The MAD Podcast with Matt Turck

In this episode, we dive deep into the story of how Datadog evolved from a single product to a multi-billion dollar observability platform with its co-founder, Olivier Pomel. Olivier shares exclusive insights on Datadog's unique approach to product development—why they avoid the "Apple approach" of building in secret and instead work closely with customers from day one.

You’ll hear about the early days when Paul Graham of Y Combinator turned down Datadog, questioning their lack of a first product. Olivier also reveals the strategies behind their iterative product launches and why they insist on charging early to ensure they’re delivering real value.

The second half of the conversation is focused on all things AI and data at Datadog - the company's initial reluctance to use AI in its products, how Generative AI changed everything, and Datadog's current AI efforts including Watchdog, Bits AI and Toto, their new time series foundational model.

We close the episode by asking Olivier about his thoughts on the topic du jour: founder mode! ▶️ Listen to 2020 Data Driven NYC episode with Oliver Pomel: https://www.youtube.com/watch?v=oXKEFHeEvMs DATADOG Website - https://www.datadoghq.com Twitter - https://x.com/datadoghq Olivier Pomel LinkedIn - https://www.linkedin.com/in/olivierpomel Twitter - https://x.com/oliveur 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

In this episode, we sit down with Ali Dasdan, CTO of ZoomInfo, a titan in the B2B sector, who harnesses vast datasets and advanced AI to redefine sales and marketing for over 35,000 global customers with $21.2 billion in annualized revenue.

We delve deep into ZoomInfo's AI initiatives, including their transformative 'Copilot,' explore sophisticated data management, and discuss their dual platforms catering to internal and customer-facing needs.

ZoomInfo

Website - https://www.zoominfo.com

Twitter - https://x.com/zoominfo

Ali Dasdan

LinkedIn - https://www.linkedin.com/in/dasdan

Twitter - https://x.com/alidasdan

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

(02:03) What is ZoomInfo

(04:47) Data as service

(06:15) Ali Dasdan's story

(07:31) Organization of ZoomInfo

(10:48) ZoomInfo Data Platform

(21:02) Lessons from building a data platform

(23:19) AI application at ZoomInfo

(27:58) ZoomInfo's Copilot

(37:43) ZoomInfo AI toolstack

(39:30) Working with small vs. big companies in the AI business

(43:39) Using data and AI for internal productivity

In this episode, we sit down with Eric Glyman, co-founder of Ramp, the company that revolutionized finance management to become a powerhouse valued at $7.6 billion.

Eric shares the tradition of counting the days since Ramp's founding and how it fosters a sense of urgency and productivity, explains the use of AI to automate expense management and fraud detection, and gives an inside look at Ramp's cutting-edge AI products, including the Ramp Intelligence Suite and experimental agentic AI use cases.

Ramp

Website - https://www.ramp.com

Twitter - https://x.com/tryramp

Eric Glyman

LinkedIn - https://www.linkedin.com/in/eglyman

Twitter - https://x.com/eglyman

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

(01:49) What is Ramp?

(04:25) How did the company start?

(09:18) Technical aspects of Ramp infrastructure

(12:17) "We can tell you if you're paying too much"

(14:20) Data privacy at Ramp

(16:13) Data infrastructure tools used at Ramp

(17:58) Traditional AI use cases

(24:51) GenAI use cases

(27:47) AI/human interaction

(33:32) Ramp Intelligence Suite

(39:38) How Ramp keeps high product release and product velocity

(42:37) How did Ramp get to product-market fit?

(45:54) Eric's perspective on building a company in NYC

In this episode, we reconnect with Sharon Zhou, co-founder and CEO of Lamini, to dive deep into the ever-evolving world of enterprise AI.

We discuss how the AI hype is evolving and what enterprises are doing to stay ahead, break down the different players in the Inference market, explore how Memory Tuning is reducing hallucinations in AI models, the role of agents in enterprise AI, and the challenges of making them real-time and reliable.

Lamini

Website - https://www.lamini.ai

Twitter - https://x.com/laminiai

Sharon Zhou

LinkedIn - https://www.linkedin.com/in/zhousharon

Twitter - https://x.com/realsharonzhou

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

(02:18) The state of the AI market in July, 2024

(10:51) What is Lamini?

