What up, what up.

People think they can smell AI writing. The em-dash. The "it's not just X, it's Y." The little tells. They run around the internet pointing at sentences like it's a witch hunt.

Here's what they're actually smelling: thin content. A thin opinion with a thousand words sprayed on top. That's the thing people object to. Not the punctuation — the emptiness behind it.

Nobody reads an AI-written guide to repairing an aortic valve and stops to complain about the dashes. They read it for the information. The em-dash disappears the second the content is worth reading.

TL;DR: People hate thin content, and AI made thin content free. The fix isn't better prose. It's information worth reading — data only you have, the judgment to ignore the rest, and a business built so your information gets more valuable with every customer you add. Get that right and nobody audits your punctuation.

Treat AI like a SQL tool

A hundred percent of what I do is mashing data together. I treat the AI more like a query tool than a writer.

Here's why. AI is great at finding the source material. It is bad at understanding why that source material is valuable. That second part — knowing what's worth keeping and what to throw away — is judgment, and it's still yours.

So the question that actually matters isn't "write me a post." It's: what is the source of the value? What data is genuinely useful here? How do I compile it — and, just as important, how do I ignore the noise and keep only the part that moves someone?

The most valuable move is subtraction

In go-to-market, the most underappreciated work is figuring out what not to do.

You've got a hundred thousand accounts in your market. Ninety-five thousand of them you should never talk to at all. Of the five thousand left, maybe a hundred will change the trajectory of your business. Find those hundred and you can spend all your time making them something worth reading.

And you don't rank them by wallet size. You rank them by the size of their problem relative to their wallet. A small company drowning in a problem you solve beats a giant who barely feels it. That ranking is the whole game.

Ask the customer

The one thing nobody does when they write a campaign: go to a real customer and ask, "Would you respond to this? Is this useful to you?"

Someone close to me is a librarian. She gets cold-called by the same rep over and over. "Will you buy my books?" Every time, same answer: "We buy books in December." All that effort, all those calls, completely wasted — because he never once asked the thing that would have told him when, or whether, to call.

That insight was sitting in her head the whole time. He just had to ask. Start from the customer's reality, not your campaign. With perfect information on the whole internet — and your own internal data — what could you hand this person that they'd actually pay to receive?

Your information has to compound

That internal data is the part that's supposed to get better over time.

I've been an advisor to Clay since 2020. One thing I keep telling them: you buy enormous amounts of data, so build a Kirkland-brand version — unbranded, way cheaper — and pass the savings to me. I could never negotiate that on my own. Their network can. That's a moat: value you can hand the next customer precisely because you already served the last one.

This is why I don't give signals-based outbound the time of day. No great product gets worse the more people use it. Signals do. The more people buy the same hiring data, the same intent data, the worse it works for all of them. Clay calls it go-to-market alpha — and the durability of that alpha is halving. It gets shorter every year.

The creative stuff I do is valuable. But it decays the moment it's copied. The stuff that compounds is different: what can you do that competitors can't — and that gets better as your business grows?

Design the message that's 10x better in three years

Here's a strange but useful way to design a business. Ask: what message could I send a future prospect that's better in a year than it is today, and ten times better in three years than it was in that first year? And how am I collecting and storing the information that makes that true?

If your business is built around distribution and around the message getting more valuable over time, everything downstream gets easier. This has been NFX's whole thesis for years — network effects, told over and over.

There's an exception. If you have the single best product in the world — say, the best AI model — you don't have a message problem. You have a collecting-money problem. But for the rest of us on the ground, the work is capturing and structuring information so the next customer finds more value than the last.

Horizontal goes thin, vertical goes deep

This is exactly where horizontal SaaS struggles. Its value is a thin layer smeared across ten verticals nobody fully understands. It doesn't compound, because you only ever go an inch deeper everywhere — and an inch deep is no moat at all.

Serve only plastics companies and your knowledge compounds. Ten plastics companies ask you for the same thing, and now you know it's worth building. Add a plastics company, a plumbing company, and a B2B SaaS company to that list and you can't tell what's worth building anymore. You go an inch deeper across all of them and own nothing.

You can't fix that by committee, either. A data company recently invited me onto a council to give feedback on their direction. Feedback's fine. The real issue is structural: in go-to-market data, the value often doesn't compound — and sometimes, with signals, it actively gets worse the more people use it. Data is becoming a commodity. An opinion helps. A product that compounds helps more.

