The Great AI Funding Divide: How 58% of VC Dollars Are Creating an Extinction Event for Traditional Software
 
Is this the end of traditional software as we know it?
 
The warning signs have been flashing red for months, and yet some founders are still operating as if it's 2019. The brutal reality: Investment in AI companies drove over 58% of global venture deal value in Q1 2025, while traditional SaaS funding has declined massively compared to previous years.
 
This isn't market cyclicality. This is investor capital flowing toward what they believe will be the future, and away from what they increasingly view as legacy technology.
 
But the funding shift is just the visible symptom of a deeper transformation happening in enterprise software markets. The most shocking evidence comes from Coatue's analysis presented on the All-In Podcast, which reveals that high-growth software companies are becoming an endangered species in public markets:
 
In 2021, 26% of public software companies achieved >25% revenue growth, with median growth at 17%. By June 2025, that figure had collapsed to just 5% of public companies hitting >25% growth, with median growth plummeting to 9%.
 
This isn't a gradual decline - it's a systematic collapse of the growth rates that made SaaS attractive to investors in the first place. Meanwhile, AI startups are posting growth curves that make traditional SaaS look pedestrian.
 
For European SaaS founders, this creates an immediate existential question: are you building the future, or are you about to become the Blockbuster in an age of Netflix?
 
 
The Death by a Thousand Cuts: How AI Is Systematically Dismantling SaaS Economics
 
The transformation isn't happening through grand announcements or board-level strategic pivots. It's death by a thousand cuts as customers quietly discover they can achieve 90% of traditional SaaS functionality for a fraction of the cost using AI-powered alternatives. The most high-profile example comes from one of Europe's most successful fintech companies.
 
Klarna dropped Salesforce and Workday in favour of AI-powered alternatives, reducing their workforce from 5,000 to 3,800 employees while increasing average annual revenue per employee from £400,000 to £700,000. CEO Sebastian Siemiatkowski's assessment was brutal: "Thanks to AI agents + AI engineers getting prolific, you can rebuild most enterprise SaaS functionality, host for super cheap, and get basically 90%+ functionality."
 
This isn't an isolated experiment. Across enterprise software categories, the same pattern is emerging:
 
Customer Service Platforms: AI voice agents are now indistinguishable from humans while handling phone-based jobs at a fraction of human cost. The $85 billion addressable market for business calls is being systematically automated.
 
Workflow Management: AI agents can orchestrate workflows without UI-heavy tools, eliminating the need for complex workflow software that requires training and configuration.
 
Business Intelligence: Real-time AI analysis is replacing static dashboards, with customers preferring natural language insights over traditional BI tools that require human interpretation.
 
The pattern is consistent: any SaaS solution that serves as an intermediary between humans and information is being replaced by AI that provides direct insight or action without the intermediary layer.
 
 
The Public Market Verdict: Investors Have Made Their Choice
 
The public markets are delivering a harsh verdict on traditional software versus AI companies. The median revenue multiple for AI companies stood at 25.8x while the median multiple for publicly-traded SaaS was 5.8x revenue in Q1 2025. This isn't just a valuation gap - it's a fundamental repricing based on growth expectations and market potential.
 
Even more telling following Q1 results: Salesforce shares fell 20% after weak earnings, marking the worst trading day since 2004, while public SaaS company valuations have retreated to 2016 levels. The market is sending a clear signal about which direction it believes software is heading.
 
European founders need to understand that this public market performance directly impacts private valuations and investor appetite. When public SaaS companies trade at historically low multiples while AI companies command premium valuations, private investors naturally gravitate toward the sector with higher return potential.
 
 
The Speed Differential That's Killing Traditional Software
 
Perhaps the most damaging evidence against traditional SaaS comes from development velocity. Nearly 90% of code at high-growth SaaS companies is now AI-generated, up from 10-15% just 12 months ago. But this productivity gain pales compared to AI-native startups that build with modern architectures optimised for intelligent behaviour from day one.

The speed differential is devastating: while incumbents spend 18-24 months retrofitting AI into legacy systems, AI-native startups can build and deploy intelligent features in 3-6 months. Traditional SaaS companies face a brutal choice between two expensive paths: retrofit AI features that feel "bolted-on" rather than native or rebuild from scratch while competing against AI-native startups.
 
