Category: Strategy

The Incumbents Survival Guide: A Roadmap for Non-Cloud-Native Scale-Ups

Over the last five parts of this series, we have charted the course of the AI Tsunami, from the rise of Agentic AI to the new playbooks for investors and challengers. We now arrive at the most difficult and urgent topic: the path forward for the established SaaS incumbent.

If you are leading a scale-up with years of history and a solid customer base, this guide is for you. This applies whether your platform is a true monolith on-prem, a “lift and shift” monolith now running in the cloud, or even if you are in the slow process of unpacking that monolith into microservices. The common thread is that your architecture was not born in the API-first, AI-native era.

Your greatest assets—your existing customers, revenue streams, and operational processes—are also your greatest liabilities in the face of disruptive change. Your battleship, while powerful, is difficult to turn, while the challengers’ speedboats are running circles around you. As we saw in Part 5, they view your legacy as a vulnerability. But it doesn’t have to be. Your experience and market knowledge are formidable assets, provided you make courageous decisions.

This is not a guide for incremental improvement. It is a survival guide for a paradigm shift.

Key Takeaways for Incumbent Leaders

• Your First Job is Defense, Not Offense. Before chasing the new, you must fortify what you have. Your immediate priority is to lock down your existing customer base and revenue by doubling down on niche expertise and customer success.
• Do Not Attempt a “Big Bang” Rewrite. Trying to rebuild your entire legacy platform from scratch is a slow, expensive path to failure. Your cloud migration strategy must be recalibrated for speed and surgical precision.
• Choose a Strategic AI Partner and Go All-In. You will not win by building foundational AI models. You will win by being the smartest, fastest integrator of a leading AI platform within your specific domain. Hedging your bets across multiple platforms will only slow you down.
• Your Business Model Must Evolve or It Will Perish. The per-seat pricing model is on borrowed time. You must begin experimenting with value-based and consumption-driven pricing now, or you will have it forced upon you by the market at the worst possible time.

The Incumbents Dilemma: When Strengths Become Anchors

The core challenge you face is that your organization is optimized for a world that is ceasing to exist. Simply moving your monolith to a cloud provider gave you operational benefits, but it did not make you agile. Your sales teams are trained to sell features and seats. Your engineering team is skilled in maintaining a complex, tightly-coupled system. Your financial model is built on predictable, recurring revenue from long-term contracts.

AI-native challengers have none of this baggage. They are building leaner solutions that deliver outcomes, not just tools. To compete, you cannot simply layer AI on top of your existing structure. You must begin the difficult process of transforming the structure itself. This requires a shift from an operational mindset focused on execution to a strategic mindset focused on reinvention.

The Survival Roadmap: A Four-Pillar Strategy

There is a path through this disruption, but it demands focus, discipline, and a willingness to challenge long-held assumptions. The strategy is built on four pillars.

Pillar 1: Fortify Your Base (Defensive Strategy)

Your first priority is to prevent churn and stabilize your core business. You have a decade of customer relationships and market knowledge; this is your fortress.

• Obsess Over Customer Retention: Use this moment to over-invest in customer success. Leverage your own data to identify at-risk accounts and proactively address their needs. You can even use simple AI tools to predict churn or analyze customer feedback at scale. A happy, loyal customer is far less likely to be tempted by a new, unproven challenger.
• Double Down on Your Niche: You understand your vertical better than any generic AI model or new startup. Deepen your moat by embedding more industry-specific workflows, compliance features, and data insights into your product. Reaffirm your position as the indispensable partner for your specific market.

Pillar 2: Modernize with Purpose (Pragmatic Technology Strategy)

Your legacy tech stack is a liability, but a full rewrite is a death sentence. Any existing cloud migration plans must be recalibrated for the AI era.

