That is how the mistake usually feels from the inside. One of the easiest mistakes in life is to believe that behavior explains itself. Someone is sharp in a meeting and we decide they are arrogant. Someone misses a deadline and we decide they are careless. Someone cuts us off in traffic and we decide they are reckless. We take a moment and turn it into a person, and the move is so fast it does not feel like interpretation. It feels like perception. But often what we are seeing is not character in isolation. It is character under pressure, behavior inside a situation, a person colliding with context we do not understand.

Two moral systems

Psychologists gave this a name. In 1977 Lee Ross named it the fundamental attribution error: our tendency to over-explain other people's behavior by their disposition and under-weight their situation. A few years earlier, Edward Jones and Richard Nisbett had described its twin, the actor-observer asymmetry. We attribute our own behavior to circumstance and other people's to character. When others behave badly, we reach for personal explanations: lazy, difficult, selfish. When we behave badly, we reach for situational ones: I was exhausted, I was under pressure, I had bad information, I am not normally like that. The double standard is obvious on paper and nearly invisible while lived. We do not feel like we are running two moral systems. We feel like we are simply being accurate. The result is predictable. Other people start to look morally simpler than they are, and we start to look more nuanced than we are.

I catch myself doing it more than I would like. It is easy, as a founder, to call someone careless when they were under-managed, or call a team slow when the priorities I gave them were unclear. I have been on the other side of it too. People inside my own company have read my shifting priorities as impulsiveness, as not being firm on a belief for long. From inside my head, the shifts are one connected thread. They see character. I see circumstance. We are both doing what this essay describes. The character story is always available, and it is always flattering to the person telling it.

One reason the bias survives is that dispositional explanations feel cleaner. If someone disappoints us, it is simpler to believe they are fundamentally unreliable than to hold a more complicated picture. Character stories give us closure and let us organize the social world quickly. That speed is useful, and it is dangerous, because human behavior is rarely produced by one thing. A person may indeed be careless. They may also be exhausted, badly managed, or afraid of escalation. The surface behavior can be real and our explanation for it still wrong. And when the explanation hardens too fast, we stop investigating. The damage is the premature certainty.

Context deepens the diagnosis

This is where people get uncomfortable, because they hear any emphasis on situation as going soft. It is not. Context does not erase responsibility. It deepens the diagnosis. If a leader humiliates someone publicly, it matters that they did it, and it also matters whether the environment normalized performative aggression and rewarded fear-based speed. If a frontline employee seems disengaged, it matters how they behaved, and it also matters whether the role has become punishing and the incentives incoherent. The person is still responsible. The explanation is no longer shallow. And shallow diagnosis is one of the great sources of recurring failure in families, teams, and institutions. A company sees poor execution and calls someone weak instead of asking whether the system is producing confusion. We are more comfortable condemning people than understanding conditions.

None of this is a case for naivety. Behavior is evidence. Actions do reveal people. Some people are exactly as careless or cruel as they appear. The point is only that a moment is evidence, not the whole case. The deepest cost of getting this wrong is not the occasional bad guess. It is that we become progressively worse at holding complexity. Once we are invested in a character story, we curate evidence around it. The colleague is no longer someone who had a bad season, they are "the kind of person who always does this." The family member under strain becomes "impossible." Moral simplification is seductive because it is efficient, and it is one of the fastest ways to become stupid about people.

Accuracy over the easy story

The fix is not to eliminate the bias, which we probably cannot, but to interrupt it before it hardens, with better questions. What else might explain this. What pressure might this person be under that I cannot see. If I had behaved this way, what situational explanation would I be tempted to give myself. Am I reading a pattern, or moralizing a moment. Those questions make the judgment less lazy. And this is why the lesson is not niceness. It is precision. You can be demanding and still contextual, hold people accountable and still resist simple explanations. Strong standards require better contextual understanding, not less, because if you misread the cause your intervention will be theatrical. You will punish where you should redesign. You will coach where you should draw a boundary.

There is a more human truth underneath all of this. Most of us have behaved in ways that did not represent the best of who we are, and we have had moments that, taken out of context, would make us look smaller or colder than the full truth. We know this intimately about ourselves and forget to extend it to others. Many people are tired, afraid, badly managed, or trapped in systems that reward the wrong thing, while being judged by someone who finds character easier to see than context. The world gets worse when we do that too often. Not only because it makes us unfair, but because it makes us inaccurate. And if you want to lead or judge well, accuracy matters more than the emotional pleasure of a quick conclusion.

I do this too. I give myself reasons and other people flaws. The work is to catch the sentence before it hardens.

Notes and Sources

• Lee Ross, "The Intuitive Psychologist and His Shortcomings," Advances in Experimental Social Psychology, 1977. The fundamental attribution error.
• Edward Jones and Richard Nisbett, "The Actor and the Observer: Divergent Perceptions of the Causes of Behavior," 1971. The actor-observer asymmetry.

This is why Patrick Lencioni's Five Dysfunctions of a Team has endured. It names a pattern most teams have lived through: low trust creates fear of conflict, weak conflict creates false commitment, false commitment weakens accountability, and weak accountability eventually shows up as missed results. The model earns its keep for one reason: the dashboard is late. By the time the metric is red, the team has already spent weeks avoiding the real conversation.

Where the miss usually hides

Most companies can recite the model. Fewer notice where they stop short of using it. In my experience the real work hides in three places.

Leaders prefer process language because it feels safer. If the launch slipped because the weekly review was weak, you can fix the weekly review. If it slipped because two executives did not trust each other enough to disagree three weeks earlier, the problem gets personal, and now you are talking about whether people hide disagreement until it is expensive, whether meetings are forums for decision or rituals of politeness, whether the team prefers false peace over useful friction. That is a far more uncomfortable diagnosis, which is why many organizations stay stuck. They keep repairing the visible machinery and leave the human structure untouched. Artificial harmony is one of the costliest lies in a company. From the outside it looks mature: polite meetings, few flare-ups, apparent alignment. Then the real movie starts after the meeting, in side conversations and private reservations and compliance without commitment. What looks like an execution problem is often an honesty problem. Healthy conflict is expensive in the moment and cheap in the aggregate. Avoided conflict feels cheap in the moment and becomes expensive later.

Commitment is not consensus. Teams confuse the two. Real commitment does not require everyone to get their preferred outcome. It requires clarity, people feeling heard, and a decision explicit enough to align behind once the debate is over. Many teams never get there. They leave with "roughly aligned" or "let's refine as we go," which sounds collaborative and is often evasive, and ambiguity travels downstream disguised as flexibility. This is why I distrust over-romanticized consensus cultures. They tend to produce less commitment, not more, because people leave without enough clarity to act or enough ownership to defend the call when it gets hard.

Accountability is hardest among peers. Teams treat accountability as a manager's job. The strongest form is peer accountability, colleagues calling one another out directly when standards slip, and it is where many teams fail, because holding a peer accountable risks tension and awkwardness. So people escalate upward or soften the feedback until it changes nothing, and the manager becomes the only source of correction. If the only person who can say the hard thing is the boss, you do not yet have a strong team. You have a reporting structure.

None of this means process is irrelevant. Clear roles, decision rights, and review rhythms matter. But process amplifies the quality of the team underneath it. It rarely saves a team from fundamental dysfunction for long. A low-trust team turns any process into bureaucracy. A conflict-avoidant team turns any cadence into theater. Amy Edmondson's work on psychological safety says the same thing: a team only voices disagreement and admits mistakes when it is safe to do so, and without that, every operating system you install runs on bad data.

The exception I live

And here is where I have to push past the model, because I live the exception to it. The chain says trust unlocks conflict. I have caught myself not saying hard things to my own co-founders, and we have about as much trust as a leadership team can build. More than a decade together. Egos worn down by the years. Nobody second-guesses intent; whatever is said, we know it is said for the right reason. By the model, candor should be free for us. It is not. The discomfort still shows up, the pause before the sentence, the butterflies. Kim Scott tells the same story from the other side: she walked through fifteen years of her career, surrounded by people who trusted and liked her, before anyone told her about the metaphorical spinach in her teeth. What that taught me is that trust is the floor, not the skill. You can believe feedback is a gift and bad news is a gift, and the belief still will not deliver the hard sentence for you. Practice does. The butterflies are the point. I now treat candor as a rep to be done, not a trait to be assumed. A team that waits to feel comfortable before being honest will wait forever. Comfort is not the precondition. It is the reward.

Many execution problems do not begin as execution problems. They begin as trust problems that have not yet become legible. The work is not to add another review meeting. The work is to make the room honest, by practice, early enough that the review meeting has something true to work with.

Notes and Sources

• Patrick Lencioni, The Five Dysfunctions of a Team, 2002. The trust-to-results causal chain.
• Amy Edmondson, "Psychological Safety and Learning Behavior in Work Teams," Administrative Science Quarterly, 1999. Disagreement and admission of error require safety.
• Kim Scott, Radical Candor, 2017. The fifteen-years-of-unspoken-feedback story.

Desire is social

This is not a new observation. The French thinker René Girard built his life's work on it, the idea he called mimetic desire: that we do not want things on our own, we want them by copying others. We usually talk about desire as if it rises from deep inside the self. Find your passion. Know what you want. Ignore the noise. That advice flatters the individual, suggesting a pure personal signal waiting underneath the confusion. Real life feels less clean. We learn what is desirable by watching what other people treat as desirable. We copy taste before we know we are copying it, and ambition before we know we have adopted it. That does not make desire fake. It makes it social, which is more uncomfortable, because it means many of our strongest ambitions arrive already shaped by other people. The question is not only what you want. The harder question is who taught you to want it.

Most people assume desire gets distorted by celebrities. That happens, but the more powerful distortions come from people much closer. This is exactly Girard's distinction between a distant model, who creates fantasy, and a close one, who creates rivalry. A billionaire in another country is a spectacle. A founder from your batch raising more capital than you can become a wound. A friend buying the house you thought you should be able to buy creates restlessness. Luke Burgis, who carried Girard's idea into business, compresses it well: conflict comes from our sameness, not our differences. A peer is dangerous precisely because his life is comparable to yours. We do not merely observe what our peers have. We inherit their scale of significance. Their milestones become plausible for us, their symbols of status newly visible, their pace a pressure. That is one reason adulthood can feel quietly more anxious than childhood even as material life improves. The circle of comparison sharpens.

