Google Quantum AI published on in 2025 on OTOC, claiming potential "quantum advantage." Basically, the idea is to use the reversibility of gate-based quantum computers to model physical time reversal symmetry of thermodynamic systems, for example, at the level of a fine-grained quantum mechanical system, as well as how this symmetry is broken. Google used a circuit and its time reversal that seems to resemble random circuit sampling, and this case is handled with Qrack's automatic circuit elision (ACE) to high cross-entropy benchmark fidelity on very deep and wide circuits. (To validate, try setting environment variable QRACK_MAX_PAGING_QB to 1/2 or 1/4 of your overall count of qubits in the benchmark, to model the case instead as 2 or 4 independent subsystems that couple between each other only semi-classically.) Alternatively, we could argue that the "real point" of OTOC is thermodynamic reversibility, like with transverse field ising model (TFIM), rather than RCS. With pyqrackising, we show that low or moderate "scrambling" returns high fidelity, while highly scrambled circuits, on TFIM, are best described in practicality by simply their mean field, to within a tiny margin, making us doubt whether this case could constitute true "advantage" for any practical purpose.