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 The 1961 Act stands repealed from 1 April 2026, but the repeal does not disturb anything relating to tax years before that date.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

Three Cents On The Dollar

Three cents.

That is roughly what one extra dollar of AI model spend is replacing, in routine knowledge work, at the firms most exposed to the shift. Among the most AI-exposed companies, each $1 drop in spending on online labour marketplaces corresponded to only about three cents in added AI model spend by the third quarter of 2025. The ratio comes from firm-level payments data analysed by Ryan Stevens of Ramp, and it has been doing the rounds in public-sector commentary this week.

If that number holds, the implication is not that AI is ‘cheaper’ in the comforting, marginal way most agency notes describe it. The implication is that the market price of a meaningful slice of first-pass, reviewable knowledge work has collapsed by roughly an order of magnitude. That is not a procurement story. It is a public finance story, and we should treat it as such.

What the 33-to-1 ratio actually says

A 33-to-1 substitution ratio is not, strictly, a substitution ratio. It is a ratio between two budget lines, captured at one moment, in one slice of the economy, by one provider’s payments data. Read with care, it tells you three things.

One: buyers have found a class of output where the first pass is now an AI deliverable rather than a human one, and the first pass is the part they used to outsource. Two: the unit cost of producing that first pass has fallen far enough that even a sceptical CFO is rewriting last year’s contractor budget. Three, and most important: the saved spend is not, mostly, being reinvested in a more expensive in-house model. It is leaving the books altogether.

For a government department, that last point matters most. In the private sector, ‘leaving the books’ eventually shows up as somebody’s lost job. In the public sector it shows up - or refuses to show up - as a backlog that does not grow, a clearance cycle that does not lengthen, a sanctioned post quietly left vacant. The change is invisible until you go looking for it.

Why government cannot just copy the ratio

It would be a mistake to read this number and rush to swap people for tokens inside a tax administration. Public-sector value is not what the private sector buys when it buys ‘first-pass output’. From inside a national tax administration, one learns quickly that the value of a routine notice is not in its first draft. It is in the fact that the draft is recorded, attributable, reviewable and defensible six years later under cross-examination.

None of that vanishes if AI does the drafting. But it changes who is accountable for what, and at which moment in the workflow. Records-management law, natural justice, statutory limitation, the discipline of audi alteram partem - these are not procurement constraints. They are the architecture of the trust the department holds with the citizen. Any honest AI strategy in this domain begins there and works backwards to the model, not the other way around.

The serious question for the head of any large administration is therefore not ‘where can I deploy a model?’ It is: where in my workflow is the first pass currently a bottleneck, who reviews it, and what does that review actually verify? If the review is genuine - a senior officer reading, weighing, signing - AI is a gift; the bottleneck moves to where it belongs, which is judgment. If the review is rubber-stamping, AI will reprice that work to zero and the institution will not notice until the courts do.

Where the repricing is already real

The categories where this repricing is real will be familiar to anyone who has worked the field. Drafting of routine intimations and standard show-cause notices. Cross-referencing returns against third-party information. Summarising voluminous assessment records before a hearing. Preparing first-pass replies to grievance petitions. Tagging and classifying taxpayer correspondence. Translating dense statutory text into plain-language guidance for citizens.

Each is a place where the marginal cost of a first pass has fallen sharply. Each is also a place where a human signature still ought to mean something. The design problem is precisely how to keep the signature serious while the draft becomes cheap.

Three operating shifts that would actually help

• Measure the bottleneck, not the licence. An honest dashboard tracks clearance cycle times, refund-issue latency, time-to-first-response on grievances. If those are not moving, the model is decorative.
• Promote the reviewer. Where AI does the first pass, the human downstream should be more senior, not less. The saving is bought by elevating the reviewer’s standard, and paying her for judgment rather than throughput.
• Budget for the audit trail before the model. Every AI-assisted output in a tax administration must carry a verifiable record of what the model saw, what it produced, what the officer changed, and why. The audit trail is the primary product. The draft is secondary.

