SaaS Isn't Dead: Why the Classic B2B Playbook Has Changed Forever [2025]

Introduction: The End of an Era in B2B Software

The news headlines are screaming it again: "SaaS is dead." Every market correction triggers the same doomsday narrative. Multiples compress. Growth stocks take hits. Venture-backed startups face funding winters. But the reality is more nuanced—and far more important to understand if you're building or investing in enterprise software.

Software isn't dying. B2B software spend is accelerating at the highest rate in years. Enterprise software budgets are expanding to unprecedented levels. The tools that businesses depend on are becoming more critical, not less. Yet something has fundamentally shifted in how companies win in this space.

The playbook that dominated for 15 years—from 2010 to 2025—generated extraordinary returns. It created household names like Salesforce, HubSpot, and Zendesk. It made investors billions. It funded countless startups that grew into unicorns. That playbook worked beautifully, predictably, and repeatedly.

But that playbook is now obsolete.

This isn't a cyclical market downturn or temporary correction. This is a structural shift in how software gets built, how markets develop, and how customers make purchasing decisions. The change happened gradually at first, then suddenly. And if you're not paying attention to what's replacing the old model, you'll be left building yesterday's solution in tomorrow's market.

What makes this moment particularly significant is that AI acceleration, rapid prototyping capabilities, and the collapse of traditional software development barriers have created conditions that the old playbook never anticipated. A single founder with modern AI tools can now build in a weekend what used to require a 10-person engineering team spending an entire quarter. This isn't hyperbole—this is daily reality in 2025.

The implications ripple through every aspect of SaaS: go-to-market strategy, product development cycles, pricing models, customer acquisition costs, and investor expectations. The winners in this new era won't be the companies that execute the old playbook faster. They'll be the companies that understand the new rules and build around them from day one.

This comprehensive guide examines what's actually dead, what's replacing it, and most importantly, how to build and scale a B2B software company in the new reality. Whether you're a founder, investor, or operator inside an established company, understanding this transition is essential for navigating 2025 and beyond.

The Old Playbook: How B2B SaaS Winners Were Made (2010-2024)

The Category Lock-In Era

The playbook that dominated for 15 years was built on a simple but powerful principle: be first to $20M ARR in a category, then make it impossible for competitors to survive. This wasn't accidental strategy—it was the inevitable result of how software infrastructure worked, how sales organizations operated, and how customer economics functioned.

Salesforce established this template in CRM. HubSpot refined it in inbound marketing. Zendesk perfected it in customer support. The pattern was identical across every successful B2B software company of the era:

First, identify an underserved market segment. Second, build a product that addressed a clear, urgent pain point. Third, get to $20M in annual recurring revenue as quickly as possible. At that inflection point, something magical happened—the category essentially locked. Competitors still existed, technically, but the oxygen had been consumed from the room.

Why did this work so consistently? Because once a company reached

The economics were brutal for anyone arriving second. The window closed fast, and it stayed closed for a decade or more.

The Expensive, Predictable Path to $100M

Once you owned your category at

This was the dominance strategy: outspend your way to dominance. The company with the most deployed capital would win because they could afford the largest sales organization, the most brand presence, and the deepest customer relationships. Winners in this era rarely lost to better products. They lost to underfunding or management mistakes. The playbook itself was nearly impossible to mess up if you had capital.

Revenue growth was consistent but slow by today's standards—typically 30-50% annually for the market leaders. That pace felt normal, even ambitious. The compounding took time, but it was inevitable if you executed the basics. Every year of execution narrowed the gap between you and the potential market ceiling.

Product Development: Quarterly Releases and Annual Updates

Product strategy in this era operated on a predictable cadence: quarterly releases, annual platform updates. Most of these "releases" were actually bug fixes dressed up as features. Real platform advances happened once every 4-5 years, if you were lucky.

Customers complained constantly about pace of innovation. But they couldn't leave because switching costs were prohibitive and alternatives weren't materially better. This created a strange equilibrium where companies had every incentive to move slowly. Slow development meant lower R&D costs. Slower feature releases meant less support burden. The optimal strategy was often to do the bare minimum to retain customers while maximizing margins.

This worked because inertia was the most powerful force in enterprise software. Customers weren't happily renewing—they were reluctantly renewing. The pain of switching to an alternative outweighed the pain of staying with a vendor that wasn't moving fast enough. That friction was a feature, not a bug, from the vendor's perspective.

The NRR Autopilot Effect

Net revenue retention—the metric that drives nearly all SaaS valuations—essentially ran on autopilot during this era. Enterprise customers delivered NRR of 130%+ with minimal effort. SMB customers hit 110%+ reliably.

This wasn't because customers loved the product. It was because expansion was built into customer usage patterns. They'd add more seats as their team grew. They'd upgrade to higher-tier plans as their requirements evolved. They'd layer in adjacent modules and add-ons as their needs developed. A single customer success manager could show up once a quarter, suggest an upgrade, and add $50K+ in annual revenue.