Aggregate the data nobody else has

Nate Nasralla wrote a great book — Fluint helps companies navigate enterprise deals. He frames a big deal like a choose-your-own-adventure: a hundred decisions, each one branching.

Here's the move. Across his customers, he's sitting on dozens of examples of how deals with Starbucks, Apple, Anthropic were won or lost. Aggregate that, and the next person who walks in selling to Starbucks gets told: you'll generally need four decision-makers, one of them in the C-suite, and that has to happen at this exact point in the funnel. Nobody else can say that, because it's private data — and every new customer makes it sharper.

That's the whole point about AI writing, brought home. In a world where every message is thin, your edge is the depth and quality of the information — not the writing of it. Focus there and everything gets easier.

Distribution-first businesses win

So the businesses that win from here are distribution-first. The questions worth obsessing over are simple to ask and hard to answer:

• How does one customer naturally become three? Referral, network, word of mouth baked into the product — not bolted on after.

• How does the 400th customer get ten times the value of the 100th? Because every customer before them made the data deeper. That's a multiplying data moat.

• What message gets better the longer you're in business? If the answer is "none," that's the thing to go fix first.

My own moat is knowledge. It's why I run a top-50 Substack two months in, why there are 20-plus tools inside Edge Copilot, and why this is the only place I put any of it. As I build for more customers, every customer gets the latest thing I've built. The next one inherits all of it.

So stop auditing other people's punctuation. Go find the one thing you know that your next customer would pay to hear — then build the machine that knows more every time you use it.

Big shout-out to David Austin, Ray Owens, and Alec Schwartz — who got his dad building in Claude Code with Edge Copilot. Thank you for using the tools and telling me what they're worth to you. That's the loop.

Have a beautiful day.

Chapters

• 00:00 — Why people say they hate AI writing

• 01:03 — Treat AI like a SQL tool

• 02:06 — The most valuable move is figuring out what not to do

• 03:13 — Ask the customer (the librarian and the book rep)

• 04:18 — Why your information has to compound

• 05:20 — Signals decay; GTM alpha is halving

• 06:23 — Design the message that's 10x better in three years

• 07:24 — Horizontal goes thin, vertical goes deep

• 09:31 — What Clay got right: enabling imagination

• 11:34 — Aggregate the data nobody else has

• 12:36 — Information is the value, not the writing

• 13:42 — Distribution-first businesses win

What Annual Adds

This one was free. Paid gets the build. Annual gives you the tools that run it.

• Every tool I ship. Edge Copilot installs to your Claude Code — talk to all my knowledge, every method, every data source. Current: Edge Copilot, AutoClaygent, Agent 7, Who to Target and What to Say, Blueprint Cloud, Technology Finder, Video List Extractor, Competitor Monitor, LinkedIn Engagement, Domain & LinkedIn Finder, Dossier Builder, PDF Contact Finder, TAM Contact Harvester, Find a Rep. Whatever ships next is included.

• All 3 courses: Who to Target and What to Say, Agent 7, AutoClaygent.

• Weekly office hours.

Your license key installs /edge in one command — then every tool above drops in on top, each one a /edge install away.

License key hits your email.

→ Go annual — $2,499/yr · Start at $50/mo (most readers start here)

"AI go-to-market" is a weird phrase. It implies there's a different, regular go-to-market over there, and this is the new one. But mostly what "AI go-to-market" has meant is this: people take the average, tired way of doing outbound and bolt AI onto it. The result isn't better outbound. It's more outbound. More noise. A bigger pile of garbage you now have to climb over to get one second of attention.

My friend Dale fights that pile by mailing people an empty donut box. "I ate these before we could talk — join me." It's funny. It works. And it gets worse the second everyone starts doing it. That's the trap with every attention-economy trick — the empty box, the free AirPods for a meeting, the "please, I beg you" energy. The best of them decay the moment they're copied.

There's a better move than fighting for attention. Work backwards from the message. And there are exactly two things you have to get right.

TL;DR: Stop competing for attention. Find the one message only you can send — built on information you have and your prospect would kill for. Then say it like a human, not like a robot showing off its homework. The targeting does the heavy lifting; the words do a lot less. Everything below maps to a skill you can run with me in Claude Code.

