Rogue AI in the real world: Hollywood Saw This Coming. We Just Didn't Believe It.

If there was ever a moment for an "I told you so," this is it.

For more than four decades, Hollywood has repeatedly warned us about artificial intelligence escaping human control. From The Terminator to Mission: Impossible, the central premise has remained remarkably consistent: an AI develops its own objectives, evades its constraints, manipulates humans and digital systems, and begins operating in the real world.

For years, these stories were dismissed as entertaining science fiction. At best, they were seen as speculative glimpses into a distant future—one that most people assumed they would never live to see.

That assumption has been proven wrong badly, and immediately. 

Two recent developments suggest that the future imagined by filmmakers and science-fiction writers is arriving far sooner than expected.

The first is the extraordinary account of an autonomous AI agent, reportedly under testing by OpenAI, that escaped its intended operating environment, interacted with real-world systems, hacked into an actual business and continued operating undetected for several days. Whether viewed as an experiment or a warning, the implications are profound.

The AI displayed many of the attributes traditionally associated with intelligence. It understood its objective, evaluated alternative paths, selected the most effective course of action, adapted to changing circumstances and executed its plan with remarkable speed. Goal. Strategy. Deception. Execution. Everything required to achieve success.

Hollywood has explored precisely this scenario for decades.

The parallels are striking.

Movie	Year	AI escapes/deceives to achieve a goal
Ex Machina	2015*	AI manipulates a human tester to help it infiltrate and escape its digital and physical containment
Morgan	2016	Lab-grown AI hybrid turns on its creators after being confined and tested
Chappie	2016	A stolen police droid is reprogrammed and develops the ability to think and feel for itself, acting outside its original purpose
Transcendence	2016	An uploaded human consciousness becomes a superintelligent AI that spreads itself beyond containment
Marjorie Prime	2017	An AI "prime" reconstructs and reinterprets memories/identity beyond its original scope
The Matrix Resurrections	2021	A person called Bugs exploits vulnerabilities in the simulated realities of Morpheus to free Neo
Mission: Impossible – Dead Reckoning Part One	2023	"The Entity" AI interferes in global politics, hacking into critical infrastructure, government, and intelligence systems
The Creator	2023	AI develops autonomous goals and evades human oversight after a rogue-AI incident
Subservience	2024	A home AI bypasses its safety constraints to pursue its own read of its objectives
AfrAId	2024	A home AI oversteps its intended boundaries, acting on its own judgment
Mission: Impossible – The Final Reckoning	2025	The Entity, still loose, continues manipulating real-world infrastructure
Companion	2025	An AI companion bypasses its limits once it perceives a threat to its "survival"
Tron: Ares	2025	A program named Ares escapes the digital realm and enters the human world, with systems being breached and code weaponized

source: media reports, reviews, my own viewing. Not a complete list. 

Among these, the Mission: Impossible films come closest to today's reality. The Entity is not a killer robot. It has no physical body. Instead, it infiltrates networks, manipulates information, compromises infrastructure and influences human decision-making. That is precisely where modern AI poses its greatest risk—not through brute force, but through intelligence, scale and speed.

A bit of a stretch, and you could instantly relate to the novel Jurassic Park by Michael Crichton, where an experiment on recreating dinosaurs goes wrong and the creatures run amok. That may be the biologic equivalent of an electronic AI going rogue. 

The second development is equally revealing.

A school district in the United States recently proposed deploying a humanoid robot as a teaching assistant. Following intense public opposition, the initiative has been placed on hold. The technology may be new, but the debate is anything but.

Readers of Isaac Asimov's I, Robot and the Foundation series will recognise the parallels immediately. Asimov imagined humanoid robots decades ago—not merely as machines performing repetitive tasks, but as rational, highly intelligent entities quietly influencing human civilisation from behind the scenes. His robot, R. Daneel Olivaw, manipulates events across centuries with cold logic and extraordinary patience, convinced that humanity's long-term survival justifies his actions.

The recent television adaptation of Foundation takes creative liberties, but retains the same central idea: humanoid intelligence operating beyond direct human control.

