The Queen or the Swarm: Why AI’s Future Depends on Who Gets to Learn

The detailed version of my oped in Transcontinental Times 

Every species that has ever competed for resources on this planet has done so with roughly the same toolkit: strength, speed, camouflage, venom, numbers. Humans are unremarkable on most of these axes. We are slower than a cheetah, weaker than a chimpanzee, blinder in the dark than an owl, and worse at smelling danger than almost anything with a snout. What we do better than any other species, by an order of magnitude no other animal comes close to, is cooperate with strangers at scale.

A wolf pack cooperates. So does a beehive. But a wolf will not lay down its life for a wolf it has never met from a pack three mountains away, coordinated by a shared story none of them witnessed. Humans do this routinely. We show up at war memorials for people we never knew, buy shares in companies run by executives we’ve never met, and hand savings to banks based on nothing but a shared belief that the institution will honor its promises. Yuval Harari’s core insight in Sapiens, that humans are unique in our capacity to organize around shared fictions: nations, currencies, corporations, human rights, describes exactly this. None of these things exist as physical objects. They exist because enough people agree to act as if they do, and that agreement is what lets seven strangers organize a supply chain across four continents.

That capacity has a name in organizational theory: institutionalized trust. And it has a delivery mechanism: communication, first spoken, then written, then encoded into the procedures, contracts, and bureaucracies that let a stranger in Rotterdam trust a signature from a stranger in Chennai. Bureaucracy earns a bad reputation as red tape, but at its root it is a trust technology, a set of standardized procedures that lets unrelated people transact without needing to personally verify each other’s character. Double-entry bookkeeping, the joint-stock company, the postal system, the passport, these are all inventions in the same category as language itself: tools that let cooperation scale past the roughly 150 people (Robin Dunbar’s famous number) that our brains can track through personal relationship alone.

Physical technology has always ridden alongside this social technology, amplifying it. The wheel didn’t just move goods faster, combined with standardized axle widths and road networks, it made regional trust networks viable at distances no courier on foot could sustain. The printing press didn’t just reproduce text,  by making the Bible, and later pamphlets, newspapers, and scientific journals, available to anyone literate, it broke the monopoly that scribal elites held over what counted as agreed-upon truth, and in doing so it re-founded whole religious and political orders. The telegraph, the telephone, the internet: each one is, underneath the marketing, a new trust-and-coordination layer stacked on the ones before it.

Now comes artificial intelligence, and it is not just another rung on that ladder. It is different in kind, because for the first time the tool doesn’t merely transmit human-generated trust signals faster ,  it can generate judgment, synthesis, and decisions on its own. That changes the question. It’s no longer just “how fast can strangers coordinate” but “who gets to do the coordinating, and on whose behalf.”

There are two directions this can go, and they lead to very different civilizations.

The first direction: the Borg model

A small number of frontier labs - right now, realistically, a handful of companies in two countries - train models on a scale of data and compute that nobody else can replicate: the entire searchable internet, increasingly private data through partnerships and acquisitions, and enough proprietary usage logs from hundreds of millions of daily conversations to know how people think, argue, and decide better than the people know themselves. Everyone else becomes a client, querying a central intelligence that has never revealed what it learned about them to anyone but itself. It is not an accident that the Star Trek Borg is the right metaphor: a distributed set of drones, individually unremarkable, whose intelligence is aggregated upward into a Queen who alone sees the whole picture and alone decides. Assimilation doesn’t require malice. It only requires that everyone’s data flows one way  upwards ,  while judgment flows back down as a service.

The second direction: diffusion

Instead of one model trained on everyone’s data and queried by everyone, imagine a nested architecture of intelligence that mirrors the way trust itself has always scaled in human societies - from individual to family to community to nation, each layer adding coordination without fully surrendering what came before it. A personal model that learns primarily from an individual’s own history and stays substantially theirs. A household-level router that reconciles the family’s shared needs,  finances, schedules, health, without exporting the raw data to any central party. Community and institutional layers that pool just enough signal to coordinate like a hospital network sharing anonymized treatment outcomes, a farming cooperative sharing yield data and so on without surrendering the underlying record. National or civilizational layers that federate further still, for the genuinely public-goods problems: pandemic response, climate modeling, financial stability. Intelligence increases with altitude, but so does the friction required to extract raw data upward. This is closer to how evolution actually organizes complexity -  through modular, semi-autonomous units that coordinate without fully centralizing control - than the single-brain model the Borg represents.

The diffusion model is the one worth betting on, but it is worth being honest about why it isn’t automatic. Model weights being “open” or a chatbot running locally on a phone does not, by itself, redistribute power. Underneath even the most local-feeling AI product today sits a stack that is still extremely concentrated: pretraining compute that only a few labs can afford, chip design and fabrication controlled by a handful of firms in a handful of countries, and energy infrastructure that is itself a scarce, geopolitically contested resource. An open-weight model trained on a closed, centrally-scraped corpus is diffusion in name and centralization in substance , a longer, more comfortable road to the same Queen. If this century’s version of the printing press turns out to require a printing press factory that only three governments can build, the diffusion story collapses into the Borg story with better marketing.

There is, however, an answer to the training problem, and it comes from the closest analogy available: how humans themselves acquire capability. Every human being is “pretrained” on a broad common corpus - language, schooling, the accumulated knowledge of a culture - before developing anything distinctive. Universal education does not centralize human intelligence; it equips each mind to then learn recursively from its own experience, in directions no curriculum planned. The base model can play the same role: a common endowment, trained once on broad public data, the way a public education system is funded once for everyone. Diffusion becomes real at the point past that endowment , when each node in the hierarchy, whether an individual, a household, a firm, or a community, holds not merely a copy of the model but the capacity to keep learning from what it alone can see, and when the owner of that node decides what portion of the learning is exposed upward. This mirrors how capability has always worked in human society. A doctor shares her diagnosis, not the decade of pattern recognition behind it; a firm sells its product, not its process knowledge; a family teaches its children things it would never publish. Skill and disclosure have always been separable, and that separability is precisely what a query-everything-through-the-center architecture destroys - the center learns from every interaction, while the individual accumulates nothing that is durably theirs.

