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Send us a text00:00 - Intro00:40 – Meta buys 49% of Scale AI11:19 – Ramp raises at $16b24:11 – AI app, Glean, raises at $7.2b
Send us a text00:00 - Intro00:51 - Scale AI gets $14.3B from Meta, hits $29B valuation02:03 - Starlink doubles subs to 6M, adds 100K in Africa03:22 - SpaceX expands Starship launch capacity in Florida04:08 - Databricks adds Google Gemini, hits $72.8B valuation05:09 - Perplexity partners with Nvidia, eyes $14B raise06:08 - Glean raises $150M at $7.2B valuation07:13 - Mistral hits $6B valuation, expands sovereign AI reach08:32 - Gecko Robotics doubles to $1.25B valuation09:28 - Bullish files confidentially for US IPO
Two founders, two wildly different paths to $100M ARR: Arvind Jain, founder of Glean, walked away from a unicorn to start over—raising $15M without revenue and ignoring lean startup rules. Kyle Hanslovan, founder of Huntress, faced brutal rejection, slept in his car, maxed out credit cards, and still crushed it. This episode is packed with raw lessons on fundraising, product-market fit, and why relentless hustle alone won't save you. If you're a founder chasing growth, stop everything and listen.Why You Should ListenLearn exactly what top founders did to get from zero to $100M ARRWhy chasing perfection won't work (and how to stop)The secret to surviving brutal fundraising rejections (over 60 VCs said no to Kyle)Why hustle culture isn't enough—here's what matters moreKeywordsproduct-market fit, startup fundraising, unicorn startups, founder hustle, lean startup method, scaling startups, early-stage growth, AI startups, SaaS growth, venture capital adviceChapters(00:00:00) Intro(00:02:05) Quitting a Unicorn to Start Again(00:05:09) From NSA Hacker to Startup Founder(00:09:18) Ignoring the Lean Startup(00:12:59) Knowing When to Launch(00:16:32) Finding Product Market Fit(00:18:01) Final Advice for FoundersSend me a message to let me know what you think!
Eric Yuan turned a simple belief into Zoom, the platform that kept the world moving through a once-in-a-century shutdown and redefined modern work. On this episode of Grit, the Zoom CEO shares why velocity beats size, how a family-first ethos powered his leadership during COVID, and why the coming wave of AI dwarfs the original internet boom. He details how he's refreshing Zoom's culture for 7,500 people, opting for virtual deal calls over in person meetings, settling into life as an empty-nester, and keeping Zoom nimble enough to outpace Big Tech and the next wave of AI startups.Guest: Eric S. Yuan, Founder & CEO of ZoomChapters: 00:00 Trailer00:44 Introduction01:47 Walking with swagger03:48 Extremely exciting moment10:05 Classic innovators' dilemma12:59 Laser-focused bandwidth17:56 Family first: lead by example22:09 Everybody was doing their road shows25:34 The entire world was dependent28:04 Community care31:57 Valuation and a co-founder35:17 A lot of unhappy days39:25 Building Zoom for consumers46:57 Holograms?52:01 Home53:23 Huge competition, high velocity1:00:33 Where companies get wrong1:04:52 Giving back1:13:12 Who Zoom is hiring1:13:24 What “grit” means to Eric1:14:24 OutroMentioned in this episode: Webex by Cisco, Glean, Apple, HP, Netscape, Yahoo, Brian Armstrong, Emilie Choi, Coinbase, New Limit, Elon Musk, Windy Hill, Magic Leap, Rony Abovitz, Jony Ive, OpenAI ChatGPT, Bill McDermott, ServiceNow, Carl EschenbachLinks:Connect with EricXLinkedInConnect with JoubinXLinkedInEmail: grit@kleinerperkins.comLearn more about Kleiner Perkins
I had the pleasure of chatting with Arvind Jain, CEO & Co-Founder of Glean, on The Ravit Show — and what a powerful discussion it was.We dove into what's real vs. what's just hype when it comes to AI agents, and how truly intelligent agents are poised to reshape how enterprises operate over the next 5–7 years.We also explored: • How search has always been core to Glean's mission — and why that foundation enables more context-aware, intelligent AI experiences. • The power of Glean's connector ecosystem with 100+ SaaS apps — and why breadth of integration really matters in the modern workplace. • Their deep partnership with Google Cloud and how it brings real value to customers. • The barriers to AI adoption in the enterprise — and how Glean is helping to lower them.And of course, I asked Arvind what excites him most about the future of AI at work — his answer? You'll have to tune in to find out!Big thanks to Arvind and the Glean team for joining me during such an exciting week here at #GoogleNext25.#data #ai #googlecloud #glean #theravitshow
The AI Breakdown: Daily Artificial Intelligence News and Discussions
OpenAI's latest product updates have people asking if startups can still compete when big platforms add features like meeting notes and document search. Companies like Glean and Granola face new pressure as OpenAI builds these tools into ChatGPT.Get Ad Free AI Daily Brief: https://patreon.com/AIDailyBriefBrought to you by:KPMG – Go to https://kpmg.com/ai to learn more about how KPMG can help you drive value with our AI solutions.Blitzy.com - Go to https://blitzy.com/ to build enterprise software in days, not months AGNTCY - The AGNTCY is an open-source collective dedicated to building the Internet of Agents, enabling AI agents to communicate and collaborate seamlessly across frameworks. Join a community of engineers focused on high-quality multi-agent software and support the initiative at agntcy.org - https://agntcy.org/?utm_campaign=fy25q4_agntcy_amer_paid-media_agntcy-aidailybrief_podcast&utm_channel=podcast&utm_source=podcast Vanta - Simplify compliance - https://vanta.com/nlwPlumb - The automation platform for AI experts and consultants https://useplumb.com/The 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/aibreakdownInterested in sponsoring the show? nlw@breakdown.network
In this episode of the pod I had incredible Glean member on Brian White on, Brian has lost over 170 pounds and is still going. We went deep on his story, how he got to be overweight and ultimatley the steps he made to change his life. This is a quality listen from start to finish and Brian is an absolute inspiration, enjoy! You can join Glean today completely risk free with our 7 day money back guarantee, simply hit the link below! Join Glean Today - https://gleanapp.com
Reach out to Cody and Buhler to tell them what's up!Can I get a little respect around here?That is what college football programs like Clemson and South Carolina might be feeling now after seeing where ESPN's Bill Connelly had them ranked in his latest SP+ rankings.While they may hail from The Palmetto State, leave it up to two guys from neighboring states who root for rival college football teams of theirs in Georgia's John Buhler (Lead Writer, FanSided.com) and North Carolina's Cody Williams (Content Director, FanSided.com) to sort this all out.Monday's episode of False Start was mostly about the SP+ rankings, new legislation coming out about the College Football Playoff, and a little bit about remembering some dudes because it was Memorial Day and all.When you fall seven times, get up eight times, because this is False Start!
This week, we are revisiting a conversation between Lightspeed partner Michael Mignano and Arvind Jain, the founder and CEO of Glean about the evolution of AI-assisted enterprise search.Arvind shares what insights helped to start Glean's journey in 2019, how the company leveraged transformer-based models early on, and how Glean developed the market for this product. They also talk about competition, the technical aspects of integrating Glean across SaaS platforms, and the monumental impact of ChatGPT on the industry. Episode Chapters(00:00) Introduction (01:15) Why Arvind Created Glean to Solve Enterprise Search Problems(03:50) Technical Foundations: Building Glean with Transformers(09:04) Product Market Fit and Early Challenges(12:16) The Impact of ChatGPT and Market Evolution(13:42) Glean's Architecture and Model Integration(17:58) The Future of AI in Enterprises(27:52) Leadership, Competition, and Company Culture(35:48) Reflections and Lessons from Rubrik to Glean(41:15) Lightning Round and Closing RemarksStay in touch:www.lsvp.comX: https://twitter.com/lightspeedvpLinkedIn: https://www.linkedin.com/company/lightspeed-venture-partners/Instagram: https://www.instagram.com/lightspeedventurepartners/Subscribe on your favorite podcast app: generativenow.coEmail: generativenow@lsvp.comThe content here does not constitute tax, legal, business or investment advice or an offer to provide such advice, should not be construed as advocating the purchase or sale of any security or investment or a recommendation of any company, and is not an offer, or solicitation of an offer, for the purchase or sale of any security or investment product. For more details please see lsvp.com/legal.
