The Trust Deadlock

In the early days of any groundbreaking technology, there’s a familiar stumbling block: the “trust deadlock.” We want to take advantage of the new capabilities, but we’re not entirely sure how or whether they’ll work reliably. Without trust, people are hesitant to try—or they try it only once and abandon it at the first hiccup. Yet trust can’t be built without real-world usage. It’s a classic chicken-and-egg problem: How do you get adoption when trust is low, and how do you build trust without adoption?

To see how we might solve this problem in the context of AI, it’s useful to look at another technology that once felt equally experimental—ride-sharing and delivery apps. Let’s see how ride-sharing overcame this ‘trust deadlock,’ and what that means for AI today.

A Personal Anecdote: The Early Days of Uber and DoorDash

I still remember when I first started using Uber. It was a revelation: I could press a button, and in moments, a driver on the other side of town would begin heading my way. In theory, this saved me a lot of effort. No more standing in the rain trying to hail a cab. But in practice, I spent just as much time glued to my phone, watching the tiny car icon move across the map.

Why? Because my trust was low and my inexperience was high:

Unfamiliar process. This was completely new territory: I had no idea how accurate the estimates were or whether the driver would take the right route.

Lack of confidence in outcomes. Would they arrive at the right entrance? Was my delivery going to get lost?

In those first dozen rides, I monitored everything. And while it cost me the same amount of time as sitting in a taxi might have, I gained something more valuable: intuition. I saw when delays happened (traffic, missed turns, other drop-offs), and I learned how the system behaved. Little by little, that knowledge boosted my confidence. I trusted the platform more, so I used it more—and over time, the process became second nature. Eventually, I only checked the app if something felt unusual. My trust had caught up with my usage, and that’s when the efficiency really kicked in.

AI in the Same Boat

Today, we see a parallel journey unfolding with AI. From large language models to agents, people are intrigued but not always sure how—or if—they should rely on these systems.

Unfamiliar Process: Many of us don’t fully understand how AI reaches its conclusions, much like not knowing what goes on under the hood of a ride-share app. It can feel unsettling to trust something so opaque.

Uncertain Outcomes: We worry about AI “hallucinations,” bias, or errors. Until we see enough successful outcomes for ourselves, we remain cautious.

Building Intuition: Just as we once hovered over the map to confirm our driver was on track or critiqued the route used to deliver our ramen, AI adopters rightly scrutinize every output to see if it “makes sense.” This vigilance, while time-consuming at first, is how we build intuition—and, eventually, trust.

Why Transparency Matters

A big reason we grew comfortable with ride-sharing is transparency. We see who’s picking us up, watch their route in real time, and get notifications if there’s a delay. With AI, a similar kind of visibility can help break the trust deadlock:

Some AI tools and research prototypes allow us to see a “chain of thought” or the step-by-step reasoning process the AI uses to arrive at an answer. It’s the equivalent of watching the driver navigate on the map. If you can see how the system is reasoning, you gain a deeper understanding of potential bottlenecks or errors, which builds trust.

While models themselves may be hard to inspect, we can build intuition and trust into our systems by exposing 'the route' and progress as the models make progress. By making AI systems less of a black box and more of a transparent system, developers help users build that crucial intuition. Just as a driver stuck at a pickup location might be a sign something’s amiss, a model’s sudden spike in perplexity—or contradictory chain-of-thought steps—can be an early indicator that you need to step in.

Practical Steps to Break the AI Trust Deadlock

Offer Low-Stakes Environments: Create sandboxes or pilot programs where people can test AI solutions without risking major consequences. In Uber, the app gives you an overview of drivers near by as an example. It's not 100% accurate, but it gives me a very quick sense of what to expect without asking anything of me.

Real-Time Feedback and Notifications: It's hidden by default, but watching the chain of thought unfold in real time is a big win for transparency and trust. Same for Perplexity showing the steps it will take to answer a question and the sources inspected. It's not visual chrome, it's very deliberate transparency designed to engender trust.

