By Christopher Penn, of Almost Timely News
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 FromOn 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:
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:
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 RecapAs a reminder, I bucket executive function into four categories that I call PODS:
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.
Part 3: The Tale of the TapeLet’s look at each of the 10 topics Claude suggested to see the threads that connect them.
AI Visibility challenges: when you read the verbatims of what people are saying about AI visibility measurement, you can tell they’re pretty much making it up. This is especially true of software vendors that are offering and peddling solutions that have very little grounding in reality - and yet, stakeholders eat this stuff up because they’d rather have certainty about a wrong number than accept uncertainty or no number at all. they are not thinking about their thinking.
Agentic oversight is degrading: the commenters on Reddit focused on the fact that as agents get more sophisticated, it’s harder and harder to follow along to see what they’re doing. So we just hit OK all the time - if we’re even thinking about a human in the loop. We’ve forfeit our authority here. In fact, some AI tools have this built in as a feature. Claude calls it dangerously skip permissions. Qwen calls it YOLO mode.
AI deployment is broken: here, the discussion is about stakeholders telling their stakeholders that the organization has deployed AI without any sense of the impact that it’s had. One poster cited a statistic that 29% of companies see significant ROI from AI, even though individual employees are claiming 5x productivity increases. The math doesn’t math. Here, people don’t want to think and reflect about what deployment even means. Katie’s been writing a lot about this in the Trust Insights newsletter the last few weeks. At its heart, we are confusing using AI with getting results out of AI.
40-60% of budget is wasted: here, folks are talking about how everyone just accepts the default model in AI tools, which is typically the most expensive one. Claude, for example, defaults to Opus 4.8, which is a much more expensive model than Sonnet 5 or Haiku 4.5. We’re not thinking. We’re not making decisions about cost trade-offs versus effectiveness. Another person pointed out that this is by design to create habits. It’s about habit formation for the most expensive models so that when the subsidization of today’s AI ends, we are accustomed to using the most expensive models. This is brain hijacking in a way.
AI is a rental: in this particular topic, the discussion centers around what you actually own in AI, which is very little if you are using today’s closed weights frontier models. Particularly Anthropic’s on-again, off-again rollout of Fable 5, thanks to U.S. export controls, was a wake-up call to the entire industry that you don’t own anything in SaaS, any more than you own music in Spotify or own videos in Netflix - but people think they do.
Sycophancy in focus groups: even though we have good academic research showing that properly prompted AI models can emulate human purchase intent with about 90% accuracy, the level of sycophancy in AI models steers them towards confirmation bias in most situations. This is especially true of synthetic focus groups; when people use AI to simulate consumer intent, what they’re really doing is reinforcing their own biases most of the time. There’s no reflection or questioning the AI output.
AI detectors don’t work: A perpetual favorite topic of mine. This thread of conversation revolved around how companies are using AI detectors to identify the use of AI in situations where it’s not appropriate, without recognizing that the detectors themselves are also broken. In testing I did 3 weeks ago now, AI detectors falsely flagged human outputs 1 out of 7 times. No one is thinking and reflecting enough about who’s watching the watchers.
AI is hollowing out companies: I really liked this quote from the agency owners subreddit:
“What’s strange is nobody decided this. There was no meeting where we discussed this. We automated one annoying task, then another, and one day the job had hollowed out from the inside.“
This erosion of tasks is all about a lack of cognition, a lack of reflection, a lack of a plan. No one’s making decisions - just leaving it up to the machines, a bit more each day.
Tokenmaxxing: this was reflecting on Meta’s most recent news story in which they were on track to spend several billion dollars in AI tokens because they measured AI productivity based on token spend, the dumbest possible way to measure AI.
Marketers as unpaid trainers: this was a whole bunch of ranting about how marketers are effectively unpaid trainers for AI platforms. The more content we produce, the more AI has to train on while simultaneously competing for the tasks we’re paid to do. Here, the thread was about how the average marketer isn’t thinking or reflecting about their relationship to AI.
And this laundry list of 10 items isn’t everything, not by a long shot. Think about how else people use AI without thinking, without thinking about their thinking. Go on LinkedIn and look at the endless streams of comment-bots all paraphrasing the same template over and over again. Look at the workslop flooding your inbox, read the reports your agencies send you that are clearly copy paste jobs.
When we put aside the direction that Claude wanted to nudge this issue of the newsletter, it becomes pretty apparent that it’s really about how much we think about thinking. How self-aware are we? How well and accurately do we perceive our relationship with AI?
