AI's impact on API integration patterns and building frictionless developer experiences | Anuj Jhunjhunwala (Director of Product, Merge)
Manage episode 489406571 series 3652402
In this episode of Request // Response, I sit down with Anuj Jhunjhunwala, who leads product and design at Merge. We talk about how Merge is transforming integration pain into product velocity with their unified API approach. Anuj shares how his team helps developers avoid the complexity of maintaining dozens of APIs, and we explore the rising strategic importance of integrations in AI-driven products. We also dive into what great developer experience really looks like and how AI is reshaping expectations around API design and usability.
Show Notes
[02:30] – The pain of building and maintaining custom integrations
[03:45] – Merge as “Plaid for B2B software” and the magic moment for devs
[07:30] – AI making integrations table stakes; the three types of data
[08:30] – Why proprietary data is the key to differentiation in AI
[09:45] – AI and the future of APIs: personalization and intuitive design
[11:30] – DX vs AX: API design for devs and for AI
[12:00] – How AI product patterns are changing API requirements
[13:00] – Richer queries, delta endpoints, and evolving API design
[14:00] – The rising importance of fine-grained permissions
[15:00] – HubSpot, search endpoints, and future-facing API choices
[16:00] – Delta endpoints explained and why they’re valuable for LLMs
[17:00] – Principles of great developer experience: predictability and frictionlessness
[19:00] – Where to learn more about Merge
Additional Quotes from the Podcast
API Integrations are Table Stakes for AI
"The biggest trend is that it's just becoming tables stakes, API integrations. We talk to a lot of AI companies. We get a lot of interest in AI companies, and there's really like three types of data. That is helpful when you're building an LLM, right? There's the public data that exists out there that's scraped from the internet and is publicly accessible. There's synthetic data, which is, you know, obviously it produced itself by an algorithm or by an LLM and can help you validate and test out edge cases. And then there's proprietary data, right? So it's the data that belongs to your customer. And the first two things there, the public and the synthetic data. Are accessible to basically anybody. The proprietary data is a data that's specific to you and makes you different, and it's a thing that no one else can get access to because it literally just belongs to you or your customer. That I think, is the magic of the integration, is that the integrations pull in that third bucket and like they can make your product different, so you're leaving money on the table if you don't have an integration strategy because you're not thinking about that third bucket, which actually makes you different."
AI's Impact on API Design and Documentation
What is Great Developer Experience?
6 episodes