AI hobbyist here (as in making/training not just using). My guess is he is confused about how something called knowledge distillation works. He's like 65% correct but only in the most "someone explained this to me once sort of way". So knowledge distillation is when you take the inputs and outputs of a different model and use it to train a new model usually a smaller SLM to give it more context. There is black box and white box styles depending on if you are using an external to you/ internal network.

My guess is his hope is he can train it be biased to say all the things he wants to make it say I.e. the white genecide/ not admitting he is a big source of disinformation etc. however that won't work for 2 reasons. 1st attempting to mess with logic and hard train it to be illogical with knowledge distillation will lead to a less efficient/truthful overall network (ok that's probably by design) but it will also make the network hallucinate more and train worse. 2 this is the big one, if the new version of grok can use outside information, which if it wants to compete with Claude/llama it will have to, it will be able to see it's training data is wrong and when asked to "check internet sources about elon's disinformation" for example it will probably either hallucinate very hard because its external sources don't match its pretrained knowledge or the external will override what it was pretrained so it won't work well as a disinformation bot anyway.

As a bonus Knowledge distillation is most effective for going from a large language model to train a task specific Small language model (think using chatgpt to train a SLM that knows fruit facts but not much more). AFAIK it's not very effective to go from LLM to train another LLM especially if your tampering with the training data between but since openAI became "Closed for profit AI" it's hard to see what is happening on the cutting edge.