Why Traditional Foundation Models Fall Short in Physical AI

Contextual Introduction

Foundation models have significantly advanced over the past decade, primarily driven by architectures for language processing and symbolic abstraction in cloud environments [1]. This approach has enabled progress in text generation, semantic analysis, and statistical reasoning, accelerating adoption across digital and service sectors. According to the Stanford Institute for Human Centered Artificial Intelligence, foundation models have reshaped AI capabilities [1].

Nevertheless, adapting these architectures to the physical world presents significant limitations. Systems interacting with real environments face strict constraints regarding response time, energy consumption, and operational reliability. As noted in a January 2024 Stanford HAI Policy Brief, these are not mere preferences but critical requirements for safe operation [2].

Domain Overview

The core challenge lies not merely in scaling but in conceptual design. Traditional models prioritize maximizing parameters, extensive training, and aggregating large data volumes. This approach often assumes constant connectivity, tolerable latencies, and nearly limitless computational resources, as McKinsey's June 2023 report highlights [3].

While effective in cloud environments, this logic frequently conflicts with infrastructure costs and energy limits for ubiquitous or critical deployments. Practical experience with generative models outside data centers highlights significant concerns about memory footprint, latency, and energy consumption. As discussed in the May 2025 *ACM Queue* article by the ACM Queue, these factors are less prominent during initial training but critical in deployment [4].

Evidence-Supported Analysis

Physical-world systems operate under constraints absent in the digital domain. Sensors, actuators, industrial machinery, and autonomous systems generate continuous data streams requiring real-time and deterministic processing. Research by D. Ngo et al. in *Electronics* (2025) highlights the need for real time, deterministic inference in resource limited edge environments [5].

Delayed inference in this context is not merely inefficient; it can compromise safety, process quality, system stability, or operational continuity. Furthermore, when parts of a processing pipeline rely on network connectivity, latency becomes a critical distribution with unpredictable tails and variability. A survey by S.B. Kamtam et al. in *Sensors* (2024) points out that this poses challenges for real-time control and supervision [6].

Current Limitations

Beyond latency, these systems must operate reliably for extended periods, often in challenging environmental conditions without consistent access to remote infrastructure. Thermal stability, energy efficiency, fault tolerance, and predictable behavior become essential requirements. These factors are crucial for maintaining continuous operation in harsh settings.

Architectures initially designed for cloud environments typically do not prioritize these factors as primary design objectives. This divergence highlights a fundamental mismatch between design principles and operational realities in physical applications, leading to potential performance gaps and reliability issues.

Emerging Conceptual Approach

An emerging conceptual approach redefines foundation models by shifting focus towards edge deployment. This neuromorphic approach aims to capture temporal dynamics and provide deterministic inference, drawing inspiration from spiking neural networks that prioritize efficiency and temporal dynamics. A review by Y. Guo et al. in *Frontiers in Neuroscience* (2023) explores these networks as a path to more efficient AI [9].

An edge-first strategy must consider sustained performance and energy efficiency as primary engineering goals. An October 2024 NVIDIA Developer Blog post emphasizes maximizing energy and power efficiency in applications with GPUs [8]. The evolution of platforms for edge inference and robotics further emphasizes balanced designs that combine processing capability with thermal and energy efficiency. The January 2026 NVIDIA Developer Blog discusses accelerating AI inference for edge and robotics, reinforcing this need [10].

Abstract Comparison

Traditional cloud-centric models prioritize scalability and data aggregation, often relying on abundant computational and network resources. Their design optimizes for throughput and capacity in a controlled data center environment. This approach has proven highly successful for digital tasks where latency and resource availability are more flexible.

In contrast, physical-world systems demand architectures optimized for resource scarcity, low latency, and deterministic operation. The design principles diverge significantly: one favors extensive parameter counts, while the other emphasizes constrained power budgets and immediate, reliable responses. This distinction underscores a need for specialized architectural considerations rather than direct translation.

• Cloud Models: Prioritize scale, data aggregation, high throughput, flexible resources.
• Physical AI: Prioritize real time response, energy efficiency, deterministic behavior, resource constraints.

Practical Implications: Autonomous Vehicles

Autonomous vehicles provide a clear illustration of why traditional models are unsuitable for physical environments. An autonomous driving system must continuously and synchronously process information from cameras, radar, and other sensors. For system viability, perception and planning must meet stringent time constraints. According to TimelyNet in *ACM* (2025), latency requirements are often less than one hundred milliseconds in autonomous driving pipelines for real-time performance [7].

When a model requires heavy or non-deterministic inference, or depends on complex software layers introducing execution variability, operational risk increases. If some computation shifts outside the vehicle, network variability and congestion events can degrade response times during critical moments. A survey by S.B. Kamtam et al. in *Sensors* (2024) points out this can be detrimental [6].

Practical power and thermal dissipation constraints further complicate matters. Within a vehicle, energy and thermal budgets are finite, and degradation from temperature or sustained consumption directly impacts system availability and reliability. This necessitates designing embedded systems with performance per watt and sustained operational efficiency in mind. Real autonomous systems therefore prioritize architectures capable of local inference, with predictable energy consumption, bounded latency, and stable behavior under continuous load. Intelligence in this context must be immediate and reliable, not intermittent.

Future Outlook

The physical world requires systems that can react, anticipate, and adapt in real time, coherently integrating perception, temporal memory, and action. This necessitates computation near data generation points and architectures adapted to environments with limited memory, power, and dissipation capabilities. Research by D. Ngo et al. in *Electronics* (2025) highlights this as a critical need for edge intelligence [5].

The next generation of artificial intelligence will not be defined solely by model size but by its ability to operate reliably, efficiently, and continuously in the real world. Recent frameworks on physical AI in industrial settings emphasize integrating sensors, hardware, and models within a stack specifically designed for real systems. A September 2025 report by the World Economic Forum highlights this shift, moving beyond data center assumptions [11].

Editorial Conclusion

The transition of artificial intelligence from abstract digital environments to tangible physical applications demands a fundamental rethinking of model design. While cloud-based foundation models have excelled in their domain, their underlying assumptions about resources and operational conditions do not align with the strict requirements of real-world interaction. This divergence necessitates a targeted approach to AI development.

Developing robust, reliable, and energy efficient AI for physical systems necessitates a paradigm shift towards edge-centric, purpose-built architectures that prioritize determinism and resource awareness. The future of AI in the physical world will be characterized by intelligence that is not just powerful, but also practical and precisely integrated.

References

1. Stanford Institute for Human Centered Artificial Intelligence. "Foundation Models (topic page)." *Stanford Institute for Human Centered Artificial Intelligence* (Undated). https://hai.stanford.edu/topics/foundation-models
2. Stanford HAI Policy Brief. "Safety Risks from Customizing Foundation Models via Fine-Tuning." *Stanford HAI Policy Brief* (January 2024). https://hai.stanford.edu/policy-brief-safety-risks-customizing-foundation-models-fine-tuning
3. McKinsey. "The economic potential of generative AI: The next productivity frontier." *McKinsey* (June 2023). https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/the%20economic%20potential%20of%20generative%20ai%20the%20next%20productivity%20frontier/the-economic-potential-of-generative-ai-the-next-productivity-frontier.pdf
4. ACM Queue. "Generative AI at the Edge: Challenges and Opportunities." *ACM Queue* (May 2025). https://queue.acm.org/detail.cfm?id=3733702
5. Ngo, D. et al. "Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments." *Electronics* (2025, 14(12), 2495). https://doi.org/10.3390/electronics14122495
6. Kamtam, S.B. et al. "Network Latency in Teleoperation of Connected and Automated Vehicles: A Survey." *Sensors* (2024, 24(12), 3957). https://www.mdpi.com/1424-8220/24/12/3957
7. TimelyNet. "Adaptive Neural Architecture for Autonomous Driving (latency requirement discussion)." *ACM* (2025). https://dl.acm.org/doi/10.1145/3762652
8. NVIDIA Developer Blog. "Maximizing Energy and Power Efficiency in Applications with NVIDIA GPUs." *NVIDIA Developer Blog* (October 2024). https://developer.nvidia.com/blog/maximizing-energy-and-power-efficiency-in-applications-with-nvidia-gpus/
9. Guo, Y. et al. "Direct learning-based deep spiking neural networks: a review." *Frontiers in Neuroscience* (2023). https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1209795/full
10. NVIDIA Developer Blog. "Accelerate AI Inference for Edge and Robotics with NVIDIA Jetson T4000 and NVIDIA JetPack 7.1." *NVIDIA Developer Blog* (January 2026). https://developer.nvidia.com/blog/accelerate-ai-inference-for-edge-and-robotics-with-nvidia-jetson-t4000-and-nvidia-jetpack-7-1/
11. World Economic Forum. "Physical AI: Powering the New Age of Industrial Operations (PDF)." *World Economic Forum* (September 2025). https://reports.weforum.org/docs/WEF_Physical_AI_Powering_the_New_Age_of_Industrial_Operations_2025.pdf