Tesla FSD v14 vs Waymo: Two Philosophies, One Goal, Very Different Paths | Taha Abbasi

The two leading autonomous driving programs in America — Tesla’s FSD and Waymo’s Driver — take fundamentally different approaches to the same problem. Taha Abbasi, who has tested FSD extensively and closely tracks Waymo’s expansion, compares the technical philosophies, current capabilities, and long-term viability of each approach.

The Philosophical Divide

Tesla’s approach: vision-only sensing, neural network-driven decision making, fleet-wide deployment at scale, supervised by human drivers. Improve through data volume.

Waymo’s approach: multi-sensor fusion (cameras, LiDAR, radar), pre-mapped operating domains, purpose-built vehicles and retrofits, no human driver in geofenced areas. Improve through domain expertise.

As Taha Abbasi frames it: Tesla is building a general-purpose autonomous driving brain. Waymo is building a perfect autonomous driver for specific streets.

Current Capabilities: Honest Assessment

Waymo today: Fully driverless robotaxi service in Phoenix, San Francisco, and Los Angeles. No safety driver. Passengers can hail rides like Uber. The experience is smooth, safe, and genuinely autonomous within the operating domain. Limited expansion to Austin and other cities underway.

Tesla FSD today: Available on millions of vehicles worldwide. Handles the vast majority of driving scenarios competently. Still requires a human supervisor. Not available as a commercial robotaxi service (yet). The V14.2.2.5 release is the most capable version to date.

Taha Abbasi‘s honest take: Waymo is winning the “fully autonomous today” race. Tesla is winning the “autonomous everywhere eventually” race. Both positions have merit.

The Sensor Debate

Tesla’s vision-only approach eliminates the cost of LiDAR sensors ($5,000-10,000+ per vehicle for Waymo’s sensor suite) but requires solving perception problems that LiDAR handles trivially — like precise 3D distance measurement in all lighting conditions.

Waymo’s LiDAR provides centimeter-accurate 3D mapping of the environment regardless of lighting, weather, or visual ambiguity. The tradeoff is cost, complexity, and the need to pre-map every operating area.

As Taha Abbasi has analyzed, Tesla’s bet is that cameras plus neural networks can eventually match LiDAR’s capabilities at a fraction of the cost. The evidence so far is promising but not conclusive.

Scalability: Tesla’s Trump Card

This is where the comparison gets interesting. Waymo operates approximately 700 vehicles across its service areas. Tesla has millions of FSD-equipped vehicles on roads worldwide, each generating training data every day.

Even if Waymo’s per-vehicle capability is currently higher, Tesla’s data volume advantage is staggering. When Tesla encounters an edge case in one vehicle, the solution trains into the entire fleet. Waymo’s smaller fleet generates proportionally less data and can only operate in pre-mapped areas.

The Cost Structure

Taha Abbasi breaks down the economic comparison: Waymo reportedly spends $100,000-200,000+ per vehicle (including sensor suite, compute hardware, and mapping), limiting fleet size. Tesla’s FSD hardware is included in every new vehicle at marginal cost, enabling fleet-wide deployment. If autonomous driving is a data problem, the cheaper sensor approach that enables wider deployment may win on data volume alone.

The Verdict: Both Win, Differently

Waymo will continue to lead in fully driverless commercial service within geofenced areas. Tesla will continue to lead in broad capability improvement across all driving scenarios. The question is which approach reaches universal autonomy first. Taha Abbasi‘s bet is that Tesla’s data volume advantage will compound over time, eventually closing the capability gap. But Waymo’s lead in commercial deployment gives it a revenue advantage that funds continued development.

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About the Author: Taha Abbasi is a technology executive, CTO, and applied frontier tech builder. Read more on Grokpedia | YouTube: The Brown Cowboy | tahaabbasi.com