Back at Amity, Twenty Years On

I passed out of Amity Business School, Lucknow in 2006. Twenty years later, on 5 October 2026, I was back on the same campus. This time I was the one holding the microphone, as the speaker in the Alumni Talk Series organised by the Corporate Resource Centre.

The title on the screen read "From Amity to Public Service: A Journey of Purpose, Passion and Perseverance." Seeing my name there, on the campus where it all started, was nostalgic and honestly quite fulfilling.

The foundation batch

I joined in 2004. We were the foundation batch, the very first MBA class of the Lucknow campus. Being first meant there were no seniors to follow and no placement record to lean on. Not much was settled yet.

The campus has grown a great deal since then.

Who was in the room

We had a good turnout of MBA and engineering students, along with faculty members and the CRC committees of both the business school and the engineering school. After the talk, several students took the mic with questions.

Here is the gist of what we talked about.

1. Your first job is not a verdict

Many students treat their first job as a final decision. It isn't. My own path has taken in an MBA, a job at India Bulls, four years of UPSC preparation and, since 2010, the Indian Revenue Service. None of it was a straight line.

A career is a series of informed bets. Make the best call you can with what you know, give it everything, and be honest with yourself about when it's time to change course.

2. Soft skills are not soft

Technical knowledge gets you shortlisted. How you speak, listen, write an email, handle a disagreement and carry yourself in a room decides how far you go. The people who rise are usually the ones others want to work with, and trust to represent them.

You can practise these skills like any other:

• Speak up in class, even when you're unsure.
• Volunteer to present.
• Write emails a busy person can read in thirty seconds.
• Learn to say, "I don't know yet, but I'll find out."

3. Don't wait for companies to come to you

Campus placements are a starting point, not the whole market. Plenty of good roles are never advertised. Make a list of the companies you actually want to work for. Find out what they do, who leads the teams you care about, and which problems they're trying to solve. Then reach out with something specific to say.

4. Cold calls and cold messages

I told the students what I used to do at India Bulls, and we talked about why cold calling matters.

Today the cold call has a cousin, the cold message on LinkedIn or email. The rules are much the same:

• Keep it short. Three or four lines.
• Make it personal. Say why you're writing to this person and not to everyone.
• Ask for something small: a fifteen-minute conversation, a pointer, an introduction.
• Follow up once, politely. Silence usually means busy, not no.

Most students never send that first message because they're afraid of being ignored. Being ignored costs nothing. Not asking costs you the opportunity.

Giving back

My thanks to the faculty and the Corporate Resource Centre of Amity University Lucknow, and especially to Ms. Archana Dhir, who extended the invitation. It was good to give something back to the campus I passed out from.

If even one student in that auditorium sends that first nervous cold message this week, the morning was worth it.

Quotient Technology: Excluding AI-Focused Comparables May Create a Characterization Risk for India's GCCs

A recurring dispute in Indian transfer pricing litigation involves the TPO selecting comparables with high margins and the taxpayer arguing that those comparables are functionally dissimilar. A recent Bangalore ITAT order in the Quotient Technology case follows this pattern, but the specific reasoning used by the Tribunal is worth examining closely. The Tribunal excluded Evoke Technologies and Great Software Laboratory from the comparable set because they were engaged in advanced work involving AI, machine learning, cloud computing, IoT and analytics. Quotient's Indian entity, by contrast, was found to be a captive performing routine development and support work for its US parent, with core R&D, intangible ownership and strategic decision-making retained offshore.

The functional comparability requirement under Rule 10B(2) has always asked whether a comparable performs similar functions, bears similar risks and deploys similar assets. What is notable in this order is the specific marker the Tribunal used to establish dissimilarity: AI/ML capability itself. A company that builds its own machine learning products or offers machine-learning-driven consulting was treated as not comparable to a routine captive, on the reasoning that AI work signals ownership of valuable intangibles, entrepreneurial risk, and margins that a cost-plus routine development model would not justify.

The reasoning that benefits captives in this case may create a different problem for captives that have themselves moved into AI-native work. Several Indian Global Capability Centres have shifted over the past two years from routine coding and support tickets toward fine-tuning models, building internal agentic workflows, running prompt-engineering and data-annotation pipelines, and in some cases owning product-level AI tooling for their global parent. If a TPO or a future Tribunal applies the Quotient reasoning symmetrically, an Indian captive that has taken on this kind of work while still operating under an intercompany agreement describing it as a 'routine software development service provider' could face a characterization problem. The functional profile recorded in its intercompany agreement, its Form 3CEB/Form 48 disclosures, and its TP documentation may no longer reflect the work the entity actually performs.

