The hidden costs of AI-generated code: what engineering leaders need to know
Learn to manage the hidden costs of AI-generated code by balancing rapid velocity with long-term maintenance and risk.
AI & Innovation
Agentic development (Claude Code, etc.), managing AI-generated code churn, the future of the IDE, and using LLMs for code exploration. Navigating the AI code explosion without losing quality or control.
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Can R&D credits help pay for your AI transformation?!
Flux uses code-based classification to show feature work, making it easier to justify and maximize your claims for R&D tax credits.
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AI transformation in software engineering: 2025 year in review
In 2025, AI transformation in software engineering redefined the industry. How are leaders navigating impacts on quality, metrics, and ROI?
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America’s AI race: global AI strategy and infrastructure
On Civitas Outlook, Rachel explores the White House’s AI strategy: aiming to secure U.S. dominance through infrastructure, exports, and foundation model regulation, while facing trade-offs around transparency, bias, and global collaboration.
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Your AI adoption roadmap for engineering
A practical roadmap for AI adoption in engineering, built around phased implementation, realistic expectations, and metrics that actually reflect long-term impact.
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AI generated code: revisiting the Iron Triangle in 2025
Does AI generated code break the Iron Triangle? Explore new research on quality, trust, and the hidden costs of AI tools.
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The Hidden Risks of AI Code Generation: What Every Developer Should Know
AI code generation risks—from security flaws to maintainability issues—pose serious challenges developers must manage to ensure long-term code quality.
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Why the AI Revolution Will Require Massive Energy Resources
Training large-scale models requires enormous computation, fueled by multimodal datasets comprising text, audio, video, images, and sensor data that often exceed a petabyte.
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The AI Code Understanding Imperative
See how AI and LLMs provide understanding engineering leaders need to manage complexity in codebases with AI code.
