Yisi Technology Co-founder Chao Guang: The Moat of AI Tennis Robots Lies Not in Specs, but in the 'Do Not Do' List
In a field report by Geek Park, Yisi Technology co-founder Chao Guang and his team explain that the true competition in the AI tennis robot sector is not hardware specifications, but product positioning—whether to build a machine as a piece of equipment or as an opponent. The team constrains its functional scope through an explicit 'do not do' list (no full-court running, no receiving shots, no serving for zero-basis users) to gain interaction depth and data accumulation, with the ultimate goal of making user time investment an untransferable moat. The first batch of mass-produced units will begin delivery in July 2026, and retention assumptions have not yet been validated by real users.
After the 2024 Paris Olympics, tennis court bookings in China multiplied several times over in the short term. Two years later, at least four AI ball robots built by Chinese teams raised millions of dollars on Kickstarter—Ponbert exceeded funding goals by 26,255% and secured $2.65 million; SwitchBot's Acemate generated $1.8 million in 30 days; Tenniix's parent company, Yinghanshi Dongli, completed three rounds of financing totaling over 100 million yuan led by Sequoia Capital; and the latest AceiiLab, priced at approximately $2,000 per unit with an early bird price of $999, is currently delivering its first batch of mass-produced machines.
However, after Geek Park reporters visited the AceiiLab A1 and met Chao Guang, co-founder of Yisi Technology, at a tennis court in Shenzhen, they found that even what this track is "selling" remains unclear. Under the label of "AI tennis robot," it could be a humanoid robot playing tennis, a portable ball machine, or even a mobile app. The conclusion drawn by Geek Park reporters on site was: hardware is merely an entry ticket; the key lies in positioning the machine as a piece of equipment or as an opponent.
This distinction is not rhetorical. The Yisi Technology team previously worked on lawn mowing robots, from which they extracted a structural difference: the relationship between a lawn mower and the user is one of employment—the grass just needs to be cut cleanly, and the user does not care how the machine cuts. It serves results, and its value ceiling is at most the wage of a laborer. In contrast, a tennis robot serves the process. Players care about the speed, landing point, and form of every shot; the machine perceives and responds every second. This "process vs. result" distinction determines all subsequent product choices.
The team codified these choices into a "do not do" list. They do not build robots that run the entire court, only moving along the baseline; they do not receive shots, only serve; and they do not serve zero-basis users, initially targeting advanced players rated 2.0 to 4.0. Each "do not" has specific justification. They avoid humanoid forms because Chao Guang believes that "on a flat court, wheels are more efficient than bipedal legs, just as using a washing machine is more efficient than hand-washing"—humanoid robots playing tennis validate robot capabilities, whereas they aim to deliver a playing experience. They avoid receiving shots because serving and receiving are distinct tasks; pursuing reception would disrupt the machine's rhythm, diminishing training value, and if reception success rates are not high enough, the dynamic reverses into humans walking the machine. They stick to the baseline because industry statistics show that 70-80% of shots from target users above level 2.5 are baseline rallies; holding the baseline secures 70-80% of the experience.
Yet why does a restrained machine invest heavily in "movement"? The team's answer is that movement is the dividing line between AceiiLab and traditional ball machines. A stationary machine interacts with humans only once in the morning—turning it on, setting parameters, then going their separate ways. A moving machine interacts every second: you hit left, it chases left; you hit right, it chases right. Movement brings a sense of opposition, enables net-rally and ball-boy modes, and fosters a subtle emotional connection. On-site observation revealed the truth of "rallying": you hit the ball over the net, and before it lands, the machine moves near the landing spot, then immediately launches a new ball from there, simulating "receiving your ball." It never truly returns your ball, but the sense of回合 (turns/rally) holds.
In terms of hardware, the team stands on the shoulders of mature industries: the chassis directly adopts mobility solutions refined by robot vacuums over ten years, uses suitcase wheels, and employs lane-keeping-like solutions for movement. Money saved here translates to price space. However, two areas are self-developed: first, a proprietary ultra-long baseline binocular camera, which calculates the first landing point within 10 milliseconds of the ball leaving the racket and maps the entire trajectory within 50 milliseconds; second, the algorithm team, where user modeling currently relies mainly on rule-based systems, distilling coaching experience into six-dimensional parameters such as footwork and gestures to simulate shots like "Nadal's left-side topspin." These two areas represent the largest costs, but what truly defines the product is the aforementioned "do not do" list.
The flywheel design logic operates as follows: after each round, users see training data including reception success rate, max streak, average speed, and landing plots; the machine records hitting videos and analyzes them across five dimensions: racket face, stability, timing, kinetic chain, and preparation action; upon submission to AI analysis, the system identifies weaknesses and pushes relevant courses. During North American road tests, a user asked, "Will I never beat it?" prompting the team to add a challenge mode and rules for the machine's defeat—if the ball lands and the machine fails to move within 50 centimeters, it counts as a loss. A machine capable of admitting defeat resembles a sports partner more than one that is always correct.
All this data accumulates into reasons why users cannot leave the machine. An executive at AceiiLab stated, "When the machine stores years of your growth data, and even data from your best friend's sparring sessions, you won't switch even if a competitor is $500 cheaper." Specs can be caught up to, molds can be copied, but only the time users invest cannot be moved away. Chinese hardware companies have followed an old path for thirty years—making a category cheap, only to be undercut by later entrants, with margins thinning generation by generation. Yisi Technology aims to take another path: sell the machine first via a thoroughly reconstructed serving experience, reduce costs through mass production, retain users via movement and interaction, and finally turn accumulated data into services.
Whether this path succeeds is too early to conclude. The first batch of mass-produced units begins delivery in July 2026; subscription renewal rates, course completion rates, and community activity levels must await numbers from real users. Reporters also noted that "being unable to react in time" was the common first impression among early testers, indicating this is not a product suitable for zero-basis users to pick up without threshold. It requires users to understand their own level and know how to set parameters—a product needing debugging configuration and mutual adaptation. Restraint is not conservatism, but patience—only users on the court can answer whether patience waits long enough for retention data.