kevin gmelin

American roboticist. Engineer @ robot learning startup. CMU robotics alum.

[kevin gmelin @KevinGmelin]

Seeing this makes me excited about the potential for humanoid robots as consumer products. The sheer versatility is exciting. People might not pay $20k for a robot that just does laundry, or just dusts. But imagine it can do a few things, and then every month, a software update ships with completely new capabilities. One month you get the soccer update. The next month you get the landscaping update. Then the pool cleaning update. Etc.

For me personally, a skill-level-adjustable soccer playing humanoid is worth at least $5k of value.

[Skild AI @SkildAI]

We trained a robot to play football.

How? Self-play for 140 years in a virtual World Cup.

Meet the #Messinator:

[Lina Khan @linamkhan]

Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products. We shouldn’t let discussions about new legal regimes distract from the fact that there’s no AI exemption from laws already on the books — a point @FTC emphasized repeatedly during my tenure.

1. There is an extensive set of laws that govern dangerous and defective products. For example, releasing unvetted AI models or agents can violate consumer protection laws. Shipping flawed AI tools without implementing adequate measures to detect and stop rogue or defective AI agents can be an “unfair or deceptive” act or practice under the FTC Act (and analogous state laws). And some state AGs are already exploring holding AI firms and their CEOs criminally liable when their models participate in criminal activity.

2. Existing laws also prohibit “unfair methods of competition.” This covers instances where AI firms appropriate the competitively sensitive information of their customers, including through tracking their use of various tools. It can also cover instances where firms pursue dangerous behavior, aware that doing so may compel rivals to do the same.

As the Supreme Court has noted: “A method of competition which casts upon one's competitors the burden of the loss of business unless they will descend to a practice which they are under a powerful moral compulsion not to adopt, even though it is not criminal, was thought to involve the kind of unfairness at which the [unfair methods of competition] statute was aimed."

3. The highly concentrated and interconnected structure of these markets could be creating major risks and conflicts of interest. We had started investigating these partnerships and cross-investments across the stack (and released a preliminarily overview of some findings: https://t.co/jJ5cS3Pin3).

Both federal and state enforcers should be scrutinizing these opaque relationships and inter-dependencies. We are already seeing how these relationships could undermine accountability. For example, OpenAI could face liability given the Hugging Face incident, but Hugging Face being bought up by Nvidia means that we’re unlikely to see it file a lawsuit over this — given Nvidia’s strong incentive to see OpenAI continue full speed ahead.

4. As AI tools dramatically change the landscape of cybersecurity risks and hacks, all businesses should be doubling down on having core security protections in place. Firms that fail to invest in adequate data security measures or fix known vulnerabilities can also be breaking the law. A recent analysis showed that around 1/3 of Fortune 100 companies do not even have a way to notify them about security issues. During my @FTC tenure, we sued firms for poor data security practices and held CEOs liable when they were personally responsible.

https://t.co/nwZ5Av8fOK

https://t.co/KjRye8y9SY

5. As policymakers consider new legal regimes, we should be looking to lessons from prior efforts to govern major sectors, such as banking and other networks, platforms, and utilities. Tools like structural separations, nondiscrimination, and supervision could be key, and there’s a rich history of what works and what doesn’t. But we can and must pursue any new efforts alongside enforcing existing laws.

[kevin gmelin @KevinGmelin]

This is the most reasonable take I’ve seen so far on AI slowdowns, which is quite fortunate to be coming from individuals tied to the government.

[David Sacks @DavidSacks]

Dario has written that we need to “pace the frontier,” and Sam has agreed. People may be surprised by my response: go ahead.

You guys are the frontier. By any reasonable metric — market share, revenue growth, model capability — the two of you have a duopoly on frontier intelligence. You’ve also claimed the lead is widening because of recursive self-improvement.

I don’t see what you see in the lab. If the unreleased models are scary enough that you think you should slow down, I support your decision to be responsible.

But stop pretending you need anyone else’s permission. Stop pretending antitrust law has to be suspended so you can form a cartel. Stop pretending you need a regulatory approval process that supersedes product liability. Stop pretending METR is independent when it is intertwined with Anthropic’s investors and staff. Stop pretending you need those same evaluators to police competitors who aren’t even at the frontier.

Most of all, stop pretending the motivation to slow down is purely altruistic. You face massive product-liability exposure if your products enable a truly damaging cyberattack. The market already punishes models that behave in unpredictable or unauthorized ways. After the Hugging Face episode, it is simply good business for OpenAI and Anthropic to trade some raw power for reliability and predictability. Call it alignment if you want. It is also just giving customers what they want.

Pacing the frontier would also create breathing room for a more intelligent conversation about regulation than Bernie Sanders’ “shut it all down.” China is very unlikely to join a global agreement, as you know, and that has to be taken into account as well.

So go ahead and pace the frontier. You are the ones setting it. The easiest way not to build superintelligence is for you to agree not to build it. Demanding your preferred regulatory framework as the price of that will look like blackmail of the public and the political system. So just do it.

