You're fundamentally assuming opaque AI, and ascribing intentions to it; this strikes me as generalizing from fictional evidence. So, let's talk about currently operational strong super-human AIs. Take, for example, Bayesian-based spam filtering, which has the strong super-human ability to filter e-mails into categories of "spam", and "not spam". While the actual parameters of every token are opaque for a human observer, the algorithm itself is transparent: we know why it works, how it works, and what needs tweaking.

This is what Holden talks about, when he says:

Among other things, a tool-AGI would allow transparent views into the AGI's reasoning and predictions without any reason to fear being purposefully misled

In fact, the operational AI R&D problem, is that you can not outsource understanding. See tried eg. neural networks, when trained with evolutionary algorithms: you can achieve a number of different tasks with these, but once you finish the training, there is no way to reverse-engineer how the actual algorithm works, making it impossible for humans to recognize conceptual shortcuts, and thereby improve performance.

Neural networks, for instance, are in the dock not only because they have been hyped to high heaven, (what hasn't?) but also because you could create a successful net without understanding how it worked: the bunch of numbers that captures its behaviour would in all probability be "an opaque, unreadable table...valueless as a scientific resource". ref