Most of all, do we see the amount of executive function we’ve ceded to AI?
Part 4: The Antidote“Nobody decided this” is haunting me. When you hand off executive functions to AI, who is making the decisions? No one. There’s no one accountable for a decision because the machine is making it for us. Whether it’s building a PowerPoint deck, assembling a report for a client, creating content for a newsletter, when the machine does it, there’s no accountability and there’s no decision making on our part other than approving it.
And this leads to a bunch of bad outcomes, everything from job loss to dissatisfaction with your own work. You know, when you use AI to offload a task, that you didn’t do the work - and you take no pride in it, any more than you’d take pride in the work that a contractor did on your behalf.
Think about this in the context of parents. Go to any parent’s house and you’ll likely see art that the kids made when they were young. The art is generally, objectively, pretty bad. But the parent values it not because of the quality of the art, but because of the level of effort made by the child. They take pride in their child’s efforts, and the child takes pride in what they did in their efforts. For good or ill, when people use AI, they themselves feel like they haven’t made an effort, and the person on the receiving end also feels like they didn’t make an effort.
Sometimes, you don’t even understand the work if you’ve outsourced it. You present it to your stakeholders, and the first question they ask that isn’t in the prepared materials leads to panic city because you can’t answer it, like buying a cake at the store instead of baking it yourself and then having someone ask if a specific allergen is in it. And you’re left scrambling, looking for the label to see what’s actually in the cake.
So my suggested antidote is this: for every task that matters, always start with someting you lead, and force the machines to educate you.
For example, when I compile monthly reports for Trust Insights clients, I turn on my voice recorder and I review the data myself. I talk out loud what I see, what I think, what makes sense and what doesn’t make sense, and then I have AI transcribe it. After the transcription is complete, I ask AI to review it and show me what I missed. I ask it to ask me questions, to record more information, to fish more information from me.
I also ask it, especially around anything in my subject matter expertise, to find me resources to learn and read about its recommendations. Recently, I was asking it to choose from a catalog I’d prepared of over 1,000 different analytical techniques, and it chose an interesting ensemble of 3 techniques, one of which I didn’t know well. So I had it teach me that, so that instead of me passively accepting its recommendations, I learned something. I got better as a professional. I grew my subject matter expertise.
If you think about it, this is not only rational from the perspective of delivering great quality work, it’s also rational from the perspective of my value. If I’m nothing more than a copy paste drone, a meat-based interface to an LLM, then why does my company need me? Why would my clients pay for me when they could just pay to ask ChatGPT or Claude the exact same things?
What they’re paying for is my expertise, my skills not only at using the technology, but the specific lens I direct it with, and the perspective that only I can bring. And if I’m using AI to constantly improve that expertise, to improve that domain knowledge, then they should keep paying for me.
Outside my subject matter expertise, I start with deep research, using AI tools to gather information and then having them create a synthesis. Once I’ve got that, then I have it create a checklist of what constitutes quality in the domain I’m working in. Finally, I sit down with the creations and I read and learn for myself. I have AI make infographics or podcast summaries to learn the domain so that I can connect it to my expertise.
Agentic AI - tools like Claude Code, OpenCode, etc. - are phenomenal researchers, far better than the web-based deep research tools folks have become accustomed to in the past couple of years. When you use a research agent, it has a lot more latitude to gather up sources, to take the time to write down notes and observations, and to synthesize conclusions from the data it has. If you use something like the Trust Insights CASINO research framework, you’ll get some amazing results from the tools that tend to have fewer hallucinations than their web-based counterparts.
Then with that research data in hand, you use it to become a better professional within your domain. You use it to level yourself up. You use it to add to your insights instead of substitute for your insights.
Part 5: Wrapping UpThe biggest problem in AI today is the delegation of our executive function to machines. Whether it’s accountability (machines have none), deskilling, or dissatisfaction with our work, the moment we forfeit executive function is the moment when AI becomes more problem than solution.
We can boil it all down to a simple set of questions:
Does the use of AI make the output better?
Does the use of AI make me better?
If the answer isn’t yes to BOTH, then you’re not using it well.
Properly used, AI is one of the greatest professional development tools ever created.
Improperly used, it’s one of the most destructive forces your career has ever known, because the moment you offload a task to AI, your own skills at that task get rusty.
And once something becomes rusty enough, it’s cheaper and easier to replace it.
