Tokyo Just Turned Off The Tap

The number is small. The symbolism is enormous.

On Tuesday, the Bank of Japan raised its benchmark rate to 1.0%, the first reading at that level since 1995. For most central banks a quarter-point move is routine. For Tokyo, it closes a chapter that has lasted longer than many working economists have been working.

The decision passed 7-1, with one dissenter citing growth risks. Governor Ueda missed the meeting after being hospitalised; Deputy Governor Himino, who chaired it, told parliament three days later that the bank remained committed to further hikes. That the move passed without drama is itself the story. Cheap yen is no longer the world's default setting.

What Tokyo actually tightened

The obvious analysis writes itself. The yen is weak, hovering near 160 to the dollar despite roughly 11.7 trillion yen of intervention in May. Wholesale inflation hit 6.3% that month, the highest since 2023, lifted by the Iran-driven oil shock. Real rates remain deeply negative even at 1%. None of that is wrong, but it misses the larger point.

For three decades Japan has effectively been the world's liquidity tap. Near-zero rates plus a deep, freely-traded currency made the yen the borrowing leg of the most successful carry trade in modern finance: borrow yen at almost nothing, swap into something higher-yielding from US Treasuries to Brazilian sovereign bonds to Indian rupee debt, pocket the spread. That trade has financed a startling share of cross-border risk for a very long time. When Tokyo moves the funding rate, every position built on it gets re-priced.

The signal beyond the number

What Tuesday closed is the optionality of staying loose. The Bank of Japan has spent a decade signalling that normalisation was conditional on wages, on inflation expectations, on the global cycle. The April hold was framed as a pause to absorb the Iran shock. The June hike says, in effect, that even with that shock unresolved, the bank judges the risk of inaction worse than the risk of action. Himino's testimony three days later, flagging the possibility that underlying inflation may deviate upward from the 2% target, was a polite way of saying more hikes are coming.

That changes the global plumbing in a way much domestic commentary will miss. A 1% policy rate today, plus a credible signal of 1.25% by year-end, is enough to make a slice of yen-funded positions uneconomic at the margin. Past BOJ hikes since 2024 have each been followed by sharp drawdowns in high-beta global assets within weeks. The mechanism is not mystery — it is forced deleveraging.

Through the India lens

The reflex in emerging markets, including ours, is to read these moves through the dollar lens. That is half the picture. The other half is who funds the dollar.

India has benefited handsomely from a long stretch in which a slice of foreign portfolio money in our bonds and equities was, somewhere up the chain, financed in cheap yen. As that funding gets more expensive, two things shift. The marginal cost of foreign capital into India rises even if the Federal Reserve does nothing further. And the patience of that capital shortens; a carry that looks attractive at a 25 basis point Japanese funding rate looks distinctly less so at 100, and brittle at 125.

There is an old lesson from any honest course on international capital markets that the funding leg of a trade often matters more than the asset leg. Tokyo just moved the funding leg. The corollary, for a finance ministry, is that long-duration, sticky foreign capital should be treated very differently in tax and regulatory design from money that is essentially a leveraged carry position. From inside a national tax administration, I have repeatedly seen the same instrument behave very differently depending on who is funding it. The funding mix is now changing.

What I would actually do

If I were sketching responses, three would be ordered ahead of the rest.

Rebuild the muscle for scenario work. Not a war-game, a quiet quarterly exercise inside the relevant departments — including the direct tax administration, which sees cross-border income in unusual granularity — on what a sustained 150 basis point repricing of yen-funded capital does to specific sectoral flows.

Read the data we already have. Indian tax filings, treaty disclosures and inbound investor reports carry a much richer real-time picture of cross-border behaviour than is generally appreciated. The question is whether anyone is reading them that way.

Stop treating every additional inflow as a win. Some inflows are savings looking for a long home. Some are leverage looking for a quick exit. Treating them identically was tolerable when Tokyo was paying the world to borrow. It is less so now.

