Twelve years later, the market gave investors another crash to respond to. In March 2020, the Nifty fell ~38% in under six weeks, one of the sharpest crashes Indian markets have ever seen.
The reactions were strikingly different. India’s SIP closure ratio jumped to 70% that month, meaning roughly two existing SIPs were being stopped for every three new ones being started. But at the very same time, new demat account openings hit a record 4.9 million that financial year, as a fresh wave of investors treated the crash as their entry point. 

Two groups of investors, watching the same kind of collapse in the same country, developed very different instincts about what a falling market actually means. Neither group was being careless. They were looking at the same crash, but their past experiences had taught them to interpret it differently. And that influence can overpower our judgement when we’re making crucial decisions.

There’s a name for this effect. In this week’s newsletter, I want to unpack where it comes from, how it shapes our decisions, and why you may start noticing it in almost every financial decision you make.

In this edition:

• What behavioral economist calls this, and why It behaves differently from every other bias
• Why fear leaves a deeper mark than facts, and why so many Indian households still trust gold over the market
• What years of market crashes have taught our instincts
• A framework for managing a bias you cannot simply think your way out of
• Why this matters more in an AI-driven market

What behavioral economist calls this, and why It behaves differently from every other bias

Most of what we know about money mistakes comes down to a fairly simple idea, that people don’t process information perfectly. We anchor on the first number we’re shown, we feel the pain of a loss more sharply than the pleasure of an equivalent gain, and we assume tomorrow will look roughly like yesterday. These are the biases behavioral economics has spent decades cataloguing, and the fix for most of them is straightforward in theory. Learn to recognize the pattern, and you can correct it.

What happened to Pai doesn’t follow that rule. Researchers call this the Experience Effect: two people can look at the same information and develop very different financial instincts simply because of what they have personally experienced.

To see how powerful this can be, let’s look at the case of Henry Wallich. In 1974, Wallich joined the board of the US Federal Reserve. He wasn’t an ordinary appointee. He had a PhD from Harvard, a professorship at Yale, and decades of experience studying economies at the highest level. If anyone in that room could be trusted to read the data correctly, it was him.

For the next twelve years, every time the Fed’s committee met to set interest rates, Wallich was looking at the same forecasts and the same models as every other governor in the room. And yet he dissented 27 times, a record that still stands, almost always pushing the same way, for higher rates, even when his colleagues, staring at identical numbers, were convinced it was safe to ease.

When researchers later studied the voting records of every Fed governor going back to 1951, the same pattern turned up again and again. People who had personally lived through high inflation stayed cautious about it for the rest of their careers, no matter what the data in front of them said in any given meeting. Wallich had lived through Germany’s hyperinflation as a child, watching prices double in a day and his family’s savings turn worthless. 

Pai had lived through five years of a portfolio that refused to recover. The decades and the currencies were different, but the mechanism underneath was the same one, and it’s what sets experience effects apart from every other bias behavioral economics has documented. Most biases weaken the moment you learn to spot them in yourself. This one doesn’t, because simply knowing about it isn’t enough to switch it off.

Why fear leaves a deeper mark than facts, and why so many Indian households still trust gold over the market

Walk into almost any Indian household built by a parent or grandparent who came of age before the 1990s, and you’ll usually find the same split, a locker full of gold, a stack of fixed deposit receipts, and a noticeable discomfort with anything resembling equity. Ask why, and there’s often a story behind it: a bank that failed, a business that collapsed, or a period when keeping money in cash felt like the safest thing to do.

That instinct becomes much easier to see when you look at the numbers. Indian households hold an estimated 30,000-35,000 tonnes of gold, worth around ₹450 lakh crore, more than the country’s entire GDP. Even within financial assets, the preference for safety is clear. Of the ₹353 lakh crore in household financial assets, ₹153 lakh crore is in bank deposits and just ₹41 lakh crore is in mutual funds. Only 5.3% of household savings goes into financial products. Much of the rest remains in gold, property and deposits, assets that have felt familiar and safe for generations.

That instinct isn’t stubbornness, and it isn’t really about intelligence either. It comes down to how the brain stores an experience in the first place. When you learn a fact from a book or an advisor, your brain files it away as information, useful, but essentially inert. 

When you live through something frightening instead, watching a family’s savings shrink or a business go under, the brain does something different. It tags that memory with the fear that came alongside it. Neuroscientists call this emotional tagging, and fear-tagged memories are both easier to recall and quicker to resurface than ordinary facts, especially when something even loosely similar happens again. Every repeat brush with that fear strengthens the pathway a little further.

The fingerprint of this shows up clearly in the data on investing behavior. One study compared people who had lived through strong stock market returns over their lifetimes with people who had lived through consistently weaker ones, holding the information available to both groups constant, and found the first group was 14 percentage points more likely to invest in stocks at all, against an average market participation rate of just 37%. 

The same pattern turns up among people whose entire job is supposed to be rational about money. Fund managers who lived through a bubble tend to carry that bubble’s shadow into every market cycle that follows. CEOs who led companies through severe downturns were found, using photo-based aging analysis, to visibly age faster than peers who hadn’t. An entire generation of Americans who grew up during the high-inflation 1970s kept avoiding cheaper adjustable-rate mortgages decades later, purely because the fear had never fully left them, a habit that cost that generation of borrowers a combined $22 billion in unnecessary interest.

None of this reflects a failure of intelligence in any of these people. It’s memory doing precisely what memory evolved to do, protecting them from something that, by the time the decision actually gets made, has usually already stopped being a real threat.

What years of market crashes have taught our instincts

The Sensex fell nearly 53% in less than a year during 2008. Across the country, investors pulled back from equity funds almost entirely. Net investment in equity mutual funds fell to just ₹1,056 crore in 2008-09, and dropped further to ₹595 crore the following year.

By September 2010, the Sensex had climbed back above 20,000, close to its pre-crash highs. Household savings in gold more than doubled in the years after, from 1.3% of GDP in 2008-09 to 2.8% by 2011-12, as investors moved money out of markets and into something that felt safer. 

And by fiscal year 2010-11, Indian investors withdrew ~₹13,500 crore more from equity mutual funds than they invested. Even though the market had already recovered, the memory of 2008 was still fresh. The numbers said it was a reasonable time to stay invested. What people remembered said otherwise, and across the country, memory won. 

Demonetisation in 2016 did something a little different but closely related. It made cash itself, the most trusted asset in most Indian households, feel unreliable overnight. 

Covid period added a different kind of shock, and this one was felt worldwide. It wasn’t just the market fall, but the stress that came with it. Researchers measuring reported stress across seventy years of inflationary periods found Covid-era inflation produced stress levels roughly three times higher than any period on record, higher than the Great Inflation of the 1970s, higher than 2008 itself. 

Then came the global rate-hike cycle in 2022. As central banks raised rates sharply, investors became less willing to pay high valuations for companies whose profits were still years away. And richly valued growth stocks fell hard within months.

These experiences don’t simply vanish when the market recovers. Behavioral research shows that lived experiences physically alter how our neural pathways process future choices, a mechanism known as long-term potentiation. The trauma of a market crash leaves a lasting biological trace that shapes our basic instincts around risk.

How to manage a bias you cannot simply think your way out of

None of this means the instinct is a flaw to be argued away, because you cannot lecture a brain out of a biological memory. No amount of spreadsheets or market commentary erases an emotional impulse wired by past stress. Awareness on its own doesn’t fix experience effects. What actually works looks a little different, and it starts before the next shock ever arrives.

Build rules while markets are calm. A portfolio decided ahead of time, with clear triggers for when to rebalance, is far less likely to be driven by fear once markets actually turn. The goal is to decide what you’ll do before emotion gets a vote in the matter.

Separate the decision from the moment. The worst financial calls tend to get made exactly when fear or excitement is at its peak. A short pause between a market event and a portfolio decision can be the difference between reacting to something and actually responding to it.

Whether a shock leaves a lasting mark also comes down to two things: how much you saw it coming, and how much control you had over your own response to it.

The matrix below maps out what that looks like in practice, and what moves you toward safer ground.

Bring in a perspective that doesn’t share your scars. You may not always see how your past is influencing a decision. Someone who didn’t live through your 2008 or your 2020 may look at the same numbers without the same emotional baggage, and spot something you can’t.

Your instincts aren’t necessarily wrong. They’re shaped by what you’ve lived through. The best defence is to have a system and another perspective that can challenge those instincts when they start driving the decision.

Why this matters more in an AI-driven market

Two people looking at the exact same stock today can pull up nearly identical research within seconds, earnings transcripts, analyst notes, sentiment scores, all surfaced by the same AI tools within minutes of each other. Whatever edge used to come from knowing something first is disappearing at the exact pace AI keeps improving.

What isn’t disappearing is the part of investing that was never really about information to begin with.

Every investor carries instincts shaped by what they’ve personally lived through, and no model can read those instincts for you, because you often can’t fully read them yourself. AI gets better at analysis every quarter. It gets no better at knowing whether your own conviction about a stock is coming from research, or from the year you happened to start investing.

This is why behavioural edges can last longer than informational or analytical ones. They don’t depend on having better data or faster models. AI may make information, analysis, and even sophisticated tools available to everyone. What it can’t easily equalise is how each person responds to uncertainty, fear, and opportunity. The real advantage lies in knowing when you’re making a considered financial decision, and when an old experience is making it for you.

Disclaimer: Investment in the securities market is subject to market risks, read all the related documents carefully before investing. The information provided herein is intended solely for educational purposes and should not be construed as solicitation, advertising, or providing any financial or investment advice or an offer to buy or sell any financial instruments. Readers are advised to consult with their financial advisor before making investment decisions based on the information provided herein. In this material, Dezerv has utilized information through publicly available sources, and other data deemed to be reliable. While reasonable care has been made to present reliable data in this article, Dezerv does not guarantee the accuracy or completeness of the data. Dezerv, along with its directors, employees, or partners or any of its affiliates, shall not be held liable for any loss, damage, or liability arising from the use of this document.

Fidelity later looked at how its actual investors had done over those same thirteen years, and found something strange. The average investor in Magellan had lost money.

Somewhere during that thirteen-year run, thousands of investors watched the fund soar, grew confident, and invested near the peak. Then came an inevitable rough patch. The fund fell, they panicked, and sold, only to watch it recover without them. Many eventually bought back in, but only after it felt safe again, paying higher prices for the same fund they had just abandoned. Lynch never had a bad year that broke the fund. His investors kept breaking their own returns by reacting to every wobble along the way.

This isn’t unique to one fund or one country. It shows up wherever markets meet human nature, and Indian mutual fund investors are not immune to it either. SIPs get paused the moment markets wobble, and withdrawals often happen right when patience matters most. 

So, on 23rd July 2026, SEBI released a consultation paper that seems to be built around exactly this problem, less about chasing better returns, more about helping people hold on to the ones already available to them.

The new consultation paper proposes to introduce a ‘MF only PMS’ framework wherein it proposes to reduce the minimum investment for portfolio management services from ₹50 lakh to ₹25 lakh, provided the client investments are done only in direct plan of mutual fund schemes, ETFs, and SIFs. This could make professional portfolio management accessible to many more investors.

So how will this actually work? Why did SEBI decide to do this now? And what does it mean for investors, and everyone else sitting in this ecosystem? That’s what today’s newsletter is about.

In this edition, we’ll cover:

• Why SEBI felt the need to introduce a new MF-PMS framework
• Who has SEBI actually built this for?
• What exactly did SEBI propose?
• How does Dezerv see this playing out and what does this mean for investors?
• The real value of a portfolio manager
  

Why SEBI felt the need to introduce a new MF-PMS framework

Professional portfolio management reaches a tiny slice of India’s investing public. SEBI’s own consultation paper puts the number of PMS clients at roughly 2.19 lakh, up from 1.5 lakh in April 2019. Against a base of crores of Indian investors, that’s a strikingly small number. The main reason is the ₹50 lakh minimum investment. Interestingly, that threshold hasn’t always been the same.

When PMS regulations were first introduced in 1993, the minimum investment was ₹5 lakh. SEBI raised it to ₹25 lakh in 2012, where it remained for nearly eight years. Then, in 2020, the minimum investment was increased again to ₹50 lakh. 

At the same time, the minimum net worth required to run a PMS was raised from ₹2 crore to ₹5 crore. The reasoning was straightforward. Traditionally, PMS has been associated with direct equity, with many strategies built around concentrated bets on individual stocks. That can make them more volatile than a diversified mutual fund. SEBI has therefore viewed PMS as a product better suited to investors with a higher risk appetite and the financial capacity to absorb losses. That’s the thinking that kept the ₹50 lakh minimum investment in place for so long.

