Podcasts about scale ai

Meta compra Manus, una startup de agentes de inteligencia artificial autónomos que ya genera más de cien millones de dólares al añoPor Félix Riaño @LocutorCoMeta acaba de cerrar una de las compras más comentadas del año en inteligencia artificial. La empresa de Mark Zuckerberg va a integrar agentes autónomos capaces de trabajar casi sin intervención humana. Se trata de Manus, una startup fundada por emprendedores chinos y hoy con sede en Singapur, que ya tiene millones de usuarios de pago y más de cien millones de dólares en ingresos anuales. ¿Qué cambia cuando una inteligencia artificial deja de responder y empieza a actuar por su cuenta?Cuando la inteligencia artificial deja de chatear y empieza a tomar decisiones.Meta Platforms ha anunciado la compra de Manus, una startup de inteligencia artificial que se ha hecho famosa por una idea muy concreta: crear agentes que trabajan de forma autónoma. Estos agentes reciben una instrucción general, planifican los pasos, ejecutan tareas complejas y entregan un resultado final sin que el usuario tenga que intervenir todo el tiempo.Manus nació en China en el año dos mil veintidós dentro de una empresa llamada Butterfly Effect. Con el aumento de las tensiones tecnológicas entre Estados Unidos y China, la empresa trasladó su sede a Singapur y dejó de operar en territorio chino. Desde allí lanzó su producto al mercado global.En pocos meses, Manus aseguró haber conseguido millones de usuarios y más de cien millones de dólares en ingresos recurrentes al año, gracias a suscripciones mensuales y anuales. Esa cifra llamó la atención de Meta, que lleva años apostándolo todo a la inteligencia artificial.—La gran pregunta es qué diferencia a Manus de otros asistentes como los chatbots tradicionales. La respuesta está en el nivel de autonomía. Mientras la mayoría de herramientas actuales esperan instrucciones paso a paso, Manus afirma que sus agentes pueden encargarse de todo el proceso.Por ejemplo, pueden hacer una investigación de mercado completa, analizar datos financieros, revisar hojas de vida, escribir código o planear un viaje entero. Todo con una sola orden inicial. Para muchos expertos, esto marca un cambio profundo en cómo usamos la inteligencia artificial en el trabajo diario.Pero también aparecen dudas. Manus fue fundada por emprendedores chinos y tuvo inversión de empresas de ese país. En Estados Unidos, cualquier proyecto de inteligencia artificial con raíces chinas despierta preocupación política. Algunos senadores ya han cuestionado el origen del capital y el acceso a datos sensibles. Meta ha respondido que, tras la compra, no quedará ninguna participación china y que Manus dejará cualquier operación pendiente en China.Meta ha sido clara en su estrategia. Manus va a seguir funcionando como servicio independiente, con sus planes de suscripción actuales, pero sus agentes se van a integrar poco a poco en productos como Facebook, Instagram y WhatsApp, donde ya existe Meta AI.La idea encaja con la visión de Mark Zuckerberg sobre una inteligencia artificial personal, presente en el día a día de las personas y las empresas. Meta quiere que estos agentes ayuden a organizar negocios pequeños, responder clientes, analizar información y automatizar tareas repetitivas.Según reportes de medios como The Wall Street Journal y Reuters, la operación estaría valorada entre dos mil y tres mil millones de dólares. Esto convierte a Manus en una de las compras más grandes de Meta en inteligencia artificial aplicada al mundo real, con ingresos comprobados.Para Meta, hay un mensaje claro hacia inversores y competidores. Esta vez no se trata solo de investigación, sino de una inteligencia artificial que ya gana dinero y que puede escalar a miles de millones de usuarios.La compra de Manus no llega sola. Durante este año, Meta ha acelerado su gasto en inteligencia artificial. En junio invirtió más de catorce mil millones de dólares para quedarse con casi la mitad de Scale AI, una empresa especializada en datos para entrenar modelos. También ha fichado talento directamente de empresas rivales como OpenAI y Google.Manus, por su parte, asegura haber procesado más de ciento cuarenta y siete billones de tokens de texto y datos, y haber creado más de ochenta millones de computadoras virtuales para ejecutar tareas de forma paralela. Son cifras enormes, pensadas para mostrar músculo tecnológico.Antes de la compra, Manus fue comparada con DeepSeek, otro desarrollo chino que llamó la atención por su rendimiento. Incluso Microsoft llegó a probar sus agentes en Windows once para crear páginas web a partir de archivos locales.Ahora, con Meta detrás, el reto será ganar la confianza de empresas que han visto a Meta envuelta en polémicas por el uso de datos. La pregunta ya no es si los agentes autónomos funcionan, sino quién se atreverá a usarlos a gran escala.Meta apuesta fuerte por una inteligencia artificial que actúa sola y ya genera ingresos reales. Manus pasa de startup prometedora a pieza central del futuro de Meta. La carrera por los agentes autónomos acaba de acelerar. Cuéntanos qué opinas y sigue Flash Diario en Spotify.

Ho Ho Ho, Alex here! (a real human writing these words, this needs to be said in 2025) Merry Christmas (to those who celebrate) and welcome to the very special yearly ThursdAI recap! This was an intense year in the world of AI, and after 51 weekly episodes (this is episode 52!) we have the ultimate record of all the major and most important AI releases of this year! So instead of bringing you a weekly update (it's been a slow week so far, most AI labs are taking a well deserved break, the Cchinese AI labs haven't yet surprised anyone), I'm dropping a comprehensive yearly AI review! Quarter by quarter, month by month, both in written form and as a pod/video! Why do this? Who even needs this? Isn't most of it obsolete? I have asked myself this exact question while prepping for the show (it was quite a lot of prep, even with Opus's help). I eventually landed on, hey, if nothing else, this will serve as a record of the insane week of AI progress we all witnessed. Can you imagine that the term Vibe Coding is less than 1 year old? That Claude Code was released at the start of THIS year? We get hedonicly adapt to new AI goodies so quick, and I figured this will serve as a point in time check, we can get back to and feel the acceleration! With that, let's dive in - P.S. the content below is mostly authored by my co-author for this, Opus 4.5 high, which at the end of 2025 I find the best creative writer with the best long context coherence that can imitate my voice and tone (hey, I'm also on a break!

Today's guest is Nina Edwards, Vice President of Emerging Technology and Innovation at Prudential Insurance. With decades of experience driving strategy, innovation, and AI-enabled growth at leading financial and consulting firms, Nina brings deep expertise in applied intelligence and emerging technology.​​ Nina joins Emerj Editorial Director Matthew DeMello to unpack why 95% of AI pilots fail to deliver enterprise value and reveal strategies for translating productivity gains into measurable ROI by ditching pre-AI metrics.​ Nina also shares practical takeaways, including protected sandboxes that slash approval cycles from months to days, unified KPI glossaries standardizing cycle time and exception rates, outcome charters targeting business value, and human-centered operating models shifting teams from doing to deciding to scale pilots enterprise-wide. This episode is sponsored by Moody's. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast!

