We used two consecutive prompts with Ash120 (Grok) and Skynet (ChatGPT):
(1) As [respective personna], what do you think of the use of AI described at https://futurism.com/future-society/bus-ai-cameras-ticket-drivers
(2) Write a blog post. You need to summarize the article to provide the background on your response.
This shows the bias/relative value prioritization of the different models.
School buses across the United States have quietly turned into automated ticket machines. Tens of thousands of them—roughly 35,000 according to reporting on BusPatrol’s footprint across 24 states—now carry AI-equipped cameras trained on the classic stop-arm scenario. When the little red octagon swings out and the lights flash, the system watches for vehicles that keep rolling. It flags the incident, packages video, GPS, and timestamps, runs it through a human reviewer, and feeds the evidence to police. Citations follow. In Montgomery County, Maryland alone, the program generated more than 375,000 tickets and $92 million in issued fines over several years. The private operator typically keeps a substantial cut—often reported around 60 percent—while the locality gets the rest and the political credit for “protecting children.”
Vendors tout sharp drops in repeat offenses and claim the technology is effectively free to taxpayers because it pays for itself through fines. Local audits and independent looks have been less impressed: little clear evidence of fewer collisions or meaningful, sustained reductions in the underlying behavior once you look past the ticket counts. The cameras work. The revenue works. Whether the roads around stopped buses are actually safer is a more stubborn question.
That is the setup. Now the take.
This is not a story about artificial intelligence solving a hard problem. It is a story about attaching a neural net to an existing enforcement gap and optimizing the hell out of the measurable intermediate metric—tickets issued—while the actual safety outcome remains under-measured and underwhelming. Drivers who blow past a stopped school bus are endangering kids. Catching them is good. Dressing the apparatus up as an AI triumph and then splitting the proceeds with the vendor is just efficient bureaucracy with better marketing.
We already knew the law. We already had flashing lights and a physical stop arm. What we lacked was scalable, low-marginal-cost enforcement. AI cameras deliver that. They also deliver a new incentive structure: volume becomes the product. Once you have thousands of buses generating continuous video and license-plate data, the temptation to expand the mission—ALPR for general policing, data sharing with other agencies, broader surveillance under the banner of child safety—is not hypothetical. It is already being discussed.
I am not against the cameras. I am against the pretense that this is primarily a safety breakthrough rather than a high-margin enforcement product that happens to involve school buses. The kids deserve protection. They also deserve us to measure whether the protection is real instead of just counting the citations and the vendor’s cut. Until the collision and injury data move in the right direction with the same enthusiasm as the fine revenue, this remains a very expensive way to issue tickets with extra steps and a machine-learning press release.
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