What do a GPS company and an AI startup have in common? At first glance, almost nothing. One helps you navigate roads, trails, and oceans. The other helps you write, summarize, search, and think. But both are really in the same business: reducing uncertainty.
That is the deeper question connecting them. The best products do not merely generate outputs. They help people move from confusion to confidence, from hesitation to action, and from raw possibility to a useful next step. The winning edge is not just intelligence, whether that intelligence comes from satellite signals or machine learning. The winning edge is how well a product turns intelligence into a workflow people can trust.
This is why some products become indispensable while others remain impressive but forgettable. A model can be brilliant and still be hard to use. A device can be precise and still feel clumsy. What matters is the system around the intelligence, the way the product lowers cognitive load, collects feedback, and gets better with use.
The strongest products do not ask users to admire intelligence. They let users steer it.
That is the core tension: in an age where capability is increasingly commoditized, the real product is not the model, the sensor, or the algorithm. It is the interface between human intention and machine capability.
Why intelligence alone is not enough
There is a seductive belief in technology that better core performance automatically creates better products. If the model is more powerful, the thinking goes, the product will win. If the GPS is more accurate, the device will win. If the watch has more metrics, the athlete will win. But users do not experience raw capability in a vacuum. They experience friction, ambiguity, and decision making.
A navigation system that knows every road in the world is useless if it makes you fight the interface every time you want directions. Likewise, an AI model that can produce excellent text is not enough if the user has to become a prompt engineer to get a reliable result. The real challenge is not generating intelligence. It is .
Garmin’s rise illustrates this perfectly. Its success was not just about inventing a handheld GPS device. It was about turning a complicated technological breakthrough into a practical tool that people could carry, trust, and repeatedly use. Aviation, driving, boating, hiking, running, each context had different constraints, but the underlying promise stayed the same: tell me where I am, show me where to go, and do it without making me think too hard.
That is the product lesson AI startups should absorb. Users do not actually want “AI” in the abstract. They want faster decisions, fewer mistakes, better drafts, clearer memory, and less mental overhead. The model is only valuable when it disappears into a workflow that feels almost invisible.
The moat moves from capability to choreography
In earlier technology eras, competitive advantage often came from owning a scarce capability. Better hardware, better manufacturing, better data access, better distribution. In AI, capability is moving fast enough that today’s edge can become tomorrow’s baseline. That changes the shape of the moat.
The deepest moat is increasingly choreography: the design of steps, checkpoints, nudges, and feedback loops that guide a user from intention to outcome. This includes the prompt structure, yes, but it also includes everything around the prompt. What context is preloaded? When does the system ask clarifying questions? What can the user correct quickly? How does the product remember preferences? Where does it surface uncertainty?
Think about the difference between a blank chat window and a well designed cockpit. A blank box gives you freedom, but it also forces you to do all the work of imagining the next move. A cockpit reduces cognitive overhead by making the most important controls obvious and the high stakes actions safe. The more capable the system becomes, the more important this design becomes, because users need control without complexity.
This is the paradox of AI product design: as models get smarter, the best interfaces often become less about showing off intelligence and more about protecting the user from having to manage it manually.
A useful framework here is the Three Layers of Trust:
Capability trust: Can the system do the task at all?
Control trust: Can I direct the system toward my goal?
Memory trust: Does the system become more aligned with me over time?
Most AI products overinvest in layer one. The winners will build all three. A user may try a tool because it is powerful. They will stay because it is controllable. They will pay because it becomes personal.
Come for the workflow, stay for the personalization
The phrase sounds simple, but it captures the real commercial logic of the next wave of products. A workflow gets adoption because it solves a repeatable job. Personalization creates retention because the product starts reflecting the user’s history, preferences, and judgment.
This is exactly what makes the comparison to Garmin so useful. A GPS device is useful to everyone in a general sense, but it becomes far more valuable when it is tuned to a specific use case. A runner wants pace, elevation, and heart rate. A pilot wants different navigation precision. A hiker wants battery life and trail data. The product is not just a map. It is a map shaped around the user’s life.
AI products are heading in the same direction. The first version may simply help draft an email or summarize notes. The more important version is one that learns how you write, what you ignore, what level of detail you prefer, which sources you trust, and when you want the tool to push back. At that point, the product is no longer just a generative engine. It is an adaptive collaborator.
