Mar 19, 2025
The Math Professor Betting Against Tesla's Playbook
An interview with Vlad Voroninski, Founder of Helm
Founder Focused
While Tesla dominated headlines with flashy full self-driving promises, a mathematician-turned-CEO was quietly building something different. Vlad Voroninski spent two years in pure R&D mode—zero product development, just research that seemed like a "pipe dream" to everyone else.
His company, Helm AI, has now raised over $100 million and partnered with Honda to help automakers compete directly with Tesla. But Voroninski's approach defies Silicon Valley's typical playbook: instead of chasing the shiny object of fully autonomous driving, he focused on the unglamorous but massive market of partial automation.
In this interview, Voroninski reveals why betting against the crowd's L4 obsession, building foundation models before they were trendy, and turning AI simulation into a competitive moat has positioned Helm as the dark horse in autonomous driving's biggest race.
Watch the full interview now on EO's YouTube channel! Below is the complete transcription of the interview. Minor edits have been made for clarity and readability.
Key Highlights:
"The end goal is achieving fully autonomous driving. We allow automakers to compete with Tesla by bringing to market cutting edge autonomous driving systems. We've raised over $100 million."
"We will see very, very scalable commercialization happen, but I would say that starting a company felt like drinking from a fire hose."
"Pitching kind of like seemed like a pipe dream, pure R&D for two years. There was zero product development during that time. Everyone thought it was crazy, but we committed to that vision."
"We actually carried out the research and we were able to make that work very much all or nothing, and it might take a long time for the world to adjust."
"We will see very, very scalable commercialization happen, but I would say that starting a company felt like drinking from a fire hose."
"Pitching kind of like seemed like a pipe dream, pure R&D for two years. There was zero product development during that time. Everyone thought it was crazy, but we committed to that vision."
"We actually carried out the research and we were able to make that work very much all or nothing, and it might take a long time for the world to adjust."
The Math Professor Who Cracked Self-Driving
Can you introduce yourself and tell us about Helm AI?
Vlad: My name is Vlad Voroninski. I'm the CEO of Helm AI. Helm is an AI software company focusing on a unified approach to autonomous driving that goes all the way from L2+ through fully autonomous driving L4. We have partnerships with companies like Honda, and we allow automakers to compete with Tesla by bringing to market cutting edge autonomous driving systems.
The end goal is achieving fully autonomous driving, and there are a number of technological and commercialization challenges along the way. These days, one of the key areas that we're focusing on is AI-based simulation, leveraging generative AI as well as our unsupervised learning IP in order to create a unified approach to solving autonomous driving that essentially unifies both partial automation and full automation.
The end goal is achieving fully autonomous driving, and there are a number of technological and commercialization challenges along the way. These days, one of the key areas that we're focusing on is AI-based simulation, leveraging generative AI as well as our unsupervised learning IP in order to create a unified approach to solving autonomous driving that essentially unifies both partial automation and full automation.
What drew you to the autonomous driving space originally?
Vlad: I first got interested in self-driving cars and computer vision during undergrad, so I was part of the UCLA Computer Vision lab while they were competing in DARPA Grand Challenges. We were in the off highway vehicle recreation area. So we just did our first autonomous path tracking test and I just thought that was a very exciting area and was focusing on computer vision at the time.
But I decided to pursue mathematics in academia for about 10 years with the intent to come back to the space when it was more mature because I saw that as the key bottleneck to AI in the sense that the biggest challenge in reading research papers in AI or computer vision was ultimately mathematical—just a question of do you understand the equations, right? So in some sense I looked at math as a tool to be able to solve AI down the road. The goal was always to come back to the autonomous driving space.
But I decided to pursue mathematics in academia for about 10 years with the intent to come back to the space when it was more mature because I saw that as the key bottleneck to AI in the sense that the biggest challenge in reading research papers in AI or computer vision was ultimately mathematical—just a question of do you understand the equations, right? So in some sense I looked at math as a tool to be able to solve AI down the road. The goal was always to come back to the autonomous driving space.
The 2016 Inflection Point
What made you decide it was the right time to jump back into autonomous driving?
Vlad: Around 2016, it became clear that the technology was really taking off in terms of deep learning. There was an inflection point in where AI technology was going with deep learning at the time, and simultaneously there was a very clear opportunity that it was the right time to jump into that space.
Because what I witnessed was a lot of companies making certain decisions that I actually didn't agree with, right? It was such an inefficient space at the time that it was very clear to me that with the right approach you can add a lot of value. It's a strategy that's kind of stood the test of time as opposed to many companies that peaked early and then died off or just kind of ran out of money and what that meant was that there was an opportunity. So after my academic career moved back to California to start Helm.
