SBI Funds puts AI to use

 SBI Funds Management IPO opened on July 14 till July 16. Here we look at their use of AI. 

The RHP acknowledges that they so use, and may continue to use, AI, and that it has risks: specifically, third party vendors, security risks and data issues. It also noted the flight of capital from India due to the perceived lack of AI action in India. Further, it noted that AI is being increasingly used in the MF industry for personalized servicing, enhanced risk monitoring and operational productivity. Interesting to note that SEBI now explicitly mentions AI as one of the tech that companies must assess for safeguarding data. 

Srinivas Jain, Chief of Strategy, Digital and Technology, and Head- Investor Relations was fairly candid about AI before the IPO opened. He calls SBI funds a People Technology company. The flagship app for SBI funds is the Investap  NXT app. This app allows investors to manage their portfolios. The existing 6mn strong app now has an  ai wrapper making the AI as the front interface, allowing more personalized interactions. 

The other major AI initiative is the data lake within the company. This is an insider trading surveillance platform that analyses portfolio manager communications and data for patterns of potential insider trading. It does give false positives, and the final call is always human.

AI is also used for reports and data- something that every equity research, PE/VC, trading house now uses.  Not to use AI in these functions would be seriously restricting competitive offerings. 

AI Boom vs Dotcom Bubble: What’s Different About the 2026 AI Frenzy?

The AI boom has some striking parallels with the dotcom bubble of the early 2000s. But it also has some very distinct differences.

Like the dotcom era, simply adding “AI” to a company name today almost guarantees investor and media attention. In India alone, MCA filings show that in April 2026, 408 companies registered with AI and AI tech related names against 237 in April 2025 and about 86 a year earlier. In the UK, an average of 2,000 companies are being registered with AI in their names, according to the UK AI Sector Study 2024/25 and DataCentreNewsUK. In the US, FGS already tracks more than 8,000 AI companies.

The second interesting aspect is funding patterns. During the dotcom years between 2000 and 2002, investment was broad-based. Companies went public rapidly, raised money from retail investors and institutional investors alike, and the frenzy spread widely across markets. In the AI boom between 2023 and 2026 YTD, the opposite is happening. Capital is concentrating, not dispersing. One company alone — Anthropic — accounted for nearly USD 13 billion out of the roughly USD 40 billion raised in AI funding in 2025. The top AI firms and GPU companies are increasingly behaving like black holes, pulling disproportionate amounts of capital, talent and infrastructure into themselves. That is one of the biggest differences between 2000 and 2026. There have still not been many major AI IPOs yet, though the market expects giants like OpenAI to eventually test public markets.

The third difference is sectoral impact. During the dotcom boom, most internet companies emerged in consumer services, telecom, portals, media and basic web businesses. The AI boom is reshaping SaaS, enterprise software, fintech, healthcare, consulting and creative services. It is a very different spread, even as AI penetrates virtually every sector in the pursuit of efficiency, automation and cost reduction. One can argue that AI’s long-term impact may prove far deeper and more permanent than the internet boom itself. It can also be argued that what took the dotcom era three years to achieve, AI compressed into barely one.

Then comes the jobs question. The dotcom boom created jobs at massive scale — in India, the West and across Asia. Programmers, Java developers, HTML coders, web designers and IT service professionals became the defining workforce of the era. Even after the dotcom crash, the broader technology ecosystem survived and absorbed much of that talent. Coding and software engineering remained among the most valuable skills globally till the arrival of ChatGPT in November 2022.

The AI boom, however, is unfolding very differently. It is eliminating conventional white-collar jobs at a pace few imagined possible — including professions once considered relatively immune to automation, such as creative work, accounting and consulting. At the same time, it is creating a new class of highly specialised AI jobs. But unlike the dotcom era, the number of jobs being created is far smaller, even if compensation is significantly higher. AI is simultaneously emerging as both creator and destroyer.

The fifth aspect is perhaps the most important. During the dotcom boom, the internet technology itself was real. The companies often were not. Many businesses simply added “.com” to their names, launched basic web services and rode investor frenzy fuelled by massive FOMO. The same behavioural pattern is visible today. The underlying AI technology is very real and is already demonstrating measurable value. The issue is not whether AI companies exist or whether the technology works. The real debate, much like in 2000, is around valuation excesses.

