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AI's breakout moment is here - but where is the real value accruing, and what's just hype?Recorded live at a16z's annual LP Summit, General Partners Erik Torenberg, Martin Casado, and Sarah Wang unpack the current state of play in AI. From the myth of the GPT wrapper to the rapid rise of apps like Cursor, the conversation explores where defensibility is emerging, how platform shifts mirror (and diverge from) past tech cycles, and why the zero-sum mindset falls short in today's AI landscape.They also dig into the innovator's dilemma facing SaaS incumbents, the rise of brand moats, the surprising role of prosumer adoption, and what it takes to pick true category leaders in a market defined by both exponential growth - and accelerated wipeouts.Resources: Find Martin on X: https://x.com/martin_casadoFind Sarah on X: https://x.com/sarahdingwangStay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
Crypto News: Bitcoin's price hits $110K, how high will it go? New XRP Futures ETF launches tomorrow. VanEck builds on Avalanche AVAX Blockchain. Worldcoin raises $135 million from a16z and others.Show Sponsor -
Recorded live at the 2025 a16z LP Summit, this episode is a candid conversation between a16z cofounders Marc Andreessen and Ben Horowitz—hosted by general partner Erik Torenberg.They cover the evolution of a16z from startup firm to multi-practice platform, how the media landscape is shaped by meme-speed narratives, why reorgs—not just returns—determine who wins, and what it takes to build an enduring venture franchise.They also share thoughts on the changing policy landscape for AI and crypto, the firm's bipartisan approach to Washington—and why Marc personally screens social media profiles before anyone joins the team. Resources: Find Marc on X: https://x.com/pmarcaFind Ben on X: https://x.com/bhorowitz Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
Are you struggling to scale your cybersecurity sales from mid-market to enterprise? Wondering how to build a high-performance sales team that consistently generates pipeline? Curious about what it really takes to win enterprise clients—and keep them? This episode of the Cybersecurity Go-To-Market Podcast dives deep into those challenges and offers candid, tactical advice from someone who's done it.Mike Ferrari is the SVP Worldwide Sales at Doppel, the social engineering defense platform, and the fastest growing cybersecurity company in the A16Z portfolio.In this conversation we discuss:
Today on the a16z Podcast, we're sharing Marc Andreessen's recent appearance on TBPN.Marc—cofounder and general partner at a16z—joins hosts John Coogan and Jordi Hays for a wide-ranging conversation, recorded live at the a16z's 2025 LP Conference in Las Vegas.They cover the rise of AI, the future of open source models, and how tech is transforming every corner of the economy—from education and defense to healthcare and housing.Marc also shares his thoughts on the evolution of venture capital, the firm's new branding, and what it takes to build enduring companies in a rapidly changing world. Resources:Watch more from TBPN: https://www.tbpn.com/Find TBPN on X: https://x.com/tbpnFind Marc on X: https://x.com/pmarca Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
Corporate spies stealing Slack messages. Adam Neumann raising another $100M (for WeWork 2.0?). AI startups hitting $34B valuations with zero revenue and ordering Ben & Jerry's ice cream over 15 payments with Klarna on DoorDash. April was wild, and Jack Kuveke joins the show to unpack the chaos, controversy, and insanity behind the biggest startup headlines. This is different than our normal episodes— definitely a much lighter twist, to be taken with a grain of salt. Let us know what you think!Why You Should ListenWhy Adam Neumann can raise billions—but you can't raise your seed roundHow a $40B valuation for AI startups might not be as insane as it soundsWhy espionage is moving from Wall St to Silicon ValleyWhat Klarna and DoorDash teaming up says about consumer debt cultureWhy A16Z thinks VCs will be the last job standing when AI takes overKeywordsAdam Neumann, AI startups, Silicon Valley espionage, A16Z, Klarna DoorDash, startup news, corporate spies, consumer debt, tech valuations, VC funding00:00 Intro01:45 Neumann's new $500 M raise and the WeWork déjà‑vu08:20 Deel‑vs‑Rippling spy saga uncovered13:00 11x growth scandal and TechCrunch backlash18:25 Marc Andreessen says only VCs are irreplaceable20:38 ChatGPT's $10 M “please & thank‑you” GPU bill26:10 Safe Super‑Intelligence and the $34 B pre‑revenue club30:00 Klarna × DoorDash lets you finance ice cream37:40 How consumer debt became America's default setting41:55 Quick survival guide for founders (and a few rants)Send me a message to let me know what you think!
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
Today's Topics: 04:44 Analysis of $3 Billion Windsurf Acquisition 12:39 Will Mega Funds Win the Future of Venture Capital 18:39 Does Every Fund Have to do Pre-Seed to Win Series A and B Today 27:53 Why AI Will Create Massive Unemployment 31:06 The $100,000 Bet on the Future of Work 35:52 Why Venture Has Become a Bundled Good 37:52 Why Stage Specific Firms Will Win: a16z vs Benchmark 40:16 What Does Harvard Losing It's For Profit Status Mean for Venture 42:57 Why AI is Maiming and Not Killing Growth Companies on the Path to IPO 45:41 Decagon Raises 100x ARR: The Breakdown 52:50 Why VCs Are Upside Junkies and What That Means Today 01:03:37 Olo Looking to Sell: What Happens When Public Companies Want to Sell
In today's episode, Jackson Ellis hosts Melissa Wasser, the Partner and Head of the Fintech Capital Network at Andreessen Horowitz (a16z), a leading venture capital firm backing bold entrepreneurs building the future through technology. Tune in to hear about: - Melissa's career journey from Goldman Sachs to FT Partners to leading fintech support at a16z - How the Fintech Capital Network helps founders navigate fundraising, M&A, and strategic partnerships - The tradeoffs between equity and debt financing, and how fintech founders can structure smarter capital strategies
In this episode of AI + a16z, Anthropic's David Soria Parra — who created MCP (Model Context Protocol) along with Justin Spahr-Summers — sits down with a16z's Yoko Li to discuss the project's inception, exciting use cases for connecting LLMs to external sources, and what's coming next for the project. If you're unfamiliar with the wildly popular MCP project, this edited passage from their discussion is a great starting point to learn:David: "MCP tries to enable building AI applications in such a way that they can be extended by everyone else that is not part of the original development team through these MCP servers, and really bring the workflows you care about, the things you want to do, to these AI applications. It's a protocol that just defines how whatever you are building as a developer for that integration piece, and that AI application, talk to each other. "It's a very boring specification, but what it enables is hopefully ... something that looks like the current API ecosystem, but for LLM interactions."Yoko: "I really love the analogy with the API ecosystem, because they give people a mental model of how the ecosystem evolves ... Before, you may have needed a different spec to query Salesforce versus query HubSpot. Now you can use similarly defined API schema to do that."And then when I saw MCP earlier in the year, it was very interesting in that it almost felt like a standard interface for the agent to interface with LLMs. It's like, 'What are the set of things that the agent wants to execute on that it has never seen before? What kind of context does it need to make these things happen?' When I tried it out, it was just super powerful and I no longer have to build one tool per client. I now can build just one MCP server, for example, for sending emails, and I use it for everything on Cursor, on Claude Desktop, on Goose."Learn more:A Deep Dive Into MCP and the Future of AI ToolingWhat Is an AI Agent?Benchmarking AI Agents on Full-Stack CodingAgent Experience: Building an Open Web for the AI EraFollow everyone on X:David Soria ParraYoko Li Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.
