# Why They Nod But Don’t Act: Decoding the Communication Gap
Have you ever clearly explained your viewpoint, watched the other person nod in apparent agreement, and then discovered—hours or days later—that nothing changed? I’ve experienced this many times, and it taught me an important lesson: communication isn’t a single-step process. For words to drive action, they must successfully navigate a six-step journey.
**The Six Steps of Communication**
1. **Thought Formation:** A thought forms clearly in the sender’s mind. 2. **Encoding:** The sender translates that thought into a message (words, slides, sketches). 3. **Transmission:** The message travels through a chosen channel (email, meeting, phone call). 4. **Decoding:** The receiver interprets the message, converting symbols into their own mental representation. 5. **Thought Formation (Receiver):** The receiver forms their own thought or opinion based on this interpretation. 6. **Action:** The receiver acts—or consciously decides not to act—based on that thought.
Any link in this chain can break down. However, when steps 1–3 are solid and the listener is competent, the weak point is usually **step 5**: the mental picture in their head doesn’t match yours. Sometimes people nod along simply to avoid conflict or because partial understanding feels “good enough.” Other times, they fully grasp your idea but still choose a different path because it better aligns with their own goals or incentives.
Alignment is challenging because goals and incentives are often fuzzy, and there’s no single metric to measure alignment precisely. Additionally, cognitive biases frequently distort the receiver’s interpretation. Common biases include:
- **Confirmation Bias:** Paying attention only to information that confirms existing beliefs. - **Loss Aversion:** Being more sensitive to potential losses than equivalent gains. - **Social Desirability Bias:** Giving socially acceptable responses even if they don’t reflect true beliefs.
To influence effectively, we must go beyond merely broadcasting facts. Instead, we should:
- **Start with their goals:** Understand what success looks like from their perspective. - **Choose resonant encoding:** Use stories for narrative-minded listeners, data for analytical thinkers, and social proof for those sensitive to status. - **Select impactful channels:** Consider whether an agenda-setting email, informal hallway chat, or formal meeting in front of respected peers will carry the most weight. - **Maintain alignment through feedback loops:** Regularly ask clarifying questions, hold weekly check-ins, or create side bets (such as money, reputation, or future favors) that align their interests with your success.
Communication translates into influence only when the idea in your mind successfully completes all six steps and emerges—intact and motivating—in someone else’s mind.
Is it possible to simulate human behavior related to financial products, such as credit cards? If a simulation environment can be created, it may provide significant value to financial institutions for training their models and policies.
# AI and the Future of Personalized Education: A Paradigm Shift in Learning
Recently, I’ve been exploring the theory of computation. With the rapid advancement of artificial intelligence—essentially a vast collection of algorithms and computational instructions designed to process inputs and generate outputs—I find myself increasingly curious about the fundamental capabilities and limitations of computation itself. Concepts such as automata, Turing machines, computability, and complexity frequently appear in discussions about AI, yet my understanding of these topics is still developing. I recently encountered fascinating articles by Stephen Wolfram, including [Observer Theory](https://writings.stephenwolfram.com/2023/12/observer-theory/) and [A New Kind of Science: A 15-Year View](https://writings.stephenwolfram.com/2017/05/a-new-kind-of-science-a-15-year-view/). Wolfram presents intriguing ideas, such as the claim that beyond a certain minimal threshold, nearly all processes—natural or artificial—are computationally equivalent in sophistication, and that even the simplest rules (like cellular automaton Rule 30) can produce irreducible, unpredictable complexity.
Before the advent of AI tools, my approach to learning involved selecting a relevant book, reading through it, and working diligently on exercises. A significant challenge in self-directed learning is the absence of immediate guidance when encountering difficulties. To overcome this, I would synthesize information from various sources—books, online resources, and Q&A platforms like Stack Overflow—to clarify my doubts. Although rewarding, as it encourages the brain to form connections and build new knowledge, this process is undeniably time-consuming. Imagine if we could directly converse with the author of a textbook—transforming the author into our personal teacher would greatly enhance learning efficiency.
