AI and Neo: The Twin Engines of Marketing’s Future

Published February 23, 2025

Overview

As martech companies find themselves trapped in an increasingly brutal red ocean of feature competition and margin erosion, I’ve explored two distinct paths to escape this commoditisation crisis. Through multiple essays examining this existential challenge, a clear framework has emerged: marketing’s future will be built on twin tracks of innovation.

Marketing stands at a critical crossroads. Despite massive investments in technology, brands face a paradoxical crisis: rising customer acquisition costs alongside declining engagement. The numbers tell a stark story: 50-80% of digital advertising budgets are wasted on wrong targeting and unnecessary reacquisition, amounting to over $350 billion in annual AdWaste. Traditional martech solutions, focused on incremental improvements in campaign management and automation, have failed to address these fundamental challenges.

Marketing’s future hinges on breaking free from the AdWaste trap by shifting from AAA (Acquire, Acquire, Acquire) to OOO (Only Once/Ones). The contrast is stark:

Aspect	AAA (Today)	OOO (Future)
Acquisition Strategy	Continuous reacquisition through adtech	Acquire once, retain through engagement
Cost Structure	50-80% spend on reacquisition	One-time CAC, then retention focus
Customer Understanding	Anonymous tracking, segments	Zero-party data, N=1 personalization
Engagement Model	Interrupt and advertise	Daily value through utilities
Economics	Rising CAC, declining ROI	Sustainable profitable growth
Platform Dependency	Reliance on Google/Meta	Direct customer relationships

The root causes of the acquisition-reacquisition-AdWaste cycle run deep. First, the “No Hotline” problem: despite collecting customer emails and phone numbers, brands lack reliable ways to engage on demand, with 90% of messages going unopened. Second, the “Not for Me” problem: basic segmentation misses 80% of opportunities, leading to generic messaging that fails to resonate. Third, brands remain trapped in expensive dependency on Google and Meta for customer acquisition and reacquisition.

These challenges cannot be solved through traditional martech improvements alone. They demand two distinct but complementary revolutions: AI-first Marketing and NeoMarketing.

AI-first Marketing leverages agentic AI to transform how marketers work and re-engineer retention. This isn’t just about automation—it’s about creating intelligent allies that enhance human capabilities. Through innovations like AI Co-Marketer and AI Twins, marketers can finally achieve true N=1 personalisation while dramatically improving their operational efficiency. This evolution can deliver 30% year-over-year growth for martech companies by making existing martech significantly better.

NeoMarketing, in contrast, represents a fundamental reimagining of marketing itself to eliminate reacquisition AdWaste. Instead of brands constantly paying to acquire and reacquire customers through expensive adtech platforms, NeoMarketing helps brands build direct relationships through consumer utilities—free products that deliver daily value while enabling precise targeting through authenticated identity. This revolution promises 10X growth for martech companies by creating entirely new revenue streams through PII-based advertising.

These two tracks serve different needs but work synergistically. AI-first Marketing optimises current operations and improves retention, while NeoMarketing eliminates wasteful reacquisition spending and creates sustainable engagement. Together, they enable the shift from AAA (Acquire, Acquire, Acquire) to OOO (Only Once/Ones)—a paradigm where brands acquire customers just once, then build lasting relationships through superior understanding and engagement.

The economic opportunity is staggering. Even capturing a small portion of the $350 billion currently lost to AdWaste could transform the martech landscape. But success requires operating these tracks differently. AI-first Marketing demands continuous improvement within existing frameworks, while NeoMarketing needs a startup mindset focused on disruptive innovation.

For martech companies, this dual transformation offers perhaps the only path to escape commoditisation and build sustainable competitive advantages. Those who successfully execute both tracks will not just survive—they’ll lead marketing’s next revolution, helping brands finally break free from expensive dependency on Big Adtech while creating genuine value for consumers.

The time for this transformation is now. With traditional advertising models under pressure from privacy changes and rising costs, brands are desperately seeking alternatives. AI-first Marketing and NeoMarketing together provide the comprehensive solution they need—not just better tools for marketers, but a fundamentally new way to build and monetise customer relationships.

Relevant Essays

Recent Writings

How Martech Companies Can Usher in the NeoMarketing Era:

Martech vendors face intense competition in an increasingly commoditised space, where differentiation proves elusive and price becomes the primary battleground. Traditional business models, still anchored in consumption metrics (CPM) or monthly active users (MAUs), fail to capture or create transformative value. Even the promise of AI, while technologically compelling, faces skepticism from brands burned by previous waves of overhyped innovation.

Most concerning is the fundamental inefficiency this system perpetuates. Brands spend billions on expensive acquisition through Google and Meta, only to see 60-65% of customers making just one purchase. Then, lacking effective retention capabilities, they resort to costly reacquisition through the same adtech platforms – creating a vicious cycle that benefits the digital advertising duopoly while draining marketing budgets.

…This has created a classic ‘red ocean’ in the retention technology space – vendors fighting for scraps of a diminishing pie, even as the core problem of customer engagement remains unsolved. The market desperately needs a breakthrough – not just in technology, but in how we fundamentally think about the relationship between acquisition, retention, and sustainable profitable growth.

The time has come for martech companies to break free from this destructive cycle. The opportunity lies in reimagining retention technology not as a cost centre to be optimised, but as a powerful engine for transforming how brands build and monetise customer relationships.

