Framework Ventures

A thesis-driven VC firm that builds alongside our founders.

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GPs: @pythianism + @im_manderson + @rajivpoc

[Framework Ventures @hiFramework]

1/ Today in @FortuneMagazine:

Framework Ventures has raised $400 Million for our 4th fund, FVIV.

We believe the future is being built at the intersection of blockchain, AI, robotics, energy, and fintech.

🧵👇

[Josh @joshavata]

Fun convo w @sonyatweetybird on how we're going to make gundams real

[Sonya Huang 🐥 @sonyatweetybird]

Biological intelligence to bootstrap robotic intelligence?

If robotics is about to enter its golden age, @MeckaAI is one of the companies feeding it, by sensorizing humans to train robots.

The biggest challenge robotics faces is the data flywheel cold start problem. LLMs got a free cold start from the internet: trillions of tokens, already written. Robotics gets nothing. Someone has to go create the training data to jumpstart the system.

@joshavata is the co-founder and CEO of Mecka, the data and deployment company for physical AI. Josh read every robotics paper he could find in late 2024, visited the labs, and made a bet: humans and humanoids look the same, so humans could act as the training fleet. Egocentric data, first-person video of people doing real work, could become vital fuel.

Two years ago, the biggest egocentric dataset was Ego4D at 4,000 hours. The field is now in the millions of hours, thanks in part to Mecka. We’re probably still orders of magnitude of training data away from robotics nirvana. But every rung on the physical intelligence ladder unlocks real commercial tasks. And eventually, Josh imagines a world with an order of magnitude more robots than people, of a plurality of shapes and sizes and use cases, as the Hitchhiker’s Guide to the Galaxy foretold :)

A few things we cover in this conversation.

- Why robotics has no cold start: pixels, actions and logic, and why pre-training data is a real market in robotics in a way it never was for LLMs

- What “teaching robots about carbon life” means: in sci-fi the final frontier is organic. The human brain runs on 20 watts and self-heals

- The egocentric bet: teleop couldn’t scale, humans and humanoids share a form, so Waymo-fy the humans. The first pilot was aunties and uncles recording themselves cooking on a mobile app

- Force data: video can’t tell a robot how hard to squeeze an egg. Wrist cameras are a hack. Mecka built the gloves instead

- Inside the data factory: a consumer app that streams your tasks, a B2B engine recruiting across “the taxonomy of all physical human work,” 100-gigabit upload lines, hundreds of petabytes

- Millions of hours vs. Tesla’s decade on two degrees of freedom, and why it’s a slow creep up, not a GPT-3 moment

- The home has the most data and is the hardest place to deploy. Laundry folding and assembly-line pick-and-place get solved in the next year or two

- Useful robots already exist: hotel delivery and cleaning bots in China and Southeast Asia, industrial cleaning in the US. Humanoids are the terminal state, not the first step

- Deployment diffusion: a modern systems integrator whose installed robot does something next week it couldn’t do last week. If Mecka ever helps deploy a robot that mines an asteroid, they’ve done their job

- The data pot of soup: a million hours of human data, tens of thousands of UMI, a few hours of in-domain teleop. Tactile and simulation haven’t entered the mix yet

- Move 37 for robotics: AlphaGo went behavior cloning, then self-play, then magic. The catch is the rules of the game are the entire physical world, and we don’t have the simulator yet

- Will the AGI labs steamroll robotics? “It takes a lot of courage to scale up data with imperfect information.”

- The Kawasaki Corleo, the robot panther-dog-horse on Mecka’s conference room wall, and why the future of robotics looks like Hitchhiker’s Guide to the Galaxy

Timestamps:

0:00 – Introduction
1:46 – What Mecka is: pixels, actions and logic
2:45 – Is robot data economically feasible without the internet?
4:15 – Teaching robots about carbon life
5:27 – Cloud kitchens, stablecoins and the path to robotics
7:03 – Entering robotics with a fresh lens
8:45 – What egocentric data is and why Mecka bet on it
10:09 – The first pilot: aunties and uncles with a mobile app
10:42 – Force data and tactile gloves
11:55 – Inside the operations of a data factory
14:41 – Heat-mapping the taxonomy of human work
15:42 – Quality gates, fraud and the McDonald’s POV videos
17:44 – Earning a margin as a data intermediary
19:33 – Millions of hours vs. Tesla’s decade
20:55 – Why it isn’t binary: unlocking commercial tasks along the way
22:02 – Home data is plentiful; home robots are the hardest
23:24 – Useful robots can be deployed today
24:35 – When robotics gets an FSD-style flywheel
26:42 – Deployment diffusion and the modern systems integrator
28:21 – Favorite vertical robot categories; mining an asteroid
30:08 – The hardware slate: Waymo-fying people
30:55 – EgoVerse
32:04 – The data pot of soup: the converged training mix
33:54 – Move 37 for robotics: AlphaGo and Federer
36:02 – World models and the training mix
36:58 – Will scaling the current mix get us to the promised land?
39:05 – Will the AGI labs steamroll robotics?
40:50 – Sci-fi roots and the Kawasaki Corleo
42:44 – How many robots in a decade?

