From: aidotengineer
The Current State: The AI Trust Gap
Currently, AI faces a significant trust gap among users [00:00:35]. Research from December 2024 by Edelman indicates that only 32% of US adults trust AI, and merely 44% of adults globally feel comfortable with how businesses utilize AI [00:00:38]. This lack of trust is often attributed to “AI slop,” where products or interfaces powered by generative AI produce incorrect or unreliable information [00:00:56]. Examples include web searches suggesting eating rocks or websites offering cars for $1, which users instinctively know are false [00:01:13].
Despite these issues, the true “magic” of generative AI lies in its potential for a UX revolution [00:01:38]. It enables users to interact with machine learning models using natural language, a capability previously unavailable [00:01:43].
The Opportunity: Redefining ‘Magic’ in Design
Drawing on UX first principles, particularly Don Norman’s concept of “invisible interfaces” from The Invisible Computer, there’s an opportunity to create software so seamless and intuitive that users forget they’re even using it [00:02:16]. Instead of simply embedding chatbots into websites, the goal is to leverage AI to design truly magical interfaces by accelerating “need finding” [00:02:31].
Accelerating Design with AI Simulation
The traditional data-driven design process, which relies on qualitative observations, quantitative data, and ethnographic studies to guide prototyping, can be evolved [00:03:33]. Inspired by pilot training through simulation, the proposed new approach empowers designers to work with “invisible users” in the form of AI simulation [00:03:19]. This turns static data artifacts from need finding into active participants in the design process, providing designers with a mini feedback cycle [00:03:54].
The Accelerated Need Finding Process
The new process maintains similarities with existing methodologies but introduces key differences with AI simulation [00:04:32]:
- Defining Audiences with Data: Start with representative data of target audiences, utilizing platforms that combine demographic, psychographic, and contextual data to simulate real-world behaviors [00:05:06].
- Applying Intent Mapping with “Intelligent Twins”: Convert this data into active simulations called “intelligent twins” [00:05:34]. These twins represent user behaviors, desired outcomes, needs, and motivations, becoming active participants in the design simulation [00:05:41].
- Evaluating Interfaces: Brief intelligent twins to evaluate interfaces by focusing on specific tasks, similar to how a human designer might conduct a heuristic analysis [00:05:56].
Advantages and Applications
This simulated methodology offers significant advantages in scale and speed [00:06:23]. Intelligent twins can operate at different velocities than human teams, allowing for broad audits, such as a global audit of sports websites [00:06:31]. For example, a sample project utilized two “personas” – a casual fan and a super fan – to evaluate three sports websites across categories like navigation, information architecture, and fan engagement, simulating 72 AI actions [00:07:06].
Key findings can be aggregated at a high level or explored granularly, revealing patterns like successful navigation performance but decreased task completion in content browsing and engagement pathways [00:07:50]. This methodology helps identify user pain points and can contribute to repairing the AI trust gap by creating better user experiences [00:08:53]. Ultimately, it enables the generation of focused design briefs for human teams, allowing them to concentrate on solving specific user issues [00:09:20].
Looking Ahead: The Future of Design and AI
Significant progress is being made in AI tools, such as Anthropic’s MCP protocol, which shows potential for converting prototype components in Figma directly into code components in React or Node.js [00:10:06]. As the creation of new designs becomes easier through AI acceleration, the emphasis shifts to the “why” – the strategy and the problem being solved for users – rather than solely the “how” [00:10:31].
Limitations and Improvements
This methodology is still experimental [00:10:53]. Future improvements include:
- Reproducibility: Standardizing parameters like briefing instructions, simulated audience dimensions, audit runs, and task completion/failure metrics in a code repository [00:11:00].
- Test and Control: Applying a test and control methodology to isolate the specific strengths of intelligent twins for design need finding [00:11:24]. This will help determine how AI simulation can complement human design teams across various industries and geographies [00:11:35].
Conclusion: Restoring Trust Through Better Design
To repair the current AI trust gap, the focus should shift from adding ineffective GenAI chatbots to websites towards creating better websites, mobile apps, and other interfaces that offer clarity and simplicity [00:11:52]. AI simulation can be a valuable tool to empower design teams, enabling them to gather insights smarter, faster, and more effectively [00:12:17]. This approach aims to create interfaces that ultimately restore and repair user trust [00:12:29].