I’ve been thinking about AutoGPT for a couple of weeks now, and I think it might be the most important AI development since ChatGPT itself. But there’s a difference between being excited about something in theory and actually putting it to work. So this week, I did exactly that.
The Task: Every Museum in the World, Indexed
Here’s what I wanted to test. Not “write me a poem” or “summarize this article” — I wanted to see if AutoGPT could handle a real, multi-step, commercially useful project. Something that would normally take a team of researchers weeks, maybe months.
The brief was pretty simple:
- Create an index of every museum in the world.
- Include each museum’s web address and physical address.
- Include hours of operation.
- Include the homepage’s featured image.
- Write a description — no longer than three paragraphs — about each museum.
That’s it. Five tasks. But if you think about the actual WORK involved — scraping directories, cross-referencing data, verifying addresses, pulling images, writing thousands of unique descriptions — you’re talking about a project that would cost tens of thousands of dollars in human labor. Minimum.
I’d put the resultant index live at a domain I own. A complete, searchable, global museum directory built almost entirely by AI.
AutoGPT’s First Move Was.. Impressive
What’s fascinating about AutoGPT isn’t just what it does — it’s how it THINKS about what it does. Here’s what it came back with as its initial plan:
- Use search engines and directories to search for museums around the world.
- Collect museum name, physical address, website URL, and hours of operation to add to the index.
- Verify all found information is correct and valid.
- Write the collected data to a JSON file suitable for index searches.
That’s a solid project plan. It didn’t just start randomly grabbing data — it outlined a methodology. Search, collect, verify, structure. Any project manager would approve that workflow.
Then it started evaluating its own code and making suggestions for improvement. It recommended using the pathlib library for better file path handling, adding docstrings for readability, using list comprehensions to simplify data transformation, and implementing a more modular architecture. It was essentially doing its own code review before I even asked.
Why This Matters Beyond Museums
Look, the museum index is cool. But here’s what I’m actually excited about — this is a LEAD GENERATION engine.
Think about it. If AutoGPT can build a comprehensive index of every museum in the world — with verified contact information, websites, physical addresses, operating hours — then it can do the same for every dentist, every law firm, every restaurant, every hotel, every real estate agency. Every industry vertical with a digital footprint becomes indexable.
If we crack this — and I’m pretty confident GPT-4 will crack it — anyone with access to this kind of tooling has one hell of a lead gen platform. The commercial applications are enormous.
The Auto Interface I’ve Been Wanting
I’ve written before about wanting to build autonomous AI systems that can handle real workflows. What I found this week is that people are already building the auto interface engine I had in mind. The AutoGPT ecosystem is moving FAST. I came across a thread cataloging dozens of AutoGPT tools and applications, and I found nearly every one of them useful. Not theoretical, not “coming soon” — useful right now.
This is the pattern I keep seeing with AI development in 2023. The gap between “interesting concept” and “working tool” is shrinking from months to days. By the time you’ve finished writing about something being possible, someone’s already shipped it.
What’s Actually Hard
I don’t want to oversell this. AutoGPT isn’t magic. It burns through API credits pretty fast when it’s running autonomously. It sometimes gets stuck in loops. The verification step — making sure the data it’s collecting is actually correct — is still the weakest link. AI is great at gathering information and terrible at knowing when that information is wrong.
But here’s the thing — those are engineering problems, not fundamental limitations. The core capability is there. An AI agent that can take a complex, multi-step research task and execute it autonomously, with its own project plan, its own code improvements, and structured output? That EXISTS now. Right now.
The Takeaway
We’re past the point of asking whether AI can do useful work autonomously. It can. The question now is how fast the tooling improves and how quickly people figure out the commercial applications.
I’m going to keep pushing on the museum index project — I want to see how complete and accurate the output actually gets. But even if it only gets 70% of the way there, the remaining 30% of human cleanup is still a fraction of doing the whole thing manually.
If you’re building anything that involves data collection, research, or content generation at scale, you need to be experimenting with AutoGPT right now. Not next quarter. Not when it’s “more mature.” Now. Because the people who figure out the workflows first are going to have a pretty significant head start.
