Box CEO Aaron Levie advises against building complex workarounds for the limitations of cheaper, older AI models. This "scaffolding" becomes obsolete with each new model release. To stay competitive, companies must absorb the cost of using the best available model, as competitors will certainly do so.

Arvind Krishna predicts that the largest AI models will become commodities with low switching costs. This belief underpins IBM's strategy to *not* compete in building frontier models, but rather to partner with providers and focus on smaller, specialized enterprise models where they can build a moat.

Just as developers use various databases for different needs, AI applications will rely on a "constellation" of specialized models. Some tasks will require expensive, high-reasoning models, while others will prioritize low-latency or low-cost models. The market will become heterogeneous, not monolithic.

As enterprises scale AI, the high inference costs of frontier models become prohibitive. The strategic trend is to use large models for novel tasks, then shift 90% of recurring, common workloads to specialized, cost-effective Small Language Models (SLMs). This architectural shift dramatically improves both speed and cost.

Paralleling the cloud adoption curve, the current surge in AI spending will inevitably be followed by an 'optimization point.' Enterprises will shift from experimentation to efficiency, scrutinizing token usage and seeking to reduce costs, forcing AI providers to help them optimize.

As AI costs rise, using one powerful frontier model for every task is no longer financially viable. The solution is to create a dedicated "Model Sommelier" role responsible for curating a portfolio of models, continuously testing and selecting the most cost-effective option for each specific business use case.

The metric for evaluating AI models is shifting. Early on, maximum quality was paramount for adoption. Now, sophisticated users are focusing on efficiency, evaluating models based on "quality per dollar spent," making cost-effectiveness a key competitive advantage.

While the cost for GPT-4 level intelligence has dropped over 100x, total enterprise AI spend is rising. This is driven by multipliers: using larger frontier models for harder tasks, reasoning-heavy workflows that consume more tokens, and complex, multi-turn agentic systems.

The AI market is bifurcating. Large, general-purpose frontier models will dominate the massive consumer sector. However, the enterprise world, where "good enough is not good enough," will increasingly adopt more accurate, cost-effective, and accountable domain-specific sovereign models to achieve real productivity benefits.