If you follow technology and development news, you’ve already noticed that Artificial Intelligence (AI) dominates the discussions. Far beyond the launch of new tools and workflows, there’s a critical debate about the practical impacts of the mass adoption of these technologies in software engineering.
Observing this scenario, a fascinating — and challenging — trend began to emerge.
The impact of AI and the code avalanche
Over the past year, Amazon faced several outages in its services, affecting both e-commerce and AWS infrastructure. The company is one of the most proactive in encouraging the use of Artificial Intelligence tools by its developers. All indications are that these blackouts were a direct consequence of the increasing use of AI in code generation, to the point of requiring a limitation on direct incorporation into production. Today, a senior engineer needs to rigorously evaluate the material before its implementation.
However, this solution raises a scalability problem. If we analyze the massive amount of code lines produced by junior and mid-level engineers with the help of AI, the senior’s review of this material will inevitably also need to be assisted by… Artificial Intelligence. Businesses already offer solutions specifically for reviewing AI-generated code, creating a continuous cycle of machine dependence.
Biology as a metaphor for software engineering
To understand where we are heading, we can look at an apparently distant field: biological and health sciences.
Humans and animals possess extremely complex characteristics. Doctors, researchers, and the pharmaceutical industry are fundamental because they deal daily with supercomplex biological systems, which are still only partially understood by science.
Consider, for example, amyloidosis. This class of diseases occurs due to protein misfolding, leading to harmful consequences in the body. The mechanism behind these diseases is difficult to treat, requiring advanced strategies, such as gene editing. Throughout our history, we have been discovering the gigantic complexity of biological systems and working intensely to, through understanding, build tools that cure diseases and improve our quality of life.
The future: The developer as a “doctor” of complex systems?
What does biology have to do with information technology? Everything.
The progressive employment of AI by developers raises an inevitable question: will we, in the future, have a scenario where programmers will act more like scientists and doctors? Professionals who will need to investigate, diagnose, and seek solutions to “cure” problems and anomalies created by the extreme complexity of AIs?
Technological systems more intricate than current ones tend to become virtually incomprehensible to humans in the future. It is exactly the same barrier we faced with biological systems some time ago. Software engineering will no longer be just about writing code, but will increasingly be about understanding and intervening in a complex ecosystem generated by machines.
Artificial intelligence has been a topic of interest for me, and I have already commented on the subject in other posts:
Read also:
- The AI Copy-Paste Problem: Killing Software Lock-In & Why Data Portability is Key
- From Cursor to Windsurf to Zed: My Journey Through AI-Enhanced Code Editors
- Why I’m Breaking Up With Vibe Coding
More references on the topic:
- Official analysis after October 19-20, 2025 event at AWS: https://aws.amazon.com/message/101925/
- Original New York Times report (Noam Scheiber, 05/25/2025) — “At Amazon, Some Coders Say Their Jobs Have Begun to Resemble Warehouse Work”: https://www.nytimes.com/2025/05/25/business/amazon-ai-coders.html
- Last Week in AWS (Corey Quinn) — critical analysis of Amazon’s response to the Q incident: https://www.lastweekinaws.com/blog/amazon-q-now-with-helpful-ai-powered-self-destruct-capabilities/
- Official Amazon response to the Financial Times: “Correcting the Financial Times report about recent Amazon.com service incidents and AI”: https://www.aboutamazon.com/news/company-news/amazon-outage-ai-financial-times-correction
- “…only one of the recent incidents involved AI tools in any way, and in that case the cause was unrelated to AI and instead our systems allowed an engineering team user error to have broader impact than it should have.”
- Learn more about amyloidosis with a fact sheet providing an accessible explanation of AL, ATTR, etc. types from Yale Medicine: https://www.yalemedicine.org/conditions/amyloidosis
- Jeyashekar NS, Sadana A, Vo-Dinh T. Protein amyloidosis misfolding: mechanisms, detection, and pathological implications. Methods Mol Biol. 2005;300:417-35. doi: 10.1385/1-59259-858-7:417. PMID: 15657495.
- Victor Jimenez-Zepeda, Vera Bril, Emilie Lemieux-Blanchard, Virginie Royal, Arleigh McCurdy, Daniel Schwartz, Margot K. Davis. A Comprehensive Multidisciplinary Diagnostic Algorithm for the Early and Efficient Detection of Amyloidosis. Clinical Lymphoma Myeloma and Leukemia, Volume 23, Issue 3, 2023, Pages 194-202. ISSN 2152-2650. https://doi.org/10.1016/j.clml.2022.12.013.
- Jiang Y, Huang L, Qiu H, Yang S, Tao J, Chen R and Hao Y (2026) Clinical safety and tolerability of in vivo gene editing drug ART001 for ATTR amyloidosis. Front. Med. 13:1783921. doi: 10.3389/fmed.2026.1783921