A Conceptual Structure: Designing the Large World Model for Domestic Life and the World it Enables

I. The Potential that Makes Deploying Large World Models Compelling

Over the past few years, there has been significant discussion about how artificial intelligence will impact every aspect of our lives. These questions have taken on a new dimension with advances in Large Language Models that have been trained on virtually every bit of information available on the internet and that can now approximate human intelligence, at least in the context of a chatbot. The capacity offered by such models has come to extend beyond dialogue and information gathering to image and video generation based on text prompts. Such still and moving images have attained an extraordinary degree of life-like accuracy.

These advances are beginning to impact how things and the world are designed. AI is being used by engineers to write program briefs, coders to write better code, contractors to optimize scheduling, and architects to quickly visualize design ideas and improve renderings. Meanwhile, it is being used by a wide array of machine learning applications to optimize mechanical systems, engineers to accelerate the rapidly approaching arrival of driverless cars, and technologists who are advancing robotics and gaming environments. In advancing the AI models that will drive these innovations, a shift has occurred from Large Language Models (LLM) to World Foundational Models (WFM) and Large World Models (LWM). Such models intend to go beyond knowing everything on the internet, to knowing everything in our physical world so that we can develop better technology to create, maintain, and navigate our world.

In pushing the capabilities of AI forward through investing in WFMs and LWMs, less attention has been paid to how such models could transform how we design, select, set up, experience, manage, and share our homes and the world in which we live at the individual level. This would be to ask how these models go beyond guiding the functionality of specific digital devices or platforms to asking the way in which these digital devices and services become fully engaged in serving the physical world and all of the non-connected dumb things, devices, surfaces, spaces, and services that define it. Moreover, it would be to explore how these models can begin to sense, digitize, and understand the world we occupy as well as how the individual contributes to this process. Such a model could then be used to make our world better at the individual and societal scale.

In what follows, I want to explore why this question is compelling, the context for asking this question, how this context goes beyond recent developments in AI and extends to the relationship between architecture and language more broadly, how this relationship can structure an LWM at the conceptual level, what the experience of such an LWM deployed in the world might be like, and the ultimate goals of doing so.

Large World Models are compelling because they can transform our limited digital understanding of the physical world. While we have an abundance of surface-level data—maps, images, coordinates, and movement patterns—this information only scratches the surface of lived experience. What remains largely inaccessible is a granular understanding of the material and spatial environments people inhabit: the precise location of objects, their functions and histories, the socio-cultural logics that shape who uses what, and the implicit hierarchies within both personal and communal spaces. We lack insight into how these elements converge to form coherent world-images—subjective constructions of reality shaped by time, practice, and meaning. Even the full digitization of literature and cultural archives cannot replace a live, adaptive model capable of interpreting these dimensions with the depth, nuance, and context that human perception affords—let alone one that can offer actionable guidance grounded in that complexity.

This situation is due, in large part, to the fact that this knowledge is embedded in individual and collective consciousness and memory. It is traced in the everyday things that we use and the artefacts of past habits and habitats that are preserved in museums. At the same time, the knowledge of the world exists in the actions that we undertake, how we use these things, and how we move through the world in the process. These motivations and things they affect drive a broader flow of materials, goods, and energy around the world to satisfy our needs. Together, they constitute the material world that we inhabit.

While digital processes increasingly shape the flow of goods, materials, people, and energy, they remain largely instrumental—task-specific and siloed—even as LLMs and leading companies push for greater interoperability. In contrast, the theories developed over centuries to explain how we inhabit and function within the world are expansive, offering insights into complex systems, human motivations, historical events, and the rise and fall of entire civilizations. Most rely on an understanding or theory of how the individual exists within their milieu with respect to space, time, consciousness, and identity that even the most advanced artificial intelligence models cannot come close to approximating. Large World Models, however, offer an opportunity to advance our understanding of how situatedness in space and time—shaped by the orientation of identity—influences how individuals and communities make sense of, respond to, and navigate specific situations. While such a model may never fully replicate human experience, it may come to understand enough about our habits and habitats to offer guidance that makes the world a little easier to live in.