Vincent founder Slava Rubin and Sacra co-founder Jan-Erik Asplund did a deep dive into Glean, the multi-billion dollar AI startup helping to companies get the most out of their enterprise data. They looked at Glean's growth trajectory, the competitive landscape and a possible IPO timeline.
No Priors: Artificial Intelligence | Machine Learning | Technology | Startups
Arvind Jain joins Sarah and Elad on this episode of No Priors. Arvind is the founder and CEO of Glean, an AI-powered enterprise search platform. He previously co-founded Rubrik and spent over a decade as an engineering leader at Google. In this episode, Arvind shares how LLMs are transforming enterprise search, why most tools in the space have failed, and the opportunity to build apps powered by internal knowledge. He discusses how much customization is still needed on top of foundation models, what made building Glean uniquely challenging compared to Arvind's previous ventures, and what's next for the company. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jainarvind Show Notes: 0:00 Introduction 0:58 How LLMs are changing search 2:05 Building out Glean's platform 5:09 Why most search companies failed 8:41 Out of the box vs. bespoke models 10:26 Creating apps on top of internal knowledge 15:34 User behaviors & insights 19:11 Unique challenges of building Glean 21:51 Product-led growth vs. enterprise sales 25:00 Succeeding in traditionally bad markets 27:08 What Glean is excited to build next
Nabeel Qureshi is an entrepreneur, writer, researcher, and visiting scholar of AI policy at the Mercatus Center (alongside Tyler Cowen). Previously, he spent nearly eight years at Palantir, working as a forward-deployed engineer. His work at Palantir ranged from accelerating the Covid-19 response to applying AI to drug discovery to optimizing aircraft manufacturing at Airbus. Nabeel was also a founding employee and VP of business development at GoCardless, a leading European fintech unicorn.What you'll learn:• Why almost a third of all Palantir's PMs go on to start companies• How the “forward-deployed engineer” model works and why it creates exceptional product leaders• How Palantir transformed from a “sparkling Accenture” into a $200 billion data/software platform company with more than 80% margins• The unconventional hiring approach that screens for independent-minded, intellectually curious, and highly competitive people• Why the company intentionally avoids traditional titles and career ladders—and what they do instead• Why they built an ontology-first data platform that LLMs love• How Palantir's controversial “bat signal” recruiting strategy filtered for specific talent types• The moral case for working at a company like Palantir—Brought to you by:• WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs• Attio—The powerful, flexible CRM for fast-growing startups• OneSchema—Import CSV data 10x faster—Where to find Nabeel S. Qureshi:• X: https://x.com/nabeelqu• LinkedIn: https://www.linkedin.com/in/nabeelqu/• Website: https://nabeelqu.co/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Nabeel S. Qureshi(05:10) Palantir's unique culture and hiring(13:29) What Palantir looks for in people(16:14) Why they don't have titles(19:11) Forward-deployed engineers at Palantir(25:23) Key principles of Palantir's success(30:00) Gotham and Foundry(36:58) The ontology concept(38:02) Life as a forward-deployed engineer(41:36) Balancing custom solutions and product vision(46:36) Advice on how to implement forward-deployed engineers(50:41) The current state of forward-deployed engineers at Palantir(53:15) The power of ingesting, cleaning and analyzing data(59:25) Hiring for mission-driven startups(01:05:30) What makes Palantir PMs different(01:10:00) The moral question of Palantir(01:16:03) Advice for new startups(01:21:12) AI corner(01:24:00) Contrarian corner(01:25:42) Lightning round and final thoughts—Referenced:• Reflections on Palantir: https://nabeelqu.co/reflections-on-palantir• Palantir: https://www.palantir.com/• Intercom: https://www.intercom.com/• Which companies produce the best product managers: https://www.lennysnewsletter.com/p/which-companies-produce-the-best• Gotham: https://www.palantir.com/platforms/gotham/• Foundry: https://www.palantir.com/platforms/foundry/• Peter Thiel on X: https://x.com/peterthiel• Alex Karp: https://en.wikipedia.org/wiki/Alex_Karp• Stephen Cohen: https://en.wikipedia.org/wiki/Stephen_Cohen_(entrepreneur)• Joe Lonsdale on LinkedIn: https://www.linkedin.com/in/jtlonsdale/• Tyler Cowen's website: https://tylercowen.com/• This Scandinavian City Just Won the Internet With Its Hilarious New Tourism Ad: https://www.afar.com/magazine/oslos-new-tourism-ad-becomes-viral-hit• Safe Superintelligence: https://ssi.inc/• Mira Murati on X: https://x.com/miramurati• Stripe: https://stripe.com/• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• Airbus: https://www.airbus.com/en• NIH: https://www.nih.gov/• Jupyter Notebooks: https://jupyter.org/• Shyam Sankar on LinkedIn: https://www.linkedin.com/in/shyamsankar/• Palantir Gotham for Defense Decision Making: https://www.youtube.com/watch?v=rxKghrZU5w8• Foundry 2022 Operating System Demo: https://www.youtube.com/watch?v=uF-GSj-Exms• SQL: https://en.wikipedia.org/wiki/SQL• Airbus A350: https://en.wikipedia.org/wiki/Airbus_A350• SAP: https://www.sap.com/index.html• Barry McCardel on LinkedIn: https://www.linkedin.com/in/barrymccardel/• Understanding ‘Forward Deployed Engineering' and Why Your Company Probably Shouldn't Do It: https://www.barry.ooo/posts/fde-culture• David Hsu on LinkedIn: https://www.linkedin.com/in/dvdhsu/• Retool's Path to Product-Market Fit—Lessons for Getting to 100 Happy Customers, Faster: https://review.firstround.com/retools-path-to-product-market-fit-lessons-for-getting-to-100-happy-customers-faster/• How to foster innovation and big thinking | Eeke de Milliano (Retool, Stripe): https://www.lennysnewsletter.com/p/how-to-foster-innovation-and-big• Looker: https://cloud.google.com/looker• Sorry, that isn't an FDE: https://tedmabrey.substack.com/p/sorry-that-isnt-an-fde• Glean: https://www.glean.com/• Limited Engagement: Is Tech Becoming More Diverse?: https://www.bkmag.com/2017/01/31/limited-engagement-creating-diversity-in-the-tech-industry/• Operation Warp Speed: https://en.wikipedia.org/wiki/Operation_Warp_Speed• Mark Zuckerberg testifies: https://www.businessinsider.com/facebook-ceo-mark-zuckerberg-testifies-congress-libra-cryptocurrency-2019-10• Anduril: https://www.anduril.com/• SpaceX: https://www.spacex.com/• Principles: https://nabeelqu.co/principles• Wispr Flow: https://wisprflow.ai/• Claude code: https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview• Gemini Pro 2.5: https://deepmind.google/technologies/gemini/pro/• DeepMind: https://deepmind.google/• Latent Space newsletter: https://www.latent.space/• Swyx on x: https://x.com/swyx• Neural networks in chess programs: https://www.chessprogramming.org/Neural_Networks• AlphaZero: https://en.wikipedia.org/wiki/AlphaZero• The top chess players in the world: https://www.chess.com/players• Decision to Leave: https://www.imdb.com/title/tt12477480/• Oldboy: https://www.imdb.com/title/tt0364569/• Christopher Alexander: https://en.wikipedia.org/wiki/Christopher_Alexander—Recommended books:• The Technological Republic: Hard Power, Soft Belief, and the Future of the West: https://www.amazon.com/Technological-Republic-Power-Belief-Future/dp/0593798694• Zero to One: Notes on Startups, or How to Build the Future: https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296• Impro: Improvisation and the Theatre: https://www.amazon.com/Impro-Improvisation-Theatre-Keith-Johnstone/dp/0878301178/• William Shakespeare: Histories: https://www.amazon.com/Histories-Everymans-Library-William-Shakespeare/dp/0679433120/• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884• Anna Karenina: https://www.amazon.com/Anna-Karenina-Leo-Tolstoy/dp/0143035002—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
