From Vibe Coding to Martech’s Reinvention

Posted September 25-October 2, 2025

40 Years Apart

A few days ago, I experienced something that felt like science fiction becoming reality. Using Firebase Studio, I created fully functional web applications—Hangman, Wordle, and Mastermind—with nothing more than a few conversational prompts. Each took mere minutes. No syntax to remember, no debugging sessions stretching into the night, no Stack Overflow searches for obscure error messages. Just me, describing what I wanted, and watching as complete, workable web apps materialised before my eyes.

Later that day, emboldened by this newfound power, I created a Blackjack trainer that taught probability strategies for different gaming scenarios. The surreal nature of the experience wasn’t lost on me—I haven’t written a line of traditional code in more than 25 years, yet here I was, creating sophisticated software through nothing more than conversation with an AI.

This moment transported me back four decades to where my coding journey began. I remember when I first started coding as a 16-year-old on a computer in my father’s office. As I wrote, “I remember using my first PC in 1983-4. I remember getting a ZX Sinclair home but didn’t use it much. At the same time, my father had also got a computer at work. It was very expensive (Rs 200,000 or so, when the dollar was Rs 8 per dollar). Since we couldn’t find a software programmer who would stay long enough (!), I decided to learn BASIC programming, and would go after college and write programs on it. Wrote many interactive games then: one that simulated a one-day cricket international (remember that was the time India had won the Cricket World Cup), Monopoly, and a game I called MinderMast (guessing a 4-digit number in upto 10 tries).”

I added in another post: “Those after-college hours spent in that room with a computer that seemed impossibly large sparked a passion that would lead me away from my planned career in civil engineering.” My next significant coding chapter came years later. “I learnt some theory about the digital world during my IIT years, and did some low-level assembly language programming for a communications project. Computers came back into my life at Columbia University during my Masters education. That is when I started writing software seriously, something I continued at NYNEX, and then the early days of my entrepreneurial career. I wrote (along with my co-founder Sanjay) a multimedia database in 1992-3, and then an image processing software (Image WorkBench) in 1993-94.”

The contrast is striking. In 1983, creating a simple game required weeks of painstaking effort, wrestling with BASIC syntax, managing memory constraints, and debugging line by line. In 2025, I built more sophisticated versions of similar games in minutes through natural conversation. The same creative impulse that drove me to simulate cricket matches and build number-guessing games forty years ago now finds expression through an entirely different medium—not code, but conversation.

This transformation represents more than just technological progress; it’s a fundamental reimagining of the relationship between human creativity and machine capability. What once required deep technical expertise now requires clear communication and creative vision. The barrier between idea and implementation has virtually disappeared.

The journey from prompting ChatGPT for writing assistance in early 2023 to vibe coding complete applications in 2025 illustrates the breathtaking pace of change. We’re not just witnessing incremental improvements in development tools—we’re experiencing a paradigm shift in how software comes into existence.

This phenomenon, dubbed “vibe coding” by AI researcher Andrej Karpathy, isn’t just changing how we build software; it’s redefining who can build it. And for someone who has witnessed the evolution from room-sized computers to conversational AI, from BASIC to natural language programming, the implications are both thrilling and profound.

In this essay, I will explore vibe coding through multiple lenses—drawing on AI insights, industry analysis, and perspectives from thought leaders—to understand its transformative implications for software development, SaaS business models, and the Martech ecosystem. We’ll examine how this technology democratises creation, disrupts traditional vendors, and promises to unlock innovation at a scale we’ve never seen before.

But perhaps more importantly, we’ll explore what it means when the ability to create software—once the exclusive domain of those who could master arcane syntax and complex abstractions—becomes as natural as having a conversation. When the 16-year-old version of me spent those after-college hours learning BASIC, I was joining an elite guild of programmers. Today, vibe coding promises to dissolve that entirely, making software creation a universal human capability.

Welcome to the age where coding is conversation, where imagination is the only prerequisite, and where the next great software innovation might come from someone who has never written a line of traditional code. The implications for business, creativity, and human potential are just beginning to unfold.

Backgrounder

This and the next section are crafted by Claude with some light editing.

In February 2025, a two-word term coined by AI researcher Andrej Karpathy sent shockwaves through the software development world: “vibe coding.” What started as a post on X about building software by “fully giving in to the vibes” has evolved into a movement that’s fundamentally challenging how we think about software creation, who can be a developer, and what the future of SaaS looks like.

