World Models: An Overview and Marketing’s Future

Published December 2-10, 2025

1

Review – 1

World models are generating significant buzz in AI circles. In this essay, I survey the key writings and explore what they could mean for marketing.

Nvidia: “World models are generative AI models that understand the dynamics of the real world, including physics and spatial properties. They use input data, including text, image, video, and movement, to generate videos. They understand the physical qualities of real-world environments by learning to represent and predict dynamics like motion, force, and spatial relationships from sensory data… World models, when used with 3D simulators, serve as virtual environments to safely streamline and scale training for autonomous machines. With the ability to generate, curate, and encode video data, developers can better train autonomous machines to sense, perceive, and interact with dynamic surroundings.”

Quanta: “The latest ambition of artificial intelligence research — particularly within the labs seeking “artificial general intelligence,” or AGI — is something called a world model: a representation of the environment that an AI carries around inside itself like a computational snow globe. The AI system can use this simplified representation to evaluate predictions and decisions before applying them to its real-world tasks… Instead of world models, today’s generative AIs appear to learn “bags of heuristics”: scores of disconnected rules of thumb that can approximate responses to specific scenarios, but don’t cohere into a consistent whole. (Some may actually contradict each other.)

Axios: “World models learn by watching video or digesting simulation data and other spatial inputs, building internal representations of objects, scenes and physical dynamics. Instead of predicting the next word, as a language model does, they predict what will happen next in the world, modeling how things move, collide, fall, interact and persist over time. The goal is to create models that understand concepts like gravity, occlusion, object permanence and cause-and-effect without having been explicitly programmed on those topics. Context: There’s a similar but related concept called a “digital twin” where companies create a digital version of a specific place or environment, often with a flow of real-time data for sensors allowing for remote monitoring or maintenance predictions.”

Sandeep Chatterjee: “Imagine if an AI could dream. Not just respond to inputs, but visualize entire worlds in its mind, predict future scenarios, and plan actions before executing them — just like a human does. This is the core idea of world modeling, a powerful paradigm that is reshaping artificial intelligence and machine learning. Instead of merely associating inputs with outputs, world models build an internal simulation of reality, allowing AI to learn, plan, and make decisions in a fundamentally more efficient and human-like way…A world model consists of three fundamental abilities: 1. Representation Learning: Compressing high-dimensional sensory data (e.g., images, text, or video) into a meaningful lower-dimensional representation. 2. Prediction: Forecasting the future state of the environment based on past and present data. 3. Planning and Decision-Making: Using the learned model to simulate different actions and choose the best course of action.”

Towards AI: “A world model is a learned internal representation that simulates the dynamics of the real world. Unlike static perception models, world models are generative and predictive — they can simulate how the world might evolve over time, allowing intelligent agents to plan actions and reason before actually performing them… World models are not just another AI architecture — they represent a paradigm shift toward internalized understanding, simulation, and prediction. In a future where physical AI must act, adapt, and learn continuously, world models offer the brain-like core needed for general-purpose agents. While not perfect yet, they form the bedrock of intelligent physical interaction, marking a vital step toward embodied AGI.”

2

Review – 2

WSJ: “Today’s AIs are book smart. Everything they know they learned from available language, images and videos. To evolve further, they have to get street smart. That requires “world models.” The key is enabling AI to learn from their environments and faithfully represent an abstract version of them in their “heads,” the way humans and animals do. To do it, developers need to train AIs by using simulations of the world… There’s an almost unanimous belief among AI pioneers that world models are crucial to creating next-generation AI. And many say they will be critical to someday creating better-than-human “artificial general intelligence,” or AGI… While the type of AI that makes large language models and ChatGPT possible gets all of the attention right now, it’s world-model-based AI that is gaining momentum in frontier research and could allow technology to take on new roles in our lives… LLMs appear to be taking over some functions in white-collar jobs already, and AIs that are world-model smart could allow them to take over yet more. Blue-collar work has been relatively safe. But as AI developers go ever deeper on world models, robots could start applying for jobs as truck drivers, plumbers or caregivers.”

