The MAD Podcast with Matt Turck

🔗 2024 MAD Landscape: https://mad.firstmark.com 📃 PDF: https://mattturck.com/landscape/mad2024.pdf 📃 Blog post: https://mattturck.com/mad2024/ In this episode, we delve into the 2024 machine learning, AI, and data scene (MAD), examining an evergrowing array of over 2011 logos, the meteoric rise of open-source AI, and the anticipated advancements in AI agents and edge AI technology. Gain valuable perspectives on the saturated AI market, the dilemmas and prospects open source AI presents, and the continuous evolution of the modern data infrastructure. This episode covers a distinctive mix of analysis, industry perspectives, and foresight into the technological future. FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck Aman Kabeer (Investor) LinkedIn - https://www.linkedin.com/in/aman-kabeer/ Twitter - https://twitter.com/AmanKabeer11 (00:00) Intro (02:06) What is MAD? (07:58) Open sourcing AI (12:29) How open source affects commercial AI? (21:02) Is the AI hype cycle over? (26:39) Was 2023 a head fake for Gen AI? What about 2024? (28:05) VC's perspective on AI (30:54) Emerging of AI stack (37:36) What are the areas VCs are excited about? (41:04) Will full-stack AI platforms kill SaaS? (42:42) Modern Data Stack: is it dead or alive? (47:17) What's next for the MAD Landscape?

In this episode, we sat down with Morgan McGuire, Chief Scientist of Roblox, and the mind behind the magic of the virtual universe. Together we explore the spectrum of creativity on Roblox, from no-code experiences to professional game development, dive deep into the cutting-edge AI tools Roblox is deploying, and how these tools are democratizing game development. Tune in to embark on a journey into the heart of creativity, technology, and community with Roblox. This is not just about playing games; it's about creating the future, one experience at a time. ROBLOX Website - https://www.roblox.com Twitter - https://twitter.com/Roblox Morgan McGuire LinkedIn - https://www.linkedin.com/in/morgan-mcguire-660120210 Twitter - https://twitter.com/casualeffects FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:05) Roblox is not a game, but a platform (10:03) How does Roblox leverage Gen AI? (13:34) How did the company start working on AI? (21:26) AI Code Assist (26:30) AI Material Generator (32:07) ControlNet (38:36) StarCoder (43:40) Who works at Roblox?

In this episode, we sat down with Benedict Evans, a leading voice in the tech industry and a former partner at Andreessen Horowitz. Known for his sharp insights and forward-thinking analysis, Benedict shares his expert perspective on what generative AI means for the future of technology, business, and society at large. Specifically, we dive deep into the evolving landscapes of generative AI, augmented and virtual reality, and the critical issue of AI bias. Join us as Benedict Evans provides a nuanced analysis of cutting-edge tech and shares his insights and perspectives on the road ahead. BENEDICT EVANS LinkedIn - https://www.linkedin.com/in/benedictevans/ Threads - https://www.threads.net/@benedictevans FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:06) The AI platform shift in 2024 (05:54) Gen AI in 2024 vs. PC-boom in the 80-s (13:24) Until AGI happens, there will be vertical-specific apps (15:12) Should companies have an AI strategy? (21:04) Platform shift OR paradigm shift? (23:55) How should we think about AGI in 2024? (34:08) Is gen AI grossly overhyped? (36:27) AI bias and the hidden problems in data (44:56) Apple Vision Pro and the future of AR/VR