But not every PMS follows that approach. Some portfolio managers build portfolios entirely using mutual funds. Since these portfolios invest only in mutual funds, and not individual stocks, they carry a very different risk profile. Recognising this difference, SEBI has proposed a separate category with a lower minimum investment of ₹25 lakh. The idea is simple: if the investment universe is more restricted and diversified, the entry barrier can also be lower.

Who has SEBI actually built this for?

There’s a sizeable population of Indians who have built meaningful wealth through mutual funds and SIPs, managing their own investments as their portfolios grew over time.

But managing it alone gets harder over time. Mutual funds were built to make investing simpler than picking individual stocks. Yet India now has roughly 2,000 mutual fund schemes chasing about 500 investible stocks. The tool designed to remove a hard decision has slowly turned into a hard decision of its own, and until now, there hasn’t been a formal way for this segment to hand that decision and execution to an external expert to make it.

When regulated wealth management isn’t accessible, people often turn to other sources of advice. A CFA Institute study found that only about 6% of India’s financial influencers are registered with SEBI, up from just 2% a year earlier. Yet a third of them still give specific investment recommendations. Around 8% of investors surveyed said they had lost money after acting on advice from someone who wasn’t regulated. 

SEBI’s new consultation paper aims to make regulated portfolio management available to more investors.

What exactly did SEBI propose?

The new structure is called MF-PMS, short for Mutual Fund only Portfolio Management Services. A regular PMS can invest in stocks, bonds, REITs, foreign securities, and more. MF-PMS strips that down to just one thing: Direct Plan mutual funds, ETFs, and a category called Specialized Investment Funds. Nothing else. That’s a deliberate choice. A portfolio manager under this structure gets discretionary control over the money, meaning they can buy and sell without checking with the client every time. But they can only do it using the same mutual funds any investor could buy on their own.

Two numbers make this proposal work. The minimum investment for a standard PMS is ₹50 lakh. For MF-PMS, it drops to ₹25 lakh. And the net worth a portfolio manager needs to even offer this service drops too, from ₹5 crore to ₹2 crore.

When it comes to switching portfolio managers, whether under Standard PMS or MF-PMS, it doesn’t mean liquidating everything and triggering a tax event. Demat portability with in-specie transfer lets an investor move the portfolio to a new manager without a forced sale.

How does Dezerv see this playing out and what does this mean for investors?

When we launched our mutual fund PMS in 2022, the idea wasn’t easy for people to understand. Competitors, other wealth managers, and even prospective clients would ask the same question: Why would anyone pay a PMS to invest in mutual funds when they could just do it themselves? SEBI’s proposal answers that question. Instead of lowering the minimum investment across all PMS categories, it lowers it only for mutual fund-only PMS, recognising it as a separate category.

We’ve had prospective clients who wanted professional portfolio management but couldn’t access it because they hadn’t crossed the ₹50 lakh minimum. Often, they already had substantial mutual fund portfolios and simply had to wait. A ₹25 lakh threshold changes that.

It also changes how the industry evolves. As more investors become eligible, portfolio managers will have to earn trust through their investment process and long-term performance, not just by offering access. Lower compliance requirements could also encourage more registered investment advisors to become portfolio managers, giving investors more choice.

For investors, the portfolio continues to stay entirely in Direct Plan mutual funds, ETFs and SIFs. A licensed portfolio manager handles asset allocation, fund selection and rebalancing, while investors continue to benefit from the lower costs of direct plans without paying distributor commissions. The investor also gets two layers of oversight instead of one, mutual fund rules at the product level, PMS rules at the portfolio level. And If they decide to switch managers, the portfolio can move without forcing a sale and creating an unnecessary tax event.

The consultation paper also expands what a standard PMS can invest in by allowing international securities and unlisted debt. Investors who want a broader mandate get more flexibility, while MF-PMS offers a simpler option for those who want to build wealth through mutual funds.

Overall, this proposal makes professional portfolio management available to many more investors while giving them more choice in how they want their money to be managed.

The real value of a portfolio manager

Ask most people what a good portfolio manager does, and they’ll probably say picking the right funds. But a recent study by ET Wealth and Crisil suggests that staying invested may matter even more. The study analysed SIP returns across 120 diversified equity mutual funds between January 2011 and March 2026, and found that the probability of losing money fell from 22.7% after one year to 2.3% after five years, and to zero after ten years. It also found that returns became much more consistent the longer an investor stayed invested.

The challenge is that many investors don’t stay invested long enough. When markets fall, it’s easy to stop SIPs or redeem investments. But those periods are often when patience matters the most. This is where an MF-PMS can help. A portfolio manager isn’t just there to choose mutual funds. They can rebalance the portfolio when needed and help investors stay invested through market ups and downs, giving them a better chance of earning the returns that long-term investing has historically delivered.

In summary

For years, mutual funds have become the preferred way for millions of Indians to build wealth. This proposal focuses on what happens after that, how those investments are managed as portfolios grow larger.

The framework is deliberately simple. Investments remain in Direct Plans, fees are capped, and the investment universe is limited to mutual funds, ETFs and SIFs. At the same time, it gives experienced advisors an easier path to become portfolio managers and offers investors with ₹25 lakh a regulated way to access professional portfolio management.

If implemented, it could help fill a gap that has existed for years. Investors who have outgrown managing their own portfolios but don’t yet qualify for a traditional PMS would finally have an option designed for them. In many ways, this is the next step in the evolution of India’s mutual fund industry. The focus is no longer just on making investing accessible, but also on making professional portfolio management accessible.

Disclaimer: This newsletter is intended solely for informational and educational purposes and should not be construed as investment, legal, tax, regulatory or financial advice, or as a recommendation, solicitation or offer to buy or sell any securities or investment products. The views expressed are based on publicly available information believed to be reliable as of the date of publication and are subject to change without notice. References to the SEBI Consultation Paper relate to proposed regulatory changes that are subject to public consultation and final approval by SEBI and should not be construed as the prevailing regulatory framework. Any historical data, illustrations, studies or third-party statistics referred to herein are for informational purposes only and are not indicative of future performance or outcomes. Investment in the securities market is subject to market risks, read all the related documents carefully before investing. The information contained in this document is for general purposes only and is not a complete disclosure of every material fact, terms and conditions. All trademarks, logos, and brand names mentioned are used for identification purposes only. Dezerv Investments Private Limited is a Portfolio Manager with SEBI Registration no. INP000007377 

Seven years later, in Gurugram, another pair of college friends watched their first startup fail. Instead of rushing into the next idea, they spent months riding with AC repairmen to understand why India’s home services market never worked. What they built next now serves millions of households every month.

Almost a decade later, a group of auto drivers in Bangalore asked two people at a fintech company to build them an app that wouldn’t eat into their earnings. Before writing a single line of code, they drove autos themselves to understand the problem.

That same year, a former employee of the original bookstore startup shut down his own company, returned $9 million to investors, and started again. Today, his travel card company is one of India’s fastest-growing startups.

Bet you got at least two of those right.  

Flipkart. Urban Company. Namma Yatri. Scapia. 

Yesterday, we hosted the founders behind each of those stories, Binny Bansal of Flipkart, Magizhan Selvan of Namma Yatri, Anil Goteti of Scapia, and Varun Khaitan of Urban Company, on our webinar. It turned out to be one of the largest we’ve ever hosted, with over 5,000 people tuning in.  In today’s newsletter, I’ll share the best lessons and insights from the session. If you missed the session, consider this your front-row seat. If you joined us live, think of it as the key takeaways worth revisiting.

In this edition:

• How Flipkart hired people before equity had value
• When salary beats equity (and when it doesn’t)
• The right time to take money off the table
• Why selling at the first buyback isn’t always the best move
• What changes after your company goes public
• How one ESOP programme created hundreds of founders

Equity means something very different to a fifth employee than to a five-hundredth. That’s the whole reason we built this webinar as an arc, tracing a company’s journey from its earliest days to a decacorn, one founder per stage.

There was no better place to start than with Binny, who has lived that arc himself — from building Flipkart from scratch to now building Opptra.

Flipkart’s story is one every founder in the room had grown up hearing. What doesn’t get talked about enough is what it took to convince people to join before there was anything to believe in. There was no funding in the early years, so there was no salary to speak of. What Binny and Sachin could offer instead was ownership, a promise that if the company worked, everyone who helped build it would share in that outcome.

That reads as obvious now. It wasn’t then. Most people in 2008 didn’t see equity as something with real value, so joining Flipkart meant betting on a currency almost nobody trusted yet. In hindsight, the people who chose equity turned out to be the ones willing to build for the long term. There wasn’t even a formal ESOP policy in those first years,  just a promise, a number, and paperwork that came later.

That same instinct — reward belief and not certainty, shaped how Flipkart handled failure. When the company acquired Mime360, a music startup meant to become something like an iTunes for India, the product never found its market. But instead of writing off the team, Flipkart moved most of them into other roles, ESOPs intact. Judge people by why something failed, not by the fact that it failed.

The same logic runs through how Binny thinks about buybacks. There’s no clean, universal schedule, a company runs one when it can afford to, and when doing so reinforces the behaviour it wants to reward. Buybacks should happen when the company is healthy enough to afford them.

What that early philosophy compounded into is worth noting. Flipkart’s ESOP programme has since created roughly ₹12,000 crore of wealth for employees. The more interesting number sits one layer beneath that: former Flipkart employees have gone on to start more than 300 companies. Six have become unicorns. Together, they employ over 40,000 people.

That’s the point where equity stops looking like compensation and starts looking like infrastructure. It doesn’t just create wealth for one generation at one company. It creates founders, who create companies, who create opportunities for thousands more people who never set foot inside the original building. 

Seed to Growth: Believing Before There’s Proof

If Flipkart showed us what happens at the end of the journey, Namma Yatri showed us what it looks like at the beginning, when choosing between salary and equity is still a very real decision.

Magizhan Selvan aka Magz thinks about this less like a job decision and more like asset allocation. Salary is the fixed income, it protects the downside and removes financial stress. ESOPs are the upside, reached for only once the downside is covered. The one place this framework breaks from normal investing logic: with equity in your own company, you’re not just holding a position and watching a scorecard. You can move the outcome yourself.

That’s why joining an early-stage startup is not just a financial decision. When Magz joined Namma Yatri, he considered taking his entire compensation as ESOPs. But instead, he kept a small salary to cover his living expenses. The logic was simple: you can believe completely in the company and still need enough cash to wait for that belief to pay off.

The instinct to leave equity alone once granted follows the same logic. Magz didn’t track the ESOPs from his previous company for two years — not something he’d recommend, read your grant letter — but the behaviour is sound. An ESOP behaves like a seed: you grow it by building the product, not by digging it up to check on it, and let the liquidation event arrive whenever it arrives.

The hardest part comes in the years before the company starts to work. Your friends at larger companies are earning more, while you’re putting in longer hours for less. This is what you call the “zone of frustration”, a phase almost every early employee goes through before the rewards start to catch up and I’ve talked about this in detail in my book, The Millionaire Employee.

Before accepting an offer with a large equity component, Magz suggests doing one thing: get to know the founders and the team. Meet them in person if possible. It’s often the best way to understand what you’re really signing up for.

Growth to Unicorn:  When Paper Wealth Turns Real

Anil Goteti has stood on both sides of this stage — first as an early Flipkart employee negotiating his own equity, later as the founder of Scapia, running his company’s first buyback.

He arrived at Flipkart with more equity literacy than most, having worked in the Bay Area, and asked for twice the ESOP he was initially offered. Binny suggested waiting a year or two before revisiting the conversation. In the end, Anil never needed to bring it up again. Flipkart made sure its employees were well rewarded.

The belief that actually reshaped how he thinks about equity came earlier, at Qualcomm, his first job. He held stock for five years, planning to cash it in for an MBA. Then he resigned,  and inside his three-month exercise window, a lawsuit hit the company. The stock fell underwater. He walked away with a tenth of what he’d planned on. 

Since then, Anil has exercised every ESOP he’s received. His advice is simple: don’t wait for the perfect moment. Sell a little over time instead of trying to time it all at once.

The same discipline shapes how he decides when Scapia should run a buyback: whether the milestones justify it, whether the company can afford it, and who’s actually earned it. Not a founder’s unilateral call,  it sits with the founder and the board together.

Unicorn to Decacorn: Staying Hungry After You’re Already Rich

Urban Company’s Varun Khaitan sits at the stage every founder eventually has to reckon with: what happens to ambition once the wealth is no longer theoretical.