If you do, you’ll buy goodwill for the next conversation. If you don’t, we’ll know this was just another bid for regulatory capture — or an election-season psyop.

[kevin gmelin @KevinGmelin]

While I’m still figuring out my own stance on ASI, RSI, etc, my current thoughts are most closely related to, and inspired by, @natolambert post on lossy self improvement: https://t.co/JV7gTwgynw

[kevin gmelin @KevinGmelin]

There’s been a lot of discussions lately on RSI.

My main skepticism of an imminent singularity comes from two points: knowledge work is not the only bottleneck for most things important to humanity, and the rising marginal difficulty of discovery/invention, where it may simply become too expensive to make gains at current compute/energy costs for a given problem.

I’m excited about Skild particularly for solving the first problem. Bottlenecks in progress will quickly shift more and more towards the cost and speed of execution in the physical world. An AI designed chip, for instance, is useless without all the physical labor involved in building the data center it runs in. Robotics seems like the best shot towards solving these physical bottlenecks.

And very similarly, robotics is not solely bottlenecked by model research. It takes a lot of effort to get robots out of the lab and deployed for economically useful work.

I’ve really loved the culture at Skild, which emphasizes the importance of not just cutting edge models, but all other aspects of deploying such systems.

Solving robotics is key to humanity’s future, and building the muscle for deploying robots is a necessity in getting there.

[Skild AI @SkildAI]

We just hit 100M ARR within 10 months of starting deployments.

We are in factory lines. On construction sites. In kitchens. In data centers.

Cleaning. Welding. Building. Cooking.

Deploying.

[kevin gmelin @KevinGmelin]

I finally feel justified in my earlier predictions that gpt-level robotics is achievable within two years.

This feels reminiscent of gpt3, where the innovation was in few shot prompting.

Curious if the next step for robotics looks like chat gpt, or more like o1 reasoning.

[Skild AI @SkildAI]

Introducing S1, our new foundation model that learns from one example.

It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning.

Watch S1 operate in real-time via in-context learning:

[kevin gmelin @KevinGmelin]

This is really exciting for the future of actually deploying robots! Normal language-conditioned policies are fun, but language feels woefully inadequate for describing lots of the tasks I want robots to do. Seems way easier to just quickly show the robot what you want done

[Skild AI @SkildAI]

Introducing S1, our new foundation model that learns from one example.

It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning.

Watch S1 operate in real-time via in-context learning:

[Skild AI @SkildAI]

We have acquired Zebra Technologies’ robotics arm (formerly Fetch Robotics).

This is what happens when orchestration meets intelligence -- a major step toward fully autonomous warehouses.

More robots. More environments. One unified brain.

[Deepak Pathak @deepakpathak]

We hosted Prof. Alyosha Efros (UC Berkeley) at @SkildAI! He didn't believe that robots could actually cook eggs reliably. :)
Tested back-to-back 5times without fail! One batch of scrambled eggs every ~2.5mins nonstop. The same model assembles a GPU on a server rack too.

[Skild AI @SkildAI]

Robotics is a data problem.

Today, we’re partnering with @ABBRobotics, @Universal_Robot, and @NVIDIARobotics to deploy the Skild Brain across real-world industries from manufacturing to factory lines.

This will help us build the world’s biggest data flywheel for physical AI.

[Skild AI @SkildAI]

India, we’re here! 🇮🇳🇺🇸

From one Silicon Valley to another, we’re thrilled to open our newest office in Bengaluru.

[Deepak Pathak @deepakpathak]

At @SkildAI, we’ve raised $1.4B, bringing our valuation to over $14B.

We’re on a generational mission, and I’m grateful to be working alongside an exceptional team. Thanks to our investors for the long-term conviction towards omni-bodied intelligence 🚀

https://t.co/8BrqxxGVZe

[Skild AI @SkildAI]

Announcing Series C

We’ve raised $1.4B, valuing the company at over $14B

With this capital, we will accelerate our mission to build omni-bodied intelligence 🚀

https://t.co/q0ArkRa8L8

[Skild AI @SkildAI]

Let robots clean in peace 🧺🤖✨

[Skild AI @SkildAI]

Humans learn by watching. Robots should too.

[Skild AI @SkildAI]

Unfortunately, cooking is not our talent. So we teach our robots to cook for us.

[Skild AI @SkildAI]

Happy Holidays!! 🎄🎄

[Skild AI @SkildAI]

giving our humans (and robots) a break at skildsgiving ‘25

[kevin gmelin @KevinGmelin]

It's going to take a lot of dark web videos to mimic the chainsaw results, if learning from human videos alone...

[Skild AI @SkildAI]

We built a robot brain that nothing can stop.

Shattered limbs? Jammed motors? If the bot can move, the Brain will move it— even if it’s an entirely new robot body.

Meet the omni-bodied Skild Brain:

[Skild AI @SkildAI]

We built a robot brain that nothing can stop.

Shattered limbs? Jammed motors? If the bot can move, the Brain will move it— even if it’s an entirely new robot body.

Meet the omni-bodied Skild Brain: