I treat the Claude output that is hard to comprehend as an encoding/encryption. Claude knows how to decipher it, humans don't. I see this frequently in design docs from inexperienced engineers who used LLMs - they will contain terms (often two words hyphenated) that aren't obvious and should be defined, or simply replaced with simple language. If you prompt claude it is able to decipher and explain / replace this gibberish.
My understanding is the water tested in this study is the water in the lavatory faucet and what they use to make hot beverages onboard. If you ask a flight attendant for water you would always get water from a can/bottle/box depending on airline, at least based on my limited experience.
Remember NFTs? It was all the rage not that long ago and anyone who questioned otherwise was said to 'not get the big picture'. NFTs were going to improve EVERYTHING.
It's not exactly the same, but the hype is similar.
I made zero mention of my driving skill. Assume I have normal driving skill, but don't drink, don't go over 80mph, don't pass aggressively, and don't text and drive.
A Tesla is currently safer than people that do that, and they want me to believe that system is safe enough to get into. But it's not as safe as a sane responsible driver.
Hiring one person to do this at a company this scale, will give you results in the next decade. BTW, when the report is complete, it will no longer be accurate. Sure you can scale the process by putting more people on it, but it won't scale linearly. Additionally you will need buy in from all the system managers to dedicate time to assist the new data privacy team. It is doable, but comes at a cost.
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