Evan Helda

Physical AI & Robotics @ Nebius $NBIS Writer & podcast host @ exploring physical AI and the future of being human. 🔗 below

[Evan Helda @evanhelda]

It’s time for a new adventure. I’m joining one of the top AI clouds, Nebius (@nebiusai), as their Head of Physical AI.

I’m also leaving my first professional love — spatial computing. At least in its most direct and advertised form, i.e. AR, VR, 3D, aka ‘XR’.

But in another sense, I’m not leaving at all.

I’m going deeper.

As I've hammered on my newsletter — Dream Machines — spatial computing is far more than XR.

This is just one end-point; just one form of a computer relying on sensors to localize, track, and understand the physical world.

Self-driving cars, mobile robots, humanoids, autonomous drones...

These are all ‘spatial computers’, each relying on a similar technical foundation, with similar computer vision tech, simulation tools, and ‘spatial’ data types, e.g. lidar, point clouds, 3D, video, IoT/sensors.

The key difference?

Autonomy and their ability to impact/interact with the physical world.

So again, I’m not leaving. I’m just moving downstream; from a ‘spatial tributary’ to a gushing river, surging with capital, progress, and commercial opportunity.

Jensen Huang popularized the term 'physical AI' about a year ago, and as it goes... Interest and awareness has exploded ever since.

To be sure, Jensen likes to live 10+ years in the future. Heck, so do I.

But physical AI remains early and is entering a hype cycle of its own. One that, at first glance, feels like the hype cycle that sucked me into XR.

But this feels different. The timing feels right: early, but not too early...

If you want to know why... check out the essay below 👇👇

It'll give you a L100-200 grasp of this new tech stack and the key innovations worth monitoring.

And if you enjoy, do subscribe!

There'll be a Part II... covering some of the challenges that remain, including the ones that I’ll be trying to solve in my new role at Nebius, as their Head of Physical AI.

Lastly, if you work in this space and want to connect. Drop me a DM.

Otherwise... See you on the front lines🫡

@EndicottInvests @mvcinvesting

[Evan Helda @evanhelda]

Imagine when intelligence is "solved". Imagine there's a final API for world: a single brain that can do anything, anywhere; the physical AI holy grail.

Even then… you've still got a problem.

You can't just load it into a robot, walk it into your lab, and hope it works.

You need rigorous, domain-specific evals. CI/CD for robots. Thousands of scenario tests before a single one touches real infrastructure — or real lives.

In this clip, the Bifrost co-founders argue the real bottleneck of the robot age isn't training. It's evaluation.

And they're already living it: most of their customers use them for evals more than training.

The moonshot? An "imagination engine" running on edge compute — where robots roll out 40 versions of a scenario in their own heads before deciding what to do.

Wild stuff. And maybe closer than we think.

For a deeper dive, check out the full episode below👇

Apple Pod: https://t.co/XnlQYcNesr

Spotify: https://t.co/WMLxSMfW74

[Evan Helda @evanhelda]

One of the most common debates in robotics...

Can you really train robots in simulation… and have them just work in the real world?

The answer is far from a clean yes or no, and the conundrum has a name: Sim2Real.

In this clip, Bifrost's co-founders demystify it. We break down:

- The 4 layers of Sim2Real (perception, dynamics, contact forces, deformables)

- Where it actually works

- Where it absolutely doesn't

- And the mind-bending trick of "sim → sim → sim → real": bouncing a policy across physics engines until reality becomes just one more.

Best line of the whole convo? "Reality is just another type of physics engine."

Make of that what you will.

Full episode here: https://t.co/zhV2bQX1rt

[Evan Helda @evanhelda]

"Sim-2-real" is one of the most common buzz words in robotics.
But very few truly understand what it means, why it matters, and how to harness it.
This episode will change that.

[Adrian Macneil — 🤖/acc @adrianmacneil]

Robot fight club - possibly the most SF thing ever?

Last week I dropped in to @UFBots (Ultimate Fighting Bots) at Temple nightclub, organized by @vitl2907 and @XeniaBulatov and powered by @nebiusai.

Pretty insane event! All of the fighting robots were @UnitreeRobotics, although in the crowd I did spot the adorable Abi by @AndromedaRobots (with her signature bubble gun).

In this video I speak with Vitaly about UFB and his vision for the future of robotics.

[Evan Helda @evanhelda]

Today’s guest is Will McDonald, co-founder and Chief Product Officer of Miris.

Miris is rethinking how high-fidelity 3D content gets delivered at scale. Their thesis: the future of 3D depends entirely on simplifying distribution.