When winning replaces wanting

This is where wanting becomes dangerous. At the start the object still seems to matter, the promotion, the company, the recognition. But if desire is being shaped through comparison, the object slowly matters less than the relative position. You do not only want to build a good company, you want to build one that beats the other company. You do not only want wealth, you want the kind that rearranges where you stand among peers. That is when desire feeds on rivalry rather than purpose, and once that happens, satisfaction becomes very hard to reach, because victory in rivalry has no natural endpoint. The comparison set keeps updating. The standard of enough keeps getting infected by somebody else's next move. People then become confused about why achievement feels flatter than expected. Often the reason is not that they wanted too much. It is that they stopped wanting the thing itself a long time ago and started wanting what it meant in relation to someone else.

The problem is old. The scale is new. Social media has made other people's desires permanently visible, so you no longer live among the handful of people physically around you but inside a rolling theater of aspiration where someone is always traveling better, building faster, succeeding more publicly. Even if you think you are immune, your nervous system is taking notes. And this is not only a consumer problem. It is a leadership and company-building problem. Founders begin by solving something real and end up trapped in valuation envy. Teams begin by serving customers and end up imitating the prestige rituals of whatever company is currently fashionable. We imagine that freedom of choice produces self-authored lives. Often it produces the opposite, more opportunities for unconscious imitation.

This is why the topic matters to me. The danger is not only that people chase shallow things. The more interesting danger is that borrowed desire can masquerade as seriousness. It can look like discipline, even like courage, because sometimes what we call courage is just the willingness to suffer for a borrowed script. So simple anti-ambition language does not help. The answer is not to want nothing. Imitation is part of being human, we learn and build and become through models. The real question is whether we are choosing them consciously. Some models leave you more grounded and more free. Others leave you more agitated and more trapped inside performance. Both can look impressive from the outside. Only one leaves you less divided against yourself.

Cleaner ambition

You do not break the loop by force. You probably do not stop comparing by sheer will. You stop by changing the conditions under which desire is formed. You change whose lives you study too closely. You become more suspicious of ambitions that intensify when a peer succeeds and fade when nobody is watching. And you ask better questions. If I achieve this and nobody knows, do I still want it. If the person I secretly compare myself to vanished, would this goal still feel alive. Is this admiration, or rivalry wearing nicer clothes. Most people do not need less ambition. They need cleaner ambition.

I do not think the goal is to become detached from desire. Desire animates life. It creates craft, invention, devotion. The problem is not wanting. The problem is sleepwalking into wants that were socially installed but personally unexamined. We are always teaching one another what matters, parents and founders and friends and whole companies, modeling not only effort but aspiration. That is how people spend years climbing toward something and feel strangely absent when they arrive.

I am not exempt. I have wanted numbers because another founder announced them first. And the most useful feedback I have ever received was a version of this essay handed back to me: stop studying other founders so closely, define your own style, and believe in it long enough for it to become yours. So I keep coming back to a question that has become more useful to me than almost any advice about ambition. Not what do I want, but who taught me to want this.

That question does not kill desire. It cleans it.

Notes and Sources

• René Girard, Deceit, Desire, and the Novel, 1961, and Things Hidden Since the Foundation of the World, 1978. Mimetic desire; distant versus close models.
• Luke Burgis, Wanting: The Power of Mimetic Desire in Everyday Life, 2021. Girard applied to business; conflict comes from sameness.

That was not an illusion. SaaS was the first category where Indian startups could build world-class companies without physically recreating the whole business in every geography. Code traveled. Distribution was digital. Support could be centralized. Product quality mattered more than office location. By 2022, Bain was describing Indian SaaS as second only to the United States in scale and maturity, while noting that only around 20% of Indian SaaS revenue came from India itself. The customer base had gone global while the company stayed built in India. That is the world in which Zoho and Freshworks became imaginable at global scale. The advantage was structural: SaaS let India export capability without exporting the full operating burden.

I think AI will create the same opening for consumer companies.

Not because physical reality disappears, or logistics stop mattering, or local trust suddenly becomes trivial. Because AI changes the cost of carrying operating context across a complex consumer system. And that is enough to reopen a question that used to look much harder: can a consumer company built in India also become a company for the world?

Why consumer stayed local

A consumer company does not only ship a product. It ships trust. That trust lives in many messy places at once: merchandising, support quality, pricing, fraud control, fulfillment, claims, localization, returns, compliance, field operations, recourse. The product is not the app. It is the whole system the customer collides with after pressing the button.

That made consumer companies brutally hard to globalize. Entering another market felt like starting another company. The catalog had to be relearned. The customer language changed. Fraud patterns changed. Local paperwork changed. And because all that complexity lived inside people, meetings, and local managers rather than in a common machine-readable system, the company kept losing coherence as it expanded.

There was a specifically Indian version of this trap. India could compensate for weak systems by throwing more people at the problem. Labor was cheaper, and smart manual work could patch over broken process for years. Outside India, that luxury collapses immediately. The same business has to survive with fewer people, higher labor cost, and far less tolerance for operational mess.

This is the part most people still underestimate. Consumer is not hard because atoms exist. Consumer is hard because context gets expensive.

AI changes the cost of operating context

Consumer companies run on context. They ingest massive amounts of messy signal: support calls, product exceptions, catalog anomalies, payment failures, refund paths, inspection outputs, fraud flags, pricing moves, local regulation, conversion drop-offs. Most of the company's complexity comes from converting that mess into decisions fast enough that the edge does not collapse.

Historically that required layers of human translation. Someone summarized what happened, someone routed it, someone escalated it, someone explained the pattern to another team. By the time the company acted, the signal was slower, flatter, and less useful.

This is no longer speculative. McKinsey's work on customer care describes gen AI already analyzing call transcripts, automating summarization, guiding agents in real time, and identifying root causes behind recurring failures. Parts of the context layer are becoming machine-readable at operating scale. AI can absorb support transcripts, classify failure modes, translate market-specific language, compare anomalies across regions, flag pricing outliers, and compress the time between event and decision. The company does not become automated. It becomes more coherent. And coherence is what global consumer systems have always struggled to preserve.

It also flips the Indian constraint. If the pre-AI way to scale was add more people until the process works, the AI-era way is build better systems until the agents work. That is a different game, and it rewards raw judgment, curiosity, and the ability to get more out of AI than most people.

The company can stay one company across markets

The real opportunity: more of the business can stay intellectually centralized while execution stays locally adapted. One pricing brain, one support memory, one quality ontology, one experimentation loop, with the last mile changing by market. The hardest thing about multi-country consumer execution was never copying the UI. It was keeping the company from becoming three different companies. AI gives you a real chance of avoiding that drift. The fraud pattern from one market updates the controls in another. The support lesson from one country translates faster into the next. The company's judgment loops start living in software and models rather than only in local heroics.

That is the part that resembles what SaaS gave software. The resemblance is in the lower penalty for coordinating local complexity.

What India can export next

The categories most likely to benefit are not the simplest ones. They are the ones with enough fragmentation, enough trust work, and enough operational repetition that AI can meaningfully compress the coordination tax: mobility, commerce, consumer finance, healthcare access, home services. These categories looked structurally local because the company had to carry too much context manually. AI gives them a chance to become programmable enough for an India-born company to hold the system together across geographies.

And India has a second advantage here. Indian companies are trained on complexity: fragmented supply, inconsistent documentation, multiple languages, non-ideal infrastructure. In a pre-AI world that looked like a handicap next to cleaner developed markets. In an AI world, some of it becomes training data. The country that learned to operate through heterogeneity may do unusually well once heterogeneity becomes machine-readable.

To be clear about what this is: a thesis, not a result. Nobody has demonstrated it at scale yet. Honestly, the multi-geography consumer company run on shared AI context is still being proven, including by us. AI will not remove regulatory differences, manufacture trust a company has not earned, fix a bad category thesis, or save poor local execution. The companies that win will still need real local market understanding, strong on-ground operators, category-specific trust rails, and regulatory seriousness. AI changes the cost of coordinating those things.

For years the exciting question from India was which software company gets built here and sold everywhere. The better question now is which consumer categories become programmable enough under AI that an India-born company can run them coherently across markets. Consumer punishes abstraction faster than software ever did. But if the shape holds, the prize is larger than another software export story.

SaaS let India export code. AI will let India export consumer companies.

Notes and Sources

• Bain & Company, India SaaS Report 2022. Indian SaaS second only to the US; ~20% of revenue from India.
• McKinsey, "Gen AI in customer care: early successes and challenges," 2024. The context layer becoming machine-readable at operating scale.

We still talk about India's used-car market as if the main unlock were inventory. List more cars. Improve discovery. Standardize inspection. Reduce transfer friction. All of it matters, and all of it is incomplete, because used-car ownership at scale is also a lending problem.

A market does not become mass merely because the sticker price is lower than new. It becomes mass when an ordinary household can bridge the gap between aspiration and liquidity without stepping into a trap. A used car can be affordable in absolute terms and still unreachable in cash terms. That gap between price and what a household can actually pay is where the market either widens or stalls.

The EMI is the real entry point

Pre-owned cars are the natural bridge into ownership because they sit where aspiration and budget can meet. CRISIL Ratings expects used-car volume to cross six million units this year, with the used-to-new ratio at about 1.4x, up from below 1.0x five years ago, and it ties that shift not only to digital adoption and confidence but to better access to finance. That last part is the point. Affordability by itself does not create access. For many households the real question is not whether a car is worth its price. It is whether paying for it empties the family's cash buffer and trades the emergency fund for mobility.

That is why financing penetration matters. On organized platforms, financing now runs above half of purchases, against roughly 20 to 25% of the used-car market as a whole, per IndianBlueBook. Credit is how the category becomes reachable. And the 20 to 25% is not evidence that buyers think car-first; it is a count of how many are still forced to answer the EMI question from savings, or stay out entirely. The buyer this market needs next settles the payment before the model.

There is a second layer here that spreadsheet logic misses. In India, loan rejection is socially uncomfortable. For a first-time buyer it is embarrassing to be told in public that you do not qualify, or qualify for less than you hoped. So people do not walk into a showroom and openly discover their budget. They try to find out quietly, through an intermediary, whether they will get a loan at all. That creates a chicken-and-egg problem. The showroom asks which car and what budget, and the buyer cannot answer, because the real budget depends on a loan decision that has not happened yet. Until that answer exists, the car search is half-fiction. This is why pre-eligibility matters so much. Letting a customer privately discover their limit and monthly affordability before the public moment of choosing removes shame, not just friction. It turns financing into a source of confidence rather than exposure.

