The iShares Expanded Tech-Software ETF (NYSE:), treated as the benchmark for the sector, has slid almost 30% from its September peak, a sharp reversal for what was considered one of the market’s safest growth franchises. Every technological cycle produces its moment of doubt. For software, that moment may be now.
Introduction
After years of uninterrupted dominance, the software sector has entered a period of sharp repricing and rising uncertainty. Artificial intelligence is becoming embedded directly into the workflows that run day-to-day operations. As this integration deepens, it changes how work is executed and how software earns its value. Markets are beginning to adjust.
AI as a Disruptor
Artificial intelligence has moved from experimentation to full deployment in a short period of time. The change goes beyond adding smarter features to existing software. Entire tasks that once required teams of employees are being automated and executed at speed.
Spending patterns highlight the acceleration. The “Magnificent 7” hyperscalers are projected to invest $650–700 billion in 2026, a 60% rise from 2025, led by Amazon’s (NASDAQ:) $200 billion budget. Capex-to-sales ratios have expanded from around 10% to above 25%. About three-quarters of that investment is directed toward chips, data centres, networking, and power.
Adoption data confirms the shift. According to Ramp, AI penetration among US businesses jumped from 5% in 2023 to 44.5% by July 2025. Early adopters report measurable productivity gains and cost efficiencies. Slower players face rising competitive pressure.
Across business functions, McKinsey estimates that more than 88% of companies use AI in at least one area. In Audit and Accounting, traditional ERP (Enterprise Resource Planning) systems such as SAP (NYSE:), Oracle Financials, and Sage rely on rule-based processes, but AI platforms like MindBridge and BlackLine now use machine learning to detect anomalies and fraud in real time, reducing manual audit work.
In Sales and CRM, Salesforce (NYSE:), Microsoft Dynamics, and HubSpot focus on pipeline tracking, but AI tools such as Salesforce Einstein GPT, Microsoft Copilot for Sales, Gong, and Clari enhance performance by analysing calls, predicting deal outcomes, and automating recommendations. In IT Service Management, ticket-based platforms like ServiceNow (NYSE:) and BMC face competition from AI agents such as Moveworks and Aisera that autonomously resolve requests.
Source: McKinsey
All of this inevitably brings the future of traditional software companies into focus. AI is no longer confined to refining existing products; it is beginning to reshape the very business functions those products were designed to support, shifting from structured workflow management toward autonomous, data-driven decision-making and execution. Irrespective of how fully that shift is reflected in valuations, the perception alone has introduced a layer of market uncertainty. The sector’s first major drawdown began around mid-2021 as generative AI technologies such as ChatGPT entered the mainstream. By mid-2025, sustained underperformance pointed to something more structural, as investors reassessed the durability of traditional software models considering AI’s expanding capabilities.
Frontier developments have reinforced that reassessment. DeepSeek shipped a $6 million AI model, signalling that advanced capability no longer requires hyperscale capital. Anthropic launched Cowork, deploying AI agents capable of autonomously handling legal review, sales operations, and compliance. Tools such as Cursor and GitHub Copilot now generate production-ready code, illustrating that AI systems can increasingly build software for AI.
Market reactions were swift. Following Claude’s Cowork launch, Nifty IT declined 6%, Infosys ADRs fell 8.4%, Cognizant dropped roughly 10%. The iShares Expanded Tech-Software ETF (IGV) is down 30% from its September high.
Source: Factset
The Two Faces of the Software Industry
The market is currently treating software as a single asset category; it is not. Beneath the surface sit two structurally different businesses valued as one: software applications, which are software humans click on, and infrastructure software, which is software other programs call. Artificial intelligence is compressing the economics of the first but structurally reinforcing the second.
Software applications encompass the tools humans interact with throughout the day: visual dashboards, customer relationship management systems, project management platforms, coding environments, legal workflow tools, and productivity suites. Think of platforms like Salesforce, HubSpot, ServiceNow, or Monday.Com (NASDAQ:). It is a segment expected to generate around $820–850 billion in revenue by 2026 and grow by 11–13% annually, with a 9.5% CAGR projected through 2029. They are typically monetised on seat-based SaaS models in which revenue scales directly with headcount. For more than a decade, the formula was powerful and predictable. Enterprise hiring drove licence expansion, which in turn drove recurring revenue growth.
