AAA: Available, Accessible, Activated - A New Lens for Understanding Knowledge in AI

AI has "all human knowledge" available to it, but in many ways so do we, us mere mortals. We have the internet, Library of Congress, Wikipedia, search engines, and millions of books. The problem isn't how much knowledge we have but rather what knowledge we should be looking for and focus on. Of course, AI is faster, but AI also doesn't know what to look for unless directed to, pretty similar to us in many ways. AI does not look through its entire training data set for every response. Instead it's directed to look into knowledge selectively.

This tension between knowledge availability and knowledge utilization led me to develop principles I call "AAA" - Available, Accessible, Activated. It's a simple but powerful lens that explains why simply "knowing" something isn't enough for either humans or machines.

The Knowledge Gap

In my work studying AI cognition, I observed an AI working through a complex coding problem. Despite having all the relevant information somewhere in its training data (available), it wasn't until I provided specific cognitive frameworks that the essential concepts became more prominent in its reasoning (accessible). Yet even then, it took a targeted prompt—a specific cue that connected theory to application — for that knowledge to be effectively deployed (activated).

This pattern mirrors human cognition remarkably well. We all experience these same three distinct states of knowledge, often without recognizing the transitions between them.

The Three A's Explained

Available Knowledge

This is the total body of information you theoretically "know" — whether stored in an AI's parameters or in the neural networks of your brain. It's everything you've learned, read, or experienced that has left some trace in memory.

Available knowledge is like having access to the entire internet or the Library of Congress. The information exists, but that doesn't mean you can retrieve or use it effectively. Without organization and retrieval mechanisms, availability alone offers little practical value. You also need to know what you're looking for and what to look for within it for in order to find exactly relevant information.

Accessible Knowledge

This second state represents knowledge that's readily retrievable in a given context. It's the information that "comes to mind" when prompted by relevant cues or situations. Kind of like if I asked, "What does this make you think of" holds up picture of apple -- your mind finds associations.

For AI systems, accessible knowledge emerges when words make specific patterns more prominent in the model's calculations. Words are concepts, words in order are connected concepts, words change relationships between concepts. With AI, every word and the order of every word are like flashlights into the landscape of Available Knowledge, becoming Accessible Knowledge through association and attention weighting.

For humans, it's the difference between recognizing "I've learned something about this" and being able to actively recall specific details and concepts related to the situation. Sometimes we don't, like when we know the name of the movie (available) but can't remember in the moment (inaccessible).

Activated Knowledge

This is the critical final state—knowledge that's not just retrievable but actively applied to the problem at hand. Activated knowledge shapes decision-making, guides actions, and generates solutions. For humans this is specifically why we practice, why we have homework, why we have quizzes and tests. It's the activation part that demonstrates the application of the knowledge to a problem. It goes beyond the pattern recognition to pattern activation and application.

The gap between accessible and activated knowledge explains many of our cognitive frustrations. You might recognize a principle is relevant (accessible) but struggle to implement it effectively (activated). It's not enough to think "I should apply systems thinking here"; you need to actually engage in systematic analysis of interconnections and feedback loops, Activate and Apply.

The Gap in Action

I witnessed this distinction while working with an AI on separating bookmarked messages from starred messages in a transcript viewer. Initially, despite understanding the problem conceptually, the AI implemented a surface-level fix addressing symptoms rather than the underlying issue.

The AI had the necessary programming principles in its training data (available). The cognitive frameworks I provided made relevant architectural patterns accessible. But it took a specific prompt that connected these patterns to the exact problem context to activate the knowledge of root cause analysis needed for a proper solution.

This mirrors my own experience as a developer. How many times have I known the principles of clean code or systematic debugging (accessible knowledge) but defaulted to expedient hacks under pressure? That's the gap between accessible and activated knowledge.

Why This Matters

The AAA principles offer practical insights for learning, teaching, and AI development:

• Simply exposing ourselves or AI to information isn't enough — we need retrieval practice to make it accessible
• Making knowledge accessible through theory and frameworks isn't sufficient — we need activation triggers tied to application contexts
• Education and AI prompting should focus not just on content delivery but on creating reliable activation pathways

Understanding these distinct knowledge states transforms how we approach learning, teaching, and AI development – shifting focus from mere information accumulation to designing the critical pathways that turn latent knowledge into applied expertise.

What patterns have you noticed in your own transitions between available, accessible, and activated knowledge? I'd love to hear about your experiences.