#14: The iPhone Moment AI Hasn't Had Yet

I read way too much AI news. Most of it is useless—just product launches dressed up as breakthroughs. Here's what actually matters: learning by doing.

While others debate GPT versions, early adopters are quietly building unfair advantages through deliberate practice. That's the game we’re playing here.

PS: I'm building an AI service for practical business challenges. Pre-launch, I'm offering free consultations to a few people with real problems they want to solve with AI. Hit reply if that's you.

💡 One Idea

The iPhone Moment AI Hasn't Had Yet

When the iPhone launched in 2007, apps mimicked physical objects – notepads with yellow lined paper, bookshelves with wooden textures. Investor Chris Dixon calls this the "skeumorphic phase" of technology adoption – making new tools resemble familiar ones.

AI is firmly in this phase today.

We're inserting AI into existing workflows, building agents that mimic human roles, and deploying them within organizational structures designed for human limitations. We think linearly, using AI to optimize tasks as they've always been done.

But what happens when we move beyond skeumorphism?

AI differs fundamentally from humans in three ways:

1. Perfect replication: AI agents can be instantly copied at scale

2. Freedom from human constraints: No need for sleep, breaks, or management hierarchies

3. Different processing patterns: Parallel rather than sequential thinking

These differences enable entirely new organizational structures:

• Capability networks instead of departments

• Algorithmic coordination replacing management hierarchies

• Parallel operations instead of linear workflows

The most successful organizations will operate with one foot in each world – pragmatically applying AI within existing systems while experimenting with entirely new structures designed specifically for AI capabilities.

Those who remain exclusively in the skeumorphic phase will eventually face structural disadvantages against competitors who've reimagined their operations from the ground up.

The real question is: what will we build tomorrow that was unimaginable yesterday?

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🛠️ One Tactic

My AI-powered LinkedIn Content System

Getting good at prompting is valuable, but the real leverage comes from delegating work to agents that run on triggers. This is where AI starts feeling like magic.

Today I'll show you exactly how I built a team of AI agents to handle my LinkedIn content workflow—not because you should copy it, but to illustrate how you can start delegating your own repetitive tasks.

I use two tools for most of my automations;

• Relay for most things (80%)

• N8N for the most complex task systems (20%)

The Core Principle: Task-Specific Agents

Rather than building one complex system, I created specialized agents that handle specific jobs. They coordinate through a shared Slack channel, creating a surprisingly effective content team.

The Content Prep Team

Newsletter Monitor: Extracts two post ideas from each new newsletter issue I publish.

Content Repurposer: Monthly scan of my top-performing posts, suggesting fresh angles and formats (short posts, carousels, different hooks).

Engagement Tracker: Weekly data pull of my LinkedIn metrics into Google Sheets—the data source for the repurposer.

Peer Tracker: Monitors industry leaders' content weekly, identifying trending formats and topics.

None of these are particularly advanced. The repurposer, for example, simply fetches posts, filters the top 5 by engagement, sends them to Claude with repurposing instructions, and drops suggestions into Slack:

The Content Writer

This is the heavy lifter—an N8N-based agent with extensive style guidelines and access to my previous posts for reference. I use N8N here because it gives more granular control and it’s easier to experiment with small improvement steps in the process. For instance, I'm currently experimenting with:

• A separate library of proven hooks and openers

• A quality-checker agent that grades outputs and requests rewrites when needed

The Integration

Everything flows through one Slack channel. Agents post their results automatically, and I can communicate with them directly through messages.

The Takeaway: Start small with one repetitive task. Build a simple agent that handles it completely, then add the next piece. The magic isn't in complexity—it's in having systems that work while you sleep.

That’s it for today. Thanks!

– Martin