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$AMD shareholders just realized Revenue from just 2 customers( $META & OpenAI= 4GW) in 2027 is already more than $AVGO entire year revenue. This is excluding $MSFT, $AMZN, $GOOGL, XAI, $ORCL, Softbank 5GW+ & EU, Anthropic(cited by Citi analyst), LumaAI, G42... and other revenue segments. And AMD is trading...

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Mike

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61,695 次观看 • 2 个月前 •via X (Twitter)

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A historic rotation is underway. On July 15th, Adam Kobeissi joined Fox Business and called for a rotation back into the Magnificent 7 to lead the S&P 500 to 8,000. The recent market leadership of chip stocks allowed the Magnificent 7 to correct, while still seeing historic earnings growth, without dragging the broader S&P 500 index lower. This was the largest such divergence between the S&P 500 and the Magnificent 7 since 2022. Less than 3 weeks since this clip with Adam Kobeissi and Charles V Payne, the S&P 500 is now above 7,700, Microsoft and Amazon are up +25% in 5 days, and the S&P 500 is worth a record $70 trillion. Many of these large cap technology names are now trading CHEAPER than the S&P 500 on a Forward P/E basis. Ironically, many stocks in this market are getting cheaper as they go up. Own assets or be left behind.
0:55

A historic rotation is underway. On July 15th, Adam Kobeissi joined Fox Business and called for a rotation back into the Magnificent 7 to lead the S&P 500 to 8,000. The recent market leadership of chip stocks allowed the Magnificent 7 to correct, while still seeing historic earnings growth, without dragging the broader S&P 500 index lower. This was the largest such divergence between the S&P 500 and the Magnificent 7 since 2022. Less than 3 weeks since this clip with Adam Kobeissi and Charles V Payne, the S&P 500 is now above 7,700, Microsoft and Amazon are up +25% in 5 days, and the S&P 500 is worth a record $70 trillion. Many of these large cap technology names are now trading CHEAPER than the S&P 500 on a Forward P/E basis. Ironically, many stocks in this market are getting cheaper as they go up. Own assets or be left behind.

The Kobeissi Letter

681,992 次观看 • 8 天前

TOM LEE: THE MARKET IS CHEAPER THAN PEOPLE THINK Thomas (Tom) Lee (not drummer) FundstratDirect.com says this rally is still earnings driven, not P/E driven. 2027 S&P 500 earnings estimates have jumped from $352 to $399 this year. But the forward P/E has fallen: 🔹 S&P 500: 6,846 → 7,365 🔹 Forward P/E: 19.4x → 18.4x 🔹 Earnings up 🔹 Multiple down That means the market is higher, but fundamentally cheaper than January 1st. Semis just dropped 7% in one day. Since 2011, 6%+ one day drops in semis have had an 88% win rate over 1, 3 and 6 months, with a median 6 month gain of 38%. Tom Lee’s view: This looks less like a top and more like a buyable dip in the AI infrastructure trade.
3:47

TOM LEE: THE MARKET IS CHEAPER THAN PEOPLE THINK Thomas (Tom) Lee (not drummer) FundstratDirect.com says this rally is still earnings driven, not P/E driven. 2027 S&P 500 earnings estimates have jumped from $352 to $399 this year. But the forward P/E has fallen: 🔹 S&P 500: 6,846 → 7,365 🔹 Forward P/E: 19.4x → 18.4x 🔹 Earnings up 🔹 Multiple down That means the market is higher, but fundamentally cheaper than January 1st. Semis just dropped 7% in one day. Since 2011, 6%+ one day drops in semis have had an 88% win rate over 1, 3 and 6 months, with a median 6 month gain of 38%. Tom Lee’s view: This looks less like a top and more like a buyable dip in the AI infrastructure trade.

BMNR Bullz

23,702 次观看 • 1 个月前

$PLTR is way too cheap at this valuation!🧵 Not Financial Advice! DYOR! Price to Sales P/S Year Revenue YoY Growth P/S 2020 $1.093B 47% 40x 2021 $1.542B 41% 24x 2022 $1.906B 24% 7x 2023 $2.225B 17% 17x 2024 $2.866B 29% 63x 2025 $4.475B 56% 102x Current SP: $137.75 or $329B Market cap Projection 2026 2027 2028. P/S is based on today MC Dr. Karp said 2027 to grow 100%+ revenue. For 2028, we can take a more conservative at 60%! 2026 $8B 79% 41x 2027 $16-$17B 100-112% 20x 2028 $26.4B 60% 12.4x ~At this kind of growth ~Rule of 40 at 145% ~Adj Operating Margin 60% and expanding ~Adj FCF Margin 57% and expanding ~EPS grew annualized rate of 76.5% to 78.9% The best margin profile vs any SaaS companies today. Is it really expensive at fwd 12.4x-20x P/S? At this kind of growth & margin: 40-50x Fwd P/S is reasonable. Of course if growth slows, Multiple will compress like 2022-2023, but that was a lovely buying opportunity for long term investor like myself :)). Absolutely not. Yes $IGV Is doing its things as Anthropic is selling fear-porn that they can destroy and bankrupt any software companies. But no software companies are delivering this kind of growth, so Palantir is the AI wave. We are experiencing a sharp pullback, but I will leave you with this clip. Are you a long term investor or trader? This isnt the first time Palantir pulls back. Not Financial Advice! DYOR!
0:58

$PLTR is way too cheap at this valuation!🧵 Not Financial Advice! DYOR! Price to Sales P/S Year Revenue YoY Growth P/S 2020 $1.093B 47% 40x 2021 $1.542B 41% 24x 2022 $1.906B 24% 7x 2023 $2.225B 17% 17x 2024 $2.866B 29% 63x 2025 $4.475B 56% 102x Current SP: $137.75 or $329B Market cap Projection 2026 2027 2028. P/S is based on today MC Dr. Karp said 2027 to grow 100%+ revenue. For 2028, we can take a more conservative at 60%! 2026 $8B 79% 41x 2027 $16-$17B 100-112% 20x 2028 $26.4B 60% 12.4x ~At this kind of growth ~Rule of 40 at 145% ~Adj Operating Margin 60% and expanding ~Adj FCF Margin 57% and expanding ~EPS grew annualized rate of 76.5% to 78.9% The best margin profile vs any SaaS companies today. Is it really expensive at fwd 12.4x-20x P/S? At this kind of growth & margin: 40-50x Fwd P/S is reasonable. Of course if growth slows, Multiple will compress like 2022-2023, but that was a lovely buying opportunity for long term investor like myself :)). Absolutely not. Yes $IGV Is doing its things as Anthropic is selling fear-porn that they can destroy and bankrupt any software companies. But no software companies are delivering this kind of growth, so Palantir is the AI wave. We are experiencing a sharp pullback, but I will leave you with this clip. Are you a long term investor or trader? This isnt the first time Palantir pulls back. Not Financial Advice! DYOR!

Mike

168,655 次观看 • 3 个月前

$AMD Valuation at $70-$100B Revenue in 2026🧵 As of December 4, 2025, AMD's stock trades at approximately $220, with a market cap of $355billion. Revised Valuation with $70B Revenue Earnings Per Share (EPS): Assuming a 40% operating margin (consistent with historical trends, probably higher), $70 billion in revenue translates to $28 billion in operating income. After taxes and interest, net income could be $20 billion, or $12.50 EPS Forward P/E: At 50x-60x (a premium due to growth), the stock price could reach $650-$750 EV/EBITDA: With $28 billion EBITDA, at 40x, EV is $1.12 trillion. Subtracting $5 billion net debt, equity value is $1.115 trillion, or $697 per share. Revised Valuation with $100B Revenue EPS: $100 billion revenue at 45% margin yields $45 billion operating income, $35 billion net income, or $22 EPS. Forward P/E: At 50x-70x, the stock price could reach $1,100-$1,540 EV/EBITDA: $40 billion EBITDA at 45x EV/EBITDA yields $1.8 trillion EV. Subtracting $5 billion net debt, equity value is $1.795 trillion, or $1,122 per share. The market's willingness to assign a high P/E multiple to AMD will be based on the anticipation that these partnerships will translate into substantial revenue and earnings growth. The P/E ratio for the semiconductor industry is approximately 58.57, a significant increase from previous years because of AI CapEx Growth and we are only 2nd year of 10 years cycle. Hence, if $AMD grew to $70B-$100B revenue in 2026, 50x-70x P/E is justified. AMD's existing partnerships with OpenAI , $Meta, $MSFT, $AMZN, $GOOGL, $DELL, $HPE, $SMCI,xAI , Oracle, Vulture combined with new collaborations with international 40+ countries like Saudi,UAE form a solid foundation for revenue growth. The OpenAI deal alone could contribute $25 billion to $28 billion(2026), while Meta's expanded allocation and Oracle's increased orders with the rest add substantial upside of 1m+ GPUs(FY2026) . Technological Leadership: The MI450 GPU, with its superior inference and training capabilities, positions AMD to disrupt Nvidia's market dominance. Benchmarks show 1.5-2x performance advantages at 35-50% lower TCO, making it an attractive choice for hyperscalers. The ROCm platform's maturity, supporting day-zero integration for major AI models, closes the software gap with CUDA, enhancing AMD's competitiveness. In conclusion, AMD's combination of strategic partnerships, technological leadership, and favorable market dynamics positions it to achieve $70 billion to $100 billion in revenue by 2026. This growth is not merely aspirational but grounded in real demand signals and execution capabilities. While risks remain, the upside potential is significant, making AMD a the best AI Name in this AI Supercycle trading at extreme cheap valuation. Not Financial Advice!
1:37