In this episode, we sit down with Gary Little, CEO of Foursquare, to discuss Foursquare's remarkable evolution from a social app to a leader in location intelligence. Gary discusses how Foursquare uses smartphone ubiquity to create a global map through crowdsourcing, covering 190 countries and over 200 million points of interest. Learn about the challenges of managing complex, real-time datasets and how Foursquare employs machine learning and knowledge graphs to analyze foot traffic and device movements. The conversation also covers the critical role of privacy and data security in location tracking, especially in light of recent regulatory changes. Gary explains Foursquare's platform strategy, drawing parallels with Amazon's AWS, to enable customers to process and utilize location data for their applications. Foursquare Website - https://location.foursquare.com Twitter - https://twitter.com/Foursquare Gary Little (CEO) LinkedIn - https://www.linkedin.com/in/gary-little-0670ba4 Twitter - https://twitter.com/garylittlefsq FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:10) Brief history of Foursquare (03:07) What makes Foursquare's location data unique? (05:17) Foursquare Platform. What is it? (08:07) A glimpse into the future of Foursquare (10:00) More customers want to process the data themselves. Why? (13:42) Data privacy of today vs 10 years ago. What has changed? (16:41) Foursquare Graph: what does it do? (19:17) How is Foursquare utilizing AI? (22:17) How will AR/VR influence location intelligence?

In this episode, we dive into the fascinating world of AI art with Cris Valenzuela, CEO of Runway. Runway is a generative AI startup that co-invented Stable Diffusion, the deep learning technology that has captured the attention of the creative industry, including luminaries such as ASAP Rocky and Madonna's teams, by pushing the boundaries of digital creativity. We explore how generative AI tools empower visual artists to unleash their imaginations without the need for Hollywood-size budgets. We also discuss the effect of AI on the entire creative industry, similar to how the camera changed things back in the day. Join us for a glimpse into the future of creativity. RUNWAY Website - https://runwayml.com Twitter - https://twitter.com/runwayml Cris Valenzuela (Co-founder & CEO) LinkedIn - https://www.linkedin.com/in/cvalenzuelab Twitter - https://twitter.com/c_valenzuelab FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck Foursquare Website - https://location.foursquare.com Twitter - https://twitter.com/Foursquare (00:00) Intro (00:55) What is Runway? (03:09) Runway started before the GenAI boom. How? (04:41) What do people get wrong about GenAI? (07:18) How AI is going to change creative software? (08:44) What is Gen-2? (12:02) Runway's role in creating Stable Diffusion (14:25) Gen-1: a model or a product? (15:11) Runway's evolution from image generation to video (18:18) Runway partnered with Getty. Why? (19:52) How has the AI video generation ecosystem evolved? (21:58) Adoption cyсle for AI video generation. Where are we now? (24:45) Challenges of building a research-focused company (26:25) How to build and maintain a soul in a startup? (28:27) "It's like an invention of new art form" -

Join us in this exciting episode as we dive into the world of enterprise AI with Florian Douetteau, co-founder and CEO of Dataiku, the leading enterprise AI platform targeting Global 2000 companies. Since its founding in 2013, Dataiku has been at the forefront of democratizing AI in the enterprise. We'll explore the current state of deployment of AI in businesses around the world, dive deep into the differences between generative AI and traditional AI, explore emerging Generative AI uses cases in the enterprise, and get a sneak peek into Dataiku's latest breakthrough, the LLM Mesh, aimed at simplifying the use of multiple Generative AI models for companies. We'll also tackle the big challenges companies face when adopting AI, from managing costs to dealing with the uncertainties of Generative AI. This episode was recorded live at a recent Data Driven NYC, the monthly in-person event organized by FirstMark since 2011, hosted this month by our partners at Foursquare, the location intelligence company, at their beautiful headquarters. Dataiku Website - https://www.dataiku.com/ Twitter - https://twitter.com/dataiku Florian Douetteau LinkedIn - https://www.linkedin.com/in/fdouetteau Twitter - https://twitter.com/fdouetteau FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck Twitter - https://twitter.com/mattturck Foursquare Website - https://location.foursquare.com Twitter - https://twitter.com/Foursquare (00:00) Intro (01:09) What is Dataiku? (02:03) Is the market ready for AI? (04:33) Traditional AI vs Generative AI (08:33) What a company should know before diving into Generative AI? (10:18) Cost of Generative AI adoption (12:10) What blocks the AI adoption? (14:31) Dataiku product tour (16:34) How to build one product for different audiences (17:45) LLM Mesh: what is it? (21:10) Evolution of platform building with Gen AI (22:17) Enterprise AI motion in 2024 (23:28) Dataiku's partnerships (24:24) Being platform-first as a startup

