By AJ Abdallat, CEO, Beyond Limits
In less than a year on the grid, we found ourselves competing against teams with over five years of racing experience. Winning was never our only goal — our mission was to use the track as a live testbed to de-risk autonomous technology before deploying it in sensitive, high-stakes operations.
So when our race car spun out at the most famous turn in American motorsport; the legendary “corkscrew” at Laguna Seca, it wasn’t just a racing setback. It was a masterclass in why the future of industrial AI must keep humans in the loop. Let me share what we learned at one of racing’s most hallowed grounds, and why it matters for every industry we serve.
To understand the significance of our experience, you need to understand Laguna Seca itself. This isn't just any racetrack, it's a cathedral of motorsport, a place where racing legends have been forged since 1957.
Built on the rolling hills of California's Central Coast, Laguna Seca emerged from necessity. The dangerous Pebble Beach Road Races, which used public roads winding through pine forests, had become too risky. So, Monterey County invested $1.5 million, a fortune in 1957, to create a purpose-built racing circuit that would be both challenging and safe.
What they created was something extraordinary: a 2.238-mile ribbon of asphalt that would test every aspect of man and machine. From that first race in November 1957, won by Pete Lovely in a Ferrari, Laguna Seca hosted the greatest names in motorsport. Can-Am legends like Bruce McLaren and Denny Hulme. Trans-Am titans like Mark Donohue and Parnelli Jones. IndyCar champions from Mario Andretti to Alex Zanardi. MotoGP superstars like Valentino Rossi and Marc Márquez.
But of all the challenges this track presents, none is more famous or more feared than Turn 8.
The corkscrew at Laguna Seca isn't just a turn it's a test of everything that makes racing great. Officially designated as Turns 8 and 8A, this downhill left-right sequence drops 59 feet in just 450 feet of track length. That's the equivalent of falling off a five-and-a-half-story building while trying to navigate a complex series of turns.
The approach is blind. Drivers crest a hill at high speed, unable to see what lies ahead until they're fully committed. Then comes the plunge, a 16% gradient that sends cars hurtling downward while demanding precise steering inputs and perfect brake modulation. The physics are brutal: cars literally lift wheels off the ground as they navigate the compression and direction changes.
This is where Alex Zanardi pulled off what many consider the greatest pass in IndyCar history. On the final lap of the 1996 CART race, trailing Bryan Herta, Zanardi dove to the inside of the corkscrew, a move that seemed impossible. He somehow found grip where none should exist, threading his car through a gap that defied logic, emerging ahead as they climbed out of the turn. It was a moment that perfectly captured what makes the corkscrew legendary: it rewards those who can find the impossible line, who can see opportunity where others see only risk.
For nearly seven decades, the corkscrew has been the ultimate test of racing skill. It's where champions are made. It's where the margin between triumph and disaster is measured in inches and milliseconds.
Our Caltech partnership team had every reason for confidence going into the Indy Autonomous Challenge at Laguna Seca. During test trials, our car posted the third-fastest lap time, a performance that put us in contention for victory. We had the speed, the technology, and the strategic vision to pursue an aggressive approach aimed at a podium finish.
For two laps, everything worked beautifully. Our AI was making thousands of calculations per second, navigating Laguna Seca's challenging layout with precision that would have impressed the track's legendary champions. Through the fast sweepers, the technical sections, the elevation changes our system was executing flawlessly, demonstrating the incredible potential of artificial intelligence in dynamic, high-stakes environments.
Then came lap three, and our appointment with destiny at the corkscrew.
As our car approached Turn 6, building speed for the run toward the track's most famous section, everything seemed normal. The AI was processing sensor data, calculating optimal racing lines, managing the delicate balance between speed and control that defines competitive racing. But as we entered the sequence leading to the corkscrew, something began to go wrong.
The system that had performed so brilliantly for two laps suddenly found itself facing a challenge it couldn't fully comprehend. The blind approach, the dramatic elevation change, the complex physics of the downhill plunge all combined to create a scenario that pushed our AI beyond its limits.
In that moment, as our car carried too much speed into the corkscrew's unforgiving embrace, physics took over. The AI system, despite its sophisticated algorithms and lightning-fast processing, couldn't overcome the fundamental laws that govern racing at the edge of performance. The car spun, our race ended, and our real education began.
But here's what made that moment so valuable: it happened at a place where the greatest drivers in history have faced similar challenges, where the margin between success and failure has always been razor-thin, where the difference between legend and footnote is often measured in the ability to adapt when conditions exceed expectations.
What we discovered in analyzing that moment at the corkscrew taught us something profound about the nature of intelligence, both artificial and human.
Our AI system was making decisions based on what it could "see" in the immediate future, a planning horizon that worked perfectly for most of Laguna Seca's challenges but proved insufficient for the complex sequence approaching the corkscrew. Like a driver focused only on the next few feet of track, the system couldn't anticipate far enough ahead to make the right decisions early enough.
