LeCun's behavior here cannot be justified, but I think it's partially explained by his background. LeCun's life experience has taught him that when others criticize his work again and again and again, he should ignore them and persist if he thinks he's right:
During much of the 1990's and the 2000's he, along with Geoff Hinton and Joshua Bengio, ignored negative criticism by many naysayers as the three of them persisted on researching deep neural networks, which a majority of AI researchers had dismissed as a dead-end. It wasn't until the late 2000's, when Hinton showed he could train restricted Boltzmann machines efficiently, that other AI researchers started paying closer attention. And of course everyone else piled on after 2012, when a deep neural network (AlexNet) won ImageNet by a wide margin over all other methods.
The difference is that since Lecun made his contribution to MNIST, an entire field of endeavor with many experts as smart as him sprung into reality, and those people are making good criticism. You'd expect him to grow and mature (especially as the leader of an AI org at a major company) and learn to recognize that sometimes, he's really overhyping the technology more than necessary.
I certainly got the same negative response when I worked in ML in the 90s- "computers aren't fast enough, we don't have enough data, and we don't have the algorithms" and to be honest, I didn't really have the capability to disprove the people saying that. So I appreciate that he persisted and was successful.
During much of the 1990's and the 2000's he, along with Geoff Hinton and Joshua Bengio, ignored negative criticism by many naysayers as the three of them persisted on researching deep neural networks, which a majority of AI researchers had dismissed as a dead-end. It wasn't until the late 2000's, when Hinton showed he could train restricted Boltzmann machines efficiently, that other AI researchers started paying closer attention. And of course everyone else piled on after 2012, when a deep neural network (AlexNet) won ImageNet by a wide margin over all other methods.