When Urban Company went public last year, the IPO didn’t feel like the finish line for Varun. Two days later, the team was back at work, focused on what came next. That was the biggest takeaway for me. Companies that keep growing don’t treat milestones as destinations. They simply move on to the next problem.

What changes after listing isn’t ambition. It’s the emotional texture of the wealth itself. Being told your equity is worth something and watching that number move on a screen every day are two different experiences. On the days it dips, people feel like they’ve “lost” money they could have taken off the table earlier, even though nothing about their underlying ownership changed. Watching your wealth go up and down every day can make even long-term investors think short term.

The mechanical shift underneath is precise: private employees can’t sell even if they want to, so a founder’s job before listing is mostly narrative, to get people to believe in the destination. Public employees can sell whenever the market allows, and no founder can stop that. So the job afterward becomes coaching people to think in four- or five-year horizons instead of daily price movements.

Urban Company has lived this directly, with its own stock swinging 20 to 30% at points, and the actual day-to-day work is keeping longtime employees calm through cycles they’ve already lived through once, and getting newer employees to stop checking the price altogether.

In Summary

Underneath all of it sits one requirement that hasn’t changed at any stage of this arc: the equity only makes sense if you believe in where the company is headed years out, not where it trades today. That’s the one thing every founder here, from a startup with no funding to a public company with a ticker, was actually asking their people to do. Believe first and the wealth will follow.

More from the session, dissected in detail,  find the full breakdown here.

A country that had been independent for barely fifteen years, still counted among the poorest in the world, decided to build a space program out of a church – one that would eventually become ISRO. The scientists were mocked in the streets for it.

Sixty-four years later, on the evening of July 18, 2026, people gathered on a coastline not far from that same stretch of India, this time at Sriharikota, watching a very different kind of rocket lift off. It was called Vikram-1. And unlike everything that had launched from Indian soil before it, this one wasn’t built by the government. 

It worked on the first try. The payload settled into a stable 450-kilometre orbit exactly as planned. The mission was named Aagaman – arrival. With that single flight, India became only the third country in the world, after the United States and China, where a private company has independently put a satellite into orbit.

The visionaries behind it were two former ISRO engineers, Pawan Chandana and Bharath Daka, who quit their jobs to start Skyroot, the company that built the rocket. At the time, what they were attempting wasn’t just difficult, it was illegal. There was no policy in India that allowed a private company to launch anything into space. They spent years designing a rocket before there was even a legal way to fly it.

This week’s newsletter tells the story of Skyroot, explores India’s private space industry, explains how a rocket business makes money, and asks whether India can build its own SpaceX.

In this edition:

• How 400 space startups appeared in 12 years
• How two ISRO engineers with no capital talked their way into ₹10 crore
• How a rocket company actually makes money
• Can Skyroot become the SpaceX of India?
• Is a successful launch the same as a successful business?
• As a wealth creator how should you look at it? 

The rise of India’s private space-tech industry

In 2014, there was exactly one organisation in India doing meaningful space work: ISRO. By March 2026, that number had crossed 400 private space startups.

That jump didn’t happen on its own. In 2023, the Indian Space Policy opened the entire space value chain – manufacturing, launch, applications, everything downstream –  to private companies for the first time.  A new regulator called IN-SPACe was set up as a single window for approvals, instead of forcing startups to navigate multiple government departments. As of mid-2026, it has registered over 4,500 organisations and cleared 133 launch and operating authorisations.

The government has also built capital specifically for this sector. There’s a seed fund offering grants of up to ₹1 crore.  A ₹1,000 crore venture fund, deploying ₹100-250 crore a year through 2030 and a separate ₹500 crore fund covering up to 60% of a startup’s technology costs. 

On the private side, Speciale Invest and Starburst are the most active investors in Indian space tech by deal count. Speciale Invest recently raised a ₹1,400 crore deep-tech fund. Strategic investors are joining in too, with Reliance backing Digantara and HDFC Bank investing in Agnikul Cosmos. Global investors are already here as well, with GIC backing Skyroot and Google investing in Pixxel.

With more than 400 startups and billions of rupees flowing into the sector, it’s worth looking at where the money has gone so far. Not all of these companies do the same thing. Some, like Skyroot and Agnikul, build rockets. Others, like Pixxel and Dhruva Space, build satellites and space applications. Across the ecosystem, Skyroot has raised the most funding so far. 

How big is this opportunity, really?

India’s space economy was worth $8.4 Bn in 2022. That’s about 2% of the global market. The government wants to grow it to $44 Bn by 2033, taking India’s share to 8%. To get there, the industry needs to grow at 16% a year for the next decade.

And the opportunity goes far beyond rockets.The government splits the space economy into three broad buckets, and launch, the business Skyroot is in, is actually one of the smaller ones by value, growing from $0.72 Bn to $3.5 Bn by 2033. That doesn’t make rockets any less important. Nothing else in the space industry works without them. But the bigger opportunity isn’t the rocket itself. It’s everything that happens after a satellite reaches orbit.

That’s where private companies like Skyroot Aerospace, Pixxel, Dhruva Space, Digantara, and SatSure come in, each building a different part of India’s space ecosystem.

Private investment in Indian space reached about $111 million in 2022, with most of the money going into early-stage startups building rockets and satellites. The next push is to bring in more venture capital, private equity, angel funding, and easier bank financing as these companies grow.

The model is simple. ISRO develops the technology, shares its infrastructure and expertise, while IN-SPACe creates the policies and approvals. Private companies then take that technology and turn it into sustainable businesses.

How two engineers with no capital built a rocket company

Chandana studied mechanical engineering at IIT Kharagpur and joined ISRO in 2012 at a starting salary of ₹35,000 a month. He worked on the LVM3, India’s largest rocket, which later carried Chandrayaan missions toward the Moon. His flatmate, Naga Bharath Daka, his flatmate and now co-founder, was an electronics engineer. From the start, Chandana focused on propulsion and structures, while Daka handled electronics and software.

In 2018, they left ISRO to start Skyroot Aerospace. They had no fundraising experience. Chandana has said they had to Google terms like “what is fundraising” and “what is a term sheet” before meeting investors. They didn’t even have a pitch deck or a website. A friend sent a LinkedIn message to Mukesh Bansal, the founder of Myntra and Cure.fit. Bansal responded, flew down, and committed to invest within an hour of meeting them.

That first cheque brought in around ₹10 crore, one of India’s largest deep-tech seed rounds at the time and gave Skyroot nearly 2.5 years of runway. The founding team had just 15 people, most with no ISRO background. They came from aviation, manufacturing, and fresh engineering colleges, joining largely through word of mouth.

After Vikram-S succeeded in 2022, GIC, Singapore’s sovereign wealth fund, backed a $50 million round, the largest the Indian space sector had seen. Skyroot has since crossed unicorn status, one of India’s fastest deep-tech companies to get there. 

How does a rocket company actually make money?

Think of Skyroot as a logistics company for space. Companies that own satellites pay Skyroot to carry them into orbit, much like businesses pay a shipping company to transport cargo.

Why do they pay for this? Because their businesses rely on satellites for two things: communication and Earth observation.

Communication covers internet, phone, and broadcast services in places where fibre networks can’t reach. Earth observation involves satellites capturing images and data that help farmers monitor crops, governments respond to floods and wildfires, businesses plan infrastructure, and weather agencies improve forecasts.

A single Skyroot launch costs around ₹100 crore. At 20 launches a year, that could translate into ~₹2,000 crore in annual revenue. The opportunity exists because satellites have become much smaller. Today, over 90% of satellites launched weigh under 300 kg, creating demand for smaller, dedicated rockets instead of giant ones.

That’s where Skyroot fits in. Vikram-1 is Skyroot’s biggest proof so far. It can carry payloads of up to 350 kg, uses an all-carbon composite structure and a 3D-printed engine, and reached orbit on its very first attempt.

Building it cost under $30 million, compared with the roughly $1 billion historically spent on similar global programs. Skyroot expects 70-80% of demand to come from international customers, and its factory can produce one Vikram-1 rocket every month.

Can Skyroot become the SpaceX of India?

Skyroot is often compared to SpaceX, but they’re solving different problems. As founder Pawan Chandana puts it, “SpaceX is like a metro. We’re more like a cab.” While SpaceX uses large rockets to carry many satellites to common orbits, Skyroot focuses on taking a smaller number of satellites directly to the exact orbit a customer needs.

The scale is also very different. SpaceX completed around 165 launches last year, generating roughly $5 billion from launches and $12 billion from Starlink. Around 70-80% of those launches were for its own Starlink satellites. A closer comparison could be Rocket Lab, the New Zealand company that built a successful launch business without a national space program. Even then, it took nearly 20 years to reach its current scale.

SpaceX’s journey shows just how hard building a rocket company is. Its first rocket, Falcon 1, failed three times before finally reaching orbit on the fourth attempt, nearly pushing the company into bankruptcy.

Skyroot’s start has been very different. Vikram-1 reached orbit on its very first launch, and the company is targeting a growing market by taking satellites directly to the exact orbit customers need.

But calling it “the next SpaceX” has its limits. Much of SpaceX’s success comes from Starlink, which creates steady demand by filling most of its own launches. SpaceX also keeps costs low by reusing rockets. Skyroot plans to build reusable rockets too, but that technology is still being developed.

Reaching orbit puts Skyroot in an exclusive club with fewer than 10 organisations worldwide. Turning that achievement into a profitable, long-lasting business is a much harder journey.

But is a successful launch the same as a successful business?

A successful launch is a huge milestone, but it’s only the beginning. Skyroot needs several more successful missions before customers see it as a reliable launch provider. In this industry, trust is earned one launch at a time.

Demand is another challenge. Its factory can build 12 Vikram-1 rockets a year, but India doesn’t launch that many small satellites, so most of Skyroot’s customers will need to come from overseas.

Competition is intense. In India, it competes with ISRO’s SSLV and PSLV. Globally, it faces players like SpaceX, whose rideshare launches are often cheaper, even if they don’t offer custom orbits. Rocket launches are also a tough business. They require huge amounts of capital, operate under strict safety and space debris rules, and leave little room for error. Unlike ISRO, which serves a national mission, Skyroot also has to build a profitable business.

The investment opportunity in India’s space economy

Skyroot isn’t building India’s space industry alone. Companies like Pixxel, Agnikul Cosmos, GalaxEye, Dhruva Space, and Bellatrix Aerospace are building different parts of the ecosystem, from rockets and satellites to imaging and propulsion.

None of them are listed yet, so you can’t invest in them directly. For most HNIs, the closest option is Alternative Investment Funds (AIFs) that invest in deep-tech startups. Funds like Speciale Invest, which has backed several space startups, are one route, though they typically require a minimum investment of ₹1 crore.

The opportunity also goes beyond rockets. Areas like in-space services (such as removing space debris), satellite services, and space-based computing could generate recurring revenue without having to launch rockets themselves.

Finally, keep an eye on the government’s role. Almost every founder in the sector wants the government to become an early customer, not just a regulator, much like NASA supported SpaceX in its early years. If that happens in India, it could become one of the biggest catalysts for the country’s private space industry.

He wasn’t alone. Across Korea that day, ordinary investors were logging into stock forums to compare losses like casualties from the same storm. 

Some had gone all-in on a Samsung product hoping for a rebound. Others had borrowed money to invest, only to receive margin calls from their brokers as prices collapsed. Margin loans across the country had already hit a record high just weeks earlier, as more and more people borrowed to get in on the excitement.

As of July 13th, over 1.2 million retail investors in Korea had hit margin call levels, about one in every 30 adults. The forced liquidation rate had jumped from 2.1% to 10% in just weeks. What began as one person’s bad night had become a nationwide crisis.

And it had all started with just two stocks. Samsung and SK Hynix, the two companies that anchor Korea’s entire stock market, had fallen nearly 12% on June 23rd, their worst since the 2008 financial crisis, dragging the KOSPI down almost 10% with it.

Over the following weeks, the Kospi fell about 25%, the Korean Won hit its weakest level since 2009, and the exchange had to halt trading several times after sharp market declines. By mid-July, South Korea’s President Lee Jae Myung was publicly calling the stock market “unstable,” admitting it would take time to settle.

For a market known for disciplined retail investors, sophisticated financial markets, and some of the world’s leading technology companies, it was an extraordinary turn of events. It’s not the kind of market you’d expect to fall apart overnight.

So what actually caused all of this? 

It all traces back to a financial product most Indian investors have never even heard of: the leveraged ETF. And here’s the reassuring part. SEBI has never allowed this product for retail investors in India, and there’s no sign that’s changing anytime soon. Watching what unfolded in Korea, it’s hard not to feel grateful for that.

This week, we’re getting into what actually happened in Korea, what these products are, how they work, the risk they carry, and why India seems to have avoided a mistake that Korea is still cleaning up.