(11:43) What is Inference?

(15:36) GPU shortage in the enterprise

(18:06) AMD vs Nvidia

(22:10) What is Lamini's final product?

(25:30) What is Memory Tuning?

(29:01) What is LoRA?

(32:39) More on Memory Tuning

(35:51) Sharon's perspective on AI agents

(40:01) What is next for Lamini?

(41:54) Reasoning vs pure compute in AI

In this episode, we sit down with Jeremy Kahn, the AI Editor at Fortune Magazine, who has recently published a book called "Mastering AI: A Survival Guide to Our Superpowered Future".

Jeremy shares his unique insights on AI's potential risks and transformative benefits, including the importance of UI design in maximizing AI's utility, the potential for AI to create a "winner takes most" economy, and the need for thoughtful AI regulation to mitigate risks without stifling innovation.

Book: https://www.amazon.com/Mastering-AI-Survival-Superpowered-Future/dp/1668053322

Jeremy Kahn

LinkedIn - https://www.linkedin.com/in/jeremy-kahn-01100462

Twitter - https://x.com/jeremyakahn

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

(01:43) Why the UI design is important for AI?

(04:32) The book is called "Mastering AI". Why?

(12:03) Automation Bias vs Automation Surprise

(20:16) The role of AI in the future of science and art

(25:32) "I think mass unemployment is a red herring, but we might see a lot of disruption"

(34:19) Jeremy's perspective on Agentic AI

(36:29) Does AI development need to be regulated?

(38:56) Should we worry about the AGI and Superintelligence?

(42:18) Who provided the most thoughtful conversation for the book?

(43:57) "I didn't use AI for the book at all"

(46:20) Jeremy's work at Fortune

In this episode, we sit down with Azeem Azhar, an expert on AI and technologies, whose weekly newsletter "The Exponential View" (www.exponentialview.co) is read by nearly two hundred thousand people from around the world.

We delve into the nuances of AI adoption, discussing how LLM's are reshaping industries and what this means for corporate leaders, the dynamics between the U.S., China, and Europe in the AI race, and the concept of sovereign AI.

Azeem Azhar

Website - https://www.exponentialview.co

Twitter - https://x.com/azeem

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

(02:05) What does the "Exponential" really mean?

(05:43) "Moore's law has not died"

(11:52) Claude is the Macintosh of AI. What does it mean?

(25:57) How does AI affect the enterprise?

(34:06) Asia is more optimistic about AI than the West. Why?

(38:42) Azeem's perspective on the sovereign AI

(45:19) AI in the modern warfare

(48:47) What is the Exponential asymmetry?

(51:59) Energy transition and the influence of AI on it

(55:21) Big Oil vs Chinese Solar: who's going to win?

(59:18) AI opens new possibilities for everyone. How?

In this episode, we sat down with Aaron Katz, the CEO of ClickHouse, a company that went from an open-source analytical database into a highly successful cloud service, utilized by Spotify, Netflix, Disney, and many more.

Aaron Katz provides intriguing insights into the challenges of transitioning an open-source project into a thriving business, ClickHouse's go-to-market strategy, the role of technical support in pre-sales, and the strategic decision to avoid traditional SDR and CSM roles.

CLICKHOUSE

Website - https://clickhouse.com/

Twitter - https://x.com/clickhousedb

Aaron Katz

LinkedIn - https://www.linkedin.com/in/aaron-katz-5762094

Twitter - https://x.com/ceo_clickhouse

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
(00:56) What is ClickHouse?
(04:28) What are the use cases for ClickHouse?
(06:17) Reducing the latency: why the world shifts to real-time
(09:05) How did ClickHouse evolve from an open-source to a cloud product?
(15:01) "Open source is the future of software"
(17:27) Self-hosted deployments
(18:45) ClickHouse's roadmap
(20:51) Is there a real-time data stack?
(22:25) ClickHouse partners in data ingestion
(24:32) Who are ClickHouse's main competitors?
(27:35) ClickHouse's sales process
(36:44) Is partnerships a good go-to-market strategy?
(37:44) When is the right time for startups to start partnering?
(38:22) Aaron's story of becoming the CEO
(43:50) Team and culture when working on two continents
(46:15) What's next?