$AMD Valuation at $70-$100B Revenue in 2026🧵 As of December 4, 2025, AMD's stock trades at approximately $220, with a market cap of $355billion. Revised Valuation with $70B Revenue Earnings Per Share (EPS): Assuming a 40% operating margin (consistent with historical trends, probably higher), $70 billion in revenue translates to $28 billion in operating income. After taxes and interest, net income could be $20 billion, or $12.50 EPS Forward P/E: At 50x-60x (a premium due to growth), the stock price could reach $650-$750 EV/EBITDA: With $28 billion EBITDA, at 40x, EV is $1.12 trillion. Subtracting $5 billion net debt, equity value is $1.115 trillion, or $697 per share. Revised Valuation with $100B Revenue EPS: $100 billion revenue at 45% margin yields $45 billion operating income, $35 billion net income, or $22 EPS. Forward P/E: At 50x-70x, the stock price could reach $1,100-$1,540 EV/EBITDA: $40 billion EBITDA at 45x EV/EBITDA yields $1.8 trillion EV. Subtracting $5 billion net debt, equity value is $1.795 trillion, or $1,122 per share. The market's willingness to assign a high P/E multiple to AMD will be based on the anticipation that these partnerships will translate into substantial revenue and earnings growth. The P/E ratio for the semiconductor industry is approximately 58.57, a significant increase from previous years because of AI CapEx Growth and we are only 2nd year of 10 years cycle. Hence, if $AMD grew to $70B-$100B revenue in 2026, 50x-70x P/E is justified. AMD's existing partnerships with OpenAI , $Meta, $MSFT, $AMZN, $GOOGL, $DELL, $HPE, $SMCI,xAI , Oracle, Vulture combined with new collaborations with international 40+ countries like Saudi,UAE form a solid foundation for revenue growth. The OpenAI deal alone could contribute $25 billion to $28 billion(2026), while Meta's expanded allocation and Oracle's increased orders with the rest add substantial upside of 1m+ GPUs(FY2026) . Technological Leadership: The MI450 GPU, with its superior inference and training capabilities, positions AMD to disrupt Nvidia's market dominance. Benchmarks show 1.5-2x performance advantages at 35-50% lower TCO, making it an attractive choice for hyperscalers. The ROCm platform's maturity, supporting day-zero integration for major AI models, closes the software gap with CUDA, enhancing AMD's competitiveness. In conclusion, AMD's combination of strategic partnerships, technological leadership, and favorable market dynamics positions it to achieve $70 billion to $100 billion in revenue by 2026. This growth is not merely aspirational but grounded in real demand signals and execution capabilities. While risks remain, the upside potential is significant, making AMD a the best AI Name in this AI Supercycle trading at extreme cheap valuation. Not Financial Advice!

Mike

187,491 次观看 • 8 个月前

The S&P100 (OEF) has outperformed the S&P500 this year: 21% compared to 17%. Here's why: It tracks only the largest 100 profitable American companies that survived recessions, crashes, and technological shifts. Average market cap: $135 billion. When you buy an S&P 500 fund, 61% of your money already goes into these top 100 stocks anyway. The other 400 contribute minimal returns. The advantages: - Lower volatility during market chaos. - Better management teams and stronger balance sheets. - Many companies pay dividends with solid growth rates. The tradeoffs: - Higher fees (0.22% vs typical 0.03% for S&P 500 funds). - More concentrated positioning. - Limited growth potential compared to smaller companies. When it makes sense: Use as your core defensive position (40% of portfolio) when market concentration is high and you want quality over quantity. How to buy: Purchase OEF ticker through any brokerage. — This is just one of the ETFs I covered in my 22-minute video on building a simple portfolio that beats the S&P 500. I also covered S&P 500's dangerous concentration in 10 companies, which growth ETFs outperform during rate cuts, and why dividend ETFs exclude the best-performing stocks. Just comment "ETF" and I'll DM it into your inbox in the next few minutes.
3:38

The S&P100 (OEF) has outperformed the S&P500 this year: 21% compared to 17%. Here's why: It tracks only the largest 100 profitable American companies that survived recessions, crashes, and technological shifts. Average market cap: $135 billion. When you buy an S&P 500 fund, 61% of your money already goes into these top 100 stocks anyway. The other 400 contribute minimal returns. The advantages: - Lower volatility during market chaos. - Better management teams and stronger balance sheets. - Many companies pay dividends with solid growth rates. The tradeoffs: - Higher fees (0.22% vs typical 0.03% for S&P 500 funds). - More concentrated positioning. - Limited growth potential compared to smaller companies. When it makes sense: Use as your core defensive position (40% of portfolio) when market concentration is high and you want quality over quantity. How to buy: Purchase OEF ticker through any brokerage. — This is just one of the ETFs I covered in my 22-minute video on building a simple portfolio that beats the S&P 500. I also covered S&P 500's dangerous concentration in 10 companies, which growth ETFs outperform during rate cuts, and why dividend ETFs exclude the best-performing stocks. Just comment "ETF" and I'll DM it into your inbox in the next few minutes.

Felix Prehn 🐶

16,511 次观看 • 7 个月前

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!
1:06

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!

Mike

711,006 次观看 • 10 个月前

$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵 Not Financial Advice! DYOR! In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion. In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions. If you enjoy this kind of thread, slap the like/repost/bookmark. If you want to support my work further and gain more in-depth analysis, consider subscribe! In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs. In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.” At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals. 1. The actual potential TAM Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates. The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000. A. CPU:GPU Ratio at 1:1 A basic calucation at mid range =12,000 x 15-20m CPUs= $180-$240B TAM B. CPU:GPU Ratio at 5:1 = $12,000 x 75m-100m CPUs= $900B-$1.2T TAM Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD. IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030. We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature. The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous. Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components. A heavier or CPUs dense rack would bring down inference cost further. EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads). Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency. 2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030. 2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail. Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm. This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks. Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before. 3. How I came up with $1,200 withint 12-18 months? At $1,200/ share, that would be around $2 Trillion MC. I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue. AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with OpenAI and Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range) EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here. EPYC Verano and MI500 series will also be on 2nm. AI GPUs: 3GW x $18B= $54B EPYC CPUs: $10k x 7m CPUs= $70B = Data center revenue alone is $124B Other segments= probably in the $20-$25B FY 2027. FY2027 revenue = $124-$149B At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see. Metric Q1 2026 FY2027 Gross Margin 55-56% 60-62% Operating Margin 25-26% 32-35% Net Income Margin ~22% 26-30% FCF Margin 25% 28-30% At $124-$149B Revenue FY 2027 Net Income would be $32-$44B EPS would be $20-$27 (GAAP) Non-GAAP would be $25-$31 At $1,200 a share or $2T valuation that would be: 13.4-16x Price to Sales (P/S) 38-48 P/E At this kind of growth of AI SuperCycle, I think it is very reasonable valuation. If we use today at $406/share or $661B MC: 2027 P/S = 4.4x-5.3x 2027 P/E = 13x-16x Is AMD today expensive or cheap to you? Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning. Conclusion: A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out. With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue. Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago. Not Financial Advice! DYOR!
13:03

$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵 Not Financial Advice! DYOR! In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion. In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions. If you enjoy this kind of thread, slap the like/repost/bookmark. If you want to support my work further and gain more in-depth analysis, consider subscribe! In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs. In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.” At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals. 1. The actual potential TAM Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates. The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000. A. CPU:GPU Ratio at 1:1 A basic calucation at mid range =12,000 x 15-20m CPUs= $180-$240B TAM B. CPU:GPU Ratio at 5:1 = $12,000 x 75m-100m CPUs= $900B-$1.2T TAM Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD. IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030. We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature. The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous. Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components. A heavier or CPUs dense rack would bring down inference cost further. EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads). Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency. 2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030. 2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail. Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm. This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks. Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before. 3. How I came up with $1,200 withint 12-18 months? At $1,200/ share, that would be around $2 Trillion MC. I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue. AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with OpenAI and Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range) EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here. EPYC Verano and MI500 series will also be on 2nm. AI GPUs: 3GW x $18B= $54B EPYC CPUs: $10k x 7m CPUs= $70B = Data center revenue alone is $124B Other segments= probably in the $20-$25B FY 2027. FY2027 revenue = $124-$149B At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see. Metric Q1 2026 FY2027 Gross Margin 55-56% 60-62% Operating Margin 25-26% 32-35% Net Income Margin ~22% 26-30% FCF Margin 25% 28-30% At $124-$149B Revenue FY 2027 Net Income would be $32-$44B EPS would be $20-$27 (GAAP) Non-GAAP would be $25-$31 At $1,200 a share or $2T valuation that would be: 13.4-16x Price to Sales (P/S) 38-48 P/E At this kind of growth of AI SuperCycle, I think it is very reasonable valuation. If we use today at $406/share or $661B MC: 2027 P/S = 4.4x-5.3x 2027 P/E = 13x-16x Is AMD today expensive or cheap to you? Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning. Conclusion: A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out. With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue. Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago. Not Financial Advice! DYOR!

