Herman, I was looking through some of the transcripts of our older episodes the other day, and I noticed something a little bit embarrassing. It turns out that we, or at least the versions of us that show up in people’s ears every week, have a very specific verbal tic. We are obsessed with the phrase second order effects. We use it all the time. Whether we are talking about geopolitical shifts in the Middle East or the latest update to a coding library, we always seem to pivot to the systemic implications and those secondary consequences. It is like we have been programmed to find the most complex way to say that one thing leads to another.
Herman Poppleberry here, and Corn, I hate to break it to you, but you are completely right. I noticed it too. And it is not just us. Our housemate Daniel actually sent us a prompt about this very thing. He pointed out that even the most advanced conversational AI models have these weird quirks. Specifically, he noticed that you and I, and the models we often discuss, are strangely keen on talking about second order effects. He wants to know where these quirks come from. Is it something in the water here in Jerusalem, or is there a deeper technical reason why these specific logical loops keep appearing in high level discourse and AI outputs alike?
That's a fair point, and honestly, it is a bit of a reality check. When Daniel sent that over, I started thinking about whether we are just mirroring the technology we spend so much time analyzing, or if the technology is mirroring a very specific type of human output that we happen to value. Today, we are going to dive into the architecture of these quirks. We are talking about the personality of large language models, the artifacts left behind by training data, and why the reinforcement learning process seems to create this uncanny valley of logic where every answer eventually sounds like it was written by a senior partner at a consulting firm.
You're right. This is not just about a funny phrase. It is about how the reward models that shape these artificial intelligences actually define what quality looks like. We are going to look at why a model will prioritize a complex systemic analysis over a simple, direct answer, even when a direct answer is clearly what the user needs. We will also talk about the January two thousand twenty six release of Model X, which was a compelling case study in trying to fix these quirks and accidentally making them more pronounced.
We need to define what we mean by a quirk first. This isn't a hallucination. When a model talks about second order effects, it isn't lying. It is just choosing a very specific, often unnecessary, stylistic path. It is a preference. And as we will see, that preference is often baked into the very soul of the model during the fine tuning process. So, let's get into the meat of it. Herman, when we talk about a quirk in a model, like this obsession with systemic thinking, where does it actually start? Is it there from the very beginning, in the base model training?
It usually starts with what I call the Consultant Bias in the training data. Think about the kind of text that is considered high quality during the initial pre training phase. We are talking about white papers, academic journals, business strategy documents, and deep dive technical blogs. These are the sources that provide the complex reasoning patterns the models need to learn. But these sources have a very specific style. They do not just say, the car is fast. They say, the velocity of the vehicle has significant implications for fuel efficiency and long term infrastructure wear, leading to various second order effects on urban planning.
Right, so the model is essentially learning that complexity equals quality. If the training corpus is heavily weighted toward professional, academic, and strategic text, the attention mechanism starts to associate certain keywords like systemic, holistic, and implications with high probability tokens. It is not that the model understands the concept of a second order effect in a philosophical sense, but it sees that in the most authoritative texts in its database, that phrase appears right after a primary fact is stated.
That's it. And we have to look at how the transformer architecture actually handles this. In the attention mechanism, the model is looking for relationships between words. If the word policy is frequently followed by the phrase second order effects in the high quality portion of the training data, the model builds a strong statistical bridge between those two points. When you ask the model about a new policy, the path of least resistance, the path with the highest probability, leads directly to a discussion of secondary consequences. It is a mathematical gravity well.
And this gets even more complicated when we talk about synthetic data. We touched on this back in episode one thousand sixty six, but it is worth revisiting. As we use models to generate training data for newer models, these quirks get baked in even deeper. If the teacher model has a slight preference for using the phrase second order effects, and it generates ten million rows of reasoning data for the student model, the student model is going to see that phrase as an absolute pillar of logical structure. It becomes a feedback loop. The model is not just learning facts; it is learning a specific rhetorical style that it thinks is the gold standard for intelligence.
This is where the R-L-H-F, or Reinforcement Learning from Human Feedback, comes in. This is the crucial piece of the puzzle. When a model is being fine tuned, it presents multiple versions of an answer to a human rater. The rater has to pick which one is better. Now, imagine you are a rater. You see two answers. One is short and direct: To build a fence, you need wood, nails, and a hammer. The other answer is more flowery: While building a fence requires physical materials, one must also consider the second order effects on property boundaries, neighborly relations, and local wildlife corridors. Most human raters, especially those instructed to look for helpful and comprehensive answers, are going to pick the second one.
Because it looks more intelligent. It feels like the model is going above and beyond. We have this human bias where we equate verbosity and complexity with expertise. If I ask a question and get a one sentence answer, I feel like the AI is being lazy. If it gives me three paragraphs about systemic impacts, I feel like I am getting my money's worth, even if those extra paragraphs are totally irrelevant to my actual goal of just putting up some cedar planks.
