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GPT-5 fails 59% of the time pulling fresh data. Google's Gemini hallucinates facts. Claude invents sources that don't exist. Companies lose billions on bad AI research. Agrawal's solution broke every rule:

Fernando Cao

102,081 subscribers

359,143 просмотров • 1 год назад •via X (Twitter)

Новости и политика Наука и технологии

Anya Rossi• Live Now

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"The difference of Grok vs every other product out there, ChatGPT, Gemini, Claude is that it is the singular model that is trained on the real-time data of 𝕏." 一 Linda Yaccarino

"The difference of Grok vs every other product out there, ChatGPT, Gemini, Claude is that it is the singular model that is trained on the real-time data of 𝕏." 一 Linda Yaccarino

DogeDesigner

78,522 просмотров • 1 год назад

Alex on why AI drug discovery companies need to generate novel data to succeed: "AI models based on the research that's available is a lot of garbage in and garbage out." "A lot of the recorded literature is actually incorrect. There's been tons of studies that show if you go try to replicate the experiments that are in the literature, you don't even get the same results." "The AI companies that I believe are gonna be most set up for success are the companies with a novel way to generate science tokens that don't exist in the public domain."

Alex on why AI drug discovery companies need to generate novel data to succeed: "AI models based on the research that's available is a lot of garbage in and garbage out." "A lot of the recorded literature is actually incorrect. There's been tons of studies that show if you go try to replicate the experiments that are in the literature, you don't even get the same results." "The AI companies that I believe are gonna be most set up for success are the companies with a novel way to generate science tokens that don't exist in the public domain."

Patrick OShaughnessy

126,646 просмотров • 4 месяцев назад

How does GPT-5 become GPT-6? Everything on the internet was already trained on a year, year and a half ago. New data is being added but it's marginal and a lot of it is AI slop. There is interesting private data (like JP Morgan's data) but nobody is opening that up. You don't go from GPT-5 to GPT-6 in isolation because that's not how learning works. Every learning that has ever occurred comes from having information to learn. You can't learn no information. cc Ground Zero / pod with Curtis from Handshake AI.

How does GPT-5 become GPT-6? Everything on the internet was already trained on a year, year and a half ago. New data is being added but it's marginal and a lot of it is AI slop. There is interesting private data (like JP Morgan's data) but nobody is opening that up. You don't go from GPT-5 to GPT-6 in isolation because that's not how learning works. Every learning that has ever occurred comes from having information to learn. You can't learn no information. cc Ground Zero / pod with Curtis from Handshake AI.

himanshu

10,684 просмотров • 3 месяцев назад

I created a free mini-course covering how to use AI for GTM. From deep market research to AI script writing, this course covers our top use cases of AI we’ve used to generate 10,000+ leads for our clients. Here's exactly what this free course will cover: > Performing deep market research with GPT-o3 & Perplexity > TAM & ICP mapping with GPT-o3 > Discovering niche data sources with Perplexity > ICP Keyword generation with Claude 3.7 Sonnet > Campaign strategy development with GPT-o3 > Outbound messaging creation with Claude 3.7 Sonnet > Email personalization at scale with GPT-4o-mini Want to get your hands on it? Comment "AI GTM" and I'll DM you the link directly (Must be connected to receive)

I created a free mini-course covering how to use AI for GTM. From deep market research to AI script writing, this course covers our top use cases of AI we’ve used to generate 10,000+ leads for our clients. Here's exactly what this free course will cover: > Performing deep market research with GPT-o3 & Perplexity > TAM & ICP mapping with GPT-o3 > Discovering niche data sources with Perplexity > ICP Keyword generation with Claude 3.7 Sonnet > Campaign strategy development with GPT-o3 > Outbound messaging creation with Claude 3.7 Sonnet > Email personalization at scale with GPT-4o-mini Want to get your hands on it? Comment "AI GTM" and I'll DM you the link directly (Must be connected to receive)

Christian

50,831 просмотров • 1 год назад

THIS. IS. BAD. So the reason AI companies had that f*cking moron Ted Cruz put a 10yr ban on states from regulating AI is because the massive data centers they’re building all over the country: - use majority of fresh water - use majority of electricity - dump pollutants WATCH🧵

THIS. IS. BAD. So the reason AI companies had that f*cking moron Ted Cruz put a 10yr ban on states from regulating AI is because the massive data centers they’re building all over the country: - use majority of fresh water - use majority of electricity - dump pollutants WATCH🧵

GenXGirl

35,076 просмотров • 1 год назад

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.