In this episode, we sit down with Bob Moore, the CEO of Crossbeam, who turned a $2.6 billion mistake into a masterclass on Ecosystem-Led Growth (ELG). Fresh off publishing his new book, Bob shares why ELG is the future of business growth, challenging traditional strategies with data-driven insights and partnerships. Bob reveals how Crossbeam can help companies of any size leverage ELG to achieve remarkable growth. He dives into the role of data in ELG, the impact of AI on marketing, and practical steps for implementing ELG in your own company. From discussing the "slow heat death" of traditional growth strategies to unveiling the potential of data-driven partnerships, this episode is packed with eye-opening revelations. Bob also tackles the practical steps companies can take to implement ELG, making this a must-watch for CEOs, leaders, and entrepreneurs aiming to catapult their businesses into a new era of growth. Book: https://www.amazon.com/Ecosystem-Led-Growth-Blueprint-Marketing-Partnerships/dp/1394226837 Crossbeam Website - https://www.crossbeam.com Twitter - https://twitter.com/crossbeam Bob Moore LinkedIn - https://www.linkedin.com/in/robertjmoore/ Twitter - https://twitter.com/robertjmoore FirstMark Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (00:43) Bob recently wrote a book. Why did he do that as a CEO? (03:20) Bob's $2.6 billion mistake (12:15) What is ELG? (17:30) How does Crossbeam work? (20:51) Why do we need another type of go-to-market motion? (25:00) AI is killing inbound/outbound marketing (31:50) Applying ELG to your company (36:13) When should you do ELG and partnerships? (43:34) Outro

In this episode, we sat down with Emi Gal, founder and CEO of Ezra, a startup that leverages AI to detect cancer early and inexpensively. Emi provides insights into the landscape of the healthcare sector and talks about the differences between building an AI startup in healthcare versus SaaS. Turns out that "(In AI skills)... are not that transferable." EZRA Website - https://ezra.com Twitter - https://twitter.com/ezrainc Emi Gal LinkedIn - https://www.linkedin.com/in/emigal Twitter - https://twitter.com/emigal FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:50) Ezra raised $21 million in series B round (02:55) The origin of Ezra (06:06) Sourcing AI talent (06:52) Building a proof of concept (09:05) The tipping point for the product market fit (10:57) Y Combinator wants more MRI startups. Why? (11:37) Ezra's vision for MRI (13:25) Is it covered by insurance? (16:15) Full stack vs Software only (20:00) Training AI (22:55) Building an MRI database (25:45) Will radiologists get replaced by AI? (27:52) Creating reports with Generative AI (30:50) Can we trust AI in healthcare? (33:44) What are the specific challenges of building an AI startup? (39:01) Healthcare entrepreneurship (43:59) Staying fit as a CEO: Emi's mental and physical health routine (48:28) Plans for 2024

In this episode, we sat down with Des Traynor, co-founder of Intercom, to explore the seismic shift towards Artificial Intelligence in customer service software. Intercom has gone all-in to embrace AI as people's expectations of what chatbots can do started growing with the release of ChatGPT. Des shares the pivotal moments and strategic decisions that led to this transition, highlighting the urgency and vision that propelled Intercom to integrate AI into their core offerings. Des also delves into the challenges of building a bicontinental startup and the strategic pivot towards becoming an AI-first company. Tune in for an enlightening discussion on the strategy and journey of adapting AI. INTERCOM Website - https://www.intercom.com Twitter - https://twitter.com/intercom Des Traynor LinkedIn - https://www.linkedin.com/in/destraynor/ Twitter - https://twitter.com/destraynor FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:16) How did Intercom make a transition to a generative AI product (Fin)? (05:34) Did the Intercom manifesto play a role in the transition? (07:16) What was the Intercom before Fin? (09:01) How much development effort did you spend on AI? (12:31) UX (15:20) People used to hate chatbots (17:51) GPT and building layers around it (20:50) The future of customer service (23:57) GPT-4/Llama/Mistral/Claude (25:58) Are multimodal AI-bots the future? (27:08) AI-hallucination (30:11) Customization (34:34) Will Fin get a voice? (36:26) Customer support cost and impact on profitability (39:58) How much should you charge? (45:26) AI-bot resolution rate (46:43) Can bots take action? (48:40) AI-adoption (51:14) How the Intercom team evolve (53:38) How did 4 Irish guys create a bi-continental startup? (56:17) Work distribution (58:38) Tech in Europe vs tech in the US

