Google's rumored "Gemini 3.2 Flash" model suggests a strategy focused on cost-efficiency rather than chasing state-of-the-art benchmarks. By offering near-frontier performance at a 15-20x lower inference cost, Google can capture a huge segment of the enterprise market focused on practical, scalable implementation.

Contrary to the belief that enterprises have unlimited budgets, they are focused on the ROI of their AI spend. As agentic workflows cause token bills to skyrocket, orchestration tools that intelligently route queries to the most cost-effective model for a given task are becoming essential infrastructure.

For years, access to compute was the primary bottleneck in AI development. Now, as public web data is largely exhausted, the limiting factor is access to high-quality, proprietary data from enterprises and human experts. This shifts the focus from building massive infrastructure to forming data partnerships and expertise.

The growth of LLM context windows has stalled not primarily due to technical barriers, but because multi-million token requests can cost users several dollars per query, leading to low demand. The industry is shifting focus to "smart context" techniques like compaction and retrieval to provide relevant information without the prohibitive cost of massive context.

Current AI models become exponentially more expensive as input size grows (quadratic scaling). New "subquadratic" architectures, however, scale linearly by pre-selecting relevant data. This change could slash compute costs by orders of magnitude, making massive context windows economically viable.

A key way to improve consumer LLM speed and cost is to cache the results for frequently asked, static questions like "When was OpenAI founded?" This approach, similar to Google's knowledge panels, would provide instant answers for a large cohort of queries without engaging expensive GPU resources for every request.

As companies deploy thousands of AI agents, their backend databases face overwhelming load. Redis is pivoting to solve this by acting as a "context engine"—a high-speed intermediary layer that serves pre-processed data to agents, protecting core systems.

By training a smaller, specialized model where company data is in the weights, firms avoid the high token costs of repeatedly feeding context to large frontier models. This makes complex, data-intensive workflows significantly cheaper and faster.

Previously, the biggest constraint in AI was compute for training next-gen models. Now, the critical bottleneck is providing enough compute for *inference*—the real-time processing of queries from a rapidly growing user base.