Physical AI is a broader field than robots alone, and its progress may depend on practical building blocks that still need work. Amulya Vishwanath, an investor who spent seven years leading product marketing for NVIDIA’s robotics platforms, describes robotics as being in its “Lego era.”
Robotics is still assembling its foundations
Vishwanath’s description points to a field whose basic parts are still being developed. She names actuators, robotic hands, software, and safety standards as pieces that are being rebuilt.
That framing shifts attention away from the idea that one powerful model could quickly make robotics mature. A robot needs more than a capable AI system: it also needs the physical components and software that allow it to act, along with safety standards. The source does not offer a timeline for those pieces to come together, but it presents them as unfinished work.
The “Lego era” comparison suggests progress built from components rather than arriving all at once. The metaphor underscores how much foundational work remains before the field can be judged by a single dramatic demonstration.
A different view of the “ChatGPT moment”
Some people believe robotics is close to a “ChatGPT moment”: the kind of sudden shift associated with the chatbot launched in late 2022. Vishwanath’s view runs counter to that expectation, emphasizing the rebuilding of fundamentals instead of an imminent leap powered by a general foundation model.
General Intuition CEO Pim de Witte told Equity earlier this year that a single general foundation model would make much of today’s specialized robotics work redundant and could be that moment. That view puts the model at the center of change. Vishwanath’s account highlights the hardware, software, and standards that would also shape what physical AI can do.
The difference matters because excitement about a general model can obscure the work needed to connect intelligence with the physical world. Models may be part of the story, but Vishwanath’s comments suggest that robotics also depends on the systems and components that let machines operate safely.
Investing across physical AI
Vishwanath discussed her investment focus on TechCrunch’s Equity podcast with Dominic-Madori Davis. As founder and managing partner of Techable VC, she is writing first checks in physical AI across robotics, materials science, and life sciences.
That range makes physical AI a larger investment category than robotics by itself. The source identifies three areas of focus, while offering no specific portfolio companies or deal details. The common thread is investment in fields where technology engages with physical systems and processes.
Vishwanath also spoke about what she has learned as one of the first women executives to spin out of Nvidia and launch her own early-stage firm. Her experience connects a career leading product marketing for robotics platforms with the decision to back companies working across physical AI.
What the “Lego era” signals
Calling robotics an early building-block stage is a reminder that progress may be uneven. Better models alone would not settle questions about robotic hands, actuators, software, or safety standards. Those are separate pieces of the foundation described in the interview.
For investors and founders, the account offers a way to think about opportunity: the next important work may involve developing the underlying pieces, not just pursuing a headline-grabbing general model. Vishwanath’s comments do not predict when robotics will reach a new stage. They describe a field still putting its fundamentals together, with investment extending into materials science and life sciences as well as robotics.