WSJ: “Yann LeCun has been telling anyone who asks that he thinks large language models, or LLMs, are a dead end in the pursuit of computers that can truly outthink humans… “We are not going to get to human-level AI just by scaling LLMs,” he said.”

Fei-Fei Li: “Today, leading AI technology such as large language models (LLMs) have begun to transform how we access and work with abstract knowledge. Yet they remain wordsmiths in the dark; eloquent but inexperienced, knowledgeable but ungrounded. Spatial intelligence will transform how we create and interact with real and virtual worlds—revolutionizing storytelling, creativity, robotics, scientific discovery, and beyond. This is AI’s next frontier…Building spatially intelligent AI requires something even more ambitious than LLMs: world models, a new type of generative models whose capabilities of understanding, reasoning, generation and interaction with the semantically, physically, geometrically and dynamically complex worlds – virtual or real – are far beyond the reach of today’s LLMs… For spatial intelligence, I define world models through three essential capabilities: 1. Generative: World models can generate worlds with perceptual, geometrical, and physical consistency…2. Multimodal: World models are multimodal by design…3. Interactive: World models can output the next states based on input actions.”

Daniel Ince-Cushman: “In the evolving landscape of artificial intelligence, two prominent paradigms have emerged although World Models remain lesser known. One is the Large Language Model (LLM), a linguistic construct trained to predict and generate human-like text. The other is the World Model, a computational framework focused on simulating environments and enabling decision-making through internalized representations of external dynamics…World models…are designed to learn representations of environments, often through multi-modal sensory data or interactive simulation. These models typically consist of perception modules (e.g., vision encoders), memory or state encoders, and predictive components that simulate future states given current inputs and actions. These systems embody fundamentally distinct conceptions of intelligence. The LLM derives its power from statistical patterns in textual data, while the world model seeks to emulate causality and the laws of physics through learned simulations. The utility of a world model lies in its ability to enable planning. Through internal simulation, an agent can forecast outcomes and select optimal actions without direct environmental interaction.”

Gilbert Traverse: “The most valuable enterprise AI investments going forward will likely focus on integrating these two paradigms: leveraging LLMs for human-centric interfaces and general knowledge, while deploying increasingly sophisticated World Models to drive the complex planning, optimization, and action needed for real-world automation in areas like manufacturing, logistics, and autonomous operations. Strategic foresight demands an understanding of both, allowing organizations to capitalize on the linguistic power of today’s AI while building the causal foundation for tomorrow’s intelligent agents.”

3

Review – 3

ChatGPT:

In artificial intelligence, world models refer to an AI system’s internal representation of how the world works – essentially a simulated reality inside the machine’s mind. A world model enables an AI agent to predict future states of its environment and imagine potential scenarios without directly experiencing them. This concept has roots in cognitive science: humans are thought to carry a mental model of the world, allowing us to anticipate outcomes and plan our actions. As system dynamics pioneer Jay Forrester described, “The image of the world around us, which we carry in our head, is just a model… [one] has only selected concepts, and relationships between them, and uses those to represent the real system.”. In other words, we do not mentally simulate every detail of reality, but we hold an abstract model that captures the essential structure of our world. In AI terms, a world model is typically a generative model of the agent’s environment. Formally, researchers define a world model as a system that, given the current state of the world and an action taken by the agent, can predict the next state. For example, if the current state includes the position of objects and the agent’s status, and the action is “move forward,” the world model should predict what the new positions and statuses will be after that action.

A world model is all about learning an internal model of the world’s dynamics so that an AI can understand, predict, and plan within its environment. This involves learning abstract representations (latents), using self-supervised prediction as a training signal, capturing causal relationships over time, and leveraging the model for imagination and planning. The end goal is an AI that doesn’t just react reflexively, but “knows” how the world works and can reason about what to do – much like humans who foresee outcomes using mental models.

