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ICT Algorithmic Price Delivery 👁 Eye Training Drill 📉GBPJPY 💎High probability BMS #theicd

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Market Maker Trading

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46,849 Aufrufe • vor 2 Jahren •via X (Twitter)

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The ICT Academyvor 2 Jahren

Learn here:

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The Professorvor 2 Jahren

That's a hell of a big trap

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Alisher Khanvor 2 Jahren

🤌🏼

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Jossi 👨‍💻👨‍💻 ✨✨vor 2 Jahren

Please what date is this

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sonuvor 2 Jahren

How you took this -ob in 5m chart brother....?

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Nayla D. Logiavor 2 Jahren

Wow 💚💚💚 thanks for sharing

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What Makes an A+++ Setup (According to a $291K Trader) A setup is the specific market condition where all your criteria align for a high-probability trade entry. Most traders don't understand the criteria that makes a solid entry. They take every setup that looks "good enough" — and end up with 1:2 or 1:3 risk-to-reward ratios, grinding for small wins. Here's the difference: An A+++ setup allowed my student Said to make $86,000 in one day from just 3 trades (with 1:18, 1:7, and 1:10 R/R). And $291k over the last 1.5 years. If there’s a mediocre setup, he won’t take it. Some weeks he only trades twice because he's waiting for perfection. In the 2-minute clip below, my student Said walks through the 5 elements that must align for an A+++ setup: 1) Inverse Fair Value Gap — An imbalance price needs to fill 2) Inducement — Liquidity sweep that triggers early traders 3) Imbalance — Gap in price delivery that draws price back 4) Protected Level — Previous inducement + break of structure creates an internal floor/ceiling 5) Break of Structure — Confirmation that one side is in control Said also looks for setups where you have TWO protected levels, not just one. This gives him the confidence to enter at the end of the fair value gap without waiting for additional confirmation. Stop loss goes just below the protected level — tight risk, massive reward potential. — This is just scratching the surface of what Said shared. In the full 1-hour interview, we also dove into: • The exact 3 trades that made him $86K in a single day (with timestamped chart breakdowns) • How he combined two different strategies into one profitable system • Why he still backtests 1-1.5 hours daily even after making $100K/month Just comment "INTERVIEW" and I'll DM you the full interview in the next few minutes.
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#NewPaper The first microscope, invented in the 16th century, was designed to unlock the secrets of the microscopic world. Today, as many fields become increasingly data-driven, there is a pressing need for new types of microscopes---tools that help us zoom in, explore, and understand complex data. We call these tools "algorithmic microscopes." Introducing the Vendiscope: The first algorithmic microscope for data collections. 🔬 The Vendiscope maximizes the probability-weighted Vendi Score of a dataset to assign a weight to each element in the collection. This weight represents a data point's contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We use them to zoom in on datasets across three domains: biology, materials science, & AI. 🧬 Biology: We used the Vendiscope on the protein universe, which contains nearly 250 million proteins. We found that nearly 200 million of the proteins are near-duplicates of each other and that AlphaFold fails on proteins that contribute most to the diversity of the protein universe. (See GIF below). 🪜 Materials Science: We used the Vendiscope on the Materials Project database, which contains 170K materials as of today. We found that 85% of crystals with formation energy data are near-duplicates of each other and that ML models for materials property prediction struggle with materials that contribute most to diversity. 🤖 Artificial Intelligence: We applied the Vendiscope to CIFAR-10, a benchmark dataset containing 50K images. We found duplicates. We applied the Vendiscope to analyze state-of-the-art generative models trained on this dataset. We found the best generative models memorize training data, as is known in the AI literature. However, we can do more with the Vendiscope and characterize the type of samples that get memorized. We found that data points contributing least to diversity are more prone to memorization by these generative models. 🧠 "Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science, providing a systematic and scalable way to identify duplicates and outliers, as well as pinpointing samples prone to memorization and those that models may struggle to predict---even before training." 💫 "The Vendiscope provides a unified framework for analyzing complex data at scale. Researchers, engineers, and data auditors can use the Vendiscope to audit datasets, identify potential biases, and refine data collection practices. For AI ethicists, the Vendiscope offers a critical lens to understand how models interact with data, particularly in the context of bias, memorization, and data fairness, enabling better mitigation strategies to prevent undesirable outcomes in AI deployment. For scientists, the Vendiscope represents a new companion in the discovery process." #VendiScoring #AlgorithmicMicroscopy Link to paper: Authors: Amey Pasarkar (Amey Pasarkar) and Adji Bousso Dieng (@adjiboussodieng)
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#NewPaper The first microscope, invented in the 16th century, was designed to unlock the secrets of the microscopic world. Today, as many fields become increasingly data-driven, there is a pressing need for new types of microscopes---tools that help us zoom in, explore, and understand complex data. We call these tools "algorithmic microscopes." Introducing the Vendiscope: The first algorithmic microscope for data collections. 🔬 The Vendiscope maximizes the probability-weighted Vendi Score of a dataset to assign a weight to each element in the collection. This weight represents a data point's contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We use them to zoom in on datasets across three domains: biology, materials science, & AI. 🧬 Biology: We used the Vendiscope on the protein universe, which contains nearly 250 million proteins. We found that nearly 200 million of the proteins are near-duplicates of each other and that AlphaFold fails on proteins that contribute most to the diversity of the protein universe. (See GIF below). 🪜 Materials Science: We used the Vendiscope on the Materials Project database, which contains 170K materials as of today. We found that 85% of crystals with formation energy data are near-duplicates of each other and that ML models for materials property prediction struggle with materials that contribute most to diversity. 🤖 Artificial Intelligence: We applied the Vendiscope to CIFAR-10, a benchmark dataset containing 50K images. We found duplicates. We applied the Vendiscope to analyze state-of-the-art generative models trained on this dataset. We found the best generative models memorize training data, as is known in the AI literature. However, we can do more with the Vendiscope and characterize the type of samples that get memorized. We found that data points contributing least to diversity are more prone to memorization by these generative models. 🧠 "Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science, providing a systematic and scalable way to identify duplicates and outliers, as well as pinpointing samples prone to memorization and those that models may struggle to predict---even before training." 💫 "The Vendiscope provides a unified framework for analyzing complex data at scale. Researchers, engineers, and data auditors can use the Vendiscope to audit datasets, identify potential biases, and refine data collection practices. For AI ethicists, the Vendiscope offers a critical lens to understand how models interact with data, particularly in the context of bias, memorization, and data fairness, enabling better mitigation strategies to prevent undesirable outcomes in AI deployment. For scientists, the Vendiscope represents a new companion in the discovery process." #VendiScoring #AlgorithmicMicroscopy Link to paper: Authors: Amey Pasarkar (Amey Pasarkar) and Adji Bousso Dieng (@adjiboussodieng)