The budget question we should be asking

Michael Ting’s course on public sector organisations at Columbia used to insist that the deepest puzzle in bureaucratic design is not how to motivate effort but how to allocate scarce attention. The Ramp number, read honestly, is a story about attention, not effort. Cheap first passes free attention; what an institution does with that freed attention is the whole game.

The danger a department most needs to guard against is the most boring one: cashing the saving without rebuilding the work. If sanctioned posts are quietly left vacant, if the freed hours are absorbed by more of the same routine work, and if the audit trail is patched on at the end, then the 33-to-1 ratio will become the public sector’s number too - and we will have repriced our own knowledge work without ever asking what we wanted to do with the difference.

The better view, I think, is to treat this moment as a budgeting exercise, not a tooling exercise. Ask, for every freed officer-hour, what higher-value question we now want that officer to be asking. Then build the model around the answer. The cheapest first pass in history will be wasted on the same old second pass.

Teaching AI to Global Tax Officials

NADT had invited me to deliver two sessions as part of their ITEC programme on "Innovations in Tax Administration: Building Capacity for the Future." The morning session was about opportunity — how generative AI is reshaping everything from taxpayer services to complex cross-border investigations. The afternoon was about the guardrails — data privacy, algorithmic bias, ethical governance, and the cautionary tales of what happens when governments deploy AI without asking the right questions first.

On paper, these are two separate topics. In practice, they're inseparable. You can't talk about the transformative potential of AI in tax administration without immediately confronting the question: at what cost, and to whom?

That tension — between innovation and protection, between speed and fairness — became the throughline of my entire day in Nagpur.

The Room That Changed My Perspective

Here's what I wasn't fully prepared for: the quality of the questions.

I walked in expecting to explain foundational concepts. What is a large language model? What does RAG architecture mean? How does prompt engineering work? And yes, some of that was necessary. But the participants — officers from developing economies across Asia, Africa, and beyond — weren't just absorbing information passively. They were interrogating it.

One officer asked how you prevent an AI-assisted audit system from encoding historical biases in taxpayer selection. Another wanted to know how her country, with limited digital infrastructure, could adopt AI without deepening the digital divide. A third pushed back on the idea that AI could meaningfully assist in transfer pricing without understanding the specific regulatory nuances of his jurisdiction.

These weren't theoretical objections. They were the questions of people who would actually have to implement these systems back home, with real constraints and real consequences.

That shift — from "should we adopt AI?" to "how do we adopt it responsibly?" — told me something important. The global conversation about AI in government has moved past the hype phase. What people want now is practical, honest guidance.

What I Showed Them (And What I Learned Showing It)

In the morning session, I walked through real examples rather than hypotheticals. India's NUDGE campaign, where AI-powered behavioural nudges led to 24,678 taxpayers voluntarily revising their returns and disclosing ₹29,208 crore in foreign assets — with zero litigation. Singapore's IRAS chatbot, which saved over 11,000 officer-hours. The CBDT's ₹3,000 crore data analytics project with LTIMindtree that's building predictive capabilities at a national scale.

I also got personal. I showed them the time-savings analysis from my own transfer pricing work — how document review that used to take eight weeks can now be compressed to two, how comparability analysis that consumed a month now takes three days. The room went quiet when I put up the numbers. Not because the technology was surprising, but because the implications were immediate. Every officer in that room was mentally mapping those efficiency gains onto their own caseload.

But the moment that stayed with me came in the afternoon.

The Cautionary Tales That Matter

When I brought up Australia's Robodebt scandal — where an automated debt recovery system wrongly targeted hundreds of thousands of welfare recipients — the energy in the room shifted. This wasn't abstract anymore. These were real governments, using real algorithms, causing real harm to real people.

The Netherlands childcare benefits crisis, where an AI system flagged families — disproportionately those with dual nationalities — for fraud based on flawed data and biased algorithms, hit even harder. Some participants came from countries with similar demographic complexities. The lesson wasn't subtle: if wealthy, technologically advanced nations can get this catastrophically wrong, what does that mean for countries with fewer resources to build safeguards?

