Email’s Next Act: How NeoMails and NeoNet Change Everything (Part 1)

How Email Lost the Plot

Email once held a privileged place in the relationship between brands and customers. It was direct, persistent, personal, and cheap. A brand did not need to rent an audience from a platform, bid for impressions, or pray that an algorithm would show its message to the right person. It could simply write to the customer. For a time, that was enough. The inbox was sparse, novelty was high, and email still carried something of a letter’s aura. Opening it felt natural.

That world is gone. Most brand email today is not written to be welcomed. It is written to be delivered. The distinction matters. A welcome message is designed around the recipient’s time and attention. A deliverable message is designed around the sender’s campaign calendar and quarterly target. Once brands shifted their mental model from relationship to throughput, the inbox began to fill with messages that were legible but forgettable — sale alerts, cart nudges, reminders, countdowns, and generic newsletters that looked slightly different but felt exactly the same.

When open rates declined, brands did not respond by improving the product. They responded by increasing the pressure. More sends, more urgency, more discounting, more automation, more triggered journeys, more subject-line tricks, more inboxing hacks. Every additional low-value message trained the customer to ignore the next one. Email did not collapse because it became technologically obsolete. It weakened because it stopped being worth opening.

The consequences extend well beyond opens and clicks. Once the inbox loses attention, the customer drifts. A customer who does not open becomes invisible to the brand — even while remaining very real in every other sense. They still know the brand. They may still like it. They are still reachable — just not through the messages the brand currently sends. This is where the reacquisition trap begins. The marketer, unable to activate the customer through owned channels, turns to Google or Meta to win back someone whose email address already sits in the database. The brand pays twice: first to acquire the customer, then again to reacquire that same customer after the relationship has gone cold. This is AdWaste at its most absurd — renting back attention you already owned.

So the crisis is not that email has failed as a medium. The crisis is that email has been reduced to two narrow roles: transaction and promotion. It can still deliver. It can still scale. But to matter again, it needs a third job — one designed not to extract, but to earn. Email did not die. It just stopped being worth opening. The next act begins by fixing that.

Thinks 1929

FT: “[Jensen] Huang’s take on AI economics is based around the production, consumption and monetisation of tokens. These are the most basic units of output from large language models: it takes about 1,300 tokens to generate 1,000 words of text. The key metric, he argues, is the cost per token of output. And as the main input into AI-powered services, he adds, tokens translate directly into revenue.”

WSJ: “Claws are autonomous agents and can plan and execute tasks on their own, and, critically, spin up their own subagents to tackle specialized tasks, access files and themselves delegate tasks to other subagents. They represent a big leap beyond question-and-answer-style AI chatbots as well as recent iterations of AI agents, which typically have narrow use cases and run for a set amount of time—although claws also come with a new set of security concerns. For claws to work as a true personal assistant, they need access to all of a user’s data. So what are people using them for today?”

WSJ: “AI tools like Anthropic’s Claude Code, Cursor and OpenAI’s Codex can now write and debug software, unlocking huge new sources of revenue. That success is pushing their makers toward a bigger ambition: automating our entire lives. What began as a way to autocomplete code quickly evolved into semiautonomous AI bots, or “agents,” that can work for hours on end with little human oversight. We can tell a bot to create a presentation for work, coordinate the family’s schedules and pick a March Madness bracket, all while it learns our personal preferences, no coding needed…The shift has permanently changed the lives of coders and sparked a $1 trillion market selloff as investors and executives contemplate the technology’s potential to reshape industries, including finance, legal and healthcare. Tens of thousands of job cuts have already been attributed to AI.”

Pete Boettke: “In the late 19th century, Italian economist Vilfredo Pareto (1848-1923) expanded on this point, observing that co-ordinating even a modest economy and matching resources to uses and preferences would soon cause an explosion in the number of equations to be solved. But today’s computers can handle quintillions of computations per second, more than Pareto could possibly have imagined. Doesn’t that make a difference? This is where Nobel laureate economist Friedrich Hayek (1899-1992) comes in. Hayek explained that the problem is not merely that the relevant knowledge is decentralized — spread out across millions of individuals — but that it is often tacit. Local shopkeepers’ understanding of their customers’ buying habits cannot be translated into one data point to feed into an AI or any other kind of model. Nor can we predict the emergence of an entrepreneur dreaming up a product that did not exist before…Prices are not lying around in the wild, waiting to be harvested and fed into an algorithm. Rather, they are the result of constantly evolving discovery. Without this process of discovery, the knowledge embedded in a price simply doesn’t come into existence…As powerful and helpful a tool as AI can be to improve logistics, better manage inventories and analyze markets, it remains just that, a tool. It can help us gain a better understanding of markets but only markets themselves can predict and co-ordinate the results of the billions and billions of voluntary exchanges that take place every day.”

 

The 4R (Rest-Reach-Reactivate-Revenue) Motion: How to Recover the 80% That Martech Forgot (Part 5)

The new economics — Alpha2, ROMS, and the flywheel

21. Alpha2 is the pricing model that aligns incentives. Traditional martech charges by input: emails sent, users active, API calls made. The vendor earns the same whether the Rest are recovered or not. Alpha2 is 100% variable, tied to recovered revenue: the vendor earns a percentage of the incremental sales generated from Rest customers reactivated through the motion. If the motion does not work, the vendor earns nothing. This is the hedge fund model applied to martech — Beta as baseline, Alpha as upside, Carry as the verified payout.

22. REACQ% reveals the hidden tax — and the Attention P&L reveals the upside. REACQ% is the share of customers appearing in adtech reacquisition campaigns who already exist in the brand’s CRM. It is AdWaste made numerical, and a brand running the 4R motion should see it fall as Rest customers are recovered through owned channels. Every percentage point reduction translates directly into recovered margin. But there is a second accounting transformation available: the inactive base stops being a dead cost and starts being managed as an Attention P&L. If a portion of the database can be reactivated, monetised through ActionAds, and converted back into purchasing behaviour, email is no longer merely a delivery cost. It becomes infrastructure with its own returns. That accounting shift is what turns 4R from a retention story into a growth economics story.

23. ROMS — Return on Martech Spend — is the new North Star Metric. ROAS measures acquisition efficiency. It cannot capture the compound value of a prevented lapse, the margin recovered through reactivation without paid media, or the LTV uplift from a Rest customer who graduates to Best. ROMS measures exactly these things: revenue generated per pound invested in owned channels and retention infrastructure. A business optimising for ROAS will keep feeding adtech. A business optimising for ROMS will invest in the 4R motion. The metric you choose determines the strategy you run.

24. The 4R motion is the commercial proof of the Three NEVERs. Never Lose Customers: the 30-day drift window is identified and addressed before Rest customers become dormant. Never Pay Twice: customers are recovered through NeoMails and NeoNet before adtech reacquisition becomes necessary. Never Pay Fixed: Alpha2 means the vendor’s economics are tied to recovery outcomes, not activity metrics. When all four stages run as designed, the brand stops feeding the reacquisition loop — and the $500 billion AdWaste crisis becomes, one brand at a time, someone else’s problem.

25. Best compounds, Rest recovers, Next becomes cleaner. This is the end-state the 4R motion is building toward. Best customers belong with Meridian — AI Twins, Context Graphs, Decision Traces, and N=1 personalisation to maximise LTV. Rest customers belong with Atrium — NeoMails, Mu, WePredict, ActionAds, and NeoNet to recover attention and reduce REACQ. Next remains the domain of acquisition, but it becomes smaller and cleaner because fewer previously known customers are leaking into it. The reacquisition loop shrinks not because adtech was defeated but because the conditions that made it necessary were removed. That is the difference between competing with the platforms and making them less necessary. Recovery becomes its own discipline. And the hidden bridge between retention and acquisition — the 4R motion — is finally recognised and operated as the commercial engine it always was.

**

The 4R motion is not a tactic. It is the operating system for the 80% of customers that martech abandoned. And the moment you start measuring REACQ%, the urgency becomes impossible to ignore.

Thinks 1928

Forbes: “As CEO, you face challenges every day. Whether it’s a meeting that spirals, a missed deadline or rising tension, it’s easy to react negatively. I’ve found that instead, it helps to pause and ask a simple question: “Isn’t this interesting?” It’s one of the most effective tools I use to stay grounded, make better decisions in fast-moving environments and build a culture that does the same. When you’re observing, you’re not in fight-or-flight. You’re not reaching for control or trying to protect your ego. You’re just seeing what’s there, reframing your frustration into curiosity. That clarity leads to smarter decisions.”

WSJ: “Getting fooled into thinking that AI is thinking is what I call the Turing Trap. Alan Turing, godfather of modern computing and AI, proposed a simple test to determine whether a computer had attained human-level intelligence: If a person chatting with a bot couldn’t tell if it was human, it might as well be declared intelligent. What became known as the Turing Test doesn’t stipulate how a machine achieves this. At the time, language was thought to be closely associated with reasoning, but modern neuroscience shows us that it’s a separate process. Speaking isn’t the same as thinking, let alone being. Rather than demonstrating that machines have achieved intelligence, the Turing Test shows that linguistic fluency is possible even in its absence.”

Naomi Klein: “A “doppelgänger” is a German word that means, literally translated, a “doublegoer” or a “double walker.” It’s the idea that out there, somewhere, you could bump into somebody who looks just like you — but isn’t you. It’s that uncanny vertigo that addresses the strangeness of that which is most familiar — which is yourself. “Mirror world” is a term I use to describe the relationship between the liberal left world and the far right world, and the ways in which, when people are ejected from our world, they end up in a world that is the exact mirror of where we live — in replica social media platforms, the same but different doppelgänger publishing worlds, doppelgänger narratives of the narratives that we tell ourselves.”

WSJ: “The core tenets of somatics are a series of slow movements designed to release tension that leads to pain and hinders flexibility and mobility. The practice proposes something more rare than perfectly toned arms: un-jangled nervous systems.”

The 4R (Rest-Reach-Reactivate-Revenue) Motion: How to Recover the 80% That Martech Forgot (Part 4)

The mechanics — how Atrium executes each R

16. NeoMails are the Reach vehicle. A brand can own the rail without owning the moment. The channel may still exist — the email address is valid, the inbox is open — but the customer’s attention has already left. That is the trap most CRM systems fall into: confusing message delivery with attention recovery. NeoMails are daily interactive emails built on the APU — Attention Processing Unit: Magnets, Mu, ActionAds, and a Ledger. The brand content comes first, and the Magnet follows — a quiz, a prediction, a daily challenge. The customer opens because the Magnet gives them something; the brand earns presence because it showed up with value. The brand gives before it asks. That reversal is the entire logic of the Relate channel.

17. ActionAds make Reach self-funding. ActionAds are in-email action units from partner brands: Subscribe, Save, Sample, Start, Book, Buy. They convert inside the email without click-through friction. A single-tap subscribe from a complementary brand generates revenue for the sending brand and a new subscriber for the ActionAd buyer. When ActionAd revenue exceeds delivery cost, the effective CPM falls to zero. ZeroCPM is not a pricing model — it is the economic outcome when Reach pays for itself.

18. Mu is the economic substrate of Reactivation. Mu is the attention currency earned through NeoMails engagement. It is displayed in the email subject line: a visible, accumulating balance that turns passive reading into active participation. The Mu balance creates the desire to return. A customer with 3,400 Mu who has not opened in two days feels the pull of the streak counter in a way that no subject line A/B test can manufacture. Mu makes the relationship visible and makes the cost of drifting tangible.

19. WePredict completes the flywheel with Mu burn. Mu earned through NeoMails can be burned in WePredict — the prediction marketplace where users stake Mu on outcomes. WePredict Private operates in closed WhatsApp and Slack groups, where predictions happen among people the customer knows. The social consequence of being right or wrong — visible to friends and colleagues, building a Predictor Score — makes Mu feel real in a way that loyalty points never do. Mu earn creates the daily engagement habit. Mu burn in WePredict creates the reason to care about that balance.

20. NeoNet extends Reach to customers who have gone dark. When a customer stops opening NeoMails after seven consecutive sends, the escalation moves to NeoNet — the cooperative brand network. NeoNet enables the brand to place an ActionAd inside another brand’s NeoMail, reaching the lapsed customer through attention that already exists elsewhere. No auction. No platform intermediary. Deterministic reach through a first-party cooperative, at a fraction of adtech reacquisition cost.

Thinks 1927

Noah Smith: “In the late 20th century, we2 invented three things that utterly changed the game. These three inventions were the lithium-ion battery, the rare-earth electric motor, and power electronics. A little over a year ago, I wrote a post about why these three inventions were such game-changers: Basically, these three things allow electric motors to replace combustion engines (and steam boilers) over a wide variety of applications. Batteries make it possible to store and transport electrical energy very compactly and extract that energy very quickly. Rare-earth motors make it possible to use electrical energy to create very strong torques — for example, the torque that turns the axles of a Tesla. And power electronics make it possible to exert fine control over large amounts of electric power — stopping and starting it, rerouting it, repurposing it for different uses, and so on. With these three technologies, combustion’s main advantages vanish in many domains. Whether it’s cars, drones, robots, or household appliances, electric technology now has both the power and the portability that only combustion technology used to enjoy.”

Siddharth Pai on OpenClaw: “The most useful term in this debate is the ‘lethal trifecta,’ popularized by Simon Willison. The three parts are precise. First, the agent has access to private or sensitive data. Second, it is exposed to untrusted content such as text, images or other material that an attacker can influence, whether through a webpage, email, document or bug report. Third, it can communicate externally; for example, by sending a message, calling an API or writing outside its trust boundary. The phrase ‘lethal trifecta’ doesn’t mean the software is evil, but that the architecture is dangerous. Private data supplies the prize, untrusted content supplies the attack path and external communication the escape route. If these features co-exist in one agent, prompt injection can turn a helpful assistant into an unwitting exfiltration channel.”

Ben Thompson: “Many of the biggest flaws from the original ChatGPT have been substantially mitigated, at least for verifiable use cases like coding: LLMs are much more likely to be right the first time, they reason over their results to increase their chances, and now agents actively verify the results without humans needing to be in the loop. That leaves one flaw: actually figuring out what to use these for.”

Asymco: “Apple turned 2 billion devices into the data center.​ Every iPhone, Mac, iPad gets distributed AI at a scale no server farm can match. While its rivals burn cash, Apple is doing the opposite. $90.7 billion in stock buybacks last fiscal year.​ Its competitors? Combined buybacks collapsed 74% from their peak.​ Apple didn’t miss the AI revolution. It just bet that the winners won’t be the ones who build the infrastructure. They’ll be the ones who own the customer and no one else on Earth owns the best customers.”

The 4R (Rest-Reach-Reactivate-Revenue) Motion: How to Recover the 80% That Martech Forgot (Part 3)

The 4R motion — what it is and what it is not

11. The 4R motion is not a campaign. A campaign has a start date, an end date, and a conversion objective. The 4R motion is a continuous operating model — a daily engagement architecture that runs beneath the campaign layer and prevents customers from drifting in the first place. It addresses all four stages of the Rest customer journey: identifying who is drifting (Rest), establishing daily presence (Reach), rebuilding the engagement habit (Reactivate), and converting recovered attention into incremental revenue (Revenue).

12. Each R is sequential and dependent on the previous one. Rest is the starting segment — customers who were engaged but are cooling. Without identifying them in real time, the motion cannot begin. Reach is the mechanism for daily presence without asking for anything in return. Without Reach, there is no surface for Reactivation. Reactivate is the process of rebuilding the engagement habit through daily micro-moments. Without it, Revenue is wishful thinking. The four stages are a logical chain, not a menu of tactics.

13. The motion is powered by the Three NEVERs. Never Lose Customers governs Rest identification — detecting drift before it becomes dormancy. Never Pay Twice governs Reach and Reactivate — exhausting owned channels before touching paid media. Never Pay Fixed governs Revenue — the Alpha2 pricing model that ties the vendor’s income to recovery outcomes, not activity metrics. The Three NEVERs are not slogans attached to the motion. They are the design principles that shaped each of its four stages.

14. The 4R motion sits inside Atrium. Meridian handles Best customers through outcome underwriting and N=1 personalisation via BrandTwins. Atrium handles Rest and Next through attention infrastructure: NeoMails, Mu, Magnets, ActionAds, and NeoNet. The 4R motion is the primary commercial motion within Atrium — the mechanism by which dormant revenue is recovered without going to adtech. Every component of Atrium was designed to serve at least one of the four stages.

15. The motion’s economic logic inverts the standard model. Standard retention marketing spends budget to send messages that may or may not work. The 4R motion generates revenue at the Reach stage — before any reactivation has occurred — through ActionAds embedded in NeoMails. The sending cost is covered by ad revenue. The brand pays nothing for the daily presence that Reach requires. Every reactivation that follows is pure recovered margin. This is ZeroCPM: the attention platform funds itself.

Thinks 1926

WSJ: “You can think of AI as a restaurant. The model is the chef. After it undergoes a period of intensive training, learning hundreds (or billions) of recipes and techniques, it is ready to begin taking orders. Inference is the day-to-day operation of the restaurant. Diners place their orders (often in the form of a query to a chatbot) and the chef prepares their meals (the chatbot’s response). Inference consists of two phases, known as prefill and decode. Prefill happens when a user enters a prompt, forcing the model to interpret the query by processing each word, symbol or image it contains. Decode is the process by which the model, using all it has learned in training, spits out a response to the query. The two phases of inference require different attributes from chips: Prefill demands more processing power, while decode requires more memory, in part because it has to draw on all the knowledge it has accumulated to serve up nice, piping-hot tokens to the user.”

Tyler Cowen: “If strong AI will lower the value of your human capital, your current wage is relatively high compared to your future wage.  That is an argument for working harder now, at least if your current and pending pay can rise with greater effort (not true for all jobs). If strong AI can at least potentially boost the value of your human capital, you should be investing in learning AI skills right now.  No need to fall behind on something so important.  You also might have the chance to use that money and buy into the proper capital and land assets. So…WORK HARDER!”

FT: “Human intellect rests on three pillars: seeing (observing the world), doing (intervening in it) and imagining (simulating what might happen under different choices). Right now, artificial intelligence inhabits only one of these pillars. Expanding existing frontier AI models will not address this problem. The breakthrough that set off today’s frenzy was the transformer architecture, developed at Google and scaled up into large language models trained on much of the public internet and used to write text and code. Then came agents that stitch these models together into automated workflows. Now the focus is on “world models”, which try to capture the physical environment from vast streams of video and other inputs. World models are an important evolution from LLMs. This so-called spatial intelligence is being used to develop technology that can enable driverless cars and robotic factory workers.”

WSJ: ““We are, hands down, the sweatiest animal on the planet,” writes Bill Gifford in “Hotwired.” His splendid book is devoted to a topic—the health benefits derived from exposure to high temperatures—that could be tedious in the wrong hands…He begins “Hotwired” with an accessible tour through the biology and evolutionary history of perspiration. There are two kinds of sweat glands in mammals: apocrine and eccrine. The vast majority of those in humans are the eccrine variety; adults have between two and four million in all. These glands are concentrated on the hands and feet and are missing from only a few places on the human body (our lips, for example). Eccrine sweat glands perform a critical function: cooling the body via evaporation. Mr. Gifford characterizes this adaptation, which dates back millions of years, as “evolution’s nuclear weapon.””

The 4R (Rest-Reach-Reactivate-Revenue) Motion: How to Recover the 80% That Martech Forgot (Part 2)

The Rest — the drifting majority no one serves

6. The BRN framework names what marketing has always had but never managed. Best customers are the top tier: consistently engaged, high lifetime value, the foundation of the business. Next customers are genuinely new — not yet acquired, or newly acquired. And then there are the Rest: formerly engaged, currently drifting, still recoverable. The largest unserved segment in any customer database, and the one martech was specifically designed to prevent from drifting. It has failed at this, almost universally.

7. Rest customers are not unhappy — they are uncommitted. An unhappy customer complains, unsubscribes, or writes a review. A drifting Rest customer does nothing. She still receives the emails. She opens occasionally. She has not decided to leave — she has simply stopped being regularly present. The relationship is cooling, not broken. That cooling is reversible. Yet CRM teams typically do the worst possible thing: they suppress the Rest to protect deliverability metrics. Suppression solves a local problem — inbox placement — while passing a much larger cost downstream to performance marketing. The brand preserved its sender score and destroyed its growth economics simultaneously.

8. Most brands have no Relate layer. The SNR diagnostic maps every message a brand sends against three categories: Sell (promotions, offers), Notify (transactional confirmations), and Relate (relationship content independent of any transaction). When you audit most brands, Relate is entirely absent. Every message either sells something or confirms something. There is no third mode — and its absence is the structural explanation for why the Rest drift.

9. The 30-day drift window is an economic threshold, not just a behavioural state. Customers do not switch from engaged to dormant overnight. They pass through a transition zone: declining open rates, longer gaps between purchases, increasingly passive behaviour. This transition — roughly 30 days in the drift phase before full dormancy — is where economics can still be changed. The longer the brand waits, the more likely the customer moves from owned attention to rented attention. Intervene during the window and recovery costs almost nothing. Let it pass and the customer crosses into dormancy, reappearing later in an adtech auction at five to ten times the retention cost.

10. The Rest are stranded capital — not a lost cause. A typical consumer brand with 500,000 customers has perhaps 100,000 Best customers and 400,000 in the Rest and beyond. The marketing budget serves the 100,000. The 400,000 are already in the database, already acquired, already identifiable — but receive almost nothing of value. This is stranded capital. The brand has already paid to create the relationship infrastructure; it simply lacks the mechanism to reactivate its economic potential. When these customers drift to dormancy and reappear in an adtech auction, the brand pays again. The Rest are not a CRM problem. They are an ignored asset waiting for a different operating model.

Thinks 1925

Dylan Patel: “The biggest bottleneck is compute. For that, the longest lead time supply chains are not power or data centers. They’re actually the semiconductor supply chains themselves. It switches back from power and data centers as a major bottleneck to chips. In the chip supply chain, there’s a number of different bottlenecks. There’s memory, logic wafers from TSMC, and the fabs themselves. Construction of the fabs takes two to three years, versus a data center which takes less than a year. We’ve seen Amazon build data centers in as fast as eight months. There’s a big difference in lead times because of the complexity of building the fab that actually makes the chips. The tools also have really long lead times. The bottlenecks, as we’ve scaled, have shifted based on what the supply chain is currently not able to do. It was CoWoS, power, and data centers, but those were all shorter lead time items. CoWoS is a much simpler process of packaging chips together. Power and data centers are ultimately way simpler than the actual manufacturing of the chips. There’s been some sliding of capacity across mobile or PC to data center chips, which has been somewhat fungible.”

Tim Wu: “Until now, companies like Meta and Google have relied on some powerful legal defenses. They do not deny that their products can be highly absorbing. But so, they contend, is a good novel, and no one suggests that a beach thriller is a public health hazard; a novel is speech protected by the First Amendment. Moreover, the companies note, unlike the publisher of a novel, which can still be responsible for defamation, social media companies cannot be held responsible for what appears on their platforms, thanks to Section 230 of the Communications Decency Act of 1996, which was intended to protect platforms from being destroyed by tort lawsuits. But changing circumstances have undercut these arguments. For one thing, if the platforms in the 1990s and 2000s were passive carriers of others’ content (albeit filtered by human moderators), they are now active purveyors. The platforms use aggressive tactics to keep users compulsively engaged — algorithmic recommendations, infinite scroll, auto video play and intermittent reinforcement (in which likes, comments and refreshed content are rewarded unpredictably rather than consistently). This goes far beyond merely hosting and moderating third-party content.”

Tom Rothman: “Windows, in entertainment lexicon, refers to a period during which a product — whether a film, TV show or sporting event — is available exclusively to the public in one place. For movies, these windows occur sequentially: first in movie theaters, then on home video, then on pay TV and streaming, then eventually on free TV. This system was meant to ensure that if you made a good film at an appropriate budget and the audience liked it, it would usually be profitable. Of course, if you made a bad film, all the windows in the world won’t save you.”

Jason Lemkin: “Let me ask you a simple question. When was the last time you were genuinely excited to buy a pre-AI SaaS tool? Not “fine with it.” Not “it gets the job done.” Genuinely excited. The way you were excited the first time you saw Slack replace email threads. Or the first time Figma made you forget Sketch existed. Or the first time Notion made you feel like your entire company’s knowledge actually lived somewhere…Now let me ask you a harder one: When was the last time you felt like you were overpaying on a renewal for a pre-AI SaaS tool — and seriously thought about cancelling?…Now ask yourself.  How do your customers feel about both these points?  Be brutally honest…Your product probably isn’t magical anymore. And your customers know it.”