Bucky Moore is a Partner @ Lightspeed Venture Partners, announced exclusively in the show today on 20VC. Prior to Lightspeed, Bucky spent an incredibly successful 7 years at Kleiner Perkins working with Mamoon Hamid to build one of the most successful early stage firms of the last decade. Bucky has made investments in the likes of Prisma, Netlify, Browserbase and more. In Today's Episode We Discuss: 03:07 Big News: Joining Lightspeed Venture Partners 04:09 Why Mega Platforms Will Win the Next 10 Years of VC 09:33 Are Foundation Model Companies Good Venture Investments 16:04 What Applications Will Model Providers Buy/Build? What Will They Not? 22:03 How to Approach Price Sensitivity in a World of AI 28:25 Why is it BS to do Market Sizing When Making Investments in AI 34:03 Is the Future of VC Domain Specialization 38:38 How to Know What Company Wins in Super Competitive Markets 41:06 Why Every Firm Has to do Pre-Seed To Win in VC Today? 44:43 The Risks of Multi-Stage Investing: Is Signalling Risk Real? 48:53 Investing Lessons from Leading Rounds in Glean and Windsurf 56:54 Quick Fire Round: Lessons from Mamoon, Fave CEO, Next 10 Years
Send us a text00:00 - Intro00:07 - Neuralink Raises $500M at $9B Valuation01:01 - Discord Appoints New CEO Amid IPO Speculation01:52 - Last of X debt sold by MStanley03:01 - Glean Raises at $7B Valuation as ARR Tops $100M03:43 - Anthropic Adds Claude Features, Projects $34.5B Revenue by 202704:37 - Epic Games Wins Against Apple, Returns to iOS App Store05:37 - Stripe Enables iOS Developers to Bypass Apple's 30% Fee
This is the very first live podcast I've recorded, with the incredible Lisa-Anne who you will all know by now! This podcast gives you a sneak peak into some of the fun stuff we do at the in person Glean events. You can join Glean today by hitting the link here - https://gleanapp.com
A week after OpenAI's o3/o4-mini volleyed with Google's Gemini 2.5 Flash, I sat down with Arvind Jain— ex-Google search luminary, Rubrik co-founder, and now CEO of Glean —just as his company released its agentic reasoning platform and swirled with rumors of a new round at a $7 billion valuation. We open on that whirlwind: why the model race is accelerating, why enterprises still gravitate to closed models, and when open-source variants finally take over. Arvind argues that LLMs should “fade into the background,” leaving application builders to pick the right engine for each task.From there, we trace Glean's three-act arc—enterprise search powered by transformers (2019), retrieval-augmented chat the moment ChatGPT hit, and now agents that have already logged 50 million real actions inside Glean enterprise customers. Arvind lifts the hood on permission-aware ranking, tool-use orchestration, and the routing layer that swaps Gemini for GPT on the fly. Along the way, he answers the hard questions: Do agents really double efficiency? Where's the moat when every startup promises the same? Why are humans still in the review loop, and for how long?The conversation crescendos with a vision of work where every employee is flanked by a team of proactive AI coworkers—all drawing from a horizontal knowledge layer that knows the firm's language better than any newcomer. If you want to know what's actually working with AI in the enterprise, how to build agents that deliver ROI, and what the next era of work will look like, this episode is packed with specifics, technical insights, and bold predictions from one of the sharpest minds in the space.GleanWebsite - https://www.glean.comX/Twitter - https://x.com/gleanaiArvind Jain LinkedIn - https://www.linkedin.com/in/jain-arvindX/Twitter - https://x.com/jainarvindFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro & Glean's $7B valuation rumor (02:01) The AI model explosion: open vs. closed in the enterprise (06:19) Why enterprises choose open source AI (and when) (10:33) The agent era: what are AI agents and why now? (12:41) Automating business processes: real-world agent use cases (16:46) Are we there yet? The reality of AI agents in 2025 (19:24) Glean's origin story: reinventing enterprise search (26:38) Glean agents: from apps to agentic platforms (31:22) Horizontal vs. vertical: Glean's strategic platform choice (34:14) How Glean's enterprise search works (39:34) Staying LLM-agnostic: integrating new AI models (42:11) The architecture of Glean agents: tool use and beyond (43:50) Data flywheels and personalization in Glean (47:06) Moats, competition, and the future of work with AI agents
Glean is a workplace search and knowledge discovery company that helps organizations find and access information across various internal tools and data sources. Their platform uses AI to provide personalized search results to assist members of an organization in retrieving relevant documents, emails, and conversations. The rise of LLM-based agentic reasoning systems now presents new The post Agentic AI at Glean with Eddie Zhou appeared first on Software Engineering Daily.
Glean is a workplace search and knowledge discovery company that helps organizations find and access information across various internal tools and data sources. Their platform uses AI to provide personalized search results to assist members of an organization in retrieving relevant documents, emails, and conversations. The rise of LLM-based agentic reasoning systems now presents new The post Agentic AI at Glean with Eddie Zhou appeared first on Software Engineering Daily.
Send us a textIn this special episode, EdTech Insiders hits the floor at ASU+GSV 2025—one of the world's premier EdTech conferences—for rapid-fire conversations with the leaders reshaping learning. From AI-native campuses to multilingual agents and human-centered R&D, we go behind the scenes with the innovators driving the future of education.
The UBS AI Podcast explores the fascinating and evolving world of artificial intelligence, how it's transforming industries, redefining what's possible and reshaping the future. On this first episode of the CEO Series, Ulrike Hoffmann-Burchardi, CIO for Global Equities with the UBS Chief Investment Office, converses with Arvind Jain, the CEO and Co-Founder of Glean. Ulrike and Arvind dive into the vision behind Glean, and discuss the future of AI and workplace productivity.
YES, AI WROTE MY SHOWNOTES TODAY±What day think>!?
