Traditionally, scaling software development meant adding engineers, time, and resources. AI is changing that equation by making code cheaper and faster to write. But generating code faster does not necessarily mean delivering software faster. Code still needs to be reviewed, tested, and deployed, creating new bottlenecks for engineering teams.

For engineering leaders, this raises a question: how can software delivery scale without a corresponding increase in headcount?

ForgeCode is addressing this challenge by building what it calls an AI-native software factory. The platform uses AI agents to manage the software development lifecycle, from specifications and implementation to testing and deployment, with humans providing oversight.

At TechSparks 2026, Founder and CEO Tushar Mathur demonstrated how the platform works and explained why the falling cost of writing code is making review, testing, and deployment more important to software delivery.

While AI has increased individual productivity, those gains have not necessarily translated into faster software delivery across organizations.

“The first thing that we are seeing is practically all code is autogenerated today,” Mathur said.

Engineering teams can now generate thousands of lines of code quickly, but the processes that follow: code reviews, testing, security checks, and deployment are struggling to keep up.

According to Mathur, the bottleneck has shifted from writing code to managing what happens after it is written.

“Earlier, writing code was the main fundamental bottleneck. But today, the cost of writing code is so low that all these other aspects have become the bottlenecks,” he explained.

As a result, teams risk producing more poorly reviewed, buggy or uncoordinated code because the systems needed to manage the increased output have not kept pace.

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