For years, the software program trade operated underneath one core assumption: writing code was the first bottleneck in software program growth. Because of this, corporations poured huge sources into hiring builders, refining growth processes, and constructing instruments that might shorten the journey from concept to product.
Then generative AI arrived.
For the primary time, the trade gained a expertise able to fixing the very drawback it had spent many years attempting to beat. The flexibility to jot down code – as soon as the trade’s most constrained useful resource – is now quicker, extra accessible, and dramatically extra environment friendly.
But when our means to generate code has elevated so dramatically, why aren’t software program organizations shifting on the identical tempo?
The info tells a extra nuanced story. A 2025 examine by researchers at Tilburg College discovered that whereas AI coding instruments enhance output, additionally they enhance the burden on senior builders. After analyzing hundreds of open-source tasks, the researchers discovered that skilled builders spent considerably extra time reviewing AI-generated code, whereas their very own coding output declined by practically 20%.
In different phrases, AI accelerates code creation, but it surely doesn’t get rid of the necessity for human experience to control, validate, and keep that code over time.
The implications lengthen nicely past senior builders. They pressure us to rethink what we truly measure.
For many years, growth groups have been measured by output: what number of options have been delivered, what number of duties have been accomplished, and the way shortly tasks moved ahead. However in a world the place AI writes a rising share of the code, these metrics inform solely a part of the story.
The true query is not how a lot software program a corporation can produce. It’s how a lot of that software program may be operated, maintained, and constantly developed over time.
That is the place the true problem of the AI period begins.
The simpler it turns into to generate code, the simpler it turns into to generate complexity. Organizations can introduce new capabilities at unprecedented velocity, however each extra element brings new necessities for testing, safety, integration, monitoring, and long-term upkeep.
AI shortens the trail to writing software program. It doesn’t shorten the trail to constructing dependable enterprise methods.
That’s the new bottleneck in software program growth – not the flexibility to generate extra code, however the means to handle the complexity that code creates.
Extra Code Would not Imply Extra Progress
Extra code is barely the symptom. The true problem is preserving it underneath management. As codebases proceed to develop, the query turns into much more urgent: if software program may be constructed quicker than ever earlier than, why are organizations nonetheless struggling to maneuver on the identical tempo?
As a result of in enterprise software program, writing code is barely the start.
Each change should undergo high quality assurance, safety opinions, regulatory compliance, and sophisticated integration processes. These steps can’t be accelerated on the identical tempo as code technology.
As AI accelerates growth, what occurs after the code is written turns into much more crucial.
Organizations are measured not by how a lot code they produce, however by their means to run steady, safe, and maintainable methods over time. That’s the reason the defining problem of the approaching years won’t be a scarcity of builders, however a scarcity of organizations that may handle complexity at scale.
This shift extends far past particular person corporations. It is going to reshape the aggressive dynamics of whole industries.
For Israel’s expertise sector, the implications are significantly important.
For years, Israel’s aggressive edge has been constructed on engineering excellence and the flexibility to develop merchandise quicker than others. However when the identical AI instruments are equally out there in Tel Aviv, London, Bengaluru, and San Francisco, velocity alone is not a aggressive benefit.
The true benefit will come from managing complexity – and turning expertise into enterprise worth.
In a world the place practically each group can generate extra code, the winners can be people who create extra worth from it.
AI has solved considered one of software program’s greatest challenges: producing extra code in much less time. Now the trade faces a tougher query: what can we do with all that code?
Itay Grushka is Basic Supervisor, CX Engineering, at NiCE.