You calibrated the lens. Now read the landscape.
Phase 8 trained you to see accurately. You built prediction tracking (L-0144), bias maps (L-0158), physiological monitoring (L-0145 through L-0148), and Bayesian updating habits (L-0157). You can now perceive with less distortion than most people around you. The closing lesson of that phase — L-0160 — made the case that this calibrated perception is a genuine competitive advantage.
It is. But it is incomplete.
Here is the problem calibration alone cannot solve: two perfectly calibrated observers can look at the same information and arrive at completely different — and completely valid — interpretations. A calibrated engineer sees "deployment failed at 3 AM" and reads an infrastructure stability issue. A calibrated product manager sees the same log entry and reads a process problem about release timing. A calibrated CEO sees it and reads a staffing question about on-call coverage. None of them are wrong. None of them are miscalibrated. They are reading the same signal through different contexts, and the context is doing the interpretive work.
This is the territory of Phase 9. Not how to see more accurately — you have that — but how to understand what your accurate observations mean. And that understanding is never in the information itself. It is always, without exception, in the relationship between the information and the context surrounding it.
The linguistic evidence: meaning is never in the words
Linguistics settled this question decades ago, and the answer is unambiguous: meaning does not live in sentences. It lives in the interaction between sentences and contexts.
Paul Grice formalized this in his theory of conversational implicature. His cooperative principle — the foundation of modern pragmatics — holds that communication works because speakers and listeners share an implicit agreement to be relevant, truthful, appropriately informative, and clear. But the critical insight is what happens when these maxims are violated. When someone says "Nice weather we're having" during a thunderstorm, the literal meaning and the communicated meaning are opposites. You decode the sarcasm not from the words but from the context: the weather outside, the speaker's tone, the shared understanding between you. The words alone are ambiguous. The context makes them precise (Grice, 1975).
Dan Sperber and Deirdre Wilson pushed this further with relevance theory. They argued that human communication is fundamentally inferential, not just decoding. When you hear an utterance, you do not simply look up word definitions. You construct meaning by combining the decoded words with your existing knowledge, the physical situation, the conversational history, and your model of the speaker's intentions. Relevance theory defines meaning as an optimization problem: you search for the interpretation that yields the greatest cognitive effect for the least processing effort, given the context you are in (Sperber & Wilson, 1986). Change the context, and the optimal interpretation changes — even when the words stay identical.
Philosophy makes the same point through indexicality. The philosopher David Kaplan demonstrated that certain expressions — "I," "here," "now," "this," "tomorrow" — have no fixed meaning at all. They are purely context-dependent. "I am tired" means something completely different depending on who says it. "Meet me here tomorrow" is meaningless without knowing where "here" is and when "tomorrow" falls. These are not edge cases. Indexical expressions are among the most common words in every human language. The fabric of daily communication is woven from words that have no meaning outside of context (Kaplan, 1989).
This is not an academic curiosity. It is a structural feature of how information works. If meaning were inherent in information — if data carried its own interpretation — then language would not need context, sarcasm would be impossible, and every reader of the same sentence would reach the same conclusion. None of these are true. Meaning is always co-constructed between information and context.
The cognitive science: your mind is not context-free
The linguistic evidence might tempt you to think context-dependence is a feature of language specifically. It is not. It is a feature of cognition itself.
The situated cognition movement, pioneered by Jean Lave, Etienne Wenger, and James Greeno, demonstrated that human thinking is not an abstract process that happens to occur inside a body. Cognition is fundamentally shaped by the physical, social, and cultural environment in which it takes place. Lave and Wenger's research on apprenticeship learning — studying Yucatec midwives, Vai and Gola tailors, naval quartermasters, and meat cutters — showed that knowledge is not a transferable object you carry between situations. Knowledge is a relationship between a person and a context. Change the context, and the knowledge itself transforms: what you can recall, how you reason, what solutions occur to you (Lave & Wenger, 1991).
Greeno extended this into a formal framework. He argued that cognition should be analyzed not as computation inside a skull but as interaction between an agent and an environment. The same person solving the same type of problem will think differently in a classroom versus a workshop versus a kitchen — not because they are being lazy or inconsistent, but because cognition is context-sensitive by design. The environment is not a backdrop to thinking. It is a component of thinking (Greeno, 1998).
This means your carefully calibrated perceptual instrument from Phase 8 does not produce context-free readings. Every observation you make is already shaped by the context you are standing in: your physical environment, your social role, your organizational position, the question you walked into the room trying to answer. Calibration reduces distortion from biases and physiological interference. It does not — it cannot — eliminate the influence of context. That influence is not a bug. It is how cognition works.
The decision-making evidence: same data, opposite choices
If you need convincing that context does not merely tint interpretation but can reverse it entirely, consider the most famous demonstration in behavioral economics.
