How Physical AI Integration Is Transforming the Real World

The conversation about artificial intelligence (AI) has evolved from hypothetical applications to tangible, real-world integrations. One notable development in this field is physical AI, an innovative area of technology that is redefining interactions among machines, robotics, and humans in various industries and in everyday life. With the increasing adoption of automation and autonomous systems, physical AI is changing our approach to productivity, efficiency, and even the fundamental aspects of human capabilities.

At the recent South by Southwest (SXSW) 2025 session, "From Cages to the Real World: The Dawn of Physical AI," a panel of leading visionaries came together to explore this emerging technology's potential and challenges. With insights from AI pioneers such as Dr. Anirudh Devgan (president and CEO of Cadence), Sir Tim Berners-Lee (inventor of the World Wide Web), Tye Brady (chief roboticist at Amazon), Dr. Andrea Thomaz (CEO and co-founder of Diligent Robotics), and moderated by Jennifer Stojkovic, the future of physical AI was discussed in vivid detail.

What Is Physical AI?

Unlike the data analytics we associate with traditional AI, physical AI operates vertically, focusing on domain-specific applications. Think of autonomous robots, drones, vehicles, or humanoid assistants that combine machine learning and robotics to function in real-world environments. These systems can sense, process, and respond to physical surroundings, acting as extensions of human capabilities.

Physical AI is more than just robots performing simple, repetitive tasks. It's about creating systems that can perceive complex, dynamic environments, make decisions, and assist humans in ways that were once limited to science fiction.

The applications are vast, spanning industries such as healthcare, manufacturing, supply chain management, and even daily home activities.

Insights from SXSW's Panel on the Dawn of Physical AI

The SXSW session brought together some of the brightest minds in the AI and robotics ecosystem, each offering unique viewpoints. Below are some of the most compelling perspectives:

Paradigm Shift in Physical AI

Dr. Anirudh Devgan emphasized the progression of AI from its current infrastructure-based stage to the era of physical AI. This transition expands beyond purely digital applications like language models, bringing AI into tangible, real-world systems such as robotics, drones, and autonomous technologies. A prime example is autonomous vehicles, which are no longer viewed as futuristic but as practical innovations on the verge of broader adoption. While scaling this technology remains a challenge, advancements in robotics and AI are paving the way for commercially viable solutions.

Dr. Devgan pointed out that it's essential to understand that physical AI represents only one part of a more extensive evolution. Technologies like AI typically undergo phased development. An illustrative comparison can be made with the evolution of the Internet. Initially, efforts were dedicated to building the foundational infrastructure, including networking systems. He explained that the second phase involved leveraging this infrastructure to enhance existing products. The most groundbreaking and influential phase, however, came later, with the creation of entirely new applications and business models—innovations like Uber and Facebook.

AI mirrors this trajectory. Dr. Devgan explained the phases of AI development. He mentioned that we are currently transitioning from the infrastructure AI phase, dominated by data centers and computational tools like GPUs, plus "agentic AI," which comprises the digital systems we experience today. The next step, physical AI, has the potential to unlock massive markets and industries valued at trillions of dollars. For AI to achieve true large-scale monetization, it must expand into trillion-dollar sectors, and physical AI could be the key. This phase is anticipated to unfold over the next two to seven years, heralding groundbreaking technological shifts. Beyond that, the third phase—termed sciences AI—will likely materialize within five to ten years. This phase will focus on applying AI to areas such as material science, life sciences, and drug discovery, marking yet another leap in technological innovation. Each phase builds on its predecessor, charting a clear path toward increasingly profound advancements and possibilities.

A Symphony of Humans and Machines—When Robots Blend into the Background

Tye Brady emphasized the harmony between humans and robotics during his discussion. Debunking the myth of full automation, he stated, "Machines should allow us to be more human. The burden is on us roboticists to make them simple and easy to use. When done right, these systems amplify human productivity instead of replacing it."

At Amazon, physical AI has scaled to unprecedented levels, with 75% of delivered packages passing through robotic systems. However, Brady underscored that the focus remains on building a "symphony" where humans and machines work seamlessly together for optimal outcomes.

