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jane street, two sigma, man group put their actual code on github 22 repos from firms running $200 billion combined - all public, all free nobody's talking about this because nobody thought to check what's in those repos isn't just tooling - it's their mental model these firms don't...

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Livsun

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61,062 次观看 • 28 天前 •via X (Twitter)

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quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask
1:27

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask

Livsun

13,176 次观看 • 1 个月前

the finance kids everyone envied in college made $120k at goldman the quiet math kids nobody noticed made $650k at firms most people can't name jane street. citadel. two sigma. d.e. shaw they don't recruit from linkedin. they recruit from math olympiads and competitive programming leaderboards the filter is 4 things: probability, coding, mental math, game theory same problems recycled every year, same structure every round the prep path is documented, free, and takes 8 months a kid from a state school who grinds this for 8 months has beaten ivy leaguers who showed up unprepared - credential matters less than the pattern recognition most people spend 3 years trying to break into banking for $120k the people who looked one level up spent 8 months and landed 5x the salary the information to do this has existed for years Bookmark this and upgrade your brain the gap isn't talent it's that nobody told you where to look
4:33

the finance kids everyone envied in college made $120k at goldman the quiet math kids nobody noticed made $650k at firms most people can't name jane street. citadel. two sigma. d.e. shaw they don't recruit from linkedin. they recruit from math olympiads and competitive programming leaderboards the filter is 4 things: probability, coding, mental math, game theory same problems recycled every year, same structure every round the prep path is documented, free, and takes 8 months a kid from a state school who grinds this for 8 months has beaten ivy leaguers who showed up unprepared - credential matters less than the pattern recognition most people spend 3 years trying to break into banking for $120k the people who looked one level up spent 8 months and landed 5x the salary the information to do this has existed for years Bookmark this and upgrade your brain the gap isn't talent it's that nobody told you where to look

Livsun

1,281,809 次观看 • 1 个月前

100 billion dollars made without predicting a single price direction not a direction call, a macro bet, or a news trade in sight they asked a completely different question than every retail trader alive retail: "will this go up?" quant: "what state is the market in, and where does that state historically lead?" same data. completely different game markets aren't random - they cycle through states: trending, ranging, compressing, reversing each state has a fixed historical probability of transitioning to the next that's the whole edge. not a signal, not an indicator - just conditional probability you don't need to know what fed will do or what earnings will print you need to know which state you're in and what that state leads to 68% of the time framework has been public since 1906 Bookmark it before it gone data is free, implementation is 200 lines of python, paper costs nothing they built 100 billion off math sitting in statistics textbooks your whole life you were staring at candles while they ran probability tables
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100 billion dollars made without predicting a single price direction not a direction call, a macro bet, or a news trade in sight they asked a completely different question than every retail trader alive retail: "will this go up?" quant: "what state is the market in, and where does that state historically lead?" same data. completely different game markets aren't random - they cycle through states: trending, ranging, compressing, reversing each state has a fixed historical probability of transitioning to the next that's the whole edge. not a signal, not an indicator - just conditional probability you don't need to know what fed will do or what earnings will print you need to know which state you're in and what that state leads to 68% of the time framework has been public since 1906 Bookmark it before it gone data is free, implementation is 200 lines of python, paper costs nothing they built 100 billion off math sitting in statistics textbooks your whole life you were staring at candles while they ran probability tables

Livsun

13,335 次观看 • 28 天前

THE MOST EXPENSIVE ENGINEERING TEAMS ON EARTH JUST PUT THEIR FINANCIAL TOOLS ON GITHUB FOR FREE. Jane Street. Goldman Sachs. JP Morgan. BlackRock. Hudson River Trading. Two Sigma. D.E. Shaw. Seven firms. Seven repos. Billions in engineering talent open sourced. Save this before you scroll past it. 1. Jane Street — magic-trace 5,300 stars. Process tracer powered by Intel PT. When your profiler is blind this sees every CPU instruction. 2. Goldman Sachs — gs-quant Derivative pricing the GS traders use at their actual desks. MIT licensed. Free. 3. JP Morgan — perspective What JPMorgan traders use to watch markets in real time. A $24,000 per year terminal. Available to anyone with a GitHub account. 4. BlackRock — lcso Rust optimizer for portfolio problems. Where scipy gives up this works. Built for problems that break standard optimization libraries. 5. Hudson River Trading — corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest US trading firms. 6. Two Sigma — flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. The data infrastructure layer behind systematic trading at scale. 7. D.E. Shaw — pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. The firm that built the tool is now giving you the enhancement for free. Here is what this list actually represents. These seven firms collectively employ thousands of engineers earning $300,000 to $1,000,000 per year. The tools they built to solve their hardest problems are the same tools you now have access to for free. The information asymmetry that used to separate a quant at Goldman from a developer at home just narrowed significantly. The infrastructure is free. The edge now belongs to whoever knows how to use it. Bookmark this before you pay for another financial data tool. Follow CyrilXBT for every elite engineering resource the moment it surfaces.
1:47

