Who Owns Your Digital Twin? Power, Control, and the Reality Gap

Published January 5, 2026
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Digital twins promise perfect mirrors of reality. But who controls what gets mirrored—and what gets left out? The answer reveals everything about power in the age of simulation.

In 2019, Singapore completed something unprecedented: a digital twin of an entire nation. Virtual Singapore integrates building information models, real-time sensor data, demographic information, and environmental systems into a single platform. Urban planners can simulate evacuation routes. Engineers can model how a new tower will cast shadows. In pilot districts, the system helped reduce energy consumption by 30%.

Meanwhile, the European Union is building something even more ambitious: Destination Earth, a digital twin of the planet itself. By 2030, it aims to simulate Earth's climate, ecosystems, and human systems at kilometer-scale resolution, updating continuously with satellite data. The goal is nothing less than modeling the future of our planet to inform policy on climate change, disaster response, and resource management.

These projects represent the apex of a $154 billion industry projected to reshape everything from manufacturing to medicine. Rolls-Royce uses engine-specific twins to predict maintenance needs so precisely that some aircraft engines now operate far beyond standard service intervals—saving fuel, reducing emissions, and preventing failures before they happen. BMW twins entire factories before building them. Philips is developing "digital patients" that model individual human physiology for personalized treatment.

The promise is extraordinary: perfect virtual mirrors of physical reality, continuously updated, predictively intelligent, capable of optimizing systems we could never understand through intuition alone.

Here's what the press releases don't mention: most of this doesn't work the way we're told it does.

What Is a Digital Twin, Actually?

The National Academies of Sciences provides the most rigorous definition:

"A digital twin is a set of virtual information constructs that mimics the structure, context, and behavior of a natural, engineered, or social system (or system-of-systems), is dynamically updated with data from its physical twin, has a predictive capability, and informs decisions that realize value. The bidirectional interaction between the virtual and the physical is central to the digital twin."

The key words: dynamically updated, predictive, bidirectional. A digital twin isn't a 3D model. It isn't a dashboard. It isn't a simulation you run once. It's a living representation that continuously ingests data from reality, makes predictions about what will happen, and—crucially—can influence the physical system it represents.

By this definition, most "digital twins" deployed today aren't twins at all. They're what researchers call digital shadows (data flows one way, from physical to virtual) or digital models (static representations with no live data connection). The industry conflates these categories constantly, inflating capabilities and obscuring limitations.

A 2025 study across engineering domains found that Technology Readiness Levels for digital twins average just 4.8 out of 9—roughly "technology validated in lab." We're not at deployment maturity. We're at promising prototype.

The Gap Has a Name

Researchers call it the reality gap: the divergence between what a simulation predicts and what the physical system actually does. Every digital twin has one. The question is how large, how consequential, and whether it's growing or shrinking.

The gap emerges from several sources:

Context mismatch. A twin trained on one set of operating conditions—temperature, load, usage patterns—will drift when conditions change. A bridge monitoring system calibrated for summer traffic behaves differently in winter. A patient twin trained on clinical trial data may not generalize to real-world populations with different demographics, comorbidities, and behaviors.

Physics we didn't model. Every simulation makes simplifying assumptions. We model the beam but not the rust. We model the flow but not the turbulence at the edges. We model the organ but not the way it moves when the patient breathes. These omissions compound. A case study of material transfer systems in remanufacturing found that minor differences in collision geometry modeling—the shape of contact surfaces, the friction coefficients—produced major discrepancies between virtual and physical behavior.

Latency and scale. High-fidelity simulation requires enormous computation. Real-time response requires speed. You can't have both. A twin that takes an hour to predict what happens in the next minute is useless for safety-critical applications. The tradeoffs are brutal: reduce resolution, simplify physics, or accept that your "real-time" twin is actually running on yesterday's data.

Data quality. Twins are only as good as their sensors. Sensors fail, drift, get occluded, report noise. Networks drop packets. Edge computing introduces latency. The chain from physical reality to virtual representation has dozens of failure points, and each one widens the gap.

What the Frontier Looks Like

The most interesting recent research doesn't promise to close the gap. It acknowledges the gap and builds systems that can detect, measure, and adapt to it.

Reality Gap Analysis (RGA). Researchers at Carnegie Mellon developed a module that continuously monitors the divergence between a digital twin's predictions and real sensor data. When the gap exceeds a threshold, the system triggers recalibration. They tested it on a steel truss bridge in Pittsburgh: the twin detected context shifts—temperature changes, traffic pattern variations—and adjusted its internal model accordingly. The gap didn't disappear, but it stopped growing.

Semantic Digital Twins. A 2025 paper on LLM-augmented twins addresses a different kind of gap: the semantic one. Traditional twins know physics but not regulations, guidelines, or domain expertise encoded in documents. By integrating large language models with twin architectures, the researchers built systems that understand not just "how does this structure behave" but "what are the regulatory constraints on modifying it." They demonstrated this for offshore wind farm planning—a domain where engineering, environmental law, maritime regulations, and local policy all intersect.

Neuro-Symbolic Reasoning. The ANSR-DT framework combines neural networks (good at pattern recognition) with symbolic reasoning (good at logic and rules) and reinforcement learning (good at adaptation). The result is a twin that can both learn from data and explain its reasoning—critical for high-stakes domains where "the AI said so" isn't an acceptable justification.

Hardware Acceleration. For applications where milliseconds matter—collision avoidance, surgical robotics, power grid stability—researchers are pushing twins onto specialized hardware. A 2025 study implemented neural twin components on FPGAs, achieving response times five times faster than human reaction time. This matters when the gap between prediction and reality has to be closed in real-time, continuously.

