The OPTIM Update Transistor (https://transistor.fm) https://feeds.transistor.fm/the-optim-update Deep conversations with the founders, investors, and operators building real-world AI - robotics, automation, industrial systems & AI infrastructure. Past the headlines, into how these technologies are really built, deployed, and scaled. Hosted by Bogdan Cristei, venture partner and former systems engineer. © 2026 OPTIM Ventures e71bb778-f296-5347-b38c-b615b68532e7 yes en Tue, 29 Sep 2026 09:42:24 -0700 Wed, 30 Sep 2026 17:29:16 -0700 https://www.optim.vc https://img.transistorcdn.com/rVqF0Ngt7vld1_CM8IxcabSekYLdFr8FGu3f0RSLqPE/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9jYTFk/YTA0N2ZmOThmMmZl/ZGVjMjA3NWMwZDUw/YmRlMS5qcGVn.jpg The OPTIM Update https://www.optim.vc episodic Bogdan Cristei Deep conversations with the founders, investors, and operators building real-world AI - robotics, automation, industrial systems & AI infrastructure. Past the headlines, into how these technologies are really built, deployed, and scaled. Hosted by Bogdan Cristei, venture partner and former systems engineer. Deep conversations with the founders, investors, and operators building real-world AI - robotics, automation, industrial systems & AI infrastructure. robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI Bogdan Cristei No No Variation Kills Automation: Teaching Robots Stiffness | Sze Cheong & Elijah Ong, Devol Robots 9 9 Variation Kills Automation: Teaching Robots Stiffness | Sze Cheong & Elijah Ong, Devol Robots full e6eea6e2-358f-418a-802c-95bc668244fb https://share.transistor.fm/s/b8d9a337 Walk into a factory that already owns robots and you'll find the end of the line automated: packaging, palletizing. Everything in between is still done by hand - plugging connectors, assembling parts, moving a lens into a jig with 10 to 20 microns of tolerance. As Sze puts it, variation kills automation.

Sze Yuan Cheong (Co-Founder & CEO, Devol Robots - a decade running manufacturing businesses before he touched a robot) and Elijah Ong (Co-Founder & CTO - turned down a Stanford PhD to help build a force-controlled arm from scratch at a three-person startup in Austin) argue that robot AI is scaling in the wrong direction. A camera can't tell a robot arm resting on a table from one pressing into it with 50 newtons. Devol teaches robots stiffness and damping, treats robot data as living in curved space instead of flat vectors, and pretrains on about 1,000 hours of data. In their own benchmark the model succeeds about 90% of the time on contact-rich tasks, where two open-source baselines land between 30 and 40%.

We cover what jigs, fixtures, and integrator hours cost a factory, the keys-in-your-pocket test for what vision can't capture, an Ethernet plug insertion phase by phase, what happens when a manufacturer calls with a new task (one demonstration, then about an hour of the robot teaching itself), whether the VLA labs can scale their way to the same place, how the model runs on position-controlled robots, where it breaks, and the hard lesson from trying to build an entire hardware stack in house.

Chapters:
00:00 Why the company is named after George Devol
00:50 Variation kills automation: what factories still do by hand
02:28 The hidden cost of jigs, fixtures, and integrator hours
04:54 Inside an optics line: 20 jigs and 10 microns of tolerance
07:22 Why a camera can't see 50 newtons: stiffness and damping explained
09:18 Finding your keys in your pocket without looking
10:03 Turning down a Stanford PhD to build a force-controlled arm
11:49 Series elastic actuators and a 3 a.m. phone call
12:46 Robot data lives in curved space
15:28 The Ethernet plug, phase by phase
17:09 Why 1,000 hours of pretraining is the floor
18:59 20 demos vs. 100: the benchmark against VLA baselines
19:52 A new task on the factory floor: one demo, then an hour of robot self-play
21:59 The bitter lesson: can the VLA labs scale their way here?
23:34 The classical robotics lesson AI forgot: find the right abstraction
24:40 Running on position-controlled robots
26:32 Where the model breaks: threading a needle
27:38 Hot take: stop scaling from pixels
28:41 Devol One: blending VLA, JEPA, and world action models
31:20 Five years out: AI rediscovers classical robotics
32:11 Slow brain, fast brain, and why low-level control matters
33:18 Nobody is working on the execution problem
34:05 Advice for founders: don't build everything yourself
35:40 Where to find Devol

Learn more about Devol Robots: https://www.devolrobots.ai

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Hosted by Bogdan Cristei of OPTIM VC.

Subscribe to the newsletter: https://www.optim.vc

]]> Walk into a factory that already owns robots and you'll find the end of the line automated: packaging, palletizing. Everything in between is still done by hand - plugging connectors, assembling parts, moving a lens into a jig with 10 to 20 microns of tolerance. As Sze puts it, variation kills automation.

Sze Yuan Cheong (Co-Founder & CEO, Devol Robots - a decade running manufacturing businesses before he touched a robot) and Elijah Ong (Co-Founder & CTO - turned down a Stanford PhD to help build a force-controlled arm from scratch at a three-person startup in Austin) argue that robot AI is scaling in the wrong direction. A camera can't tell a robot arm resting on a table from one pressing into it with 50 newtons. Devol teaches robots stiffness and damping, treats robot data as living in curved space instead of flat vectors, and pretrains on about 1,000 hours of data. In their own benchmark the model succeeds about 90% of the time on contact-rich tasks, where two open-source baselines land between 30 and 40%.

We cover what jigs, fixtures, and integrator hours cost a factory, the keys-in-your-pocket test for what vision can't capture, an Ethernet plug insertion phase by phase, what happens when a manufacturer calls with a new task (one demonstration, then about an hour of the robot teaching itself), whether the VLA labs can scale their way to the same place, how the model runs on position-controlled robots, where it breaks, and the hard lesson from trying to build an entire hardware stack in house.

Chapters:
00:00 Why the company is named after George Devol
00:50 Variation kills automation: what factories still do by hand
02:28 The hidden cost of jigs, fixtures, and integrator hours
04:54 Inside an optics line: 20 jigs and 10 microns of tolerance
07:22 Why a camera can't see 50 newtons: stiffness and damping explained
09:18 Finding your keys in your pocket without looking
10:03 Turning down a Stanford PhD to build a force-controlled arm
11:49 Series elastic actuators and a 3 a.m. phone call
12:46 Robot data lives in curved space
15:28 The Ethernet plug, phase by phase
17:09 Why 1,000 hours of pretraining is the floor
18:59 20 demos vs. 100: the benchmark against VLA baselines
19:52 A new task on the factory floor: one demo, then an hour of robot self-play
21:59 The bitter lesson: can the VLA labs scale their way here?
23:34 The classical robotics lesson AI forgot: find the right abstraction
24:40 Running on position-controlled robots
26:32 Where the model breaks: threading a needle
27:38 Hot take: stop scaling from pixels
28:41 Devol One: blending VLA, JEPA, and world action models
31:20 Five years out: AI rediscovers classical robotics
32:11 Slow brain, fast brain, and why low-level control matters
33:18 Nobody is working on the execution problem
34:05 Advice for founders: don't build everything yourself
35:40 Where to find Devol

Learn more about Devol Robots: https://www.devolrobots.ai

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Hosted by Bogdan Cristei of OPTIM VC.

Subscribe to the newsletter: https://www.optim.vc

]]> Tue, 29 Sep 2026 09:42:24 -0700 Bogdan Cristei Watch on YouTube Bogdan Cristei 2236 Walk into a factory that already owns robots and you'll find the end of the line automated: packaging, palletizing. Everything in between is still done by hand - plugging connectors, assembling parts, moving a lens into a jig with 10 to 20 microns of tolerance. As Sze puts it, variation kills automation.

Sze Yuan Cheong (Co-Founder & CEO, Devol Robots - a decade running manufacturing businesses before he touched a robot) and Elijah Ong (Co-Founder & CTO - turned down a Stanford PhD to help build a force-controlled arm from scratch at a three-person startup in Austin) argue that robot AI is scaling in the wrong direction. A camera can't tell a robot arm resting on a table from one pressing into it with 50 newtons. Devol teaches robots stiffness and damping, treats robot data as living in curved space instead of flat vectors, and pretrains on about 1,000 hours of data. In their own benchmark the model succeeds about 90% of the time on contact-rich tasks, where two open-source baselines land between 30 and 40%.

We cover what jigs, fixtures, and integrator hours cost a factory, the keys-in-your-pocket test for what vision can't capture, an Ethernet plug insertion phase by phase, what happens when a manufacturer calls with a new task (one demonstration, then about an hour of the robot teaching itself), whether the VLA labs can scale their way to the same place, how the model runs on position-controlled robots, where it breaks, and the hard lesson from trying to build an entire hardware stack in house.

Chapters:
00:00 Why the company is named after George Devol
00:50 Variation kills automation: what factories still do by hand
02:28 The hidden cost of jigs, fixtures, and integrator hours
04:54 Inside an optics line: 20 jigs and 10 microns of tolerance
07:22 Why a camera can't see 50 newtons: stiffness and damping explained
09:18 Finding your keys in your pocket without looking
10:03 Turning down a Stanford PhD to build a force-controlled arm
11:49 Series elastic actuators and a 3 a.m. phone call
12:46 Robot data lives in curved space
15:28 The Ethernet plug, phase by phase
17:09 Why 1,000 hours of pretraining is the floor
18:59 20 demos vs. 100: the benchmark against VLA baselines
19:52 A new task on the factory floor: one demo, then an hour of robot self-play
21:59 The bitter lesson: can the VLA labs scale their way here?
23:34 The classical robotics lesson AI forgot: find the right abstraction
24:40 Running on position-controlled robots
26:32 Where the model breaks: threading a needle
27:38 Hot take: stop scaling from pixels
28:41 Devol One: blending VLA, JEPA, and world action models
31:20 Five years out: AI rediscovers classical robotics
32:11 Slow brain, fast brain, and why low-level control matters
33:18 Nobody is working on the execution problem
34:05 Advice for founders: don't build everything yourself
35:40 Where to find Devol

Learn more about Devol Robots: https://www.devolrobots.ai

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Hosted by Bogdan Cristei of OPTIM VC.

Subscribe to the newsletter: https://www.optim.vc

]]> robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI No Moving a Task From One Robot Body to Another | Aurora Feng, Neural Motion 8 8 Moving a Task From One Robot Body to Another | Aurora Feng, Neural Motion full 2309b2b9-a04b-4113-9071-dd074d4e48a9 https://share.transistor.fm/s/920e9446 Aurora Feng is building a model that takes a task recorded on one robot and produces that same task on a robot it has never seen. Video and action together, with no retraining and nothing collected on the new body.
 
Every robot foundation model lab is capped by the data it physically collected. If a robot recorded 200 tasks, a policy trained on it works in the neighborhood of those 200, and the 201st is out of distribution. Aurora's argument is that there are two ways at this problem, from the data side and from the model side, and the field has spent nearly all of its attention on the model side. Co-training on every robot dataset you can find leaves the data recipe inside a black box. Her bet is that solving it at the data stage, before anything enters the pre-training pile, is what removes the ceiling.
 
We get into what changes and what stays invariant when the robot body changes, why kinematic retargeting solves correspondence in the wrong space, why real-to-sim-to-real loses the data on the way back, what happens to teleoperation data vendors if conversion works, and why she thinks compute is the only bottleneck left in five years.
 
Aurora is founder and CEO of Neural Motion. She founded Saturday Robotics, the largest robotics and world model research forum in Silicon Valley, scaling it from zero to 2,600 researchers in three months and recruiting most of her team out of it. Before that she was founding head of North America at LimX Dynamics, and invested at Pear VC and ZhenFund. Stanford '24.

CHAPTERS
 
00:00 Intro
01:30 Building the community before building the team
06:05 Embodiment, and what stays invariant when the body changes
07:32 Why co-training on every robot dataset isn't enough
10:03 Labs are dumping data across their own hardware generations
10:58 Retargeting, humanoids, and real-to-sim-to-real
14:25 The Q1 launch, and why China built hardware first
16:24 Rapid fire: teleop vendors, five years out, human video
19:33 Advice for PhDs, and what Neural Motion is hiring for

GUEST
 
Aurora Feng on LinkedIn: https://www.linkedin.com/in/aurora-feng/
Aurora Feng on X: https://x.com/aurorafeng_01
Saturday Robotics on X: https://x.com/saturdayrobotic
Neural Motion: https://neural-motion.org
 
 
THE OPTIM UPDATE
 
Newsletter: https://optim.vc
The OPTIM Update covers real-world AI, robotics, automation and AI infrastructure, hosted by Bogdan Cristei of OPTIM VC

]]> Aurora Feng is building a model that takes a task recorded on one robot and produces that same task on a robot it has never seen. Video and action together, with no retraining and nothing collected on the new body.
 
