Picking single-aspect reasoning lightens the condition (and thus, the strength of the argument) quite a bit. If you take a single learning problem domain, there

Picking single-aspect reasoning lightens the condition (and thus, the strength of the argument) quite a bit. If you take a single learning problem domain, there are a number of fully unsupervised ML/IR systems already out there with superhuman reasoning capabilities. The second part of your argument is a funky one. Fully autonomous R&D-capable AIs (AKA self-improving that is, a problem-solving / reasoning system, that is capable of creating new, and improving upon existing problem-solving / reasoning systems -which, coincidently, might include itself) are the modern equalent of the philosopher's stone, and has not only withstanded attacks in ways that few other problem domains did, but seems to be an AI-hard[1] problem by itself. As I laid down above, market forces does not favour these kind of R&D; thus I predict this problem to be unsolved for the short-term future. On the other paw, the second parameter of your cost function - "cost of data" - can be exchanged by "amount of available data" -which seems to be in an explosive boom from where I stand. Thus, while we're talking predictions, I would hypothise a short-term future with a slow, and incremental improvements upon tackling domains combined with an exponentially growing amount of available data, making even relatively naive learning methods to have results well within the optimal range. It will still require humans. It will still require R&D. But the "cost of learning" will be so much lower, that the process will be routinely employed (successfully) by developers; thus diversifying the overall software market even further. I know it doesn't make for a fancy movie, but throwing away 6.5M years of R&D by mother evolution doesn't seems to be a wise diversification move. [1] http://en.wikipedia.org/wiki/AI-complete http://en.wikipedia.org/wiki/AI-complete