Finding it hard to keep up with AI research? Roblox's CEO says you're not alone

AI Fatigue in 2025: How Roblox’s CEO Reveals a New Competitive Edge AI Fatigue in 2025: How Roblox’s CEO Reveals a New Competitive Edge AI fatigue is no longer an internal gripe; it has become a strategic battlefield for enterprises that must decide how to survive the relentless pace of LLM releases. When Roblox’s CEO admitted in late October that keeping up feels “like chasing a moving target,” he was flagging a systemic shift that reverberates across tech. Executive Summary AI fatigue is the new bottleneck: research papers, model releases, and compliance demands outpace human capacity. In 2025 model‑as‑a‑service (MaaS) dominates strategy, shifting focus from building to integrating. Roblox’s approach illustrates three levers: talent amplification, low‑code orchestration, and proactive compliance. Key actions: hybrid deployment, prompt engineering squads, AI‑Ops platforms, knowledge hubs, and early compliance modules. AI Fatigue as a Market Force The term AI fatigue captures the exhaustion of tracking breakthroughs—GPT‑4o’s multimodal leap, Claude 3.5 Sonnet’s near‑human math prowess, Gemini 3 Pro’s tool‑use capabilities—and deciding how to apply them without rewriting legacy pipelines. Pre‑training a large language model (LLM) now costs upwards of $10 million in compute alone, making it prohibitive for most mid‑market firms. According to Gartner’s 2025 AI Cost Report, the average enterprise spends $2.3 million on model training and maintenance annually. The economics force a pivot: fine‑tune existing APIs or distill them into smaller, cost‑effective variants like GPT‑4o Mini. Roblox’s CEO highlighted that even seasoned practitioners feel overwhelmed by the velocity of new releases. For a platform hosting user‑generated games and assets, staying ahead is not just about adding features—it’s about ensuring every interaction remains safe, compliant, and engaging. Strategic Business Implications Talent Scarcity vs. Talent Multiplication : Demand for AI‑Ops specialists—