Cut AI task costs over time — an agent that learns & amortizes compute

Cut AI task costs over time — an agent that learns & amortizes compute targets a $120B = 200M knowledge workers x $600/yr potential spend on AI-agent tooling and automation total addressable market with medium saturation and a year-over-year growth rate of 35%.

Key trends driving demand: Agentization -- workflows are being wrapped by autonomous agents, increasing demand for orchestration and lifecycle tooling; Open/efficient models -- cheaper fine-tunable and distilled models let teams push inference to low-cost paths; Vector search + memory -- production-grade retrieval makes cached/reused knowledge practical, lowering repeated compute; LLMops commoditization -- more standardized tooling accelerates adoption but increases need for differentiation.