I am now also on Instagram!! So, if you are a regular on Insta, you can connect here: https://instagram.com/abhishekbasumallick
My preferred poison in the social media milieu continues to be Twitter (X never caught on with me!!) and Substack.
“In all affairs it’s a healthy thing now and then to hang a question mark on the things you have long taken for granted.” - Bertrand Russell
“In order to seek truth, it is necessary once in the course of our life, to doubt, as far as possible, of all things.” - René Descartes
In India, certain purchases are milestones and not indulgences.
The first iPhone. The first international holiday. The first premium school for a child. These are not just consumer decisions. They are markers. Proof that life has moved forward. That the sacrifices of a previous generation, or of an earlier version of yourself, were not made in vain.
This is the emotional register that matters. We tend to assume the driver is aspiration looking forward. Often it is something quieter and more powerful, looking backwards—a sense of having earned the right to this. A form of self-permission that arrives only after a certain threshold has been crossed.
The premium purchase in this context is not a luxury. It is evidence. The moment the consumer says: this is who I am now.
In the West, premium often signals taste. In India, it often signals progress.
In June 1933, a new statistics journal, bearing the name Sankhyā, was founded by Prasanta Chandra Mahalanobis [PCM] in Calcutta. The name was deliberate, as the editor’s note explained in the first issue: “As we interpret it, the fundamental aim of statistics is to give determinate and adequate knowledge of reality with the help of numbers and numerical analysis. The ancient Indian word Sankhyā embodies the same idea.” Sankhyā is Sanskrit for number – but also, by philosophical extension, for adequate knowledge.
ISI did more than apply existing statistical methods to Indian problems – it developed new ones. Mahalanobis' most distinctive methodological innovation was high-quality interpretation of a network of sub-samples: instead of drawing a single sample from a population, you draw two independent sub-samples processed by separate field teams, so that any divergence between results immediately flags a non-sampling error. The resulting estimates could then be compared. Sampling theory tells you how far apart two such estimates should fall by chance alone and any divergence beyond that benchmark has to come from somewhere else – investigator bias, recording errors, inconsistent application of instructions. This converted non-sampling error into a quantity that could be measured, traced back, and corrected between rounds. Harold Hotelling, then among the foremost statisticians in the world, eventually wrote that “no technique of random sample has, so far as I can find, been developed in the United States or elsewhere, which can compare in accuracy with that described by Professor Mahalanobis.”
Paul distinguishes trading (short‑term, highly active, focused on liquidity and risk) from investing (long‑term ownership and patience).
A key formative event was the 1970s silver bubble driven by Bunker Hunt, who went from richest person in the world to near‑bankruptcy when silver crashed from about $50/oz to under $10 in eight weeks after exchange rules changed to “liquidation only.”
This taught Paul never to fully trust any asset and always to value liquidity (ability to exit fast). His grandfather’s line “you’re only worth what you can write a cheque for tomorrow” reinforced this.
He used to criticise Warren Buffett as “just lucky” for being a value investor in a long U.S. bull market, but later changed his view completely after learning Buffett understood compound interest at age 9 and sought out Benjamin Graham at 17.
Paul now calls Buffett the “OG of compound interest,” admits he himself ignored compounding for much of his career, and regrets under‑appreciating the long‑term power of staying invested.
His own fund has a strongly negative correlation to the S&P 500, meaning almost all his returns are “alpha” (independent of the market) rather than simple market exposure.
Paul compares trading to boxing: the market is your opponent, you probe, wait, and occasionally throw big punches when the opportunity is clear (e.g., long Bitcoin in 2020, short two‑year U.S. rates in 2022).
Big trades usually come when policy or behaviour is far out of line: too much fiscal stimulus, a central bank staying easy too long, or a very mispriced currency like the Japanese yen.
For him, trading is effectively constant-probability analysis and capital-flow analysis across many instruments, looking for mispricing and a clear catalyst.
Paul argues that major crashes usually come from too much leverage, often via derivatives (futures/options).
1987: driven almost entirely by “portfolio insurance” strategies using derivatives without limits.
1998: LTCM crisis, with huge, highly leveraged derivative positions.
2000: dot‑com bust, largely caused by a wave of IPOs and later share unlocks that led to sustained selling.
He sees a similar pattern building now: after 10+ years of net share buybacks reducing equity supply, we may be entering a phase of large tech and AI‑related IPOs plus unlocks, with buybacks falling due to heavy capex by “hyperscalers.” This reverses the long‑term flow supporting equities.
U.S. equity market cap is now about 252% of GDP, versus ~65% in 1929, ~85–90% in 1987, and ~170% in 2000. He views this as “over‑equitized” and dangerous for wealth effects, tax revenues, and bond markets if valuations revert.
If price‑earnings ratios fall back toward long‑run averages, he thinks a 30–35% equity drop is plausible; applied to such a high market‑cap‑to‑GDP ratio, this implies a very large negative hit to GDP and government finances.
He calls the current situation a sovereign debt bubble and notes that private equity and illiquid assets now form much larger fractions of institutional portfolios than in 2007–08, which increases systemic liquidity risk.
For a 20‑year horizon, he says simply buying the S&P 500 at today’s high valuations is risky: historically, when the S&P trades at a PE above ~22, the 10‑year forward return has been negative.
Paul is very worried about AI safety and regulation. He criticises the current “build, break, iterate” model (move fast, fix later), arguing that unlike past technologies, AI’s tail risks (low‑probability worst outcomes) could be catastrophic for humanity.
He notes that after the nuclear bomb, the U.S. created the Atomic Energy Commission within ~18 months to manage extreme risks, while AI has had several years of rapid progress without any equivalent strong global regulatory framework.
He believes a key near‑term policy priority for political leaders, including the current U.S. president Donald Trump, should be global AI regulation involving major powers like China.
His simplest, most actionable proposal: mandate watermarking of all AI‑generated content, backed by strict penalties for repeat violations. This would help distinguish human from machine output, reduce deep‑fake damage, and restore trust in public information.
Looking ahead, he worries about a “workless world” where AI replaces many jobs, removing work‑based significance for people. However, he has become more optimistic that humans may adapt by finding meaning in competition (sports, games) and deliberate acts of kindness.
Paul’s daily routine is extremely structured and intense:
Wake around 6:15, work, then 45 minutes of hard cardio, then trading for market open.
Meetings during late morning and early afternoon, with dedicated time before and after market close to plan trades and assess global markets (Tokyo, Hong Kong, London).
An evening mix of family time, light entertainment (often Netflix), and more work; he even wakes up around 2:30–3:00 a.m. to watch London open and do analysis.
He feels he works harder now than decades ago because of information overload (hundreds or thousands of emails daily), which makes it harder to focus on “exquisite execution” (buying in maximum fear, selling in maximum euphoria).
He stresses the importance of always having a pre‑defined plan for different price scenarios, especially in extreme moves like a 33% one‑day move in silver. Plans should be thought through in advance and self‑executing.
Investments in the securities market are subject to market risks. Read all the related documents carefully before investing.
Founder, Intelsense Capital, SEBI Registered RA (Cupressus Enterprises Pvt Ltd - INH000013828)
Cofounder & Fund Manager, Shree Rama Managers PMS (INP300007341)
Registration granted by SEBI and certification from NISM do not guarantee the intermediary's performance or provide any assurance of returns to investors.
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