None of this is forecasting doom. A Japanese policy rate at 1% is, in the long run, healthy normalisation — a return to a world where the price of money is set by something other than insurance against deflation. The point is simply that the era in which a non-trivial share of global risk-taking was quietly subsidised from a single building in Tokyo is closing. We should plan for the world that comes next, not the one we got used to.

#BankOfJapan #YenCarryTrade #MonetaryPolicy #EmergingMarkets #IndianEconomy #CapitalFlows #PublicFinance

Stop Building Chatbots.

Eighty-two percent. That is the share of inbound citizen queries an AI agent called Bobbi resolved in its first week across three English police forces, without a single hand-off to a human officer. The number matters less for policing than for what it telegraphs to the rest of government: the chatbot era, finally, is ending.

I say this with some scepticism about my own past. Anyone who has helped stand up a citizen-facing assistant inside a large national department knows the temptation to call any conversational widget a chatbot and declare modernisation complete. It is not. Bobbi, alongside Singapore's GovTech work and Estonia's interoperable agent network, signals that the next layer of public-sector AI will look very different from what most administrations are currently buying.

Chatbots answer. Agents act.

The technical distinction is sharper than the marketing suggests. A chatbot replies to a prompt. An agentic system is handed a goal — process this permit renewal — plans the steps, calls the databases, validates against the rules and executes the transaction. One produces text. The other completes a workflow.

This is why a recent World Economic Forum and Capgemini exercise mapping seventy core government functions sorts them more cleanly by workflow than by department. Eligibility assessment, document processing, fraud detection, permit issuance — these cut across ministries; they are the natural unit of analysis for an agent, not an org chart. Agents do not need to break silos. They operate outside them.

The chatbot habit is the trap.

The single most common mistake I see, both in India and abroad, is treating an agentic deployment as a chatbot upgrade. Same procurement template, same vendor, same knowledge base, a slightly cleverer model bolted on the back. That is not what the technology is for. The Capgemini survey of 350 public-sector organisations finds that ninety percent intend to explore or deploy agentic AI within two to three years, while Gartner forecasts that more than forty percent of those projects could be cancelled by 2027 — usually because the agency moved before understanding where the actual value sits.

The value sits in outcomes, not interactions. The right question is not can the bot answer this but what is the citizen actually trying to finish, and how many steps can we collapse into one supervised flow. An estimated one hundred and forty billion dollars in US federal benefits goes unclaimed each year because the application paths are too fragmented for the people who most need them. Most large administrations, India included, will find similar pockets if they look honestly: schemes whose stated reach far exceeds their actual delivery, not for want of policy but for want of plumbing.

What conversational AI for citizens actually teaches.

Citizen-facing conversational systems at national scale teach two unfashionable lessons. First, the volume of routine queries is far larger than any budget anticipated, and far more repetitive: a small set of questions accounts for most of the load. Second, when the citizen needs to complete something rather than learn something, the conversational layer hits a wall. The handover to forms, portals and back-office staff is where the experience breaks.

Agents are precisely the technology for that gap. In a direct-taxes context, an agent can read a notice, retrieve the relevant return, pre-fill a form against rule sets, cross-check historical filings and route only the genuinely ambiguous cases to a human officer. The administrator stays the architect. The agent does the clerical labour that nobody, on either side of the counter, particularly enjoys.

Bounded autonomy, glass-box defaults.

The discipline the field is converging on is bounded autonomy: being deliberate about what an agent is allowed to do, keeping a human meaningfully in the loop, and making every step auditable. The phrase that fits a public-sector context is glass-box governance. A chatbot answers, and the trail is shallow. An agent acts, and each action — every rule consulted, every database queried, every form submitted — should leave a perfect record. Used well, this is more accountable than the human-only system it replaces, not less.

The procurement template should change accordingly. Stop buying chatbots. Buy a workflow agent that ships with an audit log by default, with explicit, narrow permissions per task and a hard escalation rule for any case touching rights or material amounts. Bounded autonomy belongs in the contract, not in a vendor promise.

