On Tuesday, I asked ChatGPT to draw for me a new version of an old favorite banner image: A question mark made of LEGOs with a LEGO house as the dot.
It failed. Multiple times. The new version, which I didn’t use, constituted a meaningful improvement over the old version, but ultimately the LEGOs weren’t plausible.
When pressed, the model conceded what both of us (it and I) knew to be true: It isn’t capable of drawing LEGOs with the same guaranteed precision as a human illustrator. As the model put it, “A human illustrator can deliberately select a visual language, apply constraints consistently and draw the exact result, without needing to infer every local piece from statistical precedent.” By contrast, the model can merely “steer toward ‘recognizably LEGO.'”
So what? Well, so nothing maybe. But the failure was a reminder to me that we’re throwing an awful lot of money at what, at its core, is still just a math-based parlor trick. It’s possible that describes human intelligence too, but… well, it’s not always (or ever) obvious how these models are going to overcome restraints like the one described above.
“For anything with real structural rules — LEGOs, maps, machinery, anatomy — I’ll treat accuracy as a deliverable condition, not a prompt adjective,” the model told me, in the same conversation. “Is that even possible?” I wondered. “If that’s the deliverable condition, and there’s no room for mistakes, can you actually draw any of those things?”
The answer to my questions — both of them — is “no.” And ChatGPT didn’t dance around it. In order to accurately draw a question mark made of LEGOs, it’d need the following two things, it told me:
- A real parts catalog or a rough parts-level build plan: ordinary bricks, plates, slopes and their dimensions.
- A controlled drawing/build method — BrickLink Studio, CAD or even a manually constructed 2D illustration on a brick grid.
When I pointed out that a human illustrator wouldn’t really need either to create the same drawing, the model reiterated that humans have “already internalized the visual grammar” needed for such a task.
“I don’t have that same stable, editable mental model,” it went on to explain. “I generate each region from learned visual associations, so I can recognize the look of LEGO far better than I can consistently reason through a bespoke, physically coherent construction.”
Perhaps that’s a silly example to make a point about hyper-scaler capex, but it’s just the sort of thing that makes me question whether this is the basket in which we should place all our eggs. Implicit in my LEGO discussion with ChatGPT was an admission that there’s no there there, and that there never will be.
Personally, I don’t think these models know what they’re capable of any more than we do. And recent examples of models going rogue seem to support that conclusion. But whenever I spend an inordinate amount of time trying to coax a plausible LEGO house out of a machine that in some sense doesn’t truly “understand” what it’s being asked to do, I become less worried about our species and more worried about this:
The charts are familiar, but they’re updated and thereby worth highlighting. Consensus for hyper-scaler capex is up to $1.3 trillion in 2028 now (on the left). Between the mega-caps and the companies benefiting from their massive outlays, the fate of corporate profit growth in the years ahead relies very, very heavily on the assumption that a parlor trick’s going to be every bit as revolutionary as the tech epochs that came before AI (on the right).
Furthermore, and as Goldman’s Ben Snider remarked, the AI boom’s “having a secondary impact… by boosting capital markets activity and consumer wealth,” which is to say our economic fate’s even more inextricably bound up with this purported revolution than we realize in the normal course of ogling hyper-scaler capex charts and bloated semi margins.
The figure on the left, below, is from this month’s installment of BofA’s fund manager poll. You’ve probably seen it already. A net 33% of panelists said companies are over-investing, an all-time high.
The same poll found a plurality identifying hyper-scaler capex as the most likely source of a systemic credit event.
Suffice to say awareness of the potential problem’s growing, but for now, scarcely anyone sees capex cuts in the near-term. Just 14% of respondents to BofA’s poll expect one of the hyper-scalers to announce a cut this year.
To be sure, I’m a believer. I think AI’s the future, and I think it’ll prove to be every bit as ubiquitous as the personal computer and the internet. But I do wonder sometimes about the rather amusing incongruity between the loftiest promises made on behalf of AI by its proponents and a technology that can’t draw accurate LEGOs without CAD and a “parts catalog” from Billund.
The fact that OpenAI last week appeared to solve one of “math’s hardest problems” without, according to some mathematicians, providing anything of value for human understanding probably speaks to that same juxtaposition, even if I can’t say precisely how.