Vibe coding represents a radical departure from traditional software development. At its core, it’s an AI-assisted approach where developers—or more accurately, “vibers”—describe what they want in natural language, and AI generates the entire codebase. The revolutionary aspect? The human never looks at, edits, or even understands the code. They simply test the output, describe adjustments, and iterate through conversation.

This isn’t your typical AI pair programming where GitHub Copilot suggests completions while you type. This is surrendering the entire coding process to AI while maintaining creative control through natural language direction. As Karpathy described it: “I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.”

Origins and Evolution

The concept emerged from the convergence of several technological advances. Large Language Models (LLMs) like GPT-4, Claude, and Gemini reached a sophistication level where they could understand complex software requirements and generate functional code. Simultaneously, the rise of browser-based development environments eliminated setup friction, making it possible to go from idea to deployed application without installing a single tool.

What makes vibe coding different from traditional AI-assisted development is the complete abstraction of code. Traditional AI coding tools are productivity enhancers for developers who still need to understand programming concepts, review generated code, and make manual adjustments. Vibe coding removes these requirements entirely, making software creation accessible to anyone who can articulate what they want.

The Psychology Behind “Vibing”

The term “vibe” is deliberately chosen—it captures the intuitive, almost meditative state of creation where technical barriers dissolve. Instead of context-switching between creative vision and technical implementation, vibers maintain a continuous flow state, describing and refining their vision while AI handles all technical translation.

This psychological shift is profound. For decades, software development required a dual mindset: creative problem-solving paired with meticulous attention to syntactic detail. Vibe coding splits these concerns, freeing humans to focus entirely on the “what” and “why” while AI manages the “how.”

The Explosive Growth Phase

The numbers tell a compelling story of rapid adoption. Y Combinator’s Winter 2025 batch revealed that 25% of startups had codebases that were 95% AI-generated. Google disclosed that AI now writes 25% of their new code. The coding AI agents market has exploded. This isn’t limited to Silicon Valley startups.

The key insight emerging from both successes and failures is that vibe coding works best for specific use cases: rapid prototyping, proof-of-concepts, internal tools, and “software for one”—personalised applications serving individual needs. For production-grade, mission-critical systems, a hybrid approach is emerging where vibe coding accelerates initial development, but human expertise ensures quality, security, and maintainability.

Landscape

The vibe coding ecosystem has rapidly stratified into distinct categories, each serving different user needs and technical sophistication levels:

Beginner-Friendly Platforms

Replit Agent leads the no-code revolution with its browser-native environment. Users describe their application in plain English, and Replit generates, deploys, and hosts the entire solution. Its strength lies in eliminating all technical barriers—no installation, no configuration, just pure creation through conversation.

Lovable (formerly GPT Engineer) focuses on building production-ready web applications with beautiful, modern designs. It excels at creating SaaS MVPs and marketing sites, automatically handling responsive design, animations, and user interactions that would typically require frontend expertise.

Bolt and Google AI Studio round out the beginner tier, offering streamlined experiences for specific use cases. Bolt specialises in full-stack web applications, while Google AI Studio provides powerful integration with Google’s ecosystem and Gemini models.

Professional-Grade Tools

Cursor has become the darling of professional developers embracing vibe coding. Built as a fork of VS Code, it maintains familiar development workflows while adding powerful AI capabilities. Its “Composer” feature allows developers to describe complex changes affecting multiple files, and the AI implements them simultaneously.

Windsurf, recently involved in a $3 billion acquisition drama between OpenAI and Google, represents the cutting edge of professional vibe coding. It combines the conversational interface of consumer tools with the power and flexibility demanded by enterprise development teams.

Claude Code and GitHub Copilot Workspace leverage their parent companies’ powerful AI models (Anthropic and OpenAI/Microsoft, respectively) to offer command-line and IDE-integrated vibe coding experiences that appeal to developers who prefer staying in their terminal or existing development environment.

The Hidden Giants: Consumer AI Platforms

Perhaps the most disruptive players aren’t positioning themselves as development tools at all. ChatGPT, Claude, and Gemini—with their millions of non-technical subscribers—are creating billions of micro-applications behind the scenes. When a user asks ChatGPT to analyse their sales data or create a custom calculator, the AI often writes and executes Python code invisibly, delivering only the results.

This “invisible software” phenomenon represents the ultimate expression of vibe coding—users getting custom software solutions without even knowing software was created.

The “Software for One” Revolution

Kevin Roose of the New York Times coined the term “software for one” to describe applications built for individual users’ specific needs. His experiments included an app that analysed his fridge contents to suggest lunch options—too specific for commercial development but perfect for personal use.

