Technical debt is often viewed solely as a negative consequence of poor engineering—a mess that needs to be cleaned up. However, at ttoss, we view technical debt through a different lens: as a financial instrument called leverage.

This is especially true in the age of AI. As code generation becomes a commodity, the ability to strategically incur and repay debt defines the velocity of a team. When used consciously, technical debt allows us to ship faster, learn earlier, and capture market opportunities. When accumulated unconsciously, it becomes entropy that grinds development to a halt.

The difference between leverage and negligence lies in how we manage it.

In the rush to build "agentic" systems, most teams jump straight to prompting strategies, tool calling, retrieval setups, or orchestration frameworks. That's putting the cart before the horse.

The very first question you must answer, before any architecture diagram or code, is this: Is the problem (or each sub-problem) you're trying to solve well-structured or ill-structured?

This single classification determines whether traditional deterministic software, probabilistic AI (LLMs and other ML models), or a hybrid of both is the right approach.

Farming didn't disappear when tractors arrived—it evolved. Software development is undergoing the same transformation with AI. The shift is from manual coding to system architecture.

A common question in the age of AI is: "If AI writes the code, do developers just become Product Managers?"

The answer is No, and the reason lies in The Principle of Contextual Authority.

There's a moment every engineering manager experiences after adopting AI coding tools. The initial excitement—"We're shipping features twice as fast!"—slowly curdles into a disturbing realization: "Wait, why are my senior engineers spending hours manually testing for regressions that proper automated tests could catch in seconds?"

This is the AI Verification Trap, and there's only one way out.

For decades, the job of a software engineer was to write the "happy path." We spent our days scripting the exact sequence of events: fetch data, transform it, display it. We were the authors of the flow.

With the rise of Applied AI, that role is fundamentally changing. When an LLM generates the logic, we are no longer the scripters. We are the architects of the boundaries.

We are witnessing a new phenomenon in AI-assisted teams: The Principle of Zero-Cost Erosion. Because AI makes adding complexity (patching) nearly free, while refactoring remains expensive (requiring deep thought), teams are defaulting to infinite patching.

Two engineers join your team on the same day. Both have stellar résumés. Both ace the technical interviews. Both score in the 95th percentile on algorithmic problem-solving.

Six months later, one is shipping 3x more features than the other.

The difference? It's not intelligence. It's not work ethic. It's not even technical depth.

It's something we're just beginning to measure: collaborative ability with AI.

And it's exposing an uncomfortable truth: in the age of AI agents, being smart isn't enough anymore.

In the early stages of AI adoption, most teams treat AI agents as isolated tools: you give a prompt, get a result, and then the context vanishes. This "Task → Prompt → Result → Forget" cycle is inefficient because it fails to capture the intelligence generated during the interaction.

To truly leverage AI in product development, we must shift to a system where outputs are compounded. This means designing workflows where the insight from one agent becomes the context for the next, creating a shared "Memory Layer" that accumulates value over time.

Want to instantly level up your prompting skills? Stop trying to write "good" prompts. Instead, learn how to write the worst possible prompts—and then do the exact opposite.