Because what I witnessed was a lot of companies making certain decisions that I actually didn't agree with, right? It was such an inefficient space at the time that it was very clear to me that with the right approach you can add a lot of value. It's a strategy that's kind of stood the test of time as opposed to many companies that peaked early and then died off or just kind of ran out of money and what that meant was that there was an opportunity. So after my academic career moved back to California to start Helm.
How did COVID impact your early days as a company?
Vlad: COVID was a very tricky time for everyone obviously, but also in the automotive market in particular because it basically caused a halt in production. All the different auto factories and all the automakers had to immediately start dealing with that issue versus everything else, and I think it caused a bit of a delay in the deployment of autonomous driving technology.
But beyond that I mean I would say quite exciting. Ultimately I don't know if it was like—I think challenges were there but they were outweighed by how exciting it was to start a company and make a truly deep tech bet in the space.
But beyond that I mean I would say quite exciting. Ultimately I don't know if it was like—I think challenges were there but they were outweighed by how exciting it was to start a company and make a truly deep tech bet in the space.
The All-or-Nothing R&D Gamble
Tell us about those early hires and the culture you built at Helm.
Vlad: Our first 10 hires were basically all just very, very strong researchers. Any one of those people could have easily walked away and done something else. Some of those people even made certain sacrifices of their academic career to come work at Helm because they were very excited about the vision, and I think that helped us really mold the engineering culture.
When we were pitching Helm in 2016, unsupervised learning kind of seemed like a pipe dream almost, right? But we committed to that vision. We actually carried out the research and we were able to make that work, so that was quite exciting.
When we were pitching Helm in 2016, unsupervised learning kind of seemed like a pipe dream almost, right? But we committed to that vision. We actually carried out the research and we were able to make that work, so that was quite exciting.
That sounds like a risky approach—two years of pure R&D with no product development.
Vlad: I mean there was the fact that for 2 years we were essentially developing that technology and there was zero product development during that time. So it's pure R&D for 2 years, very much all or nothing. So obviously there's risk involved in that, but it was a very creative time, so I mostly just appreciate it.
Outperforming the Market by 200x
What was that breakthrough moment when you proved your technology worked?
Vlad: Back in 2018, we made a foray into foundation models, even before that term was coined. What we used that foundation model for was to essentially build an autonomous driving system that we were able to show actually outperforms the systems you were able to buy in the market by pretty wide margins.
So we essentially conducted a series of tests where we put our autonomous vehicle on a very steep and curvy mountain road—scenarios where essentially you have to make very rapid driving decisions as far as taking the various turns in a challenging landscape. We were able to achieve much better disengagement rates up to a factor of 200 better than what was out there on the market, and that is how we got the attention of some of the brand name automakers in the world in the early days.
So we essentially conducted a series of tests where we put our autonomous vehicle on a very steep and curvy mountain road—scenarios where essentially you have to make very rapid driving decisions as far as taking the various turns in a challenging landscape. We were able to achieve much better disengagement rates up to a factor of 200 better than what was out there on the market, and that is how we got the attention of some of the brand name automakers in the world in the early days.
The AI Simulation Revolution
How has your focus shifted to AI-based simulation in recent years?
Vlad: In the last couple of years we've been doing a lot of innovation in generative AI and combining that with our in-house technology which is deep teaching in order to close the gap between AI based simulation and reality. So essentially that means how do you simulate driving data or driving footage, sensor data from driving without actually having to get into a car and there are many advantages to doing that.
For example, very large fleets, right, they can be useful for collecting data in order to address difficult corner cases for autonomous driving, but the rate of occurrence of those corner cases goes down exponentially as your system improves and so you end up actually paying exponentially more to gather interesting data as you get further into the development process. So it's really not a good property and what simulation allows you to do is generate all the interesting data without actually having to deploy a fleet.
For example, very large fleets, right, they can be useful for collecting data in order to address difficult corner cases for autonomous driving, but the rate of occurrence of those corner cases goes down exponentially as your system improves and so you end up actually paying exponentially more to gather interesting data as you get further into the development process. So it's really not a good property and what simulation allows you to do is generate all the interesting data without actually having to deploy a fleet.
How does this give automakers an advantage over Tesla's approach?
Vlad: For example, Tesla has a very large fleet. Other automakers don't have access necessarily to internal fleets that are that large. So even if they wanted to take the same approach as Tesla, they would not be positioned to do so. The only alternative to doing that is essentially AI-based simulation, and until very recently it wasn't possible to generate highly realistic simulation data, but that's very much changing these days due to the advent of generative AI.
Combining generative AI with technologies like deep teaching provides a highly scalable simulation platform that allows you to essentially deploy a virtual fleet, so to say, instead of a real world fleet. You're just learning from existing data. So that's a recent inflection point that we're definitely proud to be part of and contributing to.
Combining generative AI with technologies like deep teaching provides a highly scalable simulation platform that allows you to essentially deploy a virtual fleet, so to say, instead of a real world fleet. You're just learning from existing data. So that's a recent inflection point that we're definitely proud to be part of and contributing to.
