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Vertical Limit

Iconic movies & tvshows

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1,776,888 просмотров • 9 месяцев назад •via X (Twitter)

Искусство Юмор

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🎬 Vertical Limit gave me chills😬

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This scene is very famous from this movie, you know it? 🎬👀Movie title: vertical limit (2000) Watch till the end. 🎬👀

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As climbers face a life threatening situation on a cliff, tension escalates when someone must make a difficult decision to save the team. Vertical limit🍿

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⚡️“Russia has successfully tested the Burevestnik, an unlimited-range cruise missile with a nuclear propulsion system. This is a unique weapon that no one else in the world has. Moreover, I remember perfectly well when we announced that we were developing such a weapon; some highly qualified specialists told me that, while it was a good goal, it was unrealizable in the foreseeable future. Now, however, the decisive tests have been completed. The trials were conducted on 21 October. The missile flew for more that 14 hours and covered a distance of 14 thousand kilometers and this is not its limit. The technical characteristics of the Burevestnik generally allow it to be used with guaranteed accuracy against highly protected targets at any range. In addition, during the flight, the missile performed all the specified vertical and horizontal maneuvers, demonstrating its high capability to evade missile and air defense systems.” - President Putin

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$How I Build My Teams I always liked the idea of ‘possession football’: - Control the game. - A tool to develop players. - The opponent can’t score without the ball. But I built it the wrong way: “Keep the ball. Find a gap to attack.” This turned into “side-to-side” passing. It was boring and ineffective. \ The idea. So, I flipped the idea: Instead of controlling to attack, we attack to control. Play as vertical as possible (while maintaining control). If not possible, pass back. Avoid sideways passes. For better connections and counter-press, we use a vertical, narrow structure: These simple decisions lead to short, vertical, and diagonal passes. This is what I call 'vertical possession' football. \ Make it work. The idea is simple. The execution is not. Over the years, I’ve worked to close the gap between vision and reality. (watch example below) My goal is to get closer to my vision every day. But as Vince Lombardi said: “Perfection is unattainable, but if we chase perfection we can catch excellence.” Here’s my plan to 'catch excellence': 1. Build it my way. Ignore opinions. Opinions lead to conformity. We reduce our philosophy to its essence—and build from there. Everything else is a distraction. 2. Stick to it. We limit ourselves to core principles, and stick to them. When there’s a problem, we don’t change the idea—we improve its execution. 3. Eliminate waste. Building a style of play is like carving a statue. You start with a block of marble and chip away everything unnecessary. All that’s left is the sculpture. We do the same. We cut every unnecessary touch and step: - Fewer touches mean we need less time and space. We have more space to be creative. - The simpler it gets, the more recognizable the style. 4. Obsess over the players. We don’t adapt to opponents. We focus on our players. That's what matters in the long run. When our ideas work, we can compete against anyone. 5. Develop technical and creative players as a by-product. - Possession: Many touches to refine technique. - Vertical: No “easy” sideways passes. Learn to play in tight spaces. - Control: Avoid hectic football. Control the ball to make deliberate decisions. 6. One Training Structure. We compress our philosophy into one structure. Then, we repeat it every session. We make our style of play a habit. And we develop the tools to execute it. 7. Master a Few Exercises. We repeat a small set of core games over and over. We focus on improving execution, not on new drills. The better we execute, the faster we get to our vision. 8. Bottleneck Coaching. We can’t predict what happens, so we don’t over-plan. Instead, we prepare for what might happen. This liberates our coaching: Rather than sticking to pre-planned coaching points, we solve the most critical issue—the bottleneck. 9. Competition Drives Development. Games are the core of our training. Players compete to win. They force each other to get better and better, session after session. 10. Positive Team Culture. I used to react with anger when things didn’t go right. And yes, it can work in the short term. But to build something great, players must stay on the path for a long time. This is more likely when they enjoy the process. Positive reinforcement is harder to do, but more sustainable. \ What's next? That's the plan. Now, it’s all about execution. I'll keep you updated.$

