I've been wanting to explore how people talk to Grok since asking Grok for information became common place. To test out a new scraping method, I took a look at tweets with @grok today since there have been an uptick in requesting sexual modifications to images and analyzed them w/ Opus 4.5 in R.

When is your data yours?

For enterprise, training on customer data is prohibited by default across all providers analyzed.

For consumers, the standard is an opt-out model, where data usage is assumed unless settings are manually changed. Uniquely, xAI trains on data even if you are logged out.

Next, indemnification determines the financial burden of a third-party lawsuit.

Enterprise agreements generally offer "Mutual Indemnification," sharing the burden of third-party lawsuits.

For consumers, this is nonexistent - meaning the user actually pays to defend the company.

For context, mandatory arbitration clauses waive a user's right to public court trials and class action lawsuits.

It means issues are mediated by a third-party - not a court.

In these data, OpenAI alone mandates binding arbitration for all users, regardless of tier.

Given the Dem dominance, I wanted to look deeper at Republican co-sponsors. Surprisingly, 69% of co-sponsors on Rep AI bills were Dems.

That is, when Reps introduced bills, they tended to do so solo. But Dems had a stronger tendency to build coalitions within and across party lines.

To find who bridges the partisan divide, I built a co-sponsorship network where legislators are connected if they co-sponsor the same bill.

To do this, I used a metric called betweenness centrality which measures how often someone sits on the path between others. A high score means they're a bridge

Given the recent executive order by Trump to federally regulate AI, I was curious to explore what standalone bills Congress has managed to pass - and who has led the charge in passing them.
With Opus 4.5 in R, I pulled bills with AI related terms then made topics from those will full text to analyze