Andrej Karpathy defined agentic engineering as coordinating fallible agents while preserving correctness, security, and maintainability. For enterprise B2B SaaS teams, that discipline starts in the codebase, not the process document.
Read →Technical due diligence doesn't inspect features. It inspects structure, consistency, and whether the codebase has a design or accumulated one — and those are different things to prepare for.
Read →Ethan Mollick argues that Forward Deployed AI Engineers won't deliver what companies hope because AI adoption is fundamentally an organizational design problem. He's right about the org layer. The codebase coherence layer below it — whether AI tools have patterns to generate against — is what most teams haven't named.
Read →87% of Fortune 500 teams have adopted at least one vibe coding tool. 29% of developers trust the code AI produces. The gap isn't adoption — it's that most codebases weren't designed to be AI-augmented at enterprise scale.
Read →A codebase can serve real customers, generate revenue, and still fail technical due diligence. The diligence question isn't whether the code works — it's whether an unknown engineering team can maintain, extend, and scale it under conditions the acquirer can't predict. That's a different bar.
Read →Ethan Mollick calls AI adoption decisions organizational design, not IT choices. He's right about the org layer. But there's a structural layer beneath it most teams haven't recognized: the architectural decisions in your codebase are already an AI policy.
Read →AI-generated code is fast. In enterprise contexts, 45% of it carries OWASP Top 10 vulnerabilities. The paradox is not that AI cannot write code — it is that ungrounded speed accumulates differently than a slow, planned build.
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