Consider this vivid analogy from a recent essay: An LLM is like a brilliant autocomplete that can imitate understanding by stitching together patterns in language, but it has no “imagination” beyond what it’s seen in text. A world model, by design, imagines – it simulates situations that perhaps never occurred exactly the same in its training data. It’s the difference between reading about the world versus experiencing a simulated mini-world. One paper framed it elegantly: “LLM simulates the next word in human language… [A world model] simulates the next world.” [Source]. Each has its strengths – LLMs leverage vast knowledge encoded in human text, whereas world models provide an internal sandbox for truly understanding and navigating an environment. The consensus emerging is that next-generation AI systems likely need both: the knowledge and linguistic capabilities of LLMs plus the grounded predictive planning of world models. Indeed, researchers have begun experimenting with combining the two, for example by training language models with objectives that encourage predicting missing text in a way analogous to JEPA (to imbue them with a bit of world-model-like consistency) [Source].

Claude:

Kenneth Craik proposed in 1943 that minds construct “small-scale models” to anticipate events before they occur. Philip Johnson-Laird’s mental models theory (1983) suggests we reason by mentally simulating scenarios rather than applying formal logic. The predictive processing framework in neuroscience argues the brain constantly predicts sensory input, updating beliefs only when predictions fail.

World Models translate these cognitive principles into AI architecture. They comprise nine interconnected components: latent spaces that compress high-dimensional observations into 32-64 dimensional representations filtering out unpredictable noise; predictive coding that forecasts future latent states from current state plus action; model-based reasoning enabling deliberative Mode-2 thinking versus reactive Mode-1 responses; causal structure encoding cause-effect relationships; simulation capabilities for forward prediction and counterfactual “what if” analysis; memory systems maintaining both short-term hidden states and long-term associative knowledge; planning mechanisms using gradient-based optimization or search-based methods like Monte Carlo Tree Search; self-supervision through contrastive or non-contrastive learning from unlabeled data; and embodied agent capabilities closing the sensorimotor loop from perception through planning to action.

This architecture provides what LLMs fundamentally lack: sample efficiency through imagination rather than trial-and-error (humans learn to drive in 20 hours versus millions of RL episodes for pure model-free learning), common sense grounded in intuitive physics about gravity and momentum, planning and reasoning through forward simulation of consequences, generalization by capturing underlying dynamics that transfer across tasks, safety through virtual testing of dangerous scenarios, and computational efficiency by predicting abstractions not pixels. [Source]

4

Comparison

I asked Claude to compare LLMs and World Models.

The AI landscape is witnessing a fascinating debate: are Large Language Models the path forward, or do we need something fundamentally different? To understand this, we need to grasp what each approach actually does—and more importantly, what it cannot do.

What LLMs Actually Do

Large Language Models are trained to predict the next token in sequences of text, learning statistical patterns across billions of words. Think of them as extraordinarily sophisticated pattern-matchers. When you ask ChatGPT a question, it’s not “thinking” in any conventional sense—it’s generating the most probable continuation of your prompt based on patterns it absorbed during training.

This gives LLMs surprising proficiency in diverse tasks like translation, summarization, and dialogue generation. They can write code, explain complex concepts, and even seem to reason. But here’s the catch: their understanding of phenomena like gravity or thermodynamics is inferential, derived solely from linguistic patterns rather than grounded in physical reality.

The hotly contested question is whether LLMs develop genuine “world models” internally. Some research suggests language models can develop rudimentary world models for simple concepts in their internal representations, like tracking game states in Othello. But prominent researchers argue that instead of coherent world models, today’s generative AIs learn “bags of heuristics”—scores of disconnected rules that don’t form a consistent whole.

How World Models Work Differently

World models are neural networks that understand the dynamics of the real world, including physics and spatial properties. Rather than predicting the next word, they predict what will happen next in the physical world, modeling how things move, collide, fall, interact and persist over time.

World models take inspiration from mental models humans develop naturally—our brains form abstract representations from our senses into concrete understanding of the world around us. A baseball batter has milliseconds to swing—shorter than the time for visual signals to reach the brain. The reason we can hit a fastball is our ability to instinctively predict when and where the ball will go based on our internal world model.