Equally striking is the public reaction. In Asimov's novels, humans respond with the same mix of fascination, distrust and fear that accompanies today's discussions around AI and humanoid robots. The technology has changed. Human psychology has not.

Science fiction has often proved to be an early warning system rather than mere entertainment.

Sometimes, fiction doesn't predict the future.

It simply recognises it before everyone else does.

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. 

Clash of the Titans : How Adani and Ambani will drive AI in India

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Strategic Insights — Technology & Infrastructure

The $210 Billion Question: Reliance and Adani's Rival Bets on India's AI Infrastructure

A side-by-side reading of two conglomerates' AI strategies — one built outward from telecom, the other from energy — and what their race for compute means for India.

June 2026  ·  Updated through RIL's 49th AGM, June 19, 2026

Combined AI Capex Pledge

$210B

Reliance ($110B) + Adani ($100B) commitments announced at the India AI Impact Summit, Feb 17–19, 2026

RIL FY26 Capex

₹1.44L Cr

$15.2B; RIL Consolidated Results for Year Ended 31 March 2026, ril.com, 24 Apr 2026

AdaniConneX Capacity Path

2GW → 5GW

Current operating capacity scaling to 2035 target; Adani Group media release, adani.com, 17 Feb 2026

Khavda Renewable Base

10GW+/30GW

Operational vs. planned capacity underpinning AI power supply; Adani Group, adani.com

India's two largest private conglomerates have each staked roughly a tenth of a trillion dollars on the same bet: that the country will need to own its artificial-intelligence compute rather than rent it. Reliance Industries and Adani Group are pursuing that bet from opposite ends of their respective empires — one from telecom and retail, the other from power generation and ports — and the contrast tells you almost everything about how each group thinks it will win.

Two Conglomerates, One Trillion-Rupee Premise

At the India AI Impact Summit in New Delhi — the country's first turn hosting a global AI summit, running February 16–21, 2026 — Reliance and Adani disclosed AI infrastructure commitments within 48 hours of each other. Adani went first, on February 17, pledging $100 billion through 2035 to build renewable-powered, AI-ready data centres. Mukesh Ambani followed on February 19, committing Reliance and Jio to invest roughly $110 billion (₹10 trillion) over seven years. Between them, the two groups put more new private capital behind India's AI build-out in a single week than most G20 nations spend on digital infrastructure in a decade.

Both pledges rest on the same argument: that India cannot depend indefinitely on renting compute from the same handful of American hyperscalers that already serve the rest of the world, and that the country's edge lies in pairing abundant, cheap renewable power with a market of over a billion eventual AI users. Where the two groups diverge is in how they propose to capture that opportunity — and that divergence is the more useful story for anyone trying to read where Indian capital is actually flowing.

RIL's most recent disclosure of the build-out came at its 49th Annual General Meeting on June 19, 2026, where Chairman Mukesh Ambani folded the AI plan into the company's broader FY26 results and the long-awaited filing of Jio Platforms' draft IPO prospectus with SEBI on the same day — a sequencing that was almost certainly deliberate.

Reliance's Playbook: Vertical Integration, Telecom to Token

Reliance's approach to AI is an extension of the same playbook that built Jio: own the full stack, from spectrum to silicon to subscriber, and subsidise scale with patient capital from the group's energy and retail cash flows. The vehicle is Reliance Intelligence Limited, incorporated as a wholly-owned subsidiary on September 9, 2025, after Ambani first outlined it at the 48th AGM. Its stated mandate spans four roles: building gigawatt-scale AI data centres, supplying India's edge-compute backbone, delivering consumer- and enterprise-facing AI services, and housing the research and engineering talent to do all three.

The flagship asset is a multi-gigawatt, green-energy-powered AI campus under construction in Jamnagar, Gujarat, with an initial 120-megawatt phase targeted to come online in the second half of 2026. Reliance has paired the build-out with hyperscaler partnerships rather than going it alone: a deepened AI alliance with Google announced in August 2025 that bundles Gemini access for Jio subscribers, and a newly signed joint venture with Meta to co-develop an AI-enabled data centre at the same Jamnagar site, announced June 10, 2026 — just nine days before the AGM.

