As of now, I personally feel that development of new programming languages rests on incremental paradigms. I see scope for a lot more innovation. Following ar

As of now, I personally feel that development of new programming languages rests on incremental paradigms. I see scope for a lot more innovation. Following are some thoughts: Have various computer science algorithms (graphs, trees, parsing, et al) tightly integrated into the language. (I know the popular opinion is to have these in libraries, however, I think having built-in data structures and algorithms would lead to further innovation.) The tooling should aware of run-time complexities of various algorithms and steps and thereby reason about the same. (E.g., "Warning: You have declared 'xyz' to be a graph, however, a tree is sufficient here.") Tooling should be able to say that while the programmer thought the implementation is Big-O[N^3], it's actually exponential. I wonder if programming languages can start making native use of machine learning advancements. Copilot et al already would have some internal representation as embeddings, so instead of just generating code as text, there should be value in using those representations as a part of the build tool-chain too. Better representation of 'is-a' relationships between various types. E.g., I should be able to pass a tree to a graph algorithm without writing any extra code or having run-time performance overheads. Get rid of overflows or precision constraints fundamentally from the language (and the the underlying hardware; over the time). I would like the tooling to automatically reason about which type is the best fit for the implementation during compilation -- byte, unsigned int 32, big-int, etc., via analysis of the program, and tell me what it chose and why. It may chose to typecast automatically for specific calculations when intermediary calculations need larger bit widths. Likewise, I would like innovation on how null references can be fundamentally dealt with as opposed to how each new programming language tries to work around the same in various ways. The fact that we use monospace fonts for programming seems to me to be a weakness of programming languages. We often need some alignment between adjacent lines of code to see the semantics of the code well. I would like to see a programming language which works well with variable-width fonts, without causing readability issues. Why you may ask? I think this should be explored as this would lead to innovation on how to make semantics come out independently of font width and text alignment. Having an 'alias' at many places would be super helpful. E.g. a base class may be generic method named such, whereas a derived class (more specific) may use the very same method implementation, while choosing a better name for the method. This is syntactic sugar only as this is readily doable now by having a new method that calls the base method as it is. This may be handy for example when the is-a relationship is handled between a tree and a graph in the example above. Language-native integration of units and dimensional analysis. Finally, I am a big fan of symbolic computation abilities like seen in Wolfram Language. The tooling can reason about the expressions/code, and figure an optimal strategy whether at compile-time or run-time.