AI Just Became Capital Risk

On 24 June, the Reserve Bank of India dropped a draft that does not look like an AI regulation at first read. It is titled Guidance on Regulatory Principles for Model Risk Management, 2026, and the language is pure prudential supervision: board-approved frameworks, three lines of defence, independent validation, risk tiering, model inventories retained for ten years after decommissioning. Spend twenty minutes with it and a sharper thought lands. India's central bank has just pulled AI into the family of supervisory tools normally reserved for capital and liquidity. The principle that a regulated entity remains fully accountable for the outcomes of every model it uses, including those sourced from third-party vendors, quietly closes a loophole that the entire fintech-banking AI stack had been leaning on.

What the draft actually says

The reach is wide. Eleven categories of regulated entities — commercial banks, small finance and payments banks, urban and rural co-operatives, NBFCs across all layers, AIFIs, ARCs, credit information companies — are inside the perimeter. The definition of “model” is wider still. It covers not just neural networks and generative AI but also algorithms, analytics tools, decision rules, even spreadsheet-based tools where they materially influence credit, pricing or risk decisions. Every such model must sit under a Board-approved Model Risk Management Framework. Every high-risk model must be cleared by the Risk Management Committee of the Board before deployment. Every customer-facing AI interface must disclose that it is AI, list its limitations, and offer the user a way to switch to a human. And every deployed AI system needs a working kill switch — a one-button deactivation if the outputs go wrong. Comments are invited until 24 July.

The deep idea: models as a new line on the risk ledger

This is the consequential part, and most early commentary is missing it. When a regulator says you cannot offload accountability to a vendor's API, it is doing for AI what Basel did for credit risk in the early 1990s. Models become a class of exposure. They have to be inventoried, tiered by materiality, validated independently, monitored for drift, and signed off at the highest level. That is the grammar of capital, not the grammar of code.

It is also the only grammar that scales. The RBI's own FREE-AI Committee survey last year found that nearly 21% of regulated entities were already deploying AI in production — across credit underwriting, cybersecurity, customer support, sales — and 67% wanted to go deeper. At that level of penetration, telling boards “your tech team has this” is not serious supervision. Every credit cycle eventually meets its model failure mode. A regulator that waits to discover the failure is a regulator already too late.

The kill switch rewrites the vendor market

Look at the supply side and the picture sharpens. A small set of global tech firms quietly supplies a disproportionate share of the AI models running inside Indian financial services. The draft flags this concentration explicitly as a systemic supply-chain risk. Combine it with the third-party-is-no-defence clause and the direction of travel is unmistakable: a serious push toward indigenous, auditable, swap-out-able model stacks. The new market is not the bank's market. It is a RegTech market — model validation firms, bias-testing labs, explainability auditors, red-teaming shops. A compliance officer reading this draft on Monday morning is realising that the next critical hire is not another data scientist; it is an independent validator who can sign off on someone else's data scientist.

Why this logic will travel beyond banking

I have spent enough time inside a national administration deploying AI on citizen-facing systems to read this draft as a template, not an end-point. Tax, customs, social security, urban service delivery, public health records — every government body running models on millions of files faces the same accountability question the RBI is now putting to banks. Who signs off on the model that decides a refund? A scrutiny notice? An eligibility threshold? The answer cannot be “the algorithm”. It has to be a named human, with a department behind them, with an institutional framework above them.

Three things from this draft will, I think, become the standard public-sector discipline within two budget cycles:

• the inventory rule — every active and decommissioned model on a register, with a ten-year tail;
• the explainability threshold — outputs interpretable to the extent the business process actually requires;
• the kill switch as a non-negotiable product feature, not a nice-to-have.

The trade-off, and the right side of it

The cost is real. Industry analysts already estimate a 50 to 100 basis point rise in IT spending for serious adopters. Smaller NBFCs and co-operative banks will feel it the most. Time-to-market for AI-driven products will lengthen. None of this is free, and the smaller institutions will need a transition window the draft does not yet promise.

But picture the alternative. A black-box model, bought from a vendor, denying loans to a cluster of small borrowers in a particular district, operating under no one's clear authority. Someone eventually discovers it — they always do. The regulator pays, the bank pays, the customer pays, and trust in AI itself pays the heaviest tax of all. The cost of the RBI's draft is the cost of preventing that discovery.

The cleaner way to say it: in a regulated industry, AI stops being a technology decision and becomes a capital decision. Capital decisions are made in boardrooms, not in model repositories. The window closes on 24 July. The more interesting question is not whether this framework is right for finance — it broadly is — but how quickly the rest of the public-private edge of AI adoption catches up. The RBI has, almost without saying so, written the first chapter of an Indian AI accountability code. Other regulators will write the rest, or they will inherit the failures of not having done so.