Today I had the incredible Liana on the podcast, how I've had the honour of coaching since the academy days in 2023, she has stuck with me since then, got life changing results and even won £10,000 in one of the Glean App challenges. Liana shared some great insight on her journey and how she borke free from diet culture. Evan co hosted this one with me! If you are interested in joining Glean simply hit the link below and jump straight in! https://gleanapp.com/?fbclid=PAZXh0bgNhZW0CMTEAAabGEHp5REfxU4aLd9OtSoSOu6ITZuNuYyX0M8P1SYyDFW89uzwIwfZS2VQ_aem_DMhzezdRLFpoiLlrU6kfgw
Are you tired of feeling stuck and unsure of how to achieve your goals? Glean from 62 years to shortcut your success. In this video, we'll share the 3 essential action steps you need to speed up your success in just 20 minutes. From productivity hacks to mindset shifts, we'll cover the most critical elements that will help you reach your goals faster. Whether you're an entrepreneur, queenpreneur, or just want to improve your personal life this video will provide you with actionable tips and strategies to get you moving in the right direction. So, what are you waiting for? Watch now and start speeding up your success today! Audition for Queenpreneur Accelerator: https://chontahaynes.com/queen Summary: In this impactful session, Dr. Chonta Haynes shares 62 years of experience in just 20 minutes, offering actionable strategies for success in personal, professional, and financial life. She emphasizes the importance of execution, strategy, and the right guidance to achieve goals effectively. With real-life examples and biblical wisdom, she empowers Queenpreneurs and others to reimagine, reinvent, and reframe their paths to success. Show Notes: 0:00 - 0:30 – Introduction Dr. Chonta Haynes introduces herself and her mission to help individuals succeed by applying her 62 years of experience in a condensed, actionable format. Success is about getting there quicker and smarter, and she's here to share how. 0:30 - 1:00 – Who This is For Though she primarily helps Queenpreneurs, the lessons apply to everyone—both personal and professional development is covered. 1:00 - 1:30 – The 3 Key Strategies for Success Dr. Haynes will share the top three things needed to succeed, based on her experiences, degrees, and biblical wisdom. She starts with #3 and works backward. 1:30 - 3:00 – Strategy #3: Execution Taking action is critical; writing things down isn't enough—checking them off matters. Lessons from a recent women's conference: Knowing what to do is not enough; execution is key. In finances, business, relationships, and faith, execution determines success. 3:00 - 5:30 – Execution in Different Areas of Life Business: Selling, marketing, and fulfilling services/products is key to profit. Relationships: Setting boundaries and recognizing when to shift relationships. Faith: Daily spiritual discipline and prayer lead to growth. 5:30 - 7:30 – Strategy #2: The Power of Strategy Many people fail because they don't have a clear, intentional strategy. Strategy aligns with goals—whether it's speaking on more stages, making more money, or positioning oneself for success. Having a mentor, coach, or someone who has been there before is crucial for avoiding unnecessary struggles. 7:30 - 10:00 – Why Coaching & Mentorship Matter A coach or mentor speeds up success and helps navigate blind spots. Reinventing the wheel is unnecessary—learning from those who have succeeded makes the journey easier. The key is to align with those who have already blazed the trail. Closing Remarks: Success requires execution, strategy, and guidance. The right action and the right people make all the difference. Stay tuned for the next part where she shares the #1 most important strategy for success! Call to Action: Follow Dr. Chonta Haynes for more insights on faith, finance, and business success. Apply these strategies today to start transforming your life!
Are you getting a tax refund this year? I see where many people go splurge the few thousand they get back from overpaying in federal income taxes.... but what if you could make a really powerful move with your money? What if you paid off some debt, started that emergency fund or even started a retirement account?! What could this mean for the future version of you? Will they be ecstatically thanking you for being wise and thoughtful?! Glean a few ideas from your Credit Coach on how to improve your credit and your financial profile!Questions@creditkristi.com
Guest: Ben Chestnut, Former CEO and Co-Founder of MailchimpIf you find yourself selling your startup, then Mailchimp co-founder Ben Chestnut has some important advice for you: Get a dog. When Intuit bought Mailchimp in 2021 for $12 billion, the company asked Ben if he wanted to stay on as CEO, but he chose to “walk off into the sunset” and let the new owners take over. After that, he estimates it took 6 to 12 months before he stopped checking his email, social media, and calendar with the same level of stress a CEO might have. Adopting a dog, he discovered, forces you to “get OK with the voices in your head."“After the acquisition, that's all I do, I walk the dog,” Ben says. “And the dog was good therapy ... No judgments from a dog.”Chapters:(01:09) - Growing slow (03:06) - The long journey (07:48) - Is money a burden? (09:35) - Building globally in Atlanta (11:22) - Ben's upbringing (12:59) - The first 10 years (17:58) - Scaling to one billion emails (19:22) - Freemium (23:32) - No equity (26:00) - Deciding to sell (33:55) - “I'm a sunset guy” (35:29) - Stress and support (37:25) - Time with the parents (39:07) - Get a dog (42:24) - The voices in your head (46:03) - Serial and “Mailkimp” (53:00) - Hiring interviews (57:14) - Fitness routines (59:27) - Lights off (01:01:46) - AI & reinvention (01:06:30) - The worst days (01:09:15) - What “grit” means to Ben Mentioned in this episode: Intuit, Wolt, DoorDash, LinkedIn, Dan Kurzius, Salesforce, ExactTarget, Pardot, Constant Contact, Rackspace, Free by Chris Anderson, Wired Magazine, Charles Hudson, the Freemium Summit, Drew Houston, Dropbox, Evernote, Phil Libin, TechCrunch, Brian Kane, Catalyst Partners, Georgia Pacific, Scott Cook, Bing Gordon, Vinay Hiremath, Loom, Joe Thomas, Caltrain, Flickr, Saturday Night Live, Droga5, Cannes Film Festival, Strava, Twitter, LinkedIn, Nvidia, Glean, Rubrik, Amazon AWS, and Mechnical Turk.Links:Connect with BenLinkedInConnect with JoubinTwitterLinkedInEmail: grit@kleinerperkins.com Learn more about Kleiner Perkins
In this episode of McKinsey on Building Products, host Rikki Singh and Tamar Yehoshua, President of Product and Technology at Glean, delve into the evolving landscape of product leadership in the age of AI. They discuss the pivotal role of customer obsession, the challenges of integrating AI into product management, and strategies for leveraging AI to enhance productivity and innovation.See www.mckinsey.com/privacy-policy for privacy information
Discover the future of AI startups and enterprise adoption with Arvind Jain, Founder and CEO of Glean, in CXOTalk episode 871. Arvind explains:How AI startups differ from traditional software companiesStrategies for rapid revenue scaling in AI-native businessesKey considerations for CIOs evaluating AI solutionsManaging AI ethics, security, and transparency risksPractical advice for entrepreneurs starting AI venturesThe evolving landscape of AI integration in enterprise workflowsLearn why centralizing AI strategy, focusing on small wins, and prioritizing security are crucial for successful AI implementation. Arvind emphasizes the importance of solving real business problems and adapting to the rapidly changing AI landscape.Whether you're a CIO, entrepreneur, or business leader interested in AI's transformative potential, this episode offers actionable insights to guide your AI journey.
