 This is especially true in light of a recent argument by Porter (2016: 469–70). He considers a certain schematic characterisation of computable functions, something like this: for every computable function \(f\) with property \(P_i, f\) is differentiable at \(x\) iff \(x\) corresponds to a random\(_i\) sequence. It turns out that for each sense of randomness (ML randomness, Schnorr randomness, computable randomness, etc.), there is some corresponding property of computable functions. Most importantly, none of these properties look overwhelmingly more natural or canonical than the others. For example, computable functions of bounded variation are differentiable at the Martin-Löf random points, while nondecreasing computable functions are differentiable at the computably random points. The difficulty for the Martin-Löf-Chaitin Thesis is this: these results give us the notion of a typical sequence, with respect to the differentiability of various kinds of function. Unfortunately, these notions of typical sequence diverge from one another. Unlike Church’s thesis, where all the notions of effective computability line up, here we have a case where various notions of a typical sequence do not line up with each other (though there is significant overlap). Porter concludes that ‘no single definition of randomness can do the work of capturing every mathematically significant collection of typical points’ (Porter 2016: 471). 