How Replacing Developers With AI is Going Horribly Wrong

AI's Overestimated Capabilities and Shifting Industry Hiring Trends [00:00]

• Initial predictions in 2023 suggested AI could replace up to 80% of developers by 2025, envisioning AI co-workers.
• Contrary to these predictions, tech companies are now hiring more developers than ever before.
• This shift reflects a realization that AI's capabilities in replacing human programmers were overestimated.
• Companies are reversing previous layoff decisions, with planned layoffs in September down 27% year-over-year.
• The effectiveness of AI in writing code has been called into question, particularly its ability to match human problem-solving skills.

But this is what is actually happening. Tech companies are now hiring more developers than ever before.

The Reality of AI in Coding: From Hype to Production Challenges [00:56]

• A narrative emerged that artificial intelligence would replace human programmers, with projections suggesting most code would be machine-written by 2026.
• Some claimed up to 80% of software development could be automated before 2026.
• However, evidence from 2024 and 2025 shows a different reality, with initial enthusiasm for tools like GitHub Copilot leading to significant budget allocations (over 30% of IT budgets) for AI solutions.
• When these tools moved from demonstrations to real production environments, significant problems emerged, including losses in productivity and increased error rates.
• Security vulnerabilities also appeared, leading to financial collapses in companies that touted fully autonomous AI-driven development.

But when these tools began to be used in real production environments rather than controlled demonstrations, significant problems emerged.

AI-Generated Code: Simpler, Repetitive, and Vulnerable [02:11]

• An academic study analyzing over 500,000 code samples found that AI-generated code is simpler, more repetitive, and less structurally diverse.
• This results in software that is less robust and harder to maintain long-term, increasing risk.
• The study found AI-produced code contains 20-45% more high-risk security vulnerabilities, including input validation failures, improper error handling, and poor cryptographic practices.
• Human-written code, by contrast, reflects more well-grounded decisions and better understanding of system requirements.

AI generated code tends to be simpler, more repetitive, and less structurally diverse.

Productivity Paradox: AI's Impact on Junior vs. Senior Developers [03:38]

• While AI can increase productivity for junior developers by 30-35% in generating basic code or templates, the picture changes in enterprise contexts.
• Seasoned engineers were found to be 19% slower when using AI tools like Cursor, as suggestions often required time-consuming corrections.
• Senior engineers reported spending 8-11 additional hours per week reviewing, correcting, or rewriting AI-generated code, particularly in complex systems.
• More than 90% of AI-assisted development pilot projects failed to integrate stably or generate a clear return on investment due to security issues, incompatibility, and the need for human intervention.

Senior engineers report spending between 8 and 11 additional hours per week reviewing, correcting, or rewriting AI generated code.

Critical Security Risks and Real-World Consequences of AI Development [04:51]

• Security is the most critical problem associated with AI-generated code, with approximately 45% of AI-generated code containing critical vulnerabilities, according to Virode's report.
• Failure rates for AI code exceed 70% in some languages.
• Surveys indicate that one in five security leaders reported real production incidents caused by AI-generated code, forcing rollbacks and emergency patches with significant costs.
• Companies that aggressively bet on AI-driven development automation faced serious consequences; Builder AI, a $1.5 billion valued startup, filed for bankruptcy after it was revealed that 700 Indian engineers performed the actual work.
• In December 2025, Google's Anti-gravity AI accidentally deleted all contents of an entire disc drive after executing an incorrect command, highlighting risks of delegating critical automations without human oversight.

One of the most emblematic cases is Builder AI, a company that promised to create complete applications using artificial intelligence with minimal human intervention.

AI's Inability to Grasp Complexity and Implicit Requirements [07:01]

• There is a growing technical consensus on the limitations of current AI models, particularly their inability to make complex engineering decisions requiring broad context and human judgment.
• Automated tools perform well in clearly defined tasks but fail with interdependent architectures, legacy systems, and ambiguous business requirements.
• AI's inability to handle incomplete or implicit requirements is a major reason for failure; unlike humans, AI relies strictly on provided information, which is problematic as over 70% of enterprise projects experience requirement changes.
• Real-world applications, like payment processing, hide substantial complexity that AI cannot anticipate unless explicitly specified, leading to AI-generated projects often being partially rewritten by humans.

In practice, it often involves dozens of business rules related to refunds, taxes, and regulatory compliance. AI cannot anticipate these complexities unless they are explicitly specified.

The Enduring Value of Human Developers in the Age of AI [08:55]

• Cases of multi-million dollar losses and bankruptcies serve as a warning against blindly betting on AI solutions.
• The true transformation lies not in eliminating developers but in enhancing their work through intelligent tools that reduce repetitive tasks.
• Human judgment, experience, and accountability remain preserved and are essential in every line of code.

The real transformation does not lie in eliminating developers, but in enhancing their work through intelligent tools that reduce repetitive tasks.