THE MOST EXPENSIVE ENGINEERING TEAMS ON EARTH JUST PUT THEIR FINANCIAL TOOLS ON GITHUB FOR FREE. Jane Street. Goldman Sachs. JP Morgan. BlackRock. Hudson River Trading. Two Sigma. D.E. Shaw. Seven firms. Seven repos. Billions in engineering talent open sourced. Save this before you scroll past it. 1. Jane Street — magic-trace 5,300 stars. Process tracer powered by Intel PT. When your profiler is blind this sees every CPU instruction. 2. Goldman Sachs — gs-quant Derivative pricing the GS traders use at their actual desks. MIT licensed. Free. 3. JP Morgan — perspective What JPMorgan traders use to watch markets in real time. A $24,000 per year terminal. Available to anyone with a GitHub account. 4. BlackRock — lcso Rust optimizer for portfolio problems. Where scipy gives up this works. Built for problems that break standard optimization libraries. 5. Hudson River Trading — corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest US trading firms. 6. Two Sigma — flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. The data infrastructure layer behind systematic trading at scale. 7. D.E. Shaw — pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. The firm that built the tool is now giving you the enhancement for free. Here is what this list actually represents. These seven firms collectively employ thousands of engineers earning $300,000 to $1,000,000 per year. The tools they built to solve their hardest problems are the same tools you now have access to for free. The information asymmetry that used to separate a quant at Goldman from a developer at home just narrowed significantly. The infrastructure is free. The edge now belongs to whoever knows how to use it. Bookmark this before you pay for another financial data tool. Follow CyrilXBT for every elite engineering resource the moment it surfaces.

CyrilXBT

42,296 次观看 • 1 个月前

a citadel options trader told me the one concept they test first in every quant interview and it's been sitting on a free website for years not a hedge fund textbook, not a $3,000 prep program. a free course syllabus - options greeks, volatility, quizzes - publicly available, almost nobody applying has ever opened it concept is expected value across a probability distribution retail looks at a chart and asks which direction. quant looks at expected payout across every possible outcome and asks if that number beats the cost of the trade - completely different question options pricing is just EV made rigorous fair value of any position = sum of (each outcome's probability x its payoff), discounted back. that formula is in every intro stats course and every free options curriculum these firms post publicly citadel's first round isn't a stock pitch or a DCF it's a market-making problem: "set me a bid and ask on a coin flip" if you can solve that fast and size it correctly, you can price any derivative on earth prep is documented in 6 categories: probability, greeks, volatility, mental math, coding, microstructure firms don't want you pattern-matching to old trades. they want raw EV instinct - and that's in free courses that have been online for years entry-level quant traders at these firms start at $300k. senior traders clear $650k+ most people never make it past round 1. not because they weren't smart - because nobody told them what the test was actually measuring Bookmark this they kept you reading charts while they were drilling expected value at 2am
0:56

a citadel options trader told me the one concept they test first in every quant interview and it's been sitting on a free website for years not a hedge fund textbook, not a $3,000 prep program. a free course syllabus - options greeks, volatility, quizzes - publicly available, almost nobody applying has ever opened it concept is expected value across a probability distribution retail looks at a chart and asks which direction. quant looks at expected payout across every possible outcome and asks if that number beats the cost of the trade - completely different question options pricing is just EV made rigorous fair value of any position = sum of (each outcome's probability x its payoff), discounted back. that formula is in every intro stats course and every free options curriculum these firms post publicly citadel's first round isn't a stock pitch or a DCF it's a market-making problem: "set me a bid and ask on a coin flip" if you can solve that fast and size it correctly, you can price any derivative on earth prep is documented in 6 categories: probability, greeks, volatility, mental math, coding, microstructure firms don't want you pattern-matching to old trades. they want raw EV instinct - and that's in free courses that have been online for years entry-level quant traders at these firms start at $300k. senior traders clear $650k+ most people never make it past round 1. not because they weren't smart - because nobody told them what the test was actually measuring Bookmark this they kept you reading charts while they were drilling expected value at 2am

