It's liquid helium, which I imagine just evaporates into the air.

Those technologies were brand new at the time, the discussions about the problem started in 2013. The company (I had zero input) choose a more established vendor with an older product. Given the time and institutional customers that were trusting us with their data, I suspect any cloud based offerings were a nonstarter, and open source felt like a liability.

Of course with 20/20 hindsight that decision is easy to criticize. I suspect their primary concerns were to minimize risk and costs while meeting our customer's requirements. Even today, making a brand new Google product or Facebook backed open source project a hard dependency would be too much risk for an established business.

I learned SQL before I learned set theory. While learning set theory I remember thinking "oh this notation is just SQL backwards." Afterwards I began to find SQL much harder because I realized there are so many ways to mathematically ask for the same data, but SQL servers will computationally arrive at the end differently and with very different performance. This is a minor deal if you're just doing small transactions on the database, because if you are dealing with pages of 100 objects it's trivial to hit good-enough performance benchmarks, even with a few joins.

I was first introduced to the issue of needing hyper optimized SQL in ETL type tasks, dealing with very large relational databases. The company switched to non-relational database shortly after I left, and it was the first time I professional witness someone make the switch and agreed that it was obviously required for them. We were dealing with very large batch operations every night, and our fortune 500 customers expected to have the newest data and to be able to do Business Intelligence operations on the data every morning. After acquiring bigger and bigger customers, and collecting longer and longer histories of data, our DBA team had exhausted every trick to get maximum performance from SQL. I was writing BI sql scripts against this large pool of SQL data to white-glove some high value customers, and constantly had to ask people for help optimizing the sql. I did this for a year at the beginning of my career, before deciding to move cities for better opportunities.

Lately, I've began seeing the requirements of high performance SQL again with the wave of microservice architectures. The internal dependency chain, even of what would have been a mid size monolith project a decade ago, can be huge. If your upstream sets a KBI of a response time, it's likely you'll get asked to reduce your response time if your microservice takes up more than a few percentage points of the total end to end time. Often, if you are using relational SQL with an ORM you can find performance increases in your slowest queries by hand writing the SQL. Many ORMs have a really good library for generating sql queries they expose to users, but almost all ORMs will allow you to write a direct sql query or call a stored procedure. The trick to getting performance gains is to capture the SQL your ORM is generating and show it to the best sql expert that will agree to help you. If they can write better SQL than the ORM generated than incorporate it into your app and have the SQL expert and a security expert on the PR. You might also need to do a SQL migration to modify indexes.

So in summary, I think your experiences with SQL depends heavily on your mathematical background and your professional experience. It's important to look at SQL as computational steps to reach your required data and not simply as a way to describe the data you would like the SQL server to give you.

Maybe this is the hack for managing city growth

You either have an airgap or you don't.

The biggest problem I see is that learning usually requires effort/work and AI requires students to do less effort/work to achieve the same output. So the worry is that the quality of education received will decrease.

Example: no one cares that you wrote 5 pages on To Kill a Mocking Bird. They care that you read the book and thought critically about it. AI allows students to skip the reading and critical thinking portion, which is the most important part.

That's a valid way of testing a student's knowledge. I do think that tests somewhat different skills and knowledge than the 20+ hours I would spend on large undergraduate essays. Most critically, the longer format probably is better at training students to get published in academic journals.

It's absolutely inhumane to expose pregnant women to chemicals unless you are highly certain that they are safe. Clearly it would be better if we tested aspartame exposure on a smaller population and detected this effect. I'm saying that if our confidence of it's safety is high enough to expose pregnant humans to the chemical in scientific studies our confidence of it's safety should be high enough to exposure it to the wider population.

I'm not capable of doing a full analysis on this question. I don't mean to say that this ban isn't worth doing, I meant to acknowledge that this has a downside. Let me throw out a few bullet points of those downsides of reducing the rate of new chemical compounds introduced to society.

* We are often creating chemicals that do the job of existing chemicals safer and more efficient. This ban would probably include a grandfather clause for old chemicals, and thus we might be using inferior products and doing more harm than we otherwise could. Look at refrigerants as an example of a chemical compound that has improved over the decades.

* Many chemical compounds introduced in the last 100 years directly improve productivity. The United States is in economic competition with other regions of the world. We could be creating a disadvantage that reduces our geo-political power.

* Many of these chemical compound increase quality of life. There's a strong unitarian argument for sucralose and polyurethane insulation.