Mike

301,322 次观看 • 3 个月前

$AMD is ready to break $1 Trillion MC| $TSM 2nm🧵 TLDR FY 2026(Excluding China AI Revenue) AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 The semiconductor industry is at a pivotal juncture, with advanced process nodes like TSMC's 2nm technology becoming the battleground for leadership in artificial intelligence and high-performance computing (HPC). Amid this landscape, AMD stands poised to secure early production and higher allocation of its Venice (EPYC ) and MI450 (Instinct GPUs) on TSMC's 2nm process. This strategic advantage is not merely a product of timing but a culmination of a robust partnership, market demand, technical superiority, and geopolitical dynamics. The AI and HPC markets are experiencing unprecedented growth, with inference workloads projected to constitute 80-90% of AI compute by 2030. AMD's EPYC processors and Instinct GPUs are uniquely positioned to capitalize on this trend, particularly given the demand from hyperscalers such as OpenAI , $META , $MSFT, $AMZN, and $ORCL. With $TSM starting 2nm Mass Production in Taiwan is ensuring AMD to meet FY2026 $70B to $100B revenue, driven by non-GAAP net income of $18B to $25B highlights the scale of this opportunity, starkly contrasting with analyst revenue consensus of $39-$45B. This discrepancy arises from analysts' failure to account for major orders, notably from OpenAI(Today SoftBank secured OpenAI a massive cash balance of $55-$62B).OpenAI is raising $100B, so this left $77B from UAE, Saudi, $MSFT, and others. $AMD is on track to receive higher allocation of EPYC Venice and Mi450 in 2026. AMD's acquisition of Xilinx has significantly strengthened its position in AI inference, particularly through adaptive computing technologies like FPGA-based AI Engines. The upcoming Zen 6 "Venice" generation (on TSMC 2nm, launching with MI450 in 2026) promises ~1.7× performance uplift, enhanced vector/AI capabilities, greater thread density, and open firmware innovations positioning EPYC to maintain its inference leadership while powering massive hybrid AI superclusters. TSMC's Fab 22 in Kaohsiung, Taiwan, is now the epicenter of 2nm mass production, a earlier strategic move to meet soaring demand from $AMD and $AAPL. Early production slots are typically reserved for customers with the highest revenue potential and strategic importance. AMD's early tape-out of Venice and the MI450's role as the first AMD GPU on 2nm place it at the forefront of this allocation. The 2nm process offers 10-15% higher performance or 25-30% lower power use compared to 3nm, a critical advantage for AI and HPC applications(TSMC claimed) Moreover, TSMC's recent 20% yield improvement in Versal production, as mentioned in related discussions, indicates efficient scaling. Higher yields translate to more chips produced per wafer, reducing costs and increasing allocation for key customers like AMD. This efficiency is particularly important given the aggressive timelines of customers like OpenAI, who require rapid scaling to meet their computational needs. The reopening of the China market adds another layer of demand pressure. Vendors and hyperscalers are begging for allocation of AMD's MI308X, MI300X, and MI355X, and the 2nm capacity will be critical to meet this need. TSMC's early production of 2nm ensures AMD can capitalize on this opportunity, securing higher allocation to fulfill these orders. Dr. Lisa Su's emphasis on disciplined supply chain planning for multiple gigawatt-scale customers, such as OpenAI, demonstrates AMD's readiness to scale. TSMC's confidence in AMD's ability to absorb this capacity is evident in the early 2nm production allocation. This discipline is particularly important in a market where demand outstrips supply by 10-12x. TSMC's competitors, such as Samsung and Intel, are still in the early stages of their 2nm and equivalent processes. Samsung's 2nm GAA transistors and Intel's 18A process are not yet in mass production, giving TSMC and AMD a first-mover advantage. Nvidia's acquisition of Groq Inc. is a defensive move to diversify into inference, but it does not immediately address the 2nm gap. AMD EPYC Venice and future Gen are already ahead of lowest cost for Inference along with MI450 has TCO of $0.65 to $1.00 per million inference tokens, significantly lower than Nvidia's Rubik (H2 2026) at $0.70 to $1.20 and Broadcom's XPU (2027-2029) at $0.70 to $1.30. Additionally, the MI450's TDP is estimated at 1000-1800W, compared to Nvidia's 2300-3600W (Ultra), reducing operational costs and energy consumption(TSMC 2nm vs TSMC 3nm). The MI450 features 432GB of HBM4 memory and 19.6 TB/s bandwidth, surpassing Nvidia's Rubik (288GB HBM4, 16 TB/s) and Broadcom's XPU (192/256GB HBM4, 7 TB/s est). This enhanced memory and bandwidth capacity is essential for handling the complex, data-intensive workloads of large language models and other AI applications. AMD's full-stack vision, combining EPYC hosts with Instinct accelerators, offers the lowest total cost of ownership (TCO) and thermal design power (TDP). This synergy is unbeatable for both training and inference, further justifying TSMC's prioritization. The 2nm process amplifies these advantages, ensuring AMD can maintain its competitive edge over rivals like Nvidia, whose Rubin GPUs are still on N3P (a 3nm derivative). Today, TSMC just secured $AMD to join the top 10 largest companies in the world as it begins 2nm mass production in Taiwan. AMD and Apple are to receive highest allocation. The long-standing partnership with TSMC, massive demand from hyperscalers, technical advantages of 2nm, and disciplined supply chain planning all point to AMD's favored position. The 2nm process's early mass production at Fab 22, combined with AMD's revenue potential and competitive edge, justifies TSMC's prioritization. This allocation is critical for AMD to meet aggressive demand, capture market share, and solidify its position as a leader in AI and HPC, especially in the inference-dominated future. Dr. Lisa Su "We will multiple customers/hyperscalers at GW scale" Not Financial Advice!
2:38