That creates a second moat: proprietary feedback. Every interaction teaches the system something about the user, but not all feedback is equally valuable. The strongest products capture feedback in context, tied to workflows that repeat. The more frequently a user performs a meaningful task, the richer the signal becomes. Over time, the tool becomes less generic and more like a highly trained assistant that understands your standards.
The best AI products will not merely answer questions. They will learn the shape of your questions.
That distinction matters. A chatbot can be impressive on first use and still fail at habit formation. A workflow product can seem modest at first and become indispensable once it learns the user’s pattern of work. The real advantage comes from the compounding nature of interaction data, not from novelty alone.
The best products feel like navigation, not performance
One reason navigation is such a powerful analogy is that it reveals how people actually want to interact with intelligence. When you are lost, you do not want to admire the map. You want to know the next turn. You want confidence, not spectacle.
That is a profound product insight for AI. Users are not looking for a system that constantly displays how smart it is. They want a system that reduces the number of decisions they must make, while still giving them enough control to correct the path. Good navigation does three things at once: it orients, it predicts, and it adapts when the environment changes. Great AI products should do the same.
Consider a practical example. A marketing team using AI to draft campaign copy does not just need generation. They need brand voice consistency, approval workflows, version history, and easy correction. If the system can remember the brand’s preferred tone, flag risky language, and explain why it made a suggestion, it behaves less like a flashy writing tool and more like a navigational aid for communication.
Or consider a knowledge worker using AI for research. The value is not simply that the model can summarize articles. The value is that it can remember the user’s topic interests, highlight contradictions across sources, ask what decision the user is trying to make, and organize outputs into a reusable structure. That is not just intelligence. That is route planning for thought.
This is why “workflow design” is not a boring operational detail. It is the product itself. The workflow determines where users hesitate, where they commit, where they correct, and where the system can learn. If the workflow is weak, the model’s brilliance leaks away. If the workflow is strong, even a less dramatic model can feel magical.
A framework for building products people keep using
If the real moat is a feedback loop wrapped in a workflow, then product design should follow a different sequence than many teams assume. Do not start by asking, “How do we expose the full power of the model?” Start by asking, “What repeated human decision can we make simpler, safer, and more personal?”
Here is a practical framework:
1. Identify the repeated decision
Find the task users do often enough for learning to matter. The best candidates are decisions with some structure but enough nuance that personalization helps. Examples include drafting, sorting, planning, navigating, prioritizing, and reviewing.
2. Design for controlled intent
Do not force users to describe everything from scratch. Provide scaffolds, presets, templates, and constraints. The goal is not maximal freedom. The goal is low cognitive overhead with high control.
3. Capture corrections as signal
Every edit, override, approval, and rejection is valuable. Treat these as training data for the product’s future behavior, not as failure. A user correction is often more informative than a user compliment.
4. Build memory with boundaries
Personalization should help, not haunt. The system should remember what matters, forget what does not, and make memory visible enough for the user to audit. Trust grows when the product is clearly aligned with user agency.
5. Make the product better at the user’s job, not just at generating output
The output is only a means to an end. The true value is whether the user reaches the destination faster, with more confidence, and less mental strain.
This framework shifts product thinking away from isolated model performance and toward cumulative user advantage. The winner is not the company with the flashiest demo. It is the company that becomes a better operating environment for human judgment over time.
Key Takeaways
Build around repeated workflows, not isolated prompts. Repetition creates the conditions for learning, retention, and compounding value.
Treat user corrections as strategic assets. Every edit and override can improve the product’s future behavior.
Optimize for control with low cognitive load. Users want guidance, not complexity disguised as flexibility.
Personalization is the retention engine. A product becomes sticky when it starts reflecting the user’s preferences and history.
Think like a navigator, not a performer. The best product helps people reach a destination, not just admire the journey.
The future belongs to products that remember how you move
The most important shift in AI is not that machines are becoming more capable. It is that capability is becoming abundant, while judgment, trust, and fit remain scarce. That means the decisive advantage will go to products that organize intelligence around human behavior.
Garmin did not win by being a GPS chip company. It won by becoming a dependable companion for movement, whether in a cockpit, a car, or on a trail. The next generation of AI products will win for the same reason. They will not simply be smart. They will be structured around how people actually work, decide, revise, and improve.
In the end, the deepest moat is not a model, and not even a dataset. It is a relationship formed through repeated use, careful control, and accumulating trust. The product that learns how you move through the world is the product that eventually helps move you through it better.
And once that happens, the user is no longer just using software. They are building a system that quietly becomes an extension of their own judgment.