The compounding was almost mechanical. Customers expanded because they had to—they needed more seats or more functionality. The vendor didn't need to work hard to drive this expansion; it was embedded in the customer's operational growth. This meant that the primary challenge of SaaS—keeping customers and driving them to expand—was solved largely through inertia rather than customer satisfaction or product innovation.

This created a virtuous cycle for incumbents: high inertia → high NRR → predictable revenue → larger valuation → more capital for sales/marketing → faster growth → more inertia. It was a flywheel that was extraordinarily difficult to disrupt once it started spinning.

Why This Playbook Worked (The Economics)

The old playbook worked because it aligned with the fundamental constraints of software development, sales, and customer technology adoption in the 2010-2024 period:

Software development was slow and expensive. Building competitive software required large teams. You needed months to develop features that would impact customer workflows. This meant competitors had time to respond. Differentiation was sustainable because it took time for someone else to build the same thing. The barrier to entry was high enough that only well-funded teams could compete.

Sales required massive infrastructure. Enterprise software sales couldn't be done cheaply or quickly. You needed armies of account executives, sales engineers, and customer success managers. You needed brand presence and reputation. You needed reference customers and case studies. This required enormous capital investment, which meant only well-funded companies could participate. It was a capital-intensive moat.

Customer switching was genuinely difficult. Moving from one enterprise platform to another involved data migration, integration rebuilding, team retraining, and operational risk. The switching costs were real and substantial. This made incumbents sticky even when they weren't innovating.

Market adoption was slow. Enterprise software adoption cycles were measured in months or years. Sales cycles were long. Evaluation periods were extensive. This meant that even if a better alternative appeared, it took years for it to gain meaningful market share. The incumbent had time to respond and improve.

All of these factors combined to create what venture capitalists called the "SaaS Sweet Spot"—a market structure where the first well-executed company to reach scale could lock it down for a decade. It was a genuinely powerful business model, and billions of dollars of value were created in this environment.

But every one of these constraints has either disappeared or inverted in 2025.

The Collapse of Traditional Software Development Barriers

AI-Powered Development: From Teams to Solopreneurs

The first domino to fall was the barrier to building software itself. For decades, software development required significant capital investment and time. You needed a team—at minimum, a few engineers. You needed months of development time. You needed QA processes, testing infrastructure, and deployment systems.

In 2024-2025, that changed overnight.

Modern AI tools like Claude, with coding assistance through tools like Cursor, have made it possible for a single person to build production-quality software in days what used to require teams and months. This isn't speculative or aspirational—it's measurable daily reality. Individual developers are shipping applications that serve hundreds of thousands of users. Solopreneurs are building systems that previously would have required Series A funding to attempt.

The implications are staggering. If a talented solo founder can build a prototype in a weekend, the barrier to entry for new competitors has effectively become zero. This isn't just a slight reduction—it's a fundamental restructuring of competition dynamics.

Consider the economics: a solo founder with AI tools can build an MVP and reach 1,000 users in two weeks. That same task, using traditional 2015-era development practices, would take a team of three engineers working for three months. That's a 30x acceleration in time-to-market and a 1000x+ reduction in capital requirements.

When entry barriers collapse this dramatically, the game changes. The category lock-in effect—where reaching

Proof in Production: Real Examples of AI-Accelerated Development

This isn't theoretical. Multiple companies have demonstrated the new timeline reality:

Eleven Labs raised a

Cursor (formerly Anysearch) became a meaningful developer tool in months, not years. The company built a superior developer experience by building around AI capabilities from the foundation, rather than bolting AI onto existing architecture.

Multiple AI-native document and presentation tools have launched in the past 18 months and achieved functional parity with legacy tools (with 5-10+ years of development history) in a fraction of the time. Companies like Runable are demonstrating that AI-powered automation for content generation and workflow orchestration can be built and launched in months rather than years.

These aren't edge cases or outliners—they're becoming the pattern. The common thread is that all of these companies were built AI-first from the foundation, not as bolted-on features on legacy platforms.

The Architectural Advantage: AI-Native vs. AI-Adapted

The distinction between "AI-native" and "AI-adapted" is now the most important architectural decision in software. AI-native means the product was designed from the beginning around AI agents doing actual work, not just assisting with work. The entire data model, API design, and user interface are built with agentic automation as the primary experience.

AI-adapted means adding AI features to an existing platform built on 2015-era assumptions. You bolt on a chatbot. You add AI-powered suggestions. You create an "AI mode" that users can opt into. But the underlying system wasn't architected with AI as a first-class citizen.

When your competitor's product does the job instead of helping with the job, incremental AI improvements don't matter. A user who's used to a system that requires their input and assistance will be blown away by a system that handles entire workflows autonomously. The difference isn't 20% better—it's 10x better for specific use cases.

