From One-Way Answers to Shared Knowledge

Most people use language model–based tools in a simple, one-way pattern: you open a chat, ask a question, get an answer, maybe copy a bit into a document, and move on. Knowledge flows in only one direction: from the system to the individual user.

From a distance, that is a big problem. Very few users publish or send knowledge back. Almost nobody takes what they learn and makes it available to others through the same system. The result is that knowledge does not really flow in an organization. It sits in private chats and documents, instead of being shared and reused.

If we care about knowledge, this is upside down. Knowledge should flow in all directions. It should move from system to user, from user back to the system, and between users. When that happens, the value of each answer grows, because it can be reused and improved by others instead of being consumed once and forgotten.

Today, most language model systems are built for consumption, not contribution. The tools make it easy to ask and receive, but hard to share and scale. There is usually no simple way to turn a good answer into shared knowledge, no smooth way to capture corrections or organization-specific details, and no clear path to let others benefit from what one person has already figured out.

To change this, we need systems that make it easy and natural to contribute. Adding knowledge must be almost as easy as asking for it. For example, it should be possible to save a good answer as shared knowledge with a single action, and to quickly add just enough context so others can understand and reuse it. Reusing what already exists must be simpler than starting from scratch, with search that shows both model-generated answers and user-contributed content in the same place.

When that works, every interaction can become more than a one-off answer. A conversation can turn into a reusable explanation, an internal guideline, or a small FAQ entry. Over time, this creates a layer of shared knowledge that reflects how the organization actually works, not just what the base model knows.

The goal is to share and scale knowledge, not only to consume it. We want systems that learn from their users and help knowledge circulate: in, out, and across. Instead of a one-way flow of information from model to user, we can build a two-way and many-to-many flow where each good answer has the potential to help many others.

The next time you get a useful answer from a language model, do not stop at copying it into your own document. Ask yourself: who else could use this, and how can I make it easy for them to find it? That small step is what turns a one-way knowledge system into something that truly shares and scales knowledge.

Visual and Language Models Together

Knowledge can be represented through language: text, speech, articles, blogs, stories, fairy tales, documentation. This is how we usually write things down, explain details, and share information. Language is good at being precise, describing steps, and capturing logic.

Knowledge can also be expressed visually: images, figures, diagrams. Visuals help us see structure, relationships, and patterns quickly. They give us overviews that are hard to get from text alone. A picture can say more than a thousand words.

Visual knowledge is not just a visual representation of what is already written. It is knowledge and information that cannot be effectively or efficiently expressed in writing. Things like complex interactions, flows, and spatial layouts are often easier to understand in a diagram than in paragraphs of text.

To scale knowledge, these two forms can work together. A language model can work in pair with a visual model. The language model handles text: describing, explaining, and structuring knowledge in words. The visual model handles diagrams and images: turning descriptions into visual structures and visual input into something that can be interpreted and explained.

Together, they can move back and forth between text and visuals. Text can be turned into diagrams, and diagrams can be turned into clear explanations. This pairing makes it easier to express, share, and understand knowledge in the form that fits best—sometimes as words, sometimes as pictures, and often as both.

From Abstract Models to Real Complexity

When we design a model of something, it often looks clean and simple. A couple of concepts, a few relations, and we feel we understand the whole thing. But the moment we apply that model to the real world, the complexity explodes. The complexity is not really in the abstract model itself, but in the countless concrete instances that fill it.

Take a simple example: a model of family relationships. In the abstract, this is easy to describe. You have a Person and a Relationship. The relationship can have different types: parent, child, sibling, spouse, and so on. That is basically it. A few concepts and a small set of relation types. The model is straightforward and has low complexity.

Now look at what happens when you instantiate this model in the real world. Each actual human becomes an instance of Person. Each real family connection becomes an instance of Relationship. Even in one family you quickly get many objects: parents, children, siblings, grandparents, step-parents, and more. A larger family network gives you hundreds or thousands of people and relationships.

Scale this up further. In a town, you have thousands of persons and a huge number of relationships. In a country, you have millions. In the whole world, you have billions of persons and an enormous graph of family relations between them. The abstract model has not changed at all, but the instantiated system becomes overwhelmingly complex.