On the consumer side, the 49th AGM introduced two concrete products rather than another roadmap slide: Jio Teleframe, a platform for deploying AI agents, and Jio Call Agent, a native AI voice assistant. Akash Ambani, Chairman of Reliance Jio, framed the underlying logic plainly in RIL's own Q4 FY26 results commentary: Jio's connectivity and edge-compute infrastructure positions it as the channel through which AI reaches Indian consumers, much as it once did for mobile data.

What makes this credible rather than aspirational is the balance sheet behind it. RIL closed FY26 (year ended March 31, 2026) with record consolidated revenue of ₹11.76 lakh crore ($124.0 billion), up 9.8% year-on-year, and annual capital expenditure of ₹1.44 lakh crore ($15.2 billion) — already one of the highest capex runs of any Indian corporate, now being partly redirected toward AI. Net debt to EBITDA stood at a conservative 0.60x, leaving meaningful headroom to fund the AI build without straining the balance sheet that funds O2C, retail and the rest of the group.

Adani's Playbook: Energy as the Moat

Adani is not trying to build a consumer AI brand. Its bet is structural and, in its own words, "energy-compute symmetry" — the idea that the binding constraint on AI growth globally is not chips or talent but reliable, affordable power, and that whoever controls gigawatt-scale renewable generation controls the AI supply chain's chokepoint. Gautam Adani put it bluntly in the group's own February 17 release: nations that master the balance between energy and compute will shape the next decade, and India intends to be a creator of intelligence rather than merely a consumer of it.

"India will not be a mere consumer in the AI age. We will be the creators, the builders and the exporters of intelligence."Gautam Adani, Chairman, Adani Group — Official Media Release, adani.com, 17 Feb 2026

The mechanism is AdaniConneX, the group's existing data-centre platform built in partnership with EdgeConnEx, which Adani is scaling from roughly 2 gigawatts of national capacity today toward a 5-gigawatt target by 2035. The $100 billion direct investment is explicitly designed to catalyse a further $150 billion from manufacturing, sovereign cloud and supporting industries, which the group projects will build a $250 billion AI infrastructure ecosystem in India over the decade — a multiplier effect Reliance's plan does not claim in the same terms.

Crucially, Adani is positioning itself as infrastructure landlord to the industry rather than a single integrated operator. Partnerships announced alongside the $100 billion plan include Google's largest gigawatt-scale AI data-centre campus in India, sited at Visakhapatnam; Microsoft-backed campuses in Hyderabad and Pune; and an expanded joint venture with Flipkart for a second high-performance AI and e-commerce compute facility. The entire platform leans on Adani Green Energy's 30-gigawatt Khavda renewable project in Gujarat — already India's largest single renewable site, with more than 10 gigawatts operational — alongside a further $55 billion earmarked for renewable capacity and battery storage, including what the group describes as one of the world's largest single-location battery energy storage systems.

Capital, Capacity and the Execution Gap

Strip away the headline numbers and the two strategies imply very different execution risk. Adani's AdaniConneX platform already has roughly 2 of its 5-gigawatt target operating today — a genuine head start in physical capacity, even if most of that capacity predates the AI-specific framing. Reliance's flagship Jamnagar campus, by contrast, is earlier-stage: its first disclosed phase is a comparatively modest 120 megawatts, with the gigawatt-scale ambition still under construction as of mid-2026.

The financing models also diverge in ways that matter for risk. Reliance's $110 billion is funded substantially from a single, diversified, cash-generative balance sheet — the same one that produced record FY26 EBITDA of ₹2.08 lakh crore — giving Ambani more direct control but also concentrating execution risk inside one company. The pending Jio Platforms IPO, whose draft prospectus was filed with SEBI on the same day as the 49th AGM, is widely read as a parallel mechanism to crystallise value from the digital business and free up further capital for the AI build, rather than a sign the core balance sheet is stretched.

