After the code has been tested, he still doesn't know that it works, only that it sometimes produces the expected output. (As a mathematician, it's a lot easier for me to trust a proof than an empirical verification; but I agree that what the HM type system proves is not always what one wants it to prove.)
It's an amusing anecdote. Even when using Haskell, there are some obvious constraints to relying on the compiler as the sole validation of correctness.
In particular, the kinds of errors that still may exist are ones where the implementer has a fundamental misunderstanding of what needs to happen. The code may correctly implement what he wanted it to do, but what he wanted it to do is wrong.
If you still object, consider: I'm describing the scientific method.
As he and others have pointed out, the author was pretty up-front about the scale (relatively small; looks like a few hundred LOC) and nature (implementing an extant, well-defined API) of the problem. It's not an application as you suggest, and the author doesn't suggest it's a good idea for applications, either.
The conclusion isn't "use Haskell & never test your code!". After all, he explicitly offers no conclusion. :) It's just an example of how, sometimes, you can be reasonably certain that your Haskell program is correct when it compiles.
Myself, I'd still run it, or at least QuickCheck it. I don't trust myself not to write logically sound, completely incorrect code.
However, if you prove the right thing, your code is correct. If you test the right thing, you code is correct in the cases you tested.
And yet, for whatever reason, only having tests is fine but only having proof isn't.
Only having tests is "fine" for most circumstances because, unfortunately, proofs are intractable for most systems. During a software engineering talk I recently attended, an interview candidate stated that the largest proved-correct code base is on the order of 10,000 lines of code. Most people don't have the resources to do even that.
But, philosophically, I always want tests for the same reason that I run experiments: I want empirical evidence, not just reasoning. Empirical evidence increases my confidence that we have accounted for what we think we have.