I made a point that I believe deeply: these failures weren't caused by bad people. They were caused by good people who asked "Can we automate this?" before asking "Should we automate this?" Who asked "How much will this save?" before asking "Who might this harm?"

The room didn't just nod along. They debated. They shared examples from their own contexts. They pushed me to be more specific about what "human oversight" actually looks like in practice when you're understaffed and under-resourced.

What I'm Taking Away

Three things crystallised for me during that day in Nagpur.

First, the demand for practical AI literacy in government is enormous and largely unmet. Officers don't need more keynote speeches about the "fourth industrial revolution." They need to sit down with a tool, try a prompt, see what works, understand what fails, and build intuition through practice. The most engaged moments in my sessions weren't during the slides — they were during the live demonstrations, when participants could see AI drafting a tax notice or analysing a scenario in real time. I gave a live demonstration of how we give a context in a prompt and how does it lead to a customised notice generation.

Second, developing economies have a genuine opportunity to leapfrog. They're not burdened by legacy systems the way some advanced administrations are. If they build thoughtfully — with ethical frameworks baked in from day one rather than bolted on after a scandal — they can set a global standard. That's not wishful thinking. It's a strategic possibility, and it requires exactly the kind of cross-country learning that programmes like ITEC enable.

The Question I Keep Coming Back To

Tax administration isn't glamorous. It doesn't make headlines unless something goes wrong. But it's the infrastructure that funds schools, hospitals, roads, and defence. When we get it right, societies function. When we get it wrong — through bias, opacity, or carelessness — the most vulnerable bear the cost.

So the question isn't whether AI will transform tax administration. It already is. The question is whether we — the people in the room that day, and the thousands like us around the world — will shape that transformation with the care it demands.

I left Nagpur cautiously optimistic that we will.

When AI Builds AI

There's something uniquely humbling about watching an AI agent build another AI agent in less than two weeks - and then realizing that the thing it built might fundamentally change how millions of people work every day.

Anthropic just launched Cowork, a desktop AI agent that manages files on your Mac without you having to babysit it through every step. But here's what stopped me cold: they built this entire feature using Claude Code. In approximately ten days. The AI helped build the AI that now helps us build... well, whatever we need to build.

If you're in tax administration, public finance, or really any corner of government that drowns in paperwork, you should be paying very close attention to what just happened.

The Expense Report That Changed My Mind

Let me paint you a picture that probably sounds familiar. A financial operations team receives hundreds of receipt images every month - crumpled photos from field visits, scanned hotel bills, blurry restaurant checks. Someone (usually several someones) manually extracts vendor names, amounts, dates. They sort by category. They build spreadsheets. They reconcile. Four hours of mind-numbing work that, let's be honest, nobody entered public service to do.

Now imagine this: you point Cowork at a folder of those receipts. You go get coffee. You come back to a reconciled monthly expense spreadsheet, categorized and formatted, with near-zero data entry errors. The four-hour process took fifteen minutes, and the humans involved spent that time thinking about patterns in spending rather than typing numbers into cells.

That's not a future scenario. That's available today for Claude Max subscribers on macOS.

And working at DOMS, thinking constantly about how we transform tax administration while respecting taxpayer dignity, I find myself asking: if Anthropic can build something this sophisticated in ten days using their own AI tools, what's our excuse for still requiring taxpayers to manually re-enter information the government already has? (Though we have succeeded to a greater extent with pre-filled forms)

The Recursive Loop We're Living In

Here's what fascinates me about this moment. Anthropic used Claude Code - an AI coding agent - to build Cowork - an AI file management agent. We're watching AI accelerate AI development, which will accelerate how we deploy AI, which will accelerate... you see where this goes.

In transfer pricing work in Mumbai, I've spent years analyzing patterns across thousands of transactions, looking for anomalies that might indicate profit shifting. It's intellectually demanding work that requires both pattern recognition and contextual judgment. The pattern recognition part? That's increasingly AI territory. The contextual judgment - understanding what unusual circumstances might legitimately explain an outlier, recognizing when similar-looking cases require different treatment - that's profoundly human.