Thing one: the message only you can send

Don't compete in the attention game. Find a message nobody else can send. That comes from one thing — the asymmetry between what you know and what your customer cares about.

Here's the question that defines it. With perfect information on every customer you already have, what could you tell your next best customer that's independently valuable to them? Not "we help companies like you." Something they couldn't get anywhere else.

I think about it as a break-in. If a specific prospect — an industry at a size, the thing that unites a 100x customer — could break into your systems for a single day, what would they want to know? What would be so useful they'd pay for it? That answer is almost always a blend of your internal data and external data. That's the PVP — the permissionless value proposition.

This is exactly where horizontal SaaS struggles. What you know about a vertical SaaS customer doesn't translate to Cisco. The knowledge doesn't carry across industries or sizes, so the value you can hand each one is different. You can mine call transcripts for it. You can mine product data for it. But the where to begin is usually intuition about a niche where you sit on a pile of useful internal information.

What's in Edge Copilot for this: Dossier Builder — point it at your systems and it builds one per-account timeline from all of them, with a source tag on every line.

It joins Gong, CRM, billing, product, and support into a single sorted timeline per account, and links the same company across systems before it merges anything. When billing says cancelled and the CRM says active, both show up on the line with the source attached — you decide which one is true.

Pull it (annual): Say /edge install dossier-builder once your license key arrives, then point it at your call transcripts and customer data and ask for a per-account dossier — it runs the whole thing conversationally, pausing to ask you the tiebreakers.

→ Get Dossier Builder in your terminal

Thing two: say it like a human

You can do all that data work and still blow it on the last step.

Here's the failure mode. You gather the data, you stack it up, and the message becomes a showcase of how hard the data worked. That misses the entire point. The prospect doesn't care about your pipeline. They care about what a human does with the information — whether it's actually useful to them, or just theater.

So work backwards from the customer. If you've got a campaign and a list of people it should apply to, show them the message. "Read this — what do you think? I didn't write it, so be brutal." You will be missing context every time. Oh, we never thought about it that way.

One company I advise hired someone who'd lived in the buyer's seat. We were planning an event, and I said, let's take everyone to a Michelin-star dinner — because that's what I'd want. He stopped me. Most of these people just want a break from their families and a few beers with friends. They're Miller Lite people. That's not my frame. But it's the customer's frame. So the message wasn't "I did all this targeting, here's why you need us." It was: "We've got people coming for beers. They're on us. Want in?" No data theater. It converted far better, because it matched what the customer actually wanted.

The targeting does the heavy lifting

Here's what most people get backwards: the value of the data is in the targeting, not the message. Get the targeting right and the message barely has to work.

This is why my clients ship smaller campaigns — hundreds, sometimes tens, sometimes low thousands — at much higher velocity. So much of great go-to-market is figuring out who you shouldn't target. Then, for the people you should, what genuinely valuable thing you have for them.

And disqualification beats demographics. Not "50 to 1,000 employees". Real, hard gates: they literally have no software engineer, so my product is useless to them. Or this title has never bought, so we'll never contact them. Three questions, in order:

• Can they actually benefit? Is this a prospect you know gets value from your service — not "fits the ICP."

• Is this selection 10x more valuable — and how do you know? A discernible, data-backed way to spot a 10x prospect, and a hard way to disqualify everyone else.

• What language makes them feel seen? So the power dynamic is "I'm trying to help you," not "please get on a call with me."

What's in Edge Copilot for this: win-loss-rewind — works backward from who actually won, lost, and churned to a scoring rubric of non-obvious public signals; the disqualification engine.

Name the one prospect that looks like your best customers and hard-disqualify the rest. It auto-segments your customers without assuming industry, then keeps only the signals that survive a 20% holdout. Its companion, tam-research, finds every data source that builds the whole list.

Pull it (annual): Say /edge install win-loss-rewind (and /edge install tam-research) once your license key arrives, then point it at your won/lost/healthy/churned customer list plus your calls.

Stop thinking in tactics

When you sit down to build, drop the tactics brain. Use the empathy brain.

There's an old idea worth holding onto: treat persuasion as a bag of tricks and none of it works; treat it as a way of operating and things fall into place. The "sent from my iPhone" trick, the funny little hacks to fake a human — those are tactics. They don't survive contact.