On this episode of The Ben & Marc Show, a16z co-founders Marc Andreessen and Ben Horowitz dive deep into the unfiltered story behind the founding of Andreessen Horowitz—and how they set out to reinvent venture capital itself. For the first time, Marc and Ben walk through the origins, strategy, and philosophy behind building a world-class venture capital firm designed for the future—not just the next fund. They reveal how they broke industry norms with a bold brand, a full-stack support model, and a long-term commitment to backing exceptional builders—anchored in the radical idea that founders deserved real support, not just checks. Joining them to guide the conversation is Erik Torenberg—Andreessen Horowitz's newest General Partner—who makes his Ben & Marc Show moderating debut. Erik is a technology entrepreneur, investor, and founder of the media company Turpentine. You can learn more about Erik via Marc's announcement [here]. Together, they explore: - Why traditional VC needed reinvention - How a16z scaled with a platform model, not a partner model - The "barbell strategy" reshaping venture capital today - Why venture remains a human craft, even in the age of AI We're thrilled to welcome Erik to the team—and we hope you enjoy this inside look at the structure, philosophy, and future of a16z. Resources: Marc on X: https://twitter.com/pmarca Marc's Substack: https://pmarca.substack.com/ Ben on X: https://twitter.com/bhorowitz Erik on X: https://x.com/eriktorenberg Erik's Substack: https://eriktorenberg.substack.com/ Stay Updated: Find us on X: https://twitter.com/a16z Find us on LinkedIn: https://www.linkedin.com/company/a16z This information is for general educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product.Turpentine is an acquisition of a16z Holdings, L.L.C., and is not a bank, investment adviser, or broker-dealer. Individuals and companies featured during this podcast are not endorsing AH Capital or any of its affiliates (including, but not limited to, a16z Perennial Management L.P.). Any investments or portfolio companies mentioned, referred to, or described in this podcast are not representative of all a16z investments and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by a16z is available at https://a16z.com/investment-list/. All investments involve risk, including the possible loss of capital. Past performance is no guarantee of future results and the opinions presented cannot be viewed as an indicator of future performance. Before making decisions with legal, tax, or accounting effects, you should consult appropriate professionals. Information is from sources deemed reliable on the date of publication, but a16z does not guarantee its accuracy.
Hunter Somerville is a Partner at StepStone Group, an asset management firm with over $150BN+ in assets under management. Hunter focuses on venture capital and growth equity investments which includes fund investments, directs, and secondaries.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comStepStone Group - https://www.stepstonegroup.com/Hunter Somerville on LinkedIn - https://www.linkedin.com/in/hunter-somerville-2a24117/In this episode we talk about -- The different strategies they invest in at StepStone private equity & VC team- Key things you look for in fund managers- The different risks they are undertaking when backing a fund- Common mistakes new GPs make- How big is the opportunity in the secondary market? - His advice to GPs aspiring to build the Sequoia of tomorrow& lots moreTimestamps:(00:00) Introduction(05:23) Hunter's background and career journey(06:26) StepStone's investment strategies(09:04) Key characteristics and criteria for primary fund investments(11:31) Managing DPI and liquidity in early-stage venture investments(14:09) Evaluating secondary investments and portfolio strategies(16:27) Risks associated with fund investments(20:10) Building relevance and value for venture capital funds(23:03) Common mistakes made by new General Partners (GPs)(24:45) StepStone's approach to growth stage direct investing(27:08) Comparing risk and return across different investment strategies(30:19) The secondary market opportunity and its growth(34:17) Liquidity dynamics in venture capital investments(36:10) Advice for GPs aspiring to build the next Sequoia or A16Z(37:46) Emerging trends and exciting investment areas in venture capital(40:28) Rapid fire round of questions about StepStone's investment approachFor sponsorship or guest appearance requests, write to prashantchoubey3@gmail.comSubscribe to VC10X on Youtube, Spotify, Apple Podcasts.
We're back to opining on the state of tech media! A16z has aqui-hired Erik Torenberg and his newsletter Turpentine, while the Technology Brothers with ties to Founders Fund have created a podcasting empire. Eric and Tom reminisce about the original wave of “going direct,” why it failed, and what's different this time around. Later on, Madeline shares that crypto VCs are growing frustrated with President Trump's meme coin grifts, and how hosting a private dinner for top coin holders doesn't help legitimize the industry.
We're back to opining on the state of tech media! A16z has aqui-hired Erik Torenberg and his newsletter Turpentine, while the Technology Brothers with ties to Founders Fund have created a podcasting empire. Eric and Tom reminisce about the original wave of “going direct,” why it failed, and what's different this time around. Later on, Madeline shares that crypto VCs are growing frustrated with President Trump's meme coin grifts, and how hosting a private dinner for top coin holders doesn't help legitimize the industry.
Today on the Giant Ideas podcast we are joined by Katherine Boyle, General Partner at Andreessen Horowitz. In 2022 Katherine unveiled her thesis - ‘Building American Dynamism' - which has both inspired the tech landscape and provided a central philosophical plank of Trump's America. American Dynamism is defined as “embodying the spirit of innovation, progress, and resilience that drives the United States forward”. Putting her money where her mouth is, Katherine Boyle co-founded Andreessen Horowitz's American Dynamism fund, investing in companies supporting the American national interest across aerospace, defence, manufacturing, energy, and critical infrastructure. She has backed the wildly successful defence tech startup Anduril, Elon Musk's SpaceX and many more. Katherine is one of America's most powerful investors. She previously led seed investing at another top firm, General Catalyst. She's also a big advocate of free speech: she started her career as a reporter on the Washington Post and is now on the board of The Free Press.Building a purpose driven company? Read more about Giant Ventures at www.Giant.vc.Music credits: Bubble King written and produced by Cameron McLain and Stevan Cablayan aka Vector_XING. Please note: The content of this podcast is for informational and entertainment purposes only. It should not be considered financial, legal, or investment advice. Always consult a licensed professional before making any investment decisions.
In this episode, we chat with Nile Frater is the founder of Masse, an innovative SEO agency that specializes in high-scale content campaigns for cutting-edge tech companies at Series A, B, C, and beyond. In just 12 months, Masse has grown from $0 to over $1.5 million in ARR, leveraging a unique human-AI hybrid approach to SEO.Nile's journey in the tech world began with NoCode.Tech, a hub for the no-code movement that was acquired by A16z-backed Stacker in 2022. With a background in engineering and coding, Nile has consistently pushed the boundaries of SEO, focusing on delivering tangible business results for startups and tech companies.We interviewed him about how AI is changing SEO, local SEO ranking factors, common SEO mistakes, and much more.Med Rank Interactive: https://medrankinteractive.com/ Website: https://www.masse.marketing/ #healthcaredigitalmarketingpodcast #nilefrater #lamarhull #AISEO #medicalseo #dentalseo #medrankinteractive #doctorpodcast #practicesprodcast #dentalpodcast #massemarketing
Episode #377 of The VentureFizz Podcast is Victor D. Lombard (aka DIVINE), CEO & Co-Founder of Notes. As the host of this podcast, I have the honor of being able to help tell the story of so many founders and investors and each story is unique. But this one is a whole new level. DIVINE's full story is probably the most unique entrepreneurial story that has been told on our podcast. From the streets of NYC to incarceration to master networker to fintech entrepreneur, this story has it all… including how one of Silicon Valley's top venture capitalists, that being Ben Horowitz helped change his life and built a friendship, plus his rap song that was the intro for the A16Z podcast. Oh… and it just so happens that the co-founder of his latest startup called Notes is Rakim. Yes, that same Rakim from the 80's hip-hop duo of Eric B & RAKIM… with legendary songs like Don't Sweat the Technique and Paid in Full… which is why you are hearing their song Let The Rhythm Hit ‘Em for this podcast intro and outro. Notes is a fintech and AI-powered platform empowering independent urban music artists and creators with access to capital, financial literacy, entrepreneurship and music business education, and more, for financial independence, and career growth and sustainability. The company is in pre-launch. We cover a lot of ground in this podcast including: * DIVINE's full background story * His journey into the tech industry including working with Greg Selkoe at Karmaloop * How he connected with Ben Horowitz * The details of his own career as a rap artist * The journey into entrepreneurship * All the details on Notes * And more!