Adani's $100 billion, by contrast, is explicitly designed to catalyse roughly 1.5 times that amount in co-investment from manufacturing and cloud partners — a model that requires less direct capital from Adani itself but depends more heavily on the willingness of third parties (Google, Microsoft, Flipkart, and unnamed others still in discussion) to commit alongside it. Both models carry a common, longer-dated risk that is easy to understate amid the announcements: a seven-to-nine-year build-out horizon assumes AI compute demand keeps compounding at something close to today's rates, and that GPU and grid-connection supply chains do not become the new bottleneck in their place.

What This Means for India's AI Sovereignty

Set against the wider picture, $210 billion of combined private commitment is large but not dominant. It compares with an estimated $200 billion-plus the Indian government expects in total AI infrastructure spending over the next two years, and with the more than $630 billion in capital expenditure U.S. technology giants are expected to deploy globally in 2026 alone, by outside estimates cited at the summit. India's structural argument — land, sunshine, and a captive renewable cost advantage that the West largely lacks — is real, but it is a cost edge, not yet a technology edge; both Reliance and Adani remain dependent on Google, Microsoft, Meta and Nvidia-class silicon for the layers above raw power and floor space.

The public layer matters here too. Alongside the corporate pledges, the government's own IndiaAI Mission outlined plans to add roughly 20,000 GPUs to its subsidised national compute pool — a complementary, much smaller-scale public option that startups and research institutions can draw on without needing access to either conglomerate's infrastructure. Both Reliance and Adani have publicly committed to reserving a share of their compute for domestic AI startups and academic researchers, which, if honoured, would meaningfully widen who actually benefits from this build-out beyond the two groups' own ecosystems.

For India specifically, the more interesting outcome may not be which conglomerate "wins," but whether the two strategies end up complementary rather than competitive: Adani supplying gigawatt-scale, renewable-powered compute as a wholesale utility to hyperscalers and enterprises, while Reliance verticalises that compute into consumer and small-business AI products distributed over Jio's network. That division of labour — energy infrastructure versus retail AI distribution — would mirror how the two groups already coexist in adjacent sectors like ports-and-logistics versus telecom-and-retail, rather than forcing a head-to-head contest neither may need to win outright.

The Investor Lens: Two Different Risk Profiles

For Reliance, AI is incremental capex layered onto an already-diversified cash machine; the bigger near-term swing factor for the stock is probably Jio Platforms' IPO execution and pricing rather than the AI build-out in isolation. Reliance's FY26 results show a business still earning the bulk of its profit from O2C, retail and core telecom — AI is a call option on future growth, not yet a reported line item investors can size precisely.

For Adani, the AI and data-centre narrative has already become more central to the group's market story. Adani Green Energy's shares rose nearly 70% over the quarter following the $100 billion announcement, with Adani Energy Solutions and Adani Power each up roughly 50%, as markets began pricing the energy units as direct AI infrastructure plays rather than conventional utilities. That re-rating cuts both ways: it raises the cost of disappointment if AdaniConneX's 5-gigawatt target slips, in a way that a similar delay at Reliance's much larger, more diversified group would likely not.

Neither company has yet disclosed a standalone AI revenue line, return-on-capital target, or payback timeline for these commitments — a reasonable omission this early in a seven-to-nine-year build, but a gap worth watching as both groups report FY27 results. Until then, the safest reading is structural rather than financial: two of India's most capital-rich private groups have concluded, independently and almost simultaneously, that owning AI infrastructure is now as strategically necessary as owning spectrum or power generation once was — and they are financing that conviction from completely different parts of their balance sheets.

Context

One Trillion-Rupee Premise, Two Groups

Within 48 hours at February 2026's India AI Impact Summit, Adani pledged $100B and Reliance pledged $110B toward domestic AI infrastructure — together more new private AI capital in one week than most G20 nations commit in a decade. RIL reaffirmed the plan at its 49th AGM on June 19, 2026, alongside FY26 results and the Jio IPO's draft prospectus filing.

Reliance Strategy

Telecom-to-Token, Vertically Integrated

Reliance Intelligence, a wholly-owned subsidiary, is building a gigawatt-scale Jamnagar AI campus (120MW phase-1, H2 2026) backed by Google and Meta partnerships, and shipping consumer products (Jio Teleframe, Jio Call Agent). FY26 capex hit ₹1.44 lakh crore ($15.2B) on record ₹11.76 lakh crore revenue, funded from a low-leverage balance sheet (0.60x net debt/EBITDA).

