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If your chips and models aren't built in lockstep, you're already behind. At VivaTech, Amazon SVP Peter DeSantis—who leads a new org uniting foundation models and custom chips—explained why they have to develop together, not in sequence. 🤝 "If the chips are not telling the model designers what capabilities... show more

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74,099 views • 1 month ago •via X (Twitter)

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Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Amazon

27,151 views • 3 months ago

“If we can make AI make better chips, then those chips can power the next generation of AI models.” Startup ‘Ricursive Intelligence,’ founded by Azalia Mirhoseini and Anna Goldie, has been building AI tools to do just that: to speed up the chip design process. The company’s name is a nod to “recursive self-improvement,” the idea that AI could one day bootstrap itself without human involvement. If they’re successful, Goldie and Mirhoseini could revolutionize the way the chip industry works. At the moment, AI companies mostly optimize their models for certain chips. But in a future where chip design happens far faster, it could work the other way around: chips optimized for specific models. The founders are on the TIME100 AI, the list of people shaping the development of AI in 2026. See the full list:

“If we can make AI make better chips, then those chips can power the next generation of AI models.” Startup ‘Ricursive Intelligence,’ founded by Azalia Mirhoseini and Anna Goldie, has been building AI tools to do just that: to speed up the chip design process. The company’s name is a nod to “recursive self-improvement,” the idea that AI could one day bootstrap itself without human involvement. If they’re successful, Goldie and Mirhoseini could revolutionize the way the chip industry works. At the moment, AI companies mostly optimize their models for certain chips. But in a future where chip design happens far faster, it could work the other way around: chips optimized for specific models. The founders are on the TIME100 AI, the list of people shaping the development of AI in 2026. See the full list:

TIME

38,584 views • 1 day ago

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

Amazon

47,421 views • 2 years ago

🚨 Big news for AI innovation: Claude Opus 4 and Claude Sonnet 4, Anthropic's most advanced models, are now available in Amazon Bedrock. These powerful models offer hybrid reasoning, 200K token context windows, and are designed for AI agents. From financial analysis to high-quality writing, to enhanced reasoning, coding, agentic capabilities and more—all with the enterprise-grade security of Amazon Web Services.

🚨 Big news for AI innovation: Claude Opus 4 and Claude Sonnet 4, Anthropic's most advanced models, are now available in Amazon Bedrock. These powerful models offer hybrid reasoning, 200K token context windows, and are designed for AI agents. From financial analysis to high-quality writing, to enhanced reasoning, coding, agentic capabilities and more—all with the enterprise-grade security of Amazon Web Services.

Amazon

123,056 views • 1 year ago

Demis Hassabis CEO of DeepMind $GOOG: No one ever has enough chips … and there just aren’t enough compute chips in the world for the demand There’s a reason why $AMZN and $GOOG are running their own semiconductor labs and why we’re building a ridiculous number of semiconductor fabs. AI is projected to have more than 5 billion users by 2030 and the world still not ready for that day Al resources are gonna continue to be undersupplied

Demis Hassabis CEO of DeepMind $GOOG: No one ever has enough chips … and there just aren’t enough compute chips in the world for the demand There’s a reason why $AMZN and $GOOG are running their own semiconductor labs and why we’re building a ridiculous number of semiconductor fabs. AI is projected to have more than 5 billion users by 2030 and the world still not ready for that day Al resources are gonna continue to be undersupplied

Bourbon Capital

163,842 views • 7 months ago

The founder of a $4B inference company says that if you're building agents, foundational models could become your IP. According to Lin Qiao, 90% of the world's data is still private and locked inside applications and enterprises. Foundation models are trained on public internet + labeling company data, which is barely 10% of all the data. And this is why your application and foundation models are misaligned by definition. Companies building agents don't treat models as APIs. They opt for a product-model co-design. - Models continuously learn from your private data - Pick up domain-specific intelligence - Run faster - Cost less - Scale to millions of users