How I Build My Teams I always liked the idea of ‘possession football’: - Control the game. - A tool to develop players. - The opponent can’t score without the ball. But I built it the wrong way: “Keep the ball. Find a gap to attack.” This turned into “side-to-side” passing. It was boring and ineffective. \ The idea. So, I flipped the idea: Instead of controlling to attack, we attack to control. Play as vertical as possible (while maintaining control). If not possible, pass back. Avoid sideways passes. For better connections and counter-press, we use a vertical, narrow structure: These simple decisions lead to short, vertical, and diagonal passes. This is what I call 'vertical possession' football. \ Make it work. The idea is simple. The execution is not. Over the years, I’ve worked to close the gap between vision and reality. (watch example below) My goal is to get closer to my vision every day. But as Vince Lombardi said: “Perfection is unattainable, but if we chase perfection we can catch excellence.” Here’s my plan to 'catch excellence': 1. Build it my way. Ignore opinions. Opinions lead to conformity. We reduce our philosophy to its essence—and build from there. Everything else is a distraction. 2. Stick to it. We limit ourselves to core principles, and stick to them. When there’s a problem, we don’t change the idea—we improve its execution. 3. Eliminate waste. Building a style of play is like carving a statue. You start with a block of marble and chip away everything unnecessary. All that’s left is the sculpture. We do the same. We cut every unnecessary touch and step: - Fewer touches mean we need less time and space. We have more space to be creative. - The simpler it gets, the more recognizable the style. 4. Obsess over the players. We don’t adapt to opponents. We focus on our players. That's what matters in the long run. When our ideas work, we can compete against anyone. 5. Develop technical and creative players as a by-product. - Possession: Many touches to refine technique. - Vertical: No “easy” sideways passes. Learn to play in tight spaces. - Control: Avoid hectic football. Control the ball to make deliberate decisions. 6. One Training Structure. We compress our philosophy into one structure. Then, we repeat it every session. We make our style of play a habit. And we develop the tools to execute it. 7. Master a Few Exercises. We repeat a small set of core games over and over. We focus on improving execution, not on new drills. The better we execute, the faster we get to our vision. 8. Bottleneck Coaching. We can’t predict what happens, so we don’t over-plan. Instead, we prepare for what might happen. This liberates our coaching: Rather than sticking to pre-planned coaching points, we solve the most critical issue—the bottleneck. 9. Competition Drives Development. Games are the core of our training. Players compete to win. They force each other to get better and better, session after session. 10. Positive Team Culture. I used to react with anger when things didn’t go right. And yes, it can work in the short term. But to build something great, players must stay on the path for a long time. This is more likely when they enjoy the process. Positive reinforcement is harder to do, but more sustainable. \ What's next? That's the plan. Now, it’s all about execution. I'll keep you updated.

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‼️🇺🇸 💥USS Arleigh Burke Class Destroyer | Precision Strike Platform The USS Arleigh Burke class destroyer is one of the most powerful surface combat ships in active US Navy service. In recent operations, these destroyers have been used to conduct long range precision strikes using the Tomahawk cruise missile. How it works Arleigh Burke class destroyers are equipped with the Mk 41 Vertical Launch System. This system allows the ship to fire Tomahawk missiles directly from the sea without warning and from a safe distance. Tomahawk missile role The Tomahawk is a long range, land attack cruise missile designed for high accuracy. It can strike command centers, air defense sites, radar stations, and high value infrastructure deep inside enemy territory. Guidance is achieved through GPS, terrain matching, and advanced navigation systems. Why the US Navy uses destroyers for strikes Using destroyers for missile strikes provides flexibility and speed. These ships can operate close to conflict zones while remaining hard to detect. They allow the US to project power without relying on nearby air bases or large carrier groups. Strategic impact When a Burke class destroyer launches Tomahawk missiles, it sends a clear message of deterrence and dominance. These strikes are precise, controlled, and designed to limit collateral damage while achieving military objectives. Modern naval warfare is no longer just about ships at sea. It is about reach, accuracy, and decision making within minutes. The Arleigh Burke class destroyer represents that reality.