Honesty requires admitting that this recursive-learning-at-the-edge capability does not fully exist yet. Most of what is marketed today as personalization is retrieval: the model consults an individual’s documents and history at query time without changing itself, which means the accumulated learning still lives wherever the model lives. Genuine local learning,  models that update themselves from experience, cheaply, on modest hardware, without catastrophically forgetting what they already knew remains a hard, open engineering problem. The technical trajectory, to be fair, is bending in the right direction: models keep getting smaller for a given level of capability, techniques for cheap adaptation keep improving, and consumer chips now ship with dedicated neural hardware as a matter of course. What is not bending is the commercial trajectory. The economics of every frontier lab reward keeping the learning loop at the center, because centrally accumulated learning is the moat, the more the central model learns from everyone, the harder it becomes for anyone to leave. So the two trajectories diverge: feasibility is diffusing outward while deployment keeps concentrating inward, and it is precisely in that gap that policy has work to do. Recursive learning at the edge will not be handed down by incumbents whose business model it undermines; waiting for the market to deliver it is like waiting for the scribes to distribute the printing press. It has to be pulled forward deliberately  by public research funding, by procurement rules that require publicly purchased AI systems to support local learning and owner-controlled disclosure, and by writing the principle that the learning stays with the learner into data protection law, the way purpose limitation was written in a generation ago.

So the honest version of the bet is not “small models will save us.” It is that diffusion has to be built deliberately, at every layer of the stack, the way earlier trust infrastructure was built deliberately, through standards, law, and public investment, not left to emerge on its own from a market that has every incentive to concentrate.

There is precedent for exactly this kind of deliberate construction, and it is worth pointing to because it already exists rather than remaining hypothetical. India’s approach to digital public infrastructure - a unified payments protocol that any bank or fintech can plug into rather than routing transactions through a single dominant platform, a verifiable-credentials system that lets individuals hold and share their own documents rather than surrendering them to a central database, and an open commerce network that lets buyers and sellers transact across competing apps rather than being locked into whichever platform got there first,  is essentially an attempt to build trust infrastructure as a shared, low-lock-in utility rather than as proprietary rails owned by one company. It is not a perfect model and it has real gaps, but it demonstrates something important: that population-scale coordination doesn’t require a single controlling entity if the protocol layer is deliberately kept open and interoperable. The lesson for AI is not “copy this system” so much as “copy the design principle”, build the equivalent of open rails for identity, data portability, and model access, so that intelligence can be composed from below rather than only distributed from above.

What would that take in practice? A few concrete interventions, none of them exotic:

•         Data portability as an enforceable right, not a feature,  so an individual’s interaction history can move with them between AI providers the way a phone number now moves between carriers, preventing lock-in from doing quietly what outright control could not do openly.

•         Public or multilateral investment in compute and energy capacity outside the two or three countries that currently dominate it, on the model of how public investment built highways and rural electrification rather than waiting for private markets to reach unprofitable places on their own schedule.

•         Interoperability standards for model-to-model and agent-to-agent communication, so a household-level or community-level system can coordinate with a national one without needing to be owned by the same company that owns the national one,  the AI equivalent of the postal system agreeing on envelope sizes.

•         Regulatory pressure specifically aimed at the infrastructure layer,  chips, cloud capacity, energy contracts , rather than only at the visible chatbot layer, since that is where real concentration risk is currently accumulating fastest and most invisibly.

•         A cultural shift among the capable middle tier of nations, those with talent, institutions, and ambition but not frontier-lab-scale capital, toward building shared, federated capability with each other rather than each negotiating bilaterally and separately with the handful of dominant labs, which only reproduces a hub-and-spoke Borg structure one client relationship at a time.

Sceptics of the diffusion path will point out, correctly, that some problems genuinely need a Queen, or at least a very large brain. Pandemic modelling, climate prediction, and fundamental scientific discovery benefit from the kind of massive, centralized compute that only a handful of institutions can field, and no household-level router is going to fold a protein or model a hurricane. The diffusion argument is not that centralized capability should not exist. It is that centralized capability should be treated the way we treat other infrastructure with natural concentration risk, nuclear power, undersea cables, the electrical grid,  as a public utility subject to oversight, access rules, and accountability, rather than as the private property of whichever company got there first. The European Union’s AI Act, whatever its flaws in execution, is at least an attempt to draw that line: to say that as models approach systemic scale, the obligations on their operators should scale with them. Export controls on advanced chips are a cruder version of the same instinct, aimed at slowing the concentration of the compute layer rather than the software layer, even if their current form is more about geopolitical rivalry than about distributing power more broadly.

And the early scaffolding for genuine diffusion is already visible. Federated learning in healthcare, hospitals training shared diagnostic models by exchanging model updates rather than patient records,  demonstrates that collective intelligence does not require pooling raw data in one place. On-device inference, now standard on flagship phones, means a growing share of everyday AI use never has to leave the device at all. Neither fully solves the concentration problems described above, but both show that the direction is technically viable; what is missing is the institutional will to deploy such architectures at population scale rather than leaving them as premium features for those who can already afford to ask.

Humanity’s edge was never raw intelligence. It was the invention of trust technologies that let intelligence combine across strangers without requiring a single mind to hold it all. Writing, law, currency, and bureaucracy did this by distributing judgment while standardizing the interface between people. Whether AI becomes a fifth trust technology in that lineage, or the tool that finally lets a single mind hold it all, is not a question that resolves itself as models get better. It resolves according to who builds the rails underneath them, and how deliberately the rest of us insist that those rails stay open. That is a choice still being made, right now, mostly in rooms far from public view — which is exactly why it needs to be argued for in public.

 

"We taught the whole species to read. We did not hand every book to one reader."

AI Governance and Future of Work

 

My Speech at AI-DPI – 26 Conference organised by NCEAR

Let me begin with a simple observation that I think frames everything we're about to discuss.

In the last few centuries, we witnessed multiple technological disruptions ranging from printing press to industrial revolution to computers to internet. It restructured society what work meant, where people lived, what skills had value, what governments needed to do. The economic and social ripple effects played out over decades.

Today, we are living through a transformation that is much more profound but the ripples are moving in months, not decades. And that gap between the speed of technological change and the capacity of our institutions to respond is precisely why conversations like this one matter.

Welcome to what I hope will be a frank, insightful, and perhaps uncomfortable conversation about AI, governance, and the future of work.

 Let me set the scene.

In the last three years, artificial intelligence has crossed a threshold that surprised even its creators. Large language models can now draft contracts, write code, analyse medical scans, counsel customers, generate creative content, and conduct research, tasks that, until recently, defined the upper tier of knowledge work.

We are no longer talking about AI that automates the routine. We are talking about AI that can perform the cognitive. That is a qualitatively different kind of disruption, and it demands a qualitatively different kind of response.

Three tensions sit at the heart of today's discussion.

First tension is on Governance

When it comes to governance of AI key questions that arise are

Who governs AI? Who benefits? Who bears the cost of disruption? These are political and moral questions, not just technical ones.

There lies the tension between speed and safety. AI development is moving at a pace that regulatory frameworks were simply not designed to match. The EU AI Act took years to negotiate and is already facing questions about whether its risk categories reflect the technology as it exists today, let alone as it will exist in three years. India is developing its own digital governance frameworks, and the choices made here, given the scale of this country's workforce and its digital ambitions, will matter not just domestically but globally.

The core challenge for governance is this: if you regulate too slowly, you cede the field to actors, corporate or national, who face no constraints. If you regulate too quickly, you risk encoding today's assumptions into law and stifling the innovation that could actually solve problems. There is no comfortable middle ground. There is only the hard work of trying to get it roughly right, fast enough to matter.

Here the tension is also between innovation and accountability. The companies building the most powerful AI systems are, understandably, advocates for an environment that allows rapid development. Many also, to their credit, genuinely grapple with questions of safety and responsibility. But the incentive structures of competitive markets are not naturally aligned with the kind of careful, transparent, accountable development that the stakes of this technology require.

Governance, at its best, creates the conditions under which accountability becomes not a constraint on innovation but a foundation for the trust that allows innovation to scale. We do not have that governance architecture yet. Building it nationally and internationally is one of the defining challenges of this decade.

Next tension in in he "Future of Work" that is Already Here

There are three competing narratives on this paradigm shift

• Displacement: AI replaces human jobs at scale
• Augmentation: AI makes workers more productive and valuable
• Transformation: New categories of work emerge that we can't yet name

Here lies the tension   between productivity and dignity. Every study that examines AI's impact on knowledge work shows significant productivity gains. Legal researchers, coders, financial analysts, writers when well-supported by AI tools, they produce more, faster, and often at higher quality. This is genuinely good news.

But productivity gains do not automatically translate into widely shared prosperity. The history of technological disruption is also a history of transition costs  borne disproportionately by workers who lack the resources, the retraining opportunities, or the institutional support to adapt. The question is not whether AI will transform work. It will. The question is whether that transformation will be something we navigate together or something that happens to millions of people who had no voice in shaping it.

 What does this mean for India, specifically?

India is not a passive observer in this story. It is one of the central actors.

This country has one of the world's youngest and most rapidly digitising workforces. It has a technology sector that has spent decades building the global knowledge economy's operational backbone. It has a government that has shown real ambition in digital public infrastructure, from UPI to Aadhaar to the Open Network for Digital Commerce.

And it faces a specific, urgent challenge. A significant proportion of India's IT and BPO workforce, millions of skilled, educated, middle-class workers are employed in precisely the categories of knowledge work that generative AI most directly disrupts. Customer support, document processing, software testing, data annotation, back-office operations. These jobs are not going away tomorrow. But the trajectory is clear, and the window for preparation is not infinite.

At the same time, India has something that not every country has in this moment: scale as an asset. The diversity and volume of India's linguistic, cultural, and domain-specific data; the depth of its technical talent; its position as a potential standard-setter for the Global South in AI governance, these are genuine opportunities, if they are seized with intention.

 So what do we actually need?

I'll offer three propositions to anchor our panel discussion.

First: governance must be adaptive, not just reactive. We need regulatory frameworks that are designed to evolve, that build in review cycles, that involve multistakeholder input, that distinguish between the risks of different applications rather than treating AI as a monolithic category. A diagnostic AI in a hospital has different risk parameters than a recommendation algorithm on a social platform. Governance that treats them identically will either over-regulate the beneficial or under-regulate the harmful.

Second: the future of work requires active investment, not just passive optimism. It is not enough to say that new technologies create new jobs, historically, they often do. What matters is the transition: whether workers have access to retraining, whether institutions like schools and universities adapt their curricula in time, whether social safety nets are designed for an economy where the nature of employment is changing. This is a policy challenge, not just a market one.

Third: the voices in the room must expand. The conversations that shape AI governance tend to happen in a relatively small number of rooms, boardrooms, regulatory agencies, international standards bodies, academic conferences. The people whose working lives will be most directly transformed are rarely in those rooms. That needs to change , not as a matter of procedural fairness, but because the decisions will be better if the inputs are broader.

Let me close with this.

I am neither a pessimist nor an optimist about AI. I am a realist who believes that the outcomes of this transformation are genuinely open that they will be determined not by the technology alone, but by the choices we make about how to develop it, deploy it, govern it, and distribute its benefits.

The future of work is not written. It is being written right now, in the decisions being made in companies, in legislatures, in classrooms, and in conversations like this one.

My hope for today is that we leave this room with sharper questions, not just comfortable answers and perhaps with a clearer sense of where action, not just analysis, is required.

 "AI gives us leverage, ethics gives us direction."

The Confidence You Think You Have Might Just Be Ego in a Nice Suit

 

The Confidence You Think You Have Might Just Be Ego in a Nice Suit

We often speak of confidence as if it is the defining trait of leadership. But in public life, confidence, pride, and ego are frequently mistaken for one another — and the consequences shape the lives of millions.

Today, I want to draw a line between them.
Because the difference is not academic.
It is the difference between institutions that serve the public — and institutions that serve themselves.

Confidence: The Only Trait That Strengthens Public Institutions

Real confidence is grounded in competence.
It is the leader who says:

“I know what I know. I know what I don’t. And I know who to listen to.”

Confident leadership:

• Welcomes scrutiny
• Adapts when evidence demands it
• Admits mistakes early
• Builds systems that endure beyond any individual

Confidence strengthens democracy because it strengthens accountability.