These models consist of multimodal input processing, temporal prediction that forecasts future frames from historical data, and latent representation learning that operates on abstract state spaces. This allows AI agents to “imagine” different scenarios before acting, planning by simulating future state sequences for different strategies.

The Fundamental Difference

The key distinction lies in grounding. LLMs derive their power from statistical patterns in textual data, while world models seek to emulate causality and the laws of physics through learned simulations. An LLM “knows” about gravity because countless texts mention it. A world model understands gravity because it has observed thousands of hours of objects falling and learned the underlying physics.

World models trained on photos, audio, videos, and text create internal representations of how the world works and can reason about the consequences of actions. This matters enormously for applications like robotics, autonomous vehicles, and any AI that must act in physical space rather than just generate text.

What This Means Going Forward

Yann LeCun has argued that within three to five years, world models rather than LLMs will dominate AI architectures. Whether that timeline holds, one thing is clear: text prediction alone has limits. Acting reliably in the physical world requires more than next-token prediction—it requires maintaining state, predicting dynamics, and supporting counterfactual reasoning for planning and control.

We’re likely heading toward hybrid systems that combine both approaches—language understanding from LLMs with physical reasoning from world models. The question for marketers is: which capabilities matter for your applications?

5

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:

1. 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.
2. 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.”
3. 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.

**

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

6

Applications to Marketing

This essay builds on my past writings and inputs from AIs (Claude and ChatGPT).

World models are rapidly emerging as the next frontier of artificial intelligence. Unlike large language models, which predict the next word in a sequence, world models predict what will happen next in the world—they build internal simulations of cause and effect. They imagine, forecast, and plan. If LLMs are powerful librarians of human knowledge, world models are flight simulators for decision-making. And when viewed through the lens of marketing, their impact could be nothing short of transformative.

For decades, marketing has been built on pattern recognition. We segment customers into groups, examine historical patterns, and optimise campaigns based on what has worked before. At its core, marketing has been fundamentally backward-looking: the past determines the next best action. World models flip this on its head. They move marketing from remembering to imagining—from projecting history to simulating futures.

1. From Customer Segmentation to Individual Simulation

Today, personalisation is rooted in similarity: “customers who bought X also buy Y.” With world models, similarity becomes irrelevant. Instead of predicting behaviour because other customers behaved that way, the AI models the internal dynamics of each specific customer and simulates how that individual will behave in different conditions. This is methodological individualism applied to marketing—understanding each customer as a unique decision-maker, not as a member of a segment. It is no longer “people like her”—it is her.

2. From A/B Testing to Counterfactual Experimentation

Instead of running experiments on customers, brands can run experiments inside the twin. A marketing team can test hundreds of interventions—timing, cadence, offer structure, creative style, channel mix—safely in simulation. The winning strategy reaches the customer only after the twin has proven it. This dramatically reduces AdWaste and profoundly raises confidence in execution. You’re not spending to learn; you’re learning to spend.

3. From Pattern Matching to Causal Persuasion

Current marketing predicts that a runner will click on trainers because other runners did so before. World models understand why she runs—whether for stress relief, social belonging, or competition—and generate interventions that align with her motivation. Timing becomes as important as messaging; triggers become as critical as offers; subtle nudges outperform overt pitches. The power lies not in showing the right product but in activating the underlying decision driver.

4. From Owned Channels to Adaptive Environments

Emails, apps, and websites become dynamic worlds that adjust themselves based on moment-to-moment interaction. A NeoMail is no longer a static message—it becomes a live space that responds to each user’s micro-behaviour, shaping content, rewards, and recommendations automatically. This shift turns the revenue tax paid to ad platforms into investment in owned media that learns and improves. A channel becomes a continuously adjusting environment rather than a delivery mechanism.

5. From Designed Journeys to AI-Orchestrated Futures

Today’s customer journeys are flowcharts—courses built by humans and refined through hindsight. Tomorrow’s journeys will be discovered, shaped, and adjusted by the model itself in real-time. Every customer receives the journey most likely to maximise lifetime value—not because it was built manually, but because the model has simulated thousands of futures and chosen the best one. Marketing becomes proactive navigation, not reactive optimisation.