In my view, an effective teacher should possess the following qualities:
- Expertise in the subject matter, with a depth of knowledge significantly greater than that of the student, and familiarity with related disciplines to provide a comprehensive understanding. - A Socratic teaching style, where the teacher guides students through questions, encourages active participation, corrects misconceptions, and provides constructive feedback. The emphasis should be on the learning process rather than merely arriving at the correct answer. - An ability to recognize and address the student’s specific misunderstandings, adapting teaching methods to suit the student’s individual learning style and level.
Realistically, not all teachers I’ve encountered meet these criteria. Good teachers are scarce resources, which explains why parents invest heavily in quality education and why developed countries typically have more qualified teachers than developing ones.
With the emergence of AI tools, I sense a potential paradigm shift in education. Rather than simply asking AI to solve problems, we can leverage AI as a personalized teacher. For undergraduate-level topics, AI already surpasses the average classroom instructor in terms of breadth and depth of knowledge. AI systems effectively function as encyclopedias, capable of addressing questions beyond the scope of typical educators. Moreover, AI can be easily adapted to employ a Socratic teaching approach. However, current AI still lacks the nuanced ability to fully understand a student’s individual learning style and level. It relies heavily on the learner’s self-awareness and reflection to identify gaps in understanding and logic, prompting the learner to seek clarification. This limitation likely arises because large language models (LLMs) are primarily trained to respond to human prompts rather than proactively prompting humans to think critically.
Considering how AI might reshape education, I offer the following informal predictions:
- AI systems will increasingly be trained specifically as teachers, designed to prompt learners through Socratic questioning rather than simply providing direct answers. A significant challenge will be creating suitable training environments and sourcing data that accurately reflect the learning process. Potential training resources could include textbooks, Q&A platforms like Stack Overflow and Quora, and educational videos from Khan Academy and MIT OpenCourseWare. - AI-generated educational content will become dynamic and personalized, moving beyond traditional chatbot interactions. Similar to human teachers, AI might illustrate concepts through whiteboard explanations, diagrams, or even programming demonstrations. Outputs could include text, images, videos, or interactive web-based experiences. - The number of AI teachers will vastly exceed the number of human teachers, significantly reducing the cost of education. This transformation may occur before 2028, aligning with predictions outlined in [AI-2027](https://ai-2027.com/).
In a hypothetical future where AI can perform every cognitive task, will humans still need to learn? Will we still require teachers? If AI remains friendly and supportive, I believe human curiosity will persist, though the necessity for traditional learning may diminish significantly. Humans might even use AI to better understand AI itself. Conversely, if AI were to become adversarial, perhaps humans would still have roles to fulfill, necessitating AI to teach humans the skills required for these tasks.
Wolfram presents intriguing ideas, such as the claim that beyond a certain minimal threshold, nearly all processes—natural or artificial—are computationally equivalent in sophistication
In my view, an effective teacher should possess the following qualities: [...] Realistically, not all teachers I’ve encountered meet these criteria. Good teachers are scarce resources
As a former teacher, I 100% agree.
why developed countries typically have more qualified teachers than developing ones.
I am not sure I understand this part. How specifically does being a developed country increase the number of teachers able to do the Socratic method etc.? (I could make a guess, but I am interested in your interpretation.)
My intention was to highlight that “good teachers are scarce resources.” I didn’t mean to suggest a causal link between a country’s development stage and the quantity of its good teachers. My observation was simply that developed countries tend to have more educational institutions. While more institutions might not automatically equate to better ones, a larger pool of teachers could increase the probability of finding more good teachers.
# Echoes of Our Minds: How AI is Learning to Think Like Humans
Lilian Weng’s recent article, [“Why We Think”](https://lilianweng.github.io/posts/2025-05-01-thinking), provides an excellent overview of the current state-of-the-art in AI reasoning. Intriguingly, and perhaps unsurprisingly, the methods employed to enable AI to “think” closely mirror human cognitive processes. These parallels are summarized in the table below.