This lack of knowledge is a result of the trajectory that the development of digital products and services has taken. While many of these products and services support our daily existence at work and at home—as well as a wide range of production and distribution processes—they ultimately are largely focused on specific tasks like ordering an item or changing the temperature via what happens on the screen, rather than how those digital interactions might empower the physical world in a more holistic manner. Although there was a wave of interest and innovation related to the smart home, this interest has largely faded and progress has somewhat stalled. Since many digital business models depend on screen-based engagement—and given the challenges of transforming how we construct and maintain our homes—it is understandable that few are willing to take the financial risks when opportunities continue to exist amidst the current generation of attention-based and e-commerce platforms. 

The lack of actionable data about our world is also the result of the fragmented and siloed nature of this data. There are major gaps that would make unifying this data challenging, no clear entry point for doing so, and no clear business model or captivating user experience. The  economy of the home and the marketplaces for fragmented goods and services that support the home provides an opening. This is largely the result of the collapse of the last generation of retail—from department stores to furniture marts—as well as the failure of ecommerce to effectively replace a market that relies on physical interaction and the declining effectiveness of traditional advertising models. Seizing this opportunity for innovation starts with giving people a better way of finding those things and services. It would become a gateway to a system that unifies one’s digital and physical lives around the home. It would also be an invitation leading to ongoing engagement that would help to train the LWM.

Such a platform could add significant value to our homes—not only in how they are designed and constructed, but also in how efficiently they operate. It could help realize the long-held dream of having greater agency over the spaces in which we live and extending that agency to a broader community. At the same time, it would offer a representational framework and network that acknowledges and validates each person’s home and the care and effort invested in shaping it over time. The home would become a hub of cultural memory—a personal and shared story—especially vital in an era marked by forgetting. It would be a memory rooted in lived and collective experience, within and across families. It would trace contributing to and aspiring toward something greater with one’s home and building a family legacy while connecting to a broader community.

This process of record keeping opens a temporal dimension to the home. In doing so, it allows for such a model not just to help us configure a home, but to consider a much broader arc of life and the role that our homes play in our journey. It would come to understand our actions, desires, and ambitions as well as how things, services, spaces, and people come together to support them. This  would make it possible to better understand the hierarchy of these actions and the spaces in which they occur and set up the framework for a broader understanding of everything that goes on in the spaces in which we live. It would result in a model of the domestic realm of our society as well as a record keeping framework for tracing how the domestic evolves over time—ultimately improving the model in the process.

While the platform’s utility in managing and organizing the home is compelling, its true power lies in enabling users to imagine their future. By bringing intelligence to every surface and object in the home, the platform makes one's current environment into a launchpad for dreaming, planning, and transformation. Every surface becomes a canvas—not just for fantasy, but for meaningful change—linked to a model that understands how the world works and connected to a marketplace of goods and services that can bring ideas to life. As a result, these imagined futures become actionable. The model provides intelligent guidance, removing friction and uncertainty from the process. As users engage, it learns their style, habits, and aspirations, becoming a trusted companion that supports a world tailored to their evolving needs. In doing so, it creates a pathway to reimagine not just individual lives, but the shared world we shape together over time.

This process would unfold as a gamified experience that incentivizes individuals to become active agents in training the model—by engaging with the objects, spaces, and systems that support their lives. The UI/UX would be anchored in their current home, serving as the launch point for everything they might want to manage, transform, dream about, or value. It would feel like a dialogue with one’s home—and ultimately, with one’s future self—rooted in the things we imagine doing and the spaces we imagine inhabiting. The experience would begin with a compelling visualization of where we are on that journey, seamlessly connected to an immersive retail ecosystem that brings real utility and sustains the platform.