• Recalibrate Your Cloud Strategy for Surgical Speed: The AI disruption means your multi-year plan to unpack your monolith is now too slow. You must identify the parts of your system that, if modernized and exposed via API, would unlock the most valuable AI use cases. Focus your migration efforts there. This is no longer just a technical exercise; it’s a strategic one.
• Embrace the “Strangler Fig” Pattern: Instead of a rewrite, identify these high-value parts of your monolith and gradually “strangle” them by building new, cloud-native microservices around them. Over time, the new architecture grows and the old one withers away. This minimizes risk and delivers value incrementally.
• Build an API Wrapper Now: In anticipation of the “headless” future, the most critical technical project you can undertake is to build a robust, well-documented API layer around your existing system. Your platform is not API-first, which is a major disadvantage. An API wrapper is your bridge to the agentic world, ensuring you can still provide value even if your UI is bypassed.

Pillar 3: Integrate, Dont Invent (Smart AI Adoption)

You cannot out-innovate the major AI labs, and you shouldn’t try. Your strength is not in building models; it is in applying them with deep contextual awareness.

• Choose a Strategic Partner, Not a Portfolio of Options: You now face the same dilemma you did when choosing a cloud provider: do you build a portable, multi-platform strategy, or do you go all-in with a strategic partner? For an incumbent needing to move fast with limited resources, the answer is clear. A multi-platform approach creates immense complexity and requires skills you don’t have. My advice is to pick a winner—a leading AI platform that best fits your needs—and integrate deeply. This will lower complexity, reduce costs, and give you the speed you desperately need.
• Focus on “Bolt-On” Solutions for Immediate Value: Use your chosen partner’s APIs to solve immediate customer pain points. Can you automate a tedious report? Can you add a smart search function? These “bolt-on” AI features deliver significant value quickly, buying you the time and goodwill needed for deeper transformation.

Pillar 4: Evolve Your Business Model (Courageous Commercial Strategy)

This is the most challenging pillar, as it requires you to willingly disrupt your own cash cow.

• Experiment in Parallel: Do not change the pricing for your entire customer base overnight. Instead, introduce new, value-based pricing models for new AI features or new product tiers. Start with a small segment of customers to test and learn.
• Introduce Consumption-Based Pricing for AI: Charge for the use of AI-driven services based on the volume of data processed, reports generated, or tasks automated. This aligns your revenue directly with the value your customers receive from your AI investments and prepares you for an outcome-as-a-service future.

The path for the incumbent is undeniably difficult. It demands a level of organizational change and strategic courage that many companies will fail to muster. But for those who can successfully navigate this transition—by defending their base, modernizing with purpose, integrating smartly, and evolving their business model—the reward is not just survival. It is the chance to emerge as a leaner, more resilient, and more valuable company, ready to lead in the next era of software.

The future of SaaS is a symbiosis of deep domain expertise and intelligent, autonomous execution. You already have the former. The time to build the latter is now.

The Challengers Advantage: A Blueprint for Startups and Cloud-Native Scale-Ups

In the previous parts of this series, we have dissected the forces of the AI Tsunami, explored the agentic future, and identified the market’s vulnerabilities. For established incumbents burdened by legacy systems and business models, this era represents a significant threat—a topic we will cover in our final post.

But for you, the challenger, this is not a threat. It is the single greatest opportunity you will see in your career.

Moments of profound technological disruption are the great equalizers. They create openings that disciplined execution and raw ambition can exploit. As a CTO who led through the cloud transition, I saw firsthand how nimble challengers used their agility to outmaneuver large, slow-moving incumbents. The same pattern is repeating now, but at an accelerated pace. The barriers to building sophisticated software are evaporating, and the advantage has shifted decisively from scale to speed.

This post is your blueprint. It is a guide for the AI-native startup launching today and the agile, cloud-native scale-up ready to weaponize its architectural advantage.

Key Takeaways for Founders & Builders

• Your Greatest Asset is Your Lack of Legacy. You are not tied to an outdated tech stack, a per-seat pricing model, or the expectations of an existing customer base. This freedom allows you to build for the AI-native world from day one.
• Solve a Niche Problem, Not a Broad One. The era of building another generic horizontal SaaS tool is over. Your path to victory lies in identifying a high-value, specific pain point within a vertical market and solving it 10x better with AI than any incumbent can.
• “Vibe-Code” Your MVP to Find Product-Market Fit Faster. Use low-code/no-code platforms to get a functional product in front of customers in weeks, not months. The goal is not to build a perfect, scalable architecture at the start; it is to validate your idea and find paying customers with maximum speed and minimum burn.
• Weaponize Your Cloud-Native Architecture. If you are an existing scale-up, your cloud-native stack is your primary weapon. Use it to integrate AI and launch agentic services faster than your legacy competitors, directly attacking their core revenue streams.

