See, we were loyal users of ChatGPT. Give us every chance. Update us. We registered. We built a workflow around it and trained the entire team on it. But somewhere between the forgotten context and all the reviews somehow making it worse, we had to have The Talk. It wasn’t us. It was GPT.
Here’s the thing though. When we started digging into why it wasn’t working, we realized that the problem was bigger than ChatGPT getting worse. The problem was that we were choosing AI tools completely wrong.
Short Memory and Short Attention Span
Here’s a thought: the most intelligent model wins. Better model, better output, better results. Choose the one with the largest number of parameters, build your workflow around it, the work done.
It sounds reasonable. It’s not right either.
We used ChatGPT as our daily driver for months. Built-in warnings. He coached the team. They all entered. And memory was like talking to someone who forgets your name every five minutes. You can briefly create more than ten messages, and lose the first seven. The instructions he had been following were ignored. The content should have kept the three messages that disappeared from the conversation. Getting angry.
Claude captures the context better, but the real change is custom skills: it teaches you reusable behavior in specific situations. We set ours up so that “write the client brief” triggers our template, tone, and structure. “Make a content update” follows our actual process. It is not a common assumption. AI learns your shorthand, and that includes. ChatGPT is never smart if you use it a lot. Claude does it.
Every GPT Review Feels Like a Downgrade
There’s a special kind of frustration that comes with watching a product you rely on get worse all the time. GPT-4’s cleanly managed operations began to deteriorate after the update. Every update felt less like an improvement and more like someone quietly released something overnight.
Meanwhile, other LLMs (including Claude) are making significant, tangible improvements with each release. The gap was unbridgeable. It was expanding.
But here’s something no one is talking about enough: AI models converge. Immediately. GPT-4, Claude, Gemini. They can all write a decent email, summarize a document, and pretend they understand your brand voice. The raw intelligence gap between boundary models is, in most real business use cases, ineffective.
Which explains the model is no longer a product. A product is a product. We call this The Wrapper Problem. Most AI tools are the same brain in different clothes. The model makes assumptions. The product just… wraps it up. A chat window here, a dark mode there, maybe a plugin marketplace that nobody uses. It’s like choosing a car based on the engine sheet and ignoring the fact that one of them doesn’t have a steering wheel.
The groups found it The Wrapper Problem early on, that they stop choosing engine-based AI tools and start choosing them based on the car, that’s where they’re going to get to.
Claude Actually Does Things on Your Computer
ChatGPT is a very smart text box. You type, it speaks, you copy-paste the output somewhere useful and spend an hour formatting it. That’s all. It lives in its app and is perfectly happy to stay there.
Claude, through Cowork and Claude Code, actually builds things. Presentations. Documents. Drawings. The code then runs. I built a fully functional test app in about 20 minutes, with the agent taking care of all the steps for me. There is no developer. No sprint planning. No waiting for two weeks. Twenty minutes from an idea to a working product. Try doing that in ChatGPT.
For a group of marketers and strategists (not developers), this is the difference between “here’s a script you can use” and “here’s the finished thing.” One is to save you drafts. One saves you an hour.
You don’t need to be an expert to have Claude do technical things for you. It meets you where you are. ChatGPT expects you to bridge the gap yourself.
How to Practically Test AI Tools
If you are making this decision right now, stop comparing models. Start comparing products. Specifically:
Test with real work, not party tricks. Everyone demos well with “write me a sunset poem.” Try “creating a competitive analysis of our Q3 voice in our standard format using our brand voice.” This is where the difference is embarrassing.
Count the steps between “I need this” and “it’s done.” Can the tool deliver actual deliverables, or does it create a draft and spend an hour turning it into a deliverable? A few steps win. Always.
Check if it works for a group or just one person. A great tool for you but it can’t work with the productivity island. Good for solo work. It doesn’t matter to the agency.
Watch what they post, not what they announce. ChatGPT continued to announce things. Claude continued to send us things that we actually used. There is a difference, and it seems about three weeks of daily use.
The Takeaway
We didn’t switch from ChatGPT to Claude because of the benchmark. We have changed because one tool has been making our team go slower and the other has been making us faster.
The model wars are coming together. The brand wars have just begun. And right now, Claude is winning that race not because it’s brilliantly integrated, but because it’s built around a simple idea: AI should do work, not just talk about it.
Choose the tool that gets the job done. He is not the one who starts the conversation.