Vertaix® (AI & Science)

34,762 Aufrufe • vor 1 Jahr

🚩🚩 Watching "The Big Short" in 2025 is dejavu... The 2008 housing bubble feels eerily relevant with today's sky-high stock & housing valuations 🤌 In 08, the crisis stemmed from a massively overvalued housing market fueled by risky subprime loans, lax regulation, and greed Banks bundled junk mortgages into "safe" securities, creating the massive housing bubble... Nothing has really changed. It's just a facade. TODAY: 📈📉 The Fed is warning about overleveraged hedge funds. - Michael Burry is betting on Palantir dropping in price with puts worth nearly $1 billion = 66% of his firms AUM. -Stocks are screaming "overvalued" in every way. - The S&P 500 is 63% above its long-term trend -The Warren Buffett Indicator is at a record 217%+ - Goldman & Morgan Stanley predict 20% corrections soon. 🏠🏠 Housing is bubbly and overvalued from the 2020 free money, low interest rates, and bidding wars. -Sales are crashing while prices stall. - Experts call the market "frozen" with subdued growth -Not as leveraged as '08, but affordability's tanked with higher rates. -Speculative bubbles (AI/tech stocks now vs. housing then) - Many expert warnings of crashes with scary similarities -Fed stopping QT and beginning easing amid softening economy - 1 million+ fewer jobs created in 2024 than stated by the Biden admin per revisions to 2024's fake numbers - falling new-job numbers for 2025 - Growing Repo market borrowing from desperate banks (again) -Volatility rising on potential meltdowns 2025's mess is more driven by toxic derivatives in the stock market, lax regulation, and greed... 🤌 it's just a different flavor of 2008. And still, impunity for banks and big players persists... sound familiar? Nothing changed. It's time for change - 🔥LIT🔥XCHANGE🔥 🚨 Buckle up and repost if you're ready for the housing and stock market corrections 💎 🙌
3:18

🚩🚩 Watching "The Big Short" in 2025 is dejavu... The 2008 housing bubble feels eerily relevant with today's sky-high stock & housing valuations 🤌 In 08, the crisis stemmed from a massively overvalued housing market fueled by risky subprime loans, lax regulation, and greed Banks bundled junk mortgages into "safe" securities, creating the massive housing bubble... Nothing has really changed. It's just a facade. TODAY: 📈📉 The Fed is warning about overleveraged hedge funds. - Michael Burry is betting on Palantir dropping in price with puts worth nearly $1 billion = 66% of his firms AUM. -Stocks are screaming "overvalued" in every way. - The S&P 500 is 63% above its long-term trend -The Warren Buffett Indicator is at a record 217%+ - Goldman & Morgan Stanley predict 20% corrections soon. 🏠🏠 Housing is bubbly and overvalued from the 2020 free money, low interest rates, and bidding wars. -Sales are crashing while prices stall. - Experts call the market "frozen" with subdued growth -Not as leveraged as '08, but affordability's tanked with higher rates. -Speculative bubbles (AI/tech stocks now vs. housing then) - Many expert warnings of crashes with scary similarities -Fed stopping QT and beginning easing amid softening economy - 1 million+ fewer jobs created in 2024 than stated by the Biden admin per revisions to 2024's fake numbers - falling new-job numbers for 2025 - Growing Repo market borrowing from desperate banks (again) -Volatility rising on potential meltdowns 2025's mess is more driven by toxic derivatives in the stock market, lax regulation, and greed... 🤌 it's just a different flavor of 2008. And still, impunity for banks and big players persists... sound familiar? Nothing changed. It's time for change - 🔥LIT🔥XCHANGE🔥 🚨 Buckle up and repost if you're ready for the housing and stock market corrections 💎 🙌

