The issue is not merely that you don't have ground up definitions which respect the time constraints. The issue is that you don't seem to have any ground-up definitions at all, i.e. not even for something like AIXI. The goals themselves lack any bottom up definitions.

Worst of all you build stuff from the dubious concepts like that monolithic "intelligence".

Say, we want to make better microchips. We the engineers have to build it from bottom up, so we make some partially implemented intelligence to achieve such a goal, omitting the definition of what exactly is 'best microchip', omitting the real world goals, focussing the search (heuristics are about where you search!) and instead making it design smaller logical gates and then route up the chip, and perhaps figure out manufacturing. All doable with same methods, all to the point, strongly superhuman performance on subhuman hardware.

You build Oracle AI out of that monolithic "intelligence" concept, and tell it - I want a better microchip. This monolithic intelligence figures out how to take over the world to do so. You think, how do we prevent this monolithic intelligence concept from thinking about taking over the world? That looks like an incredibly difficult problem.

Or the orthogonality thesis. You think - are the goals of that monolithic intelligence arbitrary?

Meanwhile if you try to build bottom up or at least from the concepts with known bottom up definitions, well, something like number of paperclips in the universe is clearly more difficult than f(x,n) where x is the output on the screen at the step n and f is 1 if the operator responds with the reward button, 0 otherwise (note that the state if the computer is unplugged has to be explicitly modelled, and its not trivial to build bottom-up the concept of mathematical function 'disappearing', may actually be impossible).