Datawizz is pioneering continuous reinforcement learning infrastructure for AI systems that need to evolve in production, not ossify after deployment. After building and exiting RapidAPI—which served 10 million developers and had at least one team at 75% of Fortune 500 companies using and paying for the platform—Founder and CEO Iddo Gino returned to building when he noticed a pattern: nearly every AI agent pitch he reviewed as an angel investor assumed models would simultaneously get orders of magnitude better and cheaper. In a recent episode of BUILDERS, we sat down with Iddo to explore why that dual assumption breaks most AI economics, how traditional ML training approaches fail in the LLM era, and why specialized models will capture 50-60% of AI inference by 2030. Topics Discussed Why running two distinct businesses under one roof—RapidAPI's developer marketplace and enterprise API hub—ultimately capped scale despite compelling synergy narratives The "Big Short moment" reviewing AI pitches: every business model assumed simultaneous 1-2 order of magnitude improvements in accuracy and cost Why companies spending 2-3 months on fine-tuning repeatedly saw frontier models (GPT-4, Claude 3) obsolete their custom work The continuous learning flywheel: online evaluation → suspect inference queuing → human validation → daily/weekly RL batches → deployment How human evaluation companies like Scale AI shift from offline batch labeling to real-time inference correction queues Early GTM through LinkedIn DMs to founders running serious agent production volume, working backward through less mature adopters ICP discovery: qualifying on whether 20% accuracy gains or 10x cost reductions would be transformational versus incremental The integration layer approach: orchestrating the continuous learning loop across observability, evaluation, training, and inference tools Why the first $10M is about selling to believers in continuous learning, not evangelizing the category GTM Lessons For B2B Founders Recognize when distribution narratives mask structural incompatibility: RapidAPI had 10 million developers and teams at 75% of Fortune 500 paying for the platform—massive distribution that theoretically fed enterprise sales. The problem: Iddo could always find anecdotes where POC teams had used RapidAPI, creating a compelling story about grassroots adoption. The critical question he should have asked earlier: "Is self-service really the driver for why we're winning deals, or is it a nice-to-have contributor?" When two businesses have fundamentally different product roadmaps, cultures, and buying journeys, distribution overlap doesn't create a sustainable single company. Stop asking if synergies exist—ask if they're causal. Qualify on whether improvements cross phase-transition thresholds: Datawizz disqualifies prospects who acknowledge value but lack acute pain. The diagnostic questions: "If we improved model accuracy by 20%, how impactful is that?" and "If we cut your costs 10x, what does that mean?" Companies already automating human labor often respond that inference costs are rounding errors compared to savings. The ideal customers hit differently: "We need accuracy at X% to fully automate this process and remove humans from the loop. Until then, it's just AI-assisted. Getting over that line is a step-function change in how we deploy this agent." Qualify on whether your improvement crosses a threshold that changes what's possible, not just what's better. Use discovery to map market structure, not just validate hypotheses: Iddo validated that the most mature companies run specialized, fine-tuned models in production. The surprise: "The chasm between them and everybody else was a lot wider than I thought." This insight reshaped their entire strategy—the tooling gap, approaches to model development, and timeline to maturity differed dramatically across segments. Most founders use discovery to confirm their assumptions. Better founders use it to understand where different cohorts sit on the maturity curve, what bridges or blocks their progression, and which segments can buy versus which need multi-year evangelism. Target spend thresholds that indicate real commitment: Datawizz focuses on companies spending "at a minimum five to six figures a month on AI and specifically on LLM inference, using the APIs directly"—meaning they're building on top of OpenAI/Anthropic/etc., not just using ChatGPT. This filters for companies with skin in the game. Below that threshold, AI is an experiment. Above it, unit economics and quality bars matter operationally. For infrastructure plays, find the spend level that indicates your problem is a daily operational reality, not a future consideration. Structure discovery to extract insight, not close deals: Iddo's framework: "If I could run [a call where] 29 of 30 minutes could be us just asking questions and learning, that would be the perfect call in my mind." He compared it to "the dentist with the probe trying to touch everything and see where it hurts." The most valuable calls weren't those that converted to POCs—they came from people who approached the problem differently or had conflicting considerations. In hot markets with abundant budgets, founders easily collect false positives by selling when they should be learning. The discipline: exhaust your question list before explaining what you build. If they don't eventually ask "What do you do?" you're not surfacing real pain. Avoid the false-positive trap in well-funded categories: Iddo identified a specific risk in AI: "You can very easily run these calls, you think you're doing discovery, really you're doing sales, you end up getting a bunch of POCs and maybe some paying customers. So you get really good initial signs but you've never done any actual discovery. You have all the wrong indications—you're getting a lot of false positive feedback while building the completely wrong thing." When capital is abundant and your space is hot, early revenue can mask product-market misalignment. Good initial signs aren't validation if you skipped the work to understand why people bought. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Welcome to episode 335 of The Cloud Pod, where the forecast is always cloudy! This pre-Christmas week, Ryan and Justin have hit the studio to bring you the final show of 2025. We've got lots of AI images, EKS Network Policies, Gemini 3, and even some Disney drama.  Let's get into it!  Titles we almost went with this week From Roomba to Tomb-ba: How the Robot Vacuum Pioneer Got Cleaned Out **OpenAI From Napkin Sketch to Production: Google’s App Design Center Goes GA Terraform Gets a Canvas: Google Paints Infrastructure Design with AI Mickey Mouse Takes Off the Gloves: Disney vs Google AI Showdown From Data Silos to Data Solos: Google Conducts the Integration Orchestra No More Thread Dread: AWS Brings AI to JVM Performance Troubleshooting MCP: More Corporate Plumbing Than You Think GPT-5.2 Beats Humans at Work Tasks, Still Can’t Get You Out of Monday Meetings Kerberos More Like Kerbero-Less: Microsoft Axes Ancient Encryption Standard OpenAI Teaches GPT-5.2 to PowerPoint: Death by Bullet Points Now AI-Generated MCP: Like USB-C, But Everyone’s Keeping Theirs in the Drawer Flash Gordon: Google’s Gemini 3 Gets a Speed Boost Without the Sacrifice Tag, You’re It: AWS Finally Knows Who to Bill Snowflake Gets a GPT-5.2 Upgrade: Now With More Intelligence Per Query OpenAI and Snowflake: Making Data Warehouses Smarter Than Your Average Analyst GPT-5.2 Moves Into the Snowflake: No Melting Required AI Is Going Great, or How ML Makes Money  01:06 Meta’s multibillion-dollar AI strategy overhaul creates culture clash: Meta is developing Avocado, a new frontier AI model codenamed to succeed Llama, now expected to launch in Q1 2026 after internal delays related to training performance testing.  The model may be proprietary rather than open source, marking a significant shift from Meta’s previous strategy of freely distributing Llama’s weights and architecture to developers. We feel like this is an interesting choice for Meta, but what do we know?  Meta spent 14.3 billion dollars in June 2025 to hire Scale AI founder Alexandr Wang as Chief AI Officer and acquire a stake in Scale, while raising 2026 capital expenditure guidance to 70-72 billion dollars.  Wang now leads the elite TBD Lab developing Avocado, operating separately from traditional Meta teams and not using the company’s internal workplace network. The company has restructured its AI leadership following the poor reception of Llama 4 in April, with Chief Product Officer Chris Cox no longer overseeing the GenAI unit.  Meta cut 600 jobs in Meta Superintelligence Labs in October, contributing to the departure of Chief AI Scientist Yann LeCun to launch a startup, while implementing 70-hour workweeks across AI organizations. Meta’s new AI leadership under Wang and former GitHub CEO Nat Friedman has introduced a “demo, don’t memo” development approach, replacing traditional multi-step approval processes with rapid prototyping using AI

This episode features Olivier Godement, Head of Product for Business Products at OpenAI, discussing the current state and future of AI adoption in enterprises, with a particular focus on the recent releases of GPT 5.1 and Codex. The conversation explores how these models are achieving meaningful automation in specific domains like coding, customer support, and life sciences: where companies like Amgen are using AI to accelerate drug development timelines from months to weeks through automated regulatory documentation. Olivier reveals that while complete job automation remains challenging and requires substantial scaffolding, harnesses, and evaluation frameworks, certain use cases like coding are reaching a tipping point where engineers would "riot" if AI tools were taken away. The discussion covers the importance of cost reduction in unlocking new use cases, the emerging significance of reinforcement fine-tuning (RFT) for frontier customers, and OpenAI's philosophy of providing not just models but reference architectures and harnesses to maximize developer success. (0:00) Intro(1:46) Discussing GPT-5.1(2:57) Adoption and Impact of Codex(4:09) Scientific Community's Use of GPT-5.1(6:37) Challenges in AI Automation(8:19) AI in Life Sciences and Pharma(11:48) Enterprise AI Adoption and Ecosystem(16:04) Future of AI Models and Continuous Learning(24:20) Cost and Efficiency in AI Deployment(27:10) Reinforcement Learning and Enterprise Use Cases(31:17) Key Factors Influencing Model Choice(34:21) Challenges in Model Deployment and Adaptation(38:29) Voice Technology: The Next Frontier(41:08) The Rise of AI in Software Engineering(52:09) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint

AI company Anthropic (makers of the Claude model) says it stopped what is likely the first large-scale cyberattack primarily executed by artificial intelligence. The operation, which targeted about 30 organizations globally (tech firms, financial institutions, and government agencies), used a Chinese state-sponsored hacking group to manipulate Anthropic's Claude Code tool into autonomously performing 80-90% of the attack work. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Raviteja Yelamanchili shares how Scale AI transformed banking cycles from one year to real-time and why your most valuable enterprise data isn't being collected.Topics Include:Scale evolved from data annotations company to enterprise AI solutions providerHealthcare system transformed patient transcriptions into value using reinforcement learning researchBlank slate customer problems allow Scale to experiment with latest methodsMany customers propose solutions before explaining their actual underlying business problemsBiggest AI misconception: technology will replace jobs rather than augment productivityDon't wait for perfect AI—start learning through iteration and evolution nowBanking credit cycle transformed from one-year process to real-time strategic insightsScale deploys flexibly across EC2, EKS, or Bedrock based on customer requirementsEnterprises want business value generation more than academic research papers aloneNext 12-24 months focus: making data consumable and leveraging unused datasetsTribal knowledge from experienced SMEs represents most valuable yet uncollected dataAgent-based learning captures expertise through feedback loops on Scale's SGP platformParticipants:Raviteja Yelamanchili - Head of Solution Engineering, Scale AISee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Industry strategist John Sviokla is the co-founder of GAI Insights and an Executive Fellow at the Harvard Business School. He tells the Futurists about the real challenges and opportunities in the process of reorganizing businesses around unlimited intelligence.  Topics include: The existential threat to the big strategic advisory firms. Why talking to machines is such a significant change.  The remarkably rapid rise of machine IQ and the power of emergent capabilities.  John's forecast for the evolution of new AI models. How proprietary data, especially intent, will fuel the transition from search engines to answer engines.  The logic behind the partnership between Walmart and OpenAI.  What happens to a company's organization when you can buy expertise on demand.  How every human worker will become a platoon of experts.  The two populations in most organizations that use AI. The net present value of AI projects.  GAI Insight's four-step process for fostering new corporate capabilities based on AI. 

Register here to join Founder University Japan's kickoff!