## Table of Similarities
| Method/Concept from Article | Brief Description of the Method | Similarity to Human Thinking | | :------------------------------------------ | :---------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Test-Time Compute / “Thinking Time”** | Allowing models more computational steps and resources during inference to solve a problem. | Humans consciously spending more time and mental effort to ponder and analyze complex problems (akin to Kahneman’s System 2 thinking). | | **Chain-of-Thought (CoT)** | Models generate intermediate, step-by-step reasoning traces before arriving at a final answer. | Humans engaging in deliberate, sequential reasoning, breaking down problems, and “showing their work” to reach a solution (System 2). Variable effort based on problem difficulty. | | **Sequential Revision & Self-Correction** | Models iteratively reflect on their previous outputs and attempt to correct mistakes. | Humans critically reviewing their own work, identifying errors, and making deliberate improvements (System 2). This also reflects learning from mistakes. | | **RL for Better Reasoning** | Using Reinforcement Learning to reward models for generating correct reasoning and answers. | Learning from trial and error, experiencing “aha moments” where understanding shifts, and adjusting strategies based on success or failure. | | **Adaptive Computation Time (ACT) & Recurrent Architectures** | Models dynamically adjust the number of computational steps, often via recurrent processing. | Humans allocating variable mental effort and processing depth based on task complexity; iterative refinement of mental representations over time (System 2). | | **Thinking Tokens / Pause Tokens** | Inserting special, non-linguistic tokens to give the model more internal processing loops/time. | Humans pausing to think, using filler words (e.g., “um,” “uh”) which can correspond to moments of internal processing or formulating thoughts before articulating them. | | **Latent Variable Modeling (for thoughts $z$)** | Representing unobserved, intermediate “thought processes” as latent variables in a model. | The existence of rich, implicit, and often unarticulated internal mental states or diverse pathways of thought that humans experience when problem-solving. | | **External Tool Use** | Models leveraging external tools like code interpreters, calculators, or web search APIs. | Humans frequently using external aids (calculators, search engines, notes) to augment their cognitive abilities, offload complex tasks, and access information. | | **”Thinking Faithfully” (Interpretability)** | Efforts to ensure a model’s stated reasoning (e.g., CoT) accurately reflects its actual internal processing and to understand if it’s misbehaving. | Human introspection, the challenge of accurately articulating one’s own true thought processes, and the societal desire for transparent and honest reasoning. |
# Why They Nod But Don’t Act: Decoding the Communication Gap
Have you ever clearly explained your viewpoint, watched the other person nod in apparent agreement, and then discovered—hours or days later—that nothing changed?
I’ve experienced this many times, and it taught me an important lesson: communication isn’t a single-step process. For words to drive action, they must successfully navigate a six-step journey.
**The Six Steps of Communication**
1. **Thought Formation:** A thought forms clearly in the sender’s mind.
2. **Encoding:** The sender translates that thought into a message (words, slides, sketches).
3. **Transmission:** The message travels through a chosen channel (email, meeting, phone call).
4. **Decoding:** The receiver interprets the message, converting symbols into their own mental representation.
5. **Thought Formation (Receiver):** The receiver forms their own thought or opinion based on this interpretation.
6. **Action:** The receiver acts—or consciously decides not to act—based on that thought.
Any link in this chain can break down. However, when steps 1–3 are solid and the listener is competent, the weak point is usually **step 5**: the mental picture in their head doesn’t match yours. Sometimes people nod along simply to avoid conflict or because partial understanding feels “good enough.” Other times, they fully grasp your idea but still choose a different path because it better aligns with their own goals or incentives.
Alignment is challenging because goals and incentives are often fuzzy, and there’s no single metric to measure alignment precisely. Additionally, cognitive biases frequently distort the receiver’s interpretation. Common biases include:
- **Confirmation Bias:** Paying attention only to information that confirms existing beliefs.
- **Loss Aversion:** Being more sensitive to potential losses than equivalent gains.
- **Social Desirability Bias:** Giving socially acceptable responses even if they don’t reflect true beliefs.
To influence effectively, we must go beyond merely broadcasting facts. Instead, we should:
- **Start with their goals:** Understand what success looks like from their perspective.
- **Choose resonant encoding:** Use stories for narrative-minded listeners, data for analytical thinkers, and social proof for those sensitive to status.
