Ethan Mollick has a scaffolding problem. He’s spent years making the case for AI as a learning partner — Co-Intelligence is genuinely persuasive on this — but the research on AI-assisted learning keeps surfacing a troubling pattern: when people use AI as scaffolding to learn complex skills, they often become dependent on the scaffold rather than developing the underlying capability.
This isn’t unique to AI. Apprenticeship models have been solving — and sometimes creating — this problem for centuries. The master-apprentice relationship is the oldest answer to the question of how you transfer tacit knowledge from someone who has it to someone who doesn’t. It also predates every digital learning system by millennia, and it still works better than most of them. Understanding why tells you a lot about where AI learning tools succeed and where they’re going to fail.
The Scaffolding Paradox in Practice
Here’s the problem in concrete terms: I’ve built a 20-agent AI system that handles significant portions of my workflow. I write better first drafts faster, synthesize research more thoroughly, and spot patterns across data sets that I would have missed before. These are genuine capability improvements.
But when I assess the people on my teams who have adopted AI tools most enthusiastically, I’m noticing something uncomfortable: their AI-assisted work product is improving, but their independent judgment on the same types of problems isn’t improving at the same rate. They’re getting better outputs with AI. They’re not necessarily getting better at the underlying reasoning that generates good outputs.
This is exactly what educational technology research on scaffolding has documented for decades. Scaffolding that removes the struggle of learning — that answers the question before the learner has grappled with it — produces more efficient short-term outputs and less durable long-term capability. Students who use calculator apps become faster at arithmetic and slower at mathematical reasoning. Writers who use grammar checkers produce cleaner prose and develop weaker editorial instincts. The scaffold substitutes for the learning rather than accelerating it.
What Apprenticeship Gets Right That AI Scaffolding Usually Misses
The master-apprentice model works because it’s built on three principles that most AI learning tools violate:
Context before process. Apprentices don’t get handed a manual. They observe the master working in real contexts — seeing the judgment calls, the trade-offs, the moments of uncertainty before decisions. The learning is embedded in practice, not abstracted into steps. Most AI tutoring optimizes for process efficiency: here’s the template, here’s the workflow, here are the five steps. The tacit knowledge of when to apply which approach — the judgment layer — doesn’t transfer through templates.
Struggle as pedagogy. Good mentors don’t rescue apprentices from difficulty; they use difficulty as the teaching medium. The moment the apprentice gets stuck is often the highest-value learning moment — it surfaces where their mental model diverges from reality. AI scaffolding tends to eliminate this. Stuck on a document? AI drafts it. Can’t find the right framing? AI suggests five. The struggle that would have built capability gets bypassed entirely.
Progressive responsibility transfer. The best apprenticeship relationships follow a deliberate arc from observation to supported practice to independent execution with feedback to autonomous work. This progression is designed, not accidental. Most AI tools don’t have a model of the learner’s development over time — they just answer whatever question is asked, at whatever level of support is requested, with no view toward building toward independence.
What This Means for Building AI Learning Tools
The AI learning products that will create durable value are the ones that preserve productive struggle rather than eliminating it. Practically, this means:
Design for delayed assistance. The most sophisticated AI tutoring designs I’ve seen require users to make their own attempt before unlocking AI assistance. This preserves the learning that happens in the struggle phase while preventing frustration from becoming discouragement. The friction is intentional — it’s doing work.
Ask questions instead of providing answers. An AI that responds to “how do I approach this problem?” with a clarifying question — “what have you tried so far, and where did that break down?” — is building the learner’s reasoning. An AI that just answers the question is substituting for it. Mollick’s own research on AI pedagogy points in this direction: the AI-as-Socrates model outperforms the AI-as-encyclopedia model for capability development.
Build mental models explicitly. Scaffolding that helps users understand why something works — not just what to do next — builds transferable reasoning rather than step-following. This is harder to build and harder to measure, which is why most AI tools don’t do it. But it’s the difference between producing capable practitioners and producing tool-dependent process followers.
The Stakes Are Higher Than They Look
If you’re building AI tools that people use for learning or skill development — whether that’s a sales enablement tool, a customer service training platform, an onboarding system, or an educational product — the scaffolding question determines whether your product creates lasting value or lasting dependency.
Products that make people capable compound in value. Users become more effective over time, credit the tool for helping them get there, and advocate for it because they can point to real skill growth. Products that create dependency are fragile — valuable when present, debilitating when absent, and increasingly resented when users realize they haven’t actually grown.
I’ve seen this dynamic in the platforms we’ve built for ministry and faith formation: tools designed to make practices easier often made practitioners weaker. The same dynamic shows up in corporate learning systems, sales tools, and product development processes. The question isn’t whether AI can help people work through complex tasks. It’s whether it’s helping them build the judgment to work through those tasks more independently over time.
If you’re thinking about how AI tools change the skill requirements for product roles, this connects directly to the hiring question: the PMs who will be most valuable in an AI-native organization are the ones who’ve built judgment, not just the ones who’ve built AI fluency.
Your Turn: Apply This Today
Designing for learning — rather than just task completion — requires intentional choices. Here’s where to start:
- Audit your AI features for scaffolding vs. substitution. For each AI feature in your product, ask: does this help users develop capability, or does it complete the task so thoroughly that users stop developing the underlying skill? If it’s the latter, you have a dependency risk.
- Design one “fade-the-scaffold” feature this quarter. Identify an AI assist that new users need but experienced users don’t. Build a mechanism that gradually reduces the assist as user competency grows — like training wheels that automatically come off.
- Interview users about what they’ve learned from your product. Ask: “What can you do now that you couldn’t before you started using us?” If the answer is “nothing — the AI just does it for me,” you’re building dependency, not capability. Both can be valid business models, but know which one you’re running.
- Add a “learning mode” vs. “efficiency mode” toggle to high-dependency features. Let users choose whether they want the AI to do it for them or guide them through doing it themselves. The toggle itself signals that you’ve thought about skill development.
- Evaluate your retention metrics through a dependency lens. High retention can mean high value or high lock-in. Ask: are users retained because the product makes them better, or because they can no longer function without it? The distinction matters for long-term product health and user trust.
- Build a “transfer test” into your user research. Periodically ask long-term users to attempt their core task without AI assistance, then with it. Measure the gap. If the gap is growing over time, your scaffold has become a crutch.
If you’re thinking about AI collaboration in product teams more broadly, this scaffolding question is the flip side of the AI-as-coworker conversation — both are about how humans and AI systems should divide cognitive labor over time.
Building AI tools for learning, training, or skill development — and want to design for capability growth rather than dependency? I consult with product teams on AI-assisted learning design, scaffolding strategy, and building products that create durable user value. Let’s talk.