 Bharat model is a pure OS model.

Open Weight models do not disclose training data. They can face lawsuits in markets.

Since its release in November 2022, ChatGPT has stirred the users so much that they wonder about the capabilities of Large Language models (LLMs) in particular and AI in general. It is difficult to come across someone who has not experienced the power of ChatGPT. While all these tools such as GPT, Gemini or Claude are powerful with hundreds of billions of parameters and pre-trained on vast corpora of text, they are not omnipotent. These models fall short for specific tasks — however these models can be used for speafic tasks by fine-tuning them by using techniques such quantizatron and LORA. There are some libraries for fine tuning.

Fine-tuning is an expensive process, especially a model with a large number of parameters. Models with less than 10 billion parameters can be fine tuned without any significant infrasructure changes. For larger models, we require approximately 1.5 terrabytes of GPU vRAM. It is equivalent to a cluster of 20 Nvidia A 100s, each with 80 GB of vRAM. This set up costs $ 4 lac. The assumption is the hardware is available.

Alternatively, one can use one of cloud providers (AWS, Azure or GCP). This approach too is expensive. An hour of using A 100 GPU on AWS costs $40. If a model is fine-tuned on 20 GPU for 5 days, it would cost about $1 lac.

That is why researchers use smaller LLMs with less than 10 billion parameters. A Mistral can be fine-tuned using Nvidia A10 on AWS. It takes 10 hours, costing less than $20. However, the model requires quantization.

Quantization converts model’s parameters to low-precision data types — 8-bit or 4-bit. This reduces memory consumption and speeds up execution. All 32-bit values are mapped to a smaller range of finite values — 256 for 8-bit conversion.

Another technique LoRA is low-rank adaptation. Here model’s weights are updated using matrix dimensionality reduction. Transformers, as we know, rely on matrices. Here parameters are adjusted within these matrices. In LoRa, two smaller matrices are created for updation.

A Matrix with 1000×1000 parameters (totaling 1 million parameters) can be decomposed to 1000×1000 multiplied by 100×1000 matrices. This reduces parameter count to 2*100k (a reduction of 80 per cent in parameters). The approximation is less precise but still it improves memory and computational efficiency significantly.

Quantization and LoRA can be used in combination. It is called QLoRA.

To begin fine-tuning anew, Unsloth Python library is used. After pre-training an LLM, there is supervised fine-tuning (SFT). The tools used are SFT and PEFT (Parameter Efficient Fine-tuning) from Hugging Face. In addition, LoRA and quantization can be easily applied by using BitsAndBytes (by Tim Dettmers).

Lastly, after pre-training and supervised fine-tuning, the model is informed which generated outputs are desirable and which are not. It is called preference optimization. The techniques used are RLHF — reinforcement learning from human feedback and Direct Preference Optimization (DPO).

In March 2024, a new technique has emerged called Odds Ratio Preference Optimization (ORPO) combining supervised alignment.

The Indian IT industry stands at a critical inflection point. So far Indian IT industry has utilized its talent pool by putting them on client projects domestically and offshore. It is just trading services. There is a need to shift to a product and IP-centered model out of India. This has become all the more necessary after the US launching its StarGate programme and the debut of DeepSeek from China.

India should focus on innovation by concentrating on increase in R&D expenditure, say in LLMs and SLMs, quantum computing and cyber security. It should allocate a higher percentage of revenue to fundamental research. The investment of global technology leaders to R&D is 15-20 per cent of their revenue. There should be collaboration between academia, industry and government institutions. The core research in India will act as an enabler to develop indigenous AI solutions. At present it is 0.7 per cent of GDP falling far behind the global average of 3 per cent. IP processes should be streamlined to provide protection of innovations. India should build up investor confidence.

There is a missing link to support investors through all stages of capital support. The startups here are supported by angels, venture capitalists and private equity. They require capital returns in 3-5 years. The focus should be on product development which should be incentivized. There should be a talent pool well-versed in cutting-edge technologies — it requires streamlining educational curricula as well as training within the company and massive reskilling and upskilling programmes. We lack entrepreneurs who will focus on deep tech and product thinking. Cities in China and organizations such as Alibaba have invested wider and deeper in AI than we have done as a nation. However, it is never too late.

The transformation is not going to be linear, but requires a through change in organizational culture (OC).

By an executive order, the US President has established a Strategic Bitcoin Reserve (SBR) and a stockpile of other digital assets.

As we know, Bitcoin is the original cryptocurrency and its supply is limited. Thus, having its reserve is strategically advantageous.

