Over the past year, my daily workflow as a developer has fundamentally shifted. I transitioned from writing code manually to prompting AI, developing dedicated toolkit to refine prompt quality, and integrating AI into every stage of the software development lifecycle.
Working like this every day has given me a much more grounded perspective on where software engineering is actually heading. I don't buy the narrative that AI is going to kill coding or make software engineers obsolete overnight. What I am seeing instead is a clear structural shift: an intensive Migration Era followed by a gradual Compression of the Core Engineering Team.
Here is what that looks like from the ground level. I'd love to hear how this aligns with what you're seeing in your own teams.
Phase 1: The Migration Era is a Dual-Track Reality
Before engineering teams can get smaller or move faster, most companies have to undergo a massive wave of technical cleanup and restructuring. This migration isn't headline-grabbing work, but it's happening along two parallel tracks right now:
Track 1: System Modernization (Cleaning Up Technical Debt): We are using automated scripts and AI tools to untangle legacy monolithic code, build reliable test suites, and migrate outdated dependencies to managed infrastructure. The goal here is simple: reduce the maintenance tax required just to keep existing systems running.
Track 2: Data Restructuring (Preparing for AI Capabilities): You can't deploy reliable AI features or autonomous agents without structured, accessible data. Most organizations are realizing that to build anything meaningful, they have to overhaul their internal data pipelines, consolidate APIs, and clean up domain models first.
The major risk during this phase is rushing in without a strategy. If teams just use LLMs to spit out syntax faster without fixing their underlying architecture, they end up creating a messy hybrid state.
This creates immense cognitive debt, resulting in sprawling, unmaintainable codebases that no single developer fully understands, paired with fragile data schemas and endless code review bottlenecks. The teams making real progress are the ones taking time to build clean interfaces and unified data foundations that AI assistants can safely work with.
Phase 2: Outsourced Infrastructure and Team Compression
Critics will rightly point out that it takes a village to run software. Maintaining server stability, monitoring uptime, patching endless security vulnerabilities, and handling strict enterprise compliance (like GDPR or data sovereignty) is a relentless, full-time job.
Thatβs true, but modern engineering teams rarely build or maintain that heavy machinery in-house anymore. They offload it:
- Infrastructure overhead is pushed to fully managed platforms like AWS, Cloudflare, or Vercel.
- Complex business domains are delegated to specialized APIs like Stripe for payments or Auth0/Clerk for identity management.
- Automated pipelines and monitoring bots flag, test, and patch security dependencies before engineers even open a ticket.
Because so much underlying operational weight is handled by third-party platforms and automation, internal engineering team structures are naturally changing.
The Realistic Future: Compression, Not Disappearance
Thereβs plenty of hype right now about solo developers building billion-dollar companies or small teams replacing enterprise giants overnight. Practically speaking, thatβs an edge case.
Building software at scale still involves navigating human problems, localized laws, user research, customer support, and strategic trade-offs. You canβt comfortably run a scaling enterprise product with two people without burning out.
Real businesses have to solve multiple, complex, deeply human problems at once (from localized legal compliance to nuanced user psychology). You cannot run a sprawling enterprise system with two overworked founders without facing catastrophic burnout.
Instead, the true shift will be Team Compression.
What is actually happening is Team Compression. Engineering pods are becoming leaner and more concentrated. Instead of needing ten developers to build and maintain a core service, a focused team of two or three senior engineers can achieve the same output by orchestrating AI tools and managed services. Organizations that once needed a hundred engineers to handle coordination and maintenance can operate far more efficiently with twenty.
The New Developer Skillset
As AI tools handle routine code generation, pure syntax writing is no longer the main bottleneck. Value has shifted heavily toward system architecture, data design, product intuition, and domain knowledge.
The most impactful engineers will be those who know how to connect systems securely, organize clean data pipelines, and translate real business problems into working software.
Over to You: Agree or Disagree?
This reflects what I've experienced firsthand while building alongside AI tooling over the last year.
- Do you agree that data restructuring and technical debt cleanup are the biggest roadblocks right now, and that solving them will naturally lead to smaller teams?
- Or do you think the ongoing maintenance and debugging burden of AI-assisted code will force teams to stay large just to manage the complexity?
I'd love to hear your thoughts in the comments.
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