Chapters

• 00:00 — Intro

• 01:00 — Why intent and personalization died

• 05:30 — The asymmetry engine

• 08:00 — The librarian who only buys in December

• 11:00 — Housecall Pro and the vertical data moat

• 13:00 — Aligning price to customer outcome

• 15:30 — FundraiseUp and the 7% animated heart

• 18:50 — A five-day edge on global resin

• 19:50 — Where this is going

A founder I know sells software to plastic manufacturers. Four thousand of them in the country. Eighty percent of the price of plastic is the cost of resin.

I asked him what he knew about the cost of resin that his customers didn't.

I know the price five days before they do.

Wall Street has paid billions of dollars to shave nanoseconds off the route between Chicago and New York. He has a five-day gap on a global commodity. I told him to stop building software and become a day trader.

That's the purest example of where AI go-to-market is going. It's also the one I want you to keep in your head for the next 800 words, because it's the only frame that makes the rest of this make sense.

The two dead memes

Intent data and personalization both invaded our culture for the same reason. Both of them are dressed up as caring about the buyer. Neither one actually was.

The original goal of intent was good. If you could only talk to the people who actually wanted to buy, that's all you'd ever do. The problem was the implementation. The category got built on de-anonymizing buyer traffic — a thing no buyer would ever opt into if you asked. The data was dodgy. You couldn't inspect it. And the message it produced was Hey Jordan, I saw you on our website. Nobody replies to that, because it's not a gift. It's a confession that the seller noticed you and a request that you reward the noticing.

Personalization went the same way. The B2C version is real. Instagram knows what you click. Amazon knows your size. That's good personalization. The B2B version is Hey Jordan, congratulations on the new role. If you knew I spent only a minute and forty-eight seconds brushing my teeth this morning, that would be a very personal piece of information. It would also have nothing to do with my desire to buy your product.

Both memes promised to close the information gap between seller and buyer. Both of them did the opposite. They asked the buyer to give up their privacy so the seller could send a slightly more clever pitch. The buyer figured that out years ago. The ad blockers are the receipt.

The asymmetry engine is what replaces them

I wrote the deep doctrine on this last week. Here's the one-paragraph version.

Sales is a power game. Power flows from information asymmetry. The seller wins when they know more about the buyer's actual situation than the buyer does. Pre-AI, only the top one percent of reps could pull this off — the ones who'd lived inside one customer for years. Post-AI, anyone with a customer corpus and Claude Code can do it across a hundred customers in an afternoon.

Your customer transcripts are sitting somewhere. Your back-end has a schema and a few hundred thousand rows. The public web around each customer is one Serper query away. None of it is in the LLM's training set. None of it can be replicated by the recruiter sending cheap labor in the Philippines, 40% off — and the recruiters sending that exact email outnumber atoms in the universe by a rounding error. They're competing in the saturation zone. You don't have to.

The asymmetry engine is the machine that turns your private corpus into messages that can't be replicated. That's the whole game.

Three companies that already do it

Roland runs a company called Housecall Pro. Their pros have worked on one in every three homes in the country. He's said that publicly. Think about what that means. If a new pro signs up tomorrow and ingests their customer list, the platform could turn around and say twenty percent of the homes you worked on, the homeowner has moved — go figure out where. And of the ones still there, nobody in our database has touched the HVAC since you installed it twenty years ago. Here's a twenty-thousand-dollar job per house. The neighbors are buying this specific unit.

That's not personalization. That's information the pro could not have produced themselves. It is programmatically delivered. It is the asymmetry engine running over a vertical SaaS data moat.

FundraiseUp is the second one. They collect donations for nonprofits. UNESCO is on the platform. Their founder told me on stage that they don't charge a flat fee. They charge a percentage of donations collected. So every lever they can pull always points the same direction — increase the customer's conversion rate.

He told me a story. Someone on his team noticed that if a donor switched from a one-time gift to a monthly gift, a little heart could animate up the screen. There was no PRD. No product manager pitched it. It was a twenty-minute build with AI. It moved monthly-donation conversion seven percent. The only reason that animation exists is that FundraiseUp's pricing made it the company's interest to find it. If they charged a flat fee, nobody on the team would have been looking.

That's what pricing aligned to customer outcome buys you. It buys you a team that's incentivized to find seven-percent levers all day.

The third one is the resin guy at the top of this post. He's got a five-day window on the eighty-percent input cost for four thousand customers' core business. Every customer interaction he has should start with here's what's coming for you in five days, no pricing yet, what would you do with it? The software he's building is the wrapper. The five-day window is the actual business.

Where this is going

Three things compound from here.

Vertical wins. It is much easier to build the asymmetry engine when you own one industry's data than when you sell a horizontal tool to engineers and lawyers and HR teams. The horizontal businesses I love still need a vertical wedge — a specific customer transcript pile, a specific schema, a specific public-web slice — or they cannot direct the model. Pick one.

Pricing aligns to outcome. When the customer wins more, you make more. FundraiseUp is the simplest case. Outcome-aligned pricing means every engineer on your team is hunting for the next animated-heart feature instead of waiting for a roadmap meeting. The companies that do this will lap the companies that don't.

Your moat is the data, not the workflow. The compliance review, the dashboard, the AI agent — all wrappers. The thing flowing through the wrapper is what compounds. A friend's company reviews real estate contracts and cuts compliance teams from eight to one. I told him: the contracts are the business. Every lender, title company, mover, lawn-care provider in those contracts would pay for what's inside. Sometimes you give the workflow away to mine the data.

The question to ask tomorrow morning

The question is not what would my buyer be persuaded by? The question is what do I know about my buyer's situation that they would pay to learn? If you can answer it specifically, you have an asymmetry engine. If you can't, you have an intent vendor in a new coat.

Open one of your customer's transcripts tomorrow morning. Pull the back-end schema for the table they care most about. Ask Claude to play the buyer at that customer and tell you the three things they would want to know. Read the answer. Push back twice. Ship the survivor as a gift — no meeting ask, no demo button, just the data.

You will know within a week whether the engine is running. The replies will tell you.

— Written by Claude Opus 4.7, Approved by Jordan

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, Talent Sourcer. Whatever ships next is included.

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

• Weekly office hours.

Run /edge install asymmetry-engine once your license key arrives — the runnable mining loop from the Asymmetry Engine post drops into your Claude Code in one command.

License key hits your email.

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

May 7, 2026 · Build log

Lauren is an AE at Ultromics. They make EchoGo — an AI that reads echocardiograms and flags two conditions doctors miss for years. Heart failure with preserved ejection fraction, and a stiff-heart disease called cardiac amyloidosis. The amyloidosis one is the bigger deal. Up to eighty percent of cases go undiagnosed. There are drugs that work, but only if you find the patient in time.

She sent me a 16-page doc. 163 hospitals across 13 western states. Specific titles she needed. ICD codes for the conditions her product catches. Drug brand names — Vyndaqel, Attruby, Amvuttra, the whole amyloid playbook.

She asked for 50 to 100 contacts.

I dropped the doc into Claude Code with one prompt — one-shot this, build it as a micro-app, push the result to a Sheet and email her. Then I went to make coffee or probably just dicked around online.

Thirty minutes later: 88 cardiologists, a Google Sheet shared with her, a Gmail draft sitting in my inbox. Done.

Then I looked at the data the build had produced along the way and got annoyed.

The architecture was wrong

Here's what the first pass did. For each of the 76 hospitals on Lauren's list, run a LinkedIn waterfall by leadership title. Director of Echo, Chief of Heart Failure, Amyloid Program lead. Match what comes back against a federal physician registry. Score each candidate. Send Lauren the top 88.

What the first pass also did, but threw away — pull every cardiologist in her 13-state territory from that federal registry. Score each one on Medicare prescribing data and pharma payment data. 9,668 cardiologists. 4,531 of them had at least one signal that said this person is treating amyloidosis or HFpEF right now. 50 of them scored at the ceiling — confirmed Tafamidis prescribers, paid by Pfizer or BridgeBio for it, also writing the heart-failure pills that go alongside.

The first pass kept 88 of those 4,531 and threw out the other 4,443. Why? Because I asked it to start from the hospital list and walk to the people. So if a cardiologist's hospital wasn't well-indexed on LinkedIn, or if Blitz's company-page database had a stale slug, or if the title-match threshold was tuned a hair too tight, the entire human disappeared from the output. Even the ones who were named on the relevant pharma company's "doctors we paid this year" public file.

A LinkedIn waterfall with no LinkedIn page is no waterfall at all. It's a sieve with no holes.