Every robot foundation model lab is capped by the data it physically collected. If a robot recorded 200 tasks, a policy trained on it works in the neighborhood of those 200, and the 201st is out of distribution. Aurora's argument is that there are two ways at this problem, from the data side and from the model side, and the field has spent nearly all of its attention on the model side. Co-training on every robot dataset you can find leaves the data recipe inside a black box. Her bet is that solving it at the data stage, before anything enters the pre-training pile, is what removes the ceiling.
 
We get into what changes and what stays invariant when the robot body changes, why kinematic retargeting solves correspondence in the wrong space, why real-to-sim-to-real loses the data on the way back, what happens to teleoperation data vendors if conversion works, and why she thinks compute is the only bottleneck left in five years.
 
Aurora is founder and CEO of Neural Motion. She founded Saturday Robotics, the largest robotics and world model research forum in Silicon Valley, scaling it from zero to 2,600 researchers in three months and recruiting most of her team out of it. Before that she was founding head of North America at LimX Dynamics, and invested at Pear VC and ZhenFund. Stanford '24.

CHAPTERS
 
00:00 Intro
01:30 Building the community before building the team
06:05 Embodiment, and what stays invariant when the body changes
07:32 Why co-training on every robot dataset isn't enough
10:03 Labs are dumping data across their own hardware generations
10:58 Retargeting, humanoids, and real-to-sim-to-real
14:25 The Q1 launch, and why China built hardware first
16:24 Rapid fire: teleop vendors, five years out, human video
19:33 Advice for PhDs, and what Neural Motion is hiring for

GUEST
 
Aurora Feng on LinkedIn: https://www.linkedin.com/in/aurora-feng/
Aurora Feng on X: https://x.com/aurorafeng_01
Saturday Robotics on X: https://x.com/saturdayrobotic
Neural Motion: https://neural-motion.org
 
 
THE OPTIM UPDATE
 
Newsletter: https://optim.vc
The OPTIM Update covers real-world AI, robotics, automation and AI infrastructure, hosted by Bogdan Cristei of OPTIM VC

]]> Tue, 22 Sep 2026 14:52:45 -0700 Bogdan Cristei Watch on YouTube Bogdan Cristei 1278 Aurora Feng is building a model that takes a task recorded on one robot and produces that same task on a robot it has never seen. Video and action together, with no retraining and nothing collected on the new body.
 
Every robot foundation model lab is capped by the data it physically collected. If a robot recorded 200 tasks, a policy trained on it works in the neighborhood of those 200, and the 201st is out of distribution. Aurora's argument is that there are two ways at this problem, from the data side and from the model side, and the field has spent nearly all of its attention on the model side. Co-training on every robot dataset you can find leaves the data recipe inside a black box. Her bet is that solving it at the data stage, before anything enters the pre-training pile, is what removes the ceiling.
 
We get into what changes and what stays invariant when the robot body changes, why kinematic retargeting solves correspondence in the wrong space, why real-to-sim-to-real loses the data on the way back, what happens to teleoperation data vendors if conversion works, and why she thinks compute is the only bottleneck left in five years.
 
Aurora is founder and CEO of Neural Motion. She founded Saturday Robotics, the largest robotics and world model research forum in Silicon Valley, scaling it from zero to 2,600 researchers in three months and recruiting most of her team out of it. Before that she was founding head of North America at LimX Dynamics, and invested at Pear VC and ZhenFund. Stanford '24.

CHAPTERS
 
00:00 Intro
01:30 Building the community before building the team
06:05 Embodiment, and what stays invariant when the body changes
07:32 Why co-training on every robot dataset isn't enough
10:03 Labs are dumping data across their own hardware generations
10:58 Retargeting, humanoids, and real-to-sim-to-real
14:25 The Q1 launch, and why China built hardware first
16:24 Rapid fire: teleop vendors, five years out, human video
19:33 Advice for PhDs, and what Neural Motion is hiring for

GUEST
 
Aurora Feng on LinkedIn: https://www.linkedin.com/in/aurora-feng/
Aurora Feng on X: https://x.com/aurorafeng_01
Saturday Robotics on X: https://x.com/saturdayrobotic
Neural Motion: https://neural-motion.org
 
 
THE OPTIM UPDATE
 
Newsletter: https://optim.vc
The OPTIM Update covers real-world AI, robotics, automation and AI infrastructure, hosted by Bogdan Cristei of OPTIM VC

]]> robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI No Why Pixel-Based Robot AI Is an Expensive Detour | Chao Cao, Sancho 7 7 Why Pixel-Based Robot AI Is an Expensive Detour | Chao Cao, Sancho full 3f12844c-e67a-4db7-aa98-bf42a43de104 https://share.transistor.fm/s/b7a37a9b Advanced manufacturing has automated the individual process steps. CNC, 3D printing, photolithography - however complex the step, we can build a machine for it. The handoffs between those machines still run on people, and in cell therapy that means $300,000-a-year scientists spending their days loading and unloading equipment while contamination risk and throughput bottlenecks drive up the cost of every dose.


Chao Cao (Co-Founder & CEO, Sancho - CMU robotics PhD, autonomy lead for CMU's DARPA Subterranean Challenge entries, ex-Boston Dynamics AI Institute) makes the case that the dominant approach to robot intelligence, pixel-based VLA models trained on massive datasets, is an expensive detour. Sancho bets on 3D geometric world models and test-time reasoning instead, and runs the entire stack on a single onboard compute module.


We cover the lasagna analogy for factory workflows, the math on the most expensive labor doing the lowest-value work, what five nines of reliability does to the data question, Waymo vs. Tesla as a data-quality argument, three years of sending robots into tunnels and caves, the October-to-March sprint from incorporation to NVIDIA's GTC keynote, the oversubscribed seed round co-led by Fusion Fund and Catapult, and why regulated cleanrooms are an easier place to start than a grocery store.