幻冬舎の暗号資産（仮想通貨）/ブロックチェーンなどweb3領域の専門メディア「あたらしい経済 www.neweconomy.jp/ 」がおくる、Podcast番組です。 ーーーーー 【番組スポンサー】 この番組は、モジュール型のイーサリアムL2チェーンを提供する次世代金融インフラ「Mantle」の提供でお届けします。 【Mantle】 Mantleは、モジュール型のイーサリアムL2チェーンを提供する次世代金融インフラ。Mantle Networkには、DeFi・ゲーム・NFTなど多数のDAppsが展開中。28億ドルを超えるDAOのトレジャリーが、Mantle NetworkやmETH Protocolをはじめ、多数のパートナーを支援しています。信頼、透明性、そして革新を携えたMantleと共に、次世代の経済に参加しませんか？ Web3/DeFiの未来を、Mantleと共に。 ーーーーー 【Mantle 関連リンク】 Website: https://www.mantle.xyz/ja Discord : https://discord.com/invite/0xmantle Twitter:https://x.com/0xmantlejp Medium :https://medium.com/0xmantle-jp TG: https://t.me/mantlenetwork/69759 Email: marketing@mantle.xyz ーーーーー 【紹介したニュース】 ・NTTデータとセキュリタイズJP、デジタル証券プラットフォームで「社債購入者情報提供サービス」提供開始 ・a16z crypto、LayerZero独自トークン「ZRO」を55Mドル相当取得 ・Binance Japan、Launchpoolで「イニシア（INIT）」取扱い開始 ・Solana上のDEX「Raydium」、トークンローンチパッド「LaunchLab」リリース ・EthenaとSecuritize、RWA特化チェーン「Converge」のテックスペックとロードマップ公開 ・バリュークリエーション、3度目のビットコイン購入、1億円で8.141BTC追加 ・VanEck、デジタル資産関連企業に特化したETF「NODE」を5/14にCboe BZXで上場へ ・香港SFC、ChinaAMCの「イーサリアム現物ETF」にステーキング機能承認 ・JASRAC、楽曲情報管理システム「KENDRIX」をSoneiumに対応 ・Tether社、ビットコイン分散型マイニングプール「OCEAN」へハッシュレート割り当てへ、アフリカ展開も ・a16z、登録投資顧問に対する「暗号資産カストディ規則」見直し提言 ・中国、違法取引で押収の暗号資産処理めぐる議論活発化 【あたらしい経済関連リンク】 ニュースの詳細や、アーカイブやその他の記事はこちらから https://www.neweconomy.jp/
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
Jason Lemkin is one of the leading SaaS investors of the last decade with a portfolio including the likes of Algolia, Talkdesk, Owner, RevenueCat, Saleloft and more. Rory O'Driscoll is a General Partner @ Scale where he has led investments in category leaders such as Bill.com (BILL), Box (BOX), DocuSign (DOCU), and WalkMe (WKME), among others. In Today's Episode We Discuss: 04:23 What is Wrong with Billionaires on Twitter: Are They Depressed? 08:49 Why Does product Market Fit Mean Less Than Ever 11:50 Why is Venture Capital More Risky Than Ever and No One is Discussing It 16:17 Will Private Equity Save a Generation of SaaS Companies and VCs 23:53 a16z's $20BN Fund: Seriously? 31:29 Why Josh Kushner and Thrive Capital are Masters of the World 38:21 Why is Seed Investing for Suckers 45:49 Why Are $50 Million Seed Funds Useless 46:21 Founders Fund Raises $4.6BN: Analysis 52:00 How WIll LPs Change Their Approach to Venture in the Next Five Years 59:53 When Will IPOs Comeback? 01:09:15 Why Does it Not Make Sense for the Best Companies to IPO 01:09:51 Lost Ethics and Morals in Founder Secondaries and Term Sheets 01:22:58 Quickfire: OpenAI, Cursor, Deel vs Rippling
This week on Turpentine VC, we are releasing an episode from The Cognitive Revolution, hosted by Nathan Labenz. Nathan sits down with Olivia Moore and Anish Acharya from Andreesen Horowitz to discuss the trends, investment strategies, and future potential of AI voice technology in both B2B and consumer sectors, exploring its implications for various industries including call centers, SMBs, and personalized AI companions. —
Welcome to the AI Rollup, your weekly ride through the fast-paced world of artificial intelligence! This week, we're joined by frontier tech enthusiast Josh Kale to unpack the drama around Meta's controversial launch of Llama 4, scrutinized for potentially overstating its capabilities. We dive into “AI 2027,” a provocative roadmap predicting espionage, geopolitical turmoil, and runaway intelligence, and explore Shopify's bold move making AI usage mandatory for employees. Plus, we cover Claude Education's groundbreaking efforts to boost student performance with tailored AI tutors and a16z's massive $20 billion AI startup fund aiming to shape the industry's next era.------
In this week's episode of This Week in Games, we're joined by special guest Chris Sides for a jam-packed conversation covering everything from transmedia storytelling to the latest moves in the gaming industry. We dive into the surprising momentum behind the Minecraft movie, explore Gen Z trends and market predictions, and unpack Netflix's bold push into interactive fiction. Plus, we break down A16Z's new Speed Run initiative, take a close look at Supercell's evolving investment strategy, and compare War Robots to the newly launched Steel Hunters. 00:00 Introduction and Host Introductions01:58 Special Guest Chris and His Background02:49 Transmedia and Pop Culture Discussion03:48 Industry Updates and News07:03 Podcast Highlights and Recent Episodes09:21 Minecraft Movie Success16:54 Gen Z and Market Predictions20:48 Netflix's Interactive Fiction Strategy28:44 Speed Run Initiative by A16Z30:22 Supercell's Investment Strategy31:19 Supercell's Investment Philosophy32:54 Supercell's Investment History and Performance43:14 Blueprints: The Best Rated Game of 202549:21 RE's Latest Deck Recap53:36 Steel Hunters: Early Access and Unreal Engine01:03:02 War Robots vs. Steel Hunters01:05:02 Conclusion and Farewell
Ethan Hu holds a PhD in computer graphics and AI from MIT, he is the CEO at Meshy a Silicon Valley based startup creating an AI-powered tool that generates textured 3D models from images or text, in under a minute. Meshy is the ONLY 3D AI tool featured in A16Z's 2024 AI Tools Survey and only in 2024 Over 20 million 3D assets were generated thanks to Meshy. In this conversation, we will dive into the world of 3D Gen AI. We will discover:- How are professionals using 3D assets generated with AI- How is the industry reacting to the rapid advancements- Look more broadly at 3D content for various use cases, from visualization to full games generated with AI- An ambitious prediction about the future of 3D GenAISubscribe to XR AI Spotlight weekly newsletter
Martin Casado is a General Partner at Andreessen Horowitz (a16z), where he focuses on AI and infrastructure investments. He previously co-founded Nicira which was acquired by VMware for $1.2 billion in 2012.In this episode of World of DaaS, Martin and Auren discuss:Economics of open source AIChinese AI innovation with DeepSeekModel collapse and data moatsRegulatory challenges in AILooking for more tech, data and venture capital intel? Head to worldofdaas.com for our podcast, newsletter and events, and follow us on X @worldofdaas.You can find Auren Hoffman on X at @auren and Martin Casado on X at @martin_casado.Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com)