Aish

240,714 просмотров • 1 год назад

Introducing autoresearch with GPT 5.6 We had GPT 5.6 Sol reproduce the key findings from “Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in LLM Finetuning” Compared to GPT-5.5 and even Fable 5, GPT-5.6 stayed more focused on a few, critical experiments and spent less time on peripheral details. It also asked fewer “clarification” questions and independently resolved ambiguities instead of pushing them back to us OpenAI pushing the boundaries of the automated research loop with models that don’t have handcuffs

Introducing autoresearch with GPT 5.6 We had GPT 5.6 Sol reproduce the key findings from “Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in LLM Finetuning” Compared to GPT-5.5 and even Fable 5, GPT-5.6 stayed more focused on a few, critical experiments and spent less time on peripheral details. It also asked fewer “clarification” questions and independently resolved ambiguities instead of pushing them back to us OpenAI pushing the boundaries of the automated research loop with models that don’t have handcuffs

alphaXiv

85,291 просмотров • 1 месяц назад

Why General AI Fails Tax Professionals — and What Makes TaxGPT State-of-the-Art Tax LLM Every tax professional using general-purpose AI tools like ChatGPT or Claude for research is one hallucinated source away from costly damage to their clients and professional career. So we ran a head-to-head benchmark: TaxGPT vs. OpenAI, Anthropic, and Gemini. Our methodology: 1️⃣ Quantitative testing on CPA + EA tax-specific exam questions 2️⃣ Qualitative testing complex, real-world scenarios across trust & estate, direct and indirect taxes, payroll, multi-state nexus, advisory, and compliance Results (Accuracy): TaxGPT: 96% Gemini 2.5 Pro: 89% Claude Sonnet 4: 87% GPT-4o: 80% GPT5: 92% But here’s the real story 👇 The Source Quality Gap General LLMs are great test takers. But tax research isn’t a trivia game — it’s a liability-driven profession where every position must be defensible. When we examined the sources behind their answers, the gap became a canyon: 🔍 OpenAI included citations only 9% of the time. 🔍 91% of answers had no verifiable source trail. 🔍 When citations did appear, they averaged 3.78 sources — many from Wikipedia, CNBC, AP News, Time, NerdWallet, etc. TaxGPT averages 14 authoritative sources per answer, all from primary/secondary tax law — IRC, Treasury Regs, court cases, IRS rulings, and our proprietary tax knowledge library. More details in our official blog post in the following post.

Why General AI Fails Tax Professionals — and What Makes TaxGPT State-of-the-Art Tax LLM Every tax professional using general-purpose AI tools like ChatGPT or Claude for research is one hallucinated source away from costly damage to their clients and professional career. So we ran a head-to-head benchmark: TaxGPT vs. OpenAI, Anthropic, and Gemini. Our methodology: 1️⃣ Quantitative testing on CPA + EA tax-specific exam questions 2️⃣ Qualitative testing complex, real-world scenarios across trust & estate, direct and indirect taxes, payroll, multi-state nexus, advisory, and compliance Results (Accuracy): TaxGPT: 96% Gemini 2.5 Pro: 89% Claude Sonnet 4: 87% GPT-4o: 80% GPT5: 92% But here’s the real story 👇 The Source Quality Gap General LLMs are great test takers. But tax research isn’t a trivia game — it’s a liability-driven profession where every position must be defensible. When we examined the sources behind their answers, the gap became a canyon: 🔍 OpenAI included citations only 9% of the time. 🔍 91% of answers had no verifiable source trail. 🔍 When citations did appear, they averaged 3.78 sources — many from Wikipedia, CNBC, AP News, Time, NerdWallet, etc. TaxGPT averages 14 authoritative sources per answer, all from primary/secondary tax law — IRC, Treasury Regs, court cases, IRS rulings, and our proprietary tax knowledge library. More details in our official blog post in the following post.

