Thinks 2025

NYTimes: “Wordle is a simple game in many respects. The vast majority of users play using the default settings, where you can guess any letter on any turn. But a smaller slice of players (you may be one) choose to play in “hard mode,” which adds a major constraint: Any revealed hints must be used in subsequent guesses. If letters turn yellow and green, you are forced to use them in your next guess. So you might think that hard mode is harder — it says so in the name…An analysis of 730 million games from the last year says the opposite: Players in hard mode solve in fewer turns on average.”

WSJ: “Science suggests you don’t need a long vacation to recharge. A 24-hour break that interrupts your routine—and puts you into a flow state—can be hugely restorative.”

Arnold Kling: “It could turn out that today’s tech stock darlings are also like chain letters. They transfer wealth from the broader public to the initial owners. When the supply of suckers runs out, the share prices will collapse. Even the early owners will not do well unless they cash out in time. The alternative view is that stock prices are high because the companies are creating real wealth. That could be true. It is impossible to prove it false. The very term “real wealth” is undefined. So much of the economy is intangible. We have more stuff, but mostly we are better off because of innovation, specialization, and trade. We are telling ourselves that we are wealthy. The government has borrowed money from us, and we expect to get paid. We own shares of stock, and we think that we can always sell them for a good price. It will all turn out fine in the end. Unless it doesn’t.”

Acquired (via WSJ): “The medium of animation itself is a structural reason why other film studios haven’t achieved Disney’s success: it is uniquely well-suited to form the core of such a flywheel. Mickey Mouse doesn’t age, doesn’t have bad hair days, and never asks for a raise on sequels. Animated characters happily lend the company their name, image and likeness (forever) at zero cost for commercial exploitation. They’re always available to show up at theme parks or on set, and have no problem being in two places at once. Of course, the voice actors and theme park “cast members,” as Disney calls the employees who play the characters, do share in the economics. But it’s quite a different picture than say, Mark Hamill as Luke Skywalker.”

The Living Email That Remembers: From Campaign to Learning Loop

A campaign reads from the past; a learning system reads and writes — here is how the loop actually closes, and why the memory, not the model, is the moat

1

Memory is the Difference

The living-email loop ran: open, interact, decide, act, earn, update memory. The first five steps describe a better email. The sixth describes a different kind of thing entirely — a system that learns — and it was given a single sentence.

Without that last step, a living email is only a prettier campaign — more engaging for thirty seconds, then forgotten the instant it closes. With it, every email a customer touches makes the next one sharper. But ‘it writes to the Context Graph’ is a promise, not an explanation. This essay is about how the promise is kept — what is captured, how it becomes memory, how memory becomes a better email, why it must stay visible to the customer, and why the loop compounds into something a competitor cannot easily copy.

Previous essays in this series:

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A campaign reads; a learning system reads and writes.

A campaign is read-only. It looks up a few facts about the customer — a name, a last purchase, a city — injects them into a template, and sends. The customer sees a modestly more relevant version of the same message. This is useful, and it is not memory. It is callback: the email reads from the past and changes nothing about the future. The next campaign starts from a blank canvas, knowing nothing the last one learned, because there was no mechanism for the last one to learn anything.

A learning system is read-write. It reads context before it composes, and it writes new context after the customer interacts — so the next email begins from what the last one learned rather than from nothing. The old loop was send, open, click out. The new loop is read, compose, interact, capture, write, improve. The structural difference is not that the emails are nicer; it is that the line has been bent into a circle, and the output of each turn becomes the input of the next. Personalisation borrows relevance from the past; memory compounds it into the future.

Figure 1 — A campaign reads a few facts and ends at ‘done’. A learning system reads, then writes the interaction back — the line bent into a circle.

Without memory, a living email is just theatre.

It is worth being blunt about the failure mode, because it is the likely one. A brand adopts AMP, adds polls and live status and a streak, and ships beautiful interactive emails — and learns nothing from any of them. The customer answers the poll; the answer evaporates. She sets a preference; it is not carried forward. She ignores three emails in a row; nothing adjusts. Interactivity that is not remembered is theatre — motion without consequence. The test of a learning system is not whether the email is interactive. It is whether tomorrow’s email is different because of today’s.