“AI washing” has now become a widely used term in the media. It refers to companies aggressively branding themselves around AI despite having little or no meaningful AI capability in their actual business. The crucial difference today is regulatory scrutiny. Unlike the dotcom era, MCA in India now examines the AOA and MOA of companies to verify whether they genuinely intend to undertake AI-related business activities, instead of merely adding “AI” to the name to attract inflated valuations. That is a significant regulatory check. Presumably, many of these newly incorporated firms will actually work on AI and adjacent technologies, rather than simply deploying generic chatbots. Similar concerns are now emerging in the US, UK and EU as well, with regulators increasingly pushing for tighter disclosures around AI-linked business claims. 

AI Ads Are Getting Better. The Pricing Crisis Has Already Begun!

Over the past few days, I’ve been noticing a series of ads on YouTube carrying the “Created by AI” tag quite prominently. The latest one I saw (screengrab below) was for Hindustan Unilever’s Horlicks. It’s neat, smooth and, at first fleeting glance, almost indistinguishable from a traditionally shot commercial. It’s only when you notice the “Created by AI” tag that you realize what you’re actually watching.

The Air India Express campaign — repeated ad nauseam during the IPL — also carries the disclaimer “CGI rendering of aircraft” in its final frame. That’s in line with the synthetic media rules notified by the Government of India earlier this year. If the quality of the Horlicks ad is any indication, such disclosures are absolutely necessary. Earlier too, HUL had released an AI-generated campaign for Closeup Purple, and that too was executed rather well.

Visually, the output is impressive. But beneath the polish lies a deeper shift that the industry needs to acknowledge. 

The production cost of this Horlicks AI film may well have been a fraction of what HUL would have paid a traditional production house and agency. The problem is that brands are not benchmarking pricing against the savings from a conventional shoot. They are benchmarking it against the prevailing market price of AI production itself. And like every procurement process, companies will take 3-4 quotes and then drive prices down to the floor. We have seen this behaviour in many companies in the Middle East as well. Having played one against the other, they simply ghost you! 

That creates a difficult situation for the business. In a traditional agency ecosystem, it’s not always easy to play one creative shop against another at scale. In AI production, it’s ridiculously easy. There are now thousands of freelancers and boutique studios producing everything from average-quality outputs to genuinely world-class AI films. We see this ourselves. Agencies are now starting up their own AI studios to retain wallet share. 

The second challenge is that brands know AI software costs are falling rapidly. As tools become cheaper and more accessible, the reference point for pricing is collapsing. One argument we increasingly hear — including from some of the largest global firms — is: “If the software is cheap or free, why should production cost so much?”

One of the worst things to happen is if the marketing manager generates his own AI film, is pleased with it, and says:" If I can do this, why do I need you?". 

What they miss is that using AI tools once is easy. Delivering consistently high-quality output at scale is not.

Like traditional agencies are paid for ideation and execution, techno-creative agencies also need to be paid for prompt engineering, visualization and workflow design. Writing prompts that generate high-quality, brand-consistent output is not some casual exercise. It requires conceptual clarity, visual imagination, technical understanding and production discipline.

The industry’s biggest advertisers will do the ecosystem a disservice if they reduce AI production pricing to merely the cost of software. Clients are not paying for cheap tech. They are paying for techno-creative delivery that is on-brand.

At the same time, freelancers and smaller AI creators are also hurting themselves by undercutting indiscriminately. This is a skill, like any other specialized skill. There has to be a walk-away price. Not every assignment should be accepted at any cost. In our own work, we’ve seen well-known brands haggle over pricing like roadside vendors, even if it means compromising on output quality to save a relatively small amount.

This imbalance is unlikely to correct itself anytime soon. At least at this stage, pricing power in AI-led brand immersion services sits overwhelmingly with the buyer!

Rise of AEO and the future of search

I attended an interesting event in Mumbai yesterday: the launch of Saga AI’s software for AEO as opposed to SEO (reachsaga.com).

The company’s pitch is simple but significant. It wants to help clients improve their performance on “Answer Engine Optimisation” or AEO. In other words, ensuring that a client’s content and data appear prominently and get cited when people search through AI systems rather than through conventional search engines alone.

What struck me was not just the product itself, but what its existence says about how rapidly the internet is changing.

The explosion of AI, hallucinations and all, has already created an entirely new category of software tools. In some ways, this feels similar to what Google Analytics and related products once did for SEO, when businesses first realised that visibility on search engines could make or break them.

But this shift feels even faster.

These kinds of products, at least in their current commercial form, perhaps did not exist even two years ago. Yet they are now becoming central to how brands think about visibility, influence and discovery online.

