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Early in my career, I worked on a program that endeavored to use neural networks to teach very early generation autonomous subs how to navigate on their own. That project failed to achieve its objective, but it clued me in to an important point about AI. The foundational vector and scalar math we were using for the navigation neural network was basically the same as the foundation for today’s AI systems. What we didn’t have was the requisite data storage capacity and compute power to adequately train the neural networks to achieve the desired results.

Today, the compute power and data storage capacities available to federal agencies are exponentially greater, and advances in GenAI have opened new pathways to innovation. To return to the challenge of self-navigating machines, in November 2024 reported that a trio of researchers had used GenAI models and a physics simulator to teach a robotic dog to go upstairs and climb over a box without first training the robot on real-world data. This is one example of how AI, when smartly paired with other technologies, can radically redefine the art of the possible.

To scale AI so that it can be applied to the biggest challenges our nation faces— from managing the national debt to outpacing near-peer adversaries in terms of technological supremacy—the government should look beyond the algorithms and AI models and invest in the infrastructure that will enable AI to flourish. Just as the construction of networks of fiber optic cables helped create the foundation for the internet boom, taking the following steps will position agencies to unleash the full power of AI.

• Prioritize data: Connect all enterprise networks and devices (e.g., satellites, drones) to collect and store as much operational technology (OT) data as possible. Additionally, start extending the volume of information technology (IT) data that is saved. Both OT and IT data can and should be used for AI model training and simulation. The more clear, relevant data you feed a model, the denser its parameter sets will be, which will increase its accuracy and capabilities.��
• Leverage software-defined environments: Apply software engineering practices to transform hardware-dependent systems into dynamic, software-defined environments. Certain organizations have used software-defined processes to automate complex tasks in enterprise resource planning systems. Now, it’s time to extend this concept to all aspects of physical and virtual deployments.
  
• Use digital twins: Use digital twins to create realistic virtual environments that can host simulations and testing, then use AI to optimize the performance of these systems.

This paradigm can be understood in terms of something I call the modern technology flywheel. Imagine a frictionless enterprise, where every piece of information that’s collected feeds into a virtual machine. That virtual machine uses digital twins to train AI models through thousands or even millions of simulated outcomes. It then pushes those models out to an edge device or back into the cloud where the models learn from real-world deployments and feed those insights back into the real and synthetic data environments. This sequence repeats itself over and over. Each turn generates more data that can be used to improve AI and the system software, and that new data optimizes the next turn of the wheel. By harnessing this intricate but achievable paradigm, government agencies can achieve the flexibility, scale, and acceleration essential to unleashing AI and tech at full speed.

The individual components that drive the flywheel—real and synthetic data, software-defined environments, digital twins—have matured on different paths, which is why its outsized potential wasn’t fully recognized until recently. Its interdependencies are also tremendously complex. But each time the flywheel turns, the performance gets better and the enterprise benefits—growing more efficient, more innovative, and less vulnerable to dislocation.

To realize the advantages of the modern flywheel, the right foundation must be in place. That foundation starts with data, which is the lifeblood of AI and digital twins. Agencies that have more specialized datasets at their disposal will be better positioned to create realistic simulations of mission environments, and perhaps one day, build their own AI models. In addition, software-defining assets of all sizes and scales, from networks to warships, enable the construction of systems that can be adapted, upgraded, or optimized through software layers without needing specific, costly hardware changes. It’s also critical to invest in security. Connected systems introduce new attack vectors, which is why agencies should use layered encryption and comprehensive firewalls. Scalability should be supported through multiple, physically separated availability zones, with a unified set of application programming interfaces (APIs) from edge to cloud. Given the speed and scale of cyberattacks, AI will play a critical role in the identification of threats, monitoring the attack vectors, and proactive threat hunting.

With these layers of support, federal agencies are positioned to use the modern flywheel to help AI drive mission-critical outcomes faster. To innovate in the 21st century, you need more than just a set of disparate technology tools. It’s no longer enough to scale a traditional AI application for a narrowly defined use case. Instead, it’s about integrating multiple modern AI-enabled systems to create a perpetual, self-renewing source of improvement. Blending emerging technologies across phases of data collection, simulation, training, and deployment provides the key to achieving exponential leaps in efficiency, innovation, and mission success.

Where mission success often has national implications, this technology integration isn’t just about efficiency—it’s about maintaining strategic advantage and ensuring continuous delivery of critical services. The modern flywheel approach enables federal organizations to rapidly adapt to emerging threats, scale services to meet citizen needs, and maintain technological superiority in a complex global environment.

A process digital twin models the operational processes of a physical system or workflow. It simulates the sequence of actions, interactions, and transformations within a process, allowing for real-time monitoring, analysis, and optimization. Think of a digital twin of an assembly line that simulates the interaction of machines, workers, and materials. By mirroring the behavior of the actual process, this type of digital twin enables engineers and operators to predict outcomes, identify inefficiencies, and test modifications.

A 3D structural twin models the physical geometry and structural characteristics of a real-world object or system in three dimensions. It captures detailed information about the shape, materials, and mechanical properties of the object or system, allowing for simulation and analysis of structural behavior under various conditions. By providing a virtual environment to test stress, strain, loadbearing capacity, and other physical interactions, a 3D structural twin enables engineers and designers to predict performance, optimize designs, and identify potential issues before they occur in the physical counterpart. Think of a digital twin of a bridge, simulating stresses under various conditions to predict maintenance needs.

• To innovate in the 21st century, the federal government needs to do more than scale a traditional AI application for a narrowly defined use case. Instead, agencies should invest in integrating multiple modern AI-enabled systems that can unlock the full potential of AI and other technologies.
• To build these intelligent systems, agencies must collect and store as much operational technology and informational technology data as possible, convert hardware-dependent processes into software-controlled environments, and invest in digital twins.
• This achievable technical paradigm will enable federal organizations to rapidly adapt to emerging threats, scale services to meet citizen needs, and maintain technological superiority in a complex global environment.