[Vance Spencer @pythianism]

🦾🦾🦾

https://t.co/xBmccIylS1

[Josh @joshavata]

Frontier models still struggle to accurately understand human video data for robotics

The biggest gap is actions: GPT-Astra hits 71.5% accuracy, Meckas custom trained model achieves 94%

@tkkong + @philhchen evaluates Meckas video understanding model against the frontier below

[Vance Spencer @pythianism]

The winning robotics data business has a much higher terminal value than investors are underwriting today.

Robotics will be a long race with many twists and turns, but when the ChatGPT moment hits, the winning data player will be in a very strong strategic position to capture token value. They will have large revenue, a strong balance sheet, talent density, deployments experience, and hardware supply chain knowledge. When it comes time to blitzscale they will be amongst the players that can capture the most value.

The reason this is misunderstood today is because most people are using traditional LLM data businesses as a prior for robotics data businesses and what their terminal value could be. I posit this is incorrect framing. Because the model and harness are integrated in LLMs, there is very little room for data players to play meaningfully in the token value flow in the long term.

In robotics this is exactly the opposite, the model and harness (hardware) are separate, the pretraining era is just beginning, there are many more dependencies on distributors/deployers, and there is a large surface area for other players than just models to capture value. Open source will also play a larger role earlier in the robotics race, as all prior LLM learnings will be incorporated as the race is run. This also creates opportunities for long term infrastructure players.

There is a massive amount of terminal value in winning robotics data and participating in the path of the industry from a position of strength.

From first principles:

The endgame of robotics will take place over the next decade. Empirically, today, no player has enough data, enough model intelligence, or sufficient hardware to facilitate a fast revenue takeoff in the field. No player has reasoned through the right sales motion either, which can take years to develop pilots and move to production. Serious players are correctly focused on scaling up pretraining/deep learning vs. who has the best model, deployment, or hardware today.

The robotics data business takes participants on a global hunt to build data, supply chains, and hardware. Along the way it also teaches deployment and deep knowledge of SOTA model development. Winning data participants also get the first crack at the still nascent robotics RL market, which will likely be the next large revenue stream after pretraining. As they compete to win data, data players must develop incredible talent density across both Asia and the US. The winning data business will have both Asian and North American DNA. Canada 🇨🇦 encompasses an interesting cultural mixture of both of these parts of the world. Bullish Canadian Robotics Mafias, more specifically Waterloo/Toronto.

So:

What would constitute a strong strategic position to capture value once the ChatGPT moment of robotics hits? You need a large revenue base, a strong balance sheet, and talent density + experience across data, deployments, hardware, supply chain, and RL. The hyperscalers, the frontier labs, and the winning data player all qualify.

Anyone under time pressure in what will be a long race is in a weaker strategic position in my view. Given the costs of integrated hardware+software+deployment, this would actually be most robotics-native players. They will depend on VC interest, which can be fickle.

[Framework Ventures @hiFramework]

1/Today in @FortuneMagazine:

Framework Ventures has led a $50M Series A for Ultra (@Ultraroboticsco), a company building AI-native robotics for the logistics industry.

Total funding = $62M.

Ultra is also unveiling a strategic partnership with @physical_int. 🧵👇

[Ultra @Ultraroboticsco]

@im_manderson @JonMSchwartz Thank you Michael! Couldn't ask for a better lead than Framework. Lots of work ahead, excited to build together!

[Jon Miller Schwartz @JonMSchwartz]

@im_manderson @Ultraroboticsco 🦾🦾🦾 Framework x Ultra 🦾🦾🦾

[Physical Intelligence @physical_int]

Excited to see our partners at @Ultraroboticsco announce their $50M Series A! Ultra is a great example of what's becoming possible when general-purpose robot foundation models meet real-world deployments!

