Thinks 2036

Ruchir Sharma: “It’s an AI-driven world. Of course, this monomania will not last for ever. The speculative enthusiasm will fade even as the technological revolution endures and expands in scope. As was the case following the railroad boom of the 19th century and the internet craze at the turn of this century, a more balanced global market will eventually re-emerge. But so long as investors continue to see AI as the sole foundation of the next world order, they will keep ranking nations based on their tech prowess.”

Debashis Basu: “The result is that FPI selling and DMF buying have not been mirror images of one another. Foreign investors have largely been exiting one part of the market while domestic investors have been enthusiastically accumulating another. There is another flaw in the argument that retail investors have neutralised the impact of FPI selling. When FPIs sell Indian stocks, their action weakens the rupee, as we have seen last year. DMFs, in contrast, invest in rupees; their buying can support stock prices but does not bring in foreign exchange or strengthen the currency. India has certainly become much less dependent on FPIs than before. But the idea that retail investors have simply absorbed foreign selling is flawed in multiple ways.”

WSJ: “We are witnessing an extraordinary transfer of cash from the providers of AI—and, perhaps one day, AI users—to memory-chip makers…Ultimately, there are only three ways to deal with higher prices from chip suppliers, as the airlines can attest when oil rises. Make lower profits (the short-term response), find efficiencies so you need less (in the long run), or wait for more supply (as fat profits encourage production). All three are likely in AI, and investors need to think carefully about which parts of the AI stack will make money, and how long it will last.”

TheGreySwan: “For ~20 years, enterprise software built moats around stored business memory. The switching cost was not contractual. It was existential. Leave the CRM and you leave behind the accumulated memory of your business. That was a beautiful moat. Until the reasoning layer arrived above it. AI agents now sit on top of the CRM, pulling from emails, call transcripts, calendars, support tickets and product telemetry. The database does not disappear. It gets demoted. From castle to plumbing. Any incumbent whose moat was built on holding data rather than reasoning over it is vulnerable to the same inversion.”

The Software Foundry: The Third Affordability Revolution (Part 2)

The Last Artisanal Industry

Almost every important industry has made the journey from craft to industrial production. Textiles moved from handlooms to mechanised mills. Automobiles moved from workshops to assembly lines. Consumer goods moved from local workshops to globally integrated factories. In each case, something that had been made expensively by skilled hands became something made systematically at a fraction of the price — and the world got far more of it.

Software helped make many of those transitions possible. Yet software creation itself remained stubbornly dependent on craft. A serious software product has traditionally required a large and varied team: product managers to define what should be built, designers to shape how it works, engineers to write the code, testers to find defects, security specialists to protect it, infrastructure teams to operate it, consultants to implement it and support staff to help customers use it. Seventy years into the computer era, the production process would still be recognisable to a programmer from 1985.

Four production lines. Three found their factory. The fourth is the opportunity.

It is not that nobody tried. Higher-level languages reduced how much code humans had to write. Reusable libraries stopped every developer rebuilding common functions. Offshoring moved work to lower-cost locations. Low-code platforms let predefined applications be assembled faster. Open source gave builders components created by a worldwide community. All of these mattered. None removed the central constraint. A skilled human still had to understand what was needed, translate it into software, connect the parts, test the result, diagnose its failures and maintain it over time. The craftsman acquired better tools — but the production system still revolved around the craftsman. The bottleneck survived every assault, and so did the prices built on it.

The cloud then created a second, more subtle paradox: it industrialised software’s distribution without industrialising its creation. Before the cloud, software was packaged, installed and upgraded separately for each customer. SaaS replaced that with one centrally operated product delivered over the internet; the marginal cost of another user became almost nothing. By every rule of economics, prices should have collapsed. Instead they went up. Providers learned that software could be priced on customer value rather than production cost. Perpetual licences became subscriptions; subscriptions became per-user plans; plans became bundles, editions, add-ons and usage charges; annual increases became normal. The industry achieved factory-scale distribution of handmade goods — at handmade prices.

The paradox at the heart of the software industry.

Meanwhile the products themselves kept expanding. Every new customer segment brought requests for more controls, more integrations, more reports, more configuration. Features accumulated like geological layers; the product became a suite, and the suite became a platform. This expansion was not entirely wasteful — large, complex customers do need sophisticated capabilities. But a structural imbalance emerged: the product was designed for the totality of customer requirements, while each individual customer used only a fraction of it. A company might rely on a handful of workflows, reports and integrations, yet pay for hundreds of capabilities it never touches. A smaller company might reject the product altogether as too expensive and too complicated. Software achieved abundance in features — but not affordability in outcomes.

AI coding agents are the first technology in the industry’s history that changes the production function itself. Today’s agents can inspect a codebase, write features, generate tests, diagnose bugs, produce documentation and run many tasks in parallel; Anthropic’s own research on how its coding agent is used found that the large majority of interactions were automation rather than assistance — the agent doing the work, not helping a human do it. These remain evolving tools, and complex software still demands human architecture, judgement and governance. But the direction is unmistakable: more and more of the production process can be delegated to machines.

The significance is much larger than programmer productivity. A programmer becoming thirty per cent faster improves the economics of the existing software company. A small team directing a collection of agents to produce, test and operate many applications changes the nature of the company itself. The unit of production shifts from human hours to a system: specifications, agents, reusable components, tests and quality controls. Software begins to move from workshop to foundry.

That distinction is the essential one. A workshop produces one application through concentrated craftsmanship. A foundry creates a repeatable process through which many applications are produced. The foundry does not eliminate human expertise; it elevates it. Humans specify the problem, set the architectural principles, define the quality standards, inspect the exceptions and take responsibility for consequential decisions. AI performs a growing share of the construction, testing, documentation and maintenance.

Markets have already sensed the shift: the great software sell-off of early 2026 — a trillion dollars of SaaS market value repriced in weeks — was the sound of investors realising that per-seat pricing and feature-count value propositions sit on a foundation that is dissolving. But a sell-off is only demolition. The construction is what matters, and it has a precise threshold: The industrial revolution in software will not arrive when AI writes all the code. It will arrive when the tenth reliable application is materially cheaper and faster to produce than the first. That is the line between better tools and a new industry.

Thinks 2035

FT: “Scientists have unveiled synthetic cells aimed at revolutionising industrial production by serving as tiny biological factories to make environmentally friendly materials for goods from drugs to plastics. The new invention known as SpudCell will be made available to researchers around the world, as part of an expanding effort to put micro-scale bioengineering at the heart of lower-carbon manufacturing for everyday items. SpudCell is the first synthetic system to achieve a complete cell cycle of feeding, growth and reproduction, according to a paper released on Wednesday but not yet peer-reviewed. It is built from the bottom up using known non-living chemical components, as opposed to previous methods based on pared-down living cells and their naturally occurring parts.”

NYTimes: “The discovery of electricity did not just beget lightbulbs; in time, it enabled the modern mass production system and the entire vast digital revolution. A.I.’s transformations may be even more sweeping. But generative A.I. as it currently exists cannot easily replace human beings, because it cannot manifest human intelligence. That won’t stop it, however, from destabilizing society in ways more profound than we might even imagine. The sooner we update the way we think about the current state of A.I., the sooner we can all stop freaking out about the wrong things — and start preparing ourselves for the ways it really will transform our world.”

: “The memory supply-demand gap will keep widening through 2027.”