So the key point is: to get a sense of real complexity, you cannot just look at the abstract model with its few concepts and relations. You have to look at the instances and objects that arise when the model is applied to reality. The real complexity is in the thousands, millions, or billions of concrete persons and relationships, not in the small, tidy schema that describes them.

Navigating Language Model Retirements

Language models are becoming an important part of modern solutions, but they don’t come without challenges. Azure OpenAI has announced clear retirement dates for the language models it offers, which means that once a model’s retirement date has passed, any solutions built on it will cease to function. To keep systems operational, organizations must migrate to a newer model.

For example, the current model in use, GPT-4o, is scheduled for retirement on March 31, 2026. Its replacement is GPT-5.1, which is already assigned a retirement date of May 15, 2027. For now, no successor has been announced for GPT-5.1. This illustrates a key issue: the lifecycle for language models is quite short, forcing teams to plan for updates annually. Unlike traditional software upgrades, where skipping versions is often an option to save time and effort, skipping migrations with language models isn’t typically feasible.

This pace introduces major risks for organizations. First, there’s no guarantee that a replacement model will work as well as its predecessor or align with existing use cases. For example, there’s uncertainty around whether GPT-5.1 will meet performance expectations or integrate smoothly into current setups. Second, the rapid cycle of retirements means that building long-term solutions reliant on Azure OpenAI models involves constant work to maintain compatibility.

These realities create considerable challenges. Each migration requires resources, time, and expertise to adapt solutions. The high frequency of updates can strain teams and budgets that weren’t prepared to make migrations a regular part of their operations. The lack of clarity about what comes after GPT-5.1 also makes long-term planning difficult.

Organizations can take steps to reduce these risks. It’s important to evaluate how stable a language model’s lifecycle is before building critical systems on it. Designing solutions to be modular and flexible from the start can make transitions to new models smoother. Additionally, businesses should monitor Azure’s announcements and allocate resources specifically for handling migrations. Treating migrations as a predictable part of operations, rather than a disruptive hurdle, can help mitigate potential downtime and performance issues.

Frequent updates and retirements highlight the dynamic nature of working with language models. Building solutions on this foundation requires organizations to adopt a forward-looking strategy. With adaptability, careful resource planning, and ongoing evaluation of new models, businesses can derive value from language models while staying prepared for inevitable changes.

Cat World: The Nine Lives

Welcome to Cat World: The Nine Lives, a game concept that combines survival mechanics with innovative agent-driven design. This project isn’t just a game—it’s a sandbox for exploring autonomous decision-making, emergent behavior, and long-term adaptation. The player takes on the role of a designer, creating a cat agent meant to navigate a systemic and persistent world filled with danger, opportunity, and unpredictability.

The foundation of the game is survival. The cat agent must balance core needs: food, water, rest, health, and safety. The world itself is relentless and indifferent, designed to challenge the agent without adapting to its failures or successes. Players influence the agent’s behavior by setting high-level strategies and preferences, but the agent ultimately takes autonomous actions based on its traits, instincts, memory, and learned experiences. This hands-off approach shifts the player’s role to an observer and designer, focusing on guiding the agent rather than controlling it directly.

A distinctive mechanic is the nine lives system. Each life represents a complete simulation run, and the agent’s death isn’t a reset—it’s part of its evolution. Through successive iterations, the agent inherits partial knowledge, instincts, and biases from previous lives. This creates a lineage of cats that become better adapted to survive and thrive over time. Failure, in this game, isn’t an end; it’s data for adaptation and growth.

The agent’s behavior emerges from a complex interplay of internal states like hunger, fear, thirst, and fatigue. These dynamic needs guide decision-making, ensuring the agent responds flexibly to its environment. Perception isn’t perfect—the agent relies on noisy, incomplete observations such as scent trails, limited vision, and sound cues, mimicking real-world uncertainty. Spatial memory and associative memory further enhance survival; the agent retains knowledge of safe zones, food sources, and threats, while linking patterns such as predator activity to specific locations or times of day.

Adaptation and learning are central to Cat World. Skills improve through experience, colored by traits like curiosity or memory strength. Reinforcement signals carry over between lives, shaping heuristics, biases, and decision frameworks. Traits evolve randomly across generations, introducing diversity within lineages and enabling the discovery of new strategies. Together, these systems create a dynamic, ever-evolving agent that is both unpredictable and intelligent.