… Martech companies seeking to lead the NeoMarketing revolution must undertake comprehensive product innovation across three integrated domains – email, AI-powered personalisation, and ad networks…The first track focuses on engagement and monetisation through NeoMails and NEON. This combination creates reliable brand hotlines while enabling new revenue streams through attention monetisation. The second track centres on intelligence and personalisation through AI Twins and the AI Co-Marketer, enabling true N=1 personalisation at scale.

… The transition to NeoMarketing requires martech companies to fundamentally reimagine their revenue models. Traditional SaaS-based approaches based on CPM or MAU pricing no longer align with the transformative value proposition of NeoMarketing platforms. Two new business models emerge as the foundation for this transformation: zero-cost NeoMails with NEON monetisation, and task-based pricing for AI Services.

… The future of marketing isn’t about finding better ways to buy attention – it’s about fostering lasting, profitable relationships through superior understanding and engagement. NeoMarketing isn’t just an evolution; it’s the marketing approach the industry has needed all along: acquiring customers only once, treating them as individuals, and delivering sustainable value for everyone.

From Trad to Neo to One: Rethinking B2C Martech with Free-to-Brand Consumer Utilities:

TradMartech, with its focus on selling software to businesses, will give way to NeoMartech—a revolutionary approach that provides consumer-facing solutions free to brands, monetised through precision advertising. This shift represents more than evolution; it’s a fundamental reimagining of how marketing technology creates value.

TradMartech emerged in an era where software licensing was the primary business model. Companies built tools for marketers—campaign management platforms, analytics dashboards, and automation solutions—charging monthly or annual fees based on usage or active users. While this model drove significant innovation, it also created barriers to adoption and limited network effects. It also disconnected TradMartech companies from business outcomes, unlike their adtech peers who perfected performance-based pricing.

NeoMartech takes a radically different approach. Instead of selling software to businesses, it builds consumer utilities deployed through B2B partnerships. These solutions are provided free to brands and monetised through NEON’s PII-based advertising network. This creates a powerful flywheel where better engagement enables better monetisation, which funds platform development and innovation.

… While TradMartech vendors compete in a red ocean of features and pricing, NeoMartech companies build powerful network effects through consumer engagement. Each new brand adds inventory, each new user adds value, and each interaction improves targeting—creating sustainable competitive advantages that compound over time—just like what has happened in adtech.

The two big ideas that will engineer the transformation in martech (and marketing) are AI and Neo.

Big Idea #1: AI

The AI revolution we’re witnessing today has roots stretching back decades. In the early 1990s, whilst working on neural networks for pattern recognition in images, I experienced firsthand the promise of AI. Yet those early systems, whilst theoretically powerful, remained largely confined to research labs and specific industrial applications. The landscape has transformed dramatically in the past two years, catalysed by two breakthrough moments: the publication of the “Transformer” paper by Google engineers in 2017, and OpenAI’s launch of ChatGPT in late 2022.

These developments marked a fundamental shift in AI’s accessibility and capability. The transformer architecture solved the long-standing challenge of processing sequential data efficiently, whilst attention mechanisms enabled models to focus on relevant information—much like human cognition. ChatGPT demonstrated that AI could engage in natural conversations, understand context, and generate human-like responses across an extraordinary range of topics.

The first wave of AI companies, emerging through 2022-23, focused primarily on building foundational infrastructure and basic applications. We’ve seen an explosion of chatbots for content creation, customer support automation, and coding assistance. These applications, whilst impressive, represent only the initial phase of AI’s potential—akin to the early days of the internet when static websites dominated.

The next generation of AI, emerging now in 2024-25, promises to unlock dramatically more powerful capabilities through multi-agent systems. Instead of single AI models working in isolation, we’re seeing the development of collaborative AI networks where multiple specialised agents work together, each handling specific tasks whilst coordinating towards common goals. This mirrors how human organisations function, with different experts contributing their unique capabilities to solve complex problems. [See: AI Predictions].

For marketing technology, this evolution creates unprecedented opportunities. Imagine an ecosystem where an AI Co-Marketer orchestrates campaign strategy, whilst specialised AI Twins understand different customer segments, and other agents handle content creation, channel optimisation, and performance analysis. These agents don’t just execute tasks—they learn from each other, adapt their strategies, and continuously improve their collective performance.

The breakthrough lies in agentic AI—artificial intelligence that can reason, make decisions, and take initiative rather than simply responding to prompts. This capability enables AI to become a true partner to marketers, anticipating needs, suggesting strategies, and autonomously handling complex tasks whilst maintaining alignment with human objectives.

Yet this isn’t about replacing human marketers—it’s about dramatically amplifying their capabilities. By handling routine tasks, providing deeper insights, and enabling true personalisation at scale, AI frees marketers to focus on strategy, creativity, and building genuine customer relationships. The future of marketing technology lies not in better automation, but in intelligent collaboration between human marketers and AI systems.

This sets the stage for our first major transformation: AI-first Marketing, where agentic AI fundamentally reimagines how marketers work and how brands engage with customers.

AI-first Marketing – 1

For most of marketing’s history, brands have spoken to crowds, not individuals. The era of mass marketing, birthed by newspapers and supercharged by radio and television, treated customers as vast, undifferentiated audiences. A single advertisement would reach millions, hoping that some fraction would respond. It was marketing by megaphone—loud, broad, and inevitably wasteful.

