After a couple of months deep in the trenches of vibe coding with AI agents, I’ve learned this much: scaling from a fun, magical PoC to an enterprise-grade MVP is a completely different game.
Why Scaling Remains Hard—And Costly
Getting a prototype out the door? No problem.
But taking it to something robust, secure, and maintainable? Here’s where today’s AI tools reveal their limits:
- Maintenance becomes a slog. Once you start patching AI-generated code, hidden dependencies and context loss pile up. Keeping everything working as requirements change feels like chasing gremlins through a maze.
- Context loss multiplies with scale. As your codebase grows, so do the risks of agents forgetting crucial design choices or breaking things when asked to “improve” features.
And then there’s the other elephant in the room: costs.
- The cost scaling isn’t marginal—not like the old days of cloud or Web 2.0. Powerful models chew through tokens and API credits at a rate that surprises even seasoned devs.
- That $20/month Cursor plan with unlimited auto mode? For hobby projects, it’s a steal. For real business needs, I can see why some queries rack up millions of tokens and would quickly outgrow even the $200 ultra plan.
- This is why we’re seeing big tech layoffs and restructuring: AI-driven productivity gains aren’t evenly distributed, and the cost curve for the biggest players keeps climbing.
What the Data Tells Us
That research paper—Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity—had a surprising conclusion:
Not only did experienced developers see no time savings on real-world coding tasks with AI, but costs increased as they spent more time reviewing, correcting, and adapting agent output.
The lesson:
AI shifts where the work happens—it doesn’t always reduce it. For now, scaling with agents is only as good as your processes for context, review, and cost control.
Why I Remain Optimistic
Despite the challenges, I’m genuinely excited for what’s coming next.
- The platforms and models are evolving at warp speed. Many of the headaches I face today—context loss, doc gaps, cost blind spots—will get solved just as software engineering best practices eventually became codified in our tools and frameworks.
- Agentic coding will find its place. It might not fully automate developer roles, but it will reshape teams: more focus on high-leverage decisions, design, and creative problem-solving, less on boilerplate and “busy work.”
And if you care about the craft, the opportunity is real:
- Devs who learn to manage, review, and direct agents will be in demand.
- Organizations that figure out how to blend agentic workflows with human expertise and robust process will win big.
Open Questions for the Future
- Will AI agentic coding mean smaller, nimbler teams—or simply more ambitious projects for the same headcount?
- How will the developer role evolve when so much code is “synthesized,” not hand-crafted?
- What new best practices, cost controls, and team rituals will we invent as agentic coding matures?
Final thought:
The future won’t be a return to “pure code” or a total AI handoff. It’ll be a blend—one that rewards curiosity, resilience, and the willingness to keep learning.
Where do you see your work—and your team—in this new landscape?