$AMD is ready to break $1 Trillion MC| $TSM 2nm🧵 TLDR FY 2026(Excluding China AI Revenue) AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 The semiconductor industry is at a pivotal juncture, with advanced process nodes like TSMC's 2nm technology becoming the battleground for leadership in artificial intelligence and high-performance computing (HPC). Amid this landscape, AMD stands poised to secure early production and higher allocation of its Venice (EPYC ) and MI450 (Instinct GPUs) on TSMC's 2nm process. This strategic advantage is not merely a product of timing but a culmination of a robust partnership, market demand, technical superiority, and geopolitical dynamics. The AI and HPC markets are experiencing unprecedented growth, with inference workloads projected to constitute 80-90% of AI compute by 2030. AMD's EPYC processors and Instinct GPUs are uniquely positioned to capitalize on this trend, particularly given the demand from hyperscalers such as OpenAI , $META , $MSFT, $AMZN, and $ORCL. With $TSM starting 2nm Mass Production in Taiwan is ensuring AMD to meet FY2026 $70B to $100B revenue, driven by non-GAAP net income of $18B to $25B highlights the scale of this opportunity, starkly contrasting with analyst revenue consensus of $39-$45B. This discrepancy arises from analysts' failure to account for major orders, notably from OpenAI(Today SoftBank secured OpenAI a massive cash balance of $55-$62B).OpenAI is raising $100B, so this left $77B from UAE, Saudi, $MSFT, and others. $AMD is on track to receive higher allocation of EPYC Venice and Mi450 in 2026. AMD's acquisition of Xilinx has significantly strengthened its position in AI inference, particularly through adaptive computing technologies like FPGA-based AI Engines. The upcoming Zen 6 "Venice" generation (on TSMC 2nm, launching with MI450 in 2026) promises ~1.7× performance uplift, enhanced vector/AI capabilities, greater thread density, and open firmware innovations positioning EPYC to maintain its inference leadership while powering massive hybrid AI superclusters. TSMC's Fab 22 in Kaohsiung, Taiwan, is now the epicenter of 2nm mass production, a earlier strategic move to meet soaring demand from $AMD and $AAPL. Early production slots are typically reserved for customers with the highest revenue potential and strategic importance. AMD's early tape-out of Venice and the MI450's role as the first AMD GPU on 2nm place it at the forefront of this allocation. The 2nm process offers 10-15% higher performance or 25-30% lower power use compared to 3nm, a critical advantage for AI and HPC applications(TSMC claimed) Moreover, TSMC's recent 20% yield improvement in Versal production, as mentioned in related discussions, indicates efficient scaling. Higher yields translate to more chips produced per wafer, reducing costs and increasing allocation for key customers like AMD. This efficiency is particularly important given the aggressive timelines of customers like OpenAI, who require rapid scaling to meet their computational needs. The reopening of the China market adds another layer of demand pressure. Vendors and hyperscalers are begging for allocation of AMD's MI308X, MI300X, and MI355X, and the 2nm capacity will be critical to meet this need. TSMC's early production of 2nm ensures AMD can capitalize on this opportunity, securing higher allocation to fulfill these orders. Dr. Lisa Su's emphasis on disciplined supply chain planning for multiple gigawatt-scale customers, such as OpenAI, demonstrates AMD's readiness to scale. TSMC's confidence in AMD's ability to absorb this capacity is evident in the early 2nm production allocation. This discipline is particularly important in a market where demand outstrips supply by 10-12x. TSMC's competitors, such as Samsung and Intel, are still in the early stages of their 2nm and equivalent processes. Samsung's 2nm GAA transistors and Intel's 18A process are not yet in mass production, giving TSMC and AMD a first-mover advantage. Nvidia's acquisition of Groq Inc. is a defensive move to diversify into inference, but it does not immediately address the 2nm gap. AMD EPYC Venice and future Gen are already ahead of lowest cost for Inference along with MI450 has TCO of $0.65 to $1.00 per million inference tokens, significantly lower than Nvidia's Rubik (H2 2026) at $0.70 to $1.20 and Broadcom's XPU (2027-2029) at $0.70 to $1.30. Additionally, the MI450's TDP is estimated at 1000-1800W, compared to Nvidia's 2300-3600W (Ultra), reducing operational costs and energy consumption(TSMC 2nm vs TSMC 3nm). The MI450 features 432GB of HBM4 memory and 19.6 TB/s bandwidth, surpassing Nvidia's Rubik (288GB HBM4, 16 TB/s) and Broadcom's XPU (192/256GB HBM4, 7 TB/s est). This enhanced memory and bandwidth capacity is essential for handling the complex, data-intensive workloads of large language models and other AI applications. AMD's full-stack vision, combining EPYC hosts with Instinct accelerators, offers the lowest total cost of ownership (TCO) and thermal design power (TDP). This synergy is unbeatable for both training and inference, further justifying TSMC's prioritization. The 2nm process amplifies these advantages, ensuring AMD can maintain its competitive edge over rivals like Nvidia, whose Rubin GPUs are still on N3P (a 3nm derivative). Today, TSMC just secured $AMD to join the top 10 largest companies in the world as it begins 2nm mass production in Taiwan. AMD and Apple are to receive highest allocation. The long-standing partnership with TSMC, massive demand from hyperscalers, technical advantages of 2nm, and disciplined supply chain planning all point to AMD's favored position. The 2nm process's early mass production at Fab 22, combined with AMD's revenue potential and competitive edge, justifies TSMC's prioritization. This allocation is critical for AMD to meet aggressive demand, capture market share, and solidify its position as a leader in AI and HPC, especially in the inference-dominated future. Dr. Lisa Su "We will multiple customers/hyperscalers at GW scale" Not Financial Advice!

Mike

43,219 次观看 • 7 个月前

🚨 THIS LOOKS REALLY SCARY We've seen this before: Dot-Com crash & 2008 crisis 1. The S&P 500 saw strong growth from 1995 to 1999 1995: +37.6% 1996: +23.0% 1997: +33.4% 1998: +28.6% 1999: +21.0% 5 year and then came a 50% CRASH 2. Later, since 2003, the S&P 500 rallied for another 5 straight years 2003: +28.68% 2004: +10.88% 2005: +4.91% 2006: +15.79% 2007: +5.49% 5 year and then came a 52% CRASH 3. Here's what the S&P 500 has done since 2023: 2023: +24.2% 2024: +23.3% 2025: +17.1% 2026: already +7.7% 2027: ??? Doesn't any of this concern you? Does it all really seem normal? Let's assume the S&P 500 is trading around $8,500-9,500 by the end of 2026 The market will have posted strong gains for 4 straight years, and most of that growth will have been driven by the tech sector But you're smart people - you understand that electricity demand from data centers, largely driven by AI, is growing extremely fast Many new data center projects in the U.S. are already being delayed or canceled because of grid capacity constraints, transformer shortages, and long connection wait times And if that continues, companies like Nvidia, Microsoft, Google, Amazon, and Meta could start issuing weaker AI revenue growth guidance for 2027-2028 That could force investors to reprice tech giants lower The market is already pricing in extremely aggressive AI growth expectations, so any slowdown could be painful On top of all that, SpaceX ( $SPCX ) starting trading today on Nasdaq, and it's also considered part of the tech sector Then later this year, the market could see IPOs from Anthropic and OpenAI as well Large IPO waves have historically impacted liquidity and investor sentiment Read this twice if you want to understand why I'm concerned about what's coming next I've said this before, and the cycle is still playing out exactly according to plan Turn on notifications and drop your thoughts below The next phase is gonna be very important
0:36

🚨 THIS LOOKS REALLY SCARY We've seen this before: Dot-Com crash & 2008 crisis 1. The S&P 500 saw strong growth from 1995 to 1999 1995: +37.6% 1996: +23.0% 1997: +33.4% 1998: +28.6% 1999: +21.0% 5 year and then came a 50% CRASH 2. Later, since 2003, the S&P 500 rallied for another 5 straight years 2003: +28.68% 2004: +10.88% 2005: +4.91% 2006: +15.79% 2007: +5.49% 5 year and then came a 52% CRASH 3. Here's what the S&P 500 has done since 2023: 2023: +24.2% 2024: +23.3% 2025: +17.1% 2026: already +7.7% 2027: ??? Doesn't any of this concern you? Does it all really seem normal? Let's assume the S&P 500 is trading around $8,500-9,500 by the end of 2026 The market will have posted strong gains for 4 straight years, and most of that growth will have been driven by the tech sector But you're smart people - you understand that electricity demand from data centers, largely driven by AI, is growing extremely fast Many new data center projects in the U.S. are already being delayed or canceled because of grid capacity constraints, transformer shortages, and long connection wait times And if that continues, companies like Nvidia, Microsoft, Google, Amazon, and Meta could start issuing weaker AI revenue growth guidance for 2027-2028 That could force investors to reprice tech giants lower The market is already pricing in extremely aggressive AI growth expectations, so any slowdown could be painful On top of all that, SpaceX ( $SPCX ) starting trading today on Nasdaq, and it's also considered part of the tech sector Then later this year, the market could see IPOs from Anthropic and OpenAI as well Large IPO waves have historically impacted liquidity and investor sentiment Read this twice if you want to understand why I'm concerned about what's coming next I've said this before, and the cycle is still playing out exactly according to plan Turn on notifications and drop your thoughts below The next phase is gonna be very important

Leni

81,430 次观看 • 2 个月前

🚨 SOMETHING VERY STRANGE IS HAPPENING The stock market keeps pushing to new all-time highs. But nobody is paying attention to what’s actually happening. Semiconductor stocks are now worth $13.4T. That’s 19.7% of the entire S&P 500. 4x growth in just five years. And all of that growth depends on one trade: AI. Numbers do not lie: - AI chips generate 50% of all semiconductor revenue - They represent less than 0.2% of total chip shipments - A small group of companies is carrying the entire market Nvidia. Broadcom. TSMC. The same companies every major institution already owns. Here’s how the bubble feeds itself: - Big players fund each other - Partnerships create paper revenue - Money circulates inside the same system We have seen this before: 2000: - A few tech companies carried the entire market - Massive valuations - Narratives driving everything Then reality hit. The S&P 500 collapsed 50%. Now we’re watching the same cycle again. Less than 0.2% of chip volumes are now holding up trillions in market value. And one cut in AI spending is all it takes to break the entire market. Remember, I’ve predicted all the market tops and bottoms for the last 15 years, including the exact Bitcoin bottom at $16,000 three years ago and the top at $126,000 in October. If you missed those calls, don’t worry. I’ll call the next one too. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.
0:33