Where to start.

The most useful first move for any Indian department is unglamorous: pick one workflow that already exists end-to-end on paper or in disconnected portals — refund issuance, grievance redressal, a single notice-and-reply cycle — and rebuild it as an agentic flow with a human checkpoint at the decision. Measure completions, not interactions. Compare cost not to the chatbot it replaces but to the staff hours it returns. That number is what budget committees will eventually demand, and it is the only one that matters.

The chatbot was a useful detour. It taught millions of citizens that they could speak to the state in plain language and get a sensible answer. The agent is what turns that conversation into a completed transaction. The departments that grasp the difference now, and procure for it accordingly, will spend the next decade doing more with steadier headcount. The rest will spend it explaining why their bot still cannot actually do the thing.

#AgenticAI #PublicSectorAI #DigitalGovernance #GovTech #CitizenServices #AIinGovernance #IndiaAI

The Pause Is The Plan

At 10 a.m. this morning the Monetary Policy Committee kept the repo rate at 5.25%, unchanged for the third meeting in a row, with a neutral stance and a quietly composed press conference to follow. The headlines wrote themselves: RBI holds. Fixed-income desks shrugged. Equities opened a notch firmer. To anyone watching only the rate, today's decision looked like the absence of a decision.

It was not. The pause is the plan.

The rate is the top of a layered toolkit, not the toolkit

Think of Indian monetary policy as a stack. The policy rate sits at the top, visible, dramatic, easily understood. Below it lies a thicker, less photogenic layer: variable rate repo auctions, buy-sell forex swaps, open market operations, CRR adjustments, dollar liquidity windows for oil marketers, and the steady drumbeat of intervention in the spot and forward currency markets. Above 5.25% sits one lever. Beneath it sit a dozen.

The April hold was an early signal. Today's hold confirms the doctrine: until the data forces a move, the central bank will work the lower stack and leave the top untouched. DSP Mutual Fund's fixed-income team said it plainly in a pre-policy note, that the RBI rarely jumps straight to a rate move and follows a step-by-step sequence before pulling that trigger. Today's decision is a refusal to skip steps.

The shock is arriving through the exchange rate

Crude has been hovering near 96 dollars since the conflict in West Asia escalated. The rupee has slid to about 95 to the dollar, a level no one was forecasting a quarter ago. Wholesale inflation has crossed 8 percent. Retail inflation is still inside the band, but Icra's Aditi Nayar is right to flag that the second-round effects through fuel into transport, packaging and food are only beginning to show.

The temptation, particularly for analysts who default to a textbook reaction function, is for the RBI to hike in order to defend the currency. Standard Chartered already pencils in 50 basis points across FY27 and some expected the cycle to begin today. They were wrong, and I think rightly so. A defensive rate hike to prop up the rupee is the macroeconomic equivalent of treating a fever by raising the thermostat. It hurts the patient and does not fix the cause.

Gita Gopinath made the better point earlier this week: some rupee adjustment is what should happen when the world's oil price changes. Trying to keep the currency frozen would only postpone the move and bleed reserves in the process. The professional discipline is to let the price absorb part of the shock and use the lower-stack tools to keep the adjustment orderly.

Why doing nothing is the hardest trade in the room

To anyone who has watched government respond to external shocks from inside the administrative system, the instinct to act visibly and audibly in a crisis is almost overwhelming. Holding is the harder trade because it offers nothing for the news cycle to consume. There is no announcement, no ribbon to cut, no graph to point at.

What today protects is not the rupee, which will move as it must, but the credibility of the easing cycle behind it. Between February and December 2025 the RBI cut rates by a cumulative 125 basis points. Those cuts have transmitted into home loans, MSME credit and personal borrowing. A panicked reversal of that work, on the back of a single quarter of oil-led inflation, would shake the very domestic-consumption story that gives India its growth premium. The MPC's third consecutive pause is, in effect, a statement that the easing of last year will be defended.

