Thinks 1861

WSJ: “The brain goes through five distinct stages between birth and death, a new study shows. Scientists identified the average ages—9, 32, 66 and 83—when the pattern of connections inside our brains shift. The brain’s adolescence phase, they discovered, lasts until age 32, and then it enters a period of stability until early aging begins at 66.”

Ivan Zhao: “Every miracle material required people to stop seeing the world via the rearview mirror and start imagining the new one. Carnegie looked at steel and saw city skylines. Lancashire mill owners looked at steam engines and saw factory floors free from rivers. We are still in the waterwheel phase of AI, bolting chatbots onto workflows designed for humans. We need to stop asking AI to be merely our copilots. We need to imagine what knowledge work could look like when human organizations are reinforced with steel, when busywork is delegated to minds that never sleep. Steel. Steam. Infinite minds. The next skyline is there, waiting for us to build it.”

NYTimes: “ChatGPT’s success is due in part to the power of the generative pretrained transformer (the GPT in the name), a special type of A.I. that can absorb large amounts of text and learn to reproduce it a few letters at a time. But this is only part of the story — and, as OpenAI discovered, perhaps not even the most important part. In its raw state, the output of GPTs can be off-putting and bizarre. It is only after a second, posttraining phase that A.I. is fit for human interaction. While the engines that power ChatGPT are undeniably impressive, what has made the product succeed is not its capabilities. It is ChatGPT’s personality. The core insight behind ChatGPT can be found in an OpenAI research paper from early 2022. That paper demonstrated that people preferred a small A.I., fine-tuned for human interaction, to a raw, unfiltered one with 100 times the number of parameters. Leveraging that insight, a group of engineers at OpenAI hired teams of human evaluators to grade the responses of the GPT models and nudge them toward more customer-friendly responses. This work revolutionized A.I. and ignited an arms race for control of the consumer A.I. sector.”

SiliconANGLE: “Nvidia’s moat looks reinforced by volume, experience curve effects, and years of end-to-end systems work. OpenAI’s lead looks reinforced by platform execution and enterprise pull — with a competitive landscape where model quality is table stakes, and the real battle is the software and services wrapped around the model.”

Andy Kessler: “Empathy and that elusive righteousness is about restoring lost dignity. And that often means setting up rules and then getting out of the way and letting freedom and free markets do their magic. There is little political credit for that whole “freedom” thing. Just results.”

ZAP the REACQ with NEO

Published February 4, 2026

1

The Trinity

Marketing has a problem it doesn’t know how to name.

It isn’t churn. It isn’t rising CAC. It isn’t inefficient spend.

It’s something quieter, more structural — and far more expensive.

Most brands today are paying a hidden tax every quarter. They lose customers silently, then pay again to win them back. The dashboards call it “new acquisition.” The platforms celebrate it as performance. The P&L absorbs it without protest.

This tax has a name.

REACQ: The Hidden Tax

REACQ — pronounced “re-ack” — stands for the Reacquisition Tax.

It is the money brands spend to buy back customers they already acquired, lost quietly, and failed to retain.

REACQ is not churn. Churn is visible — it triggers alerts, reports, action. REACQ happens before churn is ever acknowledged, in the long silent drift from attention to indifference.

Across categories, 60-70% of customers counted as “new” are not new at all. They are returning customers who once bought, once engaged, then slipped away unnoticed. Months later, they reappear through paid ads — retargeted, re-won, re-celebrated.

Globally, this costs brands over $500 billion a year.

Why don’t marketers talk about it? Because REACQ is invisible by design. Platforms don’t report it because they profit from it. Martech doesn’t surface it because it measures campaigns, not continuity. Everyone optimises their piece — and no one owns the whole.

Once you see it, though, you can’t unsee it.

And once you see it, you have a choice.

ZAP: The Refusal

ZAP is not a product. It is not a feature. It is not a platform upgrade.

ZAP is a refusal.

ZAP stands for Zero AdWaste Pledge — not an aspiration, but a line in the sand. A commitment to stop accepting reacquisition as normal. To stop paying twice for the same customer relationship.

ZAP has three meanings, layered deliberately:

  • Zero AdWaste — the outcome
  • ZAP — the action (eliminate, not optimise)
  • ZAP the REACQ — the rallying cry

At its core, ZAP is a moral frame with economic consequences:

Never Lose Customers. Never Pay Twice.

This is not about doing marketing better. It’s about refusing to participate in a system that profits from customer disappearance.

ZAP defines the what and the why.

Which leaves the hardest question unanswered. How?

NEO: The Operating System

The answer is NEO.

NEO stands for New Email Operating System — not email as a channel, but email as an attention and relationship OS.

The distinction matters.

Traditional martech optimises campaigns. NEO operates relationships.

ESPs send messages. NEO manages attention over time.

Martech asks: Did this campaign perform? NEO asks: Is this customer still paying attention?

NEO is built around an Attention Stack designed to eliminate REACQ at the root:

  • NeoBoost — keeps attention alive through cadence, presence, and relevance (works with any ESP)
  • NeoMails — earns daily engagement for Rest and Test customers, before drift becomes loss
  • NeoNet — enables recovery without rent, through cooperative brand-to-brand reach
  • APUs (Attention Processing Units) — make attention measurable and monetised via Magnets, Mu, ActionAds, and Ledger

Together, they replace rented reach with earned presence.

**

REACQ is the enemy. ZAP is the refusal. NEO is the operating system that makes refusal possible.

Three words. One architecture. Zero AdWaste.

2

The Playbook

ZAP is not a switch you flip.

It is a tax you stop paying — step by step.

This is not a transformation programme. It is a progressive replacement. The path follows my 4B framework: Basics, BAU Better, Boosters, Breakthrough.

Each B unlocks the next. Each maps to a segment, a product, and a goal. Together, they eliminate REACQ structurally.

**

Basics — Build the Identity & Attention Layer

Everything starts with one asset: the email address.

If you don’t have a customer’s email, you don’t own the relationship. You rent it — from Google, from Meta, from whoever intercepts them first.

Basics means capturing email — and mobile for redundancy — from every customer, at every touchpoint. Transaction. Browse. App install. Store visit. No email, no owned channel. No owned channel, no escape from REACQ.

Then establish BRTN segmentation — the lens that makes the rest of the playbook work.

BRTN segments customers by engagement:

Segment Definition Size Challenge
Best Engaged in past 30 days ~20% Keep them engaged
Rest Engaged 30-90 days ago ~30-40% Stop the drift
Test No engagement for 90+ days ~30-40% Reclaim before they’re gone forever
Next Genuine new acquisitions Variable Convert to Best

Most brands only serve Best and Next. They celebrate acquisition and reward loyalty. The middle — Rest and Test — is ignored. That’s where 60-70% of customers sit. That’s where the REACQ tax lives.

Traditional segmentation asks: How valuable is this customer? BRTN asks: Is this customer still paying attention?

The shift matters. Value-based segmentation optimises extraction. Engagement-based segmentation enables intervention. You can’t stop drift if you’re not watching for it.

Basics is not just about knowing who the customer is. It’s about being able to see when attention begins to fade — and having the infrastructure to act before silence becomes departure.

Without Basics, the other Bs don’t work.

Goal: Foundation.

**

BAU Better — Retain the Best

Your Best customers — the top 20% — are already engaged. The job isn’t to acquire them. It’s to keep them.

This is where NeoBoost works.

Every brand sends transactional emails — order confirmations, shipping updates, account notifications, password resets. These are your highest-open-rate emails. And most brands waste them on static receipts: confirming a transaction but building nothing beyond compliance.

NeoBoost embeds APUs into these emails, turning every transactional touchpoint into a relationship moment.

It works with any ESP. No migration. No platform fee. Just upgraded attention infrastructure on emails you’re already sending.

Segment: Best.
Goal: Retention.

**

Boosters — Recover the Rest

Rest and Test customers have drifted. They haven’t churned formally — they’ve just stopped paying attention. Traditional win-back campaigns shout at them with discounts. It rarely works.

This is where NeoMails come in.

NeoMails are not campaigns you run. They are a stream you maintain — lightweight, interactive, habit-forming. Sixty seconds of value. Fun, Useful, or Rewarding. Not promotional blasts. Presence.

NeoMails target the Rest and Test — the 60-70% of customers between engaged and lost. They rebuild relationships through a regular rhythm of engagement. Their goal is to lower the cost of recovery.

Segment: Rest and Test.
Goal: Reactivation.

**

Breakthrough — Reclaim the Lost

Some customers won’t respond to owned channels. They’ve tuned out completely. Traditionally, this is where brands surrender them to Google and Meta — paying full price to reacquire someone they already owned.

This is where NeoNet works.

NeoNet is a cooperative identity network. Instead of bidding on anonymous impressions, brands reach lapsed customers through other brands’ emails — authenticated, consensual, at a fraction of adtech cost.

Your lapsed customer is another brand’s engaged subscriber. NeoNet connects these dots without platform intermediaries. Recovery without rent.

When owned channels fail, NeoNet is the last line of defence before paid reacquisition. It keeps recovery in the ecosystem, not the platforms.

Segment: Test.
Goal:
Recovery without REACQ.

**

The Progression

B Action Segment Goal
Basics Identity + Attention All Foundation
BAU Better NeoBoost Best Retention
Boosters NeoMails Rest/Test Reactivation
Breakthrough NeoNet Lapsed Recovery without REACQ

Each B compounds the next. By Breakthrough, you’re no longer optimising a broken system. You’ve replaced it.

The revolving door closes. The REACQ tax drops. Marketing shifts from cost centre to profit engine. Not because marketing spent more — but because it stopped paying the same bill twice.

3

The Movement

Great products don’t change industries. Movements do.

Adtech didn’t win because it was virtuous. It won because it aligned incentives at scale — making growth easy and waste invisible. It taught brands how to buy demand efficiently, while quietly normalising the cost of losing it.

ZAP is the counter-movement.

Every mature industry eventually turns inward. First comes expansion. Then optimisation. Then the realisation that the biggest gains no longer come from doing more — but from stopping what should never have been normalised in the first place. ZAP is marketing’s inward turn. Not anti-growth, but anti-leakage.

Why Movements Beat Features

Category creators don’t sell features. They sell enemies.

Salesforce didn’t sell CRM — it declared “No Software.” HubSpot didn’t sell tools — it named Outbound as the villain and created Inbound. ZAP doesn’t sell email — it sells the end of REACQ.