The result wouldn’t just be a digital twin of the material world, but of how we feel about it—enabling it to truly know you and offer meaningful guidance that adds value over time, both individually and collectively. It would produce a model capable of mapping and optimizing organization and spatial navigation, leading to an ambient architecture that reflects human desire across the past, present, and future. Rather than relying on sensors or IoT tracking, it would emerge from the body itself, learning from individual and shared habits of interaction with space. Ultimately, this model would understand not only your style and needs, but your world—your family and your community—and play a vital role in preserving and passing on your legacy.

II. The Historical Context For Deploying Large World Models in the World

How does the evolution of Large Language Models as Large World Models extend intelligence to the world in which we live? This is to ask how the relationship between language as a descriptive communicative tool that traces motivations and guides actions relates to and even creates the spaces we inhabit. This relationship has been discussed at length by a wide range of discourses throughout the history of civilization. Religion, theology, philosophy, sociology, archaeology, aesthetics, the theory of art and architecture, linguistics, political science, economics, behavioral science, and ecology have all pondered this question. One need only walk into a Gothic Cathedral and begin reading the narratives carved on panels and etched in stone to understand how language informs space and drives actions, hierarchies, and power structures. 

The various disciplines that have tried to make sense of this relationship between language and architecture have vied for authority and validity in their explanation—often to the detriment of a deeper understanding that might come from a genuinely trans-disciplinary approach that combines a range of competing models and explanations. This tension is particularly pronounced in the split between the analytic and the continental tradition of philosophy, as well as between neuroscience, psychology, and philosophy. This split is important in the context of this discussion in large part because analytic philosophy and neuroscience have come to play an integral role in the development of symbolic logic, probability, and mathematics—game theory and graph theory in particular— that play a vital role in the evolution of Large Language Models and AI more broadly.  At the same time, the continental tradition has come to play a vital role in understanding power structures, the relationship between self and other, consciousness in space, the theory of mind, the formation and evolution of communities and societies, and the relationship between humans, nature, and ecology. Together, these insights combine to form a highly nuanced understanding of the space in which we exist and how that space is formed. 

Recent developments in LLMs have begun to bridge this long-standing divide between linguistic representation and spatial experience. By drawing on the discipline of linguistics—not just its syntactic and grammatical frameworks but also its semantic depth—LLMs now make it possible to synthesize an existential and embodied sense of space through textual description. These textual renderings are no longer just abstract narratives; they have the capacity to generate new spatial configurations and immersive experiences. Generative Adversarial Networks (GANs) have played a crucial role in this evolution, enabling the translation of these textual and conceptual models into visual and spatial forms. Together, these technologies create feedback loops between description, imagination, and design, allowing designers to work with space as something both interpreted and invented. As a result, the role of language in architecture is undergoing a significant transformation—from one primarily concerned with formal grammar, proportion, and typological structure to one driven by semantics, affect, and meaning-making. This shift opens new possibilities for how space is conceptualized, communicated, and ultimately constructed. 

In order to move the discourse forward, a model should understand the individual and collective actions of the body in space. The situatedness of this body within a micro and macro environment would be seen as the primary force that designs, constructs, maintains, and makes sense of our world–both during an individual lifetime and as accumulated across centuries. This process has resulted in a material condition that we are left to navigate and ideally improve upon. In the current context, we could use a natural language interface to bridge the gap between human consciousness, digital devices and their sensors, and action. In this sense, a dialog with agentic AI could coordinate and guide the collection of data in order to use this data to ultimately guide action. In the process, agentic AI would effectively utilize human agents to help map and learn from the physical world. In the process, the model would come to understand motivations, narratives, social hierarchies, values systems, and beliefs that humans carry with them over the course of a lifetime. The result would be a model that can sense the world and experience the existential nature of reality that creates us and that we create. It would make it possible to optimize the economy of the body and the home as a more integrated and unified entity. The model would become a bridge between the two so that our thoughts, desires, and underlying biology is mirrored in the space that we occupy.