FT: “The difference lies not in the instincts of the strongmen but in the robustness of the systems they operate in. If a strongman ruler is already governing in a largely authoritarian system…they are almost impossible to get rid of. It might take a coup or an unusually effective ruling-class conspiracy to make a change at the top. But if the strongman has come to power in a system that still has free elections — as well as courts that retain some independence and a military that will not follow unconstitutional orders — then it is still possible to evict them from office.”

The Software Foundry: The Third Affordability Revolution (Part 1)

Thirty-Five Years Ago

Some essays begin with an idea. This one begins with a memory.

While gathering my thoughts for this series, an old book surfaced from thirty-five years back: Michael Cusumano’s Japan’s Software Factories: A Challenge to U.S. Management, published in 1991. Japan had just spent two decades stunning the world in automobiles, machine tools, semiconductors and computer hardware — winning not on cheap labour but on production systems that delivered quality, variety and relentless improvement at once. Cusumano documented what happened when Hitachi, Toshiba, NEC and Fujitsu turned that same industrial ambition on code itself: the deliberate evolution, as he put it, from “craft to factory modes of software production.”

The factories rested on three pillars. Modules were designed for reuse across projects, so programs were not built from scratch. Development followed strict, standardised phases rather than individual heroics. And statistical quality control — the discipline of the Toyota line — was applied to defects, producing failure rates American software houses could not approach. Treat code as manufacture, the thesis ran, and software would yield to industry the way cars had.

The book found me at a susceptible moment. In mid-1992 I returned to India to begin my entrepreneurial journey, carrying the dream of building software products from India. I tried a multimedia database. I tried an image-processing workbench. Both failed. And with them, quietly, died my dream of building a software factory from India.

But the phrase never left me. Software factory. Every few years it resurfaces and asks the same two questions: how do you industrialise the creation of software — and what would it take for India to build such factories?

For over three decades, the honest answer to the first question was: you cannot, not fully. The Japanese factories succeeded at exactly what they controlled — process, reuse, quality — and still did not conquer software, because everything they industrialised was arranged around the programmer, while the programmer remained the unit of production. Process discipline could polish the craft. It could not replace the craftsman. Japan proved the ambition was right and the technology was missing.

Thirty-five years later, the missing piece has arrived. This essay is that old idea, returned — with the one thing it always lacked.

**

Here is the strange thing about the industry Cusumano was studying. Software went on to industrialise everyone else — factories, supply chains, finance, commerce — but its own creation remained artisanal, exactly as he found it. It is still made by teams of highly skilled people working for months or years to turn requirements into designs, designs into code, code into tested products, and products into reliable services. The cloud transformed how software was distributed. It did not transform how software was created.

Artificial intelligence is beginning to do exactly that — and the result could be the third great affordability revolution of the modern era. China transformed the economics of physical products. India transformed the economics of technology services. AI can now transform the economics of the finished software product itself.

The vehicle will be the software foundry — Cusumano’s factory, rebuilt around the ingredient it never had: a production system that uses AI not simply to help programmers write code faster, but to manufacture focused, reliable, affordable software products, repeatedly. Its promise is not every feature for every possible customer. Its promise is more useful than that: identify the 10–20% of features that carry 80–90% of the customer’s utility — and deliver that utility at 10–20% of today’s price.

This is not merely cheaper software. It is the beginning of software abundance.

Thinks 2034

FT: “Because America is based on an idea, rather than ancestral ties, its revolution triggered a quest that can never be exhausted. Much is rightly made of the fact that many of the founders, notably America’s first and third presidents, George Washington and Thomas Jefferson, owned slaves while proclaiming the equality of men…The US example helped inspire freedom movements around the world, including the Spanish American wars of the early 19th century that liberated much of the hemisphere from colonial rule. The same creed helped insulate the US from the lure of foreign ideologies, notably Marxism and fascism, and motivated its prosecution of the cold war. It is no coincidence that a nation founded on ideas became the world’s leading source of new ones, commercial and political.”

Bruce Feiler: “Everyone is leaving jobs, losing relationships, researching strange diseases, and feeling like we’ve abdicated our relationships for our smartphones, in a lot of ways. Everyone is experiencing a type of existential home sickness. That’s when I realized that I wanted a way to reconnect, to remake all of these things and restitch the bonds in my life. I thought, “I need a ritual.” Rituals are one of the few things that we know work to hold us together. A ritual is a shared unnecessary act—shared because it connects with other people—that makes us feel at home. They are unnecessary acts because you don’t need to get down on one knee to propose or wear black to mourn. Yet, they become necessary to give us meaning. They also make us feel at home.”

NYTimes: “Private credit is essentially a rebranded version of high-interest-rate lending that was first popularized under the term “junk bonds” in the 1980s and later became a subset of “distressed” or “special situations” investing. In prior iterations, these practices centered on banks, which amalgamated money from deposits and divvied out loans. This had advantages — it was heavily regulated and relatively transparent — and disadvantages, in that it put the banks’ ordinary savers at risk. Laws enacted after the 2008-9 financial crisis made it far more difficult for banks to be directly involved in such loans. The demand for financing from fledgling companies didn’t disappear, so private equity firms stepped into the void under the name private credit and began lending money.”

Business Standard: “Our monster Consumer India just got more monstrous, with another shot in the arm. After the first phase of liberalisation in the 1990s — when the prices came down and quality went up (due to lower taxes, more competition) — came Chinese goods with great price-performance points and innovative everyday products that had not been seen before. Then came Amazon, with all its incredible merchandise assortment, prices and the ability to return anything you did not want. Now comes quick commerce. Even if QC is eventually contained, it will not be for a lack of customer enthusiasm but for a lack of supplier staying power and patience through the scaling period, or their shifting to a high-margin and high-ticket-size merchandising mix. Consumer India leapfrogs in the most interesting ways, skipping stages of evolution other markets went through. Offer it customer-perceived superior value and do not demand that it change its ways, and you have a hit.”

Two Engines, Four Zones: How NeoMarketing Redefines Profitable Growth

Published July 27, 2026

1

Marketing’s Missing Middle

Strip marketing down to its essentials and most brands are running exactly two engines. CRM handles retention: app, on-site personalisation, email, WhatsApp — all owned channels, all cheap, all effective for exactly as long as the customer keeps paying attention. Adtech handles acquisition: Google, Meta, marketplaces — powerful because it can find someone even after the brand’s owned channels have gone silent, expensive because that power is rented, not owned. For a genuinely new customer, that rent may be unavoidable. For a customer the brand already knows, it has a harsher name: AdWaste — paying a platform to reach a relationship the brand had already earned.

Two engines, doing two different jobs, is a perfectly reasonable way to run marketing — until you notice what happens in between them. A customer doesn’t move directly from engaged to lost. There’s a long middle stretch where something stalls: a KYC remains incomplete, a lead goes quiet, a quote sits unclosed, a renewal hangs pending, a first buyer never repeats, a Best customer begins to drift. CRM usually tried — a reminder went out, a journey fired — and then, because that’s how campaign tooling is built, it stopped trying. At that point most brands do one of four things: escalate to a call centre, send a list to an agency, push the customer into retargeting, or nothing at all until adtech sells the customer back. The brand ends up buying back a relationship it already had.

The fix isn’t a third engine. It’s making the two you have refuse to let anyone fall through silently.