A used-car loan is not a smaller new-car loan

New-car lending sits on a cleaner foundation. The asset is standardized, the paperwork tidy, residual values easier, and the car has not yet been shaped by prior owners, repair quality, or document gaps. Used cars are different. Every one has already lived a life, and that life leaves traces. Service quality varies. Accident history may be partial. A prior loan may exist. The transfer may still depend on procedural follow-through.

So a used-car lender underwrites three things at once: the borrower's ability to repay, the truth of the asset, and the reliability of the process around ownership, transfer, and recovery. If any one is weak, the terms get worse: rejection, a heavier down payment, a higher rate, or manual friction. A used-car loan is not just a credit product. It is an underwriting decision on ambiguity. In used cars the truth of the collateral often determines whether borrower risk can be priced at all.

In India, a loan is also a trust signal

A loan here carries information as well as money. When a serious institution is willing to lend against a particular used car at a rational rate, the buyer receives more than capital. They receive a signal that the asset is legible enough to finance, that the valuation is not fantasy, that the paperwork is not hopelessly opaque, that someone with balance-sheet discipline will stand behind the transaction. The loan helps legitimize the car. That matters disproportionately in a market full of people learning ownership in real time. Bad lending does the opposite: opaque pricing or coercive collections turn ownership from aspiration into regret. The point is not more credit at any cost. It is better credit, built around the real shape of used-car ownership.

The real moat is asset intelligence, not cheap capital

Used-car lending gets described as a question of risk appetite. The better description is truth quality. To lend well you need better asset intelligence than a generic lender has: inspection good enough to surface hidden condition, pricing good enough to estimate fair value, title and lien checks good enough to flag risk early, transfer visibility good enough to reduce post-sale uncertainty, and recovery good enough to lower loss when a loan fails. The collateral is metal plus a stream of facts. Seen that way, the used-car lender looks less like a branch-led NBFC and more like a data-and-operations system attached to a balance sheet.

Most people assume the advantage starts with cheaper capital. That is true, but late in the sequence. Cheap funds only help after the lender can describe the asset truthfully enough to avoid scaling its mistakes. If valuation, document checks, and recovery are weak, a low cost of funds just lets errors compound faster. In used cars the harder problem is not raising money. It is reducing the gap between what the lender assumes about the car and what is true. Close that, and capital becomes modular: own book, co-lending, partner balance sheets, each cohort routed to the kind of capital that should carry it. The rare capability is making the asset legible enough that money can arrive with confidence.

This is also why a company sitting close to inspection, pricing, transfer, and recovery can become an unusual lender. It does not look at the same car the way a generic lender does. A standalone lender receives a file. An embedded lender sees the journey that produced the file.

Two examples of what that buys. First, the asset can testify about the borrower. A credit bureau knows the borrower's repayment history. The vehicle's own data trail knows things the bureau cannot: whether the declared kilometers match the service records, whether the challan pattern says the car has been quietly running commercial, whether the story the application tells matches the life the car has lived. In used cars, fraud usually hides in the asset, not the application, and only a lender who can read the asset catches it.

Second, the exit changes the entry. For a standalone lender, a repossessed car is a distressed sale into a market it does not control, and its losses reflect that. For a lender attached to a retail engine, the same car re-enters the funnel as inventory, inspected, refurbished, and sold at retail rather than auctioned at distress. When liquidation recovers more, the lender can approve more, at better terms, further down the income curve. The exit path is not the cleanup crew. It is part of the credit box.

Lending helps create the market it serves

Improve lending and the whole market changes. Better inspection and pricing reduce uncertainty around the asset; lower uncertainty improves approvals and lowers losses; lower losses attract cheaper, larger pools of capital; better financing lowers the cash barrier for buyers; more financed buyers improve liquidity and price discovery; better price discovery improves collateral confidence for the next loan. That is how a market gets built. The useful word is financeable. Markets do not mature first and become financeable later. Financeability is part of how they mature.

If we describe used-car ownership only in the language of listings, inspection, and retail experience, we are seeing one side. The other side is whether the customer can carry the asset into life without breaking their cash position or overpaying for uncertainty. That is where lending sits, as one of the rails that turns pre-owned mobility from a good idea into mass behavior.

Used cars are the affordability bridge. Lending decides how many people actually cross it.

Notes and Sources

• CRISIL Ratings, press release, July 2025. Used-car volume crossing six million units, the 1.4x used-to-new ratio (up from below 1.0x), and finance access as a driver of the shift.
• IndianBlueBook (Mahindra First Choice Wheels), FY25. Used-car financing penetration: above half on organized platforms, roughly 20 to 25% across the whole market.
• Cars24, Gears of Growth: 2025 Indian Used Car Market Report (our own published consumer research, labeled as such). Financing-first buyer behavior and the pre-eligibility insight.

His example was the American railroads. They had been giants, and they declined, and everyone assumed it was because demand for transportation had fallen. It had not. Demand grew. Cars, trucks, and planes carried it. The railroads missed all of it because, in Levitt's words, "they assumed themselves to be in the railroad business rather than in the transportation business. They were product oriented instead of customer oriented." They defended the object and lost the need.

Most mobility companies in India are making the same mistake right now. They think they are in the business of selling cars. List it better. Price it better. Inspect it better. Finance it faster. Move more units. All of it matters, and all of it is incomplete. The car is the visible artifact. Trust is the product.

I do not mean trust as a brand value. I mean it as the thing the customer is actually trying to buy: confidence in the asset, in the paperwork, in the financing, in the service, in the transfer, in recourse if something breaks. Confidence that reality will still make sense after the transaction is over. A transaction is an event. Ownership is a relationship, and in India it has a texture: the challan you discover months late, the PUC run, the FASTag that will not recharge, the RC still in someone else's name. Strip the sentiment out of mobility and that is what is left. People are not buying a machine. They are buying the certainty that owning the machine will not punish them.

A car is, by its very design, an uncertain machine: thousands of parts aging at rates nobody can see from outside, in the hands of a previous owner you will never meet. That is the honest reason people pay the premium for a new car. They are not buying newness. They are buying certainty, the only complete certainty this category has ever sold. This is the quiet rule of every premium category. The most premium experience is the most certain one: a five-star hotel is not selling a better bed, it is selling the guarantee that nothing about the stay will surprise you. A new car is the same purchase in a different showroom. The whole used-car opportunity is to manufacture that certainty without manufacturing a new car.

The original tax is what you cannot see

The used-car category has always run on an information problem. Buyers do not know enough. Sellers cannot prove enough. Intermediaries usually know more than both. Documents are messy, history is partial, asset quality is uneven. So the price is rarely just the price. It is also a wager on what you cannot see.

That wager is the tax everyone in the category pays, and it is why inspection, verification, warranty, and return policies exist. But those are a single layer of comfort over a much deeper structure. What the customer is really buying is a stack of certainty that runs from the bottom up: that the vehicle is what it claims to be, that the paperwork and transfer are clean, that the financing prices the asset honestly, that ownership will feel manageable rather than adversarial, and that if something goes wrong, the system will not dissolve into confusion. Solve one layer and you have a feature. Solve the stack and you have an institution.

You can see that the product is not the car by changing everything except the car. Keep the same vehicle, and make the transfer clean, the history legible, the financing fair, and watch the price, the resale confidence, and the willingness to transact all move. Nothing about the metal changed. The certainty around it did.

The proof is in the parts that have nothing to do with the sale

Take transfer. It is treated as administrative residue and it is one of the purest trust moments in the category. If the previous owner stays legally exposed after the sale, the whole market gets quietly anxious, and that anxiety leaks into pricing and resale and the social legitimacy of buying used at all. The problem is real enough that in 2022 the government had to amend the Motor Vehicle Rules to create "deemed ownership," because registration records kept lagging behind real sales. When the state invents a legal fiction to protect sellers from cars they no longer own, you are looking at a trust gap, not a paperwork delay. Policy is part of the product whenever public systems define the truth of ownership.

Or take financing. Only about a quarter of used cars in India are bought on a loan, against roughly three-quarters of new ones. That gap is not mainly about capital. It is about certainty. A lender will not underwrite an asset it cannot see clearly, so the same trust rails that calm the buyer, clean history, honest inspection, real title, are what let credit reach the people who need it most. Every clean interaction is a deposit, and the balance is what eventually makes the asset financeable, insurable, and resaleable.

And take the moment the category would rather not own: when the road turns dangerous. A mobility business that helps you buy and disappears when there is a crash, a stalled compensation claim, or an investigation that loses the thread of who owned the car, is not a full trust system. A company that takes the upside of putting more people into cars inherits some responsibility for what happens to them afterward. That is not charity. It is the same product. Safety and recourse are the last layers of the certainty stack, and they are the ones customers remember most clearly when they need them.

Why this means Indian auto is still early

Once you accept that trust is the product, the size of the prize changes too. Indian auto is not early in demand. The used market is already worth about ₹4 lakh crore and moves six million cars a year, more than the new market, on roughly 35 cars per 1,000 people. It is early in institutional completion. Ownership is still fragmented, information still uneven, transfer still manual, and financing still prices uncertainty badly. Only about a third of used-car sales even run through organized players. In developed markets most of that stack is already normalized and invisible. Here, large parts of it are still being built.

That is the opening, and it is why Indian auto will produce far larger companies than people expect. Not because cars are glamorous. Because ownership is unfinished infrastructure. The large outcomes will not come from listing inventory or moving units faster. They will come from building trust rails dense enough that the market starts behaving differently: lower fear, cleaner transfer, fairer pricing, stronger resale, more durable ownership. The company that does that does not look like an old dealership with better software. It does not even look like a platform, because a platform stands between buyer and seller and takes a toll. It looks like infrastructure: the rails the whole category runs on, the way payments rails sit under every transaction without being the transaction.

This is also the thread running under everything I have written about this category. The coming decade of ownership grows when fear falls, not just when supply rises. Lending becomes the affordability bridge only when the asset can be trusted. The greenest car is the one already built, but only if its history and its battery can be believed. Road deaths are a trust problem before they are a traffic problem. None of those are separate essays about separate topics. They are the same argument seen from different sides. The product was never the car. It is also why I write about how the company itself is built. A company cannot sell certainty outside while truth moves slowly inside.

What business we are in

So when people ask what Cars24 is, the honest answer is not a used-car company, the same way the railroads were never really in the railroad business. We are not selling cars. The car is the artifact. We are in the business of removing the fear of owning one, of making the asset legible, the paperwork survivable, the financing fair, and the system honest enough that an ordinary person can step into ownership without bracing for it to go wrong.