The Asymmetric Advantage: Why Speed Beats Scale

In stable markets, scale is a formidable advantage. Large companies have the resources, brand recognition, and distribution channels to dominate. In a disruptive market, however, these assets can become anchors. Incumbents are shackled by technical debt, quarterly earnings pressure, and a culture resistant to cannibalizing existing revenue streams.

You, the challenger, have none of these constraints. AI development tools have commoditized the act of writing code, leveling the playing field. Your competitive advantage is not a bigger engineering budget; it is the ability to move faster, learn more quickly, and make bolder decisions. While an incumbent is debating the Q3 impact of shifting its pricing model, you can be launching an AI-driven service that makes their model obsolete. This is the definition of asymmetric warfare, and you are perfectly positioned to win.

The Startup Blueprint: From Vibe-Code to Venture Scale

For the founder launching a SaaS company in 2025, the playbook has been radically simplified and accelerated. The path from idea to revenue is shorter than ever before, provided you follow a disciplined, customer-obsessed approach.

Step 1: Find the Niche, Solve the Pain.

Forget trying to build the next Salesforce. Instead, find a workflow inside a specific vertical—legal tech, construction, biotech—that is still reliant on spreadsheets and manual processes. Your mission is to find a single, acute pain point and use AI to solve it completely. Is there a repetitive, language-based task that can be automated? Is there a complex analysis that can be done autonomously? This is your entry point.

Step 2: Build Fast, Learn Faster with “Vibe-Coding.”

I call this the “vibe-coding” phase. Use low-code or no-code platforms like Bubble or Retool to build your Minimum Viable Product (MVP). The goal here is not elegant code; it is speed to feedback. You need to get a working product into the hands of your target users as quickly as humanly possible. Validate that the problem is real and that customers are willing to pay for your solution. This focus on rapid iteration to find Product-Market Fit (PMF) is everything.

Step 3: Build for an Agentic Future from Day One.

Even in the MVP phase, think like an AaaS provider. Design your solution to deliver an outcome, not just present a tool. Ensure that your low-code platform can connect to other services via APIs. This architectural foresight is critical. When it comes time to scale, you will have already validated a workflow that is ready for an agentic, “headless” world, giving you a massive head start.

Step 4: Rebuild for a Cloud-Native Scale—This is Not Optional.

Once you have clear evidence of PMF—strong retention, happy customers, and growing revenue—you must transition away from your MVP framework. This step is not a “nice to have”; it is a critical requirement for survival and growth. The “vibe-code” tools that got you here have serious limitations in scalability, performance efficiency, and security that make them unsuitable for a production-grade application.

Your goal is to build a robust, scalable, and secure platform. This requires hiring a small team of skilled developers to architect a cloud-native solution from the ground up. By embracing a cloud-native design, you can leverage higher-level managed services from cloud providers like AWS, Azure, or Google Cloud. Using serverless functions, managed databases, and integrated AI services is a major competitive advantage. It allows you to move faster than competitors by focusing your engineering resources on your unique business logic, not on managing underlying infrastructure. By waiting until you have PMF, you avoid the classic startup mistake of wasting capital on a perfect architecture for the wrong product. By not skipping this step, you avoid the fatal error of trying to scale a business on a foundation that will inevitably crumble.

The Cloud-Native Scale-Up Playbook: Weaponizing Agility

If you are an existing scale-up already operating on a modern, cloud-native stack, you are in an even more powerful position. You have an established product, a customer base, and the architectural foundation to outmaneuver the lumbering giants in your market.

1. Leverage Your Architectural Superiority.

Your microservices architecture and CI/CD pipelines are a strategic weapon. While your non-cloud-native competitors are struggling with monolithic codebases, you can integrate new AI models and ship features in a fraction of the time. Use this advantage to relentlessly add AI-driven value to your product, widening the gap between you and the competition every single week.

