That's not true - they don't lock global weights when updating, so if you have lots of cores and threads, asynchronous updates will result in very poor accuracy, making training useless.
If you train on the big dataset that is produced by demo-train-big-model-v1.sh (which includes news corpora from 2012 & 2013, the 1BN word dataset from statmt, the UMBC web corpus and the whole wikipedia) using only one thread, accuracy on the google analogy dataset drops to 68% (down from ~71.5% when using 20 threads.)
This is due to the learning rate algorithm used: Learning rate is linearly reduced with the number of processed words. When K threads are used, the input dataset is split into K parts, processed in parallel, which means that more parts of the dataset have a chance to influence the resulting vectors in the beginning of the computation (where learning rate is relatively high.)
In the limit, performance would indeed suffer as all updates would happen in parallel.
[1] Recht, Benjamin, et al. "Hogwild: A lock-free approach to parallelizing stochastic gradient descent." Advances in Neural Information Processing Systems. 2011.
The summary would be that accuracy per pass suffers slightly, but since the speedup is close to linear for the first dozen or so cores, each pass is much faster to run. The result is that the wall time to achieve a given level of accuracy is much shorter despite the slightly lower accuracy per pass.