It’s July already, and it’s hot. But this summer is unlike other summers. Because quietly, behind the scenes, artificial intelligence is still leaping, lunging, lurching ahead.
I read the most recent post from Ethan Mollick’s blog, One Useful Thing, that dropped June 30. The whole way through this crazy adventure, I’ve been following Mollick’s writing, as I consider him one of the front-runners in evaluation of the nascent technologies around us. (He also has MIT connections).
My first impression on tearing into this newest essay of his was that some of the things he said were continued points from past blogs. For one, he still notes the “jagged frontier,” where AI gets really good at some things, but not others. Another was his reference to some of the same benchmarking tools, like METR (Model Evaluation & Threat Research), a standard from an AI safety research organization that evaluates advanced AI models, measures their real-world capabilities and risks, and develops rigorous testing methods to help ensure powerful AI systems are deployed safely and responsibly.
In the Water
One thing that was absent from Mollick’s latest post is otters. In the early days, when image generation was still one of the most spectacular things that AI could do with models like Stable Diffusion, Mollick would get it to render ever-more-detailed marine mammals, like the otter reading a newspaper on an airplane, which was a favorite of mine.
I didn’t see any otters in this post, which he titled “Twilight of the Chatbots,” but Mollick did create something from the sea, a “harbor game” with various versions built by different models, to show some of the advances in open-weight and closed-weight systems.
Open and Closed Models
Mollick also presented two exponential graphs: one for American closed-weight systems, and another for a range of open-source model developments, mainly coming from China.
His overall point seemed to be that things are changing at unprecedented rates, and humans are racing, on the backfoot, to keep up.
“As AIs can do longer and longer tasks, the way people are using AI is changing,” Mollick writes. “Until recently, the dominant way to use AI was as a co-intelligence. You would ask the AI to do something, check the results, and then ask for it to do the next step of your job. By careful prompting and human attention, you could guide AIs to do complex and long-term tasks. This approach to using AI is still common and useful, but, increasingly, it is not the way AI is being used for valuable work. Long-running, smart, and self-correcting AI systems do not need constant human intervention, and they require a different way of working.”
He mentions a new buzzword in AI, the “harness” that boosts model capability and autonomy, in the agentic age.
“We are moving from a world where non-experts use chatbots to fill in gaps to one in which experts use agents to get work done,” he concludes. “And the best way to use agents is to think of yourself as a manager.”
The Frog in the Water
A corollary point that Mollick makes is that people, in general, seem to be “bad at feeling exponentials” when they are all around us, and that as a result, we tend to see AI as a series of uncomfortable jolts forward, as “turbulence” that is disturbing a new generation of Americans. Teens, in general, are feeling pretty glum about the technology, and that’s putting it mildly.
“Even though it is a curve on a graph, we keep experiencing a steady doubling of capability as a series of shocks,” he writes. “AI is not capable of being a real cybersecurity threat until suddenly it is, causing sudden and improvised policy changes at the highest level of government. Markets discount whether AI might threaten to undermine a business model until suddenly it can, leading to massive swings in stocks.”
This effectively ends the post, which is shorter than some of what Mollick is written in the past. I want to do two more things here: first, a review of a couple of comments on the piece, and then, a look back at Mollick’s other posts.
More Voices
One commenter to “Twilight of the Chatbots” had this to say:
“The exponential doesn’t just outrun institutions. It outruns the people trying to keep up with it, and the gap between what the tool can do today and what the person learned to do with it last quarter is the new form of obsolescence nobody prepared for.”
I think that’s right, and it’s leading to considerable angst.
Another writer, presumably from across the pond, wrote:
“When talking about ‘real work’ I see that you all mean coding, more or less. Whereas “Real work” for me and other humans is gardening, Planting, pulling weeds, harvesting our veggies, cooking, walking the dog, attending committee meetings, baking and putting out the supper, so on. For my adult children, female, it’s breast feeding the Bub (sic), dressing the kids for school, so on and so on, you get the drift. Oh, robots can breast feed now? Wipe up the dog piddle? Eat the cake? Taste test it? Enjoy the cake? Put the leftover cake in the fridge under gladwrap for tomorrow and put it in the lunchbox? No, but they can code.”
Notwithstanding the very human aspect of mammalian nurture, some of these items shortly will be done by AI robots. Pulling weeds was the one the occurred to me. And “attending meetings.”
Anyway…
A Use Case
With all of Mollick’s talk about being a manager and letting the AI agent do its business, I let the model go and find ten top ideas that Mollick has given us over the tenure of his blog.
Here are a couple:
15 Times to Use AI, and 5 Not To (Dec. 2024) – Instead of asking whether AI is “good” or “bad,” Mollick explains where it genuinely adds value—and where humans should remain firmly in charge.
I remember that one.
Against “Brain Damage” (July 2025) – This post helped move the conversation beyond simplistic claims that AI either destroys or enhances cognition.
I don’t remember that one.
See, right there, AI is excelling at recall. But while I found this makes things faster, it doesn’t necessarily mean that we don’t need humans anymore.
At least, I don’t think it does.
