The Derby Mill Series: Pushing AI to the Limit

Atinary Technologies, based in Lausanne and Silicon Valley, is pioneering autonomous scientific experimentation with AI-driven robotic research labs. In this episode, our season finale, the Derby Mill panel welcomes Hermann Tribukait, co-founder and CEO of Atinary, whose vision is to use the company’s technology to exponentially accelerate the discovery of breakthrough molecules.
Named after the Spanish verb “atinar” — to hit a target — Atinary’s product is SDLabs, short for “Self-Driving Labs Platform,” a code-free agentic AI solution that enables scientists to optimize their experimentation workflows without coding or machine-learning expertise. The platform has been deployed across pharma, chemicals, and materials R&D.
Earlier this year, in a significant step from the digital to the physical, Atinary opened its first Self-Driving Lab in Boston, where it runs experiments autonomously 24/7. Reported efficiencies and scaling are impressive. Atinary says it can generate in a week the data that would take a grad student an entire Ph.D. For one client, Atinary’s self-driving experimentation cut the use of an expensive catalyst by 30x, reducing cost by 97% and reaction time by 50%.
Is autonomous scientific discovery realistic? And where might the technology go at the limit? “One place where all this leads,” observes Sendhil, “Chemistry will get one step closer to a solved field—and when chemistry gets close to a solved field, boy does that look completely different.”
PARTICIPANTS
Hermann Tribukait, co-founder, CEO and chair, Atinary Technologies Inc.
Ajay Agrawal, co-founder and partner, Intrepid Growth Partners
Sendhil Mullainathan, senior advisor, Intrepid Growth Partners, MacArthur Genius grant recipient and professor, MIT
Niamh Gavin, senior advisor, Intrepid Growth Partners, Applied AI scientist and CEO, Emergent Platforms
LINKS
Atinary website
Loïc Roch, Atinary CTO and co-founder, explains the technology
Atinary launches its first self-driving lab
Subscribe to The Derby Mill Series at our Substack (main site) or on YouTube, Spotify or Apple Podcasts
Derby Mill is created by the team at Intrepid Growth Partners and produced by Ghost Bureau.
DISCUSSION POINTS
00:00 Cold open
00:52 Explaining Atinary
03:40 Sample objectives
05:54 DMTA and learn
07:22 Efficiencies
09:29 Scaling challenge
12:39 Why run experiments?
15:12 A solved field
16:30 Chemistry and RL
17:47 GenAI lessons
18:44 Football fields
20:22 Chemistry as biology
21:29 Formulation challenge
23:15 Combinatorial search
26:52 Programmable biology
28:57 OFAT
30:44 Lightning round
33:12 Eroom’s law
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