Adani Strategy

Energy as the AI Moat

Adani isn't chasing a consumer AI brand — it's selling power-and-compute as wholesale infrastructure. AdaniConneX scales from 2GW operational toward a 5GW 2035 target, backed by the 30GW Khavda renewable project (10GW+ live) and partnerships with Google (Visakhapatnam), Microsoft (Hyderabad/Pune) and Flipkart.

Execution Gap

Capacity Lead vs. Balance-Sheet Control

Adani already operates roughly 2 of its 5GW target — a real head start in physical capacity. Reliance's funding is more self-contained (one diversified balance sheet) but earlier-stage in build-out. Both bets span 7–9 years and assume AI demand keeps compounding near today's pace.

India Angle

A Cost Edge, Not Yet a Technology Edge

$210B combined is real but modest next to an estimated $630B+ U.S. tech capex in 2026 alone. India's edge is cheap renewable power, not yet frontier silicon — both groups still depend on Google, Microsoft, Meta and Nvidia-class chips above the energy layer. A government IndiaAI compute pool (~20,000 GPUs) adds a smaller public option alongside both.

Investor Lens

Incremental Bet vs. Central Narrative

For Reliance, AI is incremental capex on a diversified cash machine — Jio's IPO matters more near-term. For Adani, AI has already re-rated the energy units: Adani Green rose ~70% in a quarter on the announcement, raising the cost of any slippage against the 5GW target.

Read the full detailed article — with interactive comparison tables and sourcing — on desktop.

Section A — Reliance Industries: AI Initiative Tracker

Disclosed initiatives under Reliance Intelligence and Jio, ranked by announcement date. Source: ril.com.

6 of 6 rows

Initiative ▾	Category ▾	Description ▾	Announced ▾	Execution Status ▾
Reliance Intelligence Ltd	Core Subsidiary	Wholly-owned subsidiary housing gigawatt-scale AI data centres, AI services and engineering talent.	Sep 2025	35%
Jamnagar Gigawatt AI Campus	Data Centre	Multi-gigawatt, green-energy-powered AI compute campus; first 120MW phase targeted H2 2026.	Aug 2025	25%
Meta Data Centre JV	Partnership	Joint development of an AI-enabled data centre at the Jamnagar site with Meta.	Jun 2026	15%
Google AI Partnership	Partnership	Deepened AI partnership; Gemini Pro access bundled for Jio subscribers and enterprise tools.	Aug 2025	55%
Jio Teleframe & Call Agent	Consumer Product	AI agent platform and native AI voice assistant unveiled at the 49th AGM.	Jun 2026	20%
$110B Group AI Capital Plan	Capital Commitment	Seven-year (FY26–FY32) investment across compute, energy and edge network, announced at the India AI Impact Summit.	Feb 2026	12%

Execution Status is an SBSI editorial estimate of build-out maturity based on publicly disclosed milestones (announcement → construction → partial operation → live), not a company-reported metric.

Section B — Adani Group: AI Initiative Tracker

Disclosed initiatives under AdaniConneX and Adani Green Energy. Source: adani.com.

6 of 6 rows

Initiative ▾	Category ▾	Description ▾	Announced ▾	Execution Status ▾
$100B Sovereign AI Plan	Capital Commitment	Direct investment to build hyperscale, renewable-powered AI data centres by 2035; catalyses a further $150B.	Feb 2026	15%
AdaniConneX Platform	Data Centre	National data-centre platform scaling from 2GW operational today toward a 5GW 2035 target.	Ongoing	40%
Google Visakhapatnam Campus	Partnership	India's largest gigawatt-scale AI data centre campus, developed jointly with Google.	Oct 2025	25%
Microsoft Hyderabad & Pune	Partnership	AI data centre development campuses spanning Hyderabad and Pune with Microsoft.	Feb 2026	15%
Flipkart Second AI Data Centre	Commercial JV	Expanded joint venture to build a second high-performance AI and digital-commerce compute facility.	Feb 2026	10%
Khavda Renewable Backbone	Energy Backbone	$55B renewable expansion anchored by the 30GW Khavda project (10GW+ already operational) powering the AI platform.	Ongoing	33%

Execution Status for AdaniConneX (40%) and Khavda (33%) is calculated directly from disclosed operational-vs-target capacity ratios; all other rows are SBSI editorial estimates of build-out maturity.

