The software industry is going through one of the most confusing transitions I have seen in my career.
The market feels broken.
Engineers are applying to hundreds of roles, going through long interview processes, and often receiving no response. At the same time, companies claim they cannot find the talent they need.
Senior engineers are discovering that experience alone is no longer a differentiator. Many candidates now have 10+ years of experience, and the bar keeps rising.
But this is not just a difficult market cycle.
This is a structural shift.
A Change in the Economics of Software
The way software companies are funded has changed.
A few years ago, it was common for startups to raise $3–5 million in pre-seed rounds. Today, raising even
At the same time, the number of startups continues to grow.
This creates a new dynamic:
- More companies
- Less capital per company
- Higher expectations for efficiency
Investors are no longer funding large teams to figure things out. They are looking for teams that can move fast, build efficiently, and reach product-market fit with fewer resources.
The Role of AI in This Shift
AI is accelerating this transformation.
Companies believe AI can increase productivity by an order of magnitude. They expect smaller teams to deliver more output, automate processes, and build faster.
However, there is a clear disconnect.
Many companies know AI is important, but they do not fully understand how to integrate it effectively. This leads to:
- Unclear hiring requirements
- Unrealistic expectations
- Inefficient hiring processes
At the same time, engineers are navigating this uncertainty without clear guidance on how their role is evolving.
A Personal Perspective
I experienced this shift firsthand when I re-entered the job market.
The process was frustrating. Multiple applications, long interview loops, and often no feedback.
At some point, I started questioning the role of AI in all of this.
I saw startups building products with heavily AI-generated code, often without solid architectural foundations. It felt chaotic and unsustainable.
But my perspective changed when I joined a team that approached AI differently.
They did not see AI as a replacement for engineers, but as a way to amplify them.
We focused on:
- Reducing repetitive work
- Removing friction between tools
- Building internal workflows
- Improving engineering efficiency
The goal was not to write more code, but to spend more time on what actually matters:
- Defining the right problems
- Making better decisions
- Designing scalable systems
- Aligning product and engineering
The Real Shift: From Coding to System Design
This is the key insight.
AI is not replacing developers.
It is replacing low-leverage development work.
Tasks like:
- Boilerplate coding
- Repetitive implementations
- Basic debugging
are becoming increasingly automated.
As a result, the role of the developer is evolving.
From:
- Writing code
- Implementing tickets
To:
- Designing systems
- Defining architecture
- Understanding product and business context
- Orchestrating AI-driven workflows
Bad news for code purists:
Writing beautiful code is no longer the job.
Evidence from the Industry
This shift is not theoretical.
OpenAI recently shared their experience with what they call *Harness Engineering*:
https://openai.com/index/harness-engineering/
They describe a system where a team built a large-scale codebase with zero lines of code written directly by humans.
Instead, engineers focused on:
- Defining the environment
- Setting constraints
- Designing feedback loops
AI agents generated and maintained the code.
This does not remove engineers.
It redefines their role.
A Broader Transformation
This change extends beyond software engineering.
We are likely moving toward:
- A more fragmented, niche-driven economy
- A higher number of smaller, focused companies
- Multiple income streams instead of single career paths
Markets will be increasingly saturated with solutions, but each solution can survive by serving a specific audience.
For engineers, this means that value will be defined less by output and more by impact.
Open Questions
Despite these changes, several important questions remain:
- If AI reduces the need for entry-level work, how will new engineers learn foundational skills?
- What defines seniority in an AI-native environment?
- Are companies prepared to evaluate engineers based on system thinking rather than coding ability?
- What happens when decision-making, not implementation, becomes the main bottleneck?
- Will compensation reflect leverage and impact instead of years of experience?
Final Thoughts
AI is not the end of software engineering.
It is the end of a specific version of it.
The opportunity is still there, but it requires adaptation.
The engineers who thrive in this new environment will not be the ones who write the most code, but the ones who can:
- Design systems
- Understand problems deeply
- Make good decisions
- Use AI as a multiplier
The game has changed.
And like every shift, the opportunity is not in resisting it —
but in understanding what the new game looks like.
