How $200 of AI received four years of dev in four days

Four days, four shippable add-ons and a $200 subscription turned my someday product backlog into done. I’m not new to software, but I am new to moving this quickly. What I gained wasn’t a new skill set — it was uninterrupted access to an AI coding partner inside my editor, plus a workflow optimized for speed over ceremony.

What a $200 AI plan actually bought in capability and time

The money didn’t buy magic. It bought capacity. A higher-tier AI plan would do away with the throttles that slow you just as momentum gets going. Built into VS Code, the model took care of scaffolding and refactors and all the boring glue — while I supplied architecture, guardrails, and the “don’t-do-that” corrections that only context can provide.

I gave up on the big upfront spec. Instead, I gave the AI small sequential tasks: draw shell on UI; bump margins; wire settings; throw in data tables; add hooks into it all; business logic the this and that from some state somewhere else; now test it again, but wait there was something we forgot to do so retest. That rhythm — small asks, fast feedback — kept the assistant in the problem and me in flow.

A sprint ahead of the calendar on long-delayed projects

And historically I’ve been working at my own one-per-year pace on side projects. Context switching, maintaining a development environment and fitting deep work around a day job had it all strung out. Over 10 years, I shipped a grand total of 10 add-ons.

The score after four days in this sprint:

• Day 1: A security analytics add-on that takes logs and visualizes visitor traffic across a private site, logging events, trends, and anomalies with filterable tables and downloadable reports.
• Day 2: Content protection add-on that recognizes and governs AI crawler access through both cooperative signals (robots-style directives and emerging AI-specific flags) and active blocking for non-complying scrapers.
• Day 3: A network access add-on providing fine-grained IP, CIDR range and IPv6 controls with fast matching system and summary UI that scales up to huge address spaces.
• Day 4: A temporary access tool to provide short-term, scoped, expiring links to private sites — no accounts created, privacy-first logging, fine-grained permissions and automatic revocation.

The contrast could not be starker: four add-ons in as many days — versus one a year. The code didn’t magically materialize, but the assistant evaporated friction I’d long considered inevitable as a “side project thing.”

Why it’s plausible that time could collapse

Loose estimates allude to those gains, even if my result is toward the higher of that spectrum. Researchers at Microsoft and GitHub say developers with AI pair programmers saw task-timing decreases of about 55%. McKinsey has estimated that generative AI can automate 20% to 45% of software tasks, including (especially) boilerplate and documentation. Developers are already experimenting with AI-assisted coding, according to Stack Overflow’s developer survey.

My leap wasn’t really 2x — it was more like 10x — because AI didn’t just speed up typing; it eliminated the costly re-entry tax. No longer do I need to lose half a day getting back up on a code base. The model read through my repo, it remembered context across prompts, and handled the mundane middle bit: translations between frameworks, parameter plumbing and UI nits. I concentrated on the intent and feedback.

Limits, pitfalls, and the new bottleneck after faster coding

There were mistakes. The assistant eagerly suggested “clever” patterns that brought with the risk of security holes or performance pitfalls. The antidote was consistent human oversight, unit tests and diff discipline. I had the AI on short leashes — one file or function at a time, with explicit constraints and rapid rollbacks.

Also, the economics matter. If you use the cheapest of these, which is rate-limited. The $200 plan removed the cap enough so that you could run all day. If your billable rate is/opportunity cost for anything above this pane of glass offers returns on investment that write themselves. If you’re not simultaneously exploring, maybe one tier lower is all you need; on the other hand, if you need size yesterday, that higher-tier runway could make the difference.

And yes, the bottleneck moved. Coding is now the short pole. What is time-consuming is the product work: docs, onboarding, pricing, support macros, trial gating, analytics wiring and compliance reviews. Entities such as the OpenSSF keep reminding teams that security practices aren’t magically gone just because code is here sooner — threat modeling and SBOMs are still table stakes.

How to simulate the speed with an AI coding assistant

• UI-first: Ship the skeleton, iterate to “looks right,” attach logic.
• Decompose fiercely: demand individual steps you can validate in minutes.
• Keep that assistant lean: Let it read your repo; ground prompts in specific files and functions.
• Write tests early: Use the AI to build out scaffolding tests and fixtures; use you to make choices of what is important.
• Protect the architecture: You have control over boundaries, data flow and security decisions. The model is a power tool, not a principal chief engineer.
• Budget the go-to-market: Anticipate that launch tasks will dwarf build. That’s normal now.

The bottom line on four days that felt like four years

“Four years in four days for $200”: that might sound hyperbolic until you experience what it takes out of play, including the friction, the reacclimation and certainly any death-by-boilerplate. So, with a non-throttling AI assistant and disciplined workflow, I turned off the backlog faucet and pushed four production-ready add-ons in one damn long weekend. The code fell into place relatively smoothly; the heavy lifting now is polishing it, packaging it and supporting that which ships. As a solo builder or lean team, that’s a change worth planning around.