More than a year ago, our CEO asked me a straightforward question: “Do we have a plan for fine-tuning our models when we grow bigger?” When we started, we hadn’t planned for it for obvious reasons - we were small, and the tech was nascent. I told her that people were already moving away from the term “fine-tuning.” They were calling it “alignment.” That conversation happened way before DeepSeek-R1. Of course, in the AI era, “forever” is a concept measured in months. And then came the RL (Reinforcement Learning) rampage, and now everyone is talking about alignment.

The distinction is vital. In my opinion, with fine-tuning, you know the exact answer; you are the teacher correcting the student. With alignment, you don’t necessarily know the answer - you only decide if the output looks better or is moving in the right direction. This shift is inevitable: as models get smarter by the day, you can no longer be the teacher. You can only be the judge.

The Inception of Intent

Working with AI to build software is becoming indistinguishable from magic. The distance between you and a working prototype is just a prompt. It is like the dream levels in Inception: you can ask AI to produce what you want, ask it to create the prompt that creates what you want, or even ask for a meta-prompt to architect the whole system. What is required now is turning ambiguity into clear intent (what to build, why, success criteria, tradeoffs). While this requires skill, AI and LLMs are rapidly absorbing the execution layer. As generation and iteration become cheap and scalable, the human bottleneck shifts from “doing” to “deciding.” Humans’ enduring value concentrates at two bookends: Inception (the spark of intent) and Alignment (the verification of the outcome).

The Cost of Velocity: AI Debt

The really challenging part is keeping these powerful systems reliably within our intent - adhering to constraints, verification, and accountability. The cost curve has flipped: execution is getting cheaper, but misalignment is getting exponentially more expensive. The crux is velocity: without real-time alignment, your system drifts the moment it is deployed. The nature of failure has mutated. In the old world, a bug simply meant a feature didn’t work. In the new world, an unaligned agent creates active chaos with all sorts of issues you don’t even have time to plan for or understand. This is AI Debt. It is not just messy code, but also ambiguous behavior, and the overwhelming cognitive burden. If left unchecked, this debt doesn’t just accumulate interest - it triggers an avalanche.

The QA Renaissance

Software engineering has already weathered similar shocks. DevOps taught us that shipping is a system with feedback loops. AI now makes “building” continuous and dramatically increases the volume of change. This triggers a QA Renaissance. We already moved past the era of QA as a separate, manual gatekeeping department. Instead, QA is morphing into a core engineering capability. And now they are back, with the help of AI, defining correctness, verifying at scale, monitoring drift, and ensuring auditability.

There is a humorous irony here. For years, people predicted the QA role would disappear because testers couldn’t catch up with the speed of developers. Now, with coding agents, human developers can’t catch up either. The playing field has leveled: we both are the losers. And the boundary blurred: it is just different ways of talking to AI. The primary skill of the future developer is not writing syntax, but rigorously defining what “good” looks like so the machine can achieve it.

The Strategic Takeaway

The strategic implication is clear: we must aggressively divest from commoditized implementation-only work. The “how” is being solved; the value has moved upstream to the “what” and the “why.” Organizations must pivot their engineering culture toward Alignment Engineering, which rests on not only clear specifications and early adjustments, but also, Evaluation as Implementation: If the AI does the work, the human must build the yardstick. We need to invest heavily in “Evals” - automated test suites that score model behavior against human intent. This is the only defense against the avalanche of AI debt.

Risk Management as Architecture: We are no longer building static walls; we are managing kinetic agents. This requires designing systems with “circuit breakers” and human-in-the-loop checkpoints that trigger when an agent’s confidence drops or the stakes rise.

Ultimately, use AI throughout the process, but recognize the hard truth: AI is an engine of execution, not intent. It can run the race faster than any human, but it has no idea where the track is. Your job is no longer to run; your job is to draw the finish line.