the addition operator concatenates tuples in python:

('a', 'b')+('c', 'd')
Out[34]: ('a', 'b', 'c', 'd')

From the docstring of sum:

Return the sum of a 'start' value (default: 0) plus an iterable of numbers

It means sum doesn't start with the first element of your iterable, but rather with an initial value that is passed through start= argument.

By default sum is used with numeric thus the default start value is 0. So summing an iterable of tuples requires to start with an empty tuple. () is an empty tuple:

type(())
Out[36]: tuple

Therefore the working concatenation.

As per performance, here is a comparison:

%timeit sum(tuples, ())
The slowest run took 9.40 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 285 ns per loop


%timeit tuple(it.chain.from_iterable(tuples))
The slowest run took 5.00 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 625 ns per loop

Now with t2 of a size 10000:

%timeit sum(t2, ())
10 loops, best of 3: 188 ms per loop

%timeit tuple(it.chain.from_iterable(t2))
1000 loops, best of 3: 526 µs per loop

So if your list of tuples is small, you don't bother. If it's medium size or larger, you should use itertools.

It works because addition is overloaded (on tuples) to return the concatenated tuple:

>>> () + ('hello',) + ('these', 'are') + ('my', 'tuples!')
('hello', 'these', 'are', 'my', 'tuples!')

That's basically what sum is doing, you give an initial value of an empty tuple and then add the tuples to that.

However this is generally a bad idea because addition of tuples creates a new tuple, so you create several intermediate tuples just to copy them into the concatenated tuple:

()
('hello',)
('hello', 'these', 'are')
('hello', 'these', 'are', 'my', 'tuples!')

That's an implementation that has quadratic runtime behavior. That quadratic runtime behavior can be avoided by avoiding the intermediate tuples.

>>> tuples = (('hello',), ('these', 'are'), ('my', 'tuples!'))

Using nested generator expressions:

>>> tuple(tuple_item for tup in tuples for tuple_item in tup)
('hello', 'these', 'are', 'my', 'tuples!')

Or using a generator function:

def flatten(it):
    for seq in it:
        for item in seq:
            yield item


>>> tuple(flatten(tuples))
('hello', 'these', 'are', 'my', 'tuples!')

Or using itertools.chain.from_iterable:

>>> import itertools
>>> tuple(itertools.chain.from_iterable(tuples))
('hello', 'these', 'are', 'my', 'tuples!')

And if you're interested how these perform (using my simple_benchmark package):

import itertools
import simple_benchmark

def flatten(it):
    for seq in it:
        for item in seq:
            yield item

def sum_approach(tuples):
    return sum(tuples, ())

def generator_expression_approach(tuples):
    return tuple(tuple_item for tup in tuples for tuple_item in tup)

def generator_function_approach(tuples):
    return tuple(flatten(tuples))

def itertools_approach(tuples):
    return tuple(itertools.chain.from_iterable(tuples))

funcs = [sum_approach, generator_expression_approach, generator_function_approach, itertools_approach]
arguments = {(2**i): tuple((1,) for i in range(1, 2**i)) for i in range(1, 13)}
b = simple_benchmark.benchmark(funcs, arguments, argument_name='number of tuples to concatenate')

b.plot()

(Python 3.7.2 64bit, Windows 10 64bit)

So while the sum approach is very fast if you concatenate only a few tuples it will be really slow if you try to concatenate lots of tuples. The fastest of the tested approaches for many tuples is itertools.chain.from_iterable

That's clever and I had to laugh because help expressly forbids strings, which are also immutable, but it works

sum(...)
    sum(iterable[, start]) -> value
    
    Return the sum of an iterable of numbers (NOT strings) plus the value
    of parameter 'start' (which defaults to 0).  When the iterable is
    empty, return start.

You can add tuples to get a new, bigger tuple. And since you gave a tuple as a start value, the addition works.