In that vein… we talk about why pixel streaming falls short, how Miris uses volumetric data instead, and why that matters for everything from e-commerce and gaming to robotics and simulation.

Will and I worked together for many years at AWS, so we also dive into lessons learned from Amazon’s customer obsessed culture, what it gets right, and experience of going from big tech back into startups.

If you care about spatial computing and simulation, and where the 3D internet is heading, this one’s worth the time.

With that I bring you, Will McDonald.

Full episode here: https://t.co/fLCcX13gYm

[Evan Helda @evanhelda]

If you’re a millennial or beyond, few things are as memorable as Sesame Street.

That said, I hadn’t thought about Sesame Street or its wonderful characters in a long time. Not until this conversation with Pasquale D’Silva.

Pasquale is on a mission to recreate the magic of Sesame Street, but with the power of AI and robotics.

In our convo, he waxed poetic about Jim Henson; his vision, his philosophy, and his blueprint for creating characters that deliver joy.

Pasquale is following Jim’s blueprint, but he’s adding a unique spin: the art of clowning.

Yes, you heard me. Clowning.

Sounds strange, but bare with me… It connects back to robotics; particularly, building robots we’ll actually want.

Pasquale’s mission sparked something in me. Perhaps because my daughter Chloe is turning one and becoming a sponge. Or perhaps, because this mission is as important as any other in the AI race.

So I wrote a letter to good ol’ Jim to explain why 👇

https://t.co/ZWxJf37zrC

[Evan Helda @evanhelda]

If you’re a millennial or beyond, few things are as memorable as Sesame Street.

That said, I hadn’t thought about Sesame Street or its wonderful characters in a long time. Not until this conversation with Pasquale D’Silva.

Pasquale is on a mission to recreate the magic of Sesame Street, but with the power of AI and robotics.

In our convo, he waxed poetic about Jim Henson; his vision, his philosophy, and his blueprint for creating characters that deliver joy.

Pasquale is following Jim’s blueprint, but he’s adding a unique spin: the art of clowning.

Yes, you heard me. Clowning.

Sounds strange, but bare with me… It connects back to robotics; particularly, building robots we’ll actually want.

Pasquale’s mission sparked something in me. Perhaps because my daughter Chloe is turning one and becoming a sponge. Or perhaps, because this mission is as important as any other in the AI race.

So I wrote a letter to good ol’ Jim to explain why 👇

https://t.co/ZWxJf37zrC

[Evan Helda @evanhelda]

https://t.co/tB0sgXqh75

[Evan Helda @evanhelda]

🦾 RobotOps: Why running physical AI is nothing like running software

We've spent a decade perfecting how to run AI at scale.

Model registries. Training pipelines. Dashboards. Drift detection. The whole assembly line.

It works beautifully... for AI that lives safely behind an API in the cloud.

But physical AI? Robots? Autonomous machines moving through the real world, all day, every day?

That's a completely different animal.

And it demands an entirely new discipline: RobotOps.

---

First, a quick primer. 🏁

The existing playbook is called MLOps, how dev teams reliably train, deploy, and operate machine learning models at scale.

MLOps was built for the internet. It assumes AI lives in a clean, predictable digital bubble. Nothing explodes. Data changes slowly. When something breaks, it's a math problem, you check a dashboard, retrain the model, push an update.

The artifacts are tidy: models, datasets, training code. The feedback loop is tidy: click-through rates, accuracy scores, loss curves. Human teams decide what to fix and when.

Clean. Controllable. Comfortable.

RobotOps blows all of that up.

--

The difference starts with what you're actually operating. ⚙️

In MLOps, you're managing models.

In RobotOps, you're managing behavior and behavior lives in the physical world, which does not care about your clean abstractions.

The artifacts multiply: perception models, control models, sensor calibrations, 3D maps, world representations, simulation environments, and enormous streams of multimodal sensor data captured during real-world operation.

Code and models still matter. But they're no longer the center of gravity.

--

The feedback loop is a different beast entirely. 🔁

In MLOps, you close the loop through digital signals. The model predicts. The user clicks (or doesn't). You log it, analyze it, retrain on a human-defined schedule.

In RobotOps, the loop runs through the physical world itself.

A deployed model produces behavior. That behavior meets an unpredictable environment. Sensors capture the consequences. Those logs must be ingested, indexed, graded, and transformed into new training data and new simulation scenarios.

This loop is continuous. Not episodic.

Training, validation, and operations collapse into one always-on learning system or at least, that's the goal.

