Avsnitt
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A week after OpenAI’s o3/o4-mini volleyed with Google’s Gemini 2.5 Flash, I sat down with Arvind Jain— ex-Google search luminary, Rubrik co-founder, and now CEO of Glean —just as his company released its agentic reasoning platform and swirled with rumors of a new round at a $7 billion valuation. We open on that whirlwind: why the model race is accelerating, why enterprises still gravitate to closed models, and when open-source variants finally take over. Arvind argues that LLMs should “fade into the background,” leaving application builders to pick the right engine for each task.
From there, we trace Glean’s three-act arc—enterprise search powered by transformers (2019), retrieval-augmented chat the moment ChatGPT hit, and now agents that have already logged 50 million real actions inside Glean enterprise customers. Arvind lifts the hood on permission-aware ranking, tool-use orchestration, and the routing layer that swaps Gemini for GPT on the fly. Along the way, he answers the hard questions: Do agents really double efficiency? Where’s the moat when every startup promises the same? Why are humans still in the review loop, and for how long?
The conversation crescendos with a vision of work where every employee is flanked by a team of proactive AI coworkers—all drawing from a horizontal knowledge layer that knows the firm’s language better than any newcomer.
If you want to know what’s actually working with AI in the enterprise, how to build agents that deliver ROI, and what the next era of work will look like, this episode is packed with specifics, technical insights, and bold predictions from one of the sharpest minds in the space.
Glean
Website - https://www.glean.com
X/Twitter - https://x.com/gleanai
Arvind Jain
LinkedIn - https://www.linkedin.com/in/jain-arvind
X/Twitter - https://x.com/jainarvind
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 & Glean’s $7B valuation rumor
(02:01) The AI model explosion: open vs. closed in the enterprise
(06:19) Why enterprises choose open source AI (and when)
(10:33) The agent era: what are AI agents and why now?
(12:41) Automating business processes: real-world agent use cases
(16:46) Are we there yet? The reality of AI agents in 2025
(19:24) Glean’s origin story: reinventing enterprise search
(26:38) Glean agents: from apps to agentic platforms
(31:22) Horizontal vs. vertical: Glean’s strategic platform choice
(34:14) How Glean’s enterprise search works
(39:34) Staying LLM-agnostic: integrating new AI models
(42:11) The architecture of Glean agents: tool use and beyond
(43:50) Data flywheels and personalization in Glean
(47:06) Moats, competition, and the future of work with AI agents
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In this episode, we sit down with Aaron Levie, CEO and co-founder of Box, for a wide-ranging conversation that’s equal parts insightful, technical, and fun. We kick things off with a candid discussion about what it’s like to be a public company CEO during times of volatility, and then rewind to the early days of Box — from dorm room experiments to cold emailing Mark Cuban and dropping out of college.
From there, we dive deep into how AI is transforming the enterprise. Aaron shares how Box is layering AI agents, RAG systems, and model orchestration on top of decades of enterprise content infrastructure — and why “95% of enterprise data is underutilized.”
We explore what’s actually working with AI in production, what’s still breaking, and how companies can avoid common pitfalls. From building hubs for document-specific RAG to thinking through agent-to-agent interoperability, Aaron unpacks the architecture of Box’s AI platform — and why they’re staying out of the model training wars entirely. We also dig into AI culture inside large organizations, the trade-offs of going public, and why Levie believes every enterprise interface is about to change.
Whether you're a founder, engineer, enterprise buyer, or just trying to figure out how AI agents will reshape knowledge work, this conversation is full of practical insights and candid takes from one of the sharpest minds in tech.
Box
Website - https://www.box.com
X/Twitter - https://twitter.com/Box
Aaron Levie
LinkedIn - https://www.linkedin.com/in/boxaaron
X/Twitter - https://x.com/levie
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
(01:51) Navigating uncertainty as a public company CEO
(14:48) The Box origin story: college, cold emails, and Mark Cuban
(23:39) Cloud transformation vs. the AI wave
(30:15) The reality of AI in the enterprise: proof of concept vs. deployment
(34:37) Inside Box’s AI platform: Hubs, agents, and more
(44:15) Why Box won’t build its own model (and the dangers of fine-tuning)
(51:51) What’s working — and what’s not — with AI agents
(1:04:42) Building an AI culture at Box
(1:13:22) The future of enterprise software and Box’s roadmap
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Saknas det avsnitt?
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In this episode, we sit down with Sridhar Ramaswamy, CEO of Snowflake, for an in-depth conversation about the company’s transformation from a cloud analytics platform into a comprehensive AI data cloud. Sridhar shares insights on Snowflake’s shift toward open formats like Apache Iceberg and why monetizing storage was, in his view, a strategic misstep.
We also dive into Snowflake’s growing AI capabilities, including tools like Cortex Analyst and Cortex Search, and discuss how the company scaled AI deployments at an impressive pace. Sridhar reflects on lessons from his previous startup, Neeva, and offers candid thoughts on the search landscape, the future of BI tools, real-time analytics, and why partnering with OpenAI and Anthropic made more sense than building Snowflake’s own foundation models.
Snowflake
Website - https://www.snowflake.com
X/Twitter - https://x.com/snowflakedb
Sridhar Ramaswamy
LinkedIn - https://www.linkedin.com/in/sridhar-ramaswamy
X/Twitter - https://x.com/RamaswmySridhar
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 and current market tumult
(02:48) The evolution of Snowflake from IPO to Today
(07:22) Why Snowflake’s earliest adopters came from financial services
(15:33) Resistance to change and the philosophical gap between structured data and AI
(17:12) What is the AI Data Cloud?
(23:15) Snowflake’s AI agents: Cortex Search and Cortex Analyst
(25:03) How did Sridhar’s experience at Google and Neeva shape his product vision?
(29:43) Was Neeva simply ahead of its time?
(38:37) The Epiphany mafia
(40:08) The current state of search and Google’s conundrum
(46:45) “There’s no AI strategy without a data strategy”
(56:49) Embracing Open Data Formats with Iceberg
(01:01:45) The Modern Data Stack and the future of BI
(01:08:22) The role of real-time data
(01:11:44) Current state of enterprise AI: from PoCs to production
(01:17:54) Building your own models vs. using foundation models
(01:19:47) Deepseek and open source AI
(01:21:17) Snowflake’s 1M Minds program
(01:21:51) Snowflake AI Hub
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In this fascinating episode, we dive deep into the race towards true AI intelligence, AGI benchmarks, test-time adaptation, and program synthesis with star AI researcher (and philosopher) Francois Chollet, creator of Keras and the ARC AGI benchmark, and Mike Knoop, co-founder of Zapier and now co-founder with Francois of both the ARC Prize and the research lab Ndea. With the launch of ARC Prize 2025 and ARC-AGI 2, they explain why existing LLMs fall short on true intelligence tests, how new models like O3 mark a step change in capabilities, and what it will really take to reach AGI.
We cover everything from the technical evolution of ARC 1 to ARC 2, the shift toward test-time reasoning, and the role of program synthesis as a foundation for more general intelligence. The conversation also explores the philosophical underpinnings of intelligence, the structure of the ARC Prize, and the motivation behind launching Ndea — a ew AGI research lab that aims to build a "factory for rapid scientific advancement." Whether you're deep in the AI research trenches or just fascinated by where this is all headed, this episode offers clarity and inspiration.
Ndea
Website - https://ndea.com
X/Twitter - https://x.com/ndea
ARC Prize
Website - https://arcprize.org
X/Twitter - https://x.com/arcprize
François Chollet
LinkedIn - https://www.linkedin.com/in/fchollet
X/Twitter - https://x.com/fchollet
Mike Knoop
X/Twitter - https://x.com/mikeknoop
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
(01:05) Introduction to ARC Prize 2025 and ARC-AGI 2
(02:07) What is ARC and how it differs from other AI benchmarks
(02:54) Why current models struggle with fluid intelligence
(03:52) Shift from static LLMs to test-time adaptation
(04:19) What ARC measures vs. traditional benchmarks
(07:52) Limitations of brute-force scaling in LLMs
(13:31) Defining intelligence: adaptation and efficiency
(16:19) How O3 achieved a massive leap in ARC performance
(20:35) Speculation on O3's architecture and test-time search
(22:48) Program synthesis: what it is and why it matters
(28:28) Combining LLMs with search and synthesis techniques
(34:57) The ARC Prize structure: efficiency track, private vs. public
(42:03) Open source as a requirement for progress
(44:59) What's new in ARC-AGI 2 and human benchmark testing
(48:14) Capabilities ARC-AGI 2 is designed to test
(49:21) When will ARC-AGI 2 be saturated? AGI timelines
(52:25) Founding of NDEA and why now
(54:19) Vision beyond AGI: a factory for scientific advancement
(56:40) What NDEA is building and why it's different from LLM labs
(58:32) Hiring and remote-first culture at NDEA
(59:52) Closing thoughts and the future of AI research
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In 2022, Lin Qiao decided to leave Meta, where she was managing several hundred engineers, to start Fireworks AI. In this episode, we sit down with Lin for a deep dive on her work, starting with her leadership on PyTorch, now one of the most influential machine learning frameworks in the industry, powering research and production at scale across the AI industry.
Now at the helm of Fireworks AI, Lin is leading a new wave in generative AI infrastructure, simplifying model deployment and optimizing performance to empower all developers building with Gen AI technologies.
We dive into the technical core of Fireworks AI, uncovering their innovative strategies for model optimization, Function Calling in agentic development, and low-level breakthroughs at the GPU and CUDA layers.
Fireworks AI
Website - https://fireworks.ai
X/Twitter - https://twitter.com/FireworksAI_HQ
Lin Qiao
LinkedIn - https://www.linkedin.com/in/lin-qiao-22248b4
X/Twitter - https://twitter.com/lqiao
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) What is Fireworks AI?
(02:47) What is PyTorch?
(12:50) Traditional ML vs GenAI
(14:54) AI’s enterprise transformation
(16:16) From Meta to Fireworks
(19:39) Simplifying AI infrastructure
(20:41) How Fireworks clients use GenAI
(22:02) How many models are powered by Fireworks
(30:09) LLM partitioning
(34:43) Real-time vs pre-set search
(36:56) Reinforcement learning
(38:56) Function calling
(44:23) Low-level architecture overview
(45:47) Cloud GPUs & hardware support
(47:16) VPC vs on-prem vs local deployment
(49:50) Decreasing inference costs and its business implications
(52:46) Fireworks roadmap
(55:03) AI future predictions
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Retrieval-Augmented Generation (RAG) has become a dominant architecture in modern AI deployments, and in this episode, we sit down with Douwe Kiela, who co-authored the original RAG paper in 2020. Douwe is now the founder and CEO of Contextual AI, a startup focusing on helping enterprises deploy RAG as an agentic system.
We start the conversation with Douwe's thoughts on the very latest advancements in Generative AI, including GPT 4.5, DeepSeek and the exciting paradigm shift towards test time compute, as well as the US-China rivalry in AI.
We then dive into RAG: definition, origin story and core architecture. Douwe explains the evolution of RAG into RAG 2.0 and Agentic RAG, emphasizing the importance of self-learning systems over individual models and the role of synthetic data. We close with the challenges and opportunities of deploying AI in real-world enterprise, discussing the balance between accuracy and the inherent inaccuracies of AI systems.
Contextual AI
Website - https://contextual.ai
X/Twitter - https://x.com/ContextualAI
Douwe Kiela
LinkedIn - https://www.linkedin.com/in/douwekiela
X/Twitter - https://x.com/douwekiela
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
(01:57) Thoughts on the latest AI models: GPT-4.5, Sonnet 3.7, Grok 3
(04:50) The test time compute paradigm shift
(06:47) Unsupervised learning vs reasoning: a false dichotomy
(07:30) The significance of DeepSeek
(10:29) USA vs. China: is the AI war overblown?
(12:19) Controlling AI hallucinations at the model level
(13:51) RAG: definition and origin story
(18:46) Why the Transformers paper initially felt underwhelming
(20:41) The core architecture of RAG
(26:06) RAG vs. fine-tuning vs. long context windows
(30:53) RAG 2.0: Thinking in systems and not models
(31:28) Data extraction and data curation for RAG
(35:59) Contextual Language Models (CLMs)
(38:04) Finetuning and alignment techniques: GRIT, KTO, LENS
(40:40) Agentic RAG
(41:36) General vs. specialized RAG agents
(44:35) Synthetic data in AI
(45:51) Deploying AI in the enterprise
(48:07) How tolerant are enterprises to AI hallucinations?
(49:35) The future of Contextual AI
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In this episode, we dive into how AI is transforming video editing with Gaurav Misra, the CEO of Captions. Launched in New York in 2021, Captions already empowers over 10 million creators worldwide, leveraging AI to make video production as simple as clicking a button.
Discover the strategic framework that led to the inception of Captions, and learn how the founders identified societal changes and technological advancements to build a groundbreaking company. We explore the challenges and opportunities of building an AI product for video editing, including how Captions is outpacing traditional content production workflows.
Gaurav shares insights into the future of video editing, the role of AI in democratizing video production, and the unique approach Captions takes to differentiate itself from industry giants like Adobe and Capcut.
Captions
Website - https://www.captions.ai
X/Twitter - https://x.com/getcaptionsapp
Gaurav Misra
LinkedIn - https://www.linkedin.com/in/gamisra1
X/Twitter - https://x.com/gmharhar
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
(01:30) What is Captions?
(03:43) How did Captions start?
(08:25) The strategy behind launching Captions
(12:32) How is Captions different from other editing tools?
(14:13) How does it compare to CapCut?
(18:22) Who is the typical Captions user?
(20:13) Why ‘Captions’?
(23:47) Captions’ product suite for production and editing
(26:37) AI models powering Captions
(36:22) AI lipsync
(38:49) Personalized fine-tuned models for creators?
(39:38) Building models vs. building wrappers
(43:09) Cloud AI vs. Local AI
(45:19) Optimizing for low latency
(48:07) AI/ML stack at Captions
(51:10) “Hallucinations are a feature, not a bug”
(53:19) Prompt engineering
(54:12) Have we passed the uncanny valley for AI avatars?