In this edition:

• What a leveraged ETF is, and how it’s designed to work
• Why it’s so different from the ETFs already in your portfolio
• Why it works against long-term investors, and how Korea’s markets are still feeling that
• Why SEBI has kept leveraged ETFs out of India
• What this teaches us about using leverage wisely

What is a leveraged ETF, and how does it work?

Imagine someone offers you a simple deal: whatever the market does today, you’ll get double the return. If the market goes up 1%, you make 2%. If it falls 1%, you lose 2%.

That’s essentially what a leveraged ETF does. It aims to deliver 2x or 3x the daily return of a stock or an index.

To make this happen, the fund doesn’t buy twice as many actual shares. Instead, it uses complex financial contracts like swaps and futures, agreements that track the stock’s price, letting the fund gain massive market exposure without needing to own all the physical shares directly.

This is the idea South Korea’s regulators reached for in late May. For years, retail investors had been moving money into US markets in search of better returns. To bring some of that money back, regulators launched 2x leveraged ETFs on Samsung and SK Hynix, two of the country’s best-known companies.

The idea worked, at least at first. Within weeks, products like the KODEX SK Hynix Single-Stock Leverage ETF had pulled in billions of dollars. By the middle of the year, assets in Korea’s leveraged ETFs had reached a record $45 billion. Retail investors made up 92% of the investors, and trading in these funds and the two underlying stocks accounted for more than 70% of activity in Korea’s stock market.

But the real danger in a leveraged ETF comes down to one rule: It has to reset itself every single day. To keep delivering exactly double the return tomorrow, the fund is forced to buy more of the stock when its price rises, and sell it off when the price falls. That’s a lot of buying and selling happening at the end of every trading session, and once enough money is doing this on the same stock, that daily churn stops just reacting to the price. It starts moving it.

That’s exactly what happened in South Korea. Every sharp move in Samsung or SK Hynix triggered even more buying or selling through these ETFs, amplifying the swings.

Before the launch, the index moved in a fairly normal range. Afterwards, the swings got bigger and far more frequent. To make matters worse, regulators approved both Bull ETFs, which profit when stocks rise, and Bear ETFs, which profit when they fall. Investors were placing leveraged bets in both directions on the same two stocks. A product that was meant to keep investors at home ended up making the market far more volatile.

Why it’s so different from the ETFs in your portfolio

Most ETFs sitting in your portfolio right now are refreshingly simple. Buy a Nifty 50 ETF, and somewhere behind the scenes, the fund actually holds real shares of Reliance, HDFC Bank, Infosys, and the rest. Your investment just moves alongside them, nothing more complicated than that.

A leveraged ETF plays by a completely different set of rules. Here’s how the two stack up side by side:

How leveraged ETFs become risky over time

Let’s take a simple example. You invest ₹100 in a stock. It goes up 2% on Monday, so your investment becomes ₹102. On Tuesday, it falls 1.9%, bringing it back to about ₹100.06. After two days, you’re almost back where you started.

Now imagine you invested the same ₹100 in a 3x leveraged ETF. Monday’s 2% gain becomes 6%, so your investment grows to ₹106. Tuesday’s 1.9% fall becomes 5.7%, bringing it down to about ₹99.96.

The stock ended slightly higher than where it started. The leveraged ETF ended slightly lower. That’s because it resets every day, and small, repeated swings can slowly turn into permanent losses over time, a phenomenon known as volatility decay.

This is what investors in South Korea experienced. SK Hynix, the stock itself, continued to rise after the leveraged ETFs were launched. But the leveraged ETF tracking it was down by about 45%. The company kept doing well but investors in the leveraged ETF didn’t.

A similar story played out during the 2020 COVID market crash. The S&P 500 fell by about 34% from its peak to its trough. Because of daily compounding and amplified losses, UPRO, a real 3x leveraged S&P 500 ETF, fell by roughly three-quarters of its value over that same stretch.

By August 2020, the S&P 500 had already recovered all of its losses and was making brand-new highs. UPRO was still down more than 30% from its previous peak. The underlying market had fully recovered, but the leveraged ETF hadn’t.

And there’s one more risk. If the underlying index falls by just over 33.3% in a single day, a 3x leveraged ETF is effectively wiped out to zero. Once an investment hits zero, there might be nothing left to recover. 

That’s why leveraged ETFs are fundamentally different from ordinary investments. With standard index funds, time is your ally, and it helps you recover from downturns. With leveraged ETFs, the longer you stay invested through a highly volatile market, the harder that recovery becomes.

Why doesn’t SEBI allow this in India?

SEBI has never approved leveraged or inverse ETFs for retail investors, and there’s no sign that’s changing anytime soon. It might look like an obvious cautious regulatory approach, but there’s likely more to it.

India has already seen what happens when retail investors get easy access to leverage. Take the futures and options market. It’s a different product, but it’s built on the same idea of using leverage to make bigger bets.

SEBI’s own study found that 91% of individual traders lost money in FY24, and 93% lost money in FY25. Together, they lost more than ₹2 lakh crore. Even after losing money, more than three out of every four traders came back and kept trading.

When you compare that with South Korea’s leveraged ETF story, you can see why regulators would be concerned. Both products appealed to retail investors with the promise of bigger returns, and in both cases, the majority ended up losing money.

What should you actually take from this?

Leverage isn’t inherently bad. It just isn’t designed for most long-term investors. Leveraged ETFs can be genuinely useful for an active trader with a short-term view. They let you amplify returns, hedge positions, and gain leveraged exposure without opening a separate margin account.

But those benefits come with equally large risks.

The same leverage that boosts gains can magnify losses. Daily resets can slowly erode returns, even when the market isn’t crashing. And in extreme cases, a single bad day can wipe out your investment. When you look at the pros and cons together, one thing becomes clear: these products are built for people who watch the market every day, not for investors who buy and hold for years.

In summary

South Korea learned about the risks of leveraged ETFs the hard way, through a real crash, billions in real losses, and a regulator publicly admitting he wished he’d stopped it sooner. India may not have needed to learn that lesson at all. SEBI chose not to open these products to retail investors, and in hindsight, that decision looks like exactly the right call.

Leverage has its place, but it isn’t meant for everyone. For most investors, wealth is still built the same way it always has been: by staying invested, being patient, and letting compounding do the quiet work it’s always done best.

Before I sign off 

Last week, I launched my book, The Millionaire Employee. It’s built around an idea I feel strongly about: equity and ESOPs are becoming one of the biggest wealth creators for India’s professional class, not just its founders. Yet most employees still don’t have a good enough understanding of how they work or how to make the most of them. As a result, they’re often missing out on significant wealth.

If you’ve ever wondered how to evaluate an ESOP offer, when to hold, when to exit, or how ownership can change your financial trajectory, this book walks through all of it.

You can grab a copy here

Disclaimer – Investment in the securities market is subject to market risks, read all the related documents carefully before investing. The information provided herein is intended solely for educational purposes and should not be construed as solicitation, advertising, or providing any financial or investment advice or an offer to buy or sell any financial instruments. Readers are advised to consult with their financial advisor before making investment decisions based on the information provided herein. In this material, Dezerv has utilized information through publicly available sources, and other data deemed to be reliable. While reasonable care has been made to present reliable data in this article, Dezerv does not guarantee the accuracy or completeness of the data. Dezerv, along with its directors, employees, or partners or any of its affiliates, shall not be held liable for any loss, damage, or liability arising from the use of this document. All trademarks, logos, and brand names mentioned are used for identification purposes only and do not imply endorsement or recommendation.

Around three years ago, I started writing about ESOPs online, mostly because I kept meeting accomplished people with meaningful equity and no real framework to think about it.

If you’d told me then that this would turn into a book, I’d have laughed. But for the last two years, I’ve spent most Sundays pulling everything I’d learned into one place, because no ESOP holder in India should have to piece this together from scattered blogs, random reddit threads or feel awkward asking their seniors basic questions.

That instinct today became a book called The Millionaire Employee.

You see, ESOPs aren’t an abstract subject for me. They’re the reason I’m sitting here writing this at all. I joined IIFL Wealth (now 360 One WAM) in 2008, and equity was something I barely thought about. Then, in 2019, it turned into real money, and the first thing I did was pay off my home loan. If you’ve ever had an EMI eating into your salary every month, you know what that relief feels like.

Paying off that loan did more than clear a debt. It gave me the confidence and capital to start Dezerv with Vaibhav and Sahil, which today has over 600 employees.

For any employee, ESOPs could end up creating a lot of wealth. But the outcome depends on a few important decisions, understanding what you’ve been granted, knowing when to exercise, when to sell, and when to hold. Most people never learn these things until it’s too late.

That’s why I wrote this book, to put everything you need to know about ESOPs in one place.

And today, I’ll walk you through it, chapter by chapter, giving you a glimpse of what’s inside and why each chapter exists. If you have ESOPs today, or you might get them in the future, this is something you need.

Let’s begin.

Chapter 1: The rewards of ownership

ESOPs weren’t invented by a startup. They came out of the Great Depression, when investment banker Louis Kelso structured the first leveraged ESOP buyout for Peninsula Newspapers Inc. in 1956, letting employees buy the company using its own future profits. The US formalized the structure through ERISA in 1974.

In India, Wipro introduced ESOPs in the mid-1980s, but Infosys is really the torch-bearer, launching its Employee Stock Offer Plan in 1994 and setting the template India’s IT and financial sectors would follow for the next two decades.

Today, ESOPs in India represent a ₹14.2 lakh crore opportunity, about 22% the size of the mutual fund industry. IPOs and buybacks have created life-changing wealth overnight for most Indian employees. Swiggy’s IPO created over $1 billion for employees, and companies like Zomato, Nykaa, Policybazaar, Paytm, and Flipkart have done the same for thousands more.

We saw something similar at Dezerv during our 2024 ESOP buyback. Some employees sold their shares, while others chose to hold on because they believed the company’s future was worth more than the cash they could receive that day. 

That’s what ESOPs really are. They’re not just another part of your salary, they’re a chance to own a part of the company and share in the value you help create. 

Chapter 2: More than a salary : How ESOPs build wealth

I know dozens of people who built real wealth through ESOPs, yet very few truly understood what they owned. Most only learn the details when they’re forced to make a big decision, and by then it’s often too late to change the outcome.

Here’s the simplest way to think about an ESOP. It’s like booking a Coldplay ticket months in advance at today’s price. You pay ₹1,000 now to lock that price in. If tickets later sell for ₹5,000, you’ve won big. If they drop to ₹500, you simply skip it and buy a cheaper one instead. That locked-in price is your exercise price, sometimes also called the strike price. 

The actual ticket price on the day of the concert is the share’s fair market value (FMV). Your profit is just the gap between the two.

Face value is a completely different number, and it’s the one most people confuse. It’s just a small, fixed number stamped on the share when the company was first set up, usually ₹10. It never changes, no matter how big or valuable the company becomes later. 

Then comes vesting, the process of earning your ESOPs over time. Most companies follow a four-year schedule with a one-year cliff. A cliff is simply the minimum time you must stay before earning anything. A one-year cliff followed by monthly vesting is common, but the schedule matters. Two employees with almost the same tenure can end up with very different vested shares simply because their companies structured vesting differently. 

So, reading the fine print matters more than people realize. To make this easier, here’s a snippet from the book that shows a simple comparison of employee-friendly and unfriendly ESOP terms, so you can benchmark your own offer.

One more thing to remember: ESOP is often used as a catch-all term, but companies may actually be offering different kinds of equity.

• ESOPs / Stock Options: You get the right to buy shares later at a fixed price (exercise price).
• RSUs: You receive actual shares when they vest; no purchase required.
• ESPPs: You can buy company shares at a discount, often 10–15% below market price.
• Phantom Shares: You don’t own shares, but get a cash payout linked to the company’s value.

Each works differently and can lead to very different risks, rewards, and taxes. If your offer letter just says ESOP, ask which type of equity it actually is.

Chapter 3: Grant phase: The first step to ESOP ownership

Every ESOP eventually moves through four stages: grant, vesting, exercise, and finally, the sale.

Grant is where all of it begins, and it’s also the stage people take least seriously, mostly because it feels like a formality rather than a decision. It rarely is.

A young COO I know accepted a lower salary in exchange for ESOPs at an early-stage startup. He spent more than two years helping build the company, but his grant letter never arrived. Every time he asked, he was told it was “just a formality.” When he eventually left, he had nothing.

The lesson is simple: if you don’t have a signed grant letter, you don’t have ESOPs.