This is the lesson at the heart of Prof. Richard Robb's International Capital Markets course at Columbia: a small open economy hit by a real external shock should let the exchange rate absorb the blow while the central bank manages the volatility, not the level. That is the doctrine on display this morning, even if the press release did not say so.

The harder question is what fiscal does next

The blind spot in today's commentary is that monetary policy cannot carry this alone, and should not have to. If crude stays near 96 dollars, the second-round inflation will come through fuel cesses, GST on logistics, and the price line of every state-distributed commodity. The fiscal authority owns more of those switches than the RBI does.

A serious response over the next two quarters has to include targeted excise rebates on transport fuels rather than blanket cuts, faster GST input-tax refunds to small businesses caught in the cash-flow pinch, quicker direct-benefit transfers to insulate the bottom three deciles from food inflation, and a clear medium-term fiscal anchor so the bond market prices the borrowing programme without demanding a higher term premium. Tax and expenditure administration, for once, can be the country's first line of macroeconomic defence rather than its slowest.

The closing thought

Markets are trained to read central bank announcements for what changed. The lesson of today is to read them for what stayed the same, and to notice what is doing the work underneath. The repo rate at 5.25 percent is the surface of the water. The currents that matter run below it: forex tools, liquidity operations, and a quietly disciplined refusal to let a temporary oil shock undo a year of carefully transmitted easing.

If the next 90 days bring a calmer crude tape, today will look like skill. If oil heads higher, the hold will be tested, but the architecture for the test is visibly in place. Either way, I think the MPC made the right call. Doing less, well, is harder than doing more, badly.

#RBI #MonetaryPolicy #IndianEconomy #RepoRate #MPC #Rupee #PublicFinance #CentralBanking

The Code Goes Live

Somewhere in a finance department of a company, a junior accountant is staring at a payment portal that now asks her to select Tax Year 2026-27 instead of Assessment Year 2027-28. That single drop-down is where the new Income-tax Act, 2025, actually begins. Not on 1 April, when it commenced. On 15 June, when the first advance-tax instalment falls due and a real bank account moves money under a real new statute.

Tax law, like a building, is finished only when somebody walks in.

The first deadline is the real commencement

The Income-tax Act, 2025, has been on the books for months. The Income-tax Rules, 2026, replaced the 1962 Rules from 1 April. Hundreds of FAQs have circulated. None of that is the test. The test is the 15 June advance-tax instalment for Tax Year 2026-27, the first time a taxpayer estimates a year's liability under the new code and pays fifteen percent of it.

That is when the law moves from print to pay-in. Until then, every conversation about the new Act has been a rehearsal. From 15 June, the choreography is live.

Two statutes, one tax year

There is a peculiar transition reality that gets understated. The 1961 Act stands repealed from 1 April 2026, but the repeal does not disturb anything relating to tax years before that date. Assessments, appeals and proceedings for earlier years continue under the old Act. The advance-tax instalment due in March 2026 for FY 2025-26 was governed by the old law. The instalment due on 15 June 2026 is governed by the new one.

So the department, and every CFO of any scale, is now running two statutes simultaneously: one for past income, one for current income. From inside any large tax administration, this is the un-televised reality. A transition is not a date; it is a multi-year overlap, where the same officer handles a 2023-24 reassessment under the old code in the morning and a 2026-27 advance-tax compliance under the new code after lunch. The simplification on paper does not eliminate that doubling of cognitive load. Only time does.

The small print of a clean statute

The new Act is shorter, cleaner and more readable than the 1961 Act after six decades of grafts and provisos. But cleanness creates its own friction at the implementation layer. "Previous year" has become "tax year". Section numbers have shifted: advance tax now sits in Sections 403 to 410 rather than the familiar Section 208 onwards. Form 16 is now Form 130. Form 3CEK is now Form 173. Form 26AS is now Form 168.