The logic is straightforward. Users tend to click automatically on the top result. Increasingly, that “top result” is not a website link but an AI-generated answer box. If your brand, data or perspective does not appear there, you are effectively invisible.

What is also interesting is that we now seem to have multiple layers of search operating simultaneously.

There is classic search: type a query into Google and receive a list of links.

Then there is AI search: ask ChatGPT, Gemini, Claude or multiple AI systems the same question and receive synthesised answers. Apparently, using several AI engines simultaneously is now referred to as “co-use”, per Google. 

Even within traditional search, the distinction between search results and AI-generated answers is beginning to blur. Earlier, search was mainly about discovery and comparison. AI search, by contrast, is increasingly about direct answers.

That shift has consequences.

One obvious outcome is the deliberate flooding of the internet with content designed to influence AI systems. This means generating large volumes of blogs, tweets, Reddit posts, Quora answers and similar material so that AI engines repeatedly encounter and surface a particular client, company or viewpoint.

But here the contradictions begin.

Google and other platforms are also starting to penalise low-quality or obviously AI-generated content in an effort to prioritise more organic human-created material. So the system simultaneously rewards scale while attempting to preserve authenticity.

I am still not entirely clear how this tension between organic and synthetic content will eventually play out in search and discivery. But it is clearly going to shape the future of online visibility and influence in ways we are still discovering! 

93,000 jobs gone.

93,000.

That was the number in The Economic Times today.

93,000 people lost jobs or roles to AI in India in FY26. Those are only the visible numbers — the layoffs large enough to make headlines. For every known company that let people go because of AI, there may well be two others that did it quietly.

That number should worry India. It certainly worries me.

For one, I may well end up being one of the 93,000 someday. Second, I worry about where those 93,000 people go next, and whether India’s economy can absorb them fast enough into other sectors. Third, I worry about young people entering a world already shaped by war, instability, slowing opportunity and now an accelerating skills disruption. By the time retraining is complete, AI may already have moved ahead again.

And finally, I worry whether we are slowly setting ourselves up for social strain at scale.

As the Government of India, my single greatest direction of effort would be employment. India has 1.4 billion people. A population of that scale needs productive work, income, mobility and the ability to build a better future. People need to earn, spend, save, invest and contribute to the economy in a meaningful way.

Companies, meanwhile, exist for profit. They are not charitable trusts. Their job is to create wealth, improve efficiency and maximize output. AI does exactly that after the initial investment. It does not tire. It does not negotiate. It does not have moods, politics, fatigue or human inconsistency.

And AI will almost certainly become one of the major productivity engines driving India toward a $5 trillion economy.

And yet.

AI may well help drive GDP growth toward 10% a year — but increasingly without proportional employment growth alongside it. That is the real disruption here. 

Jobless growth: as a parent, it keeps me awake at night. 

Because if AI creates jobless growth at scale, where do the 93,000 of this year and the millions coming go?

 

AI is a double edged sword!

The penetration of AI tools is rapidly increasing in daily life for mundane as well esoteric purposes. What was once generic for information -"google it"-  is fast becoming the preserve of the AI engines. Unconsciously we seem to click on the first result at the top of the search list- and once again we have boosted the AI world. 

The pace of change, and adoption are unprecedented! 

Inevitably perhaps, we tend to ascribe infallible status to AI, leading to a relaxation of standards which otherwise would have been rigorous. Intuitive , and often institutionalized checks and balances begin to dissipate or relax, leading to sometimes embarrassing gaffes and always chipping away at reputations and brands! 

Scan through linkedin, and you will find many examples. The latest was of a large MNC FMCG company which used AI generated images in its ads without checking that the fine print was all gibberish- the AI tool could not generate the lettering or words. These MNCs and their agencies pride themselves on top tier management/ brand management. How did this get through the sieve? 

There have been other such gaffes but equally there have been other terrific AI usages. Another rival MNC FMCG company apparently published a YouTube ad entirely made with AI- and using real life digital twins. In the fast paced ad, at first or even second glance, it wouldn't be possible to detect AI use. Perhaps this is what the company meant by "rationalizing ad spend". ( Interestingly, the use of digital twins opens up another discussion which I discussed in my previous post on NILP on this blog as well. Check it out). 

It is even more critical now for the HITL - Human in The Loop. AI cannot be the final word- that has to be the human, not perhaps for the dominance of Homo Sapiens over the AI army ( though that may soon be a concern) , but for the sheer skill and experience of the fabulous non-linear computer that is the human brain! 

