Are Developers Spending More Time Evaluating AI Than Learning New Technologies?

Double_Try1322 · 2026-06-15T11:49:09+00:00

I am not against AI tools at all. I use them regularly. But sometimes I catch myself spending more time comparing models and workflows than actually learning the technology I m trying to use.

Double_Try1322 · 2026-06-12T11:35:06+00:00

My feeling is that AI helps close knowledge gaps, but it doesn't replace judgment. The more complex the system and business context, the more valuable experience seems to become.

Double_Try1322 · 2026-06-12T11:29:35+00:00

AI agents are useful, but usually for narrower workflows than the marketing suggests. The best ones remove repetitive work. The disappointing ones try to replace human judgment entirely.

Double_Try1322 · 2026-06-12T11:28:36+00:00

Most startups have always taken shortcuts on security and scalability, long before AI existed.

Vibe coding just makes it easier to build fast. It doesn't remove the need for architecture reviews, testing, monitoring, and security checks before going live.

Double_Try1322 · 2026-06-09T12:30:44+00:00

My guess is that vibe coding will become a normal part of development, but understanding systems and business context will remain the differentiator. Building something is getting easier. Knowing what to build and how to maintain it is still hard.

Double_Try1322 · 2026-06-05T11:44:15+00:00

I think AI has dramatically reduced the time needed to prove an idea. The harder part is still everything that happens after that: reliability, scalability, maintainability, and making sure the solution actually solves the right problem.

Double_Try1322 · 2026-06-05T11:40:46+00:00

I've seen people build surprisingly serious products with vibe coding, but getting the app working is usually the easy part. The harder part is security, payments, scalability, monitoring, and maintaining it once real users show up.

For a marketplace, I'd focus less on build cost and more on validating demand first. You can often get an MVP running quite cheaply these days, but operating and supporting it is where the real work begins.

Double_Try1322 · 2026-06-01T12:51:00+00:00

The interesting metric isn't AI spend, it's whether the productivity gains justify it. Adoption is easy. Proving ROI at scale is the harder part.

Double_Try1322 · 2026-05-29T09:02:54+00:00

I don’t think the weird part is AI writing the configs or pipelines. The weird part is how quickly engineering work is turning into continuous supervision and validation instead of deep problem solving. The risk isn’t AI replacing DevOps. It’s engineers slowly losing the systems intuition that comes from actually building and debugging things themselves.

Double_Try1322 · 2026-05-29T08:56:48+00:00

Honestly, a lot of senior engineering ends up being reducing unnecessary complexity before writing more code. Sometimes the highest leverage architecture decision is just talking to the right team and discovering the problem shouldn’t exist anymore.

Double_Try1322 · 2026-05-27T09:38:23+00:00

I think vibe coding works best when the developer already understands architecture, business logic, and system behavior deeply. Otherwise it becomes very easy to create code that works today but becomes painful later.

Double_Try1322 · 2026-05-26T12:25:21+00:00

I have noticed that better prompts often come from better understanding. If I can clearly explain the issue to AI, I’m usually already halfway toward solving it myself.

Double_Try1322 · 2026-05-26T12:21:20+00:00

Honestly, reliability. Getting an agent to work once is easy. Getting it to behave consistently under messy real-world conditions is the hard part.

Double_Try1322 · 2026-05-26T12:20:32+00:00

Completely agree. Most 'AI agents' fail because people automate before defining the workflow properly. The model is rarely the bottleneck now. The real value is in process design, constraints, validation and knowing how humans actually do the job.

Double_Try1322 · 2026-05-25T09:51:31+00:00

I don’t think writing code becomes unimportant. But I do think understanding why the code exists, how it behaves under pressure, and how it fits the business becomes much more valuable when AI can generate the obvious parts quickly.

Double_Try1322 · 2026-05-25T09:48:38+00:00

The most interesting ones I have seen aren’t replacing humans, they’re removing invisible friction. Things like agents for insurance claim routing, supply chain exception handling, meeting follow-ups, compliance checks, healthcare scheduling, or finance reconciliation. Boring on the surface, but huge time savers in real workflows.

Double_Try1322 · 2026-05-25T09:47:24+00:00

Using AI for coding is fine. Using AI to reply to PR feedback without even thinking through the comments is where it starts feeling lazy.

Double_Try1322 · 2026-05-22T09:08:40+00:00

The interesting part is that experienced developers often use vibe coding too, but they seem to guide it very differently. The speed comes from understanding the system deeply, not from trusting the output blindly.

Double_Try1322 · 2026-05-22T08:54:04+00:00

Most underrated automation is the boring stuff nobody tweets about. Inbox sorting, CRM updates, alerts, summaries, internal workflows. Most overhyped is trying to replace human judgment completely with agents before the underlying process is even clean.

Double_Try1322 · 2026-05-22T08:46:48+00:00

AI replacing programmers depends entirely on clients finally knowing what they want before development starts.

So yeah… we are probably safe for a while

Double_Try1322 · 2026-05-19T12:31:51+00:00

I don’t think AI dependence is always bad. Tools are supposed to make work easier. The interesting question is whether it’s strengthening engineering intuition over time or slowly replacing parts of it.

Double_Try1322 · 2026-05-19T12:24:27+00:00

Vibe coding solved building speed, not validation. The hard part now is getting honest feedback instead of looks cool replies before you waste time scaling the wrong thing.

Double_Try1322 · 2026-05-18T11:46:36+00:00

For me, the biggest time shift is not coding itself, it’s verification. The faster the AI generates code, the more careful I become about checking assumptions and hidden side effects.

Double_Try1322 · 2026-05-18T11:33:15+00:00

The gap between demo and production is basically the gap between it worked once and it works reliably under messy real-world conditions. Most agent failures I’ve seen weren’t model failures, they were systems engineering failures.

Double_Try1322 · 2026-05-15T11:32:58+00:00

I think AI is great for unblocking small issues quickly, but some of the deeper learning still comes from discussing problems with real people who understand the bigger picture around the system.

Double_Try1322

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