2

The Signals that Create the Memory

Three kinds of signal: what she said, what she did, and what she stopped doing.

Explicit signals are what the customer tells you on purpose — a poll answer, a size set, a delivery preference chosen, a prediction staked. These are high-value because they are deliberate and unambiguous. Implicit signals are what she does without meaning to tell you anything — which email she opened and how long she stayed, which block she tapped and which she skipped, the hour she engages. These are noisier but far more abundant. The living email is unusually good at capturing both, because every interaction inside it is a signal generated in a context the brand controls. This is the I and the N of the TWIN framework — the individual and the moment — captured at the source.

The most valuable signal is often the absence of one.

There is a third category campaigns systematically throw away: the signal of nothing. The email she did not open. The block she scrolled past three days running. The poll she used to answer and has stopped answering. Silence is data, and fatigue is a signal — often the most important one, because it is the early warning that attention is draining before the customer drifts away entirely. A campaign cannot hear silence, because it has no memory to notice a change against. A learning system hears it loudly, because it remembers that she used to engage and can see that she has stopped.

Figure 2 — The three signal types feed recognisable classes of memory. Negative memory — silence and fatigue — is the one campaigns discard.

Memory is richer than a profile field.

What gets remembered falls into recognisable classes, and they are richer than the attributes a CRM stores. There is preference (leave-at-door, size M, iced), intent (circling a re-order moment), constraint (opens late, avoids calls), and rhythm (engages around 9pm). There is also trust (service-first over hard-sell), reward (a streak, Mu earned), and community (her WePredict circle). A profile stores attributes; memory stores a customer becoming someone — a trajectory with a confidence level, not a static snapshot.

Signal without structure is just logs.

Here is where most ‘data-driven’ marketing stops, and why it stalls. Capturing signals is not the same as learning from them. A pile of raw events — opened at 19:34, tapped block 3, scrolled to 60% — is a log, and a log teaches nothing on its own. The signal has to be turned into structure before it becomes memory. A tap on ‘iced’ is not useful as the event ‘tap, poll-7, option-2, 19:34’. It is useful as the fact ‘this customer prefers iced’, connected to the product that satisfies that preference, with a record of why the question was asked. The move from log to memory is the move from event to fact.

3

Where Memory Lives

The Context Graph turns events into facts.

The Context Graph is the structure that makes signal into memory, and it has three substrates. The Customer Context Graph holds what is known about each individual — preferences, behaviours, state, the facts distilled from her signals. The Product Context Graph holds the brand’s catalogue as a connected structure — what relates to what, what satisfies which preference. And the Decision Trace records the choices the system itself made: which email was composed for whom, why, and what happened. When the tap on ‘iced’ arrives, it becomes a fact in the Customer graph, an edge to the cold-brew range in the Product graph, and an entry in the Decision Trace — three structured things the system can reason over, where before it was one log line nobody could use.

Figure 3 — A raw event teaches nothing. Structured into the three substrates, it becomes a fact, an edge, and a trace the system can reason over.

The Decision Trace records not just what, but why.

The Decision Trace is the substrate easiest to overlook and most important for actual learning. It does not record what the customer did; it records what the system did and why. This email was composed for this customer because the graph showed these facts. This offer was chosen over that one for this reason. This block was suppressed because of this fatigue signal. Recording the reasoning, not just the action, is what lets the system learn from outcomes rather than merely accumulate them — and it makes the system auditable, so a human can ask why a customer was shown a particular thing and get a real answer. A system that cannot explain its own choices cannot improve them, and cannot be trusted with them.

Memory is shared, not siloed per campaign.

The final property of the Context Graph is the one that most separates it from the tools it replaces: it persists. In a campaign tool, each send is its own island, its results walled off from the next. The Context Graph is not per-campaign and not per-channel; it is a single, continuous record that every email reads from and writes to, across time. The Tuesday email and the Friday email are not two separate events; they are two reads against the same growing memory — and the Friday email knows what the Tuesday email learned. That is the difference between a brand that knows its customer better every week and one that knows her exactly as well as it did a year ago.

