AI is changing what's possible in software development. A single engineer can now build a frontend, implement APIs, design a database, write tests, configure infrastructure, and deploy an application
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The distinction between wearing many hats and being an expert in all of them is the one that gets lost in a lot of current hiring talk. AI makes it possible for one engineer to produce a database schema, a pipeline and a frontend in a day. It does not give that engineer the experience to know which part of the output is quietly wrong.
Your point about tradeoffs is the honest version of the argument. If a company gives one person a whole team's responsibilities, it is choosing speed and accepting more risk in the areas that person knows least. That can be a fine choice, as long as it is made on purpose.
On client work we handle it by splitting a project into clearly defined problems and asking which ones carry real risk, usually security, data and payments. Those get a specialist review even when the rest is built quickly by a generalist.
Which area do you see generalists underestimate most often?
Mirna De Jesus Cambero
Coding, Debugging, Practicing
Thanks for sharing your perspective! I appreciate your approach to identifying high-risk areas and bringing in specialists when needed.
Three areas I think often get underestimated are observability, security, and scalability. It's relatively easy to build something that works, especially with AI. But making sure it's secure, designing it to scale as usage grows, and anticipating potential issues by setting up proper monitoring and alerts are things that require deeper expertise and are often left for later.
And the tricky part is that shortcomings in these areas aren't always obvious until the system is in production, or users start reporting problems.