NeoMarketing doesn’t propose scrapping CRM and adtech for something new. It keeps both engines and gives each one a second, accountable zone — a place to catch exactly what the original two-engine model was built to drop.

The retention engine is called Meridian. It exists for Best customers — the ones already delivering outsized value — and its job is maximising their lifetime value. Meridian runs two zones. Its normal zone is Retain: CRM 2.0, agentic marketing running on modern channels, doing what good retention has always tried to do. Its stress zone is Finish — the zone that didn’t used to exist. When a Best customer’s renewal quote goes stale, or their KYC upgrade stalls halfway through, Finish — delivered by Progency — completes the outcome rather than letting it quietly die in a queue. Both zones answer to the same discipline: Never Lose Customers.

The acquisition engine is called Atrium. It exists for Rest and Next customers — the ones who’ve gone quiet, and the ones never yet acquired — and its job is pushing the cost of reaching them toward zero. Atrium also runs two zones. Its recovery zone is Recover: Progency reactivating a dormant customer on owned channels before the brand ever reaches for its ad budget to buy them back. Its acquisition zone is Acquire: NeoNet, powered by ActionAds, finding new customers cooperatively — through attention another brand in the network has already earned — rather than renting cold attention from a platform that has none. Both zones answer to a different discipline: Never Pay Twice.

The third NEVER — Never Pay Fixed — is deliberately not a fifth zone. It’s the pricing discipline running underneath the accountable work: Beta as the baseline, Alpha as the verified uplift, Carry as the share of upside, applied through Finish and Recover and eventually through parts of NeoNet. In plain language: NeoMarketing doesn’t ask a brand to pay for activity. It asks to be paid for outcomes, proven against what would have happened anyway.

Notice the pattern in the diagram: Finish and Recover — the two zones that didn’t exist in ordinary marketing — are both delivered by the same team, Progency, even though they sit inside two different engines. That’s deliberate, and part two explains why it matters more than it first appears.

One limit worth stating honestly, up front rather than in a footnote. NeoNet reduces dependence on adtech; it doesn’t eliminate every form of paid acquisition. It works when someone in the cooperative network already knows the customer. Truly cold, net-new-to-category acquisition may still require paid media. The goal isn’t to abolish adtech overnight. The goal is to make adtech the fallback, not the first reflex.

NeoMarketing is the anti-martech, zero-AdWaste operating system for customer value: Retain what is active, Finish what is stuck, Recover what is dormant, and Acquire through trusted attention before renting reach from platforms.

The four zones at a glance

Engine Zone Offer Job NEVER
Meridian Retain CRM 2.0 Grow and protect active customers Never Lose Customers
Meridian Finish Progency-Finish Complete stuck outcomes after CRM stalls Never Lose Customers
Atrium Recover Progency-Recover Reactivate dormant customers before adtech Never Pay Twice
Atrium Acquire NeoNet + ActionAds Cooperative acquisition through trusted attention Never Pay Twice
Cross-cutting Alpha pricing Beta + Alpha + Carry Never Pay Fixed

2

From Framework to Operating System

Four zones and two engines make a clean diagram. What makes the diagram true rather than just tidy is that each zone is answering a specific, nameable failure — and that there’s a working system underneath, with real pricing discipline, a real build plan, and a real moat. This part covers both: the capabilities that make the zones function, and the operating decisions that keep them honest.

The Three A’s: why each zone actually works

Agentic closes the intelligence gap. Most brands don’t actually know their customers individually — they know segments, and segments are averages. Agentic capability, built on BrandTwins and coordinated through M-Agents, is what makes Retain and Finish possible at the level of a single customer’s specific, current context rather than a cohort’s general tendency. Without it, Retain degenerates into generic campaigns, and Finish has no way to know what, precisely, a given customer left unfinished. This is what makes Never Lose Customers achievable rather than aspirational.

Alpha closes the incentive gap. Knowing what to do isn’t the same as being paid fairly for doing it. Alpha pricing is what lets Finish and Recover be delivered without reintroducing the exact problem they exist to solve: paying for activity instead of results. Every intervention in these two zones is measured against a randomised, concurrent holdout — the brand’s current best effort, never a fictional “no contact” world — so the brand pays only for lift it can independently verify. This is Never Pay Fixed, made structural rather than promised.

Attention closes the attention gap. A customer can be technically reachable and still be drifting, because nothing in their inbox has given them a reason to keep opening. NeoMails, with habit-forming Magnets and  the currency Mu,  exists to keep a relationship warm independent of any transaction — the “Relate” category most brands send zero of today. This is what makes Recover and Acquire genuinely different from paying adtech twice for the same person: the attention being spent is owned and earned, not rented at a CPM. This is Never Pay Twice, operationalised.

Why the coral zones matter: Progency is not a fifth product

The most important detail in the four-zone diagram is the colour coding. The coral zones — Finish and Recover — are both Progency, regardless of which engine they sit inside. Finish sits inside Meridian because it deals with customers whose value is still visible but whose outcome has stalled. Recover sits inside Atrium because it deals with customers whose attention has decayed and must be rebuilt before paid media takes over. One accountable delivery function, stretched across two different customer states.

That framing prevents a failure mode worth naming: Progency becoming a fifth product bolted onto NeoMarketing with its own pitch, its own pipeline, and its own drift. It isn’t. It’s the accountable layer inside the two stress zones — and it operates only on declared leakage pools: named customer groups where the brand agrees, upfront, that value is leaking, the current best effort is insufficient, and the desired outcome can be measured. Progency doesn’t run BAU CRM. The in-house team keeps everything that’s working; Progency takes what’s stuck.

Sequencing: Finish first, Recover close behind

Finish should usually be the first proof path. Its cycles are shorter and its baselines cleaner: incomplete KYC, stuck leads, unclosed quotes, renewal completion, form completion, Pay-in-Email for active buyers, usable data capture. The customer is still warm enough to act, and the brand usually knows the unfinished job. The alternative being displaced is a call centre, a manual queue, an agency — or nothing — which means pricing anchors to the value of completed outcomes or the brand’s current cost per completion, not automatically to adtech.

Recover is the bigger strategic prize, and the harder one. Dormant customers have lower attention, staler signals, and more uncertain intent. But the alternative being displaced is usually adtech reacquisition, which makes the economic story sharper: recover this customer on owned channels before paying Google, Meta, or a marketplace to win back someone the brand already knew. The proof standard doesn’t change — treatment versus current best effort, with a holdout, and Carry only on verified Alpha.

One pricing temptation deserves explicit discipline: a flat price per email open. It looks like an easy entry point, and it quietly becomes the same exposure-based mechanic NeoMarketing argues against — a CPM by another name. Weak intent can, at most, sit inside Beta as a small activation floor. The real payment must come from what that intent produces: action, usable data, progress, revenue, or verified uplift. That’s how Never Pay Fixed stays intact under commercial pressure, not just in the doctrine document.

The build: an intelligence layer, not a new platform

The operating stack follows the same discipline. NeoMarketing should not rebuild customer engagement infrastructure from scratch. The right build is an intelligence and operating layer over rails that already exist: CE, email, WhatsApp, RCS, CDP integrations, tracking middleware, Pay-in-Email, and NeoNet and ActionAds where relevant. M-Agents automate the repeatable parts of the runbook; Martech Growth Engineers supply judgement and hold the client relationship. Automation follows repeated runbooks — it is not a prerequisite for the first pilots. The factory is the destination, not the starting requirement.

