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 HOSTS

Neil 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 Platforms

LINKS

Derby 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 POINTS

00:00 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 remarks

Nugget 01 - Sensors Reveal Hidden Data in the Skin

Traditionally, 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 Diagnoses

There'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 Delay

What 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.

DISCLAIMER

Intrepid 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.

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.

LINKS

Ada 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 Podcasts

DISCUSSION POINTS

00:00 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 remarks

NUGGET 01: Vertical vs. Horizontal Competition

Fine-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 Interpretability

Ada'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 Field

Niamh and Sendhil discuss how, despite concerns about hallucinations in AI-powered customer service, CEOs adopt GenAI more for signaling competence than for real effectiveness.

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.

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 Platforms

LINKS

Ada 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 Podcasts

DISCUSSION POINTS

00:00 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 remarks

NUGGET 01: Human Feedback for Automated Improvement

Ada 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 Quality

Rich 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: Distillation

Given 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.

In the first episode of The Derby Mill Series to post since our co-host Rich Sutton won the Turing Award, the highest distinction in the field of computer science, Sutton joins our panel of experts to further discuss the application of artificial intelligence to the task of mining exploration. What ensues is a remarkable conversation about the increasing relevance of cheap and easily discoverable data sources in an age dominated by artificial intelligence and reinforcement learning.

The analogy begins with mining, where core samples may provide an element of ground truth but are, at the same time, tough and very expensive to get. Other data sources, such as aerial imagery, chips and dust, are cheap and more easily available. So can the pattern-recognition abilities of artificial intelligence elevate the relevance of that lower-fidelity, more easily available information?

Extending the analysis, Intrepid Senior Advisors Niamh Gavin and Sendhil Mullainathan draw parallels with health care. Apple Watch’s skin sensors are certainly less accurate than an annually drawn blood test. But as the Watch conducts its tests numerous times a day, and as AI better recognizes troubling sensor patterns, the cheaper Watch data could become just as important as more expensive medical diagnostics.

Generalizing to other areas, Intrepid partner Ajay Agrawal notes that lower-fidelity data that is more easily available could become as informative as high-fidelity data that is tougher to extract. Our experts’ ultimate prediction, then, observes that cheap data plus artificial intelligence could transform the fundamental economics of many different industries.

Our panel of experts

Richard Sutton, 2024 winner of the Turing Award, pioneer of reinforcement learning and professor, University of AlbertaSendhil Mullainathan, MacArthur Genius grant recipient and professor, MITNiamh Gavin, Applied AI scientist, CEO, Emergent PlatformsAjay Agrawal, co-founder and partner, Intrepid Growth Partners

LINKS

Intrepid Growth Partners’ Senior Advisor Rich Sutton wins the Turing Award. NY Times. Financial Times. Betakit.This episode extends a discussion in Derby Mill episode 05: Mining Exploration.Referenced in this episode: Sendhil Mullainathan's heart attack studyyRich 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 Podcasts

DISCUSSION POINTS

00:00 Introductions and opening credit01:43 Recap of Mining Exploration06:01 Clips: Mining industry transformation09:52 Niamh on narrowing the search zone12:18 Rich on sequential decisions and pattern recognition15:08 Sendhil on using supervised learning to train predictors19:34 Niamh on non-invasive markers21:52 Signals in healthcare vs. mining25:02 The combination of human + AI27:47 A new age of data analysis29:50 Data sources and reinforcement learning35:53 A cognitive barrier for data42:24 The indicator analogy45:58 Closing remarks

NUGGETS (short excerpts from the full episode)

NUGGET 01: Sum > Whole of Its Parts

Niamh Gavin argues that human + AI intelligence is better than either in isolation.

NUGGET 02: A More Interactive Feedback Process

Rich Sutton advocates for an awareness that important mining exploration problems require a wide diversity of data inputs.

NUGGET 03: The Less Invasive, Far Cheaper Data Axis

“What if I had a sweat test that was 10% as good as a blood test?” asks Sendhil Mullainathan, noting the way AI can make use of data from cheap and more easily available diagnostics to improve numerous different industries.

NUGGET 04: AI and the Increasing Relevance of Little Tests

Expert intelligence is expensive today, notes Rich Sutton, but as computational power decreases, AI will help to bolster the importance of all sorts of cheap and easy tests.

