For specialized, high-stakes tasks like insurance underwriting, enterprises will favor smaller, on-prem models fine-tuned on proprietary data. These models can be faster, more accurate, and more secure than general-purpose frontier models, creating a lasting market for custom AI solutions.

While a general-purpose model like Llama can serve many businesses, their safety policies are unique. A company might want to block mentions of competitors or enforce industry-specific compliance—use cases model creators cannot pre-program. This highlights the need for a customizable safety layer separate from the base model.

Microsoft’s approach to superintelligence isn't a single, all-knowing AGI. Instead, the strategy is to develop hyper-competent AI in specific verticals like medicine. This deliberate narrowing of domain is not just a development strategy but a core safety principle to ensure control.

Instead of relying solely on massive, expensive, general-purpose LLMs, the trend is toward creating smaller, focused models trained on specific business data. These "niche" models are more cost-effective to run, less likely to hallucinate, and far more effective at performing specific, defined tasks for the enterprise.

The "agentic revolution" will be powered by small, specialized models. Businesses and public sector agencies don't need a cloud-based AI that can do 1,000 tasks; they need an on-premise model fine-tuned for 10-20 specific use cases, driven by cost, privacy, and control requirements.

The current approach of building generalist models like ChatGPT, containing all human knowledge, is inherently unsafe. A safer paradigm involves creating specialized AIs for specific tasks (e.g., translation) that lack dangerous capabilities like bioweapon design, similar to how a Pentagon janitor is denied access to nuclear codes for security.

Relying solely on expensive frontier models is unsustainable. Vertical AI companies must build a portfolio of smaller, specialized models that match frontier performance on specific tasks but cost 100x less, effectively allocating intelligence where it's needed most.

Instead of using generalist AI, LookAtMedia built a "media vertical AI model" trained on over a million journalists' writing. This focused approach yields higher quality, more authentic content with a near-zero hallucination rate (less than 0.01%), which is crucial for maintaining credibility with the media.

A proposed middle path in the AI debate is to abandon the race for Artificial General Intelligence (AGI) and instead build "narrow superintelligences." These models, trained exclusively on specific domains like protein folding, could solve major problems like disease without posing a general existential threat.