The Butcher of Wall Street | Marcel Kalinovic

505,967 Aufrufe • vor 8 Monaten

🚨 I CALIBRATED BITCOIN’S EMPIRICAL PI BOTTOM ZONE - AND THE $30K BEARS NEED A WELLNESS CHECK 🚨 Bitcoin is trading at approximately $64,900 as I write this. On June 30, Bitcoin printed what is currently the cycle low at $58,526. I believe that low may have been far more structurally significant than people realize. I took the original Pi Cycle architecture, inverted the question, and calibrated the relationship against Bitcoin’s mature terminal cycle lows. The result is what I call the: EMPIRICAL PI BOTTOM COMPRESSION ZONE 0.65 ≤ 111DMA / 350DMA ≤ 0.75 Translated from spreadsheet necromancy into English: Bitcoin’s mature cycle lows have formed when the 111-day moving average traded between approximately 65% and 75% of the 350-day moving average - a sustained compression of 25%–35% beneath the long-term trend. Here is exactly how I derived it. The traditional Pi Cycle Top compares: Bitcoin’s 111-day simple moving average and Bitcoin’s 350-day simple moving average multiplied by 2. The periods are significant because 350 / 111 = 3.153 Pi = 3.142 Hence “Pi Cycle.” The traditional top signal occurs when: 111DMA = 2 × 350DMA This means Bitcoin’s medium-term price trend has become so violently overheated that the 111-day average reaches TWICE the slower 350-day average. This is approximately when your barber discovers leverage, your dentist launches a token, and a man named “ByzantineWhale” begins financing a Lamborghini using an unsecured loan collateralized by screenshots. But what would the exact inverse look like? The clean mathematical inverse of doubling is halving: 111DMA = 0.5 × 350DMA That would create perfect multiplicative symmetry: Top: 111DMA / 350DMA = 2.00 Equilibrium: 111DMA / 350DMA = 1.00 Bottom: 111DMA / 350DMA = 0.50 Beautiful. I tested it across 5,743 daily observations from November 2010 through July 2026. The exact inverse bottom crossover occurred precisely ZERO times. Bitcoin has never pushed its 111DMA all the way down to half of its 350DMA. The lowest ratio in the dataset was approximately 0.573 on September 30, 2022. Bitcoin’s bull markets can launch the short-term average into low Earth orbit, but its bear markets stop just before the moving averages are legally declared deceased. So I abandoned the theoretically perfect inverse and measured the relationship that actually existed at Bitcoin’s mature terminal cycle lows. Define: R = 111DMA / 350DMA At the January 14, 2015 cycle low: BTC price = $171 111DMA = $347.75 350DMA = $490.36 R = 347.75 / 490.36 R = 0.709 The 111DMA traded 29.1% beneath the 350DMA. At the December 15, 2018 cycle low: BTC price = $3,180 111DMA = $5,811.31 350DMA = $7,712.15 R = 5,811.31 / 7,712.15 R = 0.754 The 111DMA traded 24.6% beneath the 350DMA. At the November 21, 2022 cycle low: BTC price = $15,766 111DMA = $20,081.11 350DMA = $30,935.25 R = 20,081.11 / 30,935.25 R = 0.649 The 111DMA traded 35.1% beneath the 350DMA. Three mature terminal cycle lows: 2015: 0.709 2018: 0.754 2022: 0.649 Rounded into an empirically observed regime: 0.65 ≤ R ≤ 0.75 Or: 25% ≤ 1 − R ≤ 35% That is my Empirical Pi Bottom Compression Zone. It does not pretend to identify whether Bitcoin bottoms at 2:37 p.m. on a Wednesday while Kevin Warsh clears his throat. It identifies a structural regime. The 350DMA represents Bitcoin’s slower long-term trend. The 111DMA represents the market’s more recent price experience. For the 111DMA to fall 25%–35% beneath the 350DMA, Bitcoin cannot merely suffer one terrible afternoon. Weak prices must persist long enough to poison an entire 111-day window. This measures the DURATION of the suffering - not merely the violence of one liquidation. That is why I exclude March 2020 from the calibration. March 2020 was a violent mid-cycle liquidity shock, not the terminal low of a completed Bitcoin bear cycle. I also treat 2011 separately because Bitcoin was still in its embryonic price-discovery era. The market and its long-term moving-average structure were not remotely comparable with the mature post-2013 cycles. Three observations are not the Ten Commandments brought down from Mount Nakamoto. This is a small-sample empirical classification... not divine law and not a guaranteed price floor. But the consistency is fascinating. Now consider June 30, 2026. BTC price: $58,526 111DMA: $71,330 350DMA: $89,805 The formal ratio was: R = 71,330 / 89,805 R = 0.794 That placed the moving-average ratio just above the 0.75 upper boundary. The full smoothed indicator had not yet formally entered the empirical zone. But look at the contemporaneous numerical price corridor created by the 350DMA: Upper boundary: 0.75 × $89,805 = $67,353 Lower boundary: 0.65 × $89,805 = $58,373 Bitcoin’s June 30 low: $58,526 Bitcoin bottomed only $153 above the lower edge of that entire $58,373–$67,353 corridor. That is absurdly close. To be precise, this does not mean spot price and the 111DMA are interchangeable. They are not. The formal metric uses the 111DMA, and moving averages are path-dependent. But as a secondary price-location confluence, Bitcoin placing its cycle low almost directly on the lower numerical boundary while the ratio was approaching the zone is incredibly interesting. Where are we now? Using the latest completed daily data: 111DMA: approximately $70,150 350DMA: approximately $86,454 R = 70,150 / 86,454 R = 0.811 Current compression: 1 − 0.811 = 18.9% With Bitcoin around $64,900, price is now approximately 10.9% above the June 30 low. The bears are nevertheless sitting online demanding $40,000, $30,000, and in some cases prices last seen when half the industry was still pretending an algorithmic stablecoin was money. Let us quantify what they are actually predicting. From approximately $64,900: $40,000 requires another 38.4% collapse. $30,000 requires another 53.8% collapse. From the June 30 low of $58,526: $40,000 requires another 31.7% decline. $30,000 requires another 48.7% decline. From the October 2025 all-time high near $126,223: $40,000 represents a total drawdown of approximately 68.3%. $30,000 represents a total drawdown of approximately 76.2%. Can Bitcoin trade at $30,000–$40,000? Of course. Bitcoin is capable of doing anything required to make the maximum number of people look stupid simultaneously. But this metric cannot honestly assign a precise probability to those prices from only three mature cycles. What it can reveal is the structural violence required. Using the current trailing price history as a simple path-dependent illustration: If Bitcoin fell to $40,000 and remained there, it would take approximately 34 consecutive daily closes around $40,000 to drag the 111DMA/350DMA ratio into the zone at 0.75. It would take approximately 98 days around $40,000 to drive the ratio to the lower 0.65 boundary. If Bitcoin fell to $30,000 and remained there, it would take approximately 25 days to reach 0.75 and approximately 54 days to reach 0.65. Those are simplified constant-price scenarios, not forecasts, but they expose what the bearish thesis actually requires. The $30K–$40K crowd is not merely predicting a wick. They are underwriting an extended structural repricing powerful enough to drag an entire 111-day average through the regime that contained Bitcoin’s mature terminal cycle lows. That is possible. Treating it as the obvious base case is statistically unserious. The market already fell approximately 53.6% from the October 2025 all-time high to the June 30 low. Spot then landed only $153 above the lower numerical boundary of the contemporaneous empirical corridor. Now Bitcoin has recovered to approximately $64,900 while the bears demand an additional 38%–54% execution in the basement because apparently the first liquidation of half the market was merely an appetizer. Their thesis requires Bitcoin to invalidate the June 30 confluence, destroy the developing compression structure, and sustain dramatically lower prices long enough to rewrite the moving-average regime. Maybe that happens. But “possible” and “probable” are not synonyms just because someone drew a red arrow on TradingView. The traditional Pi Cycle Top measures when Bitcoin’s medium-term trend becomes obscenely overheated relative to its structural trend. My Empirical Pi Bottom Zone measures when that medium-term trend has been methodically compressed beneath it for long enough to resemble Bitcoin’s mature terminal bear-market lows. One measures the mathematics of mania. The other measures whether the market has completed the psychological liquidation of everyone who bought the top using money they described as “basically free.” Derived from Philip Swift’s original 111/350 Pi Cycle architecture. Empirical bottom-zone calibration by Adam Livingston, 2026:
0:27