Over time, segmentation became more sophisticated; marketers could target specific demographics, interests, and behaviours. Print media offered specialist magazines. Cable TV brought targeted channels. The Internet revolution led to a proliferation of websites (and later apps) which enabled interest-based advertising for niche audiences. Yet even these advances dealt in groups, not individuals.

The rise of Google and Meta seemed to herald true precision. Their powerful algorithms promised to find exactly the right customers at exactly the right moments. But this targeting remained a black box—marketers poured in budget and creative, receiving clicks and conversions but little true understanding of their customers. Worse, they became dependent on these platforms even to reach their own customer base, paying premium prices through competitive auctions for diminishing returns.

The Segment Fallacy

Here’s the fundamental problem: like snowflakes, each customer is unique. They have individual preferences, behaviours, and needs that don’t fit neatly into predetermined segments. These segments were never a natural reality—they were a convenient fiction, an intermediate construct born of marketers’ limited ability to understand and engage customers as individuals.

Consider a luxury fashion brand’s “premium female customer” segment. Within this supposedly uniform group, you might find:

A busy executive who values convenience and quick delivery
An eco-conscious consumer focused on sustainability
A trend-follower who wants the latest styles first
A value seeker who purchases mainly during sales
A comfort-prioritising buyer who cares most about fit

Traditional marketing treats them identically. But their needs, preferences, and optimal engagement patterns differ dramatically. The result? Generic messages that fail to resonate, leading to poor engagement and wasted spending.

Enter AI-first Marketing

This is where AI-first Marketing creates breakthrough value. Through the combination of agentic AI, advanced analytics, and real-time personalisation, we can finally achieve true N=1 marketing—treating each customer as a unique individual rather than a segment member.

Two key innovations make this possible:

The AI Co-Marketer

The AI Co-Marketer is an intelligent partner that enhances every aspect of marketing operations:

Strategy: Analyses patterns across millions of customer interactions to identify opportunities
Execution: Orchestrates personalised journeys across channels
Optimisation: Continuously learns and adapts based on results
Innovation: Suggests new approaches based on emerging trends

For example, instead of scheduling generic email campaigns, the AI Co-Marketer might:

Identify the optimal time to reach each customer
Personalise content based on individual preferences
Select the most effective channel for each message
Adjust offers based on purchase history and browsing behaviour
Coordinate cross-channel experiences for seamless engagement

AI Twins

These digital replicas revolutionise how we understand customers:

Madtech Twins combine marketing and advertising data to provide segment-level insights
MyTwin creates personal AI companions for each customer

AI-first Marketing – 2

Imagine a marketer’s daily experience with an intelligent ecosystem of AI agents working together:

Morning Insights (9 AM)

AI Co-Marketer: “I’ve noticed an emerging pattern among premium customers planning winter travel. Shall we craft personalised experiences?”
Best Customers Madtech Twin responds: “I’ve had conversations with 2,000 customers in this segment. 40% are specifically seeking packable winter wear, with individual preferences ranging from sustainable materials to specific colour palettes.”
Together, they automatically create and deploy thousands of uniquely personalised recommendations.

Mid-day Orchestration (12 PM)

AI Co-Marketer identifies micro-moments for engagement: “Sarah just browsed winter coats but didn’t purchase. Her MyTwin indicates she typically researches extensively before buying premium items.”
MyTwin adds context: “Based on our recent conversations, Sarah prefers detailed product specifications and sustainability credentials. She also responds best to early morning communications with rich visual content.”
The system crafts a personalised early-morning email combining product details with her specific interests.

Afternoon Optimisation (3 PM)

Real-time feedback loops show engagement patterns
AI Co-Marketer: “The sustainability messaging is resonating strongly with the West Coast segment.”
Individual MyTwins report back on customer reactions, enabling instant refinement of messaging and offers
Each customer interaction automatically updates their personal preference profile

Evening Strategy (6 PM)

AI Co-Marketer synthesises daily learnings into actionable insights
AI Twins (Madtech Twins and MyTwins) predict next-day opportunities for each customer
System prepares thousands of personalised journeys, each uniquely crafted
Tomorrow’s interactions are automatically orchestrated across channels based on individual preferences

This continuous collaboration between AI Co-Marketer and AI Twins enables true N=1 marketing at scale, turning every customer interaction into an opportunity for deeper understanding and more relevant engagement.

The Essential Foundation

Making this vision reality requires solid fundamentals—what I call the House of Anti-Acquisition framework:

Unistack: Unified customer data platform integrating all touchpoints
Unichannel: Coordinated messaging across channels
Channels 2.0: Enhanced engagement capabilities in each channel
VRM (Velvet Rope Marketing): Special treatment for high-value customers
Kaizen Progency: Continuous improvement through AI-powered optimisation

This foundation enables:

Complete customer understanding
Consistent cross-channel experiences
Efficient resource allocation
Premium customer treatment
Ongoing enhancement

The Business Model Evolution

AI-first Marketing also demands new pricing models. Traditional CPM (cost per thousand) or MAU (monthly active user) pricing becomes obsolete when AI enables precise, effective communication. Instead, task-based pricing emerges:

Pay for specific AI services used
Pricing aligned with value delivered
Reduced waste in messaging
Better ROI for brands
Sustainable revenue for vendors

This shift can help martech companies accelerate from low growth to sustainable 30% annual growth by:

Delivering measurable value
Reducing customer churn
Enabling upsell opportunities
Creating competitive moats
Building strategic partnerships

The Promise and the Limit

AI-first Marketing, powered by agentic AI, represents a dramatic improvement in how brands engage customers. It transforms traditional martech from basic automation to intelligent collaboration, enabling true personalisation at scale. The result? Higher engagement, better retention, and improved ROI.