6. From Cost Centre to Profit Engine Through Predictive ROI

World models change the role of marketing measurement. Dashboards today describe the past. World models allow marketers to preview the future—predicting churn before it happens, forecasting which Rest customers can be recovered economically, and projecting the impact of expenditure on attention and retention. This makes outcome-based pricing models like Progency feasible: you can guarantee results because you’ve already seen them work in simulation. Marketing becomes a profit engine with measurable, predictable returns, not a reporting function justifying past spend.

The Core Transformation

In short: LLMs answer questions; world models answer consequences. And that single shift moves marketing from reactive communication to proactive orchestration of customer behaviour. When combined with innovations like AI Twins, NeoMails, Co-Marketer agents, and the war on AdWaste, world models may herald the most consequential marketing transformation in decades—the shift from marketing to memories to marketing to imagined futures.

The question is no longer “what worked before?” but “what will work next?” That’s the difference between looking in the rear-view mirror and having a windscreen that shows you the road ahead before you drive it.

**

I asked the AIs to bring this future to life via a story about Ria (customer) and Maya (marketer).

7

Marketing in Two Worlds (Claude)

The Old World

Ria opened her inbox on Monday morning to find seventeen promotional emails. She deleted sixteen without reading them. The seventeenth—from a running gear brand she’d bought from twice—offered her 20% off “popular items.” She’d bought trail shoes both times. The email showed road running trainers.

She ignored it.

Later that week, Instagram showed her an ad for the same brand. Different products, same problem: compression socks, when what she actually needed was a replacement headlamp for early morning trail runs. The ad took her to a generic landing page with a search bar. She closed it.

The brand had her purchase history. They had her email engagement data. They knew she opened emails about trail running but ignored road running content. They even knew she bought before dawn, suggesting early-morning runs. Yet every touchpoint felt like they were guessing.

They were.

At the brand’s office, Maya stared at her dashboard. Ria was in the “Active Customer” segment—purchased twice in six months, opened 40% of emails, never clicked an ad. The segment had 8,000 people in it. Maya’s job was to move them to “Loyal Customer” status.

She set up an A/B test: version A offered 15% off, version B offered free shipping. She’d know which worked better in two weeks. She’d been running variants of this test for three years.

Maya knew this approach was broken. She was optimising for open rates and click-through rates, not for what actually mattered: whether Ria would buy again, how much she’d spend over two years, whether she’d become an advocate. But those answers required seeing into the future. All Maya could see was the past.

The New World

Eighteen months later, Maya’s world looked different.

The brand had implemented world models and created digital twins for every customer. Ria’s twin—RiaBrandTwin—wasn’t a database record. It was a simulation of how Ria made decisions about running gear.

The twin knew Ria was a trail runner, but more importantly, it understood why she trail-ran. By analysing the cadence of her purchases (always before local trail race seasons), her email engagement patterns (higher response to endurance and challenge content than speed or aesthetics), and her browsing behaviour (she researched extensively before buying), the twin had developed a model of Ria’s underlying motivation: she ran trails for the achievement, not the aesthetic.

This changed everything.

When Ria’s headlamp was likely approaching failure (the twin simulated usage based on purchase date, typical trail runner training patterns, and equipment lifecycle), Maya didn’t just send an email. First, she tested inside the twin.

She ran fifty simulated interventions: different subject lines, sending times, offer structures, and landing page designs. She tested immediate discount offers versus “here’s what to consider when replacing a headlamp” educational content. She simulated what happened if she sent the email Tuesday morning versus Saturday evening. She tested whether including trail race calendar content increased conversion.

The twin showed her that Ria would respond best to Saturday evening (when she planned her training week), that she’d engage more with “upgrade your night running” content than “20% off,” and that featuring a comparison guide would increase consideration time but dramatically increase purchase confidence and basket size.