In this episode, we explore the dynamic world of modern analytics with Tristan Handy, CEO of dbt Labs (https://twitter.com/jthandy). DBT, which helps more than 30,000 enterprises ship trusted data products faster, has raised more than $400 million dollars, most recently at a $4B valuation.We discuss how dbt has revolutionized analytics engineering, enabling seamless data transformation and orchestration in the cloud. This innovation fosters greater collaboration among data teams and integrates software engineering principles into data analytics workflows.We also talk about dbt's Semantic Layer, a game-changer that streamlines data operations by standardizing key business metrics for consistent use across various analytical tools.In this conversation, we tackle pressing questions about the current state and future of data management and analytics. Is the "modern data stack" becoming obsolete? What's next for data engineering? And how is AI reshaping the analytics landscape?Tune in to discover our insights.📰 Is the "Modern Data Stack" Still a Useful Idea?https://roundup.getdbt.com/p/is-the-modern-data-stack-still-aDBTWebsite - https://www.getdbt.com/Twitter - https://twitter.com/getdbtTristan Handy (CEO & Co-Founder):LinkedIn - https://www.linkedin.com/in/tristanhandy/Twitter - https://twitter.com/jthandyIs the "Modern Data Stack" Still a Useful Idea? - https://roundup.getdbt.com/p/is-the-modern-data-stack-still-a?r=oc02&utm_campaign=post&utm_medium=webFIRSTMARKWebsite: https://firstmark.comTwitter: https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/Twitter - https://twitter.com/mattturckLISTEN ON:Spotify - https://open.spotify.com/show/7yLATDSaFvgJG80ACcRJtqApple - https://podcasts.apple.com/us/podcast/the-mad-podcast-with-matt-turck/id168623872400:00 - Intro02:43 - What is the Modern Data Stack?05:57 - Is the Modern Data Stack dead?12:23 - What's the alternative?16:24 - Where is analytics engineering heading?20:02 - The Reverse ETL market23:21 - The role of AI in analytics engineering27:47 - Will analytics engineers become the prompt engineers?29:78 - Is the MDS part of the emerging generative AI stack?33:51 - The Semantic Layer37:49 - dbt's plans for the near future41:17 - Hiring at different stages of the business44:21 - Going from open-source to commercial46:40 - Market situation vs. sales strategy

In this episode, we sat down with Bob van Luijt (https://twitter.com/bobvanluijt), the CEO of Weaviate, diving into the cutting-edge world of vector databases and their role in the AI revolution.Weaviate is an open source, AI-native vector database that helps developers create intuitive and reliable AI-powered applications. Weaviate sets itself apart with its vector search engine that integrates machine learning directly into its core, enabling more nuanced and context-aware search capabilities for AI-driven applications.This conversation explores vector databases (the core infrastructure behind generative models), the role of Retrieval-Augmented Generation (RAG), and how open source is driving commercial use cases.WEAVIATEWebsite - https://weaviate.ioTwitter - https://twitter.com/weaviate_ioBob van Luijt (Co-Founder & Co-CEO):LinkedIn - https://www.linkedin.com/in/bobvanluijtTwitter - https://twitter.com/bobvanluijtMatt Turck:LinkedIn - https://www.linkedin.com/in/turck/Twitter - https://twitter.com/mattturckDATA DRIVEN NYCThis episode of the MAD Podcast was recorded live at Data Driven NYC, an event series organized by FirstMark Capital. The events are free and held monthly in New York, currently with the support of Foursquare.If you wish to attend and be notified of future events, please follow FirstMark on Eventbrite at https://www.eventbrite.com/o/firstmark-capital-221557018301:00 What is RAG?06:20 Why is embedding models is such a hot topic right now?08:06 What is your assessment of RAG?09:53 Generative feedback loops11:46 What is Hybrid Search?15:15 What makes Weaviate special?16:53 What about security?17:45 Does RAG accelerated the need for real-time data?19:27 How to define good vector database? 22:11 What do you think about general purpose databases entering the field of vector-based databases?23:47 Interesting use cases of Weaviate25:27 What’s your sense of the current state of the market?26:53 Open source vs commercial product on Weaviate29:23 How did it all get started?

