London Centric's Michael Macleod delivers a rare dual-narrative that cuts through the noise of London's daily chaos: the imminent arrival of driverless taxis and a startlingly simple mathematical solution to the city's epidemic of bike theft. While the tech race grabs headlines, Macleod's investigation reveals a more profound institutional failure—the police are ignoring non-violent crime not because they lack evidence, but because they lack the time to process it, a bottleneck that a Cambridge professor claims can be solved with basic arithmetic.
The Robotaxi Race and the Human Factor
Macleod frames the upcoming arrival of Waymo's autonomous vehicles not as a futuristic utopia, but as a collision of California grid logic with London's medieval street plan. He notes that while the executive branch and central government must still grant final approval, the first test vehicles with human supervisors are already en route. The author highlights the stark contrast between the two major players: the Google-backed Waymo and the British rival Wayve, which has partnered with Uber. Macleod writes, "We're intrigued to see how Waymo's vehicles, designed for the wide grid system of Californian cities, deal with central London's narrow, congested street plan complete with winding medieval alleys."
The piece effectively dismantles the assumption that automation will automatically mean cheaper rides. Macleod points out that the expensive sensor kits and system maintenance costs will likely keep fares high, shifting the value proposition from price to privacy and safety. He quotes Waymo's stance that the service "sells itself on selling a sense of safety and privacy by removing the need to interact with another human." However, the author also captures the skepticism of the existing workforce. Steve McNamara, general secretary of the Licensed Taxi Drivers' Association, dismisses the immediate threat, arguing, "The press release is incredibly misleading. When they talk about having driverless cars in London, what they're actually going to have is a driverless car with a driver in it."
Critics might note that McNamara's dismissal of the technology as a novelty overlooks the rapid pace of regulatory approval and the potential for a sudden shift in public trust once the initial safety data is released. Macleod balances this by including McNamara's own concession: "Am I concerned about driverless cabs? In 10 to 15 years time, I can see them becoming an issue. But I certainly don't see it becoming a major problem any time soon." This framing suggests the disruption is inevitable, even if the timeline is contested.
"Nobody else I know has one. Why is that? It's because they don't trust a robot to cut their grass. Yet somehow, we're meant to believe that people who won't trust a robot to cut their lawn are suddenly going to put their kids in a driverless car to be taken to school."
A Mathematical Fix for Police Inaction
The second half of the piece pivots to a more urgent, systemic issue: the de facto decriminalization of low-level theft in London. Macleod argues that the police have effectively stopped investigating bike thefts because reviewing hours of CCTV footage is too time-consuming for current budgets. He cites British Transport Police, who state it is "not proportionate to review longer periods as it keeps officers from being available to respond to emergencies."
Here, Macleod introduces a compelling, non-technological solution proposed by Richard Weber, a mathematics professor at the University of Cambridge. The argument centers on "binary search," a method that allows an officer to find a specific event in a long video tape by repeatedly cutting the search space in half. Macleod explains the efficiency: "Suppose the tape is eight hours long and we want to identify the theft point to within a window of five minutes... The number of inspections needed to locate the theft time to within five minutes is at most seven inspections."
The implications are staggering. Macleod writes, "If a human could do one inspection in 15 seconds and then wind the tape to a new point, this would mean about two minutes to process an eight hour tape." This reframes the problem from one of resource scarcity to one of procedural inefficiency. The author also touches on the potential for artificial intelligence, quoting David Hogg from the University of Leeds, who asserts, "In principle, it should certainly be possible with current technology to automate the process of reviewing CCTV footage to detect when a bicycle theft occurs."
Yet, the piece acknowledges the bureaucratic inertia that often plagues public sector innovation. Josef Kittler, a professor at the University of Surrey, reveals that despite having working technology, a police force "failed to get funding for this trial." Macleod's reporting suggests that the barrier is not technical capability, but political will and budgetary prioritization. A counterargument worth considering is that even with faster review times, the sheer volume of thefts might still overwhelm the system if the underlying incentives for theft remain unchanged.
Bottom Line
Macleod's strongest contribution is exposing the absurdity of a police force that could solve thousands of thefts with a two-minute mathematical trick but chooses not to due to outdated workflows. While the driverless taxi story offers a glimpse into London's technological future, the real urgency lies in the present failure to protect citizens' property through simple, existing methods. The reader should watch for whether the British Transport Police adopt the binary search method, as this will be the true test of whether the institution values efficiency over the status quo.