Why I Stopped Hiring Developers
I built and managed a 45-person engineering organization across 5 countries. Now I work alone. This isn't a downgrade — it's what happens when AI changes the math.
From 2021 to 2024, I was COO of a US-based IT consultancy. I scaled the engineering org from zero to 45 people across Kazakhstan, India, Korea, and the USA. We delivered IoT systems, WebRTC platforms, mobile apps, and CRMs for US corporate clients. I hired, managed, fired, promoted, mentored, and everything in between.
Now I run carawon.tech. Just me. And I'm delivering projects that would have required a team of 5-8 people two years ago.
I didn't stop hiring because I couldn't find good people. I stopped because AI changed what one senior engineer can do.
The Scaling Problem
At some point, the business stopped being about engineering and became a business of sales and HR. That's it. Everything internally was focused on optimizing processes. More people didn't mean more output — they added overhead. We were less nimble with 45 people than we'd been with 5. Every week brought something new: burnouts, salary negotiations, personal issues, legal complications, policy updates, unhappy employees. More people meant more human factors, more risks, and — counterintuitively — less happy clients. The ratio of time spent managing versus actually building got worse every quarter. I didn't leave because the company failed. I left because I realized the model itself has a ceiling, and I was spending my best hours on everything except the work that mattered.
What AI Changed
The first sign was in 2025, when we replaced a couple of roles with AI tools. It worked, but the technology wasn't mature enough to be transformative. Then in early 2026, I built a production-ready project solo in one month — something that would have taken a team of 10 people a full year. That was the moment I realized how fast AI was maturing. I could have vibe-coded the whole thing in a week. But to keep the quality, security, and architecture at a production standard, it took 1-2 months. Still a fraction of what 10 people working for a year would cost.
The shift wasn't gradual. One day I was scoping a project and estimating 3 developers for 8 weeks. Then I sat down with AI tools and realized I could do the same work in 3 weeks. Not a rough prototype — production-grade code with tests, proper architecture, and documentation.
What AI Can't Do
Here's what I still do that no AI tool can replace:
- Architecture decisions. Which database? Monolith or microservices? How should auth work? AI will give you an answer, but it won't give you the right answer for your specific constraints.
- Scope management. Knowing what NOT to build is more valuable than knowing how to build it. AI will happily generate features nobody needs.
- Quality judgment. AI writes code that passes tests. I know when the tests themselves are wrong. I know when code that 'works' will break under load, in edge cases, or when a real user does something unexpected.
- Business context. Understanding why the client needs this feature, what problem it actually solves, and whether the technical approach aligns with their business goals.
- Saying no. AI is eager to help. A senior engineer knows when the right answer is 'we shouldn't build this.'
AI is the best junior developer who ever lived. Tireless, fast, knows every framework. But it has zero judgment. That's what 8 years of engineering leadership is for.
How I Work Now
Every project follows the same pattern: I architect, AI generates, I review. Take the car marketplace I'm building — React 19 frontend, Django backend, Firebase auth, multi-language support, payment integration. At Interspark, this would have been a 6-person team working for a year. I scoped the architecture myself, set up the data models, defined the API contracts. Then AI generated the implementation layer — components, serializers, test scaffolding, admin panels — while I reviewed every piece for security, performance, and whether it actually solved the business problem.
The result is faster than a team, cheaper than an agency, and the same quality standard I enforced at enterprise scale. Not because I'm superhuman, but because AI handles the volume and I handle the judgment calls.
Is This the Future?
Not for everyone. Large organizations will still need teams. Complex systems with regulatory requirements need multiple specialists. Some projects need 24/7 on-call coverage that one person can't provide.
But for the vast majority of custom software projects — web apps, platforms, automation, internal tools — one senior engineer with AI can deliver what used to require 5-8 people. And the economics are hard to argue with.
Want to see this model in action?
Book a call. I'll walk you through how I'd approach your project — and whether this model is the right fit.