In this episode, we sit down with Daniel Dines, the co-founder and CEO of UiPath. From a small rented apartment in Bucharest to $1.3 billion in revenue, UiPath's story is one of perseverance, innovation, and strategic pivots.

Daniel shares his insights on the pivotal moments that shaped UiPath, how to build a robust go-to-market strategy, the role of partnerships, and the lessons learned in hiring and managing a sales organization.

UIPath

Website - https://www.uipath.com/

Twitter - https://x.com/UiPath

Daniel Dines

LinkedIn - https://www.linkedin.com/in/danieldines

Twitter - https://x.com/danieldines

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

(01:38) UiPath was founded in an apartment in Bucharest. How did it all start?

(08:05) Building a global product

(11:26) The growth stage.

(18:50) "We were AI from the beginning"

(20:10) Raising the first round of funding.

(23:48) Working with the board.

(25:11) How did UiPath expand from the Romanian to the global market?

(35:00) Process Mining, Task Mining, and Communications Mining.

(41:41) The Automation Layer explained.

(45:28) The use cases for using AI in UiPath's automations

(56:22) UiPath's strategy for Gen AI adoption.

(58:27) The team.

(59:42) How important are partnerships for enterprise

(01:02:48) Recruiting the best salespeople in the industry

(01:07:10) Scaling from a software engineer to the CEO of a large company.

In this episode, we sit down with Howie Liu, co-founder and CEO of Airtable, to explore the incredible journey of Airtable from its early days to becoming a powerhouse in the enterprise software space. Howie provides a candid look at the challenges and learnings from transitioning Airtable from a PLG product to an enterprise platform, how companies are transforming their marketing operations with AI, and the transformative potential of AI in automating workflows and enhancing business processes. AIRTABLE Website - https://www.airtable.com/ Twitter - https://x.com/airtable Howie Liu LinkedIn - https://www.linkedin.com/in/howieliu/ Twitter - https://x.com/howietl 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
(02:40) What is Airtable in 2024?
(05:35) How does Airtable apply AI to its products?
(11:56) What are the AI use cases in Airtable?
(18:35) The tech behind Airtable's AI capabilities
(22:22) Is Airtable going to become an AI-first company?
(25:15) Will AI kill programming as we know it?
(29:24) How do big enterprises think about AI?
(34:46) How did Airtable go from PLG to a large enterprise product?
(41:00) AI Categories
(47:47) "We definitely had our hiccups"
(51:20) Was PLG a ZIRP-era phenomenon?
(56:29) Howie's journey as a CEO

In this episode, we sat down with Tomasz Tunguz (https://twitter.com/ttunguz), the founder of Theory Ventures and a leading voice in the tech investment space.

We discussed the transformative potential of Ethereum as a database company, the importance of data security in a decentralized world, and the evolving landscape of AI technologies from foundational models to AI-native applications.

📰 Article "What If LLMs Change the Business Model of the Internet?": https://tomtunguz.com/what-if-llms-change-the-business-model-of-the-internet/

✍️ Tomasz' blog: https://tomtunguz.com

Theory Ventures

Website - https://theory.ventures/

Twitter - https://twitter.com/Theoryvc

Tomasz Tunguz

LinkedIn - https://www.linkedin.com/in/tomasztunguz

Twitter - https://twitter.com/ttunguz

Blog - https://tomtunguz.com/

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 LISTEN ON:

YouTube - https://www.youtube.com/@DataDrivenNYC/videos

Apple - https://podcasts.apple.com/us/podcast/the-mad-podcast-with-matt-turck/id1686238724

