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Peter Thiel says the quiet part loud: AI chips will get commoditized. This is why I avoid $NVDA. It made an immense amount of money marking up its GPUs by 10x and others had to pay as alternatives weren’t good enough. Now they are getting good enough. $AMD has...

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Oguz Erkan

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460,614 görüntüleme • 8 ay önce •via X (Twitter)

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No wonder Jensen got so defensive over $NVDA GPUs against custom chips. $GOOG will start using different custom chips for inference and training. $AMZN has already adopted this approach as they have Trainium for training and Inferentia for inference. Hyperscalers obviously see better price/performance in this approach, and this is where the industry is headed. $AVGO and $MRVL are poised to be big winners, while $NVDA dominance will be under increasing pressure.
3:25

No wonder Jensen got so defensive over $NVDA GPUs against custom chips. $GOOG will start using different custom chips for inference and training. $AMZN has already adopted this approach as they have Trainium for training and Inferentia for inference. Hyperscalers obviously see better price/performance in this approach, and this is where the industry is headed. $AVGO and $MRVL are poised to be big winners, while $NVDA dominance will be under increasing pressure.

Oguz Erkan

223,244 görüntüleme • 4 ay önce

Lisa Su explains why, for at least the next few years, $AMD and $NVDA GPUs will continue to dominate the AI accelerator market. “ASICs like $GOOGL TPUs are great, but they don’t offer anywhere near the same programmability or model flexibility that GPUs provide. AI is at least a 10-year cycle, and we’re only in year two. Being so early, software developers want the flexibility to innovate on different algorithms.” Even as ASICs become competitive for inference, GPUs are on another level. $AMD and $NVDA are set to keep thriving in the years ahead.
1:04

Lisa Su explains why, for at least the next few years, $AMD and $NVDA GPUs will continue to dominate the AI accelerator market. “ASICs like $GOOGL TPUs are great, but they don’t offer anywhere near the same programmability or model flexibility that GPUs provide. AI is at least a 10-year cycle, and we’re only in year two. Being so early, software developers want the flexibility to innovate on different algorithms.” Even as ASICs become competitive for inference, GPUs are on another level. $AMD and $NVDA are set to keep thriving in the years ahead.

Daniel

125,735 görüntüleme • 7 ay önce

Groq founder Jonathan Ross explains why their architecture is better for inference than GPUs. Inference doesn't require as much compute power as training. What matters in inference is speed, cost, and energy consumption. $NVDA was going to lose market share as AI workloads shift to inference. They saw this and made a highly strategic move by striking a deal with Groq. $NVDA is already dominating training, and now it's also well-positioned to dominate inference.
1:45

Groq founder Jonathan Ross explains why their architecture is better for inference than GPUs. Inference doesn't require as much compute power as training. What matters in inference is speed, cost, and energy consumption. $NVDA was going to lose market share as AI workloads shift to inference. They saw this and made a highly strategic move by striking a deal with Groq. $NVDA is already dominating training, and now it's also well-positioned to dominate inference.

Oguz Erkan

97,186 görüntüleme • 8 ay önce

$AMZN CEO: “If you want to allow customers to use AI as expansively as they want, you need to bring down the cost of inference.” Hyperscalers are preparing for the age of inference. $AMZN has Trainium, $GOOG has TPU and $MSFT recently announced Maia 200. Once the bulk of AI workloads shifts from training to inference, they’ll depend less on $NVDA and margins will expand thanks to vertical integration. $AMZN is leading the race here as their chip business has already reached $10 billion annual run rate. Imagine being bearish on $AMZN.
1:27

$AMZN CEO: “If you want to allow customers to use AI as expansively as they want, you need to bring down the cost of inference.” Hyperscalers are preparing for the age of inference. $AMZN has Trainium, $GOOG has TPU and $MSFT recently announced Maia 200. Once the bulk of AI workloads shifts from training to inference, they’ll depend less on $NVDA and margins will expand thanks to vertical integration. $AMZN is leading the race here as their chip business has already reached $10 billion annual run rate. Imagine being bearish on $AMZN.

Oguz Erkan

130,592 görüntüleme • 6 ay önce

$AMD will be a multi-trillion $ company. The AI accelerator TAM will exceed $1T by 2030, and the market will only keep growing. As Lisa Su says: “We’re in a market where you can think of computing as equating to intelligence. If you can get more intelligence, why wouldn’t you want more computing?” While big tech like $GOOGL with TPUs and $AMZN with Trainium are building their own ASICs, GPUs—especially $NVDA and $AMD —are expected to dominate, representing 70–75% of the AI accelerator market by the end of 2030.
0:48

$AMD will be a multi-trillion $ company. The AI accelerator TAM will exceed $1T by 2030, and the market will only keep growing. As Lisa Su says: “We’re in a market where you can think of computing as equating to intelligence. If you can get more intelligence, why wouldn’t you want more computing?” While big tech like $GOOGL with TPUs and $AMZN with Trainium are building their own ASICs, GPUs—especially $NVDA and $AMD —are expected to dominate, representing 70–75% of the AI accelerator market by the end of 2030.