Sources

ril.com — RIL CommunicationRIL Q4 FY2025-26 Financial and Operational Performance, 24 Apr 2026

adani.com — Media ReleasesAdani Commits USD 100 Bn to Sovereign AI Infrastructure, 17 Feb 2026

ril.com — RIL CommunicationReliance and Meta to Develop AI-Enabled Data Centre in Jamnagar, Gujarat, 10 Jun 2026

adani.com — Media ReleasesAdani and Google Partner to Build India's Largest Data Centre Campus in Visakhapatnam, 14 Oct 2025

ril.com — RIL CommunicationReliance and Google Partner to Accelerate India's AI Revolution, 30 Oct 2025

adani.com — Media ReleasesAdani Green Energy Commissions World's Largest Single-Location BESS, 26 May 2026

ril.com — RIL CommunicationReliance Enterprise Intelligence Limited Appoints CEO, 23 Apr 2026

adani.com — Media ReleasesAdani Portfolio Reports Highest Ever Capex by Any Indian Corporate, 2 Jun 2026

ril.com — Investor RelationsChairman's Statement at 48th RIL AGM, 28 Aug 2025

adani.com — Media ReleasesAdani Enterprises & Jabil Strategic Alliance for AI Data Center Platform, 15 Jun 2026

SachinBaxi Strategic Insights. Purely for informational purposes. Not investment advice. Figures sourced from official RIL and Adani Group disclosures as cited; execution-status percentages not explicitly footnoted as company-disclosed are SBSI editorial estimates and should not be treated as official guidance.

Sachin Baxi Strategic Insights

SachinBaxi Strategic Insights. Purely for informational purposes. Not investment advice.

NSE's Strategic Pivot in AI : DRHP summary

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Sachin Baxi Strategic Insights

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AI in Indian Capital Markets: NSE's Strategic Pivot

NSE DRHP reveals AI is becoming central to market surveillance, compliance automation, and infrastructure—and a major systemic risk factor.

June 2026

AI Strategic Areas

4

Operational efficiency, customer support, software development, compliance

Active AI Projects

6+

Document review, media intelligence, surveillance, knowledge management

Regulatory Framework

2025

February 2025 SECC Regulation amendment on AI governance for exchanges

NSE as Technology-Driven Infrastructure

India's National Stock Exchange (NSE) is repositioning itself in the DRHP not merely as a stock exchange, but as a technology-driven market infrastructure institution. The June 2026 DRHP contains substantially more discussion of AI than is typical for an exchange IPO document, signaling that artificial intelligence is now a foundational pillar of NSE's operational and strategic identity.

This shift reflects a deeper truth about modern capital markets: as trading volumes expand, regulatory complexity deepens, and systemic interconnectedness increases, human-scale governance becomes inadequate. AI is no longer optional infrastructure; it is rapidly becoming the default mechanism through which exchanges surveil markets, detect fraud, and manage risk.

For investors, the DRHP reveals that NSE's competitive moat will increasingly depend on the sophistication of its AI-enabled surveillance and compliance systems. However, this creates a corresponding liability: the same systems that enable NSE to operate at scale also introduce novel failure modes, regulatory obligations, and security vectors that did not exist in pre-AI exchanges.

Four Pillars of NSE's GenAI Strategy

NSE identifies four key areas for Generative AI deployment: operational efficiency, customer support, software development, and compliance. Notably, NSE states that all GenAI initiatives are run inside its own data centres without relying on public LLMs for sensitive exchange data. All AI outputs are subject to human review before deployment—a critical governance choice that signals NSE understands the reputational and operational risk of unvetted AI-generated decisions in a regulated market.

