The Derby Mill Series

StackAdapt is an advertising platform that leverages AI to optimize digital ad campaigns across multiple channels, including display, video, native, and connected TV. With an auction system, it evaluates millions of ad opportunities each second using predictive analytics to maximize ROI and enhance audience targeting. By integrating customer data and providing privacy-conscious, scalable solutions, StackAdapt provides advertisers with data-driven insights and automated ad placement. So how can AI enhance discovery and shape awareness of digital advertising solutions that people may not yet realize they need? And what reward systems might be most effective for RL in optimising ad campaigns? The Derby Mill team talks to StackAdapt CTO and co-founder Yang Han to discuss potential answers. GUESTS AND HOSTSYang Han, CTO and co-founder, StackAdaptAjay Agrawal, co-founder and partner, Intrepid Growth PartnersRichard Sutton, senior advisor, Intrepid Growth Partners, 2024 Turing Award recipient, pioneer of reinforcement learning and professor, University of AlbertaSendhil Mullainathan, senior advisor, Intrepid Growth Partners, MacArthur Genius grant recipient and professor, MITNiamh Gavin, senior advisor, Intrepid Growth Partners, Applied AI scientist, CEO, Emergent PlatformsLINKSDerby Mill show websiteStackAdapt’s website and explainer videoRead Rich Sutton’s latest paper Welcome to the Era of ExperienceRich Sutton’s home page. Follow Rich on XSendhil Mullainathan’s website. Follow Sendhil on XBe sure to catch every episode of The Derby Mill Series by subscribing on the following platforms:YouTube // Spotify // Apple Podcasts DISCUSSION POINTS 00:00 Introduction02:00 Welcome, Yang Han, CTO and Co-Founder of StackAdapt02:45 How advertising on StackAdapt works09:10 How StackAdapt thinks about ROI11:30 Yang on ad competition and who gets the credit.13:55 Niamh on what StackAdapt will look like at the limit.18:23 Sendhil on how we can surface decision-making in advertising.24:20 Rich on the advantages of assistance-based shopping.29:13 Becoming customer-focused with the rise of AI31:53 What executives lack when perfecting the matching problem.26:47 What’s one thing investors should pay attention to in this industry? Nugget 01 - Alternative Customer-First Business ModelNugget 02 - Educating Customers with Personalized AdsNugget 03 - Disrupting the Ad Market with Agent DiscoveryDISCLAIMERIntrepid GP is an investor in StackAdapt. The content of this podcast is for informational and educational purposes only and should not be construed as marketing, solicitation, or an offer to buy or sell any securities or investments. The opinions expressed in this video are those of the participants and do not necessarily reflect the views of Intrepid Growth Partners or its affiliates. Any discussion of specific companies, technologies, or industries is for illustrative purposes and does not constitute investment advice. Viewers are encouraged to consult with their own financial, legal, and tax advisors before making any investment decisions. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit insights.intrepidgp.com

Explore the fascinating shift from human-generated data to autonomous agents in artificial intelligence. Discover how experiential learning may unlock superhuman capabilities, ushering in a new era of innovation. The discussion highlights the philosophical implications of AI's evolution and its potential to transform scientific discovery. Learn about the importance of simplicity in knowledge and how grounded motivations can enhance AI development. This engaging conversation reveals the promising future of AI driven by experience.

