Peer Perspective: It's worth noting that "Full Self-Driving" is currently a bit of a marketing misnomer. Most systems are still Level 2 (meaning the human is legally responsible), despite the "Full" in the name.
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Economic Massive job loss for 3.5 million professional truck/delivery drivers.
Privacy FSD cars are "rolling surveillance" units with 360-degree cameras recording everything.
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### Summary of Key Risks
Argument Type Core Concern
Technical Sensors are not as reliable as human eyes + brain.
Psychological Humans are incapable of staying alert while "watching" a computer drive.
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Death of Public Transit: FSD could pull funding and ridership away from buses and trains—the only truly efficient way to move people—leaving those who can't afford a high-tech car with fewer options.
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The "Zombie Car" Problem: We could see millions of empty "ghost cars" driving around to avoid parking fees or to pick up owners, clogging city streets and wasting energy.
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​## 4. Societal & Urban Degradation
​Induced Demand: If driving becomes "easy" because you can sleep or watch a movie, people will live further away and spend more time on the road. This leads to urban sprawl and more traffic, not less.
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Liability: When an FSD car crashes, manufacturers often blame the "driver" for not intervening fast enough. This creates a loop where the company gets the data and the profit, but the human gets the legal and physical risk.
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​## 3. Ethical & Legal "Black Holes"
​The Trolley Problem: If a crash is unavoidable, who does the AI kill? The passenger or the pedestrian? Letting a corporation’s hidden code make life-or-death moral decisions is a massive ethical hurdle.
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Reaction Time: A human who hasn't been driving for 20 minutes cannot suddenly take over in 0.5 seconds when the AI makes a mistake. Humans are statistically terrible at "passive monitoring."
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The Problem: The system is good enough that you get bored and stop paying attention, but bad enough that it might try to "kill you" once every 50 miles.
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​## 2. The "Safety Driver" Paradox
​Psychologists argue that FSD creates a dangerous middle ground called automation complacency.
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Depth Perception: Unlike LiDAR, which "sees" the distance to an object using light pulses, vision-only systems have to guess distance using software. If the software guesses wrong, the car can "phantom brake" or fail to see a stationary truck on the highway.
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Environmental Blindness: Cameras struggle with "edge cases" that humans or other sensors (like LiDAR and Radar) handle easily, such as blinding sun glare, heavy rain, or dense fog.
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Here are the primary technical, ethical, and social arguments against FSD:
​## 1. The "Vision-Only" Flaw (Physics vs. Software)
​A major technical argument (prominently against Tesla's FSD) is the reliance on cameras alone.
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Skepticism toward AI, the arguments against Full Self-Driving (FSD)—especially in the current "Beta" or "Supervised" state—are significant. Critics argue that we are essentially treating public roads as a laboratory and non-consenting citizens as test subjects.
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