Physical AI is often described as the next great leap for AI, with many investors and companies expecting a sudden breakthrough, similar to what ChatGPT did for language models.
However, the wait for an I, Robot-like future, where advanced AI integrates with robots, vehicles, and other devices, may be long. During a panel discussion at AWS re:Invent 2025 conference in Las Vegas, industry stakeholders agreed that Physical AI will not arrive as a single breakthrough, viral demo, or dominant model. Physical AI, they argued, is constrained by realities that digital AI never faced.
On the panel, Ryu Jung-hee, founder and chief executive officer of South Korean robotics startup RLWRLD, was joined by Kevin Peterson, CTO of Bedrock Robotics; Sri Elaprolu, director of AWS Generative AI Innovation Centre; Amit Goel, head of robotics and edge computing ecosystem at NVIDIA; and Josh Gruenstein, co-founder and CEO of Tutor Intelligence.
At the centre of this discussion was the idea that intelligence alone is no longer the hard problem. Once AI systems are expected to move, grip, and act in real environments, speed, safety, data, and hardware begin to dictate progress—and that’s where consumer expectations lie.
In an exclusive conversation with AIM on the sidelines of the event, Jung pitched that physical AI will not arrive through one viral model or a single company. Instead, it will emerge unevenly, shaped by hardware limits, real-time control, and regional data advantages.
Unlike ChatGPT, which improved rapidly by scaling data and compute, Physical AI must solve multiple problems at once. “There are two challenges,” Jung said. “Number one is data. Number two is the embodiment, the robotics system, the hardware.”
“Action is not so simple,” Jung said. “They should control the robot itself, and in real-time, because existing LLMs or vision language models are not real-time models.”
At a time when humanoid demos are entering the market, with 1X’s NEO bipedal humanoid robot capable of chores like laundry and cleaning, Jung offers a grounded counterpoint. He believes intelligence is no longer the main constraint; the harder problem is turning perception and reasoning into safe, fast, instantaneous action.
Thinking is Easy, Acting is Not
Several speakers at the panel asserted that Physical AI behaves differently from digital systems.
Goel said digital AI spread overnight because it could reach millions of users instantly. “Physically, AI is different,” he said. “There are many open challenges… that need to be solved.”
One challenge is the sheer volume of data the physical world generates. “We have to understand force. We have to understand audio,” Goel said. “The amount of data generated from the physical world obviously differs in magnitude—more than what the text data exists.” That data cannot simply be scraped from the internet. It must be produced through robots operating in factories, warehouses, and construction sites.
Jung added that robots require real-world movements that demand immediate decisions. “It also has to be fast in real-time,” he said, explaining why existing AI models cannot directly control physical systems.
ChatGPT scaled because text already existed, and mistakes carried comparatively little risk. Physical AI must deal with motion, balance, and force. Errors break machines and harm people. “How can we add in some real-time model, a real-time action model to the existing vision language model?” Jung pondered.
Even when models are trained, they cannot be deployed casually. “Unlike the digital world, where you can just wide check your models, how do you wide check a physical model? You need a simulator,” Goel enlightened. Verification and validation become central, not optional.
Dexterity is Essential
Jung noted that consumers don’t understand the complexity in integrating AI with hardware. “But we have a long way to go.”
The challenge becomes sharper when it comes to dexterity. Human hands use many joints at once. Most robots still rely on simple grippers. Jung noted that it is not a design choice but a limit of current systems.
“The hardest thing is controlling their high degree of freedom,” he said. “Providing some high level of dexterity means controlling the multiple joints of the humanoid.”
A high degree of freedom robot is one with seven or more robotic axes that offer an enhanced range of motion.
Many robots still rely on simple grippers. “None of them is now dealing with the high level of freedom hardware yet,” Jung said. “That’s why they stuck to the two fingers.” He added that even well-funded efforts struggle. “Even the Tesla Optimus team cannot provide better hardware, especially hands. They fail to [offer] high degree of freedom.”
RLWRLD is building robot hands with 15 degrees of freedom to handle complex tasks.
The reason, he argued, is not ambition but feasibility. Each extra joint multiplies the data and control space. Training those systems requires new datasets and new models. “Between the data and the hardware, we should develop the foundation model to control the robot,” Jung said.
Scaling by Region, Not Virality
Another reason Physical AI will not have a single defining moment is geography. Digital AI globalised quickly because it relied on shared infrastructure. Physical AI depends on supply chains, labour, and industrial data—all of which are unevenly distributed.
“The US is the king of software. China is the king of hardware,” Jung said in the interview. Between them sit countries like South Korea and Japan, which hold an advantage in industrial data. “Number one strength we have is industrial data,” he said, referring to East Asian manufacturing ecosystems.
India enters this picture for different reasons. “Humanoid cannot assemble humanoid,” Jung said. “Humanoid should be assembled by humans.” That labour intensity pushes companies to look for manufacturing alternatives outside mainland China. “The manufacturing inside of mainland China is not widely adopted by the US allies,” he said. “India is, I think, one of the best alternatives.”
Panellists echoed the idea that Physical AI will grow through ecosystems rather than platforms. Goel painted a hybrid future where intelligence is split between cloud and edge systems.
“You cannot be dependent on the cloud connectivity for new latency tasks,” he said. “You have to have compute on the edge.”
This architecture makes it less likely for a single model to become dominant. Instead, progress depends on how well companies assimilate training, simulation, deployment, and safety.
Gruenstein added that even industrial settings resist standardisation. “99% manufacturers in the United States are small businesses,” he said. “They can’t afford any of that stuff.” Many facilities change tasks daily, which breaks traditional automation. That variability forces robotics companies to adapt systems to local conditions rather than chase universal solutions.
This regional spread makes a single winner unlikely. Physical AI systems must adapt to local data, regulation, and infrastructure. Jung said even large US companies cannot operate alone. “In the US, even the big giants like AWS cannot survive alone,” he said. “They need some ecosystem from hardware to AI.”
Not One Single Moment
Public perception often overestimates the readiness of humanoid robots, given how digital AI products collect feedback from millions of users and iterate. Robotics cannot do the same. That’s where scalability also becomes an issue, Jung argued.
Peterson also agreed that progress would be gradual. “We’re going to see applications that roll out over time,” he said. “And then, in five years, we’re going to look back and say a lot of this work is easier.”
Some startups are pushing early robots into homes to gather data. Jung questioned that approach. “That is a very selfish idea because from the point of an end user, the product itself is not complete,” he said. He warned that such strategies trade user trust for speed.
Instead, he expects gradual progress, driven by industry use cases and regional ecosystems. “Sooner or later, we can provide a better model,” Jung predicted. “I’m very positive about providing better architecture in this market.”
For an industry searching for its ChatGPT moment, the message from founders and engineers is consistent: there may never be one.
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