This approach would form the foundation of a Large World Model that is designed specifically to serve how we create, maintain, guide, and get value from the spaces that we live within. Such a Large World Model would open up a new horizon of intelligence rooted in centuries of evolution of the relationship between mind, body, and environment. This evolution would define a deeper level of embeddedness and alignment between our technologies, our bodies, and the habitats that support us—ultimately opening a new horizon for how technology can guide our actions and transform the physical world in which we live.

III. Structuring and Commercializing this Large World Model

To make Large World Models useful to individuals, we can draw on and combine approaches from both the analytic and continental traditions to better understand, describe, and approximate the world. These frameworks can inform a model structure that is translatable into code and trainable on a wide range of datasets—ranging from text, image, and moving image archives to individual behaviors and the digitization of space. Drawing from diverse sources that offer a range of manners of understanding the relationship between language and space would aim to understand what each does best in order to chart a relational framework that can be used to know how the model should approach aspects of creating, navigating, guiding, and maintaining our world.

Describing space, and creating a framework for its construction and maintenance, begins with Descartes. Rooted in the work of Euclid and, to some extent, Plato, it uses geometry to describe both objects and the spaces they occupy. This method enabled the precise construction of spaces and was refined over centuries through its application in art, architecture, optics, navigation, mapmaking, utopian design, and mechanical systems. Descartes later introduced a coordinate system that standardized how objects are located in space, leading to the development of analytic geometry. At its core, this approach allows space to be accurately represented in plan, elevation, or axonometric projection. 

When combined with Newtonian physics, this geometry enables the analysis of how structures bear loads, and can similarly be used to model the effects of forces like heat, wind, and fire on built forms. More recently, building Information Modeling (BIM) has come to be used to describe buildings in a detailed and parametric manner while also introducing a temporal dimension that aids how construction is scheduled. 

Such models can become digital twins of the building as built that are used to operate the building while also optimizing the performance of various building systems. While this modeling generally remains content with Euclidean geometry, non-Euclidean, Hyperbolic, or Elliptical geometries have come to play an integral role in the construction of some types of architecture. In addition, graph theory, fluid dynamics, and topology, among others, have also been introduced to model how space is navigated in order to optimize its performance.

It is not enough to describe the structure of space. We need to describe what takes place within its geometry. It would model the behavior of people as they navigate this space and execute particular tasks. On one hand, this would involve capturing the individual as an object in space and assimilating the description of this form into the Cartesian model. On the other hand, it would require capturing the underlying motivations that might not be immediately evident based on how the body moves or what it manipulates. This would involve understanding the program that the individual is participating in. Such programs could range in scale from “tying one’s shoes to go for a run” to “washing the dishes to prepare for guests” to “being in a room for dining” or even the scale of the home characterized as a “vacation home,” “starter apartment,” or “luxury apartment.” 

The meaning attached to these programs for space is derived from context. This includes the specific location within a building, neighborhood, or city, as well as the culture that characterizes this context. Such a cultural context dictates social hierarchies, prohibitions, and broader traditions. They often dictate the overall appearance of these spaces based on a dominant style that in turn might be characterized by a specific structural system, layout of space, harmonization with site, orientation to the sun, water, and wind, materials used, and specific pattern and ornamentation. They also dictate who is allowed to enter such spaces and their overall value—with the most coveted and comfortable spaces taking on the highest value. In this sense, any representation of future space only has meaning insofar as it relates to these shared values that frame space. The underlying geometric description should be seen as ground on which this drama of valuation plays out.

This process of valuation ultimately drives our desire to attain status, comfort, and support for our body and mind. As a result, the model must understand the way that the human body and mind interact with and create specific spaces from birth through death. In this sense, it must model a cumulative history of this body-space interaction. At the core of accounting for this relationship, is the way that space provides comfort through protecting, nurturing, supporting, warming, cooling, hydrating, cleaning, exercising, and relaxing the body. This is joined by the reciprocal process whereby these desires and our body shape the tools that we manipulate and the space these tools build. It also accounts for how space—and manipulation of objects in space as well as the encounters with others—stimulates the mind, teaches us mental processes that apply to a wide range of tasks, problems, and social situations, and helps us understand how to navigate space. 