Last year, AI-powered sales automation startup 11x appeared to be on an explosive growth trajectory. However, nearly two dozen sources – including investors, current and former employees – tell TechCrunch that the company has experienced financial struggles, largely of its own making. Learn more about your ad choices. Visit podcastchoices.com/adchoices
In this episode of The Cognitive Revolution, host Nathan Labenz speaks with Andreessen Horowitz partners Olivia Moore and Anish Acharya about the rapid evolution of voice AI technology and its real-world applications. The conversation explores how multimodal models, reduced latency, and improved emotional intelligence are enabling more natural voice interactions across various platforms including Hume AI's Octave model, Google's NotebookLM, and Sesame. Nathan and his guests discuss compelling business use cases—from Happy Robot handling complex negotiations with truckers to SMBs deploying voice AI for after-hours support—while also addressing philosophical questions about labor displacement and the urgent need for responsible innovation to protect consumers from potential AI voice scams. SPONSORS: Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers. OCI powers industry leaders like Vodafone and Thomson Reuters with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before March 31, 2024 at https://oracle.com/cognitive Shopify: Shopify is revolutionizing online selling with its market-leading checkout system and robust API ecosystem. Its exclusive library of cutting-edge AI apps empowers e-commerce businesses to thrive in a competitive market. Cognitive Revolution listeners can try Shopify for just $1 per month at https://shopify.com/cognitive NetSuite: Over 41,000 businesses trust NetSuite by Oracle, the #1 cloud ERP, to future-proof their operations. With a unified platform for accounting, financial management, inventory, and HR, NetSuite provides real-time insights and forecasting to help you make quick, informed decisions. Whether you're earning millions or hundreds of millions, NetSuite empowers you to tackle challenges and seize opportunities. Download the free CFO's guide to AI and machine learning at https://netsuite.com/cognitive RECOMMENDED PODCAST: Second Opinion. Join Christina Farr, Ash Zenooz and Luba Greenwood as they bring influential entrepreneurs, experts and investors into the ring for candid conversations at the frontlines of healthcare and digital health every week. Spotify: https://open.spotify.com/show/0A8NwQE976s32zdBbZw6bv Apple: https://podcasts.apple.com/us/podcast/second-opinion-with-christina-farr-ash-zenooz-md-luba/id1759267211 YouTube: https://www.youtube.com/@SecondOpinionwithChristinaFarr PRODUCED BY: https://aipodcast.ing CHAPTERS: (00:00) About the Episode (03:39) Introduction and Welcome (03:50) AI Scouting Methods (08:25) Best Voice AI Experiences (11:34) Voice AI for Seniors (14:27) Sponsors: Oracle Cloud Infrastructure (OCI) | Shopify (16:54) Voice Technology Challenges (20:48) Human-Like Conversation Dynamics (24:13) AI Voice Negotiations (27:34) Apple's Siri Delays (31:13) Voice AI Stack Evolution (Part 1) (33:16) Sponsors: NetSuite (34:49) Voice AI Stack Evolution (Part 2) (37:57) Context Assembly Challenges (40:48) Enterprise Voice Applications (46:30) Labor Market Impact (49:36) SMB Voice Solutions (50:53) Creator Voice Tools (52:17) AI for Children (56:18) AI Companionship and Romance (58:58) Ethical Guidelines Discussion (01:02:46) Future of Voice Computing (01:04:49) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk
Edo Liberty left a high-paying job at AWS—where he was building AI at the highest level—to start Pinecone, a company no one understood. He pitched 40+ VCs, got rejected by every single one, and nearly ran out of money. Then, he flipped the pitch, raised $10M, and built one of the most important infrastructure companies in AI.Then ChatGPT dropped.Suddenly, Pinecone was the must-have database for AI apps, with thousands of developers signing up daily. The company exploded, leading to a $100M round led by Andreessen Horowitz and a 10x revenue surge.If you're an early-stage founder, this episode is a must-listen.Why you should listen:•How he went from from 40 VC Rejections to a $10M Seed Round• Why he quit a High-Paying Job at AWS to start a Startup• The game-changing shift that made VCs finally “get it”•What really happened inside Pinecone when AI took off•Why most founders misunderstand market timing and what to do about itKeywordsAI, Machine Learning, Startups, Entrepreneurship, Vector Databases, Fundraising, SageMaker, AWS, Technology, Innovation, Pinecone, vector database, seed funding, ChatGPT, startup growth, business model, AI, infrastructure, early stage foundersTimestamps(00:00:00) Intro(00:07:50) Edo's Story(00:12:27) The Early Days of Machine Learning(00:32:23) Seed Funding(00:42:09) Unsustainable Scaling(00:53:41) Told You So(00:59:24) A Piece of AdviceSend me a message to let me know what you think!
The AI Breakdown: Daily Artificial Intelligence News and Discussions
AI powered apps are growing fast, with ChatGPT, DeepSeek, and coding assistants leading. Reports from SimilarWeb, SensorTower, and A16Z show that AI tools for coding, data analysis, and content creation are getting the most traction. Apps like Cursor, Bolt, and Lovable make building software easier for coders and non-coders. DeepSeek's chatbot has quickly become a major player, forcing competitors to rethink pricing. Before that in the Headlines, Google cofounder has a new AI startup. Brought to you by:KPMG – Go to www.kpmg.us/ai to learn more about how KPMG can help you drive value with our AI solutions.Vanta - Simplify compliance - https://vanta.com/nlwThe Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdown
En este episodio, nos acompaña Pablo Palafox, CEO y Cofundador de Happy Robot, la startup que utiliza agentes de inteligencia artificial para revolucionar las comunicaciones en el sector logístico de Estados Unidos, junto a Corti y Frankie de Product Hackers.
