The Illusion of Replacing Developers with AI

There is a question I keep hearing in technical teams: Why do some startups still have many developers on their technical team if artificial intelligence can supposedly write a large part of the code already?

In my opinion, that is the wrong question because it assumes the bottleneck of a tech company has always been the speed of typing syntax, when in reality the question that matters is what you are trying to build and how fast you need to move in a market that does not forgive mistakes. There is a highly dangerous idea floating around the startup ecosystem that AI automatically replaces entire teams, a fantasy sold by people who do not have to maintain systems in production.

The Reality of Surviving Complexity

Although the speed of shipping code to production is important in validation stages, where the goal is to learn fast and burn as little as possible, in the long term, companies do not survive because of that. A startup does not live by generating fast code, it lives by managing and surviving the immense complexity it has created itself to stay relevant.

These teams do not spend their days writing new functions in an IDE, they spend their early mornings putting out fires related to legacy integrations, dealing with absurd enterprise support requirements, fixing broken onboarding, patching security holes, complying with compliance regulations, hunting weird bugs in production, building dashboards, designing specific workflows for the whim of a particular client and carrying a mountain of technical debt.

This is why AI does not replace the technical team: it takes away the work of typing syntax, but it imposes the enormous cognitive cost of auditing and understanding complex systems that you did not write. You stop being an engineer writing code with intention, to become an exhausted auditor.

The New Opportunity Cost in LATAM

Then we have another critical factor that in LATAM is still underestimated: the token burn rate and the true cost of operating artificial intelligence at a commercial level. While startups in San Francisco burn thousands or sometimes tens of thousands of dollars a month purely on inference to produce code with frontier models, in LATAM the discussion is still stuck on whether a $20 subscription is worth it or if we should juggle to make things work with open source models.

A developer in 2026 no longer just competes against a cheaper engineer, they now compete directly against how much additional technical leverage you could buy in tokens for your models. And that is exactly where the entire discussion about hiring and retaining talent changes absolutely. Because a very expensive developer today not only has to stand in front of you and justify that they know how to program well, they have to justify why the return on their salary would not be better replaced by more inference capacity or better AI tools to multiply the effort of the current team.