4

How Memory becomes a Better Email

The BrandTwin reads the graph and decides.

Memory that is never read is just storage. The step that turns the Context Graph into a better email is inference, and the agent that performs it is the BrandTwin — the per-customer advocate that reads that customer’s record and decides what should happen next. Before the next email is composed, the BrandTwin queries the graph: what does she prefer, what is her state, what has she been ignoring, what is the next-best thing to show her, and — just as importantly — what should she not be shown. The BrandTwin is the bridge between memory and action. It is N=1 not as a slogan but as a mechanism: one advocate, one customer, one decision composed from one accumulated memory — and its decision is only as good as the graph is rich.

Composition: the next email is assembled from what the last one learned.

The BrandTwin’s decision is handed to the composition layer — the Living Emails Factory and TwinFactory — and becomes a concrete, individual email. The blocks are chosen to match what she engages with; the offer is conditioned on her preferences and state; the timing is shifted to when she actually opens; and the things the graph says to avoid are suppressed. The customer who tapped ‘iced’ last night receives a cold-brew email tomorrow, not a generic one. The customer who set a size sees it pre-filled. None of this is personalisation in the old sense of a name in the subject line; it is composition from memory — each email built out of what the previous emails learned.

Learning what not to send is half the system.

The instinct in marketing is that learning means sending more — more relevant, more frequent, more personalised. But half of what a learning system learns is restraint. The fatigue signals, the ignored blocks, the declining engagement all feed a decision the campaign tool can never make well: when to send less, and when to go quiet. A campaign optimises for the send; a learning system optimises for the relationship, and relationships are damaged by sending into silence as surely as by silence itself. The email that remembers is also the email that knows when not to be sent — and that restraint, learned from memory, is something no campaign blast has ever been capable of.

5

Memory: See, Use, and Correct

Memory must be useful, visible, and correctable — or it becomes creepy.

There is an important constraint that the architecture essay never reached. A system that remembers can easily tip from helpful into unsettling, and the way to avoid that is not to remember less. It is to make memory serve the customer in the open. The line between remembered service and surveillance is whether the customer can see what is remembered and change it. Get that wrong and every clever inference reads as the brand watching her; get it right and the same inference reads as the brand knowing her.

Useful: the remembered state makes the next thing easier.

Memory earns its place by saving the customer effort, not by demonstrating how much the brand knows. ‘Your size M is pre-filled’ saves a step. ‘Your usual: leave at door’ removes friction. A visible streak makes a habit legible. The remembered fact should always show up as a convenience the customer receives, never as a boast about the data the brand holds. The first is service; the second is surveillance wearing a friendly face.

Figure 4 — Remembered service, not hidden personalisation: useful enough to save friction, visible enough to be recognised, correctable enough to stay accurate.

Visible and correctable: she can see it working, and change it.

Hidden inference is occasionally useful, but the relationship strengthens when the customer can recognise the continuity — ‘we remembered you prefer short evening reads.’ And memory must never become a locked assumption: preferences change, contexts change, and inference is sometimes simply wrong, so a one-tap ‘change this’ keeps the memory accurate and keeps the trust intact. The right pattern is not hidden personalisation but remembered service — visible enough to be trusted, and editable enough to stay true. This is also the cleanest defence of the whole architecture: a brand that lets the customer see and correct its memory is doing something a surveillance system structurally cannot.

6

Why the Loop Compounds

Each turn tightens the model.

Now put the stages together and watch what happens over time. More signal produces a richer graph. A richer graph produces sharper BrandTwin inference. Sharper inference produces a more relevant email. A more relevant email earns more engagement. More engagement produces more signal — and the loop has tightened by one turn. This is compounding, and it is the entire point. A single pass through the loop is a marginal improvement; a thousand passes is a categorical one, because each pass starts from a better position than the last. The brand that has run the loop for a year does not merely have a year of data; it has a year of increasingly good decisions, each built on the last.