Chapters:


00:00 Intro

01:10 Roadmap and why the company is called Sancho

02:15 The lasagna problem: the gap between machines

04:51 $300K scientists loading machines by hand

06:04 Why fixed automation isn't the answer

07:02 Bet #1: 3D geometry over pixels

09:46 Bet #2: test-time reasoning over data scaling

11:25 Running the whole stack on onboard compute

13:11 Three years in the dark: DARPA SubT lessons

16:15 October to GTC in five months

18:35 The oversubscribed seed round

19:01 Two founders, two autonomy worlds

21:08 Why start in the most regulated environments

23:48 Hot takes: Waymo vs. Tesla and wasted data

25:58 What Chao wishes he knew before starting

27:30 Hiring: perception and loco-manipulation

29:50 Where to find Sancho

 

Learn more about Sancho: https://www.sancho.com


The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Hosted by Bogdan Cristei of Optim VC.


Subscribe to the newsletter: https://www.optim.vc


Follow Bogdan on LinkedIn: https://www.linkedin.com/in/bogdancristei/

]]> Advanced manufacturing has automated the individual process steps. CNC, 3D printing, photolithography - however complex the step, we can build a machine for it. The handoffs between those machines still run on people, and in cell therapy that means $300,000-a-year scientists spending their days loading and unloading equipment while contamination risk and throughput bottlenecks drive up the cost of every dose.


Chao Cao (Co-Founder & CEO, Sancho - CMU robotics PhD, autonomy lead for CMU's DARPA Subterranean Challenge entries, ex-Boston Dynamics AI Institute) makes the case that the dominant approach to robot intelligence, pixel-based VLA models trained on massive datasets, is an expensive detour. Sancho bets on 3D geometric world models and test-time reasoning instead, and runs the entire stack on a single onboard compute module.


We cover the lasagna analogy for factory workflows, the math on the most expensive labor doing the lowest-value work, what five nines of reliability does to the data question, Waymo vs. Tesla as a data-quality argument, three years of sending robots into tunnels and caves, the October-to-March sprint from incorporation to NVIDIA's GTC keynote, the oversubscribed seed round co-led by Fusion Fund and Catapult, and why regulated cleanrooms are an easier place to start than a grocery store.


Chapters:


00:00 Intro

01:10 Roadmap and why the company is called Sancho

02:15 The lasagna problem: the gap between machines

04:51 $300K scientists loading machines by hand

06:04 Why fixed automation isn't the answer

07:02 Bet #1: 3D geometry over pixels

09:46 Bet #2: test-time reasoning over data scaling

11:25 Running the whole stack on onboard compute

13:11 Three years in the dark: DARPA SubT lessons

16:15 October to GTC in five months

18:35 The oversubscribed seed round

19:01 Two founders, two autonomy worlds

21:08 Why start in the most regulated environments

23:48 Hot takes: Waymo vs. Tesla and wasted data

25:58 What Chao wishes he knew before starting

27:30 Hiring: perception and loco-manipulation

29:50 Where to find Sancho

 

Learn more about Sancho: https://www.sancho.com


The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Hosted by Bogdan Cristei of Optim VC.


Subscribe to the newsletter: https://www.optim.vc


Follow Bogdan on LinkedIn: https://www.linkedin.com/in/bogdancristei/

]]> Mon, 13 Jul 2026 13:04:42 -0700 Bogdan Cristei Watch on YouTube Bogdan Cristei 1804 Advanced manufacturing has automated the individual process steps. CNC, 3D printing, photolithography - however complex the step, we can build a machine for it. The handoffs between those machines still run on people, and in cell therapy that means $300,000-a-year scientists spending their days loading and unloading equipment while contamination risk and throughput bottlenecks drive up the cost of every dose.


Chao Cao (Co-Founder & CEO, Sancho - CMU robotics PhD, autonomy lead for CMU's DARPA Subterranean Challenge entries, ex-Boston Dynamics AI Institute) makes the case that the dominant approach to robot intelligence, pixel-based VLA models trained on massive datasets, is an expensive detour. Sancho bets on 3D geometric world models and test-time reasoning instead, and runs the entire stack on a single onboard compute module.


We cover the lasagna analogy for factory workflows, the math on the most expensive labor doing the lowest-value work, what five nines of reliability does to the data question, Waymo vs. Tesla as a data-quality argument, three years of sending robots into tunnels and caves, the October-to-March sprint from incorporation to NVIDIA's GTC keynote, the oversubscribed seed round co-led by Fusion Fund and Catapult, and why regulated cleanrooms are an easier place to start than a grocery store.


Chapters:


00:00 Intro

01:10 Roadmap and why the company is called Sancho

02:15 The lasagna problem: the gap between machines

04:51 $300K scientists loading machines by hand

06:04 Why fixed automation isn't the answer

07:02 Bet #1: 3D geometry over pixels

09:46 Bet #2: test-time reasoning over data scaling

11:25 Running the whole stack on onboard compute

13:11 Three years in the dark: DARPA SubT lessons

16:15 October to GTC in five months

18:35 The oversubscribed seed round

19:01 Two founders, two autonomy worlds

21:08 Why start in the most regulated environments

23:48 Hot takes: Waymo vs. Tesla and wasted data

25:58 What Chao wishes he knew before starting

27:30 Hiring: perception and loco-manipulation

29:50 Where to find Sancho

 

Learn more about Sancho: https://www.sancho.com


The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Hosted by Bogdan Cristei of Optim VC.


Subscribe to the newsletter: https://www.optim.vc


Follow Bogdan on LinkedIn: https://www.linkedin.com/in/bogdancristei/

]]> robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI No Building the Missing Data Layer for Physical AI | James Kujareevanich, Vision Lab 6 6 Building the Missing Data Layer for Physical AI | James Kujareevanich, Vision Lab full fd711c5e-3409-4c51-8d21-716e638d909c https://share.transistor.fm/s/c3cebe82 James Kujareevanich is the co-founder and CEO of Vision Lab - building the missing data layer for physical AI. They go into factories, capture first-person video of real operators performing real tasks, and turn that into structured training data for frontier labs and robotics companies. The origin story starts with an MIT PhD student who strapped a camera to his head to automate lab documentation before egocentric data was even a thing. They built a human training tool first, got pulled into the robotics data business by demand from AI labs, and just closed a $6M round to scale.

