"Software will devour AI": HSBC says you are wrong amid the SaaS doomsday panic

Writing 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 many manual operations. Companies using AI have completed tasks that previously required 100 people, so naturally they need fewer software seats. The SaaS business model based on seat licensing is considered to have reached the end of its lifecycle.

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

His core point, summarized in one sentence: The market panic is a misjudgment.

The Countertrend Report

“Concerns that AI will replace enterprise software are mistaken.”

He states at the beginning of the report. 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 rival; software is the vehicle through which AI reaches the real world.

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

He draws a historical analogy from the internet era: when the internet first exploded, initial value was concentrated in physical infrastructure—servers, fiber optic cables, data centers. Massive capital flowed into hardware infrastructure, and the early struggling internet companies that endured ultimately became the long-term winners. Software is the endpoint of internet value.

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

“Software will be the main mechanism for AI to diffuse across the world’s largest enterprises. We believe 2026 will be the year software monetization kicks off.”

Why Can’t Foundational Models Replace Enterprise Software?

The most compelling argument in the report is a 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) 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 answer unfolds in three levels.

First, foundational models have “inherent flaws.”

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

The fundamental reason is the limitations of training data. LLMs are trained on publicly available internet data, but the proprietary architecture knowledge, business logic, operational norms accumulated over decades in enterprise systems—these core intellectual properties are not online and cannot be learned or replicated by AI. The moat of Oracle, SAP systems is not something that can be overtaken by coding; it’s built over time and through complex business scenarios.

Second, the capabilities of vibe coding are seriously overestimated.

The report directly points out the fatal weakness of vibe coding: it shifts all design responsibility and burden onto developers. If you tell AI “I want a system to handle 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-grade software.” They are entering a highly 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 could generate code at the same level, replacing core systems would still be prohibitively expensive—disrupting revenue, losing 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 proven uptime of 99.999%, error-free operation in complex IT environments. This trust is earned over time, not just by code.

Who Will Truly Benefit from AI Monetization?

If the first part is defensive reasoning, the latter part of the report is an offensive layout.

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

“We believe AI is the primary source of value creation in 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 constraints, 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 vector he points to is AI agents.

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

In other words, enterprises don’t need an all-powerful, free-running AI; they need an AI that can be governed, audited, and operate within compliance frameworks. Only deeply embedded enterprise software systems can provide this.

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

Additionally, he predicts that inference demand will gradually surpass training demand, becoming the main driver of computing power growth. 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 software sector’s overall valuation had already fallen to historic lows. Bersey’s view: Undervaluation combined with the upcoming monetization year is an opportunity, not a signal to exit.

“Software valuations are at historic lows, even as the industry is on the cusp of large-scale expansion.”

Regarding specific stocks, HSBC’s logic is clear: companies with deep data moats, embedded AI agent capabilities, and not relying solely on headcount-based revenue models will be the biggest beneficiaries of this AI monetization wave. Buy ratings include Oracle, Microsoft, Salesforce, ServiceNow, Palantir, CrowdStrike, Alphabet, covering nearly all core enterprise software players.

Note that HSBC also downgraded IBM and Asana, and put Palo Alto Networks on a “reduce” list—meaning not all software companies will safely weather the storm. The key is whether they can serve as foundational infrastructure for AI agents, rather than being bypassed by intelligent agents as manual interfaces.

Bersey’s reasoning is tight, timing precise, and his contrarian stance has strong dissemination potential.

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

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