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U.S. Navy Bans DeepSeek Over 'Security Concerns' As 'Substantial' Evidence Emerges Chinese AI Ripped Off ChatGPT | ZeroHedge The U.S. Navy has instructed service members to avoid using the Chinese AI platform DeepSeek, citing "potential security and ethical concerns," according to CNBC. An email sent to "shipmates" in recent...

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Owen Gregorian

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75,351 görüntüleme • 1 yıl önce •via X (Twitter)

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NeoUnrealist profil fotoğrafı
NeoUnrealist1 yıl önce

FFS if you ask it it tells you it is ChatGPT. 🤣

Hill Country Lawyer profil fotoğrafı
Hill Country Lawyer1 yıl önce

Concerning developments. Vigilance is key for American innovation.

The Gabller profil fotoğrafı
The Gabller1 yıl önce

So you're telling me that the Chinese made a cheap rip off of something?

Thrillhouse profil fotoğrafı
Thrillhouse1 yıl önce

This is where they vilify open source right?

SecBriefs | Making Cybersecurity Simple profil fotoğrafı
SecBriefs | Making Cybersecurity Simple1 yıl önce

🇺🇸 A New Chapter for America—it’s time to put cybersecurity first! 🚨 Cybersecurity = National Security A safer nation begins with informed citizens. Take the first step to understanding and protecting with the CYBERSECURITY DICTIONARY for Everyone:

Random Guy profil fotoğrafı
Random Guy1 yıl önce

1. OpenAI steals the knowledge of all writers in humanity (including modern writers online) without their consent. 2. OpenAI sells this knowledge and insight in a model. 3. DeepSeek “steals” that initial knowledge and gives it away. 4. OpenAI calls only step three theft.

Benzer Videolar

Asked if China's DeepSeek stole American IP, AI Czar David Sacks says it looks like a technique called distillation was used where a student model can "suck the knowledge" out of the parent model and there is evidence that DeepSeek distilled knowledge from OpenAI's models, which is leading to efforts to develop techniques to prevent future copycats
1:10

Asked if China's DeepSeek stole American IP, AI Czar David Sacks says it looks like a technique called distillation was used where a student model can "suck the knowledge" out of the parent model and there is evidence that DeepSeek distilled knowledge from OpenAI's models, which is leading to efforts to develop techniques to prevent future copycats

Tsarathustra

710,764 görüntüleme • 1 yıl önce

🚨🇺🇸🇨🇳 MICROSOFT & OPENAI SUSPECT DEEPSEEK OF “BORROWING” THEIR TECH Microsoft and OpenAI are investigating whether DeepSeek, a Chinese AI startup, helped itself to OpenAI’s data using its API. Microsoft’s security team flagged suspicious large-scale data extraction in late 2023, suggesting DeepSeek may have sneakily bypassed restrictions to grab more than it paid for. Not-so-coincidentally, DeepSeek just dropped “R1,” an AI model that supposedly rivals OpenAI, Google, and Meta—at a fraction of the cost. The news sent tech stocks into meltdown mode, erasing nearly $1 trillion from companies like Microsoft, Nvidia, Oracle, and Google. Investors weren’t thrilled about a potential AI leapfrogging moment. Trump’s AI czar David Sacks: "There’s substantial evidence that what DeepSeek did here is they distilled knowledge out of OpenAI models and I don’t think OpenAI is very happy about this.” OpenAI isn’t naming names but warns China is constantly trying to siphon off US AI tech and says it’s working with the US government to lock things down. Source: Bloomberg Media Source: Reuters
1:23

🚨🇺🇸🇨🇳 MICROSOFT & OPENAI SUSPECT DEEPSEEK OF “BORROWING” THEIR TECH Microsoft and OpenAI are investigating whether DeepSeek, a Chinese AI startup, helped itself to OpenAI’s data using its API. Microsoft’s security team flagged suspicious large-scale data extraction in late 2023, suggesting DeepSeek may have sneakily bypassed restrictions to grab more than it paid for. Not-so-coincidentally, DeepSeek just dropped “R1,” an AI model that supposedly rivals OpenAI, Google, and Meta—at a fraction of the cost. The news sent tech stocks into meltdown mode, erasing nearly $1 trillion from companies like Microsoft, Nvidia, Oracle, and Google. Investors weren’t thrilled about a potential AI leapfrogging moment. Trump’s AI czar David Sacks: "There’s substantial evidence that what DeepSeek did here is they distilled knowledge out of OpenAI models and I don’t think OpenAI is very happy about this.” OpenAI isn’t naming names but warns China is constantly trying to siphon off US AI tech and says it’s working with the US government to lock things down. Source: Bloomberg Media Source: Reuters