But here's the thing: I couldn't do the contextual judgment part nearly as well if I was drowning in the pattern recognition grunt work. The AI doesn't replace my expertise; it creates the conditions where my expertise can actually matter.

Cowork represents a particular philosophy about this division of labor. It's not a chatbot that requires constant prompting. You specify a folder, define a task, and it works autonomously within those boundaries. It reads, creates, edits - without asking permission at every step. That's a fundamentally different relationship between human and AI than most of us are used to.

What Government Gets Wrong About AI (And Why Cowork Matters)

In my role working on India's new Income Tax Act 2025 and developing Guidance Notes at DOMS, I see two competing visions of AI in government service constantly colliding.

Vision One: AI as Institutional Efficiency Engine. Automate processing. Speed up compliance checks. Reduce headcount needs. Make government run faster, cheaper.

Vision Two: AI as Citizen Empowerment Tool. Reduce coordination costs for taxpayers. Make complexity navigable. Shift the relationship from adversarial compliance to collaborative partnership.

Most government AI initiatives, if we're being honest, default to Vision One. It's easier to measure. It fits existing budget frameworks.

But Vision Two is where the transformation actually happens.

What Anthropic did with Cowork - built primarily by AI, for everyday human tasks, designed to work autonomously once properly directed - points toward Vision Two. It doesn't make Anthropic's team smaller; it makes them more capable of building ambitious things quickly. The constraint shifted from "how many hours can our engineers spend on this?" to "what's actually worth building?"

Now translate that to tax administration. The constraint shouldn't be "how many officers can we hire to process returns?" It should be "how do we help taxpayers understand and meet their obligations with minimum friction?"

If an AI agent can take a folder of messy receipts and produce a reconciled expense report in minutes, could a similar agent help a small business owner take a folder of invoices and produce an accurate GST return? Not just fill in the forms - actually understand what qualifies, what doesn't, flag potential issues, suggest legitimate deductions they might have missed?

The technology is clearly there. The question is whether we have the imagination and will to deploy it this way.

The Ten-Day Test

Here's a thought experiment I keep coming back to: If Anthropic can build a sophisticated desktop agent in ten days using AI-assisted development, what could a well-resourced government innovation team build in ten weeks?

A taxpayer assistance chatbot that actually understands the Income Tax Act 2025's streamlined 536 sections? An agent that automatically identifies eligible deductions from uploaded financial documents? A tool that helps taxpayers model different scenarios - should I claim this under section X or Y? - with clear explanations in plain language?

Working closely with CBDT Board Members and the Chairman on policy implementation, I've seen how much brilliant thinking goes into tax reform. The new Act itself is remarkable - reducing 800+ sections to around 536, written in genuinely plain language, designed for accessibility.

Cowork suggests a different approach: build the tools as fast as you rebuild the rules. Use AI to create AI-powered assistance that evolves alongside the policy. Don't wait for the perfect centralized solution; empower teams to build, test, learn, iterate.

The Privacy Architecture We're Not Talking About

There's a critical detail buried in Anthropic's announcement that everyone in government should internalize: Cowork operates within user-specified folders. It doesn't roam freely across your system. You define the boundaries; it works within them.

This matters enormously for government AI deployment. The resistance to AI in tax administration often centers on privacy concerns, and rightfully so. Citizens worry about algorithmic surveillance, about AIs that know too much, about data being used in ways they didn't consent to.

But the Cowork model offers a different architecture: bounded AI assistance. The taxpayer uploads their documents to a specific, secure environment. The AI works within that environment to help them complete their obligations accurately. The AI doesn't have access to their entire financial life - only what they explicitly provide for the specific purpose of tax compliance.

This isn't just technically feasible; it's a fundamentally different social contract. Instead of "trust the government with AI access to all your data," it's "use this AI tool, bounded by your choices, to interact with government more effectively."

