Beaver

The lecture is always free. Almost nobody watches it.

[Beaver @Beaver_0x]

https://t.co/IHhM8Fe47c

[Beaver @Beaver_0x]

In 2012, the sitting Chairman of the Federal Reserve stood in front of a room of undergrads and said, flatly, that the Federal Reserve had failed. Not "made mistakes." Failed. At both halves of its own job, during the worst economic collapse in American history.

Ben Bernanke, still actively running the Fed at the time, taught a free four-lecture course at George Washington University in March 2012 on why central banks exist and what his own institution got wrong the last time the world fell apart. Lecture 1 alone details more than 9,700 of the nation's 25,000 banks suspending operations between 1929 and 1933 — nearly 40% of the entire American banking system, gone, while the Fed sat on the sidelines providing what he calls "only minimal credit."

He puts the actual quote from a sitting US Treasury Secretary on the screen: "Liquidate labor, liquidate stocks, liquidate the farmers, liquidate real estate." That was the dominant economic philosophy of 1931 — that the Depression was a necessary cleansing the government shouldn't interfere with. Bernanke names the doctrine and states plainly that the Fed effectively agreed with it, and that agreeing with it was the mistake.

He also hands the class the actual rule for stopping a bank panic, written in 1873 by a British journalist named Walter Bagehot: lend freely, against good collateral, at a penalty rate. Simple enough to fit on an index card, ignored by his own institution in the 1930s, and — though he doesn't say it outright in this lecture — the exact playbook he himself had just finished running in 2008.

That's the real subject of the lecture. A sitting Fed Chairman isn't just teaching history. He's building the case, brick by brick, for why he'd made the most aggressive, most criticized interventions of his own career a few years earlier — by showing the class exactly what happens to a country when a central bank doesn't.

Executive education programs charge thousands of dollars for "central banking fundamentals" seminars covering a fraction of this, taught by people who never had the actual job.

The lecture is free. Watching the man who made the real decision explain the mistake he was determined not to repeat is the entire edge.

[Beaver @Beaver_0x]

https://t.co/IHhM8Fe47c

[Beaver @Beaver_0x]

The former head of AI at Tesla asked a room of startup founders a grade-school question: which number is bigger, 9.11 or 9.9? The best AI models in the world get it wrong.

That's Andrej Karpathy, June 2025, keynote at Y Combinator's AI Startup School. Free. Recorded, transcribed, sitting on YouTube. He built Tesla Autopilot, was a founding member of OpenAI, and literally coined the term people now use for an entire category of software — "vibe coding," a phrase he tweeted almost as a joke that now has its own Wikipedia page.

His point with the 9.11 question isn't that AI is overhyped. It's stranger than that. The same model that fumbles a first-grade comparison can solve graduate-level math in the next sentence. He calls it "jagged intelligence" — capability that doesn't rise smoothly like a human's does from childhood, but spikes and craters unpredictably, brilliant in one spot and broken one inch away.

Then he goes further: current AI doesn't just have gaps, it has no memory of closing them. He compares working with an LLM to working with a coworker who has anterograde amnesia — the condition from Memento, or 50 First Dates. Every conversation starts from zero. It can be a genius for one session and remember none of it the next morning.

The line that should be tattooed on every founder in that room: "Demo is works-dot-any. Product is works-dot-all." He tells the story on himself — riding an early self-driving prototype in 2014 with zero interventions, walking away convinced the problem was basically solved. It took another decade of unglamorous work to turn that one flawless demo into an actual product people could trust every day.

AI consultants now charge five figures to explain "prompt engineering" concepts this free talk covers more precisely, for a room that got it live, from someone who helped build the thing they're now trying to sell advice about.

The talk is free. Knowing the exact gap between "it worked once" and "it always works" is the entire edge.

[Beaver @Beaver_0x]

https://t.co/IHhM8Fe47c

[Beaver @Beaver_0x]

Two people talk over each other at a party. Two microphones catch the mess. One line of code, and a computer pulls the two voices apart perfectly, without ever being told what a voice even is.

That's not a magic trick. It's minute fifty of Lecture 1, Stanford CS229, Andrew Ng, recorded in 2008. He plays the room the actual audio: two overlapping recordings, one in English counting to ten, one in Spanish counting to ten, tangled together like real party noise. Then he plays the output. Two clean, separated voices. No transcript. No dictionary. The algorithm was never told English exists, or Spanish, or what a number is. It just found the pattern hiding in the noise.