The margin stakes run in the same direction as the Quotient dispute, only reversed. A TPO applying an entity-level or AI-adjusted comparable set to a GCC that has outgrown its routine captive label could argue for a materially higher arm's length return than the captive's existing cost-plus markup assumes. The NTT Data case illustrates the scale of this kind of gap, with segmental margins computed at 10.17% against entity-level margins of 3.90%. GCC parent groups that have not updated their functional analysis to reflect AI-related work now being performed in India are exposed to the same mismatch between documented function and actual activity that TPOs look for.

Practitioners advising GCCs need a clearer sense of where the line sits between an Indian captive using AI tools to perform routine work faster, which should remain comparable to standard BPO/SWD benchmarks, and an Indian captive performing AI functions that create value similar to the comparables excluded in Quotient, which could warrant a different characterization and markup. No authority, whether the OECD, the CBDT, or any Tribunal, has drawn that line yet. The comparables jurisprudence is nonetheless building the vocabulary that is likely to be used to draw it eventually, and that vocabulary is emerging first from the taxpayer-favourable side of the argument. TP advisors should review their GCC clients' functional profiles now and test whether the entity's actual day-to-day work would survive the same AI-based scrutiny that currently protects it.

The Facebook Royalty Ruling and the Limits of AI in Transfer Pricing

AI vendors frequently market tools that compress transfer pricing benchmarking into minutes. For certain tasks the claim holds: screening a database of five hundred potential comparables down to a defensible shortlist, or drafting a first-cut local file, is a repetitive, pattern-matching exercise that large language models and agentic workflows can handle. These tasks, however, are not where transfer pricing disputes carry the highest financial stakes. Intangible valuation, royalty characterisation and cost-sharing disputes remain the hardest interpretive questions in the field, as a recent Facebook/Meta ruling from the US Tax Court illustrates.

On September 29, 2026, the Tax Court issued a supplemental opinion closing out the long-running dispute over how Facebook Ireland should compensate Facebook US for the platform technology, user-community rights and marketing intangibles it received under a 2010 cost-sharing arrangement. The court had earlier determined the present value of these contributions at roughly $7.8 billion. The remaining question was mechanical but consequential: how to convert a single lump-sum valuation into a stream of annual royalty payments over time.

The IRS sought one aggregate flat-rate royalty on rest-of-world revenue, spread evenly over 6.29 years. The court rejected this and allowed Facebook to rely on its own contemporaneous cost-sharing documentation, which split the payment into three separate streams with different bases and durations. The regulations, the court held, require the form of payment to be specified upfront, not the precise royalty base or period. The dispute turned on reading a specific set of facts against specific regulatory text, not on matching the company against a database of comparables.

This ruling is relevant to the debate on AI adoption in transfer pricing because it marks a split in the kind of work routine automation can handle versus disputes that require sustained interpretive judgment. Routine compliance work, safe-harbour eligibility checks, comparable-set generation, local file drafting, is being automated quickly, and tax authorities are moving in the same direction. India's 2026 TP rule changes already feature rule-based automated safe-harbour processes and data-analytics-driven case referral.

At the high-value end of TP, cost sharing, platform contribution valuation, royalty mechanics, documentation form-over-substance arguments, the Facebook dispute shows that resolution depends on years of adversarial expert reconstruction. Competing discount-rate models, arguments over which revenue line is too "aspirational" to count, and disagreements over whether a specific regulatory provision requires the royalty base to be stated in the agreement itself or merely in supporting documentation, are the stuff of this kind of litigation.

Current AI tools do not attempt this kind of analysis. A recently published academic stress test gives reason for caution even where they do attempt related tasks: in a controlled simulation across 20 MNE cases, agentic AI systems performing TP functional analysis and comparable selection cut processing time by 98%, but also produced a 15% error rate in complex functional characterisations, a 22% irrelevance rate in comparable selection, and a 15% hallucination rate in legal citations.

These two developments together carry a practical implication. The parts of TP work being automated fastest are the parts where errors are least costly: an imperfect comparable set in a routine ITeS benchmarking study rarely puts significant tax at stake. The parts of TP work where errors are most costly, intangible valuation, royalty structuring, the kind of judgment call at the centre of the Facebook ruling, are precisely where current AI tools, per the error rates above, are least reliable. Courts in such disputes examine competing expert methodologies line by line rather than defer to any formulaic shortcut.

