The software market has entered a corrective, confidence-testing phase — a “software winter” in which AI-driven structural change, a dramatic reallocation of capital toward infrastructure, and a rapid re‑pricing of legacy SaaS assumptions have combined to erase hundreds of billions in market value and force a fundamental rethink of how enterprise software creates and captures value.
For much of the past decade, enterprise software followed a tidy, high‑margin script: sell seats, expand licenses as headcount grew, lock customers with high retention, and enjoy recurring revenue that translated into premium valuation multiples. That model powered outsized returns through the cloud boom and the pandemic-era digital acceleration.
That tidy equation is fraying. Across January and February 2026, headlines from corporate earnings and product launches — most notably massive hyperscaler capex plans and the arrival of agentic AI platforms — catalyzed a broad market correction in software equities. Benchmarks and sector ETFs have tumbled: the iShares Expanded Tech‑Software ETF (IGV), widely used as a shorthand for the sector, has fallen roughly thirty percent from its September peak, reflecting sudden skepticism about the durability of per‑seat economics and terminal growth assumptions.
This article synthesizes the latest market moves, validates the most consequential claims with independent industry research, and lays out a practical playbook for vendors, CIOs, and investors navigating the recalibration. Where claims are contested or inconsistent, I flag them and explain why they matter.
These adoption metrics matter because the form of AI deployment is changing. The difference between an AI feature embedded inside a UI and a true agent that reads files, calls APIs, and executes decisions is existential for some pricing models. When AI simply augments human work, seat pricing can persist. When AI performs the work autonomously, the logic of per‑seat monetization is undermined.
This matters because infrastructure benefits from usage‑based economics: AI agents do not consume “seats,” they generate compute cycles, API calls, and storage throughput. As AI workloads scale, infrastructure vendors see rising consumption — a structural tailwind that contrasts with the headwinds forming against per‑seat SaaS.
Key vulnerabilities for application software:
Why infrastructure is structurally advantaged in an AI era:
Key valuation signals:
The structure of winners is already emerging: infrastructure, security, and deeply integrated vertical solutions look well placed to capture the next wave of enterprise spend. Application vendors that proactively reframe monetization around outcomes, protect their data moats, and partner effectively with infrastructure providers can survive — and in some cases, prosper. Investors who pivot from broad thematic exposure to careful, fundamentals‑centred picks will find durable opportunities as the market sorts itself out.
For CIOs, this is an immediate operational test: treat AI like infrastructure, buy outcomes not surprises, and make governance your first priority. For vendors, the message is brutal but clear: evolve your pricing and technical architecture now, or accept the probability of a slow, value‑eroding decline.
We are witnessing a structural market correction driven by real technological change. That change is disruptive and uneven, and it rewards clarity — clarity of metrics, of pricing, and of product strategy. The next three to five years will determine which vendors adapt to an agentic world and which become lessons in the cost of complacency.
Source: Investing.com The Software Winter Marks a Structural Reset as AI Rewrites the Rules | investing.com
Background / Overview
For much of the past decade, enterprise software followed a tidy, high‑margin script: sell seats, expand licenses as headcount grew, lock customers with high retention, and enjoy recurring revenue that translated into premium valuation multiples. That model powered outsized returns through the cloud boom and the pandemic-era digital acceleration.That tidy equation is fraying. Across January and February 2026, headlines from corporate earnings and product launches — most notably massive hyperscaler capex plans and the arrival of agentic AI platforms — catalyzed a broad market correction in software equities. Benchmarks and sector ETFs have tumbled: the iShares Expanded Tech‑Software ETF (IGV), widely used as a shorthand for the sector, has fallen roughly thirty percent from its September peak, reflecting sudden skepticism about the durability of per‑seat economics and terminal growth assumptions.
This article synthesizes the latest market moves, validates the most consequential claims with independent industry research, and lays out a practical playbook for vendors, CIOs, and investors navigating the recalibration. Where claims are contested or inconsistent, I flag them and explain why they matter.