Mario Nawfal

822,553 görüntüleme • 1 yıl önce

Chinese AI company Deepseek recently made waves when it announced R1, an open-source reasoning model that it claimed achieved comparable performance to OpenAI’s o1, at a fraction of the cost. But for those following AI developments closely, Deepseek and R1 didn’t come out of nowhere. In this episode of Decoded, YC's Diana breaks down the key engineering optimizations behind Deepseek's new models — and how they fit within the broader history of recent AI breakthroughs.
12:40

Chinese AI company Deepseek recently made waves when it announced R1, an open-source reasoning model that it claimed achieved comparable performance to OpenAI’s o1, at a fraction of the cost. But for those following AI developments closely, Deepseek and R1 didn’t come out of nowhere. In this episode of Decoded, YC's Diana breaks down the key engineering optimizations behind Deepseek's new models — and how they fit within the broader history of recent AI breakthroughs.

Y Combinator

72,468 görüntüleme • 1 yıl önce

🚨TRUMP: “The release of DeepSeek AI from a Chinese company should be a wake up call for our industries that we should be laser focused on competing to win. We have the best scientists in the world. This is very unusual. We always have the ideas. We’re always first.”
0:45

🚨TRUMP: “The release of DeepSeek AI from a Chinese company should be a wake up call for our industries that we should be laser focused on competing to win. We have the best scientists in the world. This is very unusual. We always have the ideas. We’re always first.”

Autism Capital 🧩

1,863,291 görüntüleme • 1 yıl önce

What's the Big Deal with DeepSeek in AI? Here's why DeepSeek is making everyone take notice: 1. Super Smart on a Budget: DeepSeek showed you can make awesome AI without breaking the bank. Their latest model, DeepSeek-V3, was trained for only about $10 million, which is a lot less than the usual big bucks spent on AI, like the rumored $78 million for some of OpenAI's models. They did this in just two months with fewer fancy computers. 2. Open for Everyone: DeepSeek isn't keeping their tech a secret. They've made it open-source, meaning anyone can use, tweak, and learn from it. It's like they're saying, "Come join the party!" 3. Beating the Big Names: DeepSeek-V3 has done better than some top dogs from companies like OpenAI and Google in solving puzzles, math, and coding. This proves you can get great AI results without spending a fortune. 4. Challenging NVIDIA: NVIDIA's chips are usually the choice for AI because they're really powerful. But since DeepSeek did so well with less expensive chips, it might make people think twice about always going for NVIDIA's priciest options. 5. The DeepSeek Crew: The team at DeepSeek is young and smart, mostly from top Chinese schools, with brains in physics, math, and computer science. They learned AI in about six months by themselves! They use first principle thinking, which means they break down problems to the basics and build from there. This has helped them come up with cool new ways to do AI. 6. Changing AI for Good: DeepSeek is showing that AI can be cheaper and more open to everyone. They're changing how we think AI should be made and shared, which could shake up the whole AI world. So, as we watch DeepSeek, it's clear they're not just another player; they're changing the rules of the game. I predicted that this would be a make or break year for all the massive investments made in AI by American VC's. A few weeks later, DeepSeek happens! Watch the rest of my predictions in my 2025 outlook video . Link in replies #AIInnovation #DeepSeek #NVIDIA #OpenAI #TechDisruption
0:44