🚨 I CALIBRATED BITCOIN’S EMPIRICAL PI BOTTOM ZONE - AND THE $30K BEARS NEED A WELLNESS CHECK 🚨 Bitcoin is trading at approximately $64,900 as I write this. On June 30, Bitcoin printed what is currently the cycle low at $58,526. I believe that low may have been far more structurally significant than people realize. I took the original Pi Cycle architecture, inverted the question, and calibrated the relationship against Bitcoin’s mature terminal cycle lows. The result is what I call the: EMPIRICAL PI BOTTOM COMPRESSION ZONE 0.65 ≤ 111DMA / 350DMA ≤ 0.75 Translated from spreadsheet necromancy into English: Bitcoin’s mature cycle lows have formed when the 111-day moving average traded between approximately 65% and 75% of the 350-day moving average - a sustained compression of 25%–35% beneath the long-term trend. Here is exactly how I derived it. The traditional Pi Cycle Top compares: Bitcoin’s 111-day simple moving average and Bitcoin’s 350-day simple moving average multiplied by 2. The periods are significant because 350 / 111 = 3.153 Pi = 3.142 Hence “Pi Cycle.” The traditional top signal occurs when: 111DMA = 2 × 350DMA This means Bitcoin’s medium-term price trend has become so violently overheated that the 111-day average reaches TWICE the slower 350-day average. This is approximately when your barber discovers leverage, your dentist launches a token, and a man named “ByzantineWhale” begins financing a Lamborghini using an unsecured loan collateralized by screenshots. But what would the exact inverse look like? The clean mathematical inverse of doubling is halving: 111DMA = 0.5 × 350DMA That would create perfect multiplicative symmetry: Top: 111DMA / 350DMA = 2.00 Equilibrium: 111DMA / 350DMA = 1.00 Bottom: 111DMA / 350DMA = 0.50 Beautiful. I tested it across 5,743 daily observations from November 2010 through July 2026. The exact inverse bottom crossover occurred precisely ZERO times. Bitcoin has never pushed its 111DMA all the way down to half of its 350DMA. The lowest ratio in the dataset was approximately 0.573 on September 30, 2022. Bitcoin’s bull markets can launch the short-term average into low Earth orbit, but its bear markets stop just before the moving averages are legally declared deceased. So I abandoned the theoretically perfect inverse and measured the relationship that actually existed at Bitcoin’s mature terminal cycle lows. Define: R = 111DMA / 350DMA At the January 14, 2015 cycle low: BTC price = $171 111DMA = $347.75 350DMA = $490.36 R = 347.75 / 490.36 R = 0.709 The 111DMA traded 29.1% beneath the 350DMA. At the December 15, 2018 cycle low: BTC price = $3,180 111DMA = $5,811.31 350DMA = $7,712.15 R = 5,811.31 / 7,712.15 R = 0.754 The 111DMA traded 24.6% beneath the 350DMA. At the November 21, 2022 cycle low: BTC price = $15,766 111DMA = $20,081.11 350DMA = $30,935.25 R = 20,081.11 / 30,935.25 R = 0.649 The 111DMA traded 35.1% beneath the 350DMA. Three mature terminal cycle lows: 2015: 0.709 2018: 0.754 2022: 0.649 Rounded into an empirically observed regime: 0.65 ≤ R ≤ 0.75 Or: 25% ≤ 1 − R ≤ 35% That is my Empirical Pi Bottom Compression Zone. It does not pretend to identify whether Bitcoin bottoms at 2:37 p.m. on a Wednesday while Kevin Warsh clears his throat. It identifies a structural regime. The 350DMA represents Bitcoin’s slower long-term trend. The 111DMA represents the market’s more recent price experience. For the 111DMA to fall 25%–35% beneath the 350DMA, Bitcoin cannot merely suffer one terrible afternoon. Weak prices must persist long enough to poison an entire 111-day window. This measures the DURATION of the suffering - not merely the violence of one liquidation. That is why I exclude March 2020 from the calibration. March 2020 was a violent mid-cycle liquidity shock, not the terminal low of a completed Bitcoin bear cycle. I also treat 2011 separately because Bitcoin was still in its embryonic price-discovery era. The market and its long-term moving-average structure were not remotely comparable with the mature post-2013 cycles. Three observations are not the Ten Commandments brought down from Mount Nakamoto. This is a small-sample empirical classification... not divine law and not a guaranteed price floor. But the consistency is fascinating. Now consider June 30, 2026. BTC price: $58,526 111DMA: $71,330 350DMA: $89,805 The formal ratio was: R = 71,330 / 89,805 R = 0.794 That placed the moving-average ratio just above the 0.75 upper boundary. The full smoothed indicator had not yet formally entered the empirical zone. But look at the contemporaneous numerical price corridor created by the 350DMA: Upper boundary: 0.75 × $89,805 = $67,353 Lower boundary: 0.65 × $89,805 = $58,373 Bitcoin’s June 30 low: $58,526 Bitcoin bottomed only $153 above the lower edge of that entire $58,373–$67,353 corridor. That is absurdly close. To be precise, this does not mean spot price and the 111DMA are interchangeable. They are not. The formal metric uses the 111DMA, and moving averages are path-dependent. But as a secondary price-location confluence, Bitcoin placing its cycle low almost directly on the lower numerical boundary while the ratio was approaching the zone is incredibly interesting. Where are we now? Using the latest completed daily data: 111DMA: approximately $70,150 350DMA: approximately $86,454 R = 70,150 / 86,454 R = 0.811 Current compression: 1 − 0.811 = 18.9% With Bitcoin around $64,900, price is now approximately 10.9% above the June 30 low. The bears are nevertheless sitting online demanding $40,000, $30,000, and in some cases prices last seen when half the industry was still pretending an algorithmic stablecoin was money. Let us quantify what they are actually predicting. From approximately $64,900: $40,000 requires another 38.4% collapse. $30,000 requires another 53.8% collapse. From the June 30 low of $58,526: $40,000 requires another 31.7% decline. $30,000 requires another 48.7% decline. From the October 2025 all-time high near $126,223: $40,000 represents a total drawdown of approximately 68.3%. $30,000 represents a total drawdown of approximately 76.2%. Can Bitcoin trade at $30,000–$40,000? Of course. Bitcoin is capable of doing anything required to make the maximum number of people look stupid simultaneously. But this metric cannot honestly assign a precise probability to those prices from only three mature cycles. What it can reveal is the structural violence required. Using the current trailing price history as a simple path-dependent illustration: If Bitcoin fell to $40,000 and remained there, it would take approximately 34 consecutive daily closes around $40,000 to drag the 111DMA/350DMA ratio into the zone at 0.75. It would take approximately 98 days around $40,000 to drive the ratio to the lower 0.65 boundary. If Bitcoin fell to $30,000 and remained there, it would take approximately 25 days to reach 0.75 and approximately 54 days to reach 0.65. Those are simplified constant-price scenarios, not forecasts, but they expose what the bearish thesis actually requires. The $30K–$40K crowd is not merely predicting a wick. They are underwriting an extended structural repricing powerful enough to drag an entire 111-day average through the regime that contained Bitcoin’s mature terminal cycle lows. That is possible. Treating it as the obvious base case is statistically unserious. The market already fell approximately 53.6% from the October 2025 all-time high to the June 30 low. Spot then landed only $153 above the lower numerical boundary of the contemporaneous empirical corridor. Now Bitcoin has recovered to approximately $64,900 while the bears demand an additional 38%–54% execution in the basement because apparently the first liquidation of half the market was merely an appetizer. Their thesis requires Bitcoin to invalidate the June 30 confluence, destroy the developing compression structure, and sustain dramatically lower prices long enough to rewrite the moving-average regime. Maybe that happens. But “possible” and “probable” are not synonyms just because someone drew a red arrow on TradingView. The traditional Pi Cycle Top measures when Bitcoin’s medium-term trend becomes obscenely overheated relative to its structural trend. My Empirical Pi Bottom Zone measures when that medium-term trend has been methodically compressed beneath it for long enough to resemble Bitcoin’s mature terminal bear-market lows. One measures the mathematics of mania. The other measures whether the market has completed the psychological liquidation of everyone who bought the top using money they described as “basically free.” Derived from Philip Swift’s original 111/350 Pi Cycle architecture. Empirical bottom-zone calibration by Adam Livingston, 2026:

Adam Livingston

17,012 Aufrufe • vor 8 Tagen

Something interesting is going to be released soon... ✨ Kitt The Inner Circle Trader "If I had to trade only one model for the rest of my life, considering everything I've publicly disclosed, my choice would be either the second stage of re-distribution in an MMSM or the second stage of re-accumulation in an MMBM. With either of these, I believe I could consistently generate substantial profits without the need to explore alternative strategies. These models rely on specific components of both the buy and sell sides of the market curve, which are directly interconnected. This isn't a matter of identifying support and resistance levels; it's about understanding the logic of order flow. In the case of the market maker sell model, I focus on identifying a pool of liquidity beneath the initial consolidation. When I spot this sellside opportunity, I patiently await a reversal. This reversal should lead to a drop of at least 50% from the smart money's reversal point down to the sellside liquidity. If it achieves this, and then begins to rally once more, I'll look to correlate it with the other side of the curve, where the market previously rallied before reversing. This will provide me with an array that initially signaled a bullish trend but now acts as a reversal indicator. This marks the second stage of distribution or redistribution, and it usually happens swiftly, pushing prices towards the sellside. In essence, I'm waiting for a unicorn setup, where all the pieces align perfectly, and I have everything in my favor. I'll risk 5% on such a trade. This approach involves re-accumulation, where the sellside drops down to 50%, and then I match it with another array to capture the reversal. Now, picture a market maker model involving a consolidation phase where relative equal lows are formed, followed by an upward rally, possibly forming a consolidation that resembles a bull flag pattern. Subsequently, it rallies out of that consolidation. Sometimes, it may create a second stage of re-accumulation as it trades towards a premium array level—a level I consider a liquidity draw. If I'm feeling bullish, I'd aim for that level. I don't necessarily need to be there at the exact moment; I might spot the opportunity later and act accordingly. If it's reacting off of a level, that should offer sellside. So, you know where sellside delivery. The market should drop down. So, I'm anticipating price reacting and reversing at the smart money reversal once it starts to break down. If it goes back up a little bit, that's the smart money reversal. Low risk sell is the next stage and then they'll drop. When we reach the low-risk sell, it's important that the drop reaches at least 50% of the total range from the smart money reversal to the sellside I'm targeting. As long as it accomplishes this, I have confidence that the subsequent rally will reach a premium array on the left side of the curve before the market makes its high and reverses. Why would it do that? Because it's part of a larger continuation. So when and how would I determine when it's going to fail ,that first leg of re-distribution on the sell side, if it doesn't pierce 50% of that range from the smart money reversal down to the sell side liquidity. If it doesn't do that, then it's not going to go down there. It's going to be a continuation of reverse and go the other way." #ict #ICT
6:37

