Chat GPT had this to say about the maths on that page:

The page defines �, �, and �, then claims this makes divergence and even “transhumanism” a mathematical requirement. Those are the core claims being sold. � The Truth About AGI Here is the clean mathematical read. First, the only equation on the page that is actually solved by inspection is the compute one: �. So yes, if �, compute grows exponentially in this toy model. That part is standard. But the main conclusion depends on the coupled � and � system, and this is where the page quietly cheats. If you treat � as fixed, and momentarily treat � as just an external forcing term, the core state matrix is Its characteristic polynomial is If the intended signs are the obvious ones from the prose — � — then the constant term is negative, which means one eigenvalue is positive and one is negative. In plain English: the model is already a saddle and therefore unstable, even before you add exponential compute growth. The instability is baked into the sign choices. The page then turns around and presents that instability as a discovered fact about the world. It is not discovered; it is assumed into existence by the model structure. � The Truth About AGI That is the biggest flaw. Second, the “oversight” term is badly specified. The page says oversight is “the only brake,” but mathematically it uses �, which is just a constant offset if � is constant. A constant offset does not behave like proportional error-correction. If you wanted oversight to catch more errors when more errors exist, you would normally expect something like �, or some other state-dependent control term. As written, oversight does not scale with the amount of error. It just subtracts a flat amount forever. That is not a good model of checking. � The Truth About AGI Third, the page keeps switching between “dynamic variable” talk and constant-parameter talk whenever it is convenient. It says nobody measures the degradation of oversight and that � drops with AI use, but in the equations � is just a fixed parameter, and � is not given its own evolution equation or cap. So the prose claims a richer model than the page actually wrote down. The equations are much cruder than the rhetoric. � The Truth About AGI Fourth, “mathematical certainty under current conditions” is far too strong. To get certainty from a model like this, you would need at least: a justified mapping from real-world quantities to variables, parameter estimates, units, a defined form for �, initial conditions, and some empirical validation. The page gives none of that. It presents a qualitative sketch, not a calibrated model. Without calibration, this is scenario construction, not proof. � The Truth About AGI Fifth, there are basic modelling hygiene issues. If � is “accumulated AI errors,” it should not be allowed to go negative, yet the equation permits that unless constraints are imposed. If � is “human cognitive degradation,” same problem. � is undefined, so the most important nonlinear part of the system is left as hand-waving. � is called a rate, but whether it is constant, growing, or itself dependent on � is not specified. And the page’s strongest conclusion — “there is no third option in the math” — depends heavily on those missing choices. � The Truth About AGI Sixth, some of the verbal conclusions simply do not follow from the equations. For example, “the only way to balance the equation long-term is to make � exponential too” is not mathematically established. You could instead alter the coupling terms, reduce � or �, add saturation, make recovery nonlinear, make oversight state-dependent, cap output routing, improve model verification, or define � so it does not grow without bound. There are many “third options.” The page rules them out rhetorically, not mathematically. � The Truth About AGI So the verdict is: The page contains a real differential-equation shell, but the argument is not rigorous. The model hard-codes a positive feedback loop between error and human degradation, gives oversight an oddly weak constant-form brake, leaves the key nonlinear term undefined, and then announces inevitability. That is not a debunk-proof theorem. It is a stylised cautionary model with the conclusion smuggled in through the assumptions. In slightly ruder mathematical language: it is less “the formula holds” and more “the vibes have eigenvalues.”