While I agree with you that in 50 years, advances in AI will make fast hardware obsolete, it may well be that in 5 years, fast hardware will be able to avoid problems that we cannot yet solve in software. That would mean this company has 45 years to cash in before they get replaced by software.

How much neural net can you run on silicon with 20 watts?

I strongly disagree with the rest and your comments indicate a clear lack of (hardware) understanding. I often encounter this in the AI world. Where folks building ANNs just "forget" that this computational abstraction only "grew in popularity" (there were previous accelerators though) when CNNs got implemented as an accelerator in a GPU empowering newer results. The same's likely to happen in robotics.

Robots are real-time systems. Meeting time deadlines in their computations is the most important feature. (robot) Behaviors often take the form of computational graphs, with data flowing between compute abstractions (Nodes), across physical networks (communication buses) and while mapping to underlying sensors, compute technologies and actuators. ROS enables you to build these computational graphs and create robot behaviors by providing libraries, a communication infrastructure, drivers and tools to put it all together.

From a more technical compute architecture perspective, ROS 2 presents an event-driven programming interface. The resulting computational graphs built with it are "event-driven software architectures". Mapping these event-driven software architectures to hardware using CPUs leveraging classic control flow architectures (von Neumann architectures) leads to various issues. A key challenge in applying classic event-driven programming is that CPUs hardly provide real-time and safety guarantees. The de facto strategy in industry to meet the timing deadlines is a laborious, empirical, and case-by-case tuning of the system. This “whack-a-mole” approach is being realized by some, but unsustainable and hard to scale due to the lack of a hardware-supported timing-safe even driven programming interface. This is where accelerators come in. As already adopted in other industries including aerospace, automotive and healthcare, through the creation of custom compute building blocks (accelerators), a hardware/software co-design strategy provides clean behavioral specifications, describing clearly its guarantees in terms of timing (in other words, avoiding memory-centric von Neumann architectures).

Hardware is important. Note the current scarcity of semiconductors, which is one of the drivers of this research. Note also that creating custom accelerators for robotics application not only can lead to performance improvements (i.e. software that runs faster!) but also to more deterministic responses (which affects the downstream pipeline of software!). Rephrasing Alan Kay's quote, if you're serious about robotics, you should care about hardware ;).