NeoMarketing doesn’t win through ideology. It wins through economic alignment. When brands stop paying twice, profits rise. When attention is earned rather than rented, growth becomes durable. When continuity replaces campaigns as the organising principle, marketing stops leaking value.

The enemy is precise.

The Reacquisition Tax is measurable — every brand can calculate its number. It is undeniable — the maths doesn’t lie. And it is un-co-optable — platforms and legacy martech cannot lead this movement because their business models depend on REACQ continuing.

This is why ZAP cannot be a feature, a tactic, or a vendor claim. It has to be a movement.

The ZAP Campaign

For ZAP to scale, it must be bigger than any one company — including Netcore.

The mechanics are simple, deliberate, and cumulative:

  • The REACQ Calculator: Every brand calculates its number. This is the moment of seeing — the realisation that 60–70% of “growth” is a revolving door.
  • The Zero AdWaste Pledge: CMOs commit publicly: “I will never lose customers. I will never pay twice.”
  • The Scoreboard: Aggregate REACQ savings tracked over time — anonymised and indexed at first, with public recognition as legitimacy builds.
  • ZAP Certification: Brands that reduce reacquisition below 30% earn the badge — not for effort, but for outcome.

Netcore’s role is not to own the movement, but to enable it — providing the operating system that makes the pledge achievable in practice, not just in principle.

The Profit Unlock

This is not a marketing transformation. It is a P&L transformation.

If even 10% of global AdWaste is recovered, $50 billion flows back to brand margins. CFOs stop questioning marketing spend and start funding it. The Rule of 40 stops being a stretch goal and becomes a by-product of stopping the REACQ tax.

In a post-cookie world, owned identity beats rented reach. Brands that ZAP early gain an advantage others cannot buy their way into — because it is built on relationships, not auctions.

Adtech taught brands how to buy customers. ZAP teaches them how to keep them.

Never Lose Customers. Never Pay Twice.

That is not a slogan. It is the refusal that changes everything.

Thinks 1860

Dario Amodei: “Humanity needs to wake up, and this essay is an attempt — a possibly futile one, but it’s worth trying — to jolt people awake…The years in front of us will be impossibly hard, asking more of us than we think we can give.”

FT: “The global video games industry is set to be disrupted by the advent of artificial intelligence models that generate interactive 3D environments. Google DeepMind and Fei-Fei Li’s $1bn start-up World Labs are among the leading AI groups arguing that so-called “world models” — systems designed to navigate and recreate the physical world — could reshape the multibillion-dollar gaming sector. “Creating software and games in particular is changing a lot, and I expect it to change, maybe entirely, over the next few years,” said Shlomi Fruchter, co-lead of Genie 3, DeepMind’s world model. “This will go and empower creators and developers to build things faster, better and in ways that weren’t done before . . . I don’t think it [will] replace the existing experience [but we will see] more types of experiences that are not available today.””

Mint: “A brand is sharpest when defined not by what it claims to be, but by what it refuses to be…Most brands get this backwards. They brag about being ‘faster,’ ‘better,’ ‘more innovative,’ etc, which sounds like empty corporate blah-blah to consumers. Positioning isn’t about being better. It’s about being different in a way that’s meaningful.”

Pratik Bhadra: “A new era has begun: the agentic era. Now, the KPI is no longer attention; it is permission. In the agentic model, consumers are outsourcing their attention to a personal AI agent. They are setting their preferences once—”I only buy from sustainable brands,” “I’m allergic to wool,” “Never spend over $200 on shoes”—and then empowering their agent to execute. The consumer is opting out of the attention war entirely. This changes everything for brands. Our goal is no longer to interrupt a consumer’s social feed to steal eight seconds of their time. Our new goal is to be whitelisted by their personal AI agent. This is a profound shift from a “push” to a “pull” model. The old attention model pushed loud, interruptive ads to a mass audience, hoping to capture a fraction of a percent of their attention. The new permission model requires earning a user’s trust so they authorize their agent to pull your data and transact with you, often proactively.”

NeoMarketing: The Zero AdWaste Platform

Published February 3, 2026

1

Why AdWaste Is Structural, Not Accidental

I have discussed NeoMarketing in numerous essays in recent times. Each essay builds on earlier ones and incrementally improves the framework.

Maya’s Dashboard

Maya is the CMO of a mid-sized D2C brand. Her dashboard looks busy — and on the surface, healthy. Campaigns are launching on schedule. The CDP is stitched together. Journeys are flowing. AI is optimising subject lines, send times, and offers in real time.

Yet three numbers refuse to cooperate.

Customer acquisition cost is up 40% over two years. Reacquisition now accounts for 65% of performance spend. Retention is flat, despite more than $2 million invested in martech.

Maya has done everything the playbooks prescribe. She has modern tools, a capable team, and more data than ever before. But customers keep fading, budgets keep rising, and contribution margins keep shrinking.

She’s not alone. Across industries, CMOs face the same pattern. More sophistication, same decay. More spend, same leakage. More tools, same outcome.

This isn’t a failure of effort. Or intelligence. Or tooling.

Maya doesn’t have an execution problem. She has an architecture problem.

**

The AdWaste Loop

The problem shows up as a loop most marketers recognise instinctively, even if they’ve never named it:

Acquire → Ignore → Drift → Reacquire → Repeat.

A customer is acquired at high cost. They engage briefly. Then nothing sustains the relationship. They drift quietly out of view. Months later, the brand pays Google or Meta to “acquire” them again — often without realising they already had them.

This is not growth. It is buying back your own customers at auction.

Across industries, 60–70% of so-called acquisition spend is actually reacquisition. That money doesn’t build new relationships; it compensates for ones that were allowed to decay.

That is AdWaste — and it behaves less like inefficiency and more like a recurring tax. A revenue tax paid to Google, Meta, and marketplaces — funded by marketing’s failure to keep customers in the first place.

And the tax keeps rising. Because the underlying architecture guarantees decay.

**

The Three Structural Failures

AdWaste persists not because marketers are careless, but because marketing systems were built on assumptions that no longer hold. Three structural failures sit beneath the loop.

Failure What Broke Symptom
Intelligence Gap Humans can’t do N=1 Segments decay, Best customers fade undetected
Incentive Gap Vendors paid for activity No accountability for retention outcomes
Attention Gap Push without memory Inbox forgets, reacquisition becomes inevitable

The Intelligence Gap. Humans cannot manage continuous N=1 relationships at scale. Segments decay. Campaigns lag reality. Best customers fade because no system is watching them closely enough, often enough.

The Incentive Gap. Most vendors are paid for activity, not outcomes. Emails sent, messages delivered, journeys launched — revenue accrues regardless of whether customers stay or leave. Retention is optional; volume is not.

The Attention Gap. Marketing remains push-driven in a world that rewards pull. Channels forget. Engagement doesn’t compound. Customers fade silently until they reappear — expensively — in an ad auction.

These failures reinforce one another. Together, they make AdWaste inevitable, regardless of how advanced the tooling becomes.

**

Why Martech Can’t Fix This

The instinct is to solve these problems with better martech. Better AI. Better personalisation. Better targeting.

But martech optimises inside the broken structure. It does not replace it.

Backend memory is not experiential memory. Campaigns are not continuity. Channels are not relationships.

Martech didn’t fail at retention. It was never designed for it.

Fixing AdWaste does not require better marketing. It requires a new operating system.

2

Three A’s as the New Operating System

If AdWaste is structural, then the solution must be architectural.

NeoMarketing is not an upgrade to martech. It is a replacement for the assumptions martech was built on — a system designed so AdWaste becomes structurally impossible.

**

The Framework

AGENTIC → Never Lose Customers
ALPHA → Never Pay Fixed
ATTENTION → Never Pay Twice

Each pillar eliminates one structural failure. Together, they replace acquisition-first marketing with continuity-first growth.

**

AGENTIC — Never Lose Customers

Structural failure solved: the Intelligence Gap.

The maths of modern marketing is unforgiving. Ten thousand customers, multiple channels, daily decisions, real-time context — no human team can continuously optimise at N=1. Segmentation is a compromise, not a solution.

NeoMarketing replaces campaign-centric execution with Agentic systems.

M-Agents are autonomous AI agents for marketing ops that monitor intent, detect early disengagement, and orchestrate personalised actions continuously — not in batches, not in campaigns, but as a living system.

BrandTwins, created via the TwinFactory, act as persistent customer-side advocates. They learn preferences, protect attention, and ensure relevance before customers drift.

The result is not better campaigns. It is the end of campaigns as the primary unit of marketing.

Best customers stay Best. Fade is detected before it becomes churn.

Agentic doesn’t automate campaigns. It eliminates the need for them.

**

ALPHA — Never Pay Fixed

Structural failure solved: the Incentive Gap.

Traditional martech pricing is input-based. Send more, pay more. Use more features, pay more. Success or failure is irrelevant to the vendor’s revenue.

NeoMarketing introduces Alpha economics.

Pricing shifts to an outcome-based model:

  • Beta: a modest fixed baseline
  • Alpha: performance-linked upside
  • Carry: long-term profit participation

This is paired with Progency — an operating model that combines product, agents, and agency-like strategic services (via Martech Growth Engineers), paid for results rather than effort.

When vendors are paid for retention and profit, behaviour changes. Accountability becomes unavoidable.

Alpha doesn’t just change pricing. It changes who is responsible for results.

**

ATTENTION — Never Pay Twice

Structural failure solved: the Attention Gap.

Email and owned channels lost their magnet. Each interaction resets. Engagement never compounds. Customers fade quietly — and then get reacquired at full price.

NeoMarketing fixes this at the channel level.

At its core is the Attention Processing Unit (APU) — the primitive that gives the inbox memory.

  • Mu in the subject line signals accumulated value
  • Magnets create repeatable engagement
  • ActionAds monetise attention without disruption
  • Mu Ledger makes progress visible and redeemable

APU is deployed in two ways:

  • NeoBoost, embedded into existing emails on any ESP, prevents fade among Best customers
  • NeoMails, APU-native daily emails, recover Rest/Test customers

NeoNet is the ad network that enables cooperative, deterministic recovery without auction taxes.

The outcome is owned attention that compounds. Reacquisition becomes unnecessary.

Attention doesn’t fix individual emails. It fixes the channel.