What's the Big Deal with DeepSeek in AI? Here's why DeepSeek is making everyone take notice: 1. Super Smart on a Budget: DeepSeek showed you can make awesome AI without breaking the bank. Their latest model, DeepSeek-V3, was trained for only about $10 million, which is a lot less than the usual big bucks spent on AI, like the rumored $78 million for some of OpenAI's models. They did this in just two months with fewer fancy computers. 2. Open for Everyone: DeepSeek isn't keeping their tech a secret. They've made it open-source, meaning anyone can use, tweak, and learn from it. It's like they're saying, "Come join the party!" 3. Beating the Big Names: DeepSeek-V3 has done better than some top dogs from companies like OpenAI and Google in solving puzzles, math, and coding. This proves you can get great AI results without spending a fortune. 4. Challenging NVIDIA: NVIDIA's chips are usually the choice for AI because they're really powerful. But since DeepSeek did so well with less expensive chips, it might make people think twice about always going for NVIDIA's priciest options. 5. The DeepSeek Crew: The team at DeepSeek is young and smart, mostly from top Chinese schools, with brains in physics, math, and computer science. They learned AI in about six months by themselves! They use first principle thinking, which means they break down problems to the basics and build from there. This has helped them come up with cool new ways to do AI. 6. Changing AI for Good: DeepSeek is showing that AI can be cheaper and more open to everyone. They're changing how we think AI should be made and shared, which could shake up the whole AI world. So, as we watch DeepSeek, it's clear they're not just another player; they're changing the rules of the game. I predicted that this would be a make or break year for all the massive investments made in AI by American VC's. A few weeks later, DeepSeek happens! Watch the rest of my predictions in my 2025 outlook video . Link in replies #AIInnovation #DeepSeek #NVIDIA #OpenAI #TechDisruption

Dr Ola Brown

83,460 görüntüleme • 1 yıl önce

China just made Silicon Valley's entire AI industry look like a scam. The US government spent 3 years trying to stop China from building competitive AI. But this backfired HORRIBLY. Here's what happened: Yesterday, a Chinese startup called DeepSeek released a new AI model called V4. It matches the performance of OpenAI and Anthropic's best models. At 1/7th the price. And for the first time ever, it was built on Chinese chips. NOT American ones. That last part is the one that terrifies the west. For context: Since 2022, the US has banned the export of advanced AI chips to China. The entire strategy was built on the assumption that if China can't access Nvidia's best hardware, they can't build frontier AI. But DeepSeek just proved that assumption wrong. Their V4 model was trained and runs on Huawei's Ascend chips. Huawei spent months working directly with DeepSeek to make sure V4 runs across their entire line of AI processors. Jensen Huang even predicted this on a recent podcast: "The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation." That day was yesterday. And the numbers are crazy: DeepSeek V4 costs $3.48 per million output tokens. OpenAI's latest model GPT-5.5 costs $30. Anthropic's Claude charges $25. Same ballpark performance. 7x cheaper. Uber's CTO just admitted they burned through their ENTIRE 2026 AI budget in 4 months using Anthropic's tools. If Uber had used DeepSeek instead, that same budget would have lasted 7 YEARS. 4 months vs 7 years. Same work getting done. But the pricing isn't even the big thing here. The real story is what DeepSeek did with their technical report: They published the benchmarks where they LOSE. Every AI company cherry-picks the tests where their model wins. DeepSeek ran the full comparison against GPT-5.4 and Google's Gemini, found they trail frontier models by 3 to 6 months, and printed it anyway. They literally don't care because the price gap makes the performance gap irrelevant for 90% of use cases. So the US export controls didn't slow China down. They ACCELERATED China's independence. Because Chinese developers were FORCED to train models with limited resources, they had to figure out how to make AI radically more efficient. That constraint became their competitive advantage. Every generation of DeepSeek has gotten dramatically cheaper to train. V4 continues the trend. Meanwhile US companies are going the OPPOSITE direction: OpenAI's GPT-5.5 Pro costs $180 per million output tokens. That's 51x more expensive than DeepSeek V4 for comparable work. The Commerce Secretary confirmed this week that ZERO Nvidia advanced chip shipments have actually gone through to China despite being approved in January. So China built frontier AI anyway. Without American chips. At a fraction of the cost. And the market response tells you everything: Chinese chipmaker SMIC surged 10%. Huahong Semiconductor jumped 15%. DeepSeek's Chinese AI competitors Zhipu AI and MiniMax dropped 9% because V4 is destroying them too. DeepSeek is making Silicon Valley's pricing model look like a scam. US tech companies spent $650 billion on AI infrastructure this year. DeepSeek just showed the world you can match their output for pennies. The export controls were supposed to be America's ace card. Instead they taught China how to win without American chips, at American prices nobody can compete with. Jensen Huang was right. This is a horrible outcome. But it's the outcome America built for itself.
1:06

