An AI lifeguard system at a Sydney pool alerted a human lifeguard twice before anyone realised someone was trapped underneath a boom, a moveable bulkhead dividing the pool. The first alert, a lifeguard looked, saw nothing, and walked away. The AI flagged the same spot again, the lifeguard returned, found the submerged person, and pulled them out.
Royal Life Saving Australia’s RJ Houston later pointed to the incident as a clear illustration of how much glare, refraction, and blocked sightlines limit what a human eye alone can catch in a pool. Drowning still kills roughly 300,000 people worldwide every year, according to the World Health Organization, and it remains the leading cause of death for children aged 1 to 4 in the United States.
The question worth asking is not whether computer vision can technically spot a drowning swimmer. It already reliably does. The harder question is what that capability changes, and does not change, about keeping people alive in water.
How an AI Lifeguard System Actually Sees a Drowning Swimmer
Real drowning looks almost nothing like film depictions of frantic splashing and shouting. It is quiet, fast, and easy to miss even for a trained, attentive human watching the water. AI lifeguard systems are built specifically around that gap. Cameras, mounted overhead, on pool walls, or underwater, feed continuous video into deep learning models, typically convolutional neural networks paired with object-detection frameworks, that learn to distinguish normal swimming, strokes, kicks, floating, play, from the specific signatures of trouble: prolonged submersion, a sudden stop in movement, erratic limb motion, or a body drifting motionless near the bottom.
The technology’s genuine advantage over a human lifeguard is not intelligence. It is that a camera system never blinks, never gets distracted, and can watch every square metre of a pool simultaneously rather than scanning a wide area with two eyes. Lynxight, an Israeli company that layers AI analytics on top of standard CCTV rather than requiring new underwater cameras, now runs across more than 120 Australian public pools through a partnership with Royal Life Saving Australia, and sends tiered alerts directly to lifeguards’ smartwatches within seconds of detecting a concern.
What Lifeguards Who Actually Use It Say
Duncan Hutton, recreational facilities operations coordinator for the City of Stirling in Perth, has run Lynxight at his pool for over a year and describes its role in specific, modest terms, comparing it to giving his lifeguards eyes in the back of their heads, an added layer of coverage rather than a replacement for the staff who still have to respond to whatever the system flags.
Research conducted by Royal Life Saving Australia alongside Lynxight has also found a less-discussed benefit: lifeguards report reduced chronic stress from constantly worrying whether they might miss something, since the system provides a second layer of coverage across blind spots created by glare, refraction, and overlapping swimmers.
The Real Limits of the Technology
Every credible account of this technology, including from the companies building it, is explicit that AI lifeguard systems are a supplementary tool rather than a replacement for trained staff. No system can physically pull a swimmer from the water. Detection also degrades in specific, well-documented conditions: extreme crowd density where swimmers overlap extensively, turbid or heavily churned water in wave pools and surf zones, and lighting or camera angles that create persistent blind spots the system cannot see around any better than a human could.
Open water presents a substantially harder problem than a pool. A pool has clear water, known dimensions, and fixed camera positions. A beach has waves, currents, shifting weather, vast distances, and unpredictable crowd distribution.
Companies building open-water systems, such as Israeli startup SightBit, approach this with cameras trained on tens of thousands of beach images to track swimmers across long stretches of coastline and detect dangerous rip currents using optical flow analysis of water movement, but even the most capable deployed systems remain most effective in monitored beach zones with existing camera infrastructure. A solo swimmer in a remote lake or offshore ocean environment is still largely beyond the reach of current AI surveillance.
The Over-Reliance Risk Nobody in the Industry Disputes
Professor Paul Salmon of Australia’s University of the Sunshine Coast has called AI lifeguard technology one of the more genuinely positive uses of AI, while raising a concern the industry itself takes seriously rather than dismisses: that widespread adoption risks encouraging over-reliance on the technology and gradual skill degradation among lifeguards who might otherwise be scanning the water as actively themselves.
Royal Life Saving Australia has responded by building a dedicated training programme specifically designed to prevent lifeguards from deferring too heavily to the AI’s judgment instead of maintaining their own active scanning habits.
The same concern applies more acutely to open water, where a device functions as a backup rather than a monitored, staffed system. If a swimmer trusts a wearable or a beach camera system to summon help automatically and the signal drops, the battery dies, or the coverage area does not extend as far as they assumed, the technology cannot substitute for the basic water safety judgement it was meant to support rather than replace.
Regulation Is Catching Up, Slowly
The industry’s rapid growth, an estimated 486.5 million dollar global market in 2024 projected to reach 1.55 billion dollars by 2034, has begun to outpace formal standards, though that gap is closing. ASTM International published ASTM F3698-24 in 2024, the first global standard specification for computer-vision drowning detection systems in residential pools, giving buyers and regulators a benchmark against which to evaluate a specific product’s claims rather than relying entirely on vendor marketing.