Livsun

25,684 次观看 • 1 个月前

hedge funds extract their edge before the candle closes not from price, from what's moving behind it signal is called order flow toxicity every large informed trader disguises their order as retail noise - 40-80 small orders, timed to look random to a human, invisible. to an LSTM trained on cancel-to-fill ratios - readable in 400 milliseconds two sigma doesn't ask "will price go up?" - they ask "is someone with information buying right now, before the market reprices?" detection model is 3 layers: > input: bid-ask spread velocity, cancel-to-fill ratio, trade size entropy > hidden: LSTM over 500ms rolling window > output: toxicity score 0-1 above 0.67: informed flow detected. enter with it below 0.67: noise. skip or fade 74% directional accuracy. 11-minute avg hold. runs on any level 2 feed data is free - CBOE publishes it. architecture is in every ML textbook since 2012 they've been running this since you were drawing trendlines Bookmark it and add in your strategy bottleneck was never the data - it was the question you were asking
0:48

hedge funds extract their edge before the candle closes not from price, from what's moving behind it signal is called order flow toxicity every large informed trader disguises their order as retail noise - 40-80 small orders, timed to look random to a human, invisible. to an LSTM trained on cancel-to-fill ratios - readable in 400 milliseconds two sigma doesn't ask "will price go up?" - they ask "is someone with information buying right now, before the market reprices?" detection model is 3 layers: > input: bid-ask spread velocity, cancel-to-fill ratio, trade size entropy > hidden: LSTM over 500ms rolling window > output: toxicity score 0-1 above 0.67: informed flow detected. enter with it below 0.67: noise. skip or fade 74% directional accuracy. 11-minute avg hold. runs on any level 2 feed data is free - CBOE publishes it. architecture is in every ML textbook since 2012 they've been running this since you were drawing trendlines Bookmark it and add in your strategy bottleneck was never the data - it was the question you were asking

Livsun

11,425 次观看 • 1 个月前

Why VC Firms Must Have Their Own Agents: "VC firms should have their own autonomous agents. PE firms will have agents analyzing markets and opportunities. If I were starting from scratch, I would build incredible data systems to evaluate where AI companies should go and which markets are right. The deciding factor will be how well founders and investors use autonomous agents in their jobs." jerry murdock Love to hear your thoughts on this Nikhil Basu Trivedi Matt Ocko Aydin Senkut Alexis Ohanian 🗽 Jared Sleeper?
0:47

Why VC Firms Must Have Their Own Agents: "VC firms should have their own autonomous agents. PE firms will have agents analyzing markets and opportunities. If I were starting from scratch, I would build incredible data systems to evaluate where AI companies should go and which markets are right. The deciding factor will be how well founders and investors use autonomous agents in their jobs." jerry murdock Love to hear your thoughts on this Nikhil Basu Trivedi Matt Ocko Aydin Senkut Alexis Ohanian 🗽 Jared Sleeper?

Harry Stebbings

22,307 次观看 • 4 个月前

every time you hit buy, the market maker on the other side already solved for two numbers not on the asset - on you specifically the first: their internal fair value. not the midpoint you see on screen their number is adjusted for the last 30 seconds of order flow, their current inventory imbalance and which volatility regime the model thinks the market is in it's different from the displayed price, and they never show it to you the second: your adverse selection probability. a live estimate of whether you're informed or just emotional if the model flags you as a potential informed trader, the spread widens before your fill - automatically, invisibly, before you notice anything changed that bad fill you blamed on "low liquidity"? sometimes it's a market maker's algorithm deciding you look too dangerous to fill at the quoted price this is Glosten-Milgrom - published 1985, sits in every market microstructure textbook on earth, runs inside every serious market making operation that's ever quoted you a price Bookmark this feature before it lost when you click buy, you're answering a question you didn't know was being asked retail spends all its time thinking about where price is going the desk filling your order is spending that same time thinking about you
0:30

every time you hit buy, the market maker on the other side already solved for two numbers not on the asset - on you specifically the first: their internal fair value. not the midpoint you see on screen their number is adjusted for the last 30 seconds of order flow, their current inventory imbalance and which volatility regime the model thinks the market is in it's different from the displayed price, and they never show it to you the second: your adverse selection probability. a live estimate of whether you're informed or just emotional if the model flags you as a potential informed trader, the spread widens before your fill - automatically, invisibly, before you notice anything changed that bad fill you blamed on "low liquidity"? sometimes it's a market maker's algorithm deciding you look too dangerous to fill at the quoted price this is Glosten-Milgrom - published 1985, sits in every market microstructure textbook on earth, runs inside every serious market making operation that's ever quoted you a price Bookmark this feature before it lost when you click buy, you're answering a question you didn't know was being asked retail spends all its time thinking about where price is going the desk filling your order is spending that same time thinking about you

Livsun

18,906 次观看 • 14 天前

every day before market open, the CBOE publishes exactly where market makers are forced to buy and sell not a theory, not a pattern - a number that actually moves price it's called dealer gamma exposure - GEX - and it creates real price magnets quants have traded against for years here's why it works when you buy a call option, a market maker sells it to you and has to hedge that hedge shifts as price moves - they buy when it rises, sell when it falls negative GEX zones are where they sell into every rally, cap every breakout, crush every momentum move strikes with highest open interest aren't random - they're gravitational markets don't break through those levels cleanly because a billion-dollar dealer is actively hedging against it quants call them gamma walls data is free, published every morning on the CBOE site - no API, no Bloomberg terminal retail calls those same levels "resistance" and chalks it up to vibes bookmark this before it lost it's not vibes. it's math someone is paid $400k/year to understand while you do it by eyeball the whole time you thought technical analysis was detecting patterns you were just watching the shadow of dealer hedging and calling it insight
1:10

every day before market open, the CBOE publishes exactly where market makers are forced to buy and sell not a theory, not a pattern - a number that actually moves price it's called dealer gamma exposure - GEX - and it creates real price magnets quants have traded against for years here's why it works when you buy a call option, a market maker sells it to you and has to hedge that hedge shifts as price moves - they buy when it rises, sell when it falls negative GEX zones are where they sell into every rally, cap every breakout, crush every momentum move strikes with highest open interest aren't random - they're gravitational markets don't break through those levels cleanly because a billion-dollar dealer is actively hedging against it quants call them gamma walls data is free, published every morning on the CBOE site - no API, no Bloomberg terminal retail calls those same levels "resistance" and chalks it up to vibes bookmark this before it lost it's not vibes. it's math someone is paid $400k/year to understand while you do it by eyeball the whole time you thought technical analysis was detecting patterns you were just watching the shadow of dealer hedging and calling it insight

Livsun

68,819 次观看 • 22 天前

MIT has been teaching this publicly for 50 years. retail traders still draw trendlines andrei markov published the framework in 1906. hedge funds found it in the 70s built billion-dollar operations around it, never told retail it existed markets aren't random - they're state machines trending, ranging, reversing. each state has a fixed historical probability of shifting to the next you build a transition matrix from real price data: trending -> stays trending: 68% trending -> flips to range: 21% trending -> reverses: 11% now you're not predicting direction. you're entering on 68% historical completion that's not a signal, that's conditional probability compounding every single trade math is free, data is free, implementation is ~200 lines of python what's not free: knowing this framework exists while everyone around you draws support levels and hopes renaissance has run this since 1988. paper was always public edge was never secret. just never pointed out
52:24

MIT has been teaching this publicly for 50 years. retail traders still draw trendlines andrei markov published the framework in 1906. hedge funds found it in the 70s built billion-dollar operations around it, never told retail it existed markets aren't random - they're state machines trending, ranging, reversing. each state has a fixed historical probability of shifting to the next you build a transition matrix from real price data: trending -> stays trending: 68% trending -> flips to range: 21% trending -> reverses: 11% now you're not predicting direction. you're entering on 68% historical completion that's not a signal, that's conditional probability compounding every single trade math is free, data is free, implementation is ~200 lines of python what's not free: knowing this framework exists while everyone around you draws support levels and hopes renaissance has run this since 1988. paper was always public edge was never secret. just never pointed out

Livsun

13,963 次观看 • 1 个月前

So ace. Google just released Gemini Code Wiki. A free too that generates interactive docs from any GitHub URL 🤯 The tool lets you: → Chat with Gemini to understand *any* repo → Visualize code structure → Create docs from public repos 100% free. Link in 🧵↓
0:25

