Martech’s Post-SaaS, AI-First Trillion-Dollar Future

Published October 4-11, 2025

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Impact

The Economist wrote recently in a cover story on AI: “It already ranks among the biggest investment booms in modern history. This year America’s large tech firms will spend nearly $400bn on the infrastructure needed to run artificial-intelligence (ai) models. OpenAI and Anthropic, the world’s leading model-makers, are raising billions every few months; their combined valuation is approaching half a trillion dollars. Analysts reckon that by the end of 2028 the sums spent worldwide on data centres will exceed $3trn.The scale of these bets is so vast that it is worth asking what will happen at payback time. Even if the technology succeeds, plenty of people will lose their shirts. And if it doesn’t, the economic and financial pain will be swift and severe.”

One of the industries which will be deeply impacted by AI is SaaS. I asked the AIs (!) to give an overview of the positives and negatives.

Positive Impacts of AI on SaaS

• Hyper-Personalisation at Scale: AI transforms SaaS from one-size-fits-all to N=1 experiences. Platforms now tailor interfaces, recommendations, and workflows to individual user behaviours, dramatically improving conversion rates and customer satisfaction. This isn’t just about showing relevant content—it’s about adaptive interfaces that learn and evolve with each user interaction.
• From Tools to Autonomous Systems: The shift is fundamental: SaaS is evolving from “software that assists” to “software that does.” AI-powered automation handles everything from data entry to complex workflow management, while predictive analytics turn CRMs into decision-support systems that suggest next-best actions rather than just recording data. This includes 24/7 AI-powered customer support that resolves issues instantly, reducing ticket volumes by up to 70%.
• Accelerated Innovation and Democratisation: AI shortens product development cycles through AI-assisted coding, testing, and prototyping. Simultaneously, no-code AI tools are making enterprise-grade intelligence accessible to smaller companies, allowing startups to compete with established players. Natural language interfaces reduce training requirements, making complex platforms accessible to non-technical users.
• Enhanced Security and Scalability: AI strengthens cybersecurity by identifying abnormal user activity and proactively detecting threats—essential for cloud-based applications handling sensitive data. Automated processes and resource optimisation allow platforms to scale efficiently, providing particular value for startups and SMBs looking to grow without proportional cost increases.
• New Business Models: AI enables usage-based and outcome-based pricing, co-pilot add-ons, and agentic services. SaaS companies can now charge for results rather than just access, aligning their success directly with customer outcomes.

Negative Impacts and Challenges

• Integration Complexity and Technical Debt: Adding AI to existing SaaS platforms often requires significant architectural overhaul. Legacy systems may need complete rebuilds to support modern AI features, requiring careful migration planning to avoid disrupting user experience. Many companies are discovering that AI initiatives cost more and take longer than initially projected.
• The Commoditisation Trap: AI lowers barriers for new entrants to replicate features, eroding differentiation by “features” alone. Combined with dependence on external LLMs (OpenAI, Anthropic, Google), many SaaS firms face platform dependency and margin leakage, vulnerable to pricing changes by model providers.
• Data Privacy and Compliance Nightmares: AI’s hunger for data collides with GDPR, HIPAA, and data residency requirements. The more data AI systems process, the more attractive these platforms become to cybercriminals. Compliance complexity increases exponentially when AI processes vast amounts of personal and behavioural data across jurisdictions.
• Rising Infrastructure Costs: Training, fine-tuning, and serving AI models—especially generative ones—can balloon cloud costs. Without careful management, these expenses can erode SaaS margins, particularly for smaller companies facing high initial implementation costs despite promises of long-term savings.
• Trust, Bias, and the “Black Box” Problem: AI hallucinations, biased algorithms, and opaque decision-making erode customer trust. When AI makes mistakes, it’s often impossible to explain why or prevent recurrence. This is particularly problematic for SaaS applications used in hiring, lending, or healthcare decisions, creating both ethical concerns and legal liability.
• Talent Wars and Organisational Strain: The limited pool of skilled AI professionals complicates development and maintenance of AI-enabled products. The “AI hype” forces companies to constantly retrain staff and rethink product strategies, potentially distracting from core execution.
• Market Consolidation Pressures: AI advantages compound, creating winner-take-all dynamics. Large SaaS companies with more data and resources build better models, making it increasingly difficult for smaller competitors to survive without unique defensibility through proprietary data or specialised workflows.