Pride: The Silent Saboteur of Public Reform

Pride is emotional.
It wants to protect the narrative, not the nation.

Healthy pride says:
“We built something meaningful.”

Unhealthy pride says:
“We must not be seen failing.”

This is where governance falters:

• Policies stop evolving
• Programs continue long after their purpose is lost
• Institutions defend the past instead of designing the future

Pride becomes dangerous when it becomes a cage.

Ego: The Most Expensive Failure in Public Life

Ego is the loudest and the weakest of the three.

It says:

• “I cannot be wrong.”
• “Critics are enemies.”
• “Dissent is disrespect.”

Ego in public office leads to:

• Policies shaped around personalities
• Decisions made for optics instead of outcomes
• Civil servants who stop speaking truth to power
• Public trust that erodes quietly, then suddenly

Ego is not a personal flaw.
It is a governance risk.

A Simple Test for Every Public Leader

When you feel yourself reacting, to criticism, to a rival’s success, to a public setback — ask:

• Am I improving the system? → Confidence
• Am I protecting my story? → Pride
• Am I protecting my image? → Ego

If we are honest, the answer will be uncomfortable.
But that discomfort is the beginning of institutional maturity.

The strongest public institutions follow a simple architecture:

• Confidence as the foundation
• Pride as the fuel
• Ego on a leash

Or, in the plainest terms:

Do the work.
Tell yourself the truth.
Don’t govern for applause.

Ego is just confidence that refuses to stay honest. And a democracy cannot afford dishonest confidence.

Enabling a Paradigm Shift in Social Protection through BECKN

Delivery of social protection measures is a big challenge to any government; especially in a developing country.  Social protection program has three key components.

The first component is how we can uniquely identify the beneficiary and enrol them into the program. Digital Id revolutionised this part. The pandemic demonstrated that the countries which have established a foundation of digital id can manage the outreach much better than a country that is lacking in this area. Countries which have gone one step forward and have established a somewhat comprehensive registry of uniquely identified deserving beneficiaries did even better.

The second component is the payment system. Building on the foundation of Digital ID, in India we have introduced Unified Payment Interface (UPI) which is a completely open and interoperable protocol that has transformed the payment system.  As of now, UPI handles more than 1.25 billion transactions per month which is more than four times the volume of transactions handled by the Credit / Debit card network every month. We are now in the process of building a next layer on top of it to provide a digital voucher using completely open protocol. This could be a straightforward money voucher, or it could be a voucher that is meant for a specific product if the government wants to ensure that the benefits are provided in kind with respect to certain goods and services.

The third element is the market including the supply chain, and it is still a challenge. That is why the governments are forced to run the whole physical operation of procurement, supply chain management and retail shops. Often very very inefficiently.

BECKN foundation established by Nanden Nilekani has come out with a brilliant solution to this problem. It has published and open protocol specification which can completely revolutionize the marketplace.

What is the challenge it is attempting to solve?  Digital marketplace is a big boon; but the market is still heavily siloed.  It is increasingly becoming walled gardens of few players. Take for example retail. There are  a few large aggregators like amazon/ big basket/ Alibaba They are doing a great job; but, they are walled gardens. Same is the case with delivery or mobility or health service. It is a pain for the consumer who must look at multiple apps to make an ideal choice. This limits the choice and makes markets inefficient.

The service providers are also constrained. Unless you are a part of the aggregator platform you are at a big disadvantage in terms of discoverability.

BECKN protocol addresses this issue for both the service provider and the consumer. It is a paradigm shift; we are making internet, small business friendly and not small business internet friendly.

A simple adapter to the payment system or inventory management system based on BECKN protocol can provide any service provider equal access to the market whether he is big or small. Any frontend app which is comfortable to the consumer; could help him or her to access all the options seamlessly if it has a BECKN based connector to the network. It may be WhatsApp may be telegraph may be Google map or a special app provided by the government.

These mean simplification of service delivery [e.g. one stop payment, personalized workflows combining multiple services, granular view of events as and when they occur], freedom of choice for consumers, stitching of suppliers to fulfil a comprehensive service, much better (network driven) view of consumer demand and ability of suppliers to fulfil that demand

Such a marketplace has also the potential for bringing dramatic innovations in terms of option to the beneficiary. Once such an Open Benefit Delivery Network is in place, governments can focus on scheme design and enrolling the beneficiaries and reaching him the subsidy / financial assistance . Then leave the markets to make the best offer.

The beauty of this solution is that we are not talking about huge investment in a new platform or software solution. The service providers at a very low cost can be enabled to plug into the market. We are democratising the market just like HTTP democratised the internet. This is not a wishful thinking. We are helping the first solution built around BECKN to go live in India next month and few more are on the way.

It is time to give this a serious try and be an innovator to solve fundamental problems so that we can manage emergencies like the pandemic much better.

“He who is best prepared can best serve his moment of inspiration and desperation.” ― Samuel Taylor Coleridge

Uber Case and Public Policy Thoughts

A few years ago public transport in Delhi (as in most parts of India) was pathetic. The public transport buses were unsafe; especially for a girl who can be reasonably sure of being groped (at the least) if she dares to get into a city transport bus; especially alone. You had to rely on the local taxi stands to hire a taxi and they may not have a taxi available when you want. When you hail a taxi the driver would ask you where you are going. If your destination did not suit him, he would refuse to take you. When a taxi driver deigned to take you, he would refuse paying by the meter and make quote you a fare which is often 1.5 to 2 times the actual fair. When he used the meter, it would be a doctored one or he would take the most circuitous route to your destination if you were not familiar with the city. When you went shopping or visiting a friend there was very high uncertainty about getting a safe transport back. The auto rickshaw drivers also behaved the same fashion (They do that even today) .

When you get a taxi you had no idea who the driver was or what his name was. Often he brought along “his friend” along with him. If you asked him who that person was or didn't want this 'friend' to be in the car for your journey, the driver would get upset and refuse to take you. As you were desperate and had no other option you put up with these dramas. There was no authority with whom you could give a complaint. (You won't even consider complaining to the police) There have been many instances of such drivers misbehaving with passengers; especially ladies. Many of the regular black and yellow taxis still continue with such behaviour. A Canadian friend who has been visiting India for about two months told me that whenever she was travelling without an Indian friend with her, the driver refused accepting fare as per the meter.

