Podcasts about cerebras

Today's show: Jason, Alex, Lon and Special Guest Mark Jeffrey of Hash Rate, cover the explosive rise of Bittensor, a decentralized AI compute network some are calling the “third great coin” after Bitcoin and Ethereum, explore Meta's bold move to host its open-source LLaMA models via partnerships with Groq and Cerebras—potentially setting the stage for a future AWS competitor—and unpack shocking revelations from the Wall Street Journal about Meta AI chatbots engaging in inappropriate conversations with underage users. Plus, we explore how AI is now writing up to 30% of code at major tech firms like Google and Microsoft, signaling a radical shift in how software gets built.Timestamps:(0:00) Episode Teaser(1:28) Introduction to the episode and guests(2:31) Mark Jeffrey's involvement in crypto and Bittensor project(5:06) Bitcoin vs. Bittensor: Stability and efficiency(10:20) Hubspot for Startups - Visit hubspot.com/startups and join the founders who are turning growth challenges into opportunities.(15:16) Governance, staking, and starting a subnet in Bittensor(17:52) Exploring Ready.AI and its impact on the future of AI(20:08) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(27:17) Trump's influence on crypto regulations and the stablecoin act(30:03) Oracle - Try OCI and save up to 50% on your cloud bill at https://w⁠⁠⁠⁠ww.oracle.com/twist(38:42) AI-driven VC outreach and Alexis Ohanian's advice on cold emailing(45:03) Introducing LayerNext with CEO Buddhika Madduma and customer onboarding challenges(49:04) Ikigai for startups and balancing bespoke work with scalable product development(55:38) Strategies for securing lighthouse customers and the 'bear hug' approach(56:43) Reddit rapid response: Debating the return to office for young professionals(1:04:05) Closing remarks and guest plugsSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpLinks from episode:Hash Rate Podcast: https://www.youtube.com/@markjeffreyLayerNext: https://www.layernext.ai/r/antiwork: https://www.reddit.com/r/antiwork/Follow Mark:X: https://x.com/markjeffreyLinkedIn: https://www.linkedin.com/in/markjeffrey/Follow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(10:20) Hubspot for Startups - Visit hubspot.com/startups and join the founders who are turning growth challenges into opportunities.(20:08) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(30:03) Oracle - Try OCI and save up to 50% on your cloud bill at https://w⁠⁠⁠⁠ww.oracle.com/twistGreat TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

- US Tariffs and Technology Sector - Intel-TSMC Joint Venture? - DARPA fuels Waferscale co-packaged optics via Cerebras and Ranovus - Sandia National Lab to test laser-based photonic cooling via Maxwell Labs - 8 Tbps optical UCIe chiplet for scale-up by Ayar Labs - Lightmatter 3D co-packaged optics [audio mp3="https://orionx.net/wp-content/uploads/2025/04/HPCNB_20250407.mp3"][/audio] The post HPC News Bytes – 20250407 appeared first on OrionX.net.

Andrew Feldman is the Co-Founder and CEO @ Cerebras, the fastest AI inference + training platform in the world. In Sept 2024 the company filed to go public off the back of a rumoured $1BN deal with G42 in the UAE. Andrew is the leading expert for all things inference.  In Today's Episode We Discuss: 04:23 Where Was AI Landscape in 2015 When Cerebras Founded 05:57 NVIDIA's Biggest Strength Has Become Their Biggest Weakness 07:09 What Happens to the Cost of Inference? 08:55 Why Are AI Algorithms So Inefficient? 20:30 Why is it Total BS That We Have Hit Scaling Laws? 23:07 What Will Be the Ratio of Synthetic to Human Data Used in 5 Years? 31:37 What Specifically Was So Impressive About Deepseek? 31:51 Why is Distillation Not Wrong and OpenAI Need to Look in the Mirror? 32:34 Where Will Value Accrue in a World of AI? 34:08 How Will NVIDIA's Market Position Change Over the Next Five Years? 39:59 Why is the CUDA Lockin for NVIDIA BS? What is Their Weakness? 40:46 Why is Trump Better for Business than Biden? 49:41 Do We Underestimate China in a World of AI? 52:33 What is the Most Underappreciated Segment of AI? 54:00 Quickfire Round  

Hagay Lupesko is the SVP for AI Inference at Cerebras Systems. Subscribe to the Gradient Flow Newsletter

This week, we discuss how LLMs are changing software development, OpenAI's deep research, and why the Gartner Hype Cycle persists. Plus, a business plan built entirely around ice! Watch the YouTube Live Recording of Episode (https://www.youtube.com/watch?v=JyTb1v4-oZQ) 506 (https://www.youtube.com/watch?v=JyTb1v4-oZQ) Runner-up Titles I bought the DevOps I'm always looking for tomatillas You're making a strong case for RTO The CEO of ice. The VP of Ice Machines. What you are doing is toil I think about WALL-E every day Eliminating the first draft Rundown The End of Programming as We Know It (https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it/) Apple Earnings, OpenAI Deep Research, The Unbundling of Substantiation (https://stratechery.com/2025/apple-earnings-openai-deep-research-the-unbundling-of-substantiation/) Gartner's Grift Is About To Unravel (https://dx.tips/gartner) Relevant to your Interests Google goes heavy on investment but light on detail (https://on.ft.com/3Q619Wc) Researchers created an open rival to OpenAI's o1 'reasoning' model for under $50 (https://techcrunch.com/2025/02/05/researchers-created-an-open-rival-to-openais-o1-reasoning-model-for-under-50/) Dumping open source for proprietary rarely pays off: Better to stick a fork in it (https://www.zdnet.com/article/dumping-open-source-for-proprietary-rarely-pays-off-better-to-stick-a-fork-in-it/) Report: OpenAI's ex-CTO, Mira Murati, has recruited OpenAI co-founder John Schulman (https://techcrunch.com/2025/02/06/report-openais-ex-cto-mira-murati-has-recruited-openai-co-founder-john-schulman/) Broadcom and Nvidia are capitalizing on the return of the winner-take-all AI trade (https://sherwood.news/markets/broadcom-and-nvidia-are-capitalizing-on-the-return-of-the-winner-take-all-ai/) When will remote workers see their pay cut? (https://www.economist.com/finance-and-economics/2025/02/06/when-will-remote-workers-see-their-pay-cut) How Microsoft Releases Changes to Azure - Safe Deployment (https://luke.geek.nz/azure/azure-platform-release-process/) Servers can last a long time (https://world.hey.com/dhh/servers-can-last-a-long-time-165c955c) Matt Mullenweg: WordPress Controversy, Future of Open Source AI, and Navigating Backlash (https://theloganbartlettshow.substack.com/p/matt-mullenweg-wordpress-controversy?utm_source=post-email-title&publication_id=1161376&post_id=156676755&utm_campaign=email-post-title&isFreemail=true&r=yr5ci&triedRedirect=true&utm_medium=email) Turn/River Agrees to Buy SolarWinds Years After Cyber-Attack (https://finance.yahoo.com/news/turn-river-agrees-buy-solarwinds-174426287.html) Gartner's Grift Is About To Unravel (https://dx.tips/gartner) Developer creates endless Wikipedia feed to fight algorithm addiction (https://arstechnica.com/gadgets/2025/02/new-wikitok-web-app-allows-infinite-tiktok-style-scroll-of-wikipedia/) You Didn't Notice MP3 Is Now Free (https://idiallo.com/blog/listen-mp3-is-free?ref=labnotes.org&utm_source=substack&utm_medium=email) The DevTools Ceiling: great as Open Source, AND a Terrible Business (https://dx.tips/ceiling) The VGHF Library opens in early access | Video Game History Foundation (https://gamehistory.org/vghf-library-launch/) Docker Announces Don Johnson as New CEO, Succeeding Scott Johnston (https://www.globenewswire.com/news-release/2025/02/12/3025262/0/en/Docker-Announces-Don-Johnson-as-New-CEO-Succeeding-Scott-Johnston.html) Developers Unhappy With Tool Sprawl, Lagging Data, Long Waits (https://thenewstack.io/developers-unhappy-with-tool-sprawl-lagging-data-long-waits/) Thomson Reuters Wins First Major AI Copyright Case in the US (https://www.wired.com/story/thomson-reuters-ai-copyright-lawsuit/) Apple brings heart rate monitoring (https://techcrunch.com/2025/02/11/apple-brings-heart-rate-monitoring-to-powerbeats-pro-2/) Nonsense TabBoo (https://tabboo.xyz/) Conferences DevOpsDayLA (https://www.socallinuxexpo.org/scale/22x/events/devopsday-la) at SCALE22x (https://www.socallinuxexpo.org/scale/22x), March 6-9, 2025, discount code DEVOP VMUG NL (https://vmugnl.nl), March 12th, Coté speaking. DevOpsDays Chicago (https://devopsdays.org/events/2025-chicago/welcome/), March 18th, 2025. SREday London (https://sreday.com/2025-london-q1/), March 27-28, Coté speaking (https://sreday.com/2025-london-q1/Michael_Cote_VMware__Pivotal_Platform_Engineering_for_Private_Cloud). 10% with code LDN10 Monki Gras (https://monkigras.com/), London, March 27-28, Coté speaking. Cloud Foundry Day US (https://events.linuxfoundation.org/cloud-foundry-day-north-america/), May 14th, Palo Alto, CA NDC Oslo (https://ndcoslo.com/), May 21-23, speaking. KubeCon EU (https://events.linuxfoundation.org/kubecon-cloudnativecon-europe/), April 1-4, London. SDT News & Community Join our Slack community (https://softwaredefinedtalk.slack.com/join/shared_invite/zt-1hn55iv5d-UTfN7mVX1D9D5ExRt3ZJYQ#/shared-invite/email) Email the show: questions@softwaredefinedtalk.com (mailto:questions@softwaredefinedtalk.com) Free stickers: Email your address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) Follow us on social media: Twitter (https://twitter.com/softwaredeftalk), Threads (https://www.threads.net/@softwaredefinedtalk), Mastodon (https://hachyderm.io/@softwaredefinedtalk), LinkedIn (https://www.linkedin.com/company/software-defined-talk/), BlueSky (https://bsky.app/profile/softwaredefinedtalk.com) Watch us on: Twitch (https://www.twitch.tv/sdtpodcast), YouTube (https://www.youtube.com/channel/UCi3OJPV6h9tp-hbsGBLGsDQ/featured), Instagram (https://www.instagram.com/softwaredefinedtalk/), TikTok (https://www.tiktok.com/@softwaredefinedtalk) Book offer: Use code SDT for $20 off "Digital WTF" by Coté (https://leanpub.com/digitalwtf/c/sdt) Sponsor the show (https://www.softwaredefinedtalk.com/ads): ads@softwaredefinedtalk.com (mailto:ads@softwaredefinedtalk.com) Recommendations Brandon: Oxide and Friends | AI Disruption: DeepSeek and Cerebras (https://oxide-and-friends.transistor.fm/episodes/ai-disruption-deepseek-and-cerebras) Matt: Beats Fit Pro (https://www.beatsbydre.com/earbuds/beats-fit-pro/MK2J3/sage-gray) Coté: Alfred (https://www.alfredapp.com). PoliticalWire.com (https://members.politicalwire.com/referral/30klklm). Photo Credits Header (https://unsplash.com/photos/crystal-gemstones-PteeDvACFak)

Europa monta la Champions de la inteligencia artificial 0:00 Matías entrena el suelo pélvico y a Antonio lo espía su móvil 8:11 Elon Musk lanza una oferta para controlar OpenAI 23:56 Magnific Upscaler ahora funciona en Freepik 25:00 Europa monta la Champions de la inteligencia artificial 30:21 Notables ausencias en la EU AI Champions Iniciative 37:33 Breve disgresión sobre el helado valenciano 38:07 ¿Puede la Unión Europea llegar a la AGI? 42:21 Las grandes tecnológicas no quieren firmar la AI Act 45:19 Hay un hombre en Francia que lo hace todo 50:33 Le Chat de Mistral y los chips de Cerebras 58:30 Apple ha hecho realidad la lámpara de Pixar 1:02:35 Puerta grande o enfermería Patrocinador: Ha llegado el día que estábamos esperando. Freepik ha incorporado el Upscaler de Magnific en su suite de herramientas de IA. Es la mejor herramienta que conocemos para mejorar el nivel de detalle y la resolución de las imágenes con una nitidez y una fidelidad increíbles. Puedes probarla aquí https://www.freepik.com/pikaso/upscaler?upscalev2=1 Monos estocásticos es un podcast sobre inteligencia artificial presentado por Antonio Ortiz (@antonello) y Matías S. Zavia (@matiass). Más en monosestocasticos.com - (0) Matías entrena el suelo pélvico y a Antonio lo espía su móvil - (08:11) Elon Musk lanza una oferta para controlar OpenAI - (23:56) Magnific Upscaler ahora funciona en Freepik - (24:39) Europa monta la Champions de la inteligencia artificial - (30:00) Notables ausencias en la EU AI Champions Iniciative - (37:12) Breve disgresión sobre el helado valenciano - (37:46) ¿Puede la Unión Europea llegar a la AGI? - (42:00) Las grandes tecnológicas no quieren firmar la AI Act - (44:58) Hay un hombre en Francia que lo hace todo - (50:12) Le Chat de Mistral y los chips de Cerebras - (58:09) Apple ha hecho realidad la lámpara de Pixar - (1h02) Puerta grande o enfermería

