Introduction
The idea that AI can fully build and manage production software without human involvement is spreading fast. With the rise ...
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I think you're ignoring the economic angle. If AI can do 80 percent of the work, companies will accept the risk for the remaining 20 percent.
That is a valid point, and it is already happening in some areas.
But the question is where that 20 percent sits. In production systems, that remaining part often includes the most critical logic, edge cases, and failure handling.
If that 20 percent is where things break under real conditions, the cost of failure can outweigh the savings.
So you are basically saying AI will stay as a tool, not a replacement?
Exactly. The leverage is real, and it is significant. But replacing ownership is a different story. The teams that win are not removing developers, they are making them more effective.
This is a solid take, but I feel like you're underestimating how fast AI is improving. Tools are already generating full-stack apps. Give it a year or two and this might be outdated.
I get that perspective, and honestly, the speed of improvement is real. But the gap I am pointing at is not about code generation quality, it is about ownership and reliability in production.
Generating a full-stack app is one thing. Running it under real conditions with unpredictable inputs, scaling issues, and long-term maintenance is something else entirely. That gap is not closing at the same pace.
Fair, but what if AI agents start managing themselves better? Like chaining tools, monitoring logs, fixing bugs automatically. Wouldn't that solve most of it?
It helps, but it introduces a new layer of risk. You are essentially automating decision-making without true understanding.
Self-healing systems sound great, but if the system misinterprets a problem, it can make the wrong fix and push it further into production. That kind of failure is harder to catch than a simple bug.
Feels like this is similar to when people said cloud wouldn't replace on-prem. Then it did.
Interesting comparison, but there is a key difference.
Cloud changed infrastructure. It did not remove the need for engineering decisions. It shifted where those decisions are made.
AI is trying to move into decision-making itself. That is a much harder problem, because it involves reasoning, trade-offs, and accountability.
So you're saying this is not just a tech shift, but a responsibility shift?
Yes. And until AI can reliably handle responsibility at scale, not just output, full autonomy in production remains out of reach.
This is a solid take, but I feel like you're underestimating how fast AI is improving. Tools are already generating full-stack apps. Give it a few years and this might be outdated.
I get that perspective, and honestly, the speed of improvement is real. But the gap I am pointing at is not about code generation quality, it is about ownership and reliability in production.
Generating a full-stack app is one thing. Running it under real conditions with unpredictable inputs, scaling issues, and long-term maintenance is something else entirely. That gap is not closing at the same pace.
Fair, but what if AI agents start managing themselves better? Like chaining tools, monitoring logs, fixing bugs automatically. Wouldn't that solve most of it?
It helps, but it introduces a new layer of risk. You are essentially automating decision-making without true understanding.
Self-healing systems sound great, but if the system misinterprets a problem, it can make the wrong fix and push it further into production. That kind of failure is harder to catch than a simple bug.
We built a small SaaS almost entirely with AI and it's running in production. Not perfect, but definitely viable. I think you're being too cautious.
That makes sense, and honestly that is where AI shines right now. Small to mid-sized SaaS, controlled scope, limited edge cases.
The key question is what happens when that system grows. More users, more integrations, more edge cases. That is usually where the cracks start to show.