Forge Code CEO: AI-driven code generation needs a 'software factory'

Why faster code generation isn’t enough

At the TechSparks 2026 conference, Forge Code CEO Tushar Mathur addressed a critical gap in AI-driven software development. While AI tools can now generate code faster than ever, the broader software development lifecycle (SDLC) remains fraught with inefficiencies. According to Mathur, the acceleration of code generation has merely shifted bottlenecks to other stages of the process, such as reviews, testing, and coordination.

Mathur emphasized that traditional software development focused on optimizing code writing, but AI has exposed new challenges. As teams generate more code, pull requests pile up, testing cycles lengthen, and bugs slip through regression suites. Simply adding more engineers or AI agents risks increasing coordination costs or overwhelming review systems, rather than solving the core problem.

The software factory model

Forge Code’s proposed solution is a 'software factory'—a system that coordinates AI agents across the entire SDLC. This model is designed to operate autonomously, scale efficiently, and align with business goals. During his presentation, Mathur demonstrated the system’s capabilities, noting that Forge Code was running around 100 AI agents simultaneously, handling tasks like feature development, compatibility checks, and bug detection.

The factory model relies on six key principles: autonomy, scalability, cost efficiency, measurability, self-improvement, and alignment with business outcomes. Unlike traditional workflows, where engineers direct every task, this system monitors production and uses feedback loops to refine future work. Engineers shift from micromanaging tasks to overseeing outcomes, reviewing signals, and improving the system’s performance.

Mathur also highlighted the role of business leaders in this model. By contributing requests and priorities directly, they can ensure the factory translates organizational goals into a development backlog. The focus shifts from writing code faster to shipping better software—a critical distinction for companies aiming to leverage AI effectively.

What this means

Lazyfounder analysis — our interpretation, not reported fact.

Mathur’s argument challenges the prevailing narrative that AI-driven code generation alone can revolutionize software development. For founders and operators, the takeaway is clear: AI tools must be integrated into a broader system that addresses the entire SDLC. The 'software factory' model suggests that automation should extend beyond coding to include reviews, testing, and coordination. This could reshape how engineering teams are structured, reducing the need for manual oversight and allowing engineers to focus on higher-level outcomes. For startups and enterprises alike, the lesson is that AI adoption requires more than just plugging in tools—it demands a rethinking of workflows and systems.

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