While everyone is selling off software stocks, HSBC says you're wrong

Written by: Cosmic Wave Naruto, Deep Tide TechFlow

February 2026, the tech stock market is experiencing a systemic crash that some media are calling “SaaSpocalypse” (the end of SaaS).

Salesforce’s stock price has fallen nearly 40% from its 2025 peak; ServiceNow’s quarterly earnings report caused a single-day drop of over 11%, simply because management mentioned during a conference call that “AI agents are making seat visibility more complex”; Workday dropped over 22%; the entire S&P 500 Software and Services Index lost nearly $1 trillion in market value within the first six weeks of 2026.

The market logic is straightforward: AI agents can already replace a large amount of manual operations. Companies using AI have completed tasks that previously required 100 people, so naturally, they no longer need 100 software seats. The SaaS business model, which charges per seat, is considered to have reached the end of its lifecycle.

Amid this panic selling sweeping the industry, Stephen Bersey, head of US tech research at HSBC, published a highly provocative research report titled: “Software Will Eat AI.”

His core point, summarized in one sentence: the market’s panic is a misjudgment.

Countertrend Report

“Market concerns that AI will replace enterprise software are mistaken.”

He begins the report by stating that, in his view, AI will not eliminate software but will be absorbed by it, becoming an embedded capability layer within enterprise software platforms. Software is not AI’s opponent; rather, software is the vehicle through which AI reaches the real world.

This logic flips the current market narrative. The fear is “AI replacing software,” but Bersey’s judgment is “software will tame AI.”

He draws a historical analogy from the internet era: initially, the value was concentrated in physical infrastructure—servers, fiber optic cables, data centers. Massive capital flowed into hardware infrastructure, and the struggling early internet companies ultimately became the ones to generate long-term value. Software, in the end, is the ultimate value of the internet.

Bersey believes AI’s evolution is reenacting this same script. 2024 and 2025 are infrastructure-building years—computing power, models, code integration—all paving the way for explosive growth at the software layer. 2026 is the year the engine truly ignites.

“Software will be the main mechanism for AI to diffuse into the world’s largest enterprises. We believe 2026 will be the start of monetizing AI.”

Why Can’t Foundational Models Replace Enterprise Software?

The most compelling argument in the report is its layered dismantling of the logic that “AI will directly overthrow software.”

Critics’ views seem convincing: large language models can already write code, vibe coding (generating usable software directly from natural language descriptions) is emerging, AI model companies are experimenting more at the application layer. So why do enterprises still need costly traditional software systems like Oracle, SAP, Salesforce?

Bersey’s response unfolds in three levels.

First, foundational models have “inherent flaws.”

The report clearly states that foundational models “have intrinsic limitations” and cannot undertake the task of “comprehensive replacement” of large enterprise core platforms. They perform well in narrow scenarios—image generation, small application development, text processing—but for high-fidelity, enterprise-grade core platforms, this is “not realistic.”

The fundamental reason lies in the limitations of training data. LLMs are trained on publicly available internet data, but the private architecture knowledge, business logic, operational standards accumulated over decades in enterprise systems—these core intellectual properties are not on the internet and cannot be learned or replicated by AI. The system moat of Oracle and SAP is not something that can be caught up with by writing code; it is built over time and through accumulated business scenarios.

Second, the capabilities of vibe coding are seriously overestimated.

The report directly points out the fatal weakness of vibe coding: it shifts the entire design responsibility and burden onto developers. If you tell AI, “I want a system that handles global supply chains,” AI can generate code, but “how to define the system architecture, handle exceptions, ensure stability under extreme stress”—these judgments still require human input.

More critically, Bersey notes that major AI model companies “have almost no experience in creating enterprise software.” They are entering an extremely complex environment from scratch. Enterprise software has evolved over decades to achieve “almost zero errors, high throughput, high reliability,” standards that AI newcomers cannot meet in the short term.

Third, the cost of switching for enterprises is a real high wall.

Even if AI can generate code of comparable quality, the cost of replacing core systems remains extremely high—disruption of revenue, loss of productivity, compatibility issues across IT environments, trust built through brand and service quality… These are real switching costs that won’t disappear just because AI can write code.

Enterprise software demands decades of proven uptime—99.999% availability—and error-free operation in complex IT environments. This trust is earned over time, not just by stacking code.

Who Will Truly Benefit from AI Monetization?

If the first half is defensive reasoning, the second half of the report is an offensive strategic layout.

Bsey’s core judgment: the greatest share of AI’s value chain will ultimately flow into the software layer, not hardware or chips.

“We believe AI is the primary source of value creation within the software stack, and the largest long-term value share will belong to software, not hardware.”

He also points out that hardware scarcity—GPU shortages, power limitations, data center bottlenecks—will persist for years. This scarcity reinforces the strategic importance of software platforms: only software platforms can convert AI capabilities into scalable, repeatable business value.

The specific monetization vehicle the report points to is AI agents (agentic AI).

Bersey predicts that by 2026, task-oriented, workflow-embedded AI agents will see large-scale deployment in Fortune 2000 companies and SMEs. However, his characterization of agents differs sharply from mainstream narratives; he does not see agents as disruptors that replace software but as entities that must operate within parameters and permissions defined by software. Only such “boundary-limited agents” can meet enterprise needs for AI risk management.

In other words, enterprises do not need an all-powerful, free-running AI; they need an AI that can be governed, audited, and operate within compliance frameworks. And this can only be achieved by deeply embedding agents within enterprise software systems.

“Software is the key pathway for enterprises to control AI use.” This is the most core conclusion of the entire report.

Additionally, the report predicts that inference demand will gradually surpass training demand, becoming the main driver of computing power growth. This means that as agents become more widespread, computing consumption will not shrink but continue to grow, further supporting the entire software and infrastructure ecosystem.

Opportunity or Trap?

At the time of the report’s release, the overall valuation of the software sector had already fallen to historic lows. Bersey’s view is that this undervaluation, combined with the upcoming monetization year, presents an opportunity rather than a signal to exit.

“Software valuations are at historic lows, yet the industry is on the cusp of massive expansion.”

Regarding specific stock recommendations, HSBC’s logic is clear: companies that have built deep data moats, possess embedded AI agent capabilities, and do not rely solely on headcount-based revenue models will be the biggest beneficiaries of this AI monetization wave. The buy list includes Oracle, Microsoft, Salesforce, ServiceNow, Palantir, CrowdStrike, Alphabet, covering nearly all core enterprise software players.

Notably, HSBC has also downgraded IBM and Asana, and placed Palo Alto Networks on a “reduce” rating. Not all software companies can safely navigate this wave; success depends on whether they can become foundational infrastructure for AI deployment rather than just AI bypassed through manual interfaces.

Bersey’s logical rigor, timely insights, and contrarian stance have a strong propagative effect.

But one question remains unaddressed: if AI agents can truly operate efficiently within enterprise software frameworks, will enterprise demand for “seats” in software continue to quietly decline? The value of software as an AI vehicle may be valid, but whether the “per seat” business model can sustain current valuations remains an open question.

Will software swallow AI, or will AI swallow software? This debate will find new evidence in every financial report of 2026.