Your grant letter should clearly mention the number of options, exercise price, vesting schedule, exercise window, and other key terms. These details decide what you own, when you can buy your shares, and what happens if you leave the company.

This chapter also decodes a real grant letter, so you know how to read a grant letter and what to check before you sign.

Chapter 4: The exercise decision: When to convert your ESOPs.

A friend once had just 90 days after leaving his company to decide whether to exercise his ESOPs. The exercise cost and taxes came to nearly ₹15 lakh, almost all his savings.

Exercising an ESOP involves five steps: confirm your options have vested, submit the exercise request, pay the exercise price, pay tax on the difference between the exercise price and the FMV, and receive your shares. For example, if you exercise 1,000 options at ₹50 when the FMV is ₹200, you’ll pay ₹50,000 to buy the shares and owe tax on a ₹1.5 lakh perquisite gain.

There’s a full decision-factor checklist for exercising ESOPs in the book to help you think this through calmly instead of under a countdown. 

The real question is whether the risk is worth it. Startup valuations can fall sharply, as we’ve seen with companies like Byju’s, Oyo, and PharmEasy. Before you exercise, ask yourself if the potential reward is worth the capital and tax you’ll have to commit.

Chapter 5: From paper to prosperity : Navigating Liquidation

ESOP wealth becomes real money in four ways: buybacks, strategic sales, private secondary sales, and IPOs. Each offers liquidity differently

A buyback is when the company buys shares from employees, giving them a chance to cash out. Razorpay, for example, has completed four buybacks worth over $85 million, benefiting nearly 650 employees.

A strategic sale is when another company acquires the business. Employee shares are usually bought as part of the deal, and sometimes even unvested ESOPs vest early. Flipkart’s sale to Walmart led to about $800 million in ESOP payouts.

A private secondary sale lets employees sell their shares to private investors before an IPO or acquisition, without waiting for a larger liquidity event.

An IPO lets employees sell shares on the stock market, but not always immediately. Lock-in periods often apply. When Zomato’s lock-in ended in 2022, the stock fell over 11% in two days as employees and early investors sold their shares.

But before you celebrate that liquidity event, there’s one stakeholder that always gets paid first: the tax department.

Chapter 6: The hidden cost: Mastering ESOP taxation

One of the biggest surprises with ESOPs is that you’re taxed twice, at two different stages.

The first tax comes when you exercise your options. The difference between the FMV and your exercise price is treated as a prerequisite, added to your salary, and taxed at your income tax slab, even though you haven’t sold a single share. If your gain is ₹1.5 lakh and you’re in the 30% tax bracket, you’ll owe tax before you’ve received any cash.

The second tax comes when you sell the shares. Sell soon after exercising, and the gains are taxed as short-term capital gains. Hold them longer, and you may qualify for long-term capital gains tax of 12.5%, which can be significantly lower. Understanding these two tax events, and planning for them, can make a meaningful difference to your final returns.

Chapter 7: Timing your ESOP exercise

“Should I exercise now or wait?” is probably the most common ESOP question I get. And the answer depends on the numbers.

Take the same 1,000-share grant with a ₹50 exercise price, eventually sold at ₹8,000 ten years later. Exercising every year as your shares vest, exercising everything after full vesting, or waiting until a liquidity event all lead to different outcomes. In this example, waiting until the liquidity event actually delivers the highest post-tax return because you avoid locking up your money and taking valuation risk.

But there’s no one-size-fits-all answer. Early exercise gives you ownership sooner and can spread out taxes, but it also means committing your own money years before you know if there’s an exit. Waiting reduces that risk, but only if a liquidity event is likely.

I’ve built an ESOP Exercise Strategy Calculator into the book. You can enter your own grant details, tax bracket, and assumptions to compare different strategies and see what works best for you.

Chapter 8: Beyond the windfall: Transforming ESOP liquidity into financial freedom

When an early Urban Company employee received his ESOP payout, it was more than he had saved in all his previous working years combined. He paid off his home loan within days. “It wasn’t just financial freedom,” he told me, “it was mental freedom.”

What you do next matters just as much as the payout itself. Some people diversify across different assets, reducing the concentration risk they had in a single company. Others put most of the money into one property or one stock, replacing one concentration risk with another.

The size of the payout was never the differentiator I observed. What decided the outcome, every time, was the plan for the first few months after the money landed, before the excitement wore off and old spending habits crept back in.

Chapter 9: Decoding an ESOP offer

Not all ESOP offers are equal. Before you accept one, look at six things: the founders, the company’s potential, the cash-versus-equity tradeoff, the ESOP policy, how likely a liquidity event is, and the size of the market.

I learned the cash-versus-equity tradeoff the hard way. Early in my career, I chose cash over ESOPs because I had a home loan. Those ESOPs would be worth around ₹16 crore today.

It’s also worth checking the fine print. A one-year cliff, four to five years of vesting, and a 10-15% ESOP pool are common benchmarks. If your offer looks very different, ask questions before you sign.

In the book, I use this six-point framework to compare two job offers, one with higher cash and lower ESOPs, the other with lower cash and higher ESOPs, to show how to evaluate them step by step. I’ve also included an ESOP Evaluation Score Sheet so you can assess your own offer instead of relying on gut feel.

A note to every ESOP holder

When I joined IIFL Wealth in 2008, the markets were in a recession, and ESOPs were just something I didn’t really understand. A lot has changed since then. Startups are bigger, ESOP policies are more employee-friendly, and we’ve seen many success stories. 

But we’ve also seen the other side. Companies like Byju’s showed that paper wealth can disappear just as quickly as it’s created. ESOPs can create significant wealth, but they also come with risks. Taxes, vesting, lock-ins, and falling valuations can all affect your payout 

Here’s what’s concerning. In a survey of nearly 1,000 employees across corporate India, most people admitted they didn’t fully understand their vesting, taxes, or what would happen to their ESOPs if they left. Even employees holding over ₹1 crore worth of ESOPs said they were only somewhat confident about how their equity worked.

So if you take away one thing from everything I’ve shared today, let it be this: you owe it to yourself to actually understand how your ESOPs work, whether you just received your first grant or you’re sitting on a windfall you’ve been waiting years for.

That’s the one thing I hope this book helps with.

The Millionaire Employee is out now. Get your copy here

Disclaimer – The information provided herein is intended solely for educational purposes and is as on date of the document. In this material, Dezerv has utilized information through publicly available sources, and other data deemed to be reliable. Readers are advised to consult with their financial advisor before making investment decisions based on the information provided herein. All trademarks, logos, and brand names mentioned are used for identification purposes only and do not imply endorsement or recommendation. Dezerv, along with its directors, employees, or partners or any of its affiliates, shall not be held liable for any loss, damage, or liability arising from the use of this document.

For decades, owning even a small share of it was out of reach for most people. To benefit from the rent these buildings generated, you had to own the building itself. That meant investing large amounts of money, finding tenants, managing the building, and waiting years for returns.

By the mid-1980s, even the Rockefeller family faced a challenge. Ownership had been split among nearly 90 family members, and the property no longer generated enough cash for everyone. They needed liquidity, but they couldn’t sell Rockefeller Center one floor at a time.

The solution was to change how it was owned. A structure created by Congress in 1960 allowed investors to buy small units in a trust that owned large commercial properties, earn a share of the rental income, and trade those units like shares on a stock exchange.

The Rockefellers seized the opportunity. They created a publicly traded trust called Rockefeller Center Properties and took it public in a $750 million IPO. For the first time, a school teacher in Ohio could own a small stake in the same address that housed NBC and some of America’s largest companies. 

That structure is called a Real Estate Investment Trust, or REIT. India introduced its own REIT regulations in 2014, and the first listing followed in 2019. Today, you can own a floor in buildings where companies like JP Morgan and IBM pay rent, for roughly the price of a dinner.

In this edition, I will take a deep dive into how REITs work, the state of India’s REIT market today, and how to think about them as a portfolio asset.

Here’s what we’ll cover:

• What a REIT actually is, and the problem it was built to solve
• The types of REITS in India and how to invest in them
• How India’s REIT market got built
• What India’s REIT market looks like today
• Why the underlying real estate keeps getting stronger
• Where REITs fit in your portfolio and how to evaluate them
• The risks worth knowing 

Let’s begin.

What is a REIT and how does it work?

A REIT works much like a mutual fund, except it owns buildings instead of stocks. It raises money through an IPO, uses that capital to buy income-generating properties like office parks, malls, or warehouses, and earns rental income from tenants. That income is then distributed to investors, while the REIT itself trades on the stock exchange.

That simple structure solves many of the biggest problems with owning property.

1. Ticket size: A single unit replaces the crores a whole building would otherwise demand.
2. Liquidity: The units are listed on the exchange, so they can be sold on any trading day instead of waiting months for a buyer. 
3. Diversification: One unit spreads your money across many properties and tenants instead of just one flat and one tenant. 
4. Transparency: NAV and holdings are disclosed regularly, unlike private property where the actual value is mostly a guess. 
5. Regulation: SEBI governs the entire structure, mandating that the trust has to distribute at least 90% of that income back to unitholders every quarter.  

So, unlike owning a flat, the REIT handles tenants and rent collection for you, then distributes most of the rental income to unitholders. Many tenants are large companies like JP Morgan and IBM on long-term leases.

REITs in India fall into three broad categories

1. Equity REITs own income-generating properties like offices, malls, warehouses, hotels, and data centres. They earn money from the rent businesses pay to operate there, and investors receive a share of that income. It’s a way to own commercial real estate without managing the buildings yourself.

2. Mortgage REITs, or mREITs, never own a single brick. They lend against real estate instead, buying or originating mortgages and earning their return on the interest those loans generate.

3. Hybrid REITs sit in between, holding a mix of physical properties and real estate debt, picking up income from both rent and interest.

How to invest in a REITs

There are three ways to invest in REITs in India, and the entry point is different for each.

• Buy listed REITs: Purchase units through a Demat account, just like stocks. You can buy as little as one unit, with prices typically ranging from ₹100-500.
• IPOs: When a new REIT lists, the minimum application amount is usually ₹10,000-15,000.
• Mutual funds: Some mutual funds hold REITs in their portfolios, with SIPs starting from ₹500.
• SM REITs: Introduced by SEBI in 2024, these invest in smaller commercial properties (₹50–500 crore) and require a minimum investment of ₹10 lakh.

India’s REIT journey:  Embassy, the tower that started it all

Seven years ago, there was one REIT on an Indian exchange. Today, five of them trade every single day, holding office towers and shopping malls worth lakhs of crores between them. To understand how it got here, it helps to meet the buildings, and the names behind them.

Every market needs a first mover to prove the whole idea actually works, and in India that mover was a company called Embassy. In 2019, Embassy Office Parks became India’s first listed REIT, walking in with 33.3 million square feet of office space and instantly becoming Asia’s largest office REIT by area. Once Embassy proved the model worked, others followed quickly.

Mindspace Business Parks listed the next year, in August 2020. Brookfield India came in February 2021, the same year SEBI reduced the minimum application size at IPO from ₹50,000 to ₹10,000-15,000 and cut the minimum trading lot from 200 units to one, making REITs accessible to a far wider set of investors.

Nexus Select Trust arrived in 2023, India’s first retail REIT, built around malls rather than offices. And in 2025, Knowledge Realty Trust, backed by Sattva and Blackstone, oversubscribed 13 times on its final bidding day, the most subscribed REIT IPO India has seen.

Around the same time, regulators reclassified REITs as equity instruments altogether, putting India’s REITs on the same footing as how the rest of the world already treats this asset class.

What India’s REIT market actually looks like today?

Today, those five  listings have grown into a market worth over ₹1.8 lakh crore, with market capitalisation nearly tripling in the last two years. The number of unitholders has also quadrupled to more than 300,000.

And almost all of it is in one city. Bengaluru dominates India’s REIT market with 69.1 million sq. ft. of Grade A office space, followed by Hyderabad and Mumbai. 

Each REIT has followed a different strategy. Embassy focused on Bengaluru (60% portfolio), where strong demand comes from the city’s large technology sector. Mindspace built its portfolio across Hyderabad and Mumbai to benefit from growth and strong rental demand. Brookfield focused on Delhi NCR, with tenants spread across industries such as consulting, financial services, and multinational companies.

Looking across the country, the growth has been remarkable. REIT penetration across India’s top seven cities has also grown rapidly, rising from 11.2% of Grade A office stock in 2021 to 19.1% today.

But what’s more interesting than what REITs already own is what they haven’t touched yet. Bengaluru has 162 million sq. ft. of REIT-quality office space, but listed REITs own only 39% of it. Across India’s top seven cities, around 370 million sq. ft. of institutional-grade office space still sits outside listed REITs, more than twice what they currently own.

