In 2025, it seems like we all need to clearly state our stance on the role of AI in any creative, analytical, or administrative work. My direct statement is that I don’t endorse the use of AI tools to replace the expertise of readily-available humans.
But, there are some qualifications in that statement, so let’s unpack them.
AI tools or Chat GPT?
First off, what are AI tools and how are they different from Chat GPT, Copilot, Bard, and similar “general purpose” AI tools.
Machine learning and artificial intelligence are legitimate tools that can perform narrowly-defined tasks with pretty good accuracy, especially if they are carefully trained for the task they are applied to. In image processing, facial recognition algorithms are good enough to accurately apply silly masks and filters on video chat apps. In data processing, algorithms can perform multiple regressions and ANOVAs on huge data sets, find patterns, and find best-fit curves. In text generation, … well, auto-correct and text suggestions can accurately predict what word or phrase I want to use about 25% of the ducking time.
An AI- or ML-based tool is exactly that: a tool. And, just like a hammer is a tool that excels at the task it was designed for and fails miserably at other task types, general-purpose and generative AIs have a tendency to fail spectacularly when they are asked to do something other than very general content generation.
What about prompt engineering?
I’ve worked with some people who are great at developing complex prompts that cause an AI tool to deliver the type of content they need for a task.
That is a human expertise. That expertise has value.
I can prompt stable diffusion to generate images that serve my non-commercial, non-professional needs. I can prompt Chat GPT to create something more usable than lorem ipsum. But, I do not have that degree of prompt engineering expertise for the same reason that I can’t design a web app in C#—I have chosen to develop different expertises.
Why is expertise important?
In paid and organic marketing, AI companies and consultants present the tool as a replacement for some human expertise. Often, it’s in other language, like
- Use AI to write your website [implied: instead of hiring writers, a developer, a graphic designer, etc.]
- Use AI to write your emails [implied: instead of spending that time yourself]
- Use AI to plan your next event [implied: instead of working with a planner]
- Use AI to invest in crypto [implied: instead of watching my other YouTube videos about doing it yourself]
At every turn, AI is used to reduce time spent on other tasks. But, let’s unpack that sales pitch: whose time?
When I see other professionals using Chat GPT to write their emails (especially cold-call sales emails), they are reducing the time spent applying their sales and communication expertise to solve a problem.
When I see other professionals using Copilot to write SEO content titles, they are reducing the time spent applying their SEO writing expertise to solve a problem.
When developers and software engineers show me products made entirely with AI, they have reduced the time spent applying their programming expertise to solve a problem.
That’s bad under two conditions: first, if you believe that human creativity and expertise is always better than what machines can produce; second, if you have farmed out a task that the AI was not designed for.
Am I a Luddite? Is human creativity always better?
No, I don’t believe human creativity is always better. Copilot recently suggested a better sql database design than I would have come up with in my first two or three rounds of planning. Granted, my skill at sql is limited to building and maintaining WordPress plugins, so it could adequately replace my expertise.
And that’s the ideal use-case. The worst case scenario of AI getting that question wrong was that I would have an inefficient but functional database design. That’s the specific problem I was trying to avoid by asking Copilot, but it’s also the minimum outcome I could produce on my own.
Wrong function, go to 10
If I had zero sql skill and I asked Copilot to give me an answer I couldn’t evaluate, I might end up with broken code or a template design that I couldn’t adapt to my own needs.
This is where AI fails in content generation. As a writer and multimedia content creator, I think about every part of the user experience in the material I create. I consider the subject lines of emails: at what point will the text be truncated or cut off on different email platforms? Am I OK with mobile users getting text cut off at a different point than browser users? I also consider how the text of social media posts will land horizontally in the post frame, whether that might create a distracting pattern.
Basically, I think about a variety of user experiences to everything I create. Absent very specific prompting patterns (including prompting for multiple outputs which must then be evaluated by someone else), AI tools are not designed to or capable of doing the same. Some claim to be capable of that for a steep fee, but if your team lacks the expertise to evaluate what the AI produces, is the cost worth it?
Why emphasize human expertise?
Ultimately, I emphasize the importance of human expertise for one very specific reason: you can ask a human to explain their reasoning and process behind something they produce. By extension, you can ask a human to train you or someone else in creating and evaluating that same type of work. You can investigate that human’s credentials to make sure they have the expertise necessary to do those things.
AI and ML algorithms are built from mountains of data. Even if we want to make the argument that education and experience are just a mountain of data, a human’s data has typically been evaluated by yet another expert. The most popular AI tool out there, Chat GPT, uses a mountain of data that unevaluated at best and poorly constructed at worst.
Humans are accountable, humans are culpable. Humans can be questioned. Humans respond to input. Truly well-imagined, narrowly-designed AI can do some of these things, but the effort necessary to create, vet, market, and learn those tools is typically greater than just finding a human capable of the same task.
tl;dr: Boxed Cake Mixes
Ever since Chat GPT was released to the public and stable diffusion became available for download, I have thought of AI tools like boxed cake mix. Feed it the right ingredients, wait a little bit, and you get the output it claims you get. Feed it slightly different ingredients (more eggs, a different milk, different pan shape), get a slightly different output.
Brioche bread has very similar ingredients to cake, just differing in amounts. Getting boxed cake mix to produce bread is exceedingly difficult, but it’s possible if you fully understand what’s in the mix and what’s necessary to force it to behave like bread dough. If you don’t have enough flour to make bread dough but you’ve got a box of white cake mix, then it can sort of work in a pinch.
So, I don’t endorse the use of AI tools to replace the expertise of readily-available humans. I don’t endorse it to do anything other than the narrow task that it was designed to do, whether that’s finding patterns in data or imagining common sentence constructions. If no humans are available, then AI can fill the gap temporarily, but we need to be honest about what expertise we are giving up when we start typing out prompts.