In our latest episode, Intrepid’s Derby Mill podcast features the CEO and co-founder of UK-based PhysicsX, Jacomo Corbo, for a special in-person episode filmed at our Annual General Meeting in London. The interview, conducted by host Ajay Agrawal and panel members Sendhil Mullainathan and Niamh Gavin, comes just weeks after PhysicsX announced an oversubscribed $300 million Series C investment round that valued the company at $2.4 billion, which included Intrepid participation.
The PhysicsX origin story includes Corbo and his co-founder, Robin Tuluie, working on the Formula 1 racing circuit for the Renault team that won back-to-back world championships in 2005 and 2006. (Corbo took a year off from studying computer science for his Ph.D. at Harvard after the Renault team noticed his game-theory based research and asked him to apply it to F1 racing environments, eventually becoming Renault’s chief race strategist.)
Corbo and Tulule started PhysicsX in 2019 and now say that their machine-learning foundation models can accelerate simulation for industrial engineering by between 10,000 to 100,000 times. That means more efficient product design cycles, Corbo says, delivered under faster timelines, which in turn translates into better products. Based in London and New York with more than 300 employees, PhysicsX counts among its clients leading organizations in aerospace & defense, automotive, semiconductors, materials, and energy & renewables
On the agenda in today’s discussion: How do the PhysicsX AI-enabled emulation models differ from conventional simulation? What capabilities does better simulation unlock for today’s industrial engineers? And how will tomorrow’s products differ as a result? In our first-ever in-person episode filmed in London, the Derby Mill tackles it all.
PARTICIPANTS
Jacomo Corbo, co-founder and CEO, PhysicsX
Ajay Agrawal, co-founder and partner, Intrepid Growth Partners
Sendhil Mullainathan, senior advisor, Intrepid Growth Partners, MacArthur Genius grant recipient and professor, MIT
Niamh Gavin, senior advisor, Intrepid Growth Partners, Applied AI scientist and CEO, Emergent Platforms
LINKS
PhysicsX website
PhysicsX press release for its last investment round, in which Intrepid participated
Harvard School of Engineering alumni profile on Jacomo Corbo
Subscribe to The Derby Mill Series at our Substack (main site) or on YouTube, Spotify or Apple Podcasts
Derby Mill is created by the team at Intrepid Growth Partners and produced by Ghost Bureau.
DISCUSSION POINTS
00:00 Cold open
01:10 PhysicsX described
03:10 AI Numerical Inference
04:00 Governing Physics Equations
05:57 Engineering Intuition Limitations
08:07 Optimization Computational Barriers
10:01 Democratizing Engineering Simulation
11:05 Deep Learning Models
11:26 Empirical Data Training
12:28 Industrial Customer Segments
14:41 Approximating Ground Truth
15:55 Expanding Design Space
17:01 Dark Factory Vision
18:56 Key Breakthrough Requirements
21:05 Training Data Scarcity
22:10 Reinforcement Learning Approach
24:59 End-of-One Customization
26:27 Influencing Physical World
28:31 Scaling Pre-trained Models
29:21 Model Attention Mechanisms
30:35 High Dimensional Physics
32:41 Reducing Exploration Costs
35:12 Manufacturing Cost Curves
36:52 Sim-to-Real Performance
38:15 Improving Causal Inference
38:40 Quantum Computing Integration
40:01 Innovativeness and Novelty

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

UK-based Relay is a fast-growing delivery company whose mission is to lower the cost of last-mile fulfillment. CEO and co-founder Jonathan Jenssen say his team is reinventing networks for modern e-commerce. Relay covers about 23% of the UK, has a 4% share in operating postal codes and includes on its customer list such brands as Vinted, Temu, Shein, TikTok Shop, ASOS and Next.
One big challenge for Relay is that the last mile amounts to ~70% of total delivery cost. Also confounding cost decreases is the fact that the parking-to-doorstep journey amounts to ~80% of last-mile time. That all means that about half of the total cost of package delivery is in the last 100 metres of the delivery.
To decrease those costs, Jenssen’s team is harnessing the power of AI to make the last few hundred metres of the delivery more efficient. Gone are the days of hub and spoke routing and half-empty delivery trucks. Algorithms provide couriers with next-best-actions, in the form of turn-by-turn driving and parking directions, tips for building entry, and doorstep handover. They also use dynamic per-route pricing and live supply/demand matching, dynamic routing and an asset-light pit stop network of delivery depots that currently includes 10,000 local shops.
The Derby Mill team explores where Relay’s efficiency improvements may go at the limit of AI’s capabilities. Where could robots fit in? What if Relay had access to more consumer information? Could that allow the company to actually predict customer requests? And what does all this have to do with something called “the beer game,” an MIT Sloan business case? These questions and more are answered in episode 31 of The Derby Mill Series.