Joining us this week is Alex Thorn to break down why crypto markets are taking a hit. Major hacks, bearish macro signals, and fresh tariffs from the U.S. all played a role. But it's not all bad news: the SEC is backing off from lawsuits against Coinbase, OpenSea, and Uniswap, signaling a potential shift in regulatory pressure. Plus, Bybit suffered the biggest crypto hack ever, losing $1.5B—how did it happen, and who's behind it? Follow Alex: https://x.com/intangiblecoins ------
How do you turn a research idea into a successful BioTech startup? What does it take to raise funding and scale in Silicon Valley? How can a PhD background shape an entrepreneur's journey? In this episode, Nurlybek Mursali, founder of Biodock, shares his journey from being a Stanford PhD student to launching an AI-powered biotech company. He talks about the ups and downs of fundraising, how his team secured half of their investment round in just one day, and what it was like going through Y Combinator. We also get into the Kazakh startup scene, the future of Biodock, and what makes Stanford such a unique place for entrepreneurs. Plus, Nurlybek opens up about the lessons he's learned, the historical figures that inspire him, and some of his most unconventional takes on startups and innovation. Enjoy the conversation! Arman Suleimenov: https://www.instagram.com/armansu/ Nurlybek Mursali: https://www.instagram.com/nurlybek_mursaliyev/ Produced by Daniyar Akhmetzhanov: https://www.instagram.com/good.years/ Our Telegram channel: https://t.me/nfactorialpodcast Instagram: https://www.instagram.com/nfactorialpodcast/ TikTok: https://tiktok.com/@nfactorialpodcast
Technology doesn't grow in isolation—it evolves in waves. Just as mobile, cloud, and SaaS shaped the internet of the past 20 years, so too could crypto, AI, and new hardware usher in an era of the internet that's pro-innovation, pro-startup, and pro-creator. Speaking with a16z Growth General Partner David George, a16z crypto Founder and Managing Partner Chris Dixon breaks down his vision for a new internet, from using crypto to decentralize AI infrastructure and kickstart network effects, to why AI will be this era's native form of media just as film was in the 1930s. He also explores why the internet's original covenant—where content creators traded free access for search traffic—is breaking today, and how a better internet could introduce entirely new business models for creators. Right now, we have a choice to make: will the next era of the internet be shaped by a handful of centralized players, or transformed into an open ecosystem where power and control flow to creators across the globe?Resources:Watch the conversation here: https://youtu.be/gioxu1CVjhM Read more, including the full transcript, here: https://a16z.com/ai-crypto-internet-chris-dixon/Chris's recent article on blockchain innovation: https://a16zcrypto.com/posts/article/blockchain-ai-internet/Find Chris's book, Read Write Own: Building the Next Era of the Internet:Penguin Random House: https://www.penguinrandomhouse.com/books/744504/read-write-own-by-chris-dixon/Penguin UK: https://www.penguin.co.uk/books/459860/read-write-own-by-dixon-chris/9781804949245For more resources on AI & crypto visit: https://a16zcrypto.com/posts/?tag=ai-crypto,web2-to-web3Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://twitter.com/stephsmithioPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
In this week's episode of the Coin Stories News Block, we cover these major headlines related to Bitcoin and global finance: Will Tether have to sell its Bitcoin? VanEck: Bitcoin reserve bills at state level could ppark $23B in demand Government ppending surges 15% year-over-year Where more than 60% of government spending is going A16z's Brian Quintenz to be nominated for CFTC Chair ---- Join my mailing list and subscribe to our free newsletter: thenewsblock.substack.com ---- References mentioned in the episode: Tether May Have to Sell Its Bitcoin Holdings Tether's Q4 2024 Attestation Report Tether Made $13 Billion in Profits in 2024 Tether Announces Bitcoin Stacking Strategy Paolo Ardoino's Tweet on “Salty” JPM Analysts Bitcoin Chart Highlighting State Reserve Bills VanEck's Sigel Estimates Impact of State Adoption State-level Bitcoin Reserves Could Drive $23B in Bitcoin January CPI Inflation Comes in Hotter Than Expected The Era of Fiscal Dominance: Full Steam Ahead The Federal Spending Boom Rolls On Fiscal Deficit Is On Track To Top Last Fiscal Year Dalio: Cut Fiscal Deficit or Face 'Economic Heart Attack GAO Sends Warning in Report on U.S. Fiscal Health Trump Plans to Nominate a16z's Quintenz as CFTC Chairr Brian Quintenz Responds to Trump's Nomination ---- Natalie's Promotional Links: Genius (NYSE $GNS) is a Bitcoin-first business delivering AI-powered education and acceleration solutions for the future of work. Learn more and enter for a chance to win a whale pass to Bitcoin 2025 in Las Vegas: https://www.geniusgroup.ai/coinstories Secure your Bitcoin with collaborative custody and set up your inheritance plan with Casa: https://www.casa.io/natalie For easy, low-cost, instant Bitcoin payments, I use Speed Lightning Wallet. Get 5000 sats when you download using this link and promo code COINSTORIES10: https://www.speed.app/sweepstakes-promocode/ River is where I DCA weekly and buy Bitcoin with the lowest fees in the industry: https://partner.river.com/natalie Safely self-custody your Bitcoin with Coinkite and the ColdCard Wallet. Get 5% off: https://store.coinkite.com/promo/COINSTORIES Master your Bitcoin self-custody with 1-on-1 help and gain peace of mind with the help of The Bitcoin Way: https://www.thebitcoinway.com/natalie Bitcoin 2025 is heading to Las Vegas May 27-29th! Join me for my 4th Annual Women of Bitcoin Brunch! Get 10% off Early Bird passes using the code HODL: https://tickets.b.tc/affiliate/hodl/event/bitcoin-2025 Protect yourself from SIM Swaps that can hack your accounts and steal your Bitcoin. Join America's most secure mobile service, trusted by CEOs, VIPs and top corporations: https://www.efani.com/natalie Connect with Bitcoiners and Bitcoin merchants wherever you live and travel on the Orange Pill App: https://signup.theorangepillapp.com/opa/natbrunell ---- This podcast is for educational purposes and should not be construed as official investment advice. ---- VALUE FOR VALUE — SUPPORT NATALIE'S SHOWS Strike ID https://strike.me/coinstoriesnat/ Cash App $CoinStories #money #Bitcoin #investing
This week on Techish, Michael and Abadesi break down DeepSeek, China's open-source AI rival, what the rise in AI agents means for jobs, and Andreessen Horowitz (a16z) hiring Daniel Perry, the man who placed Jordan Neely in a lethal chokehold on a NYC subway. They also discuss Kendrick Lamar's Grammy sweep for his Drake diss track and why Bianca Censori, Kanye West's wife, never seems to be wearing any clothes. Chapters00:30 The Rise of Deepseek: A Game Changer in AI 07:33 Job Ads for AI Agents12:55 Kendrick Wins 5 Grammys for Dissing Drake 19:28 Andreessen Horowitz Hires Daniel Penny 26:48 Kanye West's Wife Bianca Censori Is Naked Again Extra Reading and Resources Drake Withdraws ‘Not Like Us' Petition Against Spotify And UMG [POCIT]EXCLUSIVE: Daniel Penny Gets Hired by Andreessen Horowitz [The Free Press]Donate now to The First Woke War play by Abadesi at tinyurl.com/abadesikickstarter ————————————————————Disclaimer: The information provided in this podcast episode represents the personal opinions and experiences of the presenters and is for informational and entertainment purposes only. It should not be considered professional advice. Neither host nor guests can be held responsible for any direct or incidental loss incurred by applying any of the information. Always do your own research or seek independent advice before making any decisions. ———————————————————— Watch us on YouTube: https://www.youtube.com/@techishpod/Support Techish at https://www.patreon.com/techishAdvertise on Techish: https://goo.gl/forms/MY0F79gkRG6Jp8dJ2————————————————————Stay in touch with the hashtag #Techishhttps://www.instagram.com/techishpod/https://www.instagram.com/abadesi/https://www.instagram.com/michaelberhane_/ https://www.instagram.com/hustlecrewlive/https://www.instagram.com/pocintech/Email us at techishpod@gmail.com...