And the moat is not integration complexity — integration is merely the cost of building. The moat is the Decision Trace Graph: a structured memory of context, action, channel, treatment, holdout status, cost, outcome, and next state, written back from every Finish and Recover engagement. Over time those traces become a proprietary learning system — what actually moves customers across zones, categories, and brands. Models will commoditise. Verified decision memory will not.

The redefinition, in one breath

Two engines, four zones, one Alpha discipline. Meridian maximises LTV through Retain and Finish. Atrium minimises CAC through Recover and Acquire. Alpha pricing ensures the accountable middle is paid on outcomes, not activity — and every intervention writes back a trace that makes the next one sharper. Old marketing measured what went out. NeoMarketing measures what moved, and who’s accountable if it doesn’t.

Retain. Finish. Recover. Acquire.

Max LTV. Minimise CAC. Eliminate AdWaste.

3

Mapping to the TAT

Two Engines, Four Zones is the story a CMO hears first. But it sits on top of an older, more granular instrument: the Transaction-Attention Table, or TAT — the grid that maps every identified customer by how much they’ve bought and whether they’re still paying attention. Rows are transaction depth: None, One, Repeat. Columns are attention recency: Strong (engaged within 30 days), Weakening (31 to 90 days), Lost (90-plus days of silence). Every customer lands in exactly one of nine cells, and each column already carries a verb of its own: Grow the Strong, Protect the Weakening, Recover the Lost.

The four zones and the three TAT columns are describing the same territory at two different altitudes, and leaving the relationship implicit invites a vocabulary split: one team talking in zones, another in columns, both right and unable to tell. TAT is the diagnostic grid a brand’s own analyst builds and reads. NeoMarketing is the accountable operating system built around what that grid shows — it takes the diagnosis and asks who will act, what they’ll be paid for, and how the movement gets proven. Getting the correspondence right matters more than it looks, because it settles a question that would otherwise resurface in every deck: is Retain the same thing as Grow? Is Finish just another name for Protect? The honest answers are close, but not identical, and the gap between close and identical is where a pilot’s scope gets drawn.

Retain maps to Grow, but only partly. Grow spans all three depth rows — converting known prospects who haven’t bought (the First play), moving one-time buyers across the second-purchase inflection (Second), and keeping proven repeat buyers buying (Repeat). Most of that is Track 1 work: CRM 2.0, run directly by the brand, sold as leverage rather than underwritten. Retain, in the Meridian sense — Alpha-priced, outcome-underwritten — is specifically the overlay on the Best/Repeat cell, where the stakes are highest and the measurement is cleanest. The rest of Grow stays exactly where it’s always been.

Recover maps to Recover with no qualification needed — this is the one exact match. TAT’s Lost column already carries the name; the cells inside it, R1, R2, R3, are precisely Progency’s Recover territory, prioritised by which row a lapsed customer fell from. The work runs primarily on owned channels, though NeoNet’s cooperative signal can occasionally sharpen it too — the naming system defines NeoNet itself around “deterministic customer recovery,” so a lapsed customer this brand has lost attention on may still be warm to another brand in the network. That’s a supporting mechanism, not the main one; owned channels do most of Recover’s work. (The R stands for Rest, the segment state, not for Recover, the verb applied to it. Two different words, same letter, easy to conflate in a room.)

Finish is the mapping that needs the most care. It’s tempting to write Finish equals Protect and move on, but that overstates the fit. Protect is attention-state language — a customer drifting from Strong toward Lost, regardless of whether anything specific is unfinished. Finish is outcome-state language — a named job left incomplete: a KYC form, a quote, a renewal, a cart. A customer can sit in the Grow column, fully engaged, and still have an abandoned cart from yesterday that needs finishing. So Finish isn’t contained by Protect. What’s true, and worth saying precisely, is that Finish’s richest pools concentrate in Protect — because a weakening customer with a stalled outcome is exactly where a brand’s own current effort is thinnest, and thin baselines are where a holdout can prove the most. That’s the same logic that already told us not to lead with abandoned cart: the Grow-column version of Finish is real, just rarely the best place to start.

Acquire sits outside the grid entirely, by construction. TAT only plots customers a brand has already identified — even its emptiest cell, None, is full of known people: subscribers, registered users, abandoned browsers. Acquire is for the customer this brand hasn’t met yet, found instead through someone else’s earned attention inside the NeoNet cooperative. The moment that customer responds, they stop being outside the table and become a fresh entry in the None row — Acquire’s job was only ever to be the door.

Put together, the four zones tile the entire TAT and the one population it can’t see, with no zone left unaccounted for and no cell double-claimed. TAT measures the portfolio. NeoMarketing assigns accountability. Progency underwrites the two stress zones. That completeness is the real payoff of doing this mapping properly rather than loosely.

 

Thinks 2033

Paul Graham: “One of the biggest advantages of AI will be that it lets companies get further before they cross the lines (at about 10 and about 150 people) beyond which groups become less productive.” [via Arnold Kling] More from Arnold: “Experienced software developers are used to working in teams. They have gotten accustomed to relying on Cursor. But at some point, we will see the emergence of software developers who are used to working alone. Cursor appeals to software engineers who are used to working without AI and who want to be able to see the code. My prediction is that in a few years the best software engineers will trust the best coding AI’s Instead of Cursor, what will ultimately emerge are tools that optimize for communication between a lone developer and AI.”

FT: “Uniqlo releases only 800 new designs per year, and only about half of those are changed in its six-monthly renewal cycles. By contrast Zara, a fast-fashion pioneer, churns out thousands of designs every year, with new iterations arriving every fortnight. At Shein, thousands are released each day. Smaller ranges mean Uniqlo might end up selling a million units of a single T-shirt design versus, say, less than 100,000 at fast-fashion houses, generating scale and cost advantages when it comes to raw materials, sewing and dyeing. The resulting high-quality products at reasonable prices were a winning model in the deflationary market of post-bubble Japan. Some analysts believe its lower gross profit margin relative to Inditex indicated how Uniqlo passed the price and quality benefit on to its cost-conscious consumers.”

NYTimes: “[A study] suggests that the most dangerous times for investors are when the market is high — and we may be in such a time right now. The study, by Hendrik Bessembinder, a finance professor at Arizona State University, shows that most of the biggest losers since 1926 were tech companies. They included stocks that boomed during the dot-com era and in the halcyon days just before the financial crisis that began in 2007 — and many crashed when those boom cycles ended.”

WSJ: “Unlike in previous tech cycles, corporate adoption of AI rests on all employees—not just developers—picking up on the technology. AI is increasingly being billed by usage, and the price of tokens, the basic unit of AI computing, has been volatile. That all translates to higher costs for AI.”

The Pitch That Finished the Argument: A Progency Conversation

Published July 26, 2026

CAST

Arjun — NeoMarketing sales lead, Netcore. Same audit discipline as the first meeting. Three months on, he’s back with a different kind of page.

Maya — CMO of the same D2C fashion brand. The 90-day NeoMails test closed out. She isn’t asking whether the doctrine is real any more — she’s asking who is accountable for running it.

Setting: Maya’s office, three months after the pilot closed. Her REACQ% has moved. Her Real Reach has moved. This time, she requested the meeting.

