South Park Commons has backed humanoid robots, autonomous satellites, and the data layer for robotic surgery, often before the founders had a product, a market, or even a company name. Dylan, investor at South Park Commons, talks with Ilir about why technical depth can’t be taught but storytelling can, why most PhD spinouts fail before they start, and why he’s not worried about warehouse robotics.

DYLAN is an investor at South Park Commons, a 10-year-old technical community turned early-stage fund with hubs in San Francisco, New York, and Bangalore, providing early-stage venture capital into hard tech and physical AI.

Give me the short version of what South Park Commons actually does.

We started as a community for the most curious technical minds to explore the frontier, a lot of early machine learning research, some blockchain. People from that early cohort went on to found Anthropic, OpenAI, the Ethereum Foundation. Post-GPT, our thesis shifted. Software is commoditized now. We’re more interested in the intersection of atoms and bits, not necessarily hardware, but things that are harder to build precisely because AI made software easy. We bring technical people into three physical spaces, then we’re the first check into teams building hard companies who are willing to sign up for the difficulty from day zero. We’ve funded humanoid robots, data center robots, drones, surgical robotics, and the software layer underneath all of it.

When there’s no product yet, sometimes no company yet, how do you actually evaluate someone?

We underwrite depth and clarity, the ability to train foundation models, a real pedigree in mechanical engineering. But having them in our community for a while matters more. LOIs aren’t that valuable to me. What’s interesting is what they can build with less. We have a team going for FDA approval on a surgical data-capture device, most investors won’t touch anything that needs FDA approval. We have another team building embedded cameras for construction hard hats, but instead of building custom hardware for years, they proved value with off-the-shelf gear first. And we backed a humanoid robotics team that built two fully autonomous robots, not deployed yet, but working, live, not a demo video nobody can verify. That’s the difference: proving you can do one or two of these before you ask for the money to scale. There are other ways to earn that credibility, too. The founder of our satellite company was one of five people on Earth who had done autonomous docking in space, from his time at Northrop Grumman. He didn’t need a demo, he’d already proven it in another capacity.

What actually separates a founder with a great idea from an exceptional founder?

Our bet is that you can’t teach execution, but you can potentially teach storytelling. If we find technically excellent builders who can actually execute, can we help them get better at world-building? The founders of Physical Intelligence were very academic when they started, they learned how to be CEOs, how to fundraise. We’re looking for people who spike on technical depth and execution, hoping we can bring the storytelling up to a level where it’s not embarrassing. It doesn’t have to be a pitch, a white paper, a demo, a hardware prototype all count. It’s proving you can build and articulate something that doesn’t exist yet. We use a framework internally: a tell me founder versus a show me founder. If storytelling isn’t your superpower, become a show me founder instead, get as creative as you can with what you can actually demonstrate, even without the money for a real product yet. Don’t let perfection kill momentum.

What’s the single biggest thing that keeps brilliant researchers from ever leaving academia to found something?

A tenured professor finds a postdoc, convinces them to spin the lab’s research into a company, takes a chunk of equity as co-founder, and lets the postdoc do all the work and be the face of it. It’s a free lottery ticket for the professor, cheap to test, low cost if it fails. That pushes postdocs into founding before they’re ready. Some of them are ready. Most would be better off getting real operating experience first, hiring, managing, shipping, and then going back to their old advisor later if it still makes sense. We funded a CMU PhD who went to work at NVIDIA for a couple of years first, then came back to his advisor to build the company. He’s better for it.

“Venture capital is risk capital. The question is: how many miracles need to happen for this to work?” South Park Commons’ framework for underwriting hard tech; count the miracles required, then find the team that only needs one.

How do you know a team can cross from an impressive demo to real deployment?

We ask how many miracles are required. Can they de-risk the technical miracle, can they build what they say they’ll build? That’s fine if it’s the only one. It gets harder when there’s also no market yet. Software for robotics companies that don’t exist yet is two miracles. We want teams where the only open question is technical, where if I’m convinced the demand is real, the only bottleneck is that nobody has built this affordably before, and I believe they can.

