Your Favorite AI Tool is Using Behavioral Science
How leading AI tools use behavioral science to improve their products
This is the first of two posts on how behavioral science is (or could be) used by the leading AI companies and academics based on my conversations with various folks in both fields
The technical leap in AI is clear. The latest releases from OpenAI, Anthropic, and Google build on decades of research that gave us AI beating Go champions, revolutionary protein folding, and PhD-level performance on standardized tests.
But there's another revolution happening alongside the technical one. The leading companies aren't just competing on raw capability - they're racing to make AI feel natural, trustworthy, and genuinely helpful.
Let's examine how behavioral science is already shaping the future of AI interaction and what it might mean for future product development.
Behavioral Science is Embedded in the Leading AI Tools
The core insight of behavioral science is that people won't respond solely to incentives. They are more likely to act if the action is easy, attractive, social, and timely.
1. Why ChatGPT Types Like a Human
OpenAI made a deliberate decision to use 'typewriter' functionality when showing answers in ChatGPT. In contrast, Google’s then Bard (now Gemini) initially paused and then showed the text at once.
OpenAI understood that no-one on the internet likes to wait, not even for micro-seconds. Google was slower to this insight even though it led the research on the poor impact of waiting on conversion for decades. The typewriter means the user sees progress and is the easiest path for them to get their answer.
It is a fundamental design choice for a technology that has more latency than the standard internet. And, as ChatGPT says itself, it is borne from insights into user psychology. Each word appearance triggers a tiny dopamine hit, keeping users engaged through what would otherwise be a frustrating wait time.
2. Making AI Transparent
Showing users how the sausage is made increases trust in the underlying technology. Anthropic did exactly this with their Artifacts feature (quickly copied by OpenAI). It opens a new panel to preview code in production or any other materials generated. It marked a major step forward beyond the simple chat paradigm to an easier, more transparent interface.
OpenAI's GPT-4o includes a similar product feature that allows users to see its thinking stages. This transparency helps users understand the AI's reasoning process.
This transparency creates shared mental models between human and AI. When users can see AI's reasoning process, they develop better intuitions about its capabilities and limitations. This cognitive transparency is essential for meaningful collaboration.
3. Personalization Drives Stickiness
Personalization taps into the IKEA Effect—the idea that we value things more when we help create them. ChatGPT takes this seriously. You can specify preferences for ChatGPT’s responses (e.g. be concise!), it remembers details from past interactions, and you can create your own custom GPTs. It’s like setting up a Spotify playlist for your AI interactions.
There is of course no great insight in the importance of personalization. However, it is notable that the leading tools invested in this approach so early in the product lifecycle. They know the more users invest in ensuring their AI reflects their own preferences, the less likely they are to leave for another, maybe even better, tool in the future.
Three Predictions for 2025: Using principles of behavioral science to predict future directions
Behavioral science is built off universal principles of human behavior. These universals mean they act as potential directions for product development. Especially when combined with technology advancements and user research. Here are three ideas that we may see coming to AI tools in 2025.
1. Make it Easy & Attractive: Greater Transparency and Personalization
Behavioral Insight: Users trust tools more when they understand how they work and feel ownership over them.
Why it matters: As AI becomes more adaptive and powerful, maintaining trust and retaining users means greater personalization and transparency without creating cognitive overhead.
Specific product directions:
Confidence Signaling:
Confidence scores with context for interactions ("90% confident - based on official documentation with links")
When not certain, gives alternative suggestions or approaches with trade-offs
Context Awareness:
"Simplifying response - noticed you're in back-to-back meetings"
"Using past project X as reference based on similarities"
Learning Relationship:
Progress dashboards ("Recent topics mastered")
Adaptation logs ("Changed explanation style based on preference")
Expertise evolution ("Now using more advanced terminology in domain Y")
Override Controls:
Quick mode switches ("Need simpler explanation", “Need customer-facing answer”)
Learning path adjustment ("Too basic/advanced")
2. Make it Social: Building for Teams, Not Just Users
Behavioral Insight: Teams operate on multiple invisible layers - power dynamics, expertise distribution, psychological safety, and information flows. The best collaborators understand and enhance these dynamics.
Why it matters: Most AI tools focus on individual augmentation, missing the bigger opportunity to enhance team cognition and collaboration. AI could be the catalyst for better team dynamics, not just better individual outputs.
Specific product directions:
Knowledge Bridge:
"Sarah's recent database work could help with John's current challenge"
Auto-link relevant past discussions and decisions
Cross-pollinate insights between sub-teams
Conversation Enhancement:
Balance participation in meetings
Create openings for quieter voices
Surface relevant expertise at key moments
Team Memory:
Track and resurface key decisions
Maintain context across discussions
Flag potential contradictions with past decisions
Team Cohesion:
Bridge technical/non-technical communication gaps
Translate between different team vocabularies
Surface and resolve misaligned assumptions
3. Make it Timely: Context-Aware AI
Behavioral Insight: Humans have different use cases for AI at different times of day. Sometimes it is analysis, other times it is to replace a quick search. Some of these contexts are predictable - from context-switching in the morning to deep focus periods and collaborative sessions.
Why it matters: Every manual configuration by the user breaks cognitive flow. Default settings shouldn't just be convenient - they should actively preserve users' mental state and energy.
Specific product directions:
Time-Aware Modes:
Morning briefing mode (8-10am): Shorter, actionable summaries
Deep work mode (marked focus time): Detailed technical responses, fewer interruptions
Quick check mode (mobile/between meetings): Bullet points, key decisions needed
Adjustable Defaults:
Modal modularity: Users can choose to open their AI automatically in camera and headphone mode rather than the classic chat interface
Context detection: Switch to allow your local, on device AI to adjust to your current work. Blog writer when Substack is open, analyst when Excel is open, trip planner when your favorite travel site is open.
Product Matters As Much As Technology
The next big advances in AI won't just come from better technology. They'll come from understanding how people actually work and building tools that fit naturally into our day.