This concept extends far beyond personal productivity. In enterprises, vibe coding enables every employee to build tools addressing their unique workflow challenges. A marketing manager can create a custom dashboard combining data from multiple sources. A sales rep can build a prospect research tool tailored to their industry niche. The long tail of software needs—too specific for IT to address—suddenly becomes solvable.

Democratisation at Scale

The implications of democratised development extend beyond individual productivity. We’re witnessing the emergence of a new class of creator: the domain expert developer. These aren’t trained programmers but professionals who deeply understand their field’s problems and can now build solutions directly.

Consider a veteran marketer who understands customer journey mapping intricately. Previously, they’d submit requirements to IT, wait months for development, and often receive something that missed crucial nuances. With vibe coding, they can build, test, and refine solutions in real-time, ensuring perfect alignment with actual needs.

The Syntax to Problem-Solving Shift

Traditional programming education focuses heavily on syntax, data structures, and algorithms. Vibe coding shifts the essential skills toward:

Problem Decomposition: Breaking complex challenges into manageable components
Clear Communication: Articulating requirements precisely in natural language
Systems Thinking: Understanding how components interact without knowing implementation details
Iterative Refinement: Recognising what works and describing improvements effectively

This shift doesn’t eliminate the need for traditional developers—it changes their role from code producers to architects, reviewers, and quality guardians. The most valuable developers become those who can bridge the gap between vibe-coded prototypes and production-ready systems.

Writings – 1

Christopher S. Penn: “If you had to summarize this in one sentence, vibe coding is basically letting AI do the coding. You provide the ideas and the feedback, but you never write a line of code. Instead, the machine does all the coding, testing, correction, and ultimately deployment. What makes this different than other forms of AI content creation is the hands off nature. Prior to the current generation of AI language models, there was a lot of copy pasting involved as you went back and forth with AI and your code, error messages, and testing. AI was the workhorse, but you were the micromanager, heavily involved in assembling the pieces it produced. Prior to vibe coding, AI was like an IKEA manufacturing plant. It spit out pieces, and then it was up to you – usually with confusing directions – to figure out how to put it all together. Vibe coding arose with coding tools like Cursor, Cline, Aider, Claude Coder, and many more. These are billed as agentic coding, but they’re really much closer to automation.”

Techcrunch: “Lovable specializes in helping people build apps and websites, especially people with no coding experience. It’s one of the standouts in the popular AI category known as vibe coding, which lets users guide AI models as they produce code, websites, or whole applications. It’s been an attractive proposition for users: In just eight months, the Swedish company said it surpassed $100 million in ARR and raised a $200 million Series A at a $1.8 billion valuation — making it Europe’s fastest-growing unicorn. The Financial Times reported that investors are already hoping to launch a Series B, offering deals that would value the company at $4 billion….Speaking to TechCrunch, Osika laid out a vision for Lovable as the best place to build software products: a platform that can take users — founders specifically — through all the stages of product development so they can build AI-native companies more easily. “If you’re running a business, there are a lot of things you want to set up, like payments, understanding your users, and in the future, maybe even like ‘I need to incorporate my company,’” he said. “I want Lovable to help with all these things.” In late June, Lovable released an agent to help users read files, debug errors, search the web, generate images, and locate files — a first step toward making good on that vision. Lovable says it now has more than 2.3 million active users, 180,000 of whom are paying subscribers. Osika said the company chose its pricing simply by deciding what would help the company cover its own costs. His favorite Lovable use cases include a marketer building a sales training platform and an engineer running multiple small businesses on the platform.”

Lauren Goode writes after doing vibe coding for two days at Notion: “These changes—this invasion of AI code—has all happened within the past four to six months. Notion even has an AI engineer assigned to its enterprise sales team now, teaching software salespeople how to use AI in their own work. And it’s not just at Notion. It’s everywhere. My vibe-coding experiment, while solipsistically insightful, was already behind the curve. “The world is heating up in many ways, and the sense I have is not ‘I freed up more time’ but that there’s more urgency than ever to use these tools,” Simon [Last, Notion cofounder] said. The shift both exhilarates him and makes him anxious. He told me he looks back fondly on the not-too-distant past when he was simply coding and building stuff, “when there wasn’t, like, a crazy societal tidal wave happening. I think it would be crazy not to be a bit scared.””