Can you tell us about VidGen 1 and WorldGen 1?
Vlad: VidGen 1 and WorldGen 1 are foundation models for generative AI simulation. VidGen is a foundation model that creates highly realistic video data from a multitude of different cameras, essentially arbitrary cameras, arbitrary locations.
WorldGen is a foundation model that takes a further step in that it actually simulates the entire autonomous driving stack. You can actually use WorldGen to technically drive a car because it does make predictions about what's gonna happen next. So if you input data from your autonomous driving stack, it'll tell you what's gonna happen in the next several seconds and that includes the path that the vehicle should take to perform certain actions. You can actually use it to drive so it's technically a self-driving system that not only functions as a simulator or in a simulated environment but also can function in the real world.
WorldGen is a foundation model that takes a further step in that it actually simulates the entire autonomous driving stack. You can actually use WorldGen to technically drive a car because it does make predictions about what's gonna happen next. So if you input data from your autonomous driving stack, it'll tell you what's gonna happen in the next several seconds and that includes the path that the vehicle should take to perform certain actions. You can actually use it to drive so it's technically a self-driving system that not only functions as a simulator or in a simulated environment but also can function in the real world.
Winning Over Skeptical Automakers
How do you actually convince customers in the autonomous driving space?
Vlad: I think there's two important aspects at least. So one, I think, is seeing is believing, not only having marketing materials or video demos, but actually being able to put somebody in a car and have that autonomous car navigate unforeseeable situations. I think it sends a very powerful message about the robustness of the technology and the product.
And secondly, I think that working on a production contract—there are going to be other contracts along the way, right? I think it's not really possible for a major car company to give a production contract to a supplier as the first contract, so there's going to be some sequence of contracts along the way. So I think that being able to execute on those contracts and deliver exactly what you signed up to deliver is critical because ultimately what you're entrusted with as a supplier is providing not only safety critical technology but technology that is absolutely necessary to have in a certain timeline because you're talking about a production program that has to launch in a particular year.
And secondly, I think that working on a production contract—there are going to be other contracts along the way, right? I think it's not really possible for a major car company to give a production contract to a supplier as the first contract, so there's going to be some sequence of contracts along the way. So I think that being able to execute on those contracts and deliver exactly what you signed up to deliver is critical because ultimately what you're entrusted with as a supplier is providing not only safety critical technology but technology that is absolutely necessary to have in a certain timeline because you're talking about a production program that has to launch in a particular year.
What's the third factor in building customer trust?
Vlad: Maybe a third thing I would add is just not only demonstrating your current state of where your technology is, but demonstrating the difference from one time to another, right? So being able to show, OK, here's where we are at this point in time. And then in a month we expect to be here and actually showing that differential and allowing them to measure not only your position but also your velocity when it comes to technology development.
Swimming Against the Current
What was the hardest part about building Helm, especially with such an ambitious goal?
Vlad: Especially when you're going after such an ambitious play like autonomous driving requires quite a lot of conviction. The hardest part about it was just—if you're a researcher, I think that your key job is really to perform the research and then you put it out there and there's some marketing aspect to that, but it's not nearly as significant, I think, as what you have to do for a company.
Generally as a startup founder you have to wear so many different hats. People say when they go to MIT, for example, that it's kind of like drinking from a fire hose. I never had that experience, but I would say that starting a company felt like drinking from a fire hose.
Generally as a startup founder you have to wear so many different hats. People say when they go to MIT, for example, that it's kind of like drinking from a fire hose. I never had that experience, but I would say that starting a company felt like drinking from a fire hose.
How did you handle being contrarian when most funding went to L4 companies?
Vlad: In our particular case, I think we were in some sense working against the grain in that the vast majority of the funding went toward companies taking a totally different approach—they were pursuing pure play L4. When I first started to engage people about the fact that we're gonna really focus on partial automation as the key market, everyone thought it was crazy.
But I think that it basically just emphasizes more this notion of the importance of having grit because you can't expect what you think to be the predominant worldview and it might take a long time for the world to adjust, right? I think we only started seeing signs of the world adjusting to our point of view some number of years into the company, maybe 4 years in, 5 years in, but then every year our position has improved not only as a function of the technology and the product but also because our strategy was adapted to a certain worldview that we believed would eventually materialize and that is now happening.
But I think that it basically just emphasizes more this notion of the importance of having grit because you can't expect what you think to be the predominant worldview and it might take a long time for the world to adjust, right? I think we only started seeing signs of the world adjusting to our point of view some number of years into the company, maybe 4 years in, 5 years in, but then every year our position has improved not only as a function of the technology and the product but also because our strategy was adapted to a certain worldview that we believed would eventually materialize and that is now happening.
Explore more