A major shift is coming where company-specific Small Language Models (SLMs) will run relentlessly and recursively on powerful local hardware. This creates a new paradigm of free, constantly improving, and privately-owned corporate intelligence.

The focus on achieving Artificial General Intelligence (AGI) is misplaced for consumer applications. Many existing AI tools are already "good enough." The real challenge is designing better products and interfaces that apply this existing technology effectively.

While often discussed for privacy, running models on-device eliminates API latency and costs. This allows for near-instant, high-volume processing for free, a key advantage over cloud-based AI services.

While total generation time might be similar to API calls, local models offer a superior user experience by starting responses almost immediately. This eliminates the unpredictable network latency and random slowdowns common with APIs, making the interaction feel smoother and more reliable.

The critical new AI skill isn't just using the most powerful model, but discerning when a free, private local model is sufficient versus when an expensive cloud model is necessary. This model-to-task matching instinct separates amateurs from pros by optimizing for cost, speed, and privacy.

While not as powerful as top API models, local models provide sufficient performance for many tasks. This 'good enough' capability, combined with data privacy, predictable latency, and zero per-token cost, makes them a compelling choice for specific use cases in a real workflow.

The perceived plateau in AI model performance is specific to consumer applications, where GPT-4 level reasoning is sufficient. The real future gains are in enterprise and code generation, which still have a massive runway for improvement. Consumer AI needs better integration, not just stronger models.

The most impressive AI experiences are no longer just about the raw intelligence of powerful models. Value is shifting to 'harnesses'—agentic systems like Claude Cowork that use medium-powered, cost-effective models to automate complex, practical tasks like event registration.

The hype around future model improvements overshadows a key reality: current models are already "sufficiently intelligent" for countless valuable tasks. Even if all AI innovation stopped today, we could still unlock trillions in economic value just by integrating existing technology across the economy.