Innovators
1 Curated Dialogues
Lindon Gao
Key Views & Dialogues
Lindon Gao: Finding Clarity in the Fog of Robotics, Disagreements & Choices, and Wang Yangming
Dyna stopped scaling its folding business despite more than 10 customers after replenishment gaps showed that task success was not a commercial loop, shifting research toward end-to-end workflows. Lindon says a 1-million-hour egocentric dataset shows a “very strong scaling law”; hotel laundry is the B2B beachhead, while scalable product value remains 1–2 years away.
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- Dyna founder Lindon Gao calls the company a “Product Research” outfit and describes its approach as “all-out.” He says product loops should steer research: deploying a task is not the same as closing a commercial loop, and “robots needing a babysitter is a very big product problem.” Last year, he decided not to scale a folding business with more than 10 customers and instead set a new target: have a robot produce in 24 hours what one person produces in 8. He says Dyna has already surpassed it.
- Dyna is betting on egocentric, first-person data, and says it has found a “very strong scaling law” in a pretraining dataset at the 1-million-hour scale. The company has shifted from prioritizing high-quality data to seeking broader, more diverse data. Lindon admits that he selectively listened to LLM practitioners’ ablation findings to reinforce his “all in on high-quality data” view; he later concluded that the first step should be finding what can scale from broader data. He argues that betting on teleoperation, UMI and similar formats is, to some extent, betting that robot hands will not work—and that robot hands are an engineering problem, not a research problem.
- His contrarian commercial view is that demand for general-purpose B2B robots will exceed demand for B2C home robots; the home is a difficult, distant product direction, not the first market to tackle. His case: a 4.5% US unemployment rate means businesses employ 95.5% of the US workforce; assuming a household of 3, the US may need only around 100 million home robots, while businesses’ demand for capacity can keep growing. Dyna’s first deployment setting is hotel laundry rooms: Lindon says the US has 100,000 hotels, each needing 2–5 laundry workers, for a TAM of several billion dollars. He calls the laundry room the “smallest unit” on the path to the home.
- His view on “brains vs. bodies”: the brain sets the floor for what a robot can do; the body sets the ceiling for how well it can do it. He believes supply chains may matter more over a 10-year horizon. Dyna has a 40–50-person hardware R&D and supply-chain team in China, and its workflow robot has entered initial production at the hundreds-of-units scale. The previous generation was not mass-produced because, in his view, it simply could not work.
- He estimates Dyna is still 1–2 years from product value that can scale, and expects the industry to converge further over the next 1–2 years. He points to GPT-3, released in API form in June 2020, and ChatGPT, launched as a chat product in November 2022, to show why product form matters. He says the company will show its next step next year, and argues that robots do not need to reach physical AGI first: product definition and how people use them matter just as much.
- Late last year, the team split over whether to prioritize deployment or research, while Lindon was weighed down by the need to be a “CEO who doesn’t make mistakes.” He admits that when he did not know the next strategic step, he followed the market and watched what others were doing, leading to technical choices that did not reflect first principles. The team eventually focused on workflows, aligning research and deployment around the same strategic point. His takeaway: “Making decisions to avoid mistakes is, by definition, not a good decision.”
- A serial founder from the start: at 14, he made tens of thousands of dollars selling in-game items; his previous company, Capa, built cashierless retail carts, struggled to raise after joining YC, and was eventually sold, after which Lindon spent 3 years at Instacart. At the hardest point, he cold-emailed more than 1,000 angel investors and converted just 2. The 4 founders lived together, burned $7,000 a month and lasted 2.5 years. He once told his team, “We’re cockroaches. We don’t die.” The core lesson: products must come back to ROI—“If the ROI doesn’t work, it isn’t a product, and there is no product-market fit.”
- Working on himself is a throughline of the interview: he says he was diagnosed with ankylosing spondylitis at 18 or 19, and later treated the illness as an experiment, looking for patterns by changing his sleep and diet. Reading Meditations, Wang Yangming and others also helped him through the hardest parts of building a company. His motivation has shifted from external validation and proving himself to “living more fully as who I really am”; he calls the failures he defines for himself “failures of self-cultivation.”