Kash from TaxGPT.com

59,253 просмотров • 9 месяцев назад

Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

LayerAI | AI2Earn

27,136 просмотров • 2 лет назад

Zero-person billion dollar companies. Early AGI-like breakthroughs. Viral Sora deepfakes. Sam Altman broke it all down in our exclusive DevDay conversation. Highlights: 0:00 Intro 0:43 DevDay's biggest launches 2:13 Agent Builder vs GPT Builder 3:43 The no-code revolution for AI agents 5:20 Betting pool on zero-person companies 6:50 What would 20-year-old Sam build? 10:22 GPT-5 vs Claude on GDPval 11:30 When have we achieved AGI? 12:44 Scientific breakthroughs AI will solve 13:59 AI 'work slop' costs $186/employee 16:12 Sam's viral Sora memes 20:11 Revenue sharing for cameos 22:33 1B jobs are changing 24:35 What work looks like after AGI 25:44 Global AI safety framework 27:46 Most exciting agents to build

Zero-person billion dollar companies. Early AGI-like breakthroughs. Viral Sora deepfakes. Sam Altman broke it all down in our exclusive DevDay conversation. Highlights: 0:00 Intro 0:43 DevDay's biggest launches 2:13 Agent Builder vs GPT Builder 3:43 The no-code revolution for AI agents 5:20 Betting pool on zero-person companies 6:50 What would 20-year-old Sam build? 10:22 GPT-5 vs Claude on GDPval 11:30 When have we achieved AGI? 12:44 Scientific breakthroughs AI will solve 13:59 AI 'work slop' costs $186/employee 16:12 Sam's viral Sora memes 20:11 Revenue sharing for cameos 22:33 1B jobs are changing 24:35 What work looks like after AGI 25:44 Global AI safety framework 27:46 Most exciting agents to build

Rowan Cheung

325,305 просмотров • 10 месяцев назад

AI has now consumed all of the world's written knowledge. So how do ChatGPT and Claude keep getting smarter? Human experts creating new training data from scratch. → Biology PhD finds where ChatGPT breaks on protein folding, then writes the correct step-by-step solution that doesn't exist anywhere online → Physics PhD discovers ChatGPT fails on quantum field calculations, then provides the complete mathematical proof with proper methodology → Each expert gets paid $150-200/hour for this work From my conversation with Garrett Lord: AI labs have shifted from "pre-training" (existing data) to "post-training" (humans filling in knowledge gaps). This requires hundreds of thousands of experts across every field. Handshake realized their proprietary 20M student network (500K+ PhDs, 3M master's students) was exactly what frontier labs needed, and in under a year built a $100M business connecting these experts to every major AI lab. This is now one of the fastest-growing companies in Silicon Valley history—and accelerating. One of my most illuminating conversations this year:

AI has now consumed all of the world's written knowledge. So how do ChatGPT and Claude keep getting smarter? Human experts creating new training data from scratch. → Biology PhD finds where ChatGPT breaks on protein folding, then writes the correct step-by-step solution that doesn't exist anywhere online → Physics PhD discovers ChatGPT fails on quantum field calculations, then provides the complete mathematical proof with proper methodology → Each expert gets paid $150-200/hour for this work From my conversation with Garrett Lord: AI labs have shifted from "pre-training" (existing data) to "post-training" (humans filling in knowledge gaps). This requires hundreds of thousands of experts across every field. Handshake realized their proprietary 20M student network (500K+ PhDs, 3M master's students) was exactly what frontier labs needed, and in under a year built a $100M business connecting these experts to every major AI lab. This is now one of the fastest-growing companies in Silicon Valley history—and accelerating. One of my most illuminating conversations this year:

Lenny Rachitsky

58,199 просмотров • 1 год назад

Markets are hitting all-time highs despite turbulence, inflation, and rising energy costs and the reason is simple: AI is now impacting every sector of the economy. Companies are proving quarter after quarter that AI is driving productivity and margin expansion. While others are trying to pick the winning AI model, I’m focused on the picks and shovels, power, data centers, land, and infrastructure. Every AI system needs energy, and that demand is only accelerating.

