Rajesh Jain

1

Commentary

A few days ago (Jan 25), Wall Street Journal had a story entitled “Wall Street Has Fallen Out of Love With Software Stocks” which began thus: “Software companies’ pitch to investors could use an upgrade. Once a favorite of Wall Street, software stocks have been sliding lately, with investors increasingly concerned about how the sector could be upended by their newest crush: artificial-intelligence companies. Rocked by the emergence of “vibe coding”—the practice of using AI tools to quickly produce apps and websites—software heavyweights Salesforce, Adobe and ServiceNow are all down at least 30% since the start of last year. An S&P index of small and midsize software stocks is also down more than 20% over that period, with declines accelerating this month after the introduction of Anthropic’s Claude Code, an AI tool that industry insiders have said can dramatically shrink the time it takes to build even complex software.” The story added: “In reality, few investors and analysts think that software companies will become obsolete in the foreseeable future. The more pressing risk is that it could become more difficult to increase revenue, as customers experiment with other options rather than paying more for the usual updates and add-ons, RBC’s [Rishi] Jaluria said.”

This was one in a series of such stories that I had come across in the past few weeks.

FT (Jan 21): ““Software is not at all about the code or about the technology. Software is about your domain knowledge,” said Thoma Bravo co-founder Orlando Bravo…The plunge in software valuations, driven by fear of an existential threat from artificial intelligence, is creating a “huge buying opportunity,” Orlando Bravo, the firm’s co-founder, told the FT [in Davos]… Bravo’s comments come after a plunge in software valuations in recent weeks. Software is one of the US stock market’s worst-performing sectors so far this year, with an index tracking the group down about 7 per cent over the past three weeks.”

Bloomberg (Jan 18): “All told, a group of software-as-a-service stocks tracked by Morgan Stanley is down 15% so far this year, following a drop of 11% in 2025. It’s the worst start to a year since 2022, according to data compiled by Bloomberg… The latest selloff has exacerbated an already yawning gap between the performance of software companies and other areas of the tech sector. Anxieties about competition from upstart AI services are overshadowing characteristics like hefty profit margins and recurring revenue that for years made the group attractive in the eyes of market pros.”

Sherwood (Jan 16): “The relentless slide in software stocks continues…The growing adoption of Claude Code, and more recently, the launch of Claude Cowork by Anthropic, has been an attention-grabbing moment as to the power of AI agents and how they can be housed and operated solely under one highly integrated user interface.”

Heise (Jan 22): “The sell-off can hardly be classified as more than an ordinary correction; the price losses since the beginning of the year alone seem more like they’re from a textbook for major stock market crashes. Salesforce, Adobe, and Oracle are all in double-digit losses in 2026. ServiceNow, Atlassian, and HubSpot are even losing 30, 40, 50 percent at times, as a price overview from Fiscal.ai on social media impressively illustrates. The narrative that has been playing out on Wall Street trading desks for months is: “AI eats software.”…If autonomous AI agents handle tasks in the future that used to require entire corporate departments, why pay for dozens of Salesforce or Workday licenses? If AI reviews, formulates, and closes contracts, what’s the need for widespread DocuSign subscriptions? And if generative image and design models deliver in seconds what teams of graphic designers used to do – how many Photoshop licenses does a company need from Adobe then? Investors’ fear: productivity explodes, but software providers’ revenues implode. AI is celebrated, but it could prove to be pure poison for the seat economy.”

CTech (Jan 25): “Software companies once beloved by investors, from Silicon Valley to Wall Street, are fading as talk of the “death of SaaS” gains momentum. Until recently, much of the threat seemed to come from a handful of trendy vibe-coding startups, platforms that allow non-programmers to build simple applications using verbal commands…As AI agents commoditize software, companies such as Wix, monday and Nice confront a forced reinvention of growth, pricing and value.”

Jason Lemkin: “Traditional B2B software and SaaS is under assault.  The leaders are all still growing, but in most cases slower than ever. Stock prices are under intense pressure in 2026 for anyone not growing > 20% or more.” He listed six threat vectors: fewer seats, AI budget shift, slow teams, product decay, TAM trap, price increases. He added: “The question your customers are asking themselves: “Do I want to use this product, or do I have to use this product?” If the answer is “have to,” you’re living on borrowed time.  Customers may stay because they are prisoners, or because you have their data.  But they may no longer want to.”

About the capital shift to AI, Jason Lemkin wrote: “2025 saw over $225 billion deployed into AI startups—46% of all venture capital. Five companies alone (OpenAI, Scale AI, Anthropic, Project Prometheus, and xAI) raised $84 billion. That’s 20% of all VC funding in one year going to five companies.” He added: “The market is saying: we’ll pay up for the infrastructure that makes AI possible—and we’ll make those employees generationally wealthy—but we’re deeply skeptical that incumbent software can adapt fast enough… Private AI companies are getting 3-10x the multiples of public SaaS incumbents. The market is pricing in wholesale disruption of existing software categories.”

Rohan Paul commented on a Satya Nadella interview: “”Satya Nadela is basically describing the death of the traditional SaaS model. Explains the AI agentic future, and where the “value” lives. Because business logic is moving from the software application to the AI agents. Currently, you buy software for its specific features and rules. Nadella argues that in the future, software apps will essentially become dumb databases (“CRUD”) or simple tools. The AI Agent will hold all the intelligence, orchestration, and reasoning, simply updating the databases as needed. The software becomes a commodity; the AI becomes the “brain” and the worker.”

**

My thesis

The commentary above captures what’s happening. But most analysis stops at diagnosis. I want to push toward prescription.

B2B software isn’t in a cyclical dip — it’s being structurally repriced. The subscription model that powered two decades of compounding (seats, renewals, expansion) is now being squeezed by multiple forces at once: AI-driven capability deflation, seat compression, vendor sprawl backlash, budget reallocation to AI and security, and the rise of faster, leaner AI-native competitors. Public market sentiment is reflecting this shift, with investors openly asking whether AI changes what software is “worth.”

The conventional fixes — add AI features, cut costs, buy competitors — are necessary but not sufficient. They defend existing revenue; they don’t create new profit pools. The winners will add new revenue engines that align with value created and attention captured — not only with headcount and licences.

This series examines the crisis, its root causes, the inadequacy of conventional responses, and a new revenue playbook built on two additional engines: performance fees (outcomes-based revenue) and attention yield (monetising engagement, not seats). The final part provides an implementation framework — the two-track approach — for building new revenue streams without destroying what’s working.

Sources & Influences

This series draws on public commentary and reporting including Jason Lemkin (SaaStr), Wall Street Journal, Financial Times, Bloomberg, Sherwood News, Heise, CTech, Gartner, Vertice, Zylo, Iconiq, and SemiAnalysis. Direct quotes are attributed in-text.

The interpretations, the “new revenue playbook” framework (performance fees + attention yield), and the two-track implementation model proposed in Parts 5–6 are my own, developed through work at Netcore and my writings.

As with my other writing, I combine my thinking with assistance from AI (Claude and ChatGPT).

2

The Crisis

The repricing signal: when recurring revenue stops feeling “inevitable”

For years, B2B software earned a premium because it looked like the closest thing public markets had to a perpetual motion machine: multi-year contracts, high gross margins, low churn, and expansion built on seat growth. That story hasn’t ended — but it has lost its certainty.

Over recent weeks and months, software has noticeably underperformed the broader tech indices. Several category-defining names — Salesforce, Adobe, ServiceNow, HubSpot, Atlassian, Workday, Intuit — have seen double-digit declines over short windows and meaningful drawdowns (30-50%+) from their peaks. The precise numbers will keep shifting, but the signal is stable: investors are pricing higher uncertainty into software’s future cash flows.

What’s different this time is not merely rates or rotation. It’s a narrative inversion: AI was supposed to be the accelerant for software. Instead, the market is treating AI as a potential solvent.

One quote captures the emotional turn perfectly: “The narrative has really shifted… Investors have gone from initially thinking that software companies could benefit from AI to asking, ‘Is AI just the death of software?'” — Rishi Jaluria, RBC Capital Markets [WSJ]

That single line is doing a lot of work. It doesn’t mean software disappears. It means the market is asking whether the traditional software bargain — “pay us forever for capability” — survives in a world where capability can be generated, copied, and embedded faster than ever.

The valuation collapse

The multiple compression has been brutal.

A basket of software-as-a-service stocks tracked by Morgan Stanley is now trading at roughly 18 times forward earnings — its cheapest level on record. The historical average over the past decade exceeded 55 times. That’s not a discount. That’s a fundamental repricing of what software companies are worth.

The reason software commanded lofty multiples was simple: subscription-based recurring revenue that you could extrapolate into the future almost forever. Customers got locked in. Switching costs were high. Growth was predictable.

“It is hard to know what multiple they should be trading at if they’re going up against AI agents that are running 24/7 and have the ability to complete tasks, with big projects getting done in a day.” — Bryan Wong, Osterweis Capital Management [Bloomberg]

The old certainties are gone. And so are the old valuations.

Meanwhile, AI-native companies — both foundation model providers and application-layer startups — are commanding multiples that make traditional SaaS look like deep value investing. Private AI companies are raising at 50-100x revenue while public software trades at 6-12x. The market is pricing in wholesale disruption.

The Claude Code moment

Fear becomes acute when a tool makes the abstract feel concrete.

Recent coverage around Anthropic’s Claude Code gave investors a vivid mental model: building software compresses from months to days for a widening set of use cases. Developers reported completing complex projects in a week that would have taken a year. Non-engineers built their first applications without ever learning to code.

“I cannot stress enough that Claude Code is the ChatGPT moment repeated. You must try it to understand… This is going to hurt a large part of the software industry.” — Doug O’Laughlin [SemiAnalysis]

You don’t need to believe every hyperbolic claim to accept the direction: the cost and time to produce good-enough software is falling sharply. That attacks the pricing umbrella for broad swathes of application software.

The divergence: public software vs. private AI

At the same time public software is getting hammered, private markets are pouring money into AI infrastructure and AI-native applications at valuations that imply massive future profit pools.

Foundation model companies have raised tens of billions at valuations that would have seemed absurd two years ago. Application-layer AI startups are reaching $100 million in annual recurring revenue in one to two years — versus five-plus years historically for traditional SaaS.

“We’re living through something we’ve never quite seen before in B2B software. It’s arguably the worst time in our history to be invested in public software stocks. And simultaneously, the best time in our history to be invested in private hot AI-fueled startup stocks.” — Jason Lemkin [SaaStr]

The exact valuations will change. The underlying divergence is what matters. Public markets are demanding proof. Private markets are funding possibility.

The core question

This is what the market is now forcing on every B2B software company:

If AI can increasingly do what your product does — or enable customers to assemble substitutes — what exactly are customers paying you for, and why will that payment scale?

Understanding why this is happening structurally — beyond “AI fear” — is essential.

My thesis in brief: If seats compress, software needs new revenue primitives — not just new features. I see two: outcome-tied fees (paying for measurable improvement, not capability) and attention yield (monetising engagement, not headcount). The rest of this series explains why conventional responses fall short, and how to build these new engines without destroying what’s working.