2. Institute Rigorous Operational & Financial Discipline.

Before layering expensive AI workloads onto your stack, you must commit to ruthless efficiency. Mature DevOps teams already practice value engineering, but it’s now a board-level issue. Adding the “AI tax” to an unoptimized cloud spend is a recipe for margin erosion. Furthermore, extend your operational metrics to this new discipline. If you use DORA metrics to measure your software delivery performance, apply the same principles of tracking deployment frequency, lead time, and reliability to your AI model deployment and performance. This ensures you are building a high-performing—and financially sustainable—AI practice.

3. Master the Discipline of Data Engineering.

Superior AI performance is a direct result of superior data quality. “Garbage in, garbage out” has never been more true. It is imperative to invest in data engineering as a core competency. This means optimizing the entire lifecycle of your data: its capture, cleansing, transformation, and accessibility. A robust data pipeline ensures your AI models are trained on clean, relevant, and well-structured data, which is the ultimate source of their intelligence and your competitive edge.

4. Be the First to Launch Agentic Services.

Do not wait for incumbents to pivot. Use your agility to be the first in your niche to offer a true Agent-as-a-Service solution. Transform your product from a passive tool into an autonomous service that delivers guaranteed outcomes. This move will allow you to redefine your market category and capture the highest-value customers before your competitors even know what happened.

5. Attack the Incumbent’s Cash Cow.

Identify the most overpriced and underserved segments of your market, which are often the core revenue streams of legacy players. Launch a leaner, smarter, AI-powered alternative at a more competitive price point. Your lower operational costs and superior technology allow you to be aggressive, turning an incumbent’s greatest strength—their existing customer base—into their greatest vulnerability.

For both the startup and the scale-up, the message is the same: the AI revolution is a window of opportunity. It is a moment in time where the established hierarchy of the software world is being reset. Do not be intimidated by the scale of the incumbents. In this new era, they are the ones who should be intimidated by your speed.

Coming up in our final installment: We will look at the other side of the coin. In The Incumbent’s Survival Guide: A Roadmap for Non-Cloud-Native Scale-Ups, I will lay out the difficult but necessary choices facing established players in the fight for relevance.

The Investors Playbook: How to Bet on the New SaaS Landscape

In the first three parts of this series, we’ve established a new reality: the SaaS industry is being fundamentally rewired by AI. The old moats are eroding, Agentic AI is threatening the user interface, and a great bifurcation is splitting the market between the vulnerable and the resilient.

For investors, this means the playbook that generated historic returns over the last decade is now obsolete. Traditional SaaS metrics—while still useful—are no longer sufficient to gauge long-term viability. ARR growth is vanity if your product can be replicated by a lean, AI-native competitor in six months. A healthy LTV/CAC is a lagging indicator if your customers can soon achieve the same outcomes without your software.

As an investor and advisor, my focus has shifted from evaluating a company’s current trajectory to assessing its structural resilience in the face of this AI tsunami. Investing in SaaS in 2025 requires a new thesis, a new set of questions, and a disciplined ability to distinguish durable value from sophisticated hype.

This is the playbook for placing bets in the new SaaS landscape.

Key Takeaways for Investors

• Your Old SaaS Scorecard is Obsolete. Stop leading with seat-based growth and ARR multiples. Start with data strategy, workflow entrenchment, and business model adaptability. The game has changed from capturing users to capturing unique value.
• Scrutinize the Data Moat Above All Else. In an era of commoditized code, proprietary data is one of the few truly defensible assets. Does the company have a “data flywheel” that creates a compounding advantage? Or is its data strategy an afterthought?
• Distinguish “AI-Native” from “AI-Washing.” Many incumbents are “bolt-on” AI features as a marketing tactic. You must learn to spot the difference. True AI-native solutions have AI embedded in their core architecture and value proposition, not just layered on top.
• Bet on Teams with AI Literacy, Not Just AI Experts. A siloed “AI team” is a red flag. The winning companies will be those that foster AI literacy and an experimental mindset across the entire organization, from product to sales.

The New Thesis: From Growth Metrics to Moat Durability

For years, the venture capital community has evaluated SaaS companies on a relatively standard set of metrics. We looked for predictable, recurring revenue, high gross margins, and efficient customer acquisition. These metrics measured the health of the business model. They did not, however, always measure the durability of the underlying value proposition.

