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The 'Pro' model in ChatGPT does look like a real upgrade - generations are 3-4x times faster and look at lot better. I wouldn't say it is a giant leap forward, but a material upgrade - generations are richer, with more detail and coherence. Considering that generations take c.20... show more

Peter Gostev (SF: 22-26 June)

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153,665 просмотров • 4 месяцев назад •via X (Twitter)

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Pro model in ChatGPT does feel very different - the generations are a lot faster (20 mins vs 60-80 mins for Pro Extended) and the quality is really excellent. I'll do a side by side later, but this golden gate is quite excellent vs what all other models can do in one shot.

Pro model in ChatGPT does feel very different - the generations are a lot faster (20 mins vs 60-80 mins for Pro Extended) and the quality is really excellent. I'll do a side by side later, but this golden gate is quite excellent vs what all other models can do in one shot.

Peter Gostev

239,052 просмотров • 4 месяцев назад

Voice agents are awkward, and everyone notices: You ask a question. The agent thinks. You wait. And wait... Nobody wants this. I'd rather talk to a person. If your model's response time is over 300ms, you won't make it. Unfortunately, most text-to-speech models can't get anywhere close to that. I want you to take a look at the latest model released by Inworld AI: TTS-1.5. I built a simple voice agent using the model so you can see it in action and test it on your computer. You'll find the repository link below. The latency numbers of this model are wild: • Max model → under 250ms • Mini model → under 130ms That's 4x faster than prior generations and faster than human response times!

Voice agents are awkward, and everyone notices: You ask a question. The agent thinks. You wait. And wait... Nobody wants this. I'd rather talk to a person. If your model's response time is over 300ms, you won't make it. Unfortunately, most text-to-speech models can't get anywhere close to that. I want you to take a look at the latest model released by Inworld AI: TTS-1.5. I built a simple voice agent using the model so you can see it in action and test it on your computer. You'll find the repository link below. The latency numbers of this model are wild: • Max model → under 250ms • Mini model → under 130ms That's 4x faster than prior generations and faster than human response times!

Santiago

69,455 просмотров • 7 месяцев назад

Battle of the new coding models: Cursor vs Cognition They both make big claims being near the frontier - how well do they pass the Golden Gate Bridge test? I've included 5x non-cherry picked generations from each, followed by examples from pre-release test of Gemini 3 Pro, GPT-5 and Sonnet 4.5. Generations are mostly one shot, but each had errors twice, which it then fixed in the next generation. My impressions: - Both are clearly way behind the frontier - Between the two, Windsurf's SWE-1.5 is a winner for me - no huge errors like two parts of the bridge are the wrong way round - But SWE-1.5 generations were quite low-fi and Cursor's composer did have some better generations - Overall GPT-5 seems like the best model around on these tests, and Gemini 3 Pro seems to have a lot of potential Music via Suno

Battle of the new coding models: Cursor vs Cognition They both make big claims being near the frontier - how well do they pass the Golden Gate Bridge test? I've included 5x non-cherry picked generations from each, followed by examples from pre-release test of Gemini 3 Pro, GPT-5 and Sonnet 4.5. Generations are mostly one shot, but each had errors twice, which it then fixed in the next generation. My impressions: - Both are clearly way behind the frontier - Between the two, Windsurf's SWE-1.5 is a winner for me - no huge errors like two parts of the bridge are the wrong way round - But SWE-1.5 generations were quite low-fi and Cursor's composer did have some better generations - Overall GPT-5 seems like the best model around on these tests, and Gemini 3 Pro seems to have a lot of potential Music via Suno