Guest: George Kurian, CEO of NetAppFor almost 10 years, George Kurian has been CEO of the data infrastructure firm NetApp, overseeing its pivot to cloud services. After he took the job — a surprise promotion dropped on him just days before it was announced — he had to learn on the job how the job could be.“ There are a lot more stakeholders that a CEO has to deal with than a chief product officer,” George says, referring to his previous role. “There's also a lot more external commitment ... It was a really all-consuming effort to get the company turned around.”He said the CEO job can be “fairly lonely” because you may want to be peers or friends with your team and your board — but in fact, they are sometimes your subordinates and your superiors, respectively.“ We wouldn't be here without others having contributed significantly on the journey,” George says. “[But] there are times when you have to step back and say, ‘I see a pattern that my team is not seeing,' or ‘Do I think that we can do a better job than we are doing?'”Chapters:(01:10) - Commuting to Sunnyvale (04:49) - Growing up in India (08:04) - Protect the child (09:33) - Raising kids in Silicon Valley (12:44) - Money motivation (15:04) - NetApp's renaissance (21:39) - Writing new chapters (23:15) - Culture shifts (26:38) - Coming to NetApp (29:41) - Surprise! You're the CEO (32:41) - Making sacrifices (35:04) - Work vs. family tension (37:18) - Doubt & lonely decisions (42:38) - The data wave (45:27) - Enterprise AI (51:36) - Starting your own company (53:33) - Navigating difficulty (56:28) - Who NetApp is hiring (57:11) - What “grit” means to George Mentioned in this episode: EMC, OpenAI, DeepSeek, CalTrain, the San Francisco 49ers, Princeton University, Subway, Vons, Thomas Kurian, Google Cloud, Stanford University, Brian Cox, Oliver Jay, the Quakers, Jay Chaudhry, zScaler, Manmohan Singh, Oracle, IBM, Sun, Amazon, Microsoft, Glean, Kobe Bryant, Steph Curry, McKinsey, Akamai, Cisco, Gwen McDonald, and the San Francisco Friends School.Links:Connect with GeorgeLinkedInConnect with JoubinTwitterLinkedInEmail: grit@kleinerperkins.com Learn more about Kleiner PerkinsThis episode was edited by Eric Johnson from LightningPod.fm
Guests: Winston Weinberg, CEO & co-founder of Harvey; and Ilya Fushman, partner at Kleiner Perkins“If you think about pretty much any job out there in the world, we will have some sort of [AI] copilot,” says Kleiner Perkins partner Ilya Fushman. “The question is, who are the right folks to build it, and what's their vision?”For Harvey CEO & co-founder Winston Weinberg, the vision is clear: Silicon Valley cannot and should not try to disrupt the legal profession by automating the job of lawyers. Instead, he says, they need to have “respect for the industry” before designing AI solutions that speed up specific tasks.“These industries are incredibly complex,” Winston says. “Legal is one of the oldest professions known to man. There are firms that are over a hundred years old. There are firms that are hundreds of years old, and having a brand that says, ‘We are partnering with the industry to transform it' versus ‘We are just going to steamroll the industry' is really important for us.”Chapters:(01:16) - The zeitgeist switch (02:58) - What is Harvey? (06:10) - Chief Law Officers (07:58) - Agentic workflows (09:43) - Ilya's investment thesis (12:48) - Collaborating with AI (16:05) - Task automation (20:52) - Why is it called Harvey? (23:14) - Respecting the legal industry (26:43) - Winston's past jobs (28:47) - First steps (32:13) - Scaling the company (35:02) - Scaling yourself (37:19) - Who works for Harvey (40:50) - Making mistakes (43:15) - Making sacrifices (45:51) - Growing too fast (50:50) - Setting priorities (54:54) - Harvey's competitors (57:38) - Internal virality (01:00:46) - Testing Harvey's limits (01:03:29) - Who Harvey is hiring (01:04:01) - What “grit” means to Winston Mentioned in this episode: ChatGPT, the Fortune 500, Microsoft Copilot, Gabe Pereyra, Activision, Excel, Counsel AI Corporation, Suits, Harvard University, Netflix, Dell, O'Melveny & Myers, Hueston Hennigan, Meta, Reddit, Jason Kwon, Anthropic, Marissa Mayer, Eric Schmidt, Google, Larry Page, Sergey Brin, and Glean.Links:Connect with WinstonTwitterLinkedInConnect with IlyaTwitterLinkedInConnect with JoubinTwitterLinkedInEmail: grit@kleinerperkins.com Learn more about Kleiner PerkinsThis episode was edited by Eric Johnson from LightningPod.fm
Master Chef and award-winning culinary creator, Jan Sather, "hops to it" with a rabbit dish that will blow your mind and taste buds. In this Fowl Life Podcast Provider Series Eat Wild Edition learn step by step how to easily put a 5-star wild game dinner on the table. Glean the expertise of Master Chef Jan and gain the approval of your family. Join Host Joel Kleefisch, Fowl Life Midwest Pro Staffer, Danielle Fairman, and Chef Jan Sather for a trip from the woods to the table you'll never forget. Hassenpfeffer Recipe: Four thawed Rabbit Hindquarters or one whole rabbit cut into pieces For the marinade: 2 Cups White Wine 3 T. White Vinegar 3 T. Sugar 2 Large Onions, Sliced 3-4 T. Minced Garlic (or 6 garlic cloves) 1 T. Course Black Pepper 1. Salt 1 T. THE BRIT Provider Spice For Frying: One stick butter/3 T. Olive Oil or Veg Oil For Thickening: 1/4 Cup Flour Combine marinade ingredients and pour over in 9 x 13 pan. Cover and refrigerate overnight. Next day, remove rabbit pieces from the marinade and pat dry with paper towels, and place aside. Heat butter and oil in a cast-iron skillet over medium heat and add rabbit pieces to brown on both sides. Take your time! Remove rabbit from skillet and put all of the marinade ingredients, including onions in the pan. When onions become translucent, add the flour and stir until combined, and sauce, begins to thicken. Return browned rabbit to skillet with sauce, and place in 350° oven for one and a half hours. Ready to serve and enjoy! Check out the video on Instagram, Facebook, and Youtube @Theproviderlife, & Theproviderlife.com. This Episode is brought to you by Travel Wisconsin, The Provider Culinary, Bad Boy Mowers, ZLine, Hi Viz, Banded Brands, Jacks Link's, Kershaw knives, Secureit Gun Safes, and Avery GHG Decoys!
In the latest episode of Pure Report Unplugged, the team dives into the exciting world of TechSummit Season 3 with JD's announcement of new focus areas including the Pure Storage Platform, Real-Time Enterprise File and Object services, and customer success stories from the field. The tour kicks off in Calgary, Alberta before moving to San Diego, Toronto, Atlanta, and more locations, with Andrew scheduled to appear at the inaugural Calgary event. Listeners also get insight into the team's behind-the-scenes work, from Andrew's enablement initiatives and bootcamp development to Rob's journey in production for Sales Kickoff, plus news about upcoming video podcasts and an updated web landing page. The episode's Pure News Roundup covers significant company developments, including Andrew's commentary on the enhanced Partner Program and JD's updates on Fusion features like simplified management, presets, and automation improvements. The team emphasizes the "Subscription to Innovation" philosophy available on version 6.8, discussing how security challenges and cloud technologies are transforming update strategies toward more resilient architectures. Rob also shares exciting details about Accelerate registration opening February 18th in Las Vegas at Resortsworld, highlighting expanded offerings based on attendee feedback such as more hands-on labs, customer speakers, community meetups, and certification opportunities. The Tech News Roundup segment explores fascinating AI advancements from Deepseek, Grok3, and Glean, alongside Oxford scientists' claims about quantum teleportation, drawing a nostalgic connection to Willy Wonka's Mike TeeVee character. The episode concludes with practical tips, including JD's guide to building a Pure LED wall panel using Nanoleaf Shapes Triangles with HomeKit compatibility and Pure Orange (Hex #FE5000), plus Andrew's highlight of the Genealogy View feature in Pure 1, complete with a customer anecdote about demonstrating cost savings to finance teams.
Enterprise search is a problem that's plagued companies since the advent of working on computers. Now, AI promises solutions. In this week's episode, host Michael Mignano from Lightspeed sits down with Arvind Jain, founder and CEO of Glean, to discuss the evolution of AI-assisted enterprise search. Arvind shares what insights helped to start Glean's journey in 2019, how the company leveraged transformer-based models early on, and how Glean developed the market for this product. They also talk about competition, the technical aspects of integrating Glean across SaaS platforms, and the monumental impact of ChatGPT on the industry. Episode Chapters(00:00) Introduction (01:15) Why Arvind Created Glean to Solve Enterprise Search Problems(03:50) Technical Foundations: Building Glean with Transformers(09:04) Product Market Fit and Early Challenges(12:16) The Impact of ChatGPT and Market Evolution(13:42) Glean's Architecture and Model Integration(17:58) The Future of AI in Enterprises(27:52) Leadership, Competition, and Company Culture(35:48) Reflections and Lessons from Rubrik to Glean(41:15) Lightning Round and Closing RemarksStay in touch:www.lsvp.comX: https://twitter.com/lightspeedvpLinkedIn: https://www.linkedin.com/company/lightspeed-venture-partners/Instagram: https://www.instagram.com/lightspeedventurepartners/Subscribe on your favorite podcast app: generativenow.coEmail: generativenow@lsvp.comThe content here does not constitute tax, legal, business or investment advice or an offer to provide such advice, should not be construed as advocating the purchase or sale of any security or investment or a recommendation of any company, and is not an offer, or solicitation of an offer, for the purchase or sale of any security or investment product. For more details please see lsvp.com/legal.