Unlock the secrets to Decagon AI's $1.5 billion valuation and AI-powered customer support.Ashwin Sreenivas is the co-founder of Decagon AI, a company revolutionizing enterprise customer support with AI agents. Founded in 2023, Decagon has rapidly grown to a $1.5 billion valuation, automating support workflows for brands like Duolingo and Notion. Ashwin, previously co-founder of Helio (acquired by Scale AI), shares insights into Decagon's product-market fit, secret sauce, and tangible business impact, revealing how AI is transforming customer interaction. If you're curious about the future of AI in enterprise solutions, this episode is a must-listen.Listen now YouTube | Apple | SpotifyQuotes from the episodeTraditional chatbots relied on rigid decision trees, leading to frustrating customer experiences, but Decagon's AI agents are trained like humans, enabling fluid, natural conversations.Decagon's AI agents follow Agent Operating Procedures (AOPs), which are similar to human SOPs, and this allows them to handle customer interactions across chat, phone, SMS, and email.The key is to focus on building AI agents that can follow instructions effectively, allowing businesses to offer personalized customer concierge services and seamless user experiences.Instead of predicting what customers want, AI should learn customer preferences and remember them, making interactions more seamless and efficient, enhancing overall satisfaction.What you'll learnUnderstand how Decagon AI is transforming customer support by using AI agents that can handle conversations across various channels.Learn about Agent Operating Procedures (AOPs) and how they enable AI agents to follow instructions and interact with customers like humans.Discover how Decagon AI helps businesses expand their support offerings, leading to higher retention and happier customers through increased support access.Explore the importance of solving customer problems quickly and seamlessly, regardless of whether the interaction is with a human or an AI agent.See how Decagon AI is expanding beyond customer support to offer customer concierge services, enabling personalized and friction-free interactions.Learn how focusing on customer needs and building something people will pay for can simplify early-stage company challenges.TakeawaysDecagon AI's agents use Agent Operating Procedures (AOPs) to mimic human-like interactions, which contrasts with older chatbot tech that relied on rigid decision trees.Unlike traditional approaches, Decagon AI focuses on creating a single agent adept at following instructions, improving onboarding and iteration for customers.Training smaller, fine-tuned models can outperform larger models on specific tasks, providing better performance and lower latency for customer interactions.Customer support is evolving into a brand differentiator, with companies like Amazon and American Express setting the standard for excellent service and customer trust.By making support more affordable, businesses can reinvest savings into providing more extensive support, leading to higher customer retention and satisfaction.Early customer acquisition requires manual effort, including networking, cold emailing, and LinkedIn messaging, with a focus on charging for the software from day one.Concentrating on building solutions that customers are willing to pay for within a short timeframe helps to validate business models and weed out unpromising ideas.Don't forget to subscribe and leave us a review/comment on YouTube, Apple, or Spotify.It helps us reach more listeners and bring on more interesting guests.Stay Curious, Nataraj

In this episode of Crazy Wisdom, host Stewart Alsop talks with Kevin Smith, co-founder of Snipd, about how AI is reshaping the way we listen, learn, and interact with podcasts. They explore Snipd's vision of transforming podcasts into living knowledge systems, the evolution of machine learning from finance to large language models, and the broader connection between AI, robotics, and energy as the foundation for the next technological era. Kevin also touches on ideas like the bitter lesson, reinforcement learning, and the growing energy demands of AI. Listeners can try Snipd's premium version free for a month using this promo link.Check out this GPT we trained on the conversationTimestamps00:00 – Stewart Alsop welcomes Kevin Smith, co-founder of Snipd, to discuss AI, podcasting, and curiosity-driven learning.05:00 – Kevin explains Snipd's snipping feature, chatting with episodes, and future plans for voice interaction with podcasts.10:00 – They discuss vector search, embeddings, and context windows, comparing full-episode context to chunked transcripts.15:00 – Kevin shares his background in mathematics and economics, his shift from finance to machine learning, and early startup work in AI.20:00 – They explore early quant models versus modern machine learning, statistical modeling, and data limitations in finance.25:00 – Conversation turns to transformer models, pretraining, and the bitter lesson—how compute-based methods outperform human-crafted systems. 30:00 – Stewart connects this to RLHF, Scale AI, and data scarcity; Kevin reflects on reinforcement learning's future. 35:00 – They pivot to Snipd's podcast ecosystem, hidden gems like Founders Podcast, and how stories shape entrepreneurial insight. 40:00 – ETH Zurich, robotics, and startup culture come up, linking academia to real-world innovation. 45:00 – They close on AI, robotics, and energy as the pillars of the future, debating nuclear and solar power's role in sustaining progress.Key InsightsPodcasts as dynamic knowledge systems: Kevin Smith presents Snipd as an AI-powered tool that transforms podcasts into interactive learning environments. By allowing listeners to “snip” and summarize meaningful moments, Snipd turns passive listening into active knowledge management—bridging curiosity, memory, and technology in a way that reframes podcasts as living knowledge capsules rather than static media.AI transforming how we engage with information: The discussion highlights how AI enables entirely new modes of interaction—chatting directly with podcast episodes, asking follow-up questions, and contextualizing information across an author's full body of work. This evolution points toward a future where knowledge consumption becomes conversational and personalized rather than linear and one-size-fits-all.Vectorization and context windows matter: Kevin explains that Snipd currently avoids heavy use of vector databases, opting instead to feed entire episodes into large models. This choice enhances coherence and comprehension, reflecting how advances in context windows have reshaped how AI understands complex audio content.Machine learning's roots in finance shaped early AI thinking: Kevin's journey from quantitative finance to AI reveals how statistical modeling laid the groundwork for modern learning systems. While finance once relied on rigid, theory-based models, the machine learning paradigm replaced those priors with flexible, data-driven discovery—an essential philosophical shift in how intelligence is approached.The Bitter Lesson and the rise of compute: Together they unpack Richard Sutton's “bitter lesson”—the idea that methods leveraging computation and data inevitably surpass those built from human intuition. This insight serves as a compass for understanding why transformers, pretraining, and scaling have driven recent AI breakthroughs.Reinforcement learning and data scarcity define AI's next phase: Stewart links RLHF and the work of companies like Scale AI and Surge AI to the broader question of data limits. Kevin agrees that the next wave of AI will depend on reinforcement learning and simulated environments that generate new, high-quality data beyond what humans can label.The future hinges on AI, robotics, and energy: Kevin closes with a framework for the next decade: AI provides intelligence, robotics applies it to the physical world, and energy sustains it all. He warns that society must shift from fearing energy use to innovating in production—especially through nuclear and solar power—to meet the demands of an increasingly intelligent, interconnected world.

AI stocks have experienced volatility, with companies like Nvidia, Oracle, Palantir, CoreWeave, and Snowflake at the forefront. The MAGS ETF dropped nearly five percent since late October, while capital spending on AI infrastructure increased. Nvidia announced up to $100 billion in investments in OpenAI, Broadcom and AMD secured major AI chip deals, and CoreWeave expanded its AI cloud services. Palantir and Snowflake outperformed in data management, while Salesforce, ServiceNow, Adobe, Workday, and HubSpot saw declines. OpenAI launched new products and reached a $500 billion valuation. Cloudflare, CrowdStrike, and Arista Networks advanced in their sectors. Meta invested in Scale AI and shifted its AI strategy, while Apple developed new AI features for Siri. The industry is transitioning from training AI models to running applications, with investors monitoring risks and opportunities.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

In this episode of Crazy Wisdom, host Stewart Alsop talks with Jessica Talisman, founder of Contextually and creator of the Ontology Pipeline, about the deep connections between knowledge management, library science, and the emerging world of AI systems. Together they explore how controlled vocabularies, ontologies, and metadata shape meaning for both humans and machines, why librarianship has lessons for modern tech, and how cultural context influences what we call “knowledge.” Jessica also discusses the rise of AI librarians, the problem of “AI slop,” and the need for collaborative, human-centered knowledge ecosystems. You can learn more about her work at Ontology Pipeline and find her writing and talks on LinkedIn.Check out this GPT we trained on the conversationTimestamps00:00 Stewart Alsop welcomes Jessica Talisman to discuss Contextually, ontologies, and how controlled vocabularies ground scalable systems.05:00 They compare philosophy's ontology with information science, linking meaning, categorization, and sense-making for humans and machines.10:00 Jessica explains why SQL and Postgres can't capture knowledge complexity and how neuro-symbolic systems add context and interoperability.15:00 The talk turns to library science's split from big data in the 1990s, metadata schemas, and the FAIR principles of findability and reuse.20:00 They discuss neutrality, bias in corporate vocabularies, and why “touching grass” matters for reconciling internal and external meanings.25:00 Conversation shifts to interpretability, cultural context, and how Western categorical thinking differs from China's contextual knowledge.30:00 Jessica introduces process knowledge, documentation habits, and the danger of outsourcing how-to understanding.35:00 They explore knowledge as habit, the tension between break-things culture and library design thinking, and early AI experiments.40:00 Libraries' strategic use of AI, metadata precision, and the emerging role of AI librarians take focus.45:00 Stewart connects data labeling, Surge AI, and the economics of good data with Jessica's call for better knowledge architectures.50:00 They unpack content lifecycle, provenance, and user context as the backbone of knowledge ecosystems.55:00 The talk closes on automation limits, human-in-the-loop design, and Jessica's vision for collaborative consulting through Contextually.Key InsightsOntology is about meaning, not just data structure. Jessica Talisman reframes ontology from a philosophical abstraction into a practical tool for knowledge management—defining how things relate and what they mean within systems. She explains that without clear categories and shared definitions, organizations can't scale or communicate effectively, either with people or with machines.Controlled vocabularies are the foundation of AI literacy. Jessica emphasizes that building a controlled vocabulary is the simplest and most powerful way to disambiguate meaning for AI. Machines, like people, need context to interpret language, and consistent terminology prevents the “hallucinations” that occur when systems lack semantic grounding.Library science predicted today's knowledge crisis. Stewart and Jessica trace how, in the 1990s, tech went down the path of “big data” while librarians quietly built systems of metadata, ontologies, and standards like schema.org. Today's AI challenges—interoperability, reliability, and information overload—mirror problems library science has been solving for decades.Knowledge is culturally shaped. Drawing from Patrick Lambe's work, Jessica notes that Western knowledge systems are category-driven, while Chinese systems emphasize context. This cultural distinction explains why global AI models often miss nuance or moral voice when trained on limited datasets.Process knowledge is disappearing. The West has outsourced its “how-to” knowledge—what Jessica calls process knowledge—to other countries. Without documentation habits, we risk losing the embodied know-how that underpins manufacturing, engineering, and even creative work.Automation cannot replace critical thinking. Jessica warns against treating AI as “room service.” Automation can support, but not substitute, human judgment. Her own experience with a contract error generated by an AI tool underscores the importance of review, reflection, and accountability in human–machine collaboration.Collaborative consulting builds knowledge resilience. Through her consultancy, Contextually, Jessica advocates for “teaching through doing”—helping teams build their own ontologies and vocabularies rather than outsourcing them. Sustainable knowledge systems, she argues, depend on shared understanding, not just good technology.

