It is not enough to achieve results: intelligence is partly a matter of how they are achieved. Productive laziness involves avoiding unnecessary work.
A chess champion who wins by working through all the possible sequences of moves several steps ahead and choosing the best one is not as intelligent as the player who avoids examining so many cases by noticing that the pieces form a pattern which points directly to the best move.
Why is laziness important? Given any solvable task for which a finite solution is recognizable, it is possible in principle to find a solution by enumerating all possible actions (or all possible computer programs) and checking them exhaustively until the right one turns up. In practice this is useless because the set of possibilities is too great.
This is called a `combinatorial explosion'. Any construction involving many choices from a set of options has a potentially huge array of possible constructs to choose from. If you have 4 choices each with 2 options, the total set of options is 16. If you have 20 choices each with 6 options, the total shoots up to 3,656,158,440,062,976. Clearly exhaustive enumeration is not a general solution, so lazy shortcuts have to be found.
For example, a magic square is an array of numbers all of whose rows, columns, and diagonals add up to the same total. Here is a 3 by 3 magic square made of the digits 1-9:
6 7 2
1 5 9
8 3 4
If you try to construct a 3 by 3 magic square by trying all possible ways of assigning the 9 numbers to the locations in the square, then there are 362,880 possible combinations. Trying them all would not be intelligent. A more sensible procedure would involve testing partial combinations to see whether they can possibly be extended satisfactorily, and, if not, rejecting at one blow all the combinations with that initial sequence. It is also sensible to look for symmetries in the problem. Having found that you cannot have the number 5 in the top left corner, reject all combinations that involve 5 in any corner.
Yet more subtle arguments can be used to prune the possibilities drastically. For example, since eight different triples with the same total are needed (one for each row, one for each column and the two diagonals), it is easy to show that large and small numbers must be spread evenly over the triples, and that they must in fact add up to 15. So the central number has to be in four different triples adding up to 15, the corner numbers in three triples each, and the mid-side numbers in two each. For each number we can work out how many different triples it can occur in, and this immediately restricts the locations to which they can be assigned. For example, 1 and 9 must go into locations in the middle of a side, and the only candidate for the central square is 5. In fact, a high-level symmetry shows that you need bother to do this analysis only for the numbers 1-4. You can then construct the square in a few moves, without any trial and error. What about a 2 by 2 magic square containing the numbers 1, 2, 3, and 4? Think about it!
These examples show that the ability to detect shortcuts requires the ability to describe the symmetries, relationships, and implications in the structure of the task. It also requires the ability to notice them and perceive their relevance, even though they are not mentioned in the statement of the task. This kind of productive laziness therefore depends on intentionality and flexibility, but motivates their application. Discovering relevant relationships not mentioned in the task specification (e.g., ``Location X occurs in fewer triples than location Y'') requires the use of a generative conceptual system and notation (i.e., one that enables novel descriptions to be formulated). Being lazy in this way is often harder than doing the stupid exhaustive search. But it may be very much faster. This points to a need for an analysis of the notion of intellectual difficulty.
Productive laziness often means applying previously acquired knowledge about the problem or some general class of problems. So it requires learning: the ability to form new concepts and to acquire and store new knowledge for future applications. Sometimes it involves creating a new form of representation, as has happened often in the history of science and mathematics.
Laziness motivates a desire for generality -- finding one solution for a wide range of cases can save the effort of generating new solutions. This is one of the major motivations for all kinds of scientific research. It can also lead to errors of over-generalization, prejudice, and the like. A more complete survey would discuss the differences between avoiding mental work (saving computational resources) and avoiding physical work.