#updateai #customersuccess #saas #businessLauren Kennedy (Head of CS, Glean) joins Josh Schachter (CEO & Co-Founder, UpdateAI) and Jon Johnson (Principal CSM, UserTesting) to share her insights on how Agentic AI is transforming customer success management by automating low-value tasks and enhancing productivity. She also discusses how they're leveraging their own technology to optimize internal operations and improve client engagements.**Timestamps:**0:00 - Preview, BS & Intros4:20 - Glean's X-factor8:20 - What is Agentic AI?12:00 - Goals for 202514:30 - Company Culture and Hiring Needs18:04 - Collaboration between Sales & CS20:55 - Glean for post-sales24:00 - Lauren's career path and insights___________________________
Deedy Das invests at Menlo Ventures in Seed and Series A companies in AI / SaaS / Infra and helps run the $100M Anthology Fund with Anthropic. He was on the founding team of the $4.6B enterprise search and AI company Glean, where he built and led Glean Assistant to $15M+ ARR. Deedy also writes frequently about tech, AI, startups, and immigration on X which have received 400M+ views and been featured frequently in Forbes, Wall Street Journal, TechCrunch, and more. Discover how Deedy's experience with founding an AI company sparked his passion for supporting budding startups. Gain insights into his work with the $100 million Anthology Fund with Anthropic, which offers a refreshing flexibility compared to traditional venture capital frameworks. Deedy also opens up about his immigration journey from India to the U.S., shedding light on the complex H1B visa process and its impact on talented individuals striving to establish themselves in the U.S. In this episode, you'll hear about: Deedy Das's transition from engineering to venture capital and his role at Menlo Ventures and Glean. Challenges and strategies for navigating the H1B and EB1A visa processes as an immigrant entrepreneur. Insights into the flexibility and structure of the $100 million Anthology Fund with Anthropic. The need for reform in green card pathways and exploring international models for immigration. Strategies for non-citizen entrepreneurs in the U.S., focusing on visa eligibility and fundraising. The importance of customer feedback over investor opinions for startup success. Follow and Review: We'd love for you to follow us if you haven't yet. Click that purple '+' in the top right corner of your Apple Podcasts app. We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select “Ratings and Reviews” and “Write a Review” then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast. Supporting Resources: Linkedin - https://www.linkedin.com/in/debarghyadas/ Website - https://debarghyadas.com/ https://x.com/AravSrinivas/status/1851700699756925059?lang=en https://menlovc.com/anthology-fund/ Alcorn Immigration Law: Subscribe to the monthly Alcorn newsletter Sophie Alcorn Podcast: Episode 16: E-2 Visa for Founders and Employees Episode 19: Australian Visas Including E-3 Episode 20: TN Visas and Status for Canadian and Mexican Citizens Immigration Options for Talent, Investors, and Founders Immigration Law for Tech Startups eBook
My guest today is Arvind Jain, the founder and CEO of Glean. Before Glean, Arvind spent over a decade building Google's search infrastructure. He then co-founded Rubrik, which recently passed $1B ARR.With Glean, Arvind is tackling the longstanding challenge of enterprise search. Yet his vision goes beyond this. He believes every employee should have their own team of AI agents to help them work smarter and achieve more. In our conversation, Arvind shares his journey as a technical founder and offers his unique perspective on what it takes to build a successful startup today. We also discuss where AI is heading, and where he sees the biggest opportunities for founders. Hope you find this conversation valuable! Chapters:00:00 Cold open04:42 How Arvind began his journey in search06:59 Arvind on Glean's mission08:50 The evolution of enterprise search12:56 How AI unlocks a new dimension for search16:56 Lessons for AI startup founders21:23 Navigating the AI startup landscape25:44 The "build vs. buy" decision with AI models31:09 Defining the role of AI in business34:57 The future of work with AI agents39:30 The shift from SaaS to Service-as-Software41:21 Concluding thoughts
“The AI trend still feels significantly larger than all of those previous trends that we've seen in technology over the last three decades” Glean CEO and founder Arvind Jain tells Bloomberg Intelligence. The growing enterprise-information stack that's dispersed across diverse systems adds complexity and Glean enables organizations to deploy AI-powered knowledge retrieval systems for customer workflows and systems, on the lines of ChatGPT or Google Search. In this episode of the Tech Disruptors podcast, he joins Sunil Rajgopal, BI's senior software analyst, to discuss the AI-fueled changes to enterprise knowledge management, the impact of AI agents and the broader implications of AI. They also talk about Glean's product evolution, competition landscape and pricing model. Find this and other Bloomberg Intelligence podcasts at BI PODCASTS .
Our star players try to get to the bottom of the recent pro Violenceball game which went unbroadcasted and no one in the audience seems to remember. Their search takes them from the height of the Crumblemire Consortium announcer's box to the depths of the Motorlodge's VHS dungeon. Thanks to Shayne Plunkett & Jesse Wright of Meadow Vista Media, who created this season's intro theme: www.meadowvistamedia.com Twitter: @MVM_Studio IG: @meadowvistamedia Buy our book, The Ultimate Random Encounters Book: bit.ly/RandomBook Find out more about the show at our website: www.vre.show Show pins and more: shop.vre.show Support us on Patreon: www.patreon.com/VRE Follow us @VRECast
The title basically sums it up, this podcast is your complete guide to all things nutrition for weight loss, health and life in general. I'm joined by Glean head coaches Evan & Saoirse for this episode so you'll get a bit of an insight into the craic we have on the calls on a weekly basis. Glean goes live again 3rd Feb at 10am Irish time, I'll have links on my profile and all you'll need to do is click them on the day!
The importance of seeking wisdom from godly leaders before they are no longer available
Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM
Shaun Clowes is the chief product officer at Confluent and former CPO at Salesforce's MuleSoft and at Metromile. He was also the first head of growth at Atlassian, where he led product for Jira Agile and built the first-ever B2B growth team. In our conversation, we discuss:• Why most PMs are bad, and how to fix this• Why great AI products are all about the data• Why he changed his mind about being data-driven• How to build your B2B growth team• How to choose your next career stop• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• BuildBetter—AI for product teams• Wix Studio—The web creation platform built for agencies—Find the transcript at: https://www.lennysnewsletter.com/p/why-great-ai-products-are-all-about-the-data-shaun-clowes—Where to find Shaun Clowes:• X: https://x.com/ShaunMClowes• LinkedIn: https://www.linkedin.com/in/shaun-clowes-80795014/• Website: https://shaunclowes.com/about-shaun• Reforge: https://www.reforge.com/profiles/shaun-clowes—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Shaun's background(05:08) The state of product management(09:33) Becoming a 10x product manager(13:23) Specific ways to leverage AI in product management(17:15) Feedback rivers(19:20) AI's impact on data management(24:35) The future of enterprise businesses with AI(35:41) Data-driven decision-making(45:50) Building effective growth teams(50:18) The evolution of product-led growth(56:16) Career insights and decision-making(01:07:45) Failure corner(01:12:32) Final thoughts and lightning round—Referenced:• Steve Blank's website: https://steveblank.com/• Getting Out of the Building. 