Peter Gostev

23,583 просмотров • 10 месяцев назад

Opus 4.7 - 400k vs 1m context - is there a difference? I've heard Theo - t3.gg talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model. I did a test where I toggled the 1m default model on & off in Claude Code (otherwise default settings, xHigh reasoning) and compared the outputs with 3x generations - same prompts etc. My observations: - Models feel DIFFERENT - often when you ask a model for the same generation, you get a somewhat different answer, but it feels & smells the same. Here 400k and 1m are very different every time - 400k model seems better - not that 1m is trash and 400k is amazing, but there are definitely issues with the level of ambition and accuracy that 1m model seems to have Examples of 1m failing: - Voxel Rome: the colosseum is nowhere near as impressive - Golden Gate: cars go sideways, waves not very high, bridge goes into land; though the structure of the bridge is a bit better - Stonehenge: structure is more 'wrong', lighting, shadows & textures are more flat and not as rich This isn't a conclusive evidence of course, but at least to me the two models do not behave the same way. Anecdotally as well when building 1m felt like it was doing more weird validation (e.g. going around in circles) and 400k was more straightforward. These sorts of things are harder to capture in tests, but you'd notice in Claude Code. You can review the hosted generations, see the code & prompts in the links below

Opus 4.7 - 400k vs 1m context - is there a difference? I've heard Theo - t3.gg talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model. I did a test where I toggled the 1m default model on & off in Claude Code (otherwise default settings, xHigh reasoning) and compared the outputs with 3x generations - same prompts etc. My observations: - Models feel DIFFERENT - often when you ask a model for the same generation, you get a somewhat different answer, but it feels & smells the same. Here 400k and 1m are very different every time - 400k model seems better - not that 1m is trash and 400k is amazing, but there are definitely issues with the level of ambition and accuracy that 1m model seems to have Examples of 1m failing: - Voxel Rome: the colosseum is nowhere near as impressive - Golden Gate: cars go sideways, waves not very high, bridge goes into land; though the structure of the bridge is a bit better - Stonehenge: structure is more 'wrong', lighting, shadows & textures are more flat and not as rich This isn't a conclusive evidence of course, but at least to me the two models do not behave the same way. Anecdotally as well when building 1m felt like it was doing more weird validation (e.g. going around in circles) and 400k was more straightforward. These sorts of things are harder to capture in tests, but you'd notice in Claude Code. You can review the hosted generations, see the code & prompts in the links below

Peter Gostev (SF: 22-26 June)

29,203 просмотров • 4 месяцев назад

Nano Banana Pro 2 is live in Hedra – DAY 0 🔥 4x faster. Cheaper. Better. Real text rendering. Pro-level editing. One model. We wrote the prompt guide so you don't have to figure it out. First 1,000 followers get the prompt guide. Comment "Hedra NB2" and we’ll send it instantly.

Nano Banana Pro 2 is live in Hedra – DAY 0 🔥 4x faster. Cheaper. Better. Real text rendering. Pro-level editing. One model. We wrote the prompt guide so you don't have to figure it out. First 1,000 followers get the prompt guide. Comment "Hedra NB2" and we’ll send it instantly.

Hedra

210,803 просмотров • 6 месяцев назад

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

The All-In Podcast

64,677 просмотров • 4 месяцев назад

Anthropic CEO: “At Anthropic, we essentially have Claude designing the next version of Claude itself.” Meanwhile, Anthropic says every new model is given code that trains a small AI model and asked to make it faster. Claude Opus 4 achieved a 3x speedup. Mythos Preview reached 52x. Recursive self-improvement is starting to look real The models are helping build what comes next.

Anthropic CEO: “At Anthropic, we essentially have Claude designing the next version of Claude itself.” Meanwhile, Anthropic says every new model is given code that trains a small AI model and asked to make it faster. Claude Opus 4 achieved a 3x speedup. Mythos Preview reached 52x. Recursive self-improvement is starting to look real The models are helping build what comes next.

CG

343,551 просмотров • 3 месяцев назад

HERMES DESKTOP APP JUST GOT A NEW ULTRA MODE FOR DEEPSEEK V4 PRO – AND IT COSTS ABOUT $1 A DAY TO RUN If you're using Hermes desktop app, you're probably already running DeepSeek – it's one of the top models available. The update: → DeepSeek V4 Pro now has thinking mode, fast mode, and a brand new ultra mode → There's also an extra high mode, but the real upgrade is ultra The actual cost: roughly $1/day for a normal workflow. That's about $20-25/month – basically the same as before, just with a significantly faster mode unlocked. The verdict after a full day of testing: it flies through tool calling. Noticeably faster, same price range. If you're already paying for DeepSeek through Hermes, this is a free upgrade sitting in your settings right now. Update Hermes, grab the DeepSeek API if you don't have it yet, and try V4 Pro on ultra. Bookmark this, you'll want to come back to it later.