This game concept has unique implications for agent research. Survival in Cat World is a natural multi-objective optimization problem that requires agents to balance competing priorities in challenging, non-stationary environments. Learning is embodied, grounded in physical constraints and real-time environmental interaction. The world evolves in response to resource depletion, predator activity, and other dynamics, encouraging continual adaptation and preventing static behaviors. Internal states, decision rationales, and memory models are all exposed for debugging and visualization, making the game particularly valuable for studying emergent behavior. Its modular structure also supports experimentation with novel architectures, instincts, and learning systems, extending far beyond traditional agent training methods.

In short, Cat World: The Nine Lives is both a survival simulator and a living laboratory. It turns failure into knowledge and death into progress, offering players and researchers alike the opportunity to explore the limits of autonomy, adaptation, and evolution. It’s an invitation to design, observe, and learn from agents navigating their own complex stories within a dangerous and systemic world.

How to Assess Knowledge Effectively

In any situation where learning is required, it’s essential to reflect on what kind of understanding is necessary. Before diving in, consider questions like: What knowledge is absolutely required? How wide and varied should the exploration be? Should the focus be broad, or is deeper, more specific understanding called for? Another key consideration is curiosity—how far should our natural inquisitiveness guide us in the process? Striking a balance between these factors ensures that our learning is purposeful and relevant.

There are models and methods to help guide this process. One such model is the “5 Whys” approach, which involves asking “Why?” repeatedly until you get to the root cause or deeper understanding of an issue. It’s a way to push beyond surface-level knowledge by continuously questioning the reasoning behind something. Another equally valuable method emphasizes questioning everything. This involves examining assumptions, challenging accepted norms, and looking at topics from new angles. Both methods encourage a mindset of curiosity and exploration while helping to uncover insights that could otherwise be overlooked.

Context is critical when deciding how far to go in assessing and expanding knowledge. Some situations call for a deeper dive, while others benefit from sticking to what’s sufficient for immediate needs. It’s also important to know when to stop and move forward, avoiding the trap of overanalyzing or endlessly questioning. Reflecting on your goals and tailoring your approach can make the process both efficient and effective.

By thoughtfully combining structure with curiosity, we can assess knowledge in a way that ensures deeper insights and meaningful understanding. Whether you apply the “5 Whys” or adopt a general mindset of continuous questioning, the key is to refine how you explore, keeping your focus without losing sight of what may lie beyond the obvious.

Authoritarian Models and Systems

Authoritarian models and systems operate with centralized authority, where certain entities or individuals hold more power than others. These systems rely heavily on hierarchies, with everything positioned within one or more layers of structured order. This distribution of authority ensures clarity and control in how decisions are made and enforced.

A key strength of authoritarian systems is their ability to assess situations and make decisions effectively. Their structure allows for swift evaluations and a clear chain of command. In situations that require stability and control, these models provide the discipline to maintain order and deliver results.

However, authoritarian systems are less effective when it comes to fostering change or creating something new. Their rigid frameworks make them resistant to innovation and experimentation. This limits their ability to adapt when confronted with new circumstances or challenges, and creativity often takes a backseat to maintaining structure.

The practical use of such systems depends on context. They work best when stability and decisive action are required, but they may hinder progress in situations that demand flexibility, creativity, or the exploration of alternatives. Striking a balance between authority and adaptability is key to utilizing these models effectively.

This understanding highlights the importance of knowing when authoritarian approaches can provide value and when they fall short. Recognizing their strengths and weaknesses helps ensure they are applied appropriately to achieve specific goals without hindering broader development.

Binary Models

In decision-making, we often fall back on binary models: win or lose, truth or lie, right or wrong. These simple frameworks can feel intuitive and practical, but they rarely reflect the complexity of real life. When decisions are reduced to “either-or,” we risk oversimplifying nuanced issues and ignoring the subtle shades that lie between extremes.

Binary thinking has its uses in clear and straightforward scenarios. It provides clarity and forces quick decisions. But when evaluations become strictly binary, we lose the ability to recognize middle grounds or gradations. Nuance disappears, and there’s no space for outcomes like partial success, compromise, or “draws.” Everything becomes reduced to one of two polarized outcomes.