The Metro has made a big difference in Delhi. In most other cities the metros are non-existent or limited. Then the radio taxis happened. Situation started to improve. With more and more operators joining the fray there was more competition and each of them added more features and services. Call centre support for cab booking and customer support, mobile apps, GPS tracking, online receipt, online payment and so on. With the improved supply you could be sure of getting a cab when you want. The service levels have significantly improved, there is more reliability and the quality and comfort of the vehicles too have improved. Although per kilometer charge is higher for these radio taxis, the total bill ended up being lower as the meters are not doctored and the driver is not taking circuitous routes. Even today the pre-paid cool cab charges from airport to my home in Mumbai ends up being more than the radio taxi charges including their service charge of Rs 60. With registered drivers of radio taxis who are also given some training and monitored to some extent customers feel safer than before. There had been hardly any major complaints about the behaviour of these drivers. I am not saying that everything is perfect. There are still areas for improvement.

Then the disaster hit. One unscrupulous driver has molested an unfortunate lady. It is an absolutely dastardly act. No doubt that the driver has to be punished suitably. We also have to improve the systems and processes to prevent such occurrences. But the knee-jerk reactions of administrative decision makers once again demonstrates the pathetic state of decision making founded on emotions and populism. This is not the first instance of ridiculous action by regulators and law enforcement agencies.

The Police has acted fast and has apprehended the culprit quite fast. This is commendable. DCP south has been giving twitter update on the investigation which is appreciable.  But one of the first policy decisions on this taken by the administration is banning Uber and similar services. Some states have followed suit in extending this ban to their states. By doing that, we have created a shortage in the market and this has opened avenues for un-scrupulous behaviour by cabbies. I am a regular user of taxis and I experience it. Many of my friends also share the same feeling.

Are the operations of Uber and OLA so dangerous that we need to ban it immediately? Not at all.

Let us look at this a little deeper. Uber had a system of driver verification. They relied on a document that was submitted by the driver. It appears that the character certificate that he submitted was forged and not issued by the Additional DCP as claimed in the certificate. Uber should have had a process of undertaking much more serious background check of its drivers and verification of the veracity of the documents that it receives. These service providers could also have better tracking systems. It will be easier for law enforcers and regulators to demand and monitor such good practices on organised entities like the radio taxi operators than enforcing such good practices by each cabby. By outright ban of these entities we are not reducing the risk rather increasing it.

A case has also been charged against Uber for cheating as they failed to take necessary measures to live up to the promises they have made.

It has been also reported that the same driver used forged certificate to get a permit to drive his cab in the capital. Are we taking action against the department or the officer for dereliction of duty in issuing this permit without verifying the documents presented? With commercial taxi licenses, people like him manage based on such fake documents, (which is not verified at time of issue of taxi permit) if these drivers operate as an independent taxi operator and commit such crimes, tracking them would be even more difficult and this could turn out to be even more dangerous.

When we prescribe more documents and verification processes, we also have to establish convenient and speedy mechanisms to issue these verification for genuine applicants. The sad thing is that it is a herculean task to get any kind of clearance documents and validation of the clearances from government departments unless you have “connections” or willing to pay “speed money” directly or through touts. This makes the whole process a bottleneck and avenue for corruption and encourages bypassing of such verification. Therefore we also have to establish processes for speedy verification and validation processes. For example the Police can consider making online verification of certificates issued by it. Further, the Police also could make it possible to get verification done in an easy and fair manner. It could also have exception handling mechanism to take into account practical problems. For example I came to Delhi from Mumbai and took up a rented apartment and I wanted a police verification done on my Proof of Address. I was going from pillar to post and I had to take help of a senior bureaucrat who is a friend to help me. Imagine the plight of an ordinary person who comes to a metro in search of a job. May be wider acceptance of Aadhaar Card would address this issue to some degree.

We see such knee jerk behaviour very often. Let me quote some more examples. Remember the A 320 air crash in Bangalore soon after Indian Airlines bought these aircrafts? Our reaction has been to ground the whole fleet of 320 for a very long period resulting in severe loss to the airlines. We are the only country in the world which did that.

When there was complaint against the overcharging by some Micro Finance intermediaries the reaction has been to ban such companies instead of establishing good practices and enforcing the same. When we do this the only option left to the poor customers is to fall into the clutches of money lenders with usurious interest rates.

When there is complaint against ill treatment or malpractices by some NGO we have instances of the whole community of NGOs harassed and treated shabbily resulting in genuine players avoiding such initiatives.

The government instead of improving its services, taking ownership of its duties and being accountable for its mistake place the whole blame on the private service providers. Swaminathan Anklesiaria Aiyer's comment regarding the Coal Scam in TOI is quite relevant in this context.  “The Government was found guilty of wrongful coal allocations, but suffered no penalty. Instead mine operators not found guilty of anything had to pay huge fines to the guilty government. .. Investors are increasingly apprehensive that Indian Institutions no longer provide stable decisions or secure property rights”

The regulator and the law enforcer have to keep in mind that it has a balancing act to do. On one side it should establish good practices and enforce them. It should prevent monopoly behaviour, it should ensure good practices are followed for the safety, security and convenience of the user. On the other hand they have to keep in mind that regulations and regulating agencies also have a development role to play; helping and supporting service providers to establish good practices and to evolve and thrive rectifying their mistakes.

It should be practical in managing the evolution of the regulation and market practices. As Judge Learned Hand one of the very respected American judges observed “Justice is the tolerable accommodation of the conflicting interests of society, and I don't believe there is any royal road to attain such accommodation concretely.”

If we make rules that cannot be practically followed, then we will not have decent service providers but we will be at the mercy of shady operators who operates outside the law. When mistakes and failures do happen, the reaction should not swing to the extreme. If the error is technical in nature and does not reflect willful and repeated irresponsible behaviour that can potentially cause disaster to the community then the penalty should commensurate with the mistake and solutions should be implementable. We need to help these entities to improve and not kill them. What often happens is that the regulators go heavy on technical errors  making it difficult for many genuine service providers and leaving the ground open to the fringe players who act outside the regulated market or without any service standards at all. Very often these shady operators make these regulators to clear way for them consciously or unconsciously by eliminating decent operators. 