That shows how early India’s REIT market still is. India’s five REITs account for just 0.45% of the global REIT market, compared with 72% for the US and 3% for Singapore, despite it being a city-state. 

The market could expand further in 2026. REITs have been reclassified as equity instruments for mutual funds and broader index inclusion is expected, bringing in passive investment for the first time. The RBI has also proposed allowing banks to lend directly to REITs, which could lower borrowing costs and make future acquisitions cheaper to finance.

Investment is already picking up. Indian real estate attracted $5.1 billion in Q1 2026, up 72% year-on-year. REITs currently account for 20% of India’s institutional real estate, compared with 96% in the US, 55% in Singapore, and 51% in Japan. By 2030, India’s share could rise to 25-30%, leaving plenty of room for further growth.

The Tailwinds: Why India’s commercial real estate keeps getting stronger

Commercial real estate doesn’t grow just because someone builds a new office tower. It grows as cities expand, businesses hire, and infrastructure improves. India’s real estate market is projected to reach USD 5.8 trillion by 2047. Here are some key trends driving that growth.

1. Urbanisation: India’s urban population is projected to hit ~590 million (39% of the population) by 2036, creating sustained demand for commercial real estate.
2. A rising services economy: As services keep deepening, the demand for more office space, and better quality space, keeps rising with it.
3. New roads opening new ground: Metros, highways, and airports being built right now are creating business corridors that didn’t exist a decade ago.
4. The multinationals building offices in India: Global Capability Centres (GCCs) are offices that global companies set up in India for work like engineering, research, finance, analytics, and product development. Over 500 new GCCs are expected by 2030. Companies are drawn by India’s large talent pool, lower costs, and affordable office space. GCCs now account for 40-60% of leasing across REIT portfolios, making them one of the biggest drivers of demand for REIT-owned offices.
5. The upside inside old buildings: Bengaluru has nearly 100 million sqft of aging Grade A office space where renovations could increase rents by 16-20%. In Delhi NCR, the uplift could be as high as 30%. With the capital to fund these upgrades, REITs are well placed to capture that upside.

Where REITs fit in your portfolio

A REIT makes you money in two ways. First, through regular distributions. By law, REITs must distribute at least 90% of their cash flows, giving investors annual yields of around 6%-7.5% in India, compared with 2.5%-3.5% in the US and 5%-6% in Singapore. Much of this income is also tax-efficient.

The second source of return is capital appreciation. As the value of the underlying properties grows, so can the REIT’s unit price. Since listing, Mindspace has gained 51.5%, Nexus 48.5%, and Embassy 37.3%, in addition to the distributions investors have received.

Returns matter, but so does how an investment behaves when markets fall. Through 2026, the Nifty 50 is down 10.5% year to date and Nifty Realty is down 13.9%. Listed REITs over the same stretch slipped only 1% to 6%, reflecting the stability of rental income.

This is what makes REITs different. They combine bond-like income, equity-like growth potential, and the liquidity of a listed security.

How to evaluate REITs 

Choosing the right REIT goes beyond looking at returns. Here’s a simple framework to help you evaluate one: 

The risks worth knowing

The biggest risk is concentration. Three REITs, Embassy, Mindspace, and Brookfield, make up most of the market, so the asset class depends heavily on a handful of players. The underlying asset base is also narrow, heavily weighted toward office space and technology tenants, making it vulnerable to a slowdown in either. Interest rates matter too, as higher borrowing costs can weigh on REIT valuations. And while leasing demand is strong, much of it is still driven by GCCs.

If this edition sparked your interest in REITs, you might enjoy this conversation with Karan Virwani, CEO of WeWork India and part of the Embassy Group, on the Create Wealth Podcast.

Watch the full episode here.
See you next week. 

Disclaimer – Investment in the securities market is subject to market risks, read all the related documents carefully before investing. The information provided herein is intended solely for educational purposes and should not be construed as solicitation, advertising, or providing any financial or investment advice or an offer to buy or sell any financial instruments. Any statements about future developments are speculative and should not be taken as guarantees. Past Performance is not indicative of future results. Readers are advised to consult with their financial advisor before making investment decisions based on the information provided herein.

In the preparation of this document, Dezerv has used information developed in-house and publicly available information and other sources believed to be reliable. The information is not a complete disclosure of every material fact and terms and conditions. While reasonable care has been made to present reliable data in this article, Dezerv does not guarantee the accuracy or completeness of the data. The information / data herein alone is not sufficient and shouldn’t be used for the development or implementation of an investment strategy.

Any references to names of fund houses, investment securities, or asset classes are for illustrative purposes only. Dezerv, along with its directors, employees, or partners or any of its affiliates, shall not be held liable for any loss, damage, or liability arising from the use of this document. Additionally, all trademarks, logos, and brand names mentioned are the property of their respective owners and are used for identification purposes only. The use of these names, trademarks, and logos does not imply endorsement or recommendation.

David Sinclair, a professor at Harvard Medical School who has spent thirty years studying why things age, finds trees like this genuinely useful. The genes that allow a bristlecone pine to survive five millennia are not unique to trees. Simpler versions of them exist in yeast, in worms, in mice, and in us. He calls them sirtuins, a family of seven proteins that act like the body’s damage-control team, patrolling the epigenome and keeping the right genes switched on and the wrong ones switched off. When everything is going well, when food is abundant and life is comfortable, the sirtuins go relatively quiet. When things get hard, when food runs scarce, when the body is pushed physically, when temperatures drop, they activate. They repair DNA damage, reduce inflammation, and essentially tell the cell to stop growing and start protecting itself.

In Sinclair’s lab, and in labs across Stanford and MIT, scientists have found ways to activate these mechanisms artificially. In animals, they have partially reversed the aging process, rolling back measurable biological age, regenerating the optic nerve in blind mice, restoring youthful function to old tissues. The top fifty longevity companies have collectively raised over a billion dollars in venture capital. Researchers now seriously discuss whether aging itself, rather than any individual disease, should be the target. 

And if aging can be slowed meaningfully, if the diseases that have historically ended lives at 70 are being pushed back by a decade or more, then it is worth asking whether you have a retirement corpus that will last as long as you do? That is what this edition is about.

In this edition:

• Why you may live longer than you think
• The longevity ecosystem: innovations shaping the future
• Why more years don’t mean better quality of life
• What does the future of health look like?
• Rethinking retirement for a longer life

You may live longer than you plan for

For most of human history, the challenge was dying too soon. That problem is beginning to flip. By 2050, 1 in six people worldwide will be over 65, up from 1 in ten today. And India is on a steeper curve. By the mid-2030s, India’s median age will surpass the world’s, due to falling fertility rates and increased life expectancy. Our life expectancy has gone from 47 years at independence to over 71 today. 

But a longer life only matters if those extra years remain healthy years. Researchers describe this using two measures: lifespan and healthspan. Lifespan is the number of years between birth and death. Healthspan is the number of years spent in good health. The gap between the two is where much of aging happens. 

Historically, living longer often meant spending more years managing chronic disease, taking medications, and dealing with physical decline. We added years to life, but not necessarily life to years. That trade-off is beginning to change. Much of modern healthcare is shifting from treating disease after it appears to preventing it, detecting it earlier, and helping people change behavior before serious damage occurs. 

And as medical innovation improves, the average lifespan could reach 90 by 2040, while the share of life spent in good health rises from 85% to 95%. You do not just live longer. You live well for longer, and compress the period of decline into a much shorter window at the very end.

So, for someone who is 45 today and in reasonable health, the realistic retirement planning horizon is no longer 70. It is ninety, and quite possibly beyond. 

Can we change how we age? Inside the science of longevity

Steve Horvath, a geneticist at UCLA, built a clock that measures specific patterns in your DNA and tells you how old your cells actually are, not how many birthdays you have had, but how fast your body is aging right now. 

The number can be ten years younger than your passport or ten years older, depending entirely on how you have lived. Horvath’s clock can now measure this from a blood sample, and what makes it useful is that the number moves. It responds to what you do. Which means it can go in the wrong direction, and it can go in the right one.

And here is what is actually being done to push it in the right direction. The longevity ecosystem is broad and is spread across eight distinct areas: aging therapeutics, cell and gene therapy, wellness and prevention, AI diagnostics, wearables, nanotech, age-reversal technologies, and age-tech, each operating simultaneously and beginning to reinforce each other.

And here is what is actually being done. The longevity ecosystem spans eight distinct areas: aging therapeutics, cell and gene therapy, wellness and prevention, AI diagnostics, wearables, nanotech, age-reversal technologies, and age-tech, each advancing simultaneously and beginning to reinforce each other.

Some of the promising experiments being pursued today:

1. Epigenetic reprogramming: Sinclair’s lab used three of the four Yamanaka factors, genes that can reset a cell’s identity, to partially reverse aging in living animals without causing cancer. In mice, they restored vision lost to glaucoma and regenerated damaged optic nerves. The cells’ biological age was rolled back by roughly 80%.

2. NAD precursors (NMN, NR):  NAD is a molecule cells use to produce energy, and its levels decline with age. The idea is biologically plausible, but independent studies at the Jackson Laboratory found no lifespan extension in mice. Human evidence remains limited. The commercial excitement is well ahead of the science.

3. Parabiosis and plasma exchange:  researchers connected the circulatory systems of an old and a young mouse. The old mouse became measurably younger. The young mouse aged faster. Multiple companies are now running plasma exchange trials in humans, without yet knowing exactly which factors in young blood are responsible.

4. Rapamycin and the Dog Aging Project: Rapamycin extended lifespan in mice by twenty to thirty percent, even when started late in life. Researchers are now testing it in dogs, which share our diseases and age roughly seven times faster than us, giving meaningful results in a fraction of the time a human trial would take.

5. Hyperbaric oxygen therapy: Sixty sessions of ninety minutes in a pressurised chamber did something previously thought nearly impossible in a small Israeli study: telomeres became measurably longer and senescent cells in the blood declined. First demonstration that these markers of aging could reverse in a living human.

6. Senolytics: These are the drugs designed to clear zombie cells, cells that stop dividing, refuse to die, and release inflammatory signals that damage surrounding tissue. Human trials are active across multiple companies. The biology is well established; the human drug results remain mixed.

7. GLP-1 drugs (Ozempic, Mounjaro): These drugs were originally developed to treat diabetes. But are now showing cardiovascular protection, kidney benefits, and early signals around cognitive decline well beyond what weight loss alone explains. Whether they slow aging in metabolically healthy people is still being studied.

8. DNA sequencing: In 2000, sequencing a human genome cost about $1 billion and took 13 years. Today, it can cost under $100 and be completed in about an hour.  Medicine is becoming predictive, identifying your specific genetic risks years before symptoms appear.

9. Biosensors and wearables: They continuously monitor heart rhythm, sleep architecture, and inflammatory markers. Smart mirrors, sensor-fitted bathrooms, and toilets that can already test urine for glucose, nitrites, and pH levels associated with early disease, passively, without any change in behaviour.

10. AI-powered disease detection: AI systems are increasingly being used to analyze cancer scans, ECGs, and blood tests. In several studies, they have matched or exceeded human clinicians in detecting early signs of disease. Early detection is the single most powerful lever in longevity, the difference between stage one and stage four is often the difference between treatable and terminal.

Current reality: We are living longer. But are we living healthier?

Bryan Johnson is someone worth knowing about if you are thinking seriously about longevity. He sold his payments company to PayPal for 800 million dollars, and then did something that most people found baffling: he stopped trying to build more wealth and started trying not to age. 

He hired a team of doctors, spent millions measuring every organ in his body, and built a protocol around the single goal of slowing his biological clock. He goes to bed at eight-thirty. His last meal is at noon. He takes over a hundred pills a day. He has generated more personal health data than any human in recorded history.

Most people find him easy to dismiss. What is harder to dismiss is what his doctors keep finding. His cardiovascular function scores at the level of an eighteen-year-old. His inflammation markers are near undetectable. His biological age across multiple systems is measurably younger than his chronological age of 47. You can disagree with his methods and still find the data uncomfortable to ignore.

Johnson talks about something he calls a society built for dying, the food systems, the sleep culture, the work norms, the chronic stress, and his argument is not that people are making bad choices but that the environment makes those choices almost inevitable. He is extreme. But he is pointing at something real: most of the factors that accelerate biological aging are not genetic. They are defaulted into.

And this is where the challenge becomes apparent. The average person today lives to around seventy-nine. Their health span ends around sixty-three. That is a 16 year difference spent managing chronic conditions, rising medical costs, and growing dependence on others. While global life expectancy is rising, the number of years people spend in poor health is expanding even faster. Medical advancements are keeping us alive longer, but they aren’t matching that extension with quality of life. The challenge for the future isn’t just adding years to life, but adding life to years.