More in the Almost Timely Newsletter
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Tyler Durden Sun, 07/05/2026 - 16:20In recent months one of the more frequent questions in FX trading has been the relentless collapse in the yen, which recently sank below a 40 year low despite rate differentials stubbornly headed in the opposite direction, and is increasingly flirting with levels which on previous occasions always prompted BOJ intervention.
Among the reasons cited for the chronic weakness of the Japanese currency have been the following three:
Effectively a feedback loop has emerged, whereby the weaker yen leads to an even weaker yen, and despite token resistance by the BOJ - the latest long overdue rate hike being an example - the market clearly anticipates further weakness in the currency, and is pushing it to new lows.
However, a limit to the yen's weakness is now emerging, and it goes to the growing damage on the country's households noted in point 2 above.
As Bloomberg reports, Japan’s weak currency caused the most bankruptcies for the first half of a year since 2022, underscoring the growing economic costs of the currency’s slump.
Forty-five firms failed from January to June for that reason, up more than 30% from a year earlier, according to a report by Tokyo Shoko Research published last Wednesday. The figure was the highest since 2022, when the data firm started counting companies that specifically cite currency weakness in filing for bankruptcy.
The findings suggest the smaller firms that employ most of Japan’s workers are finding it increasingly difficult to withstand the yen’s prolonged weakness, casting a shadow over the nation’s economy, even as large-cap exporters benefit.
The data also strengthen the case for continued interest-rate hikes from the Bank of Japan. While higher borrowing costs alone would typically push more firms toward insolvency, closing the gap with US rates could help support the yen.
The yen has steadily weakened against the dollar in recent years as US interest rates climbed to combat pandemic-era inflation while Japanese rates were negative to break free of deflation. While the rate differential has since narrowed, a rally in the dollar and high oil prices from the war in Iran are pressuring the yen.
The yen hit a new 40 year low of 162 per dollar on Thursday, before rising higher amid some speculation that Japan's financial authorities may finally seek to rein it in. While the weaker currency has boosted exporters’ earnings, it has also driven up import costs, squeezing profit margins across a broad range of import-dependent industries, and has also helped sustain the worst inflation in Japan's recent history.
The conflict in the Middle East has also drastically boosted costs. A price index for raw materials and merchandise purchases among a broad range of smaller firms surged in the second quarter, according to a survey by the Organization for Small & Medium Enterprises and Regional Innovation. The Bank of Japan’s producer price index has also jumped in recent months.
Tokyo Shoko Research’s report showed bankruptcies were particularly concentrated in the wholesale sector. One example was Tokyo-based Merry Time Foods, an importer of crab, shrimp and tuna from other parts of Asia. The company went bankrupt in May, citing deteriorating profitability due to the weak yen and political instability in its supplier countries.
The research firm said in the report that currency-related bankruptcies are likely to remain elevated for some time, particularly among wholesalers, retailers and manufacturers with limited pricing power.
According to Bloomberg, the strain has been acute for small- and mid-sized businesses, who are more affected by higher borrowing costs than their larger counterparts. They’re also contending with mounting wage hike pressures amid persistent labor shortages. Smaller firms often have limited ability to pass higher costs onto customers due to intense competition.
“The weak yen is one contributing factor,” said Yoshihiro Sakata, manager at Tokyo Shoko Research. “Combined with inflation and rising labor costs, it is creating a cumulative burden on businesses.”
Another source of pressure on smaller businesses may be foreign-exchange hedging, including the use of so-called reverse knockout options, according to Yuji Saito, executive adviser at SBI FXTrade. Such products are widely sold by regional banks as structured hedging products, particularly to small and regional importers seeking to minimize upfront option premiums.
Once the exchange rate reaches a preset knockout level, the option expires and the hedge ceases to provide protection. Companies needing dollars must then either purchase them in the spot market, enter into a new hedge - often at less favorable levels - or leave themselves exposed to further currency moves.
“The weaker the yen gets, the more importers roll into increasingly risky option structures,” Saito said. “Once the knockout level is breached, they are forced to buy dollars in the spot market, creating a negative spiral that puts even more downward pressure on the yen."
Analysts estimate that remaining reverse knockout levels are clustered between 163 and 170 yen per dollar, territory that many firms didn’t think the currency would reach as intervention from the central bank would likely be forthcoming due to the adverse economic impact of such unprecedented currency collapse.
“The number of knockouts could increase if the yen weakens further,” said Hiroyuki Machida, director of Japan FX and commodities sales at Australia & New Zealand Banking Group. “The situation is becoming significant for companies that are unable to pass on higher costs.”
Tyler Durden Sun, 07/05/2026 - 15:45