For Indian GCCs, IP-holding structures, and groups running long-duration cost-sharing or licensing arrangements with affiliates, AI-assisted documentation should be treated as a first draft rather than a substitute for the granular factual and methodological rigour this case rewarded. If tax administrations deploy agentic AI for risk-scoring or functional analysis in audit selection, the same 15-22% error and hallucination rates apply on their side as well. This raises governance questions, including traceability, human-in-the-loop sign-off, and explainability, that Indian TPOs and the CBDT have not yet had to address publicly but may need to as data-analytics-driven case selection expands.

Royalty or Profit-Shifting: Australia's Risk-Zone Test and Its Implications for Indian TP Characterisation Disputes

Cross-border payments for software, brand use, or know-how raise a recurring transfer pricing question: how much of the payment compensates the foreign parent for genuine IP use, and how much constitutes profit shifted out of the local entity. India has addressed this question primarily through tribunal litigation. Samsung, Sony, Vodafone Idea and other taxpayers have contested the same AMP/royalty issue across multiple assessment years, with outcomes turning on functional characterisation: whether the Indian entity operated as a full-risk licensed manufacturer or a disguised contract manufacturer, and whether the royalty comparable used by the TPO was genuinely similar. In its August 2026 ruling on Samsung India, the Delhi ITAT found that the TPO had benchmarked a consumer electronics brand licence against royalty agreements from the agricultural and biotech sectors, a comparison the tribunal rejected along with roughly ₹7,800 crore in other adjustments.

Australia has taken a structurally different approach. On 4 September 2026, the ATO finalised Taxation Ruling TR 2026/2, addressing when cross-border software and IP payments constitute royalties, and released draft Practical Compliance Guideline PCG 2026/D4 alongside it. The PCG sets out a five-zone, colour-coded risk framework, from white to red, that software distributors use to self-assess their royalty withholding exposure.

The zones rest substantially on a quantitative trigger: an Australian distributor's operating margin relative to its global group's margin, with a 10-percentage-point band used as a proxy for whether an embedded royalty sits inside an otherwise 'royalty-free' distribution arrangement. Consultation on the draft PCG closed on 2 October 2026. The same package included a Decision Impact Statement on the Oracle case, confirming that MAP and treaty arbitration remain available even where domestic litigation on the same royalty question is still running in parallel.

The two approaches differ in method and cost. India's approach is precedent-driven and functionally granular: each case requires its own FAR analysis, its own comparable search, and its own tribunal hearing, sometimes across a decade of appeals before the characterisation question is settled for that taxpayer and that year. Australia's approach is administrative and mechanical: a margin threshold either trips a risk flag or it does not, without relitigating functional characterisation in every case.

Each method has a cost. India's approach protects taxpayers from being classified on crude proxies, but generates litigation overhead that both the department and taxpayers have raised concerns about for years. Australia's approach is more scalable and predictable, but practitioners have already flagged that a single margin band applied uniformly across SaaS, cloud and subscription models may catch arrangements unrelated to embedded royalties.

The comparison is relevant to Indian practice independent of the Australian context. CBDT has, over the last two budget cycles, moved India's TP regime toward more systemised and less discretionary processes: block assessments that carry a TPO's margin determination forward two years, a rationalised safe harbour regime with fixed bands by transaction category, and a stated intent to limit automated-analytics flags from triggering full scrutiny unless paired with other evidence of evasion. This is consistent with a shift toward rules-based risk-zoning, though the department has not framed it in those terms.

If CBDT, or an AI-assisted TPO, were to adopt a quantitative first pass for royalty and FTS characterisation along the ATO's lines, drawing on CbCR and segmental data the government already collects, such a system could replace inconsistent manual judgment with predictable, published zones. It could equally reproduce the same reliance on a mismatched comparable that the Samsung tribunal had to correct, embedded instead in a model that taxpayers outside the department cannot audit.

The ATO's framework was put out for public comment; an Indian equivalent, if one emerges, may not follow the same consultative process. For TP practitioners, that difference in process, more than the underlying royalty question, is worth tracking as India's own risk-assessment tools develop.

AI-Based Comparable Screening Does Not Address India's Core Transfer Pricing Disputes

At the WU-TA Advanced Transfer Pricing Programme in Singapore this week, PwC's transfer pricing partners presented AI-based comparable screening as a significant efficiency gain for transfer pricing teams, and also set out its limits. One partner compared AI to a GPS: given the wrong address, it will still get you there efficiently, but a human has to recognise that the destination itself is wrong. A tax head from P&G compared AI's output to that of a junior associate: useful for a first draft, but requiring supervision, risk assessment and quality control before anyone relies on it.