**

The Flywheel

These pillars are not independent.

Agentic prevents loss. Attention compounds engagement. Alpha enforces discipline.

Remove any one, and AdWaste returns.

Together, they form a closed system where customers are acquired once, retained continuously, and monetised without leakage.

This is not better martech. This is what replaces it.

3

Platform, Not Products

NeoMarketing is not a product bundle. It is a platform built on primitives — and primitives compound.

**

Products, Primitives, Infrastructure

Layer Examples Role
Products NeoBoost, NeoMails, Progency Entry points
Primitives APU, BrandTwins, Alpha Structural logic
Infrastructure M-Agents, NeoNet, Mu Ledger Invisible power

Products can be copied. Primitives cannot be shortcut. Infrastructure creates moats.

This is why adding “AI personalisation” or “better journeys” does not replicate NeoMarketing. The value is not in features; it is in how the system is wired.

The question is not “which product should I buy?” It is “which layer do I need to build on?”

**

New Metrics for a New System

A new architecture demands new measures.

Open rates measure moments. CAC measures spending. Campaign ROAS measures tactics.

NeoMarketing tracks continuity and economics:

Old Metric Measures NeoMarketing Metric Measures
Open Rate Moments Attention Retention Rate Engagement that compounds
CAC Spending Reacquisition Ratio Waste made visible
Campaign ROAS Tactics N=1 Live Ledger Relationship profitability

ARR doesn’t create memory. Memory creates ARR.

**

What Zero AdWaste Actually Means

Zero AdWaste is not lower CAC or better ROAS.

It is:

  • Customers acquired once, retained continuously
  • Attention earned, not rented
  • Owned channels that compound
  • Marketing as a profit engine, not a cost centre

Zero AdWaste is not a metric. It is an architectural state.

**

Maya’s New Dashboard

Six months later, Maya’s dashboard tells a different story.

Reacquisition spend is down 35%. ARR is measured — and rising. Customer P&L is visible for the first time. Marketing contribution margin is positive.

She didn’t work harder. She didn’t optimise faster.

She changed the architecture.

Maya stopped fighting AdWaste. She made it structurally impossible.

**

Summary

The next decade of marketing will not be about better ads.

It will be about not needing them.

Traditional Martech: Lose customers. Pay twice. Repeat.

NeoMarketing: Never Lose Customers. Never Pay Fixed. Never Pay Twice.

**

NeoMarketing — The Zero AdWaste Platform

AGENTIC → Never Lose Customers
ALPHA → Never Pay Fixed
ATTENTION → Never Pay Twice

Pay Once. Profit Forever.

4

A Transformation and A Startup

Who builds NeoMarketing? If it is the answer to AdWaste, and if it cannot be replicated by adding features, then how does the industry actually get there?

The answer is uncomfortable but unavoidable: NeoMarketing requires two parallel efforts, not one. A transformation and a startup. Moving at different speeds, with different economics, toward the same destination.

**

Why Incremental Change Fails

When NeoMarketing is first described, a predictable objection arises: Can’t existing martech platforms just add these capabilities?

The answer is no — not because martech companies lack talent or technology, but because NeoMarketing breaks too many foundational assumptions to be layered on top.

You cannot simply “add” Agentic systems to an organisation designed around campaigns and quarterly planning. You cannot “pilot” outcome-based pricing inside a revenue model built on fixed SaaS fees and volume incentives. And you cannot “experiment” with inbox-level attention inside roadmap cycles optimised for enterprise feature delivery.

The Three A’s — Agentic, Alpha, Attention — are not features. They are architectural shifts. Each one changes who does the work, who bears the risk, and who is accountable for outcomes.

NeoMarketing cannot be built by doing martech better. It requires doing something different.

**

Martech 2.0 — The Transformation Engine

This does not mean traditional martech companies are obsolete. In fact, they are uniquely positioned to build part of the NeoMarketing OS.

They already have critical assets: customer relationships, data infrastructure, AI foundations, delivery expertise, and trust earned over years. But these assets must be rewired around retention and outcomes — not activity.

This is the transition from Martech 1.0 to Martech 2.0.

Martech 1.0 Martech 2.0
Campaign-centric Customer-centric
N=Many Segments N=Few and N=1 via Agents
Fixed SaaS fees Alpha-based economics
Tools vendor Growth partner

The shift is not cosmetic.

In Martech 2.0, AI is no longer used to optimise campaigns, but to power M-Agents and BrandTwins that continuously sense intent, detect early fade, and act on behalf of both brand and customer.

Just as importantly, Martech 2.0 abandons the safety of fixed pricing. Alpha economics demand a cultural shift — from selling software to sharing accountability. Revenue becomes tied to retention, growth, and profit — not emails sent or journeys launched.

This is where the Progency model emerges: product, agents, and strategic services combined — paid for results, not effort.

This is hard. It requires cultural change, not just technical change. But it is possible — for companies willing to transform rather than merely upgrade.

Martech 2.0 doesn’t sell tools. It shares responsibility.

**

Neo — Why Attention Must Be Built as a Startup

If Agentic and Alpha can be built through transformation, Attention cannot.

Attention is fundamentally different. It cannot be “evolved into.” It must be born new.

The Attention stack operates under a different physics:

  • It depends on network effects — Mu, ActionAds, and cooperative inventory grow in value only as participation increases.
  • It involves three actors simultaneously: consumers, brands, and advertisers.
  • It runs on habit formation, not procurement cycles.
  • It must be ESP-agnostic, working across existing platforms rather than reinforcing one. That positioning is impossible for a traditional martech company protecting its core business.
  • And it demands speed, experimentation, and tolerance for failure that transformation-led organisations struggle to sustain.

The Attention Processing Unit (APU) is a primitive that does not exist in today’s martech stack. NeoBoost and NeoMails are carriers for that primitive. NeoNet is not an adtech clone, but a cooperative alternative to auctions — deterministic, identity-based, and aligned with retention.

This is not a roadmap extension. It is a platform creation problem.

This is why Attention must be built as a separate entity. New team. New economics. New ambition. Startup energy applied to a problem that incumbents cannot solve from within.

Attention cannot be retrofitted into martech. It must be built as a platform — with startup energy and network economics.

**

One Vision, Two Engines

Put together, the picture becomes clear.

Engine Builds Solves
Martech 2.0 Agentic + Alpha Intelligence + Incentives
Neo Attention Memory + Magnetism

Neither engine works alone.

Agentic without Attention still leaks customers into stateless channels. Attention without Agentic lacks the intelligence to prevent fade. Alpha without both collapses back into fixed-fee SaaS.

Together, they form a closed system. One transforms what must be trusted. The other invents what must scale. They move at different speeds, but they are architecturally integrated.

NeoMarketing is not one company’s product. It is an operating system built by two engines moving at different speeds.

**

This is not one vendor’s roadmap. It is a category reset.

The martech industry does not need more tools, more dashboards, or more AI features layered onto old assumptions. It needs a new architecture — and the courage to build it in two ways at once.

Transform what must be trusted. Start fresh where new models and networks are required.

That is how NeoMarketing moves from framework to infrastructure.

5

Graphical View

Here is a ChatGPT graphic which captures the NeoMarketing vision.

And this is Claude’s take.

 

Thinks 1859

Bloomberg: “Google must prove it can monetize AI beyond ads, and Hassabis needs one of his moonshots to finally become a viable business. His track record till now is sobering. For all the prestige of AlphaFold, the protein- structure predictor that’s accelerating the work of 3 million scientists, it has yet to produce any FDA-approved drugs. But if Google’s new glasses work and sell thanks in part to world models, that could put the company in the lead to find a killer app for AI. It will also determine whether Hassabis remains one of Google’s most decorated scientists, or becomes the architect of its next era.”

SaaStr: “The rise of agentic workflows has caused token consumption per task to jump 10x-100x since December 2023. Models like o3, DeepSeek R1, and Grok 4 introduced multi-step reasoning processes that generate massive reasoning outputs — and you pay for every token. One analysis found that when comparing the same coding task, an aggressive reasoning model generated 603 tokens where a simpler model generated 60 — a 10x cost jump for identical results, purely due to token bloat. Read that again. Per-token costs are falling. But total costs per task are rising. This is the treadmill problem. As a B2B startup, you’re constantly pressured to deliver better results. Better results require better models. Better models require more reasoning tokens. And reasoning tokens are expensive.”

Neuroscience News: “New theoretical framework argues that the long-standing split between computational functionalism and biological naturalism misses how real brains actually compute. The authors propose “biological computationalism,” the idea that neural computation is inseparable from the brain’s physical, hybrid, and energy-constrained dynamics rather than an abstract algorithm running on hardware. In this view, discrete neural events and continuous physical processes form a tightly coupled system that cannot be reduced to symbolic information processing. The theory suggests that digital AI, despite its capabilities, may not recreate the essential computational style that gives rise to conscious experience. Instead, truly mind-like cognition may require building systems whose computation emerges from physical dynamics similar to those found in biological brains.” WSJ: “More than 800 million people interact with ChatGPT alone every week, and some discover consciousness-like behaviors in contexts developers never anticipated. The question whether we’re building conscious machines is scientifically tractable. Major theories of consciousness make testable predictions, and leading researchers are developing methods to probe these questions rigorously. These technologies are advancing faster than our understanding of them. We need the intellectual seriousness to treat this as an empirical question, not something we can settle with dogma.”

Pushmeet Kohli: “What I see happening is a shift in how scientists spend their time. Scientists have always played dual roles—thinking about what problem needs solving, and then figuring out how to solve it. With AI helping more on the “how” part, scientists will have more freedom to focus on the “what,” or which questions are actually worth asking. AI can accelerate finding solutions, sometimes quite autonomously, but determining which problems deserve attention remains fundamentally human. Co-scientist is designed with this partnership in mind. It’s a multi-agent system built with Gemini 2.0 that acts as a virtual collaborator: identifying research gaps, generating hypotheses, and suggesting experimental approaches. Recently, Imperial College researchers used it while studying how certain viruses hijack bacteria, which opened up new directions for tackling antimicrobial resistance. But the human scientists designed the validation experiments and grasped the significance for global health.”

Brands Remember You. Your Inbox Forgets You. Enter the APU.

Published February 2, 2026

1

Two Inboxes

Ria opens her inbox the way most people do now — not with curiosity, but with mild dread.

It’s 8:47am. She’s on the metro, coffee in one hand, phone in the other. There are 47 unread emails. Most arrived overnight. Subject lines stack on top of each other, each shouting in its own way. Flash Sale. Last Chance. Only Today. Still Thinking? We Miss You!

Each email feels like a stranger tapping her shoulder, asking for attention without acknowledging the last interruption.

She scrolls without tapping. The senders blur together. Fashion. Food. Travel. Finance. Brands she once bought from, browsed from, signed up for — now competing for the same exhausted glance. Nothing connects. No sense of history. No reason to linger. Just urgency piled on urgency.

She selects all, deletes all.

The inbox is cleared — not because anything meaningful happened, but because it needed to be emptied. That’s what the inbox has become: something to manage, not something to visit. A graveyard of unread intent. A place you clean up so you can move on.

Brands remember Ria. Her inbox doesn’t.

**

Three months later, same metro, same coffee. But something has changed.

Ria opens her inbox again — and this time, she’s looking for something.

The unread count is higher than before, but she doesn’t rush to clear it. Her eye goes to a single subject line near the top:

µ.1247 | Did you get yesterday’s prediction right?

She taps.

Inside, the email doesn’t demand anything. It continues something.

Yesterday’s prediction is revealed — she had guessed the Nifty would close up. It did. She was right. A small reward appears: +15 Mu. Her Mu balance updates: 1262. A streak badge shows Day 14. She’s opened an email from this brand — or one like it — for two weeks straight. Not because anyone asked her to. Because something was waiting.

She scrolls. A quick poll: “Weekend vibe — brunch or hiking?” She taps brunch. Another +5 Mu. A product carousel appears — not random, but relevant. She’d browsed hiking boots last week; now she sees them with a note: “Still thinking about these?” She swipes past. Not today.

At the bottom, an ActionAd from a skincare brand she’s never heard of. But it’s not irrelevant noise — it’s a product for the dry skin she mentioned in a quiz two weeks ago. She taps. Samples added to cart.

Fifty-eight seconds. Done.

She closes the email. The inbox doesn’t feel empty. It feels alive. Not busy — alive. There’s a sense that something is in motion, that today’s interaction will matter tomorrow.

She doesn’t need to remember what she did yesterday. The inbox does.

Later that evening, another email arrives. Different brand. Different category. But the experience feels familiar. The same quiet continuity. The same sense that this isn’t a one-off interruption — it’s part of an ongoing thread.

Ria doesn’t think about loyalty. She doesn’t think about engagement. She doesn’t think about brands at all.

She thinks: I’ll check again tomorrow.

**

That’s the difference.

Nothing dramatic changed. The brands didn’t suddenly get smarter. The offers weren’t louder. The copy wasn’t more clever. No one begged harder for attention.

What changed was the inbox itself.

It stopped being a pile of disconnected messages and became a place where things carried forward. Where time mattered. Where attention accumulated instead of evaporating.

The inbox stopped being something to clear.

It became something to return to.

**

This essay is about what made that shift possible — not a new campaign strategy, not better copywriting, not smarter segmentation.

Something more fundamental.

A layer that didn’t exist before. A primitive that connects emails to each other, independent of the brands that send them. A reason to return that lives in the inbox itself, not in any single message.

The inbox lost its magnet years ago, when personal communication migrated to WhatsApp, iMessage, and Slack. What remained was brand noise — each sender shouting alone, no one listening.

What Ria experienced wasn’t a better email.

It was email with its magnet restored.

Related essays: The Magnetic Inbox and 4 stories.

2

The Channel That Lost Its Magnet

When people talk about email’s decline, they usually blame brands.

Too many emails. Too much frequency. Too little relevance. Bad copy. Worse timing. Lazy segmentation. The conclusion is always the same: brands abused the channel, and customers tuned out.

That story is comforting — and incomplete.

Email didn’t weaken because brands suddenly became incompetent. It weakened because the channel itself lost the one thing that made it magnetic in the first place.

**

For most of its early life, email wasn’t primarily a marketing channel. It was a personal one.

The inbox mattered because it carried people. Messages from friends. Replies from colleagues. Notes that required response. The first thing you did each morning was check email — not out of obligation, but anticipation. Something might be waiting. Someone might have written.

That was the magnet: personal communication. The human pull that made the inbox worth returning to. Brands simply piggybacked on that gravity.

Then something structural changed.

Personal communication migrated.

One by one, the most valuable inbox messages left email and moved elsewhere — to WhatsApp, iMessage, Slack, Telegram. Conversations became faster, richer, more immediate. Group chats replaced threads. Voice notes replaced long replies. The urgent and the intimate moved elsewhere.

And when personal communication left, email’s magnet left with it.

Email didn’t die. It hollowed out.

What remained was not conversation, but content. Brand emails. Promotional messages. Transactional updates. Newsletters nobody asked for. Receipts. Password resets. Each sender speaking independently, each demanding attention, none providing a reason to return tomorrow.

**

This distinction matters.

A channel with a magnet pulls people back even when nothing urgent is happening. A channel without one must constantly push, shout, and escalate just to be noticed.

Once email lost its natural pull, decay became the default state.

Brands didn’t cause this. They inherited it.

Faced with a channel that no longer drew people in on its own, brands did the only thing they could: send more. More campaigns. More reminders. More “personalisation.” More urgency layered on top of urgency.

But volume can’t replace gravity.

The industry poured millions into making brand emails better — better subject lines, better timing, better targeting, better AI. None of it addressed the structural problem.

Email’s magnet was never content. It was the reason to return. And that reason had left the building.

**

Consider the contrast with WhatsApp.

Why do people check WhatsApp dozens of times a day? Not because brands send great messages. Because friends are there. Group chats are active. Something is always waiting — a reply, a reaction, a conversation in progress.

WhatsApp has a magnet: P2P communication and group dynamics. The magnet is not content quality; it’s anticipation. The channel pulls people back independent of any commercial content.

Email has no equivalent. Not anymore.

Each brand email arrives as an island. No connection to the previous message. No anticipation of the next. No shared sense of progress or accumulation. Even the best-crafted email must fight from zero every time.

Hope is not a magnet.

**

This explains a pattern that has puzzled marketers for years.

Why does engagement decay even when emails are “personalised”? Why do Best customers fade despite sophisticated automation? Why do open rates decline even as targeting improves?

Because the problem isn’t the emails. The problem is the channel.

Every email begins the relationship again, as if nothing came before. Opens reset. Clicks evaporate. Attention leaks. Engagement never compounds because the channel stopped remembering.

This is the mistake most analyses make: they treat email’s decline as a brand problem to be solved with better tactics. But you cannot fix a channel-level failure with sender-level optimisation.

You cannot optimise your way to a magnet.

No amount of smarter copywriting, finer segmentation, or AI-driven personalisation can restore a magnet that no longer exists. Those tools can improve individual messages. They cannot make the inbox itself worth returning to.

**

Here’s the hard truth:

Email, as a channel, is structurally disadvantaged — not because it’s old, but because its original source of gravity migrated elsewhere. WhatsApp kept its magnet. Email lost its.

And yet email remains the most valuable owned channel brands have. It’s universal. It’s permissioned. It’s persistent. It reaches customers directly, without algorithmic interference, without platform taxes. The infrastructure is sound. The channel is intact.

What’s missing is the magnetism.

Email didn’t fail because brands abused it. It weakened because its original magnet — personal communication — left the channel.

Restoring email doesn’t start with better emails. It starts with restoring the magnet.

And that requires something brands cannot build on their own — a layer that reconnects messages, creates continuity, and gives people a reason to return to the inbox itself, not just to any single sender.

The next section explores why brand-level improvements can’t solve a channel-level problem — and what kind of solution actually can.

3

Why Brand Emails Can’t Fix a Channel Problem

Once you accept that email lost its magnet at the channel level, a second truth becomes unavoidable: no individual brand can fix this on its own.

And yet, that’s exactly where the industry has spent the last decade trying.

Brands have invested heavily in making their emails better. Customer Data Platforms stitch together behaviour across touchpoints. Journey builders orchestrate increasingly complex flows. AI engines optimise subject lines, timing, and content in real time. Personalisation is deeper than ever. Automation is more sophisticated than ever.

This is real progress — but it’s progress of a specific kind.

Call it backend memory.

Backend memory is the system’s ability to remember about the customer: what they browsed, what they bought, what they opened, where they dropped off. It lives in databases. It benefits brands. And it’s excellent at improving targeting and efficiency.

What backend memory cannot do is make the inbox itself worth returning to.

**

From the brand’s point of view, things look coherent. The customer has a profile. A history. A place in a journey.

From the customer’s point of view, none of this is visible. Each email still arrives in isolation, disconnected from the last and unrelated to the next.

The brand remembers everything. The inbox reflects nothing.

This is the island problem.

Brand A sends an email. Brand B sends an email. Brand C sends an email. Each may be personalised, well-timed, and relevant in isolation. But the customer doesn’t experience isolation — they experience the inbox as a whole.

And that whole is fragmented.

There is no continuity across senders. No accumulation of value. No shared sense that opening one email makes the next more meaningful. Each brand is optimising its own message, but no one is responsible for the experience of the channel.

It’s like every shop on a dying high street improving its own window display. The individual windows get better. The street stays empty. Because the problem isn’t the shops — it’s that no one has a reason to walk down the street anymore.

The inbox is the street. And the street has lost its pull.

**

This is why even “good” emails fail to compound.

A brand can improve its open rate this week. It cannot, on its own, create anticipation for the inbox tomorrow. Because anticipation is not brand-specific — it is channel-specific.

The industry assumed that if every sender got better, the channel would recover. But that logic only works when a channel already has gravity. When the magnet is gone, optimisation turns into escalation. More urgency. More frequency. More noise.

Better subject lines? They help one email compete against other emails — but don’t change whether the inbox gets opened in the first place.

Smarter personalisation? It makes individual messages more relevant — but doesn’t create a reason to return tomorrow.

AI-powered send time optimisation? It finds the best moment to interrupt — but interruption is not attraction.

These tactics optimise the push. They don’t restore the pull.

**

What’s missing is not intelligence. It’s continuity.

Specifically, what’s missing is inbox memory — memory that lives in the experience the customer sees, not just in the systems brands operate. Memory that is visible at the moment of action. Memory that turns isolated interactions into an ongoing thread.

This distinction matters because behaviour only changes when memory is visible.

Think about why other digital experiences create habits. Social feeds show you what you liked before — memory made visible. Games display your streak, your level, your progress — memory made visible. Wallets show your balance, your rewards, your status — memory made visible.

Databases can remember forever, but invisible memory does not form habits. A customer cannot feel a journey they cannot see. They cannot value progress that never appears. They cannot anticipate what the inbox does not signal.

Backend memory helps brands target better. Inbox memory makes the channel worth returning to.

Until now, email has had the first and never the second.

**

That’s why engagement resets with every send. That’s why Best customers fade without triggering alarms. That’s why attention decays even as personalisation improves.

The problem isn’t that brands aren’t remembering. It’s that the inbox isn’t.

This is not a failure of execution. It’s a missing layer.

To restore email’s magnet, you don’t start by asking, “How can brands send better emails?” You ask a different question:

What would make the inbox itself worth coming back to — regardless of who sent the last message?

Answering that requires something email has never had before. Not a campaign. Not a tactic. Not a feature bolted onto individual messages.

It requires a new primitive — one that operates at the channel level, reconnects emails to each other, and makes memory visible where it actually changes behaviour: inside the inbox.

That primitive is what the next section introduces.

4

The Primitive That Restores the Magnet

To close this gap requires more than better emails. It requires a new layer — one that operates at the inbox level, not the message level.

This is where the Attention Processing Unit (APU) comes in.

APU is not a feature. It’s not a widget. It’s not a campaign tactic dressed up in new language.

It’s a primitive — a foundational layer that makes inbox memory possible.

Think of it this way: APU is to attention what a database is to applications. A database doesn’t create the application, but without it, nothing persists. Nothing accumulates. Nothing continues. The database is the layer that makes memory possible.

APU does the same for the inbox.

**

Here’s what APU actually does: it carries visible memory forward from one email interaction to the next — independent of the brand that sends the message.

That last part is crucial.

Until now, every attempt to improve email has been sender-specific. Brand A improves Brand A’s emails. Brand B improves Brand B’s emails. Each operates in isolation. The inbox remains fragmented.

APU works differently. It creates a continuity layer that sits across senders. A customer’s engagement with a coffee brand’s email connects to their engagement with a fashion brand’s email. Progress accumulates. Memory persists. The inbox itself becomes coherent.

This is not coordination between brands. It’s infrastructure beneath them.

**

How does this work in practice?

APU has four components, each serving a specific role in creating inbox-level magnetism:

Mu in the Subject Line — the signal that something is waiting.

When Ria sees “µ.1247” in a subject line, she knows this isn’t just another promotional email. It’s a cue. Something has accumulated. Something can be continued. The subject line becomes a beacon — a reason to open that exists before she even sees the content.

This is fundamentally different from “Don’t miss out!” or “Sale ends tonight!” Those demand attention. Mu signals that attention has been remembered.

Magnets — the reason to engage.

Inside the email, a Magnet provides interaction that pulls rather than pushes. A prediction to resolve. A quiz to complete. A game to continue. Yesterday’s interaction sets up today’s. Today’s sets up tomorrow’s.

Magnets are not brand content. They’re engagement mechanics that work across senders. The coffee brand’s email might have a prediction. The fashion brand’s email might have a quiz. But the Mu earned in one carries to the other. The streak maintained in one counts toward the other.

This is how disconnected emails become connected experiences.

ActionAds — relevance without repetition.

Traditional ads in email are interruptive and often irrelevant. ActionAds are different. They’re targeted based on identity — not cookies, not probabilistic matching, but authenticated, first-party knowledge of who the customer is and what they’ve engaged with.

An ActionAd in a coffee brand’s email might show a skincare product — because the customer mentioned dry skin in a quiz two weeks ago. The targeting is precise. The experience is relevant. And because the system remembers previous interactions, it doesn’t repeat offers already seen or declined.

This has never been possible in email (or any other platform) before.

Mu Ledger — the accumulation made visible.

Every interaction earns Mu. Every Mu is recorded. The balance is visible — in subject lines, inside emails, across the inbox. Progress doesn’t vanish after a click. It accrues.

The Ledger also shows redemption options. Mu isn’t abstract points — it’s value that can be exchanged. This closes the loop: attention is earned, remembered, and rewarded.

**

Now step back and see how these components work together.

The subject line signals continuity. The Magnet earns engagement. ActionAds monetise attention without disrupting it. The Ledger accumulates value. Each component reinforces the others.

But the real breakthrough isn’t any single component. It’s what they create in aggregate:

A reason to return to the inbox itself.

Not to Brand A’s email. Not to Brand B’s email.

Crucially, the continuity does not belong to any one brand — it belongs to the inbox.

The customer opens her inbox knowing that something is waiting — Mu to collect, a streak to maintain, a prediction to resolve. The specific sender matters less than the fact that the inbox now has gravity. Every brand benefits because the channel has regained its pull.

This is the shift from message-level optimisation to channel-level magnetism.

**

APU doesn’t replace what brands already do. It doesn’t compete with CDPs or journey builders or personalisation engines. Those tools still matter for backend memory — for helping brands decide what to send.

APU adds what they cannot provide: memory the customer can see and feel. Continuity that spans senders. A magnet that lives in the inbox, not in any individual message.

The relationship stops being merely stored in a database.

It becomes experienced in the inbox.

**

But a primitive needs carriers. APU doesn’t deliver itself. It needs to be embedded into the emails customers actually receive.

That’s where NeoBoost and NeoMails come in — two deployment modes for the same underlying primitive, each solving a different problem in the attention lifecycle.

The next section explains how they work.

5

One Primitive, Two Carriers

If APU is the primitive that restores magnetism to the inbox, then NeoBoost and NeoMails are simply how that primitive enters the real world.

They are not competing products. They are not alternative strategies. They are deployment modes — two ways to carry the same continuity layer into customer inboxes, depending on where the breakdown in attention has already occurred.

The distinction is straightforward.

NeoBoost NeoMails
Target Best (fading) Rest/Test (disengaged)
Problem Retention Recovery
Email type Existing emails New daily stream
ESP Any ESP Netcore required

NeoBoost is designed for customers who are still there — opening, clicking occasionally, but slowly fading. These customers don’t need a new stream. They need the emails they already receive to stop resetting the relationship every time. NeoBoost embeds APU into transactional, promotional, and newsletter emails sent through any ESP — no migration required, no platform fees. It prevents decay before it becomes loss.

NeoMails is designed for customers who have already drifted — the large, silent majority who haven’t churned but no longer engage. For them, brands need a fresh surface, a new reason to re-enter the inbox. NeoMails creates an APU-native email stream built entirely around continuity, rewards, and pull. It rebuilds broken relationships.

One preserves attention. The other recovers it.

Together, they close the attention loop.

**

But neither NeoBoost nor NeoMails is the real point.

They are carriers. What they carry is APU — the layer that makes continuity possible across emails, across brands, and across time.

This distinction matters because it reframes how success should be measured.

Traditional email metrics — opens, clicks, CTR — measure moments. They tell you whether a message worked once. They say nothing about whether engagement is compounding or leaking.

What matters in a world with inbox memory is Attention Retention Rate (ARR): the percentage of customers who interact with APU quarter over quarter.

ARR is not a KPI to optimise. It’s a vital sign.

ARR doesn’t create memory. Memory creates ARR.

High ARR means the inbox has regained pull. Customers are returning not because they’re being chased, but because something persists. Low ARR means silent fade is underway, even if individual campaigns look healthy.

Seen this way, ARR becomes a leading indicator of reacquisition spend. When ARR holds, reacquisition drops. When ARR decays, ad budgets quietly rise.

**

This brings us back to the bigger picture.

For twenty-five years, email has been experientially stateless. Each message a fresh start. Each brand an island. Each interaction forgotten the moment it ends. When personal communication left the channel, nothing replaced it.

APU changes that at the channel level.

Mu makes attention worth accumulating. Magnets make return worth anticipating. ActionAds make monetisation additive, not extractive. The Ledger makes progress visible.

The inbox stops being a pile of disconnected messages and becomes a place where things carry forward.

An inbox that pulls, not just pushes. A channel restored. A magnet returned.

**

This is the idea that ties everything together:

The Magnetic Inbox with Memory.

Magnetic — because it earns attention through pull, not push. Customers return because something is waiting, not because someone demanded it.

Memory — because it makes attention compound, not decay. Each interaction builds on the last. Progress persists. Relationships deepen instead of resetting.

**

Remember Ria?

Her inbox used to be a graveyard. Forty-seven unread emails, each shouting independently, none connecting to the others. A place to clear, not check.

Now she opens her inbox looking for something. A prediction to resolve. A streak to maintain. A balance to grow. The brands are still there — but the experience is transformed.

What changed wasn’t the brands. It was the inbox itself.

**

Marketing automation gave brands memory about customers.

APU gives the inbox memory that customers can feel.

Brands remember customers. Now the inbox does too.

Thinks 1858

Bloomberg: “Stablecoin-powered neobanks [are] startups that offer dollar-denominated digital banking services to customers based anywhere in the world. While the US has a vast banking industry, which includes fintechs like Chime Financial Inc. and Mercury Technologies Inc., these companies are mostly vying for customers in countries where options are more limited and currencies are inflation prone. The model relies on stablecoins to serve as rails powering faster and cheaper services such as dollar accounts, payments and cross-border transfers…“Stablecoins are one set of primitives on top of which you can build and then serve people in any region so it takes your audience from just being in one country to the whole world,” Zach Abrams, co-founder of Bridge, said in an interview. These banking startups are growing as the stablecoin market booms, fueled by fresh US legislation and backing from President Donald Trump’s administration. The total market value of stablecoins has grown about 50% since the start of the year to reach $309 billion, of which roughly 99% is dollar-denominated.”

FT: “In an era when the currency in the entertainment world is the ability to hold the attention of an audience, YouTube has become one of the few essential platforms able to knit together new content that appeals to an online generation with the sort of programming that was once the backbone of cable TV. For Broski and some of her fellow creators, the key to success has been delivering fresh takes on old TV formulas — in her case the late-night chat show — for audiences raised on memes, TikTok videos and YouTube itself. YouTube, which turned 20 this year, is already the dominant podcast platform, a major force in music and a growing presence in live sport. Almost by stealth, it has also conquered the American living room. Since 2024, Americans have been watching YouTube primarily on TVs, not their phones or other devices. YouTube now has the lead in all TV and streaming consumption in the US — above Netflix, Disney and Amazon Prime Video.”

CNBC: “Morgan Stanley expects that by 2030, nearly half of American shoppers will use AI agents and the technology could add up to $115 billion in U.S. e-commerce spending. “We believe agentic commerce — in effect the ability to have a personal digital interactive shopper — is set to be the best next substantial GenAI-enabled unlock,” Morgan Stanley analysts wrote in a report in November. They noted that a mid-single-digit percentage of consumers currently start their “purchase journey” through AI, but that could increase over time as roughly 40% to 50% of Americans currently use AI for product research.”

Business Standard: “While Uber and Ola battle for India’s metros, Rapido has penetrated 400 cities, reaching deep into Tier-III and -IV towns where it’s often the only transport option. The company now draws 30 million monthly active users and counts three million active captains, making it among India’s biggest gig-economy employers. It has created nine million jobs to date, with over one million captains active daily.  The secret weapon? A zero-commission model and intimate knowledge of India’s diversity — from problems of inconsistent place names to varying literacy levels — that Guntupalli and his team gain by constantly experiencing the service themselves.”

From AdWaste to NeoMarketing: Rebuilding Around Alpha, Agentic, and Attention

Published February 1, 2026

1

Preamble, Three Pillars

The Question We Left Unanswered

The previous essay diagnosed how martech created the $500 billion AdWaste crisis.

Eight structural failures — from input-based pricing to the reacquisition mirage — formed a self-reinforcing system that turned the industry meant to solve retention into adtech’s most valuable upstream partner.

The result:

  • 80% quarterly attention churn
  • 90% of marketing budgets flowing to adtech
  • 70% of that spend going to reacquisition
  • Half a trillion dollars in annual waste

The diagnosis ended with a question:

What must fundamentally change?

This essay provides the answer.

**

Why Fixes Won’t Work

The Instinct to Patch

After diagnosis, the instinct is to reach for familiar remedies:

  • Better segmentation
  • Smarter automation
  • More AI features
  • Improved dashboards

These feel like progress. They are not.

The Logic of Failure

The eight failures were not isolated mistakes. They were symptoms of three deeper misalignments:

  • Misaligned Economics Vendors paid for activity, not outcomes
  • Misaligned Capability Tools exceeded human capacity to operate
  • Misaligned Attention Push systems in a pull world

Why Optimisation Fails

  • You cannot fix input pricing with better dashboards The incentives stay broken
  • You cannot fix attention decay with better copy The physics of push guarantees decay
  • You cannot fix reacquisition with smarter retargeting Retargeting is reacquisition — the problem itself

Any solution that keeps the same incentives, metrics, and architecture will reproduce the same outcomes.

AdWaste cannot be optimised away. It must be structurally eliminated.

**

Introducing NeoMarketing – the Anti-Martech.

What NeoMarketing Is Not

  • “AI marketing” (AI is a means, not an end)
  • “Better martech” (it replaces, not improves)
  • “Next-gen CRM” (CRM is a subset, not the system)

What NeoMarketing Is

  • A replacement operating system
  • Built to eliminate AdWaste
  • Designed around retention, not reacquisition

The Core Doctrine: Never Lose Customers. Never Pay Twice.

The extension: Pay Once. Profit Forever.

**

The Three Pillars

NeoMarketing rests on three co-equal foundations — the Three A’s:

Pillar Function What It Fixes
ALPHA Economics Misaligned incentives
AGENTIC Intelligence Operational complexity
ATTENTION Engagement Push-based decay

Remove any one, and AdWaste returns.

2

The Pillars

ALPHA — The Economic Reversal

What It Fixes: Root Cause #1 (Input Pricing) and #7 (Adtech’s Advantage)

The Diagnosis Recap

Martech failed because it sold activity, not results.

Vendors charged for:

  • Messages sent
  • Records stored
  • MTUs / MAUs / API calls

They got paid whether:

  • Customers engaged or ignored
  • Retention improved or collapsed
  • Attention decayed or compounded

Failure was profitable. Success was optional.

The NeoMarketing Shift

From Input-Based Pricing → Outcome-Based Economics

Alpha realigns incentives by redesigning the commercial model entirely.

The Alpha Structure

Beta (Baseline) A lower fixed component covering operational costs. Ensures function, but not where profits are made.

Alpha (Uplift) Variable compensation tied directly to retention lift, revenue growth, customer recovery, and attention metrics. If customers stay, vendors earn. If customers lapse, vendors lose.

Carry (Shared Upside) Long-term participation in sustained value created. Ensures vendors think in years, not quarters.

What Changes

  • Vendors have skin in the game
  • Sending messages that don’t work hurts vendor economics
  • Retention becomes the primary objective, not an afterthought

Martech Growth Engineers (MGEs)

Alpha economics requires a different kind of engagement.

MGEs are hybrid professionals — part strategist, part operator, part analyst — who work directly with brands to ensure NeoMarketing delivers results.

They are:

  • Accountable for growth outcomes
  • Compensated on the same metrics as the platform
  • The human bridge until AI agents mature

Over time, MGE playbooks become agent playbooks. The knowledge transfers; the alignment persists.

Martech vendors collected rent. NeoMarketing partners earn yield.

**

AGENTIC — The Intelligence Reversal

What It Fixes: Root Cause #2 (Complexity), #3 (Personalisation Mirage), and #4 (Attention Blind Spot)

The Diagnosis Recap

Martech promised “Segment of One.” What it delivered was “Batch and Blast.”

Why?

  • Platforms bloated with features: 60-70% went unused
  • Teams stayed small: one person managing 500,000 subscribers
  • Content creation didn’t scale
  • Data files stayed thin
  • Attention decay went unmeasured

The result: sophisticated tools producing primitive outputs.

The NeoMarketing Shift

From Tools for Humans → Agents That Execute

The solution is not simpler tools. Simpler tools cannot deliver N=1 at scale. The solution is not more training. Training cannot close a 1:500,000 gap.

The solution is agents — AI systems that operate the complexity humans cannot.

Marketing Agents: The Operational Layer

Agent Function
Insights Agents Analyse behaviour, surface anomalies, generate intelligence
Segment/Audience Agents Create dynamic audiences based on trajectories, not static attributes
Content Agents Generate and adapt messaging at scale
Shopping Agents Optimise commerce experience based on individual context

Together, they replace the “armies of specialists” that brands couldn’t afford.

Customer Agents: The Relationship Layer

BrandTwins are AI advocates for individual customers — not segments, not personas.

Each BrandTwin:

  • Knows purchase history, engagement patterns, preferences
  • Predicts needs and identifies optimal moments
  • Detects drift before it becomes dormancy
  • Advocates for the customer within the brand’s systems

The architecture:

ArtificialPeople: Foundational consumer world models trained on behavioural patterns across populations.

Twin Factory: The customisation engine that creates and maintains millions of individual BrandTwins by overlaying brand-specific data onto ArtificialPeople foundations.

Live Ledger: Making Attention Visible

Each BrandTwin has a corresponding ledger entry tracking:

  • Attention state (engaged, drifting, dormant)
  • Trajectory (improving, stable, declining)
  • Lifetime value (historical and predicted)
  • Intervention opportunities
  • Risk signals

The Live Ledger transforms customers from campaign targets to continuously managed assets.

Martech sold Ferraris without drivers. Agentic provides the autonomous driver.

**

ATTENTION — The Engagement Reversal

What It Fixes: Root Cause #5 (Inbox Failure), #6 (Best Customer Bias), and #8 (Reacquisition Mirage)

The Diagnosis Recap

Email should have been martech’s fortress. It became its liability.

  • Batch-and-blast trained customers to ignore the inbox
  • 80% quarterly attention churn became normal
  • The “missing middle” (Rest and Test — 80% of customers) was abandoned
  • When they lapsed, the only path back was adtech auctions

Brands paid Google and Meta to reach customers they already owned, because martech had trained those customers to stop listening.

The NeoMarketing Shift

From Push Campaigns → Pull Habits

First, define Attention correctly:

Attention is sustained, voluntary, repeat engagement on channels a brand owns.

Attention is not opens. Not clicks. Not conversions. Attention is the relationship asset that makes all of those possible.

The Attention Stack

NeoMails Daily micro-engagements designed to be wanted — 60-second experiences that customers look forward to rather than ignore.

The shift:

  • From campaign to ritual
  • From promotional to valuable
  • From episodic interruption to daily habit

Magnets Pull-based hooks that create the physics of attraction:

  • Quizzes and predictions
  • Games and challenges
  • Utilities and tools
  • Personalised recommendations

Social platforms understood this. Instagram embedded magnets. TikTok created loops. Wordle created rituals. Email was left magnetless — until now.

Mu (Attention Currency) Rewards engagement with tangible value. Every interaction earns points. Points accumulate into benefits.

This transforms attention economics from extraction (brand takes time, gives nothing) to exchange (brand gives value, earns attention).

Killing the Reacquisition Trap

ActionAds: When brands build engaged NeoMail audiences, that attention becomes valuable to non-competing advertisers.

  • Advertising revenue flows to the brand, not to Google or Meta
  • The programme becomes cost-neutral or profitable
  • Retention funds itself

NeoNet: Cooperative, deterministic recovery across non-competing brands.

The logic:

  • Customer dormant for Brand A but active for Brand B
  • Brand A reaches them through Brand B’s NeoMails
  • No auction. No bidding war. No adtech intermediation.

This is deterministic recovery: reaching a known customer through a known channel at a known cost.

Martech burns attention. NeoMarketing compounds it.

3

Synthesis

The Flywheel Reversed

The Old Vicious Cycle

Martech fails at retention → Customers lapse silently → Transitions go unmeasured → Only path back is ads → AdWaste grows → Martech gets blamed but economics don’t change → More customers lapse → Cycle accelerates…

The New Virtuous Cycle

Alpha aligns incentives → Agentic retains Best → Attention recovers Rest/Test → Customers stay → No reacquisition needed → Profits compound → Investment in retention grows → Cycle accelerates…

How The Three A’s Reinforce Each Other

  • Alpha ensures vendors are incentivised to make Agentic and Attention work
  • Agentic keeps Best customers from ever becoming Rest
  • Attention catches Rest before they become Test — and recovers Test before they become adtech’s customers

Together, they shrink the customer pool that adtech can monetise.

The result: AdWaste shrinks. Profits grow. The $500 billion problem becomes the $500 billion opportunity.

The New Mantra

The Contrast

Traditional Martech: Lose customers. Pay twice. Repeat.

NeoMarketing: Never Lose Customers. Never Pay Twice. Pay Once. Profit Forever.

The Choice

Brands now face a binary decision:

  • Continue paying the AdWaste tax ($500 billion annually)
  • Adopt an operating system where retention is the default

Root Cause Resolution Map

Root Cause The Failure NeoMarketing Pillar The Solution
#1: Input Pricing Vendors paid for activity (messages, MTUs), not outcomes. No skin in the game. Failure was profitable. ALPHA Outcome-based economics (Beta + Alpha + Carry). Vendors profit only when brands profit. Retention becomes economically necessary.
#2: Complexity Gap Platforms bloated with features (60-70% unused). No services ecosystem. Teams couldn’t operate the tools they bought. AGENTIC Marketing Agents (Insights, Segment, Content, Shopping) operate the complexity humans cannot. MGEs bridge until agents mature.
#3: Personalisation Mirage Promised N=1, delivered stereotypes. 5-20 static segments for millions. “Not for me” messages trained customers to ignore. AGENTIC BrandTwins enable true N=1. Individual AI models for each customer, built in Twin Factory on ArtificialPeople foundations.
#4: Attention Blind Spot Dashboards tracked campaigns, not relationships. Attention decay unmeasured. Transitions (Best→Rest→Test) invisible. AGENTIC Live Ledger tracks real-time P&L for every customer. Drift detected before dormancy. Intervention at optimal moments.
#5: Inbox Failure Email trained to be ignored. Batch-and-blast as SOP. Push always decays, but martech pushed harder. 80% quarterly churn. ATTENTION NeoMails transform inbox from push to pull. Magnets create attraction. Mu rewards engagement. Daily habits replace quarterly campaigns.
#6: Best Customer Bias Martech focused on 20% Best (easy wins). 80% Rest/Test abandoned. Missing middle handed to adtech. ATTENTION NeoMails and NeoNet focus on Rest/Test recovery. ActionAds fund the investment. No customer abandoned to drift.
#7: Adtech’s Advantage Acquisition was easy (Agency→Budget→Clicks). Retention was complex. 90:10 budget split. ROAS culture infected retention metrics. ALPHA Alpha makes retention investment attractive. Vendors bring resources. Risk transfers from brand to partner. Retention properly funded.
#8: Reacquisition Mirage 50-70% of “acquisition” was reacquisition. Brands bid in auctions for customers they already owned. Double taxation on every lapsed customer. ATTENTION NeoNet enables cooperative, deterministic recovery. No auctions. No bidding wars. Known customers reached through known channels at known costs. Never pay twice.

 

**

The Inevitability

The question is not whether this change will happen.

The structural pressures are too great. AdWaste is too expensive. The AI capabilities are too powerful. The alternative — continuing to optimise a system designed to waste $500 billion annually — is not sustainable.

The question is who will lead.

  • Will it be incumbent martech vendors, disrupting themselves?
  • Will it be new entrants, building NeoMarketing natively?
  • Will it be brands themselves, demanding outcome alignment?

The Final Word

The diagnosis is complete. The prescription is clear.

AdWaste is optional. The cure exists.

The only question is: who moves first?

Thinks 1857

SaaStr: “The market has split into two very different worlds. In one world, companies are riding incredible tailwinds – raising at premium valuations, growing at unprecedented rates with lean teams, and accessing budgets 10x larger than traditional SaaS. In the other world, companies are fighting for scraps, dealing with flat-to-negative budgets after price increases, and watching their valuations compress. Which world you’re in is largely up to you. The technology is available. The budgets are available. The customers are in market right now. The question is whether you’re building something that deserves to win.”

WSJ: ““A properly regulated system of AI-powered choice engines could produce massive welfare benefits,” concludes Cass Sunstein in “Imperfect Oracle,” his study of what artificial intelligence can do for humanity. “It could make life less nasty, less brutish, and less short—and less hard.” Many people today see great potential in large language models and other, more ambitious, AI applications. But what does he mean by “AI-powered choice engines”? Mr. Sunstein…identifies the real benefit of AI as its capacity to overcome human “cognitive biases.” Deeply influenced by the field of behavioral economics, he argues that people tend to value avoiding losses rather than pursuing equivalent gains, pay too much attention to the examples of outcomes that are most familiar to them, and then to be “unrealistically optimistic.” They use “heuristics” that humans evolved for making snap decisions but that can mislead them at other times. “People tend to focus on the short term, not the long term,” he notes. We trust our intuitions when we should rely on rational calculation. “Intuitions and impressions should be replaced by computations,” Mr. Sunstein concludes.”

Physical Intelligence: “One of the most exciting (and perhaps controversial) phenomena in large language models is emergence. As models and datasets become bigger, some capabilities, such as in-context learning and effective chain-of-thought reasoning, begin to appear only above a particular scale. One of the things that can emerge at scale with LLMs is the ability to more effectively leverage data, both through compositionality and generalization, and by utilizing other data sources, such as synthetic data produced via RL. As we scale up foundation models, they become generalists that can soak up diverse data sources in ways that smaller models cannot. In this post, we’ll discuss some of our recent results showing that transfer from human videos to robotic tasks emerges in robotic foundation models as we scale up the amount of robot training data. Based on this finding, we developed a method for using ego-centric data from humans to improve our models, providing a roughly 2x improvement on tasks where robot data is limited.”

Jason Furman: “I’m more worried about the financial valuation bubble than I am a technological bubble…To justify financial valuations, you basically need two things: the technology works really, really well, and you can make a profit from that. The two threats to valuations are that we hit diminishing returns and a lot of the different scaling laws that have applied to date don’t apply in the future. Moreover, I don’t know that every scaling law translates economically. Every time your microchip in your computer gets two times as fast, you don’t write Word documents two times as fast or respond to emails two times as fast. In fact, a lot of that is almost like excess capacity that is building up in our computers, and that could be what happens in AI, even if it follows the law. The second thing is the current valuations assume enormous ability to monetize, which requires products that people will buy and being able to build moats so that people won’t switch to cheaper products. It’s not like I’m sure at all that there’s not an AI technology bubble — I change my thoughts on this by the day — but it’s the valuations I’m much more worried about.”

How Martech Lost the Game It Was Meant to Win

Published January 31, 2026

1

Preamble, Root Cause 1

The marketing industry wastes $500 billion every year reacquiring customers that brands already own.

This is not a rounding error. It is not inefficiency at the margins. It is not the cost of doing business. It is the dominant use of marketing budgets: nearly 70% of advertising spend flows to reacquisition, retargeting, and “win-back” campaigns — money spent bringing back customers who should never have been lost in the first place.

The instinct is to blame the usual suspects. Google and Meta have built trillion-dollar empires on advertising. Surely they are the villains in this story?

They are not.

The uncomfortable truth is that AdWaste was not created by adtech. It was created by the industry that was supposed to prevent it: martech.

Martech — the sprawling ecosystem of CRM platforms, customer engagement tools, marketing automation, CDPs, and retention solutions — was built on a singular promise: help brands build better customer relationships. Keep customers engaged. Prevent churn. Make reacquisition unnecessary.

It failed.

Not because the tools lacked features. Not because marketers lacked ambition. But because the entire system — its economics, its architecture, its incentive structures, its measurement frameworks — was designed in ways that made failure not just possible, but inevitable.

This essay is a diagnosis, not an indictment. It is a systems-level post-mortem, not a blame exercise. The goal is not to attack vendors or marketers, but to understand how an industry built to solve retention became the upstream feeder system for the very platforms it was meant to replace.

AdWaste was not an accident. It was emergent behaviour — the logical output of eight structural failures that reinforced each other over two decades until they became inescapable.

These are the eight root causes of the $500 billion AdWaste crisis.

**

ROOT CAUSE #1: Input-Based Economics with Zero Accountability

(The Original Sin)

The foundational flaw: Martech priced activity, not outcomes.

What martech charged for:

  • Messages sent
  • Contacts/records stored
  • MTUs / MAUs / events / API calls

What this created:

  • Vendors got paid whether customers engaged or ignored
  • Vendors got paid whether retention improved or collapsed
  • No economic consequence for attention decay, silent churn, or poor lifecycle design
  • Sending more was always rewarded — even when it accelerated fatigue

The perverse incentive:

  • Failure at retention became profitable
  • Success at retention became optional
  • Customer loss became an externality — pushed downstream to adtech

The contrast: Adtech, despite its flaws, aligned with the marketer’s desperation for growth (CPA, CPC, ROAS). Adtech had skin in the game; martech just collected rent.

Evidence: Gartner data shows martech utilisation dropped from 58% (2020) → 42% (2022) → 33% (2024) — yet vendors kept getting paid the same.

Net effect: Martech had no skin in the retention game. Customer loss became someone else’s problem — eventually adtech’s opportunity.

Key line: “Martech sold activity, not outcomes. The meter ran whether customers stayed or left.”

2

Root Causes 2-4

ROOT CAUSE #2: Complexity Without a Services Economy

(The Ferrari-with-No-Driver Problem)

The gap: Martech platforms became extremely powerful — and extremely unusable.

What happened:

  • Platforms bloated with features: journeys, rules, triggers, CDPs, AI engines
  • 60-70% of features went unused across the industry
  • CRM/lifecycle teams stayed small, under-skilled, lacking operational support

The missing layer:

  • Unlike ERP (SAP, Oracle), martech never built a large services ecosystem
  • SAP and Oracle spawned massive implementation partners (Accenture, TCS, Infosys, Deloitte) because contract values justified it
  • Mid-market martech pricing couldn’t sustain integrators or operators — low entry costs meant no margin for services
  • Vendors sold “self-serve sophistication” that wasn’t actually self-serve

The skills crisis:

  • 64% of organisations acknowledge significant lack of internal martech/data/marketing operations expertise
  • Most martech teams can’t distinguish between workable and harmful complexity
  • The org chart disparity: five people optimising Facebook ads, one person managing 500,000 email subscribers

The behavioural outcome: When retention tools required armies and expertise, and acquisition required only a credit card, the credit card won.

Net effect: Sophistication existed on slides, not in execution. Platforms were reduced to broadcast tools. Acquisition platforms won by default because they were easier to operate.

Key line: “Martech sold Ferraris to people who never learned to drive. Then wondered why they took taxis instead.”

**

ROOT CAUSE #3: The Personalisation Mirage

(Promised N=1, Delivered Stereotypes)

The promise: One-to-one marketing at scale. The “Segment of One.”

The reality:

  • 5-20 static segments for millions of customers
  • Batch-and-blast as standard operating procedure
  • “Women 25-34” or “loyal high-spenders” isn’t personalisation — it’s stereotyping at scale

Why true personalisation failed:

  • Content creation didn’t scale (expensive, slow, operationally painful)
  • Data files stayed thin — identity files (email, phone) rather than intent files
  • No incentive to thicken customer understanding
  • No services revenue to fund the work

The “Not for Me” problem:

  • Generic messages trained customers to ignore, skim, disengage quietly
  • Relevance collapsed fastest for Rest and Test customers — the 80% who needed personalisation most
  • Post-purchase engagement especially weak: ecommerce → “One and Done” epidemic; BFSI → broken lead nurturing

The BRTN reality: 20% Best get campaigns (they’d buy anyway), 80% Rest and Test get ignored (the ones who needed personalisation most).

Net effect: Martech solved message delivery, not meaning. Customers disengaged quietly — quarter after quarter.

Key line: “Martech promised to know each customer. Instead, it sorted millions into a dozen buckets and called it personalisation.”

**

ROOT CAUSE #4: The Attention Blind Spot

(What Martech Never Measured)

The measurement failure: Martech built dashboards for the wrong things.

What martech optimised for:

  • Opens
  • Clicks
  • Conversions
  • Campaign ROAS

What martech did NOT track:

  • Attention decay over time
  • Engagement half-life
  • Transition moments (Best → Rest → Test)
  • Click Retention Rate (who keeps clicking quarter over quarter)
  • Silent disengagement signals

The invisibility problem:

  • No dashboards for attention decay
  • No alerts for disengagement patterns
  • No ownership of customer transitions
  • Customers only became “visible” again when they were already lost — and being reacquired via ads

The timing failure:

  • By the time a customer appeared in a “win-back” segment, they’d already completed the journey to dormancy
  • The transition moments (Best→Rest, Rest→Test) — when intervention would be most effective and least costly — went unmonitored and unmanaged

Net effect: The most destructive failure mode — attention loss — remained invisible by design. Martech measured conversion, not continuity.

Key line: “Martech built dashboards for campaigns. It forgot to build dashboards for relationships.”

3

Root Causes 5-7

ROOT CAUSE #5: The Inbox Failure

(Squandering the Only Channel Brands Fully Owned)

The tragedy: Email should have been martech’s fortress. It became its greatest liability.

What email was:

  • Fully owned (no platform dependency)
  • Identity-rich (authenticated audience)
  • Low marginal cost (near-zero per message)
  • Universal (everyone has an email address)
  • Habitual by nature (checked daily)

What martech did to it:

  • Treated it as a static delivery pipe, not a relationship channel
  • Made batch-and-blast the standard operating procedure
  • No innovation around habit formation, pull, or daily value
  • No answer to attention half-life, habituation, or inbox fatigue

The physics violation:

  • Push always decays, but martech kept pushing harder
  • When engagement dropped, response was: more frequency, louder subject lines, deeper discounts
  • Expansion was the answer to every problem, never examination

The contrast: Social platforms understood pull mechanics. Instagram embedded magnets — micro-moments of reward, validation, curiosity. TikTok created daily habits. Wordle created rituals. Email was left magnetless.

The result:

  • 80% quarterly attention churn (4 of 5 clickers disappear within 90 days)
  • Not by unsubscribing, but by quietly stopping
  • 90%+ of emails became ignorable by design
  • How can an industry build itself when 90% of its messages are ignored?

Net effect: Email didn’t die. It was trained to be ignored. Owned attention eroded predictably.

Key line: “Email didn’t die. Martech killed it — one ignored message at a time.”

**

ROOT CAUSE #6: The Best Customer Bias

(Fishing Where the Fish Already Are)

The blind spot: Martech focused on the extremes and abandoned the customers who determined profitability.

What martech focused on:

  • Best customers (high-value, high-frequency)
  • High-intent moments (cart abandonment, browse abandonment)
  • Customers who would buy anyway

Why:

  • They convert easily
  • They make dashboards look good
  • It’s the marketing equivalent of fishing in a stocked pond — impressive catches, but not sustainable growth

The “Missing Middle” abandoned:

  • Rest customers (showing early disengagement) got the same generic messages as everyone else
  • Test customers (90+ days dormant) deemed lost and handed to adtech for reacquisition
  • 70-80% of customer bases received almost no strategic attention

The typical brand reality:

  • 100,000 customers: 20,000 Best keep buying, 40,000 Rest slowly disengage, 40,000 Test already gone
  • Martech focuses on the 20,000
  • Adtech profits from the 80,000

Net effect: The customers who needed martech most were abandoned earliest — and handed directly to adtech. A perfect parasitic relationship: martech’s neglect became adtech’s nourishment.

Key line: “Martech focused on the 20% who would buy anyway, while the 80% who needed nurturing silently walked away.”

**

ROOT CAUSE #7: Adtech’s Asymmetric Advantage

(The Easy Button Won)

The divergence: While martech stagnated in complexity, adtech compounded in simplicity.

What adtech perfected:

  • The ABC model: Agency → Budget → Clicks
  • Call an agency, allocate budget, receive traffic — seductively simple
  • Predictable, measurable, immediate results
  • Minimal expertise required

What adtech created:

  • A slew of agencies industrialised acquisition
  • The marketer’s job of acquisition became easy, measurable, career-safe
  • Performance marketing created ROAS culture: worship immediate results

The ROAS infection:

  • This thinking spread to retention channels — relationship-building got measured like acquisition campaigns
  • Even email and CRM got judged by short-term conversion, last-click attribution
  • The tyranny of the urgent over the important

The resource disparity:

  • Budget allocation: 90% acquisition, 10% retention — nine times more spent on the expensive activity
  • Team allocation: adtech teams are specialised and well-resourced; martech teams are “the email person”
  • Technology investment: 10:1 in favour of adtech ($200K-500K vs $20K-50K for mid-market)
  • Retention became “email — it’s basically free”

The dependency trap: Because martech wasn’t holding customers (high churn), brands had to constantly refill the “leaky bucket” via adtech.

Net effect: Growth became a budget function, not a relationship function. Attention was outsourced instead of built. Adtech filled the vacuum martech created.

Key line: “When retention required expertise and acquisition required only a credit card, the credit card won.”

4

Root Cause 8, Synthesis

ROOT CAUSE #8: Reacquisition Disguised as Growth

(The AdWaste Flywheel)

The final indignity: Most “acquisition” spend is actually reacquisition in disguise.

The silent churn reality:

  • 80% of engaged customers drift quarter over quarter
  • Drift is invisible (no dashboards, no alerts — see Root Cause #4)
  • Customers don’t complain, don’t unsubscribe — they just stop

What brands do to maintain revenue:

  • Spend more on acquisition
  • Buy traffic, retarget broadly, bid in auctions

The reacquisition mirage:

  • 50-70% of “acquisition” spend targets people already in brand databases
  • Brands are bidding in auctions to reach customers they already “own” but have tuned out
  • These customers disengaged due to Root Causes #3-6
  • Attribution models break; budgets balloon; no one questions why the “new” looks suspiciously like the old

The double taxation:

  • First, brands lose the customer’s lifetime value — all future purchases vanish
  • Then brands pay again — often more than original CAC — to bring them back through ads
  • If they don’t, competitors will
  • Either way, profit vanishes into a permanent P&L leak

The economics:

  • Every abandoned customer is worth their weight in gold to adtech platforms
  • Google and Meta happily sell them back at 5-7x the cost of retention
  • Martech’s retention failure directly subsidises adtech’s record profits

Net effect: AdWaste is not inefficiency. It is not bad execution. It is the logical output of the system. Brands pay platforms to reach customers they already own, because they trained those customers to ignore the channels they control.

Key line: “The ultimate absurdity: brands pay twice for customers they already own — because martech trained those customers to stop listening.”

**

SYNTHESIS: The System That Could Only Produce AdWaste

These eight failures weren’t isolated — they formed a self-reinforcing system:

  1. Input pricing → No accountability → Features bloated without discipline
  2. Complexity without services → Low utilisation → Defaults to batch-and-blast
  3. Personalisation failure → “Not for me” messages → Quiet disengagement
  4. Attention blind spot → Decay unmeasured → Transitions invisible
  5. Inbox abuse → Owned channels collapse → Email trained to be ignored
  6. Best-customer bias → 80% abandoned → Rest and Test hand-delivered to adtech
  7. Adtech ease → Budget addiction → Martech starved of investment and talent
  8. Reacquisition mirage → Auction dependency → AdWaste becomes structural

The flywheel of failure: Martech fails at retention → Customers lapse silently → Transitions unmeasured → Only path back is ads → AdWaste grows → Martech gets blamed but economics don’t change → More customers lapse → Cycle accelerates…

The result:

  • 80% quarterly attention churn
  • 90% of marketing budgets flow to adtech
  • 70% of that is reacquisition (buying back known customers)
  • $500 billion annual AdWaste globally

The uncomfortable truth: AdWaste isn’t a bug in martech. It’s a feature of its economics. Martech didn’t lose to adtech — it became adtech’s most valuable upstream partner.

**

The Question That Remains

Martech wasn’t beaten by adtech. It was abandoned by design:

  • Abandoned by pricing that rewarded activity over outcomes
  • Abandoned by complexity that exceeded human capacity to operate
  • Abandoned by personalisation promises that collapsed into stereotypes
  • Abandoned by measurement frameworks that couldn’t see attention decay
  • Abandoned by channel abuse that destroyed owned attention
  • Abandoned by strategic blindness that ignored 80% of customers
  • Abandoned by ease of alternatives that required no expertise
  • Abandoned by attribution that couldn’t distinguish acquisition from reacquisition

The $500 billion AdWaste crisis isn’t a market inefficiency. It’s a system working exactly as designed — just not for brands.

The question now isn’t “who’s to blame?”

It’s “what must fundamentally change?”

That’s the subject of the next essay.