‼️🇺🇸 💥USS Arleigh Burke Class Destroyer | Precision Strike Platform The USS Arleigh Burke class destroyer is one of the most powerful surface combat ships in active US Navy service. In recent operations, these destroyers have been used to conduct long range precision strikes using the Tomahawk cruise missile. How it works Arleigh Burke class destroyers are equipped with the Mk 41 Vertical Launch System. This system allows the ship to fire Tomahawk missiles directly from the sea without warning and from a safe distance. Tomahawk missile role The Tomahawk is a long range, land attack cruise missile designed for high accuracy. It can strike command centers, air defense sites, radar stations, and high value infrastructure deep inside enemy territory. Guidance is achieved through GPS, terrain matching, and advanced navigation systems. Why the US Navy uses destroyers for strikes Using destroyers for missile strikes provides flexibility and speed. These ships can operate close to conflict zones while remaining hard to detect. They allow the US to project power without relying on nearby air bases or large carrier groups. Strategic impact When a Burke class destroyer launches Tomahawk missiles, it sends a clear message of deterrence and dominance. These strikes are precise, controlled, and designed to limit collateral damage while achieving military objectives. Modern naval warfare is no longer just about ships at sea. It is about reach, accuracy, and decision making within minutes. The Arleigh Burke class destroyer represents that reality.

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Larry Ellison just said something that landed way too quietly. Ellison: “It’s the largest, fastest-growing business in human history. Bigger than the railroads, bigger than the Industrial Revolution.” He wasn’t speculating. He said it the way someone talks about the weather. Flat. Measured. Already certain. That tone is the tell. The people building this aren’t debating whether it’s real. They’re spending billions because they already know what they’re sitting on. Every revolution before this one built physical things. Rail. Steel. Wire. Engines. Objects you could see and touch and shut down if you needed to. This one is building something you can’t touch. The ability to think. Not a tool. Not a platform. The thing that designs all of them. Railroads moved freight. Factories assembled products. AI replaces the mind that engineered both. Every empire in history was built on controlling a scarce resource. Roads. Seas. Oil. Capital. The next century belongs to whoever controls intelligence itself. But there’s one thing every previous revolution had that this one doesn’t. A speed limit. You can only lay so much track. Build so many factories. Drill so many wells. Intelligence has no ceiling. Once it gets good enough to improve itself, the curve doesn’t flatten. It goes vertical. Ellison called it a whole new world dawning. I think he’s being polite. Every revolution before this one needed people to build it. This is the first one that won’t need people to finish it.

Larry Ellison just said something that landed way too quietly. Ellison: “It’s the largest, fastest-growing business in human history. Bigger than the railroads, bigger than the Industrial Revolution.” He wasn’t speculating. He said it the way someone talks about the weather. Flat. Measured. Already certain. That tone is the tell. The people building this aren’t debating whether it’s real. They’re spending billions because they already know what they’re sitting on. Every revolution before this one built physical things. Rail. Steel. Wire. Engines. Objects you could see and touch and shut down if you needed to. This one is building something you can’t touch. The ability to think. Not a tool. Not a platform. The thing that designs all of them. Railroads moved freight. Factories assembled products. AI replaces the mind that engineered both. Every empire in history was built on controlling a scarce resource. Roads. Seas. Oil. Capital. The next century belongs to whoever controls intelligence itself. But there’s one thing every previous revolution had that this one doesn’t. A speed limit. You can only lay so much track. Build so many factories. Drill so many wells. Intelligence has no ceiling. Once it gets good enough to improve itself, the curve doesn’t flatten. It goes vertical. Ellison called it a whole new world dawning. I think he’s being polite. Every revolution before this one needed people to build it. This is the first one that won’t need people to finish it.