The founder of a $4B inference company says that if you're building agents, foundational models could become your IP. According to Lin Qiao, 90% of the world's data is still private and locked inside applications and enterprises. Foundation models are trained on public internet + labeling company data, which is barely 10% of all the data. And this is why your application and foundation models are misaligned by definition. Companies building agents don't treat models as APIs. They opt for a product-model co-design. - Models continuously learn from your private data - Pick up domain-specific intelligence - Run faster - Cost less - Scale to millions of users

Ivan Burazin

54,964 views • 4 months ago

BlackRock's Tony Kim (Tony Kim): Chips are not a commodity. " This notion that they were a commodity was just a false notion." "People always said, 'Chips are a commodity.' But yet they have the highest profitability of any company in the world." "This industry is very interesting. There were hundreds of chip companies, and then systematically over 20 years, now there's just a few." " The number of companies have collapsed, and then those that have survived are behemoths with huge pricing power—the complete opposite of commodity." "And by the way, venture capital, Silicon Valley, until recently, never funded these companies."

BlackRock's Tony Kim (Tony Kim): Chips are not a commodity. " This notion that they were a commodity was just a false notion." "People always said, 'Chips are a commodity.' But yet they have the highest profitability of any company in the world." "This industry is very interesting. There were hundreds of chip companies, and then systematically over 20 years, now there's just a few." " The number of companies have collapsed, and then those that have survived are behemoths with huge pricing power—the complete opposite of commodity." "And by the way, venture capital, Silicon Valley, until recently, never funded these companies."

Molly O’Shea

50,589 views • 1 month ago

Ansem explains the thesis behind Dolphin AI “Dolphin is the provider of the uncensored model that Venice uses” “ChatGPT, Claude, all of the big labs have very strict rules on what you can ask the models, they’re censored very heavily” “We’re not in control of what that censorship is and what they’re telling the models not to say” “One of crypto’s core tenets is not having to rely on some centralized entity deciding what you can and cannot do” “They want the technology to be free and open to everyone. Uncensored models are one way crypto is looking at doing that”

Ansem explains the thesis behind Dolphin AI “Dolphin is the provider of the uncensored model that Venice uses” “ChatGPT, Claude, all of the big labs have very strict rules on what you can ask the models, they’re censored very heavily” “We’re not in control of what that censorship is and what they’re telling the models not to say” “One of crypto’s core tenets is not having to rely on some centralized entity deciding what you can and cannot do” “They want the technology to be free and open to everyone. Uncensored models are one way crypto is looking at doing that”

Market Bubble

20,688 views • 3 months ago

Elon Musk explains how data centers in Space could help the US compete with China in AI “Given that Chinese AI companies are doing as well as they are with a relatively small amount of compute, it seems that if they had a lot of compute, there’s a good chance they would be the leaders. The constraints on AI are essentially electricity and AI chips. China is very strong in digital AI, and it has far more electricity than the United States. In fact, China already has more electricity than the United States, Europe and India combined. The US government can prevent US companies from using Chinese models, but it can’t stop the rest of the world from using them. That won’t stop China from becoming the leader in AI. Digital intelligence comes down to how much AI compute, how many chips and how much electricity you have. The current constraint is electricity outside China. In China, the constraint is chips. China is also closer than most people realize to solving the lithography problem, which would enable it to make AI chips in very large volumes. Once we address the power constraint with AI data centers in space, the constraint will once again be chips outside China.”

Elon Musk explains how data centers in Space could help the US compete with China in AI “Given that Chinese AI companies are doing as well as they are with a relatively small amount of compute, it seems that if they had a lot of compute, there’s a good chance they would be the leaders. The constraints on AI are essentially electricity and AI chips. China is very strong in digital AI, and it has far more electricity than the United States. In fact, China already has more electricity than the United States, Europe and India combined. The US government can prevent US companies from using Chinese models, but it can’t stop the rest of the world from using them. That won’t stop China from becoming the leader in AI. Digital intelligence comes down to how much AI compute, how many chips and how much electricity you have. The current constraint is electricity outside China. In China, the constraint is chips. China is also closer than most people realize to solving the lithography problem, which would enable it to make AI chips in very large volumes. Once we address the power constraint with AI data centers in space, the constraint will once again be chips outside China.”