"We live in a stage of politics, where legislators seem to regard the passage of laws as much more important than the results of their enforcement." - William Howard Taft

Digital India 2.0 Hoping for the best

[Excerpts of my speech at Conference on Digital India as a part of 37th SKOCH Summit at India Habitat Center, New Delhi September 2014]

If somebody asks me my opinion about the Indian Movie Industry my reply would be; “It is big and thriving, some of its productions are world class, exciting and memorable. But it has also created a lot of crap.”  My reply would be the same if anybody asks for my opinion on e-Governance in India.

Why do I say so? Let me give two metrics in support of my statement. Firstly as per DeiTY tracking system for electronic transactions we have had just about 2 billion electronic transactions in 2013 and in 2013 this number has already reached this number in the first 8 months. Impressing in a way. But for country with more than a billion population and internet penetration of 100 million (which itself is quite low and is an expression of poor internet infrastructure  and not enough relevant and convenient service offerings compared to 8000 million mobile phones) it is 20 an average of 20 transaction per month per person. When we consider that it covers both central and state services including online utility payments, it is not something to shout from the rooftops.

Secondly, let us take a look at the eGov Development Index (EGDI) of UN which it publishes every year. In the last few years India’s ranking has been hovering in the range of 110-120. In spite of India being a powerhouse in the world of Information Technology, we are so low in the world ranking of EGDI. We have even managed a drop in our ranking from 113 in 2008 to 118 in 2014. On the other hand South Korea holds top rank for past many years.

It is in this context that we should look at the Digital India 2.0 Program that has been announced recently. It has all the Right Intentions, Great Ideas and Enough Resource Allocation. But then, Is it much different from the NEGP of yesteryears?. If we look at NEGP, it also had right intentions, right ideas and enough resources when it was launched. But then why did it fail deliver what it should? The fundamental problem was in the “Implementation”.  Let us take a look at some key learning from our experience of yesteryears. The weakness was in the foundation itself of most of the projects; which includes stable citizen focus, leadership, procurement and contracting strategy, continuous improvements and business models. Let us go a little deeper.

Most of the e-Gov initiatives are “transformational Projects” and not automation of the existing stable processes. Such transformational projects would require a re-look at the existing processes and undertake a re-engineering of the processes with a citizen focus and taking full advantage of what technology can offer. When I say citizen focus it means that the workflow and processes should attempt to make it easy and convenient for the citizen to access the services online. When he asks for a service from one government department he should not be asked to get a document/ certificate from another government department. The departmental systems should be able to talk to the other departmental systems electronically to source/ verify information available with the latter. This in addition to facility for online application could significantly reduce multiple interactions that the citizens have to make with the government departments and the related rent seeking and harassment.

One of the challenges in integrating the systems of different departmenst was the absence of a mechanism for uniquely identifying a person. Now that penetration of UID in India has reached significant levels, this problem has nearly been solved. Therefore, it is important for every departmental automation project to incorporate UID tracking of beneficiaries to the extent possible.

Any transformational project will succeed only if there is a stable and visionary leadership; especially during the conceptualisation and roll-out. Whenever government managed to place such people in the leadership roles the projects benefited. In many cases the selection and transfer policies of the government does not take this into account at all. This means poor choice of mission leaders and or key people frequently getting transferred; especially in critical stages of the project. Such changes not just affect the momentum of the project but are also counterproductive. One of the key reasons of  failure of many e-Gove projects is this.

Establishment of transformational project cannot be handled like an event management. It is an evolutionary process; a journey and not a destination.  But normally many government departments treat all projects alike an event management. There would be a study of the current processes, some kind of process re-engineering, development of specification for developing the software application , choosing of technology solution, getting the solution developed/ customised and rolling out the solution. During this process there we seed involvement of senior officers.

Once it is rolled out it is left to the operating staff to take care. But what is needed for success is continuous improvements. There should be continuous tracking and refinement of the processes, to strengthen validations, communication and clarification for the users on the basis of feedback from the field. This review and refinement should be led by the senior officers responsible for the mission. Only then would it reach adoption and acceptance of a broader cross section of clients. Often the feedback from the field is not given due importance. By the time the normal governmental process gets the approvals for these amendments it would be too late. So much negative image would have been created that adoption would have really suffered. Many users would have written this off. If we take the few projects that has demonstrated significant acceptance like Tax Information Network of CBDT, MCA 21 of Ministry of Company Affairs, Passport Seva Project of Ministry of External Affairs UIDAI etc were those which had taken this philosophy of continuous improvement seriously.

In addition to these efforts for continuous information there is also a need for education and handholding of the users so that they get used to the new system and make fewer mistakes. Even in this dimension many projects do badly.

The other major area of failure is in the procurement and contracting process of the government. Many departments select the lowest cost bidder referred to as L1. This often significantly affects the quality; especially in cases of services because it is difficult to quantify quality and monitor it. Some departments attempted Quality Cum Cost Based Selection (QCBS). But very often most of the bidders are given technical scores which are very close to each other. This is often because the evaluators would like to play safe. In this case, the bid again becomes L1.

In the selection process it is important to have experts in the field who compliment the departmental officials who may often do not have sufficient subject matter expertise. Many departments do adopt SME. But the SMEs are often selected often based on their willingness to give free/ low cost service and readiness to say yes and not on the basis of the quality of expertise. We can’t expect much value addition in such cases.

Then some departments started experimenting with outcome oriented contracts with service providers. It is a brilliant idea in theory. Even in this case most of the departments mess up. Let us see how. To make outcome oriented project a success, there are two critical pre-conditions. (i) There should be clear articulation and agreement on specific outcomes and milestones. (ii) Achievement of outcome requires that both the client and the service provider play their role on an agreed upon schedule.

But in case of most government projects both the above preconditions are not met. Firstly the outcomes are not clearly articulated. Often they are described very broadly which could be interpreted in different ways. Such vague definitions lead to so much scope creep that the service providers lose significantly. Secondly the departments fail so badly in meeting their part of the deal. Often there is significant delay / failure in giving timely approvals, giving input by users, providing feedback on proposed solutions, signing off on specifications and so on. This lead to significant delay, scope creep and increase in required effort. Taking both these together for most of the service providers government projects are loss leaders. Because of this, many good and reputed IT service providers are very cagey in bidding for government project and keep away from government projects in most cases.