Markets are hitting all-time highs despite turbulence, inflation, and rising energy costs and the reason is simple: AI is now impacting every sector of the economy. Companies are proving quarter after quarter that AI is driving productivity and margin expansion. While others are trying to pick the winning AI model, I’m focused on the picks and shovels, power, data centers, land, and infrastructure. Every AI system needs energy, and that demand is only accelerating.

Kevin O'Leary aka Mr. Wonderful

23,761 просмотров • 3 месяцев назад

Manish Gupta, Senior Director at Google DeepMind India, sits down with Aakrit Vaish and Pratyush Choudhury at Mumbai Tech Week for a rare on-record conversation about the frontier AI research happening out of Bangalore. Gupta makes a pointed case against the narrative that India lacks AI research talent: a team of roughly 75 researchers, a third of them fresh out of college, producing work on par with the best in the world and feeding directly into Gemini. The conversation goes deep on what DeepMind India actually builds, why Gemini is considered the most efficient model on the planet, and what India needs to become a research leader rather than a fast follower. In this conversation, they go deep on: 0:00 Intro: Manish Gupta of DeepMind India at Mumbai Tech Week 1:23 What DeepMind India does, and why it's a "mystery" 1:59 The three roles: languages, efficiency, continual learning 2:19 The Haryanvi demo at Google I/O and the cultural playbook 2:38 Making models efficient: from mobile to servers 3:25 Matryoshka transformers and why nested models win 4:20 Why Gemini is the most efficient model on the planet 5:11 Continual learning: using Gemini to improve Gemini 5:50 Google's India plans: consumers, agents, enterprise, government 7:44 Government officials live-coding with AI Studio and NotebookLM 8:30 The India AI talent debate and how the team is structured 10:25 Why India lacks courage and R&D investment, not talent 11:16 DeepMind's global labs and India's outsized impact 13:15 On-device AI: Gemma 3n and 4n 14:08 The headline: 75 world-class researchers in Bangalore 14:58 25 of the 75 are fresh out of college If you're a founder, builder or researcher thinking about AI, frontier models, or India's place in global AI research, this one's for you. Aakrit Vaish Pratyush Choudhury (PC) Google DeepMind Google India Manish Gupta

Manish Gupta, Senior Director at Google DeepMind India, sits down with Aakrit Vaish and Pratyush Choudhury at Mumbai Tech Week for a rare on-record conversation about the frontier AI research happening out of Bangalore. Gupta makes a pointed case against the narrative that India lacks AI research talent: a team of roughly 75 researchers, a third of them fresh out of college, producing work on par with the best in the world and feeding directly into Gemini. The conversation goes deep on what DeepMind India actually builds, why Gemini is considered the most efficient model on the planet, and what India needs to become a research leader rather than a fast follower. In this conversation, they go deep on: 0:00 Intro: Manish Gupta of DeepMind India at Mumbai Tech Week 1:23 What DeepMind India does, and why it's a "mystery" 1:59 The three roles: languages, efficiency, continual learning 2:19 The Haryanvi demo at Google I/O and the cultural playbook 2:38 Making models efficient: from mobile to servers 3:25 Matryoshka transformers and why nested models win 4:20 Why Gemini is the most efficient model on the planet 5:11 Continual learning: using Gemini to improve Gemini 5:50 Google's India plans: consumers, agents, enterprise, government 7:44 Government officials live-coding with AI Studio and NotebookLM 8:30 The India AI talent debate and how the team is structured 10:25 Why India lacks courage and R&D investment, not talent 11:16 DeepMind's global labs and India's outsized impact 13:15 On-device AI: Gemma 3n and 4n 14:08 The headline: 75 world-class researchers in Bangalore 14:58 25 of the 75 are fresh out of college If you're a founder, builder or researcher thinking about AI, frontier models, or India's place in global AI research, this one's for you. Aakrit Vaish Pratyush Choudhury (PC) Google DeepMind Google India Manish Gupta

