Author works on ML for DeepMind but doesn’t seem to be an out and out promptfondler.

Quote from this post:

I found myself in a prolonged discussion with Mark Bishop, who was quite pessimistic about the capabilities of large language models. Drawing on his expertise in theory of mind, he adamantly claimed that LLMs do not understand anything – at least not according to a proper interpretation of the word “understand”. While Mark has clearly spent much more time thinking about this issue than I have, I found his remarks overly dismissive, and we did not see eye-to-eye.

Based on this I'd say the author is LLM-pilled at least.

However, a fruitful outcome of our discussion was his suggestion that I read John Searle’s original Chinese Room argument paper. Though I was familiar with the argument from its prominence in scientific and philosophical circles, I had never read the paper myself. I’m glad to have now done so, and I can report that it has profoundly influenced my thinking – but the details of that will be for another debate or blog post.

Best case scenario is that the author comes around to the stochastic parrot model of LLMs.

E: also from that post, rearranged slightly for readability here. (the [...]* parts are swapped in the original)

My debate panel this year was a fiery one, a stark contrast to the tame one I had in 2023. I was joined by Jane Teller and Yanis Varoufakis to discuss the role of technology in autonomy and privacy. [[I was] the lone voice from a large tech company.]* I was interrupted by Yanis in my opening remarks, with claps from the audience raining down to reinforce his dissenting message. It was a largely tech-fearful gathering, with the other panelists and audience members concerned about the data harvesting performed by Big Tech and their ability to influence our decision-making. [...]* I was perpetually in defense mode and received none of the applause that the others did.

So also author is tech-brained and not "tech-fearful".