Maya sent one email to Ria, but she’d already tested fifty versions.

When Ria opened it Saturday evening, the email felt like it had been written specifically for her. Because it had. The subject line read: “Planning night runs? Here’s what changed in headlamp tech.” The content wasn’t selling; it was teaching. It acknowledged that her current headlamp was probably fine but explained what the new generation offered: better battery life for ultra-distance training, improved beam patterns for technical terrain.

Ria clicked through to a landing page that didn’t look like a shop. It looked like a guide. The twin had predicted she’d want to compare specifications, so the page offered a comparison tool. When she selected “ultra-distance trail training,” it recommended two models and explained why, with links to reviews from runners whose training patterns matched hers.

She bought the premium model. The twin had predicted an 73% probability she would.

But the transformation went deeper. The twin was now simulating Ria’s next twelve months. It predicted she’d enter a 100km trail race in eight months (based on her training progression pattern and local race calendars). It forecasted she’d need new shoes in four months, a hydration vest in six, and nutrition products throughout her training block.

Maya could see this entire journey before Ria took a single step. More importantly, she could test interventions: What if we offered a “100km training bundle” in month three? What if we sent weekly training tips? What if we introduced a premium membership with early access to new gear?

The twin let her run all these scenarios. It showed her that Ria would respond to structured training content but ignore bundle offers (she liked choosing her own gear). It revealed that a membership offer would work, but only if positioned as “early access” not “discounts”—because Ria’s motivation was performance, not savings.

Over the next year, Ria received eleven touchpoints from the brand. Every single one felt relevant. Not because they guessed right, but because they’d tested inside her twin and only sent what worked.

She never felt marketed to. She felt understood.

The Difference

In the old world, Maya optimised campaigns. In the new world, she orchestrated futures.

In the old world, Ria was a segment of 8,000. In the new world, she was a simulation of one.

In the old world, the brand tested on customers. In the new world, they tested on twins.

In the old world, marketing was spray and pray. In the new world, it was simulate and send.

Maya’s metrics transformed: Ria’s lifetime value increased 240%. Marketing cost per pound of revenue decreased 60%. But the real shift was philosophical: marketing stopped being about persuading customers to buy and started being about understanding what customers actually needed, then being there at precisely the right moment.

The twin made the invisible visible. It turned “what might work” into “what will work.” It transformed marketing from an expense optimised for efficiency into an engine optimised for customer lifetime value.

For Ria, the brand stopped being noise and became signal. For Maya, customers stopped being segments and became individuals.

That’s what happens when you can simulate the future before you live it.

8

The Day Marketing Stopped Guessing (ChatGPT)

Ria liked the brand. At least, she used to.
She had discovered FluxWear, a fast-growing D2C apparel brand, during the pandemic — comfortable athleisure, bold designs, and a mission rooted in sustainability. She had purchased from them three times and recommended them to friends. And then, as life picked up pace, she drifted away.

FluxWear didn’t notice — not immediately. They kept emailing her, pushing weekly offers, lookbook updates, and “back in stock” alerts. The irony was that Ria didn’t unsubscribe because she disliked the brand; she unsubscribed because she liked it too much to see it reduced to noise. Every promotional message made her feel like the brand didn’t really know her. She wasn’t bargain-hunting. She wasn’t addicted to trends. She bought when she felt overwhelmed and needed a reset — fitness was her way out of stress. But the brand never saw that.

And so, one Tuesday morning, she removed herself from the mailing list. Not out of anger, but disappointment.

**

Meanwhile, across the city, Maya sat staring at the dashboard in FluxWear’s marketing war room. She had done everything textbook-right.
Segmentation — check.
A/B testing — check.
Hyper-personalisation — check.
Retargeting — check.
Still, customers like Ria disengaged after the third or fourth purchase and became painfully expensive to reacquire through Meta, Google, and influencers. The CFO’s slide last month had been a punch in the gut: “28% of our revenue is now spent reacquiring customers we once had.”

Maya didn’t lack skill. She lacked certainty.

Every campaign felt like a bet — a slightly better bet than the last, but still a bet.

