Thinks 2011

FT: “What does it take to qualify as a great power? [Brendan] Simms, professor of the history of international relations at the University of Cambridge, argues that four crucial qualities are needed. Does the country have the necessary resources — military, economic and human? Does it have reach beyond the country’s own region and a sphere of influence of its own? Is it seen by its own population and by other countries as a great power — in other words does it have the necessary reputation? And does it have the resilience to absorb losses and to rebound after defeats? Simms uses this framework to explain, in a series of neat potted histories, why second-tier powers don’t make the cut. Japan and Germany, held back by their past and lacking military resources; India, not yet able to project power and a junior player to China in Asia; France, bursting with aspiration but its reach is narrowing and it is too prone to domestic turbulence. Africa and Latin America have regional powers but none qualify as “great”.”

NYTimes: “India is in the throes of Shivaji fever. Across the country, hundreds of statues of the king — usually on horseback, brandishing a sword — have begun studding the broader landscape, popping up in the country’s port cities and along its disputed borders with China and Pakistan. Such tributes to the king, a staple of school history textbooks, had previously been mostly found in Maharashtra, the Indian state dominated by the Maratha community — a broad grouping of Hindus that Shivaji was born into, and that includes farmers and warriors, some of whom are considered lower caste.”

Sam Altman: “I think that was the single biggest driver at what people, of what people realized was, the coding models have so transformed how companies are doing their work and the efficiency and the speed of which they’re able to build products. The coding models got really good late last year, early this year, and then another step forward in recent months. So I think you’re right that that’s the single biggest driver. But we are now seeing scientists really use these models and a much broader application of knowledge work beyond coding. But, yes, I think it’s fair to say that coding is the magic right now.”

FT: “The world’s top AI companies are devoting growing resources to a question many still regard as science fiction: what happens if AI becomes conscious? Google DeepMind, Anthropic and Meta have hired experts in psychology, ethics and philosophy in recent months as they expand research into machine consciousness and AI welfare, according to several people familiar with the matter. The efforts reflect a broader shift inside leading AI labs, where rapid advances in increasingly autonomous systems have revived questions about whether machines could one day possess subjective experience — and what obligations humans might have towards them if they do.”

The Intent You Rent Back (Part 8)

The honest limit, and the new question.

A doctrine is only trustworthy if it states its own limits, so here is NeoNet’s: it does not eliminate adtech. There will always be a residual case — the customer who is in-market but engaged nowhere in the network, searching only on Google, browsing only on Amazon, comparing prices on a marketplace where the brand has no owned or partner visibility. For that genuinely off-network intent, adtech remains useful and is sometimes the only available signal. Paying the tax, for that slice, is rational. The honest test is whether the customer is reachable any cheaper way at all; if not, the auction has earned the spend.

So the doctrine is not never use adtech. It is narrower and far harder to argue with: do not use adtech first for customers you already know. Use owned attention for engaged customers, intelligence for predictable windows, NeoNet for silent customers active elsewhere — and then adtech as the final rung, for the intent nothing cheaper can reach. Adtech is a last-mile detector, not a default one. Used in that position it is a sensible tool; used as the first reflex it is a tax on knowledge you already had.

The question that replaces ROAS

This changes the question a marketing team asks. For years the reflex has been: how do we improve ROAS? The better question, customer by customer, is: why are we paying ROAS at all for this one? If the customer is genuinely new, paid acquisition may be justified. If they are unknown or truly unreachable, adtech may be the only route. But if the customer is already identified, historically valuable and merely silent, the first job is not to optimise the auction — it is to avoid the auction. A brand that asks the second question will find, on inspection, that a large share of its performance spend is being paid to re-buy customers sitting in its own database, drifting through the weakening cells of its own Transaction–Attention Table while no one was watching. That single question, asked honestly of each customer, redraws a media budget faster than any optimisation ever will.

The rule of resort

The whole series reduces to a single rule. Own the intent where you can. Predict it where you can model it. Share it where a partner can route it. Rent it only for what is truly left. Build the Intent Stack in that order and the auction stops being the engine of repeat business and becomes what it should always have been: the last rung, reached for rarely and deliberately. The customer was yours. The relationship was yours. The data was yours. The next intent signal should be yours too. None of this is anti-adtech; it is adtech put back in its place — the last rung, not the first reflex.

Own it where you can. Predict it where you can model it. Share it where a partner can route it. Rent only what is left.