Generative Twins. Perhaps most striking: researchers have begun building twins that can design themselves. Using vision-language models trained on 120,000 prompt-sketch-code triplets, they demonstrated systems that convert rough layout sketches and natural language descriptions into executable simulation code. The twin doesn't just mirror reality—it generates the mirror.

The Structural Problem

Here's what the technical literature doesn't adequately address: the digital twin gap isn't just about fidelity, latency, or data quality. It's about power.

A digital twin is a representation. Representations are never neutral. They encode decisions about what matters, what gets measured, what counts as success. When GE builds a wind farm twin, it optimizes for energy output and equipment longevity—metrics that serve GE's interests. The twin doesn't model community impact, noise pollution, bird mortality, or the aesthetics of the landscape. Those aren't in the objective function.

When a hospital builds a patient twin, it models physiological parameters that can be measured, quantified, and acted upon by medical professionals. It doesn't model the patient's social support network, their financial stress, their trust in the healthcare system—factors that profoundly influence health outcomes but resist quantification.

The gap isn't just between simulation and reality. It's between the reality that gets represented and the reality that doesn't.

Who Owns the Mirror?

Virtual Singapore is built by government agencies using public funds. The data it contains—building specifications, traffic patterns, energy usage—comes from citizens and businesses. But who can access the twin? Who can query it? Who can build applications on top of it?

The Digital Twin Consortium's 2025 whitepaper on aerospace and defense identifies interoperability and data governance as critical gaps. Different contractors use different platforms, different data formats, different simulation engines. Twins can't talk to each other. Data gets siloed. The result: rather than a unified digital representation of a system, you get a collection of incompatible fragments, each owned by a different vendor, each optimized for a different purpose.

This isn't a bug. It's a business model.

The Healthcare Problem

In medicine, the digital twin gap becomes visceral. A 2025 study in JMIR examined barriers to clinical digital twins and found that the primary obstacles aren't technical—they're institutional. Data sits in silos controlled by hospitals, insurers, and device manufacturers with no incentive to share. IRB processes designed for clinical trials struggle to accommodate continuously-updated AI systems. Liability frameworks don't know how to handle decisions made "by the twin."

And beneath all of this: patients themselves. Whose body is being modeled? Who consents to the modeling? Who benefits from the predictions? The twin that helps an elite teaching hospital optimize surgery schedules may be trained on data from populations very different from the ones it will serve. The gap between the twin and the patient becomes a gap between populations—with predictable consequences for who gets helped and who gets harmed.

What We're Actually Building

Let me be direct about what the state of the art actually looks like in 2025:

Manufacturing is the success story. BMW, Siemens, and others have achieved genuine digital twins of production lines—systems that continuously ingest sensor data, predict equipment failures, and optimize throughput. The business case is clear (downtime is expensive), the physics are well-understood (machines behave more predictably than people), and the data infrastructure exists. Even here, a case study of a flexible manufacturing line found that gaps in historical data and system modeling complexity limited what the twin could actually predict.

Infrastructure is promising but early. Bentley Systems has impressive case studies: a digital twin of New Orleans' flood-gate infrastructure, modeling of the UK's Severn Tunnel, structural analysis of a Saudi port warehouse under complex soil conditions. These represent real value delivered. But they're also showcase projects with dedicated resources. The typical infrastructure operator—a municipal water authority, a regional transit agency, a power cooperative—lacks the expertise, budget, and data infrastructure to build and maintain twins at this level.

Smart cities are mostly hype. Virtual Singapore is real. Most "smart city" digital twins are 3D visualizations with some sensor overlays—useful for presentations, less useful for prediction and optimization. The computational and data requirements for genuine urban-scale twinning are staggering. Few cities can afford them. Fewer still have the governance structures to use them responsibly.

Healthcare is a minefield. Patient-specific twins for surgical planning exist and provide value. Population-scale twins for disease modeling are emerging. But the regulatory, ethical, and institutional barriers are enormous. A digital patient that predicts treatment outcomes is also a liability time bomb. The gap between technical capability and deployable system is measured in years, maybe decades.

Earth itself remains aspirational. Destination Earth is real work with real funding. But simulating the planet at kilometer resolution with continuous updates is not a 2030 problem. It's a 2040 problem, maybe 2050, maybe never. The promotional materials don't mention this.

The Deeper Question

Here's what I keep coming back to: do we actually want to close the gap?

The promise of digital twins is perfect knowledge, predictive certainty, optimal control. But perfect knowledge isn't possible for complex systems. Predictive certainty is a fantasy. And optimal control assumes we know what we're optimizing for—which requires value judgments that no simulation can make.

A bridge monitoring system that detects micro-fractures before they become failures is unambiguously good. A patient twin that predicts disease progression could save lives—or could entrench medical paternalism and erode patient autonomy. A planetary twin that models climate futures could inform policy—or could become another tool for powerful actors to justify decisions already made.

The gap between the twin and reality isn't just a technical problem. It's a feature that preserves space for human judgment, uncertainty, and the possibility of being surprised by the world. Perfect mirrors reflect only what we already expect to see. Distorted mirrors—imperfect, incomplete, obviously partial—remind us that the model is not the territory.

The question isn't whether we can close the digital twin gap. The question is what we lose when we try.

A note on this piece: I've drawn on research from the National Academies, the Digital Twin Consortium, and recent academic literature. I've tried to be precise about what's demonstrated versus what's promised. The field moves fast; some of this will be outdated within a year. But the structural questions—about power, representation, and the limits of modeling—will persist long after today's technical limitations are solved.

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