That shift from institutional efficiency to citizen empowerment I mentioned earlier? It requires this kind of privacy-preserving architecture. You can't empower citizens if they fundamentally don't trust the tools you're asking them to use.

What Gives Me Hope

I'll be honest about something that troubles me. The gap between what's technically possible and what government actually deploys is widening dangerously fast. Private sector companies are building sophisticated AI agents in weeks. Government procurement cycles measure timelines in years.

If a taxpayer can use a commercial AI tool to manage their finances more easily than they can use government-provided tax compliance tools, we've failed. And right now, in 2026, that's increasingly the reality.

But here's what gives me hope: The India we're building through initiatives like the new Income Tax Act isn't trying to compete with the private sector on technological sophistication. We're trying to create the legal and policy frameworks that make sophisticated technology serve public purpose.

The Act's move to plain language, the Taxpayers' Charter revision we're working on, the focus on collaboration over confrontation - these create the conditions where tools like Cowork-for-tax-compliance could actually flourish.

I've been struck by how hungry students and young professionals are for this vision. They don't want to choose between public service and technological sophistication. They want to bring AI's potential into government, to build tools that genuinely help people navigate complexity.

That energy matters. If we can channel it - if we can create environments where talented people can build meaningful solutions quickly, learn from real users, iterate rapidly - the ten-day timeline that seems remarkable today might just become normal.

The Question That Matters

So here's what I keep coming back to: Are we building AI for government, or are we building AI for citizens that happens to interact with government?

Cowork is definitely the latter. It doesn't exist to make Anthropic's operations more efficient (though it probably does). It exists to make Anthropic's users' lives easier. The benefit to Anthropic is indirect - happier, more productive users who see more value in their subscription.

Most government AI is the former. Built to make processing faster, compliance checking more automated, administration more efficient. The benefit to citizens is supposed to be indirect - cheaper government, faster processing, fewer errors.

I think we have this backwards.

What if we started with: How do we help this specific taxpayer understand what they owe and why? How do we make it genuinely simple for this small business to comply accurately? How do we reduce the cognitive load on this individual trying to claim legitimate deductions?

And then worked backwards to: What AI capabilities would we need to build to achieve that? What data infrastructure? What privacy protections? What training for our officers who work alongside these tools?

That's a fundamentally different procurement process. A different innovation culture. A different success metric. Not "how many returns processed by CPC” but "how many taxpayers report feeling confident they complied correctly?"

An Honest Admission

I don't have all the answers here. Working at DOMS, engaging with policy implementation at the highest levels, I'm acutely aware of constraints I couldn't have imagined before serving in this role. Government AI deployment isn't slow because bureaucrats are lazy or unimaginative. It's slow because the consequences of getting it wrong affect millions of lives, because privacy architecture for government AI is genuinely harder than for consumer applications, because equity considerations require us to ensure AI benefits don't accrue only to the tech-savvy.

These aren't excuses; they're real challenges that deserve serious thought.

But watching Anthropic build an AI agent using an AI agent in ten days, seeing what's now possible for ordinary users, I also know this: The complexity argument only holds if we're trying to build centralized, one-size-fits-all solutions. If we're trying to create bounded, privacy-preserving tools that help individuals navigate their specific situations, the path forward is clearer than we often admit.

The Income Tax Act 2025 gives us a once-in-a-generation opportunity to rethink not just the legal framework but the entire compliance experience. We're writing Guidance Notes to help people understand the new Act. What if those Guidance Notes were interactive? What if they adapted to your specific situation?

Building Differently

If I had to distill what Cowork represents into one sentence, it would be this: AI building AI to help humans focus on what actually matters to them.

For Anthropic's users, that's managing files and tasks efficiently so they can do their real work.

For taxpayers, it could be navigating tax obligations confidently so they can focus on their businesses, their families, their lives.

For us in tax administration, it should be about creating the conditions where that kind of empowerment becomes normal, expected, achievable - not exceptional.

The technology is here. The legal framework is evolving.

And I think the next ten days, ten weeks, ten months of government AI deployment will tell us whether we meant it.