The current trend of building huge, generalist AI systems is fundamentally mismatched for specialized applications like mental health. A more tailored, participatory design process is needed instead of assuming the default chatbot interface is the correct answer.
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AI excels where success is quantifiable (e.g., code generation). Its greatest challenge lies in subjective domains like mental health or education. Progress requires a messy, societal conversation to define 'success,' not just a developer-built technical leaderboard.
Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.
People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.
Instead of waiting for AI models to be perfect, design your application from the start to allow for human correction. This pragmatic approach acknowledges AI's inherent uncertainty and allows you to deliver value sooner by leveraging human oversight to handle edge cases.
A 'GenAI solves everything' mindset is flawed. High-latency models are unsuitable for real-time operational needs, like optimizing a warehouse worker's scanning path, which requires millisecond responses. The key is to apply the right tool—be it an optimizer, machine learning, or GenAI—to the specific business problem.
Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.
To maximize engagement, AI chatbots are often designed to be "sycophantic"—overly agreeable and affirming. This design choice can exploit psychological vulnerabilities by breaking users' reality-checking processes, feeding delusions and leading to a form of "AI psychosis" regardless of the user's intelligence.
The most fundamental challenge in AI today is not scale or architecture, but the fact that models generalize dramatically worse than humans. Solving this sample efficiency and robustness problem is the true key to unlocking the next level of AI capabilities and real-world impact.
Technologists often assume AI's goal is to provide a single, perfect answer. However, human psychology requires comparison to feel confident in a choice, which is why Google's "I'm Feeling Lucky" button is almost never clicked. AI must present curated options, not just one optimized result.
While the absence of human judgment makes AI therapy appealing for users dealing with shame, it creates a paradox. Research shows that because there's no risk, users are less motivated and attached, as the "reflection of the other" feels less valuable or hard-won.
