Pros
Some of the people that were here before the new "AI" theme took over were good to work with, but sadly, they are mostly all gone now.
Cons
This is only pertaining to the data science/analytics group as that is the group I'm in, and I don't know much about the other teams like the sales people who always seem happier. Anyhow, I can say without reservation that the leadership of the data science team (Managers, Directors, VP and all the way to the CTO) are deeply unqualified to do the job that they are hired for. In fact, all day long, they are very good at pretending that they are doing anything of value with things like LinkedIn posts, now they have Medium blogs (which they refer to as papers, but are just Medium articles!), frequent vendors and 3rd parties coming in like AWS to set up some new technology, platform, etc... None of this has been demonstrated to have ANY value. In fact, no one from the data science team has ever properly measured in any A/B test sense the value of any product/solution/idea they have built and put into production. Yet, the CEO is convinced frequently to shell out more and more cash to keep up with trends. With the new leadership of the data science team, ML team, "Ops" team, whatever latest term they are using these days, they have become completely shameless in their use of LLMs/AI. It is not that they are using it for development, they are using it for everything: writing documents, writing e-mails, Slack messages, and this is encouraged. The leadership of data science frequently says stupid things like "coding is painful", and "why does anyone want to code". Dude, that is because you have no knowledge of any software engineering principles, why are you trying to code to begin with? Do you have that much of an inferiority complex that you have to show you are smart but are really not? But, all day they are "vibe coding" things they have no understanding of are just copying and pasting from chatGPT and trying to build "prototypes" and show up the actual technical people. And once again, "AI"-ops, MLOps, Data science, pipelines, is just recycling known work that is either already outdated or not relevant. The other problem is that the majority of the people at this company are "chatbot"-trained. If you were to pull aside any one of the SMEs and ask them some technical question on how some algorithm works or what is the best technology to use, they wouldn't be able to tell you as most of their vocabulary is just buzzwords. They have no idea what problem they are trying to solve and have instead built ridiculous hierarchies / processes with no substance behind them.