It is evident that unless the Digital India 2.0 addresses these above implementation issues it will not be able to do any better. It can be seen from the above that n normal government processes are not capable of handling transformational projects. It has never been designed to support innovation or flexibility or agility that are essential ingredients for successful implementation of transformational projects.

One obvious solution such limitation is to carve these as independent projects and hand these over to a Special Purpose Vehicle (SPV) which has been suitably structured with sufficient flexibility to take nimble decisions and relevant mid-course corrections when needed. It is also important to have the right kind of leadership, team with relevant expertise and experience and also a supervisory body that recognizes the different approach needed for these SPVs to succeed. Especially the leadership vision and courage to take decisions will be very critical. We have seen examples of such successes. But unfortunately we see that even when SPVs are established the bureaucracy involved in the establishing these SPVs build-in structural limitations that will restrict/ prevent the nimbleness of the organisation and or install unimaginative bureaucrats to its leadership which completely vitiates the ability of these SPVS to make any significant difference.

I hope the focus of the new PMO has on implementation will galvanise the various departments towards better performance.

Related Reading

If wishes were horses

“Exogenous and blind interpretation of statutes, topped with hustled implementation of laws leads only to more turmoil and less productivity.”  ― Henrietta Newton Martin

Aadhaar or Not?

The total number of Aadhaar enrollment has reached a level of around 600 million. There are serious debates in the public domain about use of Aadhaar as mechanism to establish and verify identify for various subsidies and benefits extended to the residents of this country. Let us take a deep look at the merits and demerits of this idea.

Every entity that provides any benefit or service to a person has a need to establish the identity of the recipient at the time of enrollment into the scheme. It would also want to verify if the same person is receiving the benefit/ service every time the service is availed. For this purpose different entities use different methods and different supporting documents.  Aadhaar comes out as the best identity proof on account of the following.

·         Most of the identity documents that are commonly used like PAN card, Voter Id, passport, electricity bill and so on are only enrollment proof for a specific service. Therefore this limits the cross section of residents who can have each of these documents. This makes it essential for every service provider to allow a variety of identity document resulting in higher cost and risk. Aadhaar is issued by UIDAI which has been set up only for the purpose of issuing an identity documents to every resident. This makes it possible for every resident to receive an identity documents.

·         Aadhaar has established a centralised system and a standard process that is accessible throughout the country to verify the identity document submitted by the recipient of the service. This makes it easy for every service provider to establish uniform but strong verification mechanisms instead of establishing multiple verifications for various identity documents.  No other identity document has such a seamless verification process in place (many of them don’t give a facility to verify from the source if the identify document has been truly issued by the issuing entity) which makes it possible for the unscrupulous to get away with forged identity documents.

·         The above process also significantly reduces the cost and risk of identity verification.

Every service/ benefit provider could want to ensure that same person does not access the service under multiple identities. Aadhaar issues a unique number to a person supported by extremely strong biometric technology to ensure that one person cannot have multiple identities. The current practice of establishing uniqueness with demographic data like name, father’s name, age etc  is quite weak in comparison with this. No other identity document has such strong de-duplication mechanism in place to ensure that same person has multiple identities

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The issuing authorities for the various documents used as proof of identity have established their channel for enrolment to meet their requirement and they do not cater to the need and right of every resident to receive an identity document. For example voter id is issued to eligible voters. PAN is a tax id and tax department has no incentive to extent this as a service to all residents. Since UIDAI is established as an entity to issue identify document, it has established extensive channel for issuance and verification and is in the process of further extending this channel.

This helps any entity which has to uniquely identify a person to save significant cost and effort in the identity verification process.  Building on this Axis bank now has introduced scheme wherein an individual can walk into any Axis Bank Branch, give his / her UID and after verification of the same, the banks issues an account without any further document.  Many service providers are figuring our innovative application of this idea. With more than 600 million cards already established we can soon witness wider adoption.

But let us be clear; only thing Aadhaar can guarantee is a unique identity, nothing else. Not citizenship, not right for any entitlement. For any of these you will need other mechanism. So we should judge and decide based on what Aadhaar can do and its relevance, and not what it cannot!

“No one  can stop an idea whose time has come” Victor Hugo

Reforms in Tax Administration

[This is an excerpt of my speech at the Central Excise Day Celebration published by Chief Commissioners of Central Excise and Service Tax Mumbai and Western India Regional Council of The Institute of Chartered Accounts of India. February 2014]

The fundamental role of tax department always has been to raise revenue for the government. Over the recent past there has been fundamental shift in the working of tax administration. Tax administration is attempting see its role as an enabler in helping the citizen to comply with the tax laws than an enforcer extracting funds. Thus it has been trying to achieve balance both in taxpayer service and in enforcement. Towards this it has to give equal importance to making tax avoidance painful and making tax compliance easy.

Today’s rapidly changing environment is adding further complexities in tax administration. Growth in international trade and commerce driven by globalisation is a major contributor to this complexity. Goods and services are produced in one country using inputs from a number of other countries and consumed in a third set of countries. The management and ownership of these entities are also spread across the world. Identifying the taxability and fair computation of taxes become a cumbersome and complicated matter. 

The other challenge is related to taxing of online transactions. With the trade happening completely in the virtual world there is a need to revise and refine the laws and rules for fair tax computation and easy and practical enforcement. Similarly, the development of tax shelter products and use of tax havens is another challenge, which emanates from globalization. The new technology solutions and boundaryless commerce open new avenues of frauds and tax evasions to the unscrupulous

Tax Reform has to be seen from this paradigm shift in landscape.