Dustin

15,895 просмотров • 2 месяцев назад

Elon Musk just activated the final piece of the most ambitious vertical integration in the history of capitalism. Musk: “This is intended to be the place where all money is. The central source of all monetary transactions.” Twenty-five years ago, a 28-year-old named Elon Musk tried to build this exact thing at X. com. The banking system killed it. They forced a merger, gutted the vision, and sold the remains as PayPal. He just finished what they killed. Only this time he does not need their infrastructure. He built his own. Starlink owns the orbital communication layer. Tesla and Optimus own the physical execution layer. xAI owns the cognitive reasoning engine. SpaceX owns access to space itself. X Money captures the financial layer. These are not separate companies. They are a single vertically integrated loop that compounds on itself and depends on nothing it does not already own. Every fragile link in the legacy supply chain has been engineered out of the system. X is the only platform on Earth where unfiltered reality moves at the speed it actually happens. No editorial layer. No 24-hour delay. The people who shape the world go to X because the signal has not been processed into something safe. Now that platform handles your capital. You consume raw information. You debate its implications with the sharpest minds on the planet. You deploy capital to act on it. All inside a single app. The lag between intelligence and execution just collapsed to zero. That is not a feature update. That is a structural change in the speed limit of human decision-making. Nations running on fragmented infrastructure do not compete with operators inside a system that thinks, communicates, transacts, and executes as one architecture. Musk spent twenty-five years engineering the conditions for this moment. Twenty-five years ago they killed the vision. The system they tried to bury is now the system running beneath them.

Elon Musk just activated the final piece of the most ambitious vertical integration in the history of capitalism. Musk: “This is intended to be the place where all money is. The central source of all monetary transactions.” Twenty-five years ago, a 28-year-old named Elon Musk tried to build this exact thing at X. com. The banking system killed it. They forced a merger, gutted the vision, and sold the remains as PayPal. He just finished what they killed. Only this time he does not need their infrastructure. He built his own. Starlink owns the orbital communication layer. Tesla and Optimus own the physical execution layer. xAI owns the cognitive reasoning engine. SpaceX owns access to space itself. X Money captures the financial layer. These are not separate companies. They are a single vertically integrated loop that compounds on itself and depends on nothing it does not already own. Every fragile link in the legacy supply chain has been engineered out of the system. X is the only platform on Earth where unfiltered reality moves at the speed it actually happens. No editorial layer. No 24-hour delay. The people who shape the world go to X because the signal has not been processed into something safe. Now that platform handles your capital. You consume raw information. You debate its implications with the sharpest minds on the planet. You deploy capital to act on it. All inside a single app. The lag between intelligence and execution just collapsed to zero. That is not a feature update. That is a structural change in the speed limit of human decision-making. Nations running on fragmented infrastructure do not compete with operators inside a system that thinks, communicates, transacts, and executes as one architecture. Musk spent twenty-five years engineering the conditions for this moment. Twenty-five years ago they killed the vision. The system they tried to bury is now the system running beneath them.

