The VP Geek Speaks

Your AI-Generated Codebase Is a Liability

If a quarter of Y Combinator startups have codebases that are over 95% AI-generated, we should probably talk about what that means when those companies get acquired, audited, or sued.

AI-generated code looks clean. It follows conventions. It passes linting. It often has reasonable test coverage. By most surface-level metrics, it appears to be high-quality software.

But underneath the polished exterior, AI-generated codebases carry risks that traditional codebases don’t. Security vulnerabilities that look correct. Intellectual property questions that don’t have clear answers. Structural problems that emerge only under stress. Dependency chains that nobody consciously chose.

Will Junior Developers Survive the AI Era?

Every few months, I see another hot take claiming that junior developer roles are dead. AI can write code faster than entry-level developers, the argument goes, so why would companies hire someone who’s slower and less reliable than Copilot?

It’s a scary narrative if you’re early in your career. It’s also wrong—but not entirely wrong, which makes it worth examining carefully.

Junior developers aren’t becoming obsolete. But the path into the profession is changing, and both new developers and the leaders who hire them need to understand how.

The AI Burnout Paradox: When Productivity Tools Make Developers Miserable

Here’s an irony that nobody predicted: AI tools designed to make developers more productive are making some of them more miserable.

The promise was straightforward. AI handles the tedious parts of coding—boilerplate, repetitive patterns, documentation lookup—freeing developers to focus on the interesting, creative work. Less toil, more thinking. Less grinding, more innovating.

The reality is more complicated. Research shows that GenAI adoption is heightening burnout by increasing job demands rather than reducing them. Developers report cognitive overload, loss of flow state, rising performance expectations, and a subtle but persistent feeling that their work is being devalued.

Comprehension Debt: When Your Team Can't Explain Its Own Code

Technical debt is a concept every engineering leader understands. You take a shortcut now, knowing you’ll need to come back and fix it later. The debt is visible: you can point to the code, explain what’s wrong with it, and estimate the cost of fixing it.

AI-generated code is introducing something different—and arguably worse. Researchers have started calling it “comprehension debt”: shipping code that works but that nobody on your team can fully explain.

Vibe Coding: The Most Dangerous Idea in Software Development

Andrej Karpathy—former director of AI at Tesla and OpenAI co-founder—coined a term last year that’s become the most divisive concept in software development: “vibe coding.”

His description was disarmingly casual: an approach “where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” In practice, it means letting AI tools take the lead on implementation while you focus on describing what you want rather than how to build it. Accept the suggestions, trust the output, don’t overthink the details.

OpenClaw for Engineering Teams: Beyond Chatbots

I wrote recently about using OpenClaw (formerly Moltbot) as an automated SDR for sales outreach. That post focused on a business use case, but since then I’ve been exploring what OpenClaw can do for engineering teams specifically—and the results have been more interesting than I expected.

OpenClaw has evolved significantly since its early days. With 173,000+ GitHub stars and a rebrand from Moltbot in late January 2026, it’s moved from a novelty to a genuine platform for local-first AI agents. The key differentiator from tools like ChatGPT or Claude isn’t the AI model—it’s the deep access to your local systems and the skill-based architecture that lets you build custom workflows.

Lessons from a Year of AI Tool Experiments: What Actually Worked

Over the past year, I’ve been experimenting extensively with AI tools—trying to understand what they’re actually good for, where they fall short, and how to use them effectively. I’ve written about several of these experiments: the meeting scheduling failures, the presentation generation disappointments, and most recently, setting up Moltbot as an SDR.

Looking back at all these experiments, patterns emerge. Some things consistently worked. Others consistently didn’t. And a few things surprised me in both directions.

The Case Against Daily Standups in 2026

I’ve been thinking about daily standups lately—specifically, whether they still make sense for engineering teams in 2026.

This isn’t a “standups are terrible” rant. I’ve run teams with effective standups and teams where standups were pure theater. The question isn’t whether standups are universally good or bad; it’s whether the standard daily standup format still fits how engineering teams work today.

My conclusion: for many teams, it doesn’t. Here’s why.

AI Code Review: The Hidden Bottleneck Nobody's Talking About

Here’s a problem that’s creeping up on engineering teams: AI tools are dramatically increasing the volume of code being produced, but they haven’t done anything to increase code review capacity. The bottleneck has shifted.

Where teams once spent the bulk of their time writing code, they now spend increasing time reviewing code—much of it AI-generated. And reviewing AI-generated code is harder than reviewing human-written code in ways that aren’t immediately obvious.

From Code Writer to AI Orchestrator: The Changing Developer Role

There’s a narrative circulating in tech circles: developers are evolving from “code writers” to “AI orchestrators.” The story goes that instead of typing code ourselves, we’ll direct AI agents that write code for us. Our job becomes coordination, review, and high-level direction rather than implementation.

It’s a compelling vision. It’s also significantly oversimplified.

Research shows that developers can currently “fully delegate” only 0-20% of tasks to AI. That’s not nothing, but it’s far from the wholesale transformation some predict. The reality of how developer roles are changing is more nuanced—and more interesting—than the hype suggests.