The Hidden Costs of AI-Coded Products

Startups are often celebrated for their speed—their ability to build, iterate, and pivot rapidly. The rise of AI coding tools has only fueled this narrative, enabling founders to prototype faster than ever. But speed alone doesn’t build great companies. Great companies are built on the foundation of collaboration, thoughtful engineering, and trust between teams.

The Hidden Risks of Speed

AI-generated code promises lightning-fast development cycles, but it comes with trade-offs. It works superficially, often overlooking the nuance required for real-world scalability and stability. Startups embracing these tools without an engineering-first mindset risk piling up technical debt that becomes insurmountable when scaling begins.

This isn’t just a technical problem—it’s a business problem. Products stall, engineers burn out, and customers lose trust. While it’s tempting to think you can hire engineers later to clean up the mess, the reality is more complex:

1. Onboarding is Costly: Engineers need months to untangle poorly designed systems.
2. Retention is Fragile: No one wants to be the person brought in to fix someone else’s shortcuts.
3. Momentum is Lost: The longer it takes to resolve these issues, the more likely your competitors are to outpace you.

The Product and Engineering Dynamic

One of the biggest reasons startups fall into this trap is the disconnect between product and engineering. Product teams focus on time to market, features, and costs—metrics tied directly to their performance. Engineers, on the other hand, are measured by stability, uptime, and performance.

This misalignment creates tension. Product wants faster and cheaper solutions, while engineers resist cutting corners to ensure long-term success. The result is often animosity, with product teams frustrated by engineering “blockers” and engineers feeling undervalued and ignored.

In my experience, the most successful companies are those that bridge this gap. Product supplies vision and meaning, while engineering ensures those ideas are robust, scalable, and maintainable. Both sides must respect and trust each other, aligning their goals to drive the company forward.

An Example of Getting It Right

While leading a networking rearchitecture at Twilio, my team faced a classic product-engineering conflict. A European customer raised concerns about high latency into our US-East data center. The quick fix—a patchwork solution to appease the customer—would have been the easy way out.

Instead, I encouraged the team to think bigger. We identified related issues, such as changing PCI compliance requirements and the need for multi-regional deployments, and designed a solution that addressed all of them.

By transitioning from Layer 5 CLBs to Layer 3 NLBs and expanding our edge network to nine AWS regions using Terraform automation, we laid a foundation for scalability and resilience. When a last-minute request came in for an additional region, we were able to fulfill it immediately—something that wouldn’t have been possible with a short-term mindset.

This wasn’t just an engineering win—it was a business win. The customer’s latency issues were resolved, and they became one of the first adopters of a new direct-connect solution we introduced as part of the project.

What This Means for VCs and Founders

For venture capitalists, it’s critical to look beyond the pitch deck. Ask how product and engineering teams interact. Are they collaborators or competitors? Does the company prioritize speed at the expense of sustainability?

For founders, the lesson is clear: speed and scalability are not mutually exclusive, but they require careful planning and alignment. AI tools can accelerate development, but they can’t replace the expertise of engineers who foresee problems, ensure stability, and build for the long term.

Building a Culture of Trust

Scaling trust is one of the hardest—and most rewarding—things a leader can do. It starts with mentorship. Leaders must empower their teams, guiding them to make decisions while providing the context they need to succeed.

This trust must extend across teams. Product and engineering should work together, not at odds, with clear alignment on priorities and shared accountability for success.

The Role of Leadership

As leaders, our role is to create environments where teams thrive. Metrics and tools are important, but they should guide decisions, not dictate them. The human element—the trust, collaboration, and mentorship that drive innovation—is what separates great companies from mediocre ones.

The best leaders don’t just manage teams—they orchestrate them, creating a culture where every contribution matters and every team member feels valued. This is how great companies are built: not on speed alone, but on the strength of their people.