The MAD Podcast: Machine Learning, AI & Data with Matt Turck

AI customer service agents are quickly replacing the often clunky AI chatbots of years past, and revolutionizing how we all interact with customer service. In this episode, we dive into this rapid transformation with Mike Murchison, CEO of Ada, a fast-growing leader in the space.

Mike shares how harnessing the power of several Generative AI models enables Ada to automate up to 83% of customer interactions, providing a seamless and empathetic service that rivals, and will soon surpass, human agents. We explore the challenges and triumphs of deploying AI in customer service in this new era, from the intricacies of model orchestration to the importance of resolution and empathy. Mike also teases the future of agentic AI in the enterprise, where AI agents collaborate across departments to innovate and improve products.

Ada

Website - https://www.ada.cx

X/Twitter - https://x.com/ada_cx

Mike Murchison

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

X/Twitter - https://x.com/mimurchison

FIRSTMARK

Website - https://firstmark.com

X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

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

X/Twitter - https://twitter.com/mattturck

(00:00) Intro

(02:27) Why is customer service a perfect use case for AI?

(03:36) Why didn’t foundation models replace AI “thin wrappers” out of the box?

(05:27) What is Ada?

(10:41) Reasoning engine, model orchestration, instruction following, routing

(15:45) Hybrid systems, finetuning, customization

(18:28) Prompt engineering, observability, self-improvement

(22:07) RAG (Retrieval-Augmented Generation) and AI as a judge

(23:06) Guardrails and security

(24:33) Should we expect perfection from AI?

(26:14) Measuring “resolution”

(29:29) What actions can Ada AI Agents take?

(32:12) Authentication and personalization

(35:09) Handoff vs human delegation

(38:12) ACX (AI Customer Experience) and the future of customer service professionals

(42:13) Leveraging analytics and customer support data

(45:54) AI agents for cross-selling and upselling

(48:25) Traditional AI chatbots vs the new generation of AI Agents

(51:24) Emotion, empathy, personality

(54:56) Transparency and AI improvement

(57:58) Managing AI: the measure-coach-improve loop

(1:00:15) Ada Voice and Email

(1:06:25) Future predictions for AI

(1:07:56) Multi-agent collaboration

Replit is one of the most visible and exciting companies reshaping how we approach software and application development in the Generative AI era. In this episode, we sit down with its CEO, Amjad Masad, for an in-depth discussion on all things AI, agents, and software.

Amjad shares the journey of building Replit, from its humble beginnings as a student side project to becoming a major player in Generative AI today. We also discuss the challenges of launching a startup, the multiple attempts to get into Y Combinator, the pivotal moment when Paul Graham recognized Replit’s potential, and the early bet on integrating AI and machine learning into the core of Replit.

Amjad dives into the evolving landscape of AI and machine learning, sharing how these technologies are reshaping software development. We explore the concept of coding agents and the impact of Replit’s latest innovation, Replit Agent, on the software creation process. Additionally, Amjad reflects on his time at Codecademy and Facebook, where he worked on groundbreaking projects like React Native, and how those experiences shaped his entrepreneurial journey. We end with Amjad's view on techno-optimism and his belief in an energized Silicon Valley.

Replit

Website - https://replit.com

X/Twitter - https://x.com/Replit

Amjad Masad

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

X/Twitter - https://x.com/amasad

FIRSTMARK

Website - https://firstmark.com

X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

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

X/Twitter - https://twitter.com/mattturck

(00:00) Intro

(01:36) The origins of Replit

(15:54) Amjad’s decision to restart Replit

(19:00) Joining Y Combinator

(30:06) AI and ML at Replit

(32:31) Explain Code

(39:09) Replit Agent

(52:10) Balancing usability for both developers and non-technical users

(53:22) Sonnet 3.5 stack

(58:43) The challenge of AI evaluation

(01:00:02) ACI vs. HCI

(01:05:02) Will AI replace software development?

(01:10:15) If anyone can build an app with Replit, what’s the next bottleneck?

(01:14:31) The future of SaaS in an AI-driven world

(01:18:37) Why Amjad embraces techno-optimism

(01:20:36) Defining civilizationism

(01:23:11) Amjad’s perspective on government’s role

In this episode, we explore the cutting-edge world of data infrastructure with Justin Borgman, CEO of Starburst — a company transforming data analytics through its open-source project, Trino, and empowering industry giants like Netflix, Airbnb, and LinkedIn.