So ace. Google just released Gemini Code Wiki. A free too that generates interactive docs from any GitHub URL 🤯 The tool lets you: → Chat with Gemini to understand *any* repo → Visualize code structure → Create docs from public repos 100% free. Link in 🧵↓

Charly Wargnier

66,859 次观看 • 6 个月前

"Every company is going to build their own frontier AI unique to their secret sauce. That's exactly what the top law firms do. We're starting to talk with a law firm and their client and ask: how could we build you a joint model?" Thanks Gabe Pereyra and Harvey for joining us at our Private Frontier all-hands to share how Harvey is defining the future of Specific Intelligence for legal agents.
2:00

"Every company is going to build their own frontier AI unique to their secret sauce. That's exactly what the top law firms do. We're starting to talk with a law firm and their client and ask: how could we build you a joint model?" Thanks Gabe Pereyra and Harvey for joining us at our Private Frontier all-hands to share how Harvey is defining the future of Specific Intelligence for legal agents.

Applied Compute

19,710 次观看 • 1 个月前

ELON: “PR FIRMS = PROPAGANDA” “What the public should really understand is that public relations literally just means propaganda. Journalists are paid very little… but they can retire into PR firms and make a lot of money based on their past work and contacts. There’s a strong monetary incentive for journalists to do what PR firms say because that’s how they get rich.” Source: Elon Musk
0:50

ELON: “PR FIRMS = PROPAGANDA” “What the public should really understand is that public relations literally just means propaganda. Journalists are paid very little… but they can retire into PR firms and make a lot of money based on their past work and contacts. There’s a strong monetary incentive for journalists to do what PR firms say because that’s how they get rich.” Source: Elon Musk

Mario Nawfal

62,240 次观看 • 1 年前

The MIT professor who trains quants for Citadel, Two Sigma, and Renaissance just gave a closed-door keynote at Oxford in front of Man Group's $151B team. They left the recording on a public server. Probably by accident. 1 hour. Free. No one's talking about it. Bookmark it before Oxford notices. Then read the article below.
47:10

The MIT professor who trains quants for Citadel, Two Sigma, and Renaissance just gave a closed-door keynote at Oxford in front of Man Group's $151B team. They left the recording on a public server. Probably by accident. 1 hour. Free. No one's talking about it. Bookmark it before Oxford notices. Then read the article below.

zostaff

381,483 次观看 • 2 个月前

jane street paid a 23yo $250k last year didn't come from a target school didn't have a finance degree knew exactly what the interview was going to test while his classmates were grinding campus recruiting, he was drilling probability problems at 2am that's what nobody tells you about quant finance - the interview has nothing to do with traditional finance citadel wants to know how fast your brain handles expected value under pressure. they don't care about your DCF model 6 categories: >probability, >mental math, >statistics, >logic puzzles, >coding, >market microstructure zero to interview-ready in under a year if you actually know what to study most people apply without knowing any of this. screened out in round 1, assume they weren't smart enough the firms aren't looking for the smartest Bookmark it or will lose all potential they're looking for the most prepared
1:28

jane street paid a 23yo $250k last year didn't come from a target school didn't have a finance degree knew exactly what the interview was going to test while his classmates were grinding campus recruiting, he was drilling probability problems at 2am that's what nobody tells you about quant finance - the interview has nothing to do with traditional finance citadel wants to know how fast your brain handles expected value under pressure. they don't care about your DCF model 6 categories: >probability, >mental math, >statistics, >logic puzzles, >coding, >market microstructure zero to interview-ready in under a year if you actually know what to study most people apply without knowing any of this. screened out in round 1, assume they weren't smart enough the firms aren't looking for the smartest Bookmark it or will lose all potential they're looking for the most prepared

