
The Software Industry’s Great Reset and the New Moat That Matters
The February 2026 selloff is the clearest sign yet that AI is rewriting Software-as-a-Service (SaaS) economics. I believe what comes next will define the winners and losers for the next decade.
The software industry is undergoing its most profound valuation reset since the 2008 financial crisis. What distinguishes the 2026 downturn is not macroeconomic weakness, nor enterprise budget cuts, nor overvaluation. The root cause is far more fundamental: AI has changed the physics of the software business model itself.
GPs and LPs have been debating this vulnerability for years, but Anthropic’s February 2026 release of autonomous legal and business workflow capabilities made it a measurable reality. The implication was immediate and shocking: AI is not merely helping humans interact with software. It is replacing entire categories of software.
Within days, the share prices of legal-tech, market-data, business-intelligence, and horizontal SaaS tools were repriced sharply downward. Private equity investors reacted with equal force, and some quickly reduced software exposure. Other firms hired consultants to analyze AI risk across their portfolios. The long-expected re-underwriting of software valuations finally began in earnest.
But the selloff is not a story about panic; it is a story about rational repricing. AI is undermining three assumptions that created the SaaS boom:
- Seat-based pricing will grow forever
- Software margins are structurally fixed at 80–85%
- Recurring revenue streams are predictable
For investors, operators, and LPs, the question is no longer “Where is the growth?” but rather “Which models survive?”
Below, I dive into forces reshaping the industry, from why the economic model is under pressure to who may emerge on the other side. A bright line has emerged, and it’s one that I believe will define winners and losers for the next decade.
Why SaaS economics are breaking
The software industry’s recent downturn is the first true structural shift in SaaS economics since the model was invented. Two pillars of SaaS are under existential pressure: the seat-based pricing model and the fixed-cost margin structure.
1. The collapse of seat-based pricing
- 30% seat decline + 10% price increase = -23% revenue contraction1
- 50% seat decline + 15% price increase = -42.5% revenue contraction2
2. Variable compute costs erode margins
Historically, SaaS margins were spectacular. Software was built once, then distributed nearly free. Now, every AI interaction has a variable cost in compute. When users shift from episodic dashboard interactions to continuous AI copilot interactions, usage skyrockets. SaaS companies face new cost breakpoints:
- At $0.002 per interaction, 2,500 monthly interactions erase a $5 cost buffer
- At $0.01 per interaction, only 500 monthly interactions wipe out margin
Companies built for fixed-cost leverage must now build for cost of goods sold variability. This is a structural margin reset, not a temporary fluctuation.
Rerating ARR predictability
Together, these shifts are creating major impacts on valuation multiples. High-growth SaaS companies have historically commanded up to 15–25x revenue multiples3 because Annual Recurring Revenue (ARR) was considered predictable, contractually bound, and immune to churn.
AI introduces three new risks to ARR durability:
- Replacement Risk — AI agents replace the entire software category
- Compression Risk — seat counts fall faster than pricing can adjust
- Margin Volatility Risk — variable inference costs destabilize unit economics
In this environment, investors no longer accept ARR at face value. The “SaaS premium multiple” is gone.
Potential winners and why: Shifting cost models
Winners: Players that own end-to-end workflows, can price on usage, and can absorb variable cost structures at scale.
Losers: Software for which AI can replicate functionality directly, with collapsing seat models, or no proprietary data.
The new moat that matters: Who’s positioned to win?
Though this transition feels turbulent, the long-term opportunity in software remains. This reset is clearing out business models built on noise and rewarding those that own the deterministic core of enterprise operations.
AI doesn’t eliminate the need for software. It amplifies the value of the right software. I see the following as core predicators of who will emerge when the dust settles.
1. Mission-critical systems of record
The clearest indicator of a software company’s AI-era survivability is whether it is:
a system of record (SoR) or a bolt-on workflow tool
This divide is the new bright line for valuations.
Systems of record
- Hold proprietary, customer-specific operational data
- Run deterministic, mission-critical workflows
- Have extremely high switching costs
- Are necessary for regulatory, financial, or operational continuity
- Cannot be replaced by probabilistic AI agents
Bolt-on tools
- Do not own data
- Perform tasks where “good enough” is acceptable
- Have low switching costs
- Solve problems AI can replicate using general-purpose models
- Often rely on public or third-party data
For illustrative purposes only. Not an exhaustive list of differences.
If SAP goes down, factories stop. If Workday breaks, payroll fails. AI augments these systems; it does not replace them.
In contrast:
- Legal research? AI can synthesize statutory and case law.
- Dashboarding tools? AI generates dashboards from raw databases.
- Coding assistants? Frontier models integrate coding natively.
The replicability is the risk vector.
Potential winners and why: SoRs vs. bolt-on
Winners: Systems that own core operational data layers or are an irreplaceable backbone for companies.
Losers: Players for which AI can regenerate their insights without needing their software.
2. Vertical SaaS
Vertical SaaS has emerged as the industry’s most resilient and fastest-growing segment — and the one least disrupted by AI commoditization.
