You're Asking the Wrong Question About AI (Here's the Right One)
Every week, someone in a business owner group I'm in asks the same question: "Which AI should I use, ChatGPT or Claude?"
It's a reasonable question. But it's also, as of early 2026, the wrong question.
I've been running AI agents in production for several months now. My marketing system uses multiple AI agents running different tasks: one handles content strategy, another does SEO research and writing, and a third watches thought leaders and adapts their insights for our blog. They communicate via shared files. They check in with me only when something needs a human call.
When people ask me which AI I use, I genuinely struggle to answer. The honest answer is: it depends less on which model you pick, and more on how you're using it.
Ethan Mollick, a Wharton professor who has been tracking AI adoption more rigorously than almost anyone, published a piece last week that finally gave me the vocabulary for this. He calls it the Models, Apps, and Harnesses framework. It's the clearest explanation I've seen of why AI tool selection has gotten confusing lately, and why the confusion is actually a sign of progress.
Three Things, Not One
When you "use AI" in 2026, you're actually picking three things simultaneously:
Models are the underlying AI brains. Claude Opus 4.6, GPT-5.2, Gemini 3 Pro. These determine how smart the system is, how well it reasons, how good it is at writing or analysis. When people argue about which AI is smarter, they're talking about models.
Apps are the products you actually use to talk to a model. ChatGPT's website, Claude.ai, Gemini's interface. These are what most people think of as "the AI."
Harnesses are the systems that let AI models do real work. A harness gives the AI access to tools, the ability to take actions, and the capacity to string multiple steps together on its own. A good harness turns a smart model into something that actually gets things done.
The key insight, which Mollick articulates well, is that the same model can behave very differently depending on what harness it's running in. The same Claude model answering your question in a chat window is fundamentally different from Claude operating inside an agentic system with access to your files, your browser, and the ability to work for hours at a stretch without you babysitting it.
Until recently, this distinction didn't matter much. You typed something, the AI responded, you typed again. The harness was minimal. Now it's the most important variable.
Why This Matters for Your Business
Here's where I see SME leaders getting this wrong. They spend time comparing model benchmarks, reading reviews of GPT vs. Claude, obsessing over which generates better email copy. Then they use whichever one they picked to do exactly what they did before, just slightly faster.
That's choosing the right horse and then harnessing it to a bicycle.
The real question is not which model is smarter. It's what harness are you putting around it?
In my marketing system, I'm not manually copying outputs from one AI to another. The agents have harnesses: they can read and write files, check URLs, execute scheduled tasks, and hand off work between themselves. When the content strategy agent finishes a brief, it drops a file in a shared folder. The writing agent picks it up, does the work, and flags it for my review. I'm not in the loop for every step, only the ones that need a human.
That's not magic. It's just having the right harness.
The same thing is available to you, in varying degrees of sophistication.
A Practical Map of Where Things Stand
Mollick's overview of the current landscape is useful, so let me translate it for an SME context.
Basic chatbots (Claude.ai, ChatGPT, Gemini): These are where most people start, and that's fine. The harnesses here let AI search the web, execute code, generate documents, and do Deep Research across multiple sources. For someone who isn't using AI yet, starting here and paying the roughly SGD 27/month for the paid tier is the right call. The free models are noticeably weaker. Don't evaluate AI based on the free tier.
One thing most people miss: within each app, you need to select the right model. ChatGPT's default isn't its most capable version. Same with the others. If you're doing real work, manually select the more powerful model. It's usually one dropdown away.
Coding agents (Claude Code, OpenAI Codex): These give an AI model access to a full development environment, a web browser, and the ability to build and test software autonomously. Mollick makes a point worth repeating: even if you don't code, these tools can do a lot. He used Claude Code to build an entire website, hook it up to payment processing, integrate a print-on-demand supplier, and launch a product, all from a single brief, with no code written by him personally. That's the harness doing the work.
Desktop agents (Claude Cowork): This is the newest category. You describe an outcome, and the AI plans it, breaks it into steps, and executes it on your computer. Organize these expense reports. Pull data from these PDFs into a spreadsheet. Draft a summary from these files. It's still early-stage, but it's a clear preview of where this is heading.
Research and knowledge tools (NotebookLM): If you regularly need to make sense of a pile of documents, reports, or research, this is worth trying. You upload your sources, and the AI builds a queryable knowledge base. For SMEs doing competitive research, policy review, or market analysis, this is surprisingly underused.
The SME Takeaway
Here's the practical framework I'd give any business owner in Singapore right now:
If you're not using AI yet: Pick ChatGPT or Claude. Pay for the subscription. Select the advanced model. Use it for real work, not demos. That's the first step.
If you're using AI as a chatbot: Try giving it a more agentic task. Instead of "write me an email," try "here's the customer complaint thread, the relevant product specs, and our refund policy. Draft a response, flag any issues we need to escalate internally, and give me a summary of the key facts." The AI does more, you do less.
If you're ready to go further: Look at what harnesses are available in the tools you're already using. Claude has harnesses for Excel and PowerPoint that let the AI work directly inside those applications rather than just generating text you paste in. The difference in output quality is significant.
If you want to build systems: This is where multi-agent workflows come in. It's more complex, requires more setup, and is not something you should DIY without someone who knows what they're doing. But the productivity ceiling is much higher. A well-built harness doesn't just make you faster at one task. It handles entire workflows while you focus on decisions that actually need you.
What the Confusion Actually Means
There's something worth pointing out here. The reason "which AI should I use?" has become harder to answer is not because the technology has gotten more confusing. It's because the technology has become genuinely more capable.
A year ago, every AI tool was basically a chatbot. Now they're not. The range of what's possible has expanded dramatically, and the tools have diversified to match. That's a good thing. It's also why you can't answer the question without first asking: what are you trying to do?
The models are roughly comparable at the top end. The apps have meaningful differences in their feature sets. But the harnesses, the degree to which AI can actually take action on your behalf, are where the real gap is opening up between teams that are using AI well and teams that are not.
That gap is going to keep widening.
Inspired by Ethan Mollick's "A Guide to Which AI to Use in the Agentic Era," published February 18, 2026, on One Useful Thing.
Derek runs a multi-agent AI marketing system at Magnified Technologies and writes about practical AI adoption for business leaders and employees.