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Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a...

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124,996 views • 6 days ago •via X (Twitter)

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$NVDA has the “lowest cost per token in the world” which is really a claim about owning the economics of inference. The company that delivers the most tokens per watt, per rack and per dollar is the one that captures the most demand and the best margins as AI scales.
0:47

$NVDA has the “lowest cost per token in the world” which is really a claim about owning the economics of inference. The company that delivers the most tokens per watt, per rack and per dollar is the one that captures the most demand and the best margins as AI scales.

Shay Boloor

48,594 views • 4 months ago

“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️
0:49

“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️

NVIDIA

88,951 views • 4 months ago

🎙️ Want a full breakdown on why cost per token is a key metric for AI infrastructure TCO? We’re operating in a power-constrained world. Demand for intelligence is scaling faster than infrastructure can keep up—so metrics like cost per GPU hour or FLOPs per dollar don’t tell the full story. When it comes to TCO, Cost per Token is the metric that captures end-to-end performance across GPUs, CPUs, networking, software, and ecosystem making it the key driver of real profitability and scalability in AI. If you’re not measuring efficiency at the token level, you’re missing what actually drives cost. Watch the deep-dive conversation with Dr. Gerro Prinsloo , Nader Khalil and Carter Abdallah, live on the NVIDIA YouTube channel.
1:05

🎙️ Want a full breakdown on why cost per token is a key metric for AI infrastructure TCO? We’re operating in a power-constrained world. Demand for intelligence is scaling faster than infrastructure can keep up—so metrics like cost per GPU hour or FLOPs per dollar don’t tell the full story. When it comes to TCO, Cost per Token is the metric that captures end-to-end performance across GPUs, CPUs, networking, software, and ecosystem making it the key driver of real profitability and scalability in AI. If you’re not measuring efficiency at the token level, you’re missing what actually drives cost. Watch the deep-dive conversation with Dr. Gerro Prinsloo , Nader Khalil and Carter Abdallah, live on the NVIDIA YouTube channel.

NVIDIA

21,970 views • 3 months ago

"SpaceX AI is doing something that Anthropic and Open AI both didn't and couldn't do... what is xAI doing? xAI is building data centers, right now on Earth, that are larger, more productive, and they're built faster and at lower cost than any other data centers in the world... so when you look at that and you actually plot that out, 1.8 trillion may be low." Larry Goldberg
4:33

"SpaceX AI is doing something that Anthropic and Open AI both didn't and couldn't do... what is xAI doing? xAI is building data centers, right now on Earth, that are larger, more productive, and they're built faster and at lower cost than any other data centers in the world... so when you look at that and you actually plot that out, 1.8 trillion may be low." Larry Goldberg

Ellie in Space 🚀💫

58,309 views • 2 months ago

AI shouldn’t be controlled by a single company or person, and the reason why most AI tools are "free" is because you are the product. Your data is more valuable to them, than the tool itself. That's why Action Model is different. We use tokens so: • Every AI action has a real cost • That cost is transparent • Value flows back to contributors, not one corporation The token represents a fractional stake in the AI itself; economic rights, governance rights, and participation in its growth. And that’s how you run AI without centralized control.
0:42

AI shouldn’t be controlled by a single company or person, and the reason why most AI tools are "free" is because you are the product. Your data is more valuable to them, than the tool itself. That's why Action Model is different. We use tokens so: • Every AI action has a real cost • That cost is transparent • Value flows back to contributors, not one corporation The token represents a fractional stake in the AI itself; economic rights, governance rights, and participation in its growth. And that’s how you run AI without centralized control.

