At Panoramai's Real World AI panel, Pascal Rodriguez, Director of Engineering at Visium, delivered a sobering assessment of physical AI's current state and implementation challenges. Drawing on Visium's mission to help companies transition AI from ideation to production environments, Rodriguez illuminated the gap between laboratory perfection and real-world deployment complexities.

The Ubiquity Paradox of Physical AI

Rodriguez opened with a provocative audience poll: « Who amongst you would feel comfortable to let a machine take a decision in a split second that can probably affect your life forever? » The hesitant response revealed a fundamental disconnect—physical AI systems already make life-critical decisions daily across multiple sectors.

« Physical AI can be found already in surgical robots that can help surgeons be very precise with their movements. In factories, you have a computer vision system that can spot defects directly on production lines in real time and reject them if they are not following the right quality, » Rodriguez explained, systematically demonstrating AI's pervasive presence. From agricultural sensors optimizing soil conditions and water usage to emergency braking systems that literally save lives—as Rodriguez experienced firsthand the previous weekend—these systems operate invisibly but critically.

The Infrastructure Decision Matrix

Rodriguez outlined the fundamental computational challenge linking sensing to action in physical AI systems. Organizations face three distinct deployment architectures, each with strategic implications: cloud-based processing offering cost efficiency but higher latency, edge computing providing improved response times with moderate complexity, and on-device processing delivering optimal speed, privacy, and autonomy despite higher initial investments.

« You get the best out of the speed and entrance time. Obviously, you also get a lot of privacy and a lot of autonomy, which is quite interesting as well, » he noted regarding device-level deployment, positioning these trade-offs as critical business decisions rather than purely technical choices.

The Four Pillars of Implementation Challenge

Rodriguez identified four fundamental barriers constraining physical AI adoption. Beyond algorithmic bias—the persistent challenge of models making incorrect decisions without detection—he emphasized energy constraints for remote deployments with limited battery capacity. However, his most compelling insights addressed societal and operational challenges.

The digital divide represents what Rodriguez characterized as a philosophical challenge for the technology community. « All of these technologies for inferencing might not be available to everyone, right? They have a cost. They are quite expensive today, I would say. And then some, let's say, wealthy companies might be able to afford using them right now. But smaller organizations might just be left behind, » he observed.

This creates both competitive imbalances between organizations and geographic access gaps. « You see that in healthcare already, where, let's say, urban areas with big hospitals, they get access to bigger systems deployed on-prem. And rural areas or countries without that many technology access, cannot really afford an end-use, » Rodriguez explained, positioning inference infrastructure as a potential driver of inequality.

The Laboratory-Reality Performance Gap

Rodriguez's most practical insight addressed the persistent disconnect between controlled testing environments and real-world deployment conditions. « When you're in your lab and you're trying it out, you're always in the perfect conditions. And we just mentioned the digital twins, which are helping us to actually figure out and simulate a little bit that world, but it's still not perfect, » he acknowledged.

The solution requires obsessive attention to edge cases and consistency over peak performance. « It's probably better to have a model that is able to be accurate 95% of the time, but all the time, than something that has a 97% in some cases, and most of the time is actually quite rubbish, » Rodriguez emphasized, reflecting Visium's practical experience in production AI deployment.

Digital Twins and World Foundation Models

Rodriguez positioned digital twins as partial solutions to the lab-reality gap while acknowledging their limitations. He expressed particular enthusiasm for NVIDIA's recently announced World Foundation models, which « would allow us to basically simulate the real world. And then avoiding to have to go and deploy on-site and test and everything, we could actually do that in the lab and be closer and closer to reality. »

This represents a fundamental shift in AI development methodology—using generative AI to create more accurate simulation environments for training and testing physical AI systems before real-world deployment.

Success Metrics: Consistency Over Optimization

Rodriguez's approach to physical AI success metrics reflects Visium's production-focused philosophy. Rather than pursuing maximum theoretical performance, he advocates for systems that « are really taking care of edge cases. Be sure that the models are able to handle all of these conditions that are not very predictable, but testing them and defining them. »

This operational wisdom distinguishes between research achievements and production requirements, emphasizing that consistent reliability enables broader adoption across organizations with varying technical capabilities.

Technology Democratization Vision

Rodriguez concluded with an optimistic vision for physical AI democratization through improved simulation capabilities and consistent performance standards. « JNI basically inspires us, focused AI delivers really value, and the real world, as I just said, we need both so we can actually simulate and get the things deployed, » he summarized, positioning generative AI advances as enablers for broader physical AI adoption.

Strategic Philosophy: Production-First Innovation

Rodriguez's contribution demonstrated Visium's distinctive approach to AI implementation—prioritizing real-world deployment success over laboratory performance metrics. His emphasis on addressing the digital divide, managing the laboratory-reality gap, and achieving consistent performance reflects an engineering philosophy focused on democratizing AI benefits rather than maximizing technical capabilities.

Key Achievement: Rodriguez bridged the gap between AI research advances and practical deployment realities, providing a framework for understanding how physical AI systems can transition from laboratory demonstrations to reliable production environments while addressing critical challenges of accessibility, consistency, and real-world performance that determine widespread adoption success.