Opinion
AI solved coding. Now complexity is the bottleneck
In a world where nearly every organization can generate more code, the winners will be those that create more value from it.
For years, the software industry operated under one core assumption: writing code was the primary bottleneck in software development. As a result, companies poured enormous resources into hiring developers, refining development processes, and building tools that could shorten the journey from idea to product.
Then generative AI arrived.
For the first time, the industry gained a technology capable of solving the very problem it had spent decades trying to overcome. The ability to write code - once the industry's most constrained resource - is now faster, more accessible, and dramatically more efficient.
But if our ability to generate code has increased so dramatically, why aren't software organizations moving at the same pace?
The data tells a more nuanced story. A 2025 study by researchers at Tilburg University found that while AI coding tools increase output, they also increase the burden on senior developers. After analyzing thousands of open-source projects, the researchers found that experienced developers spent significantly more time reviewing AI-generated code, while their own coding output declined by nearly 20%.
In other words, AI accelerates code creation, but it does not eliminate the need for human expertise to govern, validate, and maintain that code over time.
The implications extend well beyond senior developers. They force us to rethink what we actually measure.
For decades, development teams have been measured by output: how many features were delivered, how many tasks were completed, and how quickly projects moved forward. But in a world where AI writes a growing share of the code, those metrics tell only part of the story.
The real question is no longer how much software an organization can produce. It is how much of that software can be operated, maintained, and continuously evolved over time.
This is where the real challenge of the AI era begins.
The easier it becomes to generate code, the easier it becomes to generate complexity. Organizations can introduce new capabilities at unprecedented speed, but every additional component brings new requirements for testing, security, integration, monitoring, and long-term maintenance.
AI shortens the path to writing software. It does not shorten the path to building reliable enterprise systems.
That is the new bottleneck in software development - not the ability to generate more code, but the ability to manage the complexity that code creates.
More Code Doesn't Mean More Progress
Excess code is only the symptom. The real challenge is keeping it under control. As codebases continue to grow, the question becomes even more pressing: if software can be built faster than ever before, why are organizations still struggling to move at the same pace?
Because in enterprise software, writing code is only the beginning.
Every change must go through quality assurance, security reviews, regulatory compliance, and complex integration processes. Those steps cannot be accelerated at the same pace as code generation.
As AI speeds up development, what happens after the code is written becomes even more critical.
Organizations are measured not by how much code they produce, but by their ability to run stable, secure, and maintainable systems over time. That is why the defining challenge of the coming years will not be a shortage of developers, but a shortage of organizations that can manage complexity at scale.
This shift extends far beyond individual companies. It will reshape the competitive dynamics of entire industries.
For Israel's technology sector, the implications are particularly significant.
For years, Israel's competitive edge has been built on engineering excellence and the ability to develop products faster than others. But when the same AI tools are equally available in Tel Aviv, London, Bengaluru, and San Francisco, speed alone is no longer a competitive advantage.
The real advantage will come from managing complexity - and turning technology into business value.
In a world where nearly every organization can generate more code, the winners will be those that create more value from it.
AI has solved one of software's biggest challenges: generating more code in less time. Now the industry faces a harder question: what do we do with all that code?
Itay Grushka is General Manager, CX Engineering, at NiCE.
