Openlayer’s Pivot from Vision Pro to AI Safety: When to Leave Big Tech to Solve Industry Problems
Working on a secret project at Apple might seem like a dream job, but for Gabriel Bayomi and his colleagues, it revealed a critical gap in AI infrastructure that would eventually lead them to found Openlayer.
In a recent episode of Category Visionaries, Gabriel revealed the systematic approach they used to validate their startup idea while still working on the Apple Vision Pro. Here’s their framework for turning corporate frustration into market opportunity:
Identify a Universal Pain Point
The key insight wasn’t in building AI models – it was in everything surrounding them. “Know building models was not the hard part,” Gabriel explains. “The hard part was everything around it. How do you test to make sure it’s good and safe? How do you monitor it in production to make sure the performance is as good as you expect?”
Crucially, this wasn’t just a personal annoyance. The team recognized a systemic problem in how AI development differed from traditional software development. While software engineers had sophisticated tools like GitHub and established CI/CD pipelines, AI engineers were “using like a Jupyter notebook, they’re just writing out on this very bad infrastructure, trying to make something work.”
Test Your Hypothesis With Peers
Before making the leap, the team validated their hypothesis with colleagues. Gabriel approached his coworkers with a simple question: “would you like to quit to start something to fix this problem once and for all?”
The enthusiastic response wasn’t just emotional support – it was market validation. These were experienced engineers working on cutting-edge AI projects, and they immediately recognized the value proposition.
Focus on Concrete Problems Over Abstract Solutions
When transitioning from internal pain point to market opportunity, the team learned to articulate their value proposition in concrete terms. “Instead of going to the abstract idea space of like, we make your AI safe, we try to market things more directly. For example, hey, get alerts when your LLM fails,” Gabriel shares.
This shift from abstract to concrete wasn’t just about marketing – it helped validate that they were solving real problems rather than theoretical ones.
Build With Clear Product Vision
The team’s experience at Apple shaped their product vision. They understood that successful AI deployment required more than just model development – it needed a comprehensive infrastructure for testing, monitoring, and safety checks.
Their goal became clear: make AI testing and monitoring as fundamental to development as unit tests are to software engineering. As Gabriel puts it, “the way that people think about code and creating tests, unit tests, or CI CD pipelines, they’re going to be thinking the same on AI, but using Openlayer.”
Validate Through Accelerators
Rather than immediately jumping ship, the team validated their idea through Y Combinator’s application process. Getting accepted provided external validation and a framework for testing their assumptions.
At YC, they received crucial advice from Paul Graham that shaped their approach: forget abstract concepts like product-market fit and focus on “things working and companies surviving… It’s all about being default alive, not default dead.”
The Decision Framework
For technical founders considering a similar leap, Gabriel’s journey suggests three key questions to validate your internal frustration as a business opportunity:
- Is the problem universal enough? Does it affect not just you but the broader industry?
- Can you articulate the solution in concrete, actionable terms?
- Are experienced professionals in your field immediately excited about the solution?
Looking ahead, Gabriel envisions Openlayer becoming “the guardrail of the AI revolution” where “people think about OpenAI and cohere and all of these companies building these huge models and think like, okay, what’s our Openlayer stack?”
The transition from Vision Pro to Openlayer illustrates a crucial lesson for technical founders: sometimes the best startup opportunities come not from building new technology, but from fixing the infrastructure problems that prevent existing technology from reaching its full potential.