Tesla is no longer just an electric vehicle manufacturer—it is positioning itself as an AI-driven company, at least according to its CEO, Elon Musk. His optimism is largely anchored in Tesla’s massive dataset: petabytes of video footage collected from millions of miles driven by Tesla owners worldwide.
Theoretically, this wealth of real-world driving data should give Tesla an edge in training AI models to achieve full autonomy. However, a critical issue remains: not all data is equally valuable. While vast in quantity, much of the collected footage lacks the depth required to address complex, unpredictable driving scenarios.
The Challenge of Autonomous Driving
Unlike natural language models such as ChatGPT, which leverage extensive internet-based datasets to refine responses, self-driving AI must handle an entirely different set of challenges. Driving involves numerous unpredictable variables, including weather conditions, road construction, changing traffic patterns, and erratic driver behaviors. Training AI on routine highway driving does little to prepare it for the edge cases—the rare but critical events that often lead to accidents.
A leading computer scientist from an autonomous vehicle technology firm, who requested anonymity, highlighted the problem: “AI can learn to drive smoothly in standard conditions, but when unexpected scenarios arise, it struggles. If it simply mimics human behavior, it may inherit bad habits—like rolling through stop signs.”
Why Tesla’s Approach Differs from Competitors
Unlike Tesla, many autonomous vehicle companies—such as Waymo, Zoox, and Aurora—rely on a combination of LiDAR and radar to generate precise 3D maps of the driving environment. While Tesla has opted for a camera-only approach, industry experts caution that such a strategy is risky. “To rely entirely on cameras, you need the best systems available, and even then, it’s a huge gamble,” stated Drago Anguelov, head of research at Waymo.
Meta’s AI chief, Yann LeCun, has also voiced skepticism about Tesla’s AI data advantage. “The impact of data is often overstated. Beyond a certain point, doubling the dataset yields diminishing returns,” he explained. While Tesla’s repository is immense, achieving full Level-5 autonomy—where a car can handle any driving situation without human intervention—remains elusive.
Musk’s Vision vs. Reality
Despite ongoing technical challenges, Musk remains unwavering in his AI vision for Tesla. He has emphasized that investors should only back the company if they believe in its ability to master autonomous driving. “If someone doesn’t think Tesla will solve autonomy, they should not be an investor in the company,” he stated in a 2024 earnings call.
Tesla recently announced the deployment of its vast data resources to its new AI-focused “Cortex” data center in Austin. The goal is to refine Tesla’s Full Self-Driving (FSD) software, which, despite its name, still requires constant driver supervision. To date, Tesla’s FSD and Autopilot systems have been linked to 52 fatal accidents worldwide.
The Data Problem: ‘Garbage In, Garbage Out’
While having access to extensive real-world driving data is an asset, quality matters more than sheer volume. Alex Ratner, CEO of Snorkel AI, warns that poor data selection can hinder progress: “AI models learn from the most common patterns they see. Without careful curation, bad driving behavior could become ingrained.”
Missy Cummings, an AI expert and professor at George Mason University, further elaborates: “There is no guarantee that the most critical edge cases will be sufficiently represented in the dataset to train AI effectively.” This challenge makes it difficult for Tesla to address problems like “phantom braking,” where the AI misinterprets environmental factors as hazards, causing unnecessary stops.
Additionally, Tesla’s approach lacks transparency. Unlike research-driven AI firms that frequently publish papers on their advancements, Tesla remains relatively absent from academic AI circles. “Tesla has virtually no presence in AI research conferences or publications,” LeCun noted. “It’s as if they don’t exist in the field.”
Musk’s Repeated Missed Deadlines
Musk’s promises regarding Tesla’s autonomy capabilities have consistently failed to materialize. In 2016, he predicted that a Tesla would complete a cross-country trip without human intervention within a year—a milestone that remains unmet. In 2019, he projected the deployment of a million robotaxis by 2020. Again, this goal has not come close to reality.
LeCun remains highly skeptical of Tesla’s claims: “For nearly a decade, Musk has exaggerated Tesla’s progress in self-driving technology. Many of us recognized early on that these claims were either misleading or wishful thinking.”
Competition in the Robotaxi Space
Tesla faces stiff competition from Alphabet’s Waymo, which leads the U.S. in robotaxi services. Waymo currently operates in Phoenix, San Francisco, Los Angeles, and Austin, booking over 200,000 paid trips per week with a fleet of about 700 vehicles. Unlike Tesla, Waymo has not been linked to any fatal accidents.
Meanwhile, Tesla’s FSD system continues to attract criticism. Numerous videos uploaded by Tesla owners show the software making dangerous maneuvers, such as running red lights or nearly colliding with vehicles on exit ramps.
Can Tesla Catch Up?
Musk remains bullish on AI’s role in Tesla’s future. He envisions massive revenue streams from autonomous ride-hailing services and humanoid robots. However, the gap between ambition and execution remains substantial.
Meta’s LeCun believes a fundamental AI breakthrough is needed before full autonomy is viable. “Current AI models lack the ability to understand the world like humans or animals do. True autonomy is still a decade away.”
While Musk’s AI vision is ambitious, its feasibility remains in question. Without significant improvements in Tesla’s approach to AI training and research, the company risks falling behind in the race for self-driving dominance.
