FEATURE: AI SCALING
FRONTIER MODELS ARE STILL IMPROVING, BUT THE GAINS ARE NOW COMING IN SMALLER STEPS.
in proprietary data, constrained by business rules and evaluated against clear acceptance criteria. Bayer, for example, weaves agronomic data into AI systems that guide crop planning, while legal-tech platforms like Luminance embed models into contract review processes with rigorous oversight. The unifying element is the move from abstract capability to dependable workflow integration, where models operate within guardrails that ensure accuracy, auditability and trust.
Think early internet, not AI winter
AI today is where the internet was in the late 1990s. The technology existed, but it took years for infrastructure, security, and business models to mature, and for the“ killer apps” like search, e-commerce and digital payments to emerge. AI is likely on a similar trajectory. The breakthroughs ahead will come less from sheer scale and more from integration into workflows, domain-specific optimization and measurable performance in the real world.
The real work ahead
If ever-larger models defined the scaling era, the next few years will look different. Without major architectural breakthroughs, which may still come, we should expect the pace of scaling-driven intelligence gains to slow. That’ s not a crisis. It’ s an opportunity.
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