Livsun

39,087 次观看 • 1 个月前

OpenAI just created a $10 billion company whose ONLY job is forcing businesses to use AI. And they're literally guaranteeing investors a 17.5% annual return to make it happen. It's called "The Deployment Company." OpenAI finalized it yesterday with 19 investors including TPG, SoftBank, Bain Capital, Brookfield, and Advent International. Here's the structure: OpenAI puts in $1.5 billion. The private equity firms put in $4 billion. In exchange, those PE firms open up their 2,000+ portfolio companies as a CAPTIVE customer base for OpenAI's products. OpenAI then embeds teams of engineers directly inside those companies, Palantir-style, to integrate their tools into daily operations. And here's the big red flag in all of this: OpenAI is GUARANTEEING those PE firms a 17.5% annual return over five years. That means even if the companies in the portfolio don't want AI, don't need AI, or get zero value from AI, OpenAI is still on the hook to pay those returns. Think about what that means for a second. OpenAI is so desperate for enterprise adoption that they're paying Wall Street to force their product into thousands of businesses. They've essentially turned private equity firms into a distribution cartel with a guaranteed commission. This has NEVER been done before in enterprise software. No software company in history has guaranteed above-market returns to financial sponsors just to get their product installed. And it gets crazier: Within MINUTES of OpenAI's announcement, Anthropic announced their own version. A $1.5 billion joint venture with Blackstone, Goldman Sachs, and Hellman & Friedman. Same playbook. Two companies worth a combined $1+ TRILLION in private valuation both concluded on the same day that organic demand for their products is not growing fast enough. If enterprises were lining up to buy AI on their own, you wouldn't need to bribe private equity firms with guaranteed returns to shove it into their portfolios. You would just sell it normally like every other software company in history. But they can't. Because the gap between what AI companies PROMISE and what enterprises actually experience is still enormous. OpenAI's COO Brad Lightcap just moved into a new role specifically to lead this push. They've also signed "Frontier Alliances" with major consulting firms to embed AI through professional services channels. Every move they're making screams the same thing: We have a demand problem. And this is all happening right before OpenAI tries to IPO at $850 billion. If they can show Wall Street that 2,000+ companies are "using OpenAI products" through this PE distribution channel, it inflates their enterprise metrics right before the roadshow. Doesn't matter if those companies actually need it or if it creates real value. What matters is the number on the S-1. This is the AI playbook entering its most dangerous phase. The tech is real but the business model is being held together by financial engineering, guaranteed returns, and captive distribution deals that look more like a pharmaceutical company paying doctors to prescribe their drug than a software company earning customers on merit. And both OpenAI and Anthropic admitted it on the same day.
0:54

OpenAI just created a $10 billion company whose ONLY job is forcing businesses to use AI. And they're literally guaranteeing investors a 17.5% annual return to make it happen. It's called "The Deployment Company." OpenAI finalized it yesterday with 19 investors including TPG, SoftBank, Bain Capital, Brookfield, and Advent International. Here's the structure: OpenAI puts in $1.5 billion. The private equity firms put in $4 billion. In exchange, those PE firms open up their 2,000+ portfolio companies as a CAPTIVE customer base for OpenAI's products. OpenAI then embeds teams of engineers directly inside those companies, Palantir-style, to integrate their tools into daily operations. And here's the big red flag in all of this: OpenAI is GUARANTEEING those PE firms a 17.5% annual return over five years. That means even if the companies in the portfolio don't want AI, don't need AI, or get zero value from AI, OpenAI is still on the hook to pay those returns. Think about what that means for a second. OpenAI is so desperate for enterprise adoption that they're paying Wall Street to force their product into thousands of businesses. They've essentially turned private equity firms into a distribution cartel with a guaranteed commission. This has NEVER been done before in enterprise software. No software company in history has guaranteed above-market returns to financial sponsors just to get their product installed. And it gets crazier: Within MINUTES of OpenAI's announcement, Anthropic announced their own version. A $1.5 billion joint venture with Blackstone, Goldman Sachs, and Hellman & Friedman. Same playbook. Two companies worth a combined $1+ TRILLION in private valuation both concluded on the same day that organic demand for their products is not growing fast enough. If enterprises were lining up to buy AI on their own, you wouldn't need to bribe private equity firms with guaranteed returns to shove it into their portfolios. You would just sell it normally like every other software company in history. But they can't. Because the gap between what AI companies PROMISE and what enterprises actually experience is still enormous. OpenAI's COO Brad Lightcap just moved into a new role specifically to lead this push. They've also signed "Frontier Alliances" with major consulting firms to embed AI through professional services channels. Every move they're making screams the same thing: We have a demand problem. And this is all happening right before OpenAI tries to IPO at $850 billion. If they can show Wall Street that 2,000+ companies are "using OpenAI products" through this PE distribution channel, it inflates their enterprise metrics right before the roadshow. Doesn't matter if those companies actually need it or if it creates real value. What matters is the number on the S-1. This is the AI playbook entering its most dangerous phase. The tech is real but the business model is being held together by financial engineering, guaranteed returns, and captive distribution deals that look more like a pharmaceutical company paying doctors to prescribe their drug than a software company earning customers on merit. And both OpenAI and Anthropic admitted it on the same day.