The AI shock: from augmentation to autonomous execution
AI adoption is real — and accelerating
Enterprise surveys from major consultancies show a clear pattern: AI is moving from pilots into everyday functions. One multinational survey of enterprise respondents found that nearly nine out of ten organizations reported AI use in at least one business function, and a growing share are experimenting with agentic systems that can plan and execute multi‑step workflows rather than merely assist. Industry trackers also confirm rapid uptake: independent adoption indices reported business AI adoption rising into the low‑to‑mid‑40 percent range by mid‑2025.These adoption metrics matter because the form of AI deployment is changing. The difference between an AI feature embedded inside a UI and a true agent that reads files, calls APIs, and executes decisions is existential for some pricing models. When AI simply augments human work, seat pricing can persist. When AI performs the work autonomously, the logic of per‑seat monetization is undermined.
Hyperscalers are placing enormous infrastructure bets
The capital flows confirm the strategic pivot. Hyperscale cloud providers and major tech incumbents have dramatically increased data‑centre, networking, and chip investments to support AI training and inference. Recent corporate guidance and analyst tallies place combined hyperscaler infrastructure spending in the high hundreds of billions for 2026 — a scale that dwarfs historic annual tech capex. One large cloud provider signalled a roughly $200 billion capex plan for 2026 alone, underscoring a shift in the axis of technology spending from application licenses to compute, storage, and networking capacity.This matters because infrastructure benefits from usage‑based economics: AI agents do not consume “seats,” they generate compute cycles, API calls, and storage throughput. As AI workloads scale, infrastructure vendors see rising consumption — a structural tailwind that contrasts with the headwinds forming against per‑seat SaaS.
The two faces of software: applications vs. infrastructure
One of the clearest takeaways from the past months is that the market has been treating “software” as a monolith when, in fact, two structurally distinct businesses sit under that label.Application software: the human‑facing seat model
Application software is what users click and log into: CRM, ERP, helpdesk platforms, productivity suites, vertical workflows and the like. For years these products benefitted from predictable, seat‑based monetization: more employees meant more licenses and higher recurring revenue.Key vulnerabilities for application software:
- Seat compression: Agentic AI that replaces the output of multiple junior roles (e.g., document reviewers, support agents, first‑pass coders) reduces the headcount tied to a function and therefore the number of seats.
- Substitution risk: When an AI agent can achieve the same outcome as several seat holders, the marginal value of additional licenses falls.
- Pricing misalignment: Traditional per‑seat contracts do not map cleanly to AI workflows that produce value independent of human headcount.
Infrastructure software: the machine‑to‑machine consumption model
Infrastructure software — databases, observability, API gateways, event streaming, identity/authentication, and cloud platform services — sits behind applications and is consumed by programs, not people. This layer is monetized on usage: bytes stored, queries served, messages published, compute seconds consumed.Why infrastructure is structurally advantaged in an AI era:
- Usage scales with compute: An AI agent may make thousands of API calls per minute; infrastructure vendors bill on that consumption.
- Indiscriminate billing: Infrastructure providers typically do not distinguish human versus machine traffic — both are revenue.
- High incremental costs for customers translate into durable vendor economics: As organizations scale AI, demand for resilient, low‑latency, high‑throughput infrastructure intensifies.
Valuation reset: what the market is pricing
The market correction in software equities has three intertwined dynamics: fear about seat erosion, large hyperscaler capex plans that raise the present‑value cost of growth, and a general re‑risking of forward multiples as interest rates remain elevated compared with the low‑rate era that supported frothy growth multiples.Key valuation signals:
- Median EV/Revenue multiples for many SaaS indices have come down into the mid‑single digits, in line with the notion that investors are applying greater scrutiny to growth sustainability and profitability.
- Sector ETFs and discretionary baskets have shed large chunks of market value; combined market cap erosion across listed software names in recent weeks exceeded hundreds of billions, driven more by investor sentiment and future terminal value assumptions than by immediate earnings misses.
- Analysts highlight that a small downward change in terminal value assumptions or the expected long‑run growth rate can materially lower current fair multiples for high‑growth, long‑duration businesses.
