What’s the biggest problem in AI today? Is it cost, with token budgets being blown out of the water by agentic AI? Is it sustainability, with AI consuming electricity and fresh water? Is it ethics, with tech companies cramming AI into everything?
I think it’s deeper than that. Those are all symptoms of a much deeper-rooted problem: nobody’s making decisions.
Or more correctly, we’ve abdicated far too much of our executive function to AI. We’ve surrendered our thinking.
Let’s dig in.
Part 1: Where This Issue Came From
On Friday afternoon, I was mulling over what I wanted to cover in this week’s issue. It’s a holiday weekend here in the USA, so not as many folks will be reading, and that’s okay. (I appreciate that YOU are) And I’ve covered a ton recently:
- How to improve advertising with AI
- Why listicles may cause more harm than good
- Setting up private, local models
- How AI detection works
- AI for GEO mental models
- AI for retail GEO
- 18 ways to save token budgets
- How to make AI write better
So on a whim, I set up a NotebookLM with the last 180 days of conversations from over 40 different subreddits, like r/marketing, r/chatgpt, etc. – everything around marketing, business, and AI. I connected it to Claude Code with the NotebookLM command line tool (the most token—efficient way for Claude to talk to NotebookLM), and then put all of my 2026 newsletters year to date into an input folder.
I asked Claude to compare what I’ve written about thus far this year with what folks are finding their hardest problems are with AI. Claude spit out a list of 10 major things derived from over 800,000 words of foaming at the mouth on Reddit that it thought might be good newsletter topics:
- AI Visibility challenges
- Agentic oversight is degrading
- AI deployment is broken
- 40-60% of company budget is wasted on the wrong models
- AI is a rental
- AI sycophancy is screwing up synthetic focus groups
- AI detectors don’t work
- AI is hollowing out corporations and no one’s hiring junior staff
- People measure AI by tokenmaxxing
- Marketers are basically unpaid labor for AI companies training data
Claude was REALLY pushing for me to write about how measurement is broken in marketing and AI today, and I might do that at some point, but that’s not what I see when I look at this laundry list. Yes, there are measurement issues in many of them, data issues in many of them, but… measurement being broken is the symptom of what I said earlier – we’ve abdicated executive function.
For those who aren’t analytics nerds, you know that measurement is a trailing indicator. It’s not a leading indicator.
Part 2: Executive Function Recap
As a reminder, I bucket executive function into four categories that I call PODS:
- Plan: you think about achieving something in the future and make a plan to get there from here
- Organize: you take what you have and try to make sense of it
- Decide: you take what you have and make decisions about it
- Solve: you solve the problems you have
Yes, there is more nuance to executive function than this, but this handy, short list is an easy way to see what our brains are doing. That’s critical thinking, one of the worst-named practices we have.
Why? Because critical thinking isn’t about being critical, per se. It’s about metacognition – the definition of which is thinking about thinking. When you’re thinking about how you think, you open the door to improvements, to growth.
Thinking about thinking means asking questions and reflecting – is this the best way to do something? How could I do this better? How could I derive more enjoyment from this thing I’m doing? It’s not criticizing yourself as much as it is recognizing what you’re doing and whether it’s working or not.
When you’re planning, organizing, deciding, and solving, you’re inherently thinking about thinking. Every time you plan, every time you bring order to chaos, you have to check in with your own brain to see if what you’re doing is moving you closer to the goal posts.
Executive function is one of the things that defines our sentience as living creatures. Every sentient creature from a mouse to us does these tasks. You’ve read or heard stories about crows fashioning tools from wire to solve problems, you’ve watched dogs and cats make decisions and plan. I’ve watched my own cat measure optically whether or not she can make a particular jump.
Properly prompted, today’s AI tools are superb at executive functions as well. Given the right frameworks, harnesses, and data, they can plan, organize, decide, and solve better than we can at most language-based tasks.
And therein lies the actual problem.
Hellbound and Down 