Valuation helps explain how we seek specific interactions—with certain people, genders, and cultures—that gradually imprint themselves on space. This becomes a process of exploration through which we build an increasingly complex understanding of the world, expanding outward from our room to our home, school, neighborhood, and eventually to other communities, cities, countries, and the world at large. Along the way, we accumulate relationships, credentials, and access points that shape our lived experience. We form a mental map—a blend of memory, perception, and embodied movement—that may not be accurate in a Cartesian sense, but helps us locate ourselves, understand our communities, and feel at home. This long-standing dialogue between mind, body, and space reveals how certain spatial values transcend culture and tap into something deeply human, tied to our evolutionary development and the ways in which space has supported our safety and success. Building a model to capture this would require drawing on insights from psychology, existentialism, and phenomenology.

Beyond spatial structure, use, and value, it is essential to incorporate the ecological context that includes geology, plant biology, zoology, and all the various forces that define the environment. In order to incorporate this information effectively, we should view humans as embedded within, rather than separate from, their environmental and infrastructural conditions. It accounts for how this embeddedness has shaped the configuration of space—from farms, pastures, pigsties, coops, and stalls to the distribution networks that deliver food, as well as the water filtration systems, power plants, solar farms, mines, and other infrastructure that enable the natural environment to support an increasingly complex and expansive global population. Ultimately, such a model could also draw on systems theory and actor-network theory to model the complex interaction of natural and human flows and how these flows drive specific configurations of space. Ideally, it would help to better map the use of resources over time and support a natural material configuration that makes our world more habitable and sustainable for a wider range of people over the coming centuries.

Finally, it is important to remember that humans value the world as it could be, just as much as they value understanding it as it currently is. In this sense, many existing spaces have become departure points for dreaming about alternative futures. This process of imagination often begins by removing a restraint, limitation, or temporal context to imagine another type of world. This other world may place the individual in a scenario that can be used to stimulate, contemplate, provoke, or scare. Examples include writers, artists, and game designers who have conjured visions of the future–either positive or negative. While often seen as outlandish or impractical, the ideas traced in these worlds can sometimes prove to be highly valuable and ultimately realized when technology advances. It may even be the case that these visions and the wonder they provoke drive future generations to experiment and realize previously impossible goals. In many ways, this aspect of the model would be the most untethered from the real world and allow for dreaming that could both inform how an individual navigates the world as well as a tool for enhancing the ability of the model to help individuals visualize and realize those dreams—thus becoming an attractive point of entry for many individuals.

The resulting model, what we have come to call The Large World Model for Domestic Life, could then be used to accurately describe how the world in which we live is constructed as well as what we hope to accomplish through the specific form that it takes. More importantly, it could be used to describe how that world exists at a given time for a specific member using the platform. This state could then be characterized via the following variables: 1) Geography (including siting, context, natural condition, ecology, and existing built environment), 2) Program (including function, goals of a project, narrative, description), 3) Geometry (including  formal description, models, plans), 4) Materialization (including construction and manufacturing process, material extraction and modification), 5) The Thing (including the completed thing, building, neighborhood, and city), 6) Regulation (including zoning, codes, borders, local laws), 7) Flows (including everything from people to deliveries, energy, and movement of money and capital), 8) The Lived Reality (including personal desires, motivations, goals, and experiences), 9) Evaluation (including  memory, judgment, and goals for the future), and, 10) The Metaphysical (including faith, belief, ontology, and the broader discourse on how to describe and improve our world). Having described the specific state, it then becomes possible to track and guide actions that seek to navigate and transform these states. Such actions might include the following: 1) creation, 2) arrangement, 3) filling, 4) moving things, 5) using things, 6) maintaining things, 7) interacting with others, 8) navigating space, 9) remaining still or engaging ambient conditions, and 10) exchanging things and currency. 

