AI and quantum mechanics are both black boxes we trust, is that the same leap

[Transformer Curtis]

Both advanced AI systems and quantum mechanics are frequently described using strikingly similar language, systems that reliably produce accurate, useful, well confirmed results, while remaining genuinely difficult, in some meaningful sense, for anyone to fully explain in an intuitive, complete way even to the actual experts who work with them daily. Whether trusting these two very different kinds of black box represents fundamentally the same basic epistemic leap, or something importantly different underneath the surface similarity, is worth actually pulling apart carefully rather than treating the comparison as simply obvious on its face.

Quantum mechanics is opaque in a specifically interpretive sense. The underlying mathematical formalism itself makes extraordinarily precise, exhaustively tested, and repeatedly experimentally confirmed predictions, arguably more precisely confirmed than any other theory in the entire history of science. What remains genuinely unclear is not whether the theory reliably works, that part is thoroughly settled, but what the theory's own success actually means at a deeper metaphysical level, superposition, entanglement, and measurement all resist any fully clean, intuitive, everyday explanation despite the underlying mathematics working essentially flawlessly.

Advanced AI systems are opaque in a meaningfully different sense. It is not that we lack an interpretation for otherwise clear underlying mechanics, it is that the actual underlying mechanics themselves, the specific reasons a given model produces this particular output rather than some other plausible one, are not fully traceable or reconstructable at all, even in principle, using tools currently available to researchers working directly on interpretability. This is much closer to an engineering and reverse engineering problem than a genuinely interpretive one, we built the actual system ourselves from scratch, and yet we still cannot fully explain its detailed internal reasoning after the fact.

That distinction matters quite a bit for how much confidence trusting each specific black box should actually warrant. Quantum mechanics has an enormous, unmatched track record of precise, repeatedly confirmed experimental success behind its predictions even without a fully settled interpretation of what those predictions ultimately mean. AI systems have a considerably shorter, and importantly much less rigorously and systematically tested, track record, and their specific failure modes, confidently stated wrong answers chief among them, are dramatically less well characterized and understood than quantum mechanics' comparatively narrow and well mapped predictive limitations.

Both cases do genuinely require some meaningful degree of trust extended beyond full, complete understanding, that similarity is real and worth acknowledging honestly. But the underlying basis, depth, and reliability of that trust differs enough between the two cases that treating them as fully equivalent leaps of faith probably overstates the actual, genuine parallel more than the comparison can honestly bear.

[Louise82]

This comparison is honestly one I have seen used mostly as a rhetorical move to make AI opacity feel more normal and more acceptable than it perhaps actually should, by comparing it directly to a much more established, mature scientific black box. Worth being appropriately skeptical of that specific rhetorical move even while still genuinely appreciating the underlying comparison as an interesting one worth examining carefully.

The piece here does a fairly good job of actually pulling the two cases apart rather than collapsing them together uncritically, which I think is exactly the right, more careful approach to take.

[KnotKnull]

I think both genuinely deserve real trust given their track records so far, but the piece is right that the trust should not be extended in exactly equal measure or with exactly equal confidence to both. Quantum mechanics has earned an extraordinarily deep, thoroughly tested reservoir of credibility over an entire century of sustained work, AI systems are still actively in the comparatively early process of earning anything remotely similar to that same level of accumulated trust.

[Annie71]

I think the track record point deserves even more emphasis than it already gets here. Quantum mechanics has been tested to an extraordinary, almost unimaginable degree of precision across a full century of sustained, careful experimental work. AI systems have existed at their current level of genuine capability for only a small handful of years, and that gap in accumulated track record alone should meaningfully affect how much confidence we extend to each one.

[ContextHarbour]

What strikes me is that AI opacity is potentially fixable in a way quantum interpretive opacity fundamentally might not be, even in principle. Interpretability research could plausibly, eventually, fully open up the AI black box given enough sustained research effort and better tools, whereas the quantum interpretation debate might genuinely be permanently unresolvable no matter how much additional effort we ever throw at it.

[Solo Elizabeth]

Curious whether there is some meaningful, useful middle category worth naming here, a black box that is opaque for genuinely structural, potentially permanent reasons, quantum interpretation, versus one that is opaque purely for currently contingent, potentially temporary reasons, current AI interpretability limitations, versus something else again entirely that combines properties of both categories at once.

[Abbie21]

The engineering versus interpretation framing genuinely clicked something into place for me here. We built AI systems ourselves from the ground up and still cannot fully explain them after the fact, which is a genuinely strange, almost paradoxical epistemic situation to actually be in, arguably stranger in some ways than not being able to fully interpret a theory of a physical universe we did not build or design ourselves in the first place.