Levitt's railroads had the customers, the track, and the trains, and they still lost, because they guarded the object instead of the need. The need in Indian mobility is not more cars on the road. It is the confidence to own them. Build that, and the cars take care of themselves.

Notes and Sources

• Theodore Levitt, "Marketing Myopia," Harvard Business Review, July-August 1960. The railroads-versus-transportation argument, quoted verbatim.
• CRISIL Ratings, July 2025. Used-car market size, volume, and the used-to-new ratio.
• MoRTH registration data via Data For India. Roughly 35 cars per 1,000 people.
• IndianBlueBook, FY25. Organized share of used-car sales and financing penetration.
• Central Motor Vehicle Rules, 2022 "deemed ownership" amendment.

I have thought about that sentence often. Not because it was dramatic. Because it was precise. He saw an open loop and closed it.

That is the role I increasingly believe matters most in the modern company. Builder. Not builder as a flattering word for founders. Not builder as a synonym for engineers. Builder as the person who can see what needs to happen and change reality directly. At Cars24 it is now the only title we have. I will come back to that. The argument comes first.

The old prestige system rewarded distance

For a long time companies rewarded a different kind of person: the one who stood further from the work, coordinated the people doing it, translated their status into the language of the next layer, and accumulated legitimacy through distance from execution. That did not happen because leaders were evil. It happened because context was expensive and coordination was scarce, so a scaled company needed translators, routers, reviewers, and summarizers to stay coherent. This is Jay Galbraith's old insight, that organization structure exists to process information under uncertainty. We turned distance from the problem into a ladder. The person closest to the customer sat below the person explaining the customer. The person writing the code sat below the person converting it into a roadmap update for someone who no longer needed to know the code at all. That ladder was never beautiful, but it was administratively useful. Now a meaningful part of that usefulness is eroding.

When context becomes cheaper to gather, move, and act on, the value of pure coordination falls. Not to zero, but enough to change the prestige system. If systems can make the state of a business visible, if messy reality can be summarized without three human relays, if the person nearest the problem can reach the context directly, then the premium shifts. The highest-value person is no longer automatically the one furthest from the work. Increasingly it is the one closest to it who can actually close the loop. The technician who finishes the car because the family needs it. The inside-sales operator who calls the customer back because the first answer did not solve the problem. The engineer who fixes the straining system before the outage becomes public. Different functions, same value: direct change to reality.

What management has to become

Any essay like this risks being read as anti-management. I do not mean it that way. Judgment, hiring, standards, coaching, conflict resolution, capital allocation, all still require real authority. What is weakening is not management. It is the prestige attached to coordination theater: chase emails, status decks, the update reformatted for the next layer up. That was never the highest form of management. It was overhead made respectable. The best managers are builders too. They build clarity, remove friction, make hard calls, and help people around them close more loops. Their contribution is not that they escaped the work. It is that they multiply it. The future is not anti-manager. It is anti-spectator.

This is also why titles matter less, and differently, than companies think. A title should be a coordinate in a system, not a coronation. It should tell people scope, responsibility, and decision rights. The day it starts deciding who gets context, who gets heard, and who is presumed intelligent regardless of proximity to the problem, it has stopped being useful and become a gate. Strong companies should grow less tolerant of gates, not because hierarchy is evil, but because unnecessary distance is expensive, on speed, accuracy, morale, learning, and truth. The customer does not care how elegant the escalation path looked. The customer cares whether the loop closed.

Agency without consequence is theater

There is another reason this matters. A company that says it values builders cannot keep pretending work becomes nobler as it moves farther from consequence. If two people are both closing loops that matter, the difference between them is domain, not dignity. Many companies claim that belief and violate it daily, praising ownership in theory while preserving status in practice. People doing indispensable work feel that lie long before leadership admits it.

And this is the part that sounds attractive until you live it. Real agency is expensive. It means there is no one to hide behind when the call was yours and the outcome was bad. It means the broken version belongs to you too, and the comfort of saying, accurately but uselessly, "it was not my job," starts to disappear. Carrying the cost is what makes it ownership. The rest is delegation. Many people say they want autonomy. Fewer want consequence. The two are inseparable. That is why builder is not a motivational label. It is a structural role with a psychological price: less permission, less ceremony, less positional status, more exposure, more responsibility, more days where the question is not your level but whether you closed what was yours to close. Some people find that exhilarating. Some find it exhausting. Both reactions are honest. The market is moving toward the first kind of person.

And this is where the argument becomes bigger than culture. In a world where coordination is cheaper, the person who can close the loop compounds faster than the person who mainly routes it. AI and cheaper context accelerate it. The strongest companies of the next decade will move dignity, context, and authority outward wherever the problem allows, while keeping hierarchy where it protects quality, safety, or coherence. They will stop pretending status should flow automatically to the people farthest from the work. The old company built prestige around commentary and distance from execution. The next strong company will build it around proximity to the problem.

What changed when the prefixes went

This is the part we made literal. We deleted every title except Builder. The old bands, grades, and designations, the vice president, the director, the manager, all went. And when the prefixes went, something important became easier to see: capability that had been waiting behind permission. The model we hold in our heads is a sports team. There is a captain, but every player has a part, and the job is to play your part, not to supervise someone else playing theirs. Fewer supervisors, more players. I say this as a reformed offender. I started this company to do things with my own hands, and as we scaled I became a master delegator, drifting further and further from the work, until my day was reviews and updates instead of building. AI handed the thing back. I can do things directly again, and so can everyone else, which is the entire point. A company no longer needs a layer of senior people whose job is to call the review and rally the troops. They do their part of the work. Everyone does.

That is what I mean by builder: the only role left that still compounds when context gets cheaper and reality gets harder to hide from.

Notes and Sources

• Jay Galbraith, "Organization Design: An Information Processing View," 1974. Organization structure exists to process information under uncertainty; the basis for the coordination-prestige argument.
• Autonauts, Cars24's public culture site (our own publication). The removal of bands, grades, and designations described in this essay, documented publicly.

Cars24 began as an engineering company. Then transactions grew, and our attention moved to operations: inspections, logistics, refurbishment, and financing. I let it happen. The company drifted from the reason it had started. Around 2023, it became clear that the old way would not take us forward. We had to rebuild how the company worked while the live business kept running.

A founder I respect told me recently his company was AI-first now. I asked what had changed. He said they had integrated GPT into support. I asked what else. He paused, then said they were still figuring that out. I do not say this to mock him. Most of us built companies for a world in which context was expensive to gather, expensive to move, and expensive to act on. That is the distinction that matters. An AI-first company uses AI. An AI-native company rebuilds around what AI changes.

A company is a machine for carrying context

That sounds semantic until you look at what a company actually is. The economist Ronald Coase asked, in 1937, why firms exist at all, why we do not just buy every task on the open market. His answer, which won him a Nobel Prize, was that using the market has costs, and a firm exists to economize on them by replacing the price mechanism with the directing authority of a manager. Hierarchy, in other words, was never simply ego or bureaucracy. It was a way to coordinate information cheaply enough to act. Jay Galbraith later made the same point in organizational language: structure is fundamentally a way of processing information under uncertainty. And Mel Conway saw the consequence back in 1968: organizations ship their own communication structures. The org chart does not just process the product. It shapes it.

So every scaled company is a context machine. It takes signals from the edge, compresses them, routes them, interprets them, and turns them into decisions. The org chart is not just a power map. It is a context architecture. It decides who gets to see reality, when they see it, and how much of it survives the trip. As a company grows, no one person can see enough of the system to decide well, so you add layers. A layer summarizes what is happening below, escalates what matters, filters noise, translates decisions back down. It is not elegant, but it kept large organizations coherent. It is also how they became slow.

A layer is now a design choice

Every layer adds latency and loses fidelity. Facts get polished on the way up, decisions get generalized on the way down, and by the time a signal reaches someone with authority to act, it is cleaner, flatter, and less useful than the reality that produced it. That was a tax companies had to pay. AI changes the economics under it, not by replacing judgment, but because a large share of what layers did was context work: summarizing, routing, triaging, drafting first-pass analysis, turning messy operational reality into something another human could absorb. That work can now be done faster, more continuously, and on routine tasks with comparable or better fidelity than the human chain it used to travel through.

Once that is true, the old shape has to be questioned, and most companies are asking the wrong question. It is not whether to use AI. Everyone will. It is what kind of company makes sense once the cost of carrying context drops this sharply. What happens to spans of control, to weekly cadences that exist mainly to move information, to permission chains built around who can see context, to decision velocity when the state of the business is continuously legible instead of periodically narrated. These are design questions, not tooling questions. The AI-first company adds software to an old shape. The AI-native company rethinks the shape.

In the old company, context travels in batches, through meetings, decks, review documents, escalation chains, and people wait for the round trip to finish before they move. A surprising number of recurring rituals turn out to be design debt once shared context is cheap. Status meetings, chase emails, escalation decks, and permission loops were all compensating for the same thing: too few people able to see enough of the system to act. Once context is cheap, meetings should default to decision, not narration.

Our monthly P&L review is the clearest example I have. It used to run three hours, and it began by waiting for a business-finance person to explain how revenue had moved and why the margin shifted. Now everyone walks in having already put those questions to an agent that can read the P&L. The diagnosis is done before the room meets. So the meeting is half an hour, and almost all of it goes to what we do next and who owns it. It has become a stand-up. The hours we used to spend establishing what happened and why have collapsed, and the time moves to the only part that needed humans in a room, deciding and committing. A company that makes in two days the decision it used to make in two weeks does not become the same company with better tooling. It gets more learning cycles per quarter, corrects faster, and compounds faster.

There is a simple test I hold us to. If AI lets a year of work happen in a month, the business must learn and improve faster too. Otherwise the projects were activity, not progress. I will not call Cars24 AI-native because of what we adopted. I will believe it only when we are visibly faster on the things that count: customer experience, unit economics, growth, and how quickly we can do new things.

This is not the end of management

The lazy version of this argument says AI replaces managers. It does not. What AI replaces is the coordination tax around management: the status collection, the reformatted update for the next layer, the meeting held because no one had a shared picture without sitting in a room for an hour. That was never the highest form of management. It was overhead. The real work, judgment, hiring, standards, coaching, conflict, the hard call under uncertainty, is still deeply human. Strip away the coordination theater and what remains is leadership.

