The VP Geek Speaks

Measuring What Matters: Getting Real About AI ROI

When a team says they don’t see performance benefits from AI, the first question to ask isn’t “Are you using it enough?” It’s “How are you measuring benefit?”

A lot of organizations track adoption (who has a license, how often they use the tool) or activity (suggestions accepted, chats per day). Those numbers go up and everyone assumes AI is working. But cycle time hasn’t improved, quality hasn’t improved, and the team doesn’t feel faster. So you get a disconnect: the dashboard says success, the team says “we don’t see it.”

OpenClaw for Teams That Gave Up on AI

Lots of teams have been here: you tried ChatGPT, Copilot, or a similar assistant. You used it for coding, planning, and support. After a few months, the verdict was “meh”—maybe a bit faster on small tasks, but no real step change, and enough wrong answers and extra verification that it didn’t feel worth the hype. So you dialed back, or gave up on “AI” as a productivity lever.

If that’s you, the next step isn’t to try harder with the same tools. It’s to try a different kind of tool: one built to do a few concrete jobs in your actual environment, with access to your systems and a clear way to see that it’s helping. OpenClaw (and tools like it) can be that next step—especially for teams that are struggling to see any performance benefits from AI in their software engineering workflows.

Why Your Team Isn't Seeing AI Benefits (And It's Not the Tools)

You rolled out AI coding tools. You got licenses, ran the demos, and encouraged the team to try them. Months later, the feedback is lukewarm: “We use it sometimes.” “It’s okay for small stuff.” “I’m not sure it’s actually faster.” Nobody’s seeing the dramatic productivity gains the vendor promised.

If this sounds familiar, you’re not alone. Research shows that while 84% of developers use or plan to use AI tools, only 55% find them highly effective—and trust in AI output has dropped sharply. Adoption doesn’t equal impact. The gap between “we have AI” and “AI is helping us ship better, faster” is where most teams get stuck.

The Documentation Problem AI Actually Solves

I’ve spent the past several weeks writing critically about AI tools—the productivity paradox, comprehension debt, burnout risks, vibe coding dangers. Those concerns are real and important.

But I want to end this series on a genuinely positive note, because there’s one area where AI tools deliver clear, consistent, unambiguous value for engineering teams: documentation.

Documentation is the unloved obligation of software development. Everyone agrees it’s important. Nobody wants to write it. The result is that most codebases are woefully underdocumented, and the documentation that does exist is often outdated, incomplete, or wrong.

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.