Thinks 2010

Manish Sabharwal: “An uneducated Indian is not a free Indian. Coaching factories, exam leaks, nursery-school interviews and unemployability mean an Indian child’s most important decisions are choosing their parents and pin code wisely. The solution is not licensing but supply.”

FT: “How much value is AI really creating? Eye-opening changes to the speed and volume of work are not always translating into genuine productivity.”

Ron Friedman on his book Superteams: “I hope readers come away realizing that great teams are not the product of luck, chemistry, or hiring a few extraordinary people. In my research, I found that Superteams share three core strengths: (1) They get more done by managing their time, energy, and attention more effectively; (2) They make each other better, and (3) they keep improving over time. And the good news is everyone one of these strengths is learnable, which means by building the right habits, any team can dramatically improve its performance.”

WSJ: “For decades, institutions such as the U.S. Federal Reserve have made enormously consequential decisions—raising or lowering interest rates—based on incomplete and often delayed information. The Fed must adhere to mandates of full employment and price stability, but inflation readings arrive weeks after the fact. Employment statistics are revised months later. This leaves policymakers operating in a world of uncertainty, interpreting imperfect signals and using models undermined by gaps in real-time knowledge. The result is that central banks sometimes wait too long to raise or cut interest rates in the face of price changes in the economy. AI has the potential to change that.”

The Intent You Rent Back (Part 7)

The Intent Stack.

The three owners are not a menu to choose from; they are an order to follow. The cleanest way to hold that order is as a single structure — the Intent Stack — which ranks every source of intent a brand can act on, from the cheapest and most owned to the most expensive and most rented.

Most brands build it upside down

At the bottom sits historical intent: what the customer has already bought and done, held in your CRM. Above it, owned intent: what the customer does on your surfaces, generated by Atrium. Above that, predicted intent: what the BrandTwin expects next, supplied by Meridian. Above that, shared intent: what the cooperative network can route, through NeoNet. And only at the very top, rented intent: what adtech sells back, the last resort. The trouble is that most brands have built the stack upside down. They begin at the rented top — performance media as the default engine of repeat business — and then wonder why margins thin year after year. They are paying auction prices for a signal they could have owned, predicted or shared at a fraction of the cost.

Rebuild it in order

Rebuilding the stack in the right order is the whole of the doctrine in one move. Own what you can see, because engaged customers are generating intent on your surfaces now. Predict what you can model, because much re-entry is a knowable window, not a surprise. Share what the network can route, because your silent customers are alive on partner surfaces. Rent only what remains — the genuinely unreachable intent that nothing cheaper can see. Each rung you build downward from the top removes spend from the most expensive layer and moves it to a cheaper, identified one. The Intent Stack is not a new channel or a new product; it is a sequencing discipline that tells a CMO, for any given customer, which rung to reach for first. In practice a brand rarely builds all five rungs at once; it builds downward from wherever it starts today, reclaiming one layer of spend at a time.

What NeoNet really competes with

The stack also clarifies what each layer actually competes against — and the sharpest case is NeoNet. Its competitor is not another email ad network, not affiliate marketing, not display, not retargeting in the narrow sense. NeoNet competes with adtech’s single most valuable monopoly: cross-surface intent visibility. Google knows when people search; Meta knows when interest stirs; Amazon and the marketplaces know when comparison begins. The brand knows its customer’s history but not always the customer’s present. NeoNet connects history to present without handing the future to the auction — it is the one rung that matches adtech’s cross-surface advantage while keeping the economics on the brand’s side. That is why it sits directly below rented intent in the stack: it is the last owned-or-shared option before a brand is forced to pay the tax. Seen this way, NeoNet is less a media channel than a co-owned answer to the question adtech has monetised for twenty years.

Thinks 2009

Marina Nitze:”Crisis engineering is harnessing a crisis to make rapid transformational change. A complex system is any system that’s made of humans and computers. The computer part is generally pretty easy to change. And the human part is very difficult to change. But if certain crisis conditions are present, you can transform the human part of a system very rapidly. Crisis engineering is about how to recognize those indicators and then harness that moment to make rapid change against the human part of your system, and probably the computer part, too.”

FT: “Emboldened by big leaps in AI’s software programming capabilities over the past six months, tech industry leaders and researchers are increasingly confident that an AI that can improve itself with little to no human input is within their grasp. These self-taught AIs could keep on building new, more powerful versions of themselves. Successive generations could repeat the trick over and over again, adding new capabilities or making themselves run more efficiently. This flywheel effect, known in the industry as “recursive self-improvement” (RSI), could quickly lead AI beyond the large language models that underpin Google’s Gemini, OpenAI’s ChatGPT and Anthropic’s Claude, into uncharted territory. AI’s optimists believe it is the key to so-called superintelligence, or the point at which AI surpasses the abilities of the human mind.”