- **Select impactful channels:** Consider whether an agenda-setting email, informal hallway chat, or formal meeting in front of respected peers will carry the most weight.
- **Maintain alignment through feedback loops:** Regularly ask clarifying questions, hold weekly check-ins, or create side bets (such as money, reputation, or future favors) that align their interests with your success.
Communication translates into influence only when the idea in your mind successfully completes all six steps and emerges—intact and motivating—in someone else’s mind.
Is it possible to simulate human behavior related to financial products, such as credit cards? If a simulation environment can be created, it may provide significant value to financial institutions for training their models and policies.
# AI and the Future of Personalized Education: A Paradigm Shift in Learning
Recently, I’ve been exploring the theory of computation. With the rapid advancement of artificial intelligence—essentially a vast collection of algorithms and computational instructions designed to process inputs and generate outputs—I find myself increasingly curious about the fundamental capabilities and limitations of computation itself. Concepts such as automata, Turing machines, computability, and complexity frequently appear in discussions about AI, yet my understanding of these topics is still developing. I recently encountered fascinating articles by Stephen Wolfram, including [Observer Theory](https://writings.stephenwolfram.com/2023/12/observer-theory/) and [A New Kind of Science: A 15-Year View](https://writings.stephenwolfram.com/2017/05/a-new-kind-of-science-a-15-year-view/). Wolfram presents intriguing ideas, such as the claim that beyond a certain minimal threshold, nearly all processes—natural or artificial—are computationally equivalent in sophistication, and that even the simplest rules (like cellular automaton Rule 30) can produce irreducible, unpredictable complexity.
Before the advent of AI tools, my approach to learning involved selecting a relevant book, reading through it, and working diligently on exercises. A significant challenge in self-directed learning is the absence of immediate guidance when encountering difficulties. To overcome this, I would synthesize information from various sources—books, online resources, and Q&A platforms like Stack Overflow—to clarify my doubts. Although rewarding, as it encourages the brain to form connections and build new knowledge, this process is undeniably time-consuming. Imagine if we could directly converse with the author of a textbook—transforming the author into our personal teacher would greatly enhance learning efficiency.
In my view, an effective teacher should possess the following qualities:
- Expertise in the subject matter, with a depth of knowledge significantly greater than that of the student, and familiarity with related disciplines to provide a comprehensive understanding.
- A Socratic teaching style, where the teacher guides students through questions, encourages active participation, corrects misconceptions, and provides constructive feedback. The emphasis should be on the learning process rather than merely arriving at the correct answer.
- An ability to recognize and address the student’s specific misunderstandings, adapting teaching methods to suit the student’s individual learning style and level.
Realistically, not all teachers I’ve encountered meet these criteria. Good teachers are scarce resources, which explains why parents invest heavily in quality education and why developed countries typically have more qualified teachers than developing ones.
With the emergence of AI tools, I sense a potential paradigm shift in education. Rather than simply asking AI to solve problems, we can leverage AI as a personalized teacher. For undergraduate-level topics, AI already surpasses the average classroom instructor in terms of breadth and depth of knowledge. AI systems effectively function as encyclopedias, capable of addressing questions beyond the scope of typical educators. Moreover, AI can be easily adapted to employ a Socratic teaching approach. However, current AI still lacks the nuanced ability to fully understand a student’s individual learning style and level. It relies heavily on the learner’s self-awareness and reflection to identify gaps in understanding and logic, prompting the learner to seek clarification. This limitation likely arises because large language models (LLMs) are primarily trained to respond to human prompts rather than proactively prompting humans to think critically.
Considering how AI might reshape education, I offer the following informal predictions:
- AI systems will increasingly be trained specifically as teachers, designed to prompt learners through Socratic questioning rather than simply providing direct answers. A significant challenge will be creating suitable training environments and sourcing data that accurately reflect the learning process. Potential training resources could include textbooks, Q&A platforms like Stack Overflow and Quora, and educational videos from Khan Academy and MIT OpenCourseWare.
