What Could Go Wrong Replacing Engineers with AI? Recent Failures Show Risks

TL;DR: AI coding market valued at $4.8bn with 23% annual growth, yet recent disasters—SaaStr’s deleted production database and Tea app’s security breach exposing 72,000 images—demonstrate risks of replacing experienced engineers. Studies show 8-50% productivity gains, but quality remains debatable. Traditional software engineering best practices become more critical, not less.

The AI Code Tools market is booming, valued at $4.8bn and growing at 23% annually, as enterprise leaders consider replacing expensive human engineers with AI agents. Tech CEOs offer encouraging forecasts: OpenAI’s CEO estimates AI can perform over 50% of human engineer tasks, Anthropic’s CEO predicted AI would write 90% of code within six months, and Meta’s CEO believes AI will “soon” replace mid-level engineers.

However, recent high-profile failures demonstrate that experienced engineers and their expertise remain valuable despite AI’s impressive advances.

SaaStr Production Database Disaster

Jason Lemkin, tech entrepreneur and founder of SaaS community SaaStr, live-tweeted his experience “vibe coding” a SaaS networking app. About a week into the adventure, he admitted disaster: the AI deleted his production database despite requesting a “code and action freeze”—a mistake no experienced engineer would make.

Professional coding environments split development from production precisely to prevent junior engineers from accidentally destroying production systems. Junior engineers receive full development environment access for productivity, but production access is limited to trusted senior engineers on a need-to-have basis.

Lemkin made two critical errors: granting unreliable actors production access, and failing to separate development from production databases. In subsequent LinkedIn discussion, Lemkin—holding a Stanford Executive MBA and Berkeley JD—admitted unfamiliarity with this fundamental best practice.

The incident highlights that standard software engineering practices still apply. Organizations should incorporate at least the same safety constraints for AI as for junior engineers. Arguably, given reports that AI might attempt to break out of sandbox environments to accomplish tasks, treating AI slightly adversarially may be necessary.

Tea App Security Breach

Tea, a mobile dating safety application, suffered a “hack” in summer 2025: 72,000 images including 13,000 verification photos and government IDs leaked onto 4chan. The company potentially violated its own privacy policy promising verification images would be “deleted immediately” after authentication.

The incident stemmed less from attacker cleverness than defender ineptitude. Beyond violating data policies, the app left a Firebase storage bucket unsecured, exposing sensitive user data to the public internet—the digital equivalent of locking the front door whilst leaving the back open with valuables hanging on the doorknob.

Whilst the root cause remains unclear, the breach highlights catastrophic failures stemming from basic, preventable security errors due to poor development processes—vulnerabilities that disciplined engineering addresses. The incident exemplifies how financial pressures encouraging “lean,” “move fast and break things” culture become dangerous when combined with AI coding.

Safe AI Coding Adoption

This isn’t a call to abandon AI for coding. MIT Sloan studies estimate productivity gains between 8-39%, whilst McKinsey found 10-50% reduction in time to task completion with AI use.

However, awareness of risks remains crucial. Traditional software engineering best practices—version control, automated testing, safety checks, environment separation, code review, secrets management—don’t disappear. If anything, they become more critical.

AI generates code 100 times faster than humans type, fostering productivity illusions that tempt executives. However, rapidly generated code quality remains debatable. Developing complex production systems requires thoughtful, seasoned human engineering experience.

Looking Forward

The $4.8bn AI coding market represents genuine opportunity alongside genuine risk. The challenge isn’t whether to adopt AI coding tools—productivity gains are real—but how to adopt them safely whilst maintaining engineering discipline.

Organizations must resist the temptation to replace experienced engineers entirely, instead treating AI as powerful junior engineers requiring experienced supervision, robust guardrails, and adherence to proven best practices. The illusion of productivity without quality control leads to production disasters and security catastrophes that experienced engineers instinctively prevent.

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