Comparable screening once meant examining databases or company websites one company at a time. AI tools can now gather that information simultaneously, cutting the search stage substantially. Participants at the conference did not dispute that this is a genuine efficiency gain. The question from an Indian perspective is what that gain addresses within the broader TP dispute process.

Indian transfer pricing litigation in 2026 continues to turn on characterization issues rather than comparable selection. The Delhi ITAT's ruling this week in Discovery Communication India illustrates this. The taxpayer had adopted TNMM, its margin came in above the comparables' average, and the TPO did not challenge the comparable set.

The dispute concerned whether the TPO could carve out AMP expenditure already included within the accepted operating cost base, treat it as a separate international transaction, and re-price it using the Bright Line Test. The Tribunal rejected this approach, following the line of Delhi High Court and ITAT precedent in Sony India, Casio, and the Special Bench ruling in LG Electronics. The reasoning turned on the terms of the intercompany agreement, whether a genuine cost-sharing arrangement existed, and whether the expenditure benefited the taxpayer or the foreign AE. Comparable benchmarking played no part in this analysis.

Much of Indian TP litigation that consumes years and significant tax amounts follows this pattern: captive power CUP arguments, GCC or FAR mischaracterization, receivables treated as separate transactions, and tested-party selection disputes. These are disputes about judgment on facts, not about identifying additional comparable companies.

AI-based comparable screening does not reach this category of dispute. If the contested, revenue-significant disputes in India are predominantly characterization disputes, concerning whether a transaction exists at all, whether an entity is genuinely a captive, or whether expenditure qualifies as AMP, a tool that shortens the comparable search by some weeks does not touch the stage of the process where litigation risk is concentrated.

Such tools make the uncontested, mechanical part of TP documentation cheaper and faster, which has value. That is a different claim from suggesting AI will reduce TP disputes or TP risk, a claim made in vendor marketing and at conference panels, including at the Singapore event. The P&G comparison of AI to a junior associate acknowledges this limitation.

The GPS comparison makes the same point more directly. It concedes that AI's efficiency is irrelevant, and potentially unhelpful, if the underlying functional characterization (the "address") is wrong, and that getting the characterization right remains a human judgment task.

The implication for Indian tax administration concerns where AI-based risk scoring may next be deployed. Tax administrations have tended to adopt AI tools some time after industry does, and CBDT or TPOs could deploy similar AI benchmarking tools to accelerate comparable searches and sharpen risk-based case selection.

If AI's comparative advantage stops where functional characterization begins, as the Singapore discussion suggested, AI-assisted TPOs may build adjustment cases faster on the same mechanical grounds that are already failing in the Tribunals, without improving their ability to predict which cases will survive judicial scrutiny on characterization. Whether any firm or tax administration is developing an AI model trained specifically on characterization outcomes, such as AMP treatment, captive recharacterization, or DEMPE allocation, rather than on comparable company financials, was not evident from the Singapore discussion.

For TP practitioners, the practical implication is to assess AI tools by the stage of the process they improve. Faster comparable searches do not reduce exposure on characterization issues, which continue to require substantive factual and legal analysis, and documentation strategy should treat that distinction as material.

Flyjac Logistics: Bombay High Court Holds That a System-Generated Notice Is Not Proof of Service in Transfer Pricing Proceedings

Transfer pricing law requires a Transfer Pricing Officer (TPO) to issue a show-cause notice before determining an arm's length price adjustment, giving the taxpayer a genuine opportunity to respond. Courts have generally dealt with breaches of this requirement where a notice was lost in transit, never issued, or deliberately bypassed. The Bombay High Court's recent ruling in Flyjac Logistics' case addresses a different failure mode: the notice was generated and logged by the department's own system, yet never reached the taxpayer because the system malfunctioned.

The TPO proposed a ₹20.16 crore adjustment to Flyjac's arm's length price and recorded that a show-cause notice had been issued and had gone unanswered. Flyjac stated that it never received any such notice and learned of its existence only because the TPO's order referred to it. In its affidavit, the Department attributed this to a technical failure rather than any deliberate lapse: the email carrying the notice had bounced, an unexplained glitch in the ITBA backend kept the notice from appearing on Flyjac's e-filing portal, and no SMS alert was triggered either.

The Bombay High Court held that the statutory show-cause requirement under Section 92C(3) is distinct from the information-seeking notices issued earlier under Section 92CA(2), and that the order could not stand without actual service on the taxpayer. A system log recording that a notice had been issued did not, in the court's view, satisfy that requirement.