In this episode of the Newcomer podcast, Eric Newcomer and Madeline Renbarger dig into all of the chaos in Washington led by Elon Musk and his team of young staffers. They push back on the attacks from the right at the press that revealing information about public employees is anything close to "doxxing," and unpack a16z's virtue signaling by hiring new right-wing cause celebrity Daniel Penny to its investing team. Later on, Eric breaks down the General Catalyst's a pitch to investors shapeshifting into a "company." They close with even more meme discourse, this time over Marc Andreessen's reading of the "heatmap" social study.Chapters00:00 DOGE's Young Staffers03:28 Is Naming Government Staffers “Doxxing”06:20 Daniel Penny's Hiring at A16z is Virtue Signaling09:16 Debating Marc Andreessen on the Heatmap12:22 Meta's AR Bets Not In Line With Venture Capital21:03 Unpacking General Catalyst's Pitch to Investors
Interview with Zack Kass, Former GTM for Open AI Why you can deep-six the DeepSeek hype Gemini 2.0 is now available to everyone OpenAI has undergone its first ever rebrand, giving fresh life to ChatGPT interactions AI Has Shown Me My Future. Here's What I've Learned. Senator Hawley Proposes Jail Time for People Who Download DeepSeek Hugging Face researchers aim to build an 'open' version of OpenAI's deep researh tool Anthropic makes 'jailbreak' advance to stop AI models producing harmful results WSJ: The Manhattan Project Was Secret. Should America's AI Work Be Too? EU AI Act: Ban on certain AI practices and requirements for AI literacy come into effect Cathy Gellis: When It's Not Just A Coup But A CFAA Violation Too a16z slides on AI and voice Microsoft AI CEO Mustafa Suleyman poaches three Google DeepMind former colleagues, including two who built NotebookLM's Audio Overviews and worked on Astra Meta's CTO said the metaverse could be a 'legendary misadventure' if the company doesn't boost sales, leaked memo shows The Salvadoran Mega-Prison Offering to Take America's Worst Criminals Hilarious analyst on Tesla How the DJI Flip uses AI Marketers will have to market to AI agents AI systems could be 'caused to suffer' if consciousness achieved, says research Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Zack Kass Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsor: zscaler.com/security
Interview with Zack Kass, Former GTM for Open AI Why you can deep-six the DeepSeek hype Gemini 2.0 is now available to everyone OpenAI has undergone its first ever rebrand, giving fresh life to ChatGPT interactions AI Has Shown Me My Future. Here's What I've Learned. Senator Hawley Proposes Jail Time for People Who Download DeepSeek Hugging Face researchers aim to build an 'open' version of OpenAI's deep researh tool Anthropic makes 'jailbreak' advance to stop AI models producing harmful results WSJ: The Manhattan Project Was Secret. Should America's AI Work Be Too? EU AI Act: Ban on certain AI practices and requirements for AI literacy come into effect Cathy Gellis: When It's Not Just A Coup But A CFAA Violation Too a16z slides on AI and voice Microsoft AI CEO Mustafa Suleyman poaches three Google DeepMind former colleagues, including two who built NotebookLM's Audio Overviews and worked on Astra Meta's CTO said the metaverse could be a 'legendary misadventure' if the company doesn't boost sales, leaked memo shows The Salvadoran Mega-Prison Offering to Take America's Worst Criminals Hilarious analyst on Tesla How the DJI Flip uses AI Marketers will have to market to AI agents AI systems could be 'caused to suffer' if consciousness achieved, says research Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Zack Kass Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsor: zscaler.com/security
Interview with Zack Kass, Former GTM for Open AI Why you can deep-six the DeepSeek hype Gemini 2.0 is now available to everyone OpenAI has undergone its first ever rebrand, giving fresh life to ChatGPT interactions AI Has Shown Me My Future. Here's What I've Learned. Senator Hawley Proposes Jail Time for People Who Download DeepSeek Hugging Face researchers aim to build an 'open' version of OpenAI's deep researh tool Anthropic makes 'jailbreak' advance to stop AI models producing harmful results WSJ: The Manhattan Project Was Secret. Should America's AI Work Be Too? EU AI Act: Ban on certain AI practices and requirements for AI literacy come into effect Cathy Gellis: When It's Not Just A Coup But A CFAA Violation Too a16z slides on AI and voice Microsoft AI CEO Mustafa Suleyman poaches three Google DeepMind former colleagues, including two who built NotebookLM's Audio Overviews and worked on Astra Meta's CTO said the metaverse could be a 'legendary misadventure' if the company doesn't boost sales, leaked memo shows The Salvadoran Mega-Prison Offering to Take America's Worst Criminals Hilarious analyst on Tesla How the DJI Flip uses AI Marketers will have to market to AI agents AI systems could be 'caused to suffer' if consciousness achieved, says research Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Zack Kass Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsor: zscaler.com/security
Interview with Zack Kass, Former GTM for Open AI Why you can deep-six the DeepSeek hype Gemini 2.0 is now available to everyone OpenAI has undergone its first ever rebrand, giving fresh life to ChatGPT interactions AI Has Shown Me My Future. Here's What I've Learned. Senator Hawley Proposes Jail Time for People Who Download DeepSeek Hugging Face researchers aim to build an 'open' version of OpenAI's deep researh tool Anthropic makes 'jailbreak' advance to stop AI models producing harmful results WSJ: The Manhattan Project Was Secret. Should America's AI Work Be Too? EU AI Act: Ban on certain AI practices and requirements for AI literacy come into effect Cathy Gellis: When It's Not Just A Coup But A CFAA Violation Too a16z slides on AI and voice Microsoft AI CEO Mustafa Suleyman poaches three Google DeepMind former colleagues, including two who built NotebookLM's Audio Overviews and worked on Astra Meta's CTO said the metaverse could be a 'legendary misadventure' if the company doesn't boost sales, leaked memo shows The Salvadoran Mega-Prison Offering to Take America's Worst Criminals Hilarious analyst on Tesla How the DJI Flip uses AI Marketers will have to market to AI agents AI systems could be 'caused to suffer' if consciousness achieved, says research Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Zack Kass Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsor: zscaler.com/security
Interview with Zack Kass, Former GTM for Open AI Why you can deep-six the DeepSeek hype Gemini 2.0 is now available to everyone OpenAI has undergone its first ever rebrand, giving fresh life to ChatGPT interactions AI Has Shown Me My Future. Here's What I've Learned. Senator Hawley Proposes Jail Time for People Who Download DeepSeek Hugging Face researchers aim to build an 'open' version of OpenAI's deep research tool Anthropic makes "jailbreak" advance to stop AI models producing harmful results WSJ: The Manhattan Project Was Secret. Should America's AI Work Be Too? EU AI Act: Ban on certain AI practices and requirements for AI literacy come into effect Cathy Gellis: When It's Not Just A Coup But A CFAA Violation Too a16z slides on AI and voice Microsoft AI CEO Mustafa Suleyman poaches three Google DeepMind former colleagues, including two who built NotebookLM's Audio Overviews and worked on Astra Meta's CTO said the metaverse could be a "legendary misadventure" if the company doesn't boost sales, leaked memo shows The Salvadoran Mega-Prison Offering to Take America's Worst Criminals Hilarious analyst on Tesla How the DJI Flip uses AI Marketers will have to market to AI agents AI systems could be "caused to suffer" if consciousness achieved, says research Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Zack Kass Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsor: zscaler.com/security
0:00 Intro3:15 - State of AI at the beginning of 202412:30 - Valuations and fundraising landscape today16:20 - Founder backgrounds for AI startups19:15 - Are AI companies defensible? What moats do they have?26:40 - Impact for large enterprise businesses30 35 - Exit options and M&A35:45 - What problems do they wish founders focused on? And not so much on?42:35 - AI therapy - is it cringe?48:55 - Stanford dropouts and starting AI companies58:35 - Olivia and Justine's favorite AI Tools1:04:00 - Outro Follow Sriram:https://www.instagram.com/sriramk/https://twitter.com/sriramkFollow Aarthi:https://www.instagram.com/aarthir/https://twitter.com/aarthirFollow the podcast:https://www.instagram.com/aarthiandsriramshow/https://twitter.com/aarthisrirampod
