What AI Tool Builders Actually Say About Vibe Coding: Practical Insight From Founders

We've all seen the viral tweet. Andrej Karpathy, former AI lead at Tesla, coined the term "vibe coding" and described a new way of building software where you "fully get into the vibe, embrace exponentials and forget that the code even exists."

It sounds promising—until you read the details and realize he's also saying things like "I don't read the diffs anymore" and "I just copy and paste error messages with no comment."

Recently, I moderated a panel titled "Vibe Coding: The Future of Development" with founders who are building AI development tools. Here's what they learned about implementing AI coding tools in real businesses.

The Expert Panel: Founders Building AI Development Tools

I moderated a discussion with four founders working on AI-assisted development:

Doug Kadlecek from Grio - CEO and co-founder of a 17-year software consulting agency that uses AI in their development workflow for enterprise clients (Google, Yahoo) and Bay Area startups.

Elmer Morales from koderAI - Software engineer turned founder, building AI tools that help non-technical people create applications from concept to deployment.

Rob Whiteley from Coder - CEO of an AI development environment platform, working with both technical and non-technical teams.

Sharon Barr from Early - Founder and CEO building AI-powered testing solutions, focused on quality assurance for AI-generated code.

What Effective AI Coding Actually Looks Like

Rob made a useful distinction between different AI coding approaches: "I would make the distinction between Agentic AI and Vibe Coding. Agentic AI is more like I have a virtual software engineer that I am trying to pair program with. That's a much better fit for senior engineers I think. But Vibe Coding is definitely sparks a little joy for the back end folks."

This distinction is useful: use agentic AI for complex development tasks where you need structured collaboration, and vibe coding for rapid exploration and prototyping, especially when working outside your technical comfort zone.

Where AI Coding Works Best Right Now

The panelists agreed on where AI coding delivers the most value today:

Sharon was direct about current capabilities: "When you look at agents and vibe coding is sort of coming to solve a pain, the pain is, I want to build a prototype right now or a demo. And for that, it's working great. But, as vibe coding evolves, I think the value will come when you want to build something for production. And to do something for production, vibe coding is not there yet."

Doug added business context from his agency's experience: "It's great for prototyping, doing things. But you need to understand your business risk. Right? You don't understand what your code is doing. It could do bad things. Right? To have security leaks, things like that."

The pattern that emerged: Start with AI coding for rapid validation and internal tools, then graduate to human-reviewed code for customer-facing applications. This isn't a limitation—it's a smart deployment approach that maximizes speed while managing risk.

The Internal Tools Sweet Spot

Rob shared specific examples of where this works in practice: "Where I do see even in like the DOD, people getting really a lot of value out of it is just internal almost like Wiki like pages that I can now make interactive. So I can create a dashboard for my team's goals or I can create a calculator to help close the books in finance."

This isn't just theory—it's a proven pattern. The DOD example is particularly telling because they have strict requirements, yet they're finding real value in specific use cases.

Elmer highlighted what this means for non-technical founders: "Our vision is that the non technical people, many of you in this audience, everything from people that are not in tech to people that are probably designers, product managers, or entrepreneurs, they now have a solution, right, to experiment. Maybe their first iteration won't go all the way to production yet, right? But they're now no longer have that bottleneck, right? Which in the past was finding that unicorn CTO or engineer to really go and build something for you."

In practice: You can now validate ideas and test assumptions before committing technical resources. Instead of writing specs and hoping developers understand your vision, you can build functioning prototypes that demonstrate exactly what you want.

Who Gets the Most Value

Rob shared an interesting observation about experience levels: "I think we're seeing something like that with developer maturity. If you're really, really junior on that scale, you're getting a ton of value from these tools. If you're really, really senior, you're getting a lot of value because you know how to break it down into concrete tasks, manage it... And then everyone in the middle is either getting frustrated or disenchanted with a lot of the AI stuff."

What this means for hiring: Consider prioritizing either very senior developers who can effectively manage AI tools, or junior developers who can grow with AI assistance. The traditional mid-level developer role might be the most disrupted.

For non-technical founders: You're actually in a good position. Your lack of preconceived notions about "how code should be written" becomes an advantage when working with AI tools that operate differently than traditional development.

Building Team Confidence

Sharon made an important point about adoption timelines: "I think the process here is trust. Right? I wouldn't drive a self driving car in 2014. Definitely won't put my kids on it. Nowadays, it happens. It took ten years to get there, and it will take time for developers to trust the production code that has been generated."

What this means: Companies that start using these tools now will be more comfortable with them later. While others are still debating whether to use AI coding, you'll already have established workflows and confidence.

The Testing Evolution

Sharon made a prediction about where the technology is heading: "I think right now we're talking about vibe coding. Maybe the next one is vibe testing. In fact, vibe testing will be 10x more than vibe coding because you want to generate so many tests around it to feel comfortable that it can actually go to production without reviewing the code."

The practical takeaway: Comprehensive AI-generated testing could be the bridge that makes AI coding production-ready. Start experimenting with AI testing tools now to prepare for this evolution.

Your Implementation Plan

Based on the discussion, here's how to implement AI coding effectively:

1. Start with Internal Tools and Rapid Prototyping

Use AI coding for internal dashboards, calculators, and workflow tools where you can iterate quickly and learn the tools' capabilities. Rob's examples were particularly compelling: "I can create a dashboard for my team's goals or I can create a calculator to help close the books in finance."

What this looks like:

• Financial modeling tools for scenario planning
• Team goal tracking dashboards
• Internal process automation (expense reporting, time tracking)
• Quick data visualization tools for executive updates
• Workflow calculators for operational decisions

These internal tools have shorter shelf lives and lower risk profiles, making them perfect learning environments. If they break, you can fix them quickly or build new ones without major business impact.

2. Use AI Coding for Rapid Validation

Leverage AI coding to test ideas and validate concepts quickly before committing development resources. Elmer captured why this matters: non-technical entrepreneurs "are now no longer have that bottleneck, right? Which in the past was finding that unicorn CTO or engineer to really go and build something for you."

Practical approach:

• Build quick prototypes to test user workflows
• Create mockups that actually function rather than static designs
• Test different approaches to the same problem rapidly
• Validate technical feasibility before hiring developers
• Create demos for investor meetings or customer conversations

The key insight: most projects don't make it to production anyway, so use AI coding to fail fast and learn quickly without major resource commitment.

3. Build Review Systems for Production Code

For customer-facing applications, implement human review processes that go beyond just checking for bugs. Doug emphasized this: "I think software engineering is more than just writing code. Right? It's about solving problems. So you have to look at things analytically and make sure you're, you know, you can write the code, but somebody has to check, is this solving the problem that we have?"

Review checklist:

• Problem validation: Does the code actually solve the intended business problem?
• Security assessment: What are the potential security vulnerabilities?
• Performance evaluation: Will this perform under expected load?
• Maintainability check: Can a human developer understand and modify this code?
• Integration testing: How does this interact with existing systems?

Think of this as a bridge strategy: use AI coding to get to a working prototype quickly, then have experienced developers review and refactor for production deployment.

4. Invest in AI Testing Capabilities

Start building AI-powered testing workflows now. Sharon's prediction was insightful: "Maybe the next one is vibe testing. In fact, vibe testing will be 10x more than vibe coding because you want to generate so many tests around it to feel comfortable that it can actually go to production without reviewing the code."

Begin experimenting with:

• AI-generated unit tests for your AI-coded prototypes
• Automated integration testing workflows
• AI-powered security scanning and vulnerability detection
• Performance testing automation
• User acceptance testing with AI-generated scenarios

The key insight: Comprehensive AI testing could be the bridge that makes AI coding production-ready. Companies that build these capabilities now will be positioned to move faster when the tools mature.

5. Build Confidence Through Small Wins

Based on Sharon's trust-building analogy, implement a gradual adoption strategy: "I wouldn't drive a self driving car in 2014. Definitely won't put my kids on it. Nowadays, it happens. It took ten years to get there, and it will take time for developers to trust the production code that has been generated."

Confidence-building approach:

• Start with low-stakes projects where failure is acceptable
• Document wins and improvements in team productivity
• Share success stories across the organization
• Gradually increase the complexity of AI coding projects
• Create clear guidelines for when to use vs. avoid AI coding

The goal is building organizational confidence through repeated successful experiences, not theoretical arguments about AI capabilities.

Timeline for Production-Ready AI Code

Elmer offered a realistic timeline: "I think we are gonna get there to the point where people can do very sophisticated applications and take them all the way to production provided that we have enough systems in place to verify, right, verify the code, do some automated testing and be very confident that this is actually gonna work and do what it needs to. We're just not there for the more complicated stuff yet, but I think we will be there probably in the next twelve months."

What this means: Build your AI coding capabilities now, so you're ready when the production-ready tools arrive.

The Volume Opportunity

Rob captured what might be the most underestimated aspect: "It's an exponential increase in the amount of software that's going to get created."

This isn't just about coding faster—it's about changing what gets built. When the cost of creating software drops dramatically, entirely new categories of solutions become viable. Internal tools that were never worth a developer's time, hyper-specific niche applications, rapid A/B testing of different approaches.

The key insight: Success won't come from coding skills alone, but from identifying which new types of software become possible when creation costs approach zero.

The question isn't whether to use AI coding—it's how quickly you can identify and capture opportunities that only become viable when software creation becomes much easier.