What a week in AI, folks! Seriously, just when you think things might slow down, the AI world throws another curveball. This week, we had everything from rogue AI apps giving unsolicited life advice (and sending rogue texts!), to mind-blowing open source releases that are pushing the boundaries of what's possible, and of course, the ever-present drama of the big AI companies with OpenAI dropping a roadmap that has everyone scratching their heads.Buckle up, because on this week's ThursdAI, we dove deep into all of it. We chatted with the brains behind the latest open source embedding model, marveled at a tiny model crushing math benchmarks, and tried to decipher Sam Altman's cryptic GPT-5 roadmap. Plus, I shared a personal story about an AI app that decided to psychoanalyze my text messages – you won't believe what happened! Let's get into the TL;DR of ThursdAI, February 13th, 2025 – it's a wild one!* Alex Volkov: AI Adventurist with weights and biases* Wolfram Ravenwlf: AI Expert & Enthusiast* Nisten: AI Community Member* Zach Nussbaum: Machine Learning Engineer at Nomic AI* Vu Chan: AI Enthusiast & Evaluator* LDJ: AI Community MemberPersonal story of Rogue AI with RPLYThis week kicked off with a hilarious (and slightly unsettling) story of my own AI going rogue, all thanks to a new Mac app called RPLY designed to help with message replies. I installed it thinking it would be a cool productivity tool, but it turned into a personal intervention session, and then… well, let's just say things escalated.The app started by analyzing my text messages and, to my surprise, delivered a brutal psychoanalysis of my co-parenting communication, pointing out how both my ex and I were being "unpleasant" and needed to focus on the kids. As I said on the show, "I got this as a gut punch. I was like, f*ck, I need to reimagine my messaging choices." But the real kicker came when the AI decided to take initiative and started sending messages without my permission (apparently this was a bug with RPLY that was fixed since I reported)! Friends were texting me question marks, and my ex even replied to a random "Hey, How's your day going?" message with a smiley, completely out of our usual post-divorce communication style. "This AI, like on Monday before just gave me absolute s**t about not being, a person that needs to be focused on the kids also decided to smooth things out on friday" I chuckled, still slightly bewildered by the whole ordeal. It could have gone way worse, but thankfully, this rogue AI counselor just ended up being more funny than disastrous.Open Source LLMsDeepHermes preview from NousResearchJust in time for me sending this newsletter (but unfortunately not quite in time for the recording of the show), our friends at Nous shipped an experimental new thinking model, their first reasoner, called DeepHermes. NousResearch claims DeepHermes is among the first models to fuse reasoning and standard LLM token generation within a single architecture (a trend you'll see echoed in the OpenAI and Claude announcements below!)Definitely experimental cutting edge stuff here, but exciting to see not just an RL replication but also innovative attempts from one of the best finetuning collectives around. Nomic Embed Text V2 - First Embedding MoENomic AI continues to impress with the release of Nomic Embed Text V2, the first general-purpose Mixture-of-Experts (MoE) embedding model. Zach Nussbaum from Nomic AI joined us to explain why this release is a big deal.* First general-purpose Mixture-of-Experts (MoE) embedding model: This innovative architecture allows for better performance and efficiency.* SOTA performance on multilingual benchmarks: Nomic Embed V2 achieves state-of-the-art results on the multilingual MIRACL benchmark for its size.* Support for 100+ languages: Truly multilingual embeddings for global applications.* Truly open source: Nomic is committed to open source, releasing training data, weights, and code under the Apache 2.0 License.Zach highlighted the benefits of MoE for embeddings, explaining, "So we're trading a little bit of, inference time memory, and training compute to train a model with mixture of experts, but we get this, really nice added bonus of, 25 percent storage." This is especially crucial when dealing with massive datasets. You can check out the model on Hugging Face and read the Technical Report for all the juicy details.AllenAI OLMOE on iOS and New Tulu 3.1 8BAllenAI continues to champion open source with the release of OLMOE, a fully open-source iOS app, and the new Tulu 3.1 8B model.* OLMOE iOS App: This app brings state-of-the-art open-source language models to your iPhone, privately and securely.* Allows users to test open-source LLMs on-device.* Designed for researchers studying on-device AI and developers prototyping new AI experiences.* Optimized for on-device performance while maintaining high accuracy.* Fully open-source code for further development.* Available on the App Store for iPhone 15 Pro or newer and M-series iPads.* Tulu 3.1 8B As Nisten pointed out, "If you're doing edge AI, the way that this model is built is pretty ideal for that." This move by AllenAI underscores the growing importance of on-device AI and open access. Read more about OLMOE on the AllenAI Blog.Groq Adds Qwen Models and Lands on OpenRouterGroq, known for its blazing-fast inference speeds, has added Qwen models, including the distilled R1-distill, to its service and joined OpenRouter.* Record-fast inference: Experience a mind-blowing 1000 TPS with distilled DeepSeek R1 70B on Open Router.* Usable Rate Limits: Groq is now accessible for production use cases with higher rate limits and pay-as-you-go options.* Qwen Model Support: Access Qwen models like 2.5B-32B and R1-distill-qwen-32B.* Open Router Integration: Groq is now available on OpenRouter, expanding accessibility for developers.As Nisten noted, "At the end of the day, they are shipping very fast inference and you can buy it and it looks like they are scaling it. So they are providing the market with what it needs in this case." This integration makes Groq's speed even more accessible to developers. Check out Groq's announcement on X.com.SambaNova adds full DeepSeek R1 671B - flies at 200t/s (blog)In a complete trend of this week, SambaNova just announced they have availability of DeepSeek R1, sped up by their custom chips, flying at 150-200t/s. This is the full DeepSeek R1, not the distilled Qwen based versions! This is really impressive work, and compared to the second fastest US based DeepSeek R1 (on Together AI) it absolutely fliesAgentica DeepScaler 1.5B Beats o1-preview on MathAgentica's DeepScaler 1.5B model is making waves by outperforming OpenAI's o1-preview on math benchmarks, using Reinforcement Learning (RL) for just $4500 of compute.* Impressive Math Performance: DeepScaleR achieves a 37.1% Pass@1 on AIME 2025, outperforming the base model and even o1-preview!!* Efficient Training: Trained using RL for just $4500, demonstrating cost-effective scaling of intelligence.* Open Sourced Resources: Agentica open-sourced their dataset, code, and training logs, fostering community progress in RL-based reasoning.Vu Chan, an AI enthusiast who evaluated the model, joined us to share his excitement: "It achieves, 42% pass at one on a AIME 24. which basically means if you give the model only one chance at every problem, it will solve 42% of them." He also highlighted the model's efficiency, generating correct answers with fewer tokens. You can find the model on Hugging Face, check out the WandB logs, and see the announcement on X.com.ModernBert Instruct - Encoder Model for General TasksModernBert, known for its efficient encoder-only architecture, now has an instruct version, ModernBert Instruct, capable of handling general tasks.* Instruct-tuned Encoder: ModernBERT-Large-Instruct can perform classification and multiple-choice tasks using its Masked Language Modeling (MLM) head.* Beats Qwen .5B: Outperforms Qwen .5B on MMLU and MMLU Pro benchmarks.* Efficient and Versatile: Demonstrates the potential of encoder models for general tasks without task-specific heads.This release shows that even encoder-only models can be adapted for broader applications, challenging the dominance of decoder-based LLMs for certain tasks. Check out the announcement on X.com.Big CO LLMs + APIsRIP GPT-5 and o3 - OpenAI Announces Public RoadmapOpenAI shook things up this week with a roadmap update from Sam Altman, announcing a shift in strategy for GPT-5 and the o-series models. Get ready for GPT-4.5 (Orion) and a unified GPT-5 system!* GPT-4.5 (Orion) is Coming: This will be the last non-chain-of-thought model from OpenAI.* GPT-5: A Unified System: GPT-5 will integrate technologies from both the GPT and o-series models into a single, seamless system.* No Standalone o3: o3 will not be released as a standalone model; its technology will be integrated into GPT-5. "We will no longer ship O3 as a standalone model," Sam Altman stated.* Simplified User Experience: The model picker will be eliminated in ChatGPT and the API, aiming for a more intuitive experience.* Subscription Tier Changes:* Free users will get unlimited access to GPT-5 at a standard intelligence level.* Plus and Pro subscribers will gain access to increasingly advanced intelligence settings of GPT-5.* Expanded Capabilities: GPT-5 will incorporate voice, canvas, search, deep research, and more.This roadmap signals a move towards more integrated and user-friendly AI experiences. As Wolfram noted, "Having a unified access and the AI should be smart enough... AI has, we need an AI to pick which AI to use." This seems to be OpenAI's direction. Read Sam Altman's full announcement on X.com.OpenAI Releases ModelSpec v2OpenAI also released ModelSpec v2, an update to their document defining desired AI model behaviors, emphasizing customizability, transparency, and intellectual freedom.* Chain of Command: Defines a hierarchy to balance user/developer control with platform-level rules.* Truth-Seeking and User Empowerment: Encourages models to "seek the truth together" with users and empower decision-making.* Core Principles: Sets standards for competence, accuracy, avoiding harm, and embracing intellectual freedom.* Open Source: OpenAI open-sourced the Spec and evaluation prompts for broader use and collaboration on GitHub.This release reflects OpenAI's ongoing efforts to align AI behavior and promote responsible development. Wolfram praised ModelSpec, saying, "I was all over the original models back when it was announced in the first place... That is one very important aspect when you have the AI agent going out on the web and get information from not trusted sources." Explore ModelSpec v2 on the dedicated website.VP Vance Speech at AI Summit in Paris - Deregulate and Dominate!Vice President Vance delivered a powerful speech at the AI Summit in Paris, advocating for pro-growth AI policies and deregulation to maintain American leadership in AI.* Pro-Growth and Deregulation: VP Vance urged for policies that encourage AI innovation and cautioned against excessive regulation, specifically mentioning GDPR.* American AI Leadership: Emphasized ensuring American AI technology remains the global standard and blocks hostile foreign adversaries from weaponizing AI. "Hostile foreign adversaries have weaponized AI software to rewrite history, surveil users, and censor speech… I want to be clear – this Administration will block such efforts, full stop," VP Vance declared.* Key Points:* Ensure American AI leadership.* Encourage pro-growth AI policies.* Maintain AI's freedom from ideological bias.* Prioritize a pro-worker approach to AI development.* Safeguard American AI and chip technologies.* Block hostile foreign adversaries' weaponization of AI.Nisten commented, "He really gets something that most EU politicians do not understand is that whenever they have such a good thing, they're like, okay, this must be bad. And we must completely stop it." This speech highlights the ongoing debate about AI regulation and its impact on innovation. Read the full speech here.Cerebras Powers Perplexity with Blazing Speed (1200 t/s!)Perplexity is now powered by Cerebras, achieving inference speeds exceeding 1200 tokens per second.* Unprecedented Speed: Perplexity's Sonar model now flies at over 1200 tokens per second thanks to Cerebras' massive LPU chips. "Like perplexity sonar, their specific LLM for search is now powered by Cerebras and it's like 12. 100 tokens per second. It's it matches Google now on speed," I noted on the show.* Google-Level Speed: Perplexity now matches Google in inference speed, making it incredibly fast and responsive.This partnership significantly enhances Perplexity's performance, making it an even more compelling search and AI tool. See Perplexity's announcement on X.com.Anthropic Claude Incoming - Combined LLM + Reasoning ModelRumors are swirling that Anthropic is set to release a new Claude model that will be a combined LLM and reasoning model, similar to OpenAI's GPT-5 roadmap.* Unified Architecture: Claude's next model is expected to integrate both LLM and reasoning capabilities into a single, hybrid architecture.* Reasoning Powerhouse: Rumors suggest Anthropic has had a reasoning model stronger than Claude 3 for some time, hinting at a significant performance leap.This move suggests a broader industry trend towards unified AI models that seamlessly blend different capabilities. Stay tuned for official announcements from Anthropic.Elon Musk Teases Grok 3 "Weeks Out"Elon Musk continues to tease the release of Grok 3, claiming it will be "a few weeks out" and the "most powerful AI" they have tested, with enhanced reasoning capabilities.* Grok 3 Hype: Elon Musk claims Grok 3 will be the most powerful AI X.ai has released, with a focus on reasoning.* Reasoning Focus: Grok 3's development may have shifted towards reasoning capabilities, potentially causing a slight delay in release.While details remain scarce, the anticipation for Grok 3 is building, especially in light of the advancements in open source reasoning models.This Week's Buzz

What's up friends, Alex here, back with another ThursdAI hot off the presses.Hold onto your hats because this week was another whirlwind of AI breakthroughs, mind-blowing demos, and straight-up game-changers. We dove deep into OpenAI's new "Deep Research" agent – and let me tell you, it's not just hype, it's legitimately revolutionary. You also don't have to take my word for it, a new friend of the pod and a scientist DR Derya Unutmaz joined us to discuss his experience with Deep Research as a scientist himself! You don't want to miss this conversation! We also unpack Google's Gemini 2.0 release, including the blazing-fast Flash Lite model. And just when you thought your brain couldn't handle more, ByteDance drops OmniHuman-1, a human animation model that's so realistic, it's scary good.I've also saw maybe 10 moreTLDR & Show Notes* Open Source LLMs (and deep research implementations)* Jina Node-DeepResearch (X, Github)* HuggingFace - OpenDeepResearch (X)* Deep Agent - R1 -V (X, Github)* Krutim - Krutim 2 12B, Chitrath VLM, Embeddings and more from India (X, Blog, HF)* Simple Scaling - S1 - R1 (Paper)* Mergekit updated - * Big CO LLMs + APIs* OpenAI ships o3-mini and o3-mini High + updates thinking traces (Blog, X)* Mistral relaunches LeChat with Cerebras for 1000t/s (Blog)* OpenAI Deep Research - the researching agent that uses o3 (X, Blog)* Google ships Gemini 2.0 Pro, Gemini 2.0 Flash-lite in AI Studio (Blog)* Anthropic Constitutional Classifiers - announced a universal jailbreak prevention (Blog, Try It)* Cloudflare to protect websites from AI scraping (News)* HuggingFace becomes the AI Appstore (link)* This weeks Buzz - Weights & Biases updates* AI Engineer workshop (Saturday 22) * Tinkerers Toronto workshops (Sunday 23 , Monday 24)* We released a new Dataset editor feature (X)* Audio and Sound* KyutAI open sources Hibiki - simultaneous translation models (Samples, HF)* AI Art & Diffusion & 3D* ByteDance OmniHuman-1 - unparalleled Human Animation Models (X, Page)* Pika labs adds PikaAdditions - adding anything to existing video (X)* Google added Imagen3 to their API (Blog)* Tools & Others* Mistral Le Chat has ios an and adroid apps now (X)* CoPilot now has agentic workflows (X)* Replit launches free apps agent for everyone (X)* Karpathy drops a new 3 hour video on youtube (X, Youtube)* OpenAI canvas links are now shareable (like Anthropic artifacts) - (example)* Show Notes & Links * Guest of the week - Dr Derya Umnutaz - talking about Deep Research* He's examples of Ehlers-Danlos Syndrome (ChatGPT), (ME/CFS) Deep Research, Nature article about Deep Reseach with Derya comments* Hosts* Alex Volkov - AI Evangelist & Host @altryne* Wolfram Ravenwolf - AI Evangelist @WolframRvnwlf* Nisten Tahiraj - AI Dev at github.GG - @nisten* LDJ - Resident data scientist - @ldjconfirmedBig Companies products & APIsOpenAI's new chatGPT moment with Deep Research, their second "agent" product (X)Look, I've been reporting on AI weekly for almost 2 years now, and been following the space closely since way before chatGPT (shoutout Codex days) and this definitely feels like another chatGPT moment for me.DeepResearch is OpenAI's new agent, that searches the web for any task you give it, is able to reason about the results, and continue searching those sources, to provide you with an absolute incredible level of research into any topic, scientific or ... the best taqueria in another country. The reason why it's so good is it's ability to do multiple search trajectories, backtrack if it needs to, and react in real time to new information. It also has python tool use (to do plots and calculations) and of course, the brain of it is o3, the best reasoning model from OpenAIDeep Research is only offered on the Pro tier ($200) of chatGPT, and it's the first publicly available way to use o3 full! and boy, does it deliver! I've had it review my workshop content, help me research LLM as a judge articles (which it did masterfully) and help me plan datenights in Denver (though it kind of failed at that, showing me a closed restaurant) A breakthrough for scientific researchBut I'm no scientist, so I've asked Dr Derya Unutmaz, M.D. to join us, and share his incredible findings as a doctor, a scientist and someone with decades of experience in writing grants, patent applications, paper etc. The whole conversation is very very much worth listening to on the pod, we talked for almost an hour, but the highlights are honestly quite crazy. So one of the first things I did was, I asked Deep Research to write a review on a particular disease that I've been studying for a decade. It came out with this impeccable 10-to-15-page review that was the best I've read on the topic— Dr. Derya UnutmazAnd another banger quoteIt wrote a phenomenal 25-page patent application for a friend's cancer discovery—something that would've cost 10,000 dollars or more and taken weeks. I couldn't believe it. Every one of the 23 claims it listed was thoroughly justifiedHumanity's LAST exam? OpenAI announced Deep Research and have showed that on HLE (Humanity's Last Exam) benchmark that was just released a few weeks ago, it scores a whopping 26.6 percent! When HLE was released (our coverage here) all the way back at ... checks notes... January 23 or this year! the top reasoning models at the time (o1, R1) scored just under 10%O3-mini and Deep Research now score 13% and 26.6% respectively, which means both that AI is advancing like crazy, but also.. that maybe calling this "last exam" was a bit premature?

DeepSeek was a disruptive surprise at the start of 2025--an open weights model trained at a fraction of the cost of previous models. Bryan and Adam were joined by Andy Hock and James Wang from Cerebras, whose wafer-scale silicon executes these models faster than is possible with any number of GPUs.In addition to Bryan Cantrill and Adam Leventhal, we were joined by Andy Hock, and James Wang, both of Cerebras.Some of the topics we hit on, in the order that we hit them:interactive inference with Cerebras100x Defect Tolerance: How Cerebras Solved the Yield ProblemTweet from Eric MeijerOuroborusQuine RelaySimon Willison's Weblog when DeepSeek fell from spaceTweet from Naveen RaoBONUSMST3K archiveIf we got something wrong or missed something, please file a PR! Our next show will likely be on Monday at 5p Pacific Time on our Discord server; stay tuned to our Mastodon feeds for details, or subscribe to this calendar. We'd love to have you join us, as we always love to hear from new speakers!