It also does not mean flatten everything. Hierarchy does more than move context. It assigns accountability, aligns incentives, and protects quality, safety, and coherence, and AI does not obviously collapse those. The point is narrower and sharper: the part of hierarchy that existed only to relay information is the part now in question.

And none of it works in a low-trust company. If people hoard context, edit bad news for the next layer, or wait for permission because being wrong is punished harder than being slow, AI will not make the company smarter. It will make it faster at producing polished summaries of the wrong story. Install better tools inside the same old permission structure and all you have built is a better-equipped waiting room. The technical shift lowers the cost of carrying context. The cultural question is whether truth is allowed to travel at all.

More shared context means more agency, not more agents

A traditional organization keeps pushing decisions upward because that is where context accumulates. The store issue escalates because headquarters has the picture. That is usually a context problem, not a character problem. AI-native companies can change it by lowering the cost of shared context, so the person closest to the problem can see the same system state that once existed only at the center. The point is not more agents. It is more agency.

This is the change I have felt most directly at Cars24. The thing that mattered was not the tools we added. It was asking which routing work still needed a human layer and which work could now be done directly, with shared context around it. Many companies will fail here. They will buy the tools and keep the old permission structure, summarize meetings that should not exist, make reporting cleaner while keeping authority trapped at the same level, and call it transformation when what they achieved was software modernization.

Start with the shape

The shape of the company is the AI strategy. Everything else is tooling. Start there and the agenda changes. You ask which meetings should disappear, not which copilots to buy. You ask why an update still moves through three people before anyone acts. You ask the hardest question of all: if you were building this company from scratch today, with AI available from day one, would you build the same layers, the same rituals, the same distance between signal and action. If the answer is no, the gap between the company you have and the company you would build is the real work of transformation, and it is slower than people admit. It touches authority, incentives, reporting rhythms, hiring, and status. It takes years, not quarters. Anyone telling you they became AI-native in six months is selling theater.

This is not only my read. McKinsey's 2025 State of AI survey tested 25 factors and found that redesigning workflows had the single biggest effect on whether a company saw bottom-line impact from generative AI. Redesign, not adoption, is what pays. The next decade will not be divided between companies that use AI and companies that do not. Everyone will use AI. It will be divided between companies that kept their old shape and companies that rebuilt for a new one. One group will still be batching context through layers, only with better software. The other will be running on a different nervous system.

That is the difference between AI-first and AI-native. One is adoption. The other is redesign.

Notes and Sources

• Ronald Coase, "The Nature of the Firm," Economica, 1937. Why firms and hierarchies exist: to economize on the costs of coordination.
• Jay Galbraith, "Organization Design: An Information Processing View," 1974. Structure as information processing under uncertainty.
• Melvin Conway, "How Do Committees Invent?", 1968. Organizations ship their communication structures.
• McKinsey, "The State of AI: How organizations are rewiring to capture value," 2025. Of 25 factors tested, workflow redesign had the largest effect on bottom-line impact from gen AI.

Most companies still believe scale comes from adding people. That belief made sense for a long time. Industrial scale was built through labor, coordination, and managerial layers. When demand rose, the organization expanded to absorb it, and the model worked well enough to become instinct. Even now, when leaders say they want to scale, they often mean more output without losing control, and when they say they are hitting limits, they mean the current team cannot carry more complexity. So the default response is familiar. Hire more people. Add a layer. Create a review. Start a meeting. Build a dashboard. The company grows, but the system underneath it does not get wiser. It gets heavier. That is why so many organizations look bigger before they look better.

The mistake is subtle, because headcount is not useless. Talent matters enormously. But scale and capability are not the same thing. A company becomes more capable when it gets better at seeing reality, learning from it, and changing behavior before complexity turns into drag. Those are systems outcomes, not hiring outcomes. A weak company can double headcount and stay confused, add managers and still make poor decisions, build planning rituals and keep rediscovering the same problems in new forms. Everyone feels busy. Nobody feels the system getting clearer.

Events are visible. Systems cause them.

This is where systems thinking becomes practical rather than philosophical, and the clearest guide is Donella Meadows, whose work on leverage points taught a generation of operators to look beneath the visible event. At the surface a company sees symptoms. Beneath that sit patterns, beneath patterns sit structures, and beneath structures sit mental models. That stack explains why some companies keep treating recurring problems as isolated surprises. Look closer and the sales miss is an information-flow problem, the leadership conflict an incentives problem, the culture problem a system that rewards concealment until self-protection is more rational than truth. The companies that keep improving are the ones that get better at diagnosing the level of the problem correctly, distinguishing a symptom you can patch from a structure you have to redesign.

The hardest part is that the deepest leverage points are rarely visible in the org chart. Most leaders are comfortable changing plans, metrics, roles, and processes. Far fewer are comfortable changing the assumptions underneath them, and the assumptions are where the system is usually hiding. Peter Senge built his idea of the learning organization on exactly this: a learning organization is not one that reads more or runs better workshops, it is one that gets better at surfacing its own mental models and updating them in public. That sounds abstract until you see how often companies are run by unexamined beliefs, growth at all costs, founders know best, bad news should be softened before it travels, disagreement is disloyalty. Those beliefs do not stay private. They shape incentives, escalation paths, hiring, and the emotional climate of the place. They become structure. Jim Collins tells the story of A&P, which ran the experiment that proved its own model obsolete, watched it succeed, and shut it down. Companies do not usually fail to see reality. They fail to face it. Which is why the highest-leverage intervention is often not a reorg. It is a clearer view of reality.

Where the truth lives

The phrase I keep coming back to is that truth has to move, not as a slogan but as operating infrastructure. A company learns when signals from the edge travel inward without being distorted, and when the center can respond without waiting for theater to finish. That means bad news has to arrive early, people have to be able to say "this is not working" without social punishment, and meetings have to exist for judgment, not ceremony. Meadows liked a small story about this. In a Dutch housing development, identical houses, identical prices, some had the electric meter in the basement and some in the front hall. The houses where the meter sat in the hall used 30 percent less electricity. Nothing changed except where the truth lived.

But complexity does not only live in software and markets. It lives in human nervous systems. Fear slows truth down, defensiveness edits reality, status distorts feedback. So the emotional quality of an organization is not separate from its strategic quality. A fearful company is not only unpleasant, it is less intelligent. Amy Edmondson's research on psychological safety matters here, but not for the sanitized reason. Safety means reality can surface while there is still time to act on it. Comfort is beside the point.

The founder has to stop being the learning loop

This is also where founder-led companies hit the same wall. In the early years the founders are the system. They carry the context, make the calls, absorb the shocks, and that concentration creates speed at small scale. Then the company grows, and if the founder keeps being the system, it stalls at exactly the point it appears to be scaling. Decisions bunch up, teams wait, information gets edited before it rises. Most companies misread this as a need for more supervision when what they need is more distributed capability: better information flow, clearer decision rights, more local context, fewer rituals built around permission, more people able to see the system well enough to act on it. The founder has to stop being the entire learning loop. The company has to become one.

I lived this one personally. For years I believed it was my job to come up with the projects, for the quarter, for the year, and hand the problem statements to the teams. I have stopped. My job now is direction, not route. Cars24's vision is better drives, better lives: in a country where so many people are buying their first car, and where a car still sits inside a family's identity and is one of the most expensive purchases they will ever make, the ownership experience should be extraordinary. One level down, the direction gets a test the teams can argue with: increase certainty in an uncertain world. From there, the teams pick the projects. There are many roads to a vision, and the people closest to the work choose better roads than I would have assigned, though I will admit they sometimes pick ones I would never have chosen, and the discipline is to let those run. This is only safe because the learning loop is fast. Leaving direction abstract is only responsible when truth moves fast enough that a wrong bet surfaces in weeks, not at the year-end review. Vision-led and learning-led are the same design. One does not work without the other.

This matters more now because AI amplifies both the upside and the penalty. If context becomes cheaper to move and coordination overhead falls, the value of a company's learning system rises sharply. A company with better learning loops, clearer systems, and more distributed agency can behave larger than it is. A company with weak ones stays small in all the ways that matter, no matter how many people it employs. The old intuition was that scale meant size. The better one is that scale means the ability to keep learning as complexity rises. That is the real challenge, not growth alone or speed alone, but building a company that gets wiser as it gets bigger. Once complexity rises, every company is eventually forced to choose. Add more weight, or learn how to learn.

Notes and Sources

• Donella Meadows, "Leverage Points: Places to Intervene in a System," 1999, and Thinking in Systems, 2008. The leverage hierarchy and the Dutch electric-meter story.
• Peter Senge, The Fifth Discipline, 1990. The learning organization and surfacing mental models.
• Jim Collins, Good to Great, 2001. The A&P story: seeing reality versus facing it.
• Amy Edmondson, "Psychological Safety and Learning Behavior in Work Teams," Administrative Science Quarterly, 1999. Safety as the condition for learning behavior.

Mine is from 2005. I was at IIT Bombay. One night a batchmate and I were riding a Kinetic back late through Mumbai. He was driving, I was on the pillion. We did not see a divider in time. The scooter hit it and he was thrown across the road. I fell on the near side. A taxi ran over him. We picked him up and I held him in my lap the whole way to the hospital. He did not survive. I walked away without a scratch.

What stayed with me was not only the grief. It was the unfairness. Same vehicle, same night, same crash. One of us lived and one did not.

That is the strange place road death occupies in Indian life. Intimate enough to wound almost every family, abstract enough to stay normal, and fragmented enough that the system rarely learns.

In 2023 the Ministry of Road Transport and Highways recorded 172,890 road deaths, the highest India has ever logged. Even that is almost certainly an undercount. The WHO once estimated India's true toll at roughly 300,000 in 2016, about double the official figure for that year, and the government's own Sample Registration System put 2022 deaths far above the police count. The exact number matters. What matters more is that we still cannot agree on it. A country that cannot count its road deaths cleanly is not just struggling with traffic. It is struggling with truth.

The conversation stays at the visible layer: overspeeding, drunk driving, bad roads, missing helmets, weak enforcement. All of it is real. None of it reaches the deeper failure, which is trust.

I do not mean trust as optimism or civic niceness. I mean it operationally. Can institutions trust the data they are using. Can a bystander trust that helping a victim will not become a punishment. Can a family trust that compensation is real and reachable. Can the engineer, the police officer, the hospital, and the judge trust they are looking at the same picture of what happened. Too often the answer is no. And a system that cannot trust its own picture of a crash cannot learn anything from it.