3

Root Causes

Six compounding pressures (only one is “AI competition”)

Jason Lemkin’s recent SaaStr analysis identified six threat vectors attacking traditional B2B software. I want to build on his framework — not just list the pressures, but explore why they compound, and what that compounding means for revenue strategy. The forces don’t operate independently; they reinforce each other in ways that make “add AI features” insufficient as a response.

The temptation is to treat the current sell-off as sentiment that will mean-revert. That’s dangerous. B2B software faces multiple converging pressures — only one is direct AI competition. The others are economic and organisational shifts that would be squeezing the sector even if ChatGPT had never launched.

My lens: from threat vectors to revenue architecture

The threat vectors are real. But diagnosing pressures isn’t the same as prescribing solutions. Most commentary stops at “software is under pressure” or “add AI features.” I want to push further: if the unit economics of seats are breaking down, what new units of value can software companies sell?

Three shifts matter most for revenue strategy:

• The CFO reversion. Enterprise spend is shifting from “experiments” to “guarantees.” Risk transfer becomes the product. Outcome-based pricing isn’t just attractive — it’s what procurement increasingly demands. CFOs are tired of paying for capability without accountability.
• The orchestration migration. As Satya Nadella suggests, apps may become systems of record while AI agents handle orchestration and reasoning. If that’s true, value migrates to measurement, guarantees, and the data layer — not workflows and UIs.
• The attention opportunity. B2B software generates enormous engagement that’s never been monetised. Email platforms see billions of opens daily. Collaboration tools capture hours of attention. That’s a latent revenue pool waiting to be unlocked — if companies can move beyond the “ads are beneath us” mindset.

With that lens, let’s examine the six pressures — and what each implies for revenue architecture.

1. Seat growth is slowing (and AI amplifies the slowdown)

Per-user pricing won’t vanish overnight — even AI-native darlings like Cursor and Anthropic charge per seat. But the seat-growth engine is no longer the dependable escalator it was. The unit of pricing may survive; the expansion dynamic has stalled.

Companies from Workday on down are seeing customers commit to lower headcount on renewals. As Workday CEO Carl Eschenbach acknowledged [SaaStr]: “We are seeing customers committing to lower headcount levels on renewals compared to what we had expected. We expect these dynamics to persist in the near term.”

Some of this is simply headcount slowdown. Tech hiring has flattened. Some companies are holding headcount flat for years while still growing revenue significantly — proof that productivity gains are real and that the old “more employees = more seats” equation is breaking.

AI agents accelerate the compression. The relationship isn’t linear or predictable, but every autonomous agent handling work previously done by humans creates downward pressure on seat counts. The maths works against traditional expansion models.

The data tells the story. Net revenue retention at leading SaaS companies has flattened or declined. Some companies are seeing enterprise customer counts actually decline while still generating strong free cash flow — the classic signs of harvest mode. Revenue holds; growth stalls.

Revenue implication: If seats don’t expand, you need revenue that scales with transactions or outcomes, not headcount. This is the core case for performance-based pricing.

In martech specifically, the seat question is even more acute: marketing teams are shrinking while marketing automation demands grow. The gap is being filled by agents, not hires. A platform priced per marketer faces structural headwinds; a platform priced per incremental sale does not.

2. SaaS sprawl backlash and “SaaS inflation”

Buyers are drowning in tools — and increasingly aware that many of those tools creep up in price annually.

SaaS prices have been rising at roughly four to five times general inflation. The average enterprise now spends significantly more per employee on SaaS than just two years ago. Analysis suggests that a substantial majority of some vendors’ recent growth came from price increases, not new customers.

As Gartner’s John-David Lovelock observed [SaaStr]: “The cost of software is going up and both the cost of features and functionality is going up as well thanks to GenAI.”

This creates a vicious cycle. Price increases eat incremental budget. Reduced budget means less room for new purchases. Less expansion pressures vendors to raise prices again. The spiral continues until something breaks.

Most IT budget uplifts are being absorbed by renewals, security posture, and foundational AI commitments. There’s less discretionary room for “yet another tool” — and even less appetite for price increases justified by AI features users didn’t ask for.

Revenue implication: The procurement trap is real. Vendor consolidation pressure makes “incremental modules” harder to sell than “revenue engines.” If you’re asking for more budget, you need to show you’re generating budget — through measurable outcomes or cost offsets. Pure capability expansion hits a ceiling.

3. The AI + security budget gravity well

Whether you love it or hate it, a growing share of incremental IT spend is being pulled into foundation models, AI tooling, and security posture.

Enterprise leaders expect dramatic growth in LLM budgets over the next year. AI has graduated from experiment to core operating expense. The majority of AI use cases are now purchased rather than built internally.

Foundation model companies alone are generating tens of billions in combined annualised revenue — consuming a material share of all incremental IT budget.

As Jason Lemkin [SaaStr] put it bluntly: “If you’re not tapping into AI budget, you’re fighting for scraps.”

If you can’t articulate how you replace humans, dramatically augment humans, or enable the previously impossible — you’re competing for a shrinking pool of non-AI budget. The “incremental productivity improvement” pitch that worked for a decade now sounds quaint next to “we eliminated three roles.”

Revenue implication: Outcome-based pricing taps into AI budget naturally, because outcomes are what AI budget is for. A platform that guarantees measurable improvement competes for AI budget. A platform that offers “AI-powered features” without measurable impact competes for scraps.

4. The efficiency gap: AI-native companies are faster and leaner

AI-native companies are showing startling revenue-per-employee figures and shipping velocity. They’re built around new production functions: smaller teams, higher leverage, faster iteration loops.

The efficiency gap is becoming a chasm. AI-native startups are averaging five to six times the revenue per employee of traditional mature SaaS. They’re reaching $100 million ARR in one to two years versus five-plus years historically.

When you can achieve the same scale with dramatically less capital and fewer people, you can move faster, experiment more, and capture market share while competitors are still in planning meetings.

The key point isn’t the precise number — it’s the magnitude of the gap. And it’s not just about headcount efficiency — it’s about iteration velocity. AI-native teams ship daily because they don’t have organisational antibodies resisting change. Traditional teams are still debating what to build while competitors are already measuring what works.

Revenue implication: Speed compounds. Companies that can prove value in 90 days will win deals over companies that need 12-month implementations. Outcome-based models force this discipline: you can’t charge for outcomes you haven’t delivered, so you’re incentivised to deliver fast. The pricing model becomes a forcing function for operational excellence.

5. TAM traps and maturity

Some categories are simply maturing.

When expansion turns into harvesting — price per seat, margin maximisation — markets stop paying for “forever growth.” When management talks about “seat expansion” and “price per seat increases” instead of customer acquisition, you’re watching a company hit its TAM ceiling.

The warning signs are clear across the industry: revenue up, customers flat or down. Growth decelerating from 30%+ to single digits. Entire strategies centred on extracting more from existing customers rather than acquiring new ones.

Markets don’t fear collapse. They fear decades of mediocrity — the slow fade of a company that’s stopped growing but hasn’t yet died.

Revenue implication: TAM traps are category-specific, but the escape route is universal: find new units of value. If you’ve saturated seat expansion in your category, you need revenue streams that don’t depend on seats. Performance fees and attention yield are TAM-agnostic — they scale with customer success and engagement, not with headcount in a saturating category.

6. The product experience gap

This is the most uncomfortable pressure to acknowledge.

AI-native products frequently feel magical in a way legacy SaaS does not. From Claude to the best AI-native applications, these products don’t just improve productivity. They create delight. Instantly.

The experience gap isn’t subjective. Chatting with data beats navigating via clicks. Natural language beats form fields. Getting an answer in seconds beats clicking through five screens. The interface paradigm has shifted, and products designed for the click-and-navigate era feel like using a fax machine.

When users can build functional apps in minutes or get meaningful output in seconds, the bar for what constitutes a “great product” has permanently shifted.

Revenue implication: Experience drives engagement. Engagement drives attention. Attention is monetisable. The companies that create delightful, habitual experiences have an asset they’re not exploiting: the attention surface. Meanwhile, companies with clunky experiences have neither the engagement to monetise nor the goodwill to raise prices.

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The meta-shift: where intelligence lives

Beneath all six pressures lies a deeper structural transformation.

Business logic is moving from the software application to AI agents. Today, you buy software for its specific features and rules. The application holds the intelligence. Tomorrow, software applications may become systems of record and CRUD (create, read, update, delete) layers — simple tools for storing and retrieving information — while AI agents hold all the intelligence, orchestration, and reasoning.

As Doug O’Laughlin wrote: “I believe that the future role of software will not have much ‘information processing’, i.e., analysis. Claude Code or Agent-Next will be doing the information synthesis, the GUI, and the workflow. That will be ephemeral and generated for the use at hand…Most SaaS companies today need to shift their business models to more closely resemble API-based models to align with the memory hierarchy of the future of software. Data’s safekeeping and longer-term storage are largely the role of software companies now, and they must learn to look much more like infrastructure software to be consumed by AI Agents. I believe that is what’s next.”

What becomes worthless in this world: faster workflows, better UIs, smoother integrations. All of that can be generated on demand.

What becomes valuable: persistent information, APIs, proprietary data — and the ability to prove outcomes and own attention. If the intelligence layer commoditises, the measurement layer and the engagement layer become the new sources of defensible value.

**

The compounding problem

The important conclusion: these forces compound.

Seat compression reduces expansion. Price hikes create backlash. AI budgets siphon incremental spend. AI-native speed widens competitive gaps. Mature categories saturate. Experience gaps accelerate switching intent. Each weakness exposes the others. Each pressure intensifies the rest.

A company facing one of these pressures can adapt. A company facing all six simultaneously — which is most of B2B software — needs more than incremental responses.

If that’s true, “add AI features” is necessary — but not sufficient. The question becomes: what is the industry doing about it.

4

What the Market Is Trying — And Why It Falls Short

Defence is not a revenue strategy

The industry is not asleep. Executives are responding. Boards are asking hard questions. Capital is being reallocated.

But most responses are defensive: they protect today’s revenue model rather than create tomorrow’s.

Response 1: “Add AI features”

The most common response is to bolt AI capabilities onto existing products. Copilots. Assistants. Agent add-ons. Generative features. Every enterprise software company now has an “AI strategy.”

The problem: adding AI doesn’t automatically change willingness-to-pay — especially if buyers believe AI features will commoditise and converge.

Even investors are noticing gaps between “AI messaging” and “AI monetisation.” Major vendors have touted AI adoption, but it hasn’t moved the revenue needle significantly. Some didn’t update AI-related measures in their earnings reports — a telling omission.

Adding features that customers expect but won’t pay more for is a treadmill, not a strategy. If AI features become table stakes — something every competitor offers — they don’t justify premium pricing.

Response 2: “Domain knowledge is our moat”

This is the Thoma Bravo argument — and it’s partly right. “Software is not at all about the code or about the technology. Software is about your domain knowledge. Most software companies know a specific vertical, a specific process, a specific function so well that there are three to five companies in the world that know it, and about 20 individuals in the world that really, really know it. That is the franchise. That is the value. That is what you cannot replicate.”

Orlando Bravo is right that payroll, compliance-heavy workflows, and regulated systems don’t evaporate overnight. Domain expertise is genuinely hard to replicate. Integration moats are real.