One striking idea came from Brady's assertion that the most successful physical AI systems are the ones that blend into the background. Instead of drawing attention, these systems quietly handle operations like warehouse sorting, delivery in hospitals, or logistics planning. "When robotics is effective, it's no longer the topic of conversation. It's simply doing its job," Brady commented.

This subtle integration of physical AI is emblematic of how it helps industries quietly evolve, creating efficiencies and empowering humans to tackle more creative and complex tasks.

Lessons from the World Wide Web

Sir Tim Berners-Lee drew parallels between the rise of physical AI and the early development of the internet. He warns against silos in AI development. "When we built the web, collaboration among tech companies was central. But in the AI scene, I don't see institutions where open collaboration is prioritized," he noted. He proposes collaborative efforts in which governments, private entities, and communities work together to create ethical, transparent AI models. Global initiatives aimed at advancing AI equitably could ensure that nations—from the United States to less developed economies—share in its bountiful rewards.

This observation underscores the importance of global coordination and ethical frameworks in physical AI. Integrating robotics into society requires a similar collective approach to maximize innovation while minimizing risks.

Real-World Impact on Healthcare

Dr. Andrea Thomaz outlined how physical AI is already making waves in healthcare. Her company's robot, Moxi, assists hospital staff by transporting medications, samples, and equipment. This has relieved healthcare workers of excessive physical tasks, allowing them to spend more time with patients.

Moxi's success is a small but powerful example of how intelligence in motion can address real-world pain points across industries. "Robots augment jobs—we're not replacing humans. Instead, we're filling critical gaps in industries with labor shortages," Thomaz explained.

Thomaz mentioned that according to their latest statistics, Moxi robots have completed approximately one million transport tasks. This equates to 1.5 billion steps that nurses and pharmacists did not have to take.

Healthcare, with its aging population and urgent need for cost-effective delivery systems, provides a model for the broader adoption of AI-driven robotics.

Ethical Challenges of Physical AI

Beyond technical applications, the SXSW panel touched on the ethical implications of physical AI. Questions around data security, equitable integration, and robotic decision-making are paramount. Sir Tim emphasized the need for trust and accountability, stating, "AI systems must work for the individual, not corporate or institutional interests. The question we should always ask is, 'Who does the AI work for?'"

This challenge will only grow as physical AI becomes more integrated into public spaces, whether in self-driving cars or grocery store robots.

What Does the Future Hold for Physical AI?

The SXSW panelists highlighted that the next decade will be pivotal for physical AI's development. The technology could contribute trillions of dollars to the global economy, disrupt labor markets, and create opportunities for innovation across sectors.

Dr. Devgan offered a hopeful and pragmatic outlook. "The full societal impact of physical AI may take years, but the possibilities are enormous. Self-driving cars, robotics in agriculture, and drone delivery systems are only the beginning."

However, challenges remain. Trust, accessibility, and interdisciplinary collaboration are critical hurdles that must be addressed. Robots must not only work efficiently but also meet ethical and safety standards to thrive in complex human environments.

Physical AI is no longer a distant idea; it's here, enabling new possibilities and pushing the boundaries of productivity. From hospitals to homes, its implications are vast and far-reaching.

Want to explore the topic further? Watch the entire SXSW session "From Cages to the Real World" to hear first-hand insights from industry leaders and what the future holds for intelligence in motion.

Cadence and the Future of Physical AI

Take your innovation to the next level with Cadence, a leader in driving the evolution of physical AI. Our customers design groundbreaking AI systems on chips (SoCs) using Cadence and its AI-driven chip-to-systems portfolio. The Cadence AI portfolio features innovative solutions, including AI-driven optimization products, agentic AI design agents powered by the Joint Enterprise Data and AI (JedAI) Platform, and the AI-driven Verisium Verification Platform. These tools are revolutionizing the design process and are becoming indispensable for advancing AI technologies.

Cadence Tensilica Intellectual Property (IP) and Neo neural processing units (NPUs) are engineered for high-performance computational tasks, offering exceptional AI performance with a low-energy footprint. These solutions empower customers to innovate with sensing and reinforcement learning (RL) SoCs that deliver unparalleled efficiency and capability.

Join the leaders designing a smarter world with Cadence—where AI meets innovation. Learn more about how Cadence can accelerate your vision for physical AI. Explore Cadence AI Solutions now.