#RBI #AIGovernance #ModelRisk #BankingRegulation #IndianEconomy #AIAccountability #PublicFinance #RegTech

Korea Was The Canary

On Tuesday, June 23, South Korea's KOSPI fell 9.99 percent in a single session, tripping circuit breakers, wiping out roughly $2.5 billion in foreign capital in hours, and qualifying as the fifth-largest single-day decline in the index's history. Samsung and SK Hynix each lost more than twelve percent. The Nasdaq followed down 2.21 percent the next session. Oracle, in the same news cycle, disclosed it had cut twenty-one thousand jobs in a year — almost thirteen percent of its workforce — and named AI as the reason. Forty-eight hours, three disclosures. None of this is a tech story. It is the beginning of a macro story we have not yet learned to read.

The capex has become the commodity

For two decades we taught ourselves that crude was the single variable that synchronised global cycles. A spike in oil touched everything: inflation in importers, fiscal space in exporters, central bank reaction functions everywhere. That intuition is still half right. But a new variable has joined it, and the last week suggests it is, in the short run, more potent.

Meta, Google, Microsoft, Amazon and Oracle are between them committing capex plans this year that could touch seven hundred billion dollars to build AI data centres. Oracle alone reported negative free cash flow of $23.7 billion last fiscal year while raising capex 162 percent to $55.7 billion. Those numbers are not technology numbers anymore. They are macroeconomic numbers — comparable in scale to the annual oil import bills of mid-sized economies — and they are decided in a handful of US boardrooms.

This is the kind of single-factor dependence Professor Richard Robb's International Capital Markets course at Columbia kept circling: when cross-border flows are tethered to a small set of decisions on a small set of US balance sheets, the receiving economies inherit volatility they did not choose and cannot hedge. Korea just lived through one rehearsal.

Why Korea fell first

Korea was not a random victim. The KOSPI was up roughly 95 percent year-to-date going into Tuesday. Samsung and SK Hynix together account for about half the index by market capitalisation. The Bank of Korea has openly said AI-related chip exports will add 0.7 percentage points to 2026 growth, more than offsetting the drag from costlier oil. Taiwan is on track for 9.6 percent GDP growth this year — its highest in sixteen — on the same trade.

When the global market began doubting whether US hyperscaler capex was sustainable, every one of those exposures got marked at once. Three triggers converged on the same morning: MSCI again excluded Korea from its developed-markets watchlist, regulators raised flags about leveraged single-stock ETFs tied to Samsung and SK Hynix, and a hawkish Federal Reserve dot-plot from June 17 was already in the bloodstream. The market did not need a new fact. It needed a coordination point.

India's awkward middle position

India's place in this story is uncomfortable. Unlike Korea or Taiwan, India is not a meaningful seller into the AI hardware stack. Unlike China, it is not building frontier models at scale. The result is the worst of both worlds: when AI capex booms, India captures little of the upside; when it wobbles, the contagion still arrives — through portfolio outflows, currency pressure and the generic risk-off impulse against emerging markets.

The numbers this month are blunt. Foreign portfolio investors pulled roughly sixty-four thousand crore rupees out of Indian equities in the first half of June alone, the heaviest exit since March, with elevated oil and "concerns over AI's impact on tech revenues" cited as the principal reasons. Two macro factors, neither of which India controls, set the direction of an enormous slice of market cap. That is not market accident; that is structural exposure.

What policymakers should actually do

One. AI capex belongs on the macroprudential dashboard. The Reserve Bank's Financial Stability Report already tracks crude, dollar moves, FII positioning and banking-sector stress. It should now also track the announced capex plans of the five US hyperscalers, because in any given quarter those plans are a bigger swing factor for emerging Asia than the OPEC+ communique. Treating this as a tech-sector story is a category error.

Two. The export-services tax base — IT, ITES, global capability centres — is more cyclically exposed to AI capex than its standard sector classification implies. Revenue projections and advance-tax assumptions should stress-test against a fifteen to twenty percent compression in this base, not as a tail risk but as a plausible scenario for the coming eighteen months. A tax administration cannot afford to be the last institution to learn that a sector's cycle has changed.

Three. The Indian debate around "missing the AI boat" oscillates uselessly between buying chips and drafting strategies. The better path runs through demand the country actually controls — large-scale public-sector AI deployment in tax, courts, health, urban services — so that compute spend, even if imported, gets monetised at home through productivity. A country that is a net buyer of AI inputs must, at minimum, be the most efficient internal consumer of them.

The bigger lesson

The KOSPI's nine-point-ninety-nine percent is not really a market story. It is a structural disclosure. Forty years ago a single oil price ran the world's inflation and growth narrative. We are not there yet with AI capex. But we are closer than is comfortable, and the trajectory is one-way. The job of policymakers in countries that neither make the chips nor own the models is to stop treating each AI-driven wobble as a curiosity and start treating it as a recurring macro shock with the same seriousness we reserve for crude.

Korea was the canary. The mine is the rest of us.

#AIcapex #KOSPI #EmergingMarkets #IndiaEconomy #GlobalMarkets #Semiconductors #MacroPolicy #ForeignFlows