# AI Safety Terminology Obscures Engineering Realities Former Microsoft exec argues AI failures should be treated as software bugs, not anthropomorphized risks. - **Source:** [a16z Podcast](https://a16z.com/podcasts) - **Published:** 2026-09-21 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/ff2ff407-12ff-4dc6-a788-d4c11d84cab5 ## Problem with anthropomorphic terms Steven Sinofsky critiques terms like 'alignment' and 'rogue agents' for anthropomorphizing AI failures. He argues these concepts describe standard software bugs that engineers have handled for decades. The framing creates unnecessary mystique around AI system failures. At Microsoft, he witnessed similar challenges during major software deployments that required rigorous debugging processes. ## Historical software parallels The discussion draws parallels between current AI challenges and past software crises like Y2K and cybersecurity threats. These were solved through systematic engineering approaches, not existential framing. Microsoft's response to major software failures in the 1990s established patterns for telemetry and incident reporting that AI labs now need to adopt as AI systems scale. ## Engineering discipline needed Sinofsky advocates applying traditional software engineering practices to AI systems: comprehensive telemetry, standardized debugging, and incident reporting. These methods proved critical for stabilizing previous generations of complex software systems. The lack of equivalent practices in AI labs creates unnecessary risks as models deploy widely across industries. ## CVE reporting model The panel discusses how AI could adopt Common Vulnerabilities and Exposures (CVE) reporting used in cybersecurity. This standardized approach helps coordinate responses to software vulnerabilities across the industry. Applying similar frameworks to AI failures would demystify incidents and accelerate solutions through shared terminology and processes. ## Terminology's regulatory impact Confusing AI terminology risks leading to equally confused regulation, the panel argues. Anthropomorphic language may prompt policymakers to create rules based on sci-fi scenarios rather than technical realities. Clear engineering framing would help regulators focus on practical safety measures comparable to other software industries. ## Industry responsibility Sinofsky emphasizes tech companies' responsibility to implement proper engineering controls before deploying AI systems widely. Drawing from Microsoft's evolution, he notes how software companies matured their operational practices after major failures. AI labs currently lack equivalent pressure despite similar scaling challenges. ## Debugging vs existential risk The discussion contrasts two approaches: viewing AI failures as engineering challenges versus existential risks. The former leads to practical solutions through established software practices, while the latter generates theoretical debates. Historical examples show engineering frameworks ultimately prove more effective for complex system reliability. ## Path forward for AI safety The conversation concludes that AI safety requires shifting from speculative terminology to concrete engineering metrics. This means adopting software industry's hard-won lessons about system reliability at scale. Such practical framing would accelerate solutions while reducing unnecessary fear about AI's technical challenges. ## Sources covering this story - https://a16z.simplecast.com/episodes/ai-safety-language-is-destroying-the-debate-steven-sinofsky-p7Rb9M_7 — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._