HOSTS AND PANELLISTS
Jonathan Jenssen, co-founder and CEO, Relay
Ajay Agrawal, co-founder and partner, Intrepid Growth Partners
Richard Sutton, senior advisor, Intrepid Growth Partners, 2024 Turing Award recipient, pioneer of reinforcement learning and professor, University of Alberta
Sendhil Mullainathan, senior advisor, Intrepid Growth Partners, MacArthur Genius grant recipient and professor, MIT
Niamh Gavin, senior advisor, Intrepid Growth Partners, Applied AI scientist and CEO, Emergent Platforms
Suzanne Gildert, CEO, Nirvanic Consciousness Technologies, quantum physicist, co-founder of Sanctuary AI and Kindred

LINKS
Relay website
MIT Sloan beer distribution game
Subscribe to The Derby Mill Series at our Substack (main site) or on YouTube, Spotify or Apple Podcasts
Derby Mill is created by the team at Intrepid Growth Partners and produced by Ghost Bureau.

DISCUSSION POINTS
00:00 Cold open
00:43 Introductions
01:35 Explaining Relay
05:02 Relay video
06:28 Clarifying questions: Rich
09:50 Optimization parameters
11:55 Clarifying question: Suzanne
16:22 Sendhil Q: Tech unlock
22:50 Hubs & spokes
26:40 Niamh: Pricing routes
29:33 Reward signals
32:23 Suzanne on robots
36:30 Coordination elements
39:03 Beer game
44:02 Information bottlenecks
47:45 Agentic info passing
49:30 JJ responds
51:40 Lightning round
53:33 Agentic AI agency
55:51 Supply chains
57:20 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.


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

How are companies using AI to improve the healthcare journey for consumers? And how will things evolve at the limit, where wearable technology integrates with electronic medical records and medical agentic AI analyzes everything on the fly to issue recommendations and improve outcomes? In other words, what are the implications of everyone becoming equipped with their own superhuman medical agent? 
Seen one way, a superhuman medical agent is what League is aiming to develop. Founded in 2014 in Toronto, League operates an AI-powered healthcare platform for insurance companies and other payers or providers. The platform allows users to manage health benefits as well as their own healthcare. As of early 2026, the company has more than 60 million contracted users and more than a billion consumer interactions annually. Customers include Highmark Health, Manulife, Shoppers Drug Mart, and Medibank. Last year, League gave 100 million healthcare recommendations, of which people clicked through or followed through about 65% of the time.   
In this episode, the Derby Mill team explores where League’s technology might go at the limit with League founder and CEO Mike Serbinis, who aims to expand the company’s capabilities to create a 24/7 infinite care platform—basically, an all-knowing doctor who knows as much as possible about you, your healthcare data, and the science of medicine. It’s a fascinating conversation that sees Sendhil observing how little the medical profession currently knows about optimizing health, or patients themselves, and Ajay challenging Rich to decide whether superhuman AI doctors would be better than having a doctor as a spouse today. (Rich says yes.)   
More on our guest: A serial entrepreneur, Michael Serbinis has also founded and run Kobo, the digital reading company, cloud storage pioneer, DocSpace, and Critical Path, a messaging service. Serbinis is chair of the board for the Perimeter Institute and on the board at the Vector Institute for Artificial Intelligence. He sits on Canada's AI Strategy Task Force.

HOSTS AND PANELLISTS
Mike Serbinis, founder and CEO, League
Ajay Agrawal, co-founder and partner, Intrepid Growth Partners
Richard Sutton, senior advisor, Intrepid Growth Partners, 2024 Turing Award recipient, pioneer of reinforcement learning and professor, University of Alberta
Sendhil Mullainathan, senior advisor, Intrepid Growth Partners, MacArthur Genius grant recipient and professor, MIT
Niamh Gavin, senior advisor, Intrepid Growth Partners, Applied AI scientist and CEO, Emergent Platforms
Suzanne Gildert, CEO, Nirvanic Consciousness Technologies, quantum physicist, co-founder of Sanctuary AI and Kindred

LINKS    
League website. League explainer video
Subscribe to The Derby Mill Series at our Substack (main site) or on YouTube, Spotify or Apple Podcasts 
Derby Mill is created by the team at Intrepid Growth Partners and produced by Ghost Bureau. 