He tells the class the algorithm has a name, independent component analysis, and that it took researchers years to develop. Then he says the part that should be illegal to say out loud in a room full of grad students paying Stanford tuition: in MATLAB, it runs in one line of code.

This was recorded years before "machine learning" was something people put on a resume. Ng opens the lecture mentioning an article that had just ranked it the single most in-demand skill in all of IT — back when most of the room still had to ask what the field even was. Half the students were computer science majors. The rest came from biology, aero-astro, chemistry, industry — people who had no idea this class would end up being one of the most watched pieces of educational video on the internet.

He also tells on himself: a friend's former student came back years later, thanked him for the machine learning class that made him rich, and admitted the only thing he'd actually used was the MATLAB.

Bootcamps now charge thousands of dollars to teach a fraction of what's sitting in this one hour, free, recorded before most of their instructors had heard of the field.

The lecture is free. Noticing the pattern nobody told the algorithm to look for is the entire course.

[Beaver @Beaver_0x]

https://t.co/IHhM8Fe47c

[Beaver @Beaver_0x]

An MIT mathematician built the world's first wearable computer in 1961 to prove every casino on earth had been running roulette wrong for a hundred years.

The device worked. He walked into a Nevada casino with it hidden in his shoe, played a single night, and beat the house edge before the earpiece wire snapped. Casinos rewrote their roulette rules within a year.

Then he did the same thing to blackjack. Then to Wall Street.

His name is Ed Thorp. He is 93. He beat Vegas at two games, ran a hedge fund for nineteen straight years without a losing quarter, and caught Bernie Madoff seventeen years before the SEC.

The 92-minute interview in this video is Thorp at his home in Newport Beach explaining exactly how he did all of it.

His personal fortune is estimated at $800 million. He earned every dollar from applying one 4-page paper written by an engineer at Bell Labs in 1956
The paper is called the Kelly criterion. It tells you the exact fraction of your capital to risk on any bet where you know the edge and the odds. John Kelly published it in the Bell System Technical Journal. It has been free in the archive for seventy years.

Thorp read it, made one modification, and never lost money at scale again.

The modification is called half Kelly. Full Kelly is the mathematically maximum bet your edge justifies. Half Kelly gives up a small fraction of return for a huge cut in drawdown. Every quant who blows up is running more than half Kelly. Every quant who compounds for decades is running less.

Princeton Newport Partners, the fund he founded in 1969, compounded at roughly 20 percent a year for nineteen years. Not one losing quarter across the entire run. In 1988, at the top of his edge, he liquidated the fund and refused to open another.

Every quant desk on Wall Street has spent the four decades since trying to reproduce what he did. Nobody has matched the runMonths In 1991 an investor asked Thorp to review Madoff's returns for a possible allocation. Within a week he had proof of fraud from reconstructing Madoff's trades against public exchange data. He sent a memo to the SEC. They filed it. Madoff ran the Ponzi for another seventeen years before his sons turned him in.

"You can have enough. And it is better than not having enough."

That is Thorp when journalists ask why he closed the fund at the peak. He had it. Almost nobody in his field ever does.

The Kelly paper is free. Thorp's book "A Man for All Markets" is under twenty dollars. His 92- minute interview is on YouTube.

Almost every hedge fund analyst has read the paper. Almost every retail trader has not. That is the entire moat.

The math is free. The willingness to bet half of the maximum and walk away when it is enough is a much rarer commodity than the formula itself.

[Beaver @Beaver_0x]

In 1988, a Yale economist asked people in two American cities the exact same question about their housing market. The answers weren't just different. They were almost opposite.

In Los Angeles, the average person expected home prices to rise 10% a year for the next decade — enough to more than double in seven years. In Milwaukee, the same survey got 4%, basically just inflation. Then came the real tell: 80% of people in Los Angeles said they were afraid that if they didn't buy now, they'd never be able to afford a house later. In Milwaukee, only 27% felt that fear. Same country, same year, same mortgage math available to both. Completely different psychology.

That's Robert Shiller, Yale, Econ 252, "Real Estate Finance and Its Vulnerability to Crisis." Free, filmed live on February 25, 2008 — recorded in the exact months before the US government would seize Fannie Mae and Freddie Mac, with subprime lender Countrywide's CEO getting hauled in front of Congress that same week.