And the extra years medicine has added to human life were not added to the end. They were added to the middle, which means more people are living longer while managing diseases that previous generations died from quickly. Non-communicable diseases now account for 67% percent of all global deaths and 82% percent of years lived in poor health. Brain health conditions alone, mental, neurological, and substance disorders, account for nearly a quarter of the total global disease burden, yet receive just two percent of healthcare funding worldwide.

Living longer and living well are not a tradeoff. In every animal model tested, the interventions that extend lifespan also extend health span. Biology does not produce more years of being unwell, it produces more years of being functional. You get both or you get neither.

What does the future of health look like? 

Think about what it means to age well versus age poorly, and you realise most people have only ever seen one version of it. The grandparent who spent their last decade largely bedridden. The parent whose seventies were defined by a revolving door of specialists and prescriptions. That picture, a long slow decline eating up years that could have been lived, is not inevitable. It is a consequence of how we have been treating aging, not how aging has to work.

McKinsey looked at roughly three hundred proven, cost-effective interventions across every major disease category and asked a simple question: if we actually scaled these, how much of the burden could we prevent? The answers are striking. 44% percent of cardiovascular disease burden avertable. 30% of cancer burden. 57% percent of chronic respiratory disease. 47% percent of mental disorders. These treatments are already available and proven to work. They’re just not reaching enough people in time.

The goal is to square the curve. Right now, health declines gradually from middle age onward, with people spending long years managing deteriorating function. The alternative, which becomes possible when prevention and early detection are taken seriously, is a life where you remain in good health for the majority of your years and experience a much shorter, sharper decline at the very end. In practical terms, that could mean six extra years of life and nine extra years of good health. The share of your life spent well rises meaningfully.

There is also a counterintuitive finding that I’d like to share. A person who is 75 today and in reasonable health has a higher expected lifespan than population averages suggest, because they have already survived the most common causes of death at every earlier age bracket. A healthy 75-year-old today has a good chance of living to 90. The challenge is that most people are not preparing for a retirement that lasts another decade and a half.

And when you scale that reality from individuals to entire populations, the implications become enormous. Improved population health could add twelve and a half trillion dollars to global GDP by 2050, 10.5 trillion from increased labour force participation alone, as healthier people work longer and more productively. The longevity market is expected to outstrip the existing healthcare market over the long run, because it is shifting from treating disease after it arrives to preventing it from arriving at all.

What extra years actually cost

The good news is that people are living longer. The harder part is ensuring their money lasts just as long. Medical costs in India are rising at 12 to 15% annually, two to three times the rate of general inflation. Someone retiring at sixty and spending ₹3 lakh a month faces a financial trajectory that standard retirement calculators, built for a twenty-year horizon, simply were not designed to model. The difference between planning for twenty years and planning for thirty is not marginal. It runs into crores, and it shows up not in year one but in year eighteen, when flexibility is lowest and medical expenses are highest.

The question most retirement plans are built around is how much you need to stop working comfortably. The question that actually determines whether the plan survives is: how much do you need if you live to ninety, spend your last decade managing significant health costs, and cannot count on supplementary income after seventy? These are not the same question. And most people have only ever answered the first one.

I’ve written extensively about how increasing life expectancy changes retirement planning, the corpus you’ll actually need, how to calculate your true retirement number, and the strategies for building it over time. 

In summary

Living longer is the best problem to have, if you plan for it. Science is moving fast. The costs are rising faster. And most retirement plans were written for a world where 75 was old age. The single most important financial decision you can make today is to take your longevity seriously, before you have to.

Disclaimer – The information provided herein is intended solely for educational purposes. Any statements about future developments are speculative and should not be taken as guarantees. In this material, Dezerv has utilized information through publicly available sources, and other data deemed to be reliable. All trademarks, logos, and brand names mentioned are used for identification purposes only and do not imply endorsement or recommendation.

Then, in 1859, a man named Edwin Drake drilled the first commercial oil well in Titusville, Pennsylvania. He wasn’t looking for a fuel to replace coal, he was after kerosene, to light lamps, because whale oil, the main lamp fuel at the time, was becoming scarce and expensive as sperm whales were hunted in large numbers.

The crude that came up out of that well also contained a thinner, more volatile fraction called gasoline, and early refiners had so little use for it that they often burned it off or dumped it straight into the nearest river. It was a waste.

Then came the combustion engine, and that waste product turned into the most valuable thing to come out of a barrel of crude. By the time Henry Ford’s Model T rolled off the line in 1908, gasoline wasn’t a byproduct anymore, it was the thing cars, factories, and eventually entire militaries couldn’t run without, and oil became the commodity that powered the global economy. 

In October 1973, when the Arab members of OPEC cut off oil exports to countries supporting Israel in the Yom Kippur War, the price of crude nearly quadrupled within months, and economies on the other side of the planet slid into recession over a decision made in a handful of desert capitals. 

Half a century later, the calculus hasn’t changed much. The Strait of Hormuz, a stretch of water you could cross by boat in twenty minutes, carries nearly a fifth of the world’s oil supply, and every time a tanker gets delayed there, markets move within hours.

What I find interesting about that story is how gasoline went from something nobody needed to something nobody could do without, once cars made it essential. I think about that whenever I read about AI today. If one of the major AI labs shut its servers down for a week today, most of us would notice an inconvenience, a chatbot goes silent, a coding assistant stops responding.

And that’s not as hypothetical as it sounds. Just this week, the US government ordered Anthropic to restrict access to its most powerful model, Mythos, and the public version built on it, Fable, for non-US users, citing national security concerns. Five or ten years from now, with far more of the economy running on models like this, would a disruption like that still feel like a brief inconvenience, or more like October 1973?

The answer depends on how deeply computing has woven itself into the economy by then. And compute, broken down to its smallest tradable unit, is measured in tokens. If oil runs on barrels and electricity runs on kilowatt-hours, AI runs on tokens, and understanding how that economy works is becoming more relevant than ever before.

This week, that’s the world I want to walk through with you.

In this edition:

• What a token actually is, and why every AI exchange has an invisible bill attached
• How tokenomics works, and the brief, strange culture of tokenmaxxing that took over corporate AI budgets
• Why falling token prices haven’t stopped AI bills from climbing
• Can you estimate if your AI is actually paying off?
• A few habits worth borrowing if you want your own AI spend to behave sensibly

Let’s begin.

What is a token, really?

AI models don’t read the way we do. We see whole words, sometimes whole sentences, in one glance. A language model breaks everything into smaller fragments first, somewhere between a syllable and a short word. “Understanding” might split into “under” and “standing.” A number like 2026 might break into two or three pieces. Punctuation gets counted too.

Think of it the way a tailor thinks about fabric. You don’t buy a shirt, you buy metres of cloth, and the shirt gets made from that. A token is the AI version of that metre of cloth. Every prompt you type gets cut into these small pieces before the model can read it, and every word it writes back gets assembled the same way before it reaches your screen.

There are two kinds you’re paying for. Input tokens are everything you feed the model: your question, your documents, the whole back and forth of a long conversation if it gets resentful each time. Output tokens are everything it generates in return. Both get counted, and both get billed.

That’s what makes AI economics different from any software you’ve bought before. A Netflix subscription charges the same whether you watch one show a month or fifty. A token meter does not. The bill depends on how much AI you use, not how many licences you’ve bought, and that changes everything.

Tokenomics, and the rise of Tokenmaxxing

Once tokens became the unit of account, AI providers started pricing them the way utilities price electricity. A token from a basic model costs a fraction of a cent. A token from an advanced reasoning model, the kind that works through several steps before it answers, costs meaningfully more, because it’s doing meaningfully more work per token. It’s also extraordinarily lucrative for the people selling it. One of the leading labs is reported to have seen its revenue jump from around $9 billion to somewhere near $35-40 billion within a single year, almost entirely on the back of this kind of usage-based pricing.

Underneath that pricing sit four pillars of tokenomics. The first is value. A token’s value depends on intelligence (model quality and context size) and speed (tokens generated per second). 

The second is demand, how much of it a business will actually need, which starts with Users × Sessions × Tokens per Session, but actual usage is much higher due to hidden reasoning tokens, agentic workflows, and context management.

The third is supply. There are three ways companies buy AI: paying for tokens as they’re consumed through APIs, committing to a certain level of usage in exchange for lower costs, and running models on your own hardware when usage becomes large enough to justify it.

And, the fourth is monetisation. In practice, companies make money from AI in four ways: selling AI access through APIs, building AI-powered products, adding AI to existing products, or using AI internally to improve productivity.

For a while, almost everyone obsessed over the second pillar, demand, and ignored the first one, value, entirely. A strange culture grew up around all this, as coding agents and AI assistants spread inside large companies, several of them started treating token consumption itself as a metric of progress. 

Amazon’s token leaderboards ranked employees by how many tokens their agents had burned through, and the heaviest spenders got celebrated as innovators, regardless of what they’d actually built. The logic was that more tokens meant more value. People started calling it Tokenmaxxing, half as a joke, before realising it wasn’t one.

The scale this reached is hard to overstate. Google alone now processes something like 1.3 quadrillion tokens a month, a number that’s jumped 130-fold in just the last year. In some companies, generative AI had turned into the fastest-growing line on the technology budget, swallowing up to half of total IT spend, with cloud bills, mostly riding on AI workloads, climbing close to 20% a year. Most of that AI bill comes from a handful of teams, especially engineering, accounting for more than 60% of total AI spending, with per-person costs often more than 10x higher than those in sales.

However, some companies are now waking up to this. Uber’s leadership admitted publicly that the company burned through its entire year’s token budget for one major AI coding tool well before the year was half over, and that tying AI usage to anything actually shipped had become hard to prove. Amazon took its internal AI leaderboard down, Microsoft pulled back on Claude Code subscriptions, once the token bills came in far higher than expected. Even Sam Altman acknowledged that many of his own customers were spending heavily on AI while unsure how much of it was waste.

Every technology cycle picks an early vanity metric and mistakes it for value, whether that was miles of railroad track in the 1800s or daily active users during the dot-com years. This cycle picked tokens. The correction tends to arrive once someone finally asks the only question that matters: what did all that spend actually buy you?

Why cheaper tokens still mean bigger bills

Token prices have been falling fast. Deloitte’s research projects the average cost of inference dropping from around four cents per million tokens in 2025 to roughly one cent by 2030. By most logic, that should shrink AI’s place on the budget.

It hasn’t worked that way, and the reason has a name nearly two centuries old: Jevons paradox. When something gets cheaper, people don’t spend the same amount for less of it, they find far more ways to use it, and total spending climbs even as the price per unit falls. Coal got cheaper and more efficient in nineteenth-century England, and total coal consumption rose rather than fell, because cheap power unlocked uses nobody had bothered with before.

AI is following the same script. A simple chatbot query in 2024 might have used around 2,000 tokens for a couple of cents. The agentic workflows running through enterprise systems today look nothing like that single exchange. One task can now pass between a primary agent, several sub-agents, tool calls, retrieval steps, validation checks, and retries, easily consuming 500,000 tokens to finish work that once took a single round trip. Here’s what that escalation looks like side by side, and why a 75% drop in price per token still adds up to a far heavier bill.

The same thing is playing out across the entire market, not just inside individual tasks. Weekly token consumption across the most used AI models has climbed from under a trillion tokens a week at the start of last year to well over twelve trillion by this May, and a growing share of that is shifting toward cheaper Chinese open-source models, as companies start routing simpler work away from the priciest frontier options.

As a result, after a massive boom in spring 2026, the market has hit a sudden cooling-off period, and money spent on LLM tokens is actively shrinking for the first time in months.

You see, there’s a simple economic logic underneath all that routing: when something becomes scarce, higher prices push people toward cheaper alternatives and reserve the limited supply for the uses that justify it most. 

That’s what is happening with AI compute today. Companies that can afford frontier models still use them for high-value tasks where the extra capability matters. Everyone else is shifting to cheaper models for routine work. Because when you compare leading AI models on intelligence, speed, and cost, an interesting pattern appears. Claude Opus 4.8 and GPT-5.5 are among the smartest models, but they cost more than $4 per million tokens. DeepSeek’s latest model scores almost as well, within about 10 points, but costs just $0.18 per million tokens, roughly 20 times cheaper.

Sending every query to the most expensive model is like hiring a surgeon to apply a band-aid. Beyond a certain point, paying more doesn’t always buy much more intelligence. Some models are extremely fast but less capable. Others are expensive despite only average performance. At the top end, intelligence and price generally rise together. After that, the relationship starts to break down.