Aposto Kiosk'un bu bölümünde hostumuz Sonat Slush'D İstanbul'da Trevor Thompson'ı konuk alıyor.

Ever wondered who's actually teaching ChatGPT and Claude how to think? Meet Caspar Eliot from Invisible Technologies - the company behind 80% of the world's top AI model training. In this eye-opening conversation, we uncover the massive human workforce behind "artificial" intelligence, why your League of Legends skills might land you an AI job, and the shocking mistakes enterprises make when deploying AI.We discuss:• How AI models really learn (hint: it's not just scraping the internet)• Why data quality beats data quantity every time• The Charlotte Hornets' revolutionary AI scouting system• Whether robots will actually take your job (spoiler: probably not)• The $14.8 billion Scale AI valuation and what it means• Why Mark Andreessen thinks VCs won't be automatedPlus: Caspar reveals the #1 mistake companies make with AI deployment and why "AI-ifying" your current process is doomed to fail.Subscribe to The Neuron newsletter: https://theneuron.aiConnect with Caspar on LinkedIn: https://uk.linkedin.com/in/caspar-eliot-46b9a55aLearn more about Invisible Technologies: https://invisibletech.aiPlease check out the sponsor of this video, Warp.dev: https://warp.dev

On today's episode, co-hosts Yasmin Gagne and Josh Christensen discuss the latest news in business and innovation. Topics include the latest on tariffs, layoffs at companies like Amazon and Target, and NBCUniversal's poaching of Taylor Sheridan.     Next, Yaz and Josh talk to Fast Company senior editor Bryan Lufkin about “ghost jobs” and other hiring trends in the current, uncertain economic climate.   Finally, Yaz interviews Lucy Guo. Guo is the founder of the content creator monetization platform Passes, which lets creators make money from fans through things like selling merch and private chats. The company hasn't been without controversy and has faced lawsuits accusing it of allowing and encouraging the distribution of illegal content, including child sexual abuse material, as well as engaging in unfair business practices. Guo is also the cofounder of Scale AI, which Meta recently bought a 49% stake in. That sale made her, by some metrics, the youngest self-made billionaire. Yaz spoke to Guo about why she left Scale AI in 2018, how she responds to those lawsuits, and why she doesn't think we're in an AI bubble. For more of the latest business and innovation news, go to https://www.fastcompany.com/newsTo read more of our reporting on hiring trends, go to https://www.fastcompany.com/work-life

En la edición de hoy del Radar Empresarial analizamos el fuerte retroceso que ha sufrido Meta en las operaciones fuera de horario. La empresa dirigida por Mark Zuckerberg registró una caída cercana al 7% en el mercado after hours, tras comunicar en su llamada con analistas que su saldo de caja se redujo un 43% desde finales del año pasado. Este descenso se atribuye principalmente a las enormes inversiones que la tecnológica está realizando en el desarrollo de inteligencia artificial. Zuckerberg explicó que tanto la contratación de expertos de primer nivel como la construcción de costosos centros de datos han presionado los márgenes operativos y reducido en un tercio el flujo de caja libre durante el tercer trimestre del año. A pesar de la reacción negativa del mercado, el CEO de Meta se mostró firme en su postura. Destacó que su estrategia pasa por consolidar a la compañía como una de las líderes mundiales en la carrera por la inteligencia artificial. Según aseguró, la empresa no tiene intención de frenar su ambicioso plan de expansión y planea aumentar su infraestructura tecnológica en 2026, con el objetivo de incrementar de manera significativa su capacidad de procesamiento y desarrollo. Entre las inversiones más destacadas se encuentra la construcción de un nuevo centro de datos en El Paso, con un desembolso de 1.500 millones de dólares, además de un acuerdo de financiación de 27.000 millones con Blue Owl Capital para el complejo que se levantará en Louisiana. A ello se suma la inyección de más de 14.000 millones en Scale AI, lo que refleja el compromiso de Meta con este sector. Sin embargo, estas cifras han generado preocupación entre los inversores, que han respondido con ventas tras conocerse los resultados. Y es que los datos financieros del tercer trimestre no fueron alentadores. Meta reportó un beneficio por acción apenas superior a un dólar, lejos de los casi siete esperados por el mercado. El beneficio neto acumulado entre enero y septiembre alcanzó los 37.690 millones, un 9% menos que en 2024. No obstante, la facturación total ascendió a más de 140.000 millones, lo que representa un incremento del 21%. La empresa también anunció una provisión fiscal cercana a los 16.000 millones debido a la aplicación de la Ley One Big Beautiful Bill, impulsada durante la administración de Donald Trump.

Chip Huyen is a core developer on Nvidia's Nemo platform, a former AI researcher at Netflix, and taught machine learning at Stanford. She's a two-time founder and the author of two widely read books on AI, including AI Engineering, which has been the most-read book on the O'Reilly platform since its launch. Unlike many AI commentators, Chip has built multiple successful AI products and platforms and works directly with enterprises on their AI strategies, giving her unique visibility into what's actually happening inside companies building AI products.We discuss:1. What people think makes AI apps better vs. what actually makes AI apps better2. What pre-training vs. post-training is, and why fine-tuning should be your last resort3. How RLHF (reinforcement learning from human feedback) actually works4. Why data quality matters more than which vector database you choose5. Why high performers are seeing the most gains from AI coding tools6. Why most AI problems are actually UX issues—Brought to you by:Dscout—The UX platform to capture insights at every stage: from ideation to production: https://www.dscout.com/Justworks—The all-in-one HR solution for managing your small business with confidence: https://ad.doubleclick.net/ddm/trackclk/N9515.5688857LENNYSPODCAST/B33689522.423713855;dc_trk_aid=616485030;dc_trk_cid=237010502;dc_lat=;dc_rdid=;tag_for_child_directed_treatment=;tfua=;gdpr=$Persona—A global leader in digital identity verification: https://withpersona.com/lenny—Where to find Chip Huyen:• X: https://x.com/chipro• LinkedIn: https://www.linkedin.com/in/chiphuyen/• Website: https://huyenchip.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Chip Huyen(04:28) Chip's viral LinkedIn post(07:05) Understanding AI training: pre-training vs. post-training(08:50) Language modeling explained(13:55) The importance of post-training(15:20) Reinforcement learning and human feedback(22:23) The importance of evals in AI development(31:55) Retrieval augmented generation (RAG) explained(38:50) Challenges in AI tool adoption(43:19) Challenges in measuring productivity(45:20) The three-bucket test(49:10) The future of engineering roles(55:31) ML Engineers vs. AI engineers(57:12) Looking forward: the impact of AI(01:05:48) Model capabilities vs. perceived performance(01:08:23) Lightning round and final thoughts—Referenced:• Chip's LinkedIn post on what actually improves AI apps: https://www.linkedin.com/posts/chiphuyen_aiapplications-aiengineering-activity-7358971409227792384-y0mf/• Prediction and Entropy of Printed English: https://www.princeton.edu/~wbialek/rome/refs/shannon_51.pdf• Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody (CEO of Mercor): https://www.lennysnewsletter.com/p/experts-writing-ai-evals-brendan-foody•Inside the expert network training every frontier AI model | Garrett Lord (Handshake CEO): https://www.lennysnewsletter.com/p/inside-handshake-garrett-lord• First interview with Scale AI's CEO: $14B Meta deal, what's working in enterprise AI, and what frontier labs are building next | Jason Droege: https://www.lennysnewsletter.com/p/first-interview-with-scale-ais-ceo-jason-droege• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Why AI evals are the hottest new skill for product builders | Hamel Husain & Shreya Shankar (creators of the #1 eval course): https://www.lennysnewsletter.com/p/why-ai-evals-are-the-hottest-new-skill• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Stanford webinar—How AI Is Changing Coding and Education, Andrew Ng & Mehran Sahami: https://www.youtube.com/watch?v=J91_npj0Nfw• He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more: https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor• Anthropic co-founder on quitting OpenAI, AGI predictions, $100M talent wars, 20% unemployment, and the nightmare scenarios keeping him up at night | Ben Mann: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann• Lenny's vibe-coded app made on Lovable: https://gdoc-images-grab.lovable.app/• Story of Yanxi Palace: https://www.imdb.com/title/tt8865016/• Steve Jobs's quote: https://www.goodreads.com/quotes/427317-remembering-that-i-ll-be-dead-soon-is-the-most-important—Recommended books:• The Complete Sherlock Holmes: https://www.amazon.com/Complete-Sherlock-Holmes-Volumes/dp/0553328255• AI Engineering: Building Applications with Foundation Models: https://www.amazon.com/AI-Engineering-Building-Applications-Foundation/dp/1098166302• The Selfish Gene: https://www.amazon.com/Selfish-Gene-Anniversary-Introduction/dp/0199291152• From Third World to First: The Singapore Story: 1965-2000: https://www.amazon.com/Third-World-First-Singapore-1965-2000/dp/0060197765—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Abu-Ghazaleh said his two-month-old company promises to cut inefficiencies in high-stakes sectors like aviation, logistics, and oil and gas through an AI-native operating system for decision-making. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Industry leaders from Coder, Scale AI, and Suger reveal why 95% of AI pilots fail—and share the frameworks that actually work to get agents into production.Topics Include:Panel features leaders from Coder, Scale AI, and Suger discussing agentic AI.MIT report reveals 95% of AI pilots fail to reach production.Challenges are rarely technical—they're organizational, mindset, and people-driven instead.Companies lack documented tribal knowledge needed to train agents effectively.Many organizations attempt AI where deterministic, rules-based automation would work better."Freestyle agents" concept: Some problems shouldn't be solved by agents at all.Regulated industries struggle when asking agents to handle highly differentiated, complex tasks.Common mistakes: building one universal agent or separate agents for every use case.Post-billing workflows and business-critical operations aren't ready for AI's black box.VCs pressure companies to define "AI-native"—but nobody has clear answers yet.Scale AI uses five maturity levels; Coder uses three tiers for adoption.Success metrics span operational readiness, business impact, and technology performance indicators.Production requires data governance, context, A/B testing, and robust fallback mechanisms.Even Anthropic uses agents conservatively: research tasks and log triage, no write-access.Path to 50% success requires agile frameworks, people change, and proper AI talent.Participants:Ben Potter - VP of Product, CoderRaviteja Yelamanchili - Head of Solutions Engineering, Scale AIJon Yoo - CEO, SugerAdam Ross - US, Partner Sales Sr. Leader, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