Figure 5 — Each turn of the loop starts from a better position than the last. The advantage is not the data you have; it is the head start in learning the data represents.

The inbox becomes a sensor and a teacher.

There is a second compounding effect worth naming. The living email turns the inbox into a sensor — capturing small, low-friction signals from customers who may never visit the website or app in that moment — and into a teacher for the wider system, showing which content resonates, which offers deserve suppression, and which rhythms build habit. Every email helps the brand learn how to act better next time — not only in the inbox, but across every channel the Context Graph feeds. The remembered email is the cheapest, highest-frequency source of first-party signal a brand has.

The moat is the memory, not the model.

It is tempting to think the advantage in all this is the AI — the model that does the inference. It is not. Models are increasingly available to everyone; a competitor can license the same capability next quarter. What a competitor cannot license is your Context Graph — the accumulated, structured, first-party memory of your customers, built one interaction at a time, owned by you and legible to no one else. It is the one thing in the system that can only be grown, never bought, and that grows faster the longer it has been growing. A rival can match your emails in a quarter and your models in a year. Your memory of your customers, they cannot match at all.

The close.

The architecture essay was right to end its loop on ‘update memory’, and wrong to spend only a sentence on it. That step is not a footnote to the living email; it is the reason the living email matters. Strip it out and you have a more beautiful campaign — motion without consequence, theatre without learning. Build it properly — capture, structure, infer, compose, restrain, and compound, in the open where the customer can see and correct it — and you have something marketing has never quite had: a relationship that gets better by itself.

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A prettier email is forgotten the moment it closes. An email that remembers is the first turn of a loop that never stops learning — and the one asset a competitor cannot buy.

Thinks 2024

FT: “Leaders who spend time in the weeds reap benefits. They inform themselves about what is going on. They meet employees, customers and suppliers, observe how managers interact with staff, and see who is underperforming or merits promotion. They spot emerging disasters or opportunities and have a shrewder idea of where investment is needed. Above all, they show the people they spend time with that they matter. That helps to build allies — and leaders need allies.”

Mint: ““For 30 years, the middle layer existed because information had to move physically through people: the junior team produces work, the manager translates it upward, leadership decides, the manager translates that decision back down. That relay function was the job,” explains Phil Fersht, chief executive of HFS Research, a Massachusetts-based IT consulting firm. “AI now does that synthesis directly, which means the core justification for the middle layer has been removed, not reduced.” For instance, the middle manager in IT services was, in practice, a relay station, says Fersht: pushing client requirements down to delivery teams and pushing status updates back up to leadership. That’s precisely the function AI now performs faster and without the distortion that comes from passing information through layers of people each managing their own position, he adds.”

CollabFund: “Everyone has a weakness. A blind spot. A vulnerability. For Achilles, it was his heel. Superman had kryptonite. Ted Williams struggled with pitches low and away. But what about the average person? I often think our Achilles heel is our inability to understand probabilities. And increasingly, it’s making us more stressed than ever.”

FT: “As workers move beyond chatbots to AI agents, which can perform complex tasks autonomously but require far more computing power, companies are being forced to scrutinise whether each query and task is worth the cost. This has intensified as groups including Anthropic and OpenAI have moved some services from flat subscriptions to token-based billing, which tracks the units of data processed by models. The change has exposed companies more directly to the cost of every prompt and automated workflow. “Compute costs are now beginning to enter the minds of both CFOs and boards. Consumers and businesses have been taught that AI is cheap or free and that is definitely not the case,” said Costi Perricos, global generative AI leader at Deloitte.”

The Living Email Factory: Easy to Build, or It Won’t Happen (Part 5)

From Editor to Factory

This is a category shift, not a feature.