The initial contribution of capital is derived from the forfeiture of assets by the US Department of Treasury since these were involved in criminal or civil proceedings. The other agencies too will transfer Bitcoin owned by them to SBR.

The order also creates a US Digital Asset Stockpile consisting of assets other than Bitcoin owned by the Department of Treasury and derived from criminal or civil asset forfeiture.

There is no clear policy right now how these assets are managed. There is lack of accountability. There is insufficient exploration of options to centralize, secure and maximize their value.

The US government, according to an estimate, owns 2 lac Bitcoins, though there is never a complete audit. The E.O. directs of full accounting of all digital asset holdings of the federal government.

There will not be any selling of Bitcoins deposited in this reserve. It will be maintained as a store of reserve assets. The Secretaries of Treasury and Commerce have been authorized to develop strategies for acquiring additional Bitcoin.

It is a strategic advantage for the US to create this Reserve since there is a fixed supply of 21 million coins. There should not be any premature sales.

The stockpile will have Ethereum, XRP, Solana and Cardano, apart from Bitcoins.

The US will not acquire additional assets for the Reserve beyond those derived from forfeitures.

The full accounting will be provided of all digital assets to the Secretory of Treasury and the Presidents Working Group on Digital Asset Market.

On one February, 2025 Sunday, President Trump announced the formation of a US Crypto Strategic Reserve. He will host the first-ever White House Conference on cryptocurrency. It has shaken up the worldwide crypto enthusiasts. He himself and his family holds substantial crypto assets.

India is cautious about promoting cryptocurrency. The RBI here feels crypto’s unchecked expansion could adversely affect monetary policy, create financial risks and bypass capital flow regulations. If practised excessively, there could be diversion of resources from the real economy –it results into economic instability.

Crypto advocates vouchsafe for its decentralization and financial freedom. The reality is different. Crypto investors fall prey to influencers and stack up high risk assets.

It is to be seen whether RBI mellows down its strict stance.

Crypto currency transfers are opaque. Though official channels could be used to buy cryptos with KYC back-up, what happens to the assets once these are stored in the wallet is dicey. Tracking these transactions is like looking for a needle in the haystack.

Cryptos affect geopolitics too. The developed countries use financial innovations as a tool for diplomacy. If India opts for such a model, it is opting for short-term speculation over long-term stability.

Cryptos thrive in a weak regulatory environment. It shifts to most lenient jurisdictions. These are high-risk traps. The US could trigger a wave of capital flight. Investors think they have entered a stable market. Crypto crashes leave investors in financial ruin.

India has built a robust finance eco-systems — UPI, Aadhar-enabled payments and the digital rupee. These should be expanded and strengthened. India should not encourage unregulated digital assets. It is not an issue of managing the risks today, but the issue is to have financial future against chaotic speculation.

No doubt, the RBI is doing a thankless job, but it is crucial. It has to protect us from financial shocks.

The White House can have a crypto party. We are better off while exercising sobriety.

Will AI affect my job? It is an issue that is echoed all over by the white-collar workers — in fact it is the fear of being obsolete (FDBO). The concern has some basis.

All knowledge workers will be affected. The omnibus term knowledge workers include administrative and managerial jobs, financial analysts, software developers, legal personnel and creative people. It is a massive upheaval and needs a back-up plan.

Knowledge workers constitute around 50 percent of the UK’s job market, and 70 jobs out of these 50 per cent could be affected or replaced by generative AI. Employees in the US making more than $2.5 lac a year constitute 50 per cent of the US consumption. You can imagine what happens when these high-net-worth individuals are affected.

Software engineering agent will be able to perform what a software engineer with several years of experience performs, that too without human supervision. Coding could be done by virtual co-workers. They could have problem-solving abilities of a mid-level engineer very soon.

AI agents can be deployed on a large scale. It means though there will be new jobs and augmentation of existing jobs on account of AI, it still would result into job losses. Already, there are signs of recession in MBA level jobs, stagnation in consultancy jobs, no jobs for the translators and lay-offs in the IT sector.

In finance, a prospectus before an IPO can be drafted by generative AI in minutes, which otherwise takes a six-member team two weeks to produce. Contract and temporary staff in banking could be curtailed while AI takes over their roles.

In the back-up plan, one must familiarize with AI tools to protect oneself. One must keep networking on LinkedIn in case one is not offered a lifeboat for another career. It is also necessary to be alive to entrepreneurial opportunities.

There is no guarantee that one will retain one’s job, but a back-up can keep FOBO at length.