Last week, we sat down with Alex Rinke (https://twitter.com/alexanderrinke), Co-founder & Co-CEO of Celonis, to explore how AI and automation are transforming business operations at large enterprises. Celonis is the pioneer of "process mining" - the technology that uses graph databases, AI, and automation to analyze processes, find inefficiencies and their root causes, and solve them.Most recently valued at $13B, Celonis is one of the most valuable startups globally. But Alexander and his two co-founders started Celonis while still in college on a $15,000 budget. In this conversation, we talked about the early days of Celonis, how Alex acquired his first enterprise clients without inside industry connections, how Celonis navigates go-to-market for a product with an expansive scope, and much more.CELONISWebsite - https://www.celonis.comTwitter - https://twitter.com/CelonisAlex Rinke (Co-Founder & Co-CEO):Twitter: https://twitter.com/alexanderrinkeLinkedIn: https://www.linkedin.com/in/alexander-rinke-10733061/DATA DRIVEN NYCThis episode of the MAD Podcast was recorded live at Data Driven NYC, an event series organized by FirstMark Capital. The events are free and held monthly in New York, currently with the support of Foursquare. If you wish to attend and be notified of future events, please follow FirstMark on Eventbrite at https://www.eventbrite.com/o/firstmark-capital-221557018300:00 - Intro02:02 - What is Process Mining?05:20 - How Celonis got started07:42 - “We had our first prototype in three weeks”09:36 - Pivotal partnership with ACP12:12 - How did Celonis find product-market-people fit?14:14 - Penetrating the global market16:19 - Technical deep dive into the Celonis’ product19:29 - Celonis finds process gaps completely automatically21:15 - Who is the average user of Celonis inside companies?22:11 - How Celonis uses Generative AI 24:54 - Acquisition of Symbio25:56 - How to keep the fire of innovation inside the team?27:49 - How to bring a very horizontal product to market?32:24 - Scaling yourself as a leader34:15 - Glimpse into the future of Celonis35:37 - Outro

We are so excited today to be joined by Brandon Duderstadt, CEO + Cofounder, and Zach Nussbaum, Machine Learning Engineer, from Nomic AI. They discuss how Nomic AI is building tools like Atlas + GPT4all that enable everyone to interact with AI scale datasets and run models on consumer computers - and - stay tuned for an exciting announcement about their newest product release later in the podcast.Thanks for joining us for the first episode of Season 2 of the MAD Podcast. We will be back to our regular weekly schedule with new conversations with leaders in the Machine Learning, AI and data landscape. If you like this show, you can find the video recording of this episode -- along with many, many more -- on the Data Driven NYC channel on YouTube.NOMIC AIwww.nomic.aitwitter.com/nomic_aiwww.linkedin.com/in/bstadt/www.linkedin.com/in/zach-nussbaum/FIRSTMARKfirstmark.comtwitter.com/FirstMarkCapMatt Turck (Managing Director)www.linkedin.com/in/turck/twitter.com/mattturckData Driven NYC YouTube ChannelFirstMark Capital Eventbrite0:46 - What is Nomic AI & how it got started5:57 - Building GPT4ALL7:23 - Running LLMs on a personal computer16:00 - Nomic Atlas21:33 - Launching Nomic Embed28:10 The Importance of Data in AI31:10 - Benchmarking LLMs32:56 - The Future of Nomic AI36: 22 - Building an AI Startup in New York39:10 - Nomic AI is hiring