Robotics is capital-intensive and slow. What has to be true for you to make that trade-off versus a pure software bet?

Not every hard tech company needs to build its own hardware. A lot of it can be dumb, off-the-shelf hardware, the value is in the operating system, the data, or the simulation environment on top. Our satellite company has to build its own hardware because nobody else builds reusable autonomous-docking satellites. Our surgical data company doesn’t, they don’t want to perform surgery, they want better data for autonomy that’s already happening. Founders need extreme clarity on why they need to build their own hardware, because that’s a much bigger capital ask. We’re happy to underwrite technical depth with a first check of one to five million dollars, but that check alone won’t get a hard tech team very far. Our satellite company, if it works, will need to raise something like four to five hundred million dollars over the life of the company. So it’s not just “can they build it”, it’s “can they also raise at that scale,” and that’s something we can coach on but they ultimately have to be great at themselves. I’m less excited about generic warehouse and packaging robotics, that’ll probably end up being one or two large players, maybe Amazon and Tesla. I’m more interested in high-cost, high-skill, low-supply labor: welding, precision manufacturing, composite work. And I’m not a believer in consumer robotics yet, the willingness to pay isn’t there.

You call yourselves a community, not just a fund. Is that actually an investment edge, or is capital still the differentiator?

Capital is a commodity, everyone’s investing in hardware now. The community is the edge. We’ve seen people come in wanting to found something, realize they’re not meant to be founders, and end up as head of hardware or CTO at one of our other companies instead. Because everyone’s been through our space, they become customers of each other, they share insight with each other. And when we’re evaluating a team outside our own expertise, we can ask other PhDs in the community what they think of the approach. That’s a depth of diligence capital alone doesn’t buy.

You could move up-market into Series B and C, where the checks are bigger. Why stay this early?

We take all the risk, we’re investing when there’s the least to go on. That builds a different kind of relationship with founders than their later investors get. It’s not that later money feels transactional exactly, but there’s more at stake for those investors, and founders sense it. Our founders come back to us first when things aren’t going well, because they feel safe doing that. The other thing I’ve learned moving from software into hardware is that the feedback cycle is so much longer. With software you get customers and revenue fast and you can raise again fast. Hardware takes way longer, it could be a year, could be a year plus before a team is in a position to raise more. We have to be patient capital and let founders know we’re comfortable with that timeline, because I don’t think every investor who now says they want to do hard tech actually knows how long this stuff takes.

Where should ambitious researchers be spending the next six to twelve months?

Getting out of the lab and talking to customers, not necessarily chasing LOIs, but validating that what they’re building is actually needed and that a hundred other people aren’t doing the same thing. That used to matter less; the field is far more crowded now. I’d also pay attention to what the major labs might do in robotics themselves, Anthropic, OpenAI, Amazon, Tesla have all signaled intent here, so know what’s uniquely yours. And drop the obsession with building your own hardware when you don’t need to. Some of our best teams have this instinct to build everything themselves because it’s an easier problem than walking into a factory and talking to people they’ve never met. Don’t reinvent the wheel, repurpose it.

Update:

Since this conversation, South Park Commons has closed its largest fund to date: $575 million for Fund IV, more than double the $275 million Fund III, bringing assets under management to roughly $2 billion.

I sat down with Silviu Homoceanu, Co-Founder & CTO of Almetra, to talk about growing up with computers in post-communist Romania, eight years inside VW's self-driving programme, and why the data most factories run on doesn't describe what actually happens on the line.

Silviu learned programming by translating English menus on his father's work PC, was selling self-built accounting software at 15, and came to Germany for a PhD in machine learning. He joined VW Group Research in 2014 and spent the next eight years on perception for autonomous driving, before a question from a manufacturing colleague sent him into factories for seven months.

Today, Almetra (formerly Deltia) combines computer vision, sensors and machine data to show how production actually runs instead of how it was planned. The company raised a $19M Series A led by blisce/ in June 2026 and is opening an office in Boston.

We cover why factory data is often wrong, what happens when the MES reports a reality that isn't real, the 100% defect detection problem, why so many robotics startups get stuck in pilot purgatory, what reliability really means on a running line, and Almetra's move from measuring manual work to automating parts of it.