Something interesting is going to be released soon... ✨ Kitt The Inner Circle Trader "If I had to trade only one model for the rest of my life, considering everything I've publicly disclosed, my choice would be either the second stage of re-distribution in an MMSM or the second stage of re-accumulation in an MMBM. With either of these, I believe I could consistently generate substantial profits without the need to explore alternative strategies. These models rely on specific components of both the buy and sell sides of the market curve, which are directly interconnected. This isn't a matter of identifying support and resistance levels; it's about understanding the logic of order flow. In the case of the market maker sell model, I focus on identifying a pool of liquidity beneath the initial consolidation. When I spot this sellside opportunity, I patiently await a reversal. This reversal should lead to a drop of at least 50% from the smart money's reversal point down to the sellside liquidity. If it achieves this, and then begins to rally once more, I'll look to correlate it with the other side of the curve, where the market previously rallied before reversing. This will provide me with an array that initially signaled a bullish trend but now acts as a reversal indicator. This marks the second stage of distribution or redistribution, and it usually happens swiftly, pushing prices towards the sellside. In essence, I'm waiting for a unicorn setup, where all the pieces align perfectly, and I have everything in my favor. I'll risk 5% on such a trade. This approach involves re-accumulation, where the sellside drops down to 50%, and then I match it with another array to capture the reversal. Now, picture a market maker model involving a consolidation phase where relative equal lows are formed, followed by an upward rally, possibly forming a consolidation that resembles a bull flag pattern. Subsequently, it rallies out of that consolidation. Sometimes, it may create a second stage of re-accumulation as it trades towards a premium array level—a level I consider a liquidity draw. If I'm feeling bullish, I'd aim for that level. I don't necessarily need to be there at the exact moment; I might spot the opportunity later and act accordingly. If it's reacting off of a level, that should offer sellside. So, you know where sellside delivery. The market should drop down. So, I'm anticipating price reacting and reversing at the smart money reversal once it starts to break down. If it goes back up a little bit, that's the smart money reversal. Low risk sell is the next stage and then they'll drop. When we reach the low-risk sell, it's important that the drop reaches at least 50% of the total range from the smart money reversal to the sellside I'm targeting. As long as it accomplishes this, I have confidence that the subsequent rally will reach a premium array on the left side of the curve before the market makes its high and reverses. Why would it do that? Because it's part of a larger continuation. So when and how would I determine when it's going to fail ,that first leg of re-distribution on the sell side, if it doesn't pierce 50% of that range from the smart money reversal down to the sell side liquidity. If it doesn't do that, then it's not going to go down there. It's going to be a continuation of reverse and go the other way." #ict #ICT

LumiTraders

387,371 Aufrufe • vor 2 Jahren

Thank you, Cde Vera, for exposing the betrayal. Every Zimbabwean must listen to this truth—Mnangagwa and ZANU-PF have abandoned the liberation cause and turned it into a personal business empire. As Cde Faith Chananda (Comrade Vera) bravely narrates, the heroes who shed blood for this country are now reduced to beggars—handed bicycles, rotten food hampers, and empty slogans in the name of “empowerment.” Meanwhile, Mnangagwa and his Zvigananda cartel loot billions, buy loyalty with cars and cash, and sow division to keep their corrupt grip on power. Let us be clear: Mnangagwa is not a liberator. He is a corrupt rogue, a national disgrace who spits on the sacrifices of the fallen. He has betrayed war veterans, betrayed the people, and betrayed the nation. While millions live in poverty, without jobs, without proper healthcare, without roads or clean water, he plots to steal more time in power through his criminal 2030 agenda. To the war veterans: you carried this nation on your backs. Do not let bicycles and food hampers be the price of your blood and sacrifice. You fought for freedom and dignity—do not allow Mnangagwa to mock you with crumbs while he builds his empire. To those still in ZANU-PF with a conscience: the party you joined to liberate Zimbabwe has been hijacked by Mnangagwa’s greed. If you remain silent while he tampers with the Constitution to extend his term beyond 2028, then you are complicit in destroying the very ideals of the struggle. The 2030 agenda is not development—it is dictatorship. It is the death of the Constitution, the betrayal of the liberation, and the enslavement of future generations. Mnangagwa must be stopped now. War veterans, ZANU-PF insiders, and all patriotic Zimbabweans must rise and declare openly: this rogue has failed, this rogue is corrupt, this rogue will not rewrite our Constitution to cling to power. Zimbabwe’s liberation cannot end in bicycles and rotten food. It must end in justice, dignity, and true freedom for the people. ZANU PF Nick Mangwana President of Zimbabwe Presidential Communications Zimbabwe 🇿🇼 dj steve easy tiger CCC Southend 🇿🇼🟨🇬🇧 LynneM 💕💝💎 Change Radio Amnesty International Zimbabwe 𝐂𝐫𝐢𝐦𝐞 𝐖𝐚𝐭𝐜𝐡 𝐙𝐖 The Mirror Masvingo TheNewsHawks Zimbabwe Third Eye News🇿🇼 👁 The Independent Parliament of Zimbabwe Varakashi4ED Mash West Updates Varakashi4ED Zimbabwe Freedom of Expression Henry Itayi Makambe Pauline Kaseke Guzha Spencer Shorayi Padare-Enkundleni 🇿🇼 ZANU PF PATRIOTS 🇿🇼 @ZanuPFYouthLeag Ali Naka Commentary African Fadzayi Mahere🇿🇼 HON Job Wiwa Sikhala Sabhuku Temba P. Mliswa Hon Lt Gen (Rtd) Amb. AN Sanyatwe Oppah Muchinguri-Kashiri (ZANUPF Nat. Chairman) Dr A. Mutambudzi ZanuPF Treasurer General - Patrick Chinamasa TENDAI BITI Thabani Mpofu TEDIOUS MUSINACHIREVO Promise Mkwananzi Cde Chibage TINO Citizens Change Champion Chibaya
21:00