Daniel

45,866 görüntüleme • 8 ay önce

$NVDA CEO: “Demand for inference will go up by a billion times.” $AMD will be one of the biggest winners as it has the upper hand in inference thanks to larger memory bandwith and capacity of its chips. Stock is still at 22x 2027 earnings. How can’t you be bullish on $AMD?
0:20

$NVDA CEO: “Demand for inference will go up by a billion times.” $AMD will be one of the biggest winners as it has the upper hand in inference thanks to larger memory bandwith and capacity of its chips. Stock is still at 22x 2027 earnings. How can’t you be bullish on $AMD?

Oguz Erkan

282,229 görüntüleme • 4 ay önce

Chamath just said the quiet part out loud, and explained why all the value from the trillions of capex will sit at the application and infrastructure layers The model layer, led by Anthropic and OpenAI, is now getting commoditized faster than ever before Open source has caught up, and there is no sustainable moat anymore. The real businesses are all being built below and above Worth giving this a listen
1:48

Chamath just said the quiet part out loud, and explained why all the value from the trillions of capex will sit at the application and infrastructure layers The model layer, led by Anthropic and OpenAI, is now getting commoditized faster than ever before Open source has caught up, and there is no sustainable moat anymore. The real businesses are all being built below and above Worth giving this a listen

Boring_Business

443,963 görüntüleme • 1 ay önce

$NBIS and $CRWV growing like crazy shouldn't be surprising. Satya Nadella publicly explained 6 months ago on a podcast that $MSFT would be leasing a lot of capacity going forward rather than building all itself. Here is what I think is happening: These neo-cloud businesses, both $NBIS and $CRWV, exist solely because $NVDA wants them to exist. Nvidia has an equity stake in both companies, and they are both members of the Nvidia Partners Program. Nvidia grants early access to its chips to the partners. Nvidia does that because it knows all hyperscalers, especially $MSFT and $AMZN, are racing to develop their own cutting-edge GPUs, as this is their biggest cost in scaling cloud operations. Nvidia knows that if they are allowed to control the supply, it will prolong the upgrade cycles and reduce orders to gain time to develop its own chips. This is why it's providing early access to the neo-clouds that are 100% dependent on Nvidia chips. Hyperscalers know that if they delay orders for the fresh capacity, the demand will shift to the neo-clouds that can build capacity fast with the support of $NVDA. Thus, $NVDA corners hyperscalers and strategically compels them to put new orders and cripples their ability to focus on their own chips. $MSFT sees this and says, "If this is the case, I'll just lease the capacity from the neo-clouds." It's basically a strategic response to $NVDA. It leases the bulk of their capacity, making them dependent on it. So, if $MSFT backs down, they'll have a really hard time staying alive. There is a small window of opportunity here for neo-clouds. If they can scale beyond hyperscalers and form a fragmented customer base, they may keep thriving even if hyperscalers reduce leases. Currently, $NBIS has a better shot at it than $CRWV, as $MSFT already makes up 72% of its revenue. I am long $NBIS, and I have already made 4x of money in the stock. I'll remain long $NBIS and even grow my position, but I'll remain skeptical of $CRWV for its unfavorable strategic position, as I explained above.
1:30

$NBIS and $CRWV growing like crazy shouldn't be surprising. Satya Nadella publicly explained 6 months ago on a podcast that $MSFT would be leasing a lot of capacity going forward rather than building all itself. Here is what I think is happening: These neo-cloud businesses, both $NBIS and $CRWV, exist solely because $NVDA wants them to exist. Nvidia has an equity stake in both companies, and they are both members of the Nvidia Partners Program. Nvidia grants early access to its chips to the partners. Nvidia does that because it knows all hyperscalers, especially $MSFT and $AMZN, are racing to develop their own cutting-edge GPUs, as this is their biggest cost in scaling cloud operations. Nvidia knows that if they are allowed to control the supply, it will prolong the upgrade cycles and reduce orders to gain time to develop its own chips. This is why it's providing early access to the neo-clouds that are 100% dependent on Nvidia chips. Hyperscalers know that if they delay orders for the fresh capacity, the demand will shift to the neo-clouds that can build capacity fast with the support of $NVDA. Thus, $NVDA corners hyperscalers and strategically compels them to put new orders and cripples their ability to focus on their own chips. $MSFT sees this and says, "If this is the case, I'll just lease the capacity from the neo-clouds." It's basically a strategic response to $NVDA. It leases the bulk of their capacity, making them dependent on it. So, if $MSFT backs down, they'll have a really hard time staying alive. There is a small window of opportunity here for neo-clouds. If they can scale beyond hyperscalers and form a fragmented customer base, they may keep thriving even if hyperscalers reduce leases. Currently, $NBIS has a better shot at it than $CRWV, as $MSFT already makes up 72% of its revenue. I am long $NBIS, and I have already made 4x of money in the stock. I'll remain long $NBIS and even grow my position, but I'll remain skeptical of $CRWV for its unfavorable strategic position, as I explained above.

