The Information Product Era Is Ending: What Replaces It Matters for Every Knowledge Business

For two decades, the path to monetizing expertise online followed a predictable arc. Now, with the rise of AI-powered learning experiences, this model is reaching structural limits.

You developed knowledge in a domain. You built an audience by sharing that knowledge. You packaged that knowledge into products: ebooks, courses, coaching programs, and communities. The format evolved from simple to complex, from one-time purchases to recurring subscriptions, but the fundamental model remained constant. You sold information.

That model is reaching structural limits.

Eugene Schwartz, the legendary copywriter, identified five stages of market sophistication that every category moves through. At stage one, you simply state what your product does. Competitors arrive, forcing bigger claims. Markets become skeptical, requiring you to explain your mechanism. Competitors copy that mechanism. Finally, everyone exhausts their claims, and the brand becomes the primary differentiator.

Information products have hit that final stage across most domains. The really exceptional ones still perform exceptionally. But average products, which by definition represent the majority, face declining returns regardless of marketing sophistication.

The data reveals why static information delivery is failing.

Self-paced online courses typically achieve completion rates of 5% to 15%. Over half of the people who enroll never even start the material. For every 100 people who enthusiastically purchase a course, 85-90 never reach the finish line.

Courses with coaching, community, or interactive elements see completion rates above 70%.

The gap isn't about content quality. It's about the delivery format. Static information, however valuable, fails to create the engagement necessary for transformation. People buy courses intending to learn. The format itself prevents most from succeeding.

Three forces converged to erode the value of packaged information.

Volume saturation. Anyone can create a course, ebook, or guide on any topic. The barrier to entry collapsed. Markets are flooded with competing products making similar claims about similar outcomes. Differentiation became increasingly difficult as the information itself grew more similar.

AI acceleration. Generative AI made it trivially easy to produce competent information products. An average person can now generate an ebook, write course content, create marketing assets, and launch in days rather than months. The baseline rose while the ceiling remained constant.

Search and discovery shifts. When someone wants to learn something specific, they can now query AI directly and receive personalized, comprehensive answers immediately. The value proposition of "I compiled this information for you" weakens when compilation happens instantly on demand.

The result: it has never been easier to start a mediocre information business. It has never been harder to differentiate.

Many creators responded to saturation by emphasizing authenticity. Personal brand. Mission. Tribe belonging. And these factors do matter at the final stage of market sophistication.

But authenticity itself has become saturated. Everyone claims it. The market grew tired of courses and coaching regardless of how authentic the creator appeared. The format creates fatigue regardless of who delivers it.

The path forward isn't more authentic information products. It's evolving beyond information products entirely.

Consider how knowledge was transferred before mass education existed.

A blacksmith didn't hand his apprentice a manual and say, "Figure it out." He worked alongside him. He corrected the grip in real time. He pointed out mistakes as they happened. Learning occurred through doing with guidance, not through consuming and then attempting.

Industrialization changed this. We needed to train thousands of workers quickly. The lecture model emerged: one teacher, many students, standardized curriculum. Efficient for scale. Terrible for actual learning. But it turned out excellent for creating compliant workers who fit industrial roles.

The internet initially replicated this model digitally. One course, many students, standardized curriculum. The same efficiency problems. The same learning limitations. The same completion failures.

AI enables something different. For the first time since the apprenticeship model, we can provide personalized, interactive guidance at scale. Not one teacher broadcasting to many students. A guide alongside each learner, correcting in real time, adapting to their specific situation.

The distinction matters.

An information product delivers content. Watch these videos. Read these chapters. Apply what you learned.

A learning experience guides action. It doesn't just tell you what to do. It helps you do it. It provides feedback. It corrects mistakes. It adapts to your progress.

The completion rate gap between static courses and coached programs isn't about the information being different. It's about the presence of guidance during implementation.

AI makes that guidance scalable.

Dan Koe, the creator whose analysis prompted this exploration, describes building what he calls "evolved information products" that function more like software than courses.

The concept: instead of a course where people watch modules, you create an AI-powered interface where people interact with your knowledge. The AI becomes a coach trained on your frameworks, your examples, and your methodology. Users don't consume passively. They engage actively, receiving feedback as they practice.