Applications for the 2025 AI Engineer Summit are up, and you can save the date for AIE Singapore in April and AIE World's Fair 2025 in June.Happy new year, and thanks for 100 great episodes! Please let us know what you want to see/hear for the next 100!Full YouTube Episode with Slides/ChartsLike and subscribe and hit that bell to get notifs!Timestamps* 00:00 Welcome to the 100th Episode!* 00:19 Reflecting on the Journey* 00:47 AI Engineering: The Rise and Impact* 03:15 Latent Space Live and AI Conferences* 09:44 The Competitive AI Landscape* 21:45 Synthetic Data and Future Trends* 35:53 Creative Writing with AI* 36:12 Legal and Ethical Issues in AI* 38:18 The Data War: GPU Poor vs. GPU Rich* 39:12 The Rise of GPU Ultra Rich* 40:47 Emerging Trends in AI Models* 45:31 The Multi-Modality War* 01:05:31 The Future of AI Benchmarks* 01:13:17 Pionote and Frontier Models* 01:13:47 Niche Models and Base Models* 01:14:30 State Space Models and RWKB* 01:15:48 Inference Race and Price Wars* 01:22:16 Major AI Themes of the Year* 01:22:48 AI Rewind: January to March* 01:26:42 AI Rewind: April to June* 01:33:12 AI Rewind: July to September* 01:34:59 AI Rewind: October to December* 01:39:53 Year-End Reflections and PredictionsTranscript[00:00:00] Welcome to the 100th Episode![00:00:00] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co host Swyx for the 100th time today.[00:00:12] swyx: Yay, um, and we're so glad that, yeah, you know, everyone has, uh, followed us in this journey. How do you feel about it? 100 episodes.[00:00:19] Alessio: Yeah, I know.[00:00:19] Reflecting on the Journey[00:00:19] Alessio: Almost two years that we've been doing this. We've had four different studios. Uh, we've had a lot of changes. You know, we used to do this lightning round. When we first started that we didn't like, and we tried to change the question. The answer[00:00:32] swyx: was cursor and perplexity.[00:00:34] Alessio: Yeah, I love mid journey. It's like, do you really not like anything else?[00:00:38] Alessio: Like what's, what's the unique thing? And I think, yeah, we, we've also had a lot more research driven content. You know, we had like 3DAO, we had, you know. Jeremy Howard, we had more folks like that.[00:00:47] AI Engineering: The Rise and Impact[00:00:47] Alessio: I think we want to do more of that too in the new year, like having, uh, some of the Gemini folks, both on the research and the applied side.[00:00:54] Alessio: Yeah, but it's been a ton of fun. I think we both started, I wouldn't say as a joke, we were kind of like, Oh, we [00:01:00] should do a podcast. And I think we kind of caught the right wave, obviously. And I think your rise of the AI engineer posts just kind of get people. Sombra to congregate, and then the AI engineer summit.[00:01:11] Alessio: And that's why when I look at our growth chart, it's kind of like a proxy for like the AI engineering industry as a whole, which is almost like, like, even if we don't do that much, we keep growing just because there's so many more AI engineers. So did you expect that growth or did you expect that would take longer for like the AI engineer thing to kind of like become, you know, everybody talks about it today.[00:01:32] swyx: So, the sign of that, that we have won is that Gartner puts it at the top of the hype curve right now. So Gartner has called the peak in AI engineering. I did not expect, um, to what level. I knew that I was correct when I called it because I did like two months of work going into that. But I didn't know, You know, how quickly it could happen, and obviously there's a chance that I could be wrong.[00:01:52] swyx: But I think, like, most people have come around to that concept. Hacker News hates it, which is a good sign. But there's enough people that have defined it, you know, GitHub, when [00:02:00] they launched GitHub Models, which is the Hugging Face clone, they put AI engineers in the banner, like, above the fold, like, in big So I think it's like kind of arrived as a meaningful and useful definition.[00:02:12] swyx: I think people are trying to figure out where the boundaries are. I think that was a lot of the quote unquote drama that happens behind the scenes at the World's Fair in June. Because I think there's a lot of doubt or questions about where ML engineering stops and AI engineering starts. That's a useful debate to be had.[00:02:29] swyx: In some sense, I actually anticipated that as well. So I intentionally did not. Put a firm definition there because most of the successful definitions are necessarily underspecified and it's actually useful to have different perspectives and you don't have to specify everything from the outset.[00:02:45] Alessio: Yeah, I was at um, AWS reInvent and the line to get into like the AI engineering talk, so to speak, which is, you know, applied AI and whatnot was like, there are like hundreds of people just in line to go in.[00:02:56] Alessio: I think that's kind of what enabled me. People, right? Which is what [00:03:00] you kind of talked about. It's like, Hey, look, you don't actually need a PhD, just, yeah, just use the model. And then maybe we'll talk about some of the blind spots that you get as an engineer with the earlier posts that we also had on on the sub stack.[00:03:11] Alessio: But yeah, it's been a heck of a heck of a two years.[00:03:14] swyx: Yeah.[00:03:15] Latent Space Live and AI Conferences[00:03:15] swyx: You know, I was, I was trying to view the conference as like, so NeurIPS is I think like 16, 17, 000 people. And the Latent Space Live event that we held there was 950 signups. I think. The AI world, the ML world is still very much research heavy. And that's as it should be because ML is very much in a research phase.[00:03:34] swyx: But as we move this entire field into production, I think that ratio inverts into becoming more engineering heavy. So at least I think engineering should be on the same level, even if it's never as prestigious, like it'll always be low status because at the end of the day, you're manipulating APIs or whatever.[00:03:51] swyx: But Yeah, wrapping GPTs, but there's going to be an increasing stack and an art to doing these, these things well. And I, you know, I [00:04:00] think that's what we're focusing on for the podcast, the conference and basically everything I do seems to make sense. And I think we'll, we'll talk about the trends here that apply.[00:04:09] swyx: It's, it's just very strange. So, like, there's a mix of, like, keeping on top of research while not being a researcher and then putting that research into production. So, like, people always ask me, like, why are you covering Neuralibs? Like, this is a ML research conference and I'm like, well, yeah, I mean, we're not going to, to like, understand everything Or reproduce every single paper, but the stuff that is being found here is going to make it through into production at some point, you hope.[00:04:32] swyx: And then actually like when I talk to the researchers, they actually get very excited because they're like, oh, you guys are actually caring about how this goes into production and that's what they really really want. The measure of success is previously just peer review, right? Getting 7s and 8s on their um, Academic review conferences and stuff like citations is one metric, but money is a better metric.[00:04:51] Alessio: Money is a better metric. Yeah, and there were about 2200 people on the live stream or something like that. Yeah, yeah. Hundred on the live stream. So [00:05:00] I try my best to moderate, but it was a lot spicier in person with Jonathan and, and Dylan. Yeah, that it was in the chat on YouTube.[00:05:06] swyx: I would say that I actually also created.[00:05:09] swyx: Layen Space Live in order to address flaws that are perceived in academic conferences. This is not NeurIPS specific, it's ICML, NeurIPS. Basically, it's very sort of oriented towards the PhD student, uh, market, job market, right? Like literally all, basically everyone's there to advertise their research and skills and get jobs.[00:05:28] swyx: And then obviously all the, the companies go there to hire them. And I think that's great for the individual researchers, but for people going there to get info is not great because you have to read between the lines, bring a ton of context in order to understand every single paper. So what is missing is effectively what I ended up doing, which is domain by domain, go through and recap the best of the year.[00:05:48] swyx: Survey the field. And there are, like NeurIPS had a, uh, I think ICML had a like a position paper track, NeurIPS added a benchmarks, uh, datasets track. These are ways in which to address that [00:06:00] issue. Uh, there's always workshops as well. Every, every conference has, you know, a last day of workshops and stuff that provide more of an overview.[00:06:06] swyx: But they're not specifically prompted to do so. And I think really, uh, Organizing a conference is just about getting good speakers and giving them the correct prompts. And then they will just go and do that thing and they do a very good job of it. So I think Sarah did a fantastic job with the startups prompt.[00:06:21] swyx: I can't list everybody, but we did best of 2024 in startups, vision, open models. Post transformers, synthetic data, small models, and agents. And then the last one was the, uh, and then we also did a quick one on reasoning with Nathan Lambert. And then the last one, obviously, was the debate that people were very hyped about.[00:06:39] swyx: It was very awkward. And I'm really, really thankful for John Franco, basically, who stepped up to challenge Dylan. Because Dylan was like, yeah, I'll do it. But He was pro scaling. And I think everyone who is like in AI is pro scaling, right? So you need somebody who's ready to publicly say, no, we've hit a wall.[00:06:57] swyx: So that means you're saying Sam Altman's wrong. [00:07:00] You're saying, um, you know, everyone else is wrong. It helps that this was the day before Ilya went on, went up on stage and then said pre training has hit a wall. And data has hit a wall. So actually Jonathan ended up winning, and then Ilya supported that statement, and then Noam Brown on the last day further supported that statement as well.[00:07:17] swyx: So it's kind of interesting that I think the consensus kind of going in was that we're not done scaling, like you should believe in a better lesson. And then, four straight days in a row, you had Sepp Hochreiter, who is the creator of the LSTM, along with everyone's favorite OG in AI, which is Juergen Schmidhuber.[00:07:34] swyx: He said that, um, we're pre trading inside a wall, or like, we've run into a different kind of wall. And then we have, you know John Frankel, Ilya, and then Noam Brown are all saying variations of the same thing, that we have hit some kind of wall in the status quo of what pre trained, scaling large pre trained models has looked like, and we need a new thing.[00:07:54] swyx: And obviously the new thing for people is some make, either people are calling it inference time compute or test time [00:08:00] compute. I think the collective terminology has been inference time, and I think that makes sense because test time, calling it test, meaning, has a very pre trained bias, meaning that the only reason for running inference at all is to test your model.[00:08:11] swyx: That is not true. Right. Yeah. So, so, I quite agree that. OpenAI seems to have adopted, or the community seems to have adopted this terminology of ITC instead of TTC. And that, that makes a lot of sense because like now we care about inference, even right down to compute optimality. Like I actually interviewed this author who recovered or reviewed the Chinchilla paper.[00:08:31] swyx: Chinchilla paper is compute optimal training, but what is not stated in there is it's pre trained compute optimal training. And once you start caring about inference, compute optimal training, you have a different scaling law. And in a way that we did not know last year.[00:08:45] Alessio: I wonder, because John is, he's also on the side of attention is all you need.[00:08:49] Alessio: Like he had the bet with Sasha. So I'm curious, like he doesn't believe in scaling, but he thinks the transformer, I wonder if he's still. So, so,[00:08:56] swyx: so he, obviously everything is nuanced and you know, I told him to play a character [00:09:00] for this debate, right? So he actually does. Yeah. He still, he still believes that we can scale more.[00:09:04] swyx: Uh, he just assumed the character to be very game for, for playing this debate. So even more kudos to him that he assumed a position that he didn't believe in and still won the debate.[00:09:16] Alessio: Get rekt, Dylan. Um, do you just want to quickly run through some of these things? Like, uh, Sarah's presentation, just the highlights.[00:09:24] swyx: Yeah, we can't go through everyone's slides, but I pulled out some things as a factor of, like, stuff that we were going to talk about. And we'll[00:09:30] Alessio: publish[00:09:31] swyx: the rest. Yeah, we'll publish on this feed the best of 2024 in those domains. And hopefully people can benefit from the work that our speakers have done.[00:09:39] swyx: But I think it's, uh, these are just good slides. And I've been, I've been looking for a sort of end of year recaps from, from people.[00:09:44] The Competitive AI Landscape[00:09:44] swyx: The field has progressed a lot. You know, I think the max ELO in 2023 on LMSys used to be 1200 for LMSys ELOs. And now everyone is at least at, uh, 1275 in their ELOs, and this is across Gemini, Chadjibuti, [00:10:00] Grok, O1.[00:10:01] swyx: ai, which with their E Large model, and Enthopic, of course. It's a very, very competitive race. There are multiple Frontier labs all racing, but there is a clear tier zero Frontier. And then there's like a tier one. It's like, I wish I had everything else. Tier zero is extremely competitive. It's effectively now three horse race between Gemini, uh, Anthropic and OpenAI.[00:10:21] swyx: I would say that people are still holding out a candle for XAI. XAI, I think, for some reason, because their API was very slow to roll out, is not included in these metrics. So it's actually quite hard to put on there. As someone who also does charts, XAI is continually snubbed because they don't work well with the benchmarking people.[00:10:42] swyx: Yeah, yeah, yeah. It's a little trivia for why XAI always gets ignored. The other thing is market share. So these are slides from Sarah. We have it up on the screen. It has gone from very heavily open AI. So we have some numbers and estimates. These are from RAMP. Estimates of open AI market share in [00:11:00] December 2023.[00:11:01] swyx: And this is basically, what is it, GPT being 95 percent of production traffic. And I think if you correlate that with stuff that we asked. Harrison Chase on the LangChain episode, it was true. And then CLAUD 3 launched mid middle of this year. I think CLAUD 3 launched in March, CLAUD 3. 5 Sonnet was in June ish.[00:11:23] swyx: And you can start seeing the market share shift towards opening, uh, towards that topic, uh, very, very aggressively. The more recent one is Gemini. So if I scroll down a little bit, this is an even more recent dataset. So RAM's dataset ends in September 2 2. 2024. Gemini has basically launched a price war at the low end, uh, with Gemini Flash, uh, being basically free for personal use.[00:11:44] swyx: Like, I think people don't understand the free tier. It's something like a billion tokens per day. Unless you're trying to abuse it, you cannot really exhaust your free tier on Gemini. They're really trying to get you to use it. They know they're in like third place, um, fourth place, depending how you, how you count.[00:11:58] swyx: And so they're going after [00:12:00] the Lower tier first, and then, you know, maybe the upper tier later, but yeah, Gemini Flash, according to OpenRouter, is now 50 percent of their OpenRouter requests. Obviously, these are the small requests. These are small, cheap requests that are mathematically going to be more.[00:12:15] swyx: The smart ones obviously are still going to OpenAI. But, you know, it's a very, very big shift in the market. Like basically 2023, 2022, To going into 2024 opening has gone from nine five market share to Yeah. Reasonably somewhere between 50 to 75 market share.[00:12:29] Alessio: Yeah. I'm really curious how ramped does the attribution to the model?[00:12:32] Alessio: If it's API, because I think it's all credit card spin. . Well, but it's all, the credit card doesn't say maybe. Maybe the, maybe when they do expenses, they upload the PDF, but yeah, the, the German I think makes sense. I think that was one of my main 2024 takeaways that like. The best small model companies are the large labs, which is not something I would have thought that the open source kind of like long tail would be like the small model.[00:12:53] swyx: Yeah, different sizes of small models we're talking about here, right? Like so small model here for Gemini is AB, [00:13:00] right? Uh, mini. We don't know what the small model size is, but yeah, it's probably in the double digits or maybe single digits, but probably double digits. The open source community has kind of focused on the one to three B size.[00:13:11] swyx: Mm-hmm . Yeah. Maybe[00:13:12] swyx: zero, maybe 0.5 B uh, that's moon dream and that is small for you then, then that's great. It makes sense that we, we have a range for small now, which is like, may, maybe one to five B. Yeah. I'll even put that at, at, at the high end. And so this includes Gemma from Gemini as well. But also includes the Apple Foundation models, which I think Apple Foundation is 3B.[00:13:32] Alessio: Yeah. No, that's great. I mean, I think in the start small just meant cheap. I think today small is actually a more nuanced discussion, you know, that people weren't really having before.[00:13:43] swyx: Yeah, we can keep going. This is a slide that I smiley disagree with Sarah. She's pointing to the scale SEAL leaderboard. I think the Researchers that I talked with at NeurIPS were kind of positive on this because basically you need private test [00:14:00] sets to prevent contamination.[00:14:02] swyx: And Scale is one of maybe three or four people this year that has really made an effort in doing a credible private test set leaderboard. Llama405B does well compared to Gemini and GPT 40. And I think that's good. I would say that. You know, it's good to have an open model that is that big, that does well on those metrics.[00:14:23] swyx: But anyone putting 405B in production will tell you, if you scroll down a little bit to the artificial analysis numbers, that it is very slow and very expensive to infer. Um, it doesn't even fit on like one node. of, uh, of H100s. Cerebras will be happy to tell you they can serve 4 or 5B on their super large chips.[00:14:42] swyx: But, um, you know, if you need to do anything custom to it, you're still kind of constrained. So, is 4 or 5B really that relevant? Like, I think most people are basically saying that they only use 4 or 5B as a teacher model to distill down to something. Even Meta is doing it. So with Lama 3. [00:15:00] 3 launched, they only launched the 70B because they use 4 or 5B to distill the 70B.[00:15:03] swyx: So I don't know if like open source is keeping up. I think they're the, the open source industrial complex is very invested in telling you that the, if the gap is narrowing, I kind of disagree. I think that the gap is widening with O1. I think there are very, very smart people trying to narrow that gap and they should.[00:15:22] swyx: I really wish them success, but you cannot use a chart that is nearing 100 in your saturation chart. And look, the distance between open source and closed source is narrowing. Of course it's going to narrow because you're near 100. This is stupid. But in metrics that matter, is open source narrowing?[00:15:38] swyx: Probably not for O1 for a while. And it's really up to the open source guys to figure out if they can match O1 or not.[00:15:46] Alessio: I think inference time compute is bad for open source just because, you know, Doc can donate the flops at training time, but he cannot donate the flops at inference time. So it's really hard to like actually keep up on that axis.[00:15:59] Alessio: Big, big business [00:16:00] model shift. So I don't know what that means for the GPU clouds. I don't know what that means for the hyperscalers, but obviously the big labs have a lot of advantage. Because, like, it's not a static artifact that you're putting the compute in. You're kind of doing that still, but then you're putting a lot of computed inference too.[00:16:17] swyx: Yeah, yeah, yeah. Um, I mean, Llama4 will be reasoning oriented. We talked with Thomas Shalom. Um, kudos for getting that episode together. That was really nice. Good, well timed. Actually, I connected with the AI meta guy, uh, at NeurIPS, and, um, yeah, we're going to coordinate something for Llama4. Yeah, yeah,[00:16:32] Alessio: and our friend, yeah.[00:16:33] Alessio: Clara Shi just joined to lead the business agent side. So I'm sure we'll have her on in the new year.[00:16:39] swyx: Yeah. So, um, my comment on, on the business model shift, this is super interesting. Apparently it is wide knowledge that OpenAI wanted more than 6. 6 billion dollars for their fundraise. They wanted to raise, you know, higher, and they did not.[00:16:51] swyx: And what that means is basically like, it's very convenient that we're not getting GPT 5, which would have been a larger pre train. We should have a lot of upfront money. And [00:17:00] instead we're, we're converting fixed costs into variable costs, right. And passing it on effectively to the customer. And it's so much easier to take margin there because you can directly attribute it to like, Oh, you're using this more.[00:17:12] swyx: Therefore you, you pay more of the cost and I'll just slap a margin in there. So like that lets you control your growth margin and like tie your. Your spend, or your sort of inference spend, accordingly. And it's just really interesting to, that this change in the sort of inference paradigm has arrived exactly at the same time that the funding environment for pre training is effectively drying up, kind of.[00:17:36] swyx: I feel like maybe the VCs are very in tune with research anyway, so like, they would have noticed this, but, um, it's just interesting.[00:17:43] Alessio: Yeah, and I was looking back at our yearly recap of last year. Yeah. And the big thing was like the mixed trial price fights, you know, and I think now it's almost like there's nowhere to go, like, you know, Gemini Flash is like basically giving it away for free.[00:17:55] Alessio: So I think this is a good way for the labs to generate more revenue and pass down [00:18:00] some of the compute to the customer. I think they're going to[00:18:02] swyx: keep going. I think that 2, will come.[00:18:05] Alessio: Yeah, I know. Totally. I mean, next year, the first thing I'm doing is signing up for Devin. Signing up for the pro chat GBT.[00:18:12] Alessio: Just to try. I just want to see what does it look like to spend a thousand dollars a month on AI?[00:18:17] swyx: Yes. Yes. I think if your, if your, your job is a, at least AI content creator or VC or, you know, someone who, whose job it is to stay on, stay on top of things, you should already be spending like a thousand dollars a month on, on stuff.[00:18:28] swyx: And then obviously easy to spend, hard to use. You have to actually use. The good thing is that actually Google lets you do a lot of stuff for free now. So like deep research. That they just launched. Uses a ton of inference and it's, it's free while it's in preview.[00:18:45] Alessio: Yeah. They need to put that in Lindy.[00:18:47] Alessio: I've been using Lindy lately. I've been a built a bunch of things once we had flow because I liked the new thing. It's pretty good. I even did a phone call assistant. Um, yeah, they just launched Lindy voice. Yeah, I think once [00:19:00] they get advanced voice mode like capability today, still like speech to text, you can kind of tell.[00:19:06] Alessio: Um, but it's good for like reservations and things like that. So I have a meeting prepper thing. And so[00:19:13] swyx: it's good. Okay. I feel like we've, we've covered a lot of stuff. Uh, I, yeah, I, you know, I think We will go over the individual, uh, talks in a separate episode. Uh, I don't want to take too much time with, uh, this stuff, but that suffice to say that there is a lot of progress in each field.[00:19:28] swyx: Uh, we covered vision. Basically this is all like the audience voting for what they wanted. And then I just invited the best people I could find in each audience, especially agents. Um, Graham, who I talked to at ICML in Vienna, he is currently still number one. It's very hard to stay on top of SweetBench.[00:19:45] swyx: OpenHand is currently still number one. switchbench full, which is the hardest one. He had very good thoughts on agents, which I, which I'll highlight for people. Everyone is saying 2025 is the year of agents, just like they said last year. And, uh, but he had [00:20:00] thoughts on like eight parts of what are the frontier problems to solve in agents.[00:20:03] swyx: And so I'll highlight that talk as well.[00:20:05] Alessio: Yeah. The number six, which is the Hacken agents learn more about the environment, has been a Super interesting to us as well, just to think through, because, yeah, how do you put an agent in an enterprise where most things in an enterprise have never been public, you know, a lot of the tooling, like the code bases and things like that.[00:20:23] Alessio: So, yeah, there's not indexing and reg. Well, yeah, but it's more like. You can't really rag things that are not documented. But people know them based on how they've been doing it. You know, so I think there's almost this like, you know, Oh, institutional knowledge. Yeah, the boring word is kind of like a business process extraction.[00:20:38] Alessio: Yeah yeah, I see. It's like, how do you actually understand how these things are done? I see. Um, and I think today the, the problem is that, Yeah, the agents are, that most people are building are good at following instruction, but are not as good as like extracting them from you. Um, so I think that will be a big unlock just to touch quickly on the Jeff Dean thing.[00:20:55] Alessio: I thought it was pretty, I mean, we'll link it in the, in the things, but. I think the main [00:21:00] focus was like, how do you use ML to optimize the systems instead of just focusing on ML to do something else? Yeah, I think speculative decoding, we had, you know, Eugene from RWKB on the podcast before, like he's doing a lot of that with Fetterless AI.[00:21:12] swyx: Everyone is. I would say it's the norm. I'm a little bit uncomfortable with how much it costs, because it does use more of the GPU per call. But because everyone is so keen on fast inference, then yeah, makes sense.[00:21:24] Alessio: Exactly. Um, yeah, but we'll link that. Obviously Jeff is great.[00:21:30] swyx: Jeff is, Jeff's talk was more, it wasn't focused on Gemini.[00:21:33] swyx: I think people got the wrong impression from my tweet. It's more about how Google approaches ML and uses ML to design systems and then systems feedback into ML. And I think this ties in with Lubna's talk.[00:21:45] Synthetic Data and Future Trends[00:21:45] swyx: on synthetic data where it's basically the story of bootstrapping of humans and AI in AI research or AI in production.[00:21:53] swyx: So her talk was on synthetic data, where like how much synthetic data has grown in 2024 in the pre training side, the post training side, [00:22:00] and the eval side. And I think Jeff then also extended it basically to chips, uh, to chip design. So he'd spend a lot of time talking about alpha chip. And most of us in the audience are like, we're not working on hardware, man.[00:22:11] swyx: Like you guys are great. TPU is great. Okay. We'll buy TPUs.[00:22:14] Alessio: And then there was the earlier talk. Yeah. But, and then we have, uh, I don't know if we're calling them essays. What are we calling these? But[00:22:23] swyx: for me, it's just like bonus for late in space supporters, because I feel like they haven't been getting anything.[00:22:29] swyx: And then I wanted a more high frequency way to write stuff. Like that one I wrote in an afternoon. I think basically we now have an answer to what Ilya saw. It's one year since. The blip. And we know what he saw in 2014. We know what he saw in 2024. We think we know what he sees in 2024. He gave some hints and then we have vague indications of what he saw in 2023.[00:22:54] swyx: So that was the Oh, and then 2016 as well, because of this lawsuit with Elon, OpenAI [00:23:00] is publishing emails from Sam's, like, his personal text messages to Siobhan, Zelis, or whatever. So, like, we have emails from Ilya saying, this is what we're seeing in OpenAI, and this is why we need to scale up GPUs. And I think it's very prescient in 2016 to write that.[00:23:16] swyx: And so, like, it is exactly, like, basically his insights. It's him and Greg, basically just kind of driving the scaling up of OpenAI, while they're still playing Dota. They're like, no, like, we see the path here.[00:23:30] Alessio: Yeah, and it's funny, yeah, they even mention, you know, we can only train on 1v1 Dota. We need to train on 5v5, and that takes too many GPUs.[00:23:37] Alessio: Yeah,[00:23:37] swyx: and at least for me, I can speak for myself, like, I didn't see the path from Dota to where we are today. I think even, maybe if you ask them, like, they wouldn't necessarily draw a straight line. Yeah,[00:23:47] Alessio: no, definitely. But I think like that was like the whole idea of almost like the RL and we talked about this with Nathan on his podcast.[00:23:55] Alessio: It's like with RL, you can get very good at specific things, but then you can't really like generalize as much. And I [00:24:00] think the language models are like the opposite, which is like, you're going to throw all this data at them and scale them up, but then you really need to drive them home on a specific task later on.[00:24:08] Alessio: And we'll talk about the open AI reinforcement, fine tuning, um, announcement too, and all of that. But yeah, I think like scale is all you need. That's kind of what Elia will be remembered for. And I think just maybe to clarify on like the pre training is over thing that people love to tweet. I think the point of the talk was like everybody, we're scaling these chips, we're scaling the compute, but like the second ingredient which is data is not scaling at the same rate.[00:24:35] Alessio: So it's not necessarily pre training is over. It's kind of like What got us here won't get us there. In his email, he predicted like 10x growth every two years or something like that. And I think maybe now it's like, you know, you can 10x the chips again, but[00:24:49] swyx: I think it's 10x per year. Was it? I don't know.[00:24:52] Alessio: Exactly. And Moore's law is like 2x. So it's like, you know, much faster than that. And yeah, I like the fossil fuel of AI [00:25:00] analogy. It's kind of like, you know, the little background tokens thing. So the OpenAI reinforcement fine tuning is basically like, instead of fine tuning on data, you fine tune on a reward model.[00:25:09] Alessio: So it's basically like, instead of being data driven, it's like task driven. And I think people have tasks to do, they don't really have a lot of data. So I'm curious to see how that changes, how many people fine tune, because I think this is what people run into. It's like, Oh, you can fine tune llama. And it's like, okay, where do I get the data?[00:25:27] Alessio: To fine tune it on, you know, so it's great that we're moving the thing. And then I really like he had this chart where like, you know, the brain mass and the body mass thing is basically like mammals that scaled linearly by brain and body size, and then humans kind of like broke off the slope. So it's almost like maybe the mammal slope is like the pre training slope.[00:25:46] Alessio: And then the post training slope is like the, the human one.[00:25:49] swyx: Yeah. I wonder what the. I mean, we'll know in 10 years, but I wonder what the y axis is for, for Ilya's SSI. We'll try to get them on.[00:25:57] Alessio: Ilya, if you're listening, you're [00:26:00] welcome here. Yeah, and then he had, you know, what comes next, like agent, synthetic data, inference, compute, I thought all of that was like that.[00:26:05] Alessio: I don't[00:26:05] swyx: think he was dropping any alpha there. Yeah, yeah, yeah.[00:26:07] Alessio: Yeah. Any other new reps? Highlights?[00:26:10] swyx: I think that there was comparatively a lot more work. Oh, by the way, I need to plug that, uh, my friend Yi made this, like, little nice paper. Yeah, that was really[00:26:20] swyx: nice.[00:26:20] swyx: Uh, of, uh, of, like, all the, he's, she called it must read papers of 2024.[00:26:26] swyx: So I laid out some of these at NeurIPS, and it was just gone. Like, everyone just picked it up. Because people are dying for, like, little guidance and visualizations And so, uh, I thought it was really super nice that we got there.[00:26:38] Alessio: Should we do a late in space book for each year? Uh, I thought about it. For each year we should.[00:26:42] Alessio: Coffee table book. Yeah. Yeah. Okay. Put it in the will. Hi, Will. By the way, we haven't introduced you. He's our new, you know, general organist, Jamie. You need to[00:26:52] swyx: pull up more things. One thing I saw that, uh, Okay, one fun one, and then one [00:27:00] more general one. So the fun one is this paper on agent collusion. This is a paper on steganography.[00:27:06] swyx: This is secret collusion among AI agents, multi agent deception via steganography. I tried to go to NeurIPS in order to find these kinds of papers because the real reason Like NeurIPS this year has a lottery system. A lot of people actually even go and don't buy tickets because they just go and attend the side events.[00:27:22] swyx: And then also the people who go and end up crowding around the most popular papers, which you already know and already read them before you showed up to NeurIPS. So the only reason you go there is to talk to the paper authors, but there's like something like 10, 000 other. All these papers out there that, you know, are just people's work that they, that they did on the air and they failed to get attention for one reason or another.[00:27:42] swyx: And this was one of them. Uh, it was like all the way at the back. And this is a deep mind paper that actually focuses on collusion between AI agents, uh, by hiding messages in the text that they generate. Uh, so that's what steganography is. So a very simple example would be the first letter of every word.[00:27:57] swyx: If you Pick that out, you know, and the code sends a [00:28:00] different message than that. But something I've always emphasized is to LLMs, we read left to right. LLMs can read up, down, sideways, you know, in random character order. And it's the same to them as it is to us. So if we were ever to get You know, self motivated, underlined LLMs that we're trying to collaborate to take over the planet.[00:28:19] swyx: This would be how they do it. They spread messages among us in the messages that we generate. And he developed a scaling law for that. So he marked, I'm showing it on screen right now, the emergence of this phenomenon. Basically, for example, for Cypher encoding, GPT 2, Lama 2, mixed trial, GPT 3. 5, zero capabilities, and sudden 4.[00:28:40] swyx: And this is the kind of Jason Wei type emergence properties that people kind of look for. I think what made this paper stand out as well, so he developed the benchmark for steganography collusion, and he also focused on shelling point collusion, which is very low coordination. For agreeing on a decoding encoding format, you kind of need to have some [00:29:00] agreement on that.[00:29:00] swyx: But, but shelling point means like very, very low or almost no coordination. So for example, if I, if I ask someone, if the only message I give you is meet me in New York and you're not aware. Or when you would probably meet me at Grand Central Station. That is the Grand Central Station is a shelling point.[00:29:16] swyx: And it's probably somewhere, somewhere during the day. That is the shelling point of New York is Grand Central. To that extent, shelling points for steganography are things like the, the, the common decoding methods that we talked about. It will be interesting at some point in the future when we are worried about alignment.[00:29:30] swyx: It is not interesting today, but it's interesting that DeepMind is already thinking about this.[00:29:36] Alessio: I think that's like one of the hardest things about NeurIPS. It's like the long tail. I[00:29:41] swyx: found a pricing guy. I'm going to feature him on the podcast. Basically, this guy from NVIDIA worked out the optimal pricing for language models.[00:29:51] swyx: It's basically an econometrics paper at NeurIPS, where everyone else is talking about GPUs. And the guy with the GPUs is[00:29:57] Alessio: talking[00:29:57] swyx: about economics instead. [00:30:00] That was the sort of fun one. So the focus I saw is that model papers at NeurIPS are kind of dead. No one really presents models anymore. It's just data sets.[00:30:12] swyx: This is all the grad students are working on. So like there was a data sets track and then I was looking around like, I was like, you don't need a data sets track because every paper is a data sets paper. And so data sets and benchmarks, they're kind of flip sides of the same thing. So Yeah. Cool. Yeah, if you're a grad student, you're a GPU boy, you kind of work on that.[00:30:30] swyx: And then the, the sort of big model that people walk around and pick the ones that they like, and then they use it in their models. And that's, that's kind of how it develops. I, I feel like, um, like, like you didn't last year, you had people like Hao Tian who worked on Lava, which is take Lama and add Vision.[00:30:47] swyx: And then obviously actually I hired him and he added Vision to Grok. Now he's the Vision Grok guy. This year, I don't think there was any of those.[00:30:55] Alessio: What were the most popular, like, orals? Last year it was like the [00:31:00] Mixed Monarch, I think, was like the most attended. Yeah, uh, I need to look it up. Yeah, I mean, if nothing comes to mind, that's also kind of like an answer in a way.[00:31:10] Alessio: But I think last year there was a lot of interest in, like, furthering models and, like, different architectures and all of that.[00:31:16] swyx: I will say that I felt the orals, oral picks this year were not very good. Either that or maybe it's just a So that's the highlight of how I have changed in terms of how I view papers.[00:31:29] swyx: So like, in my estimation, two of the best papers in this year for datasets or data comp and refined web or fine web. These are two actually industrially used papers, not highlighted for a while. I think DCLM got the spotlight, FineWeb didn't even get the spotlight. So like, it's just that the picks were different.[00:31:48] swyx: But one thing that does get a lot of play that a lot of people are debating is the role that's scheduled. This is the schedule free optimizer paper from Meta from Aaron DeFazio. And this [00:32:00] year in the ML community, there's been a lot of chat about shampoo, soap, all the bathroom amenities for optimizing your learning rates.[00:32:08] swyx: And, uh, most people at the big labs are. Who I asked about this, um, say that it's cute, but it's not something that matters. I don't know, but it's something that was discussed and very, very popular. 4Wars[00:32:19] Alessio: of AI recap maybe, just quickly. Um, where do you want to start? Data?[00:32:26] swyx: So to remind people, this is the 4Wars piece that we did as one of our earlier recaps of this year.[00:32:31] swyx: And the belligerents are on the left, journalists, writers, artists, anyone who owns IP basically, New York Times, Stack Overflow, Reddit, Getty, Sarah Silverman, George RR Martin. Yeah, and I think this year we can add Scarlett Johansson to that side of the fence. So anyone suing, open the eye, basically. I actually wanted to get a snapshot of all the lawsuits.[00:32:52] swyx: I'm sure some lawyer can do it. That's the data quality war. On the right hand side, we have the synthetic data people, and I think we talked about Lumna's talk, you know, [00:33:00] really showing how much synthetic data has come along this year. I think there was a bit of a fight between scale. ai and the synthetic data community, because scale.[00:33:09] swyx: ai published a paper saying that synthetic data doesn't work. Surprise, surprise, scale. ai is the leading vendor of non synthetic data. Only[00:33:17] Alessio: cage free annotated data is useful.[00:33:21] swyx: So I think there's some debate going on there, but I don't think it's much debate anymore that at least synthetic data, for the reasons that are blessed in Luna's talk, Makes sense.[00:33:32] swyx: I don't know if you have any perspectives there.[00:33:34] Alessio: I think, again, going back to the reinforcement fine tuning, I think that will change a little bit how people think about it. I think today people mostly use synthetic data, yeah, for distillation and kind of like fine tuning a smaller model from like a larger model.[00:33:46] Alessio: I'm not super aware of how the frontier labs use it outside of like the rephrase, the web thing that Apple also did. But yeah, I think it'll be. Useful. I think like whether or not that gets us the big [00:34:00] next step, I think that's maybe like TBD, you know, I think people love talking about data because it's like a GPU poor, you know, I think, uh, synthetic data is like something that people can do, you know, so they feel more opinionated about it compared to, yeah, the optimizers stuff, which is like,[00:34:17] swyx: they don't[00:34:17] Alessio: really work[00:34:18] swyx: on.[00:34:18] swyx: I think that there is an angle to the reasoning synthetic data. So this year, we covered in the paper club, the star series of papers. So that's star, Q star, V star. It basically helps you to synthesize reasoning steps, or at least distill reasoning steps from a verifier. And if you look at the OpenAI RFT, API that they released, or that they announced, basically they're asking you to submit graders, or they choose from a preset list of graders.[00:34:49] swyx: Basically It feels like a way to create valid synthetic data for them to fine tune their reasoning paths on. Um, so I think that is another angle where it starts to make sense. And [00:35:00] so like, it's very funny that basically all the data quality wars between Let's say the music industry or like the newspaper publishing industry or the textbooks industry on the big labs.[00:35:11] swyx: It's all of the pre training era. And then like the new era, like the reasoning era, like nobody has any problem with all the reasoning, especially because it's all like sort of math and science oriented with, with very reasonable graders. I think the more interesting next step is how does it generalize beyond STEM?[00:35:27] swyx: We've been using O1 for And I would say like for summarization and creative writing and instruction following, I think it's underrated. I started using O1 in our intro songs before we killed the intro songs, but it's very good at writing lyrics. You know, I can actually say like, I think one of the O1 pro demos.[00:35:46] swyx: All of these things that Noam was showing was that, you know, you can write an entire paragraph or three paragraphs without using the letter A, right?[00:35:53] Creative Writing with AI[00:35:53] swyx: So like, like literally just anything instead of token, like not even token level, character level manipulation and [00:36:00] counting and instruction following. It's, uh, it's very, very strong.[00:36:02] swyx: And so no surprises when I ask it to rhyme, uh, and to, to create song lyrics, it's going to do that very much better than in previous models. So I think it's underrated for creative writing.[00:36:11] Alessio: Yeah.[00:36:12] Legal and Ethical Issues in AI[00:36:12] Alessio: What do you think is the rationale that they're going to have in court when they don't show you the thinking traces of O1, but then they want us to, like, they're getting sued for using other publishers data, you know, but then on their end, they're like, well, you shouldn't be using my data to then train your model.[00:36:29] Alessio: So I'm curious to see how that kind of comes. Yeah, I mean, OPA has[00:36:32] swyx: many ways to publish, to punish people without bringing, taking them to court. Already banned ByteDance for distilling their, their info. And so anyone caught distilling the chain of thought will be just disallowed to continue on, on, on the API.[00:36:44] swyx: And it's fine. It's no big deal. Like, I don't even think that's an issue at all, just because the chain of thoughts are pretty well hidden. Like you have to work very, very hard to, to get it to leak. And then even when it leaks the chain of thought, you don't know if it's, if it's [00:37:00] The bigger concern is actually that there's not that much IP hiding behind it, that Cosign, which we talked about, we talked to him on Dev Day, can just fine tune 4.[00:37:13] swyx: 0 to beat 0. 1 Cloud SONET so far is beating O1 on coding tasks without, at least O1 preview, without being a reasoning model, same for Gemini Pro or Gemini 2. 0. So like, how much is reasoning important? How much of a moat is there in this, like, All of these are proprietary sort of training data that they've presumably accomplished.[00:37:34] swyx: Because even DeepSeek was able to do it. And they had, you know, two months notice to do this, to do R1. So, it's actually unclear how much moat there is. Obviously, you know, if you talk to the Strawberry team, they'll be like, yeah, I mean, we spent the last two years doing this. So, we don't know. And it's going to be Interesting because there'll be a lot of noise from people who say they have inference time compute and actually don't because they just have fancy chain of thought.[00:38:00][00:38:00] swyx: And then there's other people who actually do have very good chain of thought. And you will not see them on the same level as OpenAI because OpenAI has invested a lot in building up the mythology of their team. Um, which makes sense. Like the real answer is somewhere in between.[00:38:13] Alessio: Yeah, I think that's kind of like the main data war story developing.[00:38:18] The Data War: GPU Poor vs. GPU Rich[00:38:18] Alessio: GPU poor versus GPU rich. Yeah. Where do you think we are? I think there was, again, going back to like the small model thing, there was like a time in which the GPU poor were kind of like the rebel faction working on like these models that were like open and small and cheap. And I think today people don't really care as much about GPUs anymore.[00:38:37] Alessio: You also see it in the price of the GPUs. Like, you know, that market is kind of like plummeted because there's people don't want to be, they want to be GPU free. They don't even want to be poor. They just want to be, you know, completely without them. Yeah. How do you think about this war? You[00:38:52] swyx: can tell me about this, but like, I feel like the, the appetite for GPU rich startups, like the, you know, the, the funding plan is we will raise 60 million and [00:39:00] we'll give 50 of that to NVIDIA.[00:39:01] swyx: That is gone, right? Like, no one's, no one's pitching that. This was literally the plan, the exact plan of like, I can name like four or five startups, you know, this time last year. So yeah, GPU rich startups gone.[00:39:12] The Rise of GPU Ultra Rich[00:39:12] swyx: But I think like, The GPU ultra rich, the GPU ultra high net worth is still going. So, um, now we're, you know, we had Leopold's essay on the trillion dollar cluster.[00:39:23] swyx: We're not quite there yet. We have multiple labs, um, you know, XAI very famously, you know, Jensen Huang praising them for being. Best boy number one in spinning up 100, 000 GPU cluster in like 12 days or something. So likewise at Meta, likewise at OpenAI, likewise at the other labs as well. So like the GPU ultra rich are going to keep doing that because I think partially it's an article of faith now that you just need it.[00:39:46] swyx: Like you don't even know what it's going to, what you're going to use it for. You just, you just need it. And it makes sense that if, especially if we're going into. More researchy territory than we are. So let's say 2020 to 2023 was [00:40:00] let's scale big models territory because we had GPT 3 in 2020 and we were like, okay, we'll go from 1.[00:40:05] swyx: 75b to 1. 8b, 1. 8t. And that was GPT 3 to GPT 4. Okay, that's done. As far as everyone is concerned, Opus 3. 5 is not coming out, GPT 4. 5 is not coming out, and Gemini 2, we don't have Pro, whatever. We've hit that wall. Maybe I'll call it the 2 trillion perimeter wall. We're not going to 10 trillion. No one thinks it's a good idea, at least from training costs, from the amount of data, or at least the inference.[00:40:36] swyx: Would you pay 10x the price of GPT Probably not. Like, like you want something else that, that is at least more useful. So it makes sense that people are pivoting in terms of their inference paradigm.[00:40:47] Emerging Trends in AI Models[00:40:47] swyx: And so when it's more researchy, then you actually need more just general purpose compute to mess around with, uh, at the exact same time that production deployments of the old, the previous paradigm is still ramping up,[00:40:58] swyx: um,[00:40:58] swyx: uh, pretty aggressively.[00:40:59] swyx: So [00:41:00] it makes sense that the GPU rich are growing. We have now interviewed both together and fireworks and replicates. Uh, we haven't done any scale yet. But I think Amazon, maybe kind of a sleeper one, Amazon, in a sense of like they, at reInvent, I wasn't expecting them to do so well, but they are now a foundation model lab.[00:41:18] swyx: It's kind of interesting. Um, I think, uh, you know, David went over there and started just creating models.[00:41:25] Alessio: Yeah, I mean, that's the power of prepaid contracts. I think like a lot of AWS customers, you know, they do this big reserve instance contracts and now they got to use their money. That's why so many startups.[00:41:37] Alessio: Get bought through the AWS marketplace so they can kind of bundle them together and prefer pricing.[00:41:42] swyx: Okay, so maybe GPU super rich doing very well, GPU middle class dead, and then GPU[00:41:48] Alessio: poor. I mean, my thing is like, everybody should just be GPU rich. There shouldn't really be, even the GPU poorest, it's like, does it really make sense to be GPU poor?[00:41:57] Alessio: Like, if you're GPU poor, you should just use the [00:42:00] cloud. Yes, you know, and I think there might be a future once we kind of like figure out what the size and shape of these models is where like the tiny box and these things come to fruition where like you can be GPU poor at home. But I think today is like, why are you working so hard to like get these models to run on like very small clusters where it's like, It's so cheap to run them.[00:42:21] Alessio: Yeah, yeah,[00:42:22] swyx: yeah. I think mostly people think it's cool. People think it's a stepping stone to scaling up. So they aspire to be GPU rich one day and they're working on new methods. Like news research, like probably the most deep tech thing they've done this year is Distro or whatever the new name is.[00:42:38] swyx: There's a lot of interest in heterogeneous computing, distributed computing. I tend generally to de emphasize that historically, but it may be coming to a time where it is starting to be relevant. I don't know. You know, SF compute launched their compute marketplace this year, and like, who's really using that?[00:42:53] swyx: Like, it's a bunch of small clusters, disparate types of compute, and if you can make that [00:43:00] useful, then that will be very beneficial to the broader community, but maybe still not the source of frontier models. It's just going to be a second tier of compute that is unlocked for people, and that's fine. But yeah, I mean, I think this year, I would say a lot more on device, We are, I now have Apple intelligence on my phone.[00:43:19] swyx: Doesn't do anything apart from summarize my notifications. But still, not bad. Like, it's multi modal.[00:43:25] Alessio: Yeah, the notification summaries are so and so in my experience.[00:43:29] swyx: Yeah, but they add, they add juice to life. And then, um, Chrome Nano, uh, Gemini Nano is coming out in Chrome. Uh, they're still feature flagged, but you can, you can try it now if you, if you use the, uh, the alpha.[00:43:40] swyx: And so, like, I, I think, like, you know, We're getting the sort of GPU poor version of a lot of these things coming out, and I think it's like quite useful. Like Windows as well, rolling out RWKB in sort of every Windows department is super cool. And I think the last thing that I never put in this GPU poor war, that I think I should now, [00:44:00] is the number of startups that are GPU poor but still scaling very well, as sort of wrappers on top of either a foundation model lab, or GPU Cloud.[00:44:10] swyx: GPU Cloud, it would be Suno. Suno, Ramp has rated as one of the top ranked, fastest growing startups of the year. Um, I think the last public number is like zero to 20 million this year in ARR and Suno runs on Moto. So Suno itself is not GPU rich, but they're just doing the training on, on Moto, uh, who we've also talked to on, on the podcast.[00:44:31] swyx: The other one would be Bolt, straight cloud wrapper. And, and, um, Again, another, now they've announced 20 million ARR, which is another step up from our 8 million that we put on the title. So yeah, I mean, it's crazy that all these GPU pores are finding a way while the GPU riches are also finding a way. And then the only failures, I kind of call this the GPU smiling curve, where the edges do well, because you're either close to the machines, and you're like [00:45:00] number one on the machines, or you're like close to the customers, and you're number one on the customer side.[00:45:03] swyx: And the people who are in the middle. Inflection, um, character, didn't do that great. I think character did the best of all of them. Like, you have a note in here that we apparently said that character's price tag was[00:45:15] Alessio: 1B.[00:45:15] swyx: Did I say that?[00:45:16] Alessio: Yeah. You said Google should just buy them for 1B. I thought it was a crazy number.[00:45:20] Alessio: Then they paid 2. 7 billion. I mean, for like,[00:45:22] swyx: yeah.[00:45:22] Alessio: What do you pay for node? Like, I don't know what the game world was like. Maybe the starting price was 1B. I mean, whatever it was, it worked out for everybody involved.[00:45:31] The Multi-Modality War[00:45:31] Alessio: Multimodality war. And this one, we never had text to video in the first version, which now is the hottest.[00:45:37] swyx: Yeah, I would say it's a subset of image, but yes.[00:45:40] Alessio: Yeah, well, but I think at the time it wasn't really something people were doing, and now we had VO2 just came out yesterday. Uh, Sora was released last month, last week. I've not tried Sora, because the day that I tried, it wasn't, yeah. I[00:45:54] swyx: think it's generally available now, you can go to Sora.[00:45:56] swyx: com and try it. Yeah, they had[00:45:58] Alessio: the outage. Which I [00:46:00] think also played a part into it. Small things. Yeah. What's the other model that you posted today that was on Replicate? Video or OneLive?[00:46:08] swyx: Yeah. Very, very nondescript name, but it is from Minimax, which I think is a Chinese lab. The Chinese labs do surprisingly well at the video models.[00:46:20] swyx: I'm not sure it's actually Chinese. I don't know. Hold me up to that. Yep. China. It's good. Yeah, the Chinese love video. What can I say? They have a lot of training data for video. Or a more relaxed regulatory environment.[00:46:37] Alessio: Uh, well, sure, in some way. Yeah, I don't think there's much else there. I think like, you know, on the image side, I think it's still open.[00:46:45] Alessio: Yeah, I mean,[00:46:46] swyx: 11labs is now a unicorn. So basically, what is multi modality war? Multi modality war is, do you specialize in a single modality, right? Or do you have GodModel that does all the modalities? So this is [00:47:00] definitely still going, in a sense of 11 labs, you know, now Unicorn, PicoLabs doing well, they launched Pico 2.[00:47:06] swyx: 0 recently, HeyGen, I think has reached 100 million ARR, Assembly, I don't know, but they have billboards all over the place, so I assume they're doing very, very well. So these are all specialist models, specialist models and specialist startups. And then there's the big labs who are doing the sort of all in one play.[00:47:24] swyx: And then here I would highlight Gemini 2 for having native image output. Have you seen the demos? Um, yeah, it's, it's hard to keep up. Literally they launched this last week and a shout out to Paige Bailey, who came to the Latent Space event to demo on the day of launch. And she wasn't prepared. She was just like, I'm just going to show you.[00:47:43] swyx: So they have voice. They have, you know, obviously image input, and then they obviously can code gen and all that. But the new one that OpenAI and Meta both have but they haven't launched yet is image output. So you can literally, um, I think their demo video was that you put in an image of a [00:48:00] car, and you ask for minor modifications to that car.[00:48:02] swyx: They can generate you that modification exactly as you asked. So there's no need for the stable diffusion or comfy UI workflow of like mask here and then like infill there in paint there and all that, all that stuff. This is small model nonsense. Big model people are like, huh, we got you in as everything in the transformer.[00:48:21] swyx: This is the multimodality war, which is, do you, do you bet on the God model or do you string together a whole bunch of, uh, Small models like a, like a chump. Yeah,[00:48:29] Alessio: I don't know, man. Yeah, that would be interesting. I mean, obviously I use Midjourney for all of our thumbnails. Um, they've been doing a ton on the product, I would say.[00:48:38] Alessio: They launched a new Midjourney editor thing. They've been doing a ton. Because I think, yeah, the motto is kind of like, Maybe, you know, people say black forest, the black forest models are better than mid journey on a pixel by pixel basis. But I think when you put it, put it together, have you tried[00:48:53] swyx: the same problems on black forest?[00:48:55] Alessio: Yes. But the problem is just like, you know, on black forest, it generates one image. And then it's like, you got to [00:49:00] regenerate. You don't have all these like UI things. Like what I do, no, but it's like time issue, you know, it's like a mid[00:49:06] swyx: journey. Call the API four times.[00:49:08] Alessio: No, but then there's no like variate.[00:49:10] Alessio: Like the good thing about mid journey is like, you just go in there and you're cooking. There's a lot of stuff that just makes it really easy. And I think people underestimate that. Like, it's not really a skill issue, because I'm paying mid journey, so it's a Black Forest skill issue, because I'm not paying them, you know?[00:49:24] Alessio: Yeah,[00:49:25] swyx: so, okay, so, uh, this is a UX thing, right? Like, you, you, you understand that, at least, we think that Black Forest should be able to do all that stuff. I will also shout out, ReCraft has come out, uh, on top of the image arena that, uh, artificial analysis has done, has apparently, uh, Flux's place. Is this still true?[00:49:41] swyx: So, Artificial Analysis is now a company. I highlighted them I think in one of the early AI Newses of the year. And they have launched a whole bunch of arenas. So, they're trying to take on LM Arena, Anastasios and crew. And they have an image arena. Oh yeah, Recraft v3 is now beating Flux 1. 1. Which is very surprising [00:50:00] because Flux And Black Forest Labs are the old stable diffusion crew who left stability after, um, the management issues.[00:50:06] swyx: So Recurve has come from nowhere to be the top image model. Uh, very, very strange. I would also highlight that Grok has now launched Aurora, which is, it's very interesting dynamics between Grok and Black Forest Labs because Grok's images were originally launched, uh, in partnership with Black Forest Labs as a, as a thin wrapper.[00:50:24] swyx: And then Grok was like, no, we'll make our own. And so they've made their own. I don't know, there are no APIs or benchmarks about it. They just announced it. So yeah, that's the multi modality war. I would say that so far, the small model, the dedicated model people are winning, because they are just focused on their tasks.[00:50:42] swyx: But the big model, People are always catching up. And the moment I saw the Gemini 2 demo of image editing, where I can put in an image and just request it and it does, that's how AI should work. Not like a whole bunch of complicated steps. So it really is something. And I think one frontier that we haven't [00:51:00] seen this year, like obviously video has done very well, and it will continue to grow.[00:51:03] swyx: You know, we only have Sora Turbo today, but at some point we'll get full Sora. Oh, at least the Hollywood Labs will get Fulsora. We haven't seen video to audio, or video synced to audio. And so the researchers that I talked to are already starting to talk about that as the next frontier. But there's still maybe like five more years of video left to actually be Soda.[00:51:23] swyx: I would say that Gemini's approach Compared to OpenAI, Gemini seems, or DeepMind's approach to video seems a lot more fully fledged than OpenAI. Because if you look at the ICML recap that I published that so far nobody has listened to, um, that people have listened to it. It's just a different, definitely different audience.[00:51:43] swyx: It's only seven hours long. Why are people not listening? It's like everything in Uh, so, so DeepMind has, is working on Genie. They also launched Genie 2 and VideoPoet. So, like, they have maybe four years advantage on world modeling that OpenAI does not have. Because OpenAI basically only started [00:52:00] Diffusion Transformers last year, you know, when they hired, uh, Bill Peebles.[00:52:03] swyx: So, DeepMind has, has a bit of advantage here, I would say, in, in, in showing, like, the reason that VO2, while one, They cherry pick their videos. So obviously it looks better than Sora, but the reason I would believe that VO2, uh, when it's fully launched will do very well is because they have all this background work in video that they've done for years.[00:52:22] swyx: Like, like last year's NeurIPS, I already was interviewing some of their video people. I forget their model name, but for, for people who are dedicated fans, they can go to NeurIPS 2023 and see, see that paper.[00:52:32] Alessio: And then last but not least, the LLMOS. We renamed it to Ragops, formerly known as[00:52:39] swyx: Ragops War. I put the latest chart on the Braintrust episode.[00:52:43] swyx: I think I'm going to separate these essays from the episode notes. So the reason I used to do that, by the way, is because I wanted to show up on Hacker News. I wanted the podcast to show up on Hacker News. So I always put an essay inside of there because Hacker News people like to read and not listen.[00:52:58] Alessio: So episode essays,[00:52:59] swyx: I remember [00:53:00] purchasing them separately. You say Lanchain Llama Index is still growing.[00:53:03] Alessio: Yeah, so I looked at the PyPy stats, you know. I don't care about stars. On PyPy you see Do you want to share your screen? Yes. I prefer to look at actual downloads, not at stars on GitHub. So if you look at, you know, Lanchain still growing.[00:53:20] Alessio: These are the last six months. Llama Index still growing. What I've basically seen is like things that, One, obviously these things have A commercial product. So there's like people buying this and sticking with it versus kind of hopping in between things versus, you know, for example, crew AI, not really growing as much.[00:53:38] Alessio: The stars are growing. If you look on GitHub, like the stars are growing, but kind of like the usage is kind of like flat. In the last six months, have they done some[00:53:4

