The performance gap between open and closed AI models collapsed from 17.5 percentage points to 0.3% in a single year. A free downloadable model now outperforms the best paid frontier lab on software engineering benchmarks, and the cost difference for production workloads is 13x. But the open source AI story has a side most people skip: real security vulnerabilities, supply chain attacks, and AI-generated malware that breaks traditional defenses.

Alex Smith, founder of Instant AI and host of Super Confident, covers the geopolitics of Chinese open weight releases versus American closed labs, exposes open washing by Meta and OpenAI, and delivers a three-lane framework for founders choosing between open and closed models by use case. Built for anyone building with AI tools who needs the real story behind the open vs closed debate.

Chapters:

(00:00) Introduction

(00:51) The 13x Cost Gap in Real Numbers

(02:47) Where Closed Models Still Win

(04:26) AI Generated Malware Breaks Old Defenses

(05:42) Geopolitics and Open Washing

(06:54) The Three Lane Routing Framework

Should there be a responsible disclosure standard for open model releases? Leave your answer in the comments.

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