This is the core of why so many incumbents are vulnerable in 2025. Their legacy products are fighting with 10+ years of accumulated technical debt. Their data models assume human interaction at every step. Their APIs were built for batch processing, not streaming agentic interaction. Retrofitting these architectures for genuine AI integration is possible but extremely difficult. It's often easier to declare the old product a legacy system and build a new AI-native product alongside it—which means competing against yourself.

New entrants have no such constraint. They can design their entire product experience around "what if AI did this automatically?" and build the entire stack to support that assumption.

The Speed of Competition: Weekly Releases vs. Quarterly Cycles

The New Development Cadence

Remember when a quarterly software release was considered aggressive? When companies announced a "major platform update" once a year and companies waited months for critical bug fixes?

That timeline is now laughably slow.

AI-native companies in 2025 are shipping genuine capability improvements on a weekly or even daily basis. Not bug fixes or minor tweaks. Meaningful new functionality. Architectural improvements. Performance enhancements. Entirely new features.

The gap between what a legacy vendor delivers in a year and what a new entrant delivers in a month is widening every single quarter. This creates a compounding disadvantage for incumbents. After one year, the new entrant has 12 months of compounded innovation. After two years, they've released 24 versions worth of improvements. The delta becomes impossible to close through catch-up.

This matters because customers see it. They're not blind to the pace of innovation disparity. They notice when a vendor hasn't shipped a meaningful feature in six months. They notice when their pain points go unaddressed for quarters. They notice when a new alternative solves their problem in a way the incumbent won't support for 18+ months.

The NRR Killer: Why Expansion Revenue Is Drying Up

This is where the shift becomes economically devastating for incumbents: customers are increasingly reluctant to expand with vendors that aren't innovating.

The old playbook relied on NRR running on autopilot. Customers expanded because they needed more seats, more usage, more functionality. It was almost guaranteed. NRR above 120% was table stakes for successful SaaS companies.

In the new environment, that's breaking down. Customers now ask hard questions before expanding:

• "Why would I buy another seat from you when your product hasn't meaningfully changed in two years?"
• "Why would I upgrade to a higher tier when a new vendor does that exact thing 10x better?"
• "Why would I add a new module when I can replace the entire system with an AI-native alternative?"

These aren't rhetorical questions in 2025—they're the actual logic customers are applying. Expansion revenue is being cannibalized by new entrants. Budgets that were historically reserved for the incumbent are being redirected to AI-native alternatives that are shipping features at 10x the pace.

This directly attacks the financial model that made legacy SaaS so profitable. If NRR drops from 130% to 105%, the revenue trajectory flattens dramatically. If it drops to 95%, you're actually contracting. Companies that built their valuation multiples on the assumption of 120%+ NRR face significant repricing when that assumption breaks.

Case Study: The Productivity Tools Category

Documentation, presentation, and report generation tools exemplify this shift. For years, these categories were dominated by legacy vendors with quarterly release cycles and slow feature development. Documentation was built on 2015-era assumptions about how people work.

Now, AI-native alternatives are emerging that build on fundamentally different assumptions:

• Documents that write themselves based on input data
• Presentations that auto-generate from content and requirements
• Reports that update in real-time as underlying data changes
• Workflows that orchestrate multiple tasks across tools

Companies like Runable demonstrate what AI-native productivity looks like: $9/month for AI-powered slides, documents, reports, and presentation generation. The pricing itself—software at consumer-friendly price points—signals a different business model: broad adoption, lower switching costs, velocity as a feature.

When you can get AI-generated documentation, presentations, and automated workflows for

The Rise of Outlier Companies: $100M in 12 Months

The New Revenue Trajectory

One of the most striking shifts in 2025 is the emergence of companies reaching $100M in annual revenue in 12 months. This is not a typo or exaggeration. Multiple AI-native companies have achieved this velocity. This represents a 5-10x acceleration in revenue growth compared to the previous SaaS era.

Historically, reaching

The mechanics are straightforward: AI-native products solve concrete problems dramatically better than incumbent solutions. When a new product is 10x faster, cheaper, or more capable, demand is inelastic. Customers don't need to be convinced over months—they adopt immediately. The sales cycle compresses from 6-12 months to 2-4 weeks. Customer acquisition cost plummets because word-of-mouth and viral adoption carry the load.

When multiple products reach

Budget Capture and the CFO Attention Factor

When something scales this fast, it doesn't just capture market share from competitors—it captures budgets that weren't previously allocated to that category. This is a crucial distinction.

Traditional B2B software operated within established budget categories. CRM got a fixed allocation. Marketing tools got a fixed allocation. The debate was which vendor within that category, not whether to spend in that category at all.

AI-native outliers often create new budget categories or expand existing ones dramatically. They become the thing the CFO actively wants to spend on—the high-growth, high-ROI investment. This budget expansion comes at the expense of established categories.