2 Minutes to See Why: https://www.youtube.com/watch?v=TbMgWr1YVfs• OpenAI: https://openai.com/• Claude: https://claude.ai/• Sachin Rekhi on LinkedIn: https://www.linkedin.com/in/sachinrekhi/• Video: Building Your Product Intuition with Feedback Rivers: https://www.sachinrekhi.com/video-building-your-product-intuition-with-feedback-rivers• Confluent: https://www.confluent.io• Workday: https://www.workday.com/• Lenny and Friends Summit: https://lennyssummit.com/• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• Anthropic: https://www.anthropic.com/• Salesforce: https://www.salesforce.com/• Atlassian: https://www.atlassian.com/• Jira: https://www.atlassian.com/software/jira• Ashby: https://www.ashbyhq.com/• Occam's razor: https://en.wikipedia.org/wiki/Occam%27s_razor• Breaking the rules of growth: Why Shopify bans KPIs, optimizes for churn, prioritizes intuition, and builds toward a 100-year vision | Archie Abrams (VP Product, Head of Growth at Shopify): https://www.lennysnewsletter.com/p/shopifys-growth-archie-abrams• Charlie Munger quote: https://www.goodreads.com/quotes/11903426-show-me-the-incentive-and-i-ll-show-you-the-outcome• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led• Metromile: https://www.metromile.com/• Tom Kennedy on LinkedIn: https://www.linkedin.com/in/tom-kennedy-37356b2b/• Building Wiz: the fastest-growing startup in history | Raaz Herzberg (CMO and VP Product Strategy): https://www.lennysnewsletter.com/p/building-wiz-raaz-herzberg• Wiz: https://www.wiz.io• Colin Powell's 40-70 rule: https://www.42courses.com/blog/home/2019/12/10/colin-powells-40-70-rule• Detroiters on Netflix: https://www.netflix.com/title/80165019• Glean: https://www.glean.com/• Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity: https://www.amazon.com/Radical-Candor-Kick-Ass-Without-Humanity/dp/1250103509• Listen: Five Simple Tools to Meet Your Everyday Parenting Challenges: https://www.amazon.com/Listen-Simple-Everyday-Parenting-Challenges/dp/0997459301• Empress Falls Canyon and abseiling: https://bmac.com.au/blue-mountains-canyoning/empress-falls-canyon-and-abseiling—Recommended books:• The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses: https://www.amazon.com/Lean-Startup-Entrepreneurs-Continuous-Innovation/dp/0307887898• Inspired: How to Create Products Customers Love: https://www.amazon.com/Inspired-Create-Products-Customers-Love/dp/0981690408—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
Aaron and Dr. Clardy bring you a few ups and upsie daisies from Kentucky politicos on the national stage… then we chat with Jimmy Earley, the program director of Glean Kentucky, a nonprofit focused on tackling the twin challenges of food waste and food insecurity in the bluegrass –- then we close out with a call to action in support of our campaign for a caregiver voting fix for Kentucky. Also, WELCOME TO THE WORLD, GAIA PREECE!!! #ColonelsOfTruth NEWS OF THE WEAK:Governor Andy Beshear elected chair of DGA https://kentuckylantern.com/briefs/democratic-governors-pick-beshear-as-2026-chair-elect/ Rep. Brett Gutherie to Chair Energy & Commercehttps://www.kentucky.com/news/politics-government/article296850719.html https://kentuckylantern.com/2024/12/10/dc/mcconnell-falls-while-at-u-s-capitol-but-is-reported-to-be-fine/ INTERVIEW: Jimmy Earley, Glean Kentuckyhttps://gleanky.org/https://www.facebook.com/gleanKY CALL TO ACTION: Increase early voting for KY caregivers!https://actionnetwork.org/letters/early-voting-for-caregivers #ProgressKentucky - #ColonelsOfTruth Join us! http://progressky.org/Support us! https://secure.actblue.com/donate/progressky Live Wednesdays at 7pm on Facebook https://www.facebook.com/progressky/live/and on YouTube http://bit.ly/progress_ky Listen as a podcast right here, or wherever you get your pods: https://tr.ee/PsdiXaFylK Facebook - @progresskyInstagram - @progress_kyTwitter - @progress_ky Episode 190 was kinda produced by Aaron Theme music from the amazing Nato - hear more at http://www.NatoSongs.com Logo and some graphic design provided by Couchfire Media
About the Guest:Nathan Brand is a seasoned culinary expert and entrepreneur known for his transformative contributions to the restaurant industry. As a former chef and partner at the renowned Timber! restaurant, Nathan has established himself as a figure of innovation in modern dining. Currently, he embarks on a new business venture, launching a retail food and wine shop focusing on charcuterie, cheeses, wines, and gourmet products. His commitment to sourcing quality ingredients and offering unique gastronomic experiences underscores his reputation as a pioneer in the local culinary landscape.Episode Summary:Join Colin Johnson as he sits down with Nathan Brand for an engaging discussion about culinary passions, entrepreneurship, and creating a balance between work and family. In this enlightening episode, Nathan returns to the podcast to share insights about his new venture—a retail food and wine store that aims to bring gourmet experiences to Johnson City. Throughout the conversation, Nathan reveals his journey from being a partner at Timber to stepping into the world of retail with a focus on specialized, regional products like Benton's ham, unique cheeses, and natural wines, catering to discerning palates.In exploring his motivations, Nathan speaks about his new business as a returning guest on the Colin and Carly Group show. He discusses the inspiration behind his concept, touching upon the scarcity of certain high-quality products in Johnson City and his desire to make gourmet flavors accessible to the local community. The episode dives into how Nathan's background as a chef and restaurateur fuels his pursuit of innovation in the food industry. From sourcing premium ingredients to creating a space for culinary exploration, Nathan's new endeavor reflects his passion and expertise in delivering exceptional dining experiences.Key Takeaways:Shifting Career Paths: Nathan Brand moves from the restaurant industry to launching a food and wine retail venture.Regional Product Focus: The shop will spotlight regional gourmet products such as Benton's ham and North Carolina cheeses.Culinary Exploration: Nathan aims to make gourmet products and natural wines accessible and enjoyable for the local community.Balancing Personal and Professional Life: The discussion highlights Nathan's desire to spend more time with family while pursuing entrepreneurial dreams.Community Dynamics: Emphasis on the support and camaraderie of the Johnson City community in fostering business success.Notable Quotes:"I'm always looking at what's available here versus what I am looking for in my life.""I am more interested in American products that just don't get the props that they deserve.""Wine is always so stodgy… I'm really interested in making wine more accessible.""I wanted to spend more time with my kids… that's a huge value for me.""The more restaurants there are in an area, I think the better they all do."Glean amazing insights on entrepreneurship and culinary expertise by listening to this compelling episode. Stay tuned for more inspiring stories and expert discussions with the Colin and Carly Group podcast series!
I am thrilled to bring you my latest conversation with MP Eisen, VP of Partnerships at Glean, from the Google Cloud Marketplace Exchange! Glean is redefining the way enterprises access and utilize internal data, creating an "enterprise work assistant" that operates like an internal search engine, helping users find strategic insights, project updates, and account-specific information. MP shares Glean's journey as a Google Cloud partner, from their first Marketplace listing in 2023 to a rapid trajectory that's driven millions in consumption. Their approach? A laser focus on aligning Glean's offerings with Google's value propositions, from compute to Vertex AI's Gemini, resulting in a partnership that has rapidly scaled across multiple industry segments. Key takeaways from MP's insights: Clear Product-Partner Alignment: Ensuring Glean's product naturally drives value for Google's sellers and customers, particularly by tapping into incentives like quota retirement through Marketplace transactions. Focused Market Approach: Instead of broad targeting, Glean zeroes in on specific industries, regions, and segments, building success one focused area at a time—a strategy that's amplifying their presence within Google's ecosystem. Upstream Conversations: AI is shifting conversations to the C-suite, making executive buy-in critical for enterprise-wide adoption, with CIOs and CEOs seeing the transformational impact of AI in operations. Listen in on The Ultimate Guide to Partnering to learn.
