— OpenAI (@OpenAI) March 3, 2026
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The threat extends beyond accidental errors. When AI writes the software, the attack surface shifts: an adversary who can poison training data or compromise the model’s API can inject subtle vulnerabilities into every system that AI touches. These are not hypothetical risks. Supply chain attacks are already among the most damaging in cybersecurity, and AI-generated code creates a new supply chain at a scale that did not previously exist. Traditional code review cannot reliably detect deliberately subtle vulnerabilities, and a determined adversary can study the test suite and plant bugs specifically designed to evade it. A formal specification is the defense: it defines what “correct” means independently of the AI that produced the code. When something breaks, you know exactly which assumption failed, and so does the auditor.
MethodologyWe pulled monthly US organic traffic estimates from Ahrefs for ten major English-language tech publications from February 2024 through January 2026. For each site, we identified the peak traffic month and compared it to January 2026 (the most recent complete month). All figures are Ahrefs estimates for the US market.
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