This rigidity can be counterproductive, especially when dealing with complex systems. The path to achieving goals is rarely a straight line of binary decisions. In reality, systems are intricate and exist on continuums of possibilities. Binary models are simply too limited to account for the interconnected, evolving nature of such challenges.

Consider examples like human relationships or strategic planning. Relationships aren’t entirely “good” or “bad”—they’re built from layers of understanding, missteps, and growth. Similarly, no organizational decision can be fully right or wrong in isolation; success often depends on the broader context and trade-offs. In these situations, a binary framework flattens complexity into false choices.

Moving beyond binary thinking means embracing alternatives that allow for nuance and flexibility. Instead of asking whether something is true or false, we can ask to what degree it is true. Instead of assuming success or failure, we can examine factors like progress, trade-offs, and incremental improvements. Recognizing and questioning false dichotomies gives us the freedom to explore broader options and reach more thoughtful conclusions.

Binary models may help us navigate simple choices, but they falter in complex systems where absolutes rarely exist. By shifting our mindset away from “either-or,” we open ourselves to greater possibilities and a deeper understanding of the world’s intricacies. After all, life is rarely black and white—most of it exists in the gray.

Bridging Creativity and Execution with Agents

Many people have the skills and knowledge to build and execute a business idea, but they often struggle with generating innovative and fresh ideas. On the other hand, there are plenty of individuals with creative minds who frequently come up with new concepts but lack the ability, resources, or tools to implement their ideas. This gap between creativity and execution is where language models, or agents, could potentially play an important role.

For those who excel at execution but lack creativity, agents could serve as innovation scouts. They could explore the vast digital world, searching through shared ideas on forums, blogs, and social platforms to uncover concepts that can be executed. This assumes there are enough creative individuals out there willing to share their ideas online for others to act on. With an agent’s help, these ideas could be curated and presented to those who are ready to build but struggle with the “what.”

The dynamic could work the other way as well. Creative individuals who lack the skills or ability to execute their ideas could use agents to find and connect with people who can bring their concepts to life. By acting as matchmakers, agents could help pair people with complementary skills, fostering collaboration between the creative and the practical.

This approach would give executors access to a pool of actionable ideas and allow creative thinkers to see their concepts realized by partnering with the right builders. However, there are challenges to navigate: concerns about intellectual property, ensuring the quality of sourced ideas, and managing fair collaboration between parties.

Agents have the potential to bridge the gap between creativity and execution. By finding, curating, and building connections, they can unlock opportunities for collaboration and innovation that might otherwise go unnoticed or unrealized.

Language Models vs. Knowledge Models

Language models are designed to work with the coherence of text and the structure of language itself. They excel at generating outputs that appear polished, professional, and as if they come from experts. However, this doesn’t mean that these outputs are always correct. Their focus is on the language and patterns inherent in text, not on verifying or understanding the actual knowledge behind it. These models are built using vast amounts of textual data from diverse sources, which helps them to generate text that seems natural and contextually relevant.

Knowledge models, on the other hand, focus on organizing and understanding knowledge itself. They deal with things like objects, concepts, relationships, logic, causation, and even experiences. Knowledge is not limited to textual representation and can exist in other forms, although it is often represented or communicated in text for usability. Knowledge models are constructed using high-quality, well-curated data that is structured and reliable, enabling them to work with detailed and interconnected information.

The difference between language models and knowledge models lies in their focus and goals. Language models prioritize the structure of text, while knowledge models prioritize the structure and coherence of knowledge. While language models can produce text that seems to make sense, they don’t inherently understand the concepts they are describing. In contrast, knowledge models aim to provide meaningful representations of knowledge that emphasize connectivity, logic, and accuracy over language.

Language models can play a valuable supporting role in working with knowledge. For example, they can be used to summarize or simplify complex information, making knowledge more accessible. However, language models are not knowledge models; they are tools that can help process or present knowledge but lack the deeper logical coherence that comes with true knowledge organization and reasoning.

In essence, language models are a step on the path toward building richer knowledge models. The two systems complement each other, but they serve different purposes. As we continue to improve these technologies, we are likely to see even greater integration between their strengths: the fluency of language models combined with the structured reasoning of knowledge models. This advancement will bring us closer to systems that not only communicate well but also truly understand the world around them.