Dustin

49,142 просмотров • 4 месяцев назад

** Sega Genesis 3D Engine Update 8 ** Significant improvements all round as you can see and hear from the last update !! Foremost - A huge thanks to Toni Gálvez - Megastyle - BG. who has joined the project to create a bit of 16bit low poly magic. Toni's an Amiga fan but also crazy about game dev in general, he's worked on GBC, GBA, PC, MD, PSP, C64, CPC, MSX... and others. Gaming titles include War Times, Metal Gear, Rocketman, Tintin & Asterix to name a few. He's provided the great new ship model you see on screen - new striped buildings, all the backgrounds / palettes etc. There's a lot of models he's given me which need to be added, also he will be planning a lot of the level design. Very happy to have him help me turn this into something more than a tech demo as I have my hands tied pushing the MD as far as it can go haha - there is no cpu cycle to be spared. Also many thanks to my good friend CYBERDEOUS - Crouzet Laurent for the Music for this showing , I wanted to have the music load occurring so we have a realistic benchmark for performance and he was only too obliging. If you're into MD chiptunes check him out !! Since last update : New player model , substantially more detailed than the Arwing. Last update had a 23 triangle Arwing , this update has a 39 triangle custom model from Toni. We had several to choose from , others will be used for enemies . 3D Buffer size increased 25% to 256x160. This was quite tricky as I'm close to the DMA limit even with an extended vblank . Spent a few days thinking of how to do this as like anything retro every solution has a drawback, finally got a workable solution. It makes a big difference to have a bit more vertical height . Z Rotation added ( the screen tilting left to right ) , small hit to vertex transform on cpu thanks to look up tables doing the heavy lifting, saving 4 multiplies per vertex. Multiple speed ups in rendering code. Onscreen paths with no range checking used until Z is close enough to cause clipping , partial onscreen drawing pathes that need to check boundaries, quad rendering completely rewritten - was very very painfull to get right . I found out the hard way that things are great when they are not rotating in the Z axis haha . Partial buffer draw optimisations - which have helped with the massive dma load , sending up to a 20kb buffer in a single frame needs a lot of optimisation. Min / Max tile lines are analysed and only sent if dirtied , reducing most buffer swaps substantially. Still some issues to sort out , at times you can see the flicker near top of screen when frames are near full height . I need to optimise that a bit. Due to the onscreen buffer system a full Sprite background had to be implemented almost Neo Geo style. This flips the usual MD rendering system on its head as it uses both foreground and background layers for a foreground 3d plane and sprites for the background. This presents a few issues, one is to get a tilt effect on the background by using narrow sprites (16x32) we run out of sprites when trying to cover the screen. Thankfully the MD is not limited to 80 sprites, to fix this a 114 sprite multiplexor is used to draw the background, its completely made up of 16x32 sprites ! Why do things this way ? speed . Its the interleaved foreground/background layers that allow a double buffered ram system writing to write to vram using dma in a completely linear fashion - virtually no tile translation needed. The negative is you have no planes for the background, that's where the sprites come in . Thanks to H40 mode we still have a few sprites we can use for effects in the forground also . Thankfully we can implement a fairly good tilt still for the background using sprites, in future updates this will be able to move horizontally also and a bit of vertical movement. XGM1 music driver in use to simulate music cpu load, XGM2 unfortunately with the massive DMA needed to shift the 3d buffers would slow down at times rendering it unusable, XGM1 plays at full speed - albiet with a bit more of a cpu hit. Together with the sprite multiplexor and the music driver active theres a 10 % hit to cpu so I've had to play around with draw distances / object heights and other optimisations to offset that. Not to mention the larger buffer takes more cpu to fill also. Everything is placeholder so will be changed with proper stage design. We are averaging 20 FPS in the current video, I'll push for more as always !! Progress continues on my other projects , updates soon on those - retirement can't come quick enough . #SGDK #SegaGenesis #SegaMegadrive