Before the Meeting

Maya’s Monday starts the way it always has: a dashboard that looks busy enough to be reassuring. Campaigns shipped on schedule. Journeys are running. The agency sent its weekly update. Underneath the campaign layer, though, she’s started looking at a different number — 31,000 stalled KYC upgrades from the last quarter, sitting in a queue nobody owns. The dashboard didn’t lie about what went out. It just never measured what got finished. That’s the number she wants to talk about today, not the pilot recap.

1

The Handoff Problem

“So Now You’re Selling Me a Third Thing?”

Maya’s mental shift: proof of concept isn’t the same question as who runs it at scale.

Arjun sits. No printed page this time — he opens with a question instead.

Arjun:  Before I show you anything, tell me what broke when you tried to run the next stage yourselves.

Maya doesn’t hesitate. She’s been waiting to say this.

Maya:  Nothing broke. It just didn’t scale. My team ran the 100,000-ID test beautifully because it was 100,000 IDs and it had my best analyst on it for ninety days. My Rest base is 2.8 million. I don’t have four more of her. And it’s not just Rest — I pulled the KYC queue last week. Thirty-one thousand upgrades stalled, some for months. Nobody owns that number. It just sits there.

Arjun:  That’s not a people problem. It’s a bandwidth ceiling every in-house team hits at the same point — segment refresh, message variants, journey branches. It’s structural, not a reflection on your team.

Maya:  Fine. So who runs it? Because if the answer is a new vendor, I want to say now — I already have Netcore for the platform, I already have you for Atrium and Meridian. I am not adding a fourth relationship to manage a problem you told me was one system.

Arjun places a single card on the table — no deck, one page.

Arjun:  It isn’t a fourth relationship. It’s the delivery arm of the two you already have.

Maya:  Walk me through it. Slowly.

Arjun:  Meridian is the underwriting logic for your Best customers — Beta plus Alpha plus Carry, the outcome contract. Atrium is the same logic for Rest and Next. Neither of those is a team that shows up and does the work every day. Progency is that team — Martech Growth Engineers, running the actual interventions inside Meridian’s rules for your Best customers, and inside Atrium’s rules for your Rest base. One name. One accountable team. It doesn’t sit beside Meridian and Atrium — it’s how they get delivered.

Maya:  So when my Best customer’s renewal quote goes stale, that’s Progency working inside Meridian’s rules. And when a dormant subscriber needs winning back before I pay Meta for her again, that’s Progency inside Atrium’s rules. And my stalled KYC pool — those are engaged customers mid-upgrade, not dormant — so that’s Meridian’s side too.

Arjun:  Exactly right. We call the first two Finish and the second Recover. Different customers, different job, same team, same accountability line back to you.

Maya reads the card again. She just leaves it face up on the desk, which Arjun has learned to read as a good sign.

Maya:  There’s a political question underneath this too, and I want to ask it directly. My in-house team is going to hear ‘Progency’ and assume you’re taking over their job. How do I tell them that’s not what’s happening?

Arjun:  You tell them the truth, and it holds up: Progency doesn’t touch BAU. It only works declared leakage pools — named groups where your own team has already agreed the journey has stalled, the outcome is measurable, and there’s a clear baseline to beat. Your team keeps everything else: brand, strategy, the customers CRM already serves well. We’re not asking for their job. We’re asking for the pools they’ve already told you they can’t get to.

Maya:  That I can sell internally. Alright. I believe the structure. I don’t yet believe the economics. Let’s get into that.

Key Takeaway: Maya’s objection was never about whether the doctrine works. It was about vendor sprawl and internal politics. The answer that lands isn’t a better pitch — it’s proof that nothing new is being added to her stack, and nothing is being taken from her team.

2

Under Fire

“My Retargeting Already Does That. Why Do I Need You?”

Maya’s mental shift: from structural sign-off to pricing sign-off.

Maya pulls up her own dashboard — turning her screen so Arjun can see it.

Maya:  Here’s my abandoned-cart flow. Dynamic ads, product-level, running right now on Meta. You’re going to tell me Progency does this better, and I want to know exactly why, because on paper this already looks like one-to-one targeting to me.

Arjun:  It is one-to-one targeting. I’m not going to tell you adtech can’t do this — that claim doesn’t survive five minutes with your own dashboard open. What I’ll say instead: Meta is matching a device to a product probabilistically and charging you a CPM to show it again. We already know deterministically who this customer is, we know exactly which size and colour she left in the cart, the follow-up costs us next to nothing to send, and because it runs against a holdout you agree to upfront, you get a clean number for what it actually recovered — not an attribution model’s estimate.

Maya:  So the pitch isn’t ‘adtech can’t.’ It’s ‘we’re cheaper, more precise, and provable.’ Which also means cart probably isn’t where I should start — you’ve just told me my own baseline there is already decent.

Arjun:  That’s exactly right, and most brands miss it. Cart is the most-solved leak in the business, which makes it the worst place to prove Alpha — the gap between us and your current effort is narrowest exactly there. Your KYC pool is a better first test. Nobody’s retargeting a half-finished KYC form.

Maya sits back. This is the point in the last meeting where the conversation turned to money. She gets there faster this time.

Maya:  My finance team already killed one version of this. Someone on your side proposed a flat fee per email open on the dormant base. My head of ops called it ‘charging us to annoy our own customers.’ It was a hard no, and I don’t want to relitigate it.

Arjun:  You’re right to have killed it. That proposal was a mistake — an open is an impression, not an outcome. Charging for it is the exact thing Never Pay Fixed exists to stop, and I’d have told your ops team the same thing if I’d been in that room.

Maya:  Then what do I actually pay for?

Arjun:  A ladder, with names your finance team can actually look up. You never pay for an open. Pay-for-Action starts weak — a click, a reply — and climbs to strong: a completed KYC step, a qualified lead, a quote requested. Pay-for-Data sits alongside it — we hand back a qualified field your CRM was missing, like a renewal date, and it’s priced by usable fields, not raw data points. Pay-in-Email sits at the top, on the Beta-Alpha-Carry structure, whenever you’re ready for it.

Maya:  And if I never move past Pay-for-Action?

Arjun:  Then that’s what this pool is worth to you, and we don’t force the issue. But most brands climb the ladder once the first rung proves out — because the number that convinces a CFO is never the pitch, it’s the first quarter’s actual holdout comparison.

Maya:  One more thing before we move on. If I run both Finish and Recover, which one do I actually get first? I don’t have budget or attention for both to start simultaneously.

Arjun:  Finish, honestly. It’s not the bigger story — Recover, reactivating someone before Meta re-buys her, is the sentence that gets vendors like me in the door. But Finish has shorter cycles. A stalled KYC step resolves in weeks. Recover on a genuinely dormant customer takes longer to prove and longer to fund, since we’re not paid until the outcome lands. Start with Finish, let it pay for itself faster, and run Recover alongside it once the first cheque clears.

Maya writes one word on her notepad: sequencing. Underlines it once.

Key Takeaway: Maya’s finance team had already correctly rejected a flawed pricing model. Arjun’s job wasn’t to defend it — it was to agree it was wrong and show what replaced it. Conceding a bad idea landed harder than defending a good one would have.

3

The Close

“What’s Actually Yours, and What’s Everyone Else’s Too?”

Maya’s mental shift: from pricing sign-off to a test she can defend upward.

Maya leans forward — the question she always asks, arriving a little earlier than usual this time.