Silviu Homoceanu:

https://www.linkedin.com/in/silviu-homoceanu-893b7a5

Almetra:

https://www.almetra.ai/

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- CoreWeave — The Essential Cloud for AI.

CoreWeave sponsors this podcast.

Join CoreWeave at #FullyConnected:

https://coreweave.com/fully-connected-2026/agenda

👉 Free code: FCLR26

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00:00 Meeting at the AI Campus in Berlin

00:47 Who Silviu is and what Almetra does

02:44 Romania after 1989 and his father's first PC

06:08 A 286 at home, MS-DOS, then Linux

08:04 Running a computer business at 15

10:21 High school nights in the computer lab

14:44 The mentor who said: study abroad

16:15 Flipping burgers to see the MIT campus

18:58 Romania vs Germany: theory against practice

20:23 First job: cluster computing for bridge simulations

26:20 Why computer networks stopped being enough

28:00 The exam answer that led to a PhD offer

30:47 Percentages or factors: how he picks problems

36:20 VW Research comes knocking in 2014

39:05 The meeting with manufacturing that changed everything

39:52 Seven months in factories: the data is wrong

40:30 Merantix, meeting Max, and founding the company

42:08 The Goal, bottlenecks, and life on a shop floor

43:31 The 100% defect detection problem

46:14 When the MES reports a reality that isn't real

48:09 Automating stations and the robotics angle

49:33 99.999% and why factories say no to startups

52:47 What is actually automatable today

55:07 The Series A, blisce/, and the move to Boston

59:21 Closing

Follow 22Astronauts:

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I know. Wild.

I couldn’t decide what to send. It had to be something I would read myself, and I only read emails that are short and teach me something.

Robotics gets built in places that rarely talk to each other: the companies deploying it on the factory floor, the labs where the research happens, and the funds deciding what gets money. Every issue of Signal brings you one voice from each.

An operator on what happened when they deployed, where it broke and what it cost.
A researcher on work that’s about to leave the lab, and what it means for anyone building or buying robots.
An investor on where they’re putting money right now, and what they’re passing on.

The first issue goes out next Tuesday.

I’d also like this to go both ways. If something is useful, or you think it’s wrong, just reply to this email. I read every reply, and some of you will end up in a future issue.

Talk soon,
Ilir

P.S. I’m in San Francisco next week and really excited to be back in the city. If you’re there too, I’d love to meet!! I’m partnering with CoreWeave on their Fully Connected conference, so if you want to come, code FCLR26 gets you a free pass (normally $899).

I sat down with Saba Khalilnaji, Founder & CEO of Almond, inside the company's San Francisco factory to talk about building robots, scaling hardware, factory automation, and why Almond wants to become "America's robot factory."

Saba started building robots as a kid, later spent nearly seven years at DoorDash, and eventually left to work on one of the hardest problems in automation: getting robots to operate reliably in factories where the environment and tasks constantly change.

Today, Almond is building Axol, a dual-arm robot designed for physical AI, data collection, teleoperation and real-world deployment.

We cover why traditional automation struggles in high-mix manufacturing, what Saba learned from DoorDash, the rise of "neo-integrators," why deployments are Almond's North Star, manufacturing robots in San Francisco, and Almond's work on teaching Axol to help build Axol.

Saba Khalilnaji:

https://www.linkedin.com/in/saba-khalilnaji/

Almond:

https://www.almond.bot/

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• CoreWeave — The Essential Cloud for AI.

Join CoreWeave at Fully Connected in San Francisco, bringing together the people building the next generation of AI infrastructure and applications.