The ruling can be read alongside other developments this year that touch on transparency and procedural rigour in tax administration, though these do not point to a single established trend. Chile's SII has published, in some detail, the risk criteria and audit yield behind its enforcement program. HMRC has tightened the link between its TP compliance guidance and its formal error-correction facility, creating a more structured route from self-identified error to resolution. India's Supreme Court, in the Samsung India matter, dismissed the Revenue's appeals because of unexplained delays of several hundred days in filing, without reaching the underlying transfer pricing questions. Taken together, these developments suggest that as tax administrations rely more on systems, logs and published criteria to manage scale, courts and taxpayers are likely to scrutinise those systems more closely rather than less.

CBDT has publicly stated its intention to expand automated risk-flagging through initiatives such as Project Insight and NUDGE, and the broader direction of travel is toward AI-assisted scrutiny. Tax-technology vendors, including infer360 (now allied with Nexdigm) and comparable platforms, market tools for machine-assisted TP monitoring and documentation. Flyjac illustrates a specific risk that arises as these systems take on a larger role: a system can generate a notice, log that it generated the notice, and still fail to deliver it, and such a failure may not surface until the taxpayer is already contesting the resulting order in court. As automated systems take on more of the notice-generation and routing function in TP enforcement, similar service failures are likely to recur as adoption increases.

For practitioners, the ruling raises a practical evidentiary question: whether a system log showing that a notice was issued will be treated as sufficient proof of service, or whether courts will continue to require affirmative proof of actual receipt, as the Bombay High Court did in this case. Until that question is settled through further litigation, TP compliance functions may find it prudent to maintain independent records of portal access, email delivery and SMS alerts, so that service failures originating in the department's own systems can be identified and raised at the earliest stage of any proceeding.

ITAT Mumbai's Captive Power Ruling and the Limits of AI-Driven TP Benchmarking

A recent Mumbai ITAT ruling in a captive power transfer pricing dispute turned on a question that AI benchmarking tools are not built to ask: whether the comparable a taxpayer needed was already sitting inside its own books.

The case involved a cement manufacturer whose captive power plants supplied electricity to its own cement units. The dispute arose under the 2013 amendment that brought specified domestic transactions within the arm's length pricing framework. The Revenue argued that the captive generator had to be benchmarked against the rate at which other generators sell to state distribution licensees. The taxpayer argued that its own cement units paid documented market rates to state power distribution companies for electricity purchased on the open market, and that this internal rate was a valid comparable for what the captive plant charged them. The tribunal agreed. It held that the amendment does not require a generator to be benchmarked only against another generator, or that a distribution licensee's procurement rate must invariably be adopted, and that CUP selection remains governed by the transaction-specific requirements of section 92C and Rule 10B. Revenue's appeals against adjustments of roughly Rs. 64 crore and Rs. 43 crore across two years were dismissed.

The outcome is less significant than the source of the winning comparable. It was not retrieved from a database of unrelated third-party companies, the kind of external, industry-classified, margin-screened universe that commercial TP benchmarking tools are built to search. It was already in the taxpayer's own transaction records: the price its own units paid the grid for the same commodity, in the same period, in the same geography. An external comparables search, however well it ranks functional similarity, would not have surfaced this fact pattern, because the relevant exercise was not searching further afield but looking at what the group itself was already paying for the identical input elsewhere in its operations.

This points to a gap in how AI benchmarking tools are currently designed. Internal CUPs sit at the top of the comparability hierarchy because they avoid much of the functional-comparability subjectivity that external searches have to approximate statistically. Yet most AI tooling marketed to TP teams is built to screen external databases faster, not to mine a group's own intercompany and third-party transaction data for an internal benchmark. These tools automate work that used to be slow, external database screening, but they are not generally designed to surface the kind of internal comparable a case like this one turned on.

A separate observation from this filing season's TP engagements reinforces the point. Some practitioners report that adjustments are more often lost on evidence discipline than on legal interpretation: the comparable search cannot be reproduced, or the Local File and the counterparty's file tell different functional stories. Reproducibility of an external search is a baseline requirement. It says nothing about whether a better internal comparable was considered at all.

For TP practitioners evaluating AI benchmarking tools, this ruling is a useful prompt to ask a specific question: does the tool query the group's own ERP and intercompany transaction history for internal comparables before it touches an external database, or does it only speed up external screening under a new label. Before commissioning an external search, it is worth checking whether the answer was already in the ledger.