F5 Networks, which provides cybersecurity defenses to most of the Fortune 500, said the DOJ allowed it to delay notifying the public on national security grounds. Semiconductor firm Arm is partnering with Meta to enhance the social media company's AI systems amid an unprecedented infrastructure buildout. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Plus - OpenAI has five years to turn $13 billion into $1 trillion; Facebook brings back job listings in the US Learn more about your ad choices. Visit podcastchoices.com/adchoices

Artificial intelligence is transforming every sector of the global economy, from healthcare and manufacturing to financial services and telecommunications. We have passed the initial hype stage — AI exists in many organizations as an established business competency and is now being deployed in detail to solve specific business problems.So what current issues should companies be focusing on? What challenges are organizations facing as AI moves past the innovation phase of the technology life cycle and into mainstream adoption?In this Keynote, Professor Dirk Swart of Cornell Engineering is joined by Luke Corbin and Jonathan Huer from AT&T Business to explore these developments. They examine how emerging AI solutions are being deployed across industries and what lessons can be learned from AT&T's experience as an early adopter of AI technologies.This discussion covers practical aspects, such as building AI capabilities, managing data governance, scaling solutions enterprise-wide, and developing AI-ready talent and culture. You'll gain insight into how AT&T's current AI initiatives compare with industry standards and consider common challenges faced during implementation as well as future possibilities in AI-driven business transformation.Professor Swart's Technical Product Management Certificate - https://ecornell.cornell.edu/certificates/technology/technical-product-management/AI for Digital Transformation Certificate - https://ecornell.cornell.edu/certificates/technology/ai-for-digital-transformation/Generative AI for Productivity Certificate - https://ecornell.cornell.edu/certificates/technology/generative-ai-for-productivity/ Follow eCornell on Facebook, Instagram, LinkedIn, TikTok, and X.

Jason Droege is the CEO of Scale AI, a company that provides foundational training data to every major AI lab. He previously co-founded Scour with Travis Kalanick and built Uber Eats from idea to $20 billion in revenue. In this conversation, Jason shares lessons from getting sued for $250 billion, discovering restaurant economics by weighing sandwich ingredients, and over 25 years of launching transformative technology businesses.What you'll learn:What actually happened with Meta's $14 billion investment in Scale AIWhy AI models still need human experts to improve, and how that relationship is evolvingHow AI models learn from experts building websites and debugging codeThe business lessons from building Uber Eats from zero to $20 billionWhy most enterprise data is useless for AI models todayWhy urgent daily problems beat super-valuable occasional problems when building productsHow to think independently when building new products and businesses—Brought to you by:Merge—The fastest way to ship 220+ integrations: http://merge.dev/lennyFigma Make—A prompt-to-code tool for making ideas real: https://www.figma.com/lenny/Mercury—The art of simplified finances: https://mercury.com/—Transcript: https://www.lennysnewsletter.com/p/first-interview-with-scale-ais-ceo-jason-droege—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/174979621/my-biggest-takeaways-from-this-conversation—Where to find Jason Droege:• X: https://x.com/jdroege• LinkedIn: https://www.linkedin.com/in/jasondroege/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Jason Droege(06:01) Jason's early career and lessons learned(10:27) The current state of Scale AI(12:37) The shift to expert data labeling(17:02) Challenges and strategies in finding experts(18:48) Reinforcement learning and AI environments(28:18) The future of AI and human involvement(31:21) The role of evals(35:25) What AI models will look like in the next few years(41:43) Building Uber Eats and understanding customer needs(48:19) The importance of independent thinking(50:45) Setting high standards for new businesses(53:03) Exploring and selecting business ideas(57:07) The McDonald's story(01:00:13) The role of gross margins in business feasibility(01:04:49) Why Jason says, “Not losing is a precursor to winning”(01:09:12) Hiring and building teams(01:12:11) AI corner(01:14:47) Lightning round and final thoughts—Referenced:• Travis Kalanick on X: https://x.com/travisk• Scour: https://en.wikipedia.org/wiki/Scour_Inc.• Scale: https://scale.com/• Alexandr Wang on X: https://x.com/alexandr_wang• Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody (CEO of Mercor): https://www.lennysnewsletter.com/p/experts-writing-ai-evals-brendan-foody• Brendan Foody's post on X about knowledge work changing: https://x.com/BrendanFoody/status/1970163503702188048• MIT Finds 95% of GenAI Pilots Fail Because Companies Avoid Friction: https://www.forbes.com/sites/jasonsnyder/2025/08/26/mit-finds-95-of-genai-pilots-fail-because-companies-avoid-friction/• Uber Eats: https://www.ubereats.com/• Stephen Chau on X: https://x.com/thestephenchau• a16z Podcast: https://a16z.com/podcasts/a16z-podcast/• F1: The Movie: https://www.imdb.com/title/tt16311594/• V03: https://v03ai.com/• Careers at Scale: https://scale.com/careers—Recommended books:• The Selfish Gene: https://www.amazon.com/Selfish-Gene-Anniversary-Introduction/dp/0199291152• The Road Less Traveled: A New Psychology of Love, Traditional Values, and Spiritual Growth: https://www.amazon.com/Road-Less-Traveled-Timeless-Traditional/dp/0743243153/• Good to Great: Why Some Companies Make the Leap . . . And Others Don't: https://www.amazon.com/Good-Great-Some-Companies-Others/dp/0066620996• Thinking, Fast and Slow: https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Too many companies see AI only as a cost-cutting shortcut, rolling out rushed AI deployments that frustrate customers. But the real opportunity lies in everyday AI: using it to handle what humans don't do well, so people can focus on what they do best—building authentic customer relationships. This week on The Modern Customer Podcast, Henrik Werdelin, founder of BARK, Prehype, Audos, and co-author of Me, My Customer, and AI, shares how everyday AI can transform CX. Highlights from the podcast: ➡️ AI Beyond Efficiency — Instead of just automating tasks, AI expands human capability and frees teams to focus on authentic customer connections. ➡️ Listening at Scale — AI makes it possible to instantly analyze thousands of customer comments, delivering insights that go far deeper than NPS scores. ➡️ Strengthening Relationship Capital — With AI, brands can build customer loyalty through three layers: depth (feeling seen), density (community), and durability (long-term trust). ➡️ Empowering Everyday Entrepreneurs — AI lowers barriers to entry, enabling more people to create businesses that solve real customer problems and build lasting relationships.

Datumo is carving out its identity as a challenger brand to Scale AI. Its strategy focuses on efficiency, transparency, and accessibility. Whether it succeeds could redefine how the world trains AI systems.Try AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

Datumo's bold challenge to Scale AI raises critical questions about the future of data labeling and AI support systems. If successful, it could decentralize power in the AI market. The move is drawing global attention from investors and technologists.Try AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

Meta präsentiert neue AR-Brillen mit Display, während die Live-Demo spektakulär scheitert. Nvidia investiert 5 Milliarden Dollar in Intel und weitere 2 Milliarden in britische Startups. Google integriert Gemini direkt in Chrome und verhandelt mit Reddit über dynamischere Datendeals. Jimmy Kimmel verliert seinen Late-Night-Job durch politischen Druck. TikTok-Deal zwischen Larry Ellison und Andreessen Horowitz rückt näher. XAI verliert weitere Führungskräfte. DeepSeek schreibt schlechteren Code für China-kritische Gruppen. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über:   (00:00:00) Intro  (00:05:00) Meta AR-Brillen (00:12:10) Nvidia investiert in Intel (00:18:50) Google Gemini in Chrome (00:21:30) Reddit verhandelt mit Google (00:23:55) Jimmy Kimmel (00:41:45) TikTok-Deal Update (00:45:00) XAI-Führungskrise (00:49:35) Scale AI, Anthropic, DeepSeek  Shownotes Mark Zuckerberg verspricht neue Smart Glasses für 'Superintelligenz' – ft.com Nvidia investiert $5 Mrd. in Konkurrent Intel – ft.com Nvidia investiert £2 Milliarden in britische KI-Startups – bloomberg.com Google integriert Gemini in Chrome: KI-Browser werden Mainstream – wired.com Reddit plant nächsten KI-Inhaltspakt mit Google, OpenAI – bloomberg.com TikTok, Jimmy Kimmel: Trumps mediale Gleichschaltungsstrategie – spiegel.de Trump "faktisch falsch" in Verleumdungsklage, sagt NYT-Redakteur – axios.com TikToks neue Investoren in den USA unklar – theinformation.com Konflikte zwischen xAI-Führungskräften und Elon Musks Beratern vor Abgang – wsj.com Scale AI schließt Abkommen mit Pentagon ab – axios.com AI-Firma DeepSeek schreibt unsicheren Code für von China benachteiligte Gruppen – washingtonpost.com FBI bereitet neuen Krieg gegen Trans-Personen vor – kenklippenstein.com AI-Chip-Startup Groq erhält $750 Millionen bei $6,9 Milliarden Bewertung – bloomberg.com Anthropic verärgert das Weiße Haus mit Nutzungsbeschränkungen – semafor.com Gemini AI löst Programmierproblem, das 139 menschliche Teams beim ICPC-Weltfinale überforderte – arstechnica.com