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.

Intrepid partner Ajay Agrawal and senior advisors Rich Sutton, Sendhil Mullainathan and Niamh Gavin are back to dig deep in this episode all about using artificial intelligence to increase the efficiency of mining exploration. That’s the act of using machine-learning techniques to analyze information from the earth, such as core samples, to decide multi-million-dollar questions, like where to build a mine or whether to expand an existing operation.

Our guests are CEO Grant Sanden and President of Resource Modelling Solutions Jared Deutsch from GeologicAI, a Calgary-based company that redefines geological and mining decision-making with advance core-scanning technology and AI-powered analytical and modelling solutions. From Sanden:

“You've got challenges in mining… You know, you're only touching a trillionth of the deposit… And it really is a structural problem in prediction… but then once you can deal with that well, you're now characterizing uncertainty. And with these new tools, we can plan through more robustly with uncertainty properly quantified, which is a challenging endeavour.”

And from President RMS Jared Deutsch:

“The application of AI and mining is so unique because we're so data-poor spatially, but so data rich, thanks to scanning and many other technologies, where we have millimetre-scale data. Challenge is, we're 100 meters away from our next millimetre scale data. So this makes for a very challenging problem in a very non-stationary environment, where no mineral deposit is like another one. And we can't really afford to sit around for a few 100 million years and wait to see how these things evolve. So it makes for a really fun and exciting application of AI, but a bit unique compared to other areas.”

EP 05 HOSTS

Ajay Agrawal, co-founder and partner, Intrepid Growth PartnersRichard Sutton, pioneer of reinforcement learning and professor, University of AlbertaSendhil Mullainathan, MacArthur Genius grant recipient and professor, MITNiamh Gavin, Applied AI scientist, CEO, Emergent Platforms

LINKS

GeologicAI website and a short explainer video highlighting a GeologicAI use case.GeologicAI CEO Grant Sanden LinkedInRMS President Jared Deutsch LinkedInRich 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 Podcasts

DISCUSSION POINTS

00:00 Introduction01:23 Meet the GeologicAI team, and learn what the company does04:22 Challenges and opportunities in mining data05:41 Deutsch describes how unique mining exploration is as an AI application06:53 Mullainathan asks about human-algorithm interaction08:07 Sanden explains AI- and human-algorithm approaches10:41 Gavin compares mining and healthcare as AI applications12:23 Sutton on the way AI can create a super geologist14:33 Sanden on the scale of GeologicAI’s operations15:48 Mullainathan asks about optimizing the data collection17:41 Agrawal on the extreme length of learning loops in mining19:44 Mullainathan: Are there ways to reduce the 20-year lag?25:13 The challenge of optimizing the data-collection cycle27:24 Agrawal describes the mine of the future30:08 The optimal path from limited loops to ultimate loops31:00 Closing remarks

NUGGETS (short excerpts from the full episode)

NUGGET 01: Human-Algo Interaction

AIs learn from human feedback. Humans learn from AI predictions in edge cases. Sendhil Mullainathan and Grant Sanden discuss what this human-algorithm interaction might look like at the limit.

NUGGET 02: Super Geologists

AIs have the opportunity to learn from more core sample examples than any person. We know about the impact of adding more data to training sets to enhance performance. How accurately can we estimate the marginal benefit to adding a bit more data relative to the cost of collecting it? Rich Sutton questions whether we can imagine any geologist that will be better at mineral classification than the best AI.

NUGGET 03: Data Strategy

How does data collection via following the "natural order of things" differ from the optimal data collection strategy? A 20-year feedback loop seems crazy ("a little bit of a time lag"). Sendhil Mullainathan and Jared Deutsch discuss how to decrease the feedback loop.

NUGGET 04: A cheaper alternative to mining cores?

In the prior clip, we discussed the shifting value of core samples. In this case, Grant Sanden and Niamh Gavin consider other, much cheaper data sources (chips, dust, aerial imagery) that might also provide predictive power regarding hidden underground mineral deposits. Sendhil Mullainathan discusses how new forms of high-fidelity prediction that are orders of magnitude cheaper, although perhaps less accurate, might transform the fundamental economics of the mining industry.

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.