That connection weakens when AI performs the underlying task. If an AI agent completes the work of an analyst, developer, or support associate, the enterprise does not simply reduce labour costs, it also eliminates the associated software seat. Seat-based SaaS compresses as headcount compresses. Technology layoffs reached approximately 151,000 in 2024, according to Times Now News (2024), with estimates suggesting the figure may approach 120,000 in 2025. Workday (NASDAQ:) has noted customers committing to structurally lower headcount assumptions at renewal, with that discipline expected to persist into 2026. Growth that accelerated sharply after the pandemic has normalised into the mid-teens and is moderating further.
Source: Koyfin
The deeper pressure is substitution rather than augmentation. Copilots enhance user productivity within existing applications, and agentic systems execute multi-step workflows autonomously. Anthropic’s Claude Cowork is designed to complete processes end to end rather than assist incrementally. When a single agent can replicate the output of several junior employees, enterprises require fewer staff and fewer licences. The traditional seat-based moat begins to erode. Gartner forecasts that by the end of 2026, 40% of enterprise applications will transition toward outcome-based agentic pricing rather than per-user logins.
Infrastructure software operates on a fundamentally different economic model. It consists of APIs, databases, event-streaming systems, observability platforms, authentication layers, and cloud infrastructure. It is not interacted with by end users; instead, other software integrates with it directly. Companies like MongoDB (NASDAQ:), Snowflake (NYSE:), Datadog (NASDAQ:), Cloudflare (NYSE:), and Twilio (NYSE:) exemplify this group.
Unlike human-facing tools, these companies monetise usage rather than headcount. They charge per workload, per compute, or per data consumed, making their revenue more usage-sensitive and less tied to the number of human users. A human performing a task may generate a limited number of API calls per hour. An AI agent performing the same task can generate hundreds per minute, continuously querying databases, logging activity, authenticating requests, and triggering downstream services. Infrastructure providers do not distinguish between human and machine traffic. They charge on consumption. As AI workloads scale, infrastructure usage scales with them.
According to Forrester estimates, the infrastructure software segment, covering cybersecurity, data management, network monitoring, and operating systems, is expected to reach $580–610 billion by 2026, growing at a healthy ~13.3% CAGR between 2027 and 2029, outpacing traditional application software growth.
Source: Spear-invest
Drivers of Software Selloff
Recent months have brought a significant correction across US software equities, erasing approximately $300 billion in market value. The decline appears less about short-term earnings pressure and more about a structural reassessment of traditional SaaS economics amid accelerating AI adoption.
For much of the past decade, SaaS growth relied on a simple formula: enterprise hiring drove new software seats, seat expansion fuelled recurring revenue, and high retention supported premium valuation multiples, which peaked above 20x EV/Sales in 2020–2021. That clear link between headcount, revenue, and valuation is now under pressure.
What concerns investors is not simply AI’s growing presence in enterprise software, but its economic implications. As systems increasingly draft code, review contracts, reconcile financial data, and update CRM records with minimal oversight, output begins to decouple from headcount. For years, SaaS growth scaled predictably with employment expansion. That linkage is becoming less reliable. If fewer users are required to generate the same or greater productivity, per-seat pricing loses some of its structural advantage.
A central driver of the recent software correction is the shifting composition of technology spending. As companies prioritise AI capabilities, budgets are migrating toward infrastructure and model development, narrowing the space for traditional SaaS subscriptions. Investment is flowing instead to the underlying architecture that supports AI, namely data storage, scalable compute, monitoring, and automation.
The extraordinary rally of 2020–2023 had pushed software valuations to unsustainable levels. Many SaaS firms traded at lofty multiples assuming continued hypergrowth and low interest rates. As inflation drove bond yields higher, the present value of future cash flows fell, prompting investors to reassess what they were willing to pay for growth.
By early 2026, the average SaaS EV/Sales multiple was approximately 3.3x, far below the 2020 peak above 20x and near a five-year low. This suggests market pessimism may have exceeded what fundamentals justify. Core business metrics—recurring revenue, high retention and strong margins—remain solid across leading companies.
Conclusion
Artificial intelligence is not ending the software industry. It is forcing it to evolve, testing its durability and reshaping how value is created across the technology stack.
For more than a decade, application software thrived on a predictable equation: seat expansion drove recurring revenue, which justified premium valuations. As AI moves from augmenting work to executing it, that linkage is being reconsidered. Infrastructure platforms stand to benefit as AI workloads scale, while models tied closely to headcount face structural pressure.
The deeper disruption lies elsewhere. The services economy built on labour arbitrage and the billing of human hours layered on top of software is far more exposed. Software remains viable; labour arbitrage may not.