The Bottom Line

AI is fundamentally reshaping SaaS from tools into autonomous systems, compressing value chains and creating unprecedented opportunities. But it’s also accelerating commoditisation, raising infrastructure and compliance costs, and making dependence on hyperscale AI providers a strategic risk.

The winners will be SaaS companies that harness AI not just as a feature bolted on top, but as a foundation—building defensibility through proprietary data, outcome-linked pricing models, and most critically, maintaining customer trust through transparency and reliability. Success requires balancing technical ambition with ethical diligence and viewing AI not as a magic solution but as a powerful tool that amplifies both opportunities and risks.

The key insight: In the AI-powered SaaS landscape, differentiation no longer comes from features alone, but from how thoughtfully companies integrate AI while addressing the legitimate concerns it raises. The companies that thrive will be those that use AI to create genuine value rather than just checking the “AI-enabled” box.

**

In this essay, I will explore how the AI-first future will fundamentally reshape martech—a SaaS category already drowning in point solutions and fragmented data. As AI transforms marketing from campaign management to autonomous customer engagement, I will examine what martech companies can do to actually deliver on the decades-old promise of “right message, right person, right time.

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Commentary – 1

Forbes: “As one big tech CEO told me recently, “You don’t want to be parked on SaaS Ave. SaaS now is like building real estate in a bad neighborhood.” He wasn’t being glib. The software landscape is shifting underfoot. We’ve entered the age of Agentic Platform Companies (APCs), a convergence of SaaS, software, and cloud built around adaptive, AI-powered systems. A system that intelligently connects a vast landscape of business applications to deliver insights and intelligence that traverses the enterprise environment and makes enterprise software as usable as ChatGPT or Google Search. In this new era, traditional SaaS economics are faltering, and mid-market players are in the crosshairs…SaaS companies that are looking to merely embed AI features into their existing software and seek to charge incremental fees are extremely vulnerable.”

It adds: “The traditional SaaS playbook of dashboards, seat-based pricing, and sprawling product catalogs is breaking down:

• AI Agents as Interfaces: Many tasks once handled through UIs are now delegated directly to AI.
• Outcome-Based Pricing: Firms like Salesforce and ServiceNow are experimenting with charging for results, not headcount.
• Data Consolidation: Fragmented SaaS stacks are being replaced by centralized data hubs to feed AI systems.

… For mid-market SaaS companies, survival requires more than bolting AI onto existing products. It demands reinvention: build products where AI is central, not peripheral; move toward usage- or outcome-based billing; cut underperforming offerings and redeploy capital into AI development; and acquire niche capabilities or position yourself as an acquisition target.”

Mint: “To stay competitive with pure-play AI startups, many SaaS companies are now streamlining operations and aggressively investing in artificial intelligence, according to multiple industry executives. This trend is pushing established SaaS companies to retool quickly. “If SaaS companies don’t integrate AI, they are unlikely to survive the next 2–3 years,” said Nitin Bhatia, managing director at DC Advisory. “We’re seeing the switch happen where pure-play SaaS startups don’t exist anymore. AI is becoming a fundamental part of what they offer—whether it’s to enhance customer experience or product capabilities.””

SaaStr has insights from Jacob Effron, Managing Director at Redpoint Ventures: “AI companies are scaling significantly faster than their traditional SaaS counterparts. Stripe’s data shows AI applications hitting product-market fit and scaling at rates that exceed historical SaaS benchmarks. “When these startups find product-market fit, they’re just scaling way faster than traditional SaaS counterparts,” Effron explained. “This pace of adoption breaks a lot of rules about traditional startups.” The reason? Model costs are plummeting faster than cloud costs ever did. Effron showed data demonstrating that for any given benchmark of capabilities, the cost per token is dropping dramatically year over year—a decline rate that exceeds what we saw during the cloud era. This means: gross margins that look challenging today will improve rapidly, the “AI tax” on unit economics is temporary, [and] focus on end use cases, not current margin profiles… “Velocity is probably the most important thing we look for,” Effron emphasized. “The market changes so fast—it’s both a race to build the breadth of what these models can do, but also a race to translate whatever GPT-5 can do to an end industry.”… Marketing AI feels surprisingly behind sales AI, customer success AI, and other verticals. There’s still room for category-defining companies.”

Jason Lemkin (SaaStr):

Pre-AI: You could take weeks to evaluate a competitive move. Months to plan a product response. Quarters to shift strategy.