The new environment for global trade is dominating discussions in many business sectors, including artificial intelligence. To discuss the implications for global software companies and start-ups, Intrepid Growth Partners co-founders Dr. Mark Machin, Mark Shulgan and Ajay Agrawal arranged a conversation with global trade expert Marc L. Busch, a professor of international business diplomacy at Georgetown University. On the agenda: How may trade conflicts affect technology entrepreneurs and the development of machine learning applications? What can global tech companies do to protect themselves? And what are Prof. Busch’s predictions for how this all plays out? 

EP 04 HOSTS

Marc L. Busch is the Karl F. Landegger Professor of International Business Diplomacy at the Walsh School of Foreign Service, Georgetown University, and a Global Fellow at the Wilson Center’s Wahba Institute for Strategic Competition.

Dr. Mark Machin, co-founder and managing partner, Intrepid Growth Partners

Mark Shulgan, co-founder and partner, Intrepid Growth Partners

Ajay Agrawal, co-founder and partner, Intrepid Growth Partners

LINKS

Derby Mill show website: https://insights.intrepidgp.com/podcast

Marc L. Busch’s personal website. Follow Marc on X and LinkedIn

Recent Marc Busch article on how “mode 5 services” can promote U.S. manufacturing without imposing new tariffs

DISCUSSION POINTS 

00:00 Introductions 

03:10 Ajay Agrawal introduces Professor Marc L. Busch 

03:54 Busch on the legal bases for tariffs and executive orders

09:56 Non-Tariff Barriers and Intellectual Property

19:56 Impact on Software and AI Companies

26:26 Advice for Canadian and European AI Companies

29:16 Stargate, tariffs on semiconductors and subsidies as offsets for harm

33:46 Predictions on how this will play out amid political considerations

37:04 Potential for retaliation via anti-trust measures

40:23 Role of the WTO and International Compliance

45:32 Advice for Canadian AI companies

53:54 Lighting round: What should Canadian entrepreneurs ask their politicians to do?

54:53 How can Canada and the UK prevent a brain drain to US?

55:59 What should UK entrepreneurs ask their government to do?

56:41 Wrap up

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.

In the first of our “unpacked” episodes, Intrepid partner Ajay Agrawal leads our senior advisors Rich Sutton, Sendhil Mullainathan and Niamh Gavin in a conversation that further explores the themes that arose in episode one. That episode featured a conversation with Rae Jeong, CEO of Maneva, which is using AI and reinforcement learning (RL) techniques to move factories toward autonomous operations. 

In this episode, the team discusses the importance of making factories more "RLable" to enable incremental changes and ultimately achieve radical improvements. We explore the importance of continuous training data, the role of humans in active learning, and the balance between exploration and exploitation. The conversation highlights the challenges of implementing RL in manufacturing, such as the need for selective instrumentation and the potential for synthetic data. 

EP 03 HOSTS

Ajay Agrawal, co-founder and partner, Intrepid Growth Partners 

Richard Sutton, pioneer of reinforcement learning and professor, University of Alberta

Sendhil Mullainathan, MacArthur Genius grant recipient and professor, MIT

Niamh Gavin, Applied AI scientist, CEO, Emergent Platforms

LINKS    

Derby Mill show website: https://insights.intrepidgp.com/podcast

The first episode featuring Maneva CEO Rae Jeong

Maneva AI website

Maneva CEO Rae Jeong LinkedIn

A short video about Maneva’s work transforming Laura Secord chocolate production

Rich Sutton’s home page. Follow Rich on X

Sendhil Mullainathan’s website. Follow Sendhil on X

DISCUSSION POINTS 

00:00 Introductions and opening credits 

01:39 Clip: Rae Jeong discusses Maneva's approach to autonomous factories

02:01 Rich Sutton comments on the challenge of active learning in operating factories

04:54 Niamh Gavin on the use of simulated environments for experimentation

06:29 Rich Sutton: “It’s hard to compete with a human” for experimentation

08:05 Can simulation actually recreate a factory in all its complexity?