DISCUSSION POINTS
00:00 Cold open
00:38 Introductions
01:19 League explainer
01:55 Serbinis explains League
05:28 League’s AI today
08:09 More AI: Content recommendations
12:00 League’s data inputs
13:55 AB testing
19:10 Healthcare archetypes
22:15 Max health v. max engagement
25:30 Business model
27:00 At the limit: Rich 
29:00 Married to a doctor
29:42 At the limit: Sendhil
31:10 Healthcare becomes you
33:37 ROI is huge
38:06 Suzanne: Robot paramedics
42:53 Serbinis responds
46:20 Infinite care team
49:16 Sendhil’s 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.


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

A response from the Derby Mill team of AI experts to Anthropic co-founder Jack Clark’s recent Substack essay, “AI systems are about to start building themselves.” In the essay, Clark predicts that “no-human-involved AI R&D - an AI system powerful enough that it could plausibly autonomously build its own successor - happens by the end of 2028.” Clark notes: “If that happens, we will cross a Rubicon into a nearly-impossible-to-forecast future."
With that in mind, Ajay tees up a discussion among Rich, Niamh, Sendhil and Suzanne. Is Jack Clark correct that we’re heading toward a future of self-replicating, self-optimizing AI? What are the implications? Does that actually lead to the singularity? And should we, as Rich Sutton says, treat AI the way a parent treats a child, recognizing that mistakes are inevitable? 
“There’s some spookiness being implied here,” Mullainathan says, “about it coming alive and taking over a whole set of decisions we didn’t intend to cede it… and if that’s the case, that freaks me out, too.” 
“The thing that they’re scared about…” says Niamh Gavin. “It’s the fact that you have, in essence, an irreversible positive feedback loop of successive self-improvement cycles, that accelerate it toward what they call a singularity, whereby artificial intelligence exceeds human intelligence and control.”
Are the concerns outlined by Jack Clark warranted? Or are the fears about a harmful singularity overblown? The Derby Mill experts provide their views in our latest episode. 
Finally, another fascinating thread in the episode comes from Suzanne Gildert. “It all comes down to the reward function,” says Gildert. “So there’s this thing that [AI is] still limited by us telling them what to do, because we’re the ones who want something… But that’s eventually going to break because the way the whole reward function in ML works at the moment — it’s all based on our economy… It’s based on people exchanging money for goods and services. All that’s going to break if people can’t work anymore... So that’s why I think we really have to understand what the reward function should be, because the way we’re implementing it now is not going to work beyond the point where [AI] can do all human labour for us.” 
HOSTS AND PANELLISTS
Ajay Agrawal, co-founder and partner, Intrepid Growth Partners
Richard Sutton, senior advisor, Intrepid Growth Partners, 2024 Turing Award recipient, pioneer of reinforcement learning and professor, University of Alberta
Sendhil Mullainathan, senior advisor, Intrepid Growth Partners, MacArthur Genius grant recipient and professor, MIT
Niamh Gavin, senior advisor, Intrepid Growth Partners, Applied AI scientist and CEO, Emergent Platforms
Suzanne Gildert, CEO, Nirvanic Consciousness Technologies, quantum physicist, co-founder of Sanctuary AI and Kindred
LINKS    
The Jack Clark Substack post that triggered the discussion. Jack Clark is on X @jackclarkSF
Subscribe to The Derby Mill Series at our Substack (main site) or on YouTube, Spotify or Apple Podcasts. We post highlights from the show on YouTube.  
Derby Mill is created by the team at Intrepid Growth Partners and produced by Ghost Bureau. 
DISCUSSION POINTS
00:00 Cold open
01:06 Jack Clark post
02:56 Rich Sutton reaction
08:37 Sendhil’s reaction
16:22 Suzanne’s reaction
18:14 Niamh’s reaction
23:48 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.


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