Action Model

47,273 views • 6 months ago

"OpenAI and Anthropic are pretty different animals from a capital efficiency perspective. And Anthropic clearly has a dramatically lower cost per token than OpenAI. They burned maybe 80% less than OpenAI. So as businesses, they clearly have very different structural ROICs." - A solid, insightful interview featuring Gavin Baker (Gavin Baker), U.S. hedge fund manager and Chief Investment Officer of Atreides Management. ---- From "Invest Like The Best" (Invest Like the Best) YouTube channel, (link in comment)
1:12

"OpenAI and Anthropic are pretty different animals from a capital efficiency perspective. And Anthropic clearly has a dramatically lower cost per token than OpenAI. They burned maybe 80% less than OpenAI. So as businesses, they clearly have very different structural ROICs." - A solid, insightful interview featuring Gavin Baker (Gavin Baker), U.S. hedge fund manager and Chief Investment Officer of Atreides Management. ---- From "Invest Like The Best" (Invest Like the Best) YouTube channel, (link in comment)

Rohan Paul

25,185 views • 28 days ago

Gavin on how Blackwell and GB300 will end Google's position as the lowest-cost producer of tokens: "I think it's important that Google has been the lowest cost producer of tokens. And this is really important because AI is the first time in my career as a tech investor that being the low cost producer has ever mattered. What Google has been doing as the low cost producer is they have been sucking the economic oxygen out of the AI ecosystem, which is an extremely rational strategy for them -- let's make life really hard for our competitors. We'll see the first models trained on Blackwell in early 2026. I think the first Blackwell model will come from xAI because they build the data centers the fastest. The GB300 is a great chip. It is drop-in compatible in every way with those GB200 racks, and the companies that use the GB300s -- they're going to be the low cost producer of tokens. I think this has pretty profound implications because it has to change Google's strategic calculus. If you have decisive cost advantage, why not AI at a -30% margin. You make it hard for your competitors who need funding to raise the capital that they need. On the other side of that maybe you have an extremely dominant share position. Well, all of that changes once Google is no longer the low-cost producer."
3:33

Gavin on how Blackwell and GB300 will end Google's position as the lowest-cost producer of tokens: "I think it's important that Google has been the lowest cost producer of tokens. And this is really important because AI is the first time in my career as a tech investor that being the low cost producer has ever mattered. What Google has been doing as the low cost producer is they have been sucking the economic oxygen out of the AI ecosystem, which is an extremely rational strategy for them -- let's make life really hard for our competitors. We'll see the first models trained on Blackwell in early 2026. I think the first Blackwell model will come from xAI because they build the data centers the fastest. The GB300 is a great chip. It is drop-in compatible in every way with those GB200 racks, and the companies that use the GB300s -- they're going to be the low cost producer of tokens. I think this has pretty profound implications because it has to change Google's strategic calculus. If you have decisive cost advantage, why not AI at a -30% margin. You make it hard for your competitors who need funding to raise the capital that they need. On the other side of that maybe you have an extremely dominant share position. Well, all of that changes once Google is no longer the low-cost producer."

Patrick OShaughnessy

174,657 views • 7 months ago

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.
2:09

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.

Karl Mehta

16,614 views • 13 days ago

People look at me and think I'm just in a Model Y. What they don't realize is I'm in a Tesla Robotaxi. They see a guy in the driver's seat and assume he's in control, but it's Tesla's AI driving me safely around SF. They also think this ride must cost more, but it's actually the most affordable option out there - cheaper than Uber, cheaper than Lyft, cheaper than Waymo, while giving me a ride in the most advanced, best selling car on the planet, literally. I'm pretty sure I'm sitting in the middle of history right now.
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People look at me and think I'm just in a Model Y. What they don't realize is I'm in a Tesla Robotaxi. They see a guy in the driver's seat and assume he's in control, but it's Tesla's AI driving me safely around SF. They also think this ride must cost more, but it's actually the most affordable option out there - cheaper than Uber, cheaper than Lyft, cheaper than Waymo, while giving me a ride in the most advanced, best selling car on the planet, literally. I'm pretty sure I'm sitting in the middle of history right now.

