
Co-founder and CEO @atlasmotion
Long Beach, CA
Joined November 2013
- Tweets1.8K
- Following373
- Followers1.1K
- Likes3.4K
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I'm grateful to share that we've raised an $11.5M seed, led by amazing folks at Greycroft and Also Capital @nunzi46 .
The team has moved at a blistering pace over the last 5 months, and I’m excited to share more with the world on where we see this going.
Introducing Atlas
We're building motion systems for drones, robots, and autonomous machines.
Our work is in service of our customers who work factory floors, tend farmland, move medicine, and defend our country.
Atlas is built to supply them quickly, and supply them in volume. Their progress should no longer be held back by a motor or actuator.
Christian Mochen retweeted
This Veterans Day, we're honoring two veterans on the Atlas team.
Bryce McDonald served as an infantry officer in the U.S. Marine Corps. Mike Poppa served as a U.S. Navy SEAL.
From navigating complex supply chains to supporting customers around the world and shaping our culture, Bryce and Mike solve problems at every level of Atlas.
They set the standard for what it means to be part of our team. We're proud to work alongside them and grateful to all who have served.
Make art
One of the reasons you should make art is that it helps document moments of greatness so that you can inspire others
Took this pic a week ago while we took a few hours to film the unboxing of a new motor line
I’m blessed to work with savages @carlodlrs @CMochen @AveryMcmillan15 and the entire rest of the team
Hope to make art that glorifies them and their work
Christian Mochen retweeted
“Getting it manufactured still required several rounds of review and iteration.”
Design costs going to zero will make demand for profitable high mix, high vol production go to the moon
Valuations for manufacturers will follow.
Simple supply and demand.
Replying to @OpenAI
Two OpenAI team members worked with GPT-6 Astra to design a prototype device for talking to Codex in under a week, as a side project.
Astra designed the enclosure, circuit board, interface, and software. Getting it manufactured still required several rounds of review and iteration.
Christian Mochen retweeted
This panel was originally called “Industrialization of SI” until about midnight last night…
Enjoyed chatting w/ @danwrightSF and @7earse at this year’s @payloadspace Investor Summit
Incredible what @itsmoislam and team have built in a short time, privileged to be part of it
It’s genuinely impressive to see a hardware company invest this seriously in their website. @atlasmotion is one of the strongest I’ve seen from the industry in a long time.
Christian Mochen retweeted
successful robotic automation for different problems will need different actuators/reducers and to find the right answer it requires connecting the intent -> design -> manufacturing data -> performance data and back 1/
I've spent years in China building robots. I can tell you this playbook won't work:
Pre-seed $: Invent the ultimate actuator.
Seed $$: Figure out mass production.
The hard part isn't inventing a beautiful topology. It's process engineering.
The real delta between the US and China is the ability to execute the enormous amount of physical x mental work required to iterate thousands of times on product designs and manufacturing processes. Partly because much of the tacit knowledge / management systems has left the US, but also because of the sheer human labor required.
This isn't something you can solve by buying traditional automation lines either. It requires:
1. Ideation driven by human intuition. Walking up and down the line, talking to assembly workers, and developing hypotheses about what and where improvements can be made. Raw human hours and attention spent understanding a specific manufacturing process.
2. Large batches of experiments across multiple variables. Coordinating experiments that can easily involve 50+ people, issuing instructions, and tracking the execution and results of each experiment.
3. Purpose-built test design to identify key metrics. For example, a change in gear hobbing profile might require accelerated aging tests on a dyno, while a change in bearing tolerance might require NVH testing.
4. Data + human intuition-driven analysis. Interpreting test results and deciding whether to implement a process change or run the next set of experiments.
5. Strict process control. Reissuing work instructions to the line and ensuring every process is followed to the letter.
The best manufacturers in Asia, from Foxconn to TSMC, have built their competitive advantage on systematically turning thousands of physical experiments into better manufacturing processes.
Their moat isn't just production scale. It's the organizational machinery that makes manufacturing learn and improve faster than anyone else.
I don't think most people in SF clock this.
Say you're manufacturing actuator gears in-house in the US. After you've made your first article, will you hire a team of 10 process engineers to coordinate the 50 technicians running concurrent experiments with designs, processes, and materials?
To understand wear patterns across every pin in a cycloidal drive after 10,000 hours? To reduce backlash from 15 arcmin to 5, then to 1? Just to keep pace with China's progress over the next few years?
Do you have the skill set and management systems in place to do this?
That's why our approach isn't just to automate manufacturing. It's to automate the process of learning how to manufacture better.
Automate simulation and design optimization.
Automate assembly and physical testing.
Automate the design and execution of process experiments.
Feed real-world experimental data back into simulation to continuously improve sim-to-real accuracy.
The goal is to turn what currently takes 50 engineers coordinating thousands of experiments into a closed-loop system that continuously learns and improves.
Then the same robotic line mass-manufactures the product.
PS: Coming back to actuator design, many actuator designs are already commoditized in China. The project in the video is open source, and CNC machining cost in China is just $27 (watch the video).