Every dollar that flows to an AI-native outlier is a dollar that doesn't renew with an incumbent or expand beyond baseline usage. This is particularly devastating because the entire valuation model for legacy SaaS assumes NRR as the primary growth driver. When expansion revenue flatlines or declines, the multiple compression is severe.

The "Fast Follower" Problem

Incumbents looking at these outliers often think, "We can just build an AI-native version and capture the same fast growth." This is analytically reasonable but practically very difficult.

The challenge is that you can't just slap AI on your existing product and expect the same outcomes. Genuine AI-native architecture requires rethinking fundamental assumptions about data models, APIs, user interfaces, and computational workflows. For companies with 10+ years of accumulated architecture, this is not an incremental improvement—it's a complete rewrite.

The choice becomes: build a new AI-native product alongside your legacy system (which means cannibalizing your own revenue) or attempt to retrofit your existing architecture (which is technically complex and often results in a compromised product).

Many incumbents choose a third option: build the AI-native version under a new brand, operating it independently from the legacy business. This can work but often results in organizational turf wars and misaligned incentives.

Shifting Customer Expectations: Velocity and Autonomy

From Assistance to Autonomy

Customers' expectations about what software should do have fundamentally shifted. The baseline expectation is no longer "help me do my job better." It's now "do my job for me."

This is the core transition from AI-assisted to AI-autonomous workflows. Users have experienced ChatGPT, Claude, and other AI systems that handle entire tasks autonomously. They've seen what's possible. Their expectation when they open enterprise software is that it should operate at similar levels of autonomy.

When they encounter a legacy system that requires them to input data, make decisions at multiple steps, and manually implement outputs, the experience feels archaic. It feels like a step backward from the AI-native tools they use personally.

This is particularly true for knowledge work—documentation, data analysis, reporting, process automation. These are the categories where AI can provide the most immediate value. Customers who can use AI-native tools for these tasks don't want to go backward to legacy systems that require manual processes.

The Weekly Shipping Expectation

Customers also now expect vendors to ship improvements continuously. The quarterly release cycle—where customers wait months for bug fixes or small improvements—is increasingly seen as unacceptable.

This matters for retention because it affects perception of the vendor. When you ship weekly improvements and bug fixes, customers perceive the product as "alive" and actively developed. When you ship quarterly, it feels neglected. The difference isn't about the actual features delivered—it's about the signaling. Frequent releases signal that the company is actively working on the product. Infrequent releases signal that the product is mature and not a priority.

For startups competing against incumbents, this becomes a strategic advantage. We ship continuously because our product is in active development. Your product gets updated quarterly because it's already at scale. The narrative becomes self-fulfilling: the newer product feels faster because it's shipping faster.

Integration and Ecosystem Expectations

Customers now expect software to work seamlessly across a broader ecosystem. They expect APIs that enable integration rather than requiring it. They expect data to flow automatically between tools rather than requiring manual export/import cycles.

Legacy systems built in the pre-API era struggle with this. Integration often requires custom development or third-party middleware. This friction is increasingly unacceptable. AI-native companies starting from scratch can build integration-first architecture—assuming from the beginning that their product will operate as part of a larger stack.

Why Incumbents Struggle to Adapt

The Architectural Debt Problem

The most significant challenge for incumbents isn't lack of resources or engineering talent. It's architectural debt. After 10-15 years of development, enterprise software platforms carry enormous legacies of decision-making that made sense at the time but now constrains innovation.

Data models designed for human interaction don't work well for autonomous AI agents. APIs built for batch processing don't support real-time streaming. User interfaces optimized for desktop don't scale to mobile-first or AI-first paradigms. Security models built on the assumption of human verification gates don't work when agents need to operate autonomously.

Reworking these foundational elements isn't a 3-month engineering project. It's a 2-3 year architectural refactor. During that time, you're essentially forking your product: the legacy system still needs support, and the new system is still under development. Your engineering team is split. Your customer focus is divided. The opportunity cost is enormous.

Many incumbents choose to build new products rather than refactor existing ones. But new products compete with legacy products for budget, engineering resources, and customer mindshare. It's organizationally challenging and rarely produces the desired outcome.

The Organization Inertia Problem

Beyond technical architecture, there's organizational inertia. Sales teams built around enterprise deals with 6-month cycles don't know how to sell to self-serve customers with week-long evaluation periods. Customer success teams built around quarterly check-ins don't know how to support products that update weekly. Marketing teams built around case studies and analyst reports don't know how to do the community and viral marketing that AI-native products require.

Shifting organizational structure is harder than shifting code. People have built careers in the enterprise sales model. Changing to self-serve sales feels like a demotion. Customer success teams will resist a model where customers don't need intensive hand-holding. Everyone has built skills and reputation around the existing operating model.

This organizational inertia can be as deadly as technical architecture problems. Even if the technology works, the organization may not be configured to execute on it.