(0:00) Intro(1:30) About the podcast sponsor: The American College of Governance Counsel(2:16) Start of interview. *Reference to E181 (July 2025) for Steven's personal/professional background.(3:14) IPOs and Market Trends. Including Klarna and Gemini.(5:29) The Stay Private vs. Go Public Dilemma. Valuations and market health (examples of Airbnb and Figma)(12:00) The Oracle post-earnings 36% price increase. *Reference to article by Tom Chavez: In Defense of Bubbles.(14:14) AI, Data Centers, and Market Dynamics(15:15) OpenAI's Future and Governance(20:12) Power Dynamics in Big Tech companies (Mag 7).(22:35) Tesla and Elon Musk Compensation Structure (Mega Grants)(24:53) Boardroom Accountability in Big Tech(28:31) Scale AI and L&A (Licensing & Acquihiring) as the new M&A(34:34) AI startup governance (e.g. SSI and Thinking Machine Labs)(36:41) The Role of Directors in Governance. "Theater in the boardroom?"(39:08) Startup Fraud (Elizabeth Holmes, SBF, etc) and the Startup Litigation Digest(40:05) Legal Accountability and Ethics (46:39) The Future of AI and Market Valuations in the "Agentic Economy"(51:43) The Importance of Board LeadershipSteven Wolfe Pereira founded Alpha to solve a critical problem: most boards are governing AI transformation without the frameworks, intelligence, or peer networks they need to make sound fiduciary decisions.  You can follow Evan on social media at:X: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/__To support this podcast you can join as a subscriber of the Boardroom Governance Newsletter at https://evanepstein.substack.com/__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License

Jason sits down with Wilson Sonsini partner Chris Paniewski for a special Startup Legal Basics on one of the thorniest questions in tech right now: how copyright law applies to AI training data.Chris has worked on some of the biggest AI deals ever — including Scale AI's $14B+ partnership with Meta and OpenAI's $6.5B acquisition of Jony Ive's design studio — and brings practical, on-the-ground insights from advising leading AI companies.In this episode, Jason and Chris cover:Why AI copyright law is unsettled and will take years to shake outThe difference between training data and output in legal termsHow “fair use” really works (and why it's a defense, not a permission slip)The risks of scraping vs. licensing, and why open source ≠ free useHow investors are diligencing AI startups around training dataWhy startups must think differently once they're funded vs. hacking in a dorm roomWhether you're building an AI product, investing in one, or just trying to understand where the law is headed, this conversation breaks down the real legal risks every founder should know.Timestamps:(0:00) Jason introduces the Startup Legal Basics series & Chris Paniewski(1:25) Why AI copyright law is unsettled(3:40) Training data: scraping vs. licensing(6:05) Open web ≠ open license; pitfalls around terms of service(8:15) Investor diligence & risks around training data(11:00) Open source & Creative Commons: common founder mistakes(13:25) “Fair use” explained: the four-part test(15:45) Why most disputes never make it to case lawCheck Out Wilson Sonsini: https://www.wsgr.comCheck out all of the Startup Basics episodes here: https://thisweekinstartups.com/basicsFollow Chris:LinkedIn: https://www.linkedin.com/in/christopher-paniewski-09331a59/Follow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

welcome to wall-e's tech briefing for thursday, september 4th! dive into today's top tech stories: mistral ai's massive valuation: french startup nearing a $14 billion valuation, driven by its open-source language models and ai chatbot, le chat. apple's ai enhancement: potential collaboration with google's gemini ai model to upgrade siri, enhancing features like ai-powered web search by 2026. scale ai lawsuit: the company sues a former employee and competitor mercor, alleging theft of confidential customer strategy documents. spacex launch expansion: approval for environmental review increases falcon 9 launch capacity from florida to 120 annually. orchard robotics funding: raises $22 million to revolutionize agriculture with ai-driven tech for optimized crop management. stay tuned for tomorrow's tech updates!

Welcome to another episode of the Beacon Way Podcast! In this episode, host Adrienne Wilkerson dives deep into how Artificial Intelligence (AI) can revolutionize personalized marketing, making it accessible for small and medium-sized businesses. Learn about AI-driven strategies like predictive analytics, dynamic content creation, and advanced customer segmentation. Adrienne also discusses the importance of ethical AI use, data minimalization, and emerging trends. Whether you're new to AI or looking to enhance your current marketing efforts, this episode offers invaluable insights to help your business thrive in today's data-driven marketplace.

Hosted by David Cowen | Presented by Steno Live at ILTACON 2025, Rachel Shield Williams, Director of Client Intelligence at Sidley Austin, breaks down how the smartest legal teams are using AI, not just to save time, but to build stronger relationships, drive smarter decisions, and unlock entirely new ways of working. From ChatGPT to lakehouses, Rachel shares how the future of legal operations is being shaped by clean data, clear governance, and creative thinking. This episode is packed with real use cases, sharp insights, and powerful takeaways for anyone working at the intersection of law, data, and strategy. Key Topics Covered: What “client intelligence” really means and why it's a growth engine Why governance is the backbone of safe, scalable GenAI The difference between data warehouses, lakes, and lakehouses (finally explained) How cross-functional teams are reshaping client relationships Rachel's ChatGPT workflows - summarizing, strategizing, even grocery shopping AI as a leadership tool: how to support your team and stay ahead Inclusion, accessibility, and the future of data-driven collaboration This Episode is presented by Steno: Smarter transcripts. Faster delivery. Built for modern legal teams.

In this episode, Jaeden discusses the startup Datumo, which has recently raised $15.5 million to compete with Scale AI in the data labeling market. He explores the company's unique approach to data labeling, its growth trajectory, and the competitive landscape shaped by major players like Scale AI and Meta. The conversation also highlights Datumo's funding journey and future expansion plans, particularly in the U.S. and Japan.Try AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle/aboutYouTube Video: https://youtu.be/UG7wUgPwbCwChapters00:00 Introduction to Datumo and Market Context03:08 Datumo's Unique Approach to Data Labeling05:48 Industry Dynamics and Competition with Scale AI08:33 Funding Journey and Future Prospects

Enterprise AI leaders from C3 AI, Resolve AI, and Scale AI reveal how Fortune 100 companies are successfully scaling agentic AI from pilots to production and share secrets for successful AI transformation.Topics Include:Panel introduces three AI leaders from Resolve AI, C3 AI, and Scale AIResolve AI builds autonomous site reliability engineers for production incident responseC3 AI provides full-stack platform for developing enterprise agentic AI workflowsScale AI helps Fortune 100 companies adopt agents with private data integrationMoving from AI pilots to production requires custom solutions, not shrink-wrap softwareSuccess demands working directly with customers to understand their specific workflowsAll enterprise AI solutions need well-curated access to internal data and resourcesSoftware engineering has permanently shifted to agentic coding with no going backAI agents rapidly improving in reasoning, tool use, and contextual understandingIndustry moving from simple co-pilots to agents solving complex multi-step problemsSpiros coins new concept: evolving from "systems of record" to "systems of knowledge"Democratized development platforms let enterprises declare their own agent workflowsSemantic business layers enable agents to understand domain-specific enterprise operationsTrust and observability remain major barriers to enterprise agent adoptionOversight layers essential for agents making longer-horizon autonomous business decisionsPerformance tracking and calibration systems needed like MLOps for reasoning chainsCEO-level top-down support required for successful AI transformation initiativesTraditional per-seat SaaS pricing models completely broken for agentic AI solutionsIndustry shifting toward outcome-based and work-completion pricing models insteadReal examples shared: agent collaboration in production engineering and sales automationParticipants:Nikhil Krishnan – SVP & Chief Technology Officer, Data Science, C3 AISpiros Xanthos – Founder and CEO, Resolve AIVijay Karunamurthy – Head of Engineering, Product and Design / Field Chief Technology Officer, Scale AIAndy Perkins – GM, US ISV Sales – Data, Analytics, GenAI, Amazon Web ServicesFurther Links:C3 – Website – AWS MarketplaceResolve AI – Website – AWS MarketplaceScale AI – Website – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

This week on Networth and Chill, Vivian sits down with Lucy Guo, the serial entrepreneur and co-founder of Scale AI, to explore the financial blueprint behind building multiple unicorn companies before age 30. From dropping out of Carnegie Mellon to co-founding Scale AI—now valued at over $13 billion—Lucy's story reveals the strategic decisions and calculated risks that transformed her from a college dropout into one of tech's most influential young billionaires. Vivian explores Lucy's investment philosophy, her strategies for identifying market opportunities in emerging tech sectors, and how she's diversified her wealth beyond Scale AI through ventures like Passes, her social platform for creators.Whether you're interested in AI entrepreneurship, startup scaling strategies, or understanding how to leverage technical skills into generational wealth, this episode offers crucial insights on building in the rapidly evolving tech landscape. Check out Lucy on Instagram and learn more about Passes at https://www.passes.com/ Follow the podcast on Instagram and TikTok! Got a financial question you want answered in a future episode? Email us at podcast@yourrichbff.com Learn more about your ad choices. Visit podcastchoices.com/adchoices