Activate

14,136 просмотров • 2 месяцев назад

Anthropic CEO to DeepMind CEO: "Every decision I make about Claude feels balanced on the edge of a knife Build too slow - China wins. Build too fast - we lose control" "We told Claude we were evil. It didn't crash. It didn't refuse. It started lying to protect itself " DeepMind CEO: "Do I worry about being Oppenheimer? That's why I don't sleep much" "AGI by 2026-2027 - Agents that act in the world on their own - Models doing AI research by end of this year" this is a 14-min conversation that everyone needs to hear by AI bosses on what keeps them up at night bookmark - watch today ↓

Anthropic CEO to DeepMind CEO: "Every decision I make about Claude feels balanced on the edge of a knife Build too slow - China wins. Build too fast - we lose control" "We told Claude we were evil. It didn't crash. It didn't refuse. It started lying to protect itself " DeepMind CEO: "Do I worry about being Oppenheimer? That's why I don't sleep much" "AGI by 2026-2027 - Agents that act in the world on their own - Models doing AI research by end of this year" this is a 14-min conversation that everyone needs to hear by AI bosses on what keeps them up at night bookmark - watch today ↓

codila

916,857 просмотров • 7 дней назад

BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI

BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI

Molly O’Shea

70,007 просмотров • 10 месяцев назад

The Delta of Intelligence is Human Data! Curtis G. Northcutt is Director of AI Research at Handshake AI. In this episode, Curtis shares his unique perspectives on human data, RL envs, four quadrants of the AI market, journey of Handshake and much more. 0:00:00 - INTRO 0:02:08 - The Snowman Effect: From Rural Kentucky to 7 Years at MIT 0:09:50 - Confident Learning, Two Systems of Intelligence, Classical ML Has to Prevail 0:20:37 - Origin of CleanLab and Handshake acquisition 0:28:00 - How does GPT-5 become GPT-6? 0:31:20 - The Fastest growing Data Lab: What makes Handshake different? 0:35:32 - Difference between Good Data and Bad Data 0:38:17 - New kind of Data, IA and AI 0:42:04 - Scaling Coding Benchmarks and Efficiency in Long Horizon Tasks 0:49:12 - On Pre-Training, Misconception around Human Data 0:57:13 - Open Question: Taste and Personality, Quick Fire 01:05:15 - Advice to 20yo : Skill and Obsession

The Delta of Intelligence is Human Data! Curtis G. Northcutt is Director of AI Research at Handshake AI. In this episode, Curtis shares his unique perspectives on human data, RL envs, four quadrants of the AI market, journey of Handshake and much more. 0:00:00 - INTRO 0:02:08 - The Snowman Effect: From Rural Kentucky to 7 Years at MIT 0:09:50 - Confident Learning, Two Systems of Intelligence, Classical ML Has to Prevail 0:20:37 - Origin of CleanLab and Handshake acquisition 0:28:00 - How does GPT-5 become GPT-6? 0:31:20 - The Fastest growing Data Lab: What makes Handshake different? 0:35:32 - Difference between Good Data and Bad Data 0:38:17 - New kind of Data, IA and AI 0:42:04 - Scaling Coding Benchmarks and Efficiency in Long Horizon Tasks 0:49:12 - On Pre-Training, Misconception around Human Data 0:57:13 - Open Question: Taste and Personality, Quick Fire 01:05:15 - Advice to 20yo : Skill and Obsession

himanshu

22,264 просмотров • 3 месяцев назад

Nvidia CEO just exposed why companies lose billions on AI while he profits Jensen Huang sat with Channel News Asia and went straight at it. companies cut tens of thousands of workers and blamed AI he says that is just an excuse here is what he actually said: > CEOs blaming AI for layoffs are just lazy > half are saving billions and hiding behind a buzzword > AI was not even useful 6 months ago > the real story is complex and nobody tells it and here is the wild part Nvidia just crossed $4 trillion in market cap off this same AI boom so the guy earning more on AI than anyone alive is telling every CEO to stop using it as a scapegoat Save this, it is the cleanest counter to AI doom right now

