For a decade, the venture capital playbook for software investing was well understood: back SaaS businesses with strong ARR growth, healthy retention, and a clear path to operating leverage. That playbook is now being torn up. Artificial intelligence has not merely disrupted the software industry — it is redefining what a software company is, what defensibility means, and what questions investors need to ask before writing a cheque. The result is a fundamental rethink of how venture capital evaluates, prices, and supports enterprise software companies — and the stakes for founders have never been higher.
Survivability: The Brutal New Standard Replacing "Solid Fundamentals"
Partech Partner Simone Riva — who spent over a decade backing companies across the US and Europe after careers at Bain & Co and as Head of Digital Investments at Italian Family Office H14 — puts it plainly: "survivability" has replaced "solid fundamentals" as the defining metric for enterprise software investment in 2026. The question investors are now asking is not whether a company has strong unit economics today — it is whether it will still exist and matter in a world where AI can replicate, automate, or fundamentally undercut what it does.
The pressure is structural and accelerating. AI-native companies don't scale like SaaS — they scale like infrastructure and intelligence hybrids. Early costs are higher, marginal costs don't always trend to zero, and value is concentrated in proprietary models, workflows, and data — not dashboards. Horizontal SaaS without proprietary data or strong distribution is increasingly fragile. Software that merely assists is losing to software that decides. In an AI-first world, the moat is no longer the product — it is the intelligence and workflow embeddedness underneath it.
The Erosion of Software Moats: What VCs Are Now Looking For
The traditional VC scorecard for software investing was built around clear heuristics: ARR growth, net revenue retention, CAC/LTV ratios, and operating leverage. AI disrupts this entire framework. For 2026, the new investment criteria — as identified across multiple leading VC analyses — centre on a fundamentally different set of questions:
Where Does Intelligence Live?
The central VC question of 2026 is no longer "Is this a great SaaS company?" — it is "Where does intelligence live in this system, and who controls it?" Winning companies typically occupy one of three positions: infrastructure enablers (compute, tooling, orchestration), vertical AI operators deeply embedded in specific industry workflows, or platforms with proprietary data that compound in value over time.
Data Moats & Model Performance
Investors are shifting from short-term ARR obsession toward data moats, model performance, and distribution leverage. They want to see proprietary datasets that are difficult or impossible to replicate, and AI capabilities that compound in value as more data is generated through usage. Teams need a lead engineer who has built end-to-end AI systems, paired with domain experts who understand the target vertical.
SaaS-Grade Economics at AI Cost Structures
AI startups are expected to achieve gross margins of 70–80%, LTV/CAC ratios of 3× or higher, and CAC payback periods under 12 months — despite higher compute costs. Investors want concrete proof of GTM traction: AI tools convert pilots to contracts at a 47% rate — nearly double the 25% conversion rate for traditional SaaS — and VCs expect founders to demonstrate those numbers clearly.
"The next decade of venture won't be defined by who can write the largest cheque. It will be defined by who can identify real defensibility in a world where early traction is cheap, speed is abundant, and the market keeps mistaking acceleration for inevitability."
The Build vs. Buy Dilemma: How AI Shifts the CTO's Calculus
One of the sharpest tensions for enterprise software companies in 2026 is the build vs. buy dilemma facing their customers' CTOs. The rise of "vibe coding" — using natural language prompts to generate software — and AI coding tools that dramatically compress development timelines have made it easier than ever for engineering teams to consider building internal solutions instead of purchasing established software. Stockholm-based Lovable became a unicorn just eight months after launch, raising a $200 million Series A at a $1.8 billion valuation — a vivid example of how fast AI-native tooling can scale.
Simone Riva of Partech is direct about what this means for established software vendors: the total cost of ownership of an internally-built solution is almost always higher than it first appears. The best software vendors in 2026 are those who can demonstrate clearly and quantitatively why staying with them costs less than the build alternative — in time, risk, and distraction. Vendors who cannot make that case compellingly are increasingly vulnerable to being displaced — not by a competitor, but by a customer's own engineering team armed with AI tools.
This shift is also driving a new cultural divide in enterprise software adoption. High-adoption firms — those that have deeply integrated AI into their workflows and decision-making — are pulling away from competitors that are still experimenting on the margins. Code velocity is no longer sufficient as a differentiator; what matters is whether AI capability translates into genuine workflow ownership and measurable commercial outcomes for customers.
The New Founder Reality: Tiny Teams, Fast Revenue, and the Shrinking VC Window
AI is not just changing how VCs invest — it is changing what founders need to raise and when. What analyst Anu Atluru calls the "Silicon Valley Small Business" is now a genuine phenomenon: small, highly leveraged teams using AI as force-multipliers to ship faster, iterate more aggressively, and reach meaningful revenue in months rather than years. These companies don't look like classic venture rocket ships — but in many cases they outperform one, achieving product-market fit and early revenue without the traditional capital requirements.
This is compressing the window for traditional early-stage investors. When founders can reach tens of thousands of users — or even their first million in revenue — on little more than a credit card and AI leverage, the power dynamic between founders and investors shifts materially. Later-stage funds are drifting earlier to capture ownership before companies become self-sufficient. Y Combinator's Spring 2025 batch was 46% AI agent companies, a strong indicator of where the next generation of software businesses is being built.
For founders, the message from investors is clear: the bar has never been higher or more precisely set. Differentiation must be sharp, AI integration must be genuine and deep, governance and data compliance must be in place, and the path to scale must be demonstrable — not theoretical. Speed is abundant. Defensibility is rare. Defensibility wins.
What Surviving Enterprise Software Companies Must Do Now
For enterprise software companies already in the market, the challenge is existential and immediate. The strategies that built category-defining SaaS businesses over the past decade — broad horizontal features, predictable expansion revenue, product-led growth loops — are no longer sufficient defences against AI-native competitors that can replicate functionality in weeks and distribute it at marginal cost.
The path forward, according to Simone Riva and the broader investor community, requires a decisive pivot toward workflow ownership over feature competition. The vendors that will survive are those who embed so deeply into how their customers actually work — integrating with their data, their decision loops, and their operational processes — that switching becomes genuinely costly and disruptive. In an AI world, owning the workflow beats owning the feature. And the vendors who can demonstrate that ownership clearly, in terms of time saved, risk reduced, and distraction avoided, are the ones investors will continue to back.