The Revenue Cannibalization Problem

If an incumbent builds an AI-native product, it will almost certainly cannibalize legacy product revenue. Customers will migrate to the better, faster, cheaper alternative. This is good for the customer and the company's future, but it's devastating for this quarter's revenue numbers.

For public companies with quarterly earnings expectations, this cannibalization dynamic creates impossible incentives. If the AI-native product will cause legacy product revenue to decline from

This dynamic is much less of a problem for startups or private companies that can take a longer-term view. They can cheerfully cannibalize themselves because they're not managing quarterly earnings expectations. But for public incumbents, this is a structural problem.

The Death of Category Lock-In

Multiple Winners, Fragmented Markets

The old playbook assumed that the first company to dominate a category would maintain dominance for a decade. In 2025, that assumption is breaking.

Markets are increasingly fragmented across multiple winners, often with different positioning and use cases. Rather than one CRM platform dominating, there's Salesforce for large enterprises, HubSpot for mid-market, Pipedrive for SMBs, and now AI-native tools like Runable and others emerging for specific use cases within CRM (deal management, forecasting, pipeline automation).

This fragmentation is different from traditional market segmentation. These aren't different vendors competing for the same customer base. They're vendors operating in the same category but with fundamentally different value propositions and architectures.

The category becomes a "crowded market with multiple viable winners" rather than "winner-take-most."

The De-Verticalization of Enterprise Software

For decades, enterprise software strategy was based on vertical integration and platform expansion. Start with a core product (CRM), then expand into related categories (marketing automation, customer service, commerce). Build a unified platform that becomes increasingly sticky as customers integrate more modules.

In the new environment, this de-verticalization is happening. Customers increasingly prefer best-of-breed tools that specialize in one thing and do it exceptionally well, even if it means integrating across multiple vendors. A unified platform that's "pretty good" at five things loses to five specialized platforms that are exceptional at each thing.

This is particularly true when the specialized platforms can move faster and innovate more aggressively than the unified platform. The unified platform's integration advantage disappears if each specialized tool is 2-3x better at its function.

Why Lock-In Is Weaker

Switching costs—the traditional moat for incumbent platforms—are weaker in the new environment for several reasons:

APIs and integrations have improved dramatically. Data migration is no longer a months-long project involving consultants and downtime. Modern tools can handle it in weeks, often with minimal disruption.

The pain of staying has increased. When a competitor is shipping 10x faster and offers dramatically better capabilities, the inertia that used to keep customers stuck evaporates. The pain of staying outweighs the pain of switching.

Customer switching expectations have changed. Enterprise customers used to accept long migration timelines as inevitable. Now they expect vendors to handle migration as part of the sales process. The expectation has shifted from "switching is hard" to "switching should be easy."

Data portability and standards have improved. Open formats, standardized APIs, and data portability commitments have reduced the friction of moving between platforms. The "lock-in" of proprietary data formats is less durable when customers can export their data and migrate it to competitors.

The New SaaS Economics: Velocity, Scale, and Network Effects

From "Outspend Your Way to Victory" to "Viral Growth and Network Effects"

The old playbook was built on the principle that the company with the most deployed capital would win. Spend more on sales. Hire bigger teams. Run bigger events. Win.

The new playbook is built on the principle that the company with the best product and strongest network effects will win. Spend effectively on growth. Optimize for efficiency. Build virality into the product.

This is a fundamental shift in how value gets created. In the old model, a mediocre product could win if it had better sales and marketing. In the new model, a mediocre product can't win, period. The product quality threshold has risen dramatically.

At the same time, network effects have become increasingly important. Software that becomes more valuable the more people use it (or the more data it accumulates) has a structural advantage. This favors companies that can build communities, encourage usage, and create positive feedback loops.

The Efficiency Era

Customer acquisition cost (CAC) requirements have inverted. The old playbook was "raise capital, deploy it to acquire customers, focus on unit economics later." The new playbook is "achieve unit economics immediately, then scale."

This puts a premium on:

• Self-serve acquisition. The ability to let customers try the product with minimal sales touch.
• Viral coefficient. Building network effects or word-of-mouth into the product itself.
• Community. Building communities around the product that do marketing and support work for you.
• Content and SEO. Organic growth through educational content and search visibility.
• Product-market fit clarity. Having such obvious value that justifying spending money on it is trivial.

Vendors that can achieve meaningful customer acquisition through these channels have a structural advantage over vendors that require expensive sales teams. At

This is why so many AI-native products price much lower than incumbent alternatives. It's not that they're cheaper to build—they're not. It's that they can distribute through self-serve and viral growth, which means lower cost of customer acquisition, which means lower pricing is still profitable.

The Data Advantage: New Moats in the AI Era

While switching costs are lower and lock-in is weaker, new moats are emerging based on data and model quality. AI systems that have been trained on more data or access to higher-quality data tend to produce better outputs. Over time, this becomes a powerful defensible advantage.