Chapters:0:00 Introduction to Aidan Gomez, CEO of Cohere2:12 Childhood and growing up in Canada5:50 Getting into computers and internet10:30 Sending cold emails14:20 How to work with Aidan16:40 The AI paper - "Attention is all you need"18:45 Starting Cohere21:10 Why choose enterprise (vs consumer) as a market24:45 AI strategy28:20 Hallucinations in LLM models30:10 Enterprise software and security implications32:05 Deloitte, Accenture and the impact of generative AI36:40 Will LLM scaling laws hit a plateau?38:50 AGI, reasoning and inference for LLM models41:30 Synthetic data - what is it? Why is it interesting?43:25 Looking ahead - what is the Cohere's strategy?46:00 Cohere's capital structure49:15 Enterprise Use Cases52:10 Advice for founders - adaptability54:15 Thank you Follow Sriram:https://www.instagram.com/sriramk/https://twitter.com/sriramkFollow Aarthi:https://www.instagram.com/aarthir/https://twitter.com/aarthirFollow the podcast:https://www.instagram.com/aarthiandsriramshow/https://twitter.com/aarthisrirampod
The $1 billion acquisition of rent-to-own startup Divvy Homes, which was announced Wednesday, is expected to leave some shareholders without a payout, according to sources familiar with the deal. The terms — and Divvy's journey from buzzy startup to acquisition target — reflects the rollercoaster ride the proptech industry has endured over the past decade. Learn more about your ad choices. Visit podcastchoices.com/adchoices
One last Gold sponsor slot is available for the AI Engineer Summit in NYC. Our last round of invites is going out soon - apply here - If you are building AI agents or AI eng teams, this will be the single highest-signal conference of the year for you!While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.* Crossed 1m DAU in 2.5 years - William updates us on the pod that they've hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m. * Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.While they're not paying million dollar salaries, you can tell they're doing pretty well for an 11 person startup:The Chai Recipe: Building infra for rapid evalsRemember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai's usecases (therapy, assistant, roleplay, etc). It's basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:William expands upon this in today's podcast (34 mins in):Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai's routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:William is notably counter-consensus in a lot of his AI product principles:* No streaming: Chats appear all at once to allow rejection sampling* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that's it!)But chief above all is the recommender system.We also referenced Exa CEO Will Bryk's concept of SuperKnowlege:Full Video versionOn YouTube. please like and subscribe!Timestamps* 00:00:04 Introductions and background of William Beauchamp* 00:01:19 Origin story of Chai AI* 00:04:40 Transition from finance to AI* 00:11:36 Initial product development and idea maze for Chai* 00:16:29 User psychology and engagement with AI companions* 00:20:00 Origin of the Chai name* 00:22:01 Comparison with Character AI and funding challenges* 00:25:59 Chai's growth and user numbers* 00:34:53 Key inflection points in Chai's growth* 00:42:10 Multi-modality in AI companions and focus on user-generated content* 00:46:49 Chaiverse developer platform and model evaluation* 00:51:58 Views on AGI and the nature of AI intelligence* 00:57:14 Evaluation methods and human feedback in AI development* 01:02:01 Content creation and user experience in Chai* 01:04:49 Chai Grant program and company culture* 01:07:20 Inference optimization and compute costs* 01:09:37 Rejection sampling and reward models in AI generation* 01:11:48 Closing thoughts and recruitmentTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.swyx [00:02:20]: With 100k base as capital?William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...swyx [00:04:40]: Your own, all your own money?William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.swyx [00:21:03]: You found the PMF for them.William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.swyx [00:23:16]: They're the Chinese version of you. Exactly.William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.swyx [00:34:53]: There's a fourth?William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chessswyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you canswyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisinglyWilliam [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be theswyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to beswyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only knowWilliam [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.swyx [00:40:37]: It could be your models, which just weren't good enough.William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.swyx [00:42:10]: You've jumped the Apollo to cool it now.William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'mswyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to ChaiWilliam [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's superswyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.swyx [00:54:24]: You can store more things than any human can.William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction fro
Today's blockchain and cryptocurrency news Bitcoin is up slightly at $104,758 Eth is up half a percent at $3,305 XRP, up half a percent at three dollars and twelve cents Nasdaq files amendment to allow in-kind redemptions BlackRock's iShares Bitcoin Trust Litecoin and XRP ETFs files. NoOnes CE confirms security breach. a16z shutters UK office Learn more about your ad choices. Visit megaphone.fm/adchoices
Guest: Emilie Choi, president & COO of CoinbaseAfter the collapse of FTX in 2022, “the whole industry was tarnished,” recalls Coinbase COO Emilie Choi. “Politicians came out criticizing crypto, saying it was a fraud.”But unlike FTX, Coinbase was a public company in the U.S. So when the SEC served it a Wells notice, announcing its intent to charge the company with violating securities laws, the executive team took an unusual step: They went on the offensive, publicly calling BS on the agency.“Well-regarded CEOs from TradFi, they were like, ‘You don't do that,'” Emilie says. “'You don't antagonize your regulator.' ... It was a combination of chutzpah and maybe desperation that we were like, ‘We have to go tell our story, because if we don't, nobody else will.'”Chapters: (01:14) - Working with founder CEOs (04:12) - Mission first (07:16) - Reviewing candidates (09:48) - Unusual hiring (11:22) - Crypto after FTX (16:29) - Operation Choke Point 2.0 (19:19) - Grin and bear it (21:24) - Channeling negativity (24:21) - Going to war with the SEC (26:20) - Donald Trump and Gary Gensler (28:38) - Was it worth it? (31:19) - Shipping challenges (34:03) - OKRs and personal goals (36:41) - Brian Armstrong and structure (40:56) - The COO guidebook (43:30) - Removing bureaucracy (46:50) - Investing in crypto (49:41) - After Coinbase (53:03) - Constantly on (54:53) - Favorite interview questions (56:28) - Who Coinbase is hiring (58:28) - Standing for something Mentioned in this episode: Google Chat, executive coaches, Mark Zuckerberg, LinkedIn, Jeff Weiner, speed reading, Warner Bros., Elizabeth Warren, Sam Bankman-Fried, Wells notices, Paul Grewal, Chris Lehane, Airbnb, OpenAI, FOIA requests, Balaji Srinivasan, Dan Romero, Kevin Scott, Microsoft, Patrick McHenry, Ritchie Torres, Fairshake PAC, A16z, Ripple, Stand With Crypto, Dogecoin, Robinhood, Charles Schwab, JPMorgan Chase, Goldman Sachs, Paul Ryan, Faryar Shirzad, Kara Calvert, Elon Musk, Earn.com, Ben Horowitz, Bain Capital Ventures, Claire Hughes Johnson and Scaling People, Directly Responsible Individuals, Fidelity, BlackRock, Yahoo!, Stewart Butterfield, Brad Garlinghouse, Alibaba, Flickr, cognitive tests, and Loom.Links:Connect with EmilieTwitterLinkedInConnect with JoubinTwitterLinkedInEmail: grit@kleinerperkins.com Learn more about Kleiner PerkinsThis episode was edited by Eric Johnson from LightningPod.fm