Once initiated by an individual and integrated into their journey, these actions engage the model, prompting it to interpret real-world evidence and respond accordingly. Each action need not activate the entire model; rather, it could invoke a targeted sub-model designed for specific outcomes. These smaller, task-specific models could be trained more efficiently than a comprehensive, all-encompassing system. Over time, these modular engagements can be combined to form a more holistic, intelligent model of the world—one that is grounded in real behavior and capable of producing meaningful, actionable insight.

This bridge would likely take the form of an upload or capture interface that integrates existing data types into a unified architecture the model can learn from. This process would involve extracting not just what is depicted, but why it was created, how it relates to other files, individuals, and intentions, and what materials, structures, and ambient conditions define the captured spaces. Multiple media types would play a central role. Still images of spaces and objects can be processed with computer vision to classify contents, map spatial configurations, and situate them within a geometric model. These images can be stitched together or extrapolated into navigable environments, with stylistic analysis informing future actions. Vector files—such as plans and 3D models, including formats like point clouds—would provide precise spatial data. Moving images would add another layer, requiring the system to interpret motivation and action, map spatial paths, identify objects, recognize procedural steps, and anticipate obstacles.

The connection between the virtual and physical would unlock latent spatial and cultural intelligence embedded in the vast data already used to train LLMs—revealing insights about the world, the people and devices that captured it, and the intentions behind it. As new data flows in, the model would continue learning, becoming more attuned to the specificities of the built environment. Together, these media types would form the foundation for a learning system that interacts with the material world in a profoundly intelligent and responsive way.

Uploading and capturing would be initiated through a dialogue, inviting the individual to engage with the system to improve the model and their environment. The dialogue orients the optimization function to understand what to draw from each sub-model and how they combine to generate solutions quickly and effectively. It enables the system to interpret statements of intent and translate them into specific spatial configurations. The model would assess both immediate and long-term goals, recognize hierarchies among them, and balance prompted interactions with unobtrusive, background engagement. This framework would allow the model to simultaneously serve and learn, aggregating subspace models and analyzing their interactions. Over time, this creates a virtuous feedback loop: as the model guides meaningful human action, people are incentivized to contribute more data, which in turn enhances the model and enriches human experience.

IV. A New Form of the Attention Economy

If individuals and communities are going to feel compelled to explore the potential that Large World Models offer—if they are going to be convinced to alter their current relationship to technology and experiment with yet another platform that promises a better future—it will be essential that this new experience be extraordinary. It would have to do more than just harmonize our desires, reality, and ability to transform the world around us. It would have to offer a captivating gateway for everyone to dream and build their narrative of the future in a way that provokes wonder and ongoing attention. Such a gateway would go beyond supporting one’s individual narrative and allow the individual to access a broader community-wide narrative to which they can contribute. Beyond contributing, members would be invited to stand out within this narrative, attain a level of distinction, and pass on one’s contribution to the next generation.

A natural language interface would offer guidance that helps you explore how the future might appear based on your specific history, desires, and goals. It would allow the individual to do more than just imagine a generic house with kids, a nice car, and a vacation or two each year and offer a much greater level of specificity that would help anticipate the details that would have to be put in place to make this vision a reality. In this sense, it would offer the chance to have a textual and visual dialogue with a number of potential future versions of one’s self. Such a dialogue would allow one to inhabit a world that might be more compelling than what is presently available—thus offering a welcome escape. At the same time, it would offer a gamified experience of building a future that might ultimately illuminate what one really wants as opposed to what one imagined based on broader societal narratives that frame what it means to succeed.

This dialogue would play out as a cycle that begins with the screen, invites the individual to capture, gather files, and upload the current state of their world, and then leads to a digital world generated based on this information and the Large World Model. This digital world would be a set of still and moving renderings that synthesize all the available information about one’s home as a digital twin containing collections, rooms, homes, projects and plans, maintenance routines, and services. It would be an image of one’s life that can be adjusted based on past, present, and future. It would be possible to adjust the frame of reference that defines the scale of the image and what is contained within. 