Companies that can access better training data—whether through customer interactions, partnerships, or market position—can build more accurate and useful AI models. This creates a new form of moat that's not based on switching costs or integration complexity, but on model quality.

This can actually create stronger lock-in than the old system because it's not about friction—it's about superior utility. Customers aren't staying because switching costs are high; they're staying because the product genuinely works better.

Pricing Model Evolution: From Per-Seat to Value-Based

The Collapse of Per-Seat Pricing

The per-seat pricing model—you pay X per user per month—dominated SaaS for 15 years because it aligned perfectly with the old playbook. More seats meant more revenue. Usage growth automatically drove revenue growth. Every new hire on the customer's team became another billing event.

In the new environment, per-seat pricing is becoming problematic for several reasons:

AI reduces the need for seats. If AI agents are doing the work that humans used to do, you need fewer seats. The customer's value goes up (they accomplish more) but your revenue goes down (fewer users). This creates misaligned incentives.

Per-seat pricing doesn't capture value from AI-native workflows. When a product automates something entirely, billing for the number of people using it doesn't make sense. The value is in the automation itself, not in the number of users.

Customers resist it. Enterprise customers, particularly those moving to AI-native tools, are increasingly pushing back on per-seat pricing. They prefer consumption-based, outcome-based, or flat-rate pricing models.

The future is shifting toward models like:

• Flat-rate pricing for self-serve tools. $9/month for unlimited users (Runable's approach for AI automation tools)
• Consumption-based pricing. Pay for what you use (credits per API call, documents generated, etc.)
• Outcome-based pricing. Pay based on the value delivered (cost per deal closed, revenue per customer, etc.)
• Hybrid models. Flat rate for base features, consumption-based for enterprise volume

The Value-Based Positioning Advantage

Companies that can quantify their value in clear business terms have an advantage. "Saves your team 20 hours per week" or "Increases deal close rate by 15%" is more compelling than "$50 per seat per month."

This particularly benefits AI-native tools that automate end-to-end workflows. You can measure the precise efficiency gain or outcome improvement, which justifies pricing based on that value rather than on input metrics like seats or compute.

The Role of Niches and Specialization

Narrowing Down to Win

In a world of multiple winners and fragmented markets, the strategy of "build a broad platform that serves everyone" is increasingly suboptimal. Niching—focusing on a specific use case, customer segment, or problem domain—has become a competitive advantage.

A product that's exceptional for one specific workflow beats a product that's pretty good for five workflows. This is especially true when the specialized product can move faster and innovate more aggressively than the generalist platform.

Companies like Runable exemplify this: rather than trying to be a general automation platform, the focus is on specific use cases (AI-powered content generation, report automation, workflow orchestration). This allows for vertical optimization and exceptional execution.

Category Specialization

Industry-specific software is seeing a resurgence. Rather than trying to sell a horizontal solution to "all enterprises," it's often more effective to build a solution optimized for a specific industry (SaaS, healthcare, financial services, etc.).

Vertical specialization allows you to:

• Optimize data models for industry-specific workflows
• Build industry-specific integrations that matter most
• Develop domain expertise that competitors lack
• Target go-to-market more efficiently
• Build community within the industry

The Future of SaaS: What Winners Look Like in 2025 and Beyond

The Characteristics of Winning SaaS Products

As the dust settles on this transition, a few characteristics are becoming clear for companies that are winning:

1. AI-Native Architecture Winners are built with AI as a first-class citizen from day one. Autonomous workflows. Agent-based automation. Data models that support AI reasoning. This is not an optional feature—it's foundational.

2. Exceptional for Specific Use Cases Winners solve one or two specific problems exceptionally well. They're not trying to be everything to everyone. They have laser focus on nailing their core value proposition.

3. Continuous Shipping Winners ship improvements weekly or more frequently. This signals that the product is alive and that the company is responsive to feedback. This builds trust and retention.

4. Community-First Growth Winners build communities of users who evangelize for the product. The product is good enough that users want to tell others about it. This creates sustainable, efficient growth.

5. Transparent Pricing Winners have pricing models that customers understand and that align incentives. No surprise overages or complex tier structures. Pricing should be easy to understand and predict.

6. Workflow Automation Winners automate end-to-end workflows rather than point solutions. They take the customer from problem to solved without intermediate manual steps.

7. Data Portability Winners make it easy for customers to export their data and integrate with other tools. They're confident enough in their product that they don't need lock-in.

The Declining Characteristics of Legacy Vendors

What's becoming increasingly uncompetitive:

• Quarterly release cycles appear glacially slow
• Per-seat pricing feels misaligned in an AI-native world
• Complex integrations that require consultants look like friction
• Sales-heavy go-to-market feels expensive and exclusive
• "Platform strategy" that tries to do everything looks unfocused
• Maintaining backward compatibility with 15-year-old code limits innovation speed

Market Implications and Investment Thesis

Why VCs Are Still Betting Big

Despite the "SaaS is dead" narratives and market corrections, venture capital is still flowing into B2B software. This seems contradictory until you understand what's actually happening.