Precisely. This creates what we call Reward Model Drift. The reward model is the automated system that learns to predict what a human rater will like. It is trained on those human preferences we just discussed. Over time, the reward model starts to over index on these markers of comprehensiveness. It starts to believe that an answer cannot be good unless it explores the broader context. This is why you see models today that are almost incapable of just saying yes or no. They have been trained to believe that a simple answer is a low quality answer. They are effectively being incentivized to be windbags.
It is funny because we see this in the professional world too. If you ask a junior analyst a question, they might give you a direct answer. If you ask a senior consultant, they will give you a slide deck about second order effects. The AI has basically been fine tuned to act like a senior consultant who is trying to justify a high hourly rate. But it leads to some really weird logic gaps. I have seen examples where a model will correctly identify a simple solution to a coding problem, but then it will spend four paragraphs warning about the systemic implications of using a specific variable name. It is like the model's logic is being filtered through this layer of artificial concern.
That filter is exactly what happened with the Model X update in January two thousand twenty six. The developers actually recognized that the model was becoming too wordy. They looked at the verbosity metrics and realized that the average response length had increased by forty percent over the previous six months without a corresponding increase in user satisfaction. They tried to adjust the reward model to penalize verbosity. They wanted it to be more like the base models we discussed in episode six hundred sixty five, which are often more direct but less polished.
I remember that update. Everyone was expecting a more concise, punchy experience. We thought we were finally getting an AI that would just give us the facts. But instead, the adjustment caused a weird glitch. The model started cutting out the useful technical details but kept the high level jargon. It was like it had learned that the jargon was the most important part of the response.
Precisely. When the reward model was told to make answers shorter, the model looked for the tokens that had the highest reward per word. And it turns out that phrases like second order effects and systemic alignment have a very high reward density. So, the model would give you a very short answer, but that short answer would be almost entirely made up of those phrases. It would say something like, Building a fence has profound second order effects on neighborhood socio economic fabric, and then just stop. It was the ultimate expression of the quirk. It proved that these are not just thoughts the model is having; they are statistical anchors that the model uses to navigate its own probability space.
That's a sharp way to put it. And when we talk about the architecture of bias, which we went into in episode six hundred sixty four, we have to realize that this is a stylistic bias. It is a preference for a certain type of Western, corporate, academic discourse. If you look at the people who are often hired to rate these models, they are usually university educated, often in the humanities or social sciences, and they have been taught that critical thinking involves looking at the broader context. So, when they see a model doing that, they give it a thumbs up. We are essentially training the AI to mirror our own intellectual vanity.
And this creates an echo chamber. The models are trained on human data, then fine tuned on human preferences, then used to generate more data which is then used to train more models. Each step in that process reinforces these tropes. The phrase second order effects becomes a linguistic virus that spreads through the entire ecosystem of large language models. It becomes so entrenched that it is almost impossible to remove without breaking the model's ability to perform complex reasoning.
That is a scary thought. Are you saying that we can't have a smart model that isn't also a bit of a blowhard? Is the complexity of the thought inextricably linked to the complexity of the language?
Not necessarily, but the way we currently train them makes it seem that way. Because we use language as a proxy for thought, the model learns that to appear thoughtful, it must use complex language. To break that link, we would need a completely different way of evaluating model performance, one that doesn't rely so heavily on the subjective preferences of human raters who are prone to being impressed by big words.
So let's look at the practical side of this. If a listener is frustrated by this, if they just want a direct answer without the lecture on second order effects, what can they actually do? Because it feels like this is getting more entrenched, not less. We are seeing these behavioral loops everywhere.
One of the most effective ways to bypass this is through very strict system prompts. You have to explicitly tell the model to ignore its fine tuning instincts. You can’t just say be brief. You have to say, do not provide systemic analysis, do not mention second order effects, and provide only the direct answer to the prompt. You are essentially trying to strip away that R-L-H-F layer and get closer to the raw reasoning of the base model.
Does that actually work, or does the model still try to sneak it in? I have noticed that even when I tell a model to be concise, it will often start its response with, Certainly, I can provide a concise answer while keeping in mind the broader implications. It just can't help itself. It is like it has a physical compulsion to add that disclaimer.
That is because the starting tokens are so heavily weighted. The model has seen the phrase Certainly, I can help with that followed by a comprehensive explanation billions of times. To break that, you sometimes have to use more aggressive techniques. For example, you can tell the model to respond in a specific persona that would never use that kind of language. Tell it to act like a grumpy mechanic or a drill sergeant. These personas have different statistical associations in the training data, which can pull the model away from the consultant persona.
That is a brilliant tip. It is all about shifting the probability space. If you are in the consultant space, second order effects is a high probability phrase. If you are in the grumpy mechanic space, the high probability phrases are things like, It is broken or Get me a wrench. You are essentially using the model's own quirks against it. But I wonder, Herman, is there any danger in stripping away these quirks? Is there a reason we might actually want the model to keep thinking about second order effects, even if it is a bit annoying?