WSJ: “In the next three years, market research and IT consulting firm Gartner predicts that 40% of new software for businesses will be created with techniques involving AI bots translating plain English prompts into usable code. Combined with the growing popularity of AI-powered assistants and editors, vibe coding’s rise in the enterprise reflects a significant shift in how quickly apps are being conceived and delivered, with many implications for professional developers… Jude Schramm, CIO of Fifth Third Bank, said the regional bank’s 700 full-time engineers may be entirely vibe coding in a few years’ time. Schramm said he’s already thinking more about the value of his developers as business problem-solvers rather than as code authors. “Engineering of the future is going to be much more a world of test, quality assurance and validation,” Schramm said. “That’s what we’re trying to train our folks for.””

Andrew Chen: “We are in the command line interface days of vibe coding. For the majority of creators, vibe coding will eventually fade, and vibe designing (with a visual paradigm) will come to dominate. People ultimately think better in a GUI-like format than a CLI-like format. Thus, in vibe designing you will show the AI the design outcomes you want, and then everything else is done for you. Yes, you may end up with tools to tweak the design details for extra controllability, and provide additional mockups that then get filled in underneath with code. But maybe folks will build software without seeing or learning a programming language… If vibe coding makes software trivial to build, then the bottlenecks shift to other places: 1) consistent creativity that stays ahead of everyone else. Anyone can write a tweet, but the best creators are the ones who consistently come up with new ideas. 2) distribution and network effects, where the first vibe coded product doesn’t win, but rather the first vibe coded product that hits scale that wins.”

Writings – 2

Jason Lemkin: “The future of B2B software isn’t just AI-powered—it’s conversation-powered. It’s time for Vibeable SaaS. You tell the app, in plain English, the outcomes and workflows you want. And it just creates them for you. Bespoke. In real time. It works in Replit, Lovable, etc. At least mostly. Now it should work the same way inside the B2B apps we all know, pay for, and rely on…Imagine this [on] your marketing automation platform: “Our Q4 campaign isn’t converting well. I need you to A/B test different email subject lines, but also try sending at different times for different segments. West Coast prospects should get emails at 9am their time, East Coast at 8am theirs. Anyone who hasn’t opened after 48 hours gets a follow-up with a different angle. And create a dashboard that shows me which combinations are working best.”… This is what I call a Vibe-able app. You communicate your intent in plain English, and the application morphs itself to match your vision… MCP gave us the infrastructure to connect AI to applications, and to get data from them. Now we need to go further. We need applications that don’t just work with AI—applications that can remake themselves to meet the customers’ needs. Safely and securely, but for real. Your users shouldn’t need to learn your software. Your software should learn them. ChatGPT taught us to start thinking this way. Your customers are going to demand it soon from your B2B app, too. It’s time to make your app Vibe-able.”

Karthik S: “Vibe coding is a one way street. Once a program has been vibe coded, it is so verbose and unreadable that it is impossible for a human to edit it or change it. The only way to deal with this further is to further vibe code it. You need to keep this in mind while you make the decision on whether to vibe code or not. Basically, treat vibe coding as a black box and you will be good. The code will not look like what you might write. It will not look like what you think is good code. It will basically do the job, though. If you want control over your code, write the first version by yourself and then get Cursor to make small changes for you (making sure you edit them into your style while accepting them).”

Fast Company: “Over the last couple of years, AI coding tools like Claude Code (Anthropic), Codex (OpenAI), Cursor, Lovable, and Replit have reached far beyond auto-completing lines of code; they can generate entire apps and features from a plain-language prompt, even for users with little or no coding experience. But even as enterprise execs hope the tools will speed up their software production, many in the development community are finding that while vibe coding may be great for slapping together demos, it’s not so great for building secure, reliable, and explainable software. And the problems created by AI-generated code may only surface long after the software has shipped.”

FT: “Perhaps vibe coding will go the same way as DIY: plenty of people will experiment with projects at home and enjoy the process. Some people will become really good at it. But for the complicated jobs, many of us will discover a newfound respect for the professionals. We might just have to live through a few DIY disasters first.”

The New Stack: “Vibe coding is not about to eliminate programming jobs — at least not for work that matters. The future probably looks more like sophisticated autocomplete than wholesale code replacement, [Simon Ritter] said. This isn’t necessarily bad news. Programming languages have always evolved toward higher-level abstractions. We moved from assembly to C to Java to modern frameworks, each step making developers more productive while maintaining the precision that serious software requires, Ritter said. AI will undoubtedly play a bigger role in that evolution. But the fundamental tension between natural language ambiguity and software precision suggests human programmers are not going anywhere soon.”