Justin takes us through Starburst’s journey from a Yale University spin-out to a leading force in data innovation, discussing the shift from data lakes to lakehouses, the rise of open formats like Iceberg as the future of data storage, and the role of AI in modern data applications. We also dive into how Starburst is staying ahead by balancing on-prem and cloud offerings while emphasizing the value of optionality in a rapidly evolving, data-driven landscape.

Starburst Data

Website - https://www.starburst.io

X/Twitter - https://x.com/starburstdata

Justin Borgman

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

X/Twitter - https://x.com/justinborgman

FIRSTMARK

Website - https://firstmark.com

X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

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

X/Twitter - https://twitter.com/mattturck

(00:00) Intro

(01:32) What is Starburst?

(02:32) Understanding the data layer

(05:06) Justin Borgman’s story before Starburst

(10:41) The evolution of Presto into Trino

(13:20) Lakehouse vs. data lake vs. data warehouse

(22:06) Why Starburst backed the lakehouse from the start

(23:20) Starburst Enterprise

(27:31) Cloud vs. on-prem

(29:10) Starburst Galaxy

(31:23) Dell Data Lakehouse

(32:13) Starburst’s data architecture explained

(38:30) The rise of data apps

(38:54) Starburst AML

(40:41) “We actually built the Galaxy twice”

(43:13) Managing multiple products at scale

(45:14) “We founded the company on the idea of optionality”

(47:20) Iceberg

(48:01) How open-source acquisitions work

(51:39) Why Snowflake embraced Iceberg

(53:15) Data mesh

(55:31) AI at Starburst

(57:16) Key takeaways from go-to-market strategies

(01:01:18) Lessons from the Dell partnership

(01:04:40) Predictions for 2025

As AI takes over the world, data is more than ever “the new oil”, and data engineering is the discipline that makes data usable behind the scenes. In this episode, we dive deep into the present and future of data engineering with Ben Rogojan, also known as the Seattle Data Guy. A seasoned data engineering consultant, Ben has built a big brand and reputation in the field with over 100k followers on platforms like YouTube and Substack.

We started the conversation with a deep dive into data engineering as a profession: what do data engineers actually do? What is the career path, and what should aspiring data engineers learn?

We then explored some of the biggest stories of 2024 (including the rise of Iceberg) and went into some predictions for 2025, as a way to discuss some key topics everyone should be familiar with in data engineering, including the integration of AI in data workflows, the potential for automation, and why SQL isn't going anywhere. Discover how companies are navigating the complexities of data infrastructure, the rise of open table formats like Iceberg, and the ongoing battle between data giants like Snowflake and Databricks.

Ben Rogojan

Website - https://www.theseattledataguy.com

Newsletter - https://seattledataguy.substack.com

LinkedIn - https://www.linkedin.com/company/seattle-data-guy

X/Twitter - https://x.com/seattledataguy

FIRSTMARK

Website - https://firstmark.com

X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

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

X/Twitter - https://twitter.com/mattturck

(00:00) Intro

(01:20) Why 2025 will be huge for data engineering

(02:55) The story of the Seattle Data Guy

(06:51) What exactly is data engineering?

(07:41) Data, AI, and ML: where do they overlap?

(09:23) Data analyst vs. data engineer vs. data scientist: what’s the difference?

(11:20) A day in the life of a data engineer

(12:58) Data engineering: Silicon Valley vs. everywhere else

(15:27) How to become an AI engineer

(28:46) Will AI replace AI engineers?

(33:42) Why is the data world so complex?

(36:53) The functional consolidation of the data world

(38:34) Big data stories from 2024

(39:28) Why Iceberg is a game-changer

(46:02) How startups manage data in their early days

(48:44) Seattle Data Guy’s favorite tools

(50:09) Bold predictions for 2025

In this episode, we dive deep into the world of AI engineering with Chip Huyen, author of the excellent, newly released book "AI Engineering: Building Applications with Foundation Models".

We explore the nuances of AI engineering, distinguishing it from traditional machine learning, discuss how foundational models make it possible for anyone to build AI applications and cover many other topics including the challenges of AI evaluation, the intricacies of the generative AI stack, why prompt engineering is underrated, why the rumors of the death of RAG are greatly exaggerated, and the latest progress in AI agents.