We cover:
00:00 - Intro
00:46 - From McKinsey Bangkok to an MIT PhD with a camera on his head
02:36 - The Christmas pivot: from training humans to training robots
03:41 - Why robotics doesn't have its "internet" yet
04:50 - What the data product actually looks like
05:27 - Egocentric, tactile, teleoperation - every lab wants something different
07:12 - A factory capture from start to finish
07:53 - "My dad runs a factory" - the first test case
08:31 - Getting factory owners to trust you
09:40 - Industrial influencers and the factory network
10:12 - Scaling across India, Thailand, Vietnam, Indonesia
11:30 - What frontier labs learned from the pilots
12:57 - The gap between what labs want and what you can deliver
13:16 - Closing a $6M round and doubling the team in two weeks
14:00 - New verticals beyond manufacturing
14:42 - The synthetic data question
15:14 - Chaos theory and why sim data compounds errors
17:02 - Where defensibility lives in this business
17:20 - 80% of raw footage is unusable
19:29 - What the market gets wrong about robotics timelines
21:28 - Hot take: convergence to 2-3 big players
22:32 - The 10-year vision: becoming the Siemens of robotics
23:18 - Advice for robotics founders

Learn more about Vision Lab: https://thevisionlab.ai

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> James Kujareevanich is the co-founder and CEO of Vision Lab - building the missing data layer for physical AI. They go into factories, capture first-person video of real operators performing real tasks, and turn that into structured training data for frontier labs and robotics companies. The origin story starts with an MIT PhD student who strapped a camera to his head to automate lab documentation before egocentric data was even a thing. They built a human training tool first, got pulled into the robotics data business by demand from AI labs, and just closed a $6M round to scale.

We cover:
00:00 - Intro
00:46 - From McKinsey Bangkok to an MIT PhD with a camera on his head
02:36 - The Christmas pivot: from training humans to training robots
03:41 - Why robotics doesn't have its "internet" yet
04:50 - What the data product actually looks like
05:27 - Egocentric, tactile, teleoperation - every lab wants something different
07:12 - A factory capture from start to finish
07:53 - "My dad runs a factory" - the first test case
08:31 - Getting factory owners to trust you
09:40 - Industrial influencers and the factory network
10:12 - Scaling across India, Thailand, Vietnam, Indonesia
11:30 - What frontier labs learned from the pilots
12:57 - The gap between what labs want and what you can deliver
13:16 - Closing a $6M round and doubling the team in two weeks
14:00 - New verticals beyond manufacturing
14:42 - The synthetic data question
15:14 - Chaos theory and why sim data compounds errors
17:02 - Where defensibility lives in this business
17:20 - 80% of raw footage is unusable
19:29 - What the market gets wrong about robotics timelines
21:28 - Hot take: convergence to 2-3 big players
22:32 - The 10-year vision: becoming the Siemens of robotics
23:18 - Advice for robotics founders

Learn more about Vision Lab: https://thevisionlab.ai

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Mon, 15 Jun 2026 18:56:25 -0700 Bogdan Cristei Watch on YouTube Bogdan Cristei 1513 James Kujareevanich is the co-founder and CEO of Vision Lab - building the missing data layer for physical AI. They go into factories, capture first-person video of real operators performing real tasks, and turn that into structured training data for frontier labs and robotics companies. The origin story starts with an MIT PhD student who strapped a camera to his head to automate lab documentation before egocentric data was even a thing. They built a human training tool first, got pulled into the robotics data business by demand from AI labs, and just closed a $6M round to scale.

We cover:
00:00 - Intro
00:46 - From McKinsey Bangkok to an MIT PhD with a camera on his head
02:36 - The Christmas pivot: from training humans to training robots
03:41 - Why robotics doesn't have its "internet" yet
04:50 - What the data product actually looks like
05:27 - Egocentric, tactile, teleoperation - every lab wants something different
07:12 - A factory capture from start to finish
07:53 - "My dad runs a factory" - the first test case
08:31 - Getting factory owners to trust you
09:40 - Industrial influencers and the factory network
10:12 - Scaling across India, Thailand, Vietnam, Indonesia
11:30 - What frontier labs learned from the pilots
12:57 - The gap between what labs want and what you can deliver
13:16 - Closing a $6M round and doubling the team in two weeks
14:00 - New verticals beyond manufacturing
14:42 - The synthetic data question
15:14 - Chaos theory and why sim data compounds errors
17:02 - Where defensibility lives in this business
17:20 - 80% of raw footage is unusable
19:29 - What the market gets wrong about robotics timelines
21:28 - Hot take: convergence to 2-3 big players
22:32 - The 10-year vision: becoming the Siemens of robotics
23:18 - Advice for robotics founders

Learn more about Vision Lab: https://thevisionlab.ai

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI No Building the Foundry for Physical AI | Mike Xia, Anvil Robotics 5 5 Building the Foundry for Physical AI | Mike Xia, Anvil Robotics full 7f08b71b-940c-48b8-87d5-bf1e7a01b891 https://share.transistor.fm/s/9d076d6c Mike Xia is the co-founder and CEO of Anvil Robotics - building the foundry for physical AI. They make the hardware, software, and data tools that let robotics teams go from zero to model training in days vs months. They've shipped over 100 robots, manufacture in Taiwan, and just raised a $6.5M seed round. Mike gets into the economics of building and shipping a $5,000 arm, why most teams are fighting their own hardware before they can even start on AI, and what's structurally broken in the supply chain that not enough people talk about.

We cover:
00:00 - Intro
00:45 - What physical AI teams actually go through before training a model
03:16 - Why the existing robot stack was built for a different era
04:10 - What it's actually like setting up an SO-100 at home
05:21 - The leap from toy arms to real payloads
08:01 - What you get on day one with an Anvil dev kit
09:12 - What kilohertz-rate sensor fusion actually unlocks
11:19 - The false tradeoff between payload and force compliance
14:35 - Why vision alone isn't enough: the dentist analogy
16:15 - The economics of a $5,000 arm
20:01 - Scaling from 150 robots to 200 a month
21:30 - Why all customers came inbound
22:10 - Retention and repeat orders
24:47 - If open source isn't the moat, what is?
28:15 - Why the supply chain is a relationship, not a transaction
28:36 - How to do customization without becoming a services company
31:30 - How many of 1,500 new robotics startups survive 24 months?
34:52 - The most technically wrong thing teams are doing in 2026
38:57 - What happens when your whole fleet breaks and you don't know why
40:40 - What will look obvious in five years
42:29 - Where to learn more about Anvil

Anvil Robotics: https://anvil.bot

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Mike Xia is the co-founder and CEO of Anvil Robotics - building the foundry for physical AI. They make the hardware, software, and data tools that let robotics teams go from zero to model training in days vs months. They've shipped over 100 robots, manufacture in Taiwan, and just raised a $6.5M seed round. Mike gets into the economics of building and shipping a $5,000 arm, why most teams are fighting their own hardware before they can even start on AI, and what's structurally broken in the supply chain that not enough people talk about.