🚨 SOMETHING VERY STRANGE IS HAPPENING The stock market keeps pushing to new all-time highs. But nobody is paying attention to what’s actually happening. Semiconductor stocks are now worth $13.4T. That’s 19.7% of the entire S&P 500. 4x growth in just five years. And all of that growth depends on one trade: AI. Numbers do not lie: - AI chips generate 50% of all semiconductor revenue - They represent less than 0.2% of total chip shipments - A small group of companies is carrying the entire market Nvidia. Broadcom. TSMC. The same companies every major institution already owns. Here’s how the bubble feeds itself: - Big players fund each other - Partnerships create paper revenue - Money circulates inside the same system We have seen this before: 2000: - A few tech companies carried the entire market - Massive valuations - Narratives driving everything Then reality hit. The S&P 500 collapsed 50%. Now we’re watching the same cycle again. Less than 0.2% of chip volumes are now holding up trillions in market value. And one cut in AI spending is all it takes to break the entire market. Remember, I’ve predicted all the market tops and bottoms for the last 15 years, including the exact Bitcoin bottom at $16,000 three years ago and the top at $126,000 in October. If you missed those calls, don’t worry. I’ll call the next one too. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

Alex Mason 👁△

227,729 次观看 • 1 个月前

🚨 SPACEX IPO IS THE BIGGEST BULL TRAP IN U.S. HISTORY... And it may mark the top SpaceX lists June 12 $135 a share ~555 million shares ~$75 BILLION raised A $1.75 TRILLION valuation This isn't just an IPO It's the largest capital raise markets have ever seen More than 2.5x Saudi Aramco's old record Bigger than Aramco and Alibaba combined Here's what market isn't pricing in That $75B doesn't appear from nowhere To buy $SPCX, funds sell something else - Megacap tech - Index names - Whatever's liquid $75 billion pulled OUT of existing positions to fund ONE name The biggest liquidity drain in market history. In one week Now look at track record of "record" IPOs 1999–2000 - dot-com IPO frenzy. Peak greed → S&P 500 fell ~49% into 2002 June 2007 - Blackstone. The biggest deal in 5 years → Credit crunch within months. S&P 500 down ~57% into 2009 Dec 2019 - Saudi Aramco. The largest IPO ever… until now → Weeks later, the fastest bear market in history. S&P 500 -34% Nov 2021 - Rivian. Biggest US IPO since Alibaba. Barely any revenue, ~$100B → The S&P 500 topped that January. A ~25% bear market followed Four times the "biggest IPO" printed at moment of maximum greed Four times S&P paid the bill Now SpaceX is the biggest of them all There's more ~30% of deal - about $22.5B - is going to RETAIL Triple normal allocation When they hand top to crowd, ask who's selling to them Let that sink in Not calling an exact top But every ingredient of one is on the table this week Watch the tape June 12 Watch what $SPX does the day after the champagne Turn on notifications. I'll post warning before it hits headlines
0:27

🚨 SPACEX IPO IS THE BIGGEST BULL TRAP IN U.S. HISTORY... And it may mark the top SpaceX lists June 12 $135 a share ~555 million shares ~$75 BILLION raised A $1.75 TRILLION valuation This isn't just an IPO It's the largest capital raise markets have ever seen More than 2.5x Saudi Aramco's old record Bigger than Aramco and Alibaba combined Here's what market isn't pricing in That $75B doesn't appear from nowhere To buy $SPCX, funds sell something else - Megacap tech - Index names - Whatever's liquid $75 billion pulled OUT of existing positions to fund ONE name The biggest liquidity drain in market history. In one week Now look at track record of "record" IPOs 1999–2000 - dot-com IPO frenzy. Peak greed → S&P 500 fell ~49% into 2002 June 2007 - Blackstone. The biggest deal in 5 years → Credit crunch within months. S&P 500 down ~57% into 2009 Dec 2019 - Saudi Aramco. The largest IPO ever… until now → Weeks later, the fastest bear market in history. S&P 500 -34% Nov 2021 - Rivian. Biggest US IPO since Alibaba. Barely any revenue, ~$100B → The S&P 500 topped that January. A ~25% bear market followed Four times the "biggest IPO" printed at moment of maximum greed Four times S&P paid the bill Now SpaceX is the biggest of them all There's more ~30% of deal - about $22.5B - is going to RETAIL Triple normal allocation When they hand top to crowd, ask who's selling to them Let that sink in Not calling an exact top But every ingredient of one is on the table this week Watch the tape June 12 Watch what $SPX does the day after the champagne Turn on notifications. I'll post warning before it hits headlines

Klarck

26,030 次观看 • 2 个月前

$GRAB AI SuperApp Valuation is STUPID👀 Video was from CNBC w/ Howard Marks, a major shareholder on $GRAB. $GRAB is now trading at 3.5 P/S FY 2026 revenue($6.2B) after $GOTO Mobility/Delivery Acquisition with $6B+ in cash and very little debt. Expected growth after 90-95% market share consolidation is 40%+ top and bottom line. This is when $GRAB introduces higher margin services as it has the monopoly in SEA or 700m population. For example, Dine-Out has 70-80% margin (20-30% commission), and this new services drove 1/3-1/4 of all new users growth. And Grab is only 6.5% penetration(700m), 14% thesis (5 billion people TAM). We are very EARLY! Long term shareholders should also expect expansion shortly after this consolidation. Most likely H2 2026 or H1 2027. You can hate Grab or call it a Chinese company based in Singapore as much as you want, but this valuation is stupid. This balance sheet and this kind of growth deserve a premium 8-10 P/S. I will be here to enjoy the ride when the market prices this SuperApp correctly.
11:01

$GRAB AI SuperApp Valuation is STUPID👀 Video was from CNBC w/ Howard Marks, a major shareholder on $GRAB. $GRAB is now trading at 3.5 P/S FY 2026 revenue($6.2B) after $GOTO Mobility/Delivery Acquisition with $6B+ in cash and very little debt. Expected growth after 90-95% market share consolidation is 40%+ top and bottom line. This is when $GRAB introduces higher margin services as it has the monopoly in SEA or 700m population. For example, Dine-Out has 70-80% margin (20-30% commission), and this new services drove 1/3-1/4 of all new users growth. And Grab is only 6.5% penetration(700m), 14% thesis (5 billion people TAM). We are very EARLY! Long term shareholders should also expect expansion shortly after this consolidation. Most likely H2 2026 or H1 2027. You can hate Grab or call it a Chinese company based in Singapore as much as you want, but this valuation is stupid. This balance sheet and this kind of growth deserve a premium 8-10 P/S. I will be here to enjoy the ride when the market prices this SuperApp correctly.

Mike

280,725 次观看 • 8 个月前

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!
3:09

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!

Mike

102,223 次观看 • 7 个月前

🚨 S&P 500 IS NOT 500 COMPANIES ANYMORE 🚨 Think you're diversified? Look close Five companies make up 28% of the entire index NVIDIA alone is bigger than the whole energy sector And these five aren't independent bets. They're funding each other 1. NVIDIA invests in OpenAI 2. OpenAI pays Amazon 3. Amazon buys NVIDIA chips 4. NVIDIA books revenue 5. Stock pumps 6. Loop repeats That's not growth. That's a closed circuit pretending to be an economy Now look at how fast crashes are getting "fixed": - Dotcom: 1,166 days to recover - COVID: 103 days - 2025 tariffs: 55 days - 2026 Iran conflict: 11 days Every dip trained the market to buy the next one harder But OpenAI is losing $14B a year CAPE ratio is at dotcom levels. Strip out AI and the S&P is 25% lower The index looks healthy. The foundation doesn't Keep it in mind NOTIFS ON!
0:23

🚨 S&P 500 IS NOT 500 COMPANIES ANYMORE 🚨 Think you're diversified? Look close Five companies make up 28% of the entire index NVIDIA alone is bigger than the whole energy sector And these five aren't independent bets. They're funding each other 1. NVIDIA invests in OpenAI 2. OpenAI pays Amazon 3. Amazon buys NVIDIA chips 4. NVIDIA books revenue 5. Stock pumps 6. Loop repeats That's not growth. That's a closed circuit pretending to be an economy Now look at how fast crashes are getting "fixed": - Dotcom: 1,166 days to recover - COVID: 103 days - 2025 tariffs: 55 days - 2026 Iran conflict: 11 days Every dip trained the market to buy the next one harder But OpenAI is losing $14B a year CAPE ratio is at dotcom levels. Strip out AI and the S&P is 25% lower The index looks healthy. The foundation doesn't Keep it in mind NOTIFS ON!