Teslaconomics

64,908 views • 10 months ago

Gavin Baker: "which AI company actually survives? Google - already multi-trillion Microsoft, OpenAI, Meta chose open source - the only ones left is xAI and Anthropic - xAI + X will be worth over a trillion" this is him explaining what he actually invests in - and why Taiwan sells sand to Nvidia for $700 that Nvidia sells for $50,000 His words: what to invest in: "we're investing in things that increase the GPU utilization rate - because if you take a GPU from 30% utilized to 60%, you've doubled the output of that AI factory" what not to Invest in: "the models will basically go to zero - the data center is a commodity - energy might be short for a while but is also a commodity - the only thing in the whole stack with real value is the data moat and reinforcement learning" "the GPU has gotten 50 times faster in the last four years - that's a big part of why we're living through this AI revolution" he doesn't rent intelligence - he owns the thing it runs on bookmark & watch - see how below with local agent ↓
13:10

Gavin Baker: "which AI company actually survives? Google - already multi-trillion Microsoft, OpenAI, Meta chose open source - the only ones left is xAI and Anthropic - xAI + X will be worth over a trillion" this is him explaining what he actually invests in - and why Taiwan sells sand to Nvidia for $700 that Nvidia sells for $50,000 His words: what to invest in: "we're investing in things that increase the GPU utilization rate - because if you take a GPU from 30% utilized to 60%, you've doubled the output of that AI factory" what not to Invest in: "the models will basically go to zero - the data center is a commodity - energy might be short for a while but is also a commodity - the only thing in the whole stack with real value is the data moat and reinforcement learning" "the GPU has gotten 50 times faster in the last four years - that's a big part of why we're living through this AI revolution" he doesn't rent intelligence - he owns the thing it runs on bookmark & watch - see how below with local agent ↓

bodila

309,657 views • 7 days ago

⚡️ JUST IN: MUSK LAUNCHES GROK 4.5, CALLING IT "OPUS-CLASS" AI MODEL Musk just released Grok 4.5 on Grok Build, Cursor, and the SpaceXAI Console, calling it an "Opus-class" AI model. He says it is faster, more token-efficient and lower cost, with EU access expected by mid-July. Built on a 1.5 TRILLION parameter foundation model, it drops the same day as OpenAI's GPT-5.6. API pricing is $2 per million input tokens and $6 per million output tokens.
0:55

⚡️ JUST IN: MUSK LAUNCHES GROK 4.5, CALLING IT "OPUS-CLASS" AI MODEL Musk just released Grok 4.5 on Grok Build, Cursor, and the SpaceXAI Console, calling it an "Opus-class" AI model. He says it is faster, more token-efficient and lower cost, with EU access expected by mid-July. Built on a 1.5 TRILLION parameter foundation model, it drops the same day as OpenAI's GPT-5.6. API pricing is $2 per million input tokens and $6 per million output tokens.

Coin Bureau

50,488 views • 27 days ago

🚨 ELON MUSK SAYS YOU’LL SOON BE ABLE TO FALL ASLEEP IN A TESLA — AND WAKE UP AT YOUR DESTINATION. He calls Full Self-Driving “an AI-driven car.” “Tesla AI software that drives the car just by looking like a human does.” He says Tesla has “the most advanced real world AI” and Europe is about to see it. Approval in the Netherlands? Reportedly March 20. And then this: “This year, you’ll be able to fall asleep in the Tesla and wake up at your destination.” Not driver assist. Autonomous. Are we watching the iPhone moment for driving?
1:02

🚨 ELON MUSK SAYS YOU’LL SOON BE ABLE TO FALL ASLEEP IN A TESLA — AND WAKE UP AT YOUR DESTINATION. He calls Full Self-Driving “an AI-driven car.” “Tesla AI software that drives the car just by looking like a human does.” He says Tesla has “the most advanced real world AI” and Europe is about to see it. Approval in the Netherlands? Reportedly March 20. And then this: “This year, you’ll be able to fall asleep in the Tesla and wake up at your destination.” Not driver assist. Autonomous. Are we watching the iPhone moment for driving?