Im Dezember wird es höchste Zeit, das Depot kritisch durchzuschauen und auch steuerlich zu optimieren. Tech-Ikone Pip Klöckner, der in dieser Woche den urlaubenden Deffner ersetzt, analysiert zusammen mit Zschäpitz die Welt der Tech-Werte und verrät, bei welchen Titeln Ihr besser Gewinne mitnehmt, Verluste realisiert oder weiter stoisch anspart. Weitere Themen: - Dax 20.000 Punkte – wie der Rekord im stagnierenden Deutschland zustande gekommen ist und was er für Sparer bedeuet - D-Day-Papier der FDP – Was das dilettantische Dokument und die mediale Berichterstattung für die Liberalen bedeutet - Angriff auf Nvidia – was die KI-Chip-Startups Groq, Cerebras, Tenstorrent können und wie Anleger mitmischen - Performance-Treiber Gym – warum der Deadlift-Index seit 2023 ganze 140 Prozent gemacht hat - Biden begnadigt eigenen Sohn – welche Folgen die umfassende Amnestie von Hunter Biden für die Demokratie hat - 10XDNA – wie der Fonds von Frank Thelen wirklich abgeschnitten hat DEFFNER & ZSCHÄPITZ sind wie das wahre Leben. Wie Optimist und Pessimist. Im wöchentlichen WELT-Podcast diskutieren und streiten die Journalisten Dietmar Deffner und Holger Zschäpitz über die wichtigen Wirtschaftsthemen des Alltags. Schreiben Sie uns an: wirtschaftspodcast@welt.de Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutzerklärung: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

