The fundamental problem of robotics may be harder than anticipated, representing a greater risk than competition.
Current model performance is not yet reliable or efficient enough for commercial deployment.
The lack of established 'scaling laws' makes it difficult to predict the return on investment for data and compute, keeping it a research-heavy problem.
Simulation is not yet a viable data source for complex manipulation tasks due to its failure to model contact physics accurately.
Opportunities Identified
Leveraging pre-trained vision-language models to rapidly bootstrap robot intelligence and generalization.
Solving general-purpose robotics will unlock massive economic value, starting with automating all household chores.
Open-sourcing models can accelerate community engagement and uncover novel applications for the technology.
The future potential to 'vibe code' hardware, where intelligence from foundation models can be easily infused into any physical form.