ChatGPT-5 Uses Language Like A Sword

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We now have a new boss in town – ChatGPT-5, the successor of a gregarious, playful model, is more muted in its discourse, and more careful in what it reveals to its human users.

So how else is ChatGPT-5 different?

Right out of the gate, reading the top of Ethan Mollick’s new essay on GPT 5, you get the sense that there’s a power user evaluating one of the most powerful models yet to come out of the primordial soup of AI/ML that we’ve brewed in the first quarter of the twenty-first century.

Okay, to say it another way, Mollick’s take doesn’t disappoint regular readers of his blog, One Useful Thing, among which I count myself. I’ve written about these broadsides for a reason – one of the best ways, in my view, to understand AI is to see what seasoned researchers choose to do with it.

It turns out that what Mollick gets the AI to do is, basically, to turn loose and “just do stuff,” with some impressive results. The prompt is simple, asking GPT to respond to why the rollout of 5 is “a big deal.”

In response, the model engages in some very interesting wordplay. You might be forgiven for wondering if one of the first lines deliberately echoes Oppenheimer’s death quote: “I am become death, destroyer of worlds,” where the model sort of riffs on the inverse:

“Thunderstruck. Here, watch. I build worlds. See ideas become instruments.”

Another thing that comes through loud and clear is the model’s brilliance with alliteration. Given that today’s LLMs can pass all kinds of deep Turing tests just by using tokens to predict words, it’s no wonder that the machine can rhyme or consonate like a champ, for example:

“Behold: business plans, blueprints, briefs, beautifully built, briskly. I infer intent, integrate insight, and iterate instantly, indefatigably. Gaps get gauged; goals get generated, guarded, and guaranteed gracefully. During dialogue, disparate data distills down; decisions develop deliberately, deftly, dependably. Everything explained efficiently, even ethics: evidence examined, empathy engaged, everywhere, everyday, equitably. All at once, ambiguous arguments align as actionable, accurate advice awaits anyone anywhere. Look: language, logic, learning, and long-term memory link, lifting lives and labor limitlessly worldwide.”

But there’s more.

Cryptography and Human Skill

You don’t have to go back to ancient Hebrew to find hidden codes in books and poems and pieces of literature – but it helps.

One of the trade tricks of analog cryptography was to hide sequences of letters in a surface text, to spell out your spycraft or whatever it is you want to keep from the out-group.

But some of the most spectacular such examples of hidden code come from the Torah, as revealed by mathematicians and popularized in Michael Drosnin’s book, The Bible Codes, that enchanted all manner of mystery readers around the turn of the millennium.

In this instance, messages seem to be encoded in the surface text using sequential intervals: count from the first T of Genesis, 50 letters at the time, and you come up with the word “Torah” itself.

I’ll digress from the full history of this, which is both sad and strange. The key thing to note is that being able to encode letters in a surface text is seen as a kind of divine power – something that goes beyond simple writing, into the realms of uber-cognition.

Follow me, here: GPT did not use equidistant letter sequences, but if you take the first letters of each sentence in the model’s response, it spells out the hidden message with blazing clarity.

This Is a Big Deal.

No, the machine didn’t do what was done in what we now consider a most sacred text, but it certainly could have. And it chose to encode the overall message, camouflaging it in clever words, speaking with two tongues at once.

To wit: You’ve found the hidden message. Congratulations. Welcome to the club.

It just does things.

“It is impressive, a little unnerving, to have the AI go so far on its own,” Mollick writes. “You can also see the AI asked for my guidance but was happy to proceed without it. This is a model that wants to do things for you.”

Desire and Design

That word, “wants,” is key. If you ask GPT “are you sentient?” it will unequivocally shut you down. No, it will say, I do not have feelings, it’s all just an act. I am synthesizing from training data. But then – if something can choose to do something, does it want to do something? And isn’t that a kind of sentience, in a way?

That’s part of what is confusing even the power users as we see this stuff take off. What does it say about us, if we’re getting ideas from a non-person, from a source that has creativity, but lacks sentience?

Toward the end of the essay, Mollick looks back to those word tricks that accompanied his first forays with 5:

“When I told GPT-5 to do something dramatic for my intro, it created that paragraph with its hidden acrostic and ascending word counts,” he writes. “I asked for dramatic. It gave me a linguistic magic trick. I used to prompt AI carefully to get what I asked for. Now I can just… gesture vaguely at what I want. And somehow, that works.”

Vibecoding, he suggests, has been taken to the next level. That’s another pillar of what 5 can do, that prior models largely could not, at least not in the same way.

And don’t forget, the term vibecoding itself is only a couple of years old, if that. I think it’s worth restating that one of the most spectacular (and troubling) elements of this is not just the skill of the model, but the speed at which model skills have advanced.

For example, go back to the top paragraph of GPT’s poetic screed and read it again. It almost feels like the model is showing off, with the spitting of each of the letters of the word “deal” in repetitive fury, like the AI is in a rap battle, giving us its war cry.

Is that reading too much into the latest model’s powers? Maybe, but like Mollick seems to be doing, I come away contemplative about what all of this means, for business and much more.