Oguz Erkan

180,198 görüntüleme • 11 ay önce

Peter Thiel on $NVDA (about a year ago): It is probably quite tricky. If you had to concretize it, one thing that is very strange is if you just follow the money, at this point 80 to 85% of the money in AI is being made by one company, it is NVIDIA. It is all on this very weird hardware layer, which Silicon Valley does not even know very much about anymore. We do not really do hardware, we do not do silicon chips in Silicon Valley anymore. I get pitched on these companies once every three or four years, and it is always, I have no clue how to do this, it sounds like a pretty good idea, but man, I have no clue, and we never invest. There is this theory that the hardware piece makes the money initially, then gets more commodified over time, and it will shift to software. And the, I do not know, multi trillion dollar question is whether that is going to be true again this time, or whether NVIDIA will have this incredible monopoly. I suspect NVIDIA will. I think it will maintain its position for a while. I think the game theory on it is something like this. All the big tech companies are going to start trying to design their own AI chips so they do not have to pay the 10x markup to NVIDIA. How hard is it for them to do it? How long will it take? If they all do it, then the chips become a commodity and nobody makes money in chips. So do you go into hardware? You should do it if nobody else is doing it. If everybody does it, you should not do it. I am not sure how that nets out, but probably people stay stuck for a while and NVIDIA goes from strength to strength for a while.
1:55

Peter Thiel on $NVDA (about a year ago): It is probably quite tricky. If you had to concretize it, one thing that is very strange is if you just follow the money, at this point 80 to 85% of the money in AI is being made by one company, it is NVIDIA. It is all on this very weird hardware layer, which Silicon Valley does not even know very much about anymore. We do not really do hardware, we do not do silicon chips in Silicon Valley anymore. I get pitched on these companies once every three or four years, and it is always, I have no clue how to do this, it sounds like a pretty good idea, but man, I have no clue, and we never invest. There is this theory that the hardware piece makes the money initially, then gets more commodified over time, and it will shift to software. And the, I do not know, multi trillion dollar question is whether that is going to be true again this time, or whether NVIDIA will have this incredible monopoly. I suspect NVIDIA will. I think it will maintain its position for a while. I think the game theory on it is something like this. All the big tech companies are going to start trying to design their own AI chips so they do not have to pay the 10x markup to NVIDIA. How hard is it for them to do it? How long will it take? If they all do it, then the chips become a commodity and nobody makes money in chips. So do you go into hardware? You should do it if nobody else is doing it. If everybody does it, you should not do it. I am not sure how that nets out, but probably people stay stuck for a while and NVIDIA goes from strength to strength for a while.

Wall St Engine

824,912 görüntüleme • 9 ay önce

Elon: 'The Limiting Factor is Chips Now, Power in a Year' "It's not clear to me that there's enough usable electricity to turn on all the AI chips that are being made. Towards the end of this year, the chip output will exceed the ability to turn chips on. Whichever company can scale hardware the fastest will be the leader and XAI will be able to scale hardware the fastest." Source: Cheeky Pint Podcast
1:51

Elon: 'The Limiting Factor is Chips Now, Power in a Year' "It's not clear to me that there's enough usable electricity to turn on all the AI chips that are being made. Towards the end of this year, the chip output will exceed the ability to turn chips on. Whichever company can scale hardware the fastest will be the leader and XAI will be able to scale hardware the fastest." Source: Cheeky Pint Podcast

Mario Nawfal

2,737,999 görüntüleme • 6 ay önce

There are now 30+ alternatives to OpenClaw. But, I still have not found one that is as good overall. Why? Because OpenClaw hits the sweet spot. It is smooth. It is easy to use. It comes with its own UI gateway. Some alternatives are more secure, but the UI is worse. Others are more lightweight, but way more technical to set up. OpenClaw has the best balance of power, simplicity, and usability.
0:38

There are now 30+ alternatives to OpenClaw. But, I still have not found one that is as good overall. Why? Because OpenClaw hits the sweet spot. It is smooth. It is easy to use. It comes with its own UI gateway. Some alternatives are more secure, but the UI is worse. Others are more lightweight, but way more technical to set up. OpenClaw has the best balance of power, simplicity, and usability.