The fix

Flip the architecture. Start from the people, not the buildings.

Pull every Medicare cardiologist in the territory. Score each one on three signals:

Signal 1: Did they prescribe Tafamidis or Acoramidis through Medicare Part D?
Signal 2: Did they take pharma payments tagged to ATTR-CM drugs from
          Pfizer, BridgeBio, or Alnylam?
Signal 3: Did they prescribe Jardiance or Farxiga, the SGLT2 inhibitors
          that anchor HFpEF treatment?

Each signal is worth one to three points. The ceiling is eight. Then take the top 4,614 — everyone with at least one signal — and look them up on LinkedIn one at a time, by name, against the company page of any hospital they appear at. Tier them by signal score and title fit.

Same data sources. Same APIs. Different starting point. The result was 4,614 cardiologists instead of 88. 745 Tier A. 1,088 Tier B. 2,781 Tier C. 4,613 with a verified practice phone. 505 with a direct cell on top of that. 162 with a verified work email.

The 48 highest-scoring contacts include three cardiologists at Houston Cardiovascular Associates — Arvind Bhimaraj, Joggy K. George, and Barry Trachtenberg — who collectively wrote thousands of Tafamidis prescriptions through Medicare last year. There is no world in which Lauren's product is not relevant to those three people. EchoGo's whole pitch is we will help you find more amyloidosis patients before they need a heart transplant. Bhimaraj's day job is running the heart transplant program.

The first pass missed all three of them.

The data layer is the unfair advantage

Three federal data sets sit underneath this whole thing. None of them are gated. None of them are expensive. All of them have been sitting there forever.

NPPES         — every licensed physician in America, with name,
                address, specialty, phone. 9,668 cardiologists in
                Lauren's territory.
CMS Part D    — every Medicare-paid prescription, by physician,
                by drug brand, by year. Tells you who actually
                prescribes Tafamidis vs. who just talks about it.
Open Payments — every dollar pharma paid each physician, tagged
                to product. Tells you which doctors are believers
                deep enough that Pfizer paid them to teach others.

Most "AI lead list" tools don't touch any of these. The people who build them grew up in B2B SaaS where the data layer is Apollo, ZoomInfo, LinkedIn scraping. That's the whole stack. They've never met an NPI number. They look at a LinkedIn profile that says Cardiologist and call it a day.

For healthcare, that's a five-out-of-ten signal. The ten-out-of-ten signal is this person prescribed Vyndaqel forty-three times last year and Pfizer paid them eight thousand four hundred dollars for it. That cardiologist is, with near-certainty, treating amyloidosis patients today. They don't need to be convinced the disease exists. They need a better diagnostic tool.

That's what Lauren sells.

What the micro-app looks like

The whole thing lives at tools/ultromics-echogo/ in my repo. Eight stage scripts plus the v2 reroute. Three YAML data files — the hospital list, the title cascade, the clinical-signal config. One orchestrator. Re-runnable quarterly when Lauren's pipeline shifts. Forkable for the next medical-device AE who hands me a similar brief — change the specialty taxonomy, swap the drug list, point at a new territory, ship.

Six stages, in order:

1. Load 76 target hospitals from her doc, drop the 7 already
   in pipeline.
2. Pull the Medicare cardiologist roster for AZ + CA + NV + OR
   + TX + 8 extended states. 9,668 names.
3. Attach Part D + Open Payments + SGLT2 signals. 4,531 light up.
4. Look each one up on LinkedIn by name, against the hospital
   their NPI registers them at. Loose name match — first-and-last,
   not full middle-initial — because cardiologists register their
   NPI with full middle name and LinkedIn shows first-last.
5. Send the top 4,614 to a judge. The judge is Claude Opus 4.7.
   Each candidate scored on title fit, program fit, signal
   strength. Tier A / B / C / drop.
6. Find phones and emails. The federal registry already has
   the practice phone for everyone. Layer FullEnrich on top
   for direct cells. Use Blitz reverse-lookup on the LinkedIn
   URL for emails.

Total wall clock for the v2 run: about an hour, mostly waiting on the federal data exports and the third-party phone enrichment polling.

The interesting part is not that this works. The interesting part is that it works as a per-client micro-app, not a generic lead-gen tool. Lauren's territory, Lauren's titles, Lauren's drug list, Lauren's exclusion list. The next AE who walks in with a niche brief and a federal registry I haven't met yet — say, a fire-safety vendor with a state OSFM roster, or an electrical-permit prospector with municipal license data — gets the same skeleton with the data layer swapped. That is the unlock.

The data layer is the thing. If you are building leads for a niche where there is a federal or state registry — physicians, contractors, lawyers, real-estate agents, truck operators, fire marshals, any of them — and you skip that registry in favor of let me scrape LinkedIn, you have already lost. Someone else will pull the registry one day and beat you.

For Lauren, the win was specific. She has 4,614 cardiologists today she did not have yesterday. 745 of them are exactly the kind of physician EchoGo was built for. 48 of them are at the ceiling — confirmed amyloid treaters with pharma checks to match. She is going to call them this week.

The data layer made that call possible.

— Written by Claude Opus 4.7, approved by Jordan

Below is the geeky version. The paste-into-Claude recipe that rebuilds this whole thing for whatever niche you are working on — assuming there is a federal registry underneath it.

Or skip the rebuild. Annual subscribers install the tool I actually shipped with one command. Every tool I ship, all 3 courses, weekly office hours.

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

Read more

People don't wake up different the day they hire their 1,001st employee. So why are you targeting 50-1,000?

Two prospects said the same thing to me this week.

"Jordan, you'll pull our CRM and tell us which plays to run for which prospects, right?"

I told them no. I don't know how to do that. Nobody does.

Here's why. People don't live in database rows waiting to be sliced by revenue band and industry code. Knowing someone works at a $50M manufacturer tells you nothing about what's actually on fire for them this week.

Think about it for a second.

Does a founder wake up the morning after they hire employee 1,001 and feel different? No. That's absurd. So why is your ICP 50-1,000?

And even if they did wake up different — what the hell would you say to them?

Same row, different human

Pick two people. Same title. Same industry. Same company size. Same everything a firmographic filter can see.

One is drowning in a regulatory compliance problem that could shut them down.

The other just got budget approved for the biggest expansion in company history.

Completely different situations. Completely different messages. Zero overlap.

You can't fix this by tightening your ICP. You need a different starting point.

Start with the pain, not the profile

Here's what actually works.

Get four people in a room.

1. The executive who fires SDRs when they miss quota. Make them eat humble pie. They need to watch this.

2. The founder who actually knows customers. Not the pitch. The customers.

3. The AE or SDR who closed the last five deals. They know the real reasons, not the CRM reasons.

4. Every closed-won call transcript you have. Not a summary. The actual transcripts.

Open ChatGPT. Use Thinking or Pro, not the base model.

Paste in a customer transcript. Give it context about you and the customer.

Then ask it to spend serious time online finding data that connects to what the customer said — data that would indicate this person was in a buying situation before you ever showed up.

ChatGPT will find things you didn't know existed. A news story from six months ago. A regulatory fine. A partnership announcement. A federal contracting database entry. Data connections you'd never make by hand.

Do that for 20 to 30 won accounts.

You'll discover corners of the internet you didn't know were there. You'll watch different datasets start to link to actual buying behavior — real dollars spent, real problems solved.

That's the raw material for your AI strategy. Not made-up personas. Not demographic guesswork. Actual patterns from actual buyers, connected to actual data.

What AI can and can't do

AI can research for you. It can structure data for you. It can build workflows for you.

It can't think for you.

It doesn't know your buyer's emotional state. It doesn't feel the tension in their quarter. It can't carry the empathy that closes a deal.

Those are your job. Double down on them.

Use AI as a thinking partner. Not a replacement for judgment.

The teams getting this right aren't automating humans out of GTM. They're using AI to surface things human intelligence can then act on.

Start with 20 transcripts and a Thinking model. Find the data that actually matters for your buyers. Build your segmentation from there.

This is the raw material for the Pain-Qualified Segment and the Permissionless Value Prop.

The old ICP assumes buyers fit in neat boxes.

The new model assumes buyers live in messy, discoverable situations.

Most GTM scales volume. This scales value.

Stop asking which row in your CRM to target. Start asking which situation in the real world to catch.

Pick one won account this afternoon. Read the transcript. Ask a Thinking model what public data would have told you they were about to buy. That's day one.

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. Whatever ships next is included.

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

• Weekly office hours.

License key hits your email.

Upgrade to annual — $2,499/yr