There are several reasons I believe are behind this outperformance:
Vertical SaaS's growth factors
Industry-specific workflows
Generic CRM vendors cannot replicate many of vertical SaaS’s workflows without massive customization. Procore understands construction, from permitting delays, to subcontractors, to sequencing. In the same way, Toast understands restaurants and Samsara logistics.
Deep operational integration
Vertical platforms often run full operations, from POS and payroll (Toast) to FDA-compliant systems (Veeva). Switching costs increase exponentially when core operations depend on the platform.
Lower AI substitution risk
AI can replicate horizontal functionality (email sequences, dashboards, case research), but it cannot easily replicate highly specific industry rules, timing, compliance, and data structures.
Non-tech buyers are stable
Restaurants, contractors, freight operators, dentists — these industries do not cut software spend dramatically in downturns.
Network effects
Loops reinforce market share. In construction, subcontractors adopt whatever tool general contractors use. In wellness, consumers book through Mindbody’s marketplace.
For illustrative purposes only.
Potential winners and why: Vertical SaaS
Winners: Those with strong data moats, operational criticality, and embedded fintech.
Losers: Horizontal CRM or project tools without industry context or niche verticals lacking proprietary data.
3. Embedded fintech
Embedded fintech has become an important defensive layer for SaaS companies. In the U.S., embedded finance will exceed $7 trillion in transaction volume by 2026.4 For vertical SaaS companies, payment processing fees add 2-3x revenue uplift per customer compared to pure SaaS.5
Embedded fintech protects against AI disruption for a few reasons:
- Payments volume is not affected by seat compression. Restaurants process the same number of transactions regardless of staffing.
- Switching costs are massive. Shifting POS + payments + merchant accounts is painful.
- Data moats are deep. Payments create real-time cash-flow visibility.
- AI cannot replace regulated financial infrastructure. For example, AI can draft invoices, but it cannot settle payments or underwrite loans without licensed infrastructure.
Potential winners and why: Embedded fintech
Winners: Fintech revenue streams overall. They are sticky, recurring, and unaffected by AI seat reductions.
Losers: Pure-play SaaS without payments or horizontal tools with no financial hooks.
4. Research and development (R&D) investors
AI has created a structural gap between companies that invested heavily in innovation and those that optimized for short-term margins.
The leaders treat AI as a platform transformation, not a feature. They’re dedicating resources to redesigning products, updating pricing, and integrating AI throughout. Just look at Microsoft’s more than $32 million in R&D expenses in 2025.6
Though the public markets are now scrutinizing the benefits of this spending, many believe this is the best (and perhaps the only) path forward.
The laggards, particularly legacy software companies with declining R&D investment, are discovering that cost-cutting is not a strategy. AI is unforgiving to firms that underinvest.
Velocity of innovation is now a competitive moat.
Potential winners and why: R&D
Winners: Those who pursued high R&D velocity, rapid AI integration, and durable competitive positioning.
Losers: Legacy vendors with declining R&D intensity or companies caught between late cloud migration and early AI disruption.
The future: Replacing the seat with a brain
Where is this all leading us? First, I see the seat-based model giving way to a usage-based model. This could be structured in different ways:
- Per-task (invoice processed, contract reviewed)
- Per-outcome (revenue generated, costs saved, compliance maintained)
- Per-agent (AI worker pricing)
- Consumption (compute, tokens, API calls)
- Hybrid (base subscription + usage charges)
Further, AI agents’ 24/7 economics create enormous potential for platforms that can monetize it. If AI agents operate continuously, usage can grow 10–100x even as human seats decline.
In this new era, ARR loses primacy and new metrics matter more, including net dollar retention, committed vs. uncommitted spend, usage/margins by cohort, or the payback period under usage variability. Companies that can measure and communicate usage patterns win investor trust.
What this means for investors
Ultimately, I believe the software sector’s valuation reset marks the beginning of a Darwinian era. AI is the predator, and many players are at risk of becoming a meal. The bright lines are clear:
Winners are likely to have:
- Mission-critical systems of record
- Platforms with proprietary data and embedded transaction capability
- Companies investing aggressively in AI
- Hybrid or usage-based pricing innovators
Losers are likely to have:
- Tools that AI can replicate
- Products reliant on third-party data
- Low-switching-cost workflows
- Companies cutting R&D to preserve margins
Connect with HarbourVest
Private equity is already adjusting, reducing exposure, conducting AI vulnerability assessments, and reassessing underwriting assumptions. We also see selective buying where value and durability exist, but where long-term success depends on transformation aligned with the winning attributes above.
I will end with a question that our CIO has been asking for 10 years as we’ve debated SaaS investments in our investment committee:
“Is this mission critical or nice to have?”
Though so much is changing, the fundamental question remains the same.
Based on calculation: (1−0.30)×(1+0.10)−1=0.70×1.10−1=0.77−1=−0.23=-23%
Based on calculation: (1−0.50)×(1+0.15)−1=0.50×1.15−1=0.575−1=−0.425=-42.5%
Windsor Drake, SaaS Valuation Multiples: 2025, dated June 25, 2025.
Bain, Embedded Finance: What It Takes to Prosper in the New Value Chain, dated September 2022.
Windsor Drake, Vertical SaaS Valuation Report Q4 2025, dated December 2025.
Microsoft, Annual Report 2025, dated July 30, 2025.
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