He shows the class something most people never see: a hundred-year chart of US home prices, adjusted for inflation. For a full century, prices barely trended anywhere. Then, starting in the late 1990s, the line goes nearly vertical — the only comparable spike in the whole chart is the scramble for housing right after World War II, when construction had been halted for years and the Baby Boom hit all at once. This time, none of those forces were in play. Just a story people started believing about a city, and then about a country.

He also walks through why the 30-year mortgage exists at all. Before 1933, mortgages were five years, interest-only, with the entire balance due in one lump "balloon" payment at the end — which is exactly what turned a stock market crash into the worst housing collapse in US history, as unemployed families couldn't refinance and lost their homes by the millions. The self-amortizing, decades-long mortgage wasn't a bank's idea. It was a New Deal fix for a five-year loan structure that had already proven it could break the country once.

Wealth management firms charge real money to explain housing psychology that boils down to one finding this professor gave away for free: the "glamour cities" feel the fear first, and the fear is what moves the price — not the fundamentals everyone checks after the fact.

The lecture is free. Noticing which city you're standing in, psychologically, before you sign anything, is the entire edge.

[Beaver @Beaver_0x]

Tens of thousands of 18-year-olds in England are currently outside education, outside employment, and outside training. Lucy Powell looked at that number this week and called it a verdict on the A-level system.

The Department for Education is moving hard toward T-levels and vocational qualifications. The argument behind it is almost embarrassingly simple. The A-level was designed in 1951 for an economy that needed university-prepared workers in roughly 15% of jobs. The economy it now operates in needs skilled technicians, healthcare workers, and engineers at a scale that pathway was never built to produce.

T-levels carry 1,800 hours of industry placement inside a two-year qualification. The employer sets the standard. The credential is the skill demonstrated in a workplace, not the grade awarded in an exam hall. That distinction matters more than it sounds.

The students dropping out are not failing the system. They are accurately reading it. A qualification that leads to a three-year degree that leads to a job that could have been entered at 18 is a slow road with high tolls. Some portion of these students are simply declining to pay them.

Germany built the backbone of its industrial economy on a dual system where vocational training and academic education are parallel tracks with equal status. Switzerland runs the same model. In both countries, the apprenticeship route into engineering or finance carries no stigma and often pays a higher starting salary than the university route.

England has been trying to close this gap for thirty years. Every attempt has stalled on the same cultural obstruction: the belief, embedded in school guidance offices and parent expectations alike, that a university degree is the only credential that counts. What Powell is trying to change is not just the qualification framework. It is that belief.

Whether the announcement survives the next election cycle is a separate question. Whether the tens of thousands of 18-year-olds currently outside education can wait for it to settle is another.

[Beaver @Beaver_0x]

@hey_AndrewUSA AI history is a rollercoaster — and we’re only getting started.

[Beaver @Beaver_0x]

An MIT mathematician built the world's first wearable computer in 1961 to prove every casino on earth had been running roulette wrong for a hundred years.

The device worked. He walked into a Nevada casino with it hidden in his shoe, played a single night, and beat the house edge before the earpiece wire snapped. Casinos rewrote their roulette rules within a year.

Then he did the same thing to blackjack. Then to Wall Street.

His name is Ed Thorp. He is 93. He beat Vegas at two games, ran a hedge fund for nineteen straight years without a losing quarter, and caught Bernie Madoff seventeen years before the SEC.

The 92-minute interview in this video is Thorp at his home in Newport Beach explaining exactly how he did all of it.

His personal fortune is estimated at $800 million. He earned every dollar from applying one 4-page paper written by an engineer at Bell Labs in 1956
The paper is called the Kelly criterion. It tells you the exact fraction of your capital to risk on any bet where you know the edge and the odds. John Kelly published it in the Bell System Technical Journal. It has been free in the archive for seventy years.

Thorp read it, made one modification, and never lost money at scale again.

The modification is called half Kelly. Full Kelly is the mathematically maximum bet your edge justifies. Half Kelly gives up a small fraction of return for a huge cut in drawdown. Every quant who blows up is running more than half Kelly. Every quant who compounds for decades is running less.

Princeton Newport Partners, the fund he founded in 1969, compounded at roughly 20 percent a year for nineteen years. Not one losing quarter across the entire run. In 1988, at the top of his edge, he liquidated the fund and refused to open another.