Some European countries have begun mandating intelligent drowning prevention systems for public facilities above certain capacity thresholds, a regulatory trend that is likely to extend to more jurisdictions as the technology’s track record continues to build.
Why This Technology Arrives at the Right Moment for the Wrong Reason
AI lifeguard adoption is accelerating at the exact moment the human lifeguard workforce is shrinking. The lifeguard shortage in the United States has been severe enough that roughly a third of the country’s more than 309,000 public pools have been affected, with some facilities reducing hours or closing entirely rather than operating understaffed.
That timing raises an uncomfortable question every facility manager weighing this technology has to answer honestly: is an AI lifeguard system being added as a genuine second layer of protection working alongside adequate human staffing, or is it being used to justify running a pool with fewer trained lifeguards than safety would otherwise require.
The technology’s own developers and every lifeguard organisation quoted here are unanimous that the first use case is appropriate and the second is not, but a facility under budget pressure has an obvious incentive to blur that distinction, and nothing about the software itself prevents that misuse.
The Same Pattern Runs Through Nearly Every AI Safety Deployment
The gap between an AI lifeguard system’s genuine capability and how responsibly a specific facility deploys it mirrors a pattern LiveAIWire has documented repeatedly across other safety-critical uses of AI. Our coverage of the AI doctor dilemma in diagnostic medicine found the exact same underlying technology producing dramatically different real-world outcomes, sometimes a substantial accuracy improvement, sometimes a serious failure to catch a critical condition, depending entirely on how much genuine human oversight surrounds the tool rather than on the sophistication of the model itself.
AI lifeguard systems sit inside that same spectrum: the software’s usefulness is not fixed, and whether it functions as a genuine second pair of eyes or as an excuse to under-staff a pool depends on choices facility operators make that have little to do with the underlying computer vision technology.
The privacy dimension of always-on pool and beach surveillance also connects to a broader tension LiveAIWire has tracked in the expansion of AI-enabled monitoring across American public life, where cameras installed for one legitimate purpose, in this case genuinely saving lives, can create broader surveillance capability that operates with less public scrutiny than the safety justification alone would suggest is warranted.
LAIF, a Barcelona beach monitoring system, has tried to address that concern directly by processing video in real time without recording it at all, a privacy-first design choice that is not yet standard across the industry.
Who Benefits Most From This Technology
The populations at highest drowning risk do not map neatly onto the populations most likely to have access to AI lifeguard coverage. CDC data shows drowning deaths rose sharply among adults aged 65 to 74 between 2019 and 2022, a group whose risk profile, from cardiac events during swimming to reduced physical response time, differs meaningfully from the child-drowning scenarios most detection algorithms were originally trained to recognise.
LiveAIWire’s coverage of AI elderly care technology found a recurring pattern relevant here too: safety technology is frequently designed and validated around younger, more typical users first, with the specific needs of older adults added later if at all. LAIF’s ability to detect medical emergencies like cardiac events, rather than only classic drowning behaviour, is a notable exception worth watching as the broader industry catches up.
Access itself is deeply unequal along the same lines that already shape drowning risk. Black adults in the United States report not knowing how to swim at more than double the rate of the overall population, and communities already underserved by public pools and swimming lessons are, unsurprisingly, the same communities least likely to have an AI-monitored facility nearby.
A technology that genuinely reduces drowning risk at well-funded, already-safety-conscious facilities does nothing for the risk concentrated in communities that lack basic pool access in the first place, a gap LiveAIWire has also traced in consumer AI health monitoring more broadly, where the benefit of continuous, algorithmic safety monitoring tends to concentrate among people who already have the most resources and access.
What This Means for You
If you manage a pool or aquatic facility, the evidence here supports adopting AI lifeguard technology as a genuine addition to adequate human staffing, never as a substitute for it, and choosing a system validated against an independent standard like ASTM F3698-24 rather than relying solely on a vendor’s own accuracy claims.
If you are a parent or swimmer at a facility that has adopted this technology, it is a reasonable and specific question to ask directly: is the AI system running alongside full lifeguard staffing, or has staffing been reduced since its installation.
For open-water swimming specifically, the honest state of the technology is that monitored beach zones with existing camera infrastructure offer real additional protection, while a solo swim in a remote lake or unmonitored stretch of coastline remains exactly as risky as it has always been, regardless of what wearable device you might be carrying.
About the Author
Stuart Kerr is Technology Correspondent at LiveAIWire, covering artificial intelligence, cybersecurity, and the social impact of emerging technology. He publishes daily at LiveAIWire.com.