Yet even this revolution has limits. While it makes existing martech significantly better, it doesn’t address the fundamental inefficiency of paying platforms like Google and Meta to reach your own customers. For that, we need an even more radical transformation: NeoMarketing.

The future demands both tracks: AI-first Marketing to optimise current operations and NeoMarketing to create entirely new possibilities. Together, they can finally deliver marketing’s holy grail: genuine one-to-one relationships for life at global scale.

Big Idea #2: Neo

AI-first Marketing, with its powerful combination of AI Co-Marketer and AI Twins, promises to make marketers’ lives dramatically better. By enabling true N=1 personalisation, it solves the “Not for Me” problem that has long plagued traditional marketing. Yet this enhancement of TradMartech, while valuable, leaves two critical challenges unaddressed: the “No Hotline” problem and the “No Alternative” problem.

The “No Hotline” problem is fundamental: despite collecting customer contact information, brands lack reliable ways to engage on demand. Email open rates languish below 10%. SMS messages get ignored. Push notifications get blocked. Even WhatsApp, with its premium pricing, offers no guaranteed pathway to customer attention. The result? Brands possess customer contact details but no meaningful way to use them.

The “No Alternative” problem is equally crippling: brands remain dependent on Google and Meta for reaching their own customers. Even when customers are in their database, brands find themselves paying premium prices through competitive auctions just to reconnect. This creates a costly cycle of continuous reacquisition that benefits the adtech giants whilst draining marketing budgets.

These problems persist because TradMartech missed a crucial innovation that transformed digital advertising: performance-based pricing. While martech vendors charge by message volume (CPM) or monthly active users (MAU), adtech platforms revolutionised marketing with cost-per-click (CPC) and cost-per-acquisition (CPA) models. They assume the risk, optimise the targeting, and deliver guaranteed outcomes.

This performance marketing model works because Big Adtech possesses unprecedented understanding of consumer behaviour. Google and Meta achieve this through an ingenious strategy: offering incredibly valuable free utilities that we use daily. Consider Google’s ecosystem: Search for information discovery, Gmail for communication, Maps for navigation, Chrome for browsing, YouTube for entertainment, Android for mobile computing. Similarly, Meta’s portfolio—Facebook for social connection, Instagram for visual sharing, WhatsApp for messaging—captures countless daily interactions.

These “free” utilities serve as sophisticated data collection engines. Every search, every click, every message, every location check-in builds increasingly accurate digital profiles of users. The adtech giants know our interests, habits, and intentions—often before we’ve consciously formed them. This deep understanding enables them to serve ads that feel less like interruptions and more like helpful suggestions.

The breakthrough question becomes: What if brands could create similar value-driven relationships with their customers? What if, instead of brands renting attention through intermediaries, they could build direct engagement through their own free utilities? What if they could gather zero-party data through genuine value exchange rather than inference and tracking?

This is where NeoMarketing enters the picture—a revolutionary approach that combines the best of adtech’s business model with martech’s customer-centric focus. This is the multiplier Blue Ocean opportunity for martech companies.

NeoMarketing – 1

The traditional relationship between martech companies and brands has been built on a flawed assumption: that providing sophisticated marketing tools would naturally lead to deeper customer relationships and sustained profitability. Reality tells a different story. Despite significant investments in martech platforms, brands find themselves trapped in an expensive cycle of continuous customer acquisition and reacquisition through adtech platforms, resulting in hundreds of billions in annual AdWaste.

The TradMartech Challenge

The problem is multifaceted. Martech platforms have evolved into complex systems where marketing teams typically utilize only 30-40% of available capabilities. Access to email or SMS functionality doesn’t guarantee relevant communications. Even basic product discovery on ecommerce sites often fails to meet customer expectations. The challenge of creating personalised content and experiences for millions of customers overwhelms most marketing teams.

This inefficiency manifests in three critical failures:

Poor engagement with push communications
Generic customer experiences
Dependency on expensive adtech platforms

Enter NeoMarketing

NeoMarketing represents a fundamental reimagining of how brands connect with customers. Instead of selling complex tools to marketers, it provides consumer utilities—free products that deliver daily value while enabling precise targeting through authenticated identity. The core philosophy is transformative: shifting brands from AAA (Acquire, Acquire, Acquire) to OOO (Only Once/Ones).