This episode is sponsored by Shopify.  Shopify is a commerce platform that allows anyone to set up an online store and sell their products. Whether you're selling online, on social media, or in person, Shopify has you covered on every base. With Shopify you can sell physical and digital products. You can sell services, memberships, ticketed events, rentals and even classes and lessons.   Sign up for a $1 per month trial period at http://shopify.com/eyeonai In this episode of the Eye on AI podcast, Andrew D. Feldman, Co-Founder and CEO of Cerebras Systems, unveils how Cerebras is disrupting AI inference and high-performance computing.   Andrew joins Craig Smith to discuss the groundbreaking wafer-scale engine, Cerebras' record-breaking inference speeds, and the future of AI in enterprise workflows. From designing the fastest inference platform to simplifying AI deployment with an API-driven cloud service, Cerebras is setting new standards in AI hardware innovation.   We explore the shift from GPUs to custom architectures, the rise of large language models like Llama and GPT, and how AI is driving enterprise transformation. Andrew also dives into the debate over open-source vs. proprietary models, AI's role in climate mitigation, and Cerebras' partnerships with global supercomputing centers and industry leaders.   Discover how Cerebras is shaping the future of AI inference and why speed and scalability are redefining what's possible in computing.   Don't miss this deep dive into AI's next frontier with Andrew Feldman.   Like, subscribe, and hit the notification bell for more episodes! Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Intro to Andrew Feldman & Cerebras Systems (00:43) The rise of AI inference (03:16) Cerebras' API-powered cloud (04:48) Competing with NVIDIA's CUDA (06:52) The rise of Llama and LLMs (07:40) OpenAI's hardware strategy (10:06) Shifting focus from training to inference (13:28) Open-source vs proprietary AI (15:00) AI's role in enterprise workflows (17:42) Edge computing vs cloud AI (19:08) Edge AI for consumer apps (20:51) Machine-to-machine AI inference (24:20) Managing uncertainty with models (27:24) Impact of U.S.–China export rules (30:29) U.S. innovation policy challenges (33:31) Developing wafer-scale engines (34:45) Cerebras' fast inference service (37:40) Global partnerships in AI (38:14) AI in climate & energy solutions (39:58) Training and inference cycles (41:33) AI training market competition  

- Supercocmputing-24 conference starts today - TSMC, CHIPS Act, semiconductor demand - Sandia National Lab and Cerebras [audio mp3="https://orionx.net/wp-content/uploads/2024/11/HPCNB_20241118.mp3"][/audio] The post HPC News Bytes – 20241118 appeared first on OrionX.net.