DogeDesigner

245,139 views • 1 month ago

Wood chips being found in Dave's Killer Bread Not small wood shavings, not little pieces. These are pretty large wood chips (shown) and they are throughout the entire loaf This has already been reported to the FDA Dave's Killer Bread is the top-selling organic sliced bread brand in the US

Wall Street Apes

1,500,812 views • 3 months ago

They are not there, but if you have a beefy mac, Qwen3-Coder-Next has some applications due to its speed and decent tool calling capabilities. This video is in real time. (Also plenty of local models for STT etc.). They are not to be completely dismissed!

They are not there, but if you have a beefy mac, Qwen3-Coder-Next has some applications due to its speed and decent tool calling capabilities. This video is in real time. (Also plenty of local models for STT etc.). They are not to be completely dismissed!

Armin Ronacher ⇌

36,110 views • 4 months ago

This is the fastest I've seen Llama 3.3 running anywhere! Llama 3.3 70B running at 652 t/s is lightning fast. And if you want Llama 3.1, here are the speeds I was able to get: • Llama 3.1 8B: 1006 t/s • Llama 3.1 70B: 709 t/s • Llama 3.1 405B: 206 t/s (You can access all of these models for free! See the link below.) This speed is incredible, but the interesting part is what's happening behind the scenes: These models aren't running on a GPU! In this video, I'm using the SambaNova cloud to access these models. They built a custom chip (SN40L) optimized for AI workflows. A single SN40L chip can hold hundreds of models (trillions of parameters) in memory! The speed alone is a huge deal, but the big advantage is for agentic workflows running multiple specialized models. A GPU can only host a single model and switch (unload and load) to a different model if necessary. An SN40L, on the other hand, can host every model at once, making it much faster. Here is the video where you can see how fast these chips are:

This is the fastest I've seen Llama 3.3 running anywhere! Llama 3.3 70B running at 652 t/s is lightning fast. And if you want Llama 3.1, here are the speeds I was able to get: • Llama 3.1 8B: 1006 t/s • Llama 3.1 70B: 709 t/s • Llama 3.1 405B: 206 t/s (You can access all of these models for free! See the link below.) This speed is incredible, but the interesting part is what's happening behind the scenes: These models aren't running on a GPU! In this video, I'm using the SambaNova cloud to access these models. They built a custom chip (SN40L) optimized for AI workflows. A single SN40L chip can hold hundreds of models (trillions of parameters) in memory! The speed alone is a huge deal, but the big advantage is for agentic workflows running multiple specialized models. A GPU can only host a single model and switch (unload and load) to a different model if necessary. An SN40L, on the other hand, can host every model at once, making it much faster. Here is the video where you can see how fast these chips are:

Santiago

97,543 views • 1 year ago

#WATCH | Semicon India 2025 | Sandeep Kumar, CEO, L&T Semiconductor Technologies, says, "We are building a new security protocol chip. All the chips currently in use in passports, Aadhaar cards, and payment cards are imported. There are high chances of security and data breaches. At L&T, we will develop those chips for the security protocol. These chips will be released within 1.5 years. These chips will be used in passports, credit cards, and Aadhar cards, which will become electronic, and all payment gateways... The government already has plans to make new passports with electronic chips, so as soon as the chips are ready, passports will be made using these chips..."

#WATCH | Semicon India 2025 | Sandeep Kumar, CEO, L&T Semiconductor Technologies, says, "We are building a new security protocol chip. All the chips currently in use in passports, Aadhaar cards, and payment cards are imported. There are high chances of security and data breaches. At L&T, we will develop those chips for the security protocol. These chips will be released within 1.5 years. These chips will be used in passports, credit cards, and Aadhar cards, which will become electronic, and all payment gateways... The government already has plans to make new passports with electronic chips, so as soon as the chips are ready, passports will be made using these chips..."