But this is primarily a defence of durability, not a plan for new revenue expansion under seat compression. Domain knowledge protects existing revenue; it doesn’t create new revenue streams. And foundation models are building domain knowledge faster than many expected.

Bravo himself acknowledged that companies without specialisation are “absolutely vulnerable.” The question is whether even specialised companies can grow when their core revenue model is under pressure.

Protecting the franchise isn’t the same as expanding it.

Response 3: Cost cutting and “efficient growth”

Faced with slowing growth, many software companies have turned to the efficiency playbook. Layoffs. Margin expansion. “Disciplined execution.”

Some companies are generating strong free cash flow even as revenue growth stalls. Management talks about operational efficiency and 50% margins.

This is harvest mode. It works — for a while. Cash flow remains strong. Investors focused on profitability may appreciate the discipline.

But cost-cutting is an anaesthetic, not a cure. You can’t cut your way to a new growth curve. Eventually, there’s nothing left to cut. And if your category’s pricing power is under pressure, efficiency alone becomes a slow glide path to irrelevance.

Response 4: Consolidation / M&A

Private equity sees opportunity in repriced assets. Thoma Bravo has raised tens of billions for software deals, calling the sell-off a “huge buying opportunity.”

The thesis is straightforward: buy beaten-down software companies at discounted valuations, optimise operations, extract cash flow, and eventually exit. Consolidation reduces competition and creates scale.

But consolidation isn’t innovation. It’s the same revenue model with fewer players. The PE playbook — optimise margins, reduce costs, raise prices — doesn’t solve the structural challenges. It just extends the harvest phase.

And even PE isn’t immune. Some Thoma Bravo deals have reportedly soured due to AI-related issues. Other major PE firms have cut software exposure or even shorted software debt over AI fears.

When the smartest money in the room is hedging its software bets, consolidation alone isn’t the answer.

Response 5: “Wait for the narrative to turn”

Some bulls argue that the sell-off is overdone. AI will ultimately be a tailwind for software, not a headwind. The total addressable market will expand. Incumbent advantages in distribution and data will prevail.

They may be right — eventually. But hope is not a strategy.

Even if public software rebounds, the underlying structural forces remain: agentic workflows, budget shifts, buyer fatigue, speed gaps. The structural changes don’t reverse on their own. Even if AI expands the total addressable market, there’s no guarantee incumbents will capture the expansion.

Response 6: “Foundation models will struggle to build business software”

This is the most sophisticated defence, articulated by Orlando Bravo in a Financial Times op-ed.

The argument: OpenAI and Anthropic face the same challenge as every tech giant before them. Building enterprise software from scratch requires decades of industry knowledge, thousands of integrations, deep understanding of regulations, and the trust of large enterprises. Code is easy. Domain knowledge is hard.

As Orlando Bravo said, “Companies like OpenAI trying to build business software face the same challenge as every tech giant before them: creating entire business systems from scratch. What’s hard is the decades of industry knowledge, thousands of existing connections to other software, deep understanding of industry-specific regulations and the built-up trust of large enterprises.”

This is reasonable. Foundation model companies would struggle to replicate what incumbents have built over decades.

But the argument assumes the competition is foundation models building business software from scratch. It ignores the possibility of foundation models partnering with nimbler players, or enabling customers to build their own solutions, or simply commoditising the intelligence layer while incumbents are stuck with legacy architectures.

More importantly, it’s a defensive posture. “They can’t beat us” is different from “here’s how we win.”

What’s missing

Notice what all these responses have in common: they focus on defending existing subscription revenue.

Almost everything starts with: “How do we defend subscriptions?”

The better question is: What new revenue engines can a software company add that don’t depend on seat expansion and don’t require endless price hikes?

5

The New Revenue Playbook

Beyond subscriptions: two additional revenue engines

If subscriptions are under assault, what comes next?

Not “better subscriptions.” Not subscriptions with AI features. Entirely new revenue streams that align payment with value delivered.

This is the heart of the thesis — and it’s strongest when framed as additive, not as a wholesale replacement for subscriptions.

Think of the modern B2B software company as needing three revenue modes, of which two are new:

1. Subscription (stability — the foundation that funds everything)
2. Performance / outcomes (upside aligned to value created)
3. Attention / ecosystem yield (monetise engagement and distribution, not headcount)

Why these new engines?

Both “new engines” share four properties that the market is now rewarding:

Property	What it means
Variable	Scales with customer success or engagement
Aligned	Paid when value is realised
Non-linear	Can grow without seats
Defensible	Requires measurement, data, network, or workflow position

These properties escape the forces crushing traditional subscriptions. They don’t depend on headcount growth. They don’t require price increases that alienate customers. They align vendor and customer incentives. And they create moats that generic AI models can’t easily replicate.

Engine #1: Performance fees (outcomes-based revenue)

Principle: Stop charging only for capability. Charge for measurable improvement.

This isn’t new as an idea — consultancies and hedge funds have done it for decades. But software has historically avoided it because subscriptions are easier to forecast.

The point now is: forecastability is being repriced anyway. And alignment is becoming a competitive advantage.

The structure (borrowing from finance)

The terminology comes from hedge fund economics, but the application is direct:

• Beta is the baseline — what would have happened anyway. In finance, beta is market return; in outcome-based software pricing, beta is the customer’s performance without your intervention. This isn’t a fee; it’s the benchmark against which value is measured.
• Alpha is the outperformance — the incremental improvement above beta. The vendor charges a percentage of this uplift. No uplift, no payment. This is pure alignment: you only earn when you create measurable value.
• Carry is the long-tail participation — if Alpha persists over time, the vendor continues to share in the sustained improvement. This rewards durable impact, not one-time spikes.

A practical performance fee structure looks like this:

Component	What it means
Beta (Baseline)	The counterfactual — what performance would have been without intervention. This is the measurement benchmark, not a fee.
Alpha (Performance fee)	% of incremental value delivered above Beta. The vendor only earns when they create measurable uplift.
Carry (Long-tail share)	If Alpha persists over time, the vendor continues to participate in sustained improvement.

This does three useful things simultaneously:

• Makes the CFO feel safer. “Pay when it works” removes risk from the customer’s perspective. No uplift means minimal payment. This is a compelling proposition for finance leaders tired of paying for capabilities they’re not sure deliver value.
• Forces measurement rigour. Outcome pricing requires instrumentation, data trust, customer governance, and a shared definition of “incremental.” This rigour becomes a competitive advantage — and a moat.
• Escapes seat compression. Revenue is tied to transactions or outcomes, not headcount. If AI reduces the humans involved, the vendor still gets paid on results.

I’ve explored this extensively in my NeoMarketing essays, where the application to martech is direct: performance fees linked to incremental sales generated, with measurement built into the channel infrastructure. But the model generalises. Any B2B software category with measurable customer outcomes can adopt this structure.

Where it applies (beyond martech)

Performance fee models can work across B2B software wherever outcomes are measurable:

Vertical	Traditional Model	Performance Fee Model
Sales Tech	Per-seat CRM	% of pipeline conversion uplift
HR Tech	Per-employee platform fee	% of retention improvement or hiring cost reduction
Fintech (B2B)	Platform fee	% of fraud prevented or collections improved
Supply Chain	Per-user licence	% of cost savings or delivery improvement
Customer Support	Per-agent pricing	% of containment rate improvement or CSAT uplift
DevOps	Per-seat IDE	% of deployment velocity improvement or incident reduction

The common thread: tie revenue to outcomes that customers actually care about, not capabilities they may or may not use.

The key requirement

Outcome pricing is not a pricing page change — it’s an operating model.

It requires:

• Instrumentation (measuring what matters)
• Data trust (agreeing on the source of truth)
• Customer governance (pre-agreed methodology)
• Attribution clarity (defining what’s “incremental”)
• Control groups or credible quasi-experiments (proving causation)

This is hard. That’s why it’s defensible.

Engine #2: Attention yield (ads, partnerships, network monetisation)

This is the more contrarian piece — and therefore the more differentiating, if executed correctly.

Principle: If your product creates repeat engagement for a valuable audience, you can monetise the surface area — not by annoying users, but by enabling relevant, additive partner value.

The insight

Consumer platforms have understood attention economics for decades. Google and Meta built trillion-dollar businesses on attention monetisation.

But B2B software has traditionally ignored this revenue stream, viewing advertising as somehow beneath enterprise dignity. That’s changing. As subscription growth stalls, B2B companies are discovering that engaged user bases are valuable assets that can be monetised in multiple ways.

The cleanest framing: Software that owns attention can earn yield.

Yield can come from ads, partnerships, referrals, and transactions. The monetisation must be consent-driven, transparent, and value-adding.

In other words: don’t copy consumer adtech. Create utility-aligned monetisation.

Where the attention surface exists

Most B2B software never earns the right to monetise attention because users don’t choose to spend time there. But some categories do:

Surface	Why attention exists
Communication platforms	Daily, habitual engagement
Marketplaces and networks	Discovery and transaction intent
High-frequency dashboards	Operational necessity creates regular visits
Inbox-like environments	Daily attention is already there
Collaboration tools	Team workflows create repeated engagement

 How it works

Software that engages end-users creates an “attention surface.” That attention can be monetised through:

• Sponsored content from complementary vendors
• Partner revenue shares for referrals and transactions
• Contextual recommendations that add value
• Cooperative advertising networks where brands reach customers through each other’s engaged channels

The economics can be compelling. Instead of customers paying platform fees, the attention they generate subsidises their costs — while creating new revenue for the software provider.

The inbox example

Consider email — one of the few places where attention is both habitual and measurable.

Traditional model: brand pays email service provider per email sent, regardless of engagement.

Attention-based model: daily value-driven emails that customers actually want to open, with in-email transactions sponsored by complementary brands. The brand sending the email (publisher) earns revenue from the engaged attention. The brand reaching that audience (advertiser) pays for deterministic access — at a fraction of what they’d pay Google or Meta for probabilistic reach.

Both brands benefit. Attention is monetised. The network captures a share. This is the core of what I call NeoNet — a cooperative advertising layer built on authenticated engagement rather than probabilistic targeting. The economics favour everyone except the platforms currently extracting the reacquisition tax.

**

Why incumbents won’t copy easily

Even if these ideas sound straightforward, most incumbents struggle to execute because of structural barriers:

For performance fees:

• Sales compensation is built around ARR, not outcomes
• Finance teams dislike variable revenue until it becomes meaningful
• Product teams aren’t instrumented for causal measurement
• The shift requires confidence that the product actually delivers value — and willingness to stake revenue on that belief

For attention yield:

• “Ads” triggers cultural antibodies in B2B — even when it’s actually “partner yield”
• Engagement levels may not justify monetisation
• Network effects take time, and incumbents often have no incentive to start
• Different skill sets are required (media, partnerships, ad operations)

This is why the new engines require a distinct motion — not a side project for the existing team.

The moat: proprietary data and domain knowledge

Orlando Bravo is right that domain knowledge is the franchise. But he’s applying it defensively.

Applied offensively, domain knowledge becomes the foundation for new revenue models:

• Domain knowledge enables better outcome measurement. If you deeply understand a vertical, you know what “good” looks like. You can define baselines, measure uplift, and prove value in ways that generic AI models cannot.
• Proprietary data enables better targeting. If you have unique signals about customer behaviour, you can deliver more relevant recommendations and more valuable attention.
• Integration depth enables better execution. If you’re embedded in customer workflows, you can act on insights immediately.