David Oks: “Why did China get rich, and India didn’t? What explains the Sino-Indian divergence?…China’s explosive growth wasn’t simply a matter of “freeing the markets,” reducing the role of the state, and announcing that it was now glorious to get rich; nor was it simply a matter of government intervention to support the manufacturing sector and subsidies for favored companies. China succeeded because it spent decades on the basics of human development and social modernization. India did not. The rest is just commentary.”

Ben Thompson: “How many companies could actually employ that cash in a way that generated a high rate of return? It’s hard to imagine a better option than Google. The company is not only investing in AI, but has optionality in terms of outcomes: its Services business benefits from the investment, it is in contention at the model layer with Gemini, and it can sell capacity to the frontier labs. Moreover, that capacity has a sustainable cost advantage because of TPUs, which means that in a world where compute becomes a commodity — as hard as that is to imagine right now — Google is the hyperscaler that is poised to make the most profit.”

The Intent You Rent Back (Part 6)

The three states of post-purchase intent.

There is no single solution to post-purchase intent because there is no single intent state. After a purchase, a known customer sits in one of three states, and each needs a different owner. Mistaking which state a customer is in is how brands reach for the wrong tool and end up at the auction.

Engaged — manufacture owned intent

If the customer still opens, clicks, taps, browses or replies, the brand does not need to detect intent from outside. It needs to create a surface where intent can keep appearing inside. That is the job of owned attention. A conventional promotional email waits until the brand wants to sell; a NeoMail earns attention before the brand needs to sell, giving the customer a reason to open — a useful idea, a quiz, a recommendation, a small reward, a moment of recognition. Over time the inbox becomes a living surface rather than a dumping ground for offers. And every meaningful action — a click, a tap, a saved item, a preference, a reply — becomes telemetry: identified, first-party signal about what the customer is thinking and may need next. Owned attention is the only owned intent signal, which is why attention sits upstream of transactions. This is Atrium’s first job: manufacture low-cost, continuous, identified attention so that intent never has to be rented later.

Predictable — anticipate before adtech detects

Some intent does not need detecting at all, because it can be anticipated. Replenishment cycles, renewals, seasonality, usage cadence, life events, festivals, travel, expiry dates and household rhythms all create predictable windows. A brand may not know the exact day a customer will search, but it can know the likely week or trigger. This is where intelligence earns its place. A BrandTwin, fed by the Customer, Product and Decision-Trace Context Graphs and the relevant world data, models the next likely intent window and acts before the customer ever appears on Google. The shift is from catching the signal to anticipating the window. A pet-food brand should not wait for dog food delivery to be typed into a search bar; an insurance brand should not wait for the renewal comparison to begin. If the timing is modellable, the brand should move first. This is Meridian’s job: convert predictable re-intent into lifetime value before that intent becomes expensive.

Silent — route through the network

The hardest case matters most. The customer is known but silent: no opens, no clicks, no visits, no replies. Owned attention has failed and prediction is too coarse — yet the customer is in-market somewhere else. This is the moment adtech was built to monetise. But there is another possibility: your silent customer is alive on someone else’s surface. They are reading a partner brand’s email, using a partner app, engaging with a non-competing brand in the same life moment. Their attention is not gone; it is simply not with you. NeoNet turns that fact into a recovery system. The old answer was an auction — upload the audience, bid for the user, pay the tax, hope the platform finds them. The NeoNet answer is routing — recognise that a known-but-silent customer has re-engaged on a trusted partner surface, and route a relevant recovery message through that surface before the brand falls through to paid retargeting. The network need not share raw data; it shares a decision — this person, silent for Brand A, is active in a trusted context where a relevant Brand A message can be shown. Recovery stops being a retargeting problem and becomes a routing problem. And routing should always be cheaper than bidding.

Thinks 2008

NYTimes: “Scientists at Columbia University have edited the DNA of early human embryos with unprecedented accuracy, an achievement that could open the way to babies engineered with particular characteristics. The prospect has fueled controversy for years. On the one hand, the technology might one day enable parents to safely repair disease-causing mutations in embryos. But it might also be used to select desired traits — a practice that some ethicists have argued is nothing short of eugenics. Dieter Egli, a geneticist at Columbia University who led the research, called for a public conversation about the pros and cons of altering embryonic DNA. “As a scientist, you can provide the data for discussion, but then essentially there you stop and let others take over,” he said.”

