Common Pitfalls in AI Engineering: Learning from Early Adopters | Sebastian Gutierrez
Just as the software industry experienced growing pains in its early days, the AI engineering field is now experiencing maturation challenges.
The stakes are high in 2025 as companies move beyond experimental projects to integrate AI systems into their core business operations.
This transition mirrors the transition that happened with web applications in the early 2000s, when companies moved from experimental websites to mission-critical web applications.
By drawing from public case studies and personal experience, Chip Huyen provides a comprehensive overview of mistakes even experienced teams make.
The timing of this article is particularly relevant, as venture capitalists and news organizations have labeled 2025 the year of “AI Agents.”
With the surge of organizations moving from experimental AI projects to production systems, understanding these pitfalls now can help teams avoid repeating the same costly mistakes.
The article’s insights about the 80/20 rule particularly resonate because this pattern has appeared consistently throughout the evolution of software engineering.