It would be a mistake to read this as ‘a better email editor’. The Living Emails Factory is a different category of tool, the way a spreadsheet is a different category from a calculator. The template editor made documents; the factory makes applications that are plugged into the business, composed by AI, filled per recipient, governed by SNR, and learning by default. The right comparison for what email creation becomes is not Bee with more blocks — it is the move from designing a page to operating a system. It is the point where the brand’s data, its personalisation engine, its agentic layer, and its memory all meet, in a tool a marketer can use without help.

The tooling is the moat.

The living-email thesis will not be won by the team with the best deck, or even the best conceptual architecture. It will be won by the team that makes creation easy. The leap from static email to living email is not primarily a content leap; it is a tooling leap. Whoever makes that factory real will not merely improve email production — they will redefine what email can be, because the factory is the bottleneck through which every other part of the living-email argument has to pass to reach an actual inbox.

Who builds the living email?

The answer, finally, is layered. The marketer builds it, in the sense that they direct it — they bring the customer knowledge, the message, and the judgement. The factory builds it, in the sense that it supplies the live blocks, composes the draft, enforces the governance, and hides the complexity. TwinFactory builds it, in the sense that it fills the template-of-one per recipient. And the agents build it, in the sense that they do the engineering the marketer used to outsource and never sees. No single one of them builds the living email; the system does, with the marketer at the helm — the only configuration that scales, because it is the only one that keeps the marketer a marketer while the email becomes an application.

**

The static email had its editor. The living email needs its factory — and the factory is the product that lets everything else exist.

Thinks 2023

WSJ: “Ask any job applicant or manager about the hiring process today, and he or she will probably say the same thing: It’s pretty much a crapshoot. In a survey of 2,200 U.S. hiring managers by staffing firm Robert Half last spring, nearly a third said they’d made a hiring mistake in just the past two years. Failing to accurately size up the candidate’s skills or fit with the company’s culture were the biggest reasons. The idea among many hiring managers and technology experts is that if you take humans out of the equation—and replace the early, flawed generations of technology assistance with more-advanced artificial-intelligence systems—you’ll get a vastly better sense of who’s the best person for the role. That’s the hope, anyway.”

NYTimes: “Dr. Gabrielle Lyon, a celebrity physician, calls muscles “the organ of longevity.” For Dr. Peter Attia, they’re “the most important retirement accounts you can have.”…Experts say the secret to feeling strong in the long term is to focus less on the size of a muscle and more on what it can lift and how quickly, which helps you avoid chronic disease, falls and even admission into a nursing home. In other words, we want our muscles to be functional so we can move heavy things, said Michael Ormsbee, a professor of exercise physiology at Florida State University. “That’s strength. That’s power,” he said.”

Sarah O’Connor: “Humans and AI systems are both powerful, but in fundamentally different ways. In an ideal world, we would use these tools to extend our reach to achieve new things. But if we allow ourselves to be compared to machines, I fear we will come to expect too much of ourselves in some ways, and too little in others.”

WSJ: “There’s a revolution in battery technology hiding in plain sight: The 3-D printing of batteries has the potential to put energy storage inside any device. This will enable lightweight and long-lasting consumer gadgets, long-range military drones and even nanoscale robots. The promise of battery-tech 3-D printing (aka additive manufacturing) is simple: What if batteries could fill any available space, even structural elements of our gadgets, rather than always taking a rigid shape like a pouch or cylinder? The new approach has obvious appeal. The entire airframe of a drone could be filled with energy storage for increased range. Smartglasses could have sleek battery-packed frames, so they look like everyday eyewear rather than “Revenge of the Nerds” props.”

The Living Email Factory: Easy to Build, or It Won’t Happen (Part 4)

From Marketer Workflow to Agentic Workflow

AI-composed, not hand-built.

Blocks lower the floor; AI removes it. The factory’s composition layer is agentic: the marketer states an intent — ‘a Tuesday Relate email for lapsed customers, light, with a poll and a Mu reward’ — and the agentic layer drafts the email, choosing blocks, writing copy, setting the personalisation rules, and proposing the fallback. The marketer then edits and approves rather than building from a blank canvas. The marketer moves from maker to editor — from assembling the email to judging the one the factory proposed — because the fastest way to make a living email as easy as a static one is to not start from nothing at all.