Today, we’re thrilled to be joined by Eiso Kant, CTO + Co-Founder of Poolside, the buzzy new AI tool for software development. Eiso and Matt talk about Poolside’s foundational model, the critical role of data quality in AI, the importance of controlling all levels of the stack and the merits of building a global AI company out of Europe, and more. Thank you to everyone who has joined us for Season 1 of the MAD Podcast. We will be taking a short break for the winter holidays and will be back with an exciting new lineup of great speakers for Season 2 on Wednesdays in January. If you like this show, you can find the video recording of this episode -- along with many more -- on the Data Driven NYC channel on YouTube. Important links are in the show notes below. Data Driven NYC YouTube ChannelFirstMark Capital Eventbritetwitter.com/eisokantpoolside.aitwitter.com/mattturcklinktr.ee/mattturckShow Notes: [00:38:00] Introducing Eiso Kant, Co-founder and CTO of the AI startup, Poolside;[00:39:16] Eiso's Background; his journey, from starting as a young programmer to founding several companies, including Source{d}, a pioneer in applying deep learning to software source code;[00:40:33] Formation of Poolside; the collaboration between Eiso and his co-founder, Jason Warner, who was previously the CTO of GitHub and VC with Redpoint Ventures;[00:42:14] Poolside's Vision and potential to improve software development;[00:47:17] Narrowing Vision to Product Development; the importance of sequence in a company's growth, focusing on AI pair programming assistants as a start, moving towards a more autonomous future;[00:50:32] Initial Product Focus, user base, and approach to providing a vertically integrated AI stack for developers;[00:53:05] Reinforcement Learning from Code Execution Feedback;[01:02:29] Data Handling and Synthetic Data Generation; the importance of data quality and Poolside's strategy for generating and refining training data;[01:12:05] Engineering Behind Poolside's AI; the challenges and strategies Poolside is adopting, including building a team of strong engineers and creating a scalable architecture from scratch;[01:16:52] Choosing Europe as a Base for Poolside;[01:20:22] Poolside's Future Plans; the roadmap for Poolside, including launching products and APIs, exploring enterprise solutions, and creating a sustainable revenue-generating business;

Today, we’re joined by Gustavo Sapoznik, Founder and CEO of ASAPP, the generative AI platform transforming contact centers. Matt + Gustavo discuss the magnitude of challenges to overcome in this market, how their AI tech is designed to help humans, the reason smart people should choose working at a startup over Big Tech, and more. This session was recorded live at a recent Data Driven NYC, our in-person, monthly event series. If you are ever in New York, you can find us on Eventbrite by searching for "FirstMark Capital". Events run monthly and are free and open to everyone. And as always, if you enjoy the MAD podcast, please subscribe and feel free to leave us a comment or rating.Data Driven NYC YouTube ChannelFirstMark Capital Eventbriteasapp.comtwitter.com/asapptwitter.com/mattturcklinktr.ee/mattturckShow Notes: [00:00:45] Introducing Gustavo Sapoznik, Founder & CEO of ASAPP, a unicorn AI startup based in New York;[00:01:00] How ASAPP started with a mission to “end bad customer service” after a frustrating phone call Mr. Sapoznik had with his cable provider;[00:02:44] ASAPP’s product philosophy and how the customer service is a three-legged stool with companies, customers, and agents;[00:05:11] How ASAPP automates what they can and augments the rest to make agents more productive;[00:07:12] The evolution of ASAPP’s offerings including how ASAPP technology makes agents more productive;[00:9:16] How ASAPP’s technology reduces response times and improves quality for agents by including transcription, auto complete, and real-time scoring of interactions for quality assurance;[00:13:49] How ASAPP has evolved since 2014; their research-first approach, building in-house AI capabilities, training their own models, and their recent exploration of using open-source checkpoints;[00:15:05] How Mr. Sapoznik hired the guy who ran all NLP research at Google;[00:16:04] How cost, latency, and accuracy in their AI models differentiate ASAPP from common AI APIs available today;[00:18:49] Agent models v. Language models and how ASAPP AI is modularized for large teams with established tech stacks;[00:20:09] Mr. Sapoznik shares insights on selling to large enterprises and why he believes building a sales machine is equally, if not more important, than the product itself;[00:23:08] How to recruit and retain top AI talent;[00:27:42] Lessons learned from working with notable board members, including the three key dimensions of support from a good board: being a sounding board, providing tactical advice and connections, and instilling a sense of accountability and motivation;

