I'm not trained as a programmer/software engineer, but this was ChatGPT's response:
1. Added Boilerplate and Ceremony:
Simple tasks may require extra type declarations and structures, adding “ceremony” that feels unnecessary for quick one-off solutions.
2. Rigid Type Constraints:
Combining different data types or working with unclear data shapes can force complex type solutions, even for simple logic, due to strict compilation rules.
3. Complex Type Definitions for Simple Data:
Handling semi-structured data (like JSON) requires elaborate type definitions and parsing, where dynamically typed languages let you manipulate data directly.
4. Refactoring Overhead:
Small changes in data types can cause widespread refactoring, turning minor edits into larger efforts compared to flexible, dynamically typed environments.
5. Complexity of Advanced Type Systems:
Powerful type features can overwhelm trivial tasks, making a few lines of code in a dynamic language balloon into complex type arguments and compiler hints.
All of those come down to "Let the compiler guess about my data, and it may produce correct results in some of the cases."
A risk is, unexpected data (empty field instead of zero; a real number introduced in untested corner cases where only an integer will actually work etc) can cause issues after deployment.
Those 'complex' requirements mean, if you want a reliably correct program well then you'll have to put in this much work. But go ahead, that 'trivial task' may become something less trivial when your task fails during Christmas sales season or whatever.
1. Added Boilerplate and Ceremony: Simple tasks may require extra type declarations and structures, adding “ceremony” that feels unnecessary for quick one-off solutions.
2. Rigid Type Constraints: Combining different data types or working with unclear data shapes can force complex type solutions, even for simple logic, due to strict compilation rules.
3. Complex Type Definitions for Simple Data: Handling semi-structured data (like JSON) requires elaborate type definitions and parsing, where dynamically typed languages let you manipulate data directly.
4. Refactoring Overhead: Small changes in data types can cause widespread refactoring, turning minor edits into larger efforts compared to flexible, dynamically typed environments.
5. Complexity of Advanced Type Systems: Powerful type features can overwhelm trivial tasks, making a few lines of code in a dynamic language balloon into complex type arguments and compiler hints.