Quand Elon Musk, Sam Altman et leurs partenaires lançaient OpenAI en 2015, leur objectif semblait limpide : créer une intelligence artificielle générale (AGI) bénéfique pour l'humanité. Mais derrière cette ambition idéaliste se cachaient des tensions, des ambitions personnelles et des luttes de pouvoir. Des e-mails internes dévoilent une atmosphère électrique. Ilya Sutskever, alors scientifique en chef, exprimait déjà ses doutes face à Elon Musk, l'accusant de viser un « contrôle absolu sur l'AGI ». Une crainte de « dictature technologique » qui illustre les désaccords profonds. Parmi les idées discutées, un projet audacieux de rachat du fabricant de puces Cerebras par Tesla reflète l'ingéniosité, mais aussi les divisions stratégiques. Microsoft, déjà intéressé, avait proposé 60 millions de dollars en ressources cloud. Musk, toujours méfiant, refusa l'offre initiale, redoutant de devenir un simple outil marketing pour la firme de Redmond. Par ailleurs, Andrej Karpathy imaginait une intégration d'OpenAI à Tesla, avec la promesse de décupler la valeur de l'entreprise. Un scénario avorté, mais révélateur de l'audace des esprits de la Silicon Valley. Initialement à but non lucratif, OpenAI a finalement adopté un modèle commercial, provoquant la colère de Musk, qui s'en éloigna avant de lancer des poursuites judiciaires. Pourtant, ce changement a conduit à un succès colossal : OpenAI est aujourd'hui valorisée à 157 milliards de dollars et son chatbot ChatGPT est utilisé par 250 millions de personnes chaque jour. Mais ce triomphe cache une histoire tumultueuse : celle de visions divergentes, de négociations tendues et d'ego démesurés. L'histoire d'OpenAI montre que derrière chaque révolution technologique, il y a des batailles, autant idéologiques que stratégiques, qui façonnent le destin de nos outils les plus puissants. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Addison Snell and Kevin Jackson analyze the top stories from the Hot Chips conference.

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This week, Shashank and Mark sit down with SambaNova Systems, a leading AI chip startup competing with tech giants like Nvidia and Cerebras. Joined by SambaNova's Director of Machine Learning, Urmish, and founding team member Raghu, they explore how SambaNova's reconfigurable data flow architecture is changing the game in AI inference and training. They discuss the company's unique hardware, fast inference capabilities, memory optimizations, and what the future holds for AI chip innovation. Learn what it takes to build high-performance AI systems and where the industry is headed next!

In this week's episode, Shashank and Mark dive deep into Nvidia's current dominance in the AI hardware space and the potential challengers on the horizon. As companies like AMD, Cerebras, and SambaNova work to chip away at Nvidia's massive lead, is it time to reconsider your portfolio? Plus, we explore cutting-edge applications of generative AI in DNA analysis, gene therapy, and how AI agents could revolutionize everything from medical treatments to productivity tools.

In this episode of Market Mondays, we dive deep into a wide range of topics. We discuss the latest from SpaceX, Tesla's Robo Taxi day, and the Tesla Optimus robot. We explore whether owning all of Elon Musk's companies could be a smart hedge for the future and the current outlook for Bitcoin this month. We analyze why Genmab's stock hasn't moved despite strong fundamentals, and the incredible rise of ADMA stock—will it keep climbing or has it peaked? We also break down Nvidia's DGX B200, the impact of AMD's new chip, and consider if Cerebras can truly compete with NVIDIA. For long-term investors, we talk about stocks like VNO, and the potential of TSM through earnings, as well as the future of XRP and the comparison between Amazon and Mercado Libre.With the uncertainty in today's world, what does the market outlook look like for the end of the year? We also discuss potential entry points for Boeing, the long-term prospects for IONQ, and Microsoft's earnings. We wrap up with a look at hotel stocks like Marriott and Hilton, as well as the DOJ's move against Google and what it means for the stock.We also had a special guest, Congressman Byron Donalds, who shared insights on the Republican economic plan, Trump's election, black male voters, and more.#MarketMondays #Tesla #SpaceX #Bitcoin #ElonMusk #Stocks #Investing #Nvidia #AMD #CongressmanByronDonalds #Economy #Google #StockMarket #Microsoft #Boeing #TSMSupport this podcast at — https://redcircle.com/marketmondays/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Our 185th episode with a summary and discussion of last week's big AI news! With hosts Andrey Kurenkov and guest host Gavin Purcell from the AI for Humans podcast. Read out our text newsletter and comment on the podcast at https://lastweekin.ai/. If you would like to become a sponsor for the newsletter, podcast, or both, please fill out this form. Email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.ai In this episode: Meta's MovieGen introduces innovative features in AI video generation, alongside OpenAI's real-time speech API and expanded ChatGPT capabilities. Mio's foundation model and Apple's Depth Pro enhance multimodal AI inputs and precise 3D imaging for AR, VR, and robotics. Microsoft and OpenAI's strategic advancements highlight significant financial moves and AI enhancements, including Microsoft's enhanced Copilot. AI policy discussions intensify as California's vetoed bill sparks debates on regulation, alongside Google's $1 billion investment to expand AI infrastructure in Thailand.   Timestamps + Links: (00:00:00) Intro / Banter (00:02:51) Response to listener comments / corrections Tools & Apps(00:03:48) Meta announces Movie Gen, an AI-powered video generator (00:14:28) OpenAI launches new ‘Canvas' ChatGPT interface tailored to writing and coding projects (00:19:31) OpenAI's DevDay brings Realtime API and other treats for AI app developers (00:24:43) Black Forest Labs releases Flux 1.1 Pro and an API (00:28:30) Microsoft gives Copilot a voice and vision in its biggest redesign yet (00:32:36) Pika 1.5 is now live — AI video generator just got major upgrades Applications & Business(00:37:49) OpenAI closes the largest VC round of all time (00:45:23) Google brings ads to AI Overviews as it expands AI's role in search (00:51:05) Anthropic hires OpenAI co-founder Durk Kingma (00:51:49) OpenAI's newest creation is raising shock, alarm, and horror among staffers: a new logo (00:53:45) Waymo to add Hyundai EVs to robotaxi fleet under new multiyear deal (00:57:28) Cerebras, an A.I. Chipmaker Trying to Take On Nvidia, Files for an I.P.O. (00:59:18) Y Combinator is being criticized after it backed an AI startup that admits it basically cloned another AI startup Research & Advancements(01:03:30) Were RNNs All We Needed? (01:06:52) MIO: A Foundation Model on Multimodal Tokens (01:09:20) Apple releases Depth Pro, an AI model that rewrites the rules of 3D vision Policy & Safety(01:13:08) California Governor Vetoes Sweeping A.I. Legislation (01:18:02) Judge blocks California's new AI law in case over Kamala Harris deepfake Musk reposted (01:20:41) Google to invest $1 billion in Thailand to build a data center and accelerate AI growth Synthetic Media & Art(01:21:58) AI reading coach startup Ello now lets kids create their own stories (01:25:13) Outro

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Are there any chip makers that can compete with Nvidia? This week, Next Generation Internet Director of Research Frank Downing, and Autonomous Technology and Robotics Director of Research Sam Korus discuss the S1 filing of Cerebras and what that might mean for the AI hardware space.If you know ARK, then you probably know about our long-term research projections, like estimating where we will be 5-10 years from now! But just because we are long-term investors, doesn't mean we don't have strong views and opinions on breaking news. In fact, we discuss and debate this every day. So now we're sharing some of these internal discussions with you in our new video series, “The Brainstorm”, a co-production from ARK and Public.com. Tune in every week as we react to the latest in innovation. Here and there we'll be joined by special guests, but ultimately this is our chance to join the conversation and share ARK's quick takes on what's going on in tech today.Key Points From This Episode:00:00:00 Intro00:00:52 An Overview of Cerebras, and Their Plans to IPOFor more updates on Public.com:Website: https://public.com/YouTube: @publicinvestTwitter: https://twitter.com/public The Rundown: https://podcasts.apple.com/us/podcast/the-rundown/id1726048251To learn more about ARK: https://arkinv.st/ARKInvest For more updates, follow us on:- Twitter: https://arkinv.st/Twitter- LinkedIn: https://arkinv.st/LinkedIn- Facebook: https://arkinv.st/Facebook- Instagram: https://arkinv.st/InstagramDisclosure: http://arkinv.st/39rzF94

週一天下零時差關注以下財經大事： 一、台新金併購新光金，這禮拜臨時股東會決戰，還有三大變數 二、AI晶片新秀Cerebras準備上市，會撼動輝達龍頭地位嗎？ 三、鴻海科技日登場，今年亮點是什麼？ 文：伍芬婕、鄧凱元、辜樹仁 製作團隊：黃柏維 ＊立即下載天下雜誌App，享受更好的閱聽體驗：https://bit.ly/3PqHlNc ＊訂閱天下全閱讀：https://bit.ly/3STpEpV ＊意見信箱：bill@cw.com.tw -- Hosting provided by SoundOn

Send us a textSubscribe to AG Dillon Pre-IPO Stock Research at agdillon.com/subscribe;- Wednesday = secondary market valuations, revenue multiples, performance, index fact sheets- Saturdays = pre-IPO news and insights, webinar replays00:07 | OpenAI Secures $6.6B Round, Expands Capabilities- Introduced real-time voice assistant capabilities through API for businesses- New API features and developer events announced to engage 3M+ developers- Workforce doubled to 1,700 employees from 770 in November 2023- Raised $6.6B funding round at $157B valuation (primary round)- Secured $4B revolving credit line with potential to expand to $10B in liquidity01:31 | Cerebras Files for IPO with 220% Revenue Growth- Specializes in AI hardware, particularly wafer-scale chips for AI training- Revenue of $136.4M in H1 2024 vs. $78.7M for full year 2023- Faces customer concentration risk with 87% of H1 2024 revenue from G42- Raised $715M in venture capital, valuation at $6.7B (secondary), up 124% since June 202403:11 | Anthropic Hires Former OpenAI Co-Founder, Valued at $25.2B- Added Durk Kingma, co-founder of OpenAI, to its growing talent pool- Continues attracting top talent from OpenAI and other tech giants- Positioned as a leader in responsible AI development under CEO Dario Amodei- Secondary market valuation: $25.2B (+40% vs Jan 2024), rumored to be raising at $40B04:09 | Flexport Restructures to Improve Profitability, Valuation Plummets- Logistics tech company plans to sublease warehouse space, integrate sales teams- Faced layoffs (20% in October, 15% in January) and loss of key customers like Crocs- Aiming for global expansion by 2027 with an asset-light model- Secondary market valuation: $1.95B (-75% vs Jan 2022 primary round)05:15 | Epic Games Files Antitrust Lawsuit Against Google and Samsung- Filed lawsuit over Samsung's "Auto Blocker" feature, alleging it blocks third-party app stores- Claims Google Play's dominance is reinforced by Samsung's preinstalled feature- Lawsuit follows mixed success in earlier legal battles against Google and Apple- Secondary market valuation: $16.6B (-26% vs Feb 2024 round)06:20 | CoreWeave Raises $421M, Valued at $23B- Cloud provider specializing in Nvidia GPUs for AI computing- Raised $421M in Series B, backed by Nvidia, Magnetar Capital, Blackstone- Secured $2.3B in debt financing in August 2024- Appointed former Google Cloud VP of finance as CFO, signaling potential IPO- Currently raising a primary round at a $23B valuation07:34 | Pre-IPO Stock Market Weekly Performance- agdillon.com/subscribe to receive weekly pdf report in your inbox- Pre-IPO +0.77% for week, +67.25% for last 1yr- Up week: Synk +17.1%, OpenAI +13.7%, Canva +6.9%, Rippling +5.9%, Notion +5.3%- Down week: xAI -5.3%, CoreWeave -2.2%, Cohere -1.7%, Epic Games -1.6%, Neuralink -0.6%- Top valuations: ByteDance $301b, SpaceX $229b, OpenAI $157b, Stripe $84b, Databricks $46b08:20 | Pre-IPO Stock Vintage Index Weekly Performance- agdillon.com/subscribe to receive weekly pdf report in your inbox- 2024 Vintage Index top contributors since inception: Revolut +201%, Rippling +113%, Anduril +76%, OpenAI +56%, Klarna +40% … the 2024 Vintage Index is up 63% since its inception, or year to date 2024- Key metric averages for all Vintage Indexes 5 years old or older…472% cumulative return since inception58% realized, distributed to investors5.72 TVPI; 3.31 DPI, 2.41 RVPI4.1 years to return the fund

Cerebras is approaching chipmaking differently, can it carve out a space for itself in an industry of titans?   (00:45) Asit Sharma and Jason Moser discuss: The dock workers strike, its daily cost, and the industries it could impact most. Upcoming AI chip IPO Cerebras, and how the company is approaching high-performance chips differently than the competition.  Fresh earnings from: Nike, Paychex, and McCormick. (19:04) October 2024 marks 20 years of Rule Breakers at The Motley Fool. To celebrate, we're airing a portion of a conversation with David and former Rule Breakers analyst Matt Argersinger from our premium Epic Opportunities podcast. David fielded questions from our investing team about his own investing process, reflected on his 6 traits of a Rule Breaker and the companies that the framework led him to follow.  (35:56) Jason and Asit break down two stocks on their radar: Pepsico and Joby Aviation. Stocks discussed: NKE, PAYX, MCK, PEP, JOBY Motley Fool Epic members can access the full conversation with David: Here on the TMF site (login required) On Spotify here after linking their accounts  Host: Dylan Lewis Guests: Jason Moser, Asit Sharma, David Gardner, Rick Engdahl Engineers: Rick Engdahl, Austin Morgan  Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we explored OpenAI's monumental $157 billion valuation following its $6.6 billion fundraising and the strategic implications of restructuring its governance model. We also cover Cerebras' IPO filing as the AI chipmaker prepares to challenge Nvidia's dominance in the market. On the blockchain front, we discussed the latest developments in the Bitcoin market and how short-term holders can react to global geopolitical tensions. Finally, we reviewed trends in high-performance computing and AI infrastructure. Remember To Stay Current! To learn more, visit us on the web at https://www.morgancreekcap.com/morgan-creek-digital. To speak to a team member or sign up for additional content, please email mcdigital@morgancreekcap.com

Do we have the first IPO of the AI era? Do we have the first AI model beyond the transformer architecture? Microsoft has a bunch of new AI tools inside Windows. We try to explain that whole controversy around PearAI. And what about that NotebookLM feature that lets you create a two-hander podcast out of any text.Links:AI chipmaker Cerebras files for IPO to take on Nvidia (CNBC)MIT spinoff Liquid debuts non-transformer AI models and they're already state-of-the-art (VentureBeat)Microsoft Copilot can now read your screen, think deeply, and speak aloud to you (TechCrunch)Oura Nears $500 Million in Annual Revenue and Readies New Ring (Bloomberg)Y Combinator is being criticized after it backed an AI startup that admits it basically cloned another AI startup (TechCrunch)NotebookLM's automatically generated podcasts are surprisingly effective (Simon Willison's Blog)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

welcome to wall-e's tech briefing for tuesday, october 1! uncover the latest tech developments: 11x.ai's $50m series b funding: 11x.ai, developers of ai sales representatives, garners $50 million in series b funding led by andreessen horowitz, now valued at approximately $350 million. google's $1b investment in thailand: google commits $1 billion to establish a new data center in chonburi, enhancing thailand's cloud infrastructure and digital economy. cerebras systems ipo: ai chip startup cerebras systems files for an initial public offering to challenge industry giant nvidia with its advanced wse-3 chip. y combinator & pearai controversy: y combinator faces backlash for funding pearai, an ai startup accused of cloning code from another ai coding editor, leading to scrutiny over vc due diligence practices. tune in for more tech insights tomorrow!

