How a $100,000,000+ Family Office Invests Through Chaos

Tan Gera, CFA, is a former investment banker and co-founder of Decentralized Masters, the team behind Future Finance.

This week he sat down in Dubai with Aditya Parmar, Head of Investments at the Burman Family Office, who manages nine figures of generational wealth.

They discussed property, stocks, digital assets, the AI bubble, and where a family office actually puts its money when the world looks like this.

Fifteen seconds in, Aditya says real estate is genuinely the worst asset class you can buy. Most people would walk that back. He spends the next hour proving it instead.

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That's the tone for the whole episode. Very little hedging, a lot of specifics, and several moments where he says something out loud that people in his position usually keep behind closed doors.

And most of it scales down. The numbers he's working with are large, but the reasoning behind them applies just as cleanly to a first ten thousand as it does to a nine-figure book.

Here's a taste of what's in it.

The Discount Nobody Takes

Aditya's explanation for why most investors lose money is about as clean as it gets.

If you see an iPhone at 20% off, you sprint to buy it. If Apple stock falls 30%, you sell. Same discount, opposite reaction. And the phone is the depreciating asset.

His point is that this isn't an information problem, it's an emotional one, and that the people on the other side of your panic sell are buying for exactly that reason.

Where it gets interesting is how he says you actually fix it, because it isn't reading more.

He describes a specific path almost every great investor he knows has been down, and most people would rather not hear it.

That section starts around the four-minute mark.

Why He Won't Buy an Investment Property

The headline claim is easy to dismiss until he shows the arithmetic.

A property yields six or seven percent gross.

Take out maintenance, take out the person you pay to manage it, and you're at roughly five percent net, for work.

The S&P has done around seven annualised over long stretches for the effort of clicking a button. That's the opportunity cost, and he goes further into the numbers than that, including what the NASDAQ and the top seven names have done over 25 years.

He also brings up a cycle nobody mentions. One market he names fell every single year for a decade, and he explains why property downturns punish you in a way equity downturns don't.

Then it stops being about property. He and Tan get into what a mortgage does to someone in their twenties, and the argument is sharper than the usual rent-versus-buy stuff. It's about what happens to your ability to take risk once your cash flow is committed for thirty years.

Tan has watched friends do exactly this and describes where they ended up.

Worth noting he isn't telling you not to own a home. He draws a clear line between two things people constantly conflate, and it's a useful distinction to hear him make.

Where the Money Actually Went

The part that will sting for anyone who spent the last two years watching Nvidia is the energy trade.

Two years ago Aditya built a thesis on one constraint: AI needs power, and there isn't enough of it coming. He sized it at fifteen to twenty percent of the book rather than his usual three to five, and he's candid about how uncomfortable that was.

Siemens Energy returned roughly 1,700%. GE Vernova, four to five hundred percent. His own summary is that you could have thrown a dart blindfolded at anything gas or nuclear related and made money.

What makes it worth watching rather than just reading about is the reasoning, which was remarkably ordinary. A single JP Morgan report. A ten-year build time that forces capital to move now. One manufacturer with a backlog so long it tells you demand is real without needing a model.

He names the companies. He names the two he's rotating into next, one of which he describes as either going nowhere or going 50x. That whole breakdown runs from about seven minutes to twelve.

The Rest of It

There's more gems in there than we can talk about in one article.

What a $1M portfolio should look like at 40, with actual percentages.

What he'd allocate to digital assets and where. Why a $2 million missile fired at a $20,000 drone became an investment thesis. His view on trillion-dollar AI valuations, and what he was offered one of those names at three years ago.

And then the closing question, where Tan asks for his best advice on building wealth over the next four years. The answer has three parts. The third came from Aditya's mentor and it's the reason the episode opens with it.

Investing is a game of constant regret. You bought too much or you bought too little. You sold too early or you sold too late. You will never be happy, no matter how much you make.

He unpacks what to do with that in the last two minutes.

Watch it to the end, it's worth it.