Learn more: https://www.coreweave.com/fully-connected-2026

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00:00 Inside Almond's San Francisco robot factory

01:02 A first look at Axol

02:12 Building robots as a kid

07:50 Science Olympiad and early robotics

11:56 Studying bioengineering at Berkeley

14:58 From robotics to DoorDash

18:19 What DoorDash taught Saba about moving fast

22:37 Why he left to start a company

25:10 How your environment shapes ambition

29:28 Discovering the automation problem inside factories

31:06 Why traditional factory automation struggles

34:49 What Almond is building

36:03 The rise of the "neo-integrator"

38:36 Why deployments are Almond's North Star

40:17 Building America's robot factory

43:09 Why Almond believes in owning the full stack

44:05 Teaching Axol to help build Axol

46:09 Saba's advice to his younger self

47:25 Why it's called Almond

49:25 Closing

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For over a decade, Mario’s research has focused on using AI not just to analyze physics data, but at a conceptual level; building AI explorers that discover completely novel experimental setups, optics, and quantum hardware. In July 2026, alongside CEO Jonathan Klimesch and CTO Sören Arlt, he co-founded Feyer GmbH: a frontier AI lab for automated physical invention.

As one of only 10 European teams selected for SPRIND's €125M Next Frontier AI Challenge (€3M non-dilutive seed phase), Feyer couples neural explorers with ultra-fast, differentiable physics simulators. Their mission is to autonomously invent next-generation industrial hardware (from advanced microscopy and lasers to lithography and quantum sensors) scaling Europe's high-tech manufacturing strengths.

Feyer GmbH: https://feyer.ai

Mario Krenn: https://mariokrenn.wordpress.com

Ilir on X: https://x.com/IlirAliu_

Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/

Timestamps:

0:00 "AI that invents industrial hardware": What is Feyer GmbH?

3:08 How the Cyber Valley ecosystem turned an academic researcher into a founder

5:43 Growing up in Austria: Programming operating systems at age 10

11:31 Stephen Hawking, mysteries of space, and choosing physics over engineering

15:01 Joining Anton Zeilinger's lab in Vienna: "What is the next to the next step?"

18:33 March 2014: Writing a program to design a quantum experiment humans couldn't solve

21:58 Finding solution.txt & the realization that computers can be creatively scientific

28:28 Moving to Toronto & Vector Institute: Learning AI material discovery from Alán Aspuru-Guzik

34:49 Returning to Europe & building the Artificial Scientist Lab in Erlangen & Tübingen

37:47 Asking questions to the universe: Why physics experiments are fundamental

43:35 Breaking resolution limits in microscopy & applying AI to industrial high-tech

45:49 Winning SPRIND's Next Frontier AI Challenge & founding Feyer GmbH

47:25 Scaling European hardware strengths: Semiconductor, photonics, and optics leadership.

Instead of memorizing internet-scale text patterns or relying on millions of trial-and-error attempts, kausable’s models (based on the Prior-data Fitted Network / PFN approach) learn abstract cause-and-effect structures from synthetic data—enabling them to adapt to entirely new physical environments in-context from just a handful of examples.

After working in computer vision under Prof. Björn Ommer at Heidelberg University (alongside the future Black Forest Labs founders) and serving as Head of AI at AskUI, Johannes co-founded kausable with Dr. Benjamin Herdeanu (CTO) and Gregor Ramien (COO). The company recently raised a €12M seed round led by UVC Partners and Entourage, backed by top-tier angels from OpenAI, DeepMind, Black Forest Labs, and Neura Robotics.

kausable: https://kausable.ai

Johannes on LinkedIn: https://www.linkedin.com/in/jhaux/

Ilir on X: https://x.com/IlirAliu_

Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/

Timestamps:

0:00 Building foundation models in the Cyber Valley deep-tech ecosystem

1:25 Working in Prof. Ommer’s lab & meeting the future Black Forest Labs team

3:48 Early startup lessons at Sysmagine & AskUI

4:09 Why internet-scale LLMs aren't enough for general-purpose physical intelligence

4:52 Discovering Prior-data Fitted Networks (PFNs) & founding kausable

7:00 "I was a bad student": Why structure and habits matter more than raw talent

12:19 Wanting to be a movie director before choosing physics at Heidelberg

18:37 Quitting academia right before COVID lockdowns & taking the entrepreneurial leap

27:38 Why community & ecosystem matter for European deep-tech startups

29:26 How humans actually learn vs. brute-force reinforcement learning in robotics

32:01 In-context learning: Adapting to distribution drift & new sensors without retraining

33:47 TipPFN & predicting critical transitions: Seizures, blackout risks, and physical dynamics

37:37 Michael Black's perspective: Why Europe is a strong launchpad for disruptive AI

41:37 Exploratory tech vs. concrete customer problems: Making the "faster horses" bet

47:17 Moving from a research lab to early design partners

50:36 "Don't fake it, but think big": Advice for European deep-tech founders

With nearly 30 years of experience building humanoid robotics (from human-rated space manipulation at NASA Johnson Space Center to subsea autonomous robots) Nic brings a veteran, unfiltered perspective to the commercial humanoid market.