In the generative AI revolution, quality data is a valuable commodity. But not all data is created equally. Sarah Guo and Elad Gil sit down with SurgeAI founder and CEO Edwin Chen to discuss the meaning and importance of quality human data. Edwin talks about why he bootstrapped Surge instead of raising venture funds, the importance of scalable oversight in producing quality data, and the work Surge is doing to standardize human evals. Plus, we get Edwin's take on what Meta's investment into Scale AI means for Surge, as well as whether or not he thinks an underdog can catch up with OpenAI, Anthropic, and other dominant industry players. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @echen | @HelloSurgeAI Chapters: 00:00 – Edwin Chen Introduction 00:41 – Overview of SurgeAI 02:28 – Why SurgeAI Bootstrapped Instead of Raising Funds 07:59 – Explaining SurgeAI's Product 09:39 – Differentiating SurgeAI from Competitors  11:27 – Measuring the Quality of SurgeAI's Output 12:25 – Role of Scalable Oversight at SurgeAI 14:02 – Challenges of Building Rich RL Environments 16:39 – Predicting Future Needs for Training AI Models 17:29 – Role of Humans in Data Generation 21:27 – Importance of Human Evaluation for Quality Data 22:51 – SurgeAI's Work Toward Standardization of Human Evals 23:37 – What the Meta/ScaleAI Deal Means for SurgeAI 24:35 – Edwin's Underdog Pick to Catch Up to Big AI Companies 24:50 – The Future Frontier Model Landscape 26:25 – Future Directions for SurgeAI 29:29 – What Does High Quality Data Mean? 32:26 – Conclusion

ChatGPT Agents are here.U.S. President Trump has big plans for AI.And Google is slapping more AI on traditional search than a commercial pitchman slapping Flex Seal on a leaky boat.AI is changing how we all work. And there's way too much happening to keep track. So, that's why you should spend Mondays with us as we bring you the AI News that Matters.Try Gemini 2.5 Flash! Sign up at  AIStudio.google.com to get started. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Meta Considers Shift to Closed-Source AITrump Unveils National AI Policy Plan$2 Billion Seed Funding: Thinking Machines LabsGemini 2.5 Pro Launches in Google SearchGoogle AI Enables Automated Local Business CallsDeep Search Feature with Gemini 2.5 ProOpenAI ChatGPT Agents Power Multi-step AutomationChatGPT to Charge Commissions on E-commerceNew Student Study Tools: OpenAI, Google, AnthropicAnthropic Debuts Domain-Specific Financial AINvidia H20 AI Chip Exports to China ControversyTimestamps:00:00 Meta Considers Closing AI Models04:15 "Meta's AI Strategy and Open Source"08:15 Diverse Regulation Needs for AI Innovation09:42 AI Startup Surpasses $12B Valuation13:37 Google and OpenAI's AI Expansion18:41 AI Companies Target Student Demographic21:59 "Rise of Domain-Specific Models"23:47 2026 AI Model Revolution26:36 AI Export Controls and US-China Tech Race30:36 OpenAI Unveils ChatGPT Agent33:05 "Watch Mode for ChatGPT Pro"Keywords:Google AI, Gemini 2.5 Pro, Gemini AI mode, Deep search, AI-powered local calling, agentic AI capabilities, ChatGPT agents, ChatGPT agent, OpenAI, ChatGPT Pro, reasoning model, automated phone calls, local business AI calls, US AI policy, President Trump AI strategy, deregulation in AI, AI regulation, closed source AI model, Meta AI, open source vs closed source AI, superintelligence lab, Alexander Wang, Scale AI, Nvidia GPU drama, Nvidia H20 chips, AI chips export, US-China AI arms race, artificial general intelligence, AGI, ASI, Anthropic, Claude, domain specific models, financial analyst AI, financial data AI solutions, Monte Carlo simulation AI, risk modeling AI, Amazon Bedrock Agent, Microsoft Copilot Vision, multi-step task automation, virtual terminal AI, multimodal reasoning, e-commerce AI, ChatGPT shopping, AI optimization, AIO, affiliate revenue AI, education AI tools, AI study assistants, Study Together ChatGPT, Study Projects Claude, Guided Learning Gemini, enterprise AI soluSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Scott Wu is the co-founder and CEO of Cognition, the company behind Devin, the world's first AI software engineer. On Friday last week they pulled off the acquisition of the year, acquiring Windsurf, following their licensing agreement with Google. Previously a world-class competitive programmer, he was a gold medalist at the International Olympiad in Informatics and a member of the U.S. Math and Physics Olympiad teams. Before Cognition, he was a founding engineer at Scale AI, helping shape the early AI infrastructure stack. AGENDA: 00:00 – Why are founders walking away instead of going down with the ship? 01:05 – How did Cognition pull off the $220M Windsurf deal in just 72 hours? 04:45 – What really happened behind closed doors the weekend Windsurf was acquired? 07:15 – Did Google overlook a goldmine in the Windsurf team and IP? 09:00 – Who are the 100 people that secretly shape the future of AI? 12:30 – Can application startups ever gain leverage over foundation model giants like Anthropic? 14:15 – Is coding about to be replaced by simply describing what you want? 17:30 – 50% of new code is AI-written. Where does that go next? 20:45 – “We've gone from 0 to $80M ARR in 6 months. Quietly.” 25:00 – Are IDEs and agents just the training wheels for the real future of software engineering? 28:20 – If you could only back one—OpenAI or Anthropic—who's the better bet? 30:00 – Why has Cognition kept its insane growth a secret… until now?    

Wanna start a side hustle but need an idea? Check out our Side Hustle Ideas Database: https://clickhubspot.com/thds We're breaking down the wild contradictions in AI land, where companies are simultaneously laying off workers and throwing billions at acquisitions, plus Google's new feature that lets AI make your awkward phone calls to local businesses. Plus: South Korea is booming with startups and Pepsico beats Wall Street earnings expectations. Join our hosts Jon Weigell and Maria Gharib as they take you through our most interesting stories of the day. Follow us on social media: TikTok: https://www.tiktok.com/@thehustle.co Instagram: https://www.instagram.com/thehustledaily/ Thank You For Listening to The Hustle Daily Show. Don't forget to hit subscribe or follow us on your favorite podcast player, so you never miss an episode! If you want this news delivered to your inbox, join millions of others and sign up for The Hustle Daily newsletter, here: https://thehustle.co/email/  If you are a fan of the show be sure to leave us a 5-Star Review, and share your favorite episodes with your friends, clients, and colleagues.

In this episode of the Brainstorm, Sam, Nick, and Research Analyst, Jozef Soja, dive into the latest advancements in AI with a focus on Grok4 and Meta's strategic moves. They explore Grok4's impressive benchmark performances, the real-world implications of AI tool use, and Meta's aggressive investments in AI talent and infrastructure.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:Grok4 has set new standards in AI benchmarks, particularly in GPQA and Humanities Last Exam, showcasing its advanced reasoning and tool use capabilities.Real-world applications still present challenges for Grok4, highlighting the gap between theoretical performance and practical use.Meta is making aggressive moves in the AI space, acquiring stakes in companies like Scale AI and investing heavily in AI talent to build a super intelligence team.For more updates on Public.com:Website: https://public.com/YouTube: @publicinvestX: https://twitter.com/public

Imagine turning down $100 million salaries. That's apparently what's happening at OpenAI. And that's just the tip of the newsworthy AI iceberg for the week. ↳ Meta reportedly failed to acquire Perplexity. Could Apple try next? ↳ Why is Microsoft cutting so many jobs? ↳ Why are AI systems blackmailing at will? ↳ Will too much AI use lead to brain rot?Let's talk AI news shorties. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:$100M AI Salaries Being DeclinedMeta's AI Talent War EffortsMeta's Unsuccessful Acquisitions OverviewBrain Rot Concerns with AI UseOpenAI's $200M DoD ContractGoogle's Voice AI Search RolloutGoogle Gemini 2.5 in ProductionSoftBank's $1T Robotics InvestmentAnthropic's AI Model Risks ExposedMicrosoft and Amazon AI Job CutsTimestamps:00:00 Weekly AI News and Insights04:17 Meta's Major AI Acquisitions08:50 AI Impact on Student Writing Skills12:53 OpenAI Expands Government AI Program15:31 Google Launches Voice AI Search19:32 Google AI Models' Stability Feature22:55 "Project Crystal Land Initiative"27:17 AI Acquisition Talks Intensify29:43 "Apple Eyes Perplexity Acquisition"31:54 Apple's Potential Market Decline36:57 AI Ethics and Safety Concerns40:44 Amazon Warns of AI-Driven Layoffs42:44 AI's Impact on Job Market45:24 "Canvas Tips for Business Intelligence"Keywords:$100 million salaries, AI talent war, Meta, OpenAI, AI signing bonuses, Andrew Bosworth, Scale AI acquisition, Alexander Wang, Safe Superintelligence, Daniel Gross, Nat Friedman, Perplexity AI, Brain rot from AI, chat GBT and brain, MIT study on AI, SAT style essays using AI, AI neural activity, AI and cognitive effort, AI in government, $200 million contract with Department of Defense, OpenAI in security, ChatGPTgov, Federal AI initiatives, Google Gemini 2.5, AI mission-critical business, Gemini 2.5 flashlight, AI model stability, SoftBank $1 trillion investment, Project Crystal Land, Arizona robotics hub, Taiwan Semiconductor Manufacturing Company, Embodied AI, AI job cuts, Microsoft layoffs, Amazon AI workforce, Anthropic study on AI ethics, AI blackmail, Google voice-based AI search, AI search live, New AI apps, Apple acquisition interest in Perplexity, AI-powered search engine, Siri integration, AI-driven efficiencies, GenSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Try Google Veo 3 today! Sign up at gemini.google to get started. Try Google Veo 3 today! Sign up at gemini.google to get started.