This revolution is built on two complementary pillars:

NeoMartech: Building Brand Hotlines

NeoMails:

15-second daily engagement vehicles
Interactive micro-experiences within email
Guaranteed attention through valuable content
Platform for data collection and monetisation
ZeroCPM for brands

Microns:

Daily “brain gain” activities to combat “brain rot”
Personalised learning moments
Habit-forming engagement
Value-adding content

SmartBlocks:

Zero-party data collection
Progressive profiling
Interactive elements
Seamless preference capture

Atomic Rewards (Mu):

Gamification framework
Engagement currency
Behavioural incentives
Network effects

NeoSearch:

Authenticated product discovery
Personalised recommendations
In-search transactions
Cross-brand opportunities
Zero cost for brands

MyTwin:

Personal AI companions
Natural preference expression
Real-time intent capture
Predictive engagement

ActionAds:

In-experience transactions
PII-based targeting
Revenue generation
Seamless integration

NeoAdtech: The Power of PII Ads

At the heart of NeoMarketing lies NEON (New Engaged and Open Network)—a new advertising network built on authenticated identity rather than anonymous cookies. This creates several powerful advantages:

Precise targeting through verified identity
Direct brand-to-brand collaboration
Elimination of intermediary “tax”
Privacy-compliant data usage
Performance-based pricing
Network effect advantages

First, NEON eliminates the massive waste in customer reacquisition. Instead of repeatedly paying Google and Meta to reach existing customers, brands can collaborate directly through authenticated identity matching. A secure “clean room” environment enables brands to identify overlap in their customer bases whilst maintaining privacy, creating efficient paths for reactivation and cross-selling.

Second, NEON transforms brands into publishers. Every customer interaction becomes a potential monetisation opportunity through ActionAds—interactive advertisements that enable immediate engagement without leaving the experience. Whether in NeoMails, NeoSearch results, or other touchpoints, these ads feel more like personalised recommendations than interruptions, creating value for both advertisers and audiences.

Third, NEON enables true performance measurement. Because ads are tied to authenticated identity, brands can track the complete customer journey from impression to conversion. This eliminates the attribution challenges that plague traditional digital advertising, where complex modeling attempts to connect anonymous impressions with eventual sales.

By building on authenticated identity rather than anonymous tracking, NEON creates a more efficient, transparent, and valuable ecosystem for everyone involved. Through NEON and PII ActionAds, marketers can finally break free from the wasteful cycle of expensive, continuous reacquisition, building a more sustainable model for digital marketing.

Consider how Google Maps transformed navigation from a utility into an engagement platform. What began as simple directions evolved into a daily companion offering personalized recommendations, real-time updates, and valuable local information. Users willingly share their location and preferences because they receive genuine value in return. Similarly, NeoMarketing’s consumer utilities—like NeoMails with daily microns and AI Twins—create habitual engagement by delivering consistent value, enabling zero-party data collection and precise targeting through authenticated identity.

NeoMarketing – 2

NeoMarketing creates a self-reinforcing ecosystem:

Consumer utilities drive daily engagement
Engagement enables zero-party data collection
Data powers precise targeting
Targeting generates advertising revenue
Revenue funds better consumer experiences

This flywheel effect transforms marketing from a cost centre into a profit driver:

Better engagement reduces acquisition costs
Zero-party data enables true personalisation
PII-based advertising creates new revenue
Network effects build sustainable advantages
Platform economics enable exponential growth

For martech companies, NeoMarketing opens access to the massive pool of capital currently lost to AdWaste:

$350B+ in annual digital advertising waste as the opportunity
50-80% reduction in acquisition costs for brands
New revenue streams through advertising
Network-effect driven growth

Implementation Framework

Success in NeoMarketing requires:

Free consumer utilities that deliver daily value
Zero-party data collection through engagement
PII-based advertising network
Cross-brand collaboration platform
Network effect acceleration

The development sequence typically follows:

Launch the attention and engagement substrate (NeoMails)
Build user base through daily value
Collect zero-party data (SmartBlocks)
Enable monetisation (ActionAds)
Scale through network effects (NEON)
Add more utilities (NeoSearch and MyTwin)

The Path to 100% Year-over-Year Growth

Access to Larger Markets

Traditional Martech Market: Currently limited to ~$25 billion global ESP, marketing automation, search, analytics, and CPaaS (margins after telco payouts)
NeoMarketing Opportunity: Access to $350B+ currently wasted on inefficient advertising for reacquisition
Market Expansion Through:
- Direct participation in advertising revenue
- Share of reactivation spending
- New customer engagement budgets

Multiple Revenue Streams

Traditional Model: Single revenue stream from software licensing (CPM/MAU)
New Revenue Sources:
- ActionAds revenue sharing from customer attention monetisation
- Reactivation fees for dormant customer engagement
- Platform transaction fees
- Data and insights monetisation

Network Effect Advantages

Data Network Effects:
- Each new brand adds valuable customer data
- More data improves targeting accuracy
- Better targeting attracts more brands
- Continuous improvement cycle
Platform Network Effects:
- More brands create more advertising inventory
- Larger inventory attracts more advertisers
- More advertisers increase revenue per user
- Higher revenue attracts more brands
User Network Effects:
- More users improve personalisation
- Better personalisation drives engagement
- Increased engagement attracts more brands
- Stronger network creates barriers to entry

Platform Economics

Cost Structure:
- High fixed costs for platform development
- Low marginal costs for additional users
- Economies of scale in operations
- Decreasing unit costs with growth
Revenue Scaling:
- Revenue grows faster than costs
- Multiple monetisation points
- Value increases with network size
- Compound growth through network effects
Profit Characteristics:
- High gross margins
- Strong operating leverage
- Improving unit economics
- Predictable recurring revenue