China just made Silicon Valley's entire AI industry look like a scam. The US government spent 3 years trying to stop China from building competitive AI. But this backfired HORRIBLY. Here's what happened: Yesterday, a Chinese startup called DeepSeek released a new AI model called V4. It matches the performance of OpenAI and Anthropic's best models. At 1/7th the price. And for the first time ever, it was built on Chinese chips. NOT American ones. That last part is the one that terrifies the west. For context: Since 2022, the US has banned the export of advanced AI chips to China. The entire strategy was built on the assumption that if China can't access Nvidia's best hardware, they can't build frontier AI. But DeepSeek just proved that assumption wrong. Their V4 model was trained and runs on Huawei's Ascend chips. Huawei spent months working directly with DeepSeek to make sure V4 runs across their entire line of AI processors. Jensen Huang even predicted this on a recent podcast: "The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation." That day was yesterday. And the numbers are crazy: DeepSeek V4 costs $3.48 per million output tokens. OpenAI's latest model GPT-5.5 costs $30. Anthropic's Claude charges $25. Same ballpark performance. 7x cheaper. Uber's CTO just admitted they burned through their ENTIRE 2026 AI budget in 4 months using Anthropic's tools. If Uber had used DeepSeek instead, that same budget would have lasted 7 YEARS. 4 months vs 7 years. Same work getting done. But the pricing isn't even the big thing here. The real story is what DeepSeek did with their technical report: They published the benchmarks where they LOSE. Every AI company cherry-picks the tests where their model wins. DeepSeek ran the full comparison against GPT-5.4 and Google's Gemini, found they trail frontier models by 3 to 6 months, and printed it anyway. They literally don't care because the price gap makes the performance gap irrelevant for 90% of use cases. So the US export controls didn't slow China down. They ACCELERATED China's independence. Because Chinese developers were FORCED to train models with limited resources, they had to figure out how to make AI radically more efficient. That constraint became their competitive advantage. Every generation of DeepSeek has gotten dramatically cheaper to train. V4 continues the trend. Meanwhile US companies are going the OPPOSITE direction: OpenAI's GPT-5.5 Pro costs $180 per million output tokens. That's 51x more expensive than DeepSeek V4 for comparable work. The Commerce Secretary confirmed this week that ZERO Nvidia advanced chip shipments have actually gone through to China despite being approved in January. So China built frontier AI anyway. Without American chips. At a fraction of the cost. And the market response tells you everything: Chinese chipmaker SMIC surged 10%. Huahong Semiconductor jumped 15%. DeepSeek's Chinese AI competitors Zhipu AI and MiniMax dropped 9% because V4 is destroying them too. DeepSeek is making Silicon Valley's pricing model look like a scam. US tech companies spent $650 billion on AI infrastructure this year. DeepSeek just showed the world you can match their output for pennies. The export controls were supposed to be America's ace card. Instead they taught China how to win without American chips, at American prices nobody can compete with. Jensen Huang was right. This is a horrible outcome. But it's the outcome America built for itself.