None of this changes any substance. All of it changes muscle memory. Every payroll system, every ERP, every accountants-office macro, every internal departmental tutorial, every taxpayer's mental shorthand must be rewritten. The headline simplification will be felt only after that rewriting is done. The cost of clarity is paid up front, in transition friction; the dividend comes back later, in compliance ease. Reformers tend to celebrate the first. Only practitioners feel the second.

An administration learning in public

I think the more interesting question is what a new code does to the administration that runs it. A tax department is, in operational terms, a very large rule-execution machine. When the rulebook changes, every loop in that machine must be re-instructed. The portal must accept new minor-head codes. Notices must cite new sections. Officers must learn to draft orders in the new language without slipping back into the 1961 cadence they have known their whole careers. Helpline scripts must be rewritten. Internal training becomes, in effect, an ongoing exam.

This is where productivity either materialises or evaporates. If the first advance-tax cycle runs smoothly, if challans clear, refunds reconcile, mismatches are caught early, then taxpayer confidence in the new regime is established for the next decade. If it is messy, every news cycle of the next few months will be about glitches, and a reform that is substantively sound will be remembered as operationally rocky. The legislative work is over. The implementation game has just begun.

A small proposal for the transition window

One concrete suggestion. Through the first full tax year under the new Act, every taxpayer-facing communication, intimation, notice, demand, refund order, should carry the equivalent old-Act section in parentheses next to the new one. A demand under Section 405 of the 2025 Act should read "(corresponding to Section 234C of the 1961 Act)". This is not a dilution. It is a translation layer.

It would cost the department almost nothing. It would save tens of thousands of taxpayers from a private translation exercise each. It would also reduce the small but real risk of misdescription in appellate orders during cross-over years, where a citation gets stuck between two statutes. Translation layers age well. They quietly disappear once they are no longer needed, leaving a cleaner system behind. The concordance table mapping the old sections with the new one needs to get etched in the muscle memory.

For most taxpayers, 15 June 2026 will feel like any other deadline. For the new Income-tax Act, 2025, it is the day the simulation ends and the actual run begins. Everything written about the new code so far has been on a whiteboard. From that date, it is on the ledger.

#IncomeTaxAct2025 #AdvanceTax #TaxYear2026 #IndianTaxation #TaxAdministration #CBDT #PublicFinance

Who Vets The Frontier Model?

On June 2, the White House quietly published an executive order that almost no one in India will read closely, and that almost everyone in our public sector should. It puts in place an idea missing from every government's AI playbook so far: a sovereign capability to test frontier AI models before they touch the sensitive parts of the state.

The mechanism is unfussy. Within sixty days, the Director of the National Security Agency, in consultation with a handful of other officials, must develop and maintain a classified benchmarking process to assess the advanced cyber capabilities of AI models and determine the threshold at which an AI model should be designated a "covered frontier model". AI developers are invited, voluntarily, to hand a model to the federal government for up to thirty days before releasing it to anyone else. There is no licensing regime; the order expressly forbids one. But there is now a place where the state can look under the hood before the rest of the economy gets to drive the car.

The more interesting half of AI policy

India's AI conversation is loud about two things: compute and use cases. We talk about GPUs by the tens of thousands, about Bhashini, vernacular chatbots, AI in courts. Both matter. Neither answers the question every senior bureaucrat will face within the next three years.

That question goes like this. A vendor offers us a model that can read a citizen's tax history, draft a notice, recommend an action, and execute it. How do we decide whether this specific model is safe enough to be allowed into our environment? Right now, the honest answer is that we decide on vendor reputation, a procurement scorecard, and the judgment of two or three officers who have been around the file long enough to be trusted. That is not nothing. But it is not a state capability. It is hostage to the personalities and office life of the moment. And it does not scale to a country that runs DBT, GSTN, an income-tax stack, a new criminal procedure, and a thousand state-level applications on the same architectural assumptions.