[Michael Anderson @im_manderson]

We just led a $50M Series A for Ultra (@Ultraroboticsco), bringing its total raised to $62M. Very excited to partner with @JonMSchwartz and the rest of the team!

In under a year, Ultra has deployed AI-native robots across 9 states that have packed 500k+ real orders for paying logistics customers.

Ultra is also partnering with @physical_int.

[ryan.wallet @Ryan__Barney]

Robotics is compounding in real time thanks to the Data and Deployment layers. This week our portfolio company @MeckaAI announced its Series B, and today we are thrilled to be leading @Ultraroboticsco latest round alongside their deepening partnership with @physical_int. Welcome to the Framework physical AI portfolio: Ultra! Thesis on data and deployments in my pinned post.

[Rajiv Patel-O’Connor @rajivpoc]

Deployment is the ultimate test of robotics. At scale, it's highly non-trivial and requires getting financing, operations, manufacturing, and technology right, all at the same time. Thrilled to partner w/ @JonMSchwartz, Max, Oliver, @chetan_, and the entire @Ultraroboticsco team.

[Nick @ntrlsk]

Very excited to announce that Framework is leading the $50m Series A of Ultra Robotics.

@JonMSchwartz , Chetan, Oliver and Max saw where robotics was heading early. After spending years working in 3D printing/manufacturing, they decided to bring those skills over to 3PL robotics. We were impressed by their obsession with customers and how quickly they were able to put together OP-1.

With the rise of LLMs and their ability to generalize, and the sheer scale of data being gathered to train frontier models, we believe there will be more robots than people, and we will reindustrialize. The Ultra team picked the right vertical to start and hill climb. The models get better every day, and we believe the capabilities in the next year will be unimaginable relative to where they are currently, with greater dexterity, speed and planning.

Deploying robotics is different from deploying LLMs. With software it's easy to ship a product off to many companies building on top of it. Robotics is much harder: many more supply chains, physical constraints, environmental challenges, connectivity, battery, reliability, wear and tear, temperature, etc. The real world is HARD, and the clients Ultra is fortunate to work with will all have their own challenges. Robots are capex heavy, which requires creative financing, reliability and real labor output.

We believe Ultra is stacked to handle these challenges given their speed, their obsession with customers, and the ever-improving models as the wind in their back. Thank you Jon and the rest of the team for giving us the opportunity to work together.

[Chetan @chetan_]

We've seen incredible progress in robotics over the last few years -- a cambrian explosion of low cost hardware options and the birth of scaling laws in robot intelligence -- yet deployed robots doing real work are still rare. At Ultra, we are laser focused on changing that.

[Jon Miller Schwartz @JonMSchwartz]

There cannot be a ChatGPT moment for robotics without robots. That's why at Ultra, we're focused on the industry's biggest bottleneck: deployment.

Since the very beginning, we've chosen to deploy before it was comfortable, learning lessons that only customers and the chaos of the real world could teach us.

Those lessons became the foundation for OP1, a robot designed from the ground up for the realities of scaled deployment. Through repetition, we've built the operational muscle and knowledge needed to scale the installation, operation, and servicing of our robots. And most importantly, we've learned how to enable our customers to succeed with them.

It's easy in robotics to become completely engrossed with the robot itself. But technology only matters when it's useful to someone. The best robot is the one people can rely on every day to build, do, and achieve more.

It's time to deploy more robots.

[Ultra @Ultraroboticsco]

Robotics has a distribution problem. Today, Ultra is announcing $62M in funding to solve it, including a $50M Series A led by @hiFramework.

We believe that deployment is the bottleneck in robotics.

It’s relatively easy to make a robot look impressive in a controlled environment. It’s much harder to put a robot into the open world, make it useful, keep it running, earn the trust of the people working alongside it, and do that over and over and over again.

So that’s what we’re focusing on.

[Framework Ventures @hiFramework]

@physical_int 5/ Welcome to the Framework tent!

[Framework Ventures @hiFramework]

1/Today in @FortuneMagazine:

Framework Ventures has led a $50M Series A for Ultra (@Ultraroboticsco), a company building AI-native robotics for the logistics industry.

Total funding = $62M.

Ultra is also unveiling a strategic partnership with @physical_int. 🧵👇

[Framework Ventures @hiFramework]

@physical_int 4/ Why we led: most of robotics is still in the lab, chasing general-purpose or humanoid demos.

Ultra took the opposite bet: take the most capable models available today and put them to work, with paying customers.

We think 500k+ shipped orders is proof the strategy works.