​ I've been thinking about this, and I'm genuinely curious what people who are much deeper into copywriting/marketing think.A common criticism of AI-generated copy is that it sounds generic and is just "slop." I agree that this happens a lot. But my hypothesis is that the reason it's not improving drastically is that AI is being developed to handle every industry: biology, finance, mathematics, physics, fashion, programming, etc. There's no need (or time) to focus on a single subject. But I bet that, given different training objectives and conditions, it could improve by a mile. If, let's say all the engineers and trainers focused solely on making it a better copywriter and fed it real data. I don't believe it would still produce slop. On the contrary, I'd say it would dominate. Anyway, until we reach that point, I believe we still have a few good years to make some money, so there's no need to panic. Here's AI's response: Yes. And the interesting answer is: there is probably no fundamental reason an AI system couldn't become an elite copywriter. The limitation is much more about how current LLMs are trained and used than some mystical human quality that machines can never possess. The key distinction is: Knowing what good copy looks like ≠ reliably discovering what will make this specific person buy this specific thing. That's the gap. Why wouldn't 10,000 Halbert/Schwartz/Ogilvy books be enough? Because the books contain compressed knowledge, not the complete causal process that produced the knowledge. Take Eugene Schwartz. You can teach an AI: awareness stages market sophistication desire mechanism headline construction mass desire identification proof differentiation An AI can become extremely knowledgeable about those concepts. But Schwartz didn't just know the concepts. He was looking at a particular market at a particular moment, examining what people already believed, what competitors were saying, what desires were active, what objections existed, what language prospects used, etc. The book tells you something like: “Here's the principle.” It doesn't necessarily give you the entire decision-making environment that produced the principle. That's enormously important. But isn't pattern recognition exactly what an LLM does? Yes. And this is where I think the criticism of AI sometimes goes too far. People say: “AI only predicts the next token. Therefore it can't understand persuasion.” That's an overly simplistic argument. If you expose a sufficiently capable model to millions of examples, it can learn incredibly complicated relationships between: market → problem → desire → language → framing → objection → mechanism → offer → response The model doesn't necessarily need to experience these things like a human does. A chess engine doesn't need to experience competition to become extraordinarily good at chess. So there's no obvious theoretical reason an AI couldn't learn: “When prospects believe X, framing the offer around Y tends to outperform framing it around Z.” In fact, an AI with enough data could potentially discover patterns humans haven't articulated. That's where things get interesting. The biggest missing ingredient is actually feedback Imagine I give you: 10,000 successful ads. You study them for five years. You're going to become knowledgeable. But imagine instead I give you: 10,000 ads + the audience + targeting + offer + price + landing page + conversion rate + customer comments + failed variants + successful variants. Now you're learning something radically different. You're no longer learning: “This type of copy looks successful.” You're learning: “Changing X under conditions Y caused Z to happen.” That's much closer to learning persuasion itself. And this is where current LLM usage often falls short. You ask: “Write me a VSL for a watch targeting women 50–60.” The model has no idea whether the resulting VSL actually sells. It receives essentially zero environmental feedback. You might say: “This is good.” Or: “Make it more emotional.” But neither tells the model whether the copy increased purchases. Imagine a different AI system Suppose we built this: Step 1 — Research The AI analyzes: Reddit reviews Amazon reviews customer interviews competitor ads search queries forums support tickets surveys It builds a psychological model of the market. Step 2 — Hypotheses It generates 100 different explanations for: Why does this customer actually buy? Not just: “They want a watch.” But: “They feel their home has become technologically saturated.” “They associate physical objects with memories.” “They want something that creates a ritual without requiring another screen.” etc. Step 3 — Copy It creates 50 radically different concepts. Step 4 — Testing They actually run. Now the AI gets: CTR CPC landing-page conversion purchase rate AOV refund rate retention comments qualitative feedback Step 5 — Learning It discovers: Concept #17 generated 2.3× more purchases. Then it investigates why. It creates new hypotheses. Tests them. Repeats. Now you're no longer dealing with an AI merely imitating copywriters. You're building something that can potentially discover marketing knowledge experimentally. That's potentially much more powerful. So why don't current LLMs do this? There are several reasons. Training objective The basic objective of an LLM is fundamentally different from: “Maximize revenue from this