James and Al outline the dangers of Donald Trump's loyalist cabinet appointments and the risk to the military's chain of command before welcoming journalist Michael Tomasky. They discuss the dominance of right-wing media, the rise of podcasts, the triumph of misinformation over facts, and how to save journalism. They also outline the catastrophic effect of identity politics, draw comparisons between the upcoming Trump administration and Viktor Orbán's Hungary, emphasize the need to pick the right battles, and explore the ability of the Senate to act as a check on its political power. Email your questions to James and Al at politicswarroom@gmail.com or tweet them to @politicon. Make sure to include your city– we love to hear where you're from! More from James and Al: Get text updates from Politics War Room and Politicon. Watch Politics War Room & James Carville Explains on YouTube @PoliticsWarRoomOfficial CARVILLE: WINNING IS EVERYTHING, STUPID comes out on Max starting November 14th! You can also get updates and some great behind-the-scenes content by following James on Twitter @jamescarville and his new TikTok @realjamescarville James Carville & Al Hunt have launched the Politics War Room Substack Please Support Our Sponsors: Beam: Sleep better with Beam's best-selling Dream Powder and get up to 50% off for a limited time when you go to shopbeam.com/warroom and use code: WARROOM Zbiotics: Get back into action after a night out with 15% off your first order of Zbiotics when you go to zbiotics.com/pwr and use code: PWR Glean: Visit glean.com/politics to see how Glean can help your company's employees do their best work with A.I.
Glean from the invaluable insights of Mrs. Miriam Zeitlin, renowned dating coach and kallah teacher, as she delves into the power of authenticity and the crucial role of open communication in establishing relationships. Hosted by Anna Krausz.
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
AJ Tennant is the Vice President of Sales & Success at Glean, Glean has more than 20x'd its revenue and 100x'd its user base in the just two and a half years he's been there. Before Glean, AJ had incredible runs at Slack and Facebook. At Slack, AJ helped grow revenue from $6 million to more than $1 billion. In Today's Episode with AJ Tennant We Discuss: 1. How to Sell AI Tools in 2024: Are we still in the experimental budget phase for AI? How does selling AI tools differ to selling traditional SaaS? What are enterprises biggest concerns when it comes to adopting AI tools? What buzzwords get enterprises most excited in the sales process? Will we see a massive churn problem when the first renewal cycle for many of these AI products comes? 2. Outbound, Discounting, Closing: Is outbound dead in 2024? What does no one do that everyone should do? How does AJ approach discounting? Biggest lessons and advice? What can sales teams do to create a sense of urgency in a sales cycle? How does AJ do deal reviews and post-mortems? What is the difference between good and bad post-mortems? 3. How to Master Customer Success: What are the biggest mistakes founders make today in managing their CS teams? Should CS be compensated for upsell? How should the comp structure of CS teams change? What can be done to create a good handoff experience for the customer when handing from AE to CS? What are the most common ways CS teams break over time? 4. Hiring the Best Sales Teams: How does AJ structure the hiring process for all new sales hires? What questions does AJ always need to ask when hiring sales reps? What are clear signs of outperformers when hiring new reps? Does AJ give candidates a take-home assignment? What does he want to see from them?
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
Mamoon Hamid is a General Partner @ Kleiner Perkins and one of the greatest venture investors of our time. In the past, Mamoon has led rounds in Figma, Slack, Rippling, Intercom, Glean and Box. Prior to joining Kleiner Perkins, Mamoon was a Co-Founder of Social Capital, and prior to that a Partner at U.S. Venture Partners (USVP). In Today's Episode with Mamoon Hamid We Discuss: 1. The Greatest Venture Deal of All Time: Figma or Slack: What is Mamoon's highest returning deal? What did Mamoon see in Dylan and Figma when they had no revenue and very little user data? What compelled Mamoon to write Stewart the check with Slack? What did he not see with Slack that he should have seen? 2. Taking Control of the Great Brand in Venture: Kleiner Perkins: Is it true that Kleiner approached Mamoon and gave him the keys to the Kleiner kingdom? How did it go down? Will Kleiner go back to having multiple products, large growth funds, international funds? What does Mamoon want Kleiner to be in 5 years? What was the hardest element of the transition into Kleiner? What did Mamoon not know that he wishes he had known? 3. Becoming a Generational Defining Investor: Market, founder, product, how does Mamoon rank them 1-3? How has Mamoon changed most significantly as an investor? What does he know now that he wishes he had known when he became a VC 19 years ago? What is his biggest loss? How did it shape his mindset and go forward investing approach? 4. AI Supercycle: The Greatest Time to Invest Where does Mamoon believe the value will accrue in this wave of AI? Where are many investors spending a lot of time but Mamoon believes is not worthy of that time? Will scaling laws continue? Have we ever seen an incumbent set spend like this incumbent class? How does that change the game for VCs?
Politics War Room ON TOUR - live show in Boston on 11/2 at politicon.com/tour Watch Politics War Room & James Carville Explains on YouTube @PoliticsWarRoomOfficial James and Al react to the VP debate, call on Kamala's campaign to go on the offensive, and welcome Wisconsin Democratic Party Chair Ben Wikler. They strategize about how to win his state, defend the Blue Wall of northern battleground states, and expose the disastrous impact of Trump's proposed tariffs on workers and inflation. Then, they're joined by tennis legend Martina Navratilova to discuss her activism on behalf of women in sports, the desire to win, her experience defecting to the US from the Soviet Bloc, and the European perspective on the Ukraine War. Email your questions to James and Al at politicswarroom@gmail.com or tweet them to @politicon. Make sure to include your city– we love to hear where you're from! Get tickets for the Politics War Room live shows in Boston on 11/2 at politicon.com/tour Get text updates from Politics War Room and Politicon. Watch Politics War Room & James Carville Explains on YouTube @PoliticsWarRoomOfficial CARVILLE: WINNING IS EVERYTHING, STUPID, comes out 10/5 @ 7 PM EST on CNN. Get updates and some great behind-the-scenes content. Follow James on Twitter @jamescarville and his new TikTok @realjamescarville James Carville & Al Hunt have launched the Politics War Room Substack Get More From This Week's Guests: Ben Wikler: Twitter | Wisconsin Democratic Party | Website | Wisconsin Donations | MoveOn Martina Navratilova: Twitter | Website | International Tennis Hall of Fame | TopCourt.com Please Support Our Sponsors: Glean: Find answers, generate content, and automate work by connecting and understanding all your company data on Glean's Work A.I. platform when you go to glean.com/politics 3 Day Blinds: For their buy 1 get 1 50% off deal, head to 3DayBlinds.com/warroom Beam: Sleep better with Beam's best-selling Dream Powder and get up to 40% off for a limited time when you go to shopbeam.com/warroom and use code: WARROOM
Tamar Yehoshua is the president of product and technology at Glean. Prior to joining Glean, Tamar was chief product officer at Slack, where she led product, design, and research as the company scaled, including a 10x increase in revenue, its public listing, and an acquisition by Salesforce. She also led product and engineering teams at Google, working on search, identity, and privacy, and at A9.com, an Amazon company. Tamar has served on the board of directors for RetailMeNot, ServiceNow, Snyk, and Yext. In our conversation, we discuss:• Why you don't need to be a well-run company to win• The impact of AI on product management and the future of work• How to build strong cross-functional relationships, especially with engineers• Lessons learned from working with leaders like Jeff Bezos and Stewart Butterfield• Strategies for staying ahead in a rapidly evolving tech landscape• Much more—Brought to you by:• Explo—Embed customer-facing analytics in your product• Sprig—Build products for people, not data points• Sidebar—Accelerate your career by surrounding yourself with extraordinary peers—Find the transcript and show notes at: https://www.lennysnewsletter.com/p/you-dont-need-to-be-a-well-run-company-to-win-tamar-yehoshua—Where to find Tamar Yehoshua:• X: https://x.com/TYehoshua• LinkedIn: https://www.linkedin.com/in/tamar-yehoshua-886217/• Newsletter: https://tamaryehoshua.substack.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Tamar's background(02:09) Key advice for career success(06:54) Understanding people and motivations(09:33) The importance of impact(11:20) Navigating company chaos(18:40) Career planning: a different perspective(26:22) Lessons from industry leaders(37:59) Building stronger cross-functional relationships(42:00) Streamlining OKR reviews with async methods(45:26) Why you shouldn't worry so much about making users unhappy(47:50) The power of listening in leadership(52:34) How to leverage AI so you don't fall behind(01:06:39) Closing thoughts and lightning round—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
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