Ariel Cohen recently shared something that caught my attention: "BREAKING: Claude Opus 4.6 just dropped, and it's absolutely insane. I tested it on my LinkedIn system. The results were ridiculous." He went on to say he fed the model his LinkedIn strategy, his top posts, and his ICP, and in one shot it "optimized my best-performing lead magnets, rewrote my hook formulas, and built a 30-day content calendar that sounds exactly like me."

That combination (your strategy + your best work + a clear ICP) is the real takeaway. The model matters, but the bigger shift is what happens when you can keep your entire context in one place and iterate without starting from scratch.

In this post, I want to expand on Ariel's point and turn it into a practical, blog-worthy framework: how founders can use large-context AI to create consistent, voice-matched LinkedIn content that supports pipeline, not just impressions.

The founder problem Ariel is calling out

Ariel frames the pain in three blunt lines:

"Your outbound is getting ignored. Your content takes hours. Your pipeline is unpredictable."

If you are a founder or operator, you have probably felt all three.

Outbound gets colder every quarter because everyone is automating the same generic sequences.
Content feels like an extra job: ideation, drafting, editing, formatting, and then doing it again tomorrow.
Pipeline becomes streaky because the inputs (quality conversations and consistent visibility) are inconsistent.

Ariel's claim is that large-context AI changes the operating system behind content and outbound because it can hold "1 million tokens of context" and keep your voice, ICP, positioning, and past performance in the same conversation.

That is not just a speed upgrade. It is a consistency upgrade.

Why large context matters more than "better writing"

Most people use AI like a one-off copywriter: "Write a post about X." The output is often decent, but it is rarely them. It also forgets everything by the next prompt.

Ariel is describing a different use case: persistent context.

When a model can reference your:

Best-performing posts (what has already resonated)
Lead magnets (what converts)
ICP language (how buyers describe pain)
Positioning (what you want to be known for)
Offer structure (what you actually sell)

...it can generate new assets that feel connected to your body of work.

This is how you get "no more generic AI slop" and instead get drafts that sound like the same person, with the same worldview, aimed at the same buyer.

A practical way to replicate Ariel's "one conversation" setup

Ariel says he fed the model his strategy, top posts, and ICP. That is the right input set. Here is how I would structure it so the AI can actually use it.

1) Build a "Voice + Strategy" source document

Create a single document you can paste or upload that includes:

Your short bio and point of view (5-10 bullet principles)
Your ICP definition (role, company size, triggers, objections)
Your offer (what you do, for whom, typical outcomes, constraints)
Your writing rules (sentence length, formatting habits, what you never say)

Add a "negative list" too: words and tones you do not want (for example: "guru," "crushing it," or overly corporate filler).

2) Add "Proof" in the form of your best posts

Ariel mentions "reverse-engineered my viral posts." Do the same with 10-20 posts that performed well. For each post, annotate:

Topic
Hook style
Structure (bullets, story, contrarian take)
CTA
Why you think it worked

This turns your history into training data the model can mirror.

3) Add "Conversion assets" (lead magnets and outbound messages)

This is where founders often stop short. They teach the AI how to write, but not how to sell.

Include:

Your current lead magnet(s)
Landing page copy
A few DM or email threads that led to calls (anonymized)

Now the model can connect content to pipeline, not just engagement.

Turning Ariel's outputs into a repeatable workflow

Ariel lists specific deliverables he got when testing his system against a model upgrade:

"Created 5 ready-to-post lead magnets..."
"Wrote 30 hooks calibrated to my voice and ICP"
"Built a 30-day content calendar"
"Found the 3 patterns behind my top posts"

Let us translate those into a workflow you can run weekly.

H2: Step 1 - Pattern mining (what already works)

Instead of asking AI for new ideas, ask it to explain your winners.

Prompt concept:

"Analyze these 15 posts. Identify the top 3 repeatable patterns across hooks, structure, and audience triggers. Provide examples and a checklist."

Your goal is to leave with a small number of reusable templates, not 100 random ideas.

H2: Step 2 - Build a hook library that matches your ICP

Ariel mentions a "Hook Vault Builder" and "47 hooks." The number is less important than the calibration.

A strong hook library is organized by:

ICP pain (pipeline volatility, hiring, churn, long sales cycles)
Buyer awareness stage (unaware, problem-aware, solution-aware)
Angle type (contrarian, mistake, benchmark, story, teardown)

Then you can ask for 10 hooks per bucket, and you will never stare at a blank cursor again.

H2: Step 3 - Lead magnet multiplication without losing quality

Ariel says the model "optimized my best-performing lead magnets" and produced "ready-to-post lead magnets that match my top performers." That is a smart approach: start from what converts.

Instead of creating a brand new lead magnet every time, create variations:

Same promise, different format (checklist, teardown, calculator)
Same topic, different persona (founder vs head of marketing)
Same mechanism, different industry example

Your AI can do this quickly if it has both the asset and the conversion context.

H2: Step 4 - The 30-day calendar (distribution, not busywork)

A 30-day calendar is only helpful if it matches capacity and business goals.

I would structure it like this:

3 posts per week that build authority (insights, frameworks, contrarian takes)
1 post per week that demonstrates proof (case study, teardown, before-after)
1 post per week that activates demand (lead magnet CTA, event, consult offer)

Ask the model to:

Assign each post a specific ICP trigger
Include a CTA that makes sense for that trigger
Reuse your top-performing structures

That is how a calendar becomes pipeline support, not content noise.

"Outbound revival" means warming cold leads with content

One of Ariel's most practical claims is the idea of content that "warms cold leads." That is the missing bridge between posting and selling.

Here is the play:

Publish content that addresses an objection your buyer already has.
Use outbound to reference that content naturally.
Make the next step small (a question, a quick resource, a relevant teardown).

This works because you are not asking for a meeting in the dark. You are inviting them into a conversation you are already leading in public.

Where people go wrong with AI content (and how to avoid it)

Ariel's "no more starting from scratch" line is powerful, but it can also create a trap: relying on the model to do the thinking.

Avoid these failure modes:

Too much volume, not enough insight: If you post daily but say nothing specific, your ICP will feel it.
Voice drift: If you do not pin your writing rules, the model will slowly turn you into generic "creator" tone.
CTA overload: Every post does not need to sell. A predictable cadence beats constant pitching.

The fix is simple: use AI to scale execution, while you stay responsible for the point of view.

A simple starter checklist based on Ariel's approach

If you want to test this without overbuilding a system, do this in one afternoon:

Collect 10 best posts and 1 lead magnet.
Write a one-page ICP sheet.
Ask the model to identify patterns in the posts.
Generate 30 hooks tied to those patterns.
Draft 12 posts for the next 4 weeks (3 per week) using your proven structures.
Pick 2 posts to pair with 2 short outbound sequences that reference them.

You will not get perfection in one run. But you will get momentum, and momentum is what stabilizes pipeline.

This blog post expands on a viral LinkedIn post by Ariel Cohen, I Turn AI into ROI. View the original LinkedIn post →