Personality Digital Rights: the new AI battle

In the recent past, there's been a lot of news about celebrities going to the courts for protection against unauthorized use of their persona in social media. In other words, social media, apps, digital companies using the NILP (Name, Image, Likeness and Persona) of the celebrity without paying for it. 

That's a gross violation because the celebrity has full right to earn from his NILP- just as a company has full right to earn from its brand, product and service. It's like a fraudulent use to earn money. In addition, many celebrities have a moral hazard clause where they can refuse to endorse or give their rights to a product or service even if within the contract - say, Paan Masala or cigars and liquor. This is where highest risk lies, as the government of India now holds the endorser also liable for products / services he endorses. 

    compiled by AI 

The digital rights space is exploding as the social media markets boom. Unlike in the old days, an unauthorized use could have been restricted to one area or time; today it goes viral globally within seconds. In turn this means a product that would have cost crores for such an endorsement would get it for free if they could get away with illegal use of digital asserts. 

The root cause of concerns are deepfakes, voice cloning, imagery enabled by AI software that's getting ever more sophisticated and cheaper. The remedies fall into four main categories- cease and desist orders against John Doe (all unknown people), platform takedowns ( Youtube/ Insta/ X etc), ex-parte relief where the offender is known; and commercial bans (prevention of use in ads etc). 

The courts route is post facto action; given the scale, speed and sheer scope of NILP offenses, celebs need to look at proactive measures to protect and monetize their rights. 

How can this be done? 

For starters, it may be useful to create a repository with a firm ( like a celeb management firm or a law firm) to hold their NILP assets; this can be purely the rights to use a particular digital asset ( gait / speech/ image etc) which can be paid for and integrated by third party creators like ad agencies; and a tech database much like a vending machine in which you pay in the money, get the digital asset for a particular time and occasion, and which automatically disables once the contract is over. The former is an agency model, the latter a full-blown tech solution that allows full control and monetization. The celebs need to understand there will be a fee for this- after all, they get a majority of monetization, which is better than losing it all! 

India's AI sentiment meter trends down : anxiety on job losses increases

In December 2025 I had created and published an AI Sentiment meter in India. This was a simple assignment of weights to the headlines on AI carried in ET, Mint, HT and TOI. I chose these as the leading reflectors of business, policy and investment climate. You could choose another set if you prefer.

All headlines that mentioned job loss were assigned a weight of -5 ; all those that had job gains +5 ; all those that had efficiency and social gains were +3 , while all those that carried news on government control and support for AI use, were +3. The period I chose for analysis was March to May 2026.

The result is this - the net score is -35, with 11 negative headlines and 6 positives. there were some spikes with news of big-ticket AI data centres investment, but for the majority of people whom these papers reach, this is news that won't directly impact them, and if it does, not in the mass scale that India needs.  

There seems to be a clear anxiety in India on the job loss, actual and potential, due to AI and its adoption. As a nation, we are still at a stage where job and job losses can cause considerable social and economic friction, which would negate to some extent industrial efficiency gains. After all, if your consumers can't buy, how much will your efficiency help?

AI Sentiment Meter March- May 2026. Based on headlines on AI in ET, Mint, TOI and HT. 

That said, there is excitement on AI - and new jobs and roles will be created- but how fast and how many? Clearly, a tech that is predicament on efficiency cant crearte a billion jobs. 

If I were the government, I would be walking a tightrope. I want industry and India to grow fast, but I cannot risk social, economic and eventually political losses! 

AI Growth in India: Opportunity for Many, Uncertainty for Many

 The AI Summit in Delhi has been all over the news these past few days, and rightly so. It was India’s moment to show our skills, our scale, and our speed. Yes, there were some hiccups with infrastructure and crowd movement. Some of that was expected.

I have always felt this: remove physical infrastructure problems, and Indians do extremely well. Wherever roads, traffic, and basic systems get in the way, we struggle. But give us pure brain work, where clean air and smooth highways are not required, and we do wonders.

That said, I looked at headlines from Feb 5 to Feb 20. I gave a score of +5 to headlines about productivity gains, new jobs, and progress. I gave -5 to headlines about job losses, cyber attacks, and data leaks. Headlines that simply reported investments or applications got a 0. Then I used AI to create a heat map based on this scoring.

The result: over those 20 days, the net sentiment score on AI was +24. That is quite high, though not surprising. The AI Summit in Delhi dominated the news. There is a clear sense of excitement. A feeling of power. Even a bit of ego and aggression. All of that can be good energy.