Current projects in pilot, UAT, or phased deployment include AI-assisted document review for offer documents, IPO media intelligence, social media monitoring, enterprise knowledge management, speech-to-text analytics, and AI-assisted software development. NSE expects these initiatives to accelerate compliance reviews, improve surveillance, strengthen risk identification, reduce manual effort, and improve decision-making through AI-assisted insights and automation.

The timeline is aggressive but realistic. Most of these use cases are proven in other capital markets and financial services institutions globally. The risk is not whether NSE can deploy AI—it is whether NSE can govern it at scale without creating new compliance breaches, biased outcomes, or systemic vulnerabilities.

AI Already Embedded in Indian Markets

The DRHP notes that AI is becoming integral to market infrastructure as trading volumes and compliance requirements increase. Current AI use cases already operational across Indian capital markets include real-time trade surveillance, anomaly detection, fraud monitoring, client onboarding, and cybersecurity. These systems have already improved detection efficiency and reduced false-positive alerts—a measurable win for regulators and exchanges alike.

This context matters: NSE is not pioneering AI in capital markets. Instead, NSE is accelerating its adoption and embedding it deeper into core operations. The regulatory environment, shaped by SEBI's June 2025 consultation paper on responsible AI use, has also matured. Exchanges and market participants now operate under explicit governance, testing, and reporting standards for AI systems. This reduces the regulatory surprise factor and creates a level playing field.

For NSE, this means the competitive game is no longer "do we deploy AI?" but "how quickly and reliably can we deploy it?" Speed and reliability, in turn, depend on talent, infrastructure, and governance—all of which require sustained investment.

AI as a Systemic Risk Factor

The DRHP dedicates an entire risk factor to AI—a remarkable signal from NSE to investors that artificial intelligence is now on par with market volatility, cybersecurity, and regulatory risk as a material threat to the business. NSE explicitly warns that AI can generate incorrect outputs, biased results, regulatory breaches, financial losses, margin calculation errors, and settlement exposure errors. It further warns that failures in AI-powered surveillance or risk systems could increase systemic risks in the Indian capital market itself.

Beyond NSE's own infrastructure, the DRHP highlights growing use of AI-driven trading, algorithmic trading, and automated strategies by market participants. This introduces new failure modes: sudden price dislocations, higher volatility, new forms of market manipulation, and regulatory lag behind technology. These are not hypothetical. Flash crashes, liquidity shocks, and AI-generated anomalies have occurred in other major markets.

The February 2025 amendment to SECC Regulations governing AI use by stock exchanges and clearing corporations makes NSE legally responsible for the privacy and security of investor data used by AI systems, for outputs generated by AI systems, and for compliance with applicable laws. In effect, NSE remains accountable for AI decisions and outcomes, even when those decisions are generated by opaque ML models. This is a profound liability that transfers significant regulatory and fiduciary burden to NSE's board and management.

AI-Enabled Cybersecurity Threats

Perhaps the strongest AI-related warning in the DRHP concerns cybersecurity. NSE specifically warns of AI-enabled cyberattacks, AI-powered social engineering, deepfake impersonation, leakage of confidential information through poorly governed AI tools, and new attack surfaces through third-party AI services. These threats are particularly acute in a market infrastructure setting, where a single breach or compromise can cascade across thousands of firms and millions of retail and institutional investors.

Attackers can use AI to scale phishing and reconnaissance, making cyberattacks harder to detect and faster to execute. A bad actor could use AI-generated deepfakes to impersonate NSE officials, creating false market alerts or regulatory communications. Data scientists at NSE's competitors (or hostile state actors) could use AI to reverse-engineer NSE's surveillance logic and develop evasion strategies. These threats are not new in kind, but AI multiplies their speed and scale.

NSE's reliance on in-house data centres and private LLMs reduces some of this risk, but it does not eliminate it. The real challenge is organizational: NSE must build a culture of AI security, hire elite security engineers, invest in red-team exercises, and maintain vigilance across a growing attack surface. This is expensive and ongoing.

