India’s fertility rate has fallen below replacement for the first time in the country’s history, declining from a TFR of 2.3 to 1.9 in just a decade.
Delhi’s fertility rate now sits at 1.2, lower than Finland’s.
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KEN GRIFFIN WENT HOME "FAIRLY DEPRESSED" AFTER WATCHING WHAT AI IS DOING INSIDE CITADEL
The Citadel CEO says AI productivity has hit a step change in just the last few months.
Then he described what he saw inside his own firm:
"Work that we would usually do with people with master's and PhDs in finance over the course of weeks or months is being done by AI agents over the course of hours or days."
"These are not mid-tier white-collar jobs. These are extraordinarily high-skilled jobs being automated by agentic AI."
"I went home one Friday, actually fairly depressed by this, because you could just see how this was going to have such a dramatic impact on society."
"When you witness it in your own four walls, when you see work that used to be man-years of work being done in days or weeks, it's like, wow."
The guy running one of the most sophisticated quant firms on earth just told you what's coming.
This 9-minute lecture by Nassim Taleb on "Probability Distribution" will teach you more about prediction trading than 2 months as a Quant intern at Jane Street.
Bookmark it & give it 9-minutes today. It’ll be the most productive start for your week. Then read article below.
Most articles on Dan Zanger open with a lot of zeroes, followed by the tangible aspects of his strategy — the chart patterns, the volume surges, the earnings growth, and so on.
The implication is that you, too, can achieve superperformance if you just copy that strategy.
But ‘plugging and playing’ his tactics from old interviews is a recipe for disappointment.
***
First, markets change.
For example, certain patterns that had previously worked well later became less reliable — Zanger gave the cup and handle as an example in 2000. And in 2009, he felt that follow-through had become less strong on breakouts due to the rise of ETFs and algorithmic trading.
Bottom line, trying to directly apply his techniques in today’s markets is unlikely to work well.
***
Second, like many great discretionary traders, he makes his strategy sound deceptively simple.
In a 2003 interview, he summarised his approach as “I own what the big institutions want to own and go when they go”, then added he “buy[s] leading high-beta stocks coming out of well-defined patterns”. (Answers in other interviews revolve around the same idea, expressed differently.)
But if you take his more specific answers at face value, he appears to constantly contradict himself. For example, he gave different answers when asked about his favourite chart pattern. And in one interview, he described two different things as “everything” in two consecutive answers.
The true answer is that it depends on the context.
Different tactics work best in different environments. Knowing when to use which requires a deep understanding of a simple idea: being in the stocks that the market wants to move up the most.
I call this Nuanced Simplicity. And you can’t expect anyone to lay out every nuance — learnt after thousands of hours of experience — in just an interview.
***
Third, you need to do your own homework and learn from your mistakes.
Despite the simplicity of Zanger’s strategy, it took him years to “put it all together”. His ‘overnight success’ in the dot-com era was actually over a decade in the making, then he continued to learn many hard lessons in the years that followed his record-breaking run.
…because neither Zanger nor his strategy are perfect.
His secret isn’t a no-fail chart pattern. And after making his initial $42 million, he openly admitted to giving much of those gains back between taxes, getting caught in a nasty gap down in fibre optics, and his first prolonged bear market (2000–2003).
That taught him some massive lessons:
• The importance of environment — and the value of trading less overall, then aggressively at the right time.
• Pay attention to liquidity — particularly as your account grows.
You see these changes in how he talked before and after that bear market — and he had several triple-digit years after going through this school of hard knocks.
The same goes for other huge lessons that shaped his attitude. For example, when he became too enamoured with certain stocks and blew up in the mid-’90s, he became extremely quick to sell any stock not exploding higher.
Has that caused him to miss big (but slower) moves? Of course. But he accepts that trade-off.
…and that imperfect strategy obviously works. He didn’t just make his own fortune that way, but via his newsletter, has helped many other traders improve their ability to identify the market’s biggest movers. (Qullamaggie springs to mind!)
***
So, is there value in studying Zanger’s strategy?
Absolutely.
But don’t try to copy it blindly. Ask questions. Figure out the timeless principles and how Zanger applies them. Query WHY, and when, they work for him. How did he arrive at his tactics? What is their purpose?
The better you can answer such questions, the better your insight into how to make his ideas work for YOU.
Most screeners for small cap stocks are useless
Wrong floats. Outdated data. Generic filters that catch nothing.
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After hitting $12M+, @Jackaroo_Trades lost motivation from taxes—but turned $10K into $4.5M in 17 months using a Roth IRA strategy… now he mentors others to do the same.
Learn how: https://t.co/II7ASppoW8
@timothysykes
This is Leopold Aschenbrenner and this clip is from before the hedge fund, before the 13F filings, he raised $225 million and turned it into over $5.5 billion.
This is the thesis in its raw form, his point is simple, look at the jump from GPT-2 to GPT-4 (Save this).
GPT 2 was 2019 and it could sort of count to five before getting confused.
GPT 4 arrived just 3–4 years later scoring in the 90th percentile on exams like the bar, LSAT, and GRE, while solving complex math and playing chess.
That’s not incremental progress but rather a leap into an entirely new category of intelligence in less time than a college degree and his conclusion, play that forward.
Just a few more jumps like that, on a fairly short time horizon, basically this decade and we're going to hit extremely, extremely powerful systems.
Now here's where it gets interesting. Leopold didn't just say this but he put real money behind every implication of it.
His thesis was, if AI scales this fast, it will need compute at a scale the world has never built before.
The bottleneck won't be the algorithms bur rather the physical infrastructure like power, data centers and networking.
So while everyone else was buying Nvidia, Leopold was buying what Nvidia depends on.
Bloom Energy, CoreWeave, Lumentum, Core Scientific, Iren, Applied Digital, even Intel calls all ripping as AI turns power, compute, and chips into the real bottlenecks.
The whole fund is just the GPT-2 to GPT-4 chart, extended forward, and then asked, what does the world need to exist for that to happen?
He answered that question, then bought it.
Milk Road PRO is doing the same, come join us for our entire thesis Link below.
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Jane Street hired this junior at $220k-$600k /year because he uses AI to analyse TRILLIONS of data
in this 1-hour lecture - he show how to research trillion of data points thanks to his machine
Bookmark & watch it, instead of Netflix to learn how to do the same!
For @WIRED, I spoke to a med student in India who made thousands of dollars duping MAGA fans by creating a blonde AI influencer named "Emily Hart." Here's how he did it.