How US cities are deploying drones, robots, and AI to more affordably inspect, diagnose, and repair the country's aging sewer infrastructure
and sometimes fix—sewer systems more quickly and cost effectively than ever, one thing it can't do is elevate public awareness of the problem.” https://www.wsj.com/... @wsj : Cash-strapped U.S. cities are turning to drones, artificial intelligence and other innovations to help inspect and fix the country's aging, underground arteries of waste https://www.wsj.com/... Roben Farzad / @robenfarzad : “The U.S. has 875,000 miles of sewer mains” — so very much of it corroded, leaky and outdated https://www.wsj.com/... #water #waste #infrastructure @mims @WSJ #pollution #sewage #wastewater @mims : Hello good morning I would like to talk to you about sewers. WAIT don't go away, this is really important. There is a crisis under our feet and it's invisible & even the infrastructure bill won't solve it. Stay with me here: 🧵 https://www.wsj.com/... @neorsd : drones, crawling cameras, GIS, tech has a major role to play in the future of sewer monitoring and maintenance. it's not new for us, but we can't take it for granted. https://www.wsj.com/... @wsj : One reason AI is superior at inspecting sewers: For humans, “it's not intellectually stimulating to watch poo in sewer pipes and sit there thinking that's going to be the next 10 years of your life.” https://www.wsj.com/... Baiju R. Shah / @baiju_r_shah : @neorsd. Sewer tech in the @WSJ this morning! ‘When is artificial intelligence and machine learning going to get water and sewer?’ Water tech innovation is both cool and big business @H2Ohio @CLEH2OAlliance @OhioXOrg https://www.wsj.com/...
Context & Ripple Effects
This 2021 piece is an early entry in what became a running Wall Street Journal thread on autonomous inspection of physical assets: two years later the same outlet profiled the vendor ecosystem behind it — Gecko Robotics, Skydio, Anybotics, Invert Robotics — in its look at AI-powered robots maintaining critical US infrastructure. The sewer story matters because sewers are the least glamorous, most budget-constrained asset class in that set: with roughly 875,000 miles of mains corroding underground, cities cannot inspect them fast enough with human crews.
First-order effects
- Cash-strapped municipal water departments can shift from reactive dig-ups to scheduled, camera- and AI-driven condition assessments, stretching limited repair budgets across more miles of pipe per dollar.
- Robotics and drone vendors gain a new public-sector customer base beyond the power companies that pioneered this playbook when they turned to AI, drones, and sensors to run the grid earlier.
Second-order effects
- As inspection becomes cheap and continuous, the bottleneck moves from finding failures to fixing them — putting pressure on contractors and municipal capital plans, a squeeze the later reporting on [[a:893683|~$4T in AI infrastructure spending pulling skilled construction workers away from municipal projects]] makes sharper.
- Vendors that prove out sewer inspection can cross-sell into adjacent buried and hard-to-access assets — the same logic behind startups using autonomous underwater vehicles for pipeline and cable monitoring.
Third-order effects
- If the pattern holds, underground utility maintenance restructures around inspection-data platforms rather than crew hours, with cities buying condition intelligence to prioritize which pipes get replaced — and the same sensors raise adjacent questions about what the collected data reveals, as the parallel smart-toilet work on health scanning shows.
The trend: Municipal infrastructure maintenance is shifting from periodic manual inspection to always-on robotic sensing, with cash-strapped cities leading adoption where the cost of failure is highest.