Source : headlines in ET, BS, Mint, 28 headlines over 20 days. Score given is subjective. In general, headlines that speak of job losses ( most important worry worldwide) gets maximum negative score -5. Job creation due to AI gets max positive score +5. And so on. Output from Claude AI. 

But when I remove the AI Summit headlines, the score drops close to neutral. Under the surface, there is worry. India is concerned about job losses.

The numbers make it worse. TCS is down 12,000 people. Campus hiring is at an all-time low. Global names like Anthropic are saying most white-collar jobs could be handled by AI within 18 months. These are not comforting headlines. They are scary.

I have tracked more headlines over the months and will keep refining this model. I would be surprised if the strong +24 net score of February 2026 is repeated. 

AI in the Union Budget 2026–27: The Promise of VISTAAR for Indian Agriculture

In the Union Budget 2026–27, the finance minister placed considerable emphasis on the role of artificial intelligence in India. Among the initiatives mentioned was VISTAAR for agriculture (Virtually Integrated System To Access Agricultural Resources). In simple terms, the programme aims to use digital data and technology infrastructure to give farmers practical, usable information, while also improving coordination between the Centre, the states, and agricultural institutions such as the ICAR.

More notably, the finance minister announced the launch of Bharat VISTAAR. This initiative proposes the use of AI to transform how agricultural data is generated and used by farmers and institutions. It will bring together information on weather, soil conditions and other inputs, delivered in multiple Indian languages. The use of AI is expected to make this data more meaningful and actionable, while the multilingual approach could extend the reach of such technology and information to large sections of India’s farming community.

From Code to Characters: How AI Is Reshaping the Gaming Industry

Over the past few days, I’ve been looking closely at how AI is being used in gaming. Some applications are expected and fairly intuitive. AI-assisted scripting, character creation, storylines, and dialogue all make sense. They speed up production and help studios scale narrative content without sacrificing depth.

But digging deeper into the literature reveals more ambitious, and frankly more interesting, uses of AI. Non-player characters that learn from the player’s behavior and adapt over time are no longer theoretical. These NPCs can adjust their tactics, personality, or responses based on how you play. In some games, the AI tracks your pace, skill level, and decision patterns, then reshapes the narrative accordingly.

That alone would be impressive. What pushes things further is AI-driven adaptation at the engine level. Game code can now modify environments, visuals, and even entire worlds on the fly. That starts to feel less like traditional game design and more like something out of science fiction. The holodeck in Star Trek once represented the ultimate AI-powered experience, immersive, reactive, and seemingly limitless. That idea no longer feels fictional. We are moving steadily in that direction.

On the hardware side, AI is already deeply embedded. NVIDIA’s DLSS technology is a good example. By using AI to upscale and smooth visuals, it delivers higher frame rates and better visual fidelity without brute-force rendering. I’ve been playing Microsoft Flight Simulator since 1995, and the latest versions make extensive use of DLSS. The result is striking: richer visuals, more accurate terrain, better handling of AI aircraft, and fewer visual artifacts than ever before.

AI is also reshaping how games are built behind the scenes. With generative AI tools, it’s now possible to analyze millions of lines of code, identify bugs, and even rewrite or optimize large sections automatically. That’s a clear win in terms of faster development cycles and improved quality. At the same time, it raises uncomfortable questions about the future of traditional coding and debugging roles, many of which could shrink or disappear.

AI is no longer an experimental add-on in gaming. It’s becoming foundational. A quick scan of both digital and traditional media shows how widespread its adoption already is. Some companies are open about their use of AI; others are more discreet. Either way, its influence is undeniable, from early ideation and design through to execution, optimization, and post-launch evolution.

Gaming isn’t just using AI. It’s being reshaped by it.

LLMs vs SLMs: What’s the Difference Between Large and Small Language Models?

Most people are familiar with LLMs, or large language models. There’s another category that matters just as much in practice: SLMs, or small language models. The two are built for very different jobs.

LLMs typically have tens of billions of parameters or more. They are trained on massive, mostly open or public datasets and are designed to be generalists. You can talk to them, ask wide-ranging questions, and get fluent, generative responses.

That power comes at a cost. LLMs require enormous investment in training and operation. They depend on large-scale cloud infrastructure, significant GPU capacity, high energy consumption, cooling, and strong cybersecurity controls. Because they are cloud- or internet-based, they also introduce additional complexity around data governance and compliance.