**

The Future Arrives Quietly

FluxWear had been experimenting with a new AI platform. Marketing folklore claimed it had ended AdWaste for early adopters. Maya didn’t believe in folklore — she believed in evidence — but she was desperate for change.

The platform came with a feature called AI Twins — a world-model representation of each customer, not built on demographics or purchase history but on causal decision drivers. It didn’t ask what customers bought; it learned why they bought, why they paused, and what would bring them back.

The system called Ria’s twin RiaBrandTwin.

The first thing the twin showed was a shock:
Ria did not disengage because of pricing, product mismatch, or poor creative.
She disengaged because of timing.

Every time FluxWear reached out, Ria was in a stressful phase of her monthly cycle, not a recovery phase. The brand interrupted her instead of supporting her — which made her feel commercially pursued rather than cared for.

And then came the second shock:
The twin simulated 700+ interventions and none of the top-performing ones involved a discount.

The winning sequence in simulation was startlingly simple:

• A NeoMail with a calming theme
• A micro-reward for opening (Mu points she could use later)
• A video on mindful running from FluxWear’s ambassador
• A gentle invitation — “Join us when you’re ready. We’re cheering for you.”

The twin showed that this would outperform every sale-driven message by a factor of three — not because it pushed harder but because it understood why Ria runs.

This wasn’t marketing. This was empathy at scale.

Maya didn’t approve the campaign immediately. She tested again — counterfactual variations, cadence adjustments, moment windows, channel trade-offs. The twin simulated each future, not statistically but causally. It didn’t guess the next best action from the past — it imagined futures and selected the best one.

When Maya finally deployed the winning version to the real Ria, it wasn’t a risk. It was a fulfilment of a prediction already tested a thousand times in simulation.

**

The Moment Everything Changed

It was a Saturday afternoon.
Ria opened her inbox while making coffee. Most emails were promotional clutter. One stood out — the subject wasn’t a discount or claim; it simply read:

“Running is your reset. We’re here whenever you need us.”

She clicked.

The email — a NeoMail — wasn’t static. It shifted gently as she scrolled, matching her interaction rhythm. It didn’t push products. It offered calm. A short guided session. A playlist. A gamified reward. A soft reminder that she wasn’t running for fitness — she was running for herself.

By the end of the 40-second interaction, she wasn’t shopping.
She was breathing.

Two days later, she bought new shoes.

Not because the product was on sale.
Because the brand had become a source of support, not stress.

**

The Flywheel the CFO Didn’t Expect

Maya opened the dashboard again — except this time, it wasn’t a report of what had happened. It was a simulation of what would happen next.

• Ria’s likelihood to churn: ↓ 86%
• Probability of three more purchases in 90 days: ↑ 3.1×
• CAC saved from prevented reacquisition: ₹0 (because Ria was never lost)
• LTV uplift: projected, not retrospective

The model didn’t ask for more ad money.
It recommended reinvesting the AdWaste saved into more world-model-driven journeys across the customer base.

The CFO approved it instantly — because the ROI wasn’t a forecast. It was a simulation with variance bounds.

Marketing finally became predictable.

Marketing finally became profitable.

Marketing finally became human.

**

The Quiet Revolution

A few months later, the whole company could see it:

• CAC down 34% — no agency on earth could have delivered that
• Repeat purchase rate up 61% — no loyalty programme could have faked that
• AdWaste slashed — not because FluxWear spent smarter, but because it stopped losing customers in the first place

The world didn’t notice the revolution in slogan or colour palette.
It noticed it in silence — in the absence of unwanted interruptions.

Maya stopped guessing.
Ria felt understood.
The brand grew — not by shouting but by sensing.

The world model didn’t make marketing louder.
It made it wiser.

The day marketing stopped guessing was the day customers returned willingly.

And they didn’t return because they were targeted.
They returned because they were seen.

9

What Next for Marketers?