What the order gets right

The good instinct is the separation between innovation and security. The order is loudly pro-innovation; it explicitly refuses to stifle the industry with regulation and rules out any mandatory licensing of new models. At the same time, the same document sets up an AI cybersecurity clearinghouse, expands a federal Tech Force, and asks CISA to make even covered frontier models accessible to operators of critical infrastructure such as rural hospitals, community banks, and local utilities.

It treats the question of whether a model is safe enough for sensitive deployment as separate from the question of whether it can be sold at all. That separation matters. Most jurisdictions, including ours, collapse these into one bucket called "AI regulation". The result is either over-regulation in the name of safety, or no real safety work at all because the political cost of the first option is too high. The American order suggests a third path: government as a privileged tester, not as a gatekeeper.

It leaves much open, too. A classified benchmark used only by NSA, with no public-sector analogue in the agencies that will actually deploy these models - health, tax, courts, energy - is incomplete. A thirty-day pre-release window is short. And the voluntary nature of the framework will work only if industry sees a clear benefit; the order hints at this through privileged early access for "trusted partners", but the carrot is not yet sharp.

An Indian version, sketched

The better view is that we should not wait. Three concrete moves I believe should be taken, none of which need new legislation.

A state-side model evaluation cell

House it within MeitY or CERT-In, staffed by a mix of officers and contracted technical talent on three-year tours. Its job is not to police the AI industry. Its job is to build benchmarks aimed at the failure modes that hurt government most: prompt injection in workflows that touch personal data, reasoning collapse under adversarial inputs in tax assessment, hallucinated citations in legal drafting or during chatbot functioning, model behaviour under translation between Indian languages. Publish what you can. Keep the rest classified. The point is to build a small group inside the state who, over time, develop instincts no procurement officer can substitute for.

A "fit for sensitive deployment" tag

Not a licence. A signal. A model that has been tested against the cell's benchmarks receives a tag that any department or any operator of critical infrastructure can default to when shortlisting vendors. Industry will adopt the tag because the customer base is large enough to make it worth the engineering.

A pre-deployment review for the highest-risk uses

For deployments that touch tax assessment, criminal procedure, biometric matching, grid SCADA, or welfare targeting or Enforcement Directorate for that matter, require a written review by the cell, the line department, and one external technical reviewer. Thirty days. Default approval if no issues are raised in writing. Reasons recorded either way. This is, ultimately, file work. File work is something the Indian state knows how to do.

The deeper point

The instinct, when a powerful new technology arrives, is to build an external regulator. The better move, more often than we admit, is to build internal capability. Having worked on AI systems that talk to ordinary citizens inside a national administration, I am convinced that what slows public-sector AI in India is not a missing regulator. It is the absence of trusted people inside the state who can look at a specific model and say, with reasons, whether it should be allowed near a citizen's record. We need Officers with AI bent.

The American order, in its limited way, has admitted as much. The interesting thing about June 2, 2026, is not the politics around it. It is the quiet acknowledgement that even in a country with the world's deepest pool of AI talent, the state still needs its own pair of eyes. We need ours sooner than we think.

#AIGovernance #FrontierAI #PublicSectorAI #IndiaAI #StateCapacity #AISafety #DigitalIndia

Buffett's Quiet Bet On Compute

The most quietly important capital markets story of yesterday was not the eighty billion dollars. It was the ten billion inside it.

Warren Buffett's Berkshire Hathaway put ten billion dollars into Alphabet's latest equity raise: five billion of Class A shares at $351.81 and five billion of Class C at $348.20. Buffett does not normally pay for compute. For most of his career he avoided technology companies on the grounds that their capital was consumed faster than it returned. That he is now anchoring an eighty-billion-dollar stock sale designed almost entirely to fund AI data centres is a more telling signal than the headline number.

An industrial capex cycle has begun

Alphabet expects to spend roughly $180 to $190 billion on capex in 2026. Together with Microsoft, Meta and Amazon, the four hyperscalers are projected to put close to $700 billion into AI infrastructure this year, and Wall Street estimates point north of a trillion in 2027. These are not software numbers. They are railway, electricity-grid, telecom-trunk numbers, the kind of capital intensity that defines an industrial cycle, not a product cycle.