(01:01:47) The impact of deepfakes
(01:04:33) CapCut ban and its effects
(01:05:05) Evolving from paid to freemium
(01:07:42) Building a company on foundation models
(01:09:01) Running an AI company in New York
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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
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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
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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
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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
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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
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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?
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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
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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
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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
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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
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A founding engineer on Google BigQuery and now at the helm of MotherDuck, Jordan Tigani challenges the decade-long dominance of Big Data and introduces a compelling alternative that could change how companies handle data.
Jordan discusses why Big Data technologies are an overkill for most companies, how MotherDuck and DuckDB offer fast analytical queries, and lessons learned as a technical founder building his first startup.
Watch the episode with Tomasz Tunguz: https://youtu.be/gU6dGmZzmvI
Website - https://motherduck.com
Twitter - https://x.com/motherduck
Jordan Tigani
LinkedIn - https://www.linkedin.com/in/jordantigani
Twitter - https://x.com/jrdntgn
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 the Small Data?
(06:56) Marketing strategy of MotherDuck
(08:39) Processing Small Data with Big Data stack
(15:30) DuckDB
(17:21) Creation of DuckDB
(18:48) Founding story of MotherDuck
(24:08) MotherDuck's community
(25:25) MotherDuck of today ($100M raised)
(33:15) Why MotherDuck and DuckDB are so fast?
(39:08) The limitations and the future of MotherDuck's platform
(39:49) Small Models
(42:37) Small Data and the Modern Data Stack
(46:47) Making things simpler with a shift from Big Data to Small Data
(50:04) Jordan Tigani's entrepreneurial journey
(58:31) Outro
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With a $4.5B valuation, 5M AI builders and 1M public AI models, Hugging Face has emerged as the key collaboration platform for AI, and the heart of the global open source AI community.
In this episode of The MAD Podcast, we sit down with Clément Delangue, its co-founder and CEO, and delve deep into Hugging Face's journey from a fun chatbot to a central hub for AI innovation, the impact of open-source AI and the importance of community-driven development, and discuss the shift from text to other AI modalities like audio, video, chemistry, and biology. We also cover the evolution of Hugging Face's business model, and the different approach to company culture that the founders have implemented over the years.
Hugging Face
Website - https://huggingface.co
Twitter - https://x.com/huggingface
Clem Delangue
LinkedIn - https://www.linkedin.com/in/clementdelangue
Twitter - https://x.com/clemdelangue
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
(01:46) Miami vs. New York vs. San Francisco
(03:25) Current state of open source AI
(11:12) Government regulation of AI
(13:18) What is open source AI?
(15:21) Open source AI: China vs U.S.
(18:32) LLMs vs. SLMs
(22:01) Are commercial LLMs just 'Training Wheels' for enterprises?
(24:26) Software 2.0: built with AI
(28:03) Hugging Face founding story
(37:03) Are there any competitors?
(44:06) Most interesting models on Hugging Face
(50:35) Shifting focus in enterprise solutions
(55:06) Bloom & Idefix
(58:44) The culture of Hugging Face
(01:04:44) The future of Hugging Face
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This episode is a captivating conversation with Richard Socher, serial entrepreneur, investor, and AI researcher.
Richard elaborates on why he likens the impact of AI to the Industrial Revolution, the Enlightenment, and the Renaissance, discusses important current issues in AI, such as scaling laws and agents, provides a behind-the-scenes tour of YOU.com and its evolving business model, and finally describes his current investment strategy in AI startups.
You.com
Website - https://you.com/business
Twitter - https://x.com/youdotcom
Richard Socher
LinkedIn - https://www.linkedin.com/in/richardsocher
Twitter - https://x.com/richardsocher
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
(02:00) "AI era is the Industrial Revolution, Renaissance, and the Enlightenment combined"
(07:49) Top-performers in the Age of AI
(11:15) Comeback of the Renaissance Person
(13:05) People tried to stop Richard from doing deep learning research. Why?
(14:34) Jevons paradox of intelligence
(17:08) Scaling Laws in Deep Learning
(23:23) Can Deep Learning and Rule-Based AI coexist?
(25:42) Post-transformers AI Architecture
(28:20) Achieving AGI and ASI
(36:43) AI for everyday tasks: how far is it?
(44:50) AI Agents
(55:45) Evolution of You.com
(01:02:11) Technical side of You.com
(01:06:46) Is AI getting cheaper?
(01:13:05) What is AIX Ventures?
(01:16:36) VC landscape of 2024
(01:24:31) Research vs Entrepreneurship
(01:26:12) OpenAI’s transformation and its impact on the industry
- Visa fler