This is not a fringe reading. Piyush Tewari founded the SaveLIFE Foundation after losing a teenage cousin to a crash, and rebuilt a stretch of the Mumbai-Pune Expressway on what engineers call the Safe System approach: the premise that people are fallible, will make fatal mistakes no matter how well trained, and that the system around them has to absorb the error. By fixing more than 3,500 engineering defects and cutting ambulance response from about 35 minutes to under 10, that single corridor saw deaths fall by roughly half between 2016 and 2020. The lesson is not that drivers there suddenly got better. It is that the system stopped letting their mistakes turn fatal.

I did not arrive at this from a safe distance. Neither did Amar Srivastava and Deepanshu Gupta, who founded the Indian Road Safety Campaign and built Crashfree India with Gajendra Jangid, my co-founder at Cars24. The last eighteen months of that work have shown the failure at close range, and the same pattern keeps surfacing: the breakdowns are connected.

The first failure is memory

Start with the data. India runs several road-death systems that do not reconcile: police-based MoRTH and NCRB figures, the survey-based Sample Registration System, the WHO's modeled estimates. The same crash should feed the same system, and the picture still diverges, sometimes by more than a lakh of deaths. A wrong baseline makes every target performative and every evaluation a guess. Digitizing the paperwork does not fix this on its own. Systems like iRAD and eDAR point the right way, a single geo-tagged record beats disconnected forms. But bad diagnosis can be digitized too. If an official is trained to close a file rather than investigate a chain of failure, faster data entry only speeds up bad judgment.

Roads everyone knows are dangerous

Then there is the road itself. Crashfree's Project Rakshak, a youth-led infrastructure effort, works on the part India routinely ignores: the places locals already know are unsafe, which formal systems act on only after enough people have been hurt or killed. A blackspot should not have to earn its name through repeated death. By mid-2026, Rakshak teams had assessed more than 120 high-risk locations across 18 cities and secured approvals for fixes at 25 of them, sites that had likely already seen more than a thousand crashes once under-reporting is factored in, with the interventions expected to cut crash risk there by a fifth to a half. The number matters less as scale than as method: dangerous roads can be made legible to authorities before more death forces their attention. The work also found that crash data on these sites is almost certainly undercounted, because non-fatal crashes vanish from formal memory entirely. The road was telling one story. The file was telling another.

After the crash, opacity keeps killing

The same break appears after impact, and it is the most under-discussed failure in Indian road safety. A country can pass a Good Samaritan law (India's was established by the Supreme Court in 2016 and later written into Section 134A of the Motor Vehicles Act to shield bystanders who help), publish ambulance numbers, and announce compensation frameworks. But if citizens do not know their rights, hospitals behave unpredictably, and families have no way to navigate the system, the law on paper builds no trust in practice. You see it in the first minutes after a crash, when a crowd gathers and no one is sure who to call or what protecting the victim will cost them.

This is why even a first-responder card matters. Early Crashfree material distilled roadside action into three words: Check, Call, Care. Make the scene safe, call 112, control the bleeding, start CPR if needed. In a working system that would be obvious. In India that clarity is itself missing infrastructure. Helping a victim should not demand private courage and improvisation every single time.

You see it again in trauma care. A visit to the AIIMS Trauma Centre makes the abstraction concrete. By Crashfree's published account, AIIMS sees roughly 125 to 175 trauma patients a day, more than 70,000 a year, and many serious crash victims still arrive with no meaningful pre-hospital care. Ordinary people, often rickshaw drivers, end up acting as first responders with no training and no equipment. Even at a leading trauma centre, the pre-arrival chain is strikingly thin. This is why PM RAHAT, the government's cashless-treatment scheme for crash victims, matters. Its strongest form is not compensation after the fact but treatment certainty in the minutes after impact: hospitals ready, police and hospitals synchronized, zero out-of-pocket cost for eligible victims. A crash should not turn fatal because the chain is confused about who admits, who pays, and who moves first.

Then compensation, where the law exists and access is broken. The Supreme Court said as much in January 2025, observing that a negligible number of victims benefit from the schemes because public awareness is low and dissemination by authorities is weak. A 2021 World Bank study found that among poor households hit by a crash, only 14% received any compensation at all, and the financial blow came to more than seven months of household income. Crashfree's field brief, Justice Unserved, shows the machinery underneath. Hit-and-run claims run at roughly 12% of eligible crashes a year. Seventy percent of surveyed low-income households did not know compensation existed. Nine in ten pending hit-and-run claims sit stalled on paperwork, while hundreds of crores lie unclaimed in the tribunals, ₹459 crore in Bombay alone, against a national backlog of more than ten lakh cases worth over ₹80,000 crore.

And into that vacuum step the middlemen. Unregulated "ambulance chasers" intercept victims at hospitals and sell them help with claims for a commission; in 2021 the Bar Council suspended 28 advocates for filing fake motor-accident claims. Where a system is opaque, trust gets privatized and sold back to the victim. India does not mainly have a compensation-law problem. It has a compensation-access problem.

The fix is often smaller than a new law. Many families do not know a claim must be filed within six months. The biggest unlock turns out to be a 90-minute awareness session and a printed checklist; at helpdesks run inside AIIMS and Sharda hospital, more than a hundred victims' families queued for exactly that. That is what opacity looks like up close. People do not only need money. They need translation. Policy says a victim can claim. Trust means the victim knows it, believes it, can prove it, and can survive the process long enough to receive it.

Nobody owns the chain

The failure to learn is also cultural. India still treats road death as the natural tax of growth: more vehicles, wider roads, rising fatalities for a while, and a quiet shrug underneath it all. We have built speed, volume, and aspiration. We have not built a safety philosophy equal to them. A line kept recurring in the work: information is infrastructure. Weak crash data makes safety design weak, invisible hazards make prevention weak, an illegible compensation system makes justice weak. And the deeper reason all of this persists is fragmentation. Road safety in India is split across transport departments, police, hospitals, local governments, courts, insurers, and ministries. Each can point to its own slice and still leave the person on the road inside a failure that nobody owns end to end. That is why India produces so much sincere road-safety activity and so little visible outcome. Everyone owns a fragment. Too few own the chain.

A mobility company that wants the upside of the category cannot keep treating the downside as somebody else's ministry. That is why this cannot be a CSR sidecar or an awareness week. Any company that benefits from mobility at scale inherits some responsibility for making the system more legible, more responsive, or less lethal. Safety is not adjacent to the category. It is part of the product.

None of this means Crashfree has cracked the problem. It has not. But by mid-2026 it had at least shown the outline of a different model: find dangerous infrastructure before it accumulates more death, treat compensation as a justice system rather than a legal footnote, build public tools instead of only reports, and use research to move institutions rather than decorate them.

A mature road-safety system assumes human beings will make mistakes. Its job is to make those mistakes less likely, less severe, easier to respond to, and easier to learn from.

Safer roads do not begin when drivers become perfect. They begin when systems can see, respond, and learn fast enough that imperfection is no longer so often fatal.

Notes and Sources

• Ministry of Road Transport and Highways, Road Accidents in India 2023. 172,890 deaths, the highest recorded.
• WHO, Global Status Report on Road Safety 2018, and WHO India. Estimated ~300,000 deaths in 2016 against an official count near 150,000.
• Sample Registration System estimates via Data For India. ~271,300 road deaths for 2022, far above the police count; the non-reconciling data systems.
• SaveLIFE Foundation and Piyush Tewari, profiled in Stanford Social Innovation Review. The Mumbai-Pune Zero Fatality Corridor: 3,500+ engineering defects fixed, ambulance response cut from ~35 to under 10 minutes, deaths down ~52% (2016 to 2020).
• World Bank, Traffic Crash Injuries and Disabilities: The Burden on Indian Society, 2021. Only 14% of poor crash-affected households received any compensation; the seven-months-of-income burden.
• Supreme Court of India, S. Rajaseekaran v. Union of India, January 2025. A negligible number of victims benefit from compensation schemes due to low awareness.
• Section 134A, Motor Vehicles Act, and SaveLIFE Foundation v. Union of India (2016). Good Samaritan protections.
• India Legal on unclaimed tribunal funds: ₹459 crore in Bombay, the national pending backlog over ₹80,000 crore.
• Bar Council of India, 2021. 28 advocates suspended for fake motor-accident claims.
• Crashfree India public materials (our own published research, labeled as such): Project Rakshak (120+ sites assessed, 25 approvals), Justice Unserved (claim rates, paperwork stalls, awareness), the pre-hospital care primer, legal-awareness helpdesks.
• Cars24 H2 FY26 public report (our own published reporting). Carries the Project Rakshak assessment and approval figures.
• Project Rakshak live dashboard.

Andy Grove gave us the line we all live by: only the paranoid survive. I have lived by it. Hungry. Urgent. Raise the bar, and the day you clear it, raise it again.

But read Grove closely. His paranoia was never panic. He aimed it. At strategic inflection points, the moments a business is about to be remade. At specific fears worth having. It was a stance he adopted, not a state he lived in. Most leaders miss that distinction. They turn a sharp instruction about vigilance into a permanent condition of dread. Then they wonder why their best people burn out and their judgment goes.

Survival was never the point. Thriving is. And it was Naina Sahni, my coach of the last two years, who showed me Grove's law is only half the sentence. The paranoid survive. It is the regulated who thrive. The second half is hers. What follows is me learning to live it.

The most demanding team on earth runs cool

These sound like opposites. Push relentlessly, demand more, never be satisfied. And also stay calm, stay kind, look after the people next to you. The sharpest example I know is not a company. It is a rugby team. New Zealand's All Blacks have won more than three quarters of the matches they have played for over a century, a record few teams in any sport can match. They are merciless about standards. They are also known for a tool they drill called red head, blue head. Red head is tight, panicked, overwhelmed. Blue head is calm, clear, accurate. Under the heaviest pressure, they practice shifting from one to the other. The most demanding team on earth does not win by running hot. It wins by teaching its people to cool down on command.

The body explains why. Amy Arnsten at Yale has shown that even mild, uncontrollable stress causes a rapid loss of exactly the faculties a leader is paid for. Judgment. Working memory. The ability to plan. Under chronic pressure the thinking brain goes quiet and the reflexive, fearful one grabs the wheel. Athletes know this curve cold. The one who trains hardest without rest does not peak. He breaks. Effort climbing, performance falling. A company run permanently hot does the same.

Feel the emotion. Do not decide inside it.