SCOTTY BEAM

24,032 просмотров • 1 месяц назад

OpenAI are testing a new model on the Web Dev Arena Arena under the name 'Anonymous Chatbot 0717'. I can't believe I'm gonna say this, but it is genuinely at a completely different level of front end coding - far better than Sonnet, o3, Gemini 2.5 Pro, or Grok 4. To test it, I ran a great prompt borrowed from the amazing The Feature Crew YouTube channel, asking models to create a procedurally generated planet with Three.js. Take a look yourselves, but I'm pretty astonished by how big the jump is. I have featured the new model twice just because its implementations have been so interesting. Of course, this is only one test, but OpenAI models have always been a bit 'meh' at front-end work, and they seem to have finally overtaken everyone else on that front. We'll see when it comes out. Credit to Chetaslua for discovering the model

OpenAI are testing a new model on the Web Dev Arena Arena under the name 'Anonymous Chatbot 0717'. I can't believe I'm gonna say this, but it is genuinely at a completely different level of front end coding - far better than Sonnet, o3, Gemini 2.5 Pro, or Grok 4. To test it, I ran a great prompt borrowed from the amazing The Feature Crew YouTube channel, asking models to create a procedurally generated planet with Three.js. Take a look yourselves, but I'm pretty astonished by how big the jump is. I have featured the new model twice just because its implementations have been so interesting. Of course, this is only one test, but OpenAI models have always been a bit 'meh' at front-end work, and they seem to have finally overtaken everyone else on that front. We'll see when it comes out. Credit to Chetaslua for discovering the model

Peter Gostev

495,815 просмотров • 1 год назад

🔥MORE PRO-AMERICA IN IRAN. Lara Trump doesn’t hold back: “It really feels like it’s those of us who love America and those who hate America.” “I think it’s pretty fair to say right now that you have more people in Iran who are pro-America than you actually have Democrats who are pro-American right here in our own country.” “If you can’t admit that taking out terrorists who chanted ‘death to America’ is a good thing, you need to take a long hard look in the mirror.”

🔥MORE PRO-AMERICA IN IRAN. Lara Trump doesn’t hold back: “It really feels like it’s those of us who love America and those who hate America.” “I think it’s pretty fair to say right now that you have more people in Iran who are pro-America than you actually have Democrats who are pro-American right here in our own country.” “If you can’t admit that taking out terrorists who chanted ‘death to America’ is a good thing, you need to take a long hard look in the mirror.”

Laura Ingraham

117,559 просмотров • 6 месяцев назад

Andrew 'Beef' Johnston played Thursday's final nine holes blind after missing the chance to play the back nine in Wednesday's pro-am due to thunder and lightning reducing the day's play. "Look, it's all really fiddly. I wouldn't say I'm the best at maths, either. You're going with altitude, the 5%, and there's wind. A couple of times me and Jess had to double check the number three or four times. "You do have that moment on a few holes where you hit it and are like 'is that good?' and I look at Jess and he's like 'don't look at me!'. "It was unfortunate that we never got to play the nine yesterday in the pro-am but it's all good." Beef shoot level par on the back for a round of 67 to sit T25 after day one. #AustrianAlpineOpen

Andrew 'Beef' Johnston played Thursday's final nine holes blind after missing the chance to play the back nine in Wednesday's pro-am due to thunder and lightning reducing the day's play. "Look, it's all really fiddly. I wouldn't say I'm the best at maths, either. You're going with altitude, the 5%, and there's wind. A couple of times me and Jess had to double check the number three or four times. "You do have that moment on a few holes where you hit it and are like 'is that good?' and I look at Jess and he's like 'don't look at me!'. "It was unfortunate that we never got to play the nine yesterday in the pro-am but it's all good." Beef shoot level par on the back for a round of 67 to sit T25 after day one. #AustrianAlpineOpen