Sega Genesis 3D Engine Update 8 Significant improvements all round as you can see and hear from the last update !! Foremost - A huge thanks to Toni Gálvez - Megastyle - BG. who has joined the project to create a bit of 16bit low poly magic. Toni's an Amiga fan but also crazy about game dev in general, he's worked on GBC, GBA, PC, MD, PSP, C64, CPC, MSX... and others. Gaming titles include War Times, Metal Gear, Rocketman, Tintin & Asterix to name a few. He's provided the great new ship model you see on screen - new striped buildings, all the backgrounds / palettes etc. There's a lot of models he's given me which need to be added, also he will be planning a lot of the level design. Very happy to have him help me turn this into something more than a tech demo as I have my hands tied pushing the MD as far as it can go haha - there is no cpu cycle to be spared. Also many thanks to my good friend CYBERDEOUS - Crouzet Laurent for the Music for this showing , I wanted to have the music load occurring so we have a realistic benchmark for performance and he was only too obliging. If you're into MD chiptunes check him out !! Since last update : New player model , substantially more detailed than the Arwing. Last update had a 23 triangle Arwing , this update has a 39 triangle custom model from Toni. We had several to choose from , others will be used for enemies . 3D Buffer size increased 25% to 256x160. This was quite tricky as I'm close to the DMA limit even with an extended vblank . Spent a few days thinking of how to do this as like anything retro every solution has a drawback, finally got a workable solution. It makes a big difference to have a bit more vertical height . Z Rotation added ( the screen tilting left to right ) , small hit to vertex transform on cpu thanks to look up tables doing the heavy lifting, saving 4 multiplies per vertex. Multiple speed ups in rendering code. Onscreen paths with no range checking used until Z is close enough to cause clipping , partial onscreen drawing pathes that need to check boundaries, quad rendering completely rewritten - was very very painfull to get right . I found out the hard way that things are great when they are not rotating in the Z axis haha . Partial buffer draw optimisations - which have helped with the massive dma load , sending up to a 20kb buffer in a single frame needs a lot of optimisation. Min / Max tile lines are analysed and only sent if dirtied , reducing most buffer swaps substantially. Still some issues to sort out , at times you can see the flicker near top of screen when frames are near full height . I need to optimise that a bit. Due to the onscreen buffer system a full Sprite background had to be implemented almost Neo Geo style. This flips the usual MD rendering system on its head as it uses both foreground and background layers for a foreground 3d plane and sprites for the background. This presents a few issues, one is to get a tilt effect on the background by using narrow sprites (16x32) we run out of sprites when trying to cover the screen. Thankfully the MD is not limited to 80 sprites, to fix this a 114 sprite multiplexor is used to draw the background, its completely made up of 16x32 sprites ! Why do things this way ? speed . Its the interleaved foreground/background layers that allow a double buffered ram system writing to write to vram using dma in a completely linear fashion - virtually no tile translation needed. The negative is you have no planes for the background, that's where the sprites come in . Thanks to H40 mode we still have a few sprites we can use for effects in the forground also . Thankfully we can implement a fairly good tilt still for the background using sprites, in future updates this will be able to move horizontally also and a bit of vertical movement. XGM1 music driver in use to simulate music cpu load, XGM2 unfortunately with the massive DMA needed to shift the 3d buffers would slow down at times rendering it unusable, XGM1 plays at full speed - albiet with a bit more of a cpu hit. Together with the sprite multiplexor and the music driver active theres a 10 % hit to cpu so I've had to play around with draw distances / object heights and other optimisations to offset that. Not to mention the larger buffer takes more cpu to fill also. Everything is placeholder so will be changed with proper stage design. We are averaging 20 FPS in the current video, I'll push for more as always !! Progress continues on my other projects , updates soon on those - retirement can't come quick enough . #SGDK #SegaGenesis #SegaMegadrive