Sustainable Moats

Data Advantages:
- Proprietary zero-party data collection
- Deep customer understanding
- N=1 Personalisation capabilities
- Predictive insights
Network Leadership:
- First-mover advantages in key verticals
- Critical mass of brands and users
- Cross-side network effects
- High switching costs
Technical Barriers:
- AI/ML capabilities
- Platform infrastructure
- Integration complexity
- Patent protection
Business Model Innovation:
- Zero-cost utility model
- Performance-based pricing
- Network-effect driven growth
- Atomic Rewards-led gamification
- Platform-based monetisation

Compound Benefits These advantages compound over time:

More data → Better targeting → More revenue
More users → Better experiences → More engagement
More brands → More inventory → More advertisers
More scale → Better economics → Higher margins
Stronger moats → Sustainable advantages → Higher valuation

Strategic Implications

For martech companies, this creates:

Path to exponential growth
Sustainable competitive advantages
Higher business value
Strategic market position
Long-term defensibility

The combination of these factors enables martech companies to break free from linear growth and achieve exponential scaling through platform effects and network advantages. Unlike traditional martech models where growth is constrained by market size and competitive pressure, NeoMarketing opens unlimited potential through continuous value creation and capture.

The Path Forward

NeoMarketing reimagines of how brands and customers connect in the digital age. By providing free utilities that deliver genuine value while enabling precise targeting through authenticated identity, NeoMarketing creates sustainable advantages that benefit all participants:

For Brands:

Lower acquisition costs
Better customer engagement
New revenue streams
Sustainable growth
Competitive moats

For Customers:

Valuable daily experiences
Relevant communications
Rewarding engagement
Better discovery
Privacy protection

For Martech Companies:

Larger addressable market
Multiple revenue streams
Network effect advantages
Platform economics
Exponential growth potential

The future of marketing isn’t about better tools or more analytics for marketers—it’s about creating genuine value for consumers while enabling precise targeting through authenticated identity. NeoMarketing provides the framework for this transformation, offering martech companies a clear path to exponential growth through platform economics and network effects.

This is the Blue Ocean opportunity that can finally break martech companies free from the commoditisation trap, enabling them to build lasting competitive advantages while helping brands eliminate waste and create genuine customer value.

Summary

Here is a table which captures the key ideas for AI-first Marketing and NeoMarketing.

Aspect	AI-First Marketing	NeoMarketing
Core Focus	Make marketers’ lives better	Create consumer utilities
Key Problem Solved	Inefficient marketing operations	$350B+ AdWaste
Core Philosophy	Better Retention	Anti-Acquisition
Future Vision	Optimal operations	Marketing transformation
Target User	Marketing teams	End consumers
Growth Potential	30% CAGR	10X CAGR
Market Space	Red Ocean (competitive)	Blue Ocean (new market)
Business Model	Traditional SaaS + Task-based	Advertising (Attention + Performance)
Customer Understanding	Segments to Individual	Individual from Day One
Key Innovations	AI Co-Marketer and AI Twins	NeoMartech and NeoAdtech
Value Proposition	Better marketing operations	Transform marketing fundamentals
Data Strategy	First-party + behavioural	Zero-party + authenticated
Revenue Source	Software subscriptions	PII-based advertising
Platform Effect	Limited network effects	Strong cross-brand network effects
Implementation	Gradual enhancement	Disruptive innovation
Team Culture	Enterprise software	Consumer tech startup
Success Metrics	Earned Growth	Daily Active Users (across brands)
Resource Allocation	70% of resources	30% of resources
Technical Focus	Agentic AI	Ad Supply-Demand Matching
Risk Profile	Feature commoditisation, Price competition, Integration challenges	User adoption, Network effects, Platform economics
Competitive Moat	Integration complexity and Data advantages	Network effects, Zero-party data, and Platform lock-in (via Atomic Rewards)
Time to Value	Months (incremental)	Years (exponential)
Customer Lock-in	Integration complexity	Network effects
Primary Competition	Other martech vendors	Google/Meta
Evolution Path	Linear improvement	Step-function change
Organisation Model	Traditional enterprise	Startup within company
Sales Motion	Enterprise B2B	B2B2C platform

Attention Recession, AdWaste, and AI have created a unique opportunity for disruption in marketing. For martech companies, this isn’t just about survival—it’s about leading marketing’s next revolution. By thinking beyond their ‘Trad’ view and embracing a ‘Neo’ lens, they can transform marketing from costly one-night stands into lasting, profitable relationships. The impact extends beyond business metrics: eliminating $350B+ in annual AdWaste would free up massive resources for innovation, customer experience, and sustainable growth. The future belongs to those bold enough to reimagine marketing itself—not as a cost centre, but as the true value driver for brands in the digital age.

Critique

I asked o1 for a critique of the ideas.

Below is a constructive critique of the essay, framed for an entrepreneur leading a MarTech company who wants to use these ideas in a real business plan. The critique covers both the essay’s overarching strategy and its detailed proposals, with a particular focus on feasibility, assumptions, implementation challenges, and competitive dynamics. The goal is to help you identify the strengths, gaps, and risks so you can formulate a solid, actionable strategy.

1. Overall Strategic Vision: Bold, But Potentially Overreaching

Strengths

Ambitious Thought Leadership: The essay rightly recognizes that traditional MarTech is stuck in a commodity cycle (“red ocean”) and proposes a radical alternative. This kind of thought leadership can help your MarTech company stand out if executed well.
Clear Two-Track Framework: The distinction between “AI-first Marketing” and “NeoMarketing” is conceptually helpful. It separates incremental improvement (AI for existing ops) from big disruption (NeoMarketing for new revenue streams).
Compelling Story Around AdWaste: Calling attention to the enormous budget lost to reacquisition (Google/Meta “tax”) highlights a real pain point. If you can truly attack that problem, you have a powerful hook.