HustleBitch

24,619 views • 5 months ago

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."
6:17

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Molly O’Shea

12,816 views • 27 days ago

5 patterns in Text Arena's price–performance Pareto frontier since 2023: 1. GPT-4-level quality is now ~500x lower cost. - From a ~$50 blended price per million tokens in 2023 to ~$0.10 today. 2. The higher-price end is both better and lower-priced since 2023. - The leading Arena score has climbed ~170 points (1,330 → 1,500). While the price of the higher-end frontier models dropped from ~$50 to ~$20 per million tokens. 3. The low-cost end gained the most. - Under $0.20 per million tokens, the best available model went from ~1,000 Arena score in 2023 to ~1,440 today. 4. The low-cost/top performance gap has nearly closed. - In 2023, sub-$0.20 models trailed the leader by ~350 Arena points. Today, ~60. 5. The cast has rotated quite a bit. - - OpenAI set the 2023–24 benchmark. - AI at Meta strengthened the low-cost end in 2024. - Google DeepMind drove the 2025 jump. - Anthropic holds the peak in 2026. - xAI and Chinese labs like DeepSeek AI, Z.ai, Kimi.ai, Xiaomi MiMo, and Qwen are continuing to push the mid-price frontier.
0:26

5 patterns in Text Arena's price–performance Pareto frontier since 2023: 1. GPT-4-level quality is now ~500x lower cost. - From a ~$50 blended price per million tokens in 2023 to ~$0.10 today. 2. The higher-price end is both better and lower-priced since 2023. - The leading Arena score has climbed ~170 points (1,330 → 1,500). While the price of the higher-end frontier models dropped from ~$50 to ~$20 per million tokens. 3. The low-cost end gained the most. - Under $0.20 per million tokens, the best available model went from ~1,000 Arena score in 2023 to ~1,440 today. 4. The low-cost/top performance gap has nearly closed. - In 2023, sub-$0.20 models trailed the leader by ~350 Arena points. Today, ~60. 5. The cast has rotated quite a bit. - - OpenAI set the 2023–24 benchmark. - AI at Meta strengthened the low-cost end in 2024. - Google DeepMind drove the 2025 jump. - Anthropic holds the peak in 2026. - xAI and Chinese labs like DeepSeek AI, Z.ai, Kimi.ai, Xiaomi MiMo, and Qwen are continuing to push the mid-price frontier.

Arena.ai

58,133 views • 2 months ago

Boris Cherny, head of Claude Code at Anthropic, on optimizing token cost and model use. “I use Fable for everything" There is probably a 50% opportunity to reduce the investment (on token cost). However, there may be a 1,000%, 10,000%, or even 100,000% opportunity to increase the return. Therefore, I would simply use the most expensive model and focus on how to get more out of it. Ask yourself, ‘How do I increase the return?’ Do not focus primarily on cost-cutting." ---- From "Scale" YouTube channel, (full video link in comment)
1:36

Boris Cherny, head of Claude Code at Anthropic, on optimizing token cost and model use. “I use Fable for everything" There is probably a 50% opportunity to reduce the investment (on token cost). However, there may be a 1,000%, 10,000%, or even 100,000% opportunity to increase the return. Therefore, I would simply use the most expensive model and focus on how to get more out of it. Ask yourself, ‘How do I increase the return?’ Do not focus primarily on cost-cutting." ---- From "Scale" YouTube channel, (full video link in comment)

Rohan Paul

57,446 views • 8 days ago

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk
0:59

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk

X Freeze

62,736 views • 6 months ago

Tesla Chair Robyn Denholm on the proposal for Tesla to invest in xAI: "There is a misunderstanding of AI in the marketplace. AI is not one thing. It is a range of technologies. What Tesla is doin today with AI is quite different to what xAI does. xAI is working on fundamental areas of AI, but it's also working on LLM's. That's not Tesla is doing. With Optimus, we're taking real work application of AI and putting it into physical products to actually employer those products to do things."
2:00

Tesla Chair Robyn Denholm on the proposal for Tesla to invest in xAI: "There is a misunderstanding of AI in the marketplace. AI is not one thing. It is a range of technologies. What Tesla is doin today with AI is quite different to what xAI does. xAI is working on fundamental areas of AI, but it's also working on LLM's. That's not Tesla is doing. With Optimus, we're taking real work application of AI and putting it into physical products to actually employer those products to do things."

Sawyer Merritt

194,774 views • 10 months ago

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!
0:39

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,678 views • 1 month ago

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.
2:15

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Anatoli Kopadze

91,518 views • 25 days ago