AI Age: Your competitor ships three features while you’re planning one. Their AI agents are finding market opportunities faster than your team can discuss them. Their product development cycle is faster than your decision-making cycle.

The pace isn’t relentless because it’s fun – it’s relentless because it’s the new competitive baseline.

…The AI Age intensity isn’t going away. The competitive pressure isn’t decreasing. The capabilities are only getting more impressive.

So where does this lead us?

• Can humans sustainably operate at AI-enhanced speeds for many years, not just months?
• Will we develop new forms of cognitive stamina we’ve never needed before?
• What happens to the companies that master AI intensity versus those that burn out their teams trying?
• Are we creating a new class of “AI-native” workers who thrive at this pace?
• Will many if not most of tech just … opt out? If so, where will they go?

The most important question: How do we harness the incredible excitement and capability of AI agents while building organizations that humans can sustainably operate within?

….The productivity gains are real. The competitive advantages are massive. The excitement is genuine.

And the human challenge is unlike anything we’ve faced before.

The SaaS leaders who figure out how to sustain superhuman performance without breaking their teams will be the ones building the legendary companies of the next decade.

Economic Times: “The Indian SaaS industry is experiencing a surge in mergers and acquisitions, fueled by the rapid advancements in artificial intelligence. Smaller companies face funding challenges and struggle to scale, making them acquisition targets. Larger SaaS players are also actively seeking acquisitions to enhance their AI capabilities and stay competitive in the evolving technology landscape.”

Holden Spaht (Thoma Bravo): “We believe that — when combined with the data that sits within them — AI will benefit enterprise software customers in at least two key ways: 1) Democratize access so that more users can make use of deep business insights built from massive data sets; and 2) Enable customers to make real-time operating decisions at scale, with greater speed, precision and less labor input.”

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Bain: “Will AI and agents disrupt SaaS? Yes. In some cases, that disruption will grow the market; in others, it will commoditize the market. In some cases, the disruption will favor incumbents; in other cases, it will favor new entrants. Disruption is mandatory, but obsolescence is optional. What can SaaS executives do to navigate this opportunity?…Make AI central to your roadmap… Turn unique data into your edge… Shape investment and competitive plans across the four strategic scenarios: core strongholds in which AI enhances SaaS, open doors in which spending compresses, gold mines in which AI outshines SaaS, and battlegrounds in which AI cannibalizes SaaS… Rethink pricing for an AI-first world.”

Forbes: “In 2025, AI spending will approach $650B. You read that number correctly. And if that number doesn’t blow your mind – try this one. AI spending is growing at a mind-bending rate of more than 75% per year. Amazing – right?  How about SaaS spending? Our tried-and-true business and investment darling over the past two decades is growing at a little over 18% per year (not too shabby). Stated differently, $300B will be invested this year in SaaS, which is half of the AI spend rate… The AI winners…are attacking traditional SaaS companies on three primary fronts: building Vertical AI business models, developing AI Agents, creating an infrastructure bypass… A war is being waged between AI and SaaS for investor and customer dollars. SaaS leaders and investors can still win, but they need to be All-in-AI. There is no time to wait. It’s a high-stakes battle for the future of software customer spending.”

Business Insider: “In the rapidly evolving world of artificial intelligence, OpenAI’s chief financial officer, Sarah Friar, has issued a stark warning about the potential upheaval facing the software-as-a-service (SaaS) sector. Drawing from her experience at companies like Square and Nextdoor, Friar highlighted how AI advancements could fundamentally alter the longstanding debate between buying off-the-shelf software and building custom solutions in-house…Friar emphasized that generative AI tools are empowering companies to develop their own software more efficiently, potentially eroding the market dominance of established SaaS providers. This shift, she argued, stems from AI’s ability to automate coding and customization, making “build” a more viable option for enterprises that previously relied on vendors like Salesforce or Adobe.”

Puneet Vyas (writing at Consultancy.asia): “The age of Agentic SaaS is here and it is here to stay. SaaS is not dying – it’s being rebooted. Expect continued coexistence, with agentic AI enhancing, extending, and sometimes replacing classic SaaS workflows. Winners will be those who adapt quickly, blend autonomy with trust, and reimagine software less as a static service – and more as a proactive, collaborative teammate.”