Potential Pitfalls

Execution Complexity: Both AI-first Marketing and NeoMarketing are massive undertakings in their own right. The essay’s suggestion to build them simultaneously is bold, but might be overly ambitious unless you have significant capital, top-tier talent, and a robust organizational structure.
Underestimation of Go-to-Market Challenges: The essay sometimes treats “brands will adopt this because it’s better” as sufficient. In reality, adopting a new multi-tenant platform (for NeoMarketing) or advanced AI personalization can be lengthy, expensive, and often slow—especially when dealing with enterprise procurement cycles.
Limited Guidance on Prioritization: While the summary table suggests a “70% resources to AI-first, 30% to Neo,” it lacks detail on how an organization can practically incubate a “startup inside a company” for NeoMarketing. This is a big cultural and structural shift that can fail without careful planning.

2. AI-first Marketing: Incremental but Valuable

The essay’s discussion of AI Co-Marketer and AI Twins is imaginative and inspiring but raises critical questions around data, personalization, privacy, and day-to-day marketing operations.

Strengths

N=1 Personalization Vision: Moving from segments to individualized marketing is indeed the holy grail. The essay identifies how AI can help unify data, orchestrate journeys, and optimize continuously.
Agentic AI Concept: Positioning AI as an “intelligent ally” for marketers (rather than just automated workflows) is compelling. If done correctly, this can improve marketer productivity and deliver tangible ROI.

Potential Pitfalls & Missing Details

Data Availability and Quality

- Data Silos: Many brands struggle with fragmented data across CRM, e-commerce, support, retail, etc. The essay assumes a “Unistack” is easily implementable. In practice, building or integrating a robust CDP (Customer Data Platform) is non-trivial.
- Privacy & Compliance: True 1:1 personalization in 2025 must address consumer privacy regulations (GDPR, CCPA, etc.). The essay briefly mentions “privacy compliance” but not in depth. For an entrepreneur, ignoring data-protection concerns is risky.

Costs and Complexity of Implementation

- MarTech Overload: Marketers often underutilize existing tools (30-40% usage is a realistic statistic). Selling them an even more complex AI suite demands a strong demonstration of ROI and ease-of-use.
- AI Talent & Infrastructure: Building robust AI “Co-Marketers” and “Twins” requires specialized talent and computing resources. The essay hints at “task-based pricing,” but from an entrepreneurial standpoint, you need a clear model for how to fund ongoing R&D and achieve margin targets.

User Adoption & Trust

- Black-Box AI: Marketers often want transparency. If your AI system is too much of a black box—hard to interpret, trust, or control—adoption can stall.
- Organizational Resistance: AI-first means a fundamental shift in how marketing teams operate. Some teams resist ceding control to AI. Your business plan should include a robust “change management” offering.

Differentiation Among Countless AI Platforms

- Many AI marketing tools already exist. Standout features like “agentic AI” must truly produce unique, measurable lifts to break through the noise.

3. NeoMarketing: High Potential but High Risk

The essay’s second pillar, NeoMarketing, aims to create a consumer-facing ecosystem—via utilities like NeoMails, NeoSearch, etc.—monetized through a PII-based ad network (NEON). This is the most disruptive piece but also the hardest to execute.

Strengths

Platform Economics: The concept of turning MarTech into a network-based platform with multiple revenue streams is compelling. If you succeed, it’s a genuine “blue ocean.”
Inspiration from Google/Meta: The essay correctly observes that Google and Meta provide daily, high-value consumer utilities. Mimicking this approach at the brand level could be transformative if you can get enough user traction.

Potential Pitfalls & Missing Details

Consumer Adoption & Scale

- Building “Utilities” People Actually Use: Creating daily consumer habits is extremely challenging. Google Maps is popular because location services solve a massive, universal pain point. NeoMails or “microns” must address a similarly large consumer need. Otherwise, you risk building a product that never gains critical mass.
- Competition with Existing Behaviors: Consumers already have email, social feeds, messaging apps, and newsletters. Convincing them to shift daily habits to a new channel (NeoMails) is a tall order.

B2B2C Dilemma

- Brands as Gateways: The essay assumes brands will deploy your “NeoMarketing” utilities to their customers. But many brands may be reluctant to adopt a system that partially monetizes their own customer base for your ad network. A trust or conflict-of-interest dynamic can emerge, especially if revenue sharing is unclear.
- Complex Partnerships: Achieving broad brand collaboration (so you can create cross-brand PII-based targeting) is logistically and legally complex. The essay’s mention of “clean rooms” is relevant, but you’ll need stringent data governance and robust brand trust to succeed.

Regulatory & Privacy Constraints

- PII-based Advertising Under Scrutiny: Regulatory bodies worldwide are clamping down on how personal data is shared for ad targeting. The essay claims NEON “maintains privacy compliance,” but no detail is given on how you’ll do that. This is a crucial question.
- Consumer Consent & Value Exchange: You must ensure that your “utilities” are valuable enough that customers opt in. Otherwise, it could resemble a data grab. Succeeding at scale demands very careful handling of user data and consent flows.