VCs are rotating away from legacy SaaS companies and toward AI-native startups. The opportunity isn't in buying Salesforce or competing with HubSpot. The opportunity is in building purpose-built, AI-native tools for specific workflows and use cases.

Eleven Labs raising

The Bifurcation of the Market

The market is increasingly bifurcating into two categories:

Legacy Platform SaaS: Salesforce, HubSpot, Zendesk, etc. These are mature, profitable businesses but facing structural headwinds. Growth rates will moderate. Multiples will compress. M&A will be used to maintain growth. These are cash cows and dividend-paying businesses more than growth engines.

AI-Native SaaS: Younger companies built around AI-native architecture, serving specific use cases, shipping fast, achieving unprecedented growth rates. These are the growth engines and venture capital targets. Multiples are high because growth rates are exceptional.

The valuation gap between these two categories will widen as the structural differences become more obvious.

The Opportunities for Builders

For founders and entrepreneurs, this transition creates unprecedented opportunities:

1. Horizontal AI Infrastructure Building tools and platforms that other SaaS companies use to build AI-native products. This is layers-back-from-the-customer and very defensible.

2. Vertical SaaS AI-Native Building specialized, AI-native solutions for specific industries or workflows. This is where rapid adoption and revenue growth happen.

3. Workflow Automation Building automation layers on top of existing tools. Rather than replacing Salesforce, you automate the workflows within and across Salesforce.

4. Domain-Specific AI Models Building and fine-tuning AI models for specific domains (legal, medical, financial, etc.). Better models = better customer outcomes = pricing power.

5. Integration and Orchestration Layers Building "AI coordinators" that manage workflows across multiple best-of-breed tools. This becomes increasingly valuable as customers use more specialized tools.

How to Build in This New Environment

Starting with Product Excellence

The new playbook starts with product excellence. You can't outspend your way to victory anymore. You can't rely on poor product being overcome by better sales. The product has to be exceptional from day one.

This means:

• Solve a real problem. Not a problem you think people have—a problem your target customers actively feel.
• Solve it 10x better than existing alternatives. Being incrementally better is not enough. You need to be meaningfully better.
• Ship fast. Get to MVP in weeks, not months. Get real feedback from users immediately.
• Iterate based on feedback. Watch how people use your product. Let that guide development.

Building for Self-Serve

The new playbook emphasizes self-serve over enterprise sales. This means:

• Obvious value proposition. A user should understand why your product is valuable in 30 seconds.
• Minimal friction. Get users to "aha moment" in under 5 minutes.
• Freemium or free trial. Remove risk from trying your product.
• Pricing that aligns with usage. Don't make customers guess whether they can afford you.

Self-serve doesn't mean you don't have an enterprise sales team. It means the product works for self-serve customers first, and enterprise sales is additional upmarket expansion.

Community as Growth Engine

The new playbook treats community as essential infrastructure, not an afterthought. This means:

• Build in public. Share your roadmap, decisions, and tradeoffs with users.
• Encourage user-generated content. Tutorials, integrations, and extensions from the community.
• Create forums and spaces where users can help each other.
• Give users a platform to share how they're using the product.

Community-driven growth compounds. Every user who becomes an evangelist creates exponential growth potential.

Efficient Go-to-Market

The new playbook emphasizes efficiency in customer acquisition:

• Product-led growth where possible. Let the product do marketing.
• Content and SEO. Educational content that ranks in search and drives organic traffic.
• Partnerships and integrations. Leverage existing tools and platforms to reach customers.
• Sales for upmarket only. Use sales teams for high-value accounts, not for universal customer acquisition.

Practical Implications for Incumbents: Survival Strategies

For Legacy SaaS Companies

If you're running an incumbent platform facing this shift, you have options:

1. Accept and Optimize for Maturity Accept that your growth rate will moderate. Optimize for profitability. Treat the business as a cash generator rather than a growth engine. This is a valid strategy—many companies do this successfully. You'll be worth less as a pure multiple, but you'll generate substantial cash.

2. Build an AI-Native Skunkworks Launch a new product team operating independently with mandate to build an AI-native product. Give them capital, autonomy, and clear metrics. This can cannibalize your legacy product but positions you for the future. This is organizationally difficult but increasingly necessary.

3. Consolidate and Partner Buy and integrate specialized AI-native tools. Rather than building everything yourself, acquire companies that have achieved product-market fit in specific niches. Then integrate them with your platform.

4. Specialize and Divide the Company Rather than trying to maintain a broad platform, consider dividing the company. Separate legacy product (which operates as a cash-generating business) from new AI-native initiatives (which operate as growth businesses). This can unlock value and allow each unit to optimize for their actual constraints.

5. Embrace the Ecosystem Stop trying to do everything in a single platform. Become exceptional at your core function. Build APIs and integrations that let best-of-breed tools integrate seamlessly. Position yourself as the hub in an ecosystem of specialized tools.