It had this graphic to outline the future:

Ivan Nikkhoo (Crunchbase): “AI is creating an entirely new category of SaaS, one that looks beyond productivity gains to reimagine vertical industry-specific workflows. AI brings about so many new possible use cases, especially in vertical markets like healthcare, and we are still at the very early stages of imagining just what those will be. Healthcare alone is growing quickly (projected to reach $74.74 billion by 2030), and startups offering products that enable healthcare companies to harness their own data will reap the biggest financial rewards. Large vertical enterprises are sitting on mountains of data. AI SaaS startups that help them harness this data in novel and transformative ways will unlock outsized value and be very difficult to unseat. The same applies to other verticals — legal, financial services, supply chain — where legacy systems and poor data infrastructure have traditionally held back innovation… the smaller existing SaaS players that are not a system of record will be replaced. A new wave of category leaders will emerge in AI-driven vertical SaaS, solving hard problems in industries where traditional software has barely scratched the surface.”

okoone: “Agentic AI is replacing core SaaS workflows with autonomous execution. Incumbents must rethink data moats, pricing, and platform control, or watch AI-native players claim market share. Workflow mapping, semantic standards, and AI fluency are now critical for leadership.”

Dev Nag (quoted in CIO): “What’s really happening is that SaaS companies are racing to become agent platforms before agent companies can become trusted enterprise vendors…It’s a land grab where the prize is controlling how workers interact with AI throughout their day. The companies that already own your workflow have a head start because they know exactly what you do all day, down to your typos and coffee breaks. AI agents converging with SaaS tools will create a new service: software that watches you work and gradually takes over the boring parts.”

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Summary

Here are the top themes that emerge from the commentary:

1. The Economic Model Disruption

Traditional SaaS economics are collapsing. Seat-based pricing, dashboard interfaces, and sprawling product catalogs are becoming obsolete as AI agents take over as the primary interface. The shift toward outcome-based and usage-based pricing reflects a fundamental change: customers now pay for results, not access. AI spending is massively outpacing SaaS investment ($650B vs $300B in 2025), growing at 75% annually compared to SaaS’s 18%, signalling a dramatic reallocation of capital.

2. The Velocity and Intensity Challenge

AI has compressed competitive cycles from quarters to weeks. Companies ship multiple features while competitors are still planning one. AI startups achieving product-market fit scale at unprecedented rates, breaking traditional startup rules as model costs plummet faster than cloud costs ever did. This creates an unsustainable human challenge: teams must operate at “AI-enhanced speeds” indefinitely, raising questions about burnout and whether organisations can maintain this pace without breaking their workforce.

3. From Software-as-a-Service to Agentic Platforms

The fundamental shift is from “software that assists” to “software that does.” Traditional SaaS is evolving into Agentic Platform Companies where AI autonomously executes tasks rather than just providing tools. Companies are racing to become agent platforms before agent companies become trusted enterprise vendors—it’s a land grab for controlling how workers interact with AI throughout their day. The platforms that already own workflows have the advantage because they know exactly what users do.

4. Build vs Buy Revolution

Generative AI is making it increasingly viable for enterprises to build custom solutions in-house rather than purchasing off-the-shelf SaaS. AI’s ability to automate coding and customisation means companies can develop their own software more efficiently, potentially eroding the dominance of established vendors. This shift threatens the entire SaaS business model that has thrived on the complexity and cost of custom development.

5. Vertical Specialisation as Survival Strategy

Generic, horizontal SaaS players face extinction while vertical, industry-specific AI solutions represent the biggest opportunity. Companies sitting on mountains of proprietary data in healthcare, legal, financial services, and supply chain can create defensible moats. The winners will be those who harness unique industry data to solve hard problems that traditional software barely touched. Meanwhile, smaller SaaS players that aren’t systems of record will be replaced or acquired in an accelerating wave of consolidation.

The Bottom Line: SaaS isn’t dying—it’s being forcibly rebooted into Agentic SaaS where AI is the foundation, not a feature. Survival requires complete reinvention within a 2-3 year window, with success going to those who embrace vertical specialisation, proprietary data advantages, and fundamentally new economic models.

**

So, what happens to martech SaaS companies? They face a stark choice: either transform into AI-native platforms that autonomously orchestrate customer engagement across the entire journey or watch as their 8,000+ point solutions collapse into a handful of agentic systems that render traditional marketing tools irrelevant. The martech stack of 2027 won’t be a stack at all—it will be an AI orchestrator that makes today’s campaign builders, email platforms, and analytics dashboards look like spreadsheets in the age of algorithms.

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State of Martech

I asked AIs to analyse The State of Martech 2025 by Scott Brinker and Frans Riemersma and list out relevant themes.