DP World Tour

180,948 просмотров • 3 месяцев назад

Today we’re delighted to launch more new features, starting with a model capable of two-minute generations, udio-130. This makes it much easier to create tracks with long-term coherence and structure. Check out the track below for a taste of what’s possible using a single prompt. udio-130 will launch alongside our existing model. For now, two-minute generation is an experimental feature available at a discounted credit-rate to pro-subscribers only, but we will be rolling it out more broadly over the coming weeks. 1/4

Today we’re delighted to launch more new features, starting with a model capable of two-minute generations, udio-130. This makes it much easier to create tracks with long-term coherence and structure. Check out the track below for a taste of what’s possible using a single prompt. udio-130 will launch alongside our existing model. For now, two-minute generation is an experimental feature available at a discounted credit-rate to pro-subscribers only, but we will be rolling it out more broadly over the coming weeks. 1/4

udio

93,062 просмотров • 2 лет назад

Raitt "David, if you take a look at the demographics of the Maritime Provinces, they run about to 25% over 65 years of age. And those who are over 65 years of age really don't have a problem with the spending that is coming from a Liberal government." "In fact, they embrace it. They enjoy it." "And I say that because I find it very frustrating because I look at the younger demographic who are really concerned about it because they're the ones that are going to be stuck with the bill." Hon. Lisa MacCormack Raitt P.C.

Raitt "David, if you take a look at the demographics of the Maritime Provinces, they run about to 25% over 65 years of age. And those who are over 65 years of age really don't have a problem with the spending that is coming from a Liberal government." "In fact, they embrace it. They enjoy it." "And I say that because I find it very frustrating because I look at the younger demographic who are really concerned about it because they're the ones that are going to be stuck with the bill." Hon. Lisa MacCormack Raitt P.C.

cbcwatcher

112,704 просмотров • 1 год назад

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 просмотров • 2 месяцев назад

Bucke says he is tired of not being invited to events like Pro-Am but people who said slurs on stream are.😬 "How many people in the Pro-Am have said the N-Word? I'm better off in my career just saying it. At some point if I just say it and apologise I would do better"

Bucke says he is tired of not being invited to events like Pro-Am but people who said slurs on stream are.😬 "How many people in the Pro-Am have said the N-Word? I'm better off in my career just saying it. At some point if I just say it and apologise I would do better"

Stack

78,841 просмотров • 1 месяц назад

This is the correct response to what’s happening to Erika Kirk and what’s happening on the pro-Hamas Eurasianist slop “right.” And if more people have the courage to say this, I’m talking about people who are doing podcasts and who have lots and lots of viewers, we would be in a different place, but because you have a lot of people who are morally bankrupt they remain silent and pretend like that is the high ground. It’s not because your soul is not worth it a check. And if you are silent in the face of evil, that is the same thing as evil itself and God will not hold you blameless. I may not know a lot, but I definitely know that.

This is the correct response to what’s happening to Erika Kirk and what’s happening on the pro-Hamas Eurasianist slop “right.” And if more people have the courage to say this, I’m talking about people who are doing podcasts and who have lots and lots of viewers, we would be in a different place, but because you have a lot of people who are morally bankrupt they remain silent and pretend like that is the high ground. It’s not because your soul is not worth it a check. And if you are silent in the face of evil, that is the same thing as evil itself and God will not hold you blameless. I may not know a lot, but I definitely know that.