The structure might include three modes:

Learn: Interactive conversations that guide through concepts, adapting to questions and confusion points rather than delivering linear content.

Practice: Exercises where the AI evaluates attempts, provides specific feedback, and helps refine the approach.

Create: Guided implementation where users build real outputs with AI assistance, emerging with actual deliverables rather than notes they may never apply.

This isn't hypothetical. The tools to build such experiences exist today. Platforms like Replit, Cursor, or Claude's code capabilities allow non-programmers to create functional AI applications through iterative conversation rather than traditional development.

The information-to-experience shift extends far beyond selling courses.

Client onboarding. Instead of documentation that clients must read and implement themselves, create interactive guides that walk them through setup, answer questions in real time, and confirm completion of each step.

Internal training. Instead of training manuals that employees skim and forget, build AI coaching systems trained on your methodologies that help staff apply knowledge to actual work situations.

Customer support. Instead of help centers that customers must search and interpret, deploy AI trained on your knowledge base that converses with customers to solve specific problems.

Consulting delivery. Instead of reports that clients must implement independently, create tools that guide implementation step by step with your expertise embedded in the system, a key aspect of Operational AI Implementation.

The pattern is consistent: wherever you currently deliver static information that you expect independent implementation, you can create interactive experiences that guide implementation with your knowledge built in.

Your advantage in this environment isn't doing what AI can't do. That's a losing game. AI capabilities expand constantly.

Your advantage is doing what only you would think to do with AI.

Not everyone types the same prompts. Not everyone has the same domain knowledge to embed in systems. Not everyone has spent years developing the taste, judgment, and nuanced understanding that make guidance genuinely valuable.

That’s "specific knowledge": knowledge that can't be trained for, that comes from pursuing genuine curiosity, that feels like play to the person developing it. The person who spent a decade obsessing over a domain builds something far more nuanced than someone who asks AI to generate a generic solution.

If that obsessed person has also built an audience through sharing that specific knowledge, they possess distribution for their interactive tools. The combination of deep expertise, audience, and AI-enabled delivery creates a positioning that's difficult to replicate. This strategic advantage is often solidified through expert AI Strategy Consulting (visit Core Ventures).

Phase 1: Audit Your Static Content (Weeks 1-2)

Identify everywhere you currently deliver knowledge, expecting independent implementation. Courses. Documentation. Training materials. Onboarding sequences. Consulting reports. These are candidates for transformation.

Phase 2: Map the Learning Journey (Weeks 3-4)

For each content area, define what success actually looks like. Not "they understood the material." What can they now do? What have they produced? What decisions can they now make? Work backward from demonstrated capability to identify what guidance would help them get there.

Phase 3: Build a Minimal Interactive Pilot (Weeks 5-8)

Start with one focused application. A single use case where interactive guidance would dramatically improve outcomes. Build the simplest version that demonstrates value. Test with actual users. Iterate based on real feedback.

Phase 4: Expand and Systematize (Ongoing)

Use learnings from the pilot to inform broader applications. Develop your methodology for creating effective AI guidance, leveraging our expertise in Workflow Automation Design and building internal capability for rapid iteration as tools and expectations evolve.

Information products dominated for perhaps 15 years. The shift to interactive experiences may only last 2-3 years before the next evolution. The cycle is compressing.

This means two things: act quickly to capture the current opportunity, and build the organizational capability for continuous adaptation rather than one-time transformation.

Some will argue this is just repackaging, that putting information into an AI wrapper doesn't fundamentally change anything.

Consider what the wrapper actually does.

A course wrapper (the platform it's delivered on) doesn't change how learning happens. It just hosts the content. A chat interface wrapper fundamentally changes the interaction model. Users receive personalized guidance. They get feedback on their specific attempts. They engage in dialogue rather than passive consumption.

By the same logic that would dismiss AI wrappers, any software built on cloud infrastructure is just a wrapper. Cursor is a GPT wrapper. Every website is an HTML wrapper. The wrapper determines the interaction model. The interaction model determines outcomes.

Written by Dr Hernani Costa, Founder and CEO of First AI Movers. Providing AI Strategy & Execution for EU SME Leaders since 2016.

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