We cover:
00:00 - Intro
00:45 - What physical AI teams actually go through before training a model
03:16 - Why the existing robot stack was built for a different era
04:10 - What it's actually like setting up an SO-100 at home
05:21 - The leap from toy arms to real payloads
08:01 - What you get on day one with an Anvil dev kit
09:12 - What kilohertz-rate sensor fusion actually unlocks
11:19 - The false tradeoff between payload and force compliance
14:35 - Why vision alone isn't enough: the dentist analogy
16:15 - The economics of a $5,000 arm
20:01 - Scaling from 150 robots to 200 a month
21:30 - Why all customers came inbound
22:10 - Retention and repeat orders
24:47 - If open source isn't the moat, what is?
28:15 - Why the supply chain is a relationship, not a transaction
28:36 - How to do customization without becoming a services company
31:30 - How many of 1,500 new robotics startups survive 24 months?
34:52 - The most technically wrong thing teams are doing in 2026
38:57 - What happens when your whole fleet breaks and you don't know why
40:40 - What will look obvious in five years
42:29 - Where to learn more about Anvil

Anvil Robotics: https://anvil.bot

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Sun, 24 May 2026 21:52:02 -0700 Bogdan Cristei Watch on YouTube Bogdan Cristei 2592 Mike Xia is the co-founder and CEO of Anvil Robotics - building the foundry for physical AI. They make the hardware, software, and data tools that let robotics teams go from zero to model training in days vs months. They've shipped over 100 robots, manufacture in Taiwan, and just raised a $6.5M seed round. Mike gets into the economics of building and shipping a $5,000 arm, why most teams are fighting their own hardware before they can even start on AI, and what's structurally broken in the supply chain that not enough people talk about.

We cover:
00:00 - Intro
00:45 - What physical AI teams actually go through before training a model
03:16 - Why the existing robot stack was built for a different era
04:10 - What it's actually like setting up an SO-100 at home
05:21 - The leap from toy arms to real payloads
08:01 - What you get on day one with an Anvil dev kit
09:12 - What kilohertz-rate sensor fusion actually unlocks
11:19 - The false tradeoff between payload and force compliance
14:35 - Why vision alone isn't enough: the dentist analogy
16:15 - The economics of a $5,000 arm
20:01 - Scaling from 150 robots to 200 a month
21:30 - Why all customers came inbound
22:10 - Retention and repeat orders
24:47 - If open source isn't the moat, what is?
28:15 - Why the supply chain is a relationship, not a transaction
28:36 - How to do customization without becoming a services company
31:30 - How many of 1,500 new robotics startups survive 24 months?
34:52 - The most technically wrong thing teams are doing in 2026
38:57 - What happens when your whole fleet breaks and you don't know why
40:40 - What will look obvious in five years
42:29 - Where to learn more about Anvil

Anvil Robotics: https://anvil.bot

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI No Useful Now: The Case for Application-Specific Robots | Arjun Subramaniam of Factory Intelligence 4 4 Useful Now: The Case for Application-Specific Robots | Arjun Subramaniam of Factory Intelligence full 13c3fc15-667c-4e78-81d9-8e39da2e0e84 https://share.transistor.fm/s/a1912def Arjun Subramaniam is the founder and CEO of Factory Intelligence - a physical AI company training tactile foundation models for industrial manipulation. He's toured 70+ factories, deployed robots on real shop floors, and is making the contrarian bet that application-specific systems beat humanoids and general-purpose foundation models right now. His first workcell has eight robots building electrical outlets for $3/hour.

We cover:
00:00 - Intro
00:44 - What 70 factory visits taught him about deployment vs. demos
02:47 - No SLA in a research paper - why factories are a different game
04:23 - Why he put a packaging machinery veteran in the COO seat
06:34 - The "Useful Now" thesis and where the robotics narrative is wrong
08:53 - The Tesla vs. Waymo parallel for robotics
10:01 - You can't buy your way into a large enough manipulation dataset
10:27 - Why vision alone isn't enough for industrial tasks
12:54 - The pen-in-a-bin problem: why vision-only models are too slow
14:37 - Why robotics is not like LLMs - there is no single scaling law
16:32 - The application-specific full-stack quadrant: why no one else is here
17:12 - Best version of the model-first argument - and how he pushes back
19:50 - What happens to humanoids if "Useful Now" works
21:56 - Inside an electrical prefab shop - what actually happens in there
23:53 - Prefab-Cell-E1: eight robots, $3/hour, 9x productivity
24:44 - What "tailing an outlet" means - the actual task, step by step
28:01 - Wire-bending model generalizing to colors it was never trained on
29:16 - The integration trap: why custom fixtures wreck margins
31:29 - When do you know deployment economics actually work
32:08 - The data flywheel: why 50% success rate is the threshold
33:29 - Touch is filling the gap where vision saturated
35:14 - Combining neural nets with classical control - and why both matter
37:44 - The world action model: image, proprioception, tactile, action, all in
39:39 - You can't buy your way to multimodal data from the internet
40:42 - If this works: data centers on the moon

Factory Intelligence: https://factoryintelligence.com

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Arjun Subramaniam is the founder and CEO of Factory Intelligence - a physical AI company training tactile foundation models for industrial manipulation. He's toured 70+ factories, deployed robots on real shop floors, and is making the contrarian bet that application-specific systems beat humanoids and general-purpose foundation models right now. His first workcell has eight robots building electrical outlets for $3/hour.