Return on tokens: Is your AI spend actually paying off?

As AI spending grows, companies are asking a simple question: What return are we getting on all these tokens? The answer is straightforward:

Return on Tokens = (Value created − Token cost) ÷ Token cost

The gap between successful and unsuccessful AI deployments often comes down to one thing: how tokens are used.

According to Deloitte, companies are increasingly falling into two groups: value creators and value eroders. Some turn AI spending into measurable business outcomes. Others accumulate costs without creating enough value to justify them.

AI and generative AI already account for about 36% of digital transformation budgets, and that share continues to grow. What’s getting squeezed to make room for it is the less glamorous but essential spending on cybersecurity and data infrastructure.

Even business leaders remain cautious about the returns. Nearly 60% expect it will take up to three years to see meaningful value from basic AI automation, while six in ten think more advanced AI systems will take even longer. Only about one in four finance leaders say their AI investments are delivering clear, measurable value today.

Many companies use AI to solve the same problem from scratch every time. That works for prototypes and first drafts, but it becomes expensive and unreliable for business-critical tasks like fraud detection, compliance checks, or claims processing. The AI keeps consuming tokens, but very little of that work is reusable.

The better approach is to let AI do the thinking once and then turn that knowledge into a repeatable workflow. Instead of re-solving the same problem every time, the system follows established rules and only calls AI when something changes.

Companies using this approach have reported accuracy above 99% in claims processing while reducing token usage by as much as 100x. There is also a simpler way to improve returns: use the right model and infrastructure for the job. Many organizations default to the largest models even when smaller, specialized models can deliver similar results at a fraction of the cost.

Deloitte’s research highlights another important factor: infrastructure utilization. Companies running AI systems at around 85% utilization generate significantly more value per token than those that overprovision capacity and leave resources idle.

The lesson is simple: the highest returns come not from using more AI, but from using the right AI, in the right way, at the right scale.

Becoming a savvy token economist

Most leaders running AI programmes right now are still measuring success the way Tokenmaxxing taught them to, how many tokens got used, how many agents got deployed. Almost none of them are measuring what actually came out the other end.

Fixing that doesn’t require a research lab or a six-month transformation programme. It mostly comes down to a handful of habits the companies converting spend into real return have already picked up.

1. Use the right model for the job

Not every task needs the most powerful model. Simple requests can often be handled with rules or smaller, cheaper models. Reserve expensive frontier models for tasks that genuinely require advanced reasoning.

2. Reduce unnecessary tokens

A lot of AI spending comes from sending more context than necessary and generating longer responses than needed. Summarizing older conversations, limiting context windows, and keeping outputs concise can significantly reduce token usage without hurting performance.

3. Manage AI spend like any other expense 

The same rule that applies to any subscription or recurring cost applies here too: know what you’re paying, know what it’s actually giving you back, and check in on it once in a while instead of letting it run in the background. The moment a cost stops getting questioned is the moment it stops being managed.

Disclaimer – The information provided herein is intended solely for educational purposes. In this material, Dezerv has utilized information through publicly available sources, and other data deemed to be reliable. All trademarks, logos, and brand names mentioned are used for identification purposes only and do not imply endorsement or recommendation.

India is producing that kind of wealth, at a pace it has never seen before. Emerging markets will add nearly $7 trillion in financial wealth by 2030, and India alone accounts for $2.37 trillion of that, growing at 12% annually –  that is more than double Brazil, nearly four times Mexico and ahead of any other emerging market combined.

What’s driving this isn’t one thing. India’s economy has been growing at 6-7% for years, and that growth is increasingly showing up in personal wealth. The startup ecosystem has matured, creating liquidity events through ESOPs, secondary sales, and IPOs. As a result, many founders and employees are holding far more wealth than previous generations did at the same age.  

Corporate India has also expanded, creating a larger pool of senior professionals whose compensation includes equity alongside salary. Today, around 20% of Indian millionaires are under 40, and India added 26 new billionaires in 2024, compared with just 7 in 2019.

Managing this kind of wealth isn’t the same as managing a salary and some savings. It demands a different kind of attention, one most people creating this wealth don’t have the time for. So the question is no longer just where to put money, it’s how to manage it, which products to use, which relationships to trust, and how to tell the difference between advice that works for you and advice that works for someone else.

That’s what this edition is about, choosing the right wealth management model for you.

In this edition, we’ll cover:

• The four wealth management models in India and what each actually means for you
• What is a PMS and who should have access to it
• A practical map of which model fits which kind of investor
• A framework to choose the right wealth management model
• What technology can and cannot do for your wealth

The advisory gap

India’s HNI population is growing fast, from 850,000 today to a projected 1.65 million by 2027. We already rank fourth globally for individuals with assets above $10 million, behind only the US, China, and Japan.

But the more striking number is concentration. The top 1% of Indian households, roughly own close to 70% of the country’s financial assets. A small group controls a disproportionate share of the wealth that the entire wealth management industry exists to serve.

Of this group’s $11.6 trillion in total assets, only $2.7 trillion sits in things that need ongoing attention, equities, mutual funds, insurance, deposits. The rest is real estate, gold, and promoter equity, largely untouched for years. Every wealth manager, bank, and PMS provider in the country is effectively competing for a share of that $2.7 trillion. 

And the people available to do this work are limited: 1.43 lakh AMFI-registered mutual fund distributors, roughly 9,000 active stockbrokers, 932 SEBI-registered investment advisors and just ~500 PMS managers. Relationship manager attrition at private banks and wealth firms has been close to 40% in recent years. Put simply: a small, concentrated pool of wealth is being chased by an even smaller, shrinking pool of people equipped to manage it.

If experienced advisors are this scarce, you may not get to choose freely and may end up working with whoever’s available. Which makes it worth knowing what each type of professional actually does, whether the structure they operate in aligns with the kind of advice you need, and where their role ends.

Understanding wealth management landscape in India

In India the term wealth management is not one-size-fits-all. There are different models that exist for a reason, and understanding what each one does, and does not do, is the starting point for making a good choice. Each operates under a different regulatory framework, earns differently, and serves a different purpose. Here is a breakdown of four main wealth management models in India.

The Mutual Fund Distributor (MFD) is the most common model in India, and for good reason. The growth of SIPs, retail investing, and mutual fund participation has been driven in large part by the MFD network. MFDs are registered with AMFI, regulated by SEBI, and are compensated through commissions paid by mutual fund companies.

Their scope is largely limited to mutual funds and does not extend to direct equities, bonds, or most other investment products. While they may provide guidance, the investor ultimately decides what to invest in and is responsible for acting on those decisions.

A stock broker plays an even narrower role. They provide access to the market and execute trades on your instruction. They do not build portfolios, assess suitability, or provide ongoing wealth management. Their responsibility is execution.

A SEBI-Registered Investment Advisor (RIA) operates at a broader level. An RIA can help create a financial plan, recommend investments across different asset classes, and advise on portfolio construction. But, the advice stops at the recommendation stage. The investor is responsible for deciding whether to follow the advice and execute. 

For investors who are engaged, have the time to monitor their portfolio regularly, and are disciplined enough to act on advice consistently, an RIA or a direct mutual fund approach works well.

A SEBI-registered Portfolio Manager takes the process one step further, they can make and execute investment decisions on your behalf. Under a discretionary mandate, the manager handles the day-to-day management of the portfolio, so you do not need to approve every transaction. This structure is designed for investors who want professional management of their wealth without being involved in day-to-day portfolio decisions. 

Since PMS works differently from every other model in this landscape, in structure, accountability, and regulatory architecture, it’s worth understanding in more detail before making a decision.

Understanding the nuances of a PMS 

The easiest way to understand PMS is to start with a simple question: in your current wealth management arrangement, who actually makes the investment decisions?

With a stockbroker, the answer is you, the broker executes what you tell them to. With an MFD, you decide which funds to buy and the distributor helps you transact. With an RIA, the advisor builds a plan and recommends what to do, but you decide whether to act on it and you execute it yourself. In each of these models, the final decision, and often the execution, rests entirely with the investor.

A discretionary PMS works differently. The portfolio manager analyses the portfolio, makes the investment decision, and executes it. You don’t have to approve each transaction and the accountability for what happens in the portfolio sits with the manager, not with you.

This matters most for investors whose time is genuinely scarce. Within PMS, the distinction that matters in practice is what the portfolio is built around, some PMS strategies invest predominantly in direct equities, while others are built largely around mutual funds as the underlying investment vehicle.

On fees, the structure varies across models and is worth understanding clearly because it shapes incentives. Some PMS providers charge a flat fee, others charge a percentage of assets under management, and many use a combination of a fixed fee plus a performance fee, where the manager earns more only if the portfolio crosses a predefined return hurdle.”

No fee structure is inherently better than another. The important question is whether the manager’s incentives are aligned with yours and whether you are comfortable with how they are being paid.

PMS does not change how your investments are taxed. The same capital gains rules apply as they would if you were investing directly. What matters is the holding period, not the structure. A well-run PMS is not trading for activity’s sake; portfolio changes are made only when the expected benefit outweighs the costs.

The regulatory protections under a SEBI-registered PMS are also worth knowing. Client assets are kept separate from the portfolio manager’s assets, performance must be reported in a standard format, and fees and conflicts of interest have to be disclosed clearly. The goal is to ensure transparency and investor protection through regulation rather than relying solely on trust in the manager.

Which model is right for you: A simple map for a complicated choice

There’s no universal answer here. The right model depends on where you are in your financial journey, how much time, attention and decision-making you can realistically give to managing your portfolio, and what you actually need from the relationship. Here’s a rough map: 

How to choose the right wealth model for you?

Most people who build significant wealth do so by becoming exceptionally good at something, running a business, building a career, or creating a product. But the skills required to create wealth are not always the same as the skills required to manage it. As wealth grows, protecting and investing it becomes a separate discipline that demands its own time, expertise, and attention.

Each model has its place and can work well for the right investor. Use the checklist below to identify the kind of support, involvement, and expertise you need from a wealth management arrangement.

For investors who are busy running businesses or demanding careers and prefer to delegate day-to-day investment decisions, a discretionary PMS can be an effective structure. It allows a professional manager to handle portfolio decisions while the investor remains focused on their own work and priorities.

The future of wealth management: Can AI alone manage your wealth? 

Earlier this year, a product announcement from a small US startup erased more than $140 billion of market value from several listed wealth management companies. The product was not a new fund or investment strategy. It was an AI-powered tax planning tool for financial advisors.

The market’s reaction reflected a growing belief that AI could reshape wealth management, not just make it more efficient. Two years ago, LLMs hallucinated too frequently to be trusted with client-facing tasks. Today, they are helping advisors draft financial plans, prepare portfolio reviews, automate compliance work, and streamline onboarding.

Industry estimates suggest AI could increase capacity by 25–30% in portfolio management and up to 55% in onboarding and compliance. Firms that adopt these tools early may also be able to serve more clients and improve revenue (15-20%) per advisor.

But the more important distinction, the one that gets lost in the noise, is that disruption is not displacement. The firms pulling ahead are not replacing advisors with algorithms. They are using AI to handle what is systematic, so that human judgment can focus on what actually requires it. For portfolio management specifically, that line runs roughly here:

For investors, what this means practically is that the quality of a wealth management relationship is no longer measured by how much manual work the advisor does. It is measured by how well they use what technology handles automatically to focus their time on what actually matters, and how much judgment they bring to those moments when it counts.

In summary

Managing wealth well is not a one-time decision. It involves a continuous set of activities: finding the right investments, building the portfolio, deciding asset allocation, executing transactions, reviewing performance, and making adjustments as markets and goals change.  When any of these steps are done poorly, the impact is rarely immediate. It shows up gradually over time through missed opportunities, unnecessary risks, and wealth that could have been created but wasn’t. The choice of who manages your wealth, and how, is one of the few financial decisions that shapes every other one that follows.

Disclaimer – Investment in the securities market is subject to market risks, read all the related documents carefully before investing. The information provided herein is intended solely for educational purposes and should not be construed as solicitation, advertising, or providing any financial or investment advice or an offer to buy or sell any financial instruments. Any statements about future developments are speculative and should not be taken as guarantees. Readers are advised to consult with their financial advisor before making investment decisions based on the information provided herein.

In the preparation of this document, Dezerv has used information developed in-house and publicly available information and other sources believed to be reliable. The information is not a complete disclosure of every material fact and terms and conditions. While reasonable care has been made to present reliable data in this article, Dezerv does not guarantee the accuracy or completeness of the data. The information / data herein alone is not sufficient and shouldn’t be used for the development or implementation of an investment strategy.

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