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3. Proprietary AI models will "disappear" LeCun's exact words: "Proprietary platforms, I think, are going to disappear." He calls it "completely inevitable." OpenAI's closed approach? Google's secret models? All doomed. His reasoning will shock the industry:

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Karl Mehta

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104,007 просмотров • 11 месяцев назад •via X (Twitter)

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Today we are announcing Silvia has officially eclipsed all major frontier models in tax-related topics. Our big bet on specialized workflows & proprietary data is proving to be more valuable than general intelligence. The Silvia misfits are going to shock the world. $BRR
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Today we are announcing Silvia has officially eclipsed all major frontier models in tax-related topics. Our big bet on specialized workflows & proprietary data is proving to be more valuable than general intelligence. The Silvia misfits are going to shock the world. $BRR

Anthony Pompliano 🌪

101,484 просмотров • 15 дней назад

Ilya Sutskever spent 5 minutes in 2015 explaining the exact trajectory of AI models. He was completely right. Most people will scroll past this. Don't be most people. Watch & Bookmark it for this weekend:
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Ilya Sutskever spent 5 minutes in 2015 explaining the exact trajectory of AI models. He was completely right. Most people will scroll past this. Don't be most people. Watch & Bookmark it for this weekend:

Chidanand Tripathi

52,590 просмотров • 3 месяцев назад

Factory (Factory) CEO Matan Grinberg (Matan Grinberg) says "In the next 12 months, 90% of tokens will be going to open models." Enterprises are already shifting rapidly.. "At the beginning of the year, <1% of their tokens were going to open models." "Around March, it crossed 1%... & in May it crossed 10%." "These open models are incredibly performant, incredibly cheap, incredibly fast." While frontier closed models aren't going away: "They'll still have a place, but I think that place will be shrinking—at least in token share." And by the end of 2026? "At least in the enterprise, we'll probably cross the 50% threshold."
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Factory (Factory) CEO Matan Grinberg (Matan Grinberg) says "In the next 12 months, 90% of tokens will be going to open models." Enterprises are already shifting rapidly.. "At the beginning of the year, <1% of their tokens were going to open models." "Around March, it crossed 1%... & in May it crossed 10%." "These open models are incredibly performant, incredibly cheap, incredibly fast." While frontier closed models aren't going away: "They'll still have a place, but I think that place will be shrinking—at least in token share." And by the end of 2026? "At least in the enterprise, we'll probably cross the 50% threshold."

Molly O’Shea

120,949 просмотров • 1 месяц назад

I want him to continue doing this content and not mix it with his mom. The audience is not the same. He has built a great community and it will disappear if mixed.
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I want him to continue doing this content and not mix it with his mom. The audience is not the same. He has built a great community and it will disappear if mixed.

Amanda Mamzangwa 😍❤️

423,088 просмотров • 1 месяц назад

Larry Ellison $ORCL highlighted something critical: models like ChatGPT, Gemini, Grok, and Llama are all trained on largely the same public internet data. When everyone trains on the same information, models inevitably converge. That’s why AI is moving toward commoditization. The real moat isn’t the model itself. It’s the proprietary data behind it. Companies that can train on exclusive datasets gain an advantage competitors can’t replicate. Having data that no one else has will allow you to dominate your market.
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Larry Ellison $ORCL highlighted something critical: models like ChatGPT, Gemini, Grok, and Llama are all trained on largely the same public internet data. When everyone trains on the same information, models inevitably converge. That’s why AI is moving toward commoditization. The real moat isn’t the model itself. It’s the proprietary data behind it. Companies that can train on exclusive datasets gain an advantage competitors can’t replicate. Having data that no one else has will allow you to dominate your market.

Daniel

1,160,539 просмотров • 5 месяцев назад

.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."
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.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

Josh Caplan

40,039 просмотров • 1 год назад

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David Shapiro (L/0)

19,305 просмотров • 1 год назад

$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.
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$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.

Shay Boloor

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ELON: CHINA HAS THE BEST OPEN SOURCE AI MODELS RIGHT NOW “The best open source models are generally from China, which is bizarre. I think the second best one, or maybe it’s better than second best, is Grok 2.5. The open source model is actually very good, and we will continue to open source our models.” Source: The All-In Podcast
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ELON: CHINA HAS THE BEST OPEN SOURCE AI MODELS RIGHT NOW “The best open source models are generally from China, which is bizarre. I think the second best one, or maybe it’s better than second best, is Grok 2.5. The open source model is actually very good, and we will continue to open source our models.” Source: The All-In Podcast

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Putin warns that AI will force millions of people out of work and entire profession will disappear as “computer intelligence” replaces humans. According to the Russian leader, AI is already replacing junior staff in areas such as process automation, document preparation, analysis, and software development, and could soon begin replacing mid-level workers too “These processes are irreversible and inevitable,” he said.

Brian McDonald

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A 10B Chinese model beats models 20x its size. And it runs on your laptop. Step 3 VL10B uses parallel reasoning to think like 16 experts at once. → Beats 200B parameter models → Scores 92.2% on multimodal benchmarks → Runs 16 reasoning paths simultaneously → Open source on Hugging Face → Works offline on your hardware OCR. GUI understanding. STEM problems. All covered. Completely free to download. Want the technical breakdown? DM me.
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A 10B Chinese model beats models 20x its size. And it runs on your laptop. Step 3 VL10B uses parallel reasoning to think like 16 experts at once. → Beats 200B parameter models → Scores 92.2% on multimodal benchmarks → Runs 16 reasoning paths simultaneously → Open source on Hugging Face → Works offline on your hardware OCR. GUI understanding. STEM problems. All covered. Completely free to download. Want the technical breakdown? DM me.

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.Bill Gurley “I don’t think politicians understand how powerful open source is as a competitive weapon. If you have ten companies all doing open source, the minute an idea is shared, it's available to the other ten and they absorb it immediately. I think the odds that the U.S., with two proprietary models, can stay ahead of China, with ten open source ones, is very low.”

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Jason Calacanis: The outcome of NYT v. OpenAI could change everything for content creators On E215, @jason discussed the downstream impacts from a huge recent legal decision in the AI space: "Thomson Reuters just won the first major US AI copyright case." "This has huge implications for AI companies here in the United States." "I think that there is a non-zero chance, it might be 5 or 10%, that OpenAI is going to lose the The New York Times lawsuit, and they're going to lose it hard." "I think The New York Times realizes this is their payday." " The settlement might be that these language models, especially the closed ones, are going to have to pay some percentage of their revenue to the content holders." " And this could make the content industry have a massive resurgence." " I think the New York Times will make more money from licenses from LLMs than they will make from advertising or subscriptions, eventually." " This will renew the model."

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OpenAI's Noam Brown says letting AI models think for 20 seconds got the same boost in performance as scaling up the model by 100,000x and training it for 100,000 times longer, and scaling model size and compute will end up being a footnote compared to scaling model thinking
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OpenAI's Noam Brown says letting AI models think for 20 seconds got the same boost in performance as scaling up the model by 100,000x and training it for 100,000 times longer, and scaling model size and compute will end up being a footnote compared to scaling model thinking

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.Sarah Matthews: "Johnson is going to lose his gavel, and I think that he will also lose his position in leadership, because a lot of Republicans are fed up with his leadership. He has made the role of Congress completely obsolete. And all he's done is kowtow to Trump."
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.Sarah Matthews: "Johnson is going to lose his gavel, and I think that he will also lose his position in leadership, because a lot of Republicans are fed up with his leadership. He has made the role of Congress completely obsolete. And all he's done is kowtow to Trump."

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The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.
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The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.

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