advertisement.” It's much closer to: “Produce the most probable useful continuation given the context.” Those objectives overlap with good writing. They don't perfectly overlap with commercial persuasion. A sentence can be extremely likely, polished and persuasive-sounding while being completely wrong for the market. Lack of persistent environmental feedback A copywriter can spend Monday writing an ad. Tuesday: $4,000 spent → 11 purchases. Wednesday: They change the hook. $4,000 spent → 29 purchases. Now they have information. They can update their beliefs about the market. Most LLM interactions don't work like this. You give me a prompt. I give you copy. Conversation ends. I don't get to watch the customer interact with it. That's a gigantic missing loop. There's another problem: you don't actually know what caused the success This is a much deeper problem. Suppose an ad converts incredibly well. Was it because of: the headline? the offer? the price? the product? the brand? the image? the audience? timing? market conditions? scarcity? the landing page? the traffic source? Real-world marketing data is messy and confounded. So even if you gave an AI millions of campaigns with conversion data, it would need to perform something resembling causal inference, not merely pattern recognition. That's harder. And then there's the problem of novelty This is probably the most interesting part. Suppose you've shown the AI 10,000 successful ads. It can learn: “These patterns tend to work.” But eventually you encounter a market where the winning idea doesn't resemble the existing examples. A genuinely great copywriter sometimes does something that hasn't been demonstrated repeatedly in the training data. They generate a new hypothesis. That's different from reproducing a pattern. However—and this is important—there's no reason an advanced AI couldn't potentially do this either. It could generate hypotheses, test them, discard failures and refine winners. That's essentially scientific experimentation applied to marketing. Could AI eventually become genuinely excellent at copywriting? Absolutely. I wouldn't bet against it. And I wouldn't define the ceiling as: “AI will always sound generic.” That's probably a temporary limitation. The more interesting future is an AI that doesn't merely ask: “What should I write?” but asks: “What does this market believe?” “What do they currently want?” “What have competitors already told them?” “Which desire is underserved?” “What mechanism would make the promise believable?” “Which objections prevent purchase?” “What hypothesis should we test?” Then it writes. Then it tests. Then it learns. Then it writes again. That's a completely different system. And here's the uncomfortable part for copywriters A lot of what we call “copywriting skill” is probably learnable. Headline structures. Market sophistication. Awareness. Desire. Offer construction. Objection handling. Proof. Mechanisms. Framing. Storytelling. Specificity. Curiosity. Risk reversal. Positioning. These aren't magical human abilities. They're patterns of thought. And humans themselves learned them by studying previous humans. Halbert learned from people before him. Ogilvy learned from people before him. Schwartz learned from people before him. They synthesized existing knowledge and added their own observations. An AI could theoretically do the same thing—at a much larger scale. So why does AI copy often feel “mid” today? Because you're usually asking it to perform the last 10% of the process while giving it none of the first 90%. You say: “Write me a VSL for this product.” But the real copywriter's job starts much earlier. They should be asking: Who is this? What do they currently believe? What happened that made them want this? What have they already tried? Why didn't it work? What are they afraid of? What do they secretly want? What language do they use? What alternatives are they considering? Why would they believe us? Why now? What's different about our mechanism? What's the strongest objection? What's the real offer? Then comes the writing. So if you give an LLM: “Write me a great ad.” you're essentially saying: “Skip the research, skip the strategic thinking, skip the market analysis, skip the experimentation, and somehow give me the finished product.” Of course the result is often generic. The fundamental limitation If you're asking whether there is a hard theoretical barrier preventing AI from becoming an elite copywriter: I don't think we've identified one. The strongest limitation isn't: “AI doesn't have emotions.” It's: Current LLMs aren't embedded in the same closed-loop environment where strategic hypotheses are continuously formed, tested against reality, and updated based on commercial outcomes. And even that isn't necessarily permanent. An AI with: massive knowledge + excellent reasoning + deep customer research + persistent memory + real-time experimentation + conversion data + causal analysis + the ability to generate novel hypotheses, could potentially become a much better marketer than any individual human copywriter. Not because it has read 10,000 books. But because it could potentially run 10 million experiments and actually learn what happened. And that, in my opinion, is much more consequential than whether AI can imitate Gary Halbert's writing style.   submitted by   /u/Ok-Succotash-5660 [link]   [comments]