The AI revolution has exposed this gap. The central question for an investor is no longer just “Is this a good business?” but “Does this business have a right to exist in a world where AI can do its job?”

This forces a shift in due diligence, prioritizing qualitative, strategic factors over purely quantitative, backward-looking metrics. As leading VCs like Andreessen Horowitz and Sequoia have noted, the biggest opportunities will be in companies that are not just using AI, but are fundamentally re-imagined by it. This means looking for businesses that fit a new profile of success.

The AI Due Diligence Checklist: A Framework for Evaluation

To put this new thesis into practice, I use a rigorous checklist designed to probe for AI-era defensibility. These are the questions that should be front and center in every pitch meeting and due diligence process.

1. AI Strategy & Vision

• Is AI a core, integrated part of the product vision, or is it a “bolt-on” feature set designed to signal innovation?
• How, specifically, does the company’s use of AI solve a customer’s pain point in a way that was previously impossible? Can they quantify the value?
• What is the long-term vision for autonomy? Are they building towards an Agent-as-a-Service (AaaS) model, or are they content with being a human-operated tool?

2. Data Moat & Strategy

• Does the company have access to proprietary, high-quality datasets that are difficult or impossible for competitors to acquire?
• Is there a “data flywheel”? Does more usage of the product generate more unique data, which in turn improves the AI, creating a compounding advantage?
• How is data governed and secured? Is the company prepared for the ethical and regulatory complexities of training models on customer data?

3. Technology & Architecture

• Is the tech stack “AI-ready”? Can it support demanding AI workloads, or is it a legacy monolith that will require a painful and expensive refactor?
• Do they have robust, well-documented APIs? How are they preparing for a “headless” future where agents are their primary users?
• What is their model strategy? Are they building proprietary models, fine-tuning open-source models, or simply wrapping a third-party API? What are the associated costs, dependencies, and risks?

4. Team, Talent & Culture

• Does the leadership and technical team possess deep AI/ML expertise, or are they learning on the fly?
• Is AI expertise embedded within the product teams, or is it siloed in a separate “AI lab”? The former is a sign of true integration; the latter is often a sign of “innovation theater.”
• Is there a culture of rapid experimentation and data-driven decision-making across the entire organization?

5. Business Model Adaptability

• How does the company plan to price and monetize the value created by AI? Are they exploring outcome-based or consumption-based models?
• How will a shift away from per-seat pricing affect their financial model and sales strategy? Are they prepared for potential revenue contraction in some segments due to AI-driven productivity?

Signal vs. Noise: How to Spot Superficial AI-Washing

One of the biggest challenges for investors today is cutting through the noise. Every SaaS company now claims to be an “AI company.” Distinguishing genuine innovation from marketing buzz is critical.

Red Flags (AI-Washing):

• AI is primarily discussed in marketing materials, not in the product architecture.
• The use case is a generic “AI assistant” or chatbot with no deep workflow integration.
• The team cannot articulate a clear data strategy or explain how their AI creates a unique competitive advantage.
• The solution is a “thin wrapper” around a public AI model with no proprietary technology or data.

Green Flags (AI-Native Strength):

• AI is core to the founding vision and solves a problem in a fundamentally new way.
• The company has a clear strategy for acquiring a unique, defensible data asset.
• The business model is being rethought around AI, with experiments in value-based pricing.
• The team demonstrates deep AI literacy at all levels of the organization.

The massive capital flowing into AI infrastructure—with hyperscalers prioritizing GPUs for AI over CPUs for traditional cloud computing—is the ultimate tell. The platform shift is real. As investors, our job is to find the companies that are building on the new platform, not those left defending the old one. This requires discipline, a new framework for evaluation, and the courage to bet on the seismic shifts that are redefining an entire industry.

Coming up in Part 5: We’ll turn our focus to the builders. In The Challenger’s Advantage: A Blueprint for Startups and Cloud-Native Scale-Ups, I will outline the playbook for those best positioned to win in this new era: the agile, unburdened disruptors.

Vulnerability & Resilience: Who Survives the Great SaaS Shake-Up?

