Key Takeaways
- • AI-generated code is increasingly presented as a founder strength during fundraising diligence, but represents a material risk signal that institutional investors should scrutinize rather than accept at face value.
- • Due diligence reports across portfolio companies have become homogenized and formulaic, suggesting that standardized AI tools are replacing differentiated technical assessment and creating blind spots in quality evaluation.
- • Legacy technical debt—custom integrations, undocumented code paths, and bespoke engineering solutions—has paradoxically transformed into a competitive moat and valuable asset, as it cannot be easily replicated by generative AI tools.
- • Code & Co. has developed a Quality of AI (QoAI) framework to distinguish genuine engineering rigor from "AI smoke and mirrors," positioning technical code assessment as a critical new competency for private equity and venture due diligence teams.
- • A single diagnostic question can separate real engineering capabilities from AI-generated facades, highlighting a gap in conventional due diligence processes that may underestimate technical risk in modern portfolio companies.
Summary
The reliability of technology due diligence faces a novel credibility crisis as artificial intelligence-generated code becomes a standard talking point in fundraising pitches. Jason Gill and Kirby Montgomery of Code & Co. argue that what founders present as a competitive advantage—"AI wrote most of our code"—has become a warning flag rather than validation of engineering quality. This inversion reflects a fundamental misalignment between founder incentives and investor risk assessment in an era of accelerating AI deployment across development workflows.
A systemic problem has emerged in institutional due diligence processes: diligence summaries have become strikingly uniform across portfolio companies, suggesting that investors are applying identical assessment frameworks—many of which may rely on generative AI tools themselves—without sufficient differentiation or critical depth. This convergence masks heterogeneous technical realities beneath a veneer of standardized approval. The irony compounds when institutional investors recognize they are often evaluating AI-generated code summaries rather than conducting granular technical evaluation.
The strategic value proposition has inverted for engineering teams. Previously, custom integrations, undocumented code paths, and proprietary solutions were considered technical debt and optimization targets. Today, these bespoke engineering solutions represent irreplaceable competitive assets precisely because they resist AI replication and contain embedded institutional knowledge. Companies that have invested in complex, differentiated technical infrastructure have fortified themselves against commoditization and provide investors with genuine defensibility—a characteristic increasingly rare in the AI-accelerated software landscape.
Code & Co. has developed the Quality of AI (QoAI) framework to address this gap, positioning technical code assessment as an essential competency for private equity and venture diligence teams. The framework aims to distinguish legitimate engineering from generative AI output by isolating diagnostic questions that separate authentic technical capability from surface-level automation. This methodology recognizes that conventional diligence processes have failed to evolve sufficiently to capture risks unique to AI-assisted development environments.
For institutional investors, the immediate implication is straightforward: the default assumption that AI-assisted development accelerates value creation requires subordination to rigorous technical assessment. Investors should recalibrate due diligence protocols to stress-test code quality, evaluate the depth of custom versus commoditized solutions, and deploy technical expertise capable of distinguishing engineered robustness from algorithm-generated plausibility. The competitive advantage accrues to institutions that establish this differentiation in their diligence processes before it becomes table-stakes across the industry.
A systemic problem has emerged in institutional due diligence processes: diligence summaries have become strikingly uniform across portfolio companies, suggesting that investors are applying identical assessment frameworks—many of which may rely on generative AI tools themselves—without sufficient differentiation or critical depth. This convergence masks heterogeneous technical realities beneath a veneer of standardized approval. The irony compounds when institutional investors recognize they are often evaluating AI-generated code summaries rather than conducting granular technical evaluation.
The strategic value proposition has inverted for engineering teams. Previously, custom integrations, undocumented code paths, and proprietary solutions were considered technical debt and optimization targets. Today, these bespoke engineering solutions represent irreplaceable competitive assets precisely because they resist AI replication and contain embedded institutional knowledge. Companies that have invested in complex, differentiated technical infrastructure have fortified themselves against commoditization and provide investors with genuine defensibility—a characteristic increasingly rare in the AI-accelerated software landscape.
Code & Co. has developed the Quality of AI (QoAI) framework to address this gap, positioning technical code assessment as an essential competency for private equity and venture diligence teams. The framework aims to distinguish legitimate engineering from generative AI output by isolating diagnostic questions that separate authentic technical capability from surface-level automation. This methodology recognizes that conventional diligence processes have failed to evolve sufficiently to capture risks unique to AI-assisted development environments.
For institutional investors, the immediate implication is straightforward: the default assumption that AI-assisted development accelerates value creation requires subordination to rigorous technical assessment. Investors should recalibrate due diligence protocols to stress-test code quality, evaluate the depth of custom versus commoditized solutions, and deploy technical expertise capable of distinguishing engineered robustness from algorithm-generated plausibility. The competitive advantage accrues to institutions that establish this differentiation in their diligence processes before it becomes table-stakes across the industry.
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