---

And failure? Failure carries a completely different weight. 🏋️

In MLOps, failure is annoying. A user sees the wrong ad. An irrelevant search result surfaces. Embarrassing, maybe costly but recoverable.

In RobotOps, failure is physical.

Damaged hardware. Safety incidents. Regulatory nightmares. Or worse: a bad model silently poisoning future training data, compounding errors over time before anyone notices.

A bad model doesn't just output a wrong number. It creates a dangerous event.

This is why provenance, knowing exactly where your data came from, isn't a best practice in RobotOps. It's a survival mechanism. You need to know *exactly* which model, environment, and scenario caused a robot to twitch. Not for debugging convenience. For safety.

---

Now for the gap that turns into a canyon: simulation. 💥

In MLOps, you look backward. Validate models on historical data. Shadow-deploy alongside existing systems. The model sits behind an API, observing the world without touching it.

In RobotOps, you have to look forward.

You can't just ask how a model performs on past data. You have to ask: how does it *behave* when the world pushes back?

To answer that, you need simulation. You need to run candidate models through thousands of scenarios, rare edge cases, sudden obstacles, lighting changes, before that code ever touches a physical machine.

Here's the hard truth: for most teams, this is a pipe dream.

Ignore the shiny visuals in keynote demos. Building a photorealistic, physics-accurate virtual world doesn't just require engineering talent. It effectively requires building an in-house AAA game studio.

Today, most teams use simulation sparingly, a bit of synthetic data here, some basic component testing there. It lives on the periphery.

But this will invert. Simulation is moving from a supporting role to the center of the development loop. The primary environment for validation, regression, and learning.

First, though, some fundamental problems need solving, fragility in simulation pipelines, massive 3D asset dependencies, and the inability to reliably replay thousands of runs in exactly the same way.

Not trivial. But solvable.

---

Finally: automation. 🛞

MLOps automation is largely pipeline-driven and rule-based. Humans decide what data to collect, when to retrain, which models to promote. Automation speeds up execution. Intent stays human-defined.

In RobotOps, the complexity quickly exceeds what humans can manage manually. Deciding which data is missing, which edge cases matter, which scenarios to simulate next, which models should evolve, it becomes a constant cognitive bottleneck.

This is where AI-native automation becomes not just helpful, but necessary.

The early signals are already here: vision-language models auto-labeling sensor data, world models grading synthetic scenario quality, agents proposing new simulation campaigns based on observed failures.

Over time, these agents will operate entire segments of the learning loop on their own.

That's the real inflection point, when RobotOps systems start improving themselves faster than humans could ever direct.

---

So where does this leave us? 🎬

Existing MLOps tools still matter. Model registries, training pipelines, orchestration frameworks, they're not going away.

But they operate at too low a level for physical AI.

RobotOps demands higher-order abstractions:

Scenarios, not datasets. Behaviors, not predictions. Simulation campaigns, not experiments. Data grading, not drift detection. Learning loops, not deployment cycles.

In this sense, RobotOps isn't just the next evolution of MLOps.

It's the operational layer for embodied intelligence, systems that learn through action, adapt through experience, and operate under physical constraints.

The holy grail? A fully automated, physical AI data flywheel.

We're not there yet. But the discipline is being built, right now, in real time.

And the teams that figure it out first?

They won't just be building better robots.

They'll be building the infrastructure that the entire physical AI era runs on.

--
Read more about this concept, and others like it, at Dream Machines ⬇️

[Evan Helda @evanhelda]

https://t.co/Dz8ixNUB3h

[Evan Helda @evanhelda]

💻 MLOps was built for software. RobotOps was built for reality.

MLOps lives in the calm. RobotOps lives in the crash, the scrape, the signal loss, the world pushing back.

MLOps breaks in dashboards. RobotOps breaks in factories.

MLOps predicts at a distance. RobotOps acts at close range.

MLOps was designed for intelligence behind glass. RobotOps is designed for intelligence in motion, under pressure, under consequence.

In MLOps, failure is often informational. In RobotOps, failure can be physical, expensive, public.

MLOps is what happens when models stay safely behind the screen. RobotOps begins the moment those models leave the screen and enter the scene.

MLOps built the assembly line for digital intelligence. RobotOps must build the assembly line for embodied intelligence.

MLOps was born in systems that sit and observe. RobotOps was born in systems that move, act, and collide with reality.

MLOps was built for systems that predict. RobotOps is built for systems that act.

This contrast is the foundation of my latest essay below.