Together with Co-Founders Dr. Jerry Pratt (CTO, ex-IHMC & Figure AI) and Jide Akinyode (COO, ex-NASA & Nauticus), Persona AI is building modular industrial humanoids for heavy, labor-constrained industries. Rather than chasing general-purpose home helpers or $15/hr warehouse sorting, Persona targets skilled industrial trades—starting with autonomous welding humanoids for shipbuilding in partnership with HD Hyundai and POSCO.

Persona AI: https://persona.ai/

Nic on LinkedIn: https://www.linkedin.com/in/nicolaus-radford/

Ilir on X: https://x.com/IlirAliu_

Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/

Timestamps:

0:00 Why the commercial humanoid robotics market has it completely wrong

3:17 Growing up in rural Indiana & fixing mechanics out of necessity

5:31 Decathlon, severe arthritis & learning "tenacity squared" as a founder

8:16 CB radios, 286/386 PCs, and entering the early Internet at Purdue

12:35 The 2000 Wired cover that sparked a 30-year humanoid obsession

15:23 Getting lost at NASA JSC and stumbling upon Robonaut 1

23:18 Camping in the Texas desert: Building amateur rockets during the early SpaceX era

25:25 Why networking is the lifeblood of deep tech partnerships

28:00 Storytelling for VCs: Why presentation skills are learned, not innate

30:50 From 15 years at NASA (Robonaut 2 & Valkyrie) to becoming an entrepreneur

35:47 Taking space tech underwater: Building Aquanaut & taking Nauticus public on NASDAQ

36:41 Partnering with Jerry Pratt to start Persona AI

37:36 Why general-purpose "Me-Too" robots face an ROI dead end

38:37 Targeting skilled trades: Why shipbuilding and heavy industry need humanoid welders

41:46 GM assembly lines & the brutal reality of automotive cycle times

44:42 The home humanoid myth: "Can we all just stop this? There is zero ROI."

48:34 First-time founders vs. Veteran founders: Tech obsession vs. Cash flow & GTM

49:50 Raising a $42M pre-seed & closing a $100M+ seed round

51:20 Delivering humanoid welders to HD Hyundai in 24 months

52:35 "Look what we built in 90 days": Calling out fake startup timelines

With over 54,000 Google Scholar citations, Jan is one of the world’s most influential robot-learning pioneers; having co-developed foundational algorithms like Natural Actor-Critic, REPS, and Probabilistic Movement Primitives (ProMPs).

IAS Lab: https://ias.informatik.tu-darmstadt.de

Jan's Website: https://www.jan-peters.net

Ilir on X: https://x.com/IlirAliu_

Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/

Timestamps:

0:00 Combining machine learning with physical robotics before it was cool

4:05 Studying 4 Master’s degrees & dodging the German military draft

8:27 Why DLR and ATR in Japan were lightyears ahead of German universities

12:37 Meeting Kawato, Chris Atkeson, and Stefan Schaal in a bamboo forest

17:10 Turning down Stanford and CMU for a smaller lab at USC

21:46 How double-blind reviews leveled the academic playing field

23:44 Injecting physical mechanics into reinforcement learning

26:01 How 9/11 permanently shifted American culture and optimism

29:40 Returning to Max Planck: The "monastery" era of AI research

35:14 Why being authentic & speaking your mind wins long-term respect

44:01 Surviving hostile German hiring commissions in the 2000s

47:55 Why TU Darmstadt became Europe’s secret powerhouse for physical AI

57:39 High-speed table tennis, juggling, and the limits of robot perception

58:32 Raising €2M for humanoids and fighting 4 years of European bureaucracy

65:49 German red tape vs. American risk-taking: What's holding Europe back?