Insurrection Barbie

75,426 просмотров • 6 месяцев назад

【Live2D Showcase】 Polaire Vanilla 2.0 It's finally done! I had a lot of fun, and was able to experiment and learn a lot while working on this model. I'll be uploading a video explaining the model in more detail, so please look forward to it ✨ 🎨: CureYunny 🛠️: JakobHallows

【Live2D Showcase】 Polaire Vanilla 2.0 It's finally done! I had a lot of fun, and was able to experiment and learn a lot while working on this model. I'll be uploading a video explaining the model in more detail, so please look forward to it ✨ 🎨: CureYunny 🛠️: JakobHallows

Jakob Hallows 😈🦇 ジェイコブ・ハローズ

52,625 просмотров • 1 год назад

Perplexity CEO Aravind Srinivas on the brutal truth about who actually makes money in AI (and why it's not who you think): Aravind argues that the real value in AI comes from orchestration. He points to products like Codex, Claude Code, and Perplexity Computer: "What is that? It's an orchestration system. It takes a model, pairs it with an agent harness." And what is an agent harness? "The simplest way of describing it is like rules for how the agent loop should run. What are all the skills and sub-agents and connectors and tools it accesses? Without the harness, you don't necessarily capture and convert the intrinsic intelligence in the model into valuable output tokens." This leads to a blunt conclusion about who has a real business in AI, and who doesn't: "If you're literally just a reseller of model tokens, you have no business, because the model will get commoditized. So even if you're a model builder, you don't have a business. As an infra layer, you have some business on serving those output tokens. But as an application layer or model builder, you don't really have a business if you're just a reseller of tokens that come directly out of the model." So where does the value accrue? "You have a business if you know how to take the model, ground it in valuable context, orchestrate it with a really good agent harness, connected to the right set of tools and connectors (whether it's personal connectors or business connectors) and provide the experience to people in one single unified system." Aravind Srinivas then explains Perplexity's specific edge: Beyond orchestrating across tools, files, and connectors, they also orchestrate across models. "That is the differentiation that Anthropic and OpenAI cannot claim, because you wouldn't find GPT-5 inside the Claude Code harness. You wouldn't find Claude Opus inside the Codex harness. These are competing with each other. Whereas you would find both these models inside Perplexity Computer." Why does this matter? Because it all comes down to power. In Aravind's framing, the fundamental cost driver in AI is watts (the one input nobody can subsidize except the government). "Whoever provides the most valuable output tokens with the least amount of power expended to produce them generates the greatest value to the end user, has the most pricing power, has the most value. That is the orchestration problem to solve." His conclusion: "The one single most important metric in AI is token value per watt per user."

Perplexity CEO Aravind Srinivas on the brutal truth about who actually makes money in AI (and why it's not who you think): Aravind argues that the real value in AI comes from orchestration. He points to products like Codex, Claude Code, and Perplexity Computer: "What is that? It's an orchestration system. It takes a model, pairs it with an agent harness." And what is an agent harness? "The simplest way of describing it is like rules for how the agent loop should run. What are all the skills and sub-agents and connectors and tools it accesses? Without the harness, you don't necessarily capture and convert the intrinsic intelligence in the model into valuable output tokens." This leads to a blunt conclusion about who has a real business in AI, and who doesn't: "If you're literally just a reseller of model tokens, you have no business, because the model will get commoditized. So even if you're a model builder, you don't have a business. As an infra layer, you have some business on serving those output tokens. But as an application layer or model builder, you don't really have a business if you're just a reseller of tokens that come directly out of the model." So where does the value accrue? "You have a business if you know how to take the model, ground it in valuable context, orchestrate it with a really good agent harness, connected to the right set of tools and connectors (whether it's personal connectors or business connectors) and provide the experience to people in one single unified system." Aravind Srinivas then explains Perplexity's specific edge: Beyond orchestrating across tools, files, and connectors, they also orchestrate across models. "That is the differentiation that Anthropic and OpenAI cannot claim, because you wouldn't find GPT-5 inside the Claude Code harness. You wouldn't find Claude Opus inside the Codex harness. These are competing with each other. Whereas you would find both these models inside Perplexity Computer." Why does this matter? Because it all comes down to power. In Aravind's framing, the fundamental cost driver in AI is watts (the one input nobody can subsidize except the government). "Whoever provides the most valuable output tokens with the least amount of power expended to produce them generates the greatest value to the end user, has the most pricing power, has the most value. That is the orchestration problem to solve." His conclusion: "The one single most important metric in AI is token value per watt per user."

Big Brain AI

42,319 просмотров • 1 месяц назад