In the first two parts of this series, we established that AI is not just enhancing SaaS—it’s rewiring its very foundation. The commoditization of software development and the rise of the “headless” Agentic paradigm mean that the old rules for success no longer apply.

But this AI Tsunami will not impact all companies equally. For every SaaS business built on shifting sands, another stands on a bedrock of defensible value. As I’ve seen in every major technology cycle, from the shift to the cloud to the rise of mobile, moments of great disruption create a clear bifurcation in the market: there are those who are vulnerable and those who are resilient.

The strategic imperative for every leader and investor is to honestly assess which category they fall into. This post will provide a framework for that assessment. We will dissect the anatomy of a vulnerable SaaS business and contrast it with the fortress-like qualities of those built for resilience in the age of AI.

Key Takeaways for the C-Suite & Investors

• Horizontal SaaS is Ground Zero for Disruption. General-purpose tools with undifferentiated features (e.g., basic CRMs, project management) are highly susceptible to being replicated by AI or bypassed by agents.
• Vertical SaaS (VMS) is Inherently More Resilient. Deep domain expertise, proprietary industry data, and high regulatory barriers create a powerful defensive line that general-purpose AI cannot easily breach.
• Your Business Model is a Liability if it Relies on Manual Work. If your product’s core value is helping users perform tasks that AI can now automate—like manual data entry or simple content creation—you are on a collision course with obsolescence.
• The New Moats Are Not Built with Code. Defensibility is shifting from what your software does to what unique assets it leverages. This includes proprietary data, deep workflow integration, network effects, and brand trust.

Anatomy of a Vulnerable SaaS Business

The disruptive forces of AI are creating a clear profile of the companies most at risk. In my advisory work, I see these patterns emerging as the most significant threats to long-term viability. If your business shares these characteristics, urgent strategic action is required.

1. The Thin Wrapper Problem

These are companies whose product is essentially a thin user interface built on top of a third-party AI model (like OpenAI’s GPT). While they may have enjoyed first-mover advantage, their position is incredibly precarious. As the underlying foundation models become more powerful, accessible, and commoditized, there is little to stop customers—or the AI platform itself—from replicating the functionality at a fraction of the cost.

2. Reliance on Manual Data Entry and Processing

For years, a core value proposition of many SaaS tools was to provide a structured environment for humans to input and manage data. This is now AI’s sweet spot. Consider a traditional CRM: a salesperson listens to a call and then spends 15 minutes manually updating fields. Today, an AI agent can listen to the same call and automatically populate the CRM with a summary, action items, and updated contact information. The SaaS tool that merely facilitates the manual version of this workflow is facing extinction.

3. Undifferentiated Horizontal Offerings

Horizontal SaaS products are designed to serve a wide range of industries (e.g., generic project management tools, basic analytics platforms). This was once a strength, allowing for a massive Total Addressable Market (TAM). In the AI era, it is a critical vulnerability. These tools often lack the deep, contextual data of a specific industry, making their core logic easier for general-purpose AI to replicate. Why pay for a separate tool when an AI agent connected to your data warehouse can perform the same analysis on command?

4. A Moat Built Primarily on UI/UX

As we discussed in Part 2, the Agentic Shift threatens to unbundle the UI from the underlying application logic. If your primary competitive advantage is a “better user experience” rather than unique data or functionality, you are at high risk. If an agent becomes the primary interface, your beautifully designed UI may never be seen by the end-user, rendering your key differentiator moot.

These characteristics collectively paint a target on the back of many established SaaS companies. To make this tangible, I’ve created a visual framework that maps these vulnerabilities across different B2B SaaS segments. This interactive matrix clearly shows which business models are on shaky ground and which are built on a more solid foundation.

Explore the Interactive AI Disruption Vulnerability Matrix Here

The Fortress: Why Vertical SaaS is Built for the AI Era

In stark contrast to the vulnerabilities of horizontal platforms, Vertical Market Software (VMS) is uniquely positioned to thrive. VMS providers focus on solving the specific, complex problems of a single industry, such as healthcare, construction, or finance. This focus creates three powerful, AI-resistant advantages.