A workshop of named agents does the engineering the marketer used to outsource.

Behind the factory sits the M-Agents collective, pointed at composition, and it is useful to name the division of labour. An Intent Agent maps the brief to an SNR pattern. A Block Agent selects the right interactive modules. A Content Agent drafts the copy, tone, and variants. A Data Agent binds the right fields from the right systems. An AMP Agent and a Fallback Agent render the experience across client conditions. A Policy Agent checks brand rules, legal constraints, and SNR appropriateness. A QA Agent previews the output and flags issues. A Memory Agent defines which interactions count as learning signals and how they are written back. The engineering did not disappear from the living email; it moved from the marketer to the agents.

Figure 4 — The marketer directs; a workshop of agents builds; TwinFactory fills per recipient; and the SNR governance layer decides what each surface may carry.

The Factory builds it; TwinFactory fills it.

The division between the two engines is clean and worth stating plainly. The Living Emails Factory is the authoring environment — it makes the living email and produces the template-of-one, a structure with the personalisation rules expressed but the individual values unfilled. TwinFactory and the BrandTwins it maintains fill it — the BrandTwin for each customer decides which products, which tone, which offer, which timing, drawing on that customer’s record in the Context Graph. One produces the template-of-one; the other produces the one. Together they turn a single composition into a million individually-composed emails, none of which the marketer assembled by hand.

Thinks 2022

Geometric Investor: “The AI cycle is a stack of four linked ledgers, with different owners, different time horizons, and different required returns: 1. The infrastructure ledger. NVIDIA, HBM and memory suppliers, foundries, advanced packaging, networking, power equipment, and cooling…2. The hyperscaler and neocloud ledger. Cloud incumbents and GPU-rental specialists must convert installed compute into rented or sold capacity at utilization and gross margin sufficient to cover depreciation, power, financing, and the risk that the assets age out before they pay back. 3. The token buyer ledger. Enterprises and consumers must receive more value from tokens than the tokens cost — labor savings, revenue lift, faster software, automation, fewer errors. If the buyer’s return is negative, every ledger below it is being funded by a future that will not arrive. 4. The macro ledger. If token usage raises economy-wide productivity, trend growth and the neutral real rate can rise.”

NYTimes: “Twenty years ago, the words “Stage 4” almost invariably meant “end of life.” A cancer had spread far from where it formed, attacking distant parts of the body and often making treatment impossible. For some, that remains true today. But for a growing number of people, a Stage 4 diagnosis is not the immediate death sentence it once was. New therapies that target specific genes and parts of the immune system, as well as new regimens of existing cancer drugs, have given many patients far longer than the handful of months they might have once hoped for. More than a third of people diagnosed with metastatic disease now live for at least five years, compared with 17 percent in the 1990s, according to the American Cancer Society.”

FT: “We certainly have better tools than ever to try to extract the signal from the noise, even as the geopolitical climate becomes more volatile. “AI is truly a breakthrough when it comes to forecasting events,” says Anthony Vinci, a former US intelligence officer, international affairs expert and author of The Fourth Intelligence Revolution. “I look at the world and no longer trust my personal opinion of what will happen. I need an AI tool to help me.” According to Vinci, there are four ways of trying to assess the probability of future events. A few individual human superforecasters are superb at such analysis. The collective intelligence of an organisation, such as the CIA or a political risk consultancy, can also be applied. The wisdom of crowds can be harnessed through prediction betting markets, such as Polymarket or Kalshi, although these can be manipulated. And a trained AI model can parse these three sources and crunch the data for additional insights.”

Paul Graham: “The key to starting a successful startup is to understand some group of users so well that you can make exactly what they want. If you’re young you can, and should, use the hack of making something for yourself. You understand yourself. But this is just an instance of the more general rule. Only by understanding users very deeply can you make something they love so much that they tell their friends about it, and only that can get you the exponential growth you need to make a startup really successful. There are other ways to get rich than by starting startups. Some of those do require you to exploit people. But startups are the most common way to become really rich, and if you want to start a successful startup, the key is not exploitation but empathy. What do users really want? What could you do for them that would make their lives dramatically better? That kind of empathy is what we look for in founders, and what we cultivate in the ones we accept.”