We cover:
00:00 - Intro
00:44 - What 70 factory visits taught him about deployment vs. demos
02:47 - No SLA in a research paper - why factories are a different game
04:23 - Why he put a packaging machinery veteran in the COO seat
06:34 - The "Useful Now" thesis and where the robotics narrative is wrong
08:53 - The Tesla vs. Waymo parallel for robotics
10:01 - You can't buy your way into a large enough manipulation dataset
10:27 - Why vision alone isn't enough for industrial tasks
12:54 - The pen-in-a-bin problem: why vision-only models are too slow
14:37 - Why robotics is not like LLMs - there is no single scaling law
16:32 - The application-specific full-stack quadrant: why no one else is here
17:12 - Best version of the model-first argument - and how he pushes back
19:50 - What happens to humanoids if "Useful Now" works
21:56 - Inside an electrical prefab shop - what actually happens in there
23:53 - Prefab-Cell-E1: eight robots, $3/hour, 9x productivity
24:44 - What "tailing an outlet" means - the actual task, step by step
28:01 - Wire-bending model generalizing to colors it was never trained on
29:16 - The integration trap: why custom fixtures wreck margins
31:29 - When do you know deployment economics actually work
32:08 - The data flywheel: why 50% success rate is the threshold
33:29 - Touch is filling the gap where vision saturated
35:14 - Combining neural nets with classical control - and why both matter
37:44 - The world action model: image, proprioception, tactile, action, all in
39:39 - You can't buy your way to multimodal data from the internet
40:42 - If this works: data centers on the moon

Factory Intelligence: https://factoryintelligence.com

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Wed, 13 May 2026 13:33:12 -0700 Bogdan Cristei Watch on YouTube Bogdan Cristei 2535 Arjun Subramaniam is the founder and CEO of Factory Intelligence - a physical AI company training tactile foundation models for industrial manipulation. He's toured 70+ factories, deployed robots on real shop floors, and is making the contrarian bet that application-specific systems beat humanoids and general-purpose foundation models right now. His first workcell has eight robots building electrical outlets for $3/hour.

We cover:
00:00 - Intro
00:44 - What 70 factory visits taught him about deployment vs. demos
02:47 - No SLA in a research paper - why factories are a different game
04:23 - Why he put a packaging machinery veteran in the COO seat
06:34 - The "Useful Now" thesis and where the robotics narrative is wrong
08:53 - The Tesla vs. Waymo parallel for robotics
10:01 - You can't buy your way into a large enough manipulation dataset
10:27 - Why vision alone isn't enough for industrial tasks
12:54 - The pen-in-a-bin problem: why vision-only models are too slow
14:37 - Why robotics is not like LLMs - there is no single scaling law
16:32 - The application-specific full-stack quadrant: why no one else is here
17:12 - Best version of the model-first argument - and how he pushes back
19:50 - What happens to humanoids if "Useful Now" works
21:56 - Inside an electrical prefab shop - what actually happens in there
23:53 - Prefab-Cell-E1: eight robots, $3/hour, 9x productivity
24:44 - What "tailing an outlet" means - the actual task, step by step
28:01 - Wire-bending model generalizing to colors it was never trained on
29:16 - The integration trap: why custom fixtures wreck margins
31:29 - When do you know deployment economics actually work
32:08 - The data flywheel: why 50% success rate is the threshold
33:29 - Touch is filling the gap where vision saturated
35:14 - Combining neural nets with classical control - and why both matter
37:44 - The world action model: image, proprioception, tactile, action, all in
39:39 - You can't buy your way to multimodal data from the internet
40:42 - If this works: data centers on the moon

Factory Intelligence: https://factoryintelligence.com

The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI No Why the World's Most Advanced Factories Are Still Flying Blind | Jared O'Leary, SirenOpt 3 3 Why the World's Most Advanced Factories Are Still Flying Blind | Jared O'Leary, SirenOpt full 6eec5ebd-ca7e-475d-9876-1933d702b24b https://share.transistor.fm/s/08a4500c Jared O'Leary, Co-Founder and CEO of SirenOpt, breaks down why even the world's most advanced factories are still flying blind - and what a physics breakthrough out of UC Berkeley is doing about it.

We cover what advanced manufacturing actually is and why it's different from anything most people picture, the sensing gap that's been gating progress for decades, and what cold atmospheric plasma reveals about a material that no other instrument can. Then we get into the commercial reality - who's already deployed, how the business model works, and why the data moat gets harder to close with every passing month.


Jared co-invented the core technology at UC Berkeley alongside his co-founder and CTO Ali Mesbah, a tenured professor who left to build SirenOpt. Their customers include Tier-1 manufacturers across turbines, batteries, and semiconductors in North America, Europe, and Asia.


Learn more about SirenOpt: https://www.sirenopt.com

Volta Foundation presentation mentioned by Jared:
https://youtu.be/a4aW8uxGMQk?si=MGQJI7hNhzzf5pfH


The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Jared O'Leary, Co-Founder and CEO of SirenOpt, breaks down why even the world's most advanced factories are still flying blind - and what a physics breakthrough out of UC Berkeley is doing about it.

We cover what advanced manufacturing actually is and why it's different from anything most people picture, the sensing gap that's been gating progress for decades, and what cold atmospheric plasma reveals about a material that no other instrument can. Then we get into the commercial reality - who's already deployed, how the business model works, and why the data moat gets harder to close with every passing month.


Jared co-invented the core technology at UC Berkeley alongside his co-founder and CTO Ali Mesbah, a tenured professor who left to build SirenOpt. Their customers include Tier-1 manufacturers across turbines, batteries, and semiconductors in North America, Europe, and Asia.


Learn more about SirenOpt: https://www.sirenopt.com

Volta Foundation presentation mentioned by Jared:
https://youtu.be/a4aW8uxGMQk?si=MGQJI7hNhzzf5pfH


The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Wed, 15 Apr 2026 19:04:18 -0700 Bogdan Cristei Watch on YouTube Bogdan Cristei 2802 Jared O'Leary, Co-Founder and CEO of SirenOpt, breaks down why even the world's most advanced factories are still flying blind - and what a physics breakthrough out of UC Berkeley is doing about it.

We cover what advanced manufacturing actually is and why it's different from anything most people picture, the sensing gap that's been gating progress for decades, and what cold atmospheric plasma reveals about a material that no other instrument can. Then we get into the commercial reality - who's already deployed, how the business model works, and why the data moat gets harder to close with every passing month.


Jared co-invented the core technology at UC Berkeley alongside his co-founder and CTO Ali Mesbah, a tenured professor who left to build SirenOpt. Their customers include Tier-1 manufacturers across turbines, batteries, and semiconductors in North America, Europe, and Asia.