1. Deep, In-built Domain Expertise

VMS solutions are not just tools; they are encoded with deep knowledge of industry-specific workflows, regulatory requirements, and operational nuances. A general-purpose AI trained on public internet data cannot grasp the complexities of clinical trial management or construction bidding regulations. This domain expertise, built over years, is a formidable barrier to entry.

2. The Unbeatable Moat of Proprietary Data

VMS platforms often serve as the system of record for their clients. This means they accumulate vast quantities of valuable, industry-specific data that is simply not available anywhere else. This creates “data gravity.” An AI model trained on a VMS provider’s proprietary dataset of legal case files or financial compliance records will dramatically outperform a general model. This allows VMS providers to build hyper-specialized AI features that are truly unique and defensible.

3. The High Walls of Trust and Compliance

In highly regulated sectors, trust is not a feature; it is a prerequisite. Established VMS providers have spent years building relationships and navigating stringent compliance mandates (like HIPAA in healthcare). A new AI-native startup cannot easily replicate this trust, creating a significant moat that protects incumbents.

While VMS is not immune—as shown by ChatGPT’s impact on education-focused companies like Chegg—it is far better positioned to augment its offerings with AI, deepening its value proposition rather than being replaced.

Redefining Defensibility: The Moats That Matter Now

The great SaaS shake-up is forcing a re-evaluation of what constitutes a truly defensible business. The moats of the past—a faster development cycle, a slicker UI, a longer feature list—are eroding. The durable moats of the AI era are built on different foundations:

• The Data Moat: Access to unique, proprietary data that creates a compounding advantage by improving your AI models.
• The Workflow Moat: Deep entrenchment in a customer’s mission-critical daily operations, creating impossibly high switching costs.
• The Trust Moat: A hard-earned reputation for security, reliability, and compliance in a specific domain.
• The Ecosystem Moat: Strong network effects and deep integrations that make your platform the central hub for a particular business function.

Ultimately, the SaaS market is splitting in two. On one side are the vulnerable, undifferentiated tools facing a painful race to the bottom. On the other are the resilient, specialized platforms that are using AI to solidify their leadership. The time to choose which side you’re on is now.

Coming up in Part 4: We will shift from analysis to action. In The Investor’s Playbook: How to Bet on the New SaaS Landscape, I will provide a concrete due diligence framework for identifying the winners and avoiding the losers in this new paradigm.

The Agentic Shift: Why the Future of Software Has No UI

In the first part of this series, I described the AI Tsunami as two distinct waves. The first, Generative AI, is an evolutionary force that accelerates the existing SaaS paradigm. The second wave is the one we must now turn our full attention to. It is a revolutionary force with the power to fundamentally re-architect the entire software industry.

This is the Agentic Shift.

If GenAI changed how we create and interact with content and code, Agentic AI changes what software is and how it delivers value. It represents a move away from software as a passive tool that humans operate, towards software as an autonomous colleague that executes complex tasks on our behalf. As an industry, we have spent decades perfecting the Graphical User Interface (GUI). The agentic shift threatens to make it irrelevant.

Understanding this paradigm is not optional. It is the single most important strategic consideration for any software leader or investor for the remainder of this decade.

Key Takeaways for the C-Suite & Investors

• AI Agents Are Not Chatbots. An AI Agent is an autonomous system that can understand a goal, create a multi-step plan, and execute it across different applications. It moves from answering queries to achieving outcomes.
• The “No UI” Future is Metaphorical, But the Threat is Real. The prediction is not that all GUIs will literally vanish. It’s that the primary point of interaction will shift from clicking through an application to issuing a natural language command to an agent. This makes the UI a secondary, and less valuable, asset.
• Your SaaS Product Risks Becoming a “Headless” Commodity. In an agent-driven world, your application’s value may be reduced to its underlying logic and data, accessed via an API. Without a direct user relationship, brand loyalty and pricing power erode dramatically.
• A New Business Model is Emerging: Agent-as-a-Service (AaaS). The market is shifting from selling access to a tool (SaaS) to selling a guaranteed outcome delivered by an agent (AaaS). This requires a complete rethink of pricing, value propositions, and go-to-market strategies.

From Assistant to Agent: The Leap in Capability

For years, we’ve had AI “assistants” like Siri and Alexa. They are powerful but fundamentally reactive. They respond to direct, simple commands within a limited context. An AI Agent is a different class of technology entirely.