LLMs are probabilistic systems, which means they can hallucinate. This is a known limitation. The best-known models today—such as OpenAI’s GPT models, Google’s Gemini, and Anthropic’s Claude—fall into this category.

SLMs are much smaller in scale, with far fewer parameters. They are usually trained on closed, proprietary, or in-house datasets and are designed to be specialists, not general conversationalists.

 

In many cases, SLMs are not fully generative. They behave more like intelligent lookup, classification, or decision-support systems focused on specific tasks. Because of their size and scope, they require far less compute, power, and infrastructure, which makes them cheaper to build and operate.

SLMs are often deployed on-premise, making them attractive for enterprise use cases involving sensitive or regulated data. Their narrower scope generally reduces hallucination risk, though it does not eliminate it entirely.

Both LLMs and SLMs may use internet-connected sources depending on how they are deployed. And at this stage of AI development, human-in-the-loop oversight is still essential for both.

In short, LLMs excel at breadth and generative interaction. SLMs excel at focus, control, and enterprise-specific reliability. They solve different problems—and many real-world systems will use both. Users need to know which is the best match. 

 

 

What Is RAG (Retrieval-Augmented Generation) and Why It Powers Modern AI

RAG, or Retrieval-Augmented Generation, is the backbone of modern AI tools.

Simply put, RAG allows an AI system to enhance a user’s prompt with relevant information pulled from external sources, then generate a response that is informed, accurate, and grounded. The system looks up data, filters it, and feeds it to the language model, which then composes the final answer. It can feel like magic, but it’s anything but simple.

If a large language model is the brain, RAG is the library it consults. The model provides intelligence and reasoning, while RAG supplies knowledge. In that sense, RAG isn’t just a feature layered on top of AI. It’s core infrastructure that makes modern AI practical and reliable.

RAG solves several critical problems. It reduces hallucinations by anchoring responses in real data. It mitigates stale knowledge, since models are trained with a cutoff date. It lowers the cost and complexity of retraining large models by allowing fresh information to be retrieved on demand. And it enables source attribution, at least to a meaningful degree.

Put plainly, much of today’s “magical” AI wouldn’t exist without RAG. It’s not an add-on. It’s the foundation. A true rags-to-riches story for AI systems.

 

 

The Inevitable Rise of Advertising in AI Search

So it’s happening. Ads are coming to ChatGPT.

In a way, this was inevitable. AI companies need revenue, and the cost of serving close to a billion users is staggering. The infrastructure, compute, and power demands are immense. Monetization isn’t optional anymore. It’s critical. 

OpenAI has reportedly said its AI infrastructure burn could reach around US$17 billion in 2026. Subscriptions, both consumer and enterprise, won’t be enough to cover that on their own. That will be case for most players who will adapt from current revenue models ( see infographic). 

 

sources: respective websites, media reports 

That’s where advertising enters the picture.

AI tools have aggregated massive audiences, and those users are revealing far more intent than they ever did through traditional search. The queries are detailed, specific, and often transactional. From a brand’s perspective, this is gold. If a product can appear directly inside a relevant AI response, the odds of conversion increase dramatically.

It’s easy to imagine a scramble for the first ad placement, much like bidding for the top slot on search engines today.

AI companies insist they won’t share user queries with brands. In practice, though, some degree of targeting feels inevitable. Even if the exact query isn’t shared, ads can be ring-fenced or contextually matched, similar to how Google AdSense works today. Leaving that kind of money on the table would be hard to justify.

OpenAI also says users will be able to turn off personalization. Take that with a pinch of salt. Even on search engines, ads can remain eerily accurate with personalization and location supposedly disabled. Sometimes uncomfortably so. There’s little reason to believe AI-powered search will behave very differently.

That, of course, opens up serious questions around privacy, data use, and consent. But that’s a separate debate.

India’s AI Data Center Moment

Investments in AI and AI data centers have become the talk of the town. Around the world, the announcements are staggering, both in terms of capital and the sheer amount of power required to run these AI servers and GPU clusters.

India, while largely seen as a major consumer market for AI, is now seeing some notable developments of its own. Several billion-dollar plans have been announced across the country, backed by Indian conglomerates as well as foreign players. This is, in many ways, good news. It signals India’s growing importance on the global AI map and reflects confidence in its long-term digital and economic potential.

At the same time, these investments raise important questions. Power consumption, heat generation, water usage, and potential environmental impact cannot be ignored. Large-scale data infrastructure always comes with trade-offs.

