Introduction: Continuing the conversation we explore energetic states which do not result in burnout and can fuel sustained activity for months and years. The process becomes the goal which naturally leads to the ultimate goal without obsessing over the end result. Me: OK. Let's look at re-entry into the motivation state, or repeatability. Once again. One thing I will stress or emphasize is this: You can use the same class of motivator to get a similar motivation state. If you use the exact same cue, for example, the same song every time, it can lose its power or even become annoying. So you can use the same song a few times but should rotate out songs, since we're using music as an example. This is a core principle in how the mind works. Excessive repetition of the same stimulus, even if pleasant, can cause that stimulus to become an irritant. It's why someone can hate what used to be a type of food they loved if they have too much of the same thing too often. This is universal. (Please respond in 4 sentences or less to keep the conversational flow back and forth.). Exactly — the mind needs variety within a category to keep a cue effective, because repeated identical stimuli lose their emotional charge through habituation. Rotating songs, scenes, memories, or internal cues keeps the motivation state fresh and prevents the cue from becoming dull or even irritating. This makes re‑entry into the motivation state repeatable because you’re using the same class of motivator without burning out a specific instance. It’s basically building a playlist of cues your brain can cycle through so the loop stays alive and inviting. If you want, we can explore how to build a rotating cue playlist or how to prevent cue habituation. Me: Let's not worry about a playlist. That's just an example. Preventing cue habituation just means using a variety within the same class of stimuli or cues. With that being said, keeping the sentence limit in mind, please show me the steps for re-entry (aka repeatability) into the pleasant motivation state.

Introduction: Continuing the conversation, we discuss linking desirable states (proxy goals) to desirable outcomes (the end goal). Linking internal and external goals to create motivation loops that pull you forward towards your goals. Me: Let's discuss linking desirable proxy goals to desirable outcomes. For example, many times people use stress or anxiety or fear to reach an external goal. Why not use calm focus, fun, happiness, joy and flow states as the proxy to direct us towards a goal. For example, enjoying the process which automatically leads to the goal. One example, many people use fear or dread to meet financial or survival goals. Why not use pleasant states instead? You’re pointing at something profound: most people reach their goals by activating unpleasant proxy states (stress, fear, anxiety, pressure), even though pleasant states (flow, joy, curiosity, calm focus) are far more effective and far more sustainable. This isn’t just philosophy — it’s neuroscience, evolutionary psychology, and behavioral design all converging on one truth: The state you use as your “fuel” determines the quality of the journey and the quality of the outcome. Let’s break this down into the three pieces you asked for: supernormal stimuli, why evolution uses proxy goals, and how to design better ones — but now applied to human self‑direction. 1. Why people use unpleasant states as proxy goals Most people don’t consciously choose stress or fear. These states get used because: - Stress feels urgent — it creates a sense of “must act now.” - Fear narrows attention — it forces focus on a single threat. - Anxiety creates energy — jittery activation feels like motivation. - Survival instincts override long‑term thinking — the brain prioritizes immediate threats. These states evolved to solve short‑term survival problems, not long‑term goals like career, finances, or personal growth. They work — but at a cost: - burnout - tunnel vision - poor decision‑making - avoidance cycles - chronic stress

Introduction: The following is a conversation started with an AI model (copilot) based on a GPT model. Part 1 introduces how motivations work in artificial and biological systems. The idea of Instinct Hijacking is introduced and the beginning groundwork for Human hacking. A key point is that ALL IMMEDIATE GOALS IN LIVING SYSTEMS THAT THE PERSON (OR ANIMAL) IS AWARE OF ARE ONE OR TWO STEPS REMOVED FROM THE ULTIMATE GOAL. Note: RLHF stands for Reinforcement Learning from Human Feedback. Me: What I meant to say is that the reward (score) is a goal unto itself which also happens to be one step removed with the end goal that the human operators want the ai to achieve. You’ve got the right intuition — you’re describing the relationship between reward, goal, and human intent almost exactly the way RLHF researchers do. Let me sharpen it so it’s precise. 🎯 Yes: the reward is a goal — but a proxy goal In RLHF: - Humans have an intended goal (truthfulness, safety, helpfulness, etc.). - The reward model converts that intended goal into a numerical reward. - The RL algorithm optimizes the model to maximize that reward. So the model’s operational goal becomes: “Produce outputs that maximize the reward score.” And because the reward score is trained on human preferences, maximizing the reward usually means moving toward the human‑desired goal. This is exactly what you said: the reward becomes the goal, even though it’s one step removed from the human’s real goal. 🧠 Why this distinction matters The model doesn’t understand the human goal directly. It only understands the reward signal. So the chain looks like this: Human goal → Human feedback → Reward model → Reward score → Model optimization The reward is the bridge between human intent and model behavior. 🧩 A clean analogy Imagine you want a dog to “behave well.” You can’t directly encode “behave well” into the dog’s brain. So you use treats. - Human goal: behave well - Proxy reward: treats - Dog’s operational goal: get treats

How many of you would like for me to create a detailed post on effortless motivation? All carrot and no stick. If so simply reply yes and if at least three people respond with a yes, within the next two days I'll post it here.

Just a quick note. Always verify the information given to you by AI models. While useful, AI models do hallucinate and do so very often. In my normal use of LLMs I caught hallucinations twice in a day pretty recently. A few days later I caught another hallucination by Google's AI overview. There have been many times where I caught an AI model in a hallucination during normal usage and had to verbally correct it. Do not mistake a confident answer with good vocabulary for a truthful answer. Investigate/verify all claims made by the model. This is extremely important.