This technical debt problem is compounded by architectural limitations. Legacy databases weren't designed for vector embeddings and real-time ML inference. Authentication and security models built for human users struggle with autonomous agents. The result: AI features that feel like yesterday's technology with tomorrow's buzzwords attached.
 
As Chamath Palihapitiya noted on the All-In Podcast, AI's role in economic growth and productivity represents "automation's deflationary benefits" - meaning AI doesn't just compete with traditional software, it fundamentally changes the cost structure of delivering software functionality.
 
 
The European Founder's Dilemma: Fighting for Scraps in a Winner-Takes-All Market
 
For European founders, the statistical reality is sobering. Europe's venture funding plateaued at $12.6 billion in Q1 2025 — flat quarter over quarter and year over year, while the Bay Area alone attracted $55 billion, accounting for 49% of global funding.
 
When three-quarters of available capital flows to AI companies, traditional software startups are competing for an increasingly small slice of available investment.
 
The consolidation is accelerating. Q1 2025 saw just 1,852 VC deals recorded across Europe - down from 2,282 in Q4 2024, representing a ~49% decline compared to Q1 2022. Investors are placing fewer bets but writing larger cheques when they do commit, meaning the quality bar for non-AI companies has moved dramatically higher.
 
AI deals are now attracting more than 1 in 4 venture investments in Europe - a staggering shift in allocation for a single theme. This means capital isn't being added to AI; it's being diverted from other sectors. Climate, fintech, industrials, hardware, health, and infrastructure startups are swimming against a current that's getting stronger by the quarter.
 
 
The Bridge Round Crisis: A Leading Indicator of What's Coming
 
As we explored in our previous analysis of rising fundraising challenges, the bridge round phenomenon has become one of the most concerning signals in venture capital. The latest data confirms our warning: 46% of all seed deals were bridge rounds in Q1 2025, which is the highest bridge rate for any stage since Carta has been tracking such data. This dramatic increase from just 31% in 2022 suggests a growing mismatch between startup performance and investor expectations for Series A advancement.

Bridge rounds often signal a fundamental misalignment between a company's performance and investor expectations. For traditional software companies, this frequently means they can't demonstrate the exponential growth that AI companies are achieving, forcing them into extension rounds while they attempt to reach metrics that may be structurally impossible given their business model constraints.
 
The startup failure rate confirms this trend. More startups shut down in 2024 than the year prior, and 2025 looks like another brutal year. While some of these are "zombie" companies from the 2021 funding boom, many represent viable businesses that simply can't compete for investor attention in an AI-dominated market.
 
 
The Palantir Exception: How One Company Rewrote the Rules
 
While most traditional software companies struggle with AI integration, Palantir stands out with 39% revenue growth year-over-year in Q1 2025 and stock performance up over 400% in the past 12 months. Their success offers a blueprint for traditional software companies willing to bet everything on transformation.
 
Palantir's approach was radical: instead of adding AI features to existing products, they rebuilt their entire value proposition around AI-native solutions. Their AIP Bootcamps deliver working AI solutions on real customer data in just 5 days, converting prospects to paying customers at unprecedented speed. One major utility company signed a seven-figure deal during day one of bootcamp, then increased to seven figures weeks later.
 
The lesson is stark: half-measures don't work in platform transitions. Companies that survive major technological shifts are those willing to disrupt themselves before competitors do it for them.
 
 
The Survival Playbook: Five Critical Actions for European Founders
 
Investors haven't been shy about dispensing advice to their portfolio companies on retooling for the AI era. From board meetings to LP letters, the message is consistent: adapt or risk obsolescence. Jason Lemkin's survival playbook for SaaS leaders provides an excellent foundation for understanding the AI threat to traditional software. Here's our European version, adapted for the specific challenges facing founders in our market: constrained capital, longer fundraising cycles, and the need to compete against US-funded AI startups.
 
The time for incremental responses has passed. European founders face an immediate choice: transform fundamentally or risk irrelevance.
 