Today, we’re excited to chat with Scott Belsky - author, entrepreneur, investor and Chief Strategy Officer at Adobe. Matt + Scott discuss the impact of AI on creative work, how Adobe is incorporating AI across their products, and what the future creative tools landscape might look like.This session was recorded live at a recent Data Driven NYC, our in-person, monthly event series. If you are ever in New York, you can find us on Eventbrite by searching for "FirstMark Capital". Events run monthly and are free and open to everyone. And as always, if you enjoy the MAD podcast, please subscribe and leave us a comment.Data Driven NYC YouTube ChannelFirstMark Capital Eventbritetwitter.com/scottbelskyImplications, by Scott Belskytwitter.com/mattturcklinktr.ee/mattturckShow Notes: [00:53] How Adobe uses AI to enhance user experience, streamline onboarding and automate tasks across their product suite;[01:30] How AI impacts Adobe's business, making creative processes accessible with features like the context bar in Photoshop;[02:13] Firefly's journey: internal decisions, training challenges, and a commitment to using licensed material for ethical AI;[03:58] Moral considerations in Firefly's development: the decision to use licensed material, commercial viability, and addressing user comparisons;[05:52] Adobe's homegrown approach to generative AI models: in-house development and partnerships for specific capabilities like LLM;[06:08] Adobe Sensei's 10-year evolution: developing AI technologies, the non-profit Content Authenticity Initiative, and content credentials establishing asset provenance;[09:17] Adobe's new AI advancements: Firefly Image Model 2, Generative Match, and the vector model for illustration;[11:16] Firefly Editor's revolutionary image editing: dynamically generating pixels, real-time object manipulation, and Adobe's commitment to pushing technological boundaries;[12:41] Rapid integration of AI features: Firefly models and playground, surfacing on a website for user testing, and collaboration within Adobe's design organization;[14:32] How Adobe's AI and data teams are structured and leveraging in-house development for competitive advantage;[15:47] Future of work and creativity: AI's impact on raising the bar for digital experiences, accelerating creative processes, and the evolving landscape of personalized social content;[19:11] Leveraging technology to reduce friction, streamline processes, and unlock creative flow;[20:09] Impact of AI on business models: questioning time-based pricing, anticipating a shift to value-based models, and reconsidering compensation for creative professionals;[21:10] Parallels with historical Internet Service Providers, the rapid evolution of ideas, and reflections on sustainable business models;[24:53] Scott’s criteria for evaluating AI investments: valuing skeptical entrepreneurs, acknowledging temporary uniqueness, and emphasizing empathy with customers;[26:40] Navigating challenges in 2023: Tough decisions for entrepreneurs, evaluating conviction, and the importance of sticking together through the "messy middle”;

Today, we’re joined by Howard Katzenberg, CEO of Glean AI, a machine learning powered accounts payable platform. Matt + Howard discuss Glean’s founding story, how Glean helps CFOs make insight driven choices, and more. This session was recorded live at a recent Data Driven NYC, our in-person, monthly event series. If you are ever in New York, you can find us on Eventbrite by searching for "FirstMark Capital". Events run monthly and are free and open to everyone. And as always, if you enjoy the MAD podcast, please subscribe and leave us a comment. Data Driven NYC YouTube ChannelFirstMark Capital Eventbritetwitter.com/mattturck linktr.ee/mattturckShownotes: [00:00:35] Howard's background;[00:01:15] Challenges with manual FP&A;[00:02:54] Approval Process gap realization and opportunity for Glean AI;[00:04:40] How Glean AI is like “bill.com with a brain”;[00:05:06] Enhanced functionalities beyond basic AP automation;[00:06:32] Glean AI’s Inception and AI Models;[00:07:54] Why Glean AI is unique;[00:08:25] The evolution of Glean AI’s ML stack;[00:10:44] Defensibility and how Glean AI offers vendor pricing insights to its network;[00:12:23] Success stories and customer value;[00:14:47] Future plans for Glean AI;[00:16:39] Navigating industry and technical expertise;[00:18:41] Audience Q&A