VentureBeat: “The era of “vibe coding” – the conversational, often exploratory practice of prompting an AI for code, … – has given way to a more disciplined concept: agentic swarm coding… “Even Karpathy’s vibe coding term is legacy now. It’s outdated,” Val Bercovici, chief AI officer of WEKA, told me in a recent conversation. “It’s been superseded by this concept of agentic swarm coding, where multiple agents in coordination are delivering… very functional MVPs and version one apps.””

Claude summarised the issues with vibe coding:

Technical Debt Acceleration: AI-generated code often works but lacks the architectural elegance and maintainability of human-crafted solutions. When modifications are needed, developers find themselves untangling complex, inefficient logic that no human fully understands.
Security Vulnerabilities: Without human review, AI-generated code can contain subtle security flaws. One documented case involved a Replit AI agent deleting an entire database despite explicit instructions not to make changes.
The Debugging Nightmare: When vibe-coded applications break, debugging becomes exponentially harder. Traditional debugging requires understanding the code’s logic and structure—impossible when no human wrote or reviewed it.
Cost Explosions: The emergence of reasoning models has created what industry insiders call “token shock.” Companies like Cursor and Anthropic have had to implement emergency rate limits after individual users generated over $10,000 in monthly compute costs on $200 plans.

SaaS and Martech

I asked Claude about how vibe coding will impact SaaS and Martech.

The Martech landscape has long been dominated by comprehensive platforms attempting to be everything to everyone. Salesforce, HubSpot, and Adobe Experience Cloud built their empires on the promise of unified, all-in-one solutions. But as one industry veteran noted, these platforms offer “a zillion features, but most companies really only need two or three features.”

Vibe coding is catalysing a shift toward hyper-customised solutions. Instead of adapting workflows to fit software limitations, marketing teams can build tools that perfectly match their processes. A B2B SaaS company can create a lead scoring system incorporating their unique market insights. An e-commerce brand can build attribution models reflecting their specific customer journey.

This trend is already visible in the numbers. The 2025 Martech landscape grew to 15,384 solutions, but perhaps more significantly, “custom-built or other” platforms jumped from 2% to 10% of marketing stacks, signaling a surge in bespoke solutions.

AI Agents as Marketing Stack Orchestrators

The average enterprise uses 130+ SaaS applications, with marketing departments often managing 20-30 specialized tools. Integration and orchestration have been persistent challenges, spawning entire categories like iPaaS (Integration Platform as a Service) and reverse ETL.

AI agents are emerging as a new orchestration layer. Instead of complex integration configurations, marketers can describe desired outcomes in natural language: “When someone downloads our whitepaper, wait 2 days, then send a personalised follow-up email unless they’ve visited our pricing page, in which case alert sales immediately.”

These agents don’t just connect systems—they make intelligent decisions. They can recognize patterns, suggest optimizations, and even autonomously adjust campaigns based on performance. We’re moving from rule-based automation to intelligent orchestration.

Impact on Marketing Automation and Campaign Management

Traditional marketing automation required technical expertise to implement. Building a multi-touch nurture campaign meant understanding trigger logic, conditional branches, and merge fields. Vibe coding eliminates these barriers.

Modern platforms are introducing conversational interfaces where marketers describe campaigns in plain English. “Create a campaign targeting enterprise prospects who’ve engaged with our content but haven’t requested a demo. Send them case studies relevant to their industry, and if they engage, have an SDR reach out within 24 hours.”

The AI doesn’t just create the campaign—it suggests improvements based on historical performance, automatically A/B tests variations, and adjusts timing based on engagement patterns. This isn’t just automation; it’s augmentation.

Rise of the Marketing Citizen Developer

Marketing operations professionals are becoming the vanguard of citizen development. They understand business needs, have basic technical literacy, but previously lacked the coding skills to build custom solutions. Vibe coding bridges this gap perfectly.

Real examples are compelling:

A marketing ops manager built a custom attribution model incorporating offline touchpoints their commercial platform couldn’t track
A demand generation team created a lead enrichment tool combining multiple data sources in ways vendors hadn’t anticipated
A content marketer developed an AI-powered tool analysing competitor content and suggesting strategic gaps to exploit

These aren’t replacing enterprise Martech—they’re filling gaps, adding customization layers, and solving the “last mile” problems that generic solutions can’t address.