Think about what Alex Zanardi did in 1996. His legendary pass wasn't just about car control in the moment, it was about seeing an opportunity several corners earlier, positioning his car perfectly through the preceding turns, and executing a plan that required thinking multiple steps ahead. The corkscrew rewards those who can anticipate, who can see the bigger picture, who can plan for what's coming rather than just react to what's happening.
This is a fundamental challenge in AI: the balance between processing speed and planning depth. The faster you need to make decisions, the shorter your planning horizon becomes. But some situations like the approach to the corkscrew, like complex industrial processes, require you to think several steps ahead, to anticipate consequences that won't manifest for seconds, minutes, or even hours.
In industrial settings, this translates directly into the challenges our customers face every day. A manufacturing process might run smoothly for hours, but without the ability to anticipate and prepare for upcoming changes, a shift in raw material quality, a scheduled maintenance window, changing market demands, or evolving regulatory requirements even the most sophisticated AI can find itself unprepared when conditions change.
This is why Beyond Limits builds Human-in-the-Loop AI that combines the speed of artificial intelligence with the strategic thinking and long-term planning capabilities that humans excel at. Our approach recognizes that the most complex challenges require both the rapid processing power of AI and the anticipatory intelligence that comes from human experience and intuition.
Our racing experience at Laguna Seca also revealed something crucial about the gap between theoretical models and real-world performance, a lesson that resonates deeply in every industrial application we serve.
Our AI system had been trained primarily on lower-speed scenarios and more predictable track conditions. It had learned to navigate turns, manage acceleration, and maintain control but mostly under conditions that were far less extreme than racing at the edge of performance on one of the world's most challenging circuits.
When pushed to the absolute limits at Laguna Seca, the system's understanding of vehicle dynamics proved incomplete. It was like learning to drive in a parking lot and then being asked to navigate the corkscrew at racing speeds. The fundamental skills are there, but the extreme conditions reveal gaps in knowledge and experience that can only be filled through exposure to real-world complexity.
This challenge is endemic in industrial AI, and it's why our Human-in-the-Loop approach is so valuable. Laboratory conditions, simulation environments, and controlled testing can only take you so far. Real industrial environments are messy, unpredictable, and full of edge cases that no training dataset can fully capture, just like the corkscrew presents challenges that no amount of simulation can fully prepare you for.
Consider the parallels in our industrial applications. In energy operations, our AI solution might perform flawlessly under normal conditions, but when faced with extreme weather events, equipment failures, or unprecedented demand patterns, human operators bring irreplaceable real-world experience and intuition. They've seen similar situations before, they understand the broader context, and they can guide AI through scenarios that weren't fully captured in training data.
In manufacturing, the equivalent of our "corkscrew moment" might be a quality issue that emerges gradually across multiple production lines, or a supply chain disruption that requires creative problem-solving. Our AI can identify patterns and correlations, but human experts provide the context about supplier relationships, customer requirements, and production priorities that determine the best response.
Perhaps most importantly, our experience at the corkscrew highlighted the critical importance of understanding the gap between what AI wants to do and what physical systems can deliver; a lesson that applies directly to every industrial environment we serve.
Our analysis showed that in those crucial moments before the spin, there was a significant mismatch between the steering commands our AI was generating and what the car's hardware could execute. The AI was making perfectly logical decisions based on its model of the world, but the physical reality of steering response times, tire grip limits, and braking capabilities created a gap that proved decisive.
This is exactly what Zanardi understood in 1996. His legendary move “The Pass” wasn't just about having the courage to attempt an impossible maneuver, it was about understanding precisely what his car could and couldn't do in that specific moment, under those exact conditions. He knew the limits of his hardware, the capabilities of his tires, the response characteristics of his steering and brakes. That intimate understanding of physical limitations enabled him to find the impossible line through the corkscrew.
This lesson resonates deeply in industrial applications, where the gap between theoretical optimization and practical implementation can determine success or failure. AI systems can optimize processes, predict maintenance needs, and recommend operational changes with incredible sophistication. But if those recommendations don't account for the physical limitations of equipment, the practical constraints of human operators, or the economic realities of implementation, they remain theoretical rather than practical.
Our Human-in-the-Loop approach ensures that AI recommendations are filtered through human understanding of real-world constraints and capabilities. Human operators know when a theoretically optimal solution isn't practically achievable, and they can guide AI systems toward recommendations that are both intelligent and implementable.
Our decision to pursue an aggressive racing strategy at Laguna Seca, pushing for a podium finish based on our strong test performance, illustrates another crucial insight into AI and human decision-making that applies across all our industrial applications.
The AI system was executing the strategy we had planned: maintain higher speeds, take more aggressive lines, push the performance envelope. It was doing exactly what we asked it to do, following our strategic direction with precision and consistency. But conditions at the corkscrew demanded a more conservative approach.
This is where human intelligence provides irreplaceable value. A human driver approaching the corkscrew might have sensed something: a subtle change in tire grip, an intuitive feeling about the car's balance, or simply the wisdom to know when pushing the limits becomes too risky. That kind of contextual judgment, that ability to override programmed behavior when circumstances demand it, is uniquely human.