Phase 1: Confront Reality (Next 30 Days)

Assess Your Vulnerability Use these high vulnerability indicators to evaluate your existential risk:

• Workflow-heavy products that could be automated by AI agents
• UI-dependent processes that require human interpretation
• Manual data analysis that AI could perform in real-time
• Repetitive user tasks that agents could handle autonomously
• High customer service needs that voice AI could replace

If you score high on three or more indicators, you're in the danger zone. Proceed immediately to Phase 2.

Calculate Your Runway Reality For non-AI startups, fundraising timelines have increased to 6-9 months. Factor this into cash planning immediately. If you're a vulnerable non-AI startup with less than 18 months of runway, you're operating in crisis mode and need bridge funding or dramatic cost reduction.

Audit Your AI Story Can you articulate how AI makes your solution dramatically better, not just slightly improved? If your AI story feels like an afterthought, you don't have an AI story. You have marketing copy.


Phase 2: Emergency Response (Next 90 Days)
 
Accelerate AI Integration at Core Level This isn't about adding AI features. It's about rebuilding core functionality with AI-native approaches. Identify your most critical user workflows and determine how AI could eliminate intermediate steps, reduce manual effort, or provide real-time insights.
 
Prepare for Pricing Disruption AI economics will force price compression across software categories. Model scenarios where your pricing drops 30-50% and determine what operational changes would be required to maintain unit economics.
 
Prioritise Data Moats In an AI-driven world, your sustainable competitive advantage will come from proprietary data sets, unique customer insights, or regulatory compliance requirements that competitors can't easily replicate.
 
 
Phase 3: The Nuclear Option (Next 6 Months)
 
Consider Architecture Rebuilding For companies in high-risk categories, evaluate pivoting to AI-native architectures entirely, even if it means cannibalising current revenue. Historical precedent shows that companies surviving major transitions disrupted themselves first.
 
Team Transformation Your current engineering team may not be equipped for AI transformation. Top AI talent commands 40-60% salary premiums and prefers working at AI-native startups. Assess whether gradual upskilling or wholesale team changes are required.

Customer Migration Strategy Plan how to transition existing customers to AI-enhanced offerings without losing revenue. This may require running parallel systems during transition periods or offering hybrid solutions that gradually introduce AI capabilities.
 
 
Phase 4: Defensive Positioning (Ongoing)
 
Demonstrate AI Immunity If your business model depends on activities that AI cannot easily replicate - complex customer relationships, regulatory knowledge, physical world interaction, or creative problem-solving - make this explicit to investors and customers.
 
European Market Leverage Use proximity to customers, understanding of local regulations, and established trust relationships to build defensible positions before well-funded AI startups arrive. Geographic advantages become temporary moats while you build stronger competitive positions.
 
Capital Efficiency as Strategy European founders who build capital-efficient businesses often create more sustainable competitive positions than competitors who raise large rounds and burn capital quickly. Use constrained resources as a forcing function for better product-market fit.
 
 
Phase 5: The Long Game (12+ Months)
 
Platform Transformation Stop thinking about your company as a software provider. Start thinking about yourself as a solution provider who happens to use software. The most successful transitions involve redefining the entire value proposition around customer outcomes rather than software capabilities.
 
Industry Vertical Domination Focus on becoming the definitive solution for specific industry verticals where you can build deep domain expertise. Vertical specialisation creates switching costs that horizontal AI solutions struggle to overcome.
 
Partnership Strategies Rather than competing with AI platforms, explore partnership opportunities that leverage their capabilities while adding your domain expertise and customer relationships.
 
 
The Hard Truth: This Isn't Temporary
 
Unlike previous technology cycles where multiple approaches could coexist, the AI transformation appears to be creating winner-take-all dynamics. The convergence narrative is comforting but false. The future isn't AI + SaaS. The future is AI replacing Traditional SaaS.
 
European founders who treat this as a temporary market dislocation rather than a permanent shift will find themselves increasingly irrelevant. The capital allocation, customer preferences, and competitive dynamics have fundamentally changed.
 
The companies that will thrive aren't those playing defence with "AI features." They're AI-native startups and traditional companies brave enough to rebuild themselves completely. The window for halfway measures is closing.
 
The choice is binary: transform or become extinct. European founders still have time to choose transformation, but that window is shrinking rapidly. The AI funding divide isn't a trend to monitor - it's an extinction event to survive.

Let's talk.