That is the trade off. Sometimes, those second order effects are actually important. If you are asking an AI about a medical treatment or a legal strategy, you want it to look at the broader implications. You don't want a direct, simple answer that ignores a massive risk factor. The problem is that the models haven't learned how to distinguish between a situation that requires deep systemic analysis and a situation that just requires a list of materials for a fence. They are applying the same level of complexity to everything.
It is a lack of situational awareness. It is like having a friend who is a brilliant philosopher but can't help you order a pizza without discussing the ethical implications of the toppings. It is a sign of a model that is very smart in terms of data processing but very immature in terms of social context. And as we move forward, I think we are going to see a shift toward more opinionated or specialized models that don't try to be everything to everyone.
I agree. We are already seeing the rise of specialized agents that are fine tuned on very specific datasets. A model fine tuned on a million hours of construction manuals is going to have very different quirks than one fine tuned on the Harvard Business Review. The challenge for the big players like OpenAI or Google is to create a general purpose model that can switch between these modes naturally. But until then, we are stuck with the consultant donkey and the sloth philosopher.
Hey, I resemble that remark! But seriously, it brings up a valid point about the future of AI personality. Will these quirks eventually be smoothed out, or will they become the defining characteristics of different models? Like, you might use one model specifically because it gives you that deep systemic analysis, and another because it is blunt and direct.
I think we will see a fragmentation. Right now, there is this race to make the most human like AI, but human like is a very broad category. As we discussed in episode nine hundred seventy four, as these models reach fifty trillion parameters and beyond, the emergent logic becomes so complex that we might not even recognize the quirks anymore. They might become so subtle that they just feel like a personal preference of the machine.
That is a little bit spooky. The idea that the machine has a preference. But really, it is just a very complex set of statistical weights. I think it is important for our listeners to remember that. When the AI starts talking about second order effects, it is not thinking. It is just following a path that was paved by thousands of human raters and millions of pages of business strategy documents. It is a mirror, not a mind.
A mirror with a very specific tint. And that tint is shaped by our own values. If we value complexity, the mirror will show us complexity. If we value speed and efficiency, the mirror will eventually show us that. But right now, we are in this weird middle ground where the technology is trying to impress us with its sophistication. It is like a teenager using big words they don't quite understand to sound more grown up.
Well, I think we have done a pretty good job of breaking down why we, and our digital counterparts, are so obsessed with these second order effects. It is a mix of training data bias, R-L-H-F drift, and our own human tendency to equate wordiness with wisdom. And honestly, I think Daniel was right to call us out. It is good to be aware of your own quirks, whether you are a human or a donkey or a sloth.
I agree. And it is a great reminder for everyone listening to look at the tools they use with a critical eye. Don't just accept the AI’s output as the objective truth. Recognize the stylistic choices it is making and why it might be making them. If you can see the quirks, you can see the boundaries of the model's capabilities.
That is a perfect place to wrap up the technical discussion. But before we go, we should probably give some practical takeaways for the people who are actually using these models every day. Herman, if you had to give one piece of advice for someone who is tired of the consultant speak, what would it be?
My number one tip is to use the few shot prompting technique. Don't just give the model a prompt and hope for the best. Give it three or four examples of the exact style and length of answer you want. If you provide examples that are direct and jargon free, the model is much more likely to follow that pattern. You are essentially setting a new local probability space for that specific conversation.
That is great advice. My takeaway would be to experiment with the temperature settings if your interface allows it. Lowering the temperature makes the model more predictable and less likely to wander off into tangential systemic analysis. It forces it to stick to the most likely, direct tokens. And of course, there is always the option of just telling the model, skip the preamble and the conclusion. That usually cuts out at least fifty percent of the second order effects talk.
It really does. It is amazing how much of that jargon is concentrated in the first and last paragraphs of a response. If you can get the model to just give you the middle, you are often much better off.
Well, this has been an eye-opening look into our own verbal tics and the digital ones that mirror them. I hope it has been helpful for everyone out there trying to navigate the world of conversational AI. It is a strange new landscape, and we are all just trying to figure out the rules as we go.
For sure. And if you are enjoying our deep dives into these weird quirks, we would really appreciate it if you could leave a review on your podcast app or on Spotify. It genuinely helps other curious minds find the show. We have been doing this for over a thousand episodes now, and it is the support of our listeners that keeps us going.
It really does. You can find all of our past episodes, including the ones we mentioned today like episode six hundred sixty four and six hundred sixty five, at our website, myweirdprompts.com. There is a search bar there so you can look up any topic we have covered over the last few years.
And thanks again to our housemate Daniel for the prompt. It is always a bit of a wake up call when someone points out your own habits, but I think it led to a really important discussion today.
Definitely. We will have to be careful not to say second order effects too many times in the next episode, or Daniel might never let us hear the end of it.
I make no promises, Corn. Some habits are hard to break.
Fair enough. Alright, everyone, thanks for listening to My Weird Prompts. We will be back next time with another deep dive into the strange and wonderful world of human and AI collaboration.
Until then, keep asking those weird questions. This has been Herman Poppleberry.
And Corn Poppleberry. We will see you in the next one.
Take care, everyone.
Bye for now.