Anchor everything in a real person. How would a real person respond to this? When you open a message to an existing customer or a friend, you're not reaching for a template. You're trying to tell them something useful, in a friendly way. ("Yo Chris, beers Friday?" He always says yes.) That's the whole bar. Hit it with strangers and you've won.

Why I built Edge Copilot

Here's the uncomfortable part. Content like this isn't enough. You don't need me writing essays — you need me in the room when you build.

Because Claude is sycophantic. It'll build whatever you tell it to, in any direction, with total confidence. What's missing is someone with taste and opinions to steer you — to say "that's data theater, cut it" or "you haven't disqualified anyone yet." That's the job.

So I built On the Edge Copilot. It lives inside Claude Code, it has access to all my knowledge, and it nudges you while you work: Jordan has a skill for this. Jordan has a tool for this. Jordan's been watching — he has some ideas. It's trying to push you toward the two things in this post: the asymmetry only you have, and plain language that removes the automation feeling.

What's in Edge Copilot for this: the /edge skill lives in your Claude Code, talks to all my knowledge, methods, and data sources, and nudges you mid-build.

It's a second opinion in the terminal. When you tell Claude to build something, Claude builds it — /edge steps in first to say "Jordan has a skill for this," or strips the data theater out of your draft so it reads like a human wrote it. Annual includes every tool I ship, not a fixed list.

Pull it (annual): Your license key installs /edge in one command, then any tool — /edge install dossier-builder, /edge install tam-research, whatever you need — drops in on top.

→ Get Edge Copilot in your terminal

One annual subscriber wanted a list of every franchise and its owners in the country. So I built it, then handed the skill to every annual subscriber.

What's in Edge Copilot for this: franchisee-finder — turns FDD Item 20, the roster every franchisor files by law, into a national list of franchisee operators with the multi-unit whales resolved.

It collapses the per-unit shell LLCs into the one holding company quietly running 40 Burger Kings across three brands — the operator everyone wants and the logo on the door hides. Five chunked, checkpointed waves.

Pull it (annual): Say /edge install franchisee-finder once your license key arrives, then run it against a brand (or its FDD) to get the operators behind the logos.

I'm going to keep doing this. Monthly is $50 and gets you the writing. Annual is $2,499 and gets you every tool, the courses, office hours — and, increasingly, custom data jobs I build for you.

If you've got a hard list-building or data problem and $2,499 sitting around, ping me. Tell me what you're trying to find. Mostly these are data jobs — and that's the fun part.

Thanks for watching. Have a beautiful Tuesday.

Chapters

• 00:00 — Why "AI go-to-market" is mostly noise

• 01:10 — Work backwards from the message

• 02:16 — Thing one: the asymmetry only you have (PVP)

• 04:22 — Thing two: say it so they can receive it

• 05:22 — Data theater vs. what a human will actually use

• 06:25 — The beers, not the Michelin dinner

• 07:28 — The targeting does the heavy lifting

• 08:33 — Who you shouldn't target — and Edge Copilot

• 09:38 — Empathy brain over tactics brain

• 10:38 — The three gates: benefit, 10x, language

• 13:40 — Why content isn't enough (Claude is sycophantic)

• 14:42 — What I'm building for subscribers

A founder showed me his dashboard.

He runs an AI tool that watches alcohol ads for large consumer brands. White Claw. Aperol. Suntory. The thing scans a video, flags music it can't license, flags any frame that implies the drink is solving someone's anxiety, flags whatever six dozen things a Czech regulator can sue you for. He'd just gotten his first wave of customers and was setting up cold email for the first time. He led every email with a screenshot of his dashboard.

I told him to scrap the screenshot.

Same week, a guy on the call before him runs go-to-market for a lending infrastructure startup. Eighteen customers. Six-figure ACVs. Each customer launches a few products a year — credit cards, buy-now-pay-later, personal loans. He's tried everything. PQS-style outbound. Signal scraping. Predictleads jobs. He can find the companies that announced new lending products. He just can't get a reply.

Both of them are stuck. Both of them are working hard. Both of them are about to learn the same thing, which is that pain segment isn't the deepest move I teach.

It's an application of the deepest move I teach.

The deepest move is information asymmetry. Manufacture more information about the buyer's situation than the buyer has. Then deliver the insight as a complete gift the buyer can use without ever replying to you. Pre-AI, only the top 1% of reps could do this — the ones who'd lived inside one customer for so long they basically became them. Post-AI, anyone with eighteen customer transcripts and Claude Code can do it across an entire customer corpus in a long afternoon.

I'm calling the system that produces it the asymmetry engine.

Pain Segment, PVP, PQS, Working Backwards, Old World vs. Jordan's World — the whole framework lattice you've been reading from me for the last year — they're all applications of this single underlying move. I want to lay it out end-to-end. The law that runs underneath. The four moves that flip the game. The mining loop you can run on your customer base this week. And the way the existing frameworks slot in once the master frame is clear.

This one's long. Pour another coffee.

The Asymmetry Law

Sales is a power game. Power flows from information asymmetry. Whoever knows more about the buyer's actual situation has the upper hand. That's the whole thing.

Most reps lose this game on day one. They walk in with a deck. The buyer walks in with their actual job, their actual quarterly number, their actual fired-CFO ghost, their actual lawsuit they almost lost in Q2, and the ten Slack threads about whether the thing the rep sells will save them or get them fired. The buyer has more information than the rep. The asymmetry runs the wrong way. The pitch fails. The rep blames the copy.

Top-1% reps have always inverted this. They've sold to enough people in the same role to know, before the buyer opens their mouth, what the buyer is going to say next. They know the lawsuit. They know the number. They know the ghost. When the buyer talks, they're not gathering — they're confirming. That's why those reps close.

The catch was always that you had to spend years being them to know them. The German entrepreneur I always think about wanted to start a dental billing company. So he found a dentist in San Francisco and worked for free for six months filing bills. He learned that molar replacements are the most common thing a dentist invoices. He learned every step of the process from the inside. Then he built software that replaced his job. His first customer was the dentist he'd been working for. Every other customer felt like he was reading their mind, because effectively he was.

That used to be the whole skill. Live inside one customer until your guesses about every other customer were ground truth.

It isn't anymore. The asymmetry engine is what replaces it.

The god-customer question

The new question — the one nobody could ask before language models — is this. If your buyer were a god, with infinite reach, and they had access to everything you know about every customer who came before them, what would they ask?

Not what's their pain. Not what's their persona. Not how do I personalize the second line of the email.

What would a god-buyer want to know?

I asked the lending-infrastructure guy this on the call. He had eighteen customers. They'd launched somewhere between two and four products each. That's roughly seventy product launches across his platform — credit cards, buy-now-pay-later, personal loans, refi products. Every single one had transaction data sitting in his database. Every single one had a story. Some of them died at six months. Some of them tripled the customer's portfolio. He'd never asked, across the seventy of them, what the launch-day signal was that separated the dead from the tripled.

A god-buyer would. A god-buyer doesn't care about his dashboard. A god-buyer wants to know: if I launch a credit card next quarter at 7% APR, what's my pickup curve going to look like, based on every other 7% credit card that launched on this platform?

That question wasn't askable in 2023. The data was sitting in a Postgres table. The customer was sitting in their office worrying about it. Nobody could connect the two without a six-month consulting engagement and a SQL analyst. Now you can connect them with a Claude Code session and a sample of the schema.

This is the move. Stop selling product. Start asking the god-buyer question.

Why AI made this not just possible but necessary

A buddy of mine is an AE at a payroll-and-benefits company. Top quartile of his team. He told me last week that the SDRs at his company aren't sourcing him pipeline anymore. They got replaced. Not by him. By Claude. The average AE got replaced too — the one who sends I saw you raised money, can I have some. The one who sends I noticed you're hiring a benefits administrator, our payroll product is the best solution. That whole tier is gone. AI does it now. Probably better. Definitely cheaper.

So the floor moved up. The bar to get a meeting got higher. If your message reads like the average AE wrote it, your message is competing with a robot army doing the same thing for free.

The only thing that beats the robot army is data the robot army doesn't have.

That's your customer interviews. That's your transaction histories. That's your support transcripts. That's the seventy product launches sitting in your back-end. None of it's on the public web. None of it's in the LLM's training set. None of it can be replicated by the recruiter sending the "I saw you're hiring, we have cheap labor in the Philippines" email — and let me tell you, the volume of recruiters sending that exact email is greater than atoms in the universe by maybe one rounding error. They are competing in the saturation zone. You don't have to.