The combination — proprietary data, domain knowledge, and new revenue models — creates defensible differentiation. Generic AI models can replicate features. They can’t replicate years of accumulated intelligence about specific verticals and customer behaviours.

The question for CEOs

A simple restatement:

The question isn’t “how do we defend subscriptions?”

It’s “what new profit pools can we add that scale with outcomes and attention?”

Now comes the hard part: execution without wrecking the core.

6

What B2B Software Companies Must Do

The two-track approach: protect the core, build the future

The biggest mistake companies make in moments like this is either:

• Freeze (“wait it out”), or
• Flail (a panicked “pivot” that scares customers and employees)

The right approach is a two-track operating system.

Why two tracks, not a pivot

The temptation in a crisis is dramatic action. Abandon subscriptions. Go all-in on outcomes. Transform the company.

This usually fails.

Subscriptions, for all their challenges, still generate predictable cash flow. That cash flow funds operations, pays salaries, and provides the runway to experiment. Abandoning it prematurely risks the entire company.

Existing customers chose you because of the current model. They have budgets allocated, contracts signed, workflows built. Suddenly changing the deal creates confusion and churn.

New models need time to prove. Performance fees require measurement infrastructure. Attention economics require engagement levels. Neither delivers revenue on day one. Betting everything on unproven models is reckless.

And the narrative matters. “We’re pivoting because our core business is failing” terrifies investors. “We’re building a second growth engine” inspires them.

The framing: Track 1 remains the compounding base. Track 2 is a deliberate build of new profit pools that escape seat compression. Add variable upside engines while there is still credibility, customers, and cash flow.

Track 1: Protect and modernise the core

Track 1 is the subscription engine. It funds everything.

Goals

• Reduce churn and contraction
• Improve product experience
• Use AI to lower cost-to-serve and increase value
• Tighten packaging so pricing feels earned
• Add AI features that justify (but don’t depend on expanding) current pricing

Metrics

Metric	What it measures
Gross retention	Customer base stability
Net revenue retention	Expansion vs. contraction
Logo churn	Customer loss rate
Gross margin	Operational efficiency
CAC payback	Sales efficiency
Product usage health	Real adoption, not vanity metrics

 Investment

The majority of current resources — 70-80%. Track 1’s job is to buy time and fund Track 2 experimentation.

Track 2: Build new revenue engines

Track 2 is not “innovation theatre.” It is a separate revenue motion with separate economics, skills, and success criteria.

Goals

• Prove outcome pricing in a tight set of pilots
• Build measurement credibility and repeatable playbooks
• Build (or plug into) an attention/yield network where the surface exists
• Convert pilots into a scalable revenue line with clear unit economics

Metrics

Metric	What it measures
Pilots launched	Experimentation velocity
Pilot → contract conversion	Model viability
Uplift distribution (median, p75, p90)	Outcome quality — not just best case
Time-to-proof	How fast you can show measurable value
Track 2 revenue as % of total	Revenue diversification progress

 Investment

A dedicated allocation — 20-30% of resources — with a dedicated team.

**

Organisational design: the anti-antibody move

The most common mistake is letting the Track 1 team run Track 2.

It never works.

Track 1 teams are optimised for subscription metrics. They’re compensated on ARR. They’ve spent careers perfecting the current model. Asking them to cannibalise it creates impossible conflicts.

Organisational antibodies are real. New initiatives that threaten existing revenue face resistance at every level. Priorities shift back to the core business. Experiments get deprioritised. Track 2 dies a quiet death.

The solution: structural separation

Dimension	Track 1	Track 2
Team	Existing organisation	Dedicated squad (startup-within)
Leader	Current leadership	Track 2 Lead with CEO visibility
Commercial motion	Standard contracts, quotas	Separate templates, legal, finance rules
Metrics	ARR, NRR, churn	Uplift, pilot conversion, Track 2 revenue
Compensation	Standard quotas	Tied to outcomes delivered / revenue realised
Culture	Optimise and defend	Experiment and prove

 The pilot programme: how to start without drama

Track 2 should start with pilots, not transformation.

Start with 3-5 pilots where:

• Baselines are measurable (you know current performance)
• The customer has executive sponsorship (champion who will advocate)
• The domain has clear value metrics (retention, conversion, cost savings)
• You can run controlled comparisons (treatment vs. control)

Pilot structure

Element	Specification
Duration	90 days + measurement window
Scope	10% of customer base in treatment, 10% in matched control
Methodology	Pre-agreed measurement protocol
Success criteria	Defined before starting

A good pilot is designed to answer one question: Can we repeatedly create measurable value, and can we contract for it?

Kill criteria: intellectual honesty

Avoid the sunk cost fallacy. Define failure upfront.

Track 2 should be reassessed if:

• Fewer than 30% of pilots show meaningful uplift
• Average uplift falls below the threshold that justifies the economics
• Track 1 materially suffers due to distraction
• 12 months pass without repeatable proof

Write these criteria down before starting. Revisit them at each gate. Be willing to kill what isn’t working.

The 90-day starting point

For companies ready to begin:

Weeks 1-2:

• Identify Track 2 leader (credibility, autonomy, CEO access)
• Define first pilot candidates from existing customers
• Draft measurement methodology

Weeks 3-4:

• Assemble core team (5-10 people, dedicated full-time)
• Sign 3-5 pilot agreements
• Establish baseline metrics

Weeks 5-8:

• Build/configure measurement infrastructure
• Deploy initial interventions
• Begin data collection

Weeks 9-12:

• First results emerging
• Iterate based on early learnings
• Expand to additional pilots if signals positive

Week 13+:

• Full measurement cycle
• Statistical significance achieved
• Decision: scale, iterate, or kill
• Board review

The investor narrative

Your outward story is not “we’re pivoting.” It’s: “We’re building a second growth engine.

Track 1 continues to deliver predictable subscription revenue and cash flow. We’re maintaining the business, adding AI capabilities, and extracting efficiency gains. This is the foundation.

Track 2 is capturing new profit pools through outcome-based pricing and attention economics. These revenue streams escape seat-based compression because they’re tied to value delivered, not headcount. They align our incentives with our customers’ success. And they position us for growth in the AI era.

We’re running both tracks in parallel. Track 1 funds the business. Track 2 builds the future. This isn’t a pivot — it’s diversification. We’re adding variable upside engines while we still have credibility, customers, and cash flow.”

What not to say:

• “We’re pivoting” (signals desperation)
• “Subscriptions are dead” (terrifies existing customers)
• “We’ll figure out the model later” (signals no plan)

The window is closing

The software companies that win the AI era won’t be the ones who cling longest to the old model.

They’ll be the ones who:

• Keep the subscription engine healthy, and
• Build new engines that monetise value delivered and attention earned

Enterprise software spending will exceed $1.4 trillion this year. Global IT spending will surpass $6 trillion. The market is growing. The opportunity is enormous.

The question is whether you’re positioned to capture it — or watching it flow to companies that figured this out faster.

The window is 18-24 months. After that, the market will reprice based on execution, not fear. Companies that have proven new revenue models will be rewarded. Companies that haven’t will face further compression.

The time to start is now.

**

Conclusion: The Revenue Imperative

B2B software’s crisis is real, structural, and accelerating.

The six threat vectors — seat slowdown, price increase backlash, AI budget shift, efficiency gaps, TAM traps, and product experience gaps — compound each other. The conventional responses — AI features, domain knowledge, cost cuts, consolidation — are defensive. They protect what exists. They don’t create what’s needed.

What’s needed is a new revenue playbook.

Performance fees that tie vendor revenue to customer outcomes. Attention economics that monetise engagement rather than headcount. New profit pools that escape seat-based compression.

The implementation requires a two-track approach. Track 1 maintains the subscription base, generates cash flow, and buys time. Track 2 builds new revenue streams, proves new models, and creates the future.

The question isn’t whether B2B software will change. It’s whether your company will lead the change or be changed by it.

1

The Magnetic Inbox is not a product. It is a system — one that only makes sense when seen from all sides at once. Brands, consumers, advertisers, and the platform itself experience it differently, yet they are bound together by the same moments, the same interactions, the same signals rippling through the ecosystem.

To understand how pull replaces push, how reacquisition gives way to relationships, and how email quietly becomes something else entirely, we need to see the same system through four different voices. This is that story — told first by the person at the receiving end of it all.

**

Arun — The Consumer

“I never thought I’d look forward to marketing emails.”

I didn’t set out to change how I use email. It just… happened.

I’m 34, live in Mumbai, work in product management at a fintech. Like most people I know, my inbox used to be a graveyard. Promotions I never opened. OTPs. Receipts. The occasional newsletter I felt guilty unsubscribing from because I’d signed up in a burst of optimism six months earlier.

Email was background noise. Useful, but forgettable.

That changed sometime last year, when a few brands I actually liked started sending something different. Not offers. Not announcements. Something to do.

The three things I see most often are a Quiz, a WePredict card, or a Daily Fork — different formats, same rhythm: engage now, resolve later.

Now I subscribe to NeoMails from about eight or nine brands — my coffee roaster, a fitness app, two fashion labels, a financial news publisher, a skincare company, and a few others. I receive about three a day, spread across morning, afternoon, and evening — and I open all three. Not out of obligation. Out of something closer to habit.

Let me walk you through yesterday.

**

7:42am — First NeoMail: BrewCraft (Coffee)

I’m still in bed, half-awake, phone in hand. The subject line reads:

µ.1,847 | Your Daily Coffee IQ

That number — 1,847 — is my Mu balance. I’ve been accumulating it for months across all the NeoMails I receive. I don’t treat it like money exactly. It’s more like… energy. Something you spend to participate, something that grows when you show up. Right now I’m 153 Mu away from a free bag of BrewCraft’s single-origin Ethiopian. That’s maybe four or five days of engagement. I’ll get there — though honestly, I’m not chasing it. It’s more a marker of showing up than a reward I’m hunting.

I tap open.

First thing I see: my streak. Fourteen days. A small badge glows next to it. Oddly satisfying.

Then the Magnet — today it’s a WePredict card. The question: “Will NIFTY close up or down today?” Nothing to do with coffee — and that’s intentional. The Magnets don’t belong to the brand; the BrandBlocks do. They’re just… reasons to engage. I tap “Up” and stake 20 Mu. I’ll find out tomorrow if I was right.

Below that, the BrandBlocks — this is where BrewCraft shows up as a brand. There’s a short story about Maria, the farmer behind their new Colombian roast. Her family has grown coffee for three generations. I skim it — genuinely interesting. Then a product carousel: three beans I haven’t tried, curated based on my past orders. Ethiopian Yirgacheffe. Colombian Supremo. Sumatra Mandheling. I swipe through, not buying, just browsing. No pressure.

At the bottom, an ActionAd — something from a financial services brand. “Check your insurance gaps in 60 seconds.” I notice it but don’t click. Not yet.

Finally, my Mu Ledger. Today’s earnings: +15µ. Total balance: 1,862µ. The progress bar inches closer to that free bag.

