Software development inherently involves more unknowns than fields like construction, making estimation harder. However, when tasks are broken down into simpler, well-defined components with clear requirements, estimation accuracy improves significantly. The challenge lies in the fact that software projects often begin with ambiguous goals, evolving technologies, and dynamic stakeholder needs—factors that are less prevalent in physical construction.
> Software development inherently involves more unknowns than fields like construction, making estimation harder.
If by construction you mean only cookie cutter houses and garden sheds, maybe, but construction doesn't end there. As soon as you look to anything interesting, construction has all the unknowns software has and an environment that brings even more unknowns. Computing environments, in contrast, are highly predictable. Software has it easy.
The trouble here is that the original premise is flawed. Construction estimation is almost never accurate. Open today's newspaper and you're almost certainly going to read about yet another construction project that is over time and over budget by millions upon millions of dollars.
ChatGPT already has browsing, it's just way slower than Perplexity. I'm impressed how quickly a Perplexity search runs and integrates the results into the LLMs response.
Perplexity is pretty amazing. I've tried asking really obscure questions and it just answers them almost immediately and with decent sources every time.
> if you start buying things like a summer house or an apartment for renting out, those things come with maintenance tasks. This can accumulate quickly.
This is a really good point. I feel like I'm dealing with way more responsibilities now that I have a house and a family, I'm no longer the center of my own universe.