Winners, losers and the middle ground
Potential winners
- Infrastructure providers: Databases, observability, API management, GPUs/accelerators, and cloud IaaS have the clearest demand tailwinds. Usage‑based revenue models naturally capture AI-driven consumption growth.
- Security and compliance platforms: As agents proliferate and systems become more autonomous, demand for runtime protection, data lineage, and model governance rises.
- Verticalized AI vendors: Specialized agentic solutions for regulated industries (e.g., financial reconciliation, clinical decision support, claims adjudication) that demonstrate measurable ROI and certified workflows are harder to displace with generic agents.
Likely losers or highly exposed names
- Pure seat‑based SaaS without deep workflow embedding: Tools that can be replaced by a generalized agent or rebuilt internally using large foundational models face material substitution risk.
- Labor‑arbitrage service providers: Traditional outsourcing businesses that monetize headcount arbitrage are vulnerable if clients replace distributed teams with agentic automation.
- Vendors with weak data moats: If a product’s competitive advantage rests only on UX or integrations that agents can replicate, pricing pressure is likely.
The middle ground: adapt or die slowly
Most incumbents will not vanish overnight. The pragmatic path for many application vendors is to blend models: keep seat or subscription revenue for core platform access but introduce outcome elements, consumption add‑ons for agentic features, and new tiers that monetize automation benefits. Firms that successfully migrate to hybrid pricing while protecting customer ROI and predictability will survive and likely thrive; those that pivot poorly risk short‑term revenue holes and longer‑term loss of relevance.Corporate behaviour: hiring, spending and the services economy
AI’s economic implications ripple beyond software vendors to corporate budgets and hiring patterns. Several trends are visible:- Capex reallocation: Organizations and cloud providers are shifting capital toward compute and storage; this reduces the share of enterprise budgets available for application license expansions, at least in the short run.
- Headcount discipline: Some enterprises are entering renewals with structurally lower headcount forecasts, which reduces seat expansion assumptions embedded in vendor renewals.
- Services disruption: Consulting and managed‑services models that bill time and hours are at risk as automation reduces the need for large operational staffs. Demand will shift toward higher‑value advisory services (governance, integration, curated vertical solutions).
How vendors should respond: product, pricing, go‑to‑market
Vendors that want to survive and thrive must simultaneously guard near‑term revenue and invest in future architecture. Practical steps:- Reassess product value metrics
- Identify which product outcomes correlate with work completed rather than users connected. Reframe product analytics around outcomes.
- Test hybrid pricing in controlled pilots
- Offer outcome‑based or consumption‑based pilots where the vendor shares some performance risk; measure churn, gross retention, and cost to serve.
- Harden data and workflow moats
- Invest in knowledge graphs, domain‑specific training data, and deep integrations that make substitution by generic agents costly.
- Build an infrastructure partnership strategy
- For application vendors, aligning with major cloud and infrastructure partners can secure favourable cost and feature economics for large AI workloads.
- Introduce governance and explainability as product features
- Regulated customers will pay a premium for agentic systems with transparent decision trails, model auditability, and built‑in human‑in‑the‑loop controls.
What CIOs and procurement teams should do now
CIOs face a paradox: AI can deliver outsized productivity, but procurement friction, budget uncertainty, and emerging pricing models create complexity. A practical CIO checklist:- Stop buying seats as the unit of measure for AI-enabled workflows. Negotiate hybrid contracts with predictable base fees and transparent consumption caps.
- Insist on measurable KPIs for AI pilots — time saved, error rate reduction, or cost per processed item — and tie payments to those outcomes where possible.
- Build a cost‑control layer for AI consumption. Token and API billing can spike unexpectedly; internal FinOps equivalents for AI are essential.
- Prioritize vendor relationships that commit to portability and open interfaces to avoid lock‑in to a single agent or cloud ecosystem.
- Invest in governance: data hygiene, lineage, and human oversight frameworks must be in place before agentic systems are expanded.