(00:00) Intro
(02:46) Tomasz has continued to invest in blockchain through the crypto winter. Why?
(06:59) Security and privacy as the main blockchain's use case.
(09:18) Blockchain and AI: how do they work together?
(11:02) Why does Theory Ventures not invest in AI hardware?
(12:28) Why do big companies invest in cloud infrastructure?
(15:35) An investor view on the foundation models.
(18:36) Is Gen AI going to replace traditional AI?
(20:57) Does the Theory Ventures invest in AI tooling companies?
(22:53) Is investing in Cloud companies better than investing in AI-powered applications?
(26:40) Copilot AI vs full-execution AI.
(28:38) A case for specialized LLMs.
(29:54) Gross margins in Gen AI: is it profitable?
(32:34) Modern Data Stack: is it still a thing to invest in?
(37:02) Microsoft Fabric and its impact on the market.
(38:50) Tomasz's thought on Motherduck and DuckDB.
(40:37) Where do BI tools fit in the Modern Data Stack?
(44:32) Why has the democratization of BI never happened?
(45:52) How do acquisitions happen? Can you engineer them?
(49:02) Key ingredients to build data infrastructure business.
(50:40) Tomasz is a founder now! How does it feel?
(53:15) Talking numbers: Theory Ventures' financial model.

In this episode, we sat down with Renen Hallak, founder and CEO of VAST Data, a $9 billion company that's shaking the foundations of data storage, databases and compute functionality.

Through the conversation, we explore VAST's perspective on AI infrastructure, the process of selling over a billion dollars worth of software, and the technical innovations behind disaggregated, shared-everything architecture.

VAST Data

Website - https://www.vastdata.com/

Twitter - https://twitter.com/VAST_Data

Renen Hallak

LinkedIn - https://www.linkedin.com/in/renenh/

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
(01:40) What is VAST Data?
(02:56) The company was started in stealth mode. Why?
(03:42) Did VAST get lucky with the gen AI explosion?
(04:27) VAST Data founding story
(05:57) How does the company work across 2 continents?
(06:48) What made you think that you can disrupt the market?
(09:23) VAST architecture explained
(23:08) Moving from data storage to databases
(25:01) What was the hardest thing to build?
(26:32) How does VAST work with open source
(26:54) A glimpse into the future products
(28:22) The world without VAST: how it would've looked like
(29:45) Who were VAST's first customers?
(30:56) How do hedge funds use VAST?
(32:08) VAST's sales strategy
(34:04) Renen's transition from technical founder to CEO
(36:01) How do you hire great people?
(37:07) What was the hardest thing on your journey as a CEO?
(38:43) $9B CEO daily routine
(40:17) Difference between offices in NY and Israel
(42:07) Renen's learnings from sales

🔗 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-a

DBT

Website - https://www.getdbt.com/

Twitter - https://twitter.com/getdbt

Tristan Handy (CEO & Co-Founder):

LinkedIn - https://www.linkedin.com/in/tristanhandy/

Twitter - https://twitter.com/jthandy

Is 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=web

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

LISTEN ON:

Spotify - https://open.spotify.com/show/7yLATDSaFvgJG80ACcRJtq

Apple - https://podcasts.apple.com/us/podcast/the-mad-podcast-with-matt-turck/id1686238724

00:00 - Intro

02:43 - 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 market

23:21 - The role of AI in analytics engineering

27:47 - Will analytics engineers become the prompt engineers?

29:78 - Is the MDS part of the emerging generative AI stack?

33:51 - The Semantic Layer

37:49 - dbt's plans for the near future

41:17 - Hiring at different stages of the business

44:21 - Going from open-source to commercial

46:40 - 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.

WEAVIATE

Website - https://weaviate.io

Twitter - https://twitter.com/weaviate_io

Bob van Luijt (Co-Founder & Co-CEO):

LinkedIn - https://www.linkedin.com/in/bobvanluijt

Twitter - https://twitter.com/bobvanluijt

Matt Turck:

LinkedIn - https://www.linkedin.com/in/turck/

Twitter - https://twitter.com/mattturck

DATA DRIVEN NYC

This 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-2215570183

01:00 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 loops

11:46 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 Weaviate

25:27 What’s your sense of the current state of the market?

26:53 Open source vs commercial product on Weaviate

29:23 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.