**

12:34pm — Second NeoMail: FitLoop (Fitness App)

Lunchtime. I’m at my desk, eating a sandwich, scrolling through emails. The FitLoop NeoMail arrives like clockwork — always at 12:34. The subject line:

µ.1,862 | Noon Quiz — Test Your Nutrition IQ

The Magnet today is a Quiz — three quick questions about protein intake, hydration, and recovery. I get two right, miss one about creatine timing. Results show instantly: I’m in the top 23% of respondents today. Small competitive hit. Eight Mu earned.

The BrandBlocks: a “Workout of the Day” — today it’s a 15-minute mobility routine. I bookmark it for later. Below that, a progress tracker showing my activity this week (embarrassingly low) and a nudge: “You’re 2 workouts away from your weekly goal.”

There’s an ActionAd here too — the same insurance brand from this morning. This time I actually read it. It’s offering a short survey: “5 questions to find your coverage gaps. No phone number required.”

I tap through. Five questions. Takes maybe 30 seconds. At the end, it shows me I’m probably underinsured for critical illness. No hard sell. Just information. I save the link.

That’s new behaviour for me. A year ago, I would have ignored anything that looked like insurance advertising. But this felt different — it arrived inside something I already trusted. It didn’t interrupt what I was doing; it was part of it.

Total time: about 70 seconds.

**

6:48pm — Third NeoMail: ThreadCraft (Fashion)

Evening. I’m on the couch, half-watching TV, decompressing from work. The ThreadCraft NeoMail arrives:

µ.1,887 | Daily Fork — Which Look Wins?

The Magnet is a Daily Fork — two outfit options side by side. “Which works better for a Friday client meeting?” I tap the navy blazer look. Instant result: 62% of people agreed with me. Validation. Eight Mu earned.

The BrandBlocks: a carousel of new arrivals, filtered to my size and style preferences. A linen shirt I actually like. A pair of chinos on sale. Then a short feature: “How to Transition One Jacket from Office to Evening.” Useful. I read the whole thing.

The ActionAd in this one asks if I want to subscribe to a media brand’s newly started AI NeoLetter. I’m curious about AI news — I tap Yes and I’m done.

Total time: about 50 seconds.

**

What’s changed.

A year ago, I had 94 unread promotional emails. I checked once a week, mostly to delete. The inbox was something to clear, not something to visit.

Now I check three times a day — morning, lunch, evening. Not because I’m compulsive. Because there’s usually something worth 60 seconds. A prediction to make. A quiz to complete. A streak to protect. A Mu balance inching upward.

The psychology is hard to resist. The streaks create small commitments. The Mu balance creates visible progress. The predictions create anticipation — I want to know if I was right. The Forks make me feel like my opinion matters. These are tiny hooks, but they compound.

And here’s the strange part: the inbox feels calmer. Not because I get fewer emails — I probably get more. But because the emails I’ve opted into actually respect my time. They don’t shout. They don’t manufacture urgency. They give me something small, and they leave.

**

What I’ve realised.

I used to think email was broken. Now I think most email is just badly designed.

The brands that send me NeoMails have figured out something simple: if you want me to show up every day, you have to give me a reason that isn’t “buy now.” A reason that takes 60 seconds. A reason that feels like a fair exchange.

The Magnets pull me in. The BrandBlocks keep me informed. The Mu rewards me for showing up. The whole thing fits into dead moments of my day — while the kettle boils, while I eat lunch, while I decompress on the couch.

And here’s the real shift: when I am ready to buy coffee, or new clothes, or finally sort out my insurance — I already know which brands I’ll go to.

Not because they shouted the loudest. Because they showed up quietly, every day, and gave me something worth opening.

**

Next: Maya, the CMO who stopped paying to buy customers back — and didn’t quite believe it would work until it did.

2

Maya — The Brand Marketer

“I stopped paying to buy back customers I already owned.”

I used to think email was a solved channel.

I’ve been a CMO long enough to remember when email was the workhorse of growth — cheap, measurable, reliable. Then, slowly, it stopped working. Not dramatically. Quietly. Open rates slid. Clicks dried up. Revenue attribution became fuzzy. And every quarter, performance marketing took a larger share of my budget to make up the gap.

I’m the CMO at ThreadCraft, a mid-sized D2C fashion brand. By the time we started looking seriously at NeoMails, my numbers looked like this: email open rates hovering around 9%, CRM contributing less than 7% of revenue, and close to 40% of my marketing spend going into reacquisition on Meta and Google. We were paying ₹300-400 to win back customers whose email addresses we already had.

We tried everything you’re supposed to try. Better segmentation. Smarter subject lines. AI-written copy. More “personalisation.” It all felt like rearranging furniture in a room that was slowly emptying out.

Email hadn’t become bad. It had become ignored.

**

The silent churn.

Every quarter, I watched our “engaged subscribers” shrink — not because people unsubscribed, but because they just… stopped. They were still on the list. They just weren’t there anymore.

Our response? Send more. Bigger discounts. Louder subject lines. If 9% are opening, send to more people. But all we did was accelerate fatigue. The customers who were still engaged started tuning out too.

I remember a campaign last year — 30% off everything, our biggest sale of the season. Open rate: 7.4%. Lower than average. That was the moment I realised: we weren’t fighting for attention. We were training people to ignore us.

**

The heretical idea.

The first thing that made me uneasy about NeoMails was the word daily. Daily emails are how you get unsubscribes. Every marketer knows that. Or at least, every marketer trained in the old model knows that.

But we were stuck. Reacquisition costs were rising. Retention was plateauing. And email, which should have been our cheapest lever, was effectively dead weight.

So we agreed to a pilot — small, controlled, and frankly, sceptical. We picked 50,000 customers who hadn’t opened an email in 90+ days. The “dead” segment. If we burned them, we burned nothing.

The pilot ran for eight weeks.

NeoCore capped frequency at three NeoMails per customer per day, rotating across brands — so we weren’t blasting, we were participating in a shared system.

**

What happened.

Week one was modest. Open rates on the NeoMails: 24%. Way better than our 9% average. I assumed the novelty would fade.

Week eight: consistently north of 50%, peaking some days above 60%. The customers who opened once were opening again. And again. But here’s what stopped me cold — it wasn’t just opens. It was the same people opening repeatedly. Our Click Retention Rate, which had historically been around 18% quarter-over-quarter, hit 72% for the NeoMails cohort.

More than half the customers who engaged in week one were still engaging in week eight. With traditional campaigns, we’d have lost a majority of them.

**

What I didn’t expect.

I expected the Magnets to be gimmicks. Quizzes. Polls. Predictions. “Gamification” — a word that makes me cringe.

I was wrong.

The Magnets weren’t about entertainment. They were about rhythm. They gave customers a reason to open that had nothing to do with buying. A Daily Fork: “Which outfit works better for a Friday meeting?” A WePredict card: “Will cotton prices rise this week?” A quick style quiz.

None of it was about ThreadCraft specifically. The Magnets belonged to the system; our BrandBlocks carried our story. That felt counterintuitive at first. Why would I send content that isn’t about my brand?

But that’s exactly why it worked. The Magnets created the open. The BrandBlocks — our product carousels, our style tips, our brand stories — earned the attention after the customer was already there. Our role was to host, not hijack.

One customer — a guy in Mumbai, mid-thirties — opened 19 of 21 NeoMails over two months. He didn’t buy anything for the first month. Then he bought twice in week five. Then again in week seven. His lifetime value tripled, but more importantly, he never went dark. He was just… present. Regularly.

I started thinking about customers differently. Not as “purchasers” and “non-purchasers,” but as “present” and “absent.” NeoMails kept people present.

**

The ActionAds leap.

The idea of hosting ads from other brands inside our emails would have been unthinkable a year ago. But once we saw how customers interacted — the ads didn’t interrupt; they blended — the fear faded.

ActionAds from complementary brands — a premium coffee subscription, an insurance provider, a media brand’s AI newsletter — generated enough revenue to cover our entire NeoMails operation. The sending costs disappeared. The Mu rewards customers earned — for showing up, not buying — were funded by the ad revenue.

From a P&L perspective, that mattered. Email was no longer a “free but underperforming” channel. It became a self-funding one with upside.

And customers didn’t mind. The ActionAds were relevant, non-competitive, frictionless. One tap to subscribe to a newsletter. A short survey to check insurance coverage. Engagement rates of 25% — far higher than display ads, far less intrusive than retargeting.

**

The category break.

There was a moment, about a month after we expanded the pilot, when I realised this wasn’t incremental improvement.

I was reviewing our reacquisition spend for the quarter. Meta and Google costs were down 34%. Not because we’d cut budget, but because we needed less. The customers we used to lose and re-buy were still engaged. They hadn’t gone dark.

I pulled up the original pilot segment — the 50,000 “dead” customers. Thirty thousand were now active NeoMails engagers. Eight thousand had made a purchase in the last 90 days. We’d resurrected a quarter of a segment we’d written off as lost.

No discount. No retargeting. No auction. Just daily presence.

Email had stopped being a campaign channel and started behaving like a relationship surface.

**

What’s changed.

I don’t obsess over subject lines or send times the way I used to. I look at streaks. I look at how many customers are still “warm” after 30 days. I look at how many didn’t need to be reacquired at all.

The distinction we’d missed for years is now obvious: daily promotion is spam; daily interaction is habit. NeoMails aren’t asking for a purchase. They’re asking for a moment.

That difference changes everything.

We still run campaigns. We still do launches and sales and promotions. But they sit on top of something sturdier now. A daily presence. A reason to return.

And that’s the quiet shift I didn’t see coming: when customers are already opening your emails every day, you don’t have to shout when it’s time to sell.

**

Next: Ria, the advertiser who stopped chasing attention and started appearing inside it.

3

Ria — The Advertiser

“I stopped chasing attention — and started advertising inside it.”

I run growth and reactivation for a BFSI brand. Credit cards, insurance, long consideration cycles, low tolerance for noise.

For years, my job was simple to describe and painful to execute: reach customers who were almost interested, had shown some intent, but weren’t converting — without burning money on Google and Meta.

Retargeting was supposed to solve this. In reality, it became a reacquisition tax.

We paid auction prices to show ads to people who had already visited our site, already filled half a form, already existed in someone’s database somewhere. Attribution was fuzzy. Frequency was blunt. Costs kept rising. And every optimisation felt marginal.

The part that bothered me most was philosophical: I was chasing people who were actively ignoring me.

**

The context shift.

Then I saw an ActionAd — not in a feed, not in a banner, but inside a NeoMail.

It wasn’t loud. It wasn’t urgent. It didn’t even look like an ad. It said: “Check your insurance gaps in 60 seconds. No phone number required.”

What caught my attention wasn’t the copy. It was the context.

This wasn’t someone scrolling aimlessly. This was someone who had already opened an email — not because they were sold to, but because they were playing, predicting, choosing. They were present.

That’s when the model clicked for me.

NeoMails weren’t another inventory source. They were a different state of mind.

**

The experiment.

We started small. A pilot across a handful of non-competing brands — fashion, fitness, a coffee subscription, a news publisher. We tested three ActionAd formats:

Lead-light surveys — five questions, no phone number, no agent follow-up. Completion rates: 28-38%. Cost per qualified lead: 50-60% lower than Meta. And the leads were cleaner — people who’d taken 30 seconds to answer questions, not people who’d clicked accidentally while scrolling.

