Alex Podkin

founder @gearsdotco - gears and gearboxes for robots

[Alex Podkin @alexpodkin]

@dirtman yessir!

[Alex Podkin @alexpodkin]

Monday, it’s finally time to come to the shop, open the door, greet some people with big forklifts and then hide in the office until they finish doing their thing, iykyk

[Alex Podkin @alexpodkin]

@JasonPremoMFG And every time you skip ads the machine will add -0.0001 wear on a random tool 😅

[Alex Podkin @alexpodkin]

@GarrowGeer @asimovinc 🤝

[Jakob Diepenbrock @jakobdiepen]

🇺🇸 The Race for Hyper Techno Industrialism w/ @isaiah_p_taylor (CEO, @valaratomics) & @shaunmmaguire (Partner, @sequoia)

A conversation from the @DiscipulusVent Cohort in El Segundo.

Including:
> Why the best builders are congregating in the Gundo
> Hyper techno industrialism & the race against China
> Why hardware will look like software did 20 years ago
> How China actually plays the game
> OSC, raw material subsidies, & catching up
> The Caltech wind tunnel fluke that built SoCal hardware
> What’s next: biology, robotics, & gigawatts at a cent
> Why everyone is underestimating the hardware buildout

Highlights:
(00:00) The race for hyper techno industrialism
(01:07) Why El Segundo: touring every US hard tech hub
(02:15) You bump into great engineers in the Gundo
(03:07) A generation that wants to build things that matter
(04:08) 10 to 100 to 100,000: the race against China
(05:22) Hardware is about to look like software 20 years ago
(06:50) How China really plays the game
(08:38) OSC & subsidizing the supply chain
(09:43) Catching up vs. winning in the net new
(10:45) Americans as the world’s best product engineers
(11:31) Subsidizing raw materials & the Inconel problem
(13:20) Growing up with SoCal as America’s defense base
(14:31) The macro rotation back to hardware
(16:13) The Caltech wind tunnel fluke
(17:46) Designed in El Segundo, scaled in Texas
(18:13) The founding cohort of crazy people
(19:07) What’s next: biology & robotics infrastructure
(20:03) Start what seems a little crazy today
(20:23) Cheap energy & the aluminum lesson
(21:55) We’ll need 100x what people think

[Dylan Morris @Dylan_Morri]

Who loves big gears?

[Alex Podkin @alexpodkin]

@Dylan_Morri

[Alex Podkin @alexpodkin]

@jimbelosic Congrats!

[Alex Podkin @alexpodkin]

@elonmusk 🫡

[Alex Podkin @alexpodkin]

@YETIsix9ine @GarrowGeer visited the shop (it’s really cool, thanks @DenverRayburn for a tour!) a couple weeks ago together with @GarrowGeer, he posted some photos, but also there’s a bunch of photos from other accounts

[Alex Podkin @alexpodkin]

@JoshuaSteinman

[Alex Podkin @alexpodkin]

@DavidSacks is this return of the usaid? 🤔

[Alex Podkin @alexpodkin]

@MatthewChang 🤝

[Alex Podkin @alexpodkin]

@emm0sh @thesalomander is solving this

[Alex Podkin @alexpodkin]

@yeokch 🫡

[Alex Podkin @alexpodkin]

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/

[Yeok @yeokch]

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.

[𝔊𝔞𝔯𝔯𝔬𝔴 𝔊𝔢𝔢𝔯 @GarrowGeer]

This Boomer is so old he can't type "reindustrialize sweater" into his browser to see that it actually costs $16 and he wants to lecture us on reindustrialization?

Molson, you sell cheap plastic toys.

I have the cheapest plastic on Earth.

I could give you discounts you wouldn't believe.

You want to reindustrialize?

DM me.

[Alex Podkin @alexpodkin]

@GarrowGeer Good idea

[Alex Podkin @alexpodkin]

@AmericanStator 🤝

[Alex Podkin @alexpodkin]

we expect billions of robots everywhere, from our homes to the battlefield and it’s absolutely important that we can build them here in America.

Gears and gearboxes are core components and that’s why im building @gearsdotco 🫡

[Alex Podkin @alexpodkin]

i see a lot of discussions about bootstrapping / equity financing / debt / grants / etc and what should be used, i think the right answer is all of it 6/