In 1981, Amos Tversky and Daniel Kahneman presented participants with the following scenario: an unusual disease is expected to kill 600 people, and two programs are proposed to combat it. In the "gain frame," participants chose between Program A (200 people saved for certain) and Program B (one-third probability that 600 are saved, two-thirds probability that nobody is saved). In the "loss frame," participants chose between Program C (400 people will die for certain) and Program D (one-third probability that nobody dies, two-thirds probability that 600 die).
Programs A and C are mathematically identical. Programs B and D are mathematically identical. The only difference is the context — whether the outcome is framed as lives saved or lives lost. The results were dramatic: 72% of participants chose the certain option (Program A) when framed as a gain, but only 22% chose the certain option (Program C) when framed as a loss. Same information. Same expected outcomes. Opposite decisions. The context of the frame reversed the majority preference (Tversky & Kahneman, 1981).
This is not a laboratory curiosity. Framing effects have been replicated across medical decision-making, financial choices, policy preferences, legal judgments, and consumer behavior. They persist among experts. Physicians presented with survival rates versus mortality rates for the same surgical procedure recommend different treatments. Financial analysts presented with identical returns framed as gains versus reduced losses make different investment recommendations. The information is invariant. The context determines the meaning, and the meaning determines the decision.
The implication for your epistemic practice is direct: when you receive information — a statistic, a recommendation, a report, a diagnosis — you are not receiving meaning. You are receiving raw material that your context will shape into meaning. If you do not ask what context you are in, you do not know what the information means. You only know what it means to you, right now, from where you stand. That is not the same thing.
The organizational reality: one number, five departments
Scale this from individual cognition to organizational life and the principle becomes even more visible.
Consider a single metric: customer churn increased 3% this quarter. Hand that number to five departments and watch the meaning multiply.
The product team reads it as a feature gap. Users are leaving because the product does not do something they need. The context is product-market fit, and the implied action is user research and feature prioritization.
The customer success team reads it as a service failure. Users are leaving because they did not receive adequate support. The context is relationship management, and the implied action is reviewing support tickets and escalation patterns.
The finance team reads it as a revenue risk. The context is the financial model, and the implied action is revising projections and tightening spend.
The marketing team reads it as a positioning problem. The users who churned were never the right fit. The context is audience targeting, and the implied action is refining messaging and acquisition channels.
The engineering team reads it as a reliability signal. Users are leaving because of performance issues, bugs, or downtime. The context is system health, and the implied action is reviewing error logs and uptime metrics.
All five interpretations are plausible. All five are internally consistent. And all five are completely determined by the departmental context through which the number is read. The number itself — "churn increased 3%" — contains none of these meanings. It is a context-free fact waiting for a context to give it significance.
This is why cross-functional meetings so often produce conflict that feels like disagreement about facts but is actually disagreement about contexts. The product lead and the finance lead are not looking at different data. They are looking at the same data through different contextual frames, and each frame generates a different meaning, a different urgency, and a different prescribed action. The resolution is not to determine who is "right." It is to make the contexts explicit: "From a product-fit frame, this means X. From a financial-model frame, this means Y. Which context should we privilege for this decision?"
Context engineering in AI: the mirror you did not expect
The context-dependence of meaning has found its most dramatic recent validation in an unexpected domain: artificial intelligence.
Large language models do not understand anything. They predict the next token in a sequence based on the context they have been given. And the field has discovered — through billions of dollars of research and deployment — that the quality of an LLM's output depends almost entirely on the quality of the context it receives.
This realization has driven a terminological shift. Andrej Karpathy, former head of AI at Tesla, has publicly advocated replacing "prompt engineering" with "context engineering," describing it as "the delicate art and science of filling the context window with just the right information for the next step." The distinction matters: a prompt is a single instruction. Context engineering is the design of an entire information environment — task descriptions, examples, relevant data, conversational history, tool specifications, and state — that shapes what the model can produce (Karpathy, 2025).
Anthropic's engineering team has formalized this into a discipline. Their 2025 technical guide on effective context engineering for AI agents defines the challenge as: "finding the smallest possible set of high-signal tokens that maximize the likelihood of some desired outcome." The same model, given different context, will produce radically different outputs — not because it "thinks differently," but because context is the only thing determining what the model generates. Without the right context, even the most capable model produces noise. With the right context, a smaller model can outperform a larger one (Anthropic, 2025).
Research on "context rot" — the degradation of model performance as context windows grow — has reinforced the point. Larger context windows do not automatically mean better performance. Information placed in the middle of long contexts may be effectively invisible to the model during generation, a phenomenon called the "lost in the middle" effect. The quality of context matters more than the quantity. Carefully curated context outperforms exhaustive context.
