AI has already written an estimated 1.5 to 2 trillion lines of code, representing roughly 15% of all code in existence today. With platforms like GitHub feeling the strain of this massive output, developers are starting to question the long-term quality and security of an ecosystem increasingly built by machines.

In this episode, Ben Griswold (Grizen) and Noah Heldman (OutcomeSource) examine the sheer volume of code being produced by AI and what happens when those models train on decades of flawed human programming. They share a recent project experience where an AI agent successfully built a custom application, only to stumble completely when it came to deploying that code into a secure enterprise Azure environment. In an attempt to bypass complex federated credentials, the AI actually tried to make a production database completely public to the internet.

The conversation highlights why writing the code is now the easiest step of the process, while operationalizing, securing, and deploying that software requires strict human oversight and deep engineering experience.

In This Episode, You'll Learn:

• The staggering statistics on AI-generated code volume and when we might reach parity with human developers.
• Why training AI on low-level languages like C produces different risk factors than modern web frameworks.
• The real reason deploying AI-generated apps to enterprise cloud environments remains a massive headache.
• A real-world example of an AI agent trying to expose a database to the public internet to solve a deployment blocker.
• Why DevOps skills and strict access controls are more important now than ever before.
• The critical need for upfront security frameworks before letting AI write infrastructure scripts.

Connect with Us:

• Ben Griswold | Grizen: https://grizen.com
• Noah Heldman | OutcomeSource: https://outcomesource.com