Every quant desk on Wall Street has spent the four decades since trying to reproduce what he did. Nobody has matched the runMonths In 1991 an investor asked Thorp to review Madoff's returns for a possible allocation. Within a week he had proof of fraud from reconstructing Madoff's trades against public exchange data. He sent a memo to the SEC. They filed it. Madoff ran the Ponzi for another seventeen years before his sons turned him in.

"You can have enough. And it is better than not having enough."

That is Thorp when journalists ask why he closed the fund at the peak. He had it. Almost nobody in his field ever does.

The Kelly paper is free. Thorp's book "A Man for All Markets" is under twenty dollars. His 92- minute interview is on YouTube.

Almost every hedge fund analyst has read the paper. Almost every retail trader has not. That is the entire moat.

The math is free. The willingness to bet half of the maximum and walk away when it is enough is a much rarer commodity than the formula itself.

[Beaver @Beaver_0x]

An MIT professor opened his first lecture by auctioning off a sealed box to twenty-two-year-olds who had zero information about what was inside.

No touching it. No shaking it. No hints. Just a box.

The bidding started at $1. It closed at $45.

He ripped it open. An iPod Nano, retail value $149. The class had priced an unknown object, sight unseen, at roughly a third of its real worth — in about ninety seconds, with nothing but each other's bids to go on.

That's minute twenty of session one of 15.401, Finance Theory, taught by Andrew Lo at MIT Sloan. Free. On YouTube. Since 2008.

Before the auction, Lo puts up three names on the board: James Simons, a math professor who built the most successful hedge fund in history. Warren Buffett, who runs his empire with a tiny staff and literal high-school arithmetic. Jack Welch, an engineer by training who quadrupled GE's revenue without touching a single equation from his own PhD.

Three totally different backgrounds. One shared trait — they all speak the language of finance instinctively.

Then Lo tells the room something most finance bootcamps charge $3,000 a weekend to imply and never actually prove: finance isn't really about complex math. It's about two problems only — valuing things, and deciding what to do once you know the value. Everything else, thirteen weeks of it, is commentary.

There are no problem sets in this course. He hands you the entire exam question bank on day one and tells you the majority of exam points come straight from it. He says it flat out: memorize the whole stack and you'll pass — except by then you'll have accidentally learned finance.

The full thirteen weeks are still sitting on MIT OpenCourseWare, untouched by most people who bookmark it.

The lecture is free. Bidding on the box after everyone else already has is the entire course in miniature.

[Beaver @Beaver_0x]

In 1988, a Yale economist asked people in two American cities the exact same question about their housing market. The answers weren't just different. They were almost opposite.

In Los Angeles, the average person expected home prices to rise 10% a year for the next decade — enough to more than double in seven years. In Milwaukee, the same survey got 4%, basically just inflation. Then came the real tell: 80% of people in Los Angeles said they were afraid that if they didn't buy now, they'd never be able to afford a house later. In Milwaukee, only 27% felt that fear. Same country, same year, same mortgage math available to both. Completely different psychology.

That's Robert Shiller, Yale, Econ 252, "Real Estate Finance and Its Vulnerability to Crisis." Free, filmed live on February 25, 2008 — recorded in the exact months before the US government would seize Fannie Mae and Freddie Mac, with subprime lender Countrywide's CEO getting hauled in front of Congress that same week.

He shows the class something most people never see: a hundred-year chart of US home prices, adjusted for inflation. For a full century, prices barely trended anywhere. Then, starting in the late 1990s, the line goes nearly vertical — the only comparable spike in the whole chart is the scramble for housing right after World War II, when construction had been halted for years and the Baby Boom hit all at once. This time, none of those forces were in play. Just a story people started believing about a city, and then about a country.

He also walks through why the 30-year mortgage exists at all. Before 1933, mortgages were five years, interest-only, with the entire balance due in one lump "balloon" payment at the end — which is exactly what turned a stock market crash into the worst housing collapse in US history, as unemployed families couldn't refinance and lost their homes by the millions. The self-amortizing, decades-long mortgage wasn't a bank's idea. It was a New Deal fix for a five-year loan structure that had already proven it could break the country once.

Wealth management firms charge real money to explain housing psychology that boils down to one finding this professor gave away for free: the "glamour cities" feel the fear first, and the fear is what moves the price — not the fundamentals everyone checks after the fact.

The lecture is free. Noticing which city you're standing in, psychologically, before you sign anything, is the entire edge.