World models mark a profound break from how marketing has been practised for decades. They shift the discipline from guessing to simulation, from retroactive optimisation to proactive orchestration, and from segment thinking to individual motivation. The question now is: how should marketers prepare? The next era will reward those who act early and deliberately. Here is a 10-point plan to navigate the transition.

1. Replace segmentation thinking with individual-simulation thinking

Stop defining customers by similarities. Begin defining them by their internal dynamics: motivations, triggers, fears, aspirations, and decision styles. World models thrive on individuality. The fastest-growing brands will treat every customer as a market of one. This isn’t just better personalisation—it’s methodological individualism applied to marketing.

2. Build and operationalise AI Twins

Static 360-degree profiles won’t be enough. Begin creating dynamic digital twins that learn and evolve with each customer. Your goal: a living simulation that can say not just who the customer is, but why they act and how they will act next. Start with segment-level twins if individual twins seem overwhelming, then increase granularity as capabilities mature.

3. Move experimentation away from real customers and into simulation

Stop burning audiences with endless A/B tests. The ethical and economic future is test inside the twin, send after it wins. World models will allow marketers to deploy only interventions that are already proven—not guessed. You’re not spending to learn; you’re learning to spend. Even without full world models yet, begin asking counterfactual questions to build the simulation mindset.

4. Treat channels as adaptive environments, not broadcast pipes

Owned surfaces such as email, apps, and websites must become smart environments that respond to behaviour in the moment. Think NeoMails instead of newsletters, adaptive storefronts instead of static product grids. A channel becomes a continuously adjusting environment rather than a delivery mechanism. This turns the revenue tax paid to ad platforms into investment in owned media that learns and improves.

5. Shift the KPI stack from clicks to consequences

Clicks and opens are proxies. The real KPIs of the world-model era are: • Churn probability • Predicted LTV • Time to next purchase • Best→Rest→Test migration risk

Marketing is no longer about what happened; it is about what will happen. Dashboards should describe futures, not pasts.

6. Reallocate budgets from reacquisition to retention recovery

Today, the majority of the money goes to paying AdWaste—reacquiring customers already known. World models make recovery far more profitable than reacquisition. The smartest brands will invert their spending: win back Rest and Test customers on owned channels before touching paid media. Calculate your current revenue tax to ad platforms as a percentage of customer lifetime value—that’s your opportunity.

7. Give marketers copilots rather than dashboards

Dashboards describe the past. World models describe the future. Replace reporting systems with AI Co-Marketers that recommend interventions, predict ROI, and automate execution. The marketer’s job shifts from analysing what happened to orchestrating what will happen.

8. Redesign teams for a simulation-first mindset

The high-leverage skill of the next decade is journey design through imagination, not intuition. Train teams to ask: What will happen if we change X? not What happened when we changed X?

Build cross-functional fluency. Data scientists need to understand marketing objectives; marketers need to grasp simulation logic. Create forums for shared learning.

9. Integrate pricing, promotions, and product strategy into the same predictive engine

In the world-model era, marketing cannot be isolated. Promotions, launches, merchandising, logistics, and membership programmes work best when modelled together. The highest-performing businesses will run full-business simulations, not channel-specific optimisations. World models enable this holistic view.

10. Redefine the marketer’s identity

The marketer of the past executed campaigns. The marketer of the future shapes futures.

They do not send messages; they steward journeys. They do not push; they anticipate. They are no longer firefighters chasing metrics but strategists guiding customer destinies. Establish ethical boundaries for this power early—decide now how you’ll use predictive capability responsibly.

Mindset Shift

Marketing has always been built on the past—past behaviour, past data, past performance. World models invite marketers to build on the futures that haven’t yet happened. The brands that adopt this mindset first will win disproportionate advantage because they will stop losing customers in the first place. They will spend less on attention and earn more of it. And their growth will come not from louder tactics, but from deeper understanding.

The shift from marketing to memories to marketing to imagined futures won’t happen overnight. But it will happen. The next era belongs to marketers who can move from knowing customers to predicting, simulating, and supporting them.

The future isn’t for those who market better—but for those who imagine better, model better, and then act with precision.