Global technology markets have entered a phase of sharp introspection as investors reassess how artificial intelligence reshapes, rather than replaces, the foundations of the software industry. The latest selloff in software and IT services stocks reflects anxiety about speed and scale: how quickly AI capabilities are advancing, and how deeply they might cut into established business models. Against that backdrop, Nvidia chief executive Jensen Huang’s rejection of the idea that AI will make software tools obsolete has become a focal point—not as a defense of valuations, but as a reframing of how AI actually functions inside modern digital systems.
The market turbulence has been amplified by a broader re-rating of technology risk. Software stocks, long treated as predictable growth engines, suddenly found themselves priced as if core demand could evaporate. Yet Huang’s argument cuts across that fear, insisting that AI is not a replacement layer but an accelerant—one that increases the importance of tools, platforms, and structured software rather than erasing them.
A Market Selloff Rooted in Misread Disruption
The recent decline in software stocks has been driven less by earnings revisions and more by narrative shock. New AI releases, particularly those demonstrating advanced reasoning and task execution, have intensified the belief that large segments of professional and enterprise software could be bypassed. Investors extrapolated early demonstrations into sweeping conclusions: if AI can write code, analyze data, and generate reports, why would companies continue to pay for specialized software?
This line of thinking spread rapidly across global markets. IT services firms, enterprise software providers, and professional data companies were sold aggressively as investors priced in accelerated disruption. The speed of the selloff reflected how crowded software exposure had become in global portfolios, especially in markets where technology services represent a large share of equity benchmarks.
Yet what markets often do in such moments is compress complex technological change into a binary outcome—replacement or survival. That compression creates volatility, but it also opens space for correction once the underlying mechanics are examined more closely.
Huang’s Core Argument: AI Is a Tool User, Not a Tool Destroyer
At the heart of Jensen Huang’s pushback is a functional view of artificial intelligence. AI systems, no matter how advanced, do not operate in abstraction. They require structured environments, defined interfaces, databases, APIs, development frameworks, and security layers. In other words, they require software.
Huang’s dismissal of replacement fears rests on a simple logic: intelligence, whether human or artificial, is most effective when it uses tools designed for specific purposes. Software encodes decades of domain knowledge—rules, workflows, compliance requirements, and optimization logic—that AI models do not spontaneously recreate. Rebuilding those layers from scratch would be inefficient, risky, and commercially irrational.
Instead, AI systems are increasingly designed to sit on top of existing software stacks, calling tools when needed, automating sequences, and enhancing productivity within established frameworks. The most advanced AI breakthroughs have moved in this direction, emphasizing orchestration and tool use rather than end-to-end replacement.
Why Software Becomes More Valuable as AI Spreads
Paradoxically, wider AI adoption can increase demand for software rather than reduce it. As organizations deploy AI across functions, the complexity of managing data flows, permissions, auditing, and integration grows. This complexity is not solved by models alone. It requires robust platforms that define how AI interacts with real-world systems.
Enterprise software vendors that control data pipelines, identity management, compliance frameworks, and industry-specific workflows remain critical. AI does not eliminate the need for customer relationship systems, financial platforms, legal databases, or enterprise resource planning tools. It increases the need for them to be interoperable, extensible, and secure.
This distinction matters for investors. The risk is not that software disappears, but that pricing power shifts toward vendors that successfully embed AI into their offerings. Markets initially sold the sector as a whole, but longer-term performance will hinge on execution rather than existential threat.
Global Markets Reflect the Same Anxiety, With Local Nuances
The selloff’s spread across Asia highlights how universal these fears have become. In markets such as India, where IT services firms generