Thoughts on Programming Languages • Erik Meijer, Mads Torgersen & Dave Thomas • YOW! 2020

Programming languages continue to evolve with increasing focus on combining paradigms rather than staying in strict camps (functional, OO, etc.)

The challenge of efficient implementation often conflicts with elegant mathematical/theoretical models in language design

Cloud computing and distributed systems present new challenges for language design, particularly around transparency, error handling, and latency

Low-code/no-code tools serve a purpose but have limitations and won’t replace traditional programming languages

Text remains the dominant format for programming, though tooling around it has greatly improved

Compositionality is crucial for making complex programs comprehensible and maintainable

The integration of machine learning and continuous mathematics presents new challenges for programming language design

Language wars are becoming less prevalent as developers recognize different tools for different purposes

Specification languages and formal verification remain challenging to integrate effectively with implementation languages

Modern language design struggles with balancing abstraction and performance, particularly around areas like:

• Concurrency/parallelism
• Memory management
• Hardware optimization
• Cross-language interoperability

Notebooks have emerged as a practical form of literate programming, especially for data science

Programming languages need to evolve to better handle:

• Probabilistic programming
• Quantum computing
• Cloud-native development
• Machine learning workloads