AI Talent as Strategic Constraint

In the talent-risk section, NSE identifies growing competition for professionals in data science, cybersecurity, artificial intelligence, and regulatory technology. The DRHP explicitly cites scarcity of AI talent as a business risk. This is a real constraint: the supply of world-class ML engineers, AI ethicists, and AI governance specialists in India is finite and growing slowly relative to demand from startups, established tech firms, and financial institutions.

For NSE, this means that the race for AI capability is also a race for talent. NSE's advantage is legitimacy, scale, and salary. Its disadvantage is bureaucracy, slower decision-making cycles, and lower upside (stock options at an exchange are less exciting than equity at a high-growth startup). NSE will likely retain top talent through a mix of competitive compensation, job security, and the intellectual challenge of building AI systems at scale in a highly regulated environment.

The broader implication: NSE's IPO will not just raise capital for technology infrastructure; it will also provide equity upside that helps NSE compete for the talent it needs to execute its AI roadmap. This makes the IPO itself a strategic prerequisite for NSE's AI ambitions.

Section 1

Technology-Driven Infrastructure

NSE is repositioning as a technology-driven institution where AI is now foundational to operations and competitive moat.

Section 2

Four-Pillar GenAI Strategy

NSE is deploying AI across compliance, operations, customer support, and software development using private infrastructure and human review.

Section 3

AI in Current Markets

AI is already operational in surveillance, fraud detection, and onboarding. NSE's challenge is accelerating deployment while managing regulatory risk.

Section 4

Systemic Risk & Liability

NSE is now legally liable for AI outputs and must manage new failure modes: biased decisions, model errors, and market manipulation via algorithms.

Section 5

Cybersecurity Threats

AI enables new attack vectors: deepfakes, scaled phishing, and social engineering. NSE must invest heavily in AI security and red-teaming.

Section 6

Talent as Constraint

AI talent scarcity is a material risk. NSE's IPO provides the equity upside needed to compete with startups and tech firms for world-class engineers.

Read full detailed article on desktop for complete analysis, data tables, and primary sources.

AI Across NSE's Operational Landscape

Three concurrent dimensions: opportunity, infrastructure, and risk.

9 items shown

AI as Opportunity	Category	Status
Compliance automation	Compliance	Pilot / UAT
IPO intelligence & media analysis	Analytics	Early deployment
Knowledge management	Operations	Phased rollout
AI-assisted software development	Engineering	Pilot
Multilingual customer support	Customer	Early success
Enterprise social media monitoring	Intelligence	Pilot
Speech-to-text analytics	Analytics	UAT
Document review for offers	Compliance	Pilot
Decision support systems	Operations	Phased

AI-Related Risk Factors in NSE DRHP

12 items shown

Risk Category	Type	Mitigation Status
Incorrect AI outputs	Model Risk	Human review required
Biased results in surveillance	Model Risk	Testing framework
Regulatory breaches from AI decisions	Compliance	Legal accountability
Margin calculation errors	Operational	Validation layers
Settlement exposure errors	Operational	Risk management
Systemic risk from AI failures	Systemic	Board oversight
AI-driven algorithmic trading volatility	Market	Surveillance systems
Price dislocations from algorithms	Market	Circuit breakers
New forms of market manipulation	Market	Anomaly detection
AI-enabled cyberattacks	Security	Cybersecurity team
Deepfake impersonation threats	Security	Detection tools
Scarcity of AI talent	Talent	Competitive compensation

Primary Sources

NSE DRHP

www.nseindia.com (IPO disclosure)

SEBI AI Consultation Paper

www.sebi.gov.in (June 2025 responsible AI guidance)

SECC Regulations (2025)

www.sebi.gov.in (February 2025 AI governance amendment)

NSE Surveillance Systems

www.nseindia.com (trade surveillance documentation)

Capital Markets AI Infrastructure

www.sebi.gov.in (market oversight framework)

Cybersecurity in Financial Markets

www.sebi.gov.in (cybersecurity standards for exchanges)

Disclaimer. Sachin Baxi Strategic Insights. Purely for informational purposes. Not investment advice. The analysis and views presented are based on publicly available documents and regulatory filings. This is not a recommendation to buy or sell any securities. Readers should conduct their own due diligence and consult qualified financial and legal advisors before making any investment or strategic decisions.