Why robotics and physical AI will likely have revenue impact earlier than expected

Investing.com -- The anticipated revenue impact of robotics and physical AI is emerging sooner than previously expected, according to analysts at Morgan Stanley. 

A key shift in perspective followed the recent NVIDIA (NASDAQ:NVDA) Graphics Technology Conference (GTC), where discussions indicated that investment in this field is accelerating and aligning with the current business cycle rather than being a distant prospect.

Morgan Stanley previously viewed robotics and physical AI as long-term developments that might influence stock valuations but were unlikely to generate significant revenue in the current business cycle. However, shifting market dynamics are challenging this assumption.

Analysts now see two key factors accelerating the timeline: investment in robotics and physical AI is already underway, and these technologies are becoming integral to next-generation AI models. This shift suggests that their revenue impact will materialize sooner than expected.

Morgan Stanley analysts have observed a notable shift in the nature of discussions surrounding robotics. 

Just a year ago, conversations at GTC were largely conceptual. Now, companies are actively investing in AI models tailored to the physical world. 

This transition mirrors previous industry cycles, such as the rise of generative AI five years ago and early investment in autonomous driving nearly a decade ago.

“It’s our sense that a year ago, the robotics conversation was largely a conceptual framework for how things might be done. Now, it seems similar to conversations about generational AI four to five years ago, or autonomous driving seven to eight years ago. That is, companies are starting to spend money now developing models for the physical domain,” Morgan Stanley said.

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Despite skepticism stemming from past AI investments—such as autonomous driving in 2018, which has yet to yield widespread commercial success—historical patterns suggest semiconductor companies can generate revenue early in the investment phase.

“Most companies will make money when autonomous cars, or robots, are deployed, but we would argue that processor revenues can come earlier, as their hardware dominates the profits during that investment phase,” Morgan Stanley added

Much like the surge in data center spending driven by early-stage AI developments, robotics and physical AI are expected to fuel demand for AI processors well before consumer-facing products reach the market.

One of the key challenges in the physical AI space is data acquisition. Unlike language and vision-based AI, which can leverage vast amounts of existing internet and sensor data, robotics requires extensive real-world data collection and simulated environments.

Modeling the physical world presents unique challenges compared to AI for language or vision, where vast datasets are readily available. Unlike text and image AI, robotics requires extensive real-world data collection to train models effectively.

To bridge this gap, companies are investing in real-world data gathering and leveraging simulation platforms like NVIDIA’s Isaac to generate synthetic training data, accelerating development in physical AI.

At GTC, NVIDIA was the focal point of most discussions, with many companies emphasizing their reliance on NVIDIA’s ecosystem for simulation, training, and hardware integration.

“Companies presenting at an NVIDIA developers conference were mostly talking about NVIDIA, but everyone was talking about simulating the data on NVIDIA, training the data on NVIDIA, and building devices around NVIDIA” analysts said.

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However, analysts view the impact as more widespread, with semiconductor firms such as AMD (NASDAQ:AMD), Broadcom (NASDAQ:AVGO), and Marvell Technology (NASDAQ:MRVL) also positioned to benefit.

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