NoName

50,383 次观看 • 2 个月前

Paul Tudor Jones says the US is more dependent on equity prices than ever, and explains what a 35% correction would trigger in the economy: "We're 252% of stock market cap to GDP. In 1929 we were 65%. In 1987 we got to ~85-90%. In 2000, 170%. If you think about the periodicity of significant bear markets. Since 1970, we get a mean reversion about every 10 years. Let's say mean revert to the past 25 or 30-year PE. That would be a 30, 35% decline. Well, 35% on 250% of GDP is 80, 90% of GDP. 10% of our tax revenues are capital gains, they go to zero. So you can see the budget deficit blowing up. You can see the bond market getting smoked. You can see this kind of negative self-reinforcing effect. In the stock market, we're over-equitized as a country. We have the highest individual equity weightings in the history of the country. And then the real problem is if you look at private equity in 2007-2008, that was about 7% of institutional portfolios. Now it's about 16% of the institutional portfolios. We're so much more illiquid than we were in 2008. The problem is that if you buy the S&P at this current valuation, the 10-year forward return is negative when you buy the S&P with a PE of 22. That's what history shows. So yes, the S&P is spectacular long-term, if you have a hundred-year view. But that's because that's an average of a hundred years, including times when the S&P 500 PE was 6, 7 and 8, or one third of what it is right now. Valuation matters a lot, and the stock market's really high and it's gonna be really hard to make money from here with any kind of long-term view."
3:14

Paul Tudor Jones says the US is more dependent on equity prices than ever, and explains what a 35% correction would trigger in the economy: "We're 252% of stock market cap to GDP. In 1929 we were 65%. In 1987 we got to ~85-90%. In 2000, 170%. If you think about the periodicity of significant bear markets. Since 1970, we get a mean reversion about every 10 years. Let's say mean revert to the past 25 or 30-year PE. That would be a 30, 35% decline. Well, 35% on 250% of GDP is 80, 90% of GDP. 10% of our tax revenues are capital gains, they go to zero. So you can see the budget deficit blowing up. You can see the bond market getting smoked. You can see this kind of negative self-reinforcing effect. In the stock market, we're over-equitized as a country. We have the highest individual equity weightings in the history of the country. And then the real problem is if you look at private equity in 2007-2008, that was about 7% of institutional portfolios. Now it's about 16% of the institutional portfolios. We're so much more illiquid than we were in 2008. The problem is that if you buy the S&P at this current valuation, the 10-year forward return is negative when you buy the S&P with a PE of 22. That's what history shows. So yes, the S&P is spectacular long-term, if you have a hundred-year view. But that's because that's an average of a hundred years, including times when the S&P 500 PE was 6, 7 and 8, or one third of what it is right now. Valuation matters a lot, and the stock market's really high and it's gonna be really hard to make money from here with any kind of long-term view."

Patrick OShaughnessy

2,374,188 次观看 • 3 个月前

$AMD Strategic Price Positioning Long🧵 AMD is increasingly the most hated semi stock that can rival $NVDA dominance in GPUs and software(Cuda v. ROCm). $AMD is also the most under-owned among all Funds in 2025 according to Bank of America! For what I learnt for years as an investor with Dr. Lisa Su, all analysts and market are underestimate Dr. Su leadership. $AMD is capable of raising price, making high quality hardware with software. Dr. Su or AMD choice to adopt a lower price strategy to gain market share is a deliberate and multifacets approach rooted in competitive positioning, market dynamics, and long-term growth objectives. As an investor, it may take time like CPUs and embedded to see margin improving. 1. . Penetration Pricing to Challenge Dominant Competitors AMD has historically positioned itself as a cost-effective alternative to dominant players like Intel in CPUs and Nvidia in GPUs. By setting prices lower than competitors, AMD aims to attract customers and quickly gain market share. This is a classic penetration pricing strategy, where the goal is to capture a significant portion of the market by offering high-performance products at a lower price point. ~CPU Market Example: When AMD launched its Ryzen processors in 2017, it priced them competitively compared to Intel's Core processors, emphasizing a better price-to-performance ratio. Ryzen CPUs offered higher core counts and multi-core performance at lower prices, appealing to cost-conscious consumers, gamers, and professionals. This strategy helped AMD increase its CPU market share to 16.6% by early 2025, narrowing the gap with Intel. ~GPU Market Context: In the GPU market, where Nvidia holds an 88% share compared to AMD's 12%, AMD has been criticized for not launching GPUs at low enough prices to compete effectively. However, posts on X and articles suggest AMD is shifting its GPU strategy to focus on mainstream, cost-effective products rather than high-end enthusiast segments, aiming to regain market share through competitive pricing. 2. Appealing to Cost-Conscious Market Segments AMD targets price-sensitive customers, including gamers, small businesses, and enterprises looking for high-performance computing at a lower cost. This is particularly effective in segments where performance is critical, but budgets are constrained. ~Value Proposition: AMD’s Ryzen and EPYC processors, as well as Radeon GPUs, are designed to deliver performance comparable to or better than competitors in specific workloads (e.g., multi-core processing or AI compute) at a lower price. For example, Ryzen processors have been noted for their superior multi-core performance compared to Intel CPUs at similar or lower price points, making them attractive for tasks like video editing or gaming. ~AI and Data Center: In the AI and data center markets, AMD’s cost-effective Instinct MI300X GPUs and EPYC CPUs target enterprises seeking affordable alternatives to Nvidia’s expensive AI ecosystem. This strategy taps into an underleveraged market segment that Nvidia’s broad, premium-priced AI solutions may not fully address. 3. Building Scale and Developer Support AMD’s leadership, including Jack Huynh, has emphasized the importance of scale—gaining a larger market share to attract developer support and optimize software ecosystems. A lower price strategy helps AMD achieve this by increasing adoption among consumers and enterprises. ~Gaming GPUs: By focusing on mainstream GPUs with competitive pricing (e.g., targeting an 80% addressable market rather than the high-end 10%), AMD aims to build a larger user base. This scale encourages developers to optimize games for AMD’s technologies, such as FSR 3 (FidelityFX Super Resolution) and Anti-Lag 2, improving the ecosystem and competitiveness against Nvidia’s CUDA platform. ~Open Ecosystem in AI: AMD’s open-source ROCm platform contrasts with Nvidia’s proprietary CUDA, appealing to developers who prefer flexibility. Lower-priced hardware makes it easier for developers to adopt AMD’s solutions, fostering a broader AI software ecosystem. 4. Historical Context and Brand Positioning Since its founding in 1969, AMD has positioned itself as a challenger brand, often acting as a “second source” supplier to Intel. This role required competitive pricing to gain a foothold in markets dominated by established players. Over time, AMD has built a reputation for quality and affordability, reinforced by products like the Am9080 (a reverse-engineered Intel 8080) and modern Ryzen and EPYC lines. This historical strategy of undercutting competitors’ prices while delivering comparable performance continues to define AMD’s approach. 5. Countering Competitor Dominance AMD operates in highly competitive markets where Intel and Nvidia have significant advantages in brand recognition, market share, and ecosystems. A lower price strategy is a pragmatic way to disrupt this in CPUs: ~Intel’s historical dominance in the CPU market (servers, desktops, and laptops) has been challenged by AMD’s Ryzen and EPYC processors, which offer better value. For instance, AMD’s EPYC CPUs have driven a 122% year-over-year revenue increase in the data center segment, partly due to their cost-effectiveness, helping AMD capture 94% of CPU sales at some retailers. ~Nvidia in GPUs: Nvidia’s 88% GPU market share and premium pricing (e.g., high-end GPUs like the RTX 4090) leave room for AMD to compete in the mid-to-low range. However, AMD’s failure to launch GPUs at sufficiently low prices (e.g., the RX 7900 XT at $900 instead of its current $680) has limited its success, prompting a strategic shift toward more aggressive pricing in future RDNA 4 GPUs. 6. Market Share as a Long-Term Investment AMD’s lower price strategy is not just about immediate sales but also about long-term market positioning. By capturing market share, AMD can: ~Increase Brand Loyalty: Affordable, high-performance products build customer loyalty, especially among gamers and small businesses, creating a foundation for future sales. ~Drive Revenue Growth: Market share gains in CPUs (e.g., 16.6% in 2025) and data centers (e.g., $3.5 billion in Q3 revenue) translate into higher revenue, even if margins are initially lower. ~Influence Industry Standards: Greater market presence allows AMD to influence hardware and software standards, such as pushing for open-source AI frameworks or gaming optimizations, reducing reliance on competitors’ proprietary systems. 7. Challenges and Risks While effective, AMD’s lower price strategy carries risks: ~Profitability Concerns: Lower prices can compress profit margins, and some analysts note that AMD’s high stock valuation expects future profitability that may be delayed if pricing remains aggressive. ~Perception of Quality: Persistently low prices risk positioning AMD as a “budget” brand, potentially undermining its ability to compete in premium segments. ~Competitor Response: Intel and Nvidia can counter with price cuts or superior features, as seen with Nvidia’s feature-rich GPUs. AMD must balance price with innovation to avoid being outmaneuvered. 8. Strategic Shift in GPUs Recent reports indicate AMD is adjusting its GPU strategy to prioritize market share over competing in the high-end enthusiast segment. For the upcoming Radeon RX 8000 series (RDNA 4), AMD is focusing on mainstream GPUs priced competitively to appeal to a broader audience, rather than chasing Nvidia’s high-end dominance. This shift aligns with AMD’s broader goal of achieving 40–50% market share by targeting the “80%” of the market that prioritizes affordability over premium features. Lastly, AMD’s lower price strategy is a calculated move to disrupt Intel and Nvidia’s dominance, capture market share, and build scale for long-term growth. By offering high-performance CPUs and GPUs at competitive prices, AMD appeals to cost-conscious consumers and enterprises, particularly in the CPU and AI markets, where it has seen significant gains (e.g., 16.6% CPU market share and $3.5 billion in data center revenue). Recent price increase on MI350 and MI355 and more on MI400 signaled #AI chip leadership and pricing power, which will result in significant top and bottom line growth.
5:19