1. The Hypertail Revolution: From 8,000 Tools to Infinite Micro-Apps

Martech is evolving from today’s overwhelming 8,000+ point solutions into a “hypertail” of billions of AI-generated, on-demand micro-applications. Through vibe coding and natural language prompts, marketers can instantly create custom tools without technical expertise. This transforms martech from a fixed catalog of SaaS products to an infinite, generative ecosystem where the distinction between “buying” and “building” dissolves.

2. Marketing AI’s Paradox: Behind but Poised for Breakthrough

While marketing AI lags behind sales and customer success AI, this creates a unique opportunity window. With 78% of companies using AI for content production but only early-stage adoption of AI agents, martech sits at an inflection point. The category-defining companies haven’t emerged yet, meaning both incumbents and startups can still claim the autonomous marketing space.

3. From Stack to Orchestrator: The Architectural Revolution

The martech stack of 2027 won’t be a stack at all—it will be an AI orchestrator. CDPs are declining as the center, replaced by cloud data warehouses (56% integration), MAP/CEP platforms, and CRMs. AI agents don’t just assist; they autonomously orchestrate campaigns across platforms through new standards like MCP (Model Context Protocol), making today’s campaign builders and dashboards look like “spreadsheets in the age of algorithms.”

4. Data Infrastructure as the New Moat

With nearly 60% of companies running AI apps directly on cloud data warehouses/lakehouses, proprietary data has replaced product features as the primary differentiator. In an AI-first world where features can be instantly replicated, the companies sitting on rich, unique customer data will create defensible positions. This shift fundamentally changes martech’s value proposition from tool provision to data intelligence.

5. The Autonomous Engagement Transformation

AI is finally delivering on martech’s decades-old promise of “right message, right person, right time” through true N=1 personalisation. The shift from campaign management (human-driven, tool-assisted) to autonomous customer engagement (AI-driven, human-supervised) represents a fundamental change. AI agents watch, learn, and gradually take over marketing execution, creating adaptive experiences that evolve with each interaction.

6. The Great Consolidation and Category Collapse

Traditional martech categories face extinction or radical transformation. CDPs are being hollowed out by AI-driven integration, while entire categories of specialised tools will collapse into agentic platforms. The 2-3 year survival window means martech companies must either transform into AI-native platforms, position for acquisition, or face obsolescence as AI-native entrants rise.

7. The Build vs. Buy Disruption

Generative AI is making it viable for enterprises to build custom marketing solutions in-house, threatening the traditional SaaS model. Combined with citizen developers creating instant applications, this erodes the dominance of established martech vendors. The barrier between vendor and customer blurs as companies can generate their own marketing tools on demand.

8. Uneven Adoption Creates Opportunity Gaps

B2C companies lag B2B in deep AI integration, while content production dominates over more sophisticated uses like segmentation and personalisation. With AI assistants at 87.5% adoption but agents still early-stage, there’s a clear progression path. Companies that can accelerate customers along this maturity curve—from assistants to agents to fully autonomous systems—will capture the market.

The Bottom Line: Martech faces the most dramatic transformation in its history. The category must evolve from a fragmented collection of 8,000+ tools to AI-orchestrated platforms that operate on proprietary data moats, generate infinite micro-applications on demand, and deliver truly autonomous customer engagement. Success belongs to those who embrace this shift completely, not those who merely bolt AI onto existing products.

These 4 graphics are relevant for our future discussion:

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My Writings

The martech industry stands at an inflection point where three powerful forces converge. First, the vibe coding revolution is commoditising feature development—what once took months to build now takes minutes to generate through AI. Second, the agent economy is transforming marketing from human-operated dashboards to AI-orchestrated systems operating at superhuman scale. Third, B2C companies face an accountability crisis, haemorrhaging 15-20% of revenue to Google and Meta while drowning in billions of AdWaste, even as their martech vendors collect fees regardless of results.

This convergence exposes a fundamental flaw in traditional martech business models: they sell potential while customers need outcomes. They charge for messages and MAUs (monthly active users) while value comes from results. They protect features while intelligence becomes the real moat.

The vibe coding disruption accelerates this reckoning. When anyone can generate a marketing automation tool through conversation with AI, the traditional defensibility of “we built this complex feature set” evaporates. What can’t be vibe-coded? Accumulated behavioural intelligence from billions of interactions. Deliverability reputation built over years. Trust infrastructure ensuring compliance and security. And most importantly—proven ability to generate profitable outcomes.

My 200+ essays on marketing’s future have explored how martech must evolve from selling software to delivering growth. The following seven themes represent a fundamental reimagining where martech companies become true partners in profit generation, succeeding only when their customers succeed.

1. Progency: The Hybrid Product-Agency Model

The most radical reimagining—fusing product with AI-powered agency services where payment is based on alpha (incremental growth above baseline). Instead of selling software access, martech companies sell guaranteed outcomes. This model:

• Takes on execution risk and shares in upside
• Solves marketing’s “Mission Impossible” by aligning incentives completely
• Transforms martech from cost centre to profit amplifier
• Charges based on actual lift in revenue, LTV, or retention achieved

2. ZeroBase: Zero-Risk Entry with Pure Outcome Pricing

Eliminates adoption barriers by charging nothing upfront—monetising only on incremental results. This model:

• Proves value before charging anything
• Particularly powerful for retention initiatives with clear baseline measurements
• Directly attacks the “revenue tax” problem by demonstrating ROI first
• Shifts all vendor risk to performance

3. Agentic Marketing Infrastructure

AI agents operating at superhuman scale creates entirely new opportunities:

• Workflow automation-as-a-service: Where agents handle entire marketing operations
• BrandTwins: AI representatives for individual customers that negotiate with brands, with TwinLedger (maintaining P&L for every single customer, and not segments)

5. Composable API-First Architecture

Unbundling martech into microservices that AI agents and vibe-coded apps consume:

• Segmentation engines exposed as APIs
• Deliverability infrastructure as a service
• Predictive models accessible programmatically
• Campaign orchestration tools automated without human intervention
• Charging per API call rather than per seat, enabling infinite scaling

6. AdWaste Elimination as a Service

Directly attacking the billions wasted on ineffective advertising:

• Charging based on AdWaste reduced rather than features used
• Building owned media assets (e.g. The Brand Daily) that reduce paid channel dependence
• Shifting focus from acquisition (expensive) to retention (profitable)

7. Atomic Rewards and Micro-Incentive Platforms

Reimagining loyalty as precision behaviour modification:

• Micro-incentives delivered at exact moments to influence decisions
• Charging for behaviour change achieved not program management
• Real-time optimisation of incentive timing and amount
• Payment based on incremental customer actions generated

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The Race to Reinvention

These seven models converge on a singular truth: the future of martech isn’t about better software—it’s about guaranteed growth. Tomorrow’s martech companies will be growth partners, not software vendors, succeeding only when their customers succeed. This shift from “pay for potential” to “profit from performance” represents a complete inversion of the vendor-customer relationship.

The timing is critical. Clayton Christensen’s disruption theory suggests incumbents rarely survive paradigm shifts through incremental adaptation—they must fundamentally transform or be replaced. With AI-native startups already building outcome-first platforms from scratch, established martech players have a narrowing window—perhaps 2-3 years—to reinvent themselves.

The survivors won’t be those with the most features or slickest interfaces, but those solving the real problems plaguing B2C companies: revenue taxes paid to digital monopolies and billions wasted on ineffective advertising. In this new paradigm, martech companies amplify profits rather than sell tools, earn from outcomes rather than charge for access, and provide the intelligence and trust infrastructure that no amount of vibe coding can replicate.

The question isn’t whether this transformation will happen, but which companies will lead it, and which will be left behind, trying to sell dashboards in an age of autonomous agents.

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Attention, Agents, Alpha, Ads

As martech companies navigate the AI transformation, four foundations will determine who thrives and who becomes obsolete:

1. Agents: The New Operating System

The age of dashboards and manual workflows is ending. AI agents are becoming marketing’s operating system, multiplying human capabilities rather than just assisting them:

• Agents Collective: Internal co-marketers autonomously handling segmentation, campaign orchestration, and optimisation at superhuman scale
• Twin Factory: Manufacturing BrandTwins (customer AI representatives) powered by TwinLedgers, delivering true N=1 personalisation and profitability management where every customer has their own P&L
• Agent Marketplace: Creating an ecosystem where specialised agents (content creators, campaign optimisers, audience analysers) can be deployed, combined, and orchestrated like apps on a platform—turning martech from monolithic software into a composable agent economy

2. Attention: Engineering the Upstream Currency

Attention is upstream of every outcome—without it, no channel or campaign succeeds. Yet traditional channels (email, SMS, push, RCS, WhatsApp) face saturation, regulation, and declining engagement. The future lies in owning attention:

• Attention Magnets: Daily touchpoints like The Brand Daily (daily brand touchpoints) or Muniverse (gamified brand experiences) where customers return voluntarily rather than avoid
• Attention Currency: Mu powers a loyalty program which converts passive audiences into active participants, creating a sustainable attention flywheel where engagement is rewarded and measured
• The next battle isn’t about messaging more—it’s about engineering enduring attention

3. Alpha: Hedge Fund Economics for Marketing

The SaaS subscription model is giving way to outcome-driven economics. Borrowing from hedge funds’ distinction between alpha (excess returns), beta (market returns), and carry (value share):

• ZeroBase Economics: Brands pay only for incremental uplift in revenue, LTV, or retention above baseline
• Progency Partnerships: Vendor and brand co-create growth and share in the upside
• By focusing on alpha—the excess return delivered—martech transforms from cost centre to profit engine

4. Ads: The Second Revenue Revolution

Adtech built a $700B industry in two decades—with $500B being pure AdWaste spent on reacquisition. Martech, trapped in narrow SaaS thinking, remains a fraction of this. AI opens a different play:

• PII-Powered Cooperative Networks (NeoN): Brands pool first-party data for surgical reactivation, reacquisition, and lookalike acquisition, bypassing the Google/Meta duopoly
• ActionAds: Intent-rich, in-place ads that drive engagement without clicks or wasted impressions
• Twin promise: Save brands billions by cutting AdWaste while unlocking new revenue pools for brands and martech providers

The Trillion-Dollar Opportunity

Adtech scaled by capturing attention, monetising through ads, and tolerating massive waste. Martech must now scale differently—combining Agents, Attention, Alpha, and Ads into a new architecture of growth. This is the essence of post-SaaS marketing: AI-native, outcome-driven, and retention-first.

The prize isn’t incremental SaaS revenue. It’s building a trillion-dollar martech industry that finally delivers on marketing’s impossible promises: precise engagement at scale, enduring customer relationships, and profitable growth where every dollar spent generates measurable returns.

The companies that master these four foundations won’t just survive the AI disruption—they’ll lead marketing’s evolution from cost centre to profit amplifier, from message blaster to intelligence orchestrator, from vendor to growth partner. The impossible becomes inevitable, making martech companies invincible in this new future. Martech must stop thinking like SaaS and start architecting the future of customer value creation.

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AI Analysis

I asked Claude and ChatGPT to review the alignment with the ideas discussed in the vibe coding essay.

Claude

The core ideas between your two essays align remarkably well and create a coherent narrative arc. Here’s how they connect and what might be worth considering:

Strong Alignment Points

1. The Commoditization of Features

Both essays nail the central insight: AI/vibe coding makes features trivial to build, so value shifts to what can’t be instantly replicated:

• Vibe coding essay: “accumulated intelligence, validated data patterns, and institutional trust”
• Martech essay: “Accumulated behavioural intelligence from billions of interactions”

2. The Platform/OS Vision

Both position the future as platforms enabling ecosystems rather than monolithic applications:

• Vibe coding: “Marketing OS… iOS or Android of Martech”
• Martech: “Agent Marketplace… turning martech from monolithic software into a composable agent economy”

3. Outcome-Based Economics

Both emphasize the shift from capability to results:

• Vibe coding discusses outcome-based pricing emerging from the pricing crisis
• Martech makes this central with Progency and Alpha models

4. The Democratization Theme

Both recognize that AI enables non-technical users to build solutions:

• Vibe coding: “citizen developers” and “domain expert developers”
• Martech: The “hypertail” of billions of micro-apps

Potential Gaps or Tensions

1. The Speed of Transformation

• Martech essay: “2-3 year window” for transformation
• Vibe coding essay: Suggests more gradual adoption with hybrid approaches Consider reconciling these timelines or acknowledging different speeds for different segments.

2. The “Vibe Coding Hangover” Warning Your vibe coding essay extensively covers the downsides (security vulnerabilities, debugging nightmares, cost explosions), but the martech essay is more optimistic. You might want to acknowledge in the martech essay how companies will navigate these challenges.
3. The Human Element The vibe coding essay touches on developer role evolution (“architects, reviewers, and quality guardians”), while the martech essay focuses more on marketer empowerment. Consider addressing how marketing teams will need to evolve their skills.