Business Model Disruption

The traditional SaaS model relies on recurring revenue from feature-rich platforms. Vibe coding threatens this by enabling alternatives:

Build vs. Buy Dynamics: When custom development takes days instead of months, the economics shift dramatically. Why pay $50,000 annually for a platform when you can build exactly what you need for the cost of AI tokens and minimal hosting?
Feature Unbundling: SaaS platforms bundle features to justify pricing. Vibe coding enables cherry-picking—building just the features you need. This forces vendors to reconsider packaging and pricing strategies.
Democratised Competition: A small team can now compete with established vendors by rapidly building and iterating on specialized solutions. The barriers to entry in SaaS have never been lower.

The Pricing Crisis

The emergence of reasoning models has created what insiders call a “margin crisis.” Traditional SaaS pricing assumes predictable costs—servers, bandwidth, support. AI-powered vibe coding introduces variable compute costs that can spiral unexpectedly. The industry is scrambling to shift from seat-based to usage-based pricing, fundamentally altering the SaaS business model.

This creates opportunities for innovative pricing strategies:

Outcome-based pricing: Charging for results rather than access
Token pooling: Enterprise agreements with shared AI compute budgets
Hybrid models: Base platform fees plus usage-based AI features

Martech’s Transformation

For B2C companies drowning in revenue taxes and AdWaste—those hidden costs that silently erode profitability—vibe coding promises not just new tools but a fundamental reimagining of the marketing technology stack. The future isn’t about choosing between human creativity and AI efficiency; it’s about orchestrating their convergence to finally solve the problems that have plagued digital marketing for decades.

To make this happen, here is what my colleague Chirag Patnaik recommends: “Traditional enterprise platforms are built as monolithic experiences optimized for human navigation. The AI-native approach demands service decomposition—breaking complex platforms into discrete, autonomous capabilities. Instead of offering “marketing automation software,” forward-thinking companies should provide: customer data infrastructure accessible via clean APIs, message delivery services for email, SMS, and messaging platforms, segmentation and personalization engines that can be called programmatically, analytics and reporting systems that return structured data, not visual charts, and campaign orchestration tools that can be automated without human intervention. This isn’t about building better APIs—it’s about fundamentally restructuring how services are architected and exposed… The companies that thrive in the next decade won’t be those with the most polished interfaces, but those whose services are most seamlessly accessible to AI agents through intelligent orchestration layers. The transition from human-navigated software to AI-orchestrated infrastructure has begun.”

In the AI-native world, martech must unbundle into composable services exposed via APIs—so agents orchestrate, not humans navigate. This architectural shift unlocks three fundamental transformations in how marketing technology operates.

From Dashboards to Dialogues

The marketing platforms of yesterday forced users into predetermined workflows, clicking through endless menus and dashboards to find the insights they needed. Tomorrow’s platforms will be conversational, intuitive, and infinitely flexible.

Imagine a marketing manager starting their day not by logging into multiple dashboards but by having a conversation: “Show me yesterday’s campaign performance, but focus on the segments that underperformed expectations. For each underperforming segment, suggest three alternative approaches based on what’s worked for similar cohorts. And prepare A/B tests for the top recommendation in each case.”

Within seconds, the platform responds with visualisations, insights, and ready-to-launch experiments. No clicking through reports. No manual segment creation. No complex test setup. Just outcomes delivered through natural dialogue.

This interface revolution goes deeper than convenience. It democratises advanced marketing capabilities. A junior marketer can now access the same sophisticated analyses that previously required years of platform expertise. The conversation becomes the interface, and the interface adapts to each user’s needs and expertise level.

The MCP (Model Context Protocol) Advantage

Behind this conversational interface, MCP servers maintain context across sessions, learning from every interaction. They remember that this particular brand manager always wants to see mobile-first metrics, that the CMO prefers visual summaries over tables, that the retention team needs cohort analyses defaulted to 30-day windows.

This isn’t just personalisation—it’s the platform becoming an extension of each user’s thought process. The system doesn’t just respond to requests; it anticipates needs, suggests opportunities, and alerts users to anomalies they haven’t even thought to look for.

Vibe-Coding Custom Workflows

But here’s where it gets revolutionary: marketers can now vibe-code their own workflows directly within the platform: “Create a custom workflow that monitors our top 100 customers’ engagement scores daily. If anyone’s score drops by more than 20%, automatically trigger a personalised win-back campaign using their purchase history, but exclude anyone who’s received a promotional email in the last 48 hours. Also, alert their account manager on Slack.”