Thank you, Cde Vera, for exposing the betrayal. Every Zimbabwean must listen to this truth—Mnangagwa and ZANU-PF have abandoned the liberation cause and turned it into a personal business empire. As Cde Faith Chananda (Comrade Vera) bravely narrates, the heroes who shed blood for this country are now reduced to beggars—handed bicycles, rotten food hampers, and empty slogans in the name of “empowerment.” Meanwhile, Mnangagwa and his Zvigananda cartel loot billions, buy loyalty with cars and cash, and sow division to keep their corrupt grip on power. Let us be clear: Mnangagwa is not a liberator. He is a corrupt rogue, a national disgrace who spits on the sacrifices of the fallen. He has betrayed war veterans, betrayed the people, and betrayed the nation. While millions live in poverty, without jobs, without proper healthcare, without roads or clean water, he plots to steal more time in power through his criminal 2030 agenda. To the war veterans: you carried this nation on your backs. Do not let bicycles and food hampers be the price of your blood and sacrifice. You fought for freedom and dignity—do not allow Mnangagwa to mock you with crumbs while he builds his empire. To those still in ZANU-PF with a conscience: the party you joined to liberate Zimbabwe has been hijacked by Mnangagwa’s greed. If you remain silent while he tampers with the Constitution to extend his term beyond 2028, then you are complicit in destroying the very ideals of the struggle. The 2030 agenda is not development—it is dictatorship. It is the death of the Constitution, the betrayal of the liberation, and the enslavement of future generations. Mnangagwa must be stopped now. War veterans, ZANU-PF insiders, and all patriotic Zimbabweans must rise and declare openly: this rogue has failed, this rogue is corrupt, this rogue will not rewrite our Constitution to cling to power. Zimbabwe’s liberation cannot end in bicycles and rotten food. It must end in justice, dignity, and true freedom for the people. ZANU PF Nick Mangwana President of Zimbabwe Presidential Communications Zimbabwe 🇿🇼 dj steve easy tiger CCC Southend 🇿🇼🟨🇬🇧 LynneM 💕💝💎 Change Radio Amnesty International Zimbabwe 𝐂𝐫𝐢𝐦𝐞 𝐖𝐚𝐭𝐜𝐡 𝐙𝐖 The Mirror Masvingo TheNewsHawks Zimbabwe Third Eye News🇿🇼 👁 The Independent Parliament of Zimbabwe Varakashi4ED Mash West Updates Varakashi4ED Zimbabwe Freedom of Expression Henry Itayi Makambe Pauline Kaseke Guzha Spencer Shorayi Padare-Enkundleni 🇿🇼 ZANU PF PATRIOTS 🇿🇼 @ZanuPFYouthLeag Ali Naka Commentary African Fadzayi Mahere🇿🇼 HON Job Wiwa Sikhala Sabhuku Temba P. Mliswa Hon Lt Gen (Rtd) Amb. AN Sanyatwe Oppah Muchinguri-Kashiri (ZANUPF Nat. Chairman) Dr A. Mutambudzi ZanuPF Treasurer General - Patrick Chinamasa TENDAI BITI Thabani Mpofu TEDIOUS MUSINACHIREVO Promise Mkwananzi Cde Chibage TINO Citizens Change Champion Chibaya

Tatenda C.K, Hungwe

36,029 Aufrufe • vor 10 Monaten

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).
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$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

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