So regulation is not the soft sibling of ambition. It is what keeps ambition paying off. And it starts with one discipline: feel the emotion without letting it make the decision.

We are human. Bad news breaks, someone disagrees, the plan slips, and we feel it. That is not a flaw to engineer away. The flaw is collapsing the feeling and the decision into one moment. Daniel Goleman called it the amygdala hijack. The brain's alarm seizes the controls a beat before the thinking mind can vote. Kahneman gave the same warning a different name, a fast system outrunning a slow one. The work of leadership is to widen the gap between them.

But do not mistake this for going numb. Antonio Damasio studied people who lost the emotional part of the brain and kept the reasoning intact. They did not decide better. They decided worse. Many could not choose at all. The emotion is information. The skill, as James Gross's research shows, is not to bottle it up, which only drains you while the feeling stays. It is to feel it, reframe it, and act once it has passed.

Some will read regulated as a softer word for slow. It is the opposite. The dangerous kind of calm is the composed face that buries the bad news and calls it maturity. That is not regulation. That is suppression, and it makes the judgment and the company worse. Regulated does not mean you feel less. It means you feel all of it and still refuse to hand the decision to the part of you that is panicking. The frantic founder and the falsely serene one make the same mistake from opposite ends. Both let the feeling drive.

Compassion is what lets the standard survive

Here is what ambitious leaders get backwards. They treat compassion as a reward you hand out after the performance. It is the thing that makes the performance possible. Google studied what made its best teams best. The top factor was not talent. Not hours. It was psychological safety. The confidence to take a risk or admit a mistake without being humiliated. Amy Edmondson, who named the idea, is blunt that it is not about being nice. It is high care and high standards, together. Kim Scott says it in four words. Care personally, challenge directly. A frightened team hides the very threats a paranoid leader needs to see first. Because fear, the same science tells us, is what takes the thinking brain offline. Compassion is not what slows us down. It is what lets a high standard survive contact with real people.

Naina names the mechanism more precisely than I can. People do not lie to a frightened leader. They calibrate. They soften the truth, time it, route it sideways. The knowledge still exists in the company. It just stops reaching the person paid to act on it. And the leader feels none of this as loss. Fewer bad surprises feels like a well-run company. It is the symptom.

Calm is trainable, and contagious

And all of it can be trained. That is the part I take most seriously. Calm is not a temperament you are born with. It is a capacity you build, and you build it in the body. Heart rate variability is a real, if imperfect, signal of how regulated you are underneath. It responds to practice. This is why I keep a physical trainer and mental coaches close, and why I use them. My work is no longer only the hours in meetings and reviews. The hours on my body and my mind are the work too. That is the gym where calm under fire gets built.

It does not stay private either. The Navy SEALs put it in three words. Calm is contagious. So is panic. So is chaos. A team does not catch your strategy. It catches your nervous system, and amplifies it. A dysregulated founder builds a dysregulated company. A calm founder builds a calm company. The highest-leverage thing I can do for the people I lead is not to push harder. It is to be the steadiest person in the room when the news is worst.

So I want to be two things the world calls opposites. Paranoid about the work. Regulated in myself. Hungry without being frantic. Demanding without being cruel. That combination is rare, which is exactly why it wins.

The paranoid survive. The regulated thrive. I am building for both.

Notes and Sources

• Naina Sahni. The second half of the sentence comes from her, and so does much of my education in it.
• Andy Grove, Only the Paranoid Survive, 1996. Strategic inflection points; paranoia as aimed vigilance.
• Ceri Evans, Perform Under Pressure, 2019, and James Kerr, Legacy, 2013. The All Blacks' red head / blue head model. Win record: 500 wins in 652 tests since 1903, the first rugby nation to 500.
• Amy Arnsten, "Stress signalling pathways that impair prefrontal cortex structure and function," Nature Reviews Neuroscience, 2009. Even mild uncontrollable stress rapidly degrades prefrontal function.
• Daniel Goleman, Emotional Intelligence, 1995. The amygdala hijack, building on Joseph LeDoux's fear-pathway research.
• Daniel Kahneman, Thinking, Fast and Slow, 2011. System 1 and System 2.
• Antonio Damasio, Descartes' Error, 1994. Patients without emotional processing decide worse, not better.
• James Gross and Oliver John, Journal of Personality and Social Psychology, 2003. Reappraisal outperforms suppression.
• Google's Project Aristotle, building on Amy Edmondson, Administrative Science Quarterly, 1999. Psychological safety as the top factor in team effectiveness.

New EVs matter. Manufacturing should keep improving. But the biggest near-term lever in this category is not producing cleaner new vehicles. It is making the vehicles that already exist trustworthy enough to stay useful for longer.

Architects have a saying: the greenest building is the one already built. In Indian mobility, the greenest car is often the one already built.

Handled lazily, that line sounds moralistic. I mean it operationally. Every new car arrives with energy, water, minerals, logistics, and manufacturing emissions already locked inside it before anyone turns the key. If the system retires usable assets too early, mistrusts them too aggressively, or prices them incoherently, it wastes carbon the country has already paid for. Reuse is infrastructure, and India treats it like a fallback.

The climate arithmetic is the easy part

The carbon cost of a car is large before it is ever driven. The IEA estimates that roughly a fifth of a conventional car's lifetime emissions are incurred in manufacturing. For an electric car the upfront figure is higher still: the ICCT's 2025 lifecycle study finds that producing a battery EV emits about 40% more than producing a petrol car, mostly because of the battery, a debt the EV pays back over roughly its first 17,000 kilometers. After that it runs cleaner everywhere, but how much cleaner depends on the grid. On Europe's mix the lifetime advantage is about 73%. The ICCT's India analysis put it at 19 to 34% for cars sold in 2021, rising as the grid cleans up. And on a coal-heavy grid the manufacturing debt is a bigger share of the whole story, which makes wasting it through premature scrappage even harder to defend. Manufacturing is where a big share of the damage is done, and it is done whether or not the car is ever used to its full life.

That changes the question. Once a vehicle exists, the issue is no longer only whether a newer one is cleaner at the margin. It is whether the existing one can keep doing useful work without forcing unnecessary new manufacturing. Peer-reviewed lifecycle research makes the point directly: extending a vehicle's lifetime lowers its life-cycle CO2 by spreading that embodied carbon over more years and more kilometers. Reuse delays new production. Refurbishment amortizes the carbon already spent. Better diagnostics prevent premature scrappage. That is simply a faster lever than waiting for the entire fleet to turn over through fresh manufacturing.

But reuse only works if trust works

This is where the climate story usually gets too neat. People say the obvious thing: buying used is greener than buying new. And it often is. Then the real world arrives. If the buyer cannot trust the vehicle, the inspection, the paperwork, or the resale value, they walk away, and climate loses to anxiety. Reuse is only environmentally real when it becomes operationally trustworthy.

India already scraps by age, not by truth

You can watch the waste happen in policy. Delhi deregisters petrol cars at fifteen years and diesel at ten, regardless of condition. A twelve-year-old petrol car with low mileage, a clean service record, and passing emissions is, by rule, on its way to becoming waste, while the carbon already spent building it still has years of useful life to amortize. The intent is right: old vehicles can be genuinely dirty, and a vehicle that fails an honest fitness test should go. The method is wrong. Age is a guess. Condition is a fact. A country that can measure a vehicle's actual fitness, emissions, and history does not need to scrap by birthday, and every healthy car it keeps in circulation is manufacturing it avoided. That measurement layer is the trust infrastructure this essay is about, and it matters most for the petrol and diesel cars that make up nearly all of India's fleet today, not just the electric ones arriving now.

Used EVs reveal the problem most clearly

The point gets sharper with electric vehicles. India's used-EV challenge is usually described as a demand problem, a charging problem, or an awareness problem. Those matter, and they miss the deepest issue. The used-EV market is forming at the wrong prices because every participant is pricing the same battery on different assumptions: buyer, lender, insurer, manufacturer. All conservative, most wrong. That is the asset, and that is also the uncertainty. When the battery is opaque, the asset gets discounted for the doubt. Otherwise useful EVs get punished, buyers stay cautious, lenders stay conservative, and adoption weakens exactly where the system should be compounding. A resale problem becomes a climate problem.

Battery trust is the real EV infrastructure

For years the Indian EV conversation has been too charger-centric. Charging matters, but the nearer-term bottleneck is battery trust. NITI Aayog puts the battery at 35 to 40% of an EV's bill of materials, by far the most expensive part of the car, and yet its state of health is still not a portable, standardized, widely trusted fact. That is absurd for an asset this important.

The reassuring data exists. Geotab's analysis of more than 22,700 electric vehicles across 21 models found batteries degrade about 2.3% a year on average, leaving the typical pack at roughly 82% of its capacity after eight years. The same study found heat and heavy fast charging can roughly double the rate, and India is a hot-climate market building out fast charging first, which is exactly why a standard borrowed from a mild-climate market will not do. But reassurance is not the same as comparability. As TÜV SÜD points out, there is still no single accepted method for measuring battery health, and competing methods can diverge by up to seven percentage points on the same battery. When the number itself is contestable, the market stops behaving like a market and starts behaving like negotiation theater.

The fix will not be won by ad campaigns. It will be won by standards: chemistry-aware health rules, a portable battery identity, clean warranty status, standardized recertification. This is no longer hypothetical. India's Office of the Principal Scientific Adviser has issued draft guidelines for a Battery Pack Aadhaar system, giving every pack a unique Battery Pack Aadhaar Number that tracks its provenance, state of health, and second life across the battery's lifecycle. It is India's analogue of the EU's Digital Battery Passport, which becomes mandatory for EV batteries in Europe from 2027. What is still missing is resale-grade implementation. A buyer should not have to interpret a battery like folklore.

Policy and financing decide whether reuse is legible

Policy can distort reuse in both directions. If transfer is painful, resale suffers. If road tax punishes moving a vehicle across states, liquidity suffers. And tax can tilt the field directly. A new EV attracts 5% GST. The organized dealer reselling the same vehicle pays 18% on margin. A private grey-market sale pays nothing. The state, in effect, taxes the trustworthy channel hardest. Financing compounds it: the ADB and NITI Aayog report that EV loans already run 10 to 30% lower loan-to-value and 6 to 18 months shorter tenor than petrol equivalents, used EVs sit below even that, and battery opacity is added on top, so the buyer ends up paying for the system's discomfort with ambiguity. Warranty portability is often technically possible but buried in manual, brand-specific process, when it should behave like a visible field rather than a detective exercise. Insurance needs to recognize the asset for what it is, with health-banded value logic rather than generic motor cover. None of it is exciting. All of it decides whether a reused asset feels financially legible.