Ricardo

280,185 görüntüleme • 3 ay önce

Cloud capital versus AI - What DeepSeek’s spectacular success means for technofeudalism & the New Cold War DeepSeek, a Chinese artificial intelligence (AI) company, this week changed the global AI landscape, not to mention caused $1 trillion losses in the New York stock exchange and the NASDAC. In the process, it demonstrated the difference between cloud capital, which drives technofeudalism onward and upward, and AI-services, which were always a bubble waiting to burst. What remains to be seen is DeepSeek's impact on the New Cold War between the US and China which, from its beginning, was motivated by the clash between US and Chinese cloud capital. DeepSeek is China's response to OpenAI's ChatGPT. Its models perform as efficiently as their US counterparts. The difference is that DeepSeek is offered for free, making money only by selling services to developers - not to the public – at a fraction of the price OpenAI charges! The gist of DeepSeek’s arrival on the AI scene is a sudden transition from proprietary to open source technology. It is, therefore, no great wonder that, the moment DeekSeek became the most downloaded app on the Apple Store, it pulverised the market capitalisation of the, hitherto overinflated, US Big Tech companies. But how did this happen? How is a private commodity suddenly being offered for free? And does this mean that technofeudalism is in trouble? To begin with it is important to note AI was never a proprietary technology in itself. The underlying code has always been open source. What made AI quasi-private was the way these models were trained using huge amounts of privatised (that is stolen from us) data. A leaked Google memo in 2017, that was widely discussed in the industry at the time, but also widely refuted, explained: "If an open source LLM trained for a few million dollars outperforms the effectiveness of proprietary models... There will be no firewall to safeguard OpenAI either." DeepSeek pierced the US AI companies’ bubble by decommodifying the results of the model’s training, shifting them from behind a paywall to the public arena. Within days, developers around the world started building their own models on top of DeepSeek's. This is was the nightmare for US Big Tech's AI service providers who offered the results of prompts as a commodity, in the form of subscriptions. DeepSeek-type applications can now produce high-quality translations for free and, in so doing, undermine companies specialising in, for example, translation services, such as Germany's Deepl. In the broader scheme of things, this means that the morsels of cloud capital that Europe owns has lost its market value. Nevertheless, and this is a huge nevertheless, it is only AI-as-a-commodity that has lost its (grossly exaggerated) value. In sharp contrast, cloud capital utilised not as a commodity producing piece of tech but as produced means of behavioural modification is not at all threatened by companies like DeepSeek. And since technofeudalism is powered by cloud capital working that way, rather than commodity-like AI services of the ChaptGPT type, our technofeudal order is not threatened by competitors such as DeepSeek. To help understand the difference between cloud capital and AI-based commodified services it helps to compare and contrast Alexa and ChatGPT. Alexa is not offering you a commodified service. It is your free pretend-slave. Unlike ChatGPT you do not pay a subscription to Amazon for the right to order Alexa to order you milk or to switch off your lights. Instead, you train Alexa to train you to train it to know you so that it wins your trust with good recommendations so that it can modify your behaviour – ‘encourage’ you to buy a commodity from with Bezos retaining up to 40% of the price you pay (as a cloud rent). In short, the work that Alexa performs for you is not a commodity, unlike ChatGPT which works to sell you a commodity. In other words, ChatGPT is subject to market competition, to the likes of DeepSeek, but Alexa is not. This is why OpenAI, ChatGPT’s maker, is seriously damaged by the emergence of DeepSeek but Amazon is not. Thus, my basic point: Cloud capital is in a league of its own, beyond market competition from DeepSeek-like upstarts, because its power lies in its capacity to modify our behaviour and remove us from any market (e.g., to shift us from real markets to cloud fiefs like Amazon and Alibaba). In conclusion, cloud capital’s capacity to drive technofeudalism is not challenged by companies like DeepSeek. Only companies like OpenAI, which invested so much and so foolishly in providing a commodified service, stand to lose enormously. Yet another sign that capitalism is dead at the hand of cloud capital while technofeudalism is going from strength to strength and, as it does so, fuels even further the New Cold War between the US and China which in my book, Technofeudalism, I have explained away as the clash of the two huge concentrations of cloud capital: the American dollar-denominated super cloudalist power and the Chinese yuan-denominated one. Speaking of this New Cold War, which I have argued is mostly fuelled by the clash between American and Chinese cloud capital, I wonder what impact DeepSeek’s success will have on the US government. Silicon Valley and Washington DC had convinced themselves that America had a huge AI lead over China. Now, a tiny Chinese company has destroyed that confidence by producing on a shoestring better AI tech than Sillicon Valley had imagined possible. I can almost hear the whirring inside the heads of people in power on both America’s East and West Coast thinking that if the Chinese can do this out of the blue, what else can they do tomorrow? It is reminiscent of the Sputnik moment, isn’t it? It will be interesting to see how Trump reacts to this threat to companies American AI companies, especially since Elon Musk understands, and has spoken out against, the folly of commodifying AI services rather than going full on technofeudal. These are interesting times, in the traditional Chinese sense of the phrase.
9:27