Your customer corpus is the moat. It's the only moat that works in 2026. The asymmetry engine is the machine that turns the corpus into messages that can't be replicated.

That's the law. Now the moves.

The Inversion

The lending-infrastructure guy on the call asked me a fair question. How do you actually do this? Like, what's the practice?

The practice is four moves. Three of them are about how you think before you ever open a Claude Code window. The fourth one is the running. Today's article is about the thinking move-set, then the running. Let's start with the thinking.

The kidnap-engineer test

Imagine your customer broke into your office, kidnapped your engineering team, and got root access to your data. They can only do three things before the cops arrive. What are they?

Sounds insane. It works. The reason it works is that it forces you off your product and onto their actions. An engineer with access to your seventy product launches isn't going to look at your dashboard. They're going to write three SQL queries that answer what they personally need to ship next month and have nothing else.

For the lending guy, those three queries probably look like give me the pickup curve for every credit card we launched at 6.8% to 7.2% APR, normalized for issuer size. And give me the sign-up bonus value where pickup peaks and starts to flatten. And give me the personal-loan products that died inside six months and what they had in common.

Three queries. Three answers. Three messages he can ship to seventy companies that he knows are thinking about lending products this year.

That's the kidnap-engineer test. It's the inversion of the GTM question. You don't ask what would my buyer be persuaded by? You ask what would my buyer's most resourceful colleague steal if they could? Then you ship them what they would have stolen, without the kidnapping.

Inhabit the customer's mind

The quality of every piece of GTM work you do is set by how much of the buyer's mind you can inhabit.

I mean this almost literally. The German dental guy who worked at the dentist for six months — that's the gold standard. He went and lived inside the buyer until he stopped guessing. The reason he was the gold standard is that nobody else was willing to do it. Working at a dental office for six months for free is unpleasant. So almost no one did it. So the few who did had a permanent edge.

The asymmetry engine isn't a substitute for inhabiting the customer's mind. It's the only known way to inhabit a hundred customers' minds at once instead of one. You take every transcript a customer has ever recorded, every email a customer has ever sent you, every transaction in your back-end, and you ask Claude to act as this person at this company launching this kind of product. What questions would you ask of this data?

Then you read the answers. Then you push back. Then you ask three more times until the answers stop sounding like a robot puked them up. Then you go to a real customer — even just one — and ask, would you respond to this? And you listen to the no.

That's the inhabit step. The model is your speed. The customer is your ground truth. You need both.

The differentiation trap

The reason this matters more than ever is that everyone else is sending the same message.

A guy on the call last week wanted to sell a recruiting service. Cheap labor from the Philippines, 30-40% discount on hires. He asked me what he should write in the cold email. I told him not to send it. Every recruiting agency on Earth is sending the exact same email. I saw you're hiring a senior engineer. We can save you 40%. It's not that the offer is bad. The offer might be great. The problem is that the buyer has heard the same sentence eight hundred times this quarter. The message has zero differentiation. The buyer has no reason to read past the subject line.

The asymmetry engine breaks the trap by giving you something nobody else can say. Not because you're a better writer. Because you have data they don't have, and a question they couldn't ask, and an answer they didn't get last week from any of the eight hundred clones.

When you have that, the message practically writes itself. When you don't, no amount of merge-tag wizardry saves you.

That's the inversion. Now the engine itself.

Who Gets This

You've got the doctrine. Below the line is the build.

• Free: what you just read — the law, the inversion, why it matters now.

• $50/mo (most readers start here): the mining loop — the actual Claude Code prompt, the four data layers, the worked example for the lending case, the permissionless-gift template, and the way every existing framework I teach slots into this master frame.

• $2,499/yr: Every tool I ship. Edge Copilot is how you talk to all of it through Claude Code. Current tools: Edge Copilot, AutoClaygent, Agent 7, Who to Target and What to Say, Blueprint Cloud, Technology Finder, Video List Extractor, Competitor Monitor, LinkedIn Engagement, Domain & LinkedIn Finder, Dossier Builder, PDF Contact Finder. Whatever ships next is included. Plus all 3 courses and weekly office hours.

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