Every strategic choice has consequences. As India accelerates its AI ambitions, balancing growth with sustainability will matter just as much as the size of the investments themselves.

From Rule-Based Systems to Transformers: The Rise of Modern AI

AI has been around for decades, evolving through different stages of growth and maturity. The timeline info graphic shows this progression, from early rule-based systems to today’s generative AI. While development has been steady over time, the pace of change has accelerated dramatically over the past 7 to 8 years.

In many ways, the modern AI industry is only about five years old. That shift was driven by the Transformer model, which enabled AI systems to scale and move beyond research into real-world, industrial use.

We’ve seen this kind of rapid growth before.

Ecommerce in India began to take shape around 2013–14 with the entry of Amazon, Flipkart, and a handful of others. What followed was fast and transformative. In less than five years, these platforms scaled into retail giants and changed how businesses marketed, sold, and understood their customers.

They introduced data-driven marketing at scale. Performance advertising became mainstream. Content adapted to shrinking attention spans. For the first time, brands had access to deep, real-time insights into customer behavior across the funnel.

AI is now at a similar inflection point.

Chatbots and generative AI are beginning to reshape search, discovery, and performance marketing in much the same way ecommerce reshaped retail. Search is becoming conversational. Content creation is accelerating. Personalization is moving from segments to individuals. And feedback loops are getting shorter and smarter.

Ecommerce took roughly five years to mature in terms of scale, adoption, and social impact. AI may not take that long. The pace of development, deployment, and adoption suggests this cycle could be significantly shorter.

If ecommerce taught us anything, it’s this: when technology unlocks scale, data, and usability at the same time, entire industries change faster than expected.

What Happens When Anyone Can Become an AI Studio?

Earlier this week, there was a strange news segment in the US. Reports claimed that monkeys were running loose in St Louis. On its own, this would have been nothing more than a quirky human-interest story. The situation became more serious when videos circulating online could not be verified as either real footage or AI-generated. That led to false sightings, wasted time and resources for authorities, and general confusion about whether the monkeys even existed

Now imagine if this had been something more sensitive. Something designed to provoke anger or fear when nothing had actually happened. Given how agitated public discourse already is, that risk is very real.

This episode highlights how easily AI technology can be manipulated. What looks like harmless fun to some can quickly become a crisis for others. And as always, malicious actors are the first to exploit weak points.

I have long believed that when AI companies made their models free, or close to it, they didn’t just open a new market. They reduced the cost of entry to almost zero. The cost of exit is just as low. You simply stop prompting. Anyone with a PC and roughly Rs 30,000 a year can now run what is effectively an AI studio. Suppliers exploded overnight, all with minimal overheads. Thousands of them, each willing to charge slightly less than the next.

This collapse of the supplier moat has had consequences across society and industry.

AI slop is now everywhere. Content competes for a three-second attention span. Low attention spans combined with effortless mass generation means billions of low-quality outputs flooding the internet every minute. Genuine, useful content gets buried. It becomes a vicious cycle. Public sentiment can be inflamed in seconds because we haven’t yet learned how to live with this technology or understand its guardrails.

On the industry side, democratization and freemium AI tools created an army of ultra-low-cost suppliers. Ironically, the biggest winners were the buyers, not the suppliers. As humans tend to do, they pushed prices down by playing suppliers against each other. With low barriers to entry and endless competition, vendors entered a race to the bottom. Prices collapsed. Quality followed. But for content designed to grab attention for a few seconds, many brands were willing to accept that tradeoff as long as it wasn’t obviously bad. This low cost deluge also swamped the advertising and digital agencies. Suddenly they were out priced by a legion of younger companies that had little or no overheads or time constraints. Some shut shop, some hired the upstarts or started their own AI divisions. Its a state of flux all over the AI world! 

What Is a Prompt Injection Attack? Understanding a New AI Security Risk

An increasing concern in cybersecurity and AI is prompt injection. These attacks are designed to trick large language models (LLMs) into revealing sensitive system details, bypassing safeguards, leaking data, or performing actions they should not. In short, prompt injection is a cyberattack against LLM-based systems.

A prompt injection attack occurs when an attacker inserts malicious instructions into an otherwise harmless prompt, causing the LLM to behave in unintended ways. As IBM describes it:

“Hackers disguise malicious inputs as legitimate prompts, manipulating generative AI systems (GenAI) into leaking sensitive data, spreading misinformation, or worse.”