CELONIS

Website - https://www.celonis.com

Twitter - https://twitter.com/Celonis

Alex Rinke (Co-Founder & Co-CEO):

Twitter: https://twitter.com/alexanderrinke

LinkedIn: https://www.linkedin.com/in/alexander-rinke-10733061/

DATA DRIVEN NYC

This 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-2215570183

00:00 - Intro

02:02 - What is Process Mining?

05:20 - How Celonis got started

07:42 - “We had our first prototype in three weeks”

09:36 - Pivotal partnership with ACP

12:12 - How did Celonis find product-market-people fit?

14:14 - Penetrating the global market

16:19 - Technical deep dive into the Celonis’ product

19:29 - Celonis finds process gaps completely automatically

21:15 - Who is the average user of Celonis inside companies?

22:11 - How Celonis uses Generative AI

24:54 - Acquisition of Symbio

25:56 - 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 leader

34:15 - Glimpse into the future of Celonis

35:37 - 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 AI

www.nomic.ai

twitter.com/nomic_ai

www.linkedin.com/in/bstadt/

www.linkedin.com/in/zach-nussbaum/

FIRSTMARK

firstmark.com

twitter.com/FirstMarkCap

Matt Turck (Managing Director)

www.linkedin.com/in/turck/

twitter.com/mattturck

Data Driven NYC YouTube Channel

FirstMark Capital Eventbrite

0:46 - What is Nomic AI & how it got started

5:57 - Building GPT4ALL

7:23 - Running LLMs on a personal computer

16:00 - Nomic Atlas

21:33 - Launching Nomic Embed

28:10 The Importance of Data in AI

31:10 - Benchmarking LLMs

32:56 - The Future of Nomic AI

36: 22 - Building an AI Startup in New York

39:10 - 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 Channel

FirstMark Capital Eventbrite

twitter.com/eisokant

poolside.ai

twitter.com/mattturck

linktr.ee/mattturck

Show 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 Channel

FirstMark Capital Eventbrite

asapp.com

twitter.com/asapp

twitter.com/mattturck

linktr.ee/mattturck

Show 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 Channel

FirstMark Capital Eventbrite

twitter.com/scottbelsky

Implications, by Scott Belsky

twitter.com/mattturck

linktr.ee/mattturck

Show 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 Channel

FirstMark Capital Eventbrite

twitter.com/mattturck

linktr.ee/mattturck

Shownotes:

[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/RazRazcle

humanloop.com

Data Driven NYC YouTube Channel

twitter.com/mattturck

linktr.ee/mattturck

Shownotes:

[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. 

Today we're joined by Aravind Srinivas, CEO of Perplexity AI, a chatbot-style AI conversational engine that directly answers users' questions with sources and citations. Matt & Aravind discuss Perplexity's founding story, the platform itself, 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. 

Today we're joined by Mathew Lodge, CEO of Diffblue, an AI platform that uses reinforcement learning to autonomously test software. We chat about the "AI for code" landscape, the Diffblue platform, and why prompt engineering is not a thing. 

Today we're joined by Nancy Xu, AI Investor and CEO and Founder of Moonhub AI, the AI recruiting platform helping companies shorten and speed up the recruiting process while also helping employers reach a more diverse pool of candidates. We dive into how the Moonhub platform operates, Nancy's thoughts on opportunities for AI startups, her journey as an investor, and interesting projects she has her eye on.

Today we're joined by Stanislas Polu, Co-Founder of Dust, a startup building Secure AI assistants for the enterprise. We dive into Stanislas's journey to founding Dust including his experience at Open AI, the path to generative AI adoption in the enterprise, and the rise of the French AI ecosystem. 

Today we're joined by Kanjun Qiu, CEO of Imbue, an independent research company developing AI agents with general intelligence, fresh off the announcement of their $200M Series B round of financing. We talk about Kanjun's journey, Imbue's vision and the future of AI agents.

Today we're joined by Shreya Rajpal, Co-founder & CEO of Guardrails AI for a conversation on the Guardrails platform, mitigating AI hallucinations and the role fine tuning and retrieval augmented generation play in that.

Today we are joined by Ori Goshen, Co-founder and Co-CEO of AI21 Labs, for a conversation about AI 21's origin story, their differentiated approach to AI, and their ambitious platform and applications.  

