> It'll die down like web3, Big Data, cloud, mobile, etc

At least half of those the promise was realised though - mobile is substantially bigger than the market for computers and cloud turned out to be pretty amazing. AWS is not necessarily cost effective but it is everywhere and turned out to be a massive deal.

Big Data and AI are largely overlapping, so that is still to play. Only web3 hasn't had a big win - assuming web3 means a serious online use case for crypto.

"Die down" in this context means that the hype will come, go and then turn out to be mostly correct 10 years later. That was largely what happened in the first internet boom - everyone could see where it was going, the first wave of enthusiasm was just early. I don't think any technology exists right now that will take my job, but I doubt that job will exist in 20 years because it looks like AI will be doing it. There are a lot of hardware generations still to land.

> At least half of those the promise was realised though

I dunno, I think there might be different sets of "promises" here.

For example, "cloud infrastructure" is now a real thing which is useful to some people, so one could claim that "the promise of cloud infrastructure" was fulfilled.

However that's not really the same promises as when consultants preached that a company needed to be Ready For The Cloud, or when marketing was in a slapping "Cloud" onto existing product marketing, or unnecessary/failed attempts to rewrite core business logic into AWS lambda functions, etc.

"The proportion of global companies planning to increase spending on AI over the next 12 months has slipped to 63% from 93% a year earlier, according to a recent survey of 2,500 business leaders by software company Lucidworks Inc. Meanwhile, just 5% of companies in the US are using AI, according to the Census Bureau."

https://archive.ph/h6QW7#selection-2069.305-2077.1

https://www.bloomberg.com/opinion/articles/2024-06-20/google...

To a first approximation, I expect companies to spend nothing on AI and get put out of business if they are in a sector where AI does well. Over the medium-long term the disruption looks so intense that it'll be cheaper to rebuild processes from the ground up than graft AI onto existing businesses.

Which sectors are these?

AI and 'Big Data' (as trends) aren't really overlapping in my view. Of course training these LLM models requires a huge amount of data but that's very different from the prospect of spinning up a Spark cluster and writing really badly performing Python code to process something that could have easily been done in a reasonable time anyway on a decent workstation with 128gb of RAM and a large hard drive/SSD, which was a large part of what the hype train was a few years ago.

Go back further:

At a point in time the database was a bleeding edge technology.

Ingres (Postgres)... (the ofspring of Stonebreaker), Oracle, ... Db2? MSSQL? (Heavily used but not common)... So many failed DB's along the way, people keep trying to make "new ones" and they seem to fade off.

When was the last time you heard someone starting a new project with Mongo, or Hadoop? Postgres and Maria are the go to for a reason.

There's a team at my company that chose Mongo for a new data transform project about a year ago. They didn't create a schema for their output (haven't to this day) and I'm convinced they chose it purely because they could just not handle any edge cases and hope nobody would notice until it was already deployed, which is what happened. For example maybe one in a thousand of the records are just error messages - like they were calling an API and got rate limited or a 404 or whatever and just took that response and shoved it into the collection with everything else.

I... Well dev/null is an option...

https://www.youtube.com/watch?v=b2F-DItXtZs

It's all just history repeating.

still, I would have out that mess in jsonb in Postgres :D

Postgres is awesome and part of its charm is the extensibility it offers enabling the adoption of innovative features introduced by competing DB's.

Postgres adopted a lot of mongos features when it released the JSON data type and support for path expressions

Mongo is still very big, including for greenfield.

It's tiny compared to 10 years ago when it was all the rage and DBs were dead...