Maya:  Here’s what worries me longer term. The tooling you’ve described — tracking, templates, the AMP layer — none of that sounds hard for a competitor to copy in a year. What actually stops me from switching to whoever undercuts you next?

Arjun:  Nothing stops you, on the tooling. You’re right that any competent vendor can build similar utilities eventually. What they can’t copy is the record underneath it — every action Progency has run, on every type of stuck customer, across every brand we’ve touched, and what it actually produced. We call it the Decision Trace Graph. It’s inside our Context Graphs, and every intervention writes back to it. A year from now, the tenth brand we do this for benefits from what we learned on the first nine. A new entrant starts at zero.

Maya:  So the tools get me started. The trace record is what compounds.

Arjun:  That’s the honest version, yes.

Maya nods slowly — the same motion Arjun remembers from the end of the first meeting.

Maya:  Fifteen years in this industry. Every eighteen months someone tells me the delivery model has changed and this time it’s structural. Convince me this isn’t that, one more time, quickly.

Arjun:  I won’t try to convince you in the abstract. Give me one pool — not your whole Rest base, not a transformation programme. One declared leakage pool, a fixed window, a holdout your own team agrees to upfront. If it doesn’t beat the holdout, you’ve lost two months on customers who were already stuck. If it does, we’ve proven the delivery model on your own data, not a case study from someone else’s brand.

He places the second card of the meeting on the table.

Maya:  Stalled KYC upgrades. That’s the pool I already told you about — thirty-one thousand of them, and I know it’s been ignored for two quarters.

Arjun:  Then that’s the pool.

Maya:  Sixty days. No platform change, no new budget line, Pay-for-Action so my finance team doesn’t reopen the objection from last time. And I want the review to be more specific than ‘did it work.’

Arjun:  What does the review look like on your side?

 

Maya:  Five questions, not a deck. How many customers actually moved. What they actually did. What usable data we captured along the way. What revenue it produced, if any. And what happened next to the holdout. If you can’t answer those five cleanly at day sixty, I don’t care how good the pitch was.

Arjun:  Agreed — those are the right five, and they’re the same ones we’d hold ourselves to internally. One thing — do you want this framed as a Progency engagement in the write-up, or folded into the Meridian relationship you already have signed off?

Maya:  Fold it in. My board already approved Meridian. I’d rather this look like Meridian doing its job properly than a new line item.

Arjun:  Noted. It runs under Meridian.

Key Takeaway: Maya didn’t ask for a bigger commitment — she asked for the smallest one that would still tell her the truth, and she named her own success criteria rather than accepting Arjun’s. That’s the same shape as her first close, and it’s the shape Progency is built to survive: no transformation required, just a fair test on one real pool, judged on the buyer’s terms.

After the Meeting

Arjun leaves the cards on the table this time — Maya asked him to. She pulls up the KYC pool herself before her next meeting starts: names, stalled dates, how long each one has sat untouched. She’s seen this data before. She has just never looked at it as something with a price on either side of the ledger — what it’s costing her to ignore, and what it would cost to finally close. She forwards the pool definition to her ops lead with one line: “Sixty days. Five questions. Let’s find out.”

Thinks 2032

[Via Arnold Kling]: “The revolutions are led by the educated-but-blocked: young people with enough knowledge to understand the system and enough frustration to want to tear it down.”

FT: “The wisdom of crowds has never lain in the consensus. It lies in the disagreement. Once everyone thinks alike, crowds are error-prone. ”

NYTimes: “Bending Spoons is not the only firm chasing tech’s castoffs. Constellation Software, a Canadian company with $11.6 billion in annual revenue, specializes in buying business-oriented software and technology companies. A Los Angeles company called MediaLab, run by Michael Heyward, the founder of the anonymous social media app Whisper, has bought the assets of Imgur, the image-sharing site; Kik, the messaging app; and Genius, the music lyrics site. Since it was founded in 2013, Bending Spoons has purchased more than 50 companies. Last year it generated $1.3 billion in revenue.”

Ethan Mollick: “Being on an exponential means each change over a fixed window is larger than the one before it. If your organization wrote an AI plan any time before the winter of 2025, it described a system that could do a couple of hours of work with a fairly high error rate. A few months later, you can get sixteen hours or more of work from a single prompt. This is why AI keeps feeling like it is making leaps, even though it is a curve on a graph, we keep experiencing a steady doubling of capability as a series of shocks. We are very bad at feeling exponentials from the inside, and we are currently inside one.”

The Space Between: How Progency closes the gap between CRM and Adtech

Published July 25, 2026

1

The Gap No One Gets Paid to Close

A cart gets abandoned. A KYC form is left half-filled. A lead goes quiet after showing real interest. In most companies, the same thing happens next: CRM tries a few times, then stops. Weeks later, the same customer starts turning up in retargeting ads — the brand paying a platform to remind someone of a relationship it already owns.

This is the pattern behind NeoMarketing’s central complaint: brands routinely pay twice for the same customer — once to acquire them, again to remind them they exist. NeoMarketing’s Three NEVERs name the fix directly: Never Lose Customers, Never Pay Twice, Never Pay Fixed.

Turning that into a working system starts with a segmentation few brands do explicitly — BRTN: Best, Rest, Test, Next. Best customers — typically the top fifth, engaged within the last month — already deliver outsized value; the job is protecting and growing it. Rest customers have gone quiet over the past one to three months — not lost, just drifting; the job is stopping the drift before it hardens. Test customers have been silent for ninety days or more — the dormant base every brand carries and few will name; the job is reclaiming them on owned channels before adtech sells them back. Next customers haven’t been acquired at all yet. Most brands serve only Best and Next — they reward loyalty and celebrate acquisition — while the middle two segments, where sixty to seventy per cent of the base usually sits, are exactly where the leak lives. (Operationally, Test sits within Rest: two stations on the same slide from attention to silence.)

NeoCore is the engine built to run this structure. Meridian serves Best customers, underwriting outcomes to maximise lifetime value. Atrium serves Rest and Next, running an attention marketplace built to push the cost of reactivation and acquisition toward zero.

Why the relationship goes quiet in the first place

It helps to be precise about why CRM’s messages stop working, rather than treating drift as an unavoidable fact of life. Email has four jobs — SNDR: Sell, Notify, Digest, Relate. Sell is the commercial ask: an offer, a promotion, a conversion nudge. Notify is the service layer: an order confirmation, a shipping update. Digest curates the customer’s world — markets, destinations, ingredients, whatever the category knows well — with the brand as editor rather than subject. Relate builds the relationship itself: recognition, rituals, content that asks for nothing. Most brands send only the first two — and both are withdrawals from the customer’s attention, because every one either asks for something or merely reports a transaction. Digest and Relate — the two deposits — are the emails most brands have never sent. That explains something CRM dashboards usually hide: a customer can be technically “reachable” and still be drifting, because nothing in the inbox has given them a reason to keep opening. BRTN tells you where a customer has drifted to. SNDR tells you why.

What it costs to look the other way

The alternative to closing this gap isn’t free. At a typical 4-5x return on ad spend, a brand hands back roughly 20-25% of the revenue generated on that campaign, in fees, to the platform that delivered it. For a genuinely new customer, that’s simply the cost of acquisition. For a customer the brand has met before — whose email is sitting quietly in the CRM, unresponsive but not deleted — that 20-25% is money paid a second time for something already owned.

