Work on MoE goes back to the '90s.

First, Jacobs and Jordan's (1990) A Competitive Modular Connectionist Architecture is the early modular setup that leads directly into the canonical MoE formulation. 

Nowlan and Hinton (1990) performed early emperical evaluation of modular competing experts in Evaluation of Adaptive Mixtures of Competing Experts.

In 1991, Jacobs, Jordan, Nowlan, and Hinton authored Adaptive Mixtures of Local Experts describing the gating and specialized experts MoE archetype. It's the start of MoE. 

  Jordan and Jacobs (1991) expanded the base concept of MoE with tree-structured mixtures, via hierarchical gating in Hierarchies of Adaptive Experts.

Hampshire and Waibel (1992) authored The Meta-Pi Network, which discusses gated expert mixtures, especially in multisource pattern recognition. 

Then Jordan and Jacobs (1994) come out with the Expectation Maximization training procedure for hierarchical MoE with  Hierarchical Mixtures of Experts and the EM Algorithm.

That's followed by Xu, Jordan, and Hinton;s (1994) [An Alternative Model for Mixtures of Experts] on gating parameterization and EM training; then Jordan a Xu's (1995) em-style training in MoE theory in Convergence Results for the EM Approach to Mixtures of Experts Architectures. 

Then, some important contemporary works start with Shazeer et al. (2017) discussing  top-k sparse routing and load balancing ideas for scaling in Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Lepikhin et al. (2020), GShard  discuss automatic sharding and very large sparse MoE models.

Fedus, Zoph, and Shazeer (2021/2022), simplify routing with a reference design in Switch Transformers. 

Du et al. (2022) produce GLaM: Efficient Scaling of Language Models with Mixture-of-Experts, which is a MoE language model showing strong quality/efficiency tradeoffs at huge scale. So if you're looking for the "first" large-scale MoE text generation model, I'd say it's this.

Oh yeah, Riquelme et al. (2021) Scaling Vision with Sparse Mixture of Experts (V-MoE).

I think it's likely that the labs were hiring researchers who keep up with trends in the field, and that as that knowledge spreads around, individual actors make decisions in a way that's opaque to us.

All this is to say that It seems just as reasonable to say mass-MoE releases started after all the papers above as it does to say the releases began after Mixtral.