What You Might Add

• The Competitive Dynamics: While both essays mention AI-native startups vs. incumbents, you could explore more deeply how vibe coding specifically enables new martech entrants to challenge established players.
• The Data Governance Challenge: With vibe-coded tools proliferating and accessing APIs, how do companies maintain data governance, security, and compliance? This bridges both essays’ concerns.
• The Integration Reality: Vibe coding makes building easy but integration complex. How does this affect the “8,000 tools to infinite micro-apps” transition?
• The Pricing Model Transition: You could explore how companies practically transition from seat-based to outcome-based pricing while vibe coding disrupts their cost structure.

The Unified Narrative

Together, these essays tell a powerful story:

1. Vibe coding democratizes software creation
2. This forces martech to transform from features to outcomes
3. The winners will be intelligence and trust platforms that enable infinite creation while guaranteeing results

Your vision of Netcore as the “trust, intelligence, and outcomes” layer that enables vibe-coded innovation while solving real problems (AdWaste, revenue taxes) is compelling and consistent across both pieces.

The main opportunity is to more explicitly connect how vibe coding’s challenges will be solved by the new martech architecture. This would strengthen the argument that established players like Netcore have unique advantages in the vibe coding era—not despite the disruption, but because of it.

ChatGPT

Alignment of Core Ideas

1. Democratization vs. Reinvention

• Vibe Coding → Anyone can now create software via natural language, making features and interfaces instantly replicable.
• Post-SaaS Martech → Because features are commoditised, martech defensibility shifts to intelligence, outcomes, and data moats.
  👉 Alignment: Both highlight that features are no longer the moat—the future lies in outcomes, data, and orchestration.

2. Agents as the Next Layer

• Vibe Coding → Agents become orchestrators, stitching together multiple SaaS tools into workflows without human setup.
• Post-SaaS Martech → Agents become the new operating system (Agents Collective, BrandTwins, TwinLedgers).
  👉 Alignment: Both position agents as the real interface, replacing dashboards and reducing SaaS to infrastructure.

3. The Build vs. Buy Shift

• Vibe Coding → Build vs. buy tilts toward “build,” since custom solutions can be spun up cheaply via prompts.
• Post-SaaS Martech → Enterprises will increasingly build their own tools, collapsing SaaS categories into agentic orchestration.
  👉 Alignment: Both anticipate category collapse as vibe coding and agents empower bespoke, in-house solutions.

4. New Business Models

• Vibe Coding → Challenges SaaS seat-based pricing; pushes toward usage and outcomes.
• Post-SaaS Martech → Introduces Alpha (outcome-based economics), Ads (cooperative networks), and ZeroBase/Progency models.
  👉 Alignment: Both agree SaaS pricing is obsolete; the future is outcome-linked.

What You’ve Missed (Gaps)

1. Human + Organisational Strain

• In the Post-SaaS essay, you explore AI intensity (compressed cycles, burnout risk).
• In Vibe Coding, this organisational/human sustainability angle is underplayed. Adding it would highlight that the bottleneck isn’t just what AI can do, but what humans and orgs can absorb.

2. Security, Reliability, and Compliance as Differentiators

• Vibe Coding essay touches on debugging nightmares and security vulnerabilities.
• Post-SaaS essay doesn’t stress enough that in a world of infinite vibe-coded apps, established martech players like Netcore can win by being the trust, compliance, and reliability layer.

3. Ecosystem / App Store Vision

• Vibe Coding hints at an ecosystem where many micro-apps emerge.
• Post-SaaS frames martech’s future as trillions in value through Agents, Attention, Alpha, Ads.
• Missing piece: the App Store / Marketplace model—Netcore (or others) as the “iOS of Marketing,” enabling vibe-coded/agent-built apps to plug into a trusted ecosystem.

4. Atomic Rewards / Mu Layer

• Post-SaaS covers Mu under Attention, but the Vibe Coding essay doesn’t link democratized software creation with incentive systems. Connecting them (e.g., vibe-coded attention apps monetised via Mu) would strengthen coherence.

Bottom Line

Yes — the essays align beautifully. Vibe Coding explains why SaaS economics collapse (features become disposable), and Post-SaaS Martech explains what replaces them (Agents, Attention, Alpha, Ads).

The biggest gap is not conceptual but architectural: you should explicitly connect “vibe coding builds apps” with “martech provides intelligence, trust, outcomes”. That framing makes the two essays feel like two halves of one manifesto.