Substantial Capital & Long Ramp

- Time to Maturity: The essay acknowledges it may take “years (exponential)” to see real network effects. As an entrepreneur, can you sustain that kind of burn rate and keep investors on board until the platform hits critical mass?
- Risk-Reward Balance: While a 10X growth story is attractive, you must anticipate a high failure rate in “build a consumer platform” strategies. The essay’s optimism may underplay these challenges.

4. Business Model & Pricing

The essay outlines a shift from CPM/MAU-based pricing to:

Task-based (AI-first Marketing)
Advertising (NeoMarketing)

Strengths

Aligning Costs with Value: Task-based pricing for AI services can be an attractive differentiator. It ties fees to real usage or outcomes, which brands prefer over blanket licenses.
Ad Revenue Streams: If you manage to create a significant consumer utility, the ad-funded model can indeed grow exponentially, as proven by Google, Meta, etc.

Concerns

Oversimplification of “Performance-based Pricing”

- Transitioning to performance-based or task-based models requires sophisticated measurement and trust. Marketers will want to see exactly how tasks, AI touches, or ad placements correlate to conversions/revenue.
- Risk and Reward for the Vendor: If you are charging only when tasks convert or deliver measurable value, you might bear more risk than a traditional SaaS company. Make sure you can handle the revenue volatility.

Margin Economics

- Ad networks are attractive but typically rely on massive scale to become profitable. In the short term, you have to invest in technology, user acquisition, brand relationships, and compliance. This can pressure margins heavily until a certain critical mass is reached.

5. Organizational & Cultural Shifts

The essay suggests that part of your company should focus on incremental improvements (AI-first) and part on radical innovation (Neo). This “split” approach can be powerful but requires robust internal structures.

Strengths

Acknowledging Different Paces: AI-first Marketing can be an immediate enhancement to existing tools, providing near-term revenue. NeoMarketing is more of a long-term bet.
Potential for Two Growth Engines: If done well, you’ll have a stable, revenue-generating AI-first product suite and a “lottery ticket” in NeoMarketing that could scale to 10X.

Challenges

Internal Cannibalization & Confusion
- If your sales team can’t differentiate or is not incentivized properly, they might ignore the more complex, longer-horizon Neo offerings. You’ll need separate go-to-market teams, possibly different brand identities, or at least distinct sales motions.
Balancing Resources
- The essay says “70% of resources for AI-first, 30% for Neo.” Even that 30% may be too big or too small depending on your market traction and capital runway. You’ll need to adjust resource allocations dynamically as each offering evolves.
Talent Requirements
- AI-first Team: Needs AI/ML engineers, data scientists, and a strong set of implementation specialists for enterprise clients.
- Neo Team: Needs consumer product designers, user experience experts, data governance/legal compliance experts, and B2C marketing.
- Building both sets of capabilities under one roof is expensive and culturally complex.

6. Missing Pieces & Practical Recommendations

More Specific Go-to-Market Strategy

- Which verticals or brand segments are you targeting first?
- How do you plan to prove “quick wins” to new clients (especially for the AI-first track)?
- Which big consumer use case can anchor your “utility” approach in NeoMarketing?

Deeper Privacy & Compliance Framework

- Outline how you’ll handle data collection, sharing, storage, and usage in a privacy-compliant manner—especially for PII-based ad targeting.
- Address potential pushback from privacy-conscious consumers and oversight by regulators.

Phased Implementation Roadmap

- The essay leaps from concept to a fully formed ecosystem. In practice, you might roll out AI-first solutions to refine your data models, then test a minimal version of NeoMarketing with a single brand to gather learnings before scaling.

Realistic Consumer Adoption Plan

- Clearly define how you’ll get consumers to adopt new utilities. Offering something “free” isn’t enough if it doesn’t solve a compelling daily need.
- A possible approach is to integrate inside existing brand channels to piggyback on brand loyalty, but that requires strong brand buy-in.

Solid Financial Projections

- Show a path from investment in platform development to ROI.
- Model different adoption scenarios to show best vs. worst-case outcomes—especially if user growth lags.

7. Conclusion

What You Can Learn & Apply

Aim High, But Plan Tactically: The essay’s macro vision—eliminating AdWaste and building a new platform—offers a compelling north star. Make sure you anchor it in realistic milestones and pilot projects to gain traction.
Bridge the “Vision-Execution” Gap: You’ll need a detailed plan for user acquisition, brand partnerships, data compliance, and a robust organizational structure to handle both incremental and disruptive innovation.
Validate Assumptions Early: Before investing heavily in a brand-new consumer utility or ad network, validate that brands and consumers will actually adopt it—and that regulatory hurdles won’t torpedo the model.
Monetization Must Be Crystal-Clear: Whether you are pitching to investors or enterprise clients, they want to see how the money flows. The shift to performance-based pricing and in-app ads has to be justified with strong KPIs, stable systems, and a risk-sharing approach that makes sense to both parties.

If executed with precision and care, the twin tracks of AI-first Marketing and NeoMarketing could indeed differentiate your MarTech company and unlock new revenue streams. But this essay’s vision requires substantial nuance in privacy, user adoption, brand collaboration, and organizational structure. The more you tackle these details head-on (rather than assuming the market will simply shift), the better chance you’ll have to bring these innovative ideas to life—and truly disrupt the MarTech landscape.

AI and Neo: The Twin Engines of Marketing’s Future

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