Investor Joanna Lichter outlined a working hypothesis on how value may build in robotics, arguing that the sector resembles a barbell rather than a broad, evenly distributed market. On one side are horizontal robotics foundation models, or RFMs, and infrastructure platforms that can compound through data, compute, and ecosystem effects. On the other are category-leading application companies that capture value by turning intelligence into useful work. In her view, the middle layer looks less attractive, especially where models are replaceable, tools are easy to copy, or deployments lack software leverage. Lichter also drew a contrast with large language models, saying robotics faces distribution costs, scarce physical-world data, real-time compute limits, and safety constraints. Those conditions lead to a gated diffusion pattern instead of immediate mass adoption. She added that deployments can become defensible if they create a learning loop, reduce labor required per robot, and accumulate reusable data and operating knowledge over time. Vertical entry points, she said, can speed up that path.
According to ChainCatcher, investor Joanna Lichter has set out a working hypothesis for how value may accrue in robotics, arguing that the market is shaped more like a barbell.
At one end are horizontal robotics foundation models, or RFMs, along with infrastructure platforms. She said that layer can compound through data, compute, and ecosystem effects. At the other end are category-leading application companies that capture value by turning intelligence into useful work.
The middle layer looks less compelling
Lichter said the middle of the stack is less attractive. She grouped it into replaceable models, tools that are easy to replicate, and deployments that lack software leverage.
Robotics faces a different adoption curve from large language models
She said robotics differs from large language models because it must deal with distribution costs, scarce physical data, real-time compute constraints, and safety requirements. As a result, adoption follows a gated diffusion path rather than immediate large-scale uptake.
Deployment loops can become a moat
Lichter said a deployment can turn into a moat if it builds a learning loop, lowers the labor required for each robot, and keeps accumulating reusable data and operating knowledge. She added that vertical entry points can accelerate that process, allowing value to accrue at both ends of the technology stack over time.
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