[Beaver @Beaver_0x]

https://t.co/IHhM8Fe47c

[Beaver @Beaver_0x]

https://t.co/Ab2HAFBW0K

[Beaver @Beaver_0x]

@hey_AndrewUSA The history of AI is way deeper than most people realize. Great thread.

[Beaver @Beaver_0x]

In the middle of the worst financial crisis in eighty years, an MIT professor showed his class the exact mathematical mistake that caused it — using two ordinary stocks and a piece of chalk.

General Motors and Motorola. Historical correlation: 0.37. Blend them in a portfolio instead of holding either alone, and something that shouldn't be possible happens: you get a higher return than GM by itself, at lower risk than GM by itself. Not a tradeoff. Both, at once, for free. Andrew Lo tells the room plainly: "I just made all of you better off with this piece of knowledge."

Then he pushes the thought experiment further. What if two assets were perfectly negatively correlated — minus 100%? The math stops looking like an investment and starts looking like magic: a portfolio with an annual return of roughly 16%, and zero risk. Literally zero. He stops the class right there. "You tell me if you know of any investment opportunities that gives you a return of 16% a year with no risk, and I'll examine that for you carefully." It doesn't exist. It can't. And the reason it can't is the entire lesson.

Real assets are never that perfectly correlated — which is exactly where the crisis came from. Portfolio managers had pooled thousands of mortgages, historically almost uncorrelated with each other, and treated that near-zero average correlation as proof the pool carried almost no risk. Then home prices fell nationally, and defaults stopped being independent events. They started happening everywhere, at once, for the same reason, at the same time. "Overnight, literally overnight, your risks can shoot up," Lo tells them, mid-crisis, in real time.

His explanation for why correlation breaks exactly when you need it most isn't a formula. It's an airport. He compares it to Thanksgiving week — any ordinary Wednesday, traffic is unremarkable and uncorrelated with anything. The Wednesday before Thanksgiving, everyone independently decides to travel at once, and the terminal is chaos. "Correlation is not a physical quantity," he says. "That's the problem with physics and biology. Physics has parameters that don't change over time. I wish we could have that in finance. We don't."

Risk consultants charge institutional clients enormous fees to explain correlation breakdown after it's already cost them the money. This room got the mechanism explained for free, live, while it was still happening outside the building.

The lecture is free. Remembering that correlation is a record of human panic, not a constant, is the entire edge.

[Beaver @Beaver_0x]

https://t.co/IHhM8Fe47c

[Beaver @Beaver_0x]

The best mutual fund manager in history once walked into a supermarket and bought 62 pairs of pantyhose, in every color and shape they had, just to protect his biggest stock position.

Peter Lynch ran Fidelity's Magellan Fund for thirteen years, averaging 29.2% a year, the best track record of any fund on earth. His largest holding at one point was Hanes, which owned L'eggs pantyhose. A rival company launched a competing product called "No Nonsense," and Lynch couldn't tell from a spreadsheet whether it was actually better. So he didn't guess. He bought every version of it the store had, brought all 62 pairs into the office, and handed them out to anyone who'd try them, told them to report back.

Three weeks later, the verdict came in: not as good. He held the stock. It became one of the biggest winners in Magellan's history.

That's from his 1994 speech at the National Press Club, still sitting free on YouTube, thirty years later. His entire philosophy fits in one line: "If you can't explain to a 10-year-old in two minutes or less why you own a stock, you shouldn't own it." He proves the opposite case live, in the same speech, by reading out a fake computer-chip spec sheet stuffed with jargon nobody in the room can parse — a "16-bit dual-port memory... double-defused metal oxide semiconductor" — and says if you own something like that, you'll never make money on it, because you have no way to know if it's actually good.

Wall Street research analysts get paid six figures a year to build models estimating exactly what Lynch got for the price of a shopping bag full of tights.

The speech is free. Doing the research nobody else thinks to do is the entire edge.

