3 Ways AI Will Break Software Development and How to Stay Ahead in 2025
AI isn’t just another tool in the developer’s arsenal— it’s a paradigm shift rewriting the rules of software development itself. Picture this: a senior engineer uses GenAI to ship a full feature in record time. No bugs, no blockers. But a few weeks later, the code is tangled with inefficiencies, security gaps, and missed compliance requirements. The team scrambles to fix what AI didn’t catch. Welcome to the new era of development — high speed, high stakes. While AI promises to boost productivity, streamline workflows, and accelerate innovation, it also can break traditional development models. Here are 3 ways how AI will break software development:
The Death of Traditional Coding: From Developers to AI Strategists
With GenAI writing code, debugging, and even suggesting architectures, traditional coding as we know it is fading. AI-powered tools like GitHub Copilot and CodeWhisperer are transforming developers from code writers into AI orchestrators, shifting their focus from syntax to strategy.
The Risk:
Over-reliance on AI-generated code may introduce hidden vulnerabilities, inefficiencies, or lack of originality, leading to technical debt at scale.
The Solution:
Developers must evolve into AI curators – validating AI outputs, ensuring security, and leveraging AI to enhance creativity, not replace it.
The SDLC Bottleneck: AI Accelerates Coding, But Slows Everything Else
AI can churn out code faster than ever, but software development isn’t just about writing code. Tasks like requirements gathering, compliance, security reviews, and deployment pipelines are still largely manual, human-driven, and complex. The imbalance between AI-accelerated coding and slower governance processes could create a bottleneck in SDLC.
The Risk:
Faster coding doesn’t always mean faster shipping— AI-generated software may hit roadblocks when it comes to security, scalability, and compliance.
The Solution:
Organizations must rethink SDLC workflows, integrating AI into testing, security automation, and compliance checks to maintain end-to-end velocity.
The AI Bias & Quality Dilemma
AI doesn’t just mirror human biases— it amplifies them. Whether it’s subtle logic flaws or deeply embedded ethical blind spots, GenAI doesn’t always write “good” code.
The Risk:
AI-generated code can introduce bias, security vulnerabilities, and flawed logic, potentially leading to reputational damage or legal exposure.
The Solution:
Companies must implement AI governance frameworks, enforce AI-involved code reviews, and train developers to spot and mitigate AI-driven risks— technical, ethical, and reputational.
AI Won’t Replace Developers, But Developers Who Master AI Will Replace Those Who Don’t.
The rise of AI in software development is inevitable, but chaos isn’t. Developers and organizations that adapt their skill sets, workflows, and governance structures will thrive in the AI-powered future.
At Galent, we’re helping organizations reimagine the SDLC for an AI-native era – where automation accelerates innovation without compromising quality, security, or creativity. Let’s build an AI-ready SDLC together. Talk to a Galent Expert Today!