Skin Analytics is a UK company using AI to automate the diagnosis of serious skin conditions, starting with skin cancer. Its core product, DERM, is the only Class III CE mark AI medical device for autonomous dermatology in the UK’s health system. Used on more than 150,000 real-world patients, DERM achieves 99.8% negative predictive value, outperforming dermatologists. The company is expanding into general dermatology and launching in the EU and US.In the future, Skin Analytics intends to create a dermatology AI platform that is able to diagnose and treat a broader range of conditions. Based on a diverse sampling of low-cost data, the company intends its platform to transition from self-supervised to unsupervised learning, enabling ubiquitous, low-friction health monitoring.This episode features the Intrepid team exploring such questions as:* What would it take to build healthcare around AI abundance, not human bottlenecks?* How might one frame an approach to reach 99% automation in dermatological triage?* What are the tradeoffs between sensitivity, specificity, and health system efficiency?* How could reward systems (RL or pathway-based optimization) be introduced?* What’s the potential of self-supervised learning across multiple medical modalities?GUESTS AND HOSTSNeil Daly, founder and director, Skin AnalyticsJack Greenhalgh, AI director, Skin AnalyticsAjay Agrawal, co-founder and partner, Intrepid Growth PartnersRichard Sutton, senior advisor, Intrepid Growth Partners, 2024 Turing Award recipient, pioneer of reinforcement learning and professor, University of AlbertaSendhil Mullainathan, senior advisor, Intrepid Growth Partners, MacArthur Genius grant recipient and professor, MITNiamh Gavin, senior advisor, Intrepid Growth Partners, Applied AI scientist, CEO, Emergent PlatformsLINKSDerby Mill show website: insights.intrepidgp.com/podcastSkin Analytics website and explainer videoRich Sutton’s home page. Follow Rich on XSendhil Mullainathan’s website. Follow Sendhil on XBe sure to catch every episode of The Derby Mill Series by subscribing on the following platforms: YouTube // Spotify // Apple Podcasts DISCUSSION POINTS00:00 Introduction01:24 Meet the team: Skin Analytics06:12 The lead-up to image recognition10:29 Patient drop-off post-referral14:03 Getting classification right18:47 Integrating into the healthcare system22:36 Cancer detection in the limit27:55 At-home cancer detection34:10 Making dermatology RL-able45:00 Using data as proxies for other diagnoses50:21 Early detection vs. overdiagnosis55:07 Higher rates of cancer detection advantages57:00 What took so long?59:07 Final remarksNugget 01 - Sensors Reveal Hidden Data in the SkinTraditionally, dermatology has been rate-limited by the human eye and optical sensors. So incorporating a variety of additional sensors to collect more diverse and comprehensive data can open the door to a new kind of pre-primary care, potentially revealing more information about internal conditions like hypertension or liver disease.Nugget 02 - The Economic Model Behind At-Home DiagnosesThere's a massive direct-to-consumer interest in skin health, which opens the door to a potential expansion of at-home skin-monitoring apps that could be used beyond only in primary care settings. But overdiagnoses risk overwhelming the healthcare system. In order to avoid case buildup, these apps require an economic model that leverages medical systems and consumer trust.Nugget 03 - Redesigning the Treatment DelayWhat prevents people from accessing treatment is not the diagnostic delay (which often involves a lengthy wait for results), but rather the delay in seeking help: People tend to wait for a reason to address an issue, which increases the risk of lowering the survival rate as a disease spreads.DISCLAIMERIntrepid GP is an investor in Skin Analytics. The content of this podcast is for informational and educational purposes only and should not be construed as marketing, solicitation, or an offer to buy or sell any securities or investments. The opinions expressed in this video are those of the participants and do not necessarily reflect the views of Intrepid Growth Partners or its affiliates. Any discussion of specific companies, technologies, or industries is for illustrative purposes and does not constitute investment advice. Viewers are encouraged to consult with their own financial, legal, and tax advisors before making any investment decisions. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit insights.intrepidgp.com

