Today, our interactions with language models are largely limited to discrete, isolated tasks: drafting emails, generating content snippets, analyzing small datasets, or writing simple code. We type our request, receive a response, and iterate until satisfied. The stakes are relatively low, and the scope is narrow.

But this is merely the beginning. We're rapidly moving toward a future where AI systems won't just handle individual tasks—they'll operate entire functional areas within organizations. Eventually, they may manage complex organizational processes end-to-end with minimal human intervention.

This represents a shift comparable to the evolution from individual artisans crafting single items to industrial production systems manufacturing thousands of products daily. The methods appropriate for the former are entirely inadequate for the latter.

When AI transitions from generating a code snippet to architecting and maintaining mission-critical enterprise systems, from creating a single marketing asset to orchestrating integrated multi-channel campaigns that adapt in real-time to market conditions, from drafting one sales email to managing personalized outreach sequences for thousands of prospects with dynamic prioritization—our current approach to instructing these systems will collapse completely.

The resolution problem

The fundamental issue is that today's prompts are woefully low-resolution vehicles for conveying complex intent. A text prompt simply cannot capture the full dimensionality of what organizations need to accomplish.

This is both a technological and social challenge. Technologically, our specification systems need to:

- Provide structured frameworks for representing complex, multi-dimensional requirements

- Enable systematic validation of specifications against organizational policies and constraints

- Support traceability between high-level objectives and granular execution details

Socially, we need frameworks that enable:

- Collaborative authoring where multiple stakeholders can contribute their domain expertise

- Clear governance processes for reviewing and approving specifications

- Mechanisms for capturing and resolving conflicting requirements across departments

What emerges is the need for a high-resolution specification layer—substantially more sophisticated than today's prompts—that can bridge human intentions and AI capabilities.

Engineering the specification layer

The path forward requires developing a true engineering discipline around AI specification. This discipline will transform how we translate organizational requirements into executable instructions for increasingly autonomous systems.

Unlike today's ad hoc prompting, this approach will involve rigorously designed processes for developing comprehensive specifications that capture all relevant dimensions of a task or objective. These specifications will incorporate business constraints, regulatory requirements, performance metrics, fallback procedures, and integration points with other systems.

The technical infrastructure for this new discipline will need to support continuous monitoring, dynamic adaptation, and automated validation of AI behavior against specifications. It will need to identify blind spots, detect drift from intended outcomes, and create feedback loops for ongoing improvement.

This isn't just about making prompts longer or more detailed—it's about developing entirely new paradigms for human-AI communication that match the complexity of what we're asking these systems to do. Just as software engineering evolved from primitive line-by-line coding to sophisticated architectures, methodologies, and tools, AI specification will develop into a discipline with its own frameworks, best practices, and specialized roles.

The organizations that recognize this shift early and invest in building robust specification engineering capabilities will gain enormous advantages as AI moves from handling discrete tasks to managing entire operational domains. Those that continue relying on today's prompting techniques will find themselves fundamentally limited in their ability to harness increasingly powerful AI systems for consequential work.