In this podcast episode, Walt Zerbe, Sr. Directror of Technology and Standards and host of he CEDIA Podcats talks with Jim Hunter, and Rich Birra discussing their experiences at CES, focusing on the evolution of smart home technology, AI, and the impact of new innovations. Jim, attending his 29th CES, notes a decline in attendance and quieter atmosphere compared to previous years. Rich highlights changes in the show's layout and the rise of localized exhibits. They explore the concept of "matter" in smart home devices, AI's role in health and wellness, and the importance of data ownership. The conversation underscores the need for user-friendly technology and effective support systems to enhance consumer experiences. Here a few Key Points: Experiences and observations at CES (Consumer Electronics Show) Evolution and changes in the CES event over the years Innovations in smart home technology and connected devices The role of AI in enhancing user experiences and health applications Challenges and implications of implementing "matter" standards for smart home devices The impact of regulatory frameworks on technological advancements The importance of data ownership and compensation in hyper-personalization The need for skilled professionals in the installation and maintenance of smart home technologies The influence of patents on innovation in the tech industry Future trends in technology, including AI integration and user-friendly systems Here are the mentions with timestamps arranged by topic: Tools and Technologies "Matter": "00:05:42" "Cosmos OS": "00:08:54" "Coder": "00:10:45" "Crestron Systems": "00:13:20" "Lutron Systems": "00:13:20" "Control4": "00:13:20" "SmartThings (by Samsung)": "00:14:21" "ChatGPT": "00:18:00" "Neural Networks": "00:19:28" "Large Language Models": "00:19:28" "Time Series Data": "00:21:39" "Wellness Platform": "00:25:54" "Hyper Personalization": "00:26:39" "Checkbox Piracy": "00:27:20" "Wearables and Sensors": "00:29:10" "Large Language Models": "00:34:13" "Ontologies": "00:35:31" "Model Cards": "00:38:17" "Google": "00:40:23" "ADT": "00:40:23" "Ultra Human": "00:44:07" "NVIDIA": "00:44:15" "LG": "00:45:24" "Bubble": "00:50:26" "Halliday": "00:50:26" "Google Glass": "00:52:04" "Samsung XR Glasses": "00:52:56" "Instacart": "00:53:24" "Control4": "00:54:46" "Aura Ring": "00:58:27" "Cal AI": "01:00:48" Companies and Brands "LG": "00:06:16" "Samsung": "00:06:35" "Humane": "00:08:15" "SoftBank Services": "00:10:29" "A16Z": "00:12:46" "Microsoft Azure": "00:16:37" "Tesla": "00:19:28" "Waymo": "00:19:28" "T-Mobile": "00:22:38" "Starlink (by SpaceX)": "00:22:58" "Delta Airlines": "00:24:02" Events "CES (Consumer Electronics Show)": "00:00:07" "CES (Consumer Electronics Show)": "00:14:22" Notable Mentions "Black Mirror": "00:07:44" "Vivaro": "00:01:52" "Tom Brady": "00:25:54" "Poppy Crum": "00:30:21" "CTO of Panasonic": "00:32:16" "Jensen Huang's Vision": "00:58:59"
Bitcoin's plunge to $93,224 triggered a massive $483M liquidation cascade, with Ethereum taking the biggest hit at $86M. But smart money isn't running scared - major buyers are stepping in with bids at $92-93K levels, while Fidelity Investments signals bullish sentiment heading into 2025's potential inflation wave. Meanwhile, crypto powerhouse A16z is betting big on the future, launching a Spring 2025 accelerator program offering minimum $500K investments to promising startups. Despite short-term bearish signals from notable traders Bluntz and Rekt, who eye a possible dip to $90K, Real Vision's Jamie Coutts remains optimistic about Bitcoin's six-month trajectory, citing upcoming Fed rate cuts and increasing interest from high-net-worth investors.
A16z video about accounting tech https://youtu.be/OPRJI8Djfq8?si=0NEIDWo2EDbjSRpkNotebookLM https://notebooklm.google
In today's episode Dan from CMS and Ro from Hack join us to explore what's truly different about this crypto cycle and what patterns are destined to repeat. We examined the notable absence of credit markets compared to previous cycles and investigated why leverage hasn't built up in the system this time around. We unpacked the evolving L1 landscape, debated token distribution models, and analyzed the return of "hot money" flowing through various sectors from memecoins to NFTs. We closed out by diving into institutional perspectives on market structure and potential risks, providing listeners with a comprehensive framework for building their crypto portfolio strategy for 2025. Thanks for tuning in! - - Start your day with crypto news, analysis and data from Katherine Ross and David Canellis. Subscribe to the Empire newsletter: https://blockworks.co/newsletter/empire?utm_source=podcasts Follow Ro: https://x.com/roshunpatel Follow Dan: https://x.com/cmsholdings Follow Jason: https://twitter.com/JasonYanowitz Follow Santiago: https://twitter.com/santiagoroel Follow Empire: https://twitter.com/theempirepod Subscribe on YouTube: https://tinyurl.com/4fdhhb2j Subscribe on Apple: https://tinyurl.com/mv4frfv7ww Subscribe on Spotify: https://tinyurl.com/wbaypprw Get top market insights and the latest in crypto news. Subscribe to Blockworks Daily Newsletter: https://blockworks.co/newsletter/ - - Explore the SKALE Ecosystem at skale.space/ecosystem and stay up to date with the gas-free blockchain on X at @skalenetwork - - It's time for an on chain Binance to emerge. Magic Eden is getting into token trading and their vision is to become on chain Binance and then much more as the entire world ends up moving on chain - - Toku simplifies every part of global token compensation — everything from paying full time employees in stablecoins, issuing token grants to your team and investors, all in 100 countries on one platform. Talk to Toku today at: https://www.toku.com/ - - Ledger, the world leader in digital asset security, proudly sponsors Empire podcast. Celebrating 10 years of protecting over 20% of the world's crypto, Ledger ensures the security of your assets. For the best self-custody solution in the space, buy a LEDGER™ device and secure your crypto today. Buy now at Ledger.com - - Timestamps: 00:00 Introduction 01:57 Tungsten Cube & Pepe Mural Stor 04:47 Is this Cycle different? 15:20 Biggest Risks in Crypt 22:24 Eth/Btc & Sol/Eth Chart 25:26 New Altcoin 30:28 The L1 Trade 37:25 Skale Ad 38:05 Magic Eden Ad 38:49 Toku Ad 39:39 Ledger Ad 40:24 Death of Crypto Venture 55:14 Crypto's IPO Craze 58:34 A16z/s Monopol 01:05:08 Do Dino Coins Run 01:08:14 Chasing the hot ball 01:10:13 When you Retire - - Disclaimer: Nothing said on Empire is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Santiago, Jason, and our guests may hold positions in the companies, funds, or projects discussed.
SaaStr 779: 5 Things That Are Actually Working and 5 Things That Aren't in B2B SaaS AI with Ironclad's CEO and a16z Ironclad CEO and co-founder Jason Boehmig joined Seema Amble, Partner at Andreessen Horowitz at SaaStr Annual to share their observations on what's currently working – and what's not quite there yet – for Artificial Intelligence (AI) in SaaS. With Ironclad's journey from an AI-first concept in 2014 to a Series E+ company and a16z's extensive portfolio view, their insights offer a valuable perspective on the current state of AI in SaaS. -------------------------------------------------------------------------------------------- SaaStr hosts the largest SaaS community events on the planet. Hey everybody - thanks to the 10,000 of you who came out to SaaStr Annual. We had a blast and big news -- we'll be back in MAY of 2025. That's right, the SaaStr Annual will be a bit earlier next year, May 13-15 2025. We'll still be back in the same venue, in the SF bay area at the 40+ acre sprawling san mateo county events center. Grab your tickets at saastrannual.com with code JASON50 for an extra discount on our very best pricing. -------------------------------------------------------------------------------------------- This episode is sponsored by: Anrok A question for SaaS finance leaders, do you know where your customers are? Anrok tracks where your sales are creating exposure, and automates tax calculation and filing worldwide. Built for high-growth software companies, Anrok protects your revenue and saves you time. Visit anrok.com/saastr to learn more.
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