Cloud capital versus AI - What DeepSeek’s spectacular success means for technofeudalism & the New Cold War DeepSeek, a Chinese artificial intelligence (AI) company, this week changed the global AI landscape, not to mention caused $1 trillion losses in the New York stock exchange and the NASDAC. In the process, it demonstrated the difference between cloud capital, which drives technofeudalism onward and upward, and AI-services, which were always a bubble waiting to burst. What remains to be seen is DeepSeek's impact on the New Cold War between the US and China which, from its beginning, was motivated by the clash between US and Chinese cloud capital. DeepSeek is China's response to OpenAI's ChatGPT. Its models perform as efficiently as their US counterparts. The difference is that DeepSeek is offered for free, making money only by selling services to developers - not to the public – at a fraction of the price OpenAI charges! The gist of DeepSeek’s arrival on the AI scene is a sudden transition from proprietary to open source technology. It is, therefore, no great wonder that, the moment DeekSeek became the most downloaded app on the Apple Store, it pulverised the market capitalisation of the, hitherto overinflated, US Big Tech companies. But how did this happen? How is a private commodity suddenly being offered for free? And does this mean that technofeudalism is in trouble? To begin with it is important to note AI was never a proprietary technology in itself. The underlying code has always been open source. What made AI quasi-private was the way these models were trained using huge amounts of privatised (that is stolen from us) data. A leaked Google memo in 2017, that was widely discussed in the industry at the time, but also widely refuted, explained: "If an open source LLM trained for a few million dollars outperforms the effectiveness of proprietary models... There will be no firewall to safeguard OpenAI either." DeepSeek pierced the US AI companies’ bubble by decommodifying the results of the model’s training, shifting them from behind a paywall to the public arena. Within days, developers around the world started building their own models on top of DeepSeek's. This is was the nightmare for US Big Tech's AI service providers who offered the results of prompts as a commodity, in the form of subscriptions. DeepSeek-type applications can now produce high-quality translations for free and, in so doing, undermine companies specialising in, for example, translation services, such as Germany's Deepl. In the broader scheme of things, this means that the morsels of cloud capital that Europe owns has lost its market value. Nevertheless, and this is a huge nevertheless, it is only AI-as-a-commodity that has lost its (grossly exaggerated) value. In sharp contrast, cloud capital utilised not as a commodity producing piece of tech but as produced means of behavioural modification is not at all threatened by companies like DeepSeek. And since technofeudalism is powered by cloud capital working that way, rather than commodity-like AI services of the ChaptGPT type, our technofeudal order is not threatened by competitors such as DeepSeek. To help understand the difference between cloud capital and AI-based commodified services it helps to compare and contrast Alexa and ChatGPT. Alexa is not offering you a commodified service. It is your free pretend-slave. Unlike ChatGPT you do not pay a subscription to Amazon for the right to order Alexa to order you milk or to switch off your lights. Instead, you train Alexa to train you to train it to know you so that it wins your trust with good recommendations so that it can modify your behaviour – ‘encourage’ you to buy a commodity from with Bezos retaining up to 40% of the price you pay (as a cloud rent). In short, the work that Alexa performs for you is not a commodity, unlike ChatGPT which works to sell you a commodity. In other words, ChatGPT is subject to market competition, to the likes of DeepSeek, but Alexa is not. This is why OpenAI, ChatGPT’s maker, is seriously damaged by the emergence of DeepSeek but Amazon is not. Thus, my basic point: Cloud capital is in a league of its own, beyond market competition from DeepSeek-like upstarts, because its power lies in its capacity to modify our behaviour and remove us from any market (e.g., to shift us from real markets to cloud fiefs like Amazon and Alibaba). In conclusion, cloud capital’s capacity to drive technofeudalism is not challenged by companies like DeepSeek. Only companies like OpenAI, which invested so much and so foolishly in providing a commodified service, stand to lose enormously. Yet another sign that capitalism is dead at the hand of cloud capital while technofeudalism is going from strength to strength and, as it does so, fuels even further the New Cold War between the US and China which in my book, Technofeudalism, I have explained away as the clash of the two huge concentrations of cloud capital: the American dollar-denominated super cloudalist power and the Chinese yuan-denominated one. Speaking of this New Cold War, which I have argued is mostly fuelled by the clash between American and Chinese cloud capital, I wonder what impact DeepSeek’s success will have on the US government. Silicon Valley and Washington DC had convinced themselves that America had a huge AI lead over China. Now, a tiny Chinese company has destroyed that confidence by producing on a shoestring better AI tech than Sillicon Valley had imagined possible. I can almost hear the whirring inside the heads of people in power on both America’s East and West Coast thinking that if the Chinese can do this out of the blue, what else can they do tomorrow? It is reminiscent of the Sputnik moment, isn’t it? It will be interesting to see how Trump reacts to this threat to companies American AI companies, especially since Elon Musk understands, and has spoken out against, the folly of commodifying AI services rather than going full on technofeudal. These are interesting times, in the traditional Chinese sense of the phrase.