Think about what separates great racing drivers from merely fast ones. It's not just speed, it's knowing when to push and when to back off, when to take risks and when to preserve equipment, when to follow the plan and when to adapt to changing conditions. The legends of Laguna Seca from Pete Lovely to Alex Zanardi to Valentino Rossi all shared this ability to read situations and make strategic adjustments in real-time.
This is why our industrial AI solutions are designed to provide intelligent recommendations while keeping human operators in control of strategic decisions. Our Operations Advisor can suggest optimizations and identify opportunities, but human experts make the final calls about when to push performance and when to prioritize safety and reliability.
In energy operations, this might mean our AI identifies an opportunity to increase output during peak demand periods, but human operators decide whether current equipment conditions and weather forecasts make that aggressive approach advisable. In manufacturing, our systems might recommend accelerating production to meet a rush order, but human experts consider the broader implications for quality, worker safety, and long-term equipment health.
The beauty of pushing AI systems to their limits at a place like Laguna Seca whether at the corkscrew or in industrial applications is that you discover exactly where those limits are and why they exist. Our racing experience has taught us that the most valuable insights come from understanding failure modes, not just success stories.
This philosophy drives our approach to industrial AI development. We don't just test our systems under normal operating conditions, we explore edge cases, stress scenarios, and failure modes. We want to understand not just how our AI performs when everything goes right, but how it behaves when things go wrong.
More importantly, we design our Human-in-the-Loop systems to handle these edge cases gracefully. When our AI encounters situations beyond its training or experience, the equivalent of approaching the corkscrew at racing speeds doesn't just fail. It escalates to human operators who can provide guidance, override decisions, or implement fallback strategies. Our AI systems learn the same lessons, but with the crucial addition of human oversight and intervention capabilities.
The lessons from our Autonomous Indy Racing Challenges are already transforming how we design and deploy industrial AI solutions, just as that legendary turn has shaped generations of racing drivers and engineers.
We're building systems with longer planning horizons that can anticipate complex sequences of events and prepare for them appropriately like a racing driver who begins preparing for the corkscrew several turns in advance. We're expanding our training to include more extreme scenarios and edge cases, ensuring our AI systems have broader experience to draw upon when facing challenging conditions.
Most importantly, we're designing better integration between AI capabilities and human oversight, creating systems that know when to escalate decisions to human operators just as a smart racing driver knows when to push the limits and when to exercise caution.
These improvements aren't just about building better AI, they're about building smarter human-AI partnerships that leverage the unique strengths of both artificial and human intelligence, much like the best racing teams combine advanced technology with human expertise and judgment.
Every lesson from Laguna Seca translates directly into the industrial challenges our customers face, because the fundamental principles of performance under pressure remain constant whether you're racing at 150 mph or managing critical infrastructure.
In energy operations, the equivalent of our "corkscrew moment" might be an unexpected equipment failure during peak demand periods, or a cyber security threat that emerges during a critical maintenance window. Our Human-in-the-Loop systems can detect the early warning signs, model potential responses, and present options to human operators who understand the broader implications for grid stability, customer service, and regulatory compliance.
The future of industrial AI isn't about replacing human intelligence with artificial intelligence, it's about creating partnerships that amplify the best of both, much like the greatest racing achievements combine advanced technology with human skill and judgment.
Our racing experience at Laguna Seca reinforced our conviction that the most powerful AI systems are those that work with humans, not instead of them. AI provides speed, consistency, and the ability to process vast amounts of data. Humans provide context, judgment, anticipation, and the ability to adapt to novel situations like finding the impossible line through the corkscrew.
Together, they create something more powerful than either could achieve alone, whether navigating the challenges of legendary racetracks or managing the complexities of modern industrial operations.
The Indy Autonomous Race Challenge allows us to follow in the footsteps of racing legends who understood that the greatest achievements come from combining advanced technology with human expertise, judgment, and intuition.
We're not just building AI. We're building the future of human-AI collaboration and that future recognizes that the most challenging problems require the best of both human and artificial intelligence working together.
At Beyond Limits, we're racing toward a future where AI doesn't just automate, it collaborates, anticipates, and empowers human experts to achieve what neither could accomplish alone. And we're doing it with the same spirit of innovation and excellence that has made Laguna Seca a legend in the world of motorsport.
Laguna Seca isn’t just a racetrack, it’s part of the fabric of the Monterey community, a place where history, passion, and local pride come together. Today, a dedicated nonprofit, Friends of Laguna Seca, is working tirelessly to restore and improve the track’s infrastructure, ensuring it remains a world-class venue for motorsport and a driver of the local economy. Their vision is community-driven, privately funded, and rooted in preserving this iconic landmark for future generations. If Laguna Seca has ever inspired you, thrilled you, or given you a memory worth keeping, consider helping the people who are fighting to keep it alive. > https://friendsoflagunaseca.org/