FT: “For most people working in markets, business or government, the canonical examples of a global financial crisis are 1929 and 2007-09. Liaquat Ahamed’s new book brings to life a third global cataclysm that was not like either of its successors and raises intriguing parallels with today’s technology and geopolitically charged world. The cast of 1873 includes Mark Twain, Karl Marx, Egyptian accountants and Prussian officers turned speculators. The boom and the bust they lived through helped create the modern world.”

WSJ: “Finance chiefs are trying to get a better read on how much AI their companies are using to avoid a sticker shock moment as vendors begin charging for the technology by tokens. The shift to pricing based on usage, and measured by tokens—the basic unit of measurement for AI computing—is creating new challenges for even the most experienced finance teams. CFOs used to paying flat amounts for technology are finding costs more unpredictable and harder to model as they build agents and embark on ambitious AI investments. Twenty-six percent of companies say they have a comprehensive view of their AI costs, while 50% have some visibility and 22% report no visibility or visibility after billing, according to an as-yet-unreleased survey from KPMG. “It’s a new resource that needs to be managed that didn’t exist quite that way, and we’re seeing exponential growth,” said Steve Chase, KPMG’s global head of AI.”

Mint: “India already sees $15 billion in annual philanthropic giving, nearly 100 times the annual need estimate. This is comparable to India’s startup funding, placed at around $12 billion dollars every year. Yet, 80% of non-profits struggle to scale due to funding constraints. The issue is not capital but a lack of ‘flexible funding.’ Flexible funding, akin to venture capital for startups, is a distant dream in the non-profit sector. Most funders restrict grants to specific programmes, tightly defined budgets and short timelines. We have argued previously that building scalable, low-cost solutions requires iteration, experimentation and adaptability—conditions incompatible with line-item funding.”

The Intent You Rent Back (Part 5)

A worse signal than the one you should own.

There is a final insult buried in the economics. The intent a brand re-buys from adtech is not just expensive; it is a worse signal than the one the brand could have owned.

The adtech signal is probabilistic, delayed and anonymous. The platform infers that someone may be in-market by matching devices, cookies, cohorts and behaviours. It does not tell you what it knows. It rarely hands over the underlying customer truth. It sells you an audience and then charges you, again and again, to reach it. You are buying a guess about a stranger who is probably your customer — an audience you must keep renting, because you never take possession of it.

The signal a brand could have owned is the opposite on every axis: deterministic and identified. This is your customer. This is their email and mobile. This is their order history and their value. This is the last meaningful attention event, and this is the likely next window. This is the best route to the next transaction. Nothing is inferred, because nothing needs to be — the relationship already exists.

Paying a premium for a downgrade

Put the two side by side and the absurdity is plain. A brand pays a premium for a poorer version of a signal it had the right to build for nothing. The re-bought customer arrives stripped of identity, with no history attached, mediated by a platform that keeps the customer truth for itself. The brand has effectively handed its own customer knowledge to the auction and is now buying a thin slice of it back at a markup. That is the tragedy of AdWaste, stated at the level of the signal: not merely that you pay twice, but that the second thing you buy is worse than the first thing you gave away. Worse still, the brand loses the learning: every recovery handled by the auction teaches the platform about your customer and teaches you nothing.

The problem is sequencing, not adtech

None of this makes adtech the villain. Adtech is genuinely useful for what no owned or shared system can see — a true new prospect, a customer active only on surfaces the brand cannot reach. The problem is not that adtech exists; it is that brands use it as the first detector of repeat intent instead of the last resort. NeoMarketing simply reverses the order. Spend adtech last. First own the intent you can see, because an engaged customer is generating it on your surfaces right now. Then predict the intent you should expect, because much re-entry is modellable. Then share the intent your partners can see, because your silent customer is often alive on someone else’s surface. Only after those three fail should you rent intent from the auction — and then for the narrow slice that genuinely nothing cheaper could reach. The next three parts take those three owners in turn, beginning with the engaged customer. Reversing the order is not a tactic; it is a change in what marketing treats as its first instinct.