Book: https://www.oreilly.com/library/view/ai-engineering/9781098166298/

Chip Huyen

Website - https://huyenchip.com

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

Twitter/X - https://x.com/chipro

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

(02:45) What is new about AI engineering?

(06:11) The product-first approach to building AI applications

(07:38) Are AI engineering and ML engineering two separate professions?

(11:00) The Generative AI stack

(13:00) Why are language models able to scale?

(14:45) Auto-regressive vs. masked models

(16:46) Supervised vs. unsupervised vs. self-supervised

(18:56) Why does model scale matter?

(20:40) Mixture of Experts

(24:20) Pre-training vs. post-training

(28:43) Sampling

(32:14) Evaluation as a key to AI adoption

(36:03) Entropy

(40:05) Evaluating AI systems

(43:21) AI as a judge

(46:49) Why prompt engineering is underrated

(49:38) In-context learning

(51:46) Few-shot learning and zero-shot learning

(52:57) Defensive prompt engineering

(55:29) User prompt vs. system prompt

(57:07) Why RAG is here to stay

(01:00:31) Defining AI agents

(01:04:04) AI agent planning

(01:08:32) Training data as a bottleneck to agent planning

In this episode, we sit down with Florian Douetteau, co-founder and CEO of Dataiku, a global category leader in enterprise AI and a fixture on the Forbes Cloud 100 list and in the Gartner Leader Quadrant.

Florian shares his journey from a Parisian student fascinated by functional programming to leading a global enterprise software company. We discuss how Dataiku bridges the gap between technical and business teams to democratize AI in the enterprise, the challenges of selling to enterprise clients, and how Dataiku acts as an orchestration layer for Generative AI, helping businesses manage complex data processes and control AI, so they can build more with AI.

Dataiku

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

X/Twitter - https://twitter.com/dataiku

Florian Douetteau

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

X/Twitter - https://twitter.com/fdouetteau

FIRSTMARK

Website - https://firstmark.com

X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

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

X/Twitter - https://twitter.com/mattturck

(00:00) Intro

(02:08) Florian's life before Dataiku

(06:58) Creation of Dataiku

(12:08) Secret behind the Dataiku's name

(12:47) How does Dataiku stay insightful about the future?

(14:46) Building a platform, not just a tool

(17:26) How to sell to the enterprise from the beginning

(20:09) Dataiku platform today

(26:55) Data is always the problem

(28:50) LLM Mesh

(36:02) Will Gen AI replace ML?

(39:41) Managing Gen AI and traditional AI on one platform

(40:37) Gen AI deployment in the enterprise

(48:33) Dataiku's roadmap

(50:28) What has changed with the company's growth?

In this episode, we dive into the world of generative AI with May Habib, co-founder of Writer, a platform transforming enterprise AI use. May shares her journey from Qordoba to Writer, emphasizing the impact of transformers in AI. We explore Writer's graph-based RAG approach, and their AI Studio for building custom applications.

We also discuss Writer's Autonomous Action functionality, set to revolutionize AI workflows by enabling systems to act autonomously, highlighting AI's potential to accelerate product development and market entry with significant increases in capacity and capability.

Writer

Website - https://writer.com

X/Twitter - https://x.com/get_writer 

May Habib

LinkedIn - https://www.linkedin.com/in/may-habib

X/Twitter - https://x.com/may_habib

FIRSTMARK

Website - https://firstmark.com

X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

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

X/Twitter - https://twitter.com/mattturck

This session was recorded live at a recent Data Driven NYC, our in-person, monthly event series, hosted at Ramp's beautiful HQ. If you are ever in New York, you can join the upcoming events here: https://www.eventbrite.com/o/firstmark-capital-2215570183

(00:00) Intro

(01:47) What is Writer?

(02:52) Writer's founding story

(06:54) Writer is a full-stack company. Why?

(07:57) Writer's enterprise use cases

(10:51) Knowledge Graph

(17:59) Guardrails

(20:17) AI Studio

(23:16) Palmyra X 004

(27:18) Current state of the AI adoption in enterprises

(28:57) Writer's sales approach

(31:25) What May Habib is excited about in AI

(33:14) Autonomous Action use cases

Nathan Benaich, founder and GP at VC firm Air Street Capital, publishes every year "State of AI", one of the most widely-read and comprehensive reports on all things AI across research, industry, and policy. In this episode, we sit down with Nathan to discuss some of the highlights of the 2024 edition of the report, including the "vibes" shift in the industry from existential risk concerns last year to the current monetization race, the financial success of the foundation model labs, how a generative AI app could top the Apple Store charts in 2025, and the challenges facing humanoid robotics.

