Digital Twins
I next asked Claude to discuss Digital Twins in the context of World Models.
Digital twins have been around since NASA used simulators to troubleshoot Apollo 13 in 1970, though the term wasn’t coined until 1997. By the 2000s, manufacturers were creating virtual replicas of jet engines and factory floors to monitor performance and predict maintenance needs. Today, roughly 75% of businesses employ digital twins in some capacity, from monitoring turbines to simulating entire cities.
But here’s what most digital twins are today: sophisticated data visualization systems. They mirror current states, track historical performance, and run rule-based simulations. A digital twin of a wind turbine displays real-time performance data and can simulate the impact of changing wind speeds based on pre-programmed physics equations. Useful, certainly. Transformative? Not quite.
The Static Twin Problem
Current digital twins have a fundamental limitation—they’re built on static models. Whether it’s a Digital Twin of an Organization mapping business processes or a Digital Twin of a Customer representing buyer behaviors, these systems operate on fixed rules and historical patterns. They can tell you what happened and predict outcomes based on what has happened before, but they struggle with genuine novelty.
A customer digital twin today might predict that Sarah will buy running shoes because she bought running shoes before. It’s pattern matching wrapped in fancy visualization. The twin doesn’t understand why Sarah runs, what motivates her purchases, or how her behavior might evolve as her life circumstances change. It’s a database with a better interface.
Enter World Models
World models change the equation fundamentally. Instead of rule-based simulations, they learn the underlying dynamics of environments. They don’t just store facts about how customers behave—they develop an internal understanding of what drives that behavior, similar to how humans build mental models of cause and effect.
Imagine a customer digital twin powered by world models. Rather than simply mirroring past purchases and predicting repeat behavior, it would understand the customer’s decision-making process. It could simulate how that customer would respond to different marketing interventions, not because similar customers responded that way in the past, but because it has learned how this customer actually thinks and decides.
The twin could run counterfactual scenarios: “If we change the email cadence, how does Sarah’s engagement evolve over three months?” “If we introduce this new product category, which existing customers will find it relevant based on their underlying preferences, not just their purchase history?”
The Convergence
The convergence of world models and digital twins represents a shift from backward-looking analytics to forward-looking simulation. Digital twins have always promised the ability to “test before you deploy”—but with static models, you’re testing against the past, not the future.
World models bring three critical capabilities to digital twins:
- Dynamic understanding rather than static rules. The twin doesn’t just know that customers like personalized emails—it understands how personalization affects decision-making and can predict when it becomes intrusive.
- Genuine prediction rather than pattern matching. The difference between “customers like Sarah usually do X” and “Sarah will likely do X because of how she perceives value and makes decisions.”
- Causal reasoning rather than correlation. World model-powered twins can distinguish between what drives behavior and what merely correlates with it, enabling true experimentation and optimization.
What This Means for Marketing
For marketers, this convergence opens a new frontier. Customer journey optimization today means analyzing past journeys and tweaking touchpoints. With world model-powered digital twins, you could simulate entire journeys before customers take them, testing not just messaging variations but strategic interventions—timing, sequencing, channel choices, offer structures.
The digital twin becomes less like a dashboard and more like a flight simulator—a place to test piloting strategies in realistic conditions before committing resources. You’re not just looking at customer data differently; you’re creating an environment where you can experiment with customer dynamics.
The question is no longer “what did customers do?” but “what would customers do if we changed the system?” That’s the shift from digital twins as mirrors to digital twins as laboratories.
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My previous essays on Digital/AI Twins and Marketing:
Marketing’s AI Triad: Large Customer Model, Digital Twins, Co-Marketer
AI-Powered Digital Twins: Marketing’s Marvel
AI Twins: Digital Customer Representations That Will Transform Marketing
Digital Twins in Marketing: Magical Minions
AI Twins: The Future of Marketing Intelligence
AI Twins in Action: Daily Allies for Smarter Marketing and Meaningful Connections