It goes deep into all the gaps between MLOps and RobotOps, and then follows Maya, a robotics engineer, through a long day of trying to spin the physical AI data flywheel. 🛞

Link here ⬇️

[Evan Helda @evanhelda]

New essay, with a sci-fi twist — 2033: The Future of Robot Learning. 🤖 👇

My last essay contrasted MLOps with ‘RobotOps’: the discipline required to break intelligence out of tiny screens and into the physical world.

It also followed a day-in-the-life of a robotics developer (named Maya) and a stressful one at that; fumbling and bumbling her way through the lifecycle of robotics data, i.e. a physical AI ‘data flywheel’.

Today, we’re blasting Maya into the future.

This is what it feels like when the data flywheel runs itself.

Enjoy.

PS: When I first wrote this, it was before the release of agentic tools/harnesses like OpenClaw. Suddenly, this stuff is not nearly as sci-fi as you might think.

Much of ‘2033’ will likely be pulled into 2028-29, if not sooner. Curious if you agree... let's hear your thoughts in the comments.

https://t.co/QCv52jHTz0

[Evan Helda @evanhelda]

My last essay contrasted MLOps with ‘RobotOps’: the discipline required to break intelligence out of tiny screens and into the physical world.

It also followed a day-in-the-life of a robotics developer (named Maya) and a stressful one at that; fumbling and bumbling her way through the lifecycle of robotics data, i.e. a physical AI ‘data flywheel’.

Today, we’re blasting Maya into the future.

This is what it feels like when the data flywheel runs itself.

Essay below. Enjoy. 👇

PS: When I first wrote this, it was before the release of agentic tools like OpenClaw.

Suddenly, this stuff is not nearly as sci-fi as you might think. Much of ‘2033’ will likely be pulled into 2028-29, if not sooner.

https://t.co/WPdGzyyt14

[Evan Helda @evanhelda]

My last essay contrasted MLOps with ‘RobotOps’: the discipline required to break intelligence out of tiny screens and into the physical world.

It also followed a day-in-the-life of a robotics developer (named Maya) and a stressful one at that; fumbling and bumbling her way through the lifecycle of robotics data, i.e. a physical AI ‘data flywheel’.

Today, we’re blasting Maya into the future.

This is what it feels like when the data flywheel runs itself.

Essay below. Enjoy. 👇

PS: When I first wrote this, it was before the release of agentic tools like OpenClaw.

Suddenly, this stuff is not nearly as sci-fi as you might think. Much of ‘2033’ will likely be pulled into 2028-29, if not sooner.

https://t.co/WPdGzyyt14

[Evan Helda @evanhelda]

https://t.co/0YwDibjFvW

[Evan Helda @evanhelda]

Character is the final AI Frontier

Most companies building AI characters are asking: "How do we make the technology more convincing?"

Illusion of Life is asking a different question entirely.

"How do we puppeteer humanness into the machine?"

Their process starts with humans LARPing as the character. Acting it out. Feeling it. Then working backwards to figure out what's worth automating and what isn't.

Eye contact, for example. Before building a computer vision system, they just... pointed the eyes manually.

And asked: does this actually change how people feel? Is it worth solving technically?

That's not a tech-first approach. That's a theatre-first approach.

Their bet: in a world of infinite AI agents, Character is the final frontier. Character, with a capital C.

Not the model. Not the hardware. But the spirit flowing through it.

This might be the one of the most important ideas in AI nobody is talking about.

🎧 Full conversation on Dream Machines, link below ⬇️

[Evan Helda @evanhelda]

The people Silicon Valley actually needs... are the ones most afraid of AI 👀

Artists. Storytellers. Film makers. The creatives who've been panicking, they're the secret weapon.

Because personality can't be coded.

It has to be felt.

🎧 Hear the full conversation at Dream Machines - link in bio.

[Evan Helda @evanhelda]

Amen #PhysicalAI

[Lenny Rachitsky @lennysan]

My biggest takeaways from @qasar:

1. The real AI revolution over the next 5 to 10 years will happen in the physical world, not in software. While everyone obsesses over ChatGPT, Claude and coding agents, the real impact will come from autonomous vehicles, mining robots, and farming equipment. They’ll save lives (over 30,000 die annually in U.S. car accidents), enable mobility for disabled people, solve labor shortages in dangerous industries where nobody wants to work, and much more.

