How Merge uses AI (w/ Co-founder & CTO Gil Feig)

Merge helps companies add hundreds of integrations through a single API. The product powers data connectivity for orgs like OpenAI, Perplexity, Ramp, and Brex.

We spoke to Gil Feig, Merge’s co-founder and CTO, about how his team is building with AI.

Here are some of the highlights from our conversation:

AI as a company-wide mandate

At Merge, using AI isn’t optional.

Gil calls it their “mini Shopify moment.” Every employee, from engineering to recruiting, is required to use AI tools in their day-to-day work.

“It’s an expectation that you’re going to use AI. Not just for Merge, but for your own career and your future.”

The goal is to help people build lasting skills. Merge plans to eventually include AI proficiency in performance reviews, but the team is being given time and training to get comfortable first.

Training engineers to prompt, not just code

Merge uses Windsurf for both code generation and code review. Gil jokes that sometimes Windsurf writes the code, then reviews it on GitHub.

But the bigger shift has been cultural, not technical. Many engineers lose trust after one failed prompt. Gil’s team is learning to see prompting as a skill in itself.

“If you tell it what not to do, that doesn’t help. You have to tell it exactly what to do next.”

By improving how they communicate with AI (adding structure, context, and examples) the Merge team has gone from small productivity gains to dramatic speedups in how quickly they can ship.

Code gen thrives in greenfield work

AI’s biggest impact so far has been on new products, where there’s little tech debt.

Gil estimates their newest product, Agent Handler, was 90% AI-built. The combination of Windsurf and strong prompting allows the team to ship new features in days instead of weeks.

By contrast, applying AI to older codebases is still challenging. (Something Vanta's Iccha Sethi told us when we spoke to her). Large, complex repos often exceed context-windows, and subtle changes can’t yet be fully automated.

For now, Gil’s rule of thumb: AI accelerates creation more than maintenance.

Beyond engineering: recruiting as an unexpected AI use case

Outside engineering, one department at Merge has fully embraced AI: recruiting.

Recruiting shares a lot of similarities with sales (outreach, qualification, and pipeline management for instance) and AI excels in those areas. It helps Merge’s team identify candidates, personalize outreach, and fill roles faster.

“It’s great for top-of-funnel sourcing. Recruiting is like sales - but for non-executive hires, AI can run most of the playbook.”

Interestingly, sales hasn’t seen the same lift yet. For enterprise deals, nuanced relationships still matter more than volume. AI is great for scale, but it can't be used for persuasion, especially the kind that is needed for high touch, high value deals.

Hiring for curiosity, not credentials

Probably the most common thing we've heard from leaders we've talked to. AI hasn’t changed the skills Merge hires for. The meta-skills required to be a great hire (curiosity, agency etc) have become even more important post-AI.

The best hires aren’t the ones who already know how to use AI; they’re the ones eager to learn it.

“We’ve had people become AI experts here just by experimenting, reading, and talking to AI itself.”

Gil prioritizes drive, curiosity, and adaptability over hard credentials. Tools will change; the meta-skills won't.

Low-stakes experimentation builds momentum

Some of the most surprising wins come from employees using AI to solve small, everyday problems.

When one teammate built a web app with Lovable to generate company email signatures (something that used to take hours to standardize), Gil realized how powerful these low-stakes experiments can be.

“You’d never waste time building an app for that.. now someone can in ten minutes - and it works.”

Those small experiments build confidence. They also create a bottom-up culture of continuous tinkering.

Actionable takeaways for builders

• Make AI usage a cultural default. Adoption shouldn’t be a side quest. It needs to be how work gets done.
• Treat prompting like a craft. Give teams concrete frameworks and examples to improve prompt quality over time.
• Use AI for creation, not maintenance. AI performs best on new surfaces where there’s less legacy complexity.
• Expand beyond engineering. Recruiting and operations often see ROI faster than product teams.
• Hire learners, not experts. Curiosity and adaptability compound faster than credentials.
• Celebrate micro-experimentation. Encourage small, creative AI wins that build the tinkering muscle.

We often talk about AI adoption as a technical shift. Gil reminded us it’s also a cultural one. By encouraging use, normalizing learning, and celebrating small wins, Merge is teaching its people to think alongside machines.

We had a great time jamming with Gil! We’ve been having similar conversations with product and engineering leaders at top engineering orgs (like Vercel, Wix, Vanta, etc) to unpack how they actually build with AI. You can find previous episodes on YouTube.