As I outlined in my research, an agent possesses three core capabilities that set it apart:

1. Memory: It maintains both short-term context for the task at hand and long-term knowledge to learn and improve over time.
2. Planning: It can decompose a high-level goal (e.g., “increase sales in the EMEA region”) into a sequence of logical sub-tasks.
3. Action: It can interact with the digital world to execute those tasks—calling APIs, accessing databases, and even using other software tools.

Consider the practical difference. You tell a SaaS application: “Show me my sales dashboard.” You then manually analyze the data, formulate a plan, and execute it in other applications.

You tell an AI Agent: “Find my top three underperforming sales reps in Europe, identify the root cause by analyzing their call logs and pipeline data, and draft a performance improvement plan for each.”

The agent does the work. This is not science fiction; it is what top technology firms are building towards right now. Gartner has named Agentic AI the number one strategic tech trend for 2025, predicting that agents will autonomously handle 80% of standard customer service requests by 2029. This is the new benchmark for software intelligence.

The No UI Paradigm: What Happens When Your Interface Isnt Yours?

For decades, the UI has been the heart of a SaaS product. It’s where we invested millions in R&D and design, and it’s how we built user habits and brand loyalty. The agentic shift threatens to completely unbundle the user experience from the application itself.

Microsoft CEO Satya Nadella has described this as the emergence of a new “AI tier” in the software stack. This layer, powered by agents, sits between the user and the application layer. Instead of logging into your CRM, the user simply tells their agent what they need. The agent then interacts with your CRM’s backend via its API.

In this scenario, your SaaS product becomes “headless.”

Your beautifully designed dashboards, intuitive forms, and carefully crafted user flows are bypassed. The user’s experience is with the agent, not with your product. This has devastating implications for defensibility:

• Erosion of Stickiness: User habits are tied to the agent, not your app.
• Intensified Competition: If multiple SaaS products offer similar functionality via APIs, an agent could theoretically swap between them based on price or performance, turning your service into a commodity.
• Loss of Brand Identity: Your brand is relegated to a line item in an agent’s execution log.

This doesn’t mean GUIs will disappear overnight. As I’ve seen in every major technology transition, from mainframe to cloud, there will be a long period of hybrid use. Users will still need visual interfaces for complex data analysis, configuration, and oversight. However, the balance of power will shift. The primary value will migrate from the interface to the underlying data, logic, and outcomes.

The Rise of Agent-as-a-Service (AaaS)

If the primary value of software is shifting to autonomous execution, then the business model must evolve accordingly. This is where Agent-as-a-Service (AaaS) comes into play.

Instead of buying a license for a project management tool, a company might subscribe to a “Project Management Agent” tasked with ensuring projects are delivered on time and within budget. The pricing model shifts from a per-seat fee to a fee based on the outcomes achieved or the complexity of the tasks managed.

This is a far more scalable and valuable model. It aligns the software’s price directly with the economic value it creates, a goal the SaaS industry has chased for years. However, succeeding as an AaaS provider requires a fundamentally different mindset and architecture. You are no longer selling a tool; you are selling a trusted, reliable, autonomous service.

The Challenges Are Real, But the Direction is Clear

The vision of a fully agentic future faces significant hurdles. The quality and structure of enterprise data remain a mess. Building trust in autonomous systems for mission-critical operations will take time. Real-world workflows are often more nuanced and complex than current agents can handle.

Because of this, the transition will be gradual. Agents will start by augmenting human capabilities, then automate more defined tasks, and eventually orchestrate complex, cross-functional processes. But make no mistake: the direction of travel is clear.

As a leader, your strategic imperative is to prepare for this “headless” future now. You must begin architecting your products to be “agent-native,” with robust, well-documented APIs. You must ask yourself: if my UI disappeared tomorrow, what unique value would remain?

If you don’t have a good answer, you are building on borrowed time.

Coming up in Part 3: I will analyze the impact of these forces on the market. In Vulnerability & Resilience: Who Survives the Great SaaS Shake-Up?, we will identify which SaaS segments are most at risk and where the pockets of durable value lie.