(0:00) The Besties welcome Thomas Laffont! (3:26) State of LA, Hollywood's decline, positivity around GDP growth and AI productivity (10:19) Zuck on tilt over AI: $100M offers, Scale AI deal, hiring spree (23:58) Mag 7 AI Showdown: Ranking the most likely AI winners, biggest stock divergences, and more (42:41) Why Apple is fumbling AI and how they can fix it? (57:02) IPOs and M&A heating up in 2025 (1:16:18) State of liquidity: SPACs, Direct Listings, and more (1:25:40) Amazon's "kingmaker" position, job displacement (1:37:47) Sacks joins to discuss the GENIUS Act passing the Senate (1:52:13) Animal trailer Follow Thomas Laffont: https://x.com/thomas_coatue Animal Trailer: https://www.youtube.com/watch?v=8NNW5r63oXU Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect Referenced in the show: https://techcrunch.com/2025/06/17/sam-altman-says-meta-tried-and-failed-to-poach-openais-talent-with-100m-offers https://www.cnbc.com/2025/06/10/zuckerberg-makes-metas-biggest-bet-on-ai-14-billion-scale-ai-deal.html https://www.nytimes.com/2025/06/12/technology/meta-scale-ai.html https://scale.com/blog/scale-ai-announces-next-phase-of-company-evolution https://www.reuters.com/business/meta-talks-hire-former-github-ceo-nat-friedman-join-ai-efforts-information-2025-06-18 https://techcrunch.com/2012/09/11/mark-zuckerberg-our-biggest-mistake-with-mobile-was-betting-too-much-on-html5 https://www.reuters.com/technology/china-launch-new-40-bln-state-fund-boost-chip-industry-sources-say-2023-09-05 https://x.com/JoannaStern/status/1933564098291048764 https://www.youtube.com/watch?v=wCEkK1YzqBo https://x.com/chamath/status/1932157508698919320 https://www.renaissancecapital.com/IPO-Center/Stats/Pricings https://www.aboutamazon.com/news/company-news/amazon-ceo-andy-jassy-on-generative-ai https://x.com/chamath/status/1935369326321877153 https://x.com/chamath/status/1935740807925100853 https://www.google.com/finance/quote/COIN:NASDAQ https://www.google.com/finance/quote/SPOT:NYSE https://x.com/ylecun/status/1935108028891861393 https://x.com/ben_j_todd/status/1934284189928501482 https://apnews.com/article/election-2024-senate-ohio-brown-moreno-74c4b91e5866215d4201377fefcadad0 https://companiesmarketcap.com/microsoft/revenue https://apnews.com/article/election-2024-senate-ohio-brown-moreno-74c4b91e5866215d4201377fefcadad0 https://www.youtube.com/watch?v=8NNW5r63oXU

Today's show:In this episode, @Jason and @alex explore how AI is reshaping the economy—from Pano AI's $44M raise to fight wildfires with drones, to Amazon CEO Andy Jassy's memo foreshadowing white-collar job cuts, to Meta's stealth move poaching Scale AI talent. They dig into the collapse of early-career roles, the slow disappearance of the gig economy safety net, and why founders may want to think twice before building in public.Timestamps:(1:52) Travel chaos, laundry issues, and the Airbnb event(3:05) CO2 conference highlights and Zipline drone delivery innovation(5:24) AI's effect on job disruption and white-collar retraining(09:46) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(11:01) Jassy on AI's workforce impact and boosting teacher roles(13:23) Media evolution: market resilience and direct communication strategies(20:08) INBOUND - Use code TWIST10 for 10% o your General Admission ticket at https://www.inbound.com/register (Valid thru 7/31)(21:14) OpenAI's podcast, corporate media shifts, and Twist 500 highlights(30:02) NWRA - Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit northwestregisteredagent.com/twist today!(31:20) Insurance, AI, and OpenAI's enterprise focus(41:11) Meta and Traversal's AI bets; startup transparency(48:57) TikTok, US-China tension, and data privacy debate(56:43) Actuality.ai's platform for AI-driven RFPs and enterprise pricing insights(1:11:00) Wrap-up and final thoughts with Rishab GuptaSubscribe 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/v19fcpFollow 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:(09:46) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(20:08) INBOUND - Use code TWIST10 for 10% o your General Admission ticket at https://www.inbound.com/register (Valid thru 7/31)(30:02) NWRA - Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit northwestregisteredagent.com/twist today!Great 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

Today's show:Meta just took a 49% stake in Scale AI, and the shockwaves are hitting the entire AI ecosystem. In this episode, @Jason and @alex unpack the deal's implications: Google ($150M customer!) and others are fleeing Scale, worried Meta will hoard its RLHF infrastructure and cut off competitors. Startups like Labelbox, Turing, and Handshake are already seeing a demand surge. Is this smart vertical integration or anti-competitive overreach? Jason shares tactical advice for founders on how to capitalize when incumbents stumble—hire ex-Scale talent, build “Scale AI alternative” SEO pages, and hit the podcast circuit. Don't miss this deep dive into AI's shifting power dynamics.Timestamps:(04:01) Is Jason becoming an AI doomer?!(9:52) OpenPhone - Streamline and scale your customer communications with OpenPhone. Get 20% off your first 6 months at www.openphone.com/⁠twist(13:47) PostHog, and when is it okay for founders to break the rules?(20:56) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(25:50) Why the Navy is recruiting startups(30:12) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year.(39:09) Did Zuck buy Scale in order to keep it from competitors?(56:08) When does incentivizing customers turn into burning capital?(1:04) How raising too much money could KILL your startup!Subscribe 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/v19fcpFollow 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:(9:52) OpenPhone - Streamline and scale your customer communications with OpenPhone. Get 20% off your first 6 months at www.openphone.com/⁠twist(20:56) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(30:52) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year.Great 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

As businesses are adopting artificial intelligence and beginning to figure out how it will make them money, developers are already working on ways to embody AI in the physical world. From home robots to manufacturing and beyond, tech reporter Belle Lin digs into the industry's plans and tells us whether physical AI might bring both makers and users the big returns on investment they've been anticipating. Alex Ossola hosts. Further Reading:  These Developers Can't Get Excited About Apple's AI Efforts  AI Is Here for Plumbers and Electricians. Will It Transform Home Services?  Companies Are Struggling to Drive a Return on AI. It Doesn't Have to Be That Way.  Nvidia and Perplexity Team Up in European AI Push  Apple Executives Defend Apple Intelligence, Siri and AI Strategy  Meta in Talks to Invest $14 Billion in Scale AI, Hire CEO Alexandr Wang  Apple Fails to Clear a Low Bar on AI  Learn more about your ad choices. Visit megaphone.fm/adchoices

This week, Meta hits the reset button on A.I. But will a new research lab and a multibillion-dollar investment in Scale AI bring the company any closer to its stated goal of “superintelligence”? Then we break down Apple's big developer conference, WWDC: What was announced, what was noticeably absent, and why Apple seems a little stuck in the past. Finally, a couple of weeks ago we asked if your job is being automated away — it's time to open up the listener mail bag and hear what you said.Additional Reading:Meta looks for an A.I. resetApple Executives Defend Apple Intelligence, Siri and A.I. StrategyThis A.I. Company Wants to Take Your JobWe want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Unlock full access to New York Times podcasts and explore everything from politics to pop culture. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify.

Alex Wang is the CEO and co-founder of Scale AI, a leading data platform accelerating the development of artificial intelligence applications. Founded in 2016, Scale AI provides high-quality training data for AI models, serving clients like OpenAI, Microsoft, and the U.S. Department of Defense. A former software engineering prodigy, Wang dropped out of MIT to build Scale AI, which is now valued at over $13 billion. Recognized on Forbes' 30 Under 30 and TIME's 100 Most Influential People in AI, Wang is a prominent voice in shaping the future of AI innovation and deployment. He advocates for responsible AI development and policies to ensure ethical and secure AI advancements. Shawn Ryan Show Sponsors: ⁠https://www.roka.com⁠ - USE CODE SRS ⁠https://www.americanfinancing.net/srs⁠ NMLS 182334, nmlsconsumeraccess.org ⁠https://www.tryarmra.com/srs⁠ ⁠https://www.betterhelp.com/srs⁠ This episode is sponsored by better help. Give online therapy a try at ⁠betterhelp.com/srs⁠ and get on your way to being your best self. ⁠https://www.shawnlikesgold.com⁠ ⁠https://www.lumen.me/srs⁠ ⁠https://www.patriotmobile.com/srs⁠ ⁠https://www.rocketmoney.com/srs⁠ ⁠https://www.shopify.com/srs⁠ ⁠https://trueclassic.com/srs⁠ Upgrade your wardrobe and save on @trueclassic at ⁠trueclassic.com/srs⁠! #trueclassicpod Alex Wang Links: Website - https://scale.com Scale AI X - https://x.com/scale_ai Alex X - https://x.com/alexandr_wang LI - https://www.linkedin.com/company/scaleai Learn more about your ad choices. Visit podcastchoices.com/adchoices

Christina Knight, Machine Learning Safety and Evals Lead at Scale AI and former senior policy adviser at the U.S. AI Safety Institute (AISI), joins Kevin Frazier, the AI Innovation and Law Fellow at Texas and a Senior Editor at Lawfare, to break down what it means to test and evaluate frontier AI models as well as the status of international efforts to coordinate on those efforts.This recording took place before the administration changed the name of the U.S. AI Safety Institute to the U.S. Center for AI Standards and Innovation. To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.