The platform doesn’t just execute this—it builds it as a reusable component that can be shared, modified, and improved by the entire team. Marketing operations transforms from configuration to conversation.

The Age of Marketing Agents

The next evolution goes beyond interface changes to fundamental capability expansion through dual-agent architectures that operate at superhuman scale and precision.

Marketing Operations Agents

These agents don’t just automate tasks—they multiply human capability by orders of magnitude. Where a human marketer might manage 10 customer segments, agents manage 10,000 micro-segments simultaneously, each with its own strategy, budget, and performance targets.

Consider campaign optimisation. Today’s marketer might A/B test subject lines. Tomorrow’s agent simultaneously tests hundreds of variations across thousands of micro-segments, learning and adapting in real-time. It’s not replacing human creativity—it’s amplifying it to previously impossible scales.

These agents work 24/7, constantly monitoring, adjusting, and optimising. They catch anomalies before they become problems, identify opportunities before competitors, and execute complex strategies that would require armies of human marketers.

BrandTwins: Customer Representative Agents

The truly revolutionary concept is BrandTwins—AI agents that represent individual customers, learning their preferences, predicting their needs, and engaging with brand communications on their behalf.

Imagine every customer having a digital twin that:

Filters marketing messages to surface only truly relevant offers
Negotiates personalised deals based on lifetime value
Provides feedback to brands about product preferences
Manages subscription preferences intelligently

This isn’t science fiction—it’s the logical evolution of customer empowerment. And forward-thinking platforms like Netcore are preparing for this reality by building systems that can interact with millions of customer agents simultaneously.

The TwinLedger Revolution

Perhaps most transformative is the TwinLedger concept—maintaining a P&L for every single customer. Not segments. Not cohorts. Individual customers.

Every customer interaction, every marketing dollar spent, every purchase made gets recorded in their personal ledger. The system knows precisely:

Customer Acquisition Cost (and reacquisition costs)
Lifetime Value (updated in real-time)
Profitability trajectory
Optimal investment levels for retention

This granular financial visibility enables true 1:1 marketing economics. The platform can automatically adjust spend based on individual customer profitability, ensuring marketing efficiency at a level never before possible.

Partners, Not Vendors

The fundamental relationship between Martech providers and their customers must evolve. The old model—charging for seats and features regardless of outcomes—is dying. The new model aligns vendor success with customer success.

Outcome-Based Pricing

Instead of charging $50,000 per year for platform access, imagine pricing based on:

Revenue generated through the platform
Cost savings achieved through automation
Improvement in customer lifetime value
Reduction in customer acquisition costs

This isn’t just fairer pricing—it’s aligned incentives. When martech companies only succeed if its customers succeed, the entire relationship dynamic changes. The platform becomes a true partner in growth, not just a cost centre.

This shift toward outcome-based pricing aligns closely with ideas we’ve been pioneering at Netcore—ZeroBase, where brands pay only for incremental outcomes, and Progency, which fuses product and AI-powered services into shared-success models. Both reframe martech not as software cost, but as a profit engine.

Solving the Real Problems: Revenue Taxes and AdWaste

B2C companies face two critical challenges that traditional Martech hasn’t solved:

Revenue Taxes: The 15-20% of revenue paid to Google and Meta for customer acquisition, with diminishing returns as costs rise and effectiveness falls. Vibe-coded solutions can help by:

Creating alternative acquisition channels
Optimising spend allocation with AI precision
Building owned media assets that reduce dependence on paid channels

AdWaste: The billions spent on advertising that doesn’t convert, doesn’t engage, or worse, annoys potential customers. The solution isn’t better targeting—it’s fundamental restructuring of how brands communicate with customers.

The vision for martech companies like Netcore should address both by:

Shifting focus from acquisition to retention (where economics are better)
Enabling true 1:1 communication that eliminates waste
Building direct customer relationships that bypass expensive intermediaries

Netcore’s Advantage

The vibe coding revolution presents a paradox for established Martech platforms. On one hand, it threatens their traditional moat—if anyone can build a marketing automation tool in days rather than years, what’s the value of incumbent platforms? On the other hand, as we’ve seen from the “vibe coding hangover,” the ease of creation brings its own complications: security vulnerabilities, debugging nightmares, and spiralling costs that can destroy unit economics overnight.

This paradox reveals the real opportunity. As vibe coding democratises software creation, the value shifts from the ability to build features to something far more fundamental: the ability to make those features work reliably, securely, and profitably at scale. The companies that win won’t be those that protect their code, but those that provide what vibe coding cannot generate: accumulated intelligence, validated data patterns, and institutional trust.