2. AI isn’t replacing jobs in industries like trucking and farming—it’s arriving just in time to fill a labor gap that already exists. The average age of a farmer in the U.S. is in the late 50s. Long-haul trucking jobs go unfilled not because people can’t do them but because the tradeoff isn’t worth it anymore; a family can choose DoorDash or Uber so the parent can pick up their kid. Qasar’s view is that physical AI will fill gaps created by demographic shifts and changing preferences, not displace workers who want those roles. He’s careful to say this doesn’t mean there are no downsides, but that the framing of “AI is coming for your job” misses the more immediate reality.

3. Comparing Chinese AI companies to American AI companies is a category error. Qasar uses Huawei as his example: the company’s name means “China’s ambition,” roughly a quarter of its employees are Communist Party members, and its goal is not to grow profits but to extend the state. So when people say Chinese EVs are outcompeting Detroit, they’re comparing a government-backed entity with no profit constraint to companies like Rivian that get hammered by public investors for losing money. Qasar says that if American companies were freed from profit expectations the same way, they’d field comparable products. The point isn’t that China is incompetent or not a serious competitor; it’s that the comparison framework most people use is wrong.

4. The Industrial Revolution is the best mental model for AI. Just like the late 1800s brought child labor and monopolies but also unprecedented access to healthcare, heating, cooling, and material goods, AI will have downsides we must address while delivering massive benefits. The key: don’t pump the brakes on technology to protect jobs—that hurts the people you’re trying to help most. Find solutions that account for workers while enabling progress.

5. Building under the radar can be your competitive advantage. Qasar built Applied Intuition for nearly a decade without a social media presence. One of the company’s early core values was “Our best work is done alone and quietly.” His reasoning: every minute spent on a podcast, a post, or content for public consumption is a minute not spent on customers and the product. Qasar adds an important caveat—he could afford to stay quiet because he was already known in the ecosystem. Founders without an existing network may need the visibility that public presence creates.

6. Qasar thinks most Silicon Valley CEOs lack taste—both in the artistic sense and in the sense of making good operational decisions—because their life experience is too narrow. A founder who grew up in Cupertino, went to Berkeley, and immediately started a company has never experienced what it’s like to be at the bottom of a 100,000-person organization. Qasar spent over a decade at GM and Bosch and says that experience—the bureaucracy, the bad tools, the disconnected leadership—directly informs how he leads Applied Intuition today. His broader point is that taste comes from exposure to a wide range of human experience: backpacking, reading old books, working in different cultures and industries.

7. Successful companies almost always show traction early. If you’re two years in and the market isn’t giving you increasingly specific signals about what to build, consider resetting. The foundation might be wrong—co-founders, market, or life phase. Your first startup is practice; treat it as building the muscle of being a founder, not as your magnum opus.

8. Emotions are a filter that distorts decision-making, and the goal should be to remove that filter so the “raw image” of the decision comes through. Qasar doesn’t mean leaders shouldn’t have empathy; he means that attachment to your own idea, the desire to be right, and the tribal instinct to follow the loudest voice are all emotional distortions. His practical heuristic: the same decision, presented to multiple people independently in the company, should produce the same result. If it doesn’t, some emotional filter is warping the signal. This connects to his broader philosophy of creating a culture where the best idea wins regardless of who proposed it or how senior they are.

9. Qasar’s advice on company values: don’t invent them philosophically. Instead, write down the 5 to 10 things that explain why your company is already successful, and those become your values. Applied Intuition’s values include “Move fast, move safe,” “Never disappoint the customer,” “Technical mastery,” “High output matters,” “Laugh a lot,” and “Half of the work is follow-up.”

10. Treat your first startup as a zero—a practice round, not destiny. Qasar tells founders leaving Applied Intuition to start companies that their first three years will likely produce nothing, and that’s fine. Founding is a craft, like woodworking. If your first table is wobbly, you don’t quit—you build another one. He thinks a lot of founders, especially first-timers, put so much pressure on themselves to succeed immediately that they miss the real value of the experience: learning and building the muscle. His own third company is the most successful by far, and he sees this pattern repeatedly. There are entire funds focused exclusively on multi-time founders for exactly this reason.

[Lenny Rachitsky @lennysan]

My biggest takeaways from @qasar:

1. The real AI revolution over the next 5 to 10 years will happen in the physical world, not in software. While everyone obsesses over ChatGPT, Claude and coding agents, the real impact will come from autonomous vehicles, mining robots, and farming equipment. They’ll save lives (over 30,000 die annually in U.S. car accidents), enable mobility for disabled people, solve labor shortages in dangerous industries where nobody wants to work, and much more.