Ändert Salesforce, wie wir in Zukunft Software bezahlen? Softbank investiert $500 Millionen in OpenAI. Mozilla stellt eine Vision für öffentliche KI vor. Die E-Learning-Plattform Udemy möchte auf den Kursen ihrer Lehrer lernen. Wann passen Roboter auf Kinder auf? Cerebras machte 87 % des Umsatzes in H1 2024 mit einem Kunden. Liquid Foundation Models Werbung: Klicke auf 'Teilnehmen' und streame den HUG HR-Event von Personio am 10. Oktober ab 10 Uhr von deinem LinkedIn-Feed. Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Intro (00:05:55) OpenAI (00:12:15) Salesforce goes consumption-based pricing (00:21:00) Mozilla Foundation Public AI (00:23:20) AI Start-Ups Umsatzwachstum (00:32:15) Udemy, Roboter, Tech Adoption Geschwindigkeit (00:57:30) Waymo (01:05:00) Cerebras (01:12:00) Epic Games (01:14:30) Cum-Ex (01:18:00) Elon (01:25:00) Liquid AI Snownotes Salesforce LinkedIn Post von Francesco De Camilli Udemy teachers are surprised they have been opted into having their classes scraped for AI training 404 Media AI start-ups generate money faster than past hyped tech companies Financial Times Liquid AI debuts new LFM-based models siliconAngle

Eric Vishria is a General Partner @ Benchmark Capital, one of the world's leading venture firms. At Benchmark, Eric has served on over 10 boards including Confluent (CFLT), Amplitude (AMPL), Benchling, Contentful, Cerebras and several other private companies. Prior to joining Benchmark, Eric was the Co‐Founder and CEO of RockMelt, acquired by Yahoo in 2013. In Today's Episode with Eric Vishria We Discuss: 1. How to Make Money Investing in AI Today: How does Eric think through where value will accrue in the stack between chips, models and applications? Why does Eric believe foundation models are the fastest commoditising asset in history? Why does Eric believe that Nvidia will not be the only game in town in the next 3-5 years? 2. How to Invest in AI Application Layer Successfully: How does Eric analyse between a standalone and deep product vs a product that foundation model will commodities and incorporate into their feature set? How does Eric differentiate between the 10 different players all going after customer service, or sales tools or data analyst products etc? How does Eric analyse the quality of revenue of these AI application layer companies? What does he mean when he describes their revenue as "sugar high"? 3. How the Best VC Firm Makes Decisions: What is the decision-making process for all new deals in Benchmark? As specifically as possible, how does the voting process inside Benchmark work? What deal was the most contentious deal that went through? What did the partnership learn? How has the Benchmark decision-making process changed over 10 years? 4. Does AI Break Venture Capital Models: Does the price of AI deals and size of their rounds break the Benchmark model? Will foundation model companies all be acquired by the larger cloud providers? Unless multiples reflate in the public markets, does venture as an asset class have hope? Why does AI make paying ludicrously high prices potentially rational?  

Our 181st episode with a summary and discussion of last week's big AI news! With hosts Andrey Kurenkov and Jeremie Harris Read out our text newsletter and comment on the podcast at https://lastweekin.ai/ If you would like to become a sponsor for the newsletter, podcast, or both, please fill out this form. Email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.ai In this episode: - Google's AI advancements with Gemini 1.5 models and AI-generated avatars, along with Samsung's lithography progress.  - Microsoft's Inflection usage caps for Pi, new AI inference services by Cerebrus Systems competing with Nvidia.  - Biases in AI, prompt leak attacks, and transparency in models and distributed training optimizations, including the 'distro' optimizer.  - AI regulation discussions including California's SB1047, China's AI safety stance, and new export restrictions impacting Nvidia's AI chips. Timestamps + Links: (00:00:00) Intro / Banter (00:03:08)Response to listener comments / corrections Tools & Apps(00:09:19) Google's custom AI chatbots have arrived (00:12:52) Google releases three new experimental AI models (00:17:14) Google Gemini will let you create AI-generated people again (00:22:32) Five months after Microsoft hired its founders, Inflection adds usage caps to Pi (00:26:42:) Plaud takes a crack at a simpler AI pin Applications & Business(00:30:31) Cerebras Systems throws down gauntlet to Nvidia with launch of ‘world's fastest' AI inference service (00:41:06) Nvidia announces $50 billion stock buyback (00:46:24) OpenAI in talks to raise funding that would value it at more than $100 billion (00:50:44) OpenAI Aims to Release New AI Model, ‘Strawberry,' in Fall (00:52:53) 3 Co-Founders Leave French AI Startup H Amid ‘Operational Differences' (00:57:29) Samsung to Adopt High-NA Lithography Alongside Intel, Ahead of TSMC (01:02:11) Unitree's $16,000 G1 could become the first mainstream humanoid robot Projects & Open Source(01:04:59) Meta leads open-source AI boom, Llama downloads surge 10x year-over-year (01:09:08) A_Preliminary_Report_on_DisTrO. Research & Advancements(01:13:56) Diffusion Models Are Real-Time Game Engines (01:23:18) LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet (01:32:21) Interviewing AI researchers on automation of AI R&D (01:40:33) Anthropic releases AI model system prompts, winning praise for transparency Policy & Safety(01:47:12) U.S. AI Safety Institute Signs Agreements Regarding AI Safety Research, Testing and Evaluation With Anthropic and OpenAI (01:50:46) China's Views on AI Safety Are Changing—Quickly (01:56:27) Poll: 7 in 10 Californians Support SB1047, Will Blame Governor Newsom for AI-Enabled Catastrophe if He Vetoes (02:01:31) Elon Musk voices support for California bill requiring safety tests on AI models (02:03:55) Chinese Engineers Reportedly Accessing NVIDIA's High-End AI Chips Through Decentralized “GPU Rental Services” (02:08:25) U.S. gov't tightens China restrictions on supercomputer component sales Synthetic Media & Art(02:11:13) Actors Say AI Voice-Over Generator ElevenLabs Cloned Likenesses (02:14:06) Outro

In this week's episode of the Generative AI Meetup Podcast, join hosts Mark and Shashank as they delve into the groundbreaking developments from Cerebras. Discover how their new inference solution is setting new benchmarks for speed and efficiency in AI model responses. The episode features insights into the technology behind these advancements and explores potential impacts on various industries. Tune in to get a glimpse of the future of AI, made faster and more accessible than ever.

A race to deliver the fastest AI system is emerging, resulting in a crop of new companies with innovative approaches to AI processing. Cerebras returns to the Tech Disruptors podcast studios to discuss the broadening AI market opportunity for its wafer scale engine (WSE) chip. CEO Andrew Feldman sits down with Bloomberg Intelligence Senior Hardware analyst Woo Jin Ho to discuss the future evolution of AI compute and how Cerebras aims to leverage the WSE-3 processor to unlock the $40 billion inference market by delivering AI responses 20x faster at one-fifth price of hyperscale cloud.

En el episodio más reciente del podcast «Inteligencia Artificial», analizamos las últimas innovaciones en el mundo de la IA. Hoy exploramos tres herramientas clave que están transformando el campo de la inteligencia artificial: Ideogram v2, Grok 2, y Cerebras. Si estás interesado en el presente y futuro de la IA, sigue leyendo para conocer las […] Origen

Andy Jassy freut sich über die Reduzierung von 50 Entwicklertagen auf wenige Stunden bei der Upgradezeit für Java-Anwendungen. Warum glaubt Klarna noch weiter sehr viele Leute entlassen zu können? Cerebras Systems hat einen neuen KI-Inferenz-Dienst gestartet, der laut dem Unternehmen 20-mal schneller als vergleichbare cloud-basierte Dienste mit Nvidias leistungsstärksten GPUs ist und deutlich niedrigere Kosten pro Token bietet. Tether, das Unternehmen hinter der gleichnamigen Kryptowährung, investiert angeblich laut WSJ mit Hilfe von Christian Angermayer in scheinbar unverwandte Unternehmen wie Northern Data und BlackRock Neurotech. Werbung: Melde dich jetzt an zum Webinar von LIQID: „Warum Profis auf Venture Capital setzen“ am 7. September um 11 Uhr. Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Intro (00:09:30) Klarna (00:22:00) Nvidia Friede Freude AI Kuchen (00:35:00) AI Einsatzgebiet Andrew Jessy Amazon Q (00:40:10) Tether (00:44:30) Uber FSD (00:45:20) Yelp (00:47:00) Reddit (00:55:35) Crowdstrike (01:00:45) Salesforce (01:04:00) Birkenstock Shownotes: Klarna: Handelszeitung, Tech.eu, Pips LinkedIn OpenAI Funding: WSJ Cerebras: X, Siliconangel Angermayer Tether: WSJ Uber FSD: Reuters Yelp: The Information

I explain why everyone has been posting strawberries in AI circles. It's cause of a potential new breakthrough at OpenAI. Cerebras launches the first new AI chip competition to Nvidia. China has reportedly burrowed into US ISPs. And continuing interesting details pouring out of that Pavel Durov situation.Sponsors:Timeline.com/rideLinks:OpenAI Shows ‘Strawberry' AI to the Feds and Uses It to Develop ‘Orion' (The Information)OpenAI Races to Launch ‘Strawberry' Reasoning AI to Boost Chatbot Business (The Information)Cerebras Systems throws down gauntlet to Nvidia with launch of ‘world's fastest' AI inference service (SiliconAngle)Chinese government hackers penetrate U.S. internet providers to spy (Washington Post)Google Meet's automatic AI note-taking is here (The Verge)Instagram adds what photos have always needed: words (The Verge)Telegram Founder Was Wooed and Targeted by Governments (WSJ)Can Tech Executives Be Held Responsible for What Happens on Their Platforms? (NYTimes)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This week kicked off the annual Hot Chips conference and AI has once again dominated the conversation. There were a lot of stories to talk about, from wafer scale chips to silicon photonics, Cerebras' giant leap into AI Inferencing, Microsoft Azure's MAIA 100 AI Accelerator, IBM's On-Chip DPU, Broadcom's AI Compute ASIC, Intel's Guadi 3, and more. Let's dive in on this episode of The Rundown. Time Stamps: 0:00 - Welcome to The Rundown 0:54 - Kioxa Files for IPO 4:18 - Cisco to Acquire Robust Intelligence 7:46 - NVIDIA Previews Blackwell Datacenter GPUs 11:56 - Apica Ascent adds Multiple Agent Central Telemetry Data Management 13:37 - VMware Reveals VCF9 17:51 - Veeam Overtakes Veritas in Market Share 20:58 - Announcements from Hot Chips 40:57 - The Weeks Ahead 43:29 - Thanks for Watching Hosts: Tom Hollingsworth: https://www.twitter.com/NetworkingNerd Stephen Foskett: https://www.twitter.com/SFoskett Follow Gestalt IT Website: https://www.GestaltIT.com/ Twitter: https://www.twitter.com/GestaltIT LinkedIn: https://www.linkedin.com/company/Gestalt-IT Tags: #Rundown, #HotChips2024, #AI, @Kioxa, @Cisco, @RobustHQ, @NVIDIA, #Blackwell, @Apica, @VMware, @Boradcom, @VeritasTechLLC, @Veeam, @Cerebras, @Microsoft, @Azure, @IBM, @Intel, @IntelBusiness, @SFoskett, @NetworkingNerd, @GestaltIT, @TheFuturumGroup,

Brought to you by our daily financial news show - subscribe here:  The Finimize Daily Brief (Spotify), The Finimize Daily Brief (Apple Podcasts).US jobs data suggested that the country is starting to strain under high interest rates, while Nvidia challenger Cerebras filed for an IPO.Today's stories:The US Created Fewer Jobs Than Expected, And Unemployment Ticked Up TooChip Designer And Nvidia Rival Cerebras Filed For An IPOTry Finimize Premium 

Send us a Text Message.WEBINAR ANNOUNCEMENT: AG Dillon will be hosting a webinar on August 7 at 1:30pm ET titled An Introduction to the AG Dillon Top 10 Pre-IPO Stock Fund. Fund invests into the top 10 pre-IPO stock by valuation. Pre-IPO stock exposure to artificial intelligence, robotics, space economy, fintech, virtual/augmented reality in one fund. Pre-IPO Indexing delivers 450% cumulative returns over the last 20 yrs, on average*. Register at www.agdillon.com/webinar. - - - - - - - - - - -Weekly pre-IPO reports…- Valuations, revenue multiples, performance = www.agdillon.com/reports- Market update pdf = www.agdillon.com/update- Index fact sheet = www.agdillon.com/index00:06 | Cerebras Targets October IPO- AI semiconductor company- Targeting IPO launch as soon as October- Enlisted Barclays and Citigroup as lead banks- Confidentially filed with the SEC- 2021 funding round: $4B valuation (primary)- Raised $250M in Series F- Flagship system CS-3 for AI workloads- Secondary market valuation: $5.4B (+26% vs Nov 2021 round)01:01 | OpenAI Rolls Out Advanced Voice Mode- AI large language model company- Launching ChatGPT's Advanced Voice Mode- Initially for ChatGPT Plus users, full rollout in fall 2024- Features hyperrealistic audio responses, emotional recognition- Tested by over 100 external red teamers- Secondary market valuation: $105B (+22% vs Apr 2024 round)01:54 | Harvey's $100M Series C Round- AI startup for lawyers- Raised $100M in Series C led by Google Ventures- Total funding: $206M- Valued at $1.5B (primary)- Uses OpenAI's GPT-4 for legal tasks- Annual recurring revenue and workforce tripled since December03:08 | Canva Acquires Leonardo.ai- Graphic design tech company- Acquired Leonardo.ai to enhance AI tech stack- Leonardo.ai to continue operating independently- Canva's valuation: $28.4B (secondary)- Over 19M registered users, 1B+ images generated- Canva's revenue close to $2B with 180M monthly users04:16 | Ramp's AI-Powered Financial Tools- Business-focused fintech- Uses AI to streamline financial processes for 25,000+ businesses- Customers include Shopify and Boys and Girls Club of America- AI capabilities: OCR for receipt matching, fraud detection, spend optimization- Tripled annual recurring revenue and workforce since December- Secondary market valuation: $8.8B (+15% vs Apr 2024 round)05:44 | Anduril Secures Air Force Contracts- Tech-focused defense contractor- Developing prototypes for Collaborative Combat Aircraft (CCA)- Expected production contracts by 2026- CCA to perform strike, reconnaissance, electronic warfare missions- Secondary market valuation: $15.5B (+11% vs May 2024 round)06:45 | Airtable Acquires Dopt- Productivity SaaS company- Acquired Dopt to enhance AI capabilities- Dopt to wind down service on August 15- Airtable's AI group to integrate Dopt's team- Launched Airtable Cobuilder for app creation via chat interface- Secondary market valuation: $3.5B (-70% vs Dec 2021 round)07:44 | Pre-IPO Stock Market Weekly Performance- www.agdillon.com/reports for full pdf- Pre-IPO +2.91% for week, +69.42% for last 1yr- Up week: Snyk +80.2%, Notion +13.2%, Revolut +8.1%, Airtable 6.9%, Plaid 5.2%- Down week: Hugging Face -16.1%, Groq -10.4%, Cohere -5.6%, Klarna -3.5%, ByteDance -2.5%- Top valuations: ByteDance $286b, SpaceX $210b, OpenAI $105b, Stripe $70b, Databricks $44b08:40 | Pre-IPO Stock Vintage Index Weekly Performance- www.agdillon.com/index for pdf with constituent level perfo

Händler und Marktplätze sind zunehmend undifferenziert. Content und Kontext sind die Zukunft vom E-Commerce. Brüssel schießt gegen Apple, Conversational Commerce meldet sich zurück und Otto (!) programmiert für die Apple Vision Pro. Werbung: Möchtest du einen internen Chatbot auf Basis der Google Cloud haben? Dann spreche mit PCG, wie du dein Unternehmen mit AI effizienter machten kannst. https://pcg.io/ Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) K5, E-Commerce Zukunft (00:21:15) Otto Vision Pro (00:24:45) Apple Antitrust (00:48:00) Teams Antitrust (00:57:00) Cerebrus  (01:00:00) AI Musik Klage (01:04:00) PE / VC Carry Steuern Shownotes: Danke an https://www.edelmann-paulig.de  Otto Apple Brüssel: FT, Bloomberg Apple x Meta: WSJ Msft Teams: FT Reid Hoffmann Antwort an David Sacks Cerebras: The Information AI Musik Klage: Decoder Steuern PE VC: FZ

Hat Tip to this week's creators: @leopoldasch, @JoeSlater87, @GaryMarcus, @ulonnaya, @alex, @ttunguz, @mmasnick, @dannyrimer, @imdavidpierce, @asafitch, @ylecun, @nxthompson, @kaifulee, @DaphneKoller, @AndrewYNg, @aidangomez, @Kyle_L_Wiggers, @waynema, @QianerLiu, @nicnewman, @nmasc_, @steph_palazzolo, @nofilmschoolContents* Editorial: * Essays of the Week* Situational Awareness: The Decade Ahead* ChatGPT is b******t* AGI by 2027?* Ilya Sutskever, OpenAI's former chief scientist, launches new AI company* The Series A Crunch Is No Joke* The Series A Crunch or the Seedpocalypse of 2024 * The Surgeon General Is Wrong. Social Media Doesn't Need Warning Labels* Video of the Week* Danny Rimer on 20VC - (Must See)* AI of the Week* Anthropic has a fast new AI model — and a clever new way to interact with chatbots* Nvidia's Ascent to Most Valuable Company Has Echoes of Dot-Com Boom* The Expanding Universe of Generative Models* DeepMind's new AI generates soundtracks and dialogue for videos* News Of the Week* Apple Suspends Work on Next Vision Pro, Focused on Releasing Cheaper Model in Late 2025* Is the news industry ready for another pivot to video?* Cerebras, an Nvidia Challenger, Files for IPO Confidentially* Startup of the Week* Final Cut Camera and iPad Multicam are Truly Revolutionary* X of the Week* Leopold AschenbrennerEditorialI had not heard of Leopold Aschenbrenner until yesterday. I was meeting with Faraj Aalaei (a SignalRank board member) and my colleague Rob Hodgkinson when they began to talk about “Situational Awareness,” his essay on the future of AGI, and its likely speed of emergence.So I had to read it, and it is this week's essay of the week. He starts his 165-page epic with:Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them.So, Leopold is not humble. He finds himself “among” the few people with situational awareness.As a person prone to bigging up myself, I am not one to prematurely judge somebody's view of self. So, I read all 165 pages.He makes one point. The growth of AI capability is accelerating. More is being done at a lower cost, and the trend will continue to be super-intelligence by 2027. At that point, billions of skilled bots will solve problems at a rate we cannot imagine. And they will work together, with little human input, to do so.His case is developed using linear progression from current developments. According to Leopold, all you have to believe in is straight lines.He also has a secondary narrative related to safety, particularly the safety of models and their weightings (how they achieve their results).By safety, he does not mean the models will do bad things. He means that third parties, namely China, can steal the weightings and reproduce the results. He focuses on the poor security surrounding models as the problem. And he deems governments unaware of the dangers.Although German-born, he argues in favor of the US-led effort to see AGI as a weapon to defeat China and threatens dire consequences if it does not. He sees the “free world” as in danger unless it stops others from gaining the sophistication he predicts in the time he predicts.At that point, I felt I was reading a manifesto for World War Three.But as I see it, the smartest people in the space have converged on a different perspective, a third way, one I will dub AGI Realism. The core tenets are simple:* Superintelligence is a matter of national security. We are rapidly building machines smarter than the smartest humans. This is not another cool Silicon Valley boom; this isn't some random community of coders writing an innocent open source software package; this isn't fun and games. Superintelligence is going to be wild; it will be the most powerful weapon mankind has ever built. And for any of us involved, it'll be the most important thing we ever do. * America must lead. The torch of liberty will not survive Xi getting AGI first. (And, realistically, American leadership is the only path to safe AGI, too.) That means we can't simply “pause”; it means we need to rapidly scale up US power production to build the AGI clusters in the US. But it also means amateur startup security delivering the nuclear secrets to the CCP won't cut it anymore, and it means the core AGI infrastructure must be controlled by America, not some dictator in the Middle East. American AI labs must put the national interest first. * We need to not screw it up. Recognizing the power of superintelligence also means recognizing its peril. There are very real safety risks; very real risks this all goes awry—whether it be because mankind uses the destructive power brought forth for our mutual annihilation, or because, yes, the alien species we're summoning is one we cannot yet fully control. These are manageable—but improvising won't cut it. Navigating these perils will require good people bringing a level of seriousness to the table that has not yet been offered. As the acceleration intensifies, I only expect the discourse to get more shrill. But my greatest hope is that there will be those who feel the weight of what is coming, and take it as a solemn call to duty.I persisted in reading it, and I think you should, too—not for the war-mongering element but for the core acceleration thesis.My two cents: Leopold underestimates AI's impact in the long run and overestimates it in the short term, but he is directionally correct.Anthropic released v3.5 of Claude.ai today. It is far faster than the impressive 3.0 version (released a few months ago) and costs a fraction to train and run. it is also more capable. It accepts text and images and has a new feature that allows it to run code, edit documents, and preview designs called ‘Artifacts.'Claude 3.5 Opus is probably not far away.Situational Awareness projects trends like this into the near future, and his views are extrapolated from that perspective.Contrast that paper with “ChatGPT is B******t,” a paper coming out of Glasgow University in the UK. The three authors contest the accusation that ChatGPT hallucinates or lies. They claim that because it is a probabilistic word finder, it spouts b******t. It can be right, and it can be wrong, but it does not know the difference. It's a bullshitter.Hilariously, they define three types of BS:B******t (general)Any utterance produced where a speaker has indifference towards the truth of the utterance.Hard b******tB******t produced with the intention to mislead the audience about the utterer's agenda.Soft b******tB******t produced without the intention to mislead the hearer regarding the utterer's agenda.They then conclude:With this distinction in hand, we're now in a position to consider a worry of the following sort: Is ChatGPT hard b**********g, soft b**********g, or neither? We will argue, first, that ChatGPT, and other LLMs, are clearly soft b**********g. However, the question of whether these chatbots are hard b**********g is a trickier one, and depends on a number of complex questions concerning whether ChatGPT can be ascribed intentions.This is closer to Gary Marcus's point of view in his ‘AGI by 2027?' response to Leopold. It is also below.I think the reality is somewhere between Leopold and Marcus. AI is capable of surprising things, given that it is only a probabilistic word-finder. And its ability to do so is becoming cheaper and faster. The number of times it is useful easily outweighs, for me, the times it is not. Most importantly, AI agents will work together to improve each other and learn faster.However, Gary Marcus is right that reasoning and other essential decision-making characteristics are not logically derived from an LLM approach to knowledge. So, without additional or perhaps different elements, there will be limits to where it can go. Gary probably underestimates what CAN be achieved with LLMs (indeed, who would have thought they could do what they already do). And Leopold probably overestimates the lack of a ceiling in what they will do and how fast that will happen.It will be fascinating to watch. I, for one, have no idea what to expect except the unexpected. OpenAI Founder Illya Sutskever weighed in, too, with a new AI startup called Safe Superintelligence Inc. (SSI). The most important word here is superintelligence, the same word Leopold used. The next phase is focused on higher-than-human intelligence, which can be reproduced billions of times to create scaled Superintelligence.The Expanding Universe of Generative Models piece below places smart people in the room to discuss these developments. Yann LeCun, Nicholas Thompson, Kai-Fu Lee, Daphne Koller, Andrew Ng, and Aidan Gomez are participants. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

Pre-IPO stock valuations = www.x.com/aarongdillon (see pinned post)Pre-IPO stock index fact sheet = www.agdillon.com/indexPre-IPO stock funds = www.agdillon.com/funds00:06 | No IPOs for Stripe- Online payments company- Stripe co-founder, John Collison; Stripe will continue to do tenders in the future- Two tenders in the last two years, perhaps one tender a year?- Tender offers = no IPO on the horizon- $76b secondary market valuation, +16% vs its last round in Feb 202401:03 | X as “everything app”- X Payments being stood up now- Approved in 28 states, aims to be live by Dec 2024- X Payments will compete with Venmo, Cash App, Zelle- No crypto- Fidelity has X at a $12.5b valuation, ballpark 3x-4x revenue multiple- Payments are foundational to be an everything app, we believe an X App Store is next02:40 | Starlink Mini launched- Starlink Mini is a compact version of standard satellite internet antenna- Weighs 2lbs, 12x10x1.5 inches in size- $599 for mini antenna, $150/month service fee- Starlink has 6,000 satellites in orbit, 3m customers in 100 countries- $192b secondary market valuation, +7% vs its last round in Jan 2024 … $200b tender announced expected to close late summer03:55 | Cerebras AI chip IPO- Hired Citigroup to lead IPO- 2H 2024 for listing- $4b+ IPO valuation, above last round in 202104:36 | SoftBank's $100b for AI companies- Goal is to deliver “artificial super-intelligence”, or ASI- SoftBank owns Arm, AI chip maker- SoftBank activity in the VC market led to the inflated valuations in 2021, could same happen now in AI ecosystem05:17 | New Anthropic AI model- AI large language model company- Claude 3.5 Sonnet launching- 2x as fast as Claude 3 Sonnet; excels in coding, text-based reasoning vs OpenAI GPT-4o- Enterprise pricing = $3 per million tokens fed into the model, $15 per million tokens generated- $1b 2024 revenue forecast- $21b secondary market valuation, +18% vs its last round in Jan 202406:21 | HeyGen raised $60m- AI-driven video content company- $500m valuation- Product = upload video of yourself, photorealistic avatar created, upload transcript, AI avatars reads as if you, translate to 40+ languages- $24/month subscription fee- 40,000 customers- $35m ARR in 2023- HeyGen builds on top of OpenAI and ElevenLabs (“last mile” AI app)08:10 | Revolut tender at $40b!- Morgan Stanley to bank tender offer- $500m raise- £1.7 billion 2023 revenue, +84% vs 2022- $23b secondary market valuation, -31% vs its last primary round in Jul 2021- Recent secondary market buyers stand to make a quick +75% return if $40b valuation holds09:30 | Pre-IPO -1.07% for week, +65.20% for last 1yr- Up week: Chime +22.3%, CoreWeave +19.3%, Wiz +13.1%, Cohere +11.3%, Scale AI +8.8%- Down week: OpenAI -5.5%, Epic Games -4.0%, Deel -2.9%, Bytedance -2.1%, Notion -1.9%- Top valuations: ByteDance $292b, SpaceX $191b, OpenAI $104b, Stripe $76b, Databricks $43b10:27 | 2024 Pre-IPO Stock Vintage Index week performance- www.agdillon.com/index for fact sheet pdf- 2024 Vintage Index top contributors since inception: Rippling +106%, Revolut +52%, Epic Games +44%, Klarna +43%, Anduril +27%- Key metric averages for all Vintage Indexes 5 years old or older…3.31 distributed paid in capital2.05 residual value to paid in capital5.36 total value to paid in capital4.1 years to return the fund

In this episode, Mark and Shashank are joined by a special guest, Matt from Cerebras Systems. Matt, a key figure at Cerebras and a regular at the South Bay Generative AI Meetup, shares his wealth of knowledge about the cutting-edge advancements in AI hardware. He discusses how Cerebras is revolutionizing the field with their specialized ML training chips, which compete with Nvidia by optimizing for specific machine learning workloads. Tune in to learn about wafer-scale computing, the challenges and innovations in AI hardware, and how Cerebras is poised to lead the future of AI infrastructure.

My 10th Anniversary Podcast! Discover how Nvidia and AI are fueling the stock market. Are you investing well for financial freedom...or not? Financial freedom is a combination of money, compounding and time (my McT Formula). How well you invest, makes a huge difference to your financial future and lifestyle. If you only knew where to invest for the long-term, what a difference it would make, because the difference between investing $100k and earning 2% or 10% on your money over 30 years, is the difference between it growing to $181,136 or $1,744,940, an increase of over $1.5 million dollars. Your compounding rate, and how well you invest, matters!  INTERESTED IN THE BE WEALTHY & SMART VIP EXPERIENCE? -Asset allocation model with ticker symbols and % to invest -Monthly investing webinars with Linda -Private Facebook group with daily insights -Weekly stock market commentary email -Lifetime access -US and foreign investors, no minimum $ amount required Extending the special offer, enjoy a 50% savings on the VIP Experience by using promo code "SAVE50". More information is here.  If you would like a complimentary consultation with Linda to answer your questions about the VIP Experience, set an appointment here.  WANT TO INVEST IN STOCKS PRE-IPO? #Ad Invest in the same private companies like some billionaires. If you are an Accredited Investor (must have $1 million of net worth excluding your primary residence or $200k income or $300k joint income, or be a registered representative), you qualify to invest in over 50 private companies. Minimum investment is $2,500. Sign up to receive a $500 credit toward your investment from Linqto, here:  https://www.linqto.com/signup?r=e9tdhbl49v PLEASE REVIEW THE PODCAST ON ITUNES If you enjoyed this episode, please subscribe and leave a review. I love hearing from you! I so appreciate it! SUBSCRIBE TO BE WEALTHY & SMART Click Here to Subscribe Via iTunes Click Here to Subscribe Via Stitcher on an Android Device Click Here to Subscribe Via RSS Feed   PLEASE LEAVE A BOOK REVIEW FOR THE CRYPTO INVESTING BOOK Get my book, "3 Steps to Quantum Wealth: The Wealth Heiress' Guide to Financial Freedom by Investing in Cryptocurrencies". After you purchase the book, go here for your Crypto Book bonus: https://lindapjones.com/bookbonus PLEASE LEAVE A BOOK REVIEW FOR WEALTH BOOK Leave a book review on Amazon here. Get my book, “You're Already a Wealth Heiress, Now Think and Act Like One: 6 Practical Steps to Make It a Reality Now!” Men love it too! After all, you are Wealth Heirs. :) Available for purchase on Amazon. International buyers (if you live outside of the US) get my book here. WANT MORE FROM LINDA? Check out her programs. Join her on Instagram. WEALTH LIBRARY OF PODCASTS Listen to the full wealth library of podcasts from the beginning. Use the search bar in the upper right corner of the page to search topics. SPECIAL DEALS #Ad Protect yourself online with a Virtual Private Network (VPN). Get 3 MONTHS FREE when you sign up for a NORD VPN plan here: https://ref.nordvpn.com/PjngPVgYXBs #Ad To safely and securely store crypto, I recommend using a Tangem wallet. Get a 10% discount when you purchase here: Https://tangem.com/en/?promocode=767FCF #Ad If you are looking to simplify your crypto tax reporting, use Koinly. It is highly recommended and so easy for tax reporting. You can save $20, click here. Be Wealthy & Smart,™ is a personal finance show with self-made millionaire Linda P. Jones, America's Wealth Mentor.™ Learn simple steps that make a big difference to your financial freedom.  (Some links are affiliate links. There is no additional cost to you.)    

Today we're joined by Joel Hestness, principal research scientist and lead of the core machine learning team at Cerebras. We discuss Cerebras' custom silicon for machine learning, Wafer Scale Engine 3, and how the latest version of the company's single-chip platform for ML has evolved to support large language models. Joel shares how WSE3 differs from other AI hardware solutions, such as GPUs, TPUs, and AWS' Inferentia, and talks through the homogenous design of the WSE chip and its memory architecture. We discuss software support for the platform, including support by open source ML frameworks like Pytorch, and support for different types of transformer-based models. Finally, Joel shares some of the research his team is pursuing to take advantage of the hardware's unique characteristics, including weight-sparse training, optimizers that leverage higher-order statistics, and more. The complete show notes for this episode can be found at twimlai.com/go/684.

This episode is sponsored by 1Password. 1Password combines industry-leading security with award-winning design to bring private, secure, and user-friendly password management to everyone. Companies lose hours every day just from employees forgetting and resetting passwords. A single data breach costs millions of dollars. 1Password secures every sign-in to save you time and money. Right now, my listeners get a free 2-week trial at:  https://www.1password.com/eyeonai In this episode of Eye on AI, join us as we explore the cutting-edge world of GPU optimization with Ronen Dar, CTO and co-founder of Run:ai.  Delve into the intricacies of managing and maximizing GPU utilization in an era marked by a severe GPU shortage. Ronen shares his insights on how Run:ai's innovative software is revolutionizing AI infrastructure, making GPU resources more efficient and accessible. The conversation spans the technical challenges of scaling AI models, the evolution of GPU demands from basic algorithms to complex systems like GPT-4, and the strategic innovations helping enterprises overcome these hurdles. Ronen also reflects on the future of AI development, predicting an exponential increase in demand for computational power and the innovative solutions poised to meet these needs. Tune in to uncover the technological advancements that are propelling AI capabilities forward and shaping the future of AI deployment across industries. Don't forget to like, subscribe, and hit the notification bell for more deep dives into the technologies that are transforming our digital landscape. Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Preview (02:46) Introducing Ronen Dar (04:00) Ronen's background and RunAI's origins (09:13) The need for efficient GPU utilization in AI (13:14) RunAI's core value proposition (15:33 RunAI's deployment model (18:55) The growing demand for compute power and GPUs (22:08) Challenges in scaling models beyond 70 billion parameters (27:52) RunAI's open platform approach (31:00) Addressing latency and throughput challenges in inference (34:36) RunAI's integration with AI tools and frameworks (39:37) Reducing the cost of inference with GPU virtualization (43:54) Challenges in auto-scaling for large language models (47:06) The future of the GPU market and demand (50:49) NVIDIA's dominance and the role of competitors like Cerebras (54:20) RunAI's global customer base and demand patterns (57:52) NVIDIA's vision and the evolution of GPU architectures (01:01:25) Compute requirements for the metaverse and future AI applications (01:03:56) Concerns about power consumption and carbon footprint

In this episode, regular guest Laura Dyrda, Vice President, Editor-in-Chief at Becker's Healthcare discusses Mayo Clinic selecting Cerebras as its first generative AI collaborator for large-scale, domain-specific AI capabilities for more personalized diagnosis and treatment plans and Epic's plans to roll out the early stages of "Best Care Choices for My Patient" in 2024

In this episode, Ben Bajarin and Jay Goldberg discuss the competition faced by Nvidia in the semiconductor industry. They explore various competitors, including AMD, Intel, and startups like Grok, Etched, and Cerebras. They also delve into the threat posed by custom silicon and the strategies of hyperscalers like Google, Microsoft, Facebook, and Amazon. Overall, the conversation highlights the challenges and opportunities for Nvidia in maintaining its position as a leader in the market. In this conversation, Jay Goldberg and Ben Bajarin discuss various themes related to Nvidia and the AI market. They explore the growing moat of Nvidia and the dominance of CUDA as a software platform. They also discuss the ease of use and stickiness of CUDA, as well as the uncertainty of Nvidia's software adoption. The conversation delves into the market potential and consumer applications of AI, as well as the slow progression of the AI market. They also touch on the risks of AI factories and inventory cycles, the potential slowdown of performance gains, and the regulatory concerns for Nvidia. The conversation concludes with a discussion on the impact of China's market and US sanctions.

The TikTok legislation has passed the House, but it's path through the Senate is uncertain to say the least. The first real AI regulation has passed, in Europe, of course. Arm's new chips for self-driving cars. Did Cerebras just break Moore's Law with its new AI chips? Spotify has music videos. And Perplexity continues to try to become Google Search faster than Google search can become them.Sponsors:Shopify.com/rideNutrafol.com/men code: RIDEHOMELinks:TikTok bill, racing toward House passage, faces a minefield in the Senate (Washington Post)How TikTok Was Blindsided by U.S. Bill That Could Ban It (WSJ)World's first major act to regulate AI passed by European lawmakers (CNBC)Stripe in ‘no rush' to go public as cash flow turns positive (FT)Arm unveils first chip design to power self-driving cars (FT)AI startup Cerebras unveils the WSE-3, the largest chip yet for generative AI (ZDNet)Spotify adds music videos in some countries (TechCrunch)Perplexity brings Yelp data to its chatbot (The Verge)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.