Capabilities-Value-Scale
"Software always goes through capabilities stage first, so what is possible to actually create with this? Then, it needs to transition into value, of how is it that am I going to get value out of this? And then you can start thinking about scaling it, of which aspects of my life and my work that can actually go in?" - Elena Verna (citing John Cutler)
What It Is
Capabilities-Value-Scale is a framework for understanding how users adopt new software, especially emerging technologies like AI. Users don't immediately extract maximum value from new tools—they move through distinct phases of exploration and understanding before fully integrating software into their workflows.
This framework, attributed to John Cutler, explains why new technology adoption follows predictable patterns and why rushing users to "value" before they've explored "capabilities" often fails.
How It Works
Stage 1: Capabilities "What is possible to actually create with this?"
- Users explore the boundaries of what the technology can do
- Experimentation is the goal, not productivity
- Questions are: "Can it do X? What happens if I try Y?"
- Users build mental models of the technology's potential
- For AI: "What can I build with vibe coding?"
Stage 2: Value "How am I going to get value out of this?"
- Users connect capabilities to their actual needs
- Focus shifts from "what's possible" to "what's useful to me"
- Questions are: "How does this solve my problem? Where does this fit?"
- Users identify specific use cases worth investing in
- For AI: "How can this help me ship faster?"
Stage 3: Scale "Which aspects of my life and work can actually go in?"
- Users expand the technology across multiple domains
- Focus is on integration and habit formation
- Questions are: "Where else can I use this? How do I make this standard?"
- Users build systems and workflows around the technology
- For AI: "How do I make this part of how my team works?"
How to Apply It
Identify where your users are - Are they exploring capabilities, seeking value, or ready to scale? Design experiences accordingly.
Don't rush the stages - Users in capabilities mode won't respond to value messaging. Let them explore first.
Support exploration - For new technology, create low-friction ways to discover capabilities. Free trials, sandboxes, and playground modes.
Bridge to value - Once users understand capabilities, help them find their specific value. Templates, use case guides, and success stories.
Enable scale - For users ready to expand, provide integrations, team features, and workflow tools.
Recognize regression - When capabilities change (new AI model release), users return to stage 1. The cycle restarts.
When to Use It
- New technology launches - Understanding why adoption feels slow
- Product marketing - Matching messaging to user stage
- Onboarding design - Creating stage-appropriate experiences
- AI products - Where capabilities constantly expand
- User research - Understanding where users are in their journey
Implications for AI Products
In AI, this framework has special significance:
- Capabilities change constantly - With each model release, users return to exploring what's newly possible
- Value evolves - As capabilities expand, so do potential value propositions
- Scale is deferred - The constant capability change keeps many users in early stages
- This explains "hype" behavior - Users exploring capabilities can appear frivolous but are following natural adoption patterns
Source
- Guest: Elena Verna
- Episode: "10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey)"
- Key Discussion: (00:10:06) - Elena describes this framework from John Cutler to explain AI user behavior
- YouTube: Watch on YouTube
- Framework Origin: John Cutler
Related Frameworks
- Product-Market Fit Treadmill - PMF must be recaptured as capabilities evolve
- Adjacent User Theory - Users at different stages need different approaches
- AI Adoption Stages - Three-stage progression for AI adoption