ANI

54,188 views • 1 year ago

$AMZN CEO Andy Jassy reveals customers want Trainium chips outside AWS and says there's a real chance Amazon sells them that way. "On the question about selling Trainium, we're quite excited about what's happening in our chips business." "As I mentioned earlier, it's over $25 billion in annual revenue at this point. We think we have the leading price performance chip in both the AI space with Trainium and in the CPU space with Graviton." "The fact that we have multi-year, multi-gigawatt commitments from the two largest AI labs, Anthropic and OpenAI, and more and more companies, as I mentioned in my opening comments, using Trainium is exciting and promising." "And we just have an incredible amount of demand for Trainium. So there are a lot of customers who are very excited about using it in the form that we're providing right now." "We do have an increasing number of customers who are interested in us providing the Trainium chips to them separate from our cloud. And we're actively having those conversations and exploring. And I expect there's a real chance we'll do that in the future."

$AMZN CEO Andy Jassy reveals customers want Trainium chips outside AWS and says there's a real chance Amazon sells them that way. "On the question about selling Trainium, we're quite excited about what's happening in our chips business." "As I mentioned earlier, it's over $25 billion in annual revenue at this point. We think we have the leading price performance chip in both the AI space with Trainium and in the CPU space with Graviton." "The fact that we have multi-year, multi-gigawatt commitments from the two largest AI labs, Anthropic and OpenAI, and more and more companies, as I mentioned in my opening comments, using Trainium is exciting and promising." "And we just have an incredible amount of demand for Trainium. So there are a lot of customers who are very excited about using it in the form that we're providing right now." "We do have an increasing number of customers who are interested in us providing the Trainium chips to them separate from our cloud. And we're actively having those conversations and exploring. And I expect there's a real chance we'll do that in the future."

Fireside Alpha

11,669 views • 25 days ago

Nvidia CEO Jensen Huang: There’s a belief that somehow if we provided American chips to foreign countries — and specifically China — that the Chinese military would be building their military, building airplanes and aircraft carriers using American chips. First of all, they have plenty of their own chips, and their companies are formidable. The idea that China does not have a technology industry — we now know — is lunacy. Source: TIME

Nvidia CEO Jensen Huang: There’s a belief that somehow if we provided American chips to foreign countries — and specifically China — that the Chinese military would be building their military, building airplanes and aircraft carriers using American chips. First of all, they have plenty of their own chips, and their companies are formidable. The idea that China does not have a technology industry — we now know — is lunacy. Source: TIME

Clash Report

62,118 views • 7 months ago

Inferact CEO Simon Mo says open and closed models are converging. The moat is environment: "What really differentiates open-weight models from closed-weight models? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy. Capability-wise, I don't really see a big gap, not even today." "One of the most important parts is just the data. It's about who gets what data, and then what are the environments you are building to let the model improve on itself." "For Moonshot, they have built some of the best environments for front-end coding... the ability for this model to code and then see what the rendered code is, and then continue looping in this iterative process." "Now, this is about their environment to improve the model. It's not about just source data. It's not about where they get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "The next year is all gonna be about that: how open-weight model labs are differentiating and really getting the model to meet the real world." Simon Mo

Inferact CEO Simon Mo says open and closed models are converging. The moat is environment: "What really differentiates open-weight models from closed-weight models? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy. Capability-wise, I don't really see a big gap, not even today." "One of the most important parts is just the data. It's about who gets what data, and then what are the environments you are building to let the model improve on itself." "For Moonshot, they have built some of the best environments for front-end coding... the ability for this model to code and then see what the rendered code is, and then continue looping in this iterative process." "Now, this is about their environment to improve the model. It's not about just source data. It's not about where they get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "The next year is all gonna be about that: how open-weight model labs are differentiating and really getting the model to meet the real world." Simon Mo

a16z

39,424 views • 17 days ago