Linas Beliūnas on AI’s HR Overhaul: A Beautiful Failure?

In a recent LinkedIn post, Linas Beliūnas shares a humorous yet cautionary tale about the capabilities and limitations of advanced AI in enterprise software development, specifically focusing on a hypothetical HR system replacement.

Beliūnas recounts an experiment where he utilized an early access version of “ChatGPT-6 Astra” to code a replacement for the Workday HR platform. The results, while visually impressive, highlight a critical gap between AI-generated aesthetics and functional reliability in complex business systems.

“3 hours and 90,000 lines of code later, Astra rebuilt our entire HR stack: New hire onboarding flows, PTO tracking, Org charts with drag-and-drop edits, Admin dashboards, Auto-generated policies, A surprisingly tasteful UI”

The Allure of AI-Generated Design

The initial output from Astra, as described by Beliūnas, was remarkably comprehensive, covering essential HR functions from onboarding to policy generation. The AI reportedly delivered a user interface that was not only functional in concept but also aesthetically pleasing. This rapid development cycle, producing a vast amount of code in just a few hours, speaks to the potential of AI in accelerating software creation.

However, Beliūnas quickly pivots from the impressive facade to the underlying reality of the system’s performance. The success in generating code and a good UI was overshadowed by critical functional failures.

When AI Code Fails in the Real World

Despite the seemingly robust code generation, the practical application of the AI-built HR system proved disastrous. Beliūnas highlights the severe consequences of these failures, underscoring the current chasm between AI’s coding prowess and its ability to deliver mission-critical, error-free business solutions.

“None of it worked. Payroll broke, and we missed paying the staff for 4 weeks.”

This outcome, according to Beliūnas, serves as a stark reminder that while AI can generate complex structures and appealing interfaces, ensuring the core functionality—especially for sensitive operations like payroll—remains a significant challenge. The incident demonstrates that the ‘vibe code’ approach, while perhaps efficient for prototyping or less critical applications, falls short when absolute accuracy and reliability are paramount.

The Gap Between Form and Function

Beliūnas’s post, framed with a touch of humor, points to a broader discussion in the tech industry about the maturity of AI in enterprise environments. As he notes, the AI produced a system that was ‘beautiful’ but fundamentally non-operational for core business needs. This dichotomy between appearance and performance is a key consideration for businesses looking to integrate AI into their critical workflows.

The experience shared by Beliūnas suggests that while AI tools are rapidly evolving, human oversight, rigorous testing, and a deep understanding of business logic are still indispensable. The failure in payroll, a non-negotiable business process, emphasizes that current AI, even advanced versions, cannot yet fully replace the nuanced judgment and meticulous validation required for enterprise-grade software.

“But boy was it beautiful.”

In conclusion, Linas Beliūnas uses his simulated experience with ChatGPT-6 Astra to illustrate that while AI can be a powerful tool for code generation and design, its application in critical business systems requires careful consideration of its current limitations. The post serves as a humorous yet insightful commentary on the state of AI in enterprise software development, highlighting that functionality and reliability remain the ultimate arbiters of success, regardless of how aesthetically pleasing the code might be.

Beliūnas also shared a link to a resource titled “How to Build an Agentic OS with Claude Fable 5”, indicating an ongoing interest in exploring the practical applications and architectures of advanced AI systems.