I hope people understand this reality. I'll link more examples in the replies.
People are starting to realize.
This is why we built Atlas the way we did.
I've spent years in China building robots. I can tell you this playbook won't work:
Pre-seed $: Invent the ultimate actuator.
Seed $$: Figure out mass production.
The hard part isn't inventing a beautiful topology. It's process engineering.
The real delta between the US and China is the ability to execute the enormous amount of physical x mental work required to iterate thousands of times on product designs and manufacturing processes. Partly because much of the tacit knowledge / management systems has left the US, but also because of the sheer human labor required.
This isn't something you can solve by buying traditional automation lines either. It requires:
1. Ideation driven by human intuition. Walking up and down the line, talking to assembly workers, and developing hypotheses about what and where improvements can be made. Raw human hours and attention spent understanding a specific manufacturing process.
2. Large batches of experiments across multiple variables. Coordinating experiments that can easily involve 50+ people, issuing instructions, and tracking the execution and results of each experiment.
3. Purpose-built test design to identify key metrics. For example, a change in gear hobbing profile might require accelerated aging tests on a dyno, while a change in bearing tolerance might require NVH testing.
4. Data + human intuition-driven analysis. Interpreting test results and deciding whether to implement a process change or run the next set of experiments.
5. Strict process control. Reissuing work instructions to the line and ensuring every process is followed to the letter.
The best manufacturers in Asia, from Foxconn to TSMC, have built their competitive advantage on systematically turning thousands of physical experiments into better manufacturing processes.
Their moat isn't just production scale. It's the organizational machinery that makes manufacturing learn and improve faster than anyone else.
I don't think most people in SF clock this.
Say you're manufacturing actuator gears in-house in the US. After you've made your first article, will you hire a team of 10 process engineers to coordinate the 50 technicians running concurrent experiments with designs, processes, and materials?
To understand wear patterns across every pin in a cycloidal drive after 10,000 hours? To reduce backlash from 15 arcmin to 5, then to 1? Just to keep pace with China's progress over the next few years?
Do you have the skill set and management systems in place to do this?
That's why our approach isn't just to automate manufacturing. It's to automate the process of learning how to manufacture better.
Automate simulation and design optimization.
Automate assembly and physical testing.
Automate the design and execution of process experiments.
Feed real-world experimental data back into simulation to continuously improve sim-to-real accuracy.
The goal is to turn what currently takes 50 engineers coordinating thousands of experiments into a closed-loop system that continuously learns and improves.
Then the same robotic line mass-manufactures the product.
PS: Coming back to actuator design, many actuator designs are already commoditized in China. The project in the video is open source, and CNC machining cost in China is just $27 (watch the video).
I hope people understand this reality. I'll link more examples in the replies.
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If you believe robotics can maximize human potential, have exceptional ability in an area you love, have a passion for solving problems, and want to have fun—send me a dm.
I promise we will chat. Carpe diem!
Christian Mochen retweeted
Technical moats are mostly over at this point. If you’re building something that is meant to be durable, the only moats now are:
• Proprietary data
• Network effects
• A physical component
Christian Mochen retweeted
Extremely excited for these guys @atlasmotion - western indsutrial supply chains are constrained and not getting better overnight.
@CMochen @tombaron @carlodlrs are setting out to change just that - starting with a facility in Long Beach and bridging the Pacific divide with joint manufacturing 🇺🇸🇵🇭
great hat @nunzi46
30 days of Hard Tech: Day 3 🚀
Varda announced their $250m Series D
I break it down with Mike Annunziata (Mike is your favorite VC's favorite VC)
He was the first check in Varda and now a board member
"At its core, Varda is a company doing in-space manufacturing and reentry and that capability unlocks very cool business models across defense, pharmaceuticals, and semiconductors"
"We'll be generating significant revenue off of those missions and it's not all R&D. Every mission we launch, we learn. All that we learn goes into the next vehicle and into the next series of vehicles"
"We will know we've been successful when reentry is as boring as launch"
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Christian Mochen retweeted
I watched the state of the force, then I had the kids watch it. The three most important words: Autonomous War Command.
Christian Mochen retweeted
Panasonic, Dyson, Nidec, Toshiba, Mitsubishi and Toyota all manufacture in the Philippines. An ecosystem producing motors, components and finished products by the millions.
Neil and EJ are two of the engineers building Atlas who came out of that world.
Neil came to Atlas from Dyson, where he led electromagnetic design for the motor inside the famous Airwrap. He obsesses over the unglamorous details in winding, overmoulding, busbar fusing, that turn a great motor design into something you can actually make thousands of times.
EJ started out building ultra-efficient vehicles for the Shell Eco-marathon at UST. He later joined Dyson as a mechatronics engineer, working across electromechanical design, process engineering, and line automation.
When we decided to start by building in the Philippines, the bet was that world-class engineering and manufacturing talent was already there, with years of experience building really difficult products.
We feel very lucky to have them.




