2. AI isn’t replacing jobs in industries like trucking and farming—it’s arriving just in time to fill a labor gap that already exists. The average age of a farmer in the U.S. is in the late 50s. Long-haul trucking jobs go unfilled not because people can’t do them but because the tradeoff isn’t worth it anymore; a family can choose DoorDash or Uber so the parent can pick up their kid. Qasar’s view is that physical AI will fill gaps created by demographic shifts and changing preferences, not displace workers who want those roles. He’s careful to say this doesn’t mean there are no downsides, but that the framing of “AI is coming for your job” misses the more immediate reality.

3. Comparing Chinese AI companies to American AI companies is a category error. Qasar uses Huawei as his example: the company’s name means “China’s ambition,” roughly a quarter of its employees are Communist Party members, and its goal is not to grow profits but to extend the state. So when people say Chinese EVs are outcompeting Detroit, they’re comparing a government-backed entity with no profit constraint to companies like Rivian that get hammered by public investors for losing money. Qasar says that if American companies were freed from profit expectations the same way, they’d field comparable products. The point isn’t that China is incompetent or not a serious competitor; it’s that the comparison framework most people use is wrong.

4. The Industrial Revolution is the best mental model for AI. Just like the late 1800s brought child labor and monopolies but also unprecedented access to healthcare, heating, cooling, and material goods, AI will have downsides we must address while delivering massive benefits. The key: don’t pump the brakes on technology to protect jobs—that hurts the people you’re trying to help most. Find solutions that account for workers while enabling progress.

5. Building under the radar can be your competitive advantage. Qasar built Applied Intuition for nearly a decade without a social media presence. One of the company’s early core values was “Our best work is done alone and quietly.” His reasoning: every minute spent on a podcast, a post, or content for public consumption is a minute not spent on customers and the product. Qasar adds an important caveat—he could afford to stay quiet because he was already known in the ecosystem. Founders without an existing network may need the visibility that public presence creates.

6. Qasar thinks most Silicon Valley CEOs lack taste—both in the artistic sense and in the sense of making good operational decisions—because their life experience is too narrow. A founder who grew up in Cupertino, went to Berkeley, and immediately started a company has never experienced what it’s like to be at the bottom of a 100,000-person organization. Qasar spent over a decade at GM and Bosch and says that experience—the bureaucracy, the bad tools, the disconnected leadership—directly informs how he leads Applied Intuition today. His broader point is that taste comes from exposure to a wide range of human experience: backpacking, reading old books, working in different cultures and industries.

7. Successful companies almost always show traction early. If you’re two years in and the market isn’t giving you increasingly specific signals about what to build, consider resetting. The foundation might be wrong—co-founders, market, or life phase. Your first startup is practice; treat it as building the muscle of being a founder, not as your magnum opus.

8. Emotions are a filter that distorts decision-making, and the goal should be to remove that filter so the “raw image” of the decision comes through. Qasar doesn’t mean leaders shouldn’t have empathy; he means that attachment to your own idea, the desire to be right, and the tribal instinct to follow the loudest voice are all emotional distortions. His practical heuristic: the same decision, presented to multiple people independently in the company, should produce the same result. If it doesn’t, some emotional filter is warping the signal. This connects to his broader philosophy of creating a culture where the best idea wins regardless of who proposed it or how senior they are.

9. Qasar’s advice on company values: don’t invent them philosophically. Instead, write down the 5 to 10 things that explain why your company is already successful, and those become your values. Applied Intuition’s values include “Move fast, move safe,” “Never disappoint the customer,” “Technical mastery,” “High output matters,” “Laugh a lot,” and “Half of the work is follow-up.”

10. Treat your first startup as a zero—a practice round, not destiny. Qasar tells founders leaving Applied Intuition to start companies that their first three years will likely produce nothing, and that’s fine. Founding is a craft, like woodworking. If your first table is wobbly, you don’t quit—you build another one. He thinks a lot of founders, especially first-timers, put so much pressure on themselves to succeed immediately that they miss the real value of the experience: learning and building the muscle. His own third company is the most successful by far, and he sees this pattern repeatedly. There are entire funds focused exclusively on multi-time founders for exactly this reason.

[Nebius @nebiusai]

Robotics innovation depends on more than better models.

On 3/17 at #NVIDIAGTC, @evanhelda of Nebius joins leaders from @huggingface, @HyundaiEurope and more on a virtual panel to discuss how open ecosystems, shared infrastructure and cloud-first pipelines accelerate physical AI.

Add to your cal: https://t.co/Eg3aU7yq7B