In this unpacked episode, the team further expands its discussion of themes that came up in episode seven, which explored the automation of customer support with artificial intelligence. Our guests in that episode were a duo that is leading efforts in that space: CEO Mike Murchison and chief product and technology officer Mike Gozzo from Ada.In this episode, Intrepid Growth Partners cofounder and partner Ajay Agrawal leads the discussion with Intrepid Senior Advisors Rich Sutton (Turing Award winner), Sendhil Mullainathan (MacArthur genius grant recipient) and Niamh Gavin (CEO, Emergent Platforms).In the previous episode, we learned that Ada’s north star is “percent automated resolutions”, or the percentage of customer inquiries that are fully resolved by AI without human intervention. One challenge is that Ada relies on large language models (LLMs) rather than action-based goals, often requiring human agents to step in when confidence is low.“It’s a mistake to think that [Ada’s AI agents] have goals,” says Sutton. ”What we have instead … is we have [AI agents] mimicking people.”All of which raises the question of how customer support will evolve as this technology advances towards the limit.Our team also debates the need for clear, objective measures of AI performance and the challenges of achieving true goal-oriented AI systems.Our panel of experts:Ajay Agrawal, co-founder and partner, Intrepid Growth PartnersRichard Sutton, 2024 Turing Award recipient, pioneer of reinforcement learning and professor, University of AlbertaSendhil Mullainathan, MacArthur Genius grant recipient and professor, MITNiamh Gavin, Applied AI scientist, CEO, Emergent PlatformsSutton, Mullainathan and Gavin are all Intrepid Growth Partners’ senior advisors.LINKSAda websiteThis episode extends the discussion from Derby Mill episode 07: Customer Support Rich Sutton’s home page. Follow Rich on XSendhil Mullainathan’s website. Follow Sendhil on XBe sure to catch every episode by subscribing on the following platforms:YouTube // Spotify // Apple PodcastsDISCUSSION POINTS00:00 Introduction and opening credit02:00 Ada refresher03:46 Clip: Testing harness06:50 Clip discussion begins10:15 What are goal-based objectives?13:30 Is this the year of the agent?17:40 What makes agents goal-oriented19:20 Decision-making fundamentals in AI21:27 Clip: Automating system improvement over time23:32 Clip discussion begins30:13 Automating the evaluation process34:08 What could Ada look like in the limit?41:03 Closing remarksNUGGET 01: Vertical vs. Horizontal CompetitionFine-tuning used to be costly and impractical, pushing companies to open-source solutions—only to revert to OpenAI due to complexity. Now, companies like Ada build on top of model providers, offering flexibility while managing AI’s complexity. Niamh discusses the competitiveness between verticalized AI (industry-specific applications) and horizontal AI (broad sector models).NUGGET 02: The Challenge of InterpretabilityAda's evaluations rely on human judgment. The challenge here is interpretability—determining whether an outcome is truly good without direct human input. Rich Sutton offers potential solutions, including using reinforcement learning with human feedback (RLHF) as a proxy measure trained on high-quality data.NUGGET 03: Benchmarking vs. Deployment in the FieldNiamh and Sendhil discuss how, despite concerns about hallucinations in AI-powered customer service, CEOs adopt GenAI more for signaling competence than for real effectiveness.DISCLAIMERThe content of this podcast is for informational and educational purposes only and should not be construed as marketing, solicitation, or an offer to buy or sell any securities or investments. The opinions expressed in this video are those of the participants and do not necessarily reflect the views of Intrepid Growth Partners or its affiliates. Any discussion of specific companies, technologies, or industries is for illustrative purposes and does not constitute investment advice. Viewers are encouraged to consult with their own financial, legal, and tax advisors before making any investment decisions. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit insights.intrepidgp.com