68:33 Why TU Darmstadt rivals top US universities in AI breadth

Backing their bet that tabletop manufacturing cells beat humanoids for real-world reliability, MicroFactory is backed by Naval Ravikant and Hugging Face CEO Clément Delangue, using continuous human teleoperation feedback loops to reach near-100% precision.

MicroFactory: https://microfactory.com

Igor on X: https://x.com/ihorbeaver

Ilir on X: https://x.com/IlirAliu_

Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/

Timestamps:

0:00 Why we don't need humanoid robots for tabletop assembly

2:24 Inspired by "Back to the Future": Building pinball machines and telescopes at 15

6:30 Creating indie games and viral social media apps for 1M daily users

11:40 The pain of physical hardware: Running a wedding light startup in Ukraine for 9 years

21:16 Moving to San Francisco to be at the frontier of physical AI

32:06 Discovering the problem: Why hardware startups struggle with assembly

36:18 The viral photo frame demo that launched MicroFactory at Founders, Inc.

39:38 Why big AGI labs ignore precision and 99.9% reliability

43:07 Beyond pure data collection: Why reinforcement learning damages hardware

45:00 Human-in-the-loop DAgger: Correcting errors on Jetson Nano for $5 per retraining

52:15 Positive ROI vs. perpetual demos: Why shipping matters

62:41 Advice to founders: Change your environment if your experiments aren't working

Formerly a researcher at UW and the Allen Institute for AI (Ai2), Jiafei co-led the open-source "MolmoAct" and "MolmoAct2" robotics foundation models; action reasoning models designed to provide a transparent, fully reproducible alternative to proprietary "black box" physical AI systems.

MAGIC Lab: https://magic-ailab.github.io/

RoboPapers Podcast: https://www.youtube.com/@RoboPapers

Jiafei on X: https://x.com/DJiafei

Ilir on X: https://x.com/IlirAliu_

Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/

Timestamps:

0:00 Why true robotics progress requires radically open science

1:45 The RoboPapers podcast: Asking the hard research questions

3:34 Building my first mobile manipulator from PVC pipes at 16

7:19 The Leap Motion epiphany: Controlling plywood with Arduino

10:15 Writing the survey that introduced "Embodied AI" to Singapore

14:55 Why Singapore is investing heavily in physical intelligence

21:36 Moving from cognitive science to robotics at UW with Dieter Fox

24:51 AR2-D2: Collecting robot manipulation data using AR & inpainting

28:41 The PhD shift: Learning how to think and communicate science

32:49 Why robotics isn't ready for industry to consolidate yet

39:11 Vision researchers are migrating to ICRA: The field is shifting

41:26 Running open VLA models on a $200 SO-100 hobbyist robot

46:38 Leaving Ai2: The philosophy behind fully open data and weights

50:52 Launching the MAGIC Lab at NUS: Finding efficient model architectures

54:36 Why robotics won't have its ChatGPT moment without open source

The ATARI Lab focuses on AI planning in dynamic environments, bridging optimal control with modern foundation models to make general-purpose humanoid robots safe, adaptable, and truly autonomous.

ATARI Lab: https://ce.cit.tum.de/en/aipd/

Majid on LinkedIn: https://www.linkedin.com/in/majid-khadiv-a92a2a62/

Ilir on X: https://x.com/IlirAliu_

Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/

Timestamps:

0:00 Building national humanoids in Iran without resources

2:11 Why playing soccer beats being a tech kid

3:03 How my sister proved you can break through the entrance exam

5:10 Thinking deeply about a single math problem for five hours

7:02 Visualizing physics: Why Newtonian mechanics just clicked

8:38 Why traditional schooling fails curious minds

10:50 Should AI tutors replace the classroom experience?