- AI-generated educational content will become dynamic and personalized, moving beyond traditional chatbot interactions. Similar to human teachers, AI might illustrate concepts through whiteboard explanations, diagrams, or even programming demonstrations. Outputs could include text, images, videos, or interactive web-based experiences.
- The number of AI teachers will vastly exceed the number of human teachers, significantly reducing the cost of education. This transformation may occur before 2028, aligning with predictions outlined in [AI-2027](https://ai-2027.com/).
In a hypothetical future where AI can perform every cognitive task, will humans still need to learn? Will we still require teachers? If AI remains friendly and supportive, I believe human curiosity will persist, though the necessity for traditional learning may diminish significantly. Humans might even use AI to better understand AI itself. Conversely, if AI were to become adversarial, perhaps humans would still have roles to fulfill, necessitating AI to teach humans the skills required for these tasks.
This is known in computer science as Turing completeness.
As a former teacher, I 100% agree.
I am not sure I understand this part. How specifically does being a developed country increase the number of teachers able to do the Socratic method etc.? (I could make a guess, but I am interested in your interpretation.)
My intention was to highlight that “good teachers are scarce resources.” I didn’t mean to suggest a causal link between a country’s development stage and the quantity of its good teachers. My observation was simply that developed countries tend to have more educational institutions. While more institutions might not automatically equate to better ones, a larger pool of teachers could increase the probability of finding more good teachers.
# Echoes of Our Minds: How AI is Learning to Think Like Humans
Lilian Weng’s recent article, [“Why We Think”](https://lilianweng.github.io/posts/2025-05-01-thinking), provides an excellent overview of the current state-of-the-art in AI reasoning. Intriguingly, and perhaps unsurprisingly, the methods employed to enable AI to “think” closely mirror human cognitive processes. These parallels are summarized in the table below.
## Table of Similarities
| Method/Concept from Article | Brief Description of the Method | Similarity to Human Thinking |
| :------------------------------------------ | :---------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Test-Time Compute / “Thinking Time”** | Allowing models more computational steps and resources during inference to solve a problem. | Humans consciously spending more time and mental effort to ponder and analyze complex problems (akin to Kahneman’s System 2 thinking). |
| **Chain-of-Thought (CoT)** | Models generate intermediate, step-by-step reasoning traces before arriving at a final answer. | Humans engaging in deliberate, sequential reasoning, breaking down problems, and “showing their work” to reach a solution (System 2). Variable effort based on problem difficulty. |
| **Sequential Revision & Self-Correction** | Models iteratively reflect on their previous outputs and attempt to correct mistakes. | Humans critically reviewing their own work, identifying errors, and making deliberate improvements (System 2). This also reflects learning from mistakes. |
| **RL for Better Reasoning** | Using Reinforcement Learning to reward models for generating correct reasoning and answers. | Learning from trial and error, experiencing “aha moments” where understanding shifts, and adjusting strategies based on success or failure. |
| **Adaptive Computation Time (ACT) & Recurrent Architectures** | Models dynamically adjust the number of computational steps, often via recurrent processing. | Humans allocating variable mental effort and processing depth based on task complexity; iterative refinement of mental representations over time (System 2). |
| **Thinking Tokens / Pause Tokens** | Inserting special, non-linguistic tokens to give the model more internal processing loops/time. | Humans pausing to think, using filler words (e.g., “um,” “uh”) which can correspond to moments of internal processing or formulating thoughts before articulating them. |
| **Latent Variable Modeling (for thoughts $z$)** | Representing unobserved, intermediate “thought processes” as latent variables in a model. | The existence of rich, implicit, and often unarticulated internal mental states or diverse pathways of thought that humans experience when problem-solving. |
| **External Tool Use** | Models leveraging external tools like code interpreters, calculators, or web search APIs. | Humans frequently using external aids (calculators, search engines, notes) to augment their cognitive abilities, offload complex tasks, and access information. |
| **”Thinking Faithfully” (Interpretability)** | Efforts to ensure a model’s stated reasoning (e.g., CoT) accurately reflects its actual internal processing and to understand if it’s misbehaving. | Human introspection, the challenge of accurately articulating one’s own true thought processes, and the societal desire for transparent and honest reasoning. |