The raise itself is structured to look like one: thirty billion in underwritten public offerings, of which fifteen billion is mandatory convertible preferred stock, a forty-billion at-the-market programme to be drawn through the year, and the Berkshire anchor on top. Mandatory convertibles are the financing instrument of a company that wants equity-like permanence without immediate dilution. ATM programmes let you tap the market the way a utility taps a balance sheet. This is not a tech firm raising opportunistically. It is a hyperscaler behaving the way a power utility did in the 1920s.

The compute concentration that follows

Capital flows are not neutral. A trillion dollars of capex absorbed annually into four American balance sheets, almost entirely deployed inside the United States with custom silicon and dedicated power, is a redirection of the world's marginal investable capital toward one geography and one stack. The natural counterpart is already visible. Foreign investors pulled more than twenty billion dollars out of Indian equities in the first four months of this year alone, eclipsing the full-year record of 2025, and the rupee has touched all-time lows against the dollar under the combined pressure of energy shocks and capital outflows.

It would be a mistake to credit all of this to the Iran war. The war is the proximate shock. The deeper current is that risk capital globally is being pulled toward an AI capex story that, for now, is built and owned almost entirely by a handful of American firms. In Prof. Richard Robb's International Capital Markets class at Columbia, the point worth remembering was that cross-border capital follows narrative as much as yield. The narrative now has one destination.

Where the sovereignty instinct misfires

The reflex in capitals from Delhi to Brasília will be to demand domestic equivalents. Indian compute sovereignty. Indian foundation models. Indian sovereign silicon. Some of this is genuinely necessary. Most of it, at the scale being discussed, is not feasible.

Consider the arithmetic. Alphabet alone will commit more capex to AI infrastructure in 2026 than the Union government's entire capital expenditure outlay for the year. A press release calling for a Bharat-class hyperscale buildout that can sit at parity with the four American giants is not a strategy. Even a credible five percent of that footprint would consume a meaningful share of domestic risk capital and crowd out infrastructure, housing and credit to small firms.

The harder question is the right one. If we cannot build at parity, and the world's compute will sit on a small number of foreign balance sheets, what is the negotiating position?

The smarter move

I think the correct frame is to treat compute the way India once thought about energy: as a strategic input that does not have to be domestically owned to be domestically governed. A few concrete moves follow from that.

• Sovereign compute partnerships, not sovereign compute factories. Long-duration capacity contracts with hyperscalers, partly funded by domestic institutional capital, locking in priced access to GPUs and inference for Indian public workloads over a decade. The mental model is closer to an LNG offtake agreement than to Make-in-India.
• Co-investment vehicles routed through sovereign and pension capital. When the most capital-hungry sector in the world is issuing equity in eighty-billion-dollar tranches with Berkshire on the cover page, the disciplined move is to be a calibrated buyer of those issues, not an admiring spectator.
• Domestic compute concentrated on what only India can do. Indian-language models, public-data fine-tuning, citizen-facing inference at low cost, audit and compliance tooling fitted to Indian law. Not foundation-model parity. Sovereignty at the layer that actually touches citizens.
• A tax architecture that recognises compute as infrastructure. Depreciation schedules, transfer pricing rules and withholding treatment for cross-border compute services were drafted for an earlier world. They need a careful refresh, because in five years a large share of value added in Indian sectors will be running on rented foreign GPUs, and the statute should not be ambiguous about where that value is taxed.

The Buffett tell, again

Return to Buffett. The investor who built a career on insisting that capital must compound, not be incinerated, has now decided that the compute build is the rare case where the spend itself is the moat. For an Indian observer that is the cue, not to argue with the cycle, but to find the cleanest seat inside it. The wrong response is national chest-thumping about sovereign models. The right one is the unglamorous work of contracts, capital allocation, and statute. The cycle is industrial now. Treat it as such.

#CapitalMarkets #AICompute #IndianEconomy #Alphabet #BerkshireHathaway #ComputeSovereignty #Hyperscalers #EmergingMarkets

The First Job Isn't a Verdict

India's joining season is in full swing. Trains and flights to Bangalore, Pune, Hyderabad and Gurgaon are unusually full of nervous twenty-two-year-olds with new wheeled suitcases. Some are taking up offers they want. Many are taking up offers they will accept because the ones they wanted did not arrive. Almost everyone is asking, quietly, the same question: have I picked correctly?

I want to take that question apart, because it is the wrong one.

The fear is real. It is also pointed the wrong way.

The macro numbers do not help. Industry reports this year suggest only about 42.6 percent of graduates are considered job-ready, and major IT services firms have trimmed entry-level mass hiring by close to a quarter as AI quietly rewrites the bottom of the pyramid. Only roughly 30 to 40 percent of engineering graduates are placed through campus drives; the rest assemble their own ladder. None of this is news to you if you spent April watching half your batch get one offer and the other half get none.

But fear about which first job is the wrong target. Your first job is a sample. It is not a sentence. The variable that compounds, and ultimately decides the next ten years, is not the company name printed on the offer letter. It is the rate at which you become useful.

Optimise for velocity, not for identity

Spend your first twenty-four months optimising for four things, in this order: reps, proximity, feedback, and ownership.

Reps

How many real problems will you actually solve in a year? An analyst at a small firm who closes thirty messy projects will be sharper in twenty-four months than a peer at a brand-name firm who is the seventh name on three big slide decks. Volume of completed work is the most under-rated input to early skill.

Proximity

Who do you sit next to? At twenty-three, the people in the room you happen to be in are your second university. A mediocre role next to a brilliant boss beats a glossier role next to a checked-out one. Ask, in every final interview, who you will report to and what their last three hires now do.

Feedback

Does the work tell you, within days, whether you were right or wrong? Sales, building products, writing, litigation, trading, and certain operating roles have that quality. Many fresher strategy roles do not. Choose closer to the customer, the code, or the courtroom; wherever the world keeps score.

Ownership

Will anything bear your name? One small line item that you fully own, end to end, will teach you more than a rotational program of twelve passenger seats.

None of this requires choosing the right industry. All of it requires choosing the right shape of work.

Read the employer, do not only let them read you

The hiring market has spent two years reading you through aptitude tests, GitHub repos and structured interviews. You are allowed to read back. Two minutes of honesty from a current employee tells you more than two days reading a Glassdoor page.

Before you sign, find one person who joined the same role twenty-four months ago and is still there, and one person who joined and left within twelve. Ask both: what did you actually do in the first six months, and what made you stay or go. If neither call happens, you are not too humble; you are uninformed.

Take the offer where the work is honest about itself. A role that admits to repetitive client servicing in year one but a real shot at owning a portfolio in year two is more truthful than a role that promises transformation and strategic impact on day one. Be careful with brochures.

One unfashionable thing I would ask you to do anyway

Write things down. By hand or on a screen, but write. A weekly note to yourself, ten minutes, on what you learned, who taught you, where you embarrassed yourself, what you would do differently. Nothing performative. No one else reads it.

Over years inside institutions, I have watched many careers form and a handful go genuinely far. The single shared habit, far more reliable than raw talent, is a private practice of reflection that builds judgment quietly. People who arrive at thirty-five with judgment did not get there by accident. They earned it one Sunday evening at a time. Writing Journals, reflecting on what went right and what is it that's going wrong.

If you must hold a worry, hold this one: not that you picked the wrong first job, but that you will sleepwalk through it. Sleepwalking is the actual risk. Bad job plus alert person becomes a good career. Good job plus bored person becomes a long, unhappy LinkedIn.

Take the offer in front of you. Show up on Monday. Be the one who asks the most precise question in the meeting. The first job is not the verdict. The first decade is.

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