Ricardo

52,488 次观看 • 2 个月前

Toughness. Effort. Hard work. That’s my standard. So I’m excited to team up with Every Man Jack (EVERY MAN JACK) for a partnership that's all about turning grit into a routine. Their products hold up to the work I put in, on and off the field. Clean ingredients, no nonsense, and built to keep me ready. I've always believed that if you want something, you don’t just talk about it–you put in the work. So to all those out there who are putting in the work every day, this one’s for you.
0:30

Toughness. Effort. Hard work. That’s my standard. So I’m excited to team up with Every Man Jack (EVERY MAN JACK) for a partnership that's all about turning grit into a routine. Their products hold up to the work I put in, on and off the field. Clean ingredients, no nonsense, and built to keep me ready. I've always believed that if you want something, you don’t just talk about it–you put in the work. So to all those out there who are putting in the work every day, this one’s for you.

Baker Mayfield

215,950 次观看 • 6 个月前

"It's basically what we call the Blackstone model, which is, you don't take over the whole company right, you buy the largest stake in it and you use that to control it. And so that's the model starting with the US, but basically every country in the world is being subject to this. And so they have planted through these small focus groups of private intelligence, and it's a combination of private intelligence, private equity, and IT firms, and they're basically coming together as one. And they set up their teams in each country to basically take it over strategically. There's a playbook for this..." Emanuel Pastreich of 🥷 🔗
8:37

"It's basically what we call the Blackstone model, which is, you don't take over the whole company right, you buy the largest stake in it and you use that to control it. And so that's the model starting with the US, but basically every country in the world is being subject to this. And so they have planted through these small focus groups of private intelligence, and it's a combination of private intelligence, private equity, and IT firms, and they're basically coming together as one. And they set up their teams in each country to basically take it over strategically. There's a playbook for this..." Emanuel Pastreich of 🥷 🔗

Geopolitics & Empire

18,051 次观看 • 4 个月前

Jane Street needed a 4,032 GPU liquid-cooled data center just to run their trading models > that tells you everything about how much money this firm makes and why their quants start at $400K/year someone just got inside and filmed it > custom AI and trading agents trained specifically for this firm - off-the-shelf doesn't cut it > packets processed in under 100 nanoseconds > copper over fiber because electrons beat light at that scale > automated kill switches to shut algorithms down before downtime costs millions > financial privacy of their positions is protected at infrastructure level - nothing leaks 20 years ago their cluster was 6 Dell boxes on an office floor > this is the machine now what do you think - is this level of infrastructure even possible for smaller firms?
14:54

Jane Street needed a 4,032 GPU liquid-cooled data center just to run their trading models > that tells you everything about how much money this firm makes and why their quants start at $400K/year someone just got inside and filmed it > custom AI and trading agents trained specifically for this firm - off-the-shelf doesn't cut it > packets processed in under 100 nanoseconds > copper over fiber because electrons beat light at that scale > automated kill switches to shut algorithms down before downtime costs millions > financial privacy of their positions is protected at infrastructure level - nothing leaks 20 years ago their cluster was 6 Dell boxes on an office floor > this is the machine now what do you think - is this level of infrastructure even possible for smaller firms?

Mr. Buzzoni

241,402 次观看 • 1 个月前

Apple Intelligence is over-hated mostly because Apple never gave it to the users properly. Look at this example. This is the most capable on-device model I have ever seen; it eats 1000 Gemini Nano in breakfast. And from my short interaction, their server model is just as good. I can't say it’s comparable to ChatGPT or Grok because I haven't used it much. And see how fast it is considering it's running all offline.
0:49

Apple Intelligence is over-hated mostly because Apple never gave it to the users properly. Look at this example. This is the most capable on-device model I have ever seen; it eats 1000 Gemini Nano in breakfast. And from my short interaction, their server model is just as good. I can't say it’s comparable to ChatGPT or Grok because I haven't used it much. And see how fast it is considering it's running all offline.

Apple Mis-Intelligence

148,166 次观看 • 1 年前