NeoLetter subscriptions — opt-in to our “Financial Wellness” newsletter. Simple ActionAd: one tap, no form. Subscription rate: 12%. Not leads in the traditional sense, but permission. A foot in the door.

Soft intent checks — “Planning to review your insurance this year? Yes / No / Maybe.” Low friction, high signal. The “Maybe” responses became our nurture pool.

The results weren’t explosive. They were clean. Drop-offs were honest. People who weren’t interested simply didn’t engage. No accidental clicks. No forced funnels.

**

The Arun moment.

One campaign stands out.

A customer — mid-thirties, Mumbai — saw our survey ActionAd inside a fitness app’s NeoMail at lunchtime. Completed the five questions. Didn’t opt in immediately.

That evening, the same offer appeared again — this time inside a fashion brand’s NeoMail. This time, he tapped through and requested the detailed report. Two days later, he was on a call with our advisor. A week after that, he upgraded his health cover.

One customer. Two touchpoints. Both inside emails he was already opening for entirely different reasons.

The difference was trust. Not in us as a brand — but in the environment. We weren’t interrupting his day. We were participating in a ritual he’d already chosen.

**

What didn’t work.

Not every format performed.

Direct offers — “Get 20% off your first premium” — underperformed. Insurance isn’t an impulse purchase. Discounts felt out of place inside an email about coffee or fitness.

Long forms — anything requiring more than five fields — had terrible completion rates. The NeoMail environment is built for 60-second interactions. Ten data points broke the rhythm.

Generic creative — we initially used the same ad units we ran on Meta. They looked like ads. The ActionAds that worked felt native: simple copy, single ask, one-tap action.

The lesson: ActionAds aren’t a new placement for old creative. They’re a different format entirely.

**

What’s changed.

I still run Meta and Google campaigns. They’re not going away. But my budget allocation has shifted. Last quarter, 18% of my acquisition spend went to ActionAds. This quarter, it’s 27%.

More importantly, my mental model has shifted.

NeoNet isn’t about targeting people. It’s about entering relationships.

Traditional ads try to manufacture attention. ActionAds borrow attention that already exists — earned by someone else, maintained by the system. And because everything is deterministic — real emails, real engagement, real actions — the learning loop is tighter.

There are still limits. This isn’t for mass reach or brand blasts. It’s for consideration, reactivation, and trust-building. But those are exactly the hardest problems in BFSI.

BFSI lives in the gap between awareness and action — and that gap is exactly where NeoMails operates best.

I don’t think of ActionAds as ads anymore. I think of them as polite questions asked at the right moment.

And that’s a very different game.

**

Next: Kiran — the builder behind the system, watching all of this happen at once.

4

Kiran — The Platform CEO

“We’re not building more email. We’re trying to make the inbox livable again.”

When we started Neocore, the problem wasn’t technology. It was incentives.

Email failed not because it couldn’t do more — but because everyone involved was paid whether it worked or not. ESPs charged for volume. Brands optimised for campaigns. Adtech profited from churn.

No one was responsible for presence.

The original insight was simple: if customers drift quietly, and brands pay loudly to get them back, something is fundamentally broken.

NeoMails was our answer — but it only works as part of a system.

**

The orchestration problem.

Arun’s behaviour, Maya’s economics, Ria’s ROI — none of them make sense in isolation. The challenge is holding them together.

Every day, we’re balancing three forces:

Brands want engagement and sales. Left unchecked, they’d fill every NeoMail with product carousels and promotional offers.

Consumers want value without friction. They’ll engage with Magnets, accumulate Mu, tolerate BrandBlocks — but only if the experience stays light. The moment it feels like work, they’re gone.

Advertisers want leads and conversions. They’d love aggressive offers in every email. But if ActionAds become intrusive, consumers stop opening, and the whole system degrades.

If any one side is optimised too hard, the system collapses. Our job is not to please any one party — it’s to preserve the loop.

**

The 60-second constraint.

The discipline that makes it work is time.

Every NeoMail is designed to take no more than 60 seconds. That’s the budget. Roughly a third goes to the Magnet, a third to BrandBlocks, a small slice to ActionAds, and the rest to tracking progress in the Mu Ledger.

This constraint is religious for us. Brands push back — “Can we add a third BrandBlock?” Advertisers push back — “Can we have a longer form?” The answer is almost always no.

Because the moment a NeoMail takes 90 seconds, completion rates drop. When completion drops, habit breaks. When habit breaks, the system stops working.

**

The AI engine.

The hardest part wasn’t building Magnets. It was making them repeatable.

Quizzes, WePredict cards, Daily Forks — these aren’t content. They’re moves. Each one creates unfinished business. Each one respects time. And each one must work whether the brand is coffee, fashion, or finance.

AI helps — but not by being clever. We don’t optimise for novelty. We optimise for rhythm. Any Magnet that requires explanation, surprise, or learning how to play is rejected.

Our AI assists with Magnet creation — calibrated to category, difficulty level, and freshness. A customer might predict stock movements today, cricket match outcomes a few days later, and movie box office performance the following week. Different content, same mechanic.

The goal is familiar novelty — the same format, endlessly varied. That’s what sustains habit. Customers know what to expect, but they don’t know exactly what they’ll get.

**

Mu as memory.

Mu is the glue. Not as currency — but as memory.

It makes progress visible across brands, across days. It tells Arun: you’ve been here before. It gives Maya’s customers a reason to return that isn’t a discount. It funds itself through ActionAd revenue.

If earning outpaces burning significantly, customers are hoarding — redemption options aren’t compelling. If burning outpaces earning, rewards feel too cheap. Healthy velocity is roughly 1.2:1 earn-to-burn.

Mu isn’t about incentives. It’s about making attention remembered.

**

What we got wrong.

Early on, we let brands control too much of the NeoMail composition. They could choose which Magnets to include, how many BrandBlocks to add, where to place the ActionAd.

The result was chaos. Some NeoMails took two minutes. Others had three BrandBlocks and no Magnet. The experience was inconsistent, and engagement suffered.

The fix was standardisation. We created templates: Magnet first, then BrandBlocks, then ActionAd, then Mu Ledger. Brands could customise within the template, but they couldn’t break the structure.

The lesson: in a multi-sided system, too much flexibility destroys coherence. Constraints aren’t limitations. They’re what make the system legible.

**

What I watch daily.

Every morning, three numbers:

System-wide open rate. If it drops meaningfully for a few days in a row, something is wrong — stale Magnets, broken mechanics, or fatigue.

Mu velocity. Earn-to-burn ratio. Healthy is 1.2:1.

ActionAd engagement. Below 3%, the ads are becoming noise. Above 8%, the targeting is working.

These three numbers are my vital signs. Everything else ladders up to one question: Did we give people a reason to come back tomorrow?

**

Where this goes.

The goal isn’t to maximise email. It’s to minimise regret.

Every metric, every constraint, every “no” we say to brands and advertisers — it all serves one purpose: making sure that when someone opens a NeoMail, they don’t wish they hadn’t.

If Arun opens one NeoMail today, we’ve done our job. If he opens three, that’s a bonus. If he skips, we don’t punish him. We wait.

Because relationships don’t scale through force. They scale through patience.

That’s what the Magnetic Inbox is really about. Not pull versus push. Not ads versus email. But designing a system where everyone benefits by showing up — quietly, repeatedly, and by choice.

**

Four voices. One system. The same week.

Arun opens three NeoMails a day and doesn’t think of them as marketing. Maya watches her reacquisition spend shrink and wonders why she waited so long. Ria finds leads inside attention she didn’t have to chase. And Kiran holds the system together, balancing the forces that would pull it apart.

The Magnetic Inbox isn’t magic. It’s alignment — of incentives, of timing, of value exchanged. Brands earn the right to show up. Consumers get reasons to return. Advertisers appear inside trust they didn’t have to build. The platform enforces the constraints that keep everyone honest.

Email didn’t die. It was waiting to be redesigned.

Pull replaces push. Habit replaces decay. And the inbox, finally, becomes a place worth returning to.

1

The Ignored Inbox

Every marketer knows this feeling.

You send an email. It is well-designed, carefully written, perfectly timed. The subject line has been A/B tested. The audience has been segmented. The dashboard refreshes.

And then… nothing.

No opens. No clicks. No response. No signal.

The modern inbox is not hostile. It is indifferent.

This indifference is usually blamed on the recipient. People are busy. Attention spans are shrinking. Email is dying. The customer, we are told, is distracted, fickle, unreachable.

That diagnosis is comforting — and completely wrong.

Email isn’t ignored because people hate email. It’s ignored because email has been stripped of its pulling power. It has become pure push.

Over time, marketing trained customers to expect only one thing from the inbox: demands. Buy now. Limited time. Last chance. We replaced curiosity with campaigns, conversation with conversion, rhythm with randomness. Frequency went up. Relevance went down. Attention decayed.

The data tells the story. Across hundreds of brands, only about 20% of customers who click in one quarter click again in the next. Four out of five disappear — not by unsubscribing, not by complaining, but by quietly stopping. This is not churn in the traditional sense. It is something more dangerous: attention decay.

And attention decay compounds.

When emails are ignored, brands respond the only way they know how: send more. Louder subject lines. Bigger discounts. More urgency. Push harder. The inbox becomes a graveyard where every message fights every other message for a fraction of a second — and loses.

Eventually, these customers are written off as “inactive” and handed back to ad platforms for reacquisition. Brands pay again to reach people they already had a direct line to. This is the $500 billion AdWaste loop — created not by bad acquisition, but by broken attention.

The uncomfortable truth: email didn’t fail because it stopped working. It failed because we used it wrong.

**

Email is not a feed. It is not a billboard. It is not a delivery mechanism for offers. It is the most intimate digital space brands have access to — closer than social, more personal than apps, more universal than messaging. The inbox is checked daily, often dozens of times. It is habitual by nature.

But habit only forms when there is pull.

Social platforms understood this early. Instagram did not grow by pushing messages asking people to open the app. It grew by embedding magnets inside the experience — micro-moments of reward, validation, curiosity, and progress that trained the brain to return. TikTok did the same with short-form video. Wordle did it with a daily ritual.

Email, by contrast, was left magnetless.

For decades, brands survived on default attention — people opened because there was little else competing for it. That era is over. The inbox now competes with infinite feeds, endless notifications, and algorithmically optimised distractions. Today, attention must be earned every day, or it disappears silently. This is why tactics that worked before no longer do.

Most emails contain no reason to open beyond obligation or discount. No anticipation. No continuity. No reward for attention. Once the initial novelty fades, the rational response is silence.

This is not a content problem. It is a physics problem.

Push always decays. Pull compounds.

Which leads to a simple but heretical conclusion:

Email doesn’t fail because people hate email. It fails because it has no magnets.

Until magnets exist inside the inbox — forces that pull attention rather than demand it — no amount of better copy, smarter segmentation, or more AI will fix the problem. Campaigns will decay. Attention will leak. And brands will keep paying twice for customers they should never have lost.

The solution is not to send better emails.

It is to redesign the inbox itself.

2

The Fatal Assumption

Modern email marketing is built on a quiet assumption so deeply embedded that it is rarely questioned:

If something isn’t working, do more of it.