The Living Email Factory: Easy to Build, or It Won’t Happen (Part 3)

The Factory

The bar: as easy as making an HTML email today.

Start with the design constraint that governs everything else. A living email must be as easy to make as a static one is today — not almost as easy, not easy-with-training, but genuinely as fast and as approachable as dragging blocks into Bee. If composing a living email takes a marketer materially longer than composing a static one, the static one wins every deadline, and living emails stay rare. This means the factory cannot expose any of the underlying complexity — not the AMP, not the fallback, not the data plumbing, not the memory wiring. Familiar on the surface; entirely new underneath.

Seven layers, from intent to memory.

The cleanest way to describe the factory is as a stack of layers, each one absorbing a discipline the marketer used to lack. At the top sits the Intent Layer: the marketer states the job — ‘a Notify email for a delayed shipment’ — and the system begins from purpose, not a blank canvas. Below it, the Block Layer supplies governed, reusable live components. The AI Composition Layer turns them into a customer-specific draft. The Data Layer binds the email to CRM, CDP, catalogue, orders, loyalty, and automation. The Rules and Governance Layer decides what each surface is allowed to carry. The Render and Fallback Layer emits valid AMP and a genuinely good HTML fallback in lockstep. And the Memory and Measurement Layer writes every interaction back to the Context Graph. Each layer is a thing the marketer would otherwise have had to be an engineer to handle — absorbed into the tool, where it belongs.

Figure 2 — The seven layers of the Living Emails Factory. The marketer states intent at the top; a sendable, living, learning email falls out of the bottom.

Block-based, but the blocks are alive.

The factory keeps the one thing about the old editor that works — composition by block — and changes what a block is. In the old editor, a block is an image or a piece of text: pure appearance. In the factory, a block is a live component — a poll, a product carousel, a live-status panel, a size picker, an ActionAd slot — and each carries its own logic, its own data binding, its own AMP build, its own HTML fallback, and its own memory hook, all packaged inside it. The marketer drags in a ‘poll’ the way they used to drag in an image, but the poll arrives complete. The complexity does not vanish — it is packed inside the block, where the marketer never has to see it.

Figure 3 — Anatomy of a live block. The marketer drags it in like an image; the logic, dual render, data binding, and memory hook are pre-engineered once and ride along inside.

Governance is not a constraint bolted on; it is a layer.

The Rules and Governance Layer is where SNR stops being a diagnostic and becomes enforcement. A Notify email may carry live status and a service next-step, but not a random reward mechanic. A Sell email may carry preference capture and first-party cross-sell, but not open third-party demand. A Relate email can carry a Magnet, Mu, one governed ActionAd, and a funding layer, because that is the surface designed to hold them. The factory reads the SNR mode of the email and only offers the blocks that mode permits. The factory does not just make a living email possible; it makes the wrong living email impossible — which is what keeps a bank’s Notify surface from ever sprouting a points game, and a Sell email from leaking a competitor’s ad.

Thinks 2021

Mint: “Every age creates its own managerial obsession. The 1980s worshipped scale. The 1990s celebrated globalization. The first decades of the twenty-first century chased disruption and digital transformation. Today, artificial intelligence has become the new altar before which executives gather. Strategy decks are being rewritten, business schools are redesigning curricula, and consultants are repackaging old wisdom in new algorithmic wrappers. Yet as AI makes knowledge increasingly accessible and expertise increasingly replicable, a more fundamental question emerges: what remains uniquely human in leadership? The answer may lie not in thinking harder but in seeing better. The future may not belong to leaders who know the most. It may belong to those who notice the most.”

