While there are some key concepts to things like databases, the fact remains that your 1985 database would not be considered sufficient in todays world: It would have too many limitations, lack features we now take for granted, would not scale to modern data requirements, etc. Supporting all that "modern" functionality is non-trivial and requires a huge amount of effort. You can't just say "Well, we figured out space and computationally-efficient hashing, so relational databases are well on their way to being feature-complete"
There's a reason we haven't stuck with 1.0 on our platforms, and it's not just security or a desire for a bigger version number: New demands required new functionality and new ways of building things.
iOS was basically AppKit, so anyone already developing for the Mac knew most of what they needed to know to develop for iPhone.
Pretty much every programming innovation is incremental, and doesn't require throwing out all of your previous knowledge and starting over.
Maybe. But AppKit was not the Mac Toolbox.
When my career began being good at memory management was a skill to be proud of. I would say now, being good at concurrency is a skill to be proud of.
I don't really have to worry about memory management any longer but didn't worry about threading when I started my career.
As I see the younger generation entering the programming field I wonder in what ways the craft will be different when they've had a few decades under their belt.
Will parameter tuning datasets for machine learning be the coveted skill? Who knows.
But the demand for developing in AppKit suddenly increased by orders of magnitude.
That's the sort of shift in the environment that the grandparent is talking about. Fundamental CS tech was arguably better in the 1970s and 1980s, because it moved more slowly and you had time to get the details right. That doesn't matter if you're building say a mobile Ethereum wallet in 2018, because you're building for the user expectations of today, they don't care about data integrity or security as long as it doesn't fail during the period where they're deciding which tech to use, and software that solves the problem (poorly) now is better than software that doesn't exist.
I believe you are a victim of survivorship bias.
There was plenty of shitty software in the 70s and 80s. The difference between then and now is that we haven't been able to wait for 4 decades, to see what software of 2018 stood the test of time.
In the 1980s there was a lot of "foundational research" (poorly re-inventing the wheel) for microcomputer people who did not know about the work done on large computers in the 1960s. Move fast and break things was also very much a thing for microcomputer manufacturers and most microcomputer software vendors. Look at how many releases software packages went through, and at what rate.
That has always been the case and is about as good a one-line summary of the software industry as I can think of.
The "modern" database systems are now going back to the exact design principles that the books you refer to solved long time ago. There is tons of research, dissertations,.. that focuses on this from decades ago.
Its just now that the new systems realize that these problems actually exist.
If you dont know the history of a certain field and what came out, you repeat and make the same mistakes again. This seems to also apply to software engineering.
A great example of this is the evolution of FB/Google/Amazon. Portions of their core tech have been completely re-written over the years for marginal gain, but there is a large premium to being the best in tech.
In other parts of the industry every new cycle enables some new area of tech, and those marginal gains become the minimum bar for entry. e.g. Deep Learning and Computer Vision, distributed systems and cloud computing/SaaS.
Many of the underlying problems and solutions exist for decades. Database systems you mention are a good example of this.