More emails. More content. More segments. More automation.

When engagement drops, the instinctive response is expansion, not examination. Increase frequency. Add another journey. Layer on another campaign. The logic feels sound — after all, visibility precedes conversion. If people aren’t responding, surely they haven’t seen enough.

This is the first fatal assumption: that attention scales linearly with output.

It doesn’t.

Attention is not additive. It is fragile. Every message does not increase the chance of engagement; it competes with every message that came before it. In a push-only system, more doesn’t amplify results — it accelerates decay. The brands that send the most email are often the brands with the worst engagement, trapped in a spiral where volume compensates for declining response until the entire list goes cold.

**

The second assumption follows closely behind: content is king.

If people are ignoring emails, the answer must be better content. Smarter copy. Better design. More storytelling. More value. This belief fuels endless content calendars and creative marathons. Teams are told to “be more relevant,” as if relevance were something that could be produced on demand by working harder.

But content is not king in a vacuum. Context is.

The same piece of content can delight or be ignored depending on when, why, and what came before it. A brilliant email sent without anticipation is still an interruption. A beautifully written message arriving without continuity still feels disposable.

Consider: a fashion brand’s Instagram post and its promotional email often contain identical images, identical offers, identical messaging. One gets engagement. One gets ignored. The difference isn’t content quality. It’s mechanics. Instagram embeds micro-rewards into every scroll. Email embeds nothing.

Content without pull is just noise — however well-crafted.

**

The third assumption is the most seductive: segmentation solves everything.

For two decades, martech promised that the path to relevance lay in slicing audiences ever more finely. From broad lists to personas, from personas to micro-segments, from micro-segments to “segments of one.” The theory was simple: the smaller the segment, the more relevant the message; the more relevant the message, the higher the engagement.

In practice, segmentation hit a hard ceiling.

Segments are static snapshots in a dynamic world. They describe what customers were, not what they are becoming. They do not account for mood, timing, fatigue, or familiarity. A customer does not wake up as “Segment B, High Intent, Discount Sensitive.” They wake up as a human with limited time, limited patience, and a long memory of being interrupted.

Even the most finely tuned segment delivers content at customers, not for them. It assumes relevance can be predicted in advance rather than earned in the moment. It assumes the inbox is empty, waiting.

Segmentation solves what to send. It does not solve why anyone would open.

**

The harsh reality is this: email engagement does not collapse because marketers lack data, creativity, or tools. It collapses because the entire system is optimised for output, not attraction.

More frequency without pull creates fatigue. Better content without continuity creates forgettability. Finer segmentation without habit creates silence.

And yet the industry doubles down — because the metrics reward activity. Emails sent. Campaigns launched. Journeys activated. Dashboards glow green while relationships quietly decay.

This is why email feels broken even when executed perfectly.

The problem is not execution. It is the mental model.

The real enemy is not bad email. It is the Broadcast Doctrine — the belief that attention is something you can demand repeatedly without consequence. This doctrine has governed marketing for decades. It is time to abandon it.

Email has been treated like a pipe: push content in, hope value comes out. But attention does not flow through pipes. It obeys different laws — laws of attraction, anticipation, and reward.

To fix the inbox, we must stop thinking like broadcasters and start thinking like physicists.

Once attention decays, no optimisation can bring it back. Better subject lines do not revive dead habits. Better timing does not recreate anticipation. Better AI does not resurrect trust. This is not a performance problem with a performance solution. It is a category break.

3

The Physics of Attention

Once the old assumptions fall away, a different way of thinking becomes possible.

Attention is not persuasion. It is not preference. It is not even interest.

Attention is a force.

Like all forces, it follows laws. Ignore those laws and no amount of effort compensates. Respect them, and small inputs compound into large effects. Marketing has spent decades optimising messages while ignoring mechanics — polishing words while violating physics.

The most important distinction in attention physics is simple:

Push versus Pull.

Push requires continuous energy. Every message must overcome resistance — the resistance of time, distraction, fatigue, and memory. Push decays naturally. The more often it is applied, the faster it loses effectiveness. This is not a failure of creativity; it is entropy at work.

Pull behaves differently. Pull stores energy. It creates expectation, anticipation, and return behaviour. Once established, pull reduces effort rather than increasing it. Frequency strengthens rather than weakens the effect.

This is why habits matter so much. Habits are attention in a stable orbit.

The difference can be visualised simply: push systems are funnels — attention pours in at the top and leaks out at the bottom, requiring constant refilling. Magnetic systems are orbits — once attention enters, it stays in motion, returning reliably without additional force.

**

Social platforms understood this intuitively. They stopped asking, “What message should we send?” and started asking, “What makes someone come back?”

TikTok did not grow by pushing notifications. It grew by creating an experience so magnetic that users opened it involuntarily, dozens of times per day. The content was short — fifteen seconds, thirty seconds — because brevity reduces friction. The algorithm was precise — serving exactly what would trigger the next view. The reward was variable — sometimes brilliant, sometimes mundane, always uncertain enough to compel one more scroll.

Wordle took a different path to the same destination. One puzzle per day. No more, no less. The constraint created scarcity. The daily rhythm created ritual. The shareable grid created social proof. Millions of people now start their morning with a word game — not because Wordle pushes them, but because the habit pulls.

Different surfaces. Same physics.

Email never made this shift. Instead of designing for return, email was optimised for delivery. Instead of building anticipation, it focused on immediacy. Instead of continuity, it relied on novelty. Each email was treated as a standalone event, disconnected from what came before and indifferent to what would come after.

This violates a fundamental law of attention:

Attention without continuity always resets to zero.

When there is no memory, no progression, no accumulation, every message must earn attention from scratch. That is an exhausting game — for sender and recipient alike. Over time, the rational response is disengagement.

**

A second law follows closely:

Attention is conserved, not created.

There is a finite amount of attention available each day. When a brand demands attention without offering value in return, it is borrowing against future attention — and paying interest in the form of fatigue. Discounts, urgency, and emotional pressure can spike engagement temporarily, but they drain the system. The bill always arrives.

Pull-based systems behave differently because they pay for attention — not with money, but with meaning, progress, recognition, or reward. They make attention feel voluntary rather than coerced. The user does not feel interrupted; they feel invited.

This is why frequency is so misunderstood in email marketing.

In a push system, increasing frequency accelerates decay. In a pull system, increasing frequency strengthens habit.

Same inbox. Same person. Radically different outcomes.

**

The difference lies not in the message, but in the presence of an attractive force — something that gives the recipient a reason to open beyond obligation or discount.

Marketing has spent years debating subject lines, send times, personalisation tokens, and AI-generated copy. These are optimisations within a push framework. They do not change the underlying physics.

What email lacks is not intelligence. It lacks magnetism.

A magnet does not persuade. It attracts. It does not shout. It exerts a quiet force that works even when nothing else is happening. Importantly, magnets do not demand energy every time — they store it.

Without magnets, email behaves like friction. With magnets, email becomes gravity.

The next section introduces the missing element — the smallest unit of pull capable of transforming the inbox from push medium into magnetic field.

I call them Magnets.

4

Introducing Magnets

Once attention is understood as a force, the absence becomes obvious.

Email has messages. Email has content. Email has data, automation, and AI.

What it does not have is pull.

This is where Magnets enter — not as a feature, not as a format, but as a missing primitive.

**

A Magnet is the smallest unit of attention pull — a micro-interaction designed to convert seconds of attention into habit, signal, and reward.

This definition is precise. Every word matters.

A Magnet is not “content.” It is not a widget bolted onto an email. It is not gamification layered on top of messaging. Magnets are designed around behaviour, not messaging. Their job is not to persuade or inform, but to attract and retain attention.

Think of a Magnet as a reason to open that exists independent of the offer.

A daily trivia question. A prediction to be made. A streak to be maintained. A poll whose outcome you want to see. A puzzle that progresses day by day. A scratch card that might reveal a reward. A swipeable carousel that invites browsing without clicking away.

None of these demand a purchase. None require long commitment. Each can be completed in seconds — the 60-second window is sacred. Yet each creates a reason to return. Not once, but again and again.

This is the crucial shift: from message-led to behaviour-led email.

Traditional emails ask, “What do we want to say today?” Magnets ask, “What will make someone come back tomorrow?”

**

Magnets work because they respect three immutable laws of attention.

First, attention precedes intention. People do not decide to care and then pay attention; they pay attention and then decide to care. Magnets secure the first step reliably, without coercion. They create the opening that content alone cannot.

Second, signals emerge from interaction, not inference. Clicks, swipes, answers, choices — these are explicit signals of preference and state. Which option did they pick? What do they prefer? When do they engage? Magnets generate this zero-party data naturally, without surveillance or guesswork. Every interaction teaches the system something real.

Third, reward stabilises habit. When attention is acknowledged — through progress, recognition, or micro-rewards — it becomes sustainable. The reward can be intrinsic (the satisfaction of solving a puzzle) or extrinsic (points accumulating toward prizes). Without reward, attention decays. With reward, it compounds.

**

This leads to a simple but powerful law:

The Law of the Magnetic Inbox: Without embedded pull, frequency accelerates decay.

Most email strategies fail because they increase frequency without increasing pull. Magnets invert that equation. They make frequency work for the relationship instead of against it.

Importantly, Magnets are modular. They can live inside any email, across any category, without changing the brand’s core message. A financial services company can use predictions. A fashion brand can use daily style challenges. A media brand can use polls and quizzes. A retailer can use streaks and scratch cards.

The surface changes. The physics does not.

**

Magnets also do something subtle but profound: they shift the emotional posture of email.

The inbox stops feeling like a place where brands demand attention and starts feeling like a place where value appears. Opening becomes optional — and therefore desirable. The brand’s name becomes a trigger for anticipation, not dismissal.

This is why Magnets matter more than personalisation, AI, or creative polish. Those tools optimise what is pushed. Magnets change why someone pulls.

And once Magnets exist, something else becomes possible. Emails stop being standalone events and start becoming connected moments. Today’s interaction sets up tomorrow’s. Progress accumulates. Memory forms. The inbox develops continuity — something it has been missing for decades.

Magnets do not replace content. They anchor it. They do not eliminate messaging. They earn the right to message.

On their own, Magnets are powerful. But their full impact is realised when embedded inside a new kind of email — one designed not as a campaign, but as a daily relationship surface.

That system is NeoMails.

5

In Action

A Magnetic Inbox is not created by sending different emails. It is created by changing what an email does.

In a traditional inbox, each email is a standalone event. It arrives, asks for attention, and disappears — successful or ignored. There is no memory. No accumulation. No reason to return unless something is being sold.

In a Magnetic Inbox, emails behave very differently. Each one is a continuation, not an interruption.

This is where NeoMails come in.

**

NeoMails are not campaigns. They are not newsletters. They are daily relationship surfaces — lightweight, interactive touchpoints designed for sixty seconds or less. Their purpose is not conversion in the moment, but continuity over time.

Every NeoMail is built around at least one Magnet.

A morning trivia question. A prediction with results revealed tomorrow. A streak that grows with daily participation. A poll that influences next week’s content. A puzzle that unfolds across the week.

The content around the Magnet can change — tips, stories, recommendations, offers — but the Magnet stays consistent. It becomes the reason to open. The message earns attention because attention was already pulled.

