Back in 2017, I wrote No, AI Won’t Take over the World.
My opinion has not really changed, but it has become more nuanced.
Since I wrote that post, AI companies, especially OpenAI and Anthropic, have taken over the cultural milieu, promising AGI is right around the corner, others thinking AI is an existential crisis, and every other option along the spectrum.
Jobs and processes that people see as grunt work and repetitive are becoming easier with AI. Whether it’s creating a Powerpoint presentation or doing data analysis, AI can help either automate it or make it not-sucky. Most Powerpoint presentations are terrible, not because Powerpoint sucks, but how people try to communicate with presentations is bad. Communicating is a skill and hard to do well.
The base nature of LLMs is predicting what is next piece of text for output based on its training dataset. If we assume everything in the training set averages out to…well, average, the opportunity is someone who isn’t good at a skill, can achieve average. Therefore, the floor is raised.
That also means, we will have more average, making more of the average, reinforcing average more and more, and make the average more of what will be predicted.
In a way, average is the same as standards or best practices. It’s some agreed upon right way an activity or process should be done. It’s a safe tested way of doing things. You are setting a floor to expected results rather than optimizing for the best. Nothing wrong with that, especially if you don’t think the activity is a core function.
To raise the bar, we will need specialized models trained on curated data sets with what “good” and “best” looks like, not simply ingesting everything on the Internet and every piece of content in existence.
That is why marketing AI tools suck. Finding data that can be labeled “good” or “best” is hard to find. No company is going to share that data publicly. You look at any marketing plan generated by AI and it’s…fine? It’s not technically wrong in any way, but its basically what everyone else would do. In marketing, doing and looking like everyone else is the worst.
But if your skills don’t include marketing, an AI generated marketing plan is better than nothing.
If you know what you are looking for though, changing variables like temperature and top-p, can produce more interesting, if not novel, results. Better prompting and asking LLMs to take contrarian positions can provide variant and diverse results. Unfortunately, most users stick with the default and end up with mush.
I keep thinking longer term, AI will be invisible tool applied to all sorts inputs and outputs. Chat interfaces will be thin interfaces, conductors similar to agent orchestrators today.
Smart homes are a common but good example. I like different temperatures in different rooms in different seasons. And as you get older, those preferences change. I don’t want to program this into any tool at all. It’s not complicated but it is complex. And of course, it gets harder once you have multiple people in a room. This seems like the perfect use for AI, lots of data, with lots of variables, changing over time.
We are only at the beginning of AI being integrated into everything we touch.
Of course, AI has lots and lots of problems, and if psychos like Zuck are pushing it, you have to be wary.
It’s going to be a crazy ride.
Sidenote: While everyone dumps on the idea of tokenmaxxing, the idea of leaderboards, and the unsustainability of it all, I do believe quantity gets you to quality. You gotta try and have a whole bunch of failures to get to real success. The point of tokenmaxxing is to push everyone to try a new way of solving problems. You can’t nickel-and-dime the process. Make AI do everything and see where the bottlenecks are and where the opportunities are. The exploration can’t happen if you try to justify tokenmaxxing for a specific project or feature.