Yanis Varoufakis

96,249 görüntüleme • 1 yıl önce

A Chinese AI company has unveiled its new model, Kimi K2.5, which quickly climbed to the forefront in global AI rankings. Notably, this is an open-source model, currently positioned as the world’s strongest in that category. China’s AI development still trails the U.S. by about 6-7 months, thus the Sputnik moment has yet to arrive. Taking the U.S. restrictions on exporting the most advanced AI chips to China into consideration, this gap is likely to persist for a considerable time. However, China appears to be charting a distinct path in AI evolution, seeking alternative approaches. Nearly all leading Chinese AI large models follow an open-source strategy. This means the source code is publicly available, allowing anyone to use or modify it freely. As a result, users benefit from accessible technology while also contributing as refiners and new developers. Global downloads of Chinese open-source large models have cumulatively reached 10 billion, a figure that has just surpassed the U.S. Compared to AI behind the screen, China seems to prioritize embedding AI into humanoid and industrial robots, a concept they term “embodied intelligence.” In the past, assembly-line robotic arms could only perform preset, singular tasks; if a screw unexpectedly fell, they were helpless. Now, AI serves not just as the robots’ eyes and skin—enabling them to see the world clearly and sense force, temperature, and more—but also endows them with reasoning capabilities: they can figure out what they’re doing, what they should do, and how to do it better. In this new era of tech competition, the role of the Chinese government has subtly shifted. Whether developing nuclear weapons, satellites, rockets, or space stations, the government traditionally assembled top experts, poured in vast funds and resources, and treated it as a “political imperative.” In recent AI advancements, however, we’re seeing a government that’s more in the background—focusing primarily on removing institutional barriers that hinder AI progress—backing enterprises from the sidelines. And the leading large models—such as DeepSeek, Qwen, Dola, and others—are developed by private-owned enterprises.
1:03

A Chinese AI company has unveiled its new model, Kimi K2.5, which quickly climbed to the forefront in global AI rankings. Notably, this is an open-source model, currently positioned as the world’s strongest in that category. China’s AI development still trails the U.S. by about 6-7 months, thus the Sputnik moment has yet to arrive. Taking the U.S. restrictions on exporting the most advanced AI chips to China into consideration, this gap is likely to persist for a considerable time. However, China appears to be charting a distinct path in AI evolution, seeking alternative approaches. Nearly all leading Chinese AI large models follow an open-source strategy. This means the source code is publicly available, allowing anyone to use or modify it freely. As a result, users benefit from accessible technology while also contributing as refiners and new developers. Global downloads of Chinese open-source large models have cumulatively reached 10 billion, a figure that has just surpassed the U.S. Compared to AI behind the screen, China seems to prioritize embedding AI into humanoid and industrial robots, a concept they term “embodied intelligence.” In the past, assembly-line robotic arms could only perform preset, singular tasks; if a screw unexpectedly fell, they were helpless. Now, AI serves not just as the robots’ eyes and skin—enabling them to see the world clearly and sense force, temperature, and more—but also endows them with reasoning capabilities: they can figure out what they’re doing, what they should do, and how to do it better. In this new era of tech competition, the role of the Chinese government has subtly shifted. Whether developing nuclear weapons, satellites, rockets, or space stations, the government traditionally assembled top experts, poured in vast funds and resources, and treated it as a “political imperative.” In recent AI advancements, however, we’re seeing a government that’s more in the background—focusing primarily on removing institutional barriers that hinder AI progress—backing enterprises from the sidelines. And the leading large models—such as DeepSeek, Qwen, Dola, and others—are developed by private-owned enterprises.

Sinical

96,267 görüntüleme • 5 ay önce

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.
1:57

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

elvis

140,692 görüntüleme • 1 yıl önce

Chinese AI Model DeepSeq: Despite U.S. sanctions restricting access to Nvidia AI chips, Chinese researchers developed DeepSeq, an AI model reportedly more powerful than OpenAI’s. Overcoming Hardware Limitations: With only a limited supply of Nvidia chips, Chinese engineers achieved groundbreaking efficiency optimizations, reducing training resource requirements to just 1/100th of what OpenAI, Microsoft, or Google typically use. Open-Sourcing the Innovation: In a bold move, the developers open-sourced their AI models, making both the code and methodology freely available to the public, significantly reducing the competitive edge of proprietary models like OpenAI’s. Impact on AI Industry: The release of high-quality free alternatives disrupts the market, potentially devaluing OpenAI’s models and forcing American AI companies to rethink their strategies. China’s Rapid AI Innovation: The success of DeepSeq underscores China’s growing self-sufficiency in AI and its ability to leapfrog hardware limitations through advanced software and algorithmic breakthroughs.
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Chinese AI Model DeepSeq: Despite U.S. sanctions restricting access to Nvidia AI chips, Chinese researchers developed DeepSeq, an AI model reportedly more powerful than OpenAI’s. Overcoming Hardware Limitations: With only a limited supply of Nvidia chips, Chinese engineers achieved groundbreaking efficiency optimizations, reducing training resource requirements to just 1/100th of what OpenAI, Microsoft, or Google typically use. Open-Sourcing the Innovation: In a bold move, the developers open-sourced their AI models, making both the code and methodology freely available to the public, significantly reducing the competitive edge of proprietary models like OpenAI’s. Impact on AI Industry: The release of high-quality free alternatives disrupts the market, potentially devaluing OpenAI’s models and forcing American AI companies to rethink their strategies. China’s Rapid AI Innovation: The success of DeepSeq underscores China’s growing self-sufficiency in AI and its ability to leapfrog hardware limitations through advanced software and algorithmic breakthroughs.

