Absolutely nailed the concept, Hodman and Wyndo! We're writing better prompts when we should be onboarding AI like a new analyst with proper definitions, examples, and edge cases. Curious to try this framework myself!
Thanks Hodman and Wyndo for this excellent post. Really great to see risk management play such a prominent part as well. Looking forward to trying to test out some of this workflow myself.
Appreciate that! The risk management piece is often overlooked but it's where the framework really proves its value. Would love to hear how it works for your specific use case once you test it out. What's the first concept you're planning to teach your AI?
AI didn’t fail here because it lacked a shared vocabulary or better definitions. It failed because it flattened growth into a smooth surface, obscuring time as the axis on which individual events occurred.
This is a familiar limitation. In Flatland, the inhabitants are intelligent, articulate, and mathematically capable, yet incapable of perceiving dimensions beyond their own. When confronted with evidence of a higher dimension, they reduce it to what fits their plane.
Today’s AI behaves much the same way. It is excellent at projecting complex phenomena onto a surface it can analyze, and just as poor at recognizing when something essential has been flattened away. Viral growth is not a strategy error or a metric problem. It is a time-bound event. Treating it otherwise is a dimensional mistake.
Thank you for this observation. Appreciate the Flatland analogy. You're absolutely right that AI flattened the temporal dimension. It saw "47% growth" as a continuous trend rather than a discrete spike event.
I'd argue that's precisely why the shared vocabulary matters, though. When I taught my AI what "sustainable growth" vs. "viral spike" means (with temporal context baked into the definition), it learned to ask: "Was this growth distributed over time or concentrated?" That's teaching it to look for the time dimension.
But your point stands: AI's default is dimensional reduction. The framework helps, but we're still fighting against that fundamental limitation. The question becomes: can we teach AI to recognize when it's flattening something critical? That's the harder problem.
Curious how you'd approach preserving temporal context systematically in AI analysis?
I appreciate how you framed this, especially the idea of recognizing when something critical has been flattened rather than simply misclassified.
My instinct is that preserving temporal context has to be enforced procedurally, not just semantically. Vocabulary helps, but definitions alone are static. Temporal meaning emerges from relationships: sequence, spacing, and adjacency.
Practically, that suggests inserting a deliberate temporal pass before aggregation or summarization. For example, forcing analysis to answer questions like: What happened first? What immediately followed? Which events cluster tightly in time versus disperse? What changes if nothing is reordered except time?
One additional wrinkle is that not all datasets actually encode time in a way that supports this kind of analysis. Some domains record timestamps that reflect administrative events rather than meaningful sequence, and others omit temporal information altogether. In those cases, the first step is not preservation but detection: asking whether temporal relationships are knowable from the data at hand, or whether they are simply being inferred.
In that sense, time feels less like metadata and more like a load-bearing dimension. Once sequence is collapsed into totals or trends, you cannot reliably reconstruct it from summaries alone. Making the act of flattening visible may be the most realistic first step toward teaching AI to recognize when compression itself is the error.
Absolutely nailed the concept, Hodman and Wyndo! We're writing better prompts when we should be onboarding AI like a new analyst with proper definitions, examples, and edge cases. Curious to try this framework myself!
I'm so glad! Yes, please let me know. You can download it here: https://hodman.gumroad.com/l/dataliteracyloop
What a collab 😍😍😍 Can't wait to try the workflow! Thank you :)
Let me know how it goes!
Congratulations on the 2025 growth and the collab Wyndo !
He has done something remarkable with AI Maker, hasn't he? Congrats, Wyndo!
He certainly has ✨
Thanks Hodman and Wyndo for this excellent post. Really great to see risk management play such a prominent part as well. Looking forward to trying to test out some of this workflow myself.
Appreciate that! The risk management piece is often overlooked but it's where the framework really proves its value. Would love to hear how it works for your specific use case once you test it out. What's the first concept you're planning to teach your AI?
Definitely what its boundaries are in terms of what questions it should be asking me (identifying info, family details, etc.)
Thanks for the effectiveness and efficiency and services to the same for the good 😊
Thank you! Really appreciate you reading 😊
AI didn’t fail here because it lacked a shared vocabulary or better definitions. It failed because it flattened growth into a smooth surface, obscuring time as the axis on which individual events occurred.
This is a familiar limitation. In Flatland, the inhabitants are intelligent, articulate, and mathematically capable, yet incapable of perceiving dimensions beyond their own. When confronted with evidence of a higher dimension, they reduce it to what fits their plane.
Today’s AI behaves much the same way. It is excellent at projecting complex phenomena onto a surface it can analyze, and just as poor at recognizing when something essential has been flattened away. Viral growth is not a strategy error or a metric problem. It is a time-bound event. Treating it otherwise is a dimensional mistake.
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Thank you for this observation. Appreciate the Flatland analogy. You're absolutely right that AI flattened the temporal dimension. It saw "47% growth" as a continuous trend rather than a discrete spike event.
I'd argue that's precisely why the shared vocabulary matters, though. When I taught my AI what "sustainable growth" vs. "viral spike" means (with temporal context baked into the definition), it learned to ask: "Was this growth distributed over time or concentrated?" That's teaching it to look for the time dimension.
But your point stands: AI's default is dimensional reduction. The framework helps, but we're still fighting against that fundamental limitation. The question becomes: can we teach AI to recognize when it's flattening something critical? That's the harder problem.
Curious how you'd approach preserving temporal context systematically in AI analysis?
I appreciate how you framed this, especially the idea of recognizing when something critical has been flattened rather than simply misclassified.
My instinct is that preserving temporal context has to be enforced procedurally, not just semantically. Vocabulary helps, but definitions alone are static. Temporal meaning emerges from relationships: sequence, spacing, and adjacency.
Practically, that suggests inserting a deliberate temporal pass before aggregation or summarization. For example, forcing analysis to answer questions like: What happened first? What immediately followed? Which events cluster tightly in time versus disperse? What changes if nothing is reordered except time?
One additional wrinkle is that not all datasets actually encode time in a way that supports this kind of analysis. Some domains record timestamps that reflect administrative events rather than meaningful sequence, and others omit temporal information altogether. In those cases, the first step is not preservation but detection: asking whether temporal relationships are knowable from the data at hand, or whether they are simply being inferred.
In that sense, time feels less like metadata and more like a load-bearing dimension. Once sequence is collapsed into totals or trends, you cannot reliably reconstruct it from summaries alone. Making the act of flattening visible may be the most realistic first step toward teaching AI to recognize when compression itself is the error.
❦❦❦
Grateful you named the gap between clean numbers and real understanding.
That gap is exactly where most AI collaboration breaks down. Thanks for reading!
These prompts and systems are on fire. Totally setting us up for success.
Love to hear it! The prompt templates in the downloadable one pager are ready to copy/paste and customize. Hope they save you tons of time 🔥