Between how CRM behaves and how Meridian and Atrium operate sits a gap most martech stacks quietly ignore. CRM’s automation gives up on a customer after a fixed number of retries — that’s how campaign tools are built. Adtech has the opposite economics: it takes over exactly when CRM stops trying, and starts charging the brand a media fee to reach the same person again. In between — after CRM has stalled, before the brand pays adtech to re-acquire — sits a stretch of unfinished value that neither system is actually built to close.

Call it the post-CRM, pre-Adtech gap. It’s also pre-call-centre, pre-agency, and pre-manual-escalation in a great many cases — any point where the brand still owns the identity, the history and the context, but has no accountable operator for what’s left unfinished. Nobody in the standard stack is accountable for it, priced for it, or even measuring it, because it falls between systems, each designed to hand it off rather than close it.

This is the specific space Progency exists to close. Progency is NeoCore’s managed-service layer, run by Martech Growth Engineers (MGEs) working with M-Agents — the agent collective that does the machine-scale work of cohort discovery, message variants, and journey decisioning, while the MGEs supply judgement, governance, and the accountability line back to the brand. The pairing matters: without the agents, a managed-service team is simply the client’s own CRM team outsourced — same bandwidth ceiling, same economics, same give-up point. With them, a small number of MGEs can run outcome-based interventions across pools no in-house team has the capacity to touch — exactly the customers CRM has stopped trying and adtech hasn’t yet re-bought. It isn’t a new platform and it isn’t a new segment. It’s a dedicated operating function sitting precisely in the space the rest of the stack leaves empty.

Naming the gap explicitly turns an invisible leak into something measurable, sellable, and priceable. Once you can point to this customer, this unfinished journey, this exact moment CRM gave up, you can ask a sharper question than “how do we get more customers?” You can ask: how much of the value we already have is leaking through this gap right now, and what would it cost to close it?

The answer differs depending on who the customer is. A customer who has gone quiet entirely is a different problem from one who was actively transacting right up until the journey broke. Progency treats them as two distinct books of business, priced against two different alternatives — which is where the next part picks up.

2

Two Books, Two Alternatives

Not every customer who falls into the post-CRM, pre-Adtech gap got there the same way, and treating them as one undifferentiated pool is the fastest route to underpricing the harder cases and overpaying for the easy ones. Progency splits the gap into two books.

Finish is for the engaged-but-stuck base — customers mid-journey when things broke: an abandoned cart, an incomplete KYC form, a lead that cooled after real interest, a quote never followed up. These customers haven’t drifted away; a specific transaction simply never completed. Here the alternative isn’t adtech at all — it’s whatever the brand’s own fallback happens to be: a call-centre follow-up, an agency retainer, a manual outreach queue, or in a lot of cases, nothing.

Recover is for the non-engaged base — lapsed buyers, dormant subscribers, one-time purchasers who never returned. For this book, the alternative the brand is weighing is almost always Adtech: pay a platform to put this same customer back in view via a paid ad. Recover’s job is to win the customer back on owned channels first, at a fraction of that cost, before the brand ever reaches for its ad budget. It’s priced against what paid reacquisition would have cost — a return equivalent to roughly 6x has proven realistic, or roughly half of what an equivalent paid campaign would run.

That distinction corrects a claim that’s tempting to make and doesn’t survive scrutiny: that these plays “never go to adtech, because adtech can only target cohorts.” That isn’t accurate. Dynamic retargeting — the kind that shows someone an ad for the exact product left in their cart — already does precise, one-to-one targeting. Adtech is entirely capable of chasing an abandoned cart.

The honest differentiator isn’t that adtech can’t do this. It’s that owned channels do it better, on four counts. Identity: Progency already knows exactly who this customer is, deterministically, with no probabilistic ad-matching involved. Context: it knows precisely what was left unfinished — which product, which form field, which step — not just a broad interest signal. Cost: another message to a known customer costs a fraction of a paid impression. Proof: because the intervention runs on owned channels against a proper holdout group, the lift is measured cleanly, rather than inferred through an ad platform’s own attribution model.

The declared leakage pool

Neither book starts with Progency simply being handed a customer list. It starts with a declared leakage pool — a specific, named group of customers where the brand, the in-house CRM team, and Progency agree on three things upfront: the current journey has genuinely stalled, the desired outcome is measurable, and the comparison baseline (what the brand’s own current best effort achieves) is clear. Incomplete KYC applications from the last quarter. Leads that went cold after a product demo. A suppressed email segment nobody has touched in six months. Each is a pool with a name, a size, and an agreed definition of success — not a vague mandate to “help with retention.”

That boundary does two jobs at once. First, it protects the in-house marketing team politically: Progency isn’t taking over BAU CRM, brand, or strategy — it’s working the specific pools the team has already agreed are stuck, dormant, or too costly to chase through existing methods. Second, it protects Progency from becoming generic services. Without the declared-pool discipline, any outcome-shaped request could get pitched as “Progency,” which dilutes the model into something that looks like an agency retainer with extra steps. A declared pool, an agreed baseline, and a measurable outcome are what keep this a specific, underwritten service rather than marketing-as-a-vague-favour.

Put simply: adtech can chase these moments. Progency finishes them — faster, cheaper, and with cleaner proof of what actually worked.

The practical upshot for a CMO is that Finish and Recover aren’t two versions of the same pitch dressed up differently — they solve different problems, against different competitors, on different customer states. Most brands run both problems simultaneously: a live stream of engaged customers falling out of half-finished journeys every day, and a Rest segment slowly decaying toward being repurchased by adtech.

Because the alternative each book displaces is different, the price for each has to be different too — and that’s where an earlier temptation, pricing everything the same way, runs into a problem the next part addresses directly.

3

Paying for Progress, Not Attention

One of the Three NEVERs is Never Pay Fixed — the idea that brands shouldn’t pay a platform for exposure regardless of whether it produced anything. It’s the core complaint against adtech’s CPM model: pay for the impression, hope for the result.

It would be easy for Progency to quietly reintroduce the same problem from the other side — charging a flat fee per email opened, regardless of what happens next. An open is a rendering event. It tells you the message reached an inbox and someone glanced at it. It doesn’t tell you whether the customer moved a step closer to finishing the KYC form, replying to the follow-up, or returning to the cart. Billing for it anyway would be paying for attention, not outcome — the exact pattern NeoMarketing exists to end, wearing a different logo. It would also make the pricing indistinguishable from a CPM by another name.

So opens stay where they belong: on the diagnostic dashboards NeoMarketing already runs to track attention health (Click Retention Rate, Real Reach, and the rest of the NEVER Metrics) — not on the invoice. What Progency prices instead is a ladder of named, verified commercial units, each closer to revenue than the last.

Pay-for-Action covers two of the rungs. At its weakest, that’s a click, an in-message tap, or a reply — a real, verified action, but not yet meaningful progress. At its strongest, it’s a completed form step, a qualified lead, or a quote requested — the customer visibly moving through the exact journey that had stalled. Pay-for-Data sits alongside it: a preference answered, a renewal date confirmed, a missing KYC field filled in — first-party information the brand didn’t have before, written back into the Customer Context Graph, priced by qualified usable fields rather than raw data points. At the top of the ladder, Pay-in-Email prices a transaction completed inside the message itself, and Carry pays a share of verified uplift on larger pools with a clean holdout.