There are various factors that should be taken into account in this reform process They include:

a.      Simple tax system to facilitate administration and reduce compliance cost: It has been proven across the world that simple tax systems will improve compliance and make tax administration more efficient. A simple tax system will have few taxes, limited number of rates for each tax and limited exemptions. Forms and Procedures would be simple. Information required on tax forms would be kept to a minimum and readily available from taxpayer’s books and records. Taxpayers, to the extent possible, may not be asked to present data that would require them to keep special records for tax purposes.

b.      Taxpayer Communication: A well thought out communication strategy is a key element of taxpayer service by making information available to and addressing concerns of the taxpayers and business community

Objectives of Communication Strategy: The broad objectives are           

                                                          i.             Complete coverage: Ensure communication reaches all potential taxpayers

                              ii.           Uniform understanding: Ensure there is uniform understanding of  Laws and compliance across all taxpayers

                                 iii.             Mobilise taxpayers community for registration and voluntary compliance by preparing an environment to overcome  apprehensions at the taxpayer’s end

                                               iv.            Sustain the momentum: Reassure and amplify positive experiences of registration and voluntary compliance to interest and excite large taxpayer community to follow suit

Channels: Today multiple channels are available to reach the citizen. The department should have a well thought out strategy to use these multiple channels instead of just limiting to statutory publication in official communiques..

In addition there should be a channel to provide reliable answers to frequently asked queries and doubts that would be generated by the public awareness campaign

c.       Taxpayer Service: The quality of taxpayer service will be a significant determinant of the taxpayer compliance. Taxpayer services shall adopt a customer centric approach and shall foster to establish the following:

                                            i.             Consistency of service between what is expected by taxpayer and what is perceived by tax authority.

                               ii.             Translation of service expectations into service specifications

                                                iii.             Adherence to service specifications consistently during service delivery

                             iv.             Consistency of service delivery with taxpayer communications

The elements of taxpayer service will include:

Taxpayer Rights and Obligations: Taxpayers rights and obligations and the procedures for redressal of their grievances could be explicitly declared and published and adequately communicated

Ease in compliance: The policies, forms and procedures relating to compliances could be easy to understand and easy to comply with.

                                             i.      All key administrative processes including the procedure for taxpayer registration, tax payment, filing of tax returns/ grievances/ appeals and applications for rulings etc could be automated, reducing the need for manual intervention by the taxpayer and the tax administration to the barest minimum.

                                                ii.     The tax department should allow taxpayers opportunities to provide feedback relating to administrative policies and procedures.

                         iii.      The department could continuously strive to refine administrative processes from the Taxpayers’ perspective so as to minimise compliance burden

Service Level Standards: The department could publish service standards with respect to various services to taxpayers like assessment, grievance resolution, refunds and be accountable to honour these service standards. The extent to which these levels are achieved may be made a part of performance measurement for the department and it may be made available in public domain.

Reliable and up- to-date taxpayer information

                                                              i.      Provide taxpayer information (self)services – a real-time convenient online login-based secure access to taxpayer on its registration details (e.g., legal full name, physical address, date of incorporation, industrial sector), tax payment history, status of returns, refunds, etc, specific communications from the department  and with an online facility for query and grievance handling.

                                                            ii.      Up-to-date information relating to law, procedures and regulations could be made readily available to taxpayers in a simple and coherent manner through a mix of identified channels including online modes.

                                                          iii.      The department could consistently deliver quality information and respond to inquiries and requests from taxpayers in an accurate and timely fashion to  help  them meet  their obligations;

                                                           iv.      The department could aim to resolve Taxpayer queries at the first point of contact, avoiding the need for subsequent contacts on the same subject

Assured confidentiality and privacy of taxpayer information

                                                              i.      The department should use taxpayer information only to the extent permitted by law;

                                                            ii.      Privacy of taxpayer information will  be  protected  to  a  level consistent with the standards applicable to comparable public and private sector organisations such as banks

Time bound Grievance redressal A Grievance Redressal Mechanism to be made available to taxpayers for time bound redressal of grievances.

Education: Continuous education of various stakeholders is critical for any tax administration. Therefore it is imperative to have a focused effort in educational initiatives that creates an ecosystem to help augment the department’s capacity to maximise the outreach.

Certification Program: The department could consider establishing a certification with programs tailored for finance professionals responsible for tax compliance, tax agents etc. This will help in ensuring a certain level of learning and provide a bench mark to measure level of expertise.

d.      Enforcement and Compliance Management: Simple tax system, ease of compliance, self-assessment and high quality taxpayer service will make compliance painless and incentivize compliance. Equally important are means to make non-compliance painful which is an equally important lever for better compliance.

                                            i.      A robust compliance management mechanism will identify the risks associated with non-compliance, and strategies should be developed to mitigate and counter those risks.

                                          ii.      These may involve interventions by way of profiling risk, auditing, developing third-party information sources, general anti-avoidance efforts, prosecution and proposals for legislative change.

                                        iii.      The department should monitor compliance activity to detect and deter noncompliance.

Development of information system to counter risk to revenue

A well designed information system with the provision to receive and integrate financial data from diverse sources and develop risk profiles using data mining and other profiling techniques will be the main feature of compliance risk management strategy. The department could take steps to integrate the taxpayer data in different tax systems. It could also take steps to identify data sources in public domain and define legal framework to access to key third Party information (like database of ministries/departments dealing with corporate affairs in the country, relevant databases of regulatory authorities in the sectors of interest, etc)

Tackling Tax Fraud

The department could adopt a zero tolerance policy for tax frauds. For this purpose it should-

                                 i.            Design and implement efficient strategies to deal with organised tax evasion and fraud and prosecute under criminal law persons found involved in fraudulent activities. In such cases it will use powers of search of business premises and arrest of suspects.

                               ii.            Tax frauds require specialised multi-disciplinary investigation skills including tax investigations, computer forensics, and criminal investigations etc. Therefore, the department may set up specialised Enforcement Unit having the necessary skill sets for investigation into tax fraud and recovery of difficult tax debt.

                              iii.            The findings of investigations into tax frauds could also be used for re-determination and recovery of evaded tax liabilities.

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e.      People In the current environment with fast-paced change in the business domain and technology tools it is important that the department has cohesive strategy to attract, develop and retain resources with the relevant skill set so as to provide competent and trained managers and staff with high professional and ethical standards. It should also provide continuous skill up-gradation of its employees. This could  to a great extent limit incentives and opportunities for rent seeking and inappropriate behaviour

Emphasis will have to be placed on proper and continual training and development of the personnel for achieving professional competence for dealing with the complex issues in the legal landscape. Training programs should lay emphasis on behavioural aspects involved in dealing with taxpayers with courtesy, fairness, and consideration..

The tax administration thus should evaluate these dimensions on a regular basis and reform the tax administration if it has to stay relevant and effective.