Today, we have the pleasure of chatting with Raza Habib, CEO of Humanloop, the platform for LLM collaboration and evaluation. Matt and Raza cover how to understand and optimize model performance, lessons learned about model evaluation and feedback, and explore the future of model fine-tuning.twitter.com/RazRazclehumanloop.comData Driven NYC YouTube Channeltwitter.com/mattturcklinktr.ee/mattturckShownotes: [00:00:47] How Humanloop helps product and engineering teams build reliable applications on top of large language models by providing tools to find, manage, and version prompts;[00:03:05] Where Humanloop fits into the MAD landscape as LM / LLM Ops;[00:02:40] The challenges of evaluating and monitoring LLM;[00:03:40] Why evaluating LLMs and generative AI is subjective given its stochastic attributes;[00:04:40] Why evaluation is important during development and production stages of LLMs to make informed design decisions, and how that challenge evolves In production to monitoring system behavior;[00:05:40] The need for regression testing with LLMs;[00:06:10] How Humanloop makes it easy for users to capture feedback including Implicit signals of user satisfaction, such as post-interaction actions and edits to generated content;[00:07:40] Why and how Humanloop uses guardrails in the app to ensure effective LLM use and implementation;[00:08:38] Why using an LLM as part of the evaluation process can introduce additional uncertainty and noise; with turtles all the way down;[00:09:40] How evaluators on Humanloop are restricted to binary yes-or-no style questions or numerical scores to maintain reliability with LLMs in production.[00:10:40] Why a new set of tools were needed to monitor and observe LLM performance;[00:11:40] How Humanloop’s interactive environment allows users to find and fix bugs in a prompt, including logs to support issue identification, and then run what-if style analysis by changing the prompt or information retrieval system — allowing for quick interventions and turnaround times within minutes to hours instead of days/weeks;[00:12:40] Why having evaluation and observability closely connected to prompt engineering tools is critical for speed;[00:13:40] How prompt engineering is like writing software specifications for the model, enabling domain experts to have a more direct impact on product development, and democratizing access and reducing reliance on engineers to implement the desired features;[00:15:40] The key differences between popular LLMs on the market today;[00:18:40] How the quality of open-source models has been rapidly improving, and how LLMs use tools or function calling to access APIs to go beyond simple text-based interactions;[00:21:22] How Humanloop empowers non-technical experts;[00:22:40] Where Humanloop fits within the AI ecosystem as an collaborative tool for enterprises building language models where collaboration and robust evaluation are crucial;[00:25:40] How Humanloop customers are often problem-aware, and how the go-to-market motion is mainly inbound, but sales-led[00:27:48] How Humanloop serves as a central place for storing prompts and sharing learnings across teams;[00:28:24] Raza’s thoughts on Open Source v. Closed Source models in the AI community;[00:30:40] The potential consequences of restricting access to models and Raza’s case for regulating end use cases and punishing malicious use rather than banning the technology altogether;[00:33:40] Next steps for Humanloop;

Today we're joined by Akilesh Bapu, CEO and Founder of DeepScribe, the platform using AI and Natural Language Processing to doctor/ patient transcripts. Matt and Akilesh go into DeepScribe's clinical use cases, supervised vs. unsupervised learning, and how critical it still is to have a human in the loop in a medical setting.

Today we have the pleasure of chatting with Sharon Zhou, CEO of Lamini, an LLM platform for the enterprise. Matt and Sharon go over the battle between prompting and fine-tuning, how the Lamini platform enables fine-tuning to be done "one billion times faster", and their recently-announced "LLM Super-station" in partnership with AMD.