State of AI 2024 report: https://www.stateof.ai/2024-report-launch

State of AI 2024 video: https://youtu.be/EVMbnPOuUl0

Air Street Capital

Website - https://www.airstreet.com

X/Twitter - https://x.com/airstreet

Nathan Benaich

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

X/Twitter - https://x.com/nathanbenaich

FirstMark

Website - https://firstmark.com

X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

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

X/Twitter - https://twitter.com/mattturck

(01:08) Who is Nathan Benaich?

(04:57) "Vibe" shift in AI

(09:13) Current state of the foundation models

(22:01) AI companies vs. SaaS

(23:31) AI consumer apps

(25:49) AI applications from a VC's perspective

(29:25) "You don't need to be an AI engineer to build an AI company"

(30:46) AI in robotics

(34:36) AI regulations in Europe

(40:55) Predictions on the future of AI

(49:30) Nathan Benaich's favorite sources of information

In this special episode of the MAD Podcast, Matt Turck and Aman Kabeer from FirstMark delve into the AI market from a venture investor perspective, in the final weeks of an incredibly packed and exciting 2024. They comment on their favorite news stories, such as OpenAI's record-breaking $6.6 billion funding round and the massive $200B investments in AI infrastructure by Meta, Google, and Amazon. They tackle the latest trends in funding and valuations in both public and private markets, debate the critical question of whether we're in an AI bubble, examine the current state of AI demand, the potential of scaling laws, and the future of AI-driven innovation. They then discuss where they see opportunities for startups and investors across AI hardware, compute, foundation models, AI tooling, and both consumer and enterprise AI applications.

FIRSTMARK

Website - https://firstmark.com

X/Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

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

X/Twitter - https://twitter.com/mattturck

Aman Kabeer (Investor)

LinkedIn - https://www.linkedin.com/in/aman-kabeer/

X/Twitter - https://x.com/AmanKabeer11

(00:00) Intro

(02:20) The Year of Record-Breaking Evaluations and Investments

(05:23) AI's Environmental Impact and Nuclear Revival

(06:48) AI Valuations and Market Dynamics

(17:01) Are We in an AI Bubble?

(25:01) AI Progress and Demand

(35:06) AI's Role in Consumer Applications

(41:02) AI's Influence on SaaS and Business Models

(50:55) AI's Role in Enterprise Transformation

(01:04:00) The Future of AI: Apps and Agents

Before he founded Modal, Erik Bernhardsson created Spotify's music recommendation system. Today he's bringing a consumer app approach to radically simplifying developer experience for data and AI projects on the Modal platform.

In this episode, we dive into the broader AI compute landscape, discussing the roles of hyperscalers, GPU clouds, inference platforms, and the emergence of alternative AI cloud providers. Erik gives us a product tour of the Modal platform, provides insights into the AI industry's shift from training to inference as the primary use case, and speculates on the future of AI-native consumer applications. Learn about Modal's commitment to fast feedback loops, their cloud maximalist approach, their dedication to building a product that developers truly love, as well as founder lessons Erik learned along the way.

Erik's blog: https://erikbern.com

"It's hard to write code for humans": https://erikbern.com/2024/09/27/its-hard-to-write-code-for-humans

Modal

Website - https://modal.com

Twitter - https://x.com/modal_labs

Erik Bernhardsson

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

Twitter - https://x.com/bernhardsson

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

(01:35) What is Modal?

(02:18) Current state of AI compute space

(09:54) Erik's path to starting Modal

(13:57) Core elements of the Modal platform

(28:52) Is serverless the right level of abstraction for AI compute?

(33:35) Balancing costs: GPU vendor fees vs. customer pricing

(37:56) Designing products for humans

(42:43) Modal's early go-to-market motion

(45:32) Managing early engineering team

(48:26) The only correct way to add a new function to the company

(50:07) Building company in NYC

(52:05) Modal's roadmap

(54:04) Erik's predictions on AI