The End of Switching Costs: Why UX Is the Last Moat in Gen AI

Which AI Services Will Survive?
The End of Switching Costs in 2025

You may have experienced this: you’ve used a certain tool for years, and one day you try a new AI-powered alternative—and it’s so convenient that you end up letting go of the old one without even realizing it.

You didn’t make a conscious decision to switch; it just happened.

We’ve entered an era where the switching cost—the effort, time, or loss of familiarity associated with moving to a new tool—is rapidly diminishing.

Traditionally, switching costs referred to the sacrifices a user had to make when adopting a new service or product.

How AI Evolution Is Eliminating Software Switching Costs

It used to take significant time to get used to a new tool: learning shortcuts, setting preferences, and training the system to suit your needs. This “learning curve” was part of a product’s PMF (Problem-Market Fit).

Over time, with repeated use and customization such as rule setting and shortcuts, a tool would become personalized—essentially evolving into “your tool.” In other words, the post-market fit revolved around user history.

But what about now?
Today, AI delivers high satisfaction even without prior user history.

Take email organization tools as an example. While Superhuman focuses on training users with keyboard shortcuts and manual configuration, Shortwave imports your inbox once and uses AI to automatically infer and apply your organizational logic.

There’s no need to “teach” the system your methods anymore. PMF has evolved from something built slowly and gradually over time to something that delivers a 9/10 immediately. And as a result, switching costs are becoming increasingly irrelevant.

3 Types of AI Services Built to Last in the Gen AI Era

A lower switching cost means that there is less reason users have to stay with any one service.

In the past, once a user became accustomed to a tool, they rarely switched away. Now, the transition itself is almost frictionless—and users are quick to move toward whatever is better, right now.

So which AI services are built to last in this new landscape?

1. Why AI History Training No Longer Creates Competitive Advantage

Traditional AI services were refined over time as users repeatedly interacted with them, gradually honing their features. History and context, such as in a personalized workflow, were its core assets.

However, such accumulated data can now be instantly surpassed by today’s AI with its overwhelming performance, right from the start.

• The old way: Satisfaction starts at 50 → user feedback → incremental improvement

• Gen AI: Deliver a 90-100 satisfaction score right out of the gate

If AI nails it on the first try, there’s no reason to go back to the old tools.

2. 3 Domains Where AI Switching Costs Still Matter

That said, AI isn’t invincible across the board. There are still blind spots where AI cannot easily reach, where thoughtful UX design takes priority over technical prowess.

(1) Healthcare and Legal AI: Where Data Access Creates Switching Costs

In industries like healthcare or law, access to data itself can be a vital challenge. For instance, time-series data in healthcare is crucial for monitoring patient progress.

Major hospitals such as the Mayo Clinic have developed algorithms that analyze data from frequently visiting patients to screen those likely to develop arrhythmia within the next two weeks.

This kind of data utilization enables preventative intervention, allowing potential risks to be identified and addressed in advance.

But most of this data remains siloed and is not easily shared.

In such closed-off industries, success depends less on AI capabilities and more on how naturally data can be collected.

One example is ScribeHealth, which automatically records doctor–patient conversations and syncs them with EMRs in a single click.

Its strength lies in capturing valuable data with minimal disruption to user behavior. These are precisely the kinds of environments where vertical SaaS solutions still shine.

(2) AI in Consulting and Investing: Why Tacit Knowledge Still Wins

In fields like psychotherapy, early-stage investing, or apprenticeship-based education, structured data is often nonexistent. What matters most is human judgment, intuition, and contextual sensitivity—things that live in people’s minds, not in spreadsheets. No matter how advanced Gen AI becomes, it struggles to replicate this kind of tacit knowledge.

While explicit knowledge can often be codified and automated, tacit knowledge remains much harder to replace.

Take early-stage investing, for example. Investors don’t rely solely on numbers or pitch decks; they also pick up on intangibles—like the founder’s presence, vision, or the energy in a conversation.

These are things that can’t be easily quantified, and they play a critical role in decision-making. Such insight comes from lived experience and deep human understanding—the very essence of tacit knowledge.

To serve these tacit knowledge domains, AI must focus on creating UX that seamlessly draws out the user’s internal context.

ChatGPT's conversational prompts are a good example. By asking lightweight, guiding questions, it enables users to gradually explore and articulate their thoughts—without requiring perfect answers up front.

In this way, the interaction becomes an exploratory dialogue, and tacit knowledge is surfaced. The more immersed users become in the process, the more likely they are to reveal their insights—allowing the AI to learn and adapt along the way.

(3) B2B AI Integration: Overcoming Complex Workflow Challenges

In many organizations, tools, processes, and formats vary widely—making AI adoption more challenging.

Take customer data management, for example. One company might use Salesforce, while another relies on HubSpot. If an AI tool can’t seamlessly integrate with both systems, it becomes difficult to automatically update or analyze shared data in a collaborative setting.

In such cases, the value of AI quickly diminishes. No matter how advanced the technology, it won’t gain traction unless it can reduce friction with existing workflows.

Especially in the B2B space, one of the biggest challenges in adopting AI is earning the trust of the sales organization. Convincing teams purely on the promise of improved productivity is often not enough—mainly because it’s difficult to quantify the impact with clear metrics.

With so many variables still in play, companies remain hesitant, and strong sales execution continues to play a critical role in driving adoption.

The Future of AI Services:
UX Design Over Technology

What insight can be gleaned here?
That survival of AI Services no longer depends on technology alone.

AI is accomplishing things once thought impossible and is rapidly reshaping entire industries. But the barriers of data, context, and human behavior still remain.

What truly matters now is how we design experiences that guide user behavior—drawing out richer context and deeper data—while minimizing disruption and integrating seamlessly into existing workflows.

Design—not just technology—will be the real competitive edge in the age of AI.

To all the founders striving to build truly user-centered products—we’re cheering you on. If you're facing challenges, don’t hesitate to reach out to us at Kakao Ventures. We’re here to think through the tough questions with you and help turn problems into progress, together.

Building User-First AI Products:
Kakao Ventures' Perspective

To all the founders out there building user-first AI services: we’re cheering you on.

And if you’re grappling with these challenges, Kakao Ventures is here to help.