Daniel Matias recently shared something that caught my attention: "AI + Linkedin = easiest extra $10K+/mo in 2026.

(This system shows you how to do it)

Yet 90% of people won't make it work for themselves."

I have seen the same pattern play out with founders, operators, and creators. They want the upside of consistent LinkedIn publishing, but they fear that using AI will turn their voice into generic noise. Daniel pushes back on that fear with a line worth repeating: "Bad prompts ruin authenticity. AI just scales what you give it."

That is the real conversation we should be having. Not "Should I use AI?" but "What system am I feeding into AI so the output is still me?" Below, I am expanding on Daniel's framework and translating it into a blog-style playbook you can actually run, even if you are posting only 3 times per week.

The real reason AI content feels generic

Daniel argues that most people fail because they assume AI equals "generic slop," so they swing to the other extreme and do everything manually. The result is predictable:

They burn out after 2 weeks of posting
Their content still sounds like everyone else
Revenue stays inconsistent

I would add one more: they never build a repeatable pipeline. They rely on motivation and bursts of inspiration, which is not a strategy.

When AI output feels bland, it is usually because the inputs are bland:

No point of view (only summaries and tips)
No lived examples (wins, losses, numbers, constraints)
No audience specificity (who exactly is this for?)
No voice constraints (what words you do not use, what you always do)

Key insight: Authenticity is not the absence of AI. Authenticity is the presence of clear, specific inputs.

What "an AI system" actually means (beyond "use ChatGPT")

Daniel is explicit that this is not another "use ChatGPT for posts" guide. He is talking about a system with parts that work together:

Research that finds topics worth writing about
Training AI on your tone so it sounds like you
Repurposing one idea into many angles
Automating the workflow (research, writing, scheduling)
Optimizing your profile to convert attention into leads

I like this framing because it treats LinkedIn like a growth channel. Channels need operations: sourcing, production, QA, distribution, and conversion. AI simply reduces the cost of each step.

1) Build an AI research engine for trend-aware topics

Daniel mentions an "AI research engine that finds trending topics in your niche automatically." The practical takeaway is: stop guessing what to write.

A simple research engine can pull signals from:

Your clients' recurring questions (sales calls, support, onboarding)
Competitor posts with high engagement (especially comments, not likes)
Podcasts and newsletters in your niche (extract repeated themes)
Product updates and industry news (explain implications, not headlines)

The AI part is not magical. It is pattern recognition at scale. You can ask AI to:

Cluster topics into 5-10 pillars (for consistency)
Identify contrarian angles ("everyone says X, but in my experience Y")
Generate a weekly content queue with hooks and thesis statements

Where most people go wrong is they ask for "10 viral post ideas" with no niche context. Instead, provide:

Your ICP (industry, role, seniority)
The outcomes they want (and what blocks them)
The offers you sell (so posts connect to revenue)

2) Train AI on your voice, not just your knowledge

Daniel highlights "How to train AI on your tone of voice (so it sounds like YOU, not ChatGPT)." This is the part that turns AI from a content generator into a writing amplifier.

To train voice, you need examples and rules.

Collect voice assets

Give the model a small, high-signal dataset such as:

10-20 of your best posts (or threads, emails, or memos)
3-5 posts you dislike (and why)
A list of phrases you use often
A list of phrases you never use

Define your voice spec

Write a "voice card" that includes:

Default tone (direct, calm, playful, analytical, etc.)
Sentence length preference (short and punchy vs longer and structured)
Formatting habits (line breaks, bullets, questions)
Your point of view (what you believe that others do not)

Then instruct the model to follow the spec and to ask clarifying questions when inputs are missing.

Daniel's claim that "AI just scales what you give it" is literally true here: the model will mirror your specificity.

3) Repurpose one idea into 10 posts without sounding repetitive

Daniel calls out a repurposing framework that turns 1 idea into 10 posts. The trick is to repurpose by angle, not by rewording.

Take one core idea (example: "AI does not ruin authenticity, bad prompts do") and create variants:

A contrarian claim post (state the belief, defend it)
A story post (the moment you realized it)
A teardown post (what bad prompts look like, with examples)
A checklist post (inputs that prevent generic output)
A case study post (before and after metrics)
A myth vs reality post
A framework post (system diagram: research - voice - repurpose - automate)
A Q and A post (answer common objections)
A mistakes post (3 ways people break authenticity)
A process post (your weekly workflow)

This approach keeps your message consistent while your delivery stays fresh.

4) Automate the workflow, but keep quality control

Daniel mentions "the exact workflow that automate everything (research, writing, scheduling)." Automation is where the time savings show up, but it is also where low quality can sneak in.

A practical workflow looks like this:

Weekly topic pull (30-60 minutes): AI surfaces topics, you pick 3
Outline generation (10 minutes): hook, thesis, 3-5 bullets, CTA
Drafting (20 minutes): AI drafts in your voice spec
Human QA (10 minutes): you add examples, numbers, and edge cases
Scheduling (10 minutes): queue posts and set reminders to reply to comments

The quality control step is non-negotiable. Your job is to add the "human-only" ingredients:

A specific story (what happened, what changed)
A hard constraint (time, budget, team size)
A real opinion (what you would do differently)
A clear call to action (what should the reader do next)

5) Profile optimization: convert visitors into leads

Daniel includes "Profile optimization that converts visitors into leads." This is the part many creators ignore, then wonder why high impressions do not translate into pipeline.

If your content works, people will click your profile. Make the next step obvious:

Headline: who you help + outcome + method (clear, not clever)
About section: problems you solve, proof, process, simple CTA
Featured: 1-3 best posts, a lead magnet, a case study, or booking link
Social proof: numbers, results, testimonials, recognizable logos (if applicable)

Think of your profile as the landing page for your content.

A simple way to start this week (without overengineering)

If Daniel's results ("20+ inbound calls per week") feel far away, start smaller but build the same system.

Pick 3 content pillars for the next 30 days
Write a one-page voice card
Create 5 reusable post templates (story, checklist, teardown, framework, myth)
Batch one week at a time, then iterate based on comments and inbound DMs

The goal is not to post more. The goal is to make posting sustainable and tied to revenue.

My takeaway from Daniel Matias: stop debating whether AI is authentic. Build inputs that are unmistakably yours, then let AI scale them.

This blog post expands on a viral LinkedIn post by Daniel Matias, Freelance AI Engineer | Helping Founders Scale with AI Systems to Drive Growth | 100K+ users served | Agentic Systems. View the original LinkedIn post →