These are different sorts of alignments, with different sorts of math behind them.
Genome assembly is the shortest common super sequence problem. It involves finding the best rearrangement and overlap of reads which minimize the overall sequence, given the expected errors in the read technology. It would still be hard even if all of the reads were perfect.
Sequence alignment looks at two or more sequences in their entirety, and does a best fit alignment using a given model of how substitutions and gaps can occur. This model may be based on chemical or evolutionary knowledge.
A "super-efficient solution to sequence alignment" doesn't lead to a way to tell how the reads should be assembled into a single large sequence, even ignoring possible read errors.
