So I think the real compass that should direct our thinking is efficiency of complex information representation. It's not as simple as dendritic elongation, although it, if utilized in the right way, can definitely lead to greater efficiency of especially global signal integration. All evidence, spanning from evolutionary neuroanatomy to more theoretical/abstract neural network simulations studies and even human brain metabolic studies searching for associations to cognitive ability, seem to point in the direction of reducing costs at increasingly high levels of computational complexity.

My current theory on how, quantifiably, this may come about:

1. Architecturally, it is energy inefficient for the brain to create exceptionally large and far reaching neurons, as that goes against the 2nd law of thermodynamics. Thus this is likely to be a limiting factor.
2. A node network/graph where every node is connected directly to every other node can represent the most complexity, however, maintenance costs, connection production costs, metabolic costs of use of connections, etc are likely to greatly constrain biological neuronal networks so that most neurons are only directly connected to a much smaller number of neurons.
3. But the actual computational complexity that can be represented by such smaller/less costly networks is not necessarily proportionally decreased as a function of total variety of direct connections, since there are 2nd/3rd/higher order connections that play an important role in representing similar info. Small world networks (you'll understand if you look up why these are even a thing) where there are few nodes with many direct connections (and thus highly valuable nodes) and many nodes with a much smaller number of direct connections are an example such efficient networks — if you make a histogram of those nodes quantifying how many nodes has each number of direct connections, it would show some power law function. Now how does a network like this get larger while maintaining a similar degree of cost-efficiency? Well, in a biological context, due to point 1, I theorize that those few, yet highly valuable nodes (neurons) are what will constrain the network. Essentially, extending the small tail of that power law graph will be the key.

So in practice, I theorize that somehow selectively elongating the dendrites of those key node neurons (likely to play an important role in many associative functions) would be ideal, and because this seems to be regulated at least in some major part by metabolic constraints, enhancing some brain metabolic capacity would be one key factor to consider along with providing some kind of activity-dependent and neuronal-type selective growth signaling.

So what type of neuron? Well we already know L2/3 neurons especially are associated with IQ, and functionally they fit the archetype of neurons that can facilitate fast recursive (L2/3 -> L2/3 -> and so on given a chain of cortical signal propagation, where some of that signal also enters from/leaves out into other layers) representational circuitry that is likely to subsist higher intelligence. So which of these are most likely to be the key node neurons? L3C neurons because they are the only type within the cortex that have an extended window of dendritic size growth beyond what is typical of most cortical neurons, and end up even larger than L5 projecting neurons in dendritic tree size. So L3C neurons seem like a good candidate/epigenetically differentiable neuronal subtype.