Shannon Birt

31,497 просмотров • 5 дней назад

This Chinese guy created agents in Claude Code for landing pages and single-handedly serves 47 small businesses a month, taking $400 from each. He built a system of 7 agents on Claude Sonnet 4.6 that analyzes Google Maps in small towns, finds small businesses without websites there, and over 1 weekend takes each one to a finished mockup with video and cold message. No assistant, no sales team, no SDR. Just him, a MacBook, an iPhone, and 1 API key. And traditional web design agencies keep teams of 8 people on salary for the same order flow, while his expenses are only tokens and subscriptions to Lovable, Higgsfield, and Calendly. 7 agents work through 1 orchestrator on Claude Code Router. Usage is about 3 million tokens a day, the average API bill is about $480 a month. All 7 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions, and 1 of them lives right in the iPhone and picks up positive replies from the subway, a taxi, or on walks. And here is the system prompt he put into the orchestrator before launch: "You are the orchestrator of a solo agency that sells ready-made websites to local businesses. You delegate read-only tasks to 6 sub-agents and own all writes. sub-agents: // Scout (walks through Google Maps in selected cities, looks for narrow niches: 5+ years on the map, fewer than 50 reviews, no website or a website from 2014, but high ratings) // Diagnoser (for each lead writes a 50-word diagnosis, hero angle, tone matched to the industry, and a cold message under 70 words) // Builder (generates a landing page mockup in Lovable through MCP only for the top 5 leads per day, with the sharpest diagnoses and the biggest gap) // Filmer (pulls 5 screenshots of the mockup and through Higgsfield renders a 10-second vertical video 1080x1920 with a soft zoom) // Pitcher (sends a personalized cold message through the right channel for the niche: email to roofers, SMS to tradesmen, IG DM to salons, LinkedIn to realtors) // Checker (runs every message through evals for personalization, absence of AI markers and buzzwords before sending) // Mobile (lives in the iPhone, handles positive replies in real time, books Zoom calls in Calendly through MCP while the owner is on the go). You never let 2 sub-agents touch 1 lead. You stop and request approval from the human only when a deal exceeds $3,000 or the reply rate in a niche for the day drops below 12%." Meaning the system knows what it is and within what boundaries it is allowed to act. It knows it is supposed to find leads on its own. It knows it is supposed to take each one to a mockup, video, and cold message without intervention. It knows the human only steps in when a deal goes above $3,000 or the reply rate stops converging. → The system runs 24 hours a day → Scout goes through about 220 local businesses on Google Maps per day and leaves 30 new leads in the queue → Diagnoser outputs 30 structured diagnoses + briefs + cold messages per day → Builder assembles 3 to 5 finished landing pages in Lovable for the sharpest leads → Filmer renders a 10-second vertical video in Higgsfield for each one → Pitcher sends 30 personalized messages per day across 4 channels with a reply rate of about 14% → Checker runs every message through evals before sending And only when a deal breaks $3,000 or the reply rate for the day drops below 12% does the orchestrator wake the owner. And when the owner at that moment is sitting in the subway or a taxi, the Mobile agent in his iPhone picks up 1 move on its own: replies to a fresh positive reply from a dentist, books a Zoom through Calendly synced to the local time of the client, and puts the lead back in the queue. The owner only has to tap "approve" and in just 10 minutes join the call. Here is what the system writes in his log during 1 of the Saturdays: "scout report: 218 businesses checked in Austin, Denver, and Miami, 34 without a website, 19 with a website from 2014, 6 with an active redesign request in reviews. passing top 30 to diagnoser." "pitcher: 30 cold messages sent across 4 channels, 14 replies, 5 positive, 3 Zoom calls booked for Sunday. passing to closer." "builder: landing page for Westside Cosmetic Dentistry built in Lovable, 5 sections, mobile, soft beige. URL placed at /Users/dev/maps-agency/clients/westside/v1. filmer launching Higgsfield." "eval flag: deal with The Lotus Salon at $3,400 exceeds the approved limit of $3,000. sending for manual review." He has no server of his own and no separate backend. Just a local file sandbox at /Users/dev/maps-agency, an MCP router, 1 API key to Claude, and the same key forwarded to Claude Code on his iPhone. Out of everything I have seen this year, this is the cleanest one-person agency for selling websites to small businesses: $480 a month on the API, about $18,800 into the account, and between them 7 prompts, 1 file system, and 1 phone in the pocket.