Consider a coffee brand’s NeoMail arriving at 7am. The subject line shows the customer’s Mu balance: µ.247 | Your Daily Coffee IQ. Inside: yesterday’s prediction resolves (they were right — Arabica futures rose). A quick quiz follows. Then a brew tip. A swipeable product carousel. Total time: 58 seconds. No pressure. No urgency. Just value delivered, habit reinforced, relationship maintained.

**

This flips the economics of engagement.

Instead of fighting for attention every time, the brand builds stored attention. The customer opens not to see what the brand wants, but to complete what they started. Yesterday’s interaction creates today’s anticipation. Today’s action sets up tomorrow’s return.

Importantly, this happens without pressure.

There is no urgency language. No discount bait. No artificial scarcity. The pull comes from progress and curiosity, not persuasion. The inbox feels lighter, not heavier — even though the brand shows up more often.

This is where many marketers pause and ask the wrong question:

“Won’t daily emails increase fatigue?”

In a push system, yes — absolutely. More frequency accelerates decay. But in a magnetic system, the opposite happens. Frequency stabilises habit. Familiarity replaces friction. Silence, not presence, becomes the thing that feels wrong.

The inbox stops being something to clear and starts becoming something to check.

**

Magnets also change what the brand learns.

Traditional emails infer intent indirectly — opens, clicks, time-of-day heuristics. NeoMails generate explicit signals through interaction. Answers reveal preferences. Choices reveal state. Behaviour reveals readiness. The system no longer guesses what might matter; it listens to what customers actually do.

This matters because relevance improves after attention is secured, not before. Magnets solve the first problem — presence. Intelligence solves the second — meaning.

Over time, something else emerges: rhythm.

The inbox develops a cadence. A predictable arrival time. A familiar format. A small moment of value that fits naturally into the day — with coffee, during a commute, between meetings. The brand becomes part of the customer’s routine, not a disruption to it.

And when offers do appear, they land differently.

They are no longer cold asks. They are contextual suggestions made inside a relationship that already exists. Conversion stops feeling extractive and starts feeling timely.

**

This is also where Mu — the micro-rewards currency — quietly amplifies the effect.

Each interaction earns recognition. Streaks are acknowledged. Participation is valued. The balance climbs visibly: µ.247 yesterday, µ.262 today, µ.280 tomorrow. Attention is no longer free labour; it is a fair exchange. The inbox becomes a place where value flows both ways.

Taken together, this is what a Magnetic Inbox looks like in practice:

• Magnets pull attention reliably
• NeoMails provide rhythm and continuity
• Mu rewards attention and stabilises habit
• Signals accumulate naturally

No single email carries the burden of performance. Value compounds across days, not clicks.

The result is subtle but profound. Attention decay slows. Engagement becomes durable. Customers stop drifting — not because they are locked in, but because they are connected.

This is how email becomes something it hasn’t been for a long time: a place people return to by choice.

The next section maps the full architecture — and shows how these components combine to eliminate AdWaste permanently.

6

The Attention Stack

Once the Magnetic Inbox is understood in action, a deeper pattern becomes visible.

Magnets are not a tactic. NeoMails are not a format. Rewards are not an incentive scheme.

Together, they form a stack.

For decades, marketing stacks have been built around execution: channels, journeys, campaigns, messages. They answer the question, “How do we send more effectively?” But they never answer the more important one: “Why should anyone pay attention at all?”

The Attention Stack exists to answer that question.

It is a layered system designed around how attention is earned, retained, and compounded — not how messages are delivered. Each layer plays a distinct role. Remove any one, and the system collapses back into push.

**

At the base is NeoMarketing — the architectural layer.

NeoMarketing defines the economic logic of the system. Its principles are simple but uncompromising: Never Lose Customers. Never Pay Twice. Attention is treated as capital, not as a free resource to be burned. Retention is not a KPI; it is the objective. Without this alignment, every other layer degrades into optimisation theatre.

Above that sits Mu — the currency layer.

Mu exists for one reason: to acknowledge attention fairly. Time, interaction, and intent are valuable, yet marketing has historically treated them as free. Mu corrects this imbalance. It transforms attention from an invisible input into a recognised exchange. Progress, streaks, and participation are no longer abstract; they are remembered. Mu gives attention memory — and memory is what makes habit possible.

Next comes NeoNet — the network layer.

NeoNet connects Magnetic Inboxes into a cooperative system. Instead of each brand fighting alone for diminishing attention, they participate in a shared recovery and monetisation layer. Attention earned in one inbox can be respected in another. Reacquisition shifts from auction-based bidding to network-based collaboration. ActionAds flow between complementary brands — a grinder offer inside a coffee brand’s email, a jewellery offer inside a fashion brand’s email — funding the entire system at ZeroCPM. This is how the stack scales without reintroducing AdWaste.

Above the network sits Magnets — the attention unit layer.

Magnets are the atomic building blocks of pull. They are where physics meets behaviour. Every Magnet is designed to do three things simultaneously: create a reason to open, generate a signal through interaction, and reinforce return behaviour. Magnets are small by design — seconds, not minutes — because habit is built in micro-moments, not campaigns.

Magnets create attention. Mu preserves it. Without Mu, Magnets attract but do not retain. With Mu, they compound.

At the top of the stack sit NeoMails — the relationship surface.

NeoMails are where everything becomes visible to the customer. They provide rhythm, familiarity, and continuity. NeoMails are not judged by single-email performance but by what happens over time: attention half-life, return frequency, habit strength. They are the daily expression of the stack, not its strategy.

**

Seen together, the Attention Stack looks like this:

Layer	Name	Role
Relationship surface	NeoMails	Daily rhythm
Attention units	Magnets	Habit + signal + reward
Network layer	NeoNet	Monetisation + recovery
Currency	Mu	Incentive + memory
Architecture	NeoMarketing	Economic alignment

Each layer enables the next. Without NeoMarketing’s economic alignment, Mu becomes a gimmick. Without Mu’s memory, NeoNet cannot value attention fairly. Without NeoNet’s funding, Magnets cannot scale sustainably. Without Magnets’ pull, NeoMails collapse into push.

This structure matters because it explains why traditional email optimisation fails. You cannot fix a missing layer by over-investing in another. Better copy cannot compensate for absent pull. More data cannot replace habit. Automation cannot create attraction.

Push systems optimise horizontally. Attention systems compound vertically.

The Attention Stack replaces fragile campaigns with durable relationships. It shifts the marketer’s job from “driving engagement” to designing return. And once return exists, relevance, conversion, and growth follow naturally.

Most importantly, the stack is additive. Brands do not need to abandon their existing channels or tools. They need to add the missing layers — the ones that turn effort into force.

**

Which brings us to the final question.

What happens when attention stops decaying — and starts compounding?

The answer is not just better email. It is a fundamentally different marketing outcome.

7

The Attention Reset

Every era of marketing is defined by what it takes for granted.

For the past two decades, the industry accepted three assumptions as inevitable: customers will drift, attention will decay, and reacquisition is simply the cost of growth. The tools improved. The data got richer. The AI got smarter. And yet the outcome barely changed.

Customers still disappeared. Attention still evaporated. Budgets still flowed back to intermediaries to buy back relationships that should never have been lost.

This wasn’t incompetence. It was architecture.

Marketing was built as a push system in a world that increasingly runs on pull. It optimised messages while ignoring mechanics. It measured activity while missing decay. It treated attention as free — and paid dearly for it later.

The Magnetic Inbox marks a clean break from that past.

**

It begins with a refusal: we will no longer blame customers for ignoring what gave them no reason to care. Silence is not apathy; it is feedback. It tells us the system failed before the message ever arrived.

Magnets change that equation.

They restore pull to the inbox. They turn seconds into signals, signals into habits, and habits into relationships. They make frequency safe again. They make continuity possible. They allow brands to show up daily without demanding daily decisions.

Most importantly, they prevent the quiet drift that feeds AdWaste.

When attention is earned and remembered, customers do not vanish invisibly. They do not need to be reacquired expensively. They stay connected — lightly, voluntarily, and consistently. Retention stops being a campaign outcome and becomes a property of the system.

**

This is why the Magnetic Inbox is not an email innovation. It is a marketing reset.

It replaces interruption with invitation. It replaces extraction with exchange. It replaces reacquisition with relationship.

And it forces a harder but healthier discipline on brands: design for return, not response.

The implications are profound.

When customers return by habit, relevance becomes easier. When signals are explicit, personalisation becomes truthful. When attention compounds, conversion becomes contextual rather than coercive. And when customers are not lost, the most expensive line item in marketing — reacquisition — quietly disappears.

**

This is how the economics change.

Never Lose Customers is not a slogan. It is what happens when attention stops decaying.

Never Pay Twice is not a provocation. It is what follows when relationships persist.

These outcomes do not require louder messaging, deeper discounts, or larger budgets. They require a different mental model — one that treats attention as a force to be designed for, not a resource to be consumed.

The Magnetic Inbox is that model made concrete.

It says: email is not dead — it was misused. It says: frequency is not the enemy — push without pull is. It says: AI will not save marketing unless it is anchored to attraction.

Most of all, it says this:

Attention is not taken. It is earned. And what is earned can be kept.

**

Marketing’s future will not be won on feeds we do not control, through auctions we cannot escape, or via algorithms that rent us our own customers. It will be won quietly, daily, in spaces where trust still exists — starting with the inbox.

Not through more messages. Through better mechanics.

The era of ignored email is ending — not because people suddenly love brands more, but because brands are finally learning how attention actually works.

Welcome to the Magnetic Inbox.

Pull replaces push. Habit replaces decay. And marketing finally stops paying for its own forgetting.

From Netcore’s Agentic Predictions 2026: “We’ve entered the Agentic Era defined by Autonomous Systems and Outcome-Based growth.” 

5 defining shifts that will reshape how brands connect, convert, and grow.

1. Multi Agents
2. Brand Twins
3. Agentic Commerce
4. Human Attention
5. Outcome Pricing

Bonus Prediction: CMO Reimagined

Many of these are themes I have discussed.

Full report.

From my article in Economic Times:

Marketing is changing. Brands waste billions chasing customers they already have. The future is Agentic Marketing. Intelligent agents will decide the best action for each customer. This means fewer, better interactions. It will reduce waste and improve customer loyalty. This shift will guide commerce and focus on customer retention. It is a permanent change for marketing leaders.

…Think of agentic marketing like a smart team working quietly behind the scenes. Each agent has a clear role one creates content, another ensures it stays on brand, others optimize media, analyze insights, build smarter segments, and personalize every interaction. Instead of just reporting the past, these agents learn from customer behavior and suggest what to do next. At the center is an orchestrator that coordinates everything, activating the right agent at the right moment. Working together, they adapt in real time to deliver relevant, timely, and meaningful experiences at scale with far less manual effort and guesswork.

…Agentic commerce introduces guidance. Intelligent agents help customers discover, evaluate, and decide much like a knowledgeable salesperson in a physical store. They reduce friction, simplify choice, and respect intent. As these agents become the primary interface between brands and buyers, competition will shift. The question will no longer be who has the biggest catalogue or the loudest promotions, but whose system understands the customer best. Trust becomes the differentiator.