09:42 Sendhil Mullainathan is confused where Maneva actually uses RL

10:41 Balancing exploration and exploitation

14:52 Discussion of temporal credit assignment in manufacturing 

15:54 Clip: Sendhil asks how Maneva uses labels and exploration

17:42 Clip: AI needs to conduct exploration to achieve continuous improvement 

16:34 Exploring the future of manufacturing with reinforcement learning

19:29 The challenge of making factories more “RL-able”

23:01 Why prediction tends to come before control 

28:55 Discussion of selective instrumentation and the role of humans

32:09 Sendhil asks, do you know why EKG leads are placed where they are?

34:28 Clip: Temporal credit assignment and taking RL to the limit in factories

38:28 Sendhil emphasizes the need for a CEO-level sale for RL in manufacturing

44:00 Challenges of fully instrumenting a factory

49:00 Algorithms identifying valuable measurements

52:42 Conclusion and final thoughts

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.

The Chinese tech start-up DeepSeek used reinforcement learning and other creative techniques to develop an ultra-efficient chatbot that was less costly to make, uses fewer chips and requires much less electrical power than better-known alternatives, such as OpenAI’s ChatGPT or Anthropic’s Claude.

To discuss the implications, Intrepid Growth Partners gathered the Derby Mill team, including the pioneer of reinforcement learning himself, Richard Sutton, as well as Sendhil Mullainathan and Niamh Gavin, all of whom are Intrepid GP senior advisors. The team joins show host and Intrepid partner Mark Shulgan along with special guest Kevin Bryan, economist and co-founder of the AI start-up, All Day TA.

Questions our hosts discussed:

* How can DeepSeek perform as well as OpenAI's most advanced technology for many tasks—but operate at a fraction of the cost?

* What potential economic effects could result from DeepSeek’s open-source code and technical specifications?

* What implications does DeepSeek have for investors seeking strategies to invest in the future of AI?

* We’ll also make some predictions about where things go from here.

LINKS

* Derby Mill show website: https://insights.intrepidgp.com/podcast.

* The DeepSeek academic paper that started it all. Plus: DeepSeek’s website.

* Find Kevin Bryan on X @afinetheorem and on Bluesky @afinetheorem.bsky.social. His website is kevinbryanecon.com.

* See Kevin Bryan’s start-up, All Day TA, which provides AI assistants for university professors.

* Rich Sutton’s home page. Follow Rich on X @RichardSSutton.

* Sendhil Mullainathan’s website. Follow Sendhil on X @m_sendhil.

* Ajay Agrawal’s website. Follow Ajay on X @professor_ajay.

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.

In the premiere of the Intrepid Growth Partners’ podcast, we meet with Rae Jeong, the CEO of Maneva AI, a Toronto-based company whose mission is to use artificial intelligence to make factories autonomous—completely run, maintained and improved by machine-learning algorithms and manufacturing robots. 

Rae joins our panel of experts:

Ajay Agrawal, co-founder and partner, Intrepid Growth Partners 

Richard Sutton, pioneer of reinforcement learning and professor, University of Alberta

Sendhil Mullainathan, MacArthur Genius grant recipient and professor, MIT

Niamh Gavin, Applied AI scientist, CEO, Emergent Platforms

“The ambition that we have… is to leverage some of the work that [Derby Mill host Richard Sutton] pioneered in reinforcement learning,” says Jeong, who foresees a future with zero workforce injuries and where the cost of production is simply the cost of energy.

LINKS    

Derby Mill show website: insights.intrepidgp.com/podcast

Maneva AI website

Maneva CEO Rae Jeong LinkedIn

A short video about Maneva’s work transforming Laura Secord chocolate production

Rich Sutton’s home page. Follow Rich on X

Sendhil Mullainathan’s website. Follow Sendhil on X

Intrepid Growth Partners is excited to release the official sizzle reel for our new podcast: The Derby Mill Series: Intrepid Pioneers of the Next Economy. Featuring discussions with entrepreneurs at the forefront of deploying machine intelligence and brainstorming sessions about where the technology will go—at the limit. 

 Intrepid Senior Advisors Rich Sutton (pioneer of reinforcement learning), Sendhil Mullainathan (MacArthur Genius recipient), and Niamh Gavin (Applied AI scientist) join Intrepid co-founder and partner Ajay Agrawal to explore the possibility frontier. Join them as they connect with guest founders and practitioners that are currently implementing AI-based solutions and pushing out the productivity frontier.