The parallel to human cognition is striking and instructive. You, like an LLM, do not process information in a vacuum. Every interpretation you make happens inside a context window: your recent experiences, your current emotional state, your organizational role, your physical environment, the question you are trying to answer. The quality of your interpretations depends on the quality of your context — not on the volume of information you have access to. And just as an LLM can be misled by poorly structured context, you can be misled when you interpret information inside the wrong frame, or without recognizing which frame you are in.
The AI mirror reveals something humans have always done but rarely made explicit: you are always doing context engineering. Every time you walk into a meeting, open a document, start a conversation, or read a report, you are assembling a context window that will determine what the incoming information means. The question is whether you do this deliberately or by default.
Context collapse: what happens when contexts merge
If context determines meaning, then collapsing multiple contexts into one should cause interpretive chaos. It does. The phenomenon has a name: context collapse.
Alice Marwick and danah boyd coined the term in their 2011 study of Twitter users. In face-to-face life, you naturally manage multiple contexts: you speak differently to your manager than to your partner, differently to your students than to your friends, differently to your parents than to your teammates. You are not being deceptive. You are performing context-appropriate communication — adjusting meaning to match the interpretive frame your audience inhabits.
Social media flattens all of these audiences into a single stream. Your manager, your partner, your college friends, your industry peers, and your extended family all read the same post. The multiple contexts that gave your words their precise meaning collapse into one undifferentiated space. The result is that every statement is simultaneously interpreted through every possible context — and many of those interpretations conflict. A joke your friends understand through shared history is read as offensive by professional contacts who lack that context. A professional achievement you share for industry peers reads as bragging to family members in a different socioeconomic context (Marwick & boyd, 2011).
Context collapse is not a social media problem. It is a context problem that social media made visible. The same mechanism operates whenever contexts merge without warning: when an email intended for one team gets forwarded to another, when a quarterly report designed for internal use leaks to investors, when a casual conversation in a hallway gets quoted in a formal meeting. In each case, information designed for one context enters another, and the meaning deforms because the receiving context imposes a different interpretive frame.
Understanding context collapse is understanding context-dependence in reverse: instead of asking "how does context create meaning?" you observe what happens when the context is stripped away or scrambled. The result is not ambiguity. It is misinterpretation — confident, systematic, and often invisible to the person doing it.
The principle, stated precisely
Here is what the evidence converges on:
Information is not meaning. Information is the raw material from which meaning is constructed, and context is the construction site.
This is true in linguistics, where the same sentence means different things depending on speaker, listener, and situation. It is true in cognitive science, where the same person thinks differently in different environments. It is true in decision-making, where the same data produces opposite choices depending on how it is framed. It is true in organizations, where the same metric generates different actions depending on which department reads it. And it is true in AI, where the same model produces different outputs depending entirely on the context it is given.
The practical implication is a discipline: never interpret information without first identifying the context you are interpreting it inside. This is not a suggestion to be more careful. It is a structural requirement of accurate understanding. If meaning depends on context, then interpreting without context awareness is interpreting blind — and the fact that you will still feel confident in your interpretation makes it worse, not better.
The bridge forward
Phase 8 trained you to see accurately. Phase 9 trains you to understand what you are seeing — by understanding the contexts that shape meaning.
This lesson established the foundational principle: context determines meaning. Every lesson that follows in Phase 9 builds on this foundation. L-0162 turns the principle into a practice: the habit of asking "what context am I in?" before interpreting any information. From there, Phase 9 will explore context switching (L-0163), written context as misinterpretation prevention (L-0164), cultural context (L-0165), temporal context (L-0166), emotional context (L-0167), and the cascading implications of context sensitivity across communication, organizations, and personal epistemology.
You spent twenty lessons calibrating your perception. Now spend twenty learning that calibrated perception is always perception from somewhere — and that the somewhere matters as much as the accuracy.
Sources:
- Grice, H. P. (1975). "Logic and Conversation." In P. Cole & J. L. Morgan (Eds.), Syntax and Semantics, Vol. 3: Speech Acts. New York: Academic Press, 41-58.
- Sperber, D., & Wilson, D. (1986). Relevance: Communication and Cognition. Cambridge, MA: Harvard University Press. (2nd ed., 1995).
- Kaplan, D. (1989). "Demonstratives." In J. Almog, J. Perry, & H. Wettstein (Eds.), Themes from Kaplan. Oxford: Oxford University Press, 481-563.
- Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press.
- Tversky, A., & Kahneman, D. (1981). "The Framing of Decisions and the Psychology of Choice." Science, 211(4481), 453-458.
- Marwick, A. E., & boyd, d. (2011). "I Tweet Honestly, I Tweet Passionately: Twitter Users, Context Collapse, and the Imagined Audience." New Media & Society, 13(1), 114-133.
- Karpathy, A. (2025). Commentary on context engineering. Via X (formerly Twitter) and public presentations.
- Anthropic. (2025). "Effective Context Engineering for AI Agents." Anthropic Engineering Blog. https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents