You are living in an electromagnetic world, and you have never actually seen it or understood how it behaves
Electromagnetism enables energy and information to move through the world, and Maxwell’s equations describe how it works
Electromagnetism is the idea that there are invisible forces all around us that allow energy and information to move from one place to another, and even though we cannot see these forces, they are working every time we use electricity, send a message, or connect to WiFi, because what is really moving is not physical objects but patterns in invisible fields that travel through space and interact with everything around them.
Electromagnetism is not just about energy, but about how information moves through the world, which means that every communication system, from wireless networks to satellites, depends on how well we can shape and control these invisible fields.
A scientist named James Clerk Maxwell discovered that all of this behavior follows a small set of rules called Maxwell’s equations, which explain how electric and magnetic effects are created, how they interact, and how they propagate, and from these rules we get light, radio waves, wireless communication, and almost every modern technology that defines modern life.
The laws are known, applying them in reality is the real challenge
The surprising part is that these rules are elegant and well understood, but applying them in real-world situations becomes extremely difficult because electromagnetic waves do not behave in simple or isolated ways when they interact with objects, materials, and environments, and instead they reflect, interfere, combine, cancel, and reshape themselves in ways that are highly sensitive to even very small changes in geometry, position, or material properties.
Electromagnetic behavior is fundamentally shaped by geometry, which means that the physical form of a system directly determines how fields propagate, interact, and distribute energy, and even small changes in shape can lead to completely different outcomes, making design less about simple parameters and more about understanding how geometry controls the invisible field.
At its core, electromagnetism is geometry shaping invisible fields
Electromagnetic behavior also depends strongly on the surrounding environment, which means that the same design can perform very differently depending on where and how it is used, making prediction and optimization even more difficult.
Electromagnetic systems can also exhibit sharp and non-intuitive behaviors such as resonance and interference effects, where small changes can lead to disproportionately large impacts, making reliable design even more challenging.
This creates a system where everything interacts with everything else, meaning that a small change in one part of a design can affect the entire field behavior, making it difficult to predict outcomes in a clean or modular way, and this complexity is further amplified by the fact that the same physical laws must operate consistently across very different scales, from nanometer-level structures inside chips to meter-scale environments like rooms to kilometer-scale communication systems.
Because of this, engineers cannot directly reason about outcomes in their heads and instead rely heavily on simulation, which means recreating these physical interactions numerically inside a computer, but this process is computationally expensive and must often be repeated for every new design, leading to a workflow that is dominated by trial and error, where systems are designed, simulated, adjusted, and re-tested many times before they perform as intended.
Even after design and prediction, systems must be verified through simulation and physical testing, because small errors or overlooked interactions can lead to failure in real-world conditions.
The real bottleneck is not computation, it is human intuition
Beyond the computational and physical challenges, there is a more fundamental limitation, which is that humans do not naturally understand electromagnetic fields, because we cannot see or directly experience them, and unlike motion or gravity, which we can intuitively grasp, electromagnetic behavior remains abstract and indirect, forcing even experienced engineers to rely on tools, approximations, and accumulated experience rather than direct mental models.
This perspective has been discussed in recent industry conversations, including writing by Packy McCormick, which frames the problem not just as computation, but as a gap in human intuition.
This means that progress is not only limited by computation and methods, but also by human cognition, since only a small number of experts develop partial intuition over many years, and even that intuition is incomplete and often dependent on specific domains.
We rely on multiple approaches today, and none of them is sufficient alone
Because each of these challenges creates a different constraint, no single method is sufficient, and instead, a combination of approaches has evolved, where each one addresses a specific limitation of working with electromagnetic systems.
Simulation remains the most reliable method for capturing the full complexity of electromagnetic interactions, but this comes with high computational cost and slow iteration.
Engineers also rely on experience, heuristics, and simplified analytical models to guide design decisions, which helps compensate for the lack of direct intuition, although these methods do not generalize well to new or complex scenarios.
Reduced or surrogate models approximate behavior based on prior computations, allowing faster predictions while still retaining a connection to physical principles, and helping avoid repeatedly solving the full physics problem.
Optimization techniques systematically explore design spaces and improve performance without relying purely on manual iteration, making it possible to search through solutions that are difficult to discover manually.
Real-world testing provides ground truth, ensuring that systems perform correctly in real environments where models and simulations may fall short, but this approach is expensive and slow.
We are moving from solving physics every time to learning how it behaves
The challenge is not just solving equations, but navigating an enormous design space, where small changes in geometry and configuration can produce entirely different outcomes, making it difficult to systematically discover optimal designs.
AI helps reduce iteration and approximate intuition across a much larger design space, allowing systems to explore possibilities that would be impractical through simulation alone.
Companies such as Arena Physica, Anduril Industries, Ansys, etc., are exploring this direction, not to replace physics, but to build systems that approximate how fields behave and assist in generating designs more efficiently.
The shift is subtle but important. AI is not just speeding up computation; it is attempting to approximate intuition, allowing systems to recognize patterns across many scenarios and propose solutions that may not be obvious to human designers, including designs that appear unusual or non-intuitive but perform well when validated against physical laws.
This does not replace physics, but it changes how often we need to solve it from scratch.
Controlling electromagnetism means controlling how the modern world operates
Improving how we design and understand electromagnetic systems has direct and wide-ranging effects on the real world, because these fields carry both energy and information, which means that better control over them translates into better communication, computation, and infrastructure.
In communication systems, this means faster and more reliable networks, fewer dead zones, and more efficient use of limited spectrum.
In computing, especially at the level of chips and data centers, better electromagnetic design reduces interference, improves signal integrity, and enables faster and more energy-efficient systems.
In transportation, space, and defense, electromagnetic performance directly impacts safety, coordination, detection, and resilience.
More broadly, as the world becomes increasingly digital and connected, the importance of this invisible layer continues to grow, and we are beginning to reach limits in performance and efficiency that cannot be overcome without better ways of designing and controlling electromagnetic systems.
Many modern systems are already approaching limits in efficiency, performance, and spectrum usage, and further improvements increasingly depend on our ability to better understand and control electromagnetic behavior.
This is not just about better engineering; it is about who gets to shape the invisible layer that modern life depends on.
Physics still decides what actually works
Simulation and physical testing do not go away, because electromagnetic systems must ultimately obey physical laws, and any approximation must be verified to ensure correctness, especially in high-stakes systems.
The future is not about replacing Maxwell’s equations. It is about combining approaches, where faster approximations and pre-learned models reduce the need for repeated computation, while physics-based methods continue to provide the final ground truth.
If these challenges are significantly reduced, the shift is not just incremental improvement, but a change in how systems are designed, moving from slow, trial-and-error processes to more direct and intent-driven design, where desired outcomes can be translated into physical systems more efficiently.
This also changes the speed of innovation, reducing the time required to explore and validate designs, and enabling a much larger design space to be explored.
At a broader level, improving our ability to control electromagnetic systems increases our ability to control how information and energy move through the world, which has implications not just for engineering but for economic and strategic advantage.
The real problem is no longer knowing the rules, it is thinking within them
Electromagnetism is the invisible system that enables energy and information to move through the world. Maxwell’s equations describe how it works. The main challenge lies in applying these rules to complex and interconnected real-world systems, and humans lack natural intuition for this domain.
The problem is no longer knowing the rules; it is developing the ability to think within them.
We already understand the rules of the invisible world, but we are still learning how to think inside it, and whoever learns to think inside it first will shape what comes next