The fact it would probably be software, executing on a digital platform. These things typically lend themselves to optimization and scaling.
Seems to me that the chance we'll have just enough computing power to run human-level AI—without sufficient resources to go beyond that—is a small one. It is far more likely that we will either have too little, or more than ample computing power available.
What evidence do you have that human-level AI won't scale easily?
The best argument I can think of in your favor would be prohibitive computational complexity requirements, but the aforementioned dynamic still applies—it's unlikely that we'd have only just enough computing power to achieve human-level and not go beyond that.
And that's just talking about a raw increase in computing power. We don't have any idea whether a human level intelligence program would even be able to put increased processing power to good use (and it could theoretically be detrimental; look at what happens to humans who have too many neuronal connections).
Simulating the brain isn't necessarily the path to human-level AGI though. Simulating biology in digital form tends to be inefficient.
Modern machine learning scales easily, or at least relatively easy.
>(and it could theoretically be detrimental; look at what happens to humans who have too many neuronal connections)
Perhaps a constraint of our brain's cognitive architecture and not intrinsic to cognition itself. Still, interesting point.
Now as I said, "adding another person doesn't necessarily make things go faster." It can, but not always. But what evidence do we have that a human level AI would be able to scale easily? That seems to be a pretty huge assumption that gets glossed over.