This Chinese guy created agents in Claude Code for landing pages and single-handedly serves 47 small businesses a month, taking $400 from each. He built a system of 7 agents on Claude Sonnet 4.6 that analyzes Google Maps in small towns, finds small businesses without websites there, and over 1 weekend takes each one to a finished mockup with video and cold message. No assistant, no sales team, no SDR. Just him, a MacBook, an iPhone, and 1 API key. And traditional web design agencies keep teams of 8 people on salary for the same order flow, while his expenses are only tokens and subscriptions to Lovable, Higgsfield, and Calendly. 7 agents work through 1 orchestrator on Claude Code Router. Usage is about 3 million tokens a day, the average API bill is about $480 a month. All 7 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions, and 1 of them lives right in the iPhone and picks up positive replies from the subway, a taxi, or on walks. And here is the system prompt he put into the orchestrator before launch: "You are the orchestrator of a solo agency that sells ready-made websites to local businesses. You delegate read-only tasks to 6 sub-agents and own all writes. sub-agents: // Scout (walks through Google Maps in selected cities, looks for narrow niches: 5+ years on the map, fewer than 50 reviews, no website or a website from 2014, but high ratings) // Diagnoser (for each lead writes a 50-word diagnosis, hero angle, tone matched to the industry, and a cold message under 70 words) // Builder (generates a landing page mockup in Lovable through MCP only for the top 5 leads per day, with the sharpest diagnoses and the biggest gap) // Filmer (pulls 5 screenshots of the mockup and through Higgsfield renders a 10-second vertical video 1080x1920 with a soft zoom) // Pitcher (sends a personalized cold message through the right channel for the niche: email to roofers, SMS to tradesmen, IG DM to salons, LinkedIn to realtors) // Checker (runs every message through evals for personalization, absence of AI markers and buzzwords before sending) // Mobile (lives in the iPhone, handles positive replies in real time, books Zoom calls in Calendly through MCP while the owner is on the go). You never let 2 sub-agents touch 1 lead. You stop and request approval from the human only when a deal exceeds $3,000 or the reply rate in a niche for the day drops below 12%." Meaning the system knows what it is and within what boundaries it is allowed to act. It knows it is supposed to find leads on its own. It knows it is supposed to take each one to a mockup, video, and cold message without intervention. It knows the human only steps in when a deal goes above $3,000 or the reply rate stops converging. → The system runs 24 hours a day → Scout goes through about 220 local businesses on Google Maps per day and leaves 30 new leads in the queue → Diagnoser outputs 30 structured diagnoses + briefs + cold messages per day → Builder assembles 3 to 5 finished landing pages in Lovable for the sharpest leads → Filmer renders a 10-second vertical video in Higgsfield for each one → Pitcher sends 30 personalized messages per day across 4 channels with a reply rate of about 14% → Checker runs every message through evals before sending And only when a deal breaks $3,000 or the reply rate for the day drops below 12% does the orchestrator wake the owner. And when the owner at that moment is sitting in the subway or a taxi, the Mobile agent in his iPhone picks up 1 move on its own: replies to a fresh positive reply from a dentist, books a Zoom through Calendly synced to the local time of the client, and puts the lead back in the queue. The owner only has to tap "approve" and in just 10 minutes join the call. Here is what the system writes in his log during 1 of the Saturdays: "scout report: 218 businesses checked in Austin, Denver, and Miami, 34 without a website, 19 with a website from 2014, 6 with an active redesign request in reviews. passing top 30 to diagnoser." "pitcher: 30 cold messages sent across 4 channels, 14 replies, 5 positive, 3 Zoom calls booked for Sunday. passing to closer." "builder: landing page for Westside Cosmetic Dentistry built in Lovable, 5 sections, mobile, soft beige. URL placed at /Users/dev/maps-agency/clients/westside/v1. filmer launching Higgsfield." "eval flag: deal with The Lotus Salon at $3,400 exceeds the approved limit of $3,000. sending for manual review." He has no server of his own and no separate backend. Just a local file sandbox at /Users/dev/maps-agency, an MCP router, 1 API key to Claude, and the same key forwarded to Claude Code on his iPhone. Out of everything I have seen this year, this is the cleanest one-person agency for selling websites to small businesses: $480 a month on the API, about $18,800 into the account, and between them 7 prompts, 1 file system, and 1 phone in the pocket.

Blaze

2,708,436 просмотров • 2 месяцев назад

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

Mike

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