Today we are excited to welcome Carly Taylor for a broad discussion covering the numerous ways she's ingrained in the AI & data world including AI at Activision’s Call of Duty franchise, consulting at Rebel Data Science and being a prominent voice for data science on social media. 

We're joined by Milos Rusic, CEO & Co-founder of deepset AI for a conversation on deepset's origin story as a bootstrapped company, a deep dive into the Haystack open source project and deepset's Cloud platform, and emerging applications for NLP and LLMs in the enterprise.

Dimitri Sirota, Co-Founder and CEO of BigID, which has raised $280+ to date, joins us for a chat about the importance and complexities around knowing and controlling your enterprise data.

Today we are diving into the world of generative AI in Healthcare with CEO and Co-founder of Hippocratic AI, Munjal Shah. In this episode Matt and Munjal discuss the vision for Hippocratic AI, and the unique challenges and opportunities of deploying generative AI in the world of healthcare.

This week’s guest is top AI researcher, entrepreneur and investor Richard Socher, CEO of AI search engine You.com. In this conversation, we go behind the scenes and discuss some core design principles and building blocks of the You.com platform, as well as its market positioning. We close the discussion with Richard’s investment thesis and approach in the fast moving AI market

Today we have the pleasure of talking to Lukas Biewald, CEO of Weights and Biases for a conversation about Lukas' entrepreneurial journey building two companies in the MLOps space, the current capabilities of the Weights & Biases platform, lessons learned on the Go to Market front, and more!

Relational databases, data cloud's effect on infrastructure, serverless databases, and GTM strategies: Matt Turck and CockroachDB's Spencer Kimball cover it all in today's episode.

Today we're joined by Mike Murchison, Co-Founder & CEO of AI-native customer service platform Ada, for a talk about about the Ada platform, reinventing customer service in the age of Generative AI, and how AI should be onboarded and trained like an employee.

Today we're joined by Victor Riparbelli, CEO of the generative AI video platform Synthesia that just last month hit a $1B valuation after raising their Series C funding round. Matt and Victor go into the Synthesia platform, what it takes to build a successful AI company, and dive into the ethics of generative AI videos.

This week we're joined by Jerry Liu, Co-Founder & CEO of LlamaIndex, a startup that offers a data framework for connecting custom data sources to large language models, for a conversation about the emerging Generative AI infrastructure stack, how startup founders navigate a field as new and fast paced as Generative AI, and more.

Today we're joined by Florian Douetteau, CEO and Co-Founder of Dataiku, for a conversation about the Dataiku platform, emerging use cases for Generative AI in the enterprise and some leadership lessons learned along the way.

You can find Florian's essay, The Children of AI, here: https://children-of-ai.florian-douetteau.com

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We're joined by Jeff Huber, Co-founder of Chroma, for a chat on open-source AI-native databases.

Today we sit down with George Sivulka, CEO of the AI productivity tool, Hebbia, for a conversation on generative AI in fintech & government how how Hebbia keeps "smart people from doing stupid tasks" in fintech & government.

New York Times Chief Data Officer and Author of "How Data Happened" joins us for a conversation on the multifaceted impact data has had on our society.

Today we're joined by Daniel Sternberg, Head of Data at Notion, for a conversation on the release of Notion AI and the infrastructure and processes needed to launch and integrate an AI product.

This week we welcome Edo Liberty, Founder & CEO of Pinecone, the vector database that receives 10,000+ sign-ups a day.

Today we're joined by the "Godfather of Cloud Computing" Amr Awadallah for a conversation on LLM powered search, AI hallucinations, and more.

We're joined by Sarah Catanzaro, General Partner at Amplify Partners and one of the leading investors in AI, ML, and data to talk about the startup landscape, LLMs, and more.

AssemblyAI Founder & CEO, Dylan Fox joined FirstMark Managing Partner, Matt Turck for Data Driven NYC! AssemblyAI is the fastest way to build with AI for audio. With a simple API, get access to production-ready AI models to transcribe and understand speech. AssemblyAI has raised $63M+.

William Falcon, Founder of Lightning AI joins Matt Turck for a conversation on pytorch, LLaMAs, the future of large language models, and more.