Learn more about SirenOpt: https://www.sirenopt.com

Volta Foundation presentation mentioned by Jared:
https://youtu.be/a4aW8uxGMQk?si=MGQJI7hNhzzf5pfH


The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI No Why Heavy Industrial Work Demands a Different Kind of Robot | Gary Chen & Conley Oster, Raise Robotics 2 2 Why Heavy Industrial Work Demands a Different Kind of Robot | Gary Chen & Conley Oster, Raise Robotics full 20479225-6c6e-4468-a555-b7c1243876b3 https://share.transistor.fm/s/92405a5a Gary Chen and Conley Oster are co-founders of Raise Robotics - they build mobile manipulators for construction sites, steel fabrication facilities, and shipyards. Environments where the work is genuinely dangerous, the labor shortage is acute, and the dominant solution being pitched to investors right now - humanoid robots - may be fundamentally the wrong answer.

In this episode we cover:

  • What it actually feels like to spend a day on a heavy industrial job site
  • Why humanoid form factor is the wrong frame for most of these applications
  • The self-driving car parallel: we didn't solve autonomous driving by putting a robot in a taxi
  • Why "simple" tasks like drawing a line or drilling a hole are deceptively complex
  • The tribal knowledge problem - losing the human LLMs built over decades on job sites
  • What a job site looks like in 2030
  • The shipbuilder who got 2 applicants for "welder" and thousands for "robot welding technician"

Raise Robotics: https://raiserobotics.ai/


The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Gary Chen and Conley Oster are co-founders of Raise Robotics - they build mobile manipulators for construction sites, steel fabrication facilities, and shipyards. Environments where the work is genuinely dangerous, the labor shortage is acute, and the dominant solution being pitched to investors right now - humanoid robots - may be fundamentally the wrong answer.

In this episode we cover:

  • What it actually feels like to spend a day on a heavy industrial job site
  • Why humanoid form factor is the wrong frame for most of these applications
  • The self-driving car parallel: we didn't solve autonomous driving by putting a robot in a taxi
  • Why "simple" tasks like drawing a line or drilling a hole are deceptively complex
  • The tribal knowledge problem - losing the human LLMs built over decades on job sites
  • What a job site looks like in 2030
  • The shipbuilder who got 2 applicants for "welder" and thousands for "robot welding technician"

Raise Robotics: https://raiserobotics.ai/


The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Tue, 17 Mar 2026 15:36:17 -0700 Bogdan Cristei Watch on YouTube Bogdan Cristei 2506 Gary Chen and Conley Oster are co-founders of Raise Robotics - they build mobile manipulators for construction sites, steel fabrication facilities, and shipyards. Environments where the work is genuinely dangerous, the labor shortage is acute, and the dominant solution being pitched to investors right now - humanoid robots - may be fundamentally the wrong answer.

In this episode we cover:

  • What it actually feels like to spend a day on a heavy industrial job site
  • Why humanoid form factor is the wrong frame for most of these applications
  • The self-driving car parallel: we didn't solve autonomous driving by putting a robot in a taxi
  • Why "simple" tasks like drawing a line or drilling a hole are deceptively complex
  • The tribal knowledge problem - losing the human LLMs built over decades on job sites
  • What a job site looks like in 2030
  • The shipbuilder who got 2 applicants for "welder" and thousands for "robot welding technician"

Raise Robotics: https://raiserobotics.ai/


The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI No How AI Models for Robotics Are Really Built, Deployed, and Sold | Dr. Asad Tirmizi, Trener Robotics 1 1 How AI Models for Robotics Are Really Built, Deployed, and Sold | Dr. Asad Tirmizi, Trener Robotics full d33b1dc9-7ae4-44c7-b1ff-cd06c59b5f97 https://share.transistor.fm/s/4bfb9db5 Dr. Asad Tirmizi, Co-Founder and CEO of Trener Robotics, breaks down the full lifecycle of AI in industrial robotics.

We cover how models are actually built - the training pipelines, the data, and what separates people who've done it from people who've read about it. Then we get into deployment reality - what happens when AI hits a real factory floor. And we close with the part nobody wants to discuss - how do you actually sell this stuff through integrators and OEMs.

Asad previously worked at Vicarious (acquired by Google) and ByteDance's robotics program. Trener recently raised a $32M Series A co-led by Engine Ventures and IAG Capital Partners to scale their Acteris platform - a robot-agnostic AI skills platform that works across ABB, Universal Robots, FANUC and others.

Learn more about Trener Robotics: https://trener.ai

The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Dr. Asad Tirmizi, Co-Founder and CEO of Trener Robotics, breaks down the full lifecycle of AI in industrial robotics.

We cover how models are actually built - the training pipelines, the data, and what separates people who've done it from people who've read about it. Then we get into deployment reality - what happens when AI hits a real factory floor. And we close with the part nobody wants to discuss - how do you actually sell this stuff through integrators and OEMs.

Asad previously worked at Vicarious (acquired by Google) and ByteDance's robotics program. Trener recently raised a $32M Series A co-led by Engine Ventures and IAG Capital Partners to scale their Acteris platform - a robot-agnostic AI skills platform that works across ABB, Universal Robots, FANUC and others.

Learn more about Trener Robotics: https://trener.ai

The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> Thu, 05 Mar 2026 23:20:56 -0800 Bogdan Cristei Watch on YouTube Bogdan Cristei 2936 Dr. Asad Tirmizi, Co-Founder and CEO of Trener Robotics, breaks down the full lifecycle of AI in industrial robotics.

We cover how models are actually built - the training pipelines, the data, and what separates people who've done it from people who've read about it. Then we get into deployment reality - what happens when AI hits a real factory floor. And we close with the part nobody wants to discuss - how do you actually sell this stuff through integrators and OEMs.

Asad previously worked at Vicarious (acquired by Google) and ByteDance's robotics program. Trener recently raised a $32M Series A co-led by Engine Ventures and IAG Capital Partners to scale their Acteris platform - a robot-agnostic AI skills platform that works across ABB, Universal Robots, FANUC and others.

Learn more about Trener Robotics: https://trener.ai

The OPTIM Update covers real-world AI, automation, robotics, and AI Infrastructure for founders, investors, and operators.

Subscribe: https://www.optim.vc

]]> robotics, AI, automation, industrial AI, physical AI, venture capital, deep tech, manufacturing, founders, startups, real-world AI No