18:48 Discovering the intersection of control and dynamics in robotics

20:30 Building the Surena III humanoid from scratch with PhD students

23:44 Honda's Asimo vs our 98kg robot: The engineering reality

26:01 How the 2015 DARPA Robotics Challenge humbled everyone

28:55 Why grades don't reflect true deep-thinking ability

34:01 How Ludovic Righetti transformed my research mindset at Max Planck

37:09 The price to pay for staying at the forefront of academia

52:19 Why general purpose humanoids are the holy grail

58:26 The hand is the pinnacle of human intelligence

62:03 Fusing language models with evolutionary search for physics planning

64:27 Why safety and OOD cases will keep humanoids out of homes for years

70:19 Advice to my younger self: Have confidence in your curiosity

RoboStrategy already holds concentrated positions in industry-defining companies including ⁨Figure AI, ⁨Apptronik, ⁨Standard Bots⁩, and Dyna Robotics.

It is the first publicly listed vehicle purpose-built to give investors liquid exposure to private robotics and physical AI giants, applying the MicroStrategy capital-markets playbook to automation.

RoboStrategy: https://www.robostrategy.ai

Andrew on X: https://x.com/Rewkang

Andrew on LinkedIn: https://www.linkedin.com/in/rewkang/

Ilir on X: https://x.com/IlirAliu_

Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/

Timestamps

0:00 Why we built a public fund for private robotics

0:35 Building the MicroStrategy for robotics

1:22 Escaping the tiger parent trap

2:11 Video games are elite training for game theory and critical thinking

6:30 Learning arbitrage in RuneScape and Neopets

7:59 Don't believe experts just because of their titles

10:24 Why investing is just a video game with real money

12:40 Harvard nutrition professors don't know what they're talking about

18:48 Recreating global finance from first principles

19:57 Why robotics is the early days of crypto all over again

21:50 Realizing humanoids are no longer sci-fi

22:58 Why I wrote a $19M check when every VC told me not to

27:24 How SPVs are democratizing venture investing

30:55 Bet on the founder and their execution speed, not the pitch deck

36:39 The math behind a $50 trillion physical labor market

41:44 Applying the MicroStrategy playbook to private markets

45:48 Humanoids are the GPUs of physical labor

47:48 Building a new SoftBank for the robotics era

50:51 Why robotics startups need engineers for attention

54:35 Don't be satisfied with surface level answers

We talk about the early years spent building prototypes in a small apartment, running more than one hundred calls with manufacturers, and discovering the same problem every time. Companies wanted automation, but robots were too expensive and too difficult to program 

Evan explains why Standard Bots focuses on usable automation instead of chasing hype. The goal was not a slightly better robot but something ten times easier to deploy, trained through demonstration and physical AI rather than complex programming 

We also discuss surviving the near-death phase, raising funding weeks before running out of money, rebuilding electronics during the chip shortage, and learning electrical engineering while debugging exploding circuit boards remotely 

A conversation about persistence, customer obsession, and why the future of robotics will be decided by real ROI on factory floors, not humanoid spectacle.

In this episode, Grace shares a founder journey that started long before a company existed. She had been building robots since her teenage years, but the real motivation appeared during COVID lockdowns. People in care facilities were physically supported, yet emotionally isolated. The goal became simple. Build a robot people actually want around them.

We talk about learning outside the curriculum, reaching out to mentors early, and running long real-world pilots before the product was ready. Instead of waiting for perfection, Andromeda deployed unfinished systems into nursing homes to understand behavior, trust, and human reaction.

Grace explains why the hardest problem in social robotics is not intelligence but comfort. A robot can function technically and still fail if people feel uneasy around it. Design, personality, and interaction determine adoption more than raw capability.

We also discuss building a company as a young founder, hiring commercial leadership early, and how moving into the US startup ecosystem changed the pace of decisions and iteration.

You don't want to miss this one!

In this episode, Camilla shares a founder journey shaped long before robotics.

She grew up as a competitive alpine skier, leaving home early to train at elite level. Discipline, pressure, and repetition defined her daily life. Losing was normal. Learning how to reset and keep going became second nature.

After an injury ended her professional sports career, that same intensity moved into engineering. Camilla talks about discovering robotics through building, not theory. Working on factory floors, programming robots across vendors, and seeing firsthand how slow and fragmented industrial automation really is.

