Depth-First AI Fluency Rule
Master a few powerful models before adding specialized tools
- Difficulty
- Moderate
- Time to result
- ~months to results
- Steps
- 6
- Confidence
- 97%
Limit the initial toolset and develop depth through repeated use in real contexts. Select two or three strong general-purpose models, then apply them to recurring professional and personal tasks rather than constantly switching platforms. Starting suitable work with AI exposes capabilities, limitations, and prompting patterns that passive training cannot teach. Personal uses—such as analyzing sanitized financial data—can build confidence without waiting for an enterprise project. When a model misses, improve the context and learn why rather than immediately returning to the old workflow or replacing the tool. Only add a niche product when a clearly defined requirement remains unmet. The result is adoption fluency: a practical understanding of how to extract value consistently from a small set of powerful systems.
Origin
Extracted from Marketing Against The Grain as the speakers discussed living with AI, avoiding tool noise, and mastering a small number of frontier models.
Core principles
- 01Fluency comes from repeated real use, not passive awareness
- 02Start important tasks with AI to discover practical capabilities
- 03Use personal problems as a low-friction learning environment
- 04Prefer depth in a few powerful models over shallow exposure to many
- 05Add niche tools only when a specific unmet need appears
How to run it
- 1
Choose a narrow toolset
Select no more than two or three capable frontier tools that cover the majority of likely tasks.
Pro tip Favor broad capability and organizational approval over novelty.
Watch out Do not begin by collecting every specialized product mentioned online.
- 2
Pick recurring use cases
Identify professional and personal tasks that happen often enough to support deliberate practice.
Pro tip Use lower-risk personal data only after removing identifying information.
Watch out Do not expose confidential, medical, or financial records to an unapproved service.
- 3
Start suitable work with AI
Before using the old process, test whether the selected tool can accelerate research, analysis, writing, planning, or organization.
Pro tip Use the real task rather than an artificial tutorial exercise.
Watch out Do not force AI into tasks where it creates more risk than value.
- 4
Learn from misses
When an answer fails, diagnose missing context, unclear instructions, model limitations, or unsuitable task design.
Pro tip Keep the model constant long enough to distinguish skill gaps from model gaps.
Watch out Six misses do not justify blind persistence if the use case is fundamentally unsafe or unsupported.
- 5
Deepen feature fluency
Learn projects, files, long context, reasoning modes, integrations, and other advanced features in the chosen tools.
Pro tip Build reusable projects for repeated contexts.
Watch out Basic chat usage understates what frontier systems can do.
- 6
Add tools by exception
Introduce a specialized product only when a repeated, valuable requirement remains unsolved.
Pro tip Define the missing capability before evaluating vendors.
Watch out Novelty-driven expansion recreates overload.
In the wild
A leader spends several weeks learning Claude, Grok, and Gemini through a real personal problem. He cleans household transaction data, removes personally identifiable information, uploads CSV files, and asks where overspending and repeat purchases occur. The analysis challenges an assumption about Amazon purchases and reveals unexpectedly high restaurant and Uber spending.
→ A practical personal exercise builds AI fluency while producing an actionable view of household spending.
Common mistakes
Chasing every release
Constant switching prevents users from learning the deeper capabilities and workflows of any one model.
Retreating after early misses
Users often return to familiar habits before improving the context, task design, or fluency required for useful output.
Uploading sensitive data casually
Personal experimentation still requires data minimization, de-identification, and an appropriate privacy boundary.
Is it for you?
Best for
Leaders and knowledge workers who recognize AI's importance but feel distracted by rapid releases and conflicting tool recommendations.
Not ideal for
Specialists whose work genuinely requires benchmarking many models or maintaining vendor-independent infrastructure.
From the transcript
“Look, we've shared a lot of names, a lot of platforms, but just focus yourself on two or three. That's it.”
“And so if I just took one and just obsessed about becoming deeper in my adoption fluency expertise in just one, I'm probably far better…”
“And then you get overboden, then you go back to your old habits.”
From the episode
How a $1B+ Crypto Company Really Uses AI in Marketing