This image of one’s life is not merely a tool for imagining the future, but for actualizing it through the way in which past, present, and future images are annotated and made actionable via connections to products and services. This marketplace would defragment the furniture and home goods industry as well as the many services that are required to support the operation and maintenance of the home. The image itself and the dialogue that guides action would drive this defragmentation through how our desires attain structure around our goals for our habits and habitat. The result would be a common point of orientation for both buyers and sellers of goods and services that would open a wide range of new opportunities for growth for small and medium producers of furniture and homes goods and related services that are ideally suited to serve local needs and address the wide variations in personal style and goals for the future.

Ultimately, these actionable images would prompt engagement with the world in which we live. They would lead to new things being delivered, rooms redecorated, homes renovated, landscaping transformed, moving between homes, helping friends and family with their homes, setting up events at home, building collections, and passing on or inheriting these homes and collections. This engagement with the world could be enhanced through utilizing augmented and virtual reality to enhance the dialogue between the digital and physical both individually and in collaboration with others. It may even be the case that individuals install digital projection equipment to support this immersion–perhaps turning entire walls into portals to the homes of friends and family and hosting events in blended realities across locations.

This enhanced experience could occur both at home and in a range of retail stores that have also been mapped via a Large World Model. These stores would ideally be configured based on the goals and desires of nearby individuals rather than based on whatever the retailer wants to sell. This would involve constantly reconfiguring these stores based on these desires as individuals pull things into their orbit, experience those things as they make a final decision, and ultimately purchase those items. This process would help drive sales of high end items that benefit from touch while also helping to coordinate long lead times and the assembly of complex collections of things across brands for projects like home renovations or setting up new spaces for the first time. As this occurs, additional data would be captured and the individual would realize a new understanding of their current and future ambitions that would lead to dialogue, uploading, a new rendering, and repetition of the process.

While there will be ongoing incentives to engage this dialogue, the model will also continue to function and learn in an ambient manner. It will know where one is on their journey and prompt specific actions related to near or long term goals. As it does so, it will track how one uses space, interacts with things, engages services, and enjoys life with friends. Each data point will become instrumental in further refining the ability of the model to support one's existence. It will make it possible for everyone to experience the world in a unique and enchanted way—one that feels deeply personal, as if crafted just for them—without sacrificing a sense of community. Rather than isolating individuals in personalized silos, the platform fosters a shared, collective engagement, where each person’s distinct perspective contributes to a richer, more connected experience for all.

V. Creating Value and Wealth

While the experience of imaging and executing one’s vision for the future will be rewarding, deploying Large World Models has the capacity to support a few more fundamental goals. While we explore many of these in “The Global Nature of OurThings,” it is worth noting here how the model can help individuals, families, and communities build wealth. Through accounting for everything one owns and the spaces that contain these collections and then providing a means for visualizing the future and achieving goals, it will be possible to more accurately track and increase wealth in real time. Moreover, it will be possible to share this success with others, set benchmarks based on community performance, and embark on challenges to surpass these benchmarks and achieve distinction. This support building wealth would be driven by aligning wealth with personality, style, and goals related to habit and habitat rather than strictly based on a desire to increase the quantity in an account. It would help ensure that spending actually builds value across collections and homes over time. A significant aspect of doing so would be the accurate and collective record keeping of all those things and spaces as an immutable narrative and provenance that increases the value of these items and spaces.

At a much deeper level, this model will allow one to be remembered beyond one’s lifetime by a community that extends beyond one’s immediate family and via one’s contributions to the model itself. In many ways, doing so would be to update the long tradition of homes serving as the sites of cultural memory and generational wealth to which members of a family orient themselves over time. This model would serve a world where families may move more often, where local conditions and desirability of a home evolve more rapidly, and where people are less tethered to a physical home, neighborhood, or city. It would also create a path by which both elite homes built over the last generation that trace an extraordinary accumulation of wealth can be preserved beyond features in glossy architecture and design magazines while also becoming an ongoing beacon of inspiration that encourages people to strive for success. This could occur both at the level of the image and at the level of the data that plays an integral role in forming and training the model in order to continue to enhance guidance for everyone.