# Yesterday's Technology, Tomorrow: The Challenge of AI Obsolescence in Government Local governments eager to modernize with artificial intelligence face an ironic dilemma: by the time AI systems clear procurement hurdles, they may already be obsolete. Unlike traditional IT infrastructure that remains functional for 5-7 years, AI applications can become outdated within months of deployment. This accelerated obsolescence cycle presents unique challenges for government agencies accustomed to multi-year technology planning. ## The Acceleration Problem "AI is advancing at a pace that makes Moore's Law look downright sluggish," notes industry expert Steve Vassallo. While traditional computing power historically doubled every 18-24 months, AI capabilities have grown exponentially faster—computational demands of cutting-edge models increased by a staggering 40,000× over just five years. This rapid advancement creates a fundamental mismatch with government procurement cycles. As the Department of Defense discovered, "long procurement cycles and delays can lead to obsolete AI tools" before they ever reach implementation. The traditional government motto—"buying yesterday's technology tomorrow"—becomes particularly problematic with AI, where yesterday's technology may be multiple generations behind. ## Model Decay: A Unique Challenge Unlike hardware infrastructure, AI systems don't just become comparatively slower over time—they can actively deteriorate in performance. This phenomenon, known as "model drift," occurs when real-world conditions change from the data the AI was trained on. Consider Idaho's election chatbot initiative. In early 2020, the state deployed an IBM Watson-based virtual assistant to answer voter questions. While initially effective, within two years the system appeared primitive compared to newer models like ChatGPT. What was cutting-edge at deployment became legacy technology at remarkable speed. Similarly, federal agencies exploring AI for technical writing have discovered that internally-built tools from 2022 are already outclassed by commercial offerings like Microsoft's Copilot. This rapid obsolescence forces agencies to choose between continuous upgrades or complete replacement far sooner than with traditional IT investments. ## Breaking the Cycle Forward-thinking government bodies are adapting to this challenge through several strategies: 1. **Pilot-to-Production Acceleration**: The Government Accountability Office developed "Project Galileo," a generative AI tool, with a rapid three-month prototype phase followed by iterative testing—recognizing that extended development timelines guarantee obsolescence. 2. **Leveraging Commercial Platforms**: The Office of Personnel Management found employees "clamoring" to use Microsoft's Copilot AI assistant, recognizing that commercial offerings advance faster than custom-built solutions. 3. **Retrieval-Augmented Generation**: By grounding AI in current organizational data rather than relying solely on pre-trained knowledge, agencies can extend an AI system's relevance even as underlying models age. 4. **Streamlined Procurement**: The Department of Defense created specialized vehicles like the Tradewind contract specifically to accelerate AI acquisition. ## Planning for Obsolescence For local governments considering AI investments, acknowledging the accelerated obsolescence cycle is essential. Success requires treating AI not as a one-time purchase but as an evolving capability requiring continuous investment. Budgets must account for regular model updates, staff training, and potentially complete replacement within 1-3 years. By contrast, storage infrastructure or traditional software can reasonably be expected to function adequately for 5-10 years. The rewards of well-implemented AI—enhanced efficiency, improved citizen services, better decision support—can justify this compressed timeline. But only if governments abandon the traditional "set it and forget it" approach to technology investments. In the AI era, yesterday's technology truly arrives tomorrow—and becomes obsolete the day after. --- **Sources:** - Haniyeh Mahmoudian (DataRobot), Congressional testimony on DoD procurement challenges - National Association of State Chief Information Officers (NASCIO), "AI in State Government Procurement" report - Steve Vassallo, LinkedIn analysis of AI progress vs. Moore's Law - The Guardian, "UK Public Accounts Committee: AI rollout threatened by outdated IT" (March 2025) - FedTech Magazine, "GAO's Project Galileo generative AI pilot" (August 2024) - StateScoop, "Idaho's IBM Watson chatbot for election Q&A" (September 2020)