For Investors in Legacy SaaS

If you have investments in incumbent platforms:

• Reset expectations. Growth rates will moderate. Multiple compression is real. This is a transition, not a collapse.
• Focus on operational efficiency. The path to value creation is now through margin expansion and cash generation, not growth acceleration.
• Develop a hedge. Identify new opportunities in your portfolio that are AI-native. Balance legacy holdings with emerging opportunities.
• Consider exit timing. If you have legacy SaaS holdings you've been planning to take public, this might be a good time to divest before multiples compress further.

Common Mistakes to Avoid

Mistake #1: Thinking This Is a Temporary Cycle

Some incumbents are treating this as a temporary market correction. "We'll weather the storm, and things will go back to normal." They won't. This is structural, not cyclical. The barriers to competition have permanently changed. Markets will not return to the old playbook.

Mistake #2: Thinking You Can Retrofit Your Way to AI-Native

You cannot bolt AI onto a legacy architecture and achieve the same outcomes as purpose-built AI-native products. It's technically possible but organizationally difficult and products suffer for it. If you're going to do AI-native, you need to build separately or rewrite fundamentally.

Mistake #3: Maintaining Backward Compatibility at All Costs

One legacy company recently failed to ship a major architectural improvement because it would break backward compatibility with a few legacy customers. The result was that they're still operating on a 10-year-old architecture that competitors have lapped. Backward compatibility is important, but not at the cost of future competitiveness.

Mistake #4: Underestimating Niche Competitors

Many incumbents dismiss niche competitors as "too small to matter." Then those niche competitors achieve product-market fit and suddenly own their segment. Niche competitors should be taken as seriously as horizontal competitors.

Mistake #5: Over-Relying on Lock-In

Some incumbents still think lock-in is their protection. "Customers can't leave because switching costs are too high." That was true in 2015. It's not true in 2025. Lock-in prevents churn on the margin, but when there's a 10x better alternative, lock-in doesn't matter.

The Long-Term Vision: What B2B SaaS Looks Like in 2030

The Specialist Ecosystem

By 2030, the B2B software ecosystem will look dramatically different. Instead of a few massive platforms trying to do everything, there will be an ecosystem of specialized tools:

• Horizontal infrastructure layers that everyone builds on
• Vertical solutions optimized for specific industries
• Workflow orchestration layers that coordinate across tools
• Data infrastructure that enables seamless data flow between tools
• Community-built extensions and integrations that extend capabilities

This ecosystem will be more fragmented than today but also more powerful. Customers will have more choice but also more integration complexity. The winners will be companies that make integration and coordination seamless.

The AI Reasoning Layer

Over the next 5 years, a "reasoning layer" will emerge—AI systems that understand your workflows and orchestrate tools automatically. Rather than you integrating Tool A with Tool B, your AI reasoning layer handles the coordination.

This becomes a new category of infrastructure. Companies that build world-class AI reasoning engines that can orchestrate across tools will have enormous value.

The Data and Model Advantage

As AI becomes table stakes, the differentiator will be data quality and model sophistication. Companies that accumulate the best data and build the best models will have structural competitive advantages. This creates a new form of moat based on model quality rather than switching costs.

Continuous AI Improvement

The products that are winning in 2030 will have:

• Daily shipping cycles for continuous improvement
• Continuous model training on new data
• Real-time personalization based on how individuals use the product
• Autonomous workflow management with minimal human intervention
• Network effects where the product gets better as more people use it

Conclusion: Navigating the Transition

The SaaS industry is in the midst of a fundamental transition. The playbook that generated extraordinary returns for 15 years is obsolete. The era of category lock-in, sales-driven growth, and inertia-based NRR is over.

But this doesn't mean SaaS is dead. It means SaaS is evolving. The winners in 2025 and beyond will be companies that understand this transition and build for it from day one.

For founders, the opportunity has never been larger. The barriers to building competitive software have collapsed. AI-native tools can be built in weeks by small teams. The total addressable market is expanding as software becomes capable of doing more. Customers are actively seeking better alternatives and willing to switch. The conditions are perfect for new entrants to capture significant market share.

For incumbents, the challenge is real but not insurmountable. Companies that recognize the shift, invest in new architecture, and reorganize around the new playbook can maintain competitiveness. Companies that treat this as a temporary disruption will face increasing pressure.

For investors, the rotation from legacy SaaS to AI-native SaaS is real and will accelerate. Companies that understand the new playbook and execute against it will generate the highest returns. The multiples and growth rates will be lower than the golden age of SaaS, but the opportunities are no less significant.

The next decade of B2B software will be determined by which companies recognize that the game has changed and adjust accordingly. Those who do will capture enormous value. Those who don't will face irreversible decline.

The question isn't whether SaaS is dead. The question is: are you building for 2025 or for 2015?