Meet Ada, a Canadian AI agent platform automating the resolution of customer service interactions. When customers have complex requests—such as resetting passwords, checking order status, or requesting a refund—Ada uses large language models to radically reduce the amount of human effort required to fulfill the customer’s inquiry.Here, Ada CEO Mike Murchison and Chief Product & Technology Officer Mike Gozzo join the Derby Mill podcast to discuss the intersection of AI and customer support—and where the technology may go, at the limit.Our panel of experts:Ajay Agrawal, co-founder and partner, Intrepid Growth PartnersRichard Sutton, 2024 Turing Award recipient, pioneer of reinforcement learning and professor, University of AlbertaSendhil Mullainathan, MacArthur Genius grant recipient and professor, MITNiamh Gavin, Applied AI scientist, CEO, Emergent PlatformsLINKSAda websiteAda CEO Mike Murchison LinkedInAda Chief Product & Technology Officer Mike Gozzo LinkedInRich Sutton’s home page. Follow Rich on X.Sendhil Mullainathan’s website. Follow Sendhil on X.Sendhil’s article on Algorithms Need Managers, Too published in the Harvard Business Review Be sure to catch every episode by subscribing on the following platforms:YouTube // Spotify // Apple PodcastsDISCUSSION POINTS00:00 Introduction01:49 Meet Ada, the company automating customer support05:05 Customer service & books: an analogy05:41 Murchison describes automated resolution08:50 Human feedback for automated improvement 23:17 LLMs in customer service26:10 The difference between language and action26:34 Ada’s use of LLMs30:01 Murchison on how “deterministic” Ada’s actions are30:59 Improving decision quality37:06 Protecting against LLM’s unreliability 44:40 Closing remarksNUGGET 01: Human Feedback for Automated ImprovementAda describes the role of humans "coaching" their AIs. Why this is one of the first areas for "automated improvement," and how can the preference data they are collecting through the coaching process be used to "drive automated improvements throughout the entire system."NUGGET 02: Decision-Making QualityRich Sutton asks how Ada improves the quality of the system's decisions, and questions the role of humans vs. AI in terms of evaluating versus improving the quality of decisions.NUGGET 03: DistillationGiven the cost and latency virtues of smaller models, when do we anticipate applications to use large foundation models at the limit? Is the Ada case a good example of using large models to bootstrap a commercial solution en route to smaller, more specialized models?DISCLAIMER The content of this podcast is for informational and educational purposes only and should not be construed as marketing, solicitation, or an offer to buy or sell any securities or investments. The opinions expressed in this video are those of the participants and do not necessarily reflect the views of Intrepid Growth Partners or its affiliates. Any discussion of specific companies, technologies, or industries is for illustrative purposes and does not constitute investment advice. Viewers are encouraged to consult with their own financial, legal, and tax advisors before making any investment decisions. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit insights.intrepidgp.com

The conversation dives into how artificial intelligence is transforming mining exploration by utilizing lower-fidelity data like aerial imagery and dust analysis. Experts discuss the parallels between mining and healthcare, highlighting how devices like the Apple Watch can offer valuable insights despite their lower accuracy. There's a focus on enhancing predictive models by integrating diverse data sources, and how AI, when combined with human intuition, can revolutionize decision-making. The discussion emphasizes the potential of cost-effective data in both geological exploration and medical diagnostics.

Grant Sanden, CEO of GeologicAI, shares insights on revolutionizing mining through AI. He discusses how advanced core-sampling technology can significantly enhance geological predictions while navigating uncertainties. The conversation dives into the unique challenges of mining data—rich in some areas yet sparse in others—and how AI helps integrate massive datasets for better decision-making. They also highlight innovative methods like using drones and IoT sensors to improve resource exploration and operational efficiency, addressing both accuracy and sustainability in mining.

In this lively discussion, Marc L. Busch, a Georgetown University professor and global trade expert, dives into the complexities of tariffs and their impact on the AI industry. He shares insights on how trade conflicts shape the entrepreneurial landscape, particularly regarding software and machine learning. The conversation also touches on regulatory challenges for tech companies and the geopolitical dynamics at play, especially with China. Busch offers strategic advice on navigating these turbulent waters to help global tech firms thrive.

The discussion dives into making factories more responsive to reinforcement learning, focusing on how it can lead to significant improvements. Experts tackle the role of humans in active learning and the challenges of implementing adaptive systems. They delve into the complexities of factory dynamics, emphasizing a balance between automation and human insight. The conversation also highlights the importance of sensor technology and the delicate interplay between exploration and exploitation for optimal efficiency in manufacturing.

Kevin Bryan, an economist and co-founder of All Day TA, shares insights on the groundbreaking AI chatbot DeepSeek, which rivals larger platforms with its cost-effective and efficient design. The discussion reflects on how DeepSeek’s open-source approach could reshape economic strategies and investment opportunities in AI. They delve into the need for meaningful benchmarks in evaluating AI models and caution against complacency in the West regarding global tech advancements. Furthermore, predictions are made about the tech landscape's future and the ethical challenges of originality in AI development.