Rajesh Jain

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.”

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.”

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.

MartechAI has an interview with me. From the intro:

Martech is at an inflection point, says Rajesh Jain, Founder & Managing Director of Netcore Cloud. The problem is not a shortage of tools, but a mindset that overpays for reacquiring customers brands already have. In this conversation with MarTechAi.com’s Brij Pahwa, Jain argues that the next wave will be agentic AI that delivers true one-to-one engagement through “brand twins,” a data and decisioning layer that represents each customer to the brand in real time. The payoff he wants marketers to chase: profitable growth, not vanity metrics.

Jain’s thesis is blunt. Most budgets still chase acquisition on ad platforms while neglecting retention, referrals and lifetime value. He calls for CMOs to become “chief AI and profits officers,” build zero and first-party data pipelines, and unify identity across touchpoints. He believes multi-agent systems will make the “impossible” possible, letting a team of one orchestrate thousands of hyper-relevant campaigns every day. He also sets a new profitability rule he abbreviates as OONA (edited): acquire a customer only once, never pay again to reacquire.

Here is the video.

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?