Johannes Maria

24,512 görüntüleme • 1 yıl önce

I'm running Llama 4 Maverick at 620 t/s! I'm living in the future! Honestly, a large language model running this fast is something straight out of a sci-fi movie. Speeds like this will enable a whole new world of applications that aren't possible today. For reference, GPT-4o, which is probably the most popular OpenAI model, runs between 60 and 110 t/s. The secret here: I'm not running AI at Meta's Llama 4 Maverick on a GPU. I'm using the SambaNova Cloud (my sponsor) and their custom SN40L chips. They are optimized from the ground up for running AI workflows. Right now, SambaNova Cloud runs DeepSeek, Qwen, Whisper, and the entire family of Llama models on these chips. You can check the speed of each of these models using SambaNova Cloud's Playground (see the attached video). It's completely free, and that's how I'm measuring their speeds. For example, I also tried DeepSeek R1 (the latest version from May) and, oh boy! DeepSeek R1 is a huge 671B parameter model. It's probably the best open reasoning model in the world, and it runs at 140 tokens per second! !!! Inference time on an SN40L is night and day from what you'll get from a GPU. Here is why this is big: If you are running an agentic workflow that uses multiple models simultaneously on a GPU, it will need to swap models in and out of memory (because not every model fits). A single SNL40 chip can simultaneously hold over 100 models (trillions of parameters) in memory. If you are using open models, try the SambaCloud API to see what lightning speed looks like. Here is how: 1. Create a free account at: 2. Check the QuickStart guide: If you try the playground, check the speed you're getting with Llama 4 and DeepSeek, and post the results below. I've seen much higher numbers than I posted here, so I'm curious to see whether geography affects the speed.
1:10

I'm running Llama 4 Maverick at 620 t/s! I'm living in the future! Honestly, a large language model running this fast is something straight out of a sci-fi movie. Speeds like this will enable a whole new world of applications that aren't possible today. For reference, GPT-4o, which is probably the most popular OpenAI model, runs between 60 and 110 t/s. The secret here: I'm not running AI at Meta's Llama 4 Maverick on a GPU. I'm using the SambaNova Cloud (my sponsor) and their custom SN40L chips. They are optimized from the ground up for running AI workflows. Right now, SambaNova Cloud runs DeepSeek, Qwen, Whisper, and the entire family of Llama models on these chips. You can check the speed of each of these models using SambaNova Cloud's Playground (see the attached video). It's completely free, and that's how I'm measuring their speeds. For example, I also tried DeepSeek R1 (the latest version from May) and, oh boy! DeepSeek R1 is a huge 671B parameter model. It's probably the best open reasoning model in the world, and it runs at 140 tokens per second! !!! Inference time on an SN40L is night and day from what you'll get from a GPU. Here is why this is big: If you are running an agentic workflow that uses multiple models simultaneously on a GPU, it will need to swap models in and out of memory (because not every model fits). A single SNL40 chip can simultaneously hold over 100 models (trillions of parameters) in memory. If you are using open models, try the SambaCloud API to see what lightning speed looks like. Here is how: 1. Create a free account at: 2. Check the QuickStart guide: If you try the playground, check the speed you're getting with Llama 4 and DeepSeek, and post the results below. I've seen much higher numbers than I posted here, so I'm curious to see whether geography affects the speed.

Santiago

34,148 görüntüleme • 1 yıl önce

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below
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AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

starmex

978,917 görüntüleme • 1 ay önce