Why the ladder needs more than one rung

Pay-in-Email is the cleanest outcome — money moves, the result is visible — but it can’t be the only rung, because plenty of brands hesitate to commit to it on day one. The hesitation is rarely about the economics; it’s operational. Finance wants to know who owns the payment flow. Legal wants sign-off on a new transaction path. Whoever runs the dormant base doesn’t want the first message a lapsed customer receives in months to ask for money — that reads as pushing them further away, not winning them back. None of that is irrational, and Progency shouldn’t need a brand to clear every internal hurdle before a pilot can start.

That’s the entire argument for the ladder existing: a brand that isn’t ready for Pay-in-Email can start on Pay-for-Action or Pay-for-Data, see the mechanism work on a small, safe slice of its Rest base or a single stuck-lead pool, and climb toward Revenue and Carry once trust is established. Underneath all four tiers sits the same contract logic NeoCore uses for Meridian’s Best-customer outcomes: a small baseline payment (Beta), an upside tied to verified lift above what the brand’s own current effort would have achieved (Alpha), and a payout on that lift (Carry) — always measured against a randomised, concurrent holdout, never against doing nothing at all. That comparison to the brand’s actual current best effort, not to silence, is what makes the lift figure defensible in front of a CFO rather than a marketing team’s own optimistic assumption.

What doesn’t change is the underlying discipline: every tier is still an outcome, verified against a holdout, never a payment for reach alone. The ladder makes the model easier to start. It doesn’t make it easier to cheat.

That leaves the practical question of where to start climbing — which pool to run first, and why the obvious answer usually isn’t the right one.

4

The Operating Layer

Progency shouldn’t begin by building a new customer engagement platform. Netcore already has the rails — email, CE, CPaaS, WhatsApp, RCS, CDP integrations, Unbxd. The right move is an intelligence and operating layer above what already exists, not a rebuilt stack underneath it. Every new idea is tempted to become a new platform; Progency has to resist that temptation deliberately, because the first version isn’t software sold to the brand — it’s an accountable operating system, run by people, that gets more automated as it proves itself.

The declared leakage pool is where every engagement starts — named, sized, with an agreed stall point and a known current best effort to measure against. From there, the action surface follows a simple discipline: email as the low-cost owned room for attention, data capture, and completing the outcome in place, with WhatsApp, RCS, and SMS held back as escalation rails for the moments that genuinely need immediacy — an urgent KYC deadline, a lead about to go cold for good. The rule is channel-fit, not channel-loyalty: use the cheapest owned or cooperative surface that can actually finish the job.

Underneath that sits the utility layer — the Living Email Factory, AMP components, Pay-in-Email infrastructure, tracking middleware, and the Attention Processing Unit (Magnets, Mu, ActionAds, and the Ledger that records it all). These tools are necessary. They are not, on their own, defensible, because a competent competitor can build broadly similar tooling given enough time and budget.

M-Agents and Martech Growth Engineers run the pool day to day. M-Agents handle the repeatable work — cohort discovery, message variants, channel selection, response scoring, test monitoring. MGEs make the judgement calls: what a specific customer’s context actually means, when to escalate a channel, how to read an ambiguous response, how to keep the brand’s tone and consent rules intact. Automation follows this pattern, rather than preceding it — the point isn’t to declare an all-automated system on day one, but to automate the runbook one proven outcome at a time: finish KYC, then finish leads, then finish renewals; recover dormant buyers, then recover old leads.

The Decision Trace Graph is what all of this writes back to — customer state, pool, context, channel, message, offer, holdout status, cost, response, outcome, and the customer’s next state, every time. This is the actual compounding asset, sitting inside NeoCore’s wider Context Graphs alongside the Customer CG and Product CG. Integrating many tools is the cost of building Progency. The corpus of verified decisions and outcomes is the moat — the record that makes the tenth brand’s first pilot smarter than the first brand’s tenth pilot, because every trace sharpens what the system already knows about which action moves which kind of stuck customer.

Choosing the first pool

A serious brand almost always already has an abandoned-cart flow, and often dynamic retargeting alongside it — which makes cart recovery the most familiar leak, and, for that exact reason, usually the wrong one to lead with. The brand’s current best effort there is already strong, so the incremental lift a holdout can prove is thin. Alpha is widest wherever the brand’s current best effort is weakest, not wherever the leak happens to be most visible.

Incomplete KYC and stuck leads make better first pools precisely because they’re less discussed: the current alternative is often a call centre, a manual queue, or nothing at all, which gives a randomised holdout real room to show a difference. Renewals and form completion follow — clear events, clear value, clear counterfactuals. Dormant-base recovery comes after: strategically the larger prize, but it requires re-earning attention before anything resembling revenue is realistic, which makes it a slower pool to prove first.

None of this is about permanently ignoring cart recovery or dormant reactivation — both matter, and both eventually run. It’s about sequencing the first proof where the brand’s own current effort gives Progency the least competition, so the first holdout comparison is unambiguous rather than marginal.

5

Where Progency Lives

 It would be tempting to describe Progency as a third pillar of NeoCore, sitting beside Meridian and Atrium as an independent offering with its own sales motion. That would be the wrong way to think about it — worth saying plainly rather than leaving vague.

Progency isn’t a third engine. It’s the delivery arm of the two engines that already exist.

Finish operates inside Meridian’s domain. Meridian’s job is protecting and maximising the value of Best customers — and a Best customer whose renewal quote goes unanswered, or whose KYC step stalls mid-upgrade, is exactly the leak Meridian is built to prevent. Finish is how that protection gets delivered day to day: MGEs and M-Agents running the specific interventions that stop a valuable, engaged customer’s journey from quietly dying in a queue.

Recover operates inside Atrium’s domain. Atrium exists to push the cost of reactivation and acquisition toward zero for Rest and Next customers. Recover is Atrium’s operating layer for the specific customers who have gone dormant and are drifting toward being re-bought by a paid channel — the same mission as Atrium, delivered as a hands-on managed service rather than a self-serve marketplace mechanic.

One name, one delivery team, two engines it plugs into, two pricing ladders reflecting the two different alternatives each book displaces. That’s a materially different structure than treating Progency as a stand-alone product line — and it has a real consequence: Progency doesn’t need a sales pitch separate from Meridian and Atrium. It’s the answer to “how does this actually get done” for both.

There’s a sequencing question worth being direct about, because the honest answer isn’t the tidy one. Recover — reactivating a customer before a platform re-buys them — is the story that gets a sceptical CMO into the room in the first place. No adtech vendor will ever offer to make itself unnecessary; that’s a structurally unique pitch, and it should stay the headline.

But inside an actual engagement, Finish often ships first. Its cycles are shorter — a stalled KYC form can resolve in weeks, while winning back a genuinely dormant customer takes longer to prove and to fund, since outcome-only pricing means the cost of delivery is carried upfront and collected only once results land. Running Finish first improves how quickly a pilot pays for itself, while the longer Recover proof cycle plays out alongside it. The story that opens the door and the plan that ships first don’t have to be the same thing — and pretending otherwise is how good doctrine quietly drifts.

Put together, the pitch is simple enough to say in one breath: before a known customer is handed to a call centre, an agency, a manual queue, or an adtech platform, give Progency the first right to finish or recover the outcome. You pay only for verified results.

That’s the whole idea. Not a new department. Not a new promise. Just the place in the stack where NeoMarketing’s outcome discipline finally reaches the customers who had been falling through the middle all along.