$AMD Strategic Price Positioning Long🧵 AMD is increasingly the most hated semi stock that can rival $NVDA dominance in GPUs and software(Cuda v. ROCm). $AMD is also the most under-owned among all Funds in 2025 according to Bank of America! For what I learnt for years as an investor with Dr. Lisa Su, all analysts and market are underestimate Dr. Su leadership. $AMD is capable of raising price, making high quality hardware with software. Dr. Su or AMD choice to adopt a lower price strategy to gain market share is a deliberate and multifacets approach rooted in competitive positioning, market dynamics, and long-term growth objectives. As an investor, it may take time like CPUs and embedded to see margin improving. 1. . Penetration Pricing to Challenge Dominant Competitors AMD has historically positioned itself as a cost-effective alternative to dominant players like Intel in CPUs and Nvidia in GPUs. By setting prices lower than competitors, AMD aims to attract customers and quickly gain market share. This is a classic penetration pricing strategy, where the goal is to capture a significant portion of the market by offering high-performance products at a lower price point. ~CPU Market Example: When AMD launched its Ryzen processors in 2017, it priced them competitively compared to Intel's Core processors, emphasizing a better price-to-performance ratio. Ryzen CPUs offered higher core counts and multi-core performance at lower prices, appealing to cost-conscious consumers, gamers, and professionals. This strategy helped AMD increase its CPU market share to 16.6% by early 2025, narrowing the gap with Intel. ~GPU Market Context: In the GPU market, where Nvidia holds an 88% share compared to AMD's 12%, AMD has been criticized for not launching GPUs at low enough prices to compete effectively. However, posts on X and articles suggest AMD is shifting its GPU strategy to focus on mainstream, cost-effective products rather than high-end enthusiast segments, aiming to regain market share through competitive pricing. 2. Appealing to Cost-Conscious Market Segments AMD targets price-sensitive customers, including gamers, small businesses, and enterprises looking for high-performance computing at a lower cost. This is particularly effective in segments where performance is critical, but budgets are constrained. ~Value Proposition: AMD’s Ryzen and EPYC processors, as well as Radeon GPUs, are designed to deliver performance comparable to or better than competitors in specific workloads (e.g., multi-core processing or AI compute) at a lower price. For example, Ryzen processors have been noted for their superior multi-core performance compared to Intel CPUs at similar or lower price points, making them attractive for tasks like video editing or gaming. ~AI and Data Center: In the AI and data center markets, AMD’s cost-effective Instinct MI300X GPUs and EPYC CPUs target enterprises seeking affordable alternatives to Nvidia’s expensive AI ecosystem. This strategy taps into an underleveraged market segment that Nvidia’s broad, premium-priced AI solutions may not fully address. 3. Building Scale and Developer Support AMD’s leadership, including Jack Huynh, has emphasized the importance of scale—gaining a larger market share to attract developer support and optimize software ecosystems. A lower price strategy helps AMD achieve this by increasing adoption among consumers and enterprises. ~Gaming GPUs: By focusing on mainstream GPUs with competitive pricing (e.g., targeting an 80% addressable market rather than the high-end 10%), AMD aims to build a larger user base. This scale encourages developers to optimize games for AMD’s technologies, such as FSR 3 (FidelityFX Super Resolution) and Anti-Lag 2, improving the ecosystem and competitiveness against Nvidia’s CUDA platform. ~Open Ecosystem in AI: AMD’s open-source ROCm platform contrasts with Nvidia’s proprietary CUDA, appealing to developers who prefer flexibility. Lower-priced hardware makes it easier for developers to adopt AMD’s solutions, fostering a broader AI software ecosystem. 4. Historical Context and Brand Positioning Since its founding in 1969, AMD has positioned itself as a challenger brand, often acting as a “second source” supplier to Intel. This role required competitive pricing to gain a foothold in markets dominated by established players. Over time, AMD has built a reputation for quality and affordability, reinforced by products like the Am9080 (a reverse-engineered Intel 8080) and modern Ryzen and EPYC lines. This historical strategy of undercutting competitors’ prices while delivering comparable performance continues to define AMD’s approach. 5. Countering Competitor Dominance AMD operates in highly competitive markets where Intel and Nvidia have significant advantages in brand recognition, market share, and ecosystems. A lower price strategy is a pragmatic way to disrupt this in CPUs: ~Intel’s historical dominance in the CPU market (servers, desktops, and laptops) has been challenged by AMD’s Ryzen and EPYC processors, which offer better value. For instance, AMD’s EPYC CPUs have driven a 122% year-over-year revenue increase in the data center segment, partly due to their cost-effectiveness, helping AMD capture 94% of CPU sales at some retailers. ~Nvidia in GPUs: Nvidia’s 88% GPU market share and premium pricing (e.g., high-end GPUs like the RTX 4090) leave room for AMD to compete in the mid-to-low range. However, AMD’s failure to launch GPUs at sufficiently low prices (e.g., the RX 7900 XT at $900 instead of its current $680) has limited its success, prompting a strategic shift toward more aggressive pricing in future RDNA 4 GPUs. 6. Market Share as a Long-Term Investment AMD’s lower price strategy is not just about immediate sales but also about long-term market positioning. By capturing market share, AMD can: ~Increase Brand Loyalty: Affordable, high-performance products build customer loyalty, especially among gamers and small businesses, creating a foundation for future sales. ~Drive Revenue Growth: Market share gains in CPUs (e.g., 16.6% in 2025) and data centers (e.g., $3.5 billion in Q3 revenue) translate into higher revenue, even if margins are initially lower. ~Influence Industry Standards: Greater market presence allows AMD to influence hardware and software standards, such as pushing for open-source AI frameworks or gaming optimizations, reducing reliance on competitors’ proprietary systems. 7. Challenges and Risks While effective, AMD’s lower price strategy carries risks: ~Profitability Concerns: Lower prices can compress profit margins, and some analysts note that AMD’s high stock valuation expects future profitability that may be delayed if pricing remains aggressive. ~Perception of Quality: Persistently low prices risk positioning AMD as a “budget” brand, potentially undermining its ability to compete in premium segments. ~Competitor Response: Intel and Nvidia can counter with price cuts or superior features, as seen with Nvidia’s feature-rich GPUs. AMD must balance price with innovation to avoid being outmaneuvered. 8. Strategic Shift in GPUs Recent reports indicate AMD is adjusting its GPU strategy to prioritize market share over competing in the high-end enthusiast segment. For the upcoming Radeon RX 8000 series (RDNA 4), AMD is focusing on mainstream GPUs priced competitively to appeal to a broader audience, rather than chasing Nvidia’s high-end dominance. This shift aligns with AMD’s broader goal of achieving 40–50% market share by targeting the “80%” of the market that prioritizes affordability over premium features. Lastly, AMD’s lower price strategy is a calculated move to disrupt Intel and Nvidia’s dominance, capture market share, and build scale for long-term growth. By offering high-performance CPUs and GPUs at competitive prices, AMD appeals to cost-conscious consumers and enterprises, particularly in the CPU and AI markets, where it has seen significant gains (e.g., 16.6% CPU market share and $3.5 billion in data center revenue). Recent price increase on MI350 and MI355 and more on MI400 signaled #AI chip leadership and pricing power, which will result in significant top and bottom line growth.