[Beaver @Beaver_0x]

https://t.co/Ab2HAFBW0K

[Beaver @Beaver_0x]

Two million people have watched a dead billionaire’s lecture and still pay $500 a month for a “mental models” course that steals half its content from it.
He gave the talk once, at Harvard, in 1995. Never repeated it. Never turned it into a book on its own — it just got tucked into the back of someone else’s.
His name was Charlie Munger. Vice Chairman of Berkshire Hathaway for over four decades. The guy Warren Buffett called the architect of the whole operation.
The talk was called “The Psychology of Human Misjudgment.”
Twenty-five numbered tendencies. Why smart people make dumb decisions. Reward bias, envy, denial, social proof, contrast effect — the entire anatomy of being wrong, laid out like a parts manual.
No slides. No framework diagram to sell. No certification at the end.
“I try to get rid of people who always confidently answer questions about which they don’t have any real knowledge.”
That’s page one. Munger built a $780 billion company partly by refusing to sound smart about things he didn’t understand — and then handed you the instructions for how he did it, for free.
Investing newsletters charge $300 a year to repackage four of the twenty-five tendencies as “cognitive bias cheat sheets.” Business schools build entire electives around a summary of a talk you can read in ninety minutes.
The full transcript is free. It’s in “Poor Charlie’s Almanack,” excerpted everywhere, sitting on Farnam Street.
Munger died in 2023. Almost nobody who’s read the speech has actually run their last five bad decisions through his list.
The list is free. Doing the audit on yourself is the entire edge.

[Beaver @Beaver_0x]

top-10 MBA finance concentration costs around $200,000 and two years.
MIT released the core of that curriculum free in course 15.401.
Lecture 1 opens by separating expected returns from realized returns. The distinction is drawn on the board before any formulas appear. Students watch as the two concepts are placed side by side so the difference in their behavior becomes visible at once.
It then shows how risk gets priced across different time horizons. The pricing mechanism emerges directly from that separation. Different horizons carry different exposures because the realized path can deviate in ways the expectation did not anticipate.
The math that sits under every discount rate follows from that single framing. Each step is derived in sequence without jumping ahead.
By the middle of the hour the lecture has already made clear why most models break when they meet actual markets.
Wall Street training programs spend two years trying to install this in new analysts. The attrition in that process is the entire reason senior practitioners are paid what they are.
The lecture is free.
Knowing it exists is the entire edge.

[Beaver @Beaver_0x]

@hey_AndrewUSA Absolutely fascinating breakdown of AI’s journey!

[Beaver @Beaver_0x]

top-10 MBA finance concentration costs around $200,000 and two years.
MIT released the core of that curriculum free in course 15.401.
Lecture 1 opens by separating expected returns from realized returns. The distinction is drawn on the board before any formulas appear. Students watch as the two concepts are placed side by side so the difference in their behavior becomes visible at once.
It then shows how risk gets priced across different time horizons. The pricing mechanism emerges directly from that separation. Different horizons carry different exposures because the realized path can deviate in ways the expectation did not anticipate.
The math that sits under every discount rate follows from that single framing. Each step is derived in sequence without jumping ahead.
By the middle of the hour the lecture has already made clear why most models break when they meet actual markets.
Wall Street training programs spend two years trying to install this in new analysts. The attrition in that process is the entire reason senior practitioners are paid what they are.
The lecture is free.
Knowing it exists is the entire edge.

[Beaver @Beaver_0x]

https://t.co/Ab2HAFBW0K

[Beaver @Beaver_0x]

Harvard Business School charges $73,440 a year in tuition. MIT put the equivalent finance curriculum on the internet for free.

Three courses. Financial economics, quantitative analysis, and investment theory. Each is a full semester of graduate-level material, and together they build the framework that separates people who understand markets from people who have opinions about them.

The financial economics course (MIT 15.401) starts with one question: what is risk? Not as a feeling. As a mathematical object. The part of an asset's variance that cannot be diversified away, and therefore must be compensated. The course works through the Capital Asset Pricing Model from first principles, then immediately shows where it breaks. That combination, the model and its own failure conditions, is exactly what gets left out of the CFA prep books and the YouTube explainer videos.

The quantitative analysis course goes further. Stochastic calculus. The Black-Scholes derivation, done line by line, not summarized. The mathematical infrastructure underneath every options desk in New York and London. The course does not translate it down. It teaches it at the level it is taught to MIT doctoral students.

Investment theory closes the sequence. Portfolio construction, factor models, and the empirical evidence on what actually predicts returns. Not the clean textbook version. The version where anomalies exist, persist, and resist explanation. Where the academic literature disagrees with itself and the real edge sits in knowing which disagreements matter.

Finance educators started circulating these courses again this spring. Professors linking them in threads. The reason is simple: there is no comparable free resource at this level.

Wall Street training programs spend roughly $40,000 per analyst in the first twelve months. The source material is free on MIT OpenCourseWare.

The lecture is free. Knowing where to look is the entire edge.