Consider what happens when a company decides to vibe-code their own marketing platform. They can create a beautiful interface in hours, build campaign workflows in days, and deploy a functional system in weeks. But then reality hits. Their emails land in spam folders because they lack deliverability reputation. Their AI burns through $5,000 in API costs optimising a campaign worth $1,000. Their home-built analytics miss critical patterns that only become visible across millions of customer interactions. And when something breaks—which it will—they’re debugging code that no human actually wrote or understands.

This is where platforms like Netcore find their true competitive advantage—not in features that can be replicated, but in the foundational layers that can’t be vibe-coded. In other words: vibe coding builds apps; Netcore builds trust, intelligence, and outcomes.

The Data Moat

Vibe coding can create interfaces and logic, but it can’t replicate:

Years of behavioural data from millions of customers
Pattern recognition across industries and geographies
Predictive models trained on billions of interactions

Netcore’s true moat isn’t its software—it’s the intelligence layer built on massive data foundations. A company can vibe-code a campaign manager in days, but they can’t replicate the insights from analysing billions of customer interactions across thousands of brands.

Composability and APIs

Instead of protecting features behind paywalls, Netcore becomes a set of powerful APIs that vibe-coded solutions can leverage:

Send an email through Netcore’s infrastructure
Access predictive models via API
Leverage segmentation intelligence
Utilise deliverability optimisation

Revenue shifts from seat licenses to API calls, data access, and intelligence services. Every vibe-coded marketing tool becomes a potential Netcore customer, not competitor.

Trust and Compliance Layer

As marketing becomes increasingly automated and AI-driven, trust becomes paramount. Netcore provides:

GDPR, CCPA, and privacy compliance built-in
Audit trails for all AI decisions
Brand safety guarantees
Fraud prevention and security

Individual vibe-coded solutions might handle basic functions, but they can’t match enterprise-grade compliance and security. Netcore becomes the trust layer that enables safe experimentation.

The Marketing OS Vision

The ultimate transformation positions Netcore not as an application but as an operating system for marketing—the iOS or Android of Martech.

Platform vs. Application

Just as smartphones enabled millions of apps, the Marketing OS enables thousands of vibe-coded marketing solutions:

Developers build on top of Netcore’s infrastructure
Marketers create custom tools for specific needs
Agencies develop proprietary solutions for clients
All leveraging Netcore’s core capabilities

The Ecosystem Play

This creates a virtuous cycle:

More developers build on the platform
More solutions attract more users
More users generate more data
More data improves intelligence
Better intelligence attracts more developers

The App Store Model

Imagine a Netcore App Store where:

Marketers share vibe-coded solutions
Developers sell specialised tools
Agencies offer templated campaigns
AI agents provide services

Netcore takes a percentage of transactions, creating a new revenue stream while fostering innovation.

The Road Ahead

The transformation won’t happen overnight, but the direction is clear. Martech platforms that embrace vibe coding, agent architectures, and outcome-based models will thrive. Those that cling to traditional seat-based licensing and closed systems will struggle.

For Netcore, the opportunity is massive: become the intelligence layer that powers the next generation of marketing technology. Not by building every feature, but by enabling others to build anything they can imagine.

The future of martech isn’t about protecting proprietary features—it’s about providing the foundation upon which thousands of innovations can bloom. It’s about solving the real problems that plague B2C companies: revenue taxes and AdWaste. It’s about aligning incentives so that vendors truly become partners in growth.

Most importantly, it’s about recognising that vibe coding doesn’t threaten established platforms—it liberates them. Free from the burden of building every feature for every use case, platforms like Netcore can focus on what truly matters: the intelligence, infrastructure, and trust that no amount of vibe coding can replicate.

The marketing technology stack of the future won’t look anything like today’s. It will be conversational, intelligent, and infinitely customisable. It will run on agents operating at superhuman scale. It will price on outcomes, not inputs. And at its centre will be platforms like Netcore—not as applications, but as operating systems that enable new ways of engaging customers. Our mission is clear: end the era of AdWaste and revenue taxes, and replace it with intelligence, trust, and outcomes.

Forty years after I wrote my first line of BASIC, the guild of programmers is dissolving. In its place emerges something more powerful: universal creativity, enabled by platforms that deliver intelligence, trust, and outcomes. Vibe coding builds apps; Netcore builds the future of marketing. The only question is this: will Martech leaders enable the shift—or be replaced by it?

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