FT: “Policymakers who rely on those indicators to judge whether AI is delivering benefits may be missing a deeper shift. The great achievement of modern capitalism was to move activity from the household into the market — converting domestic production into paid specialisation, creating jobs and making output visible to the national accounts. AI-enabled self-service is quietly reversing that centuries-long trend. The automation question — can a machine do this job? — would never have predicted the laundress’s decline. No robot could walk to the well and handwash linens. But the washing machine did not need to. The self-service question — can the customer do without this job? — would have predicted it. If we keep asking the first question about AI, we will keep looking in the wrong place.”

WSJ: “When it comes to the diseases that threaten to steal our healthy years—Alzheimer’s, heart disease, cancer, arthritis—they all have one thing in common: By the time we get diagnosed, often much of the damage is already done. But a wave of new scientific advances have the potential to shift that timeline far earlier.  In the near future, doctors may be able to predict the speed at which your individual organs are aging, and detect cancer, Alzheimer’s and other diseases long before you develop symptoms. GLP-1 drugs, now used for diabetes and weight loss, might be prescribed to protect your heart or brain or to treat a range of chronic conditions. And instead of a knee or hip replacement, you could get new bone and joint treatments designed to reverse physical decline entirely by regenerating tissue in damaged joints. “We’re entering a new era of prediction and prevention,” says Dr. Eric Topol.”

Molly Kinder: “I think what we’re really entering is this messy middle period. It’s a world in which AI gets better and it’s more capable of taking on more work, but we don’t overnight see a jobs apocalypse. Instead, we find something that’s still painful, but more narrow. It’s a world of partial automation, where AI starts to get capable enough to do certain types of jobs, and that is still very painful. A world where most jobs are intact but there’s a concentrated loss is still a world that is politically, societally, and economically explosive. So I’m trying to call attention to this messy middle period, which could last for decades, depending on how good the technology gets. Even in a world where we don’t see a full jobs apocalypse, if we’re seeing a lot of pain in the knowledge sector or early career, that is still going to be something that we feel as a country — and that we’re not prepared for.”

The Living Email Factory: Easy to Build, or It Won’t Happen (Part 2)

Why the Old Editor breaks

A template produces a document; a living email is an application.

Figure 1 — The template editor produces a document: fixed, identical, inert. The factory produces an application: live, per-recipient, learning.

The cleanest way to see the problem is to name the category. A template editor produces a document — a fixed artefact, complete at the moment of saving. A living email is an application — a small program that runs when opened, responds to the recipient, talks to a data source, and records what happened. These are different kinds of thing, made by different kinds of tool. This is why the instinct to ‘add AMP support’ to an existing editor never quite works. You cannot bolt application behaviour onto a document tool and get an application tool; you get a document tool with a confusing new tab that produces broken applications. The right mental model is not ‘next-generation template builder’. It is an experience compiler: the marketer states what the email should do, and the tool assembles the experience that does it.

Five things a living email needs that a template cannot give.

Be specific about what is missing. First, interactivity — the blocks must do something when tapped, which means each block carries logic, not just appearance. Second, per-recipient composition — the email is a template-of-one, filled differently for each person. Third, live data binding — a balance, a delivery status, a price, fetched at the moment of opening rather than baked in at send. Fourth, memory write-back — every interaction routed back to the brand’s record of the customer. Fifth, dual rendering — an AMP version and an HTML fallback, kept in lockstep, from a single definition. Each is a discipline a template editor has no concept of. The missing capability is not in the marketer. It is in the instrument.

The trap: asking the marketer to become an engineer.

Faced with this, the tempting answer is the wrong one: give the marketer the raw materials and let them assemble the application by hand. Hand-author the AMP. Maintain the HTML fallback in parallel. Wire the data calls. Set up the memory routing. Test across a dozen clients that each render AMP slightly differently. This does not scale, for a simple reason. The people who make marketing emails are marketers, and they will not become engineers to send a newsletter — nor should they. A handful of sophisticated brands will hire the talent and produce a few beautiful living emails a quarter. That is not a channel; it is a craft project. For living emails to be the default, the engineering has to disappear from the marketer’s job entirely. The marketer’s job is to know the customer and the message. The tool’s job is everything else.