Mike

38,006 次观看 • 11 个月前

My dad looked at my screen and said "what is this, a hacker game?" It was a Polymarket bot making $400 while he was standing behind me He sat down. I explained Polymarket runs on one equation - softmax. The same math Claude uses to pick the next word C(q) = b · ln Σ e^(qi/b) 93% of traders don't know this formula exists They look at 40¢ and think "cheap" My bot looks at 40¢ and calculates the theoretical price is 58¢ I'm use for copytrade bots: That's an 18-cent edge per share Two more formulas do the rest: f* = (p·b − q) / b - Kelly Criterion. P(H|E) = P(E|H)·P(H) / P(E) - Bayes. I gave Claude all 4 and said "find every contract the market has wrong" $600 → $10,190. 425 trades. Still running 61.2% win rate - wrong 39% of the time. Doesn't matter. The sizing formula makes sure wins pay more than losses cost 87% of wallets lose money trading against this math The 4 formulas are on Wikipedia. The code fits in one file The only edge: my bot reads them at 3 AM when the market is mispriced and nobody's looking My dad still calls it a hacker game. But he stopped asking when I'm getting a "real job" Only math + code, no more
0:43

My dad looked at my screen and said "what is this, a hacker game?" It was a Polymarket bot making $400 while he was standing behind me He sat down. I explained Polymarket runs on one equation - softmax. The same math Claude uses to pick the next word C(q) = b · ln Σ e^(qi/b) 93% of traders don't know this formula exists They look at 40¢ and think "cheap" My bot looks at 40¢ and calculates the theoretical price is 58¢ I'm use for copytrade bots: That's an 18-cent edge per share Two more formulas do the rest: f* = (p·b − q) / b - Kelly Criterion. P(H|E) = P(E|H)·P(H) / P(E) - Bayes. I gave Claude all 4 and said "find every contract the market has wrong" $600 → $10,190. 425 trades. Still running 61.2% win rate - wrong 39% of the time. Doesn't matter. The sizing formula makes sure wins pay more than losses cost 87% of wallets lose money trading against this math The 4 formulas are on Wikipedia. The code fits in one file The only edge: my bot reads them at 3 AM when the market is mispriced and nobody's looking My dad still calls it a hacker game. But he stopped asking when I'm getting a "real job" Only math + code, no more

Lunar

108,420 次观看 • 4 个月前

Growth ETFs are perfect if you want higher potential returns and can handle the ups and downs that come with it. Vanguard Growth ETF (VUG) is my primary growth pick. VUG owns 185 large-cap growth companies—businesses growing earnings faster than the overall market. Their expense ratio is 0.04% (meaning if you invest $10,000, you pay only $4 per year in fees—extremely cheap). Their top holdings are Apple, Microsoft, Nvidia, Meta, Google, Tesla (which makes sense). The key difference between them and the S&P 100 is they don't include the other 94 stocks like Coca-Cola, Procter & Gamble, and Walmart (which provide more stability but slower growth). In VUG, you're overweighting specifically the growth companies. You're saying "I believe in technology and innovation, and I want concentrated exposure to that theme." So here's the trade-off: VUG is more volatile. When markets rally, it outperforms. When markets crash, it drops harder than the S&P 100. That's why you don't put 100% of your portfolio into VUG. It's your growth sleeve, not your whole strategy. Here's what I'd recommend: - Under 35: Up to 50% in growth - Over 50: Maybe 20-30% VUG gives you growth across multiple sectors—tech, consumer (Amazon), healthcare, communication. Another great growth ETF is VGT, which is the Vanguard Information Technology ETF. It has a 0.09% expense ratio and focuses purely on tech companies—more concentrated, more risk. I prefer VUG because it gives you growth across multiple sectors, not just tech. — This is just one of the ETFs I covered in my 22-minute video on ETFs that beats the S&P 500. I also covered how the S&P 500 is dangerously concentrated in 10 companies, which growth ETFs outperform during rate cuts, and why dividend ETFs exclude the best-performing stocks. Just comment "ETF" and I'll DM it into your inbox in the next few minutes.
3:18

Growth ETFs are perfect if you want higher potential returns and can handle the ups and downs that come with it. Vanguard Growth ETF (VUG) is my primary growth pick. VUG owns 185 large-cap growth companies—businesses growing earnings faster than the overall market. Their expense ratio is 0.04% (meaning if you invest $10,000, you pay only $4 per year in fees—extremely cheap). Their top holdings are Apple, Microsoft, Nvidia, Meta, Google, Tesla (which makes sense). The key difference between them and the S&P 100 is they don't include the other 94 stocks like Coca-Cola, Procter & Gamble, and Walmart (which provide more stability but slower growth). In VUG, you're overweighting specifically the growth companies. You're saying "I believe in technology and innovation, and I want concentrated exposure to that theme." So here's the trade-off: VUG is more volatile. When markets rally, it outperforms. When markets crash, it drops harder than the S&P 100. That's why you don't put 100% of your portfolio into VUG. It's your growth sleeve, not your whole strategy. Here's what I'd recommend: - Under 35: Up to 50% in growth - Over 50: Maybe 20-30% VUG gives you growth across multiple sectors—tech, consumer (Amazon), healthcare, communication. Another great growth ETF is VGT, which is the Vanguard Information Technology ETF. It has a 0.09% expense ratio and focuses purely on tech companies—more concentrated, more risk. I prefer VUG because it gives you growth across multiple sectors, not just tech. — This is just one of the ETFs I covered in my 22-minute video on ETFs that beats the S&P 500. I also covered how the S&P 500 is dangerously concentrated in 10 companies, which growth ETFs outperform during rate cuts, and why dividend ETFs exclude the best-performing stocks. Just comment "ETF" and I'll DM it into your inbox in the next few minutes.

Felix Prehn 🐶

20,926 次观看 • 7 个月前

🚨 MAN WHO DIED IN 1940 IS CURRENTLY MANAGING THE S&P 500 🚨 Sounds insane until you look at this chart Jesse Livermore mapped a 14 step speculative cycle in the 1900s Every bubble in history followed it! Now look at where the S&P 500 is right now Step 1 through 6 printed perfectly Accumulation, breakout, distribution, test before selling short Each one exactly where Livermore said it would be We just hit step 6 That's the point Livermore called "test before selling short" And the macro is confirming it right now New Fed Chair just held rates while inflation climbed back above 4% Consumer sentiment hit a record low, below every recession trough in 75 years Five stocks are carrying the entire index while the average S&P member is already down 21% Livermore didn't need to know any of that He just knew how bubbles end If you've been following me closely you knew when and why S&P 500 would start correcting Many of my followers also took profit from the GOLD pump The next stock market updates will be the most important ones The reason is simple - we're entering a correction phase Turn on notifications and you'll realize how much valuable info you've been missing by not doing it sooner
0:44

🚨 MAN WHO DIED IN 1940 IS CURRENTLY MANAGING THE S&P 500 🚨 Sounds insane until you look at this chart Jesse Livermore mapped a 14 step speculative cycle in the 1900s Every bubble in history followed it! Now look at where the S&P 500 is right now Step 1 through 6 printed perfectly Accumulation, breakout, distribution, test before selling short Each one exactly where Livermore said it would be We just hit step 6 That's the point Livermore called "test before selling short" And the macro is confirming it right now New Fed Chair just held rates while inflation climbed back above 4% Consumer sentiment hit a record low, below every recession trough in 75 years Five stocks are carrying the entire index while the average S&P member is already down 21% Livermore didn't need to know any of that He just knew how bubbles end If you've been following me closely you knew when and why S&P 500 would start correcting Many of my followers also took profit from the GOLD pump The next stock market updates will be the most important ones The reason is simple - we're entering a correction phase Turn on notifications and you'll realize how much valuable info you've been missing by not doing it sooner

NoName

117,758 次观看 • 1 个月前

🚨 THIS IS HOW THE AI BUBBLE WILL BREAK THE S&P 500 Read this carefully before buying stocks like $SPCX. S&P 500 keeps hitting new all-time highs. The crowd thinks they are buying a resilient index. In reality, they are holding a ticking time bomb. And Big Tech is the one lighting the fuse. Apple, Microsoft, Google, Meta are no longer cash machines. Now they are just massive factories drowning in debt. By end of 2026, every cent of profit burns on chips and data centers. Even worse: their own billions aren't enough anymore. Big Tech borrowing exploded 357% in a single year. They are literally begging the market for cash. Why? This is pure FOMO. They're buried in debt for infrastructure that may never turn a profit. If AI doesn't fix productivity, this will be the most expensive bubble in history. And when this bubble bursts, it won't just hit tech. It will drag the entire S&P 500 into the abyss. Reminder: I’ve called all the market tops and bottoms for the last 15 years, including the Bitcoin bottom at $16,000 and the top at $126,000. The next call will be even more important. When I exit the markets completely, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.
0:22

🚨 THIS IS HOW THE AI BUBBLE WILL BREAK THE S&P 500 Read this carefully before buying stocks like $SPCX. S&P 500 keeps hitting new all-time highs. The crowd thinks they are buying a resilient index. In reality, they are holding a ticking time bomb. And Big Tech is the one lighting the fuse. Apple, Microsoft, Google, Meta are no longer cash machines. Now they are just massive factories drowning in debt. By end of 2026, every cent of profit burns on chips and data centers. Even worse: their own billions aren't enough anymore. Big Tech borrowing exploded 357% in a single year. They are literally begging the market for cash. Why? This is pure FOMO. They're buried in debt for infrastructure that may never turn a profit. If AI doesn't fix productivity, this will be the most expensive bubble in history. And when this bubble bursts, it won't just hit tech. It will drag the entire S&P 500 into the abyss. Reminder: I’ve called all the market tops and bottoms for the last 15 years, including the Bitcoin bottom at $16,000 and the top at $126,000. The next call will be even more important. When I exit the markets completely, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

DANNY

42,647 次观看 • 1 个月前