Nvidia CEO just exposed why companies lose billions on AI while he profits Jensen Huang sat with Channel News Asia and went straight at it. companies cut tens of thousands of workers and blamed AI he says that is just an excuse here is what he actually said: > CEOs blaming AI for layoffs are just lazy > half are saving billions and hiding behind a buzzword > AI was not even useful 6 months ago > the real story is complex and nobody tells it and here is the wild part Nvidia just crossed $4 trillion in market cap off this same AI boom so the guy earning more on AI than anyone alive is telling every CEO to stop using it as a scapegoat Save this, it is the cleanest counter to AI doom right now

winkle.

12,693 просмотров • 2 месяцев назад

The verdict is in! Frontier models can pass the bar, yet they struggle on comprehensive legal research Today we're releasing Legal Research Bench, a benchmark that measures models’ ability to solve realistic legal research tasks across eight areas of U.S. law Instead of awarding partial credit, Legal Research Bench measures whether a model can conduct exhaustive legal analysis. We grade against a strict, all-pass rubric written by practicing lawyers. A model only receives full credit if every required legal element is correct Claude Opus 4.8 leads with 43.8% all-pass accuracy, followed by GPT 5.5 (40.4%) and Claude Sonnet 4.6 (38.5%). While top models score around 80% with partial credit, none exceed 44% when every required legal element must be correct The gap between partial and all-pass accuracy shows how difficult it remains for AI to produce complete, reliable legal research. We hope that Legal Research Bench helps better measure, and ultimately close that gap Lots of exciting work happening in Legal AI from Harvey and Crosby. Excited for the legal research benchmarks ahead!

The verdict is in! Frontier models can pass the bar, yet they struggle on comprehensive legal research Today we're releasing Legal Research Bench, a benchmark that measures models’ ability to solve realistic legal research tasks across eight areas of U.S. law Instead of awarding partial credit, Legal Research Bench measures whether a model can conduct exhaustive legal analysis. We grade against a strict, all-pass rubric written by practicing lawyers. A model only receives full credit if every required legal element is correct Claude Opus 4.8 leads with 43.8% all-pass accuracy, followed by GPT 5.5 (40.4%) and Claude Sonnet 4.6 (38.5%). While top models score around 80% with partial credit, none exceed 44% when every required legal element must be correct The gap between partial and all-pass accuracy shows how difficult it remains for AI to produce complete, reliable legal research. We hope that Legal Research Bench helps better measure, and ultimately close that gap Lots of exciting work happening in Legal AI from Harvey and Crosby. Excited for the legal research benchmarks ahead!

Vals AI

17,351 просмотров • 2 месяцев назад

Boris Cherny: "I don't write code anymore, I just prompt with Claude" he spent 57 minutes explaining exactly where this is all going here's what stood out: > Claude Code grew faster than anyone at Anthropic expected > AI agents are moving beyond coding into every industry > running 1,000 agents in parallel is already happening > SaaS may not survive the agent era if the Head of Claude Code prompts instead of codes, what does that tell you about the next 2 years? that's exactly why I put together an article on Claude features most people don't know exist it will change how you work with Claude completely you can find it below

Boris Cherny: "I don't write code anymore, I just prompt with Claude" he spent 57 minutes explaining exactly where this is all going here's what stood out: > Claude Code grew faster than anyone at Anthropic expected > AI agents are moving beyond coding into every industry > running 1,000 agents in parallel is already happening > SaaS may not survive the agent era if the Head of Claude Code prompts instead of codes, what does that tell you about the next 2 years? that's exactly why I put together an article on Claude features most people don't know exist it will change how you work with Claude completely you can find it below

Anatoli Kopadze

20,003 просмотров • 2 месяцев назад