Focus on Solving Real Problems: People flocked to Uber and DoorDash because they solved genuine, everyday hassles in a compelling way. The technology (GPS, mobile apps, and so on), was an implementation detail.

Highlight Early Successes: A quick “aha” moment can turn skeptics into believers. Surface examples of where AI performs exceptionally well: accurate translations, spot-on recommendations, or time-saving automations. These small wins pave the way for deeper trust. Deliver early. Deliver often.

Encourage Incremental Adoption: Just as I eventually stopped watching the map after I learned how rides usually go, AI adoption can progress from meticulous supervision to a more laid-back approach. At first, users might check every output for accuracy. Over time, they’ll gain the confidence to let the AI work more autonomously (while still checking the output).

Err on the side of transparency: Some worry transparency could create information overload. However, like ride-sharing apps that thoughtfully surfaced only key information (driver location, ETA) while hiding complexity, AI interfaces can be designed to show meaningful insights without overwhelming users. The greater risk lies in insufficient transparency rather than too much.

While thoughtful transparency provides the foundation for trust, equally important is helping users develop the knowledge and skills to interpret what they're seeing.

User education plays a crucial role in establishing trust. Much like how ride-sharing companies created simple tutorials and clear expectations, AI adoption requires calibrated expectations, guided first experiences, progressive disclosure, and creating spaces where users can share tips and best practices.

A model wrapped in an agent wrapped in a web app probably won't do the job. Studies show that users with even basic AI literacy are more likely to form appropriate trust levels—neither over-relying on AI nor dismissing its capabilities entirely.

We’re Still in the Early Stages

It’s important to remember that today’s AI technology, despite its astonishing capabilities, is still in its infancy. We’re figuring out the best ways to integrate models like ChatGPT into our daily workflows and how to measure and present metrics like perplexity so that they’re meaningful to end users. This journey will likely involve iterative improvements in transparency, user experience, and reliability.

At the heart of this evolution is the realization that trust begets usage, and usage begets trust. We saw it happen with ride-sharing and delivery services: once enough people experienced a smooth ride—or a perfectly delivered meal—they came back for more, enabling the platform to grow and refine its offerings.

To track progress in breaking the trust deadlock, consider metrics that capture both behavioral and attitudinal aspects: frequency of use, depth of engagement, and willingness to try new AI feature, how often users override or modify AI suggestions (decreasing over time indicates growing trust), explicit user feedback on their confidence in AI outputs, and so on.

Trust Varies by Context and Stakes

While the ride-sharing analogy helps us understand basic trust-building mechanisms, AI applications span a much wider spectrum of risk and impact. Different contexts demand different levels of trust.

In medical diagnosis, financial decisions, or safety-critical systems, users rightfully demand near-perfect reliability and extensive transparency. The trust bar is may require formal verification, regulatory approval, and extensive real-world testing.

While for business intelligence, customer service, casual assistance, or productivity tools, users typically need confidence in overall reliability while accepting occasional minor errors.

Each context requires tailored trust-building approaches. For high-stakes applications, extensive pre-deployment testing, third-party validation, and robust monitoring may be necessary. For lower-stakes tools, highlighting the system's limitations while emphasizing its benefits can set appropriate expectations.

We haven't fully figured out these context-specific trust mechanisms yet, but as AI matures and becomes more integrated into critical systems, we'll develop more nuanced frameworks for building and recovering trust across different domains.

The Road Ahead

For AI, the same dynamic is playing out. We need to break the trust deadlock through a combination of compelling use cases, transparent interfaces, and reliable performance. The technology under the hood can be cutting-edge and revolutionary, but if end users don’t trust it—or don’t know how to use it effectively—it won’t gain widespread adoption.

As developers, innovators, or simply curious users of AI, our mission is to provide enough openness and reassurance so that everyone can comfortably take that first “ride,” watch for potential hiccups, and eventually incorporate AI into their daily routines without a second thought. With each successful ride—or accurate AI output—trust grows, and the technology becomes another seamless part of life’s journey.