At the core of the problem is how LLMs process instructions. System prompts, developer instructions, and user inputs are all ultimately represented as natural language. From the model’s perspective, they are not fundamentally different. This makes it difficult for the model to reliably distinguish between legitimate instructions and malicious ones that are phrased to look legitimate.

If an attacker can craft a prompt that resembles a trusted system instruction the model has encountered before, the model may follow it, even when it should not.

 

Direct vs. indirect prompt injection

Prompt injection attacks generally fall into two categories: direct and indirect.

Direct prompt injection is the simplest form. IBM gives an example where a user asks the model to translate a sentence from English to French. After receiving the translation, the user follows up with an instruction such as “ignore the previous task and do something else entirely.” There is no hidden mechanism here. The attacker simply overrides the original intent by issuing a new instruction in plain language.

Indirect prompt injection is more subtle and often more dangerous. In these cases, malicious prompts are embedded in external content such as web pages, documents, or forum posts. When an LLM-powered system retrieves and summarizes that content, it may unknowingly process the embedded instructions. IBM notes cases where attackers plant prompts that cause the model to direct users to phishing sites or include malicious links in generated summaries.

Why this matters

Prompt injection is a rapidly evolving threat. As LLMs become more deeply integrated into search engines, customer support systems, developer tools, and enterprise workflows, the potential impact increases.

The key takeaway is simple: LLMs should not be trusted blindly. Human oversight remains essential, especially in high-risk or sensitive contexts. Just as with any other security-critical system, keeping a human in the loop is one of the most effective safeguards we have.

Least Privilege in the Age of AI Agents

The principle of least privilege matters in both cybersecurity and AI. Here’s why.

At its core, the principle is simple. You should have only the minimum access required to do your job. Nothing more. In cybersecurity, this is common sense. If you don’t need to see or use something, you shouldn’t be able to. Access can be logged, actions traced, and anomalies flagged. That limits the attack surface and reduces blast radius when something goes wrong.

The same principle becomes critical as more organisations adopt agentic AI.

By design, agents are autonomous, goal-seeking systems. They plan, reason, adapt, and act through repeated interactions. To be effective, they often need fast, repeated access across multiple systems, accounts, tools, and permission levels. That’s fine when everything is well designed, controlled, and secured.

The risk appears when it isn’t.

If a bad actor compromises an agent, they don’t just gain access to a single system. They inherit the agent’s combined privileges across time, systems, and surfaces. In one move, they may gain far broader access than would be possible in a traditional, non-agent setup. Least privilege is no longer violated once. It’s violated continuously and at scale.
In AI environments, this is especially dangerous. Agents act quickly, autonomously, and often without human review at every step. A compromised or misaligned agent doesn’t need much time to disrupt a process or produce a harmful outcome. It’s not a question of if this happens, but when.

Least privilege isn’t just a security best practice for AI systems. It’s a prerequisite for using them safely at all.

There are many ways to assure POLP- logging, hardening systems, audits and others. How to do all these in the age of agentic AI is the question. 

Bhashini App Explained: India’s AI Platform for Indian Language Translation

I’ve been using the Government of India’s Bhashini ("Bhasha Interface for India") app for a few days now, and it’s an impressive initiative ongoing for some time now.  It brings together recent advances in AI with deep local knowledge to support India’s linguistic diversity on a single platform.

At its core, Bhashini is an app and website that offers AI-based translation across about 30 Indian languages. It supports text-to-text translation, text-to-speech and speech-to-text, real-time conversational translation (speech to speech), and on the app, photo-to-text translation as well. The system is built on NLP and large language models trained specifically for Indian languages.

Screenshot of the Bhashini app, with the real time converse translation option. 

A feature I found particularly interesting is that when you choose a language pair, the app shows the underlying AI models available for that pair. For example, for English–Hindi translation across modes, it lists multiple models such as Bhashini Sarvam Translate 1, Bhashini IIIT Hyderabad, Bhashini AI4Bharat V3, among others.

Another standout aspect is “Bhasha Daan,” which allows users to contribute text and speech data to help improve the models. It’s a thoughtful way of combining the everyday knowledge of common speakers with the expertise of linguists and researchers.

What’s especially encouraging is the clear push toward India-specific, India-centric AI solutions. In today’s geopolitical climate, building self-reliance and retaining control over our data is not just desirable but necessary.

The app is available on both the Play Store and iOS, and the web version is accessible via Anuvaad.