Reuse is social infrastructure too

There is another reason this matters in India. A reused vehicle is often how mobility begins. In a first-time ownership market, pre-owned access is not a thrift decision, it is an entry door. If reused vehicles stay hard to trust, climate and access lose together, because a category that cannot build confidence in reuse pushes people into either delay or overextension. A mature mobility system extracts more life from the assets it has already paid to create, with enough rigor that second and third lives become normal. India is still building that maturity.

The climate upside is an information opportunity

The effect of reuse compounds through ordinary market behavior. Every vehicle that stays useful longer through trustworthy resale avoids some premature new production. Battery health that becomes legible enough to support a fair price keeps EVs from being stranded too early, and policy fixes that lower transfer and tax friction quietly improve the carbon efficiency of the whole category. India's climate opportunity in mobility is not only a manufacturing opportunity. It is also an information one.

Markets waste assets when they cannot measure them properly. That is true of used cars, and even more true of used EVs, where an opaque battery makes the market punish the asset more than it deserves. So the real climate work here looks less glamorous than the public narrative wants: better inspection, standardized battery health, portable warranties, cleaner transfer rails, insurance that understands the asset, and finance that can price confidence instead of fear. It looks like a reused car or EV feeling legible enough that a buyer chooses it without feeling they are gambling blind.

India will not decarbonize mobility only by building more. It will decarbonize faster by learning to preserve useful life with far more intelligence and far more trust.

Notes and Sources

• IEA. Manufacturing accounts for roughly a fifth of a conventional car's lifetime emissions; lifecycle tooling at the EV Life Cycle Assessment Calculator.
• Carl Elefante. The built-environment line this essay adapts: the greenest building is one already built.
• ICCT, Negri and Bieker, "Life-cycle greenhouse gas emissions from passenger cars in the European Union," July 2025. BEV production emissions ~40% higher than petrol, the ~17,000 km payback, and the ~73% lifetime advantage on the EU grid.
• ICCT, global lifecycle assessment of passenger cars, July 2021. The 19 to 34% lifecycle advantage for EVs registered in India in 2021, rising with grid decarbonization.
• Kagawa et al., "Role of Motor Vehicle Lifetime Extension in Climate Change Policy," Environmental Science & Technology. Extending vehicle life lowers lifecycle CO2.
• National Green Tribunal (2015) and Supreme Court of India (2018) orders deregistering ten-year diesel and fifteen-year petrol vehicles in Delhi-NCR. The age-based scrappage example.
• Geotab, battery health study, January 2026. 2.3% average annual degradation across 22,700+ EVs and 21 models; heat and high-power fast charging roughly double the rate.
• TÜV SÜD. No standardized method for battery state of health; methods diverge by up to ~7 percentage points.
• NITI Aayog EV supply-chain analysis. Battery pack at 35 to 40% of an EV's bill of materials.
• ADB and NITI Aayog, EV financing report. EV loans run 10 to 30% lower LTV and 6 to 18 months shorter tenor than petrol equivalents.
• GST Council, December 2024. 18% GST on the dealer margin for used EVs, against 5% on new EVs.
• Office of the Principal Scientific Adviser, Government of India. Draft guidelines for the Battery Pack Aadhaar (BPAN) system.
• EU Battery Regulation. Digital Battery Passport mandatory for EV batteries from 2027.

It will not begin with every household deciding it needs a new car and financing one through a familiar, well-oiled system. It will not ride on perfect paperwork, clean resale markets, confident first-time buyers, and ownership habits passed down across generations.

India is not that market. That is exactly why the opportunity is so large.

The standard story says demand is obvious and the infrastructure will eventually catch up. The causality runs the other way. Ownership grows when the system around it becomes trustworthy enough for ordinary people to attempt it.

India is still a first-time ownership country

In developed markets, people grow up around cars and enter adulthood already fluent in the rituals. Insurance, servicing, registration, resale, breakdowns, renewals: annoying, but not alien. The customer is usually replacing one owned car with another.

In India, a large share of the market is crossing into ownership for the first time. IndianBlueBook finds that for 64% of non-metro buyers, a used car is the first car they ever own. That changes the emotional logic of the purchase.

When someone buys their first car in India, the fear is not only that a shady dealer will cheat them. That fear is real. But another one matters just as much: the fear inside the buyer. Will I know how to manage this. What if I make a costly mistake. What happens when the insurance expires, a challan appears, the FASTag stops working, or the transfer paperwork gets stuck.

The closest analogy is not routine shopping. It is skydiving for the first time. You can choose the best operator in the world, read the reviews, trust the instructor, and still feel the fear sitting inside you. The question is not only whether the operator is credible. It is whether you can trust yourself inside a new experience with real consequence.

Ownership support is a trust event, not an add-on

Most people treat ownership services as adjacent. Insurance renewal, PUC reminders, service scheduling, challan discovery, FASTag, RC paperwork all get filed under after-sales convenience or margin.

That understates what they do. In a first-time market, each of those interactions is a trust event. Buy a car, then get help navigating the next small problem, and something shifts. The next form feels less intimidating. The next renewal feels manageable. The car becomes part of life instead of an object demanding constant vigilance. Confidence builds in small deposits. Insurance handled. Challan found and resolved. Service booked without confusion. Over time the customer is no longer only trusting a seller. They are trusting themselves as an owner. A market grows when ownership feels survivable, then normal, then desirable.

The second-owner decade

This is why India does not become a mass car-owning country by copying the rich world's new-car-first path. It becomes one through second owners.

The economics already point there. India has roughly 35 cars per 1,000 people, among the lowest rates in the world, against more than 800 in the United States. The used market is now worth about ₹4 lakh crore and crosses six million units a year, around 1.4 used cars sold for every new one, according to CRISIL Ratings. The same ratio runs about 2.5 in the United States and 4.0 in the United Kingdom, so India's used market is already the bigger one at home and still early by world standards. Set that against a record 4.3 million new passenger vehicles in FY2024-25 and the direction is unmistakable. Used cars are the affordability bridge. They let households step into ownership without absorbing the full cost of a new vehicle, and they fit the emotional reality of a first-time buyer better, because people do not only need aspiration, they need a manageable entry point.

But a pre-owned market does not become trustworthy just because cars are cheaper. It needs rails.

Transfer decides legitimacy

One of the deepest mistakes in Indian mobility is treating transfer as administrative detail. RC transfer decides whether a used-car market feels legitimate or risky. And transfer is no longer occasional: CRISIL notes the average used car now changes hands at around 3.7 years old, which makes ownership a recurring cycle of handovers, not a one-time event. If the prior owner stays legally exposed after the sale, if insurance continuity is unclear, if NOCs are painful, if state systems are too manual, the market stays structurally anxious even when demand exists.

The problem is real enough that in 2022 the government amended the Motor Vehicle Rules to create "deemed ownership," precisely because registration records kept lagging behind actual sales, even though the law sets hard deadlines for reporting a transfer. When the state has to invent a legal fiction to protect sellers from cars they no longer own, the rails are not yet trustworthy. A transfer-heavy category cannot scale on aspiration alone. It needs legitimacy.

Community memory is infrastructure too

There is another kind of infrastructure that gets ignored because it does not look like infrastructure. Community memory. What Team-BHP and its community built is not just a forum, it is one of the rare public-trust institutions in Indian auto. People call it India's Reddit for cars, but that misses it. No car advertisements, no paid placements, tight moderation, open searchable archives, standards strong enough that the knowledge stays useful years later. The result is a durable memory of ownership: which cars age badly, which service centres misbehave, what maintenance really costs, what holds value, what owners regret, and what they would buy again.

The community has teeth, too. Years ago a group of bikers, defrauded by a Kawasaki dealer who took their money and delivered nothing, skipped the courts and posted their accounts on Team-BHP. Within 45 days Kawasaki took the loss on its own balance sheet and delivered the bikes rather than risk its reputation on the forum. That is community memory operating as enforcement. In a first-time market, that knowledge is part of the category's trust layer, reducing asymmetry before any platform touches the transaction.

Why the opportunity is larger than commerce

Look at the market this way and it stops looking like a dealership story, or even a marketplace story. It starts looking like institution-building.

The prize is making ownership less frightening and less opaque at national scale. That means building across the whole stack: discovery that reduces confusion, financing that expands access, transfer that creates legitimacy, service that builds confidence, and information that makes the asset more legible over time. The headroom is enormous. Only about a third of used-car sales run through organized players, and only about a quarter of used cars are bought on a loan, against roughly three-quarters of new ones.

And the shift is structural, not a blip. As CRISIL's Anuj Sethi put it, the rising ratio of used to new cars "signals a structural shift, driven by rising consumer confidence and digital adoption." Confidence is the operative word. The category here is not finished infrastructure. Large parts of ownership are still manual, fragmented, mistrusted, or emotionally intimidating. That leaves room for institutions, not just transactions, which is why Indian auto will produce far larger companies than many still expect.

What actually grows the market

Ask what will grow the Indian car market and the usual answer is the visible things. More income, more financing, more supply, better roads, more choice. All true. But another answer matters just as much. The market grows when ownership gets easier to live with. When first-time buyers stop feeling that one wrong decision will punish them for years, when resale stops feeling like a legal gamble, and when paperwork starts behaving like rails rather than obstacles.

India's next decade of ownership will be second-owner, transfer-heavy, and trust-led.

The future of car ownership in India is not just more cars.

It is less fear.

Notes and Sources

• CRISIL Ratings, press release, July 2025. Used-car market at ~₹4 lakh crore and ~6 million units, the 1.4x used-to-new ratio, the US ~2.5x and UK ~4.0x comparisons, the ~3.7 year average transaction age, and the Anuj Sethi quote.
• SIAM. Record 4.3 million passenger vehicles sold in FY2024-25.
• Ministry of Road Transport and Highways registration data, via Data For India. Roughly 35 cars per 1,000 people (2022).
• IndianBlueBook (Mahindra First Choice Wheels), FY25 report. 64% of non-metro buyers' first car is used; ~29% organized share; financing penetration.
• Cars24, Gears of Growth: 2025 Indian Used Car Market Report (our own published consumer research, labeled as such). Behavioral findings on how first-time buyers approach ownership.
• Central Motor Vehicle Rules, 2022 amendment creating "deemed ownership" of vehicles pending transfer.