Julian Goldie SEO

10,741 görüntüleme • 5 ay önce

ELON MUSK: "xAI will be the leader, we'll be able to turn on more chips than other people and faster, because we're good at hardware. The current limiting factor that I see in the sort of three to four year time frame is chips. In one day time it's the energy, power production, electricity. It's not clear to me that there's enough usable electricity to turn on all the AI chips that are being made, towards the end of this year. xAI will be able to scale hardware the fastest, and therefore most likely will be the leader."
1:43

ELON MUSK: "xAI will be the leader, we'll be able to turn on more chips than other people and faster, because we're good at hardware. The current limiting factor that I see in the sort of three to four year time frame is chips. In one day time it's the energy, power production, electricity. It's not clear to me that there's enough usable electricity to turn on all the AI chips that are being made, towards the end of this year. xAI will be able to scale hardware the fastest, and therefore most likely will be the leader."

DogeDesigner

52,128 görüntüleme • 6 ay önce

Money is the coordination layer for agents. As multi-agent systems take on real tasks, they will need to quote, budget, pay, and settle as part of their reasoning loop. We are building this reasoning infrastructure in the open, for everyone.
0:45

Money is the coordination layer for agents. As multi-agent systems take on real tasks, they will need to quote, budget, pay, and settle as part of their reasoning loop. We are building this reasoning infrastructure in the open, for everyone.

Sentient

14,712 görüntüleme • 6 ay önce

The UK Government has just announced a £1.1bn package for AI Hardware! LETS GO Here's what is in the package: > £750m for a national AI supercomputer that will run on a heterogeneous mixed chip system (which Danyal Akarca and Callosum are building the future for) > Of the £750m, £400m will go towards equipping the UK’s AI supercomputer with next-generation chips, which could include buying chips from UK companies OLIX and Fractile > £120m will fund a new AI Hardware Innovation Programme to give british companies the funding to design, develop and test innovative novel chips > £20m+ of the programme will expand the Scaling Inference Lab, delivered by ARIA and CommonAI > £45 million in new support for skills including backing doctoral training and undergraduate bursaries to train more engineers, chip designers and technicians I caught up with the Minister of AI Kanishka Narayan MP to get all the details. LETS GO amazing news 👏
15:13

The UK Government has just announced a £1.1bn package for AI Hardware! LETS GO Here's what is in the package: > £750m for a national AI supercomputer that will run on a heterogeneous mixed chip system (which Danyal Akarca and Callosum are building the future for) > Of the £750m, £400m will go towards equipping the UK’s AI supercomputer with next-generation chips, which could include buying chips from UK companies OLIX and Fractile > £120m will fund a new AI Hardware Innovation Programme to give british companies the funding to design, develop and test innovative novel chips > £20m+ of the programme will expand the Scaling Inference Lab, delivered by ARIA and CommonAI > £45 million in new support for skills including backing doctoral training and undergraduate bursaries to train more engineers, chip designers and technicians I caught up with the Minister of AI Kanishka Narayan MP to get all the details. LETS GO amazing news 👏

Seb Johnson

10,145 görüntüleme • 2 ay önce

J-Cal: Nvidia is taking the gloves off with its open source model. They will own the whole stack. “You will not be able to tell the difference between Jensen Huang's open source LLM and Claude for 95% of your searches, I guarantee you. Now, why has Nvidia and Jensen downplayed their open source model until this moment? Why would he do that? Why would he never bring it up in the All-In interview, never bring it up? Because his top customers (OpenAI, Anthropic, etc.) were very concerned, from what I understand, about the fact that Nvidia had made so much progress on their open source model. But suddenly, after OpenAI announced their Jalapeno chips, after Anthropic started making chips, after AMD did successful projects with both of these companies, after Elon said he's going to do his own fab, Nvidia's taking the gloves off. They are going to own the whole stack. You get the hardware from them, and you're going to get a model that's competitive with OpenAI's for free.”
0:54

J-Cal: Nvidia is taking the gloves off with its open source model. They will own the whole stack. “You will not be able to tell the difference between Jensen Huang's open source LLM and Claude for 95% of your searches, I guarantee you. Now, why has Nvidia and Jensen downplayed their open source model until this moment? Why would he do that? Why would he never bring it up in the All-In interview, never bring it up? Because his top customers (OpenAI, Anthropic, etc.) were very concerned, from what I understand, about the fact that Nvidia had made so much progress on their open source model. But suddenly, after OpenAI announced their Jalapeno chips, after Anthropic started making chips, after AMD did successful projects with both of these companies, after Elon said he's going to do his own fab, Nvidia's taking the gloves off. They are going to own the whole stack. You get the hardware from them, and you're going to get a model that's competitive with OpenAI's for free.”

The All-In Podcast

227,477 görüntüleme • 1 ay önce

$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 görüntüleme • 10 ay önce

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.
0:49

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.

Mike

511,082 görüntüleme • 10 ay önce