Thinks 2007

Aakrit Vaish (Activate): “AI does not kill IT services. It collapses the delivery pyramid, splits pricing along the bespoke/commodity line, and converges the whole industry on professional-services economics. The margin trapped in headcount-heavy managed services and implementation is being released. The firms that capture it will not be better staffing businesses, but vertically deep, partner-led, agentic delivery firms that own workflow completion rather than workflow effort, operating as the intelligence layer inside enterprise GCCs. The $98.4B GCC ecosystem is the distribution. The agent stack is the engine. Run-time is the real estate. And India, with the domain depth, the talent cost structure, the enterprise trust, and the GCC density already in place, is holding the deed.”

NYTimes: “Consultants and executive coaches who don’t have the bandwidth to address every inquiry are referring some clients to their A.I. doubles. Harvard Business School professors have incorporated A.I. versions of themselves into courses and office hours. And executives are using their A.I. avatars to address employees in other countries in their own languages. Whipping up an A.I. chatbot or avatar is easy. Allaire built his using Claude. A handful of start-ups provide interfaces that make it even easier and offer more control: Delphi takes your content and instructions and creates a voice and text chatbot that mimics you, while A.I. video generators like HeyGen and Synthesia will do the same for a digital avatar that copies your appearance.”

WSJ: “Run-A-Muck’s bigger plan is to see which stories land with readers and turn them into the backbone of other money-making projects. Short stories from Hemingway’s tales to “Brokeback Mountain” and 2017’s “Cat Person” have long been source material for movies. Run-A-Muck thinks they could also expand into full-length novels, podcasts, TV shows, immersive events, digital shorts, microdramas and other vertical-video formats. It also hopes to flip the script, so to speak, publishing short stories based on new and upcoming series and films…The company is betting that the generation raised on a diet of YouTube channels and Instagram captions will also embrace the novel’s shorter sibling.”

FT: “Under-30s make up about half of India’s 1.4bn people — the world’s largest youth population — and according to a March report by Azim Premji University in Bengaluru, nearly 40 per cent of graduates aged 15-25 and 20 per cent of those aged 25-29 are jobless. While those unemployment rates are higher than for those less educated, many young people and their families see success in public exams as their best hope for economic advancement and security. Every year about 200,000 late-teen students travel by train, bus, car or motorbike from all over India to cram in Kota, a city in Rajasthan.”

The Intent You Rent Back (Part 4)

The purchase starts the silence.

A purchase feels like the end of a successful journey. In reality it is the start of the next-purchase clock — and brands consistently misread the moment.

The customer got what they came for. The immediate need is satisfied. They stop browsing, stop opening, stop visiting. They may not need the brand for weeks or months, and the better the purchase experience, the more complete the closure. So the brand goes blindest at exactly the moment it should be watching most carefully. Many CRM programmes enter a quiet zone after purchase: an order update, perhaps a review request, perhaps a replenishment nudge, then silence. If the customer keeps engaging they stay visible; if they stop, the brand quietly files them as inactive.

Relationship silence is not market silence

This is the costliest confusion in marketing: brands mistake relationship silence for market silence. Inactivity in your channels does not mean inactivity in the market. It only means the customer is no longer paying attention to you. A customer who does not open your email may be browsing a competitor. A customer who has not opened your app may be searching the category. A customer who ignores your WhatsApp may be comparing options on a marketplace. To martech, all three look dormant. To adtech, all three are warming up. The customer has not gone cold; they have gone elsewhere — and elsewhere is precisely where your owned systems cannot follow.

How an owned customer becomes a rented one

That confusion is the mechanism by which an owned customer becomes a rented one, and the sequence is mundane and expensive. The customer buys. Engagement falls, as it always does after a purchase. The brand reads the fall as disinterest and eases off — or worse, keeps up the same promotional pressure and trains the customer to ignore it. The real re-entry, when it comes, surfaces off-property, where adtech sees it first. The platform packages it as a retargeting audience and sells it back. The brand pays the tax and books the result as performance, never noticing it just re-bought a customer it already owned and had merely stopped watching.

Watch attention, not transactions

The fix begins with measuring the right thing. The next-purchase clock should not run on the order book; it should run on attention. A brand that watches days-since-last-transaction will always be late, because by the time the transaction gap is visible the attention gap has already done its damage. A brand that watches days-since-last-meaningful-attention has an early-warning system — it can see the drift from engaged to weakening to silent before the revenue stops, which is the only window in which intervention is still cheap. The post-purchase period is not a lull to be left alone. It is the most important surveillance window a brand has, and the one most programmes sleep through. Treat the quiet as the signal, not the absence of one. Most dashboards are built to report the transaction gap; almost none are built to report the attention gap, which is why the damage is usually found too late to be cheap to fix.