For years, we’ve talked about “moving up the stack.” Last weekend, I experienced what that actually means.
After a long break from hands-on development, I built a complete, production-quality application in a matter of hours with the help of modern AI coding agents. My total investment was refreshingly modest: a $60 Cursor Pro license and a free Alpha Vantage API key to pull real-time market prices.
What changed wasn’t my ability to code. It was the economics of software creation.
The last time I wrote serious production code was in the late 2000s, back in the C#, VC++ and MS SQL Server era. Since then, my career has evolved through solution architecture, pre-sales & marketing, program leadership, sales leadership, and executive roles. Over time, I moved away from writing code — not because I stopped caring about software, but because my role shifted toward shaping systems, teams, and outcomes, rather than functions and classes.
Out of curiosity — and prompted by a very practical consideration while looking for a replacement for the much loved and retired Mint app (it used to track budgets and portfolio’s beautifully) — I decided to build something again last weekend.
I didn’t build a demo. I built something I actually use.
I started with a simple idea: a portfolio tracker that could calculate returns and compare performance against market indices. Within hours, it evolved into a full portfolio management application that handles real-world complexity — multiple asset types, missing purchase dates, manual overrides, and account-level aggregation — all presented in a clean, usable interface.
At a feature level, the application includes:
- Real-time stock prices using a free Alpha Vantage API
- Excel/CSV imports
- Annualized return calculations with edge-case handling
- Multiple asset classes, including stocks, other assets, and cash
- Account-level aggregation and intelligent classification
- Weighted returns with manual overrides where math doesn’t apply
- Export/import for data portability
By the end of the weekend, I had written over 4,000 lines of working code and had a production-quality React application that I genuinely use to track my own portfolio.
Before tools like this, building something of this completeness even as a serious side project would have required weeks of sustained effort and constant context switching — and realistically, it’s the kind of thing that often never quite gets finished.
What changed for me wasn’t ambition or capability. It was the ability to move from “someday” to “done” in a single weekend.
What struck me most was that the real shift wasn’t speed. It was compression.
AI coding agents didn’t replace development. They collapsed the distance between intent and execution. Instead of spending time remembering syntax, wiring libraries, debugging boilerplate (all of which i was terrible at even when i was coding during my early years), or setting up infrastructure, I stayed focused on data models, business logic, UX decisions, and architectural trade-offs.
The agent handled the mechanics. I stayed in the judgment loop.
That distinction matters — because clarity, intent, and trade-offs are where real value is created. This experience reinforced something important: most organizations don’t suffer from a lack of ideas. They suffer from the cost — financial, cognitive, and organizational — of turning ideas into software.
When a production-quality application can be built with a modest tool subscription, free or low-cost APIs, and a few hours of focused intent, the economics shift. Small internal tools suddenly feel worth building. Prototypes replace long documents. Experimentation becomes cheap enough to encourage learning rather than delay it. This isn’t just faster delivery. It’s a reframing of what’s now feasible.
When building becomes this easy, the bottleneck moves. It shifts away from raw engineering capacity and toward organizational clarity and guardrails. The question becomes less about who has time to build and more about what should be built, why, and with what constraints. This wasn’t about going back to coding.
It was about recognizing that the human role has moved up the stack — from writing code to shaping systems, defining intent, and exercising judgment. AI doesn’t eliminate the need for engineering excellence. It amplifies it.
The people and organizations that benefit most won’t be the ones chasing tools. They’ll be the ones who learn how to convert intent into working systems — clearly, quickly, and with purpose.