Investment framework: how investors can parse the storm
For investors, the sector dislocation demands sharper differentiation and a return to fundamental analysis over narrative buying. Consider this framework:- Tier 1 (Structural Infra Winners): Companies with clear usage‑based revenue, sticky developer adoption, and high switching costs. These are long‑term beneficiaries of AI scale.
- Tier 2 (Transitioning Apps): Vendors with strong workflow embedding and the ability to monetize agentic features through hybrid models. Evaluate execution risk: can they reprice without destroying renewals?
- Tier 3 (High Substitution Risk): Purely seat‑driven, horizontal applications with light technical moats. These businesses face the greatest uncertainty; position sizing should reflect that risk.
- Focus on retention metrics that are value‑adjusted: net revenue retention in an AI environment must reflect not just expansion but whether expansion is durable when automation reduces headcount.
- Favor companies with transparent unit economics and an established path to profitability; the market is punishing story stocks that lack cashflow visibility.
- Treat episodic selloffs as opportunities to reweight into structurally advantaged infrastructure and security plays, but verify execution and governance.
Systemic risks and policy considerations
The shift to agentic AI and the resulting business model changes carry wider societal and market risks that deserve policy attention:- Labor market disruption: Rapid substitution for routine cognitive tasks may outpace reskilling programs. Policymakers and industry should coordinate on accelerated retraining and transitional support.
- Concentration risk: Massive hyperscaler capex can create platform concentration, where a few cloud providers control both the compute stack and many downstream monetization levers.
- National security and export control issues: Cases where advanced models and chips circulate across jurisdictional boundaries highlight geopolitical risks and the tension between open innovation and national controls.
- Regulatory clarity: As agents perform higher‑risk tasks (legal review, medical recommendations), regulators need to set meaningful standards for accountability and auditability.
What we know, what is contested, and where to be cautious
Verified, cross‑checked points:- Enterprise AI adoption surveys show rapid expansion of use cases and broad experimentation across business functions.
- Hyperscalers have signalled vastly increased capex commitments to AI infrastructure; at least one major cloud company announced capex guidance in the hundreds of billions for 2026.
- The emergence of agentic AI platforms capable of multi‑step workflows has shifted investor sentiment; product launches from major model providers have coincided with broad sector selloffs.
- Exact layoff tallies attributed solely to AI vary widely across trackers and press reports; take single‑source totals with skepticism.
- Specific forecasting claims about the percentage of enterprise apps moving entirely to outcome‑based pricing by a fixed date (for example, the oft‑repeated “40% by year‑end” figure) reflect extrapolations of analyst models and vendor surveys; they are plausible directionally but are sensitive to buyer behaviour, regulatory constraints, and execution realities.
- Cost‑of‑training numbers for certain models (reported by vendors or third parties) may omit amortization, developer tooling, or follow‑on tuning costs; treat low headline figures as informative but not definitive proof that hyperscale budgets are unnecessary.
Conclusion: the winter is a transition, not the end
“Software winter” is a useful metaphor for the current market shock because it captures both the pain of repricing and the ecological pressure on legacy forms of value capture. But winters end — and they sterilize only the weakly adapted. AI is not killing software; it is re‑shaping it.The structure of winners is already emerging: infrastructure, security, and deeply integrated vertical solutions look well placed to capture the next wave of enterprise spend. Application vendors that proactively reframe monetization around outcomes, protect their data moats, and partner effectively with infrastructure providers can survive — and in some cases, prosper. Investors who pivot from broad thematic exposure to careful, fundamentals‑centred picks will find durable opportunities as the market sorts itself out.
For CIOs, this is an immediate operational test: treat AI like infrastructure, buy outcomes not surprises, and make governance your first priority. For vendors, the message is brutal but clear: evolve your pricing and technical architecture now, or accept the probability of a slow, value‑eroding decline.
We are witnessing a structural market correction driven by real technological change. That change is disruptive and uneven, and it rewards clarity — clarity of metrics, of pricing, and of product strategy. The next three to five years will determine which vendors adapt to an agentic world and which become lessons in the cost of complacency.
Source: Investing.com The Software Winter Marks a Structural Reset as AI Rewrites the Rules | investing.com
