TL;DR
Erik Wilde joined Stefan and Jens to talk about why many enterprise APIs are not ready for AI, why so many AI projects fail prematurely, and how strong API fundamentals (design, governance) play a more critical role than ever. Drawing on decades in the API space, Erik argued that AI readiness emerges mainly from strong API design and organizational alignment, rather than advances in models alone. The conversation explored why fine-grained APIs break down for agents, why intent-driven design and business capabilities matter more than individual endpoints, and why using less AI can often lead to more reliable systems.
The AI bubble will burst. The useful parts will survive
Erik sees clear parallels between the dotcom boom and AI boom, including the inevitability of a correction.
I think it’s clear that it’s a bubble. Right. Like it’s going to be ugly. There’s going to be companies that will not fare well.
This is a reminder that hype compresses timelines and inflates expectations. When that pressure releases, some companies disappear, but the underlying shift often remains.
That is how he frames AI. The excess will get stripped away, but the parts that actually help people get work done will stick around.
Why Jentic’s bet is to use less AI, not more
His move to Jentic was not about chasing AI for its own sake. It was about putting boundaries around it.
He argues that most systems work best when the predictable parts stay predictable. Workflows that must run the same way every time should live in deterministic code. AI should be reserved for places where ambiguity or creativity is unavoidable.
This is not an anti-AI position. It is a cost-aware and reliability-driven one. Especially in enterprise environments, using less AI can actually make systems more robust.
What we do is kind of allows you to do as little AI as possible, right? By saying for those things where AI create value, it’s good to use it, but for all the other things, just use deterministic code.
APIs are business capabilities, not endpoints
Much of Erik’s career has been spent correcting a quiet misunderstanding inside large organizations: teams often believe that once they expose APIs, the hard part is done.
In reality, APIs only create leverage when they reflect real business capabilities. If they are misaligned with how the business works, teams still move slowly, even if the technology is structurally sound.
This is why API design, governance, and organizational structure are inseparable topics for him. Poor alignment shows up as friction everywhere else.
When we talk about APIs, we really talk more about business capabilities, right? Like some kind of thing that is made available in a digital way.
Why many enterprise APIs are not AI ready
When the discussion turns to AI readiness, Erik shifts the focus away from models and protocols and toward API shape.
He points out that many enterprise APIs are too fine grained. They expose large numbers of low-level endpoints that humans can piece together during development, but that agents struggle to reason about at runtime.
As the surface area grows, LLMs struggle with context and focus. Discovery becomes harder, because there is a mismatch between how APIs were designed and how agents consume tools.
APIs are many organizations kind of too low level. Right. They’re kind of very fine grained. And that oftentimes makes it hard. In particular, if you look at stuff like MCP.
The fix is not rewriting 50,000 APIs
What happens when an organization already has tens of thousands of APIs that were never designed for the AI world?
Erik’s answer is pragmatic. You do not rewrite everything. You do not expose the mess directly to AI. You introduce a new layer.
By placing well-described, business-level workflows on top of fine-grained APIs, teams can improve AI readiness without destabilizing existing systems. The same layer also makes life easier for human developers.
Instead of even exposing those to AI, let’s put some workflows on top of those which are well-described, right, are more at the business level, and then those will be the things that AI actually gets exposed.
The bigger picture
As the episode wraps up, Erik makes it clear that none of this points to a dramatic break with the past. APIs are not going away. Design still matters. Organizational alignment still shapes technical outcomes.
What is changing is how systems are consumed. Agents behave differently than human developers and that shift exposes weaknesses that already existed in many API landscapes.
AI raises the stakes, but it does not change the fundamentals.
This episode was directed by Jacob Javor. Transcript lightly edited for clarity and flow.