We talk about how Forgis came to life at ETH Zurich. Why hardware is not the bottleneck in factories. Why software fragmentation is. And how Forgis sits on top of existing systems as a hardware-agnostic, edge-based AI layer that upgrades how factories operate instead of tearing them apart.

A great conversation about discipline, switching paths, and why Europe’s manufacturing future depends on intelligence, not replacement.

In this episode, Stephen shares his path from growing up in Wales to spending a decade at Imperial, a postdoc at Berkeley, and eventually founding Neuracore. Not because he wanted to be a startup founder, but because he kept running into the same problem again and again: every robotics team rebuilding the same infrastructure from scratch.

We talk about what actually slows robotics teams down, why data pipelines matter more than clever algorithms, and how Neuracore aims to become the infrastructure layer that lets teams focus on deployment instead of plumbing.

Stephen also reflects on imposter syndrome, work ethic, moving between academia and industry, and why Europe can and should build its own robotics infrastructure instead of copying Silicon Valley playbooks.

A very honest conversation about building foundations, not hype, and why scaling robotics is mostly about removing friction.

In this episode, Steve shares a rare founder journey that starts far from robotics. From studying physics at Peking University, struggling to stand out, and rebuilding confidence through sheer consistency, to a PhD at Columbia and an early failed startup built out of love for his dog. A detour that taught him the cost of building without a business model.

We talk about his path through Cruise, NVIDIA, and NIO, where he led large-scale simulation efforts for autonomous driving. Steve explains how those years shaped his conviction that simulation, data quality, and evaluation are the real bottlenecks in physical AI.

He then breaks down how Lightwheel AI came to life. Why sim-ready assets matter more than solvers. How synthetic data actually closes the sim-to-real gap. And why robotics teams hit a ceiling without proper evaluation and scaling infrastructure.

A deep conversation about resilience, delayed gratification, and why the hardest part of building is often unlearning what made you successful before.

We talk about growing up in India, building his first autonomous vehicle on a $280 budget after being rejected from a CMU program, and how that failure pushed him toward Berkeley, Stanford, and eventually NVIDIA Research. Animesh shares why he avoided computer science early on, what drew him to mechatronics, and how curiosity rather than planning shaped his entire career.

ORBIT and Isaac Lab, why simulation is now the backbone of robot learning, and how world models, reinforcement learning, and foundation models are lowering the barrier for people outside robotics to build real systems.

Animesh explains why he believes the most important robotics breakthroughs will come from people who are still in high school today.

A deeply personal conversation about grit, risk, redefining success, and why chasing interesting problems beats chasing money.

Marco shares his journey from a middle-class upbringing with no academic role models to becoming a leading figure in applied AI for manufacturing. We talk about discovering computer science through a single physics teacher, why he almost went to vocational school, and how a mix of personal drive and mentors shaped his path.

We spend some time on his years between academia and industry, what he learned working in high-pressure startups, and why real innovation happens when theory meets factory floors. Marco explains how Germany still leads in fundamental AI research but is at risk of losing the race when it comes to turning research into scalable industrial products.

A conversation about explainable AI, robotics in production, and why Europe has only a small window left to turn Physical AI into a competitive advantage.

In this episode, Stefan shares how his path into robotics did not start with founding ambitions, but with curiosity for how machines work. From studying mechanical engineering at TUM to hands-on autonomous driving research at BMW and years at Fraunhofer IPA, his motivation was always the same: building systems that work in the real world, not just on paper.

We talk about the transition from research to entrepreneurship, why understanding the market matters more than perfect technology, and how NODE Robotics emerged as a Fraunhofer spin-off with real customers from day one. Stefan explains why many mobile robot projects fail at scale, how software complexity is the real bottleneck, and why modular, hardware-agnostic autonomy is critical for industrial adoption.

The conversation also looks into decision-making under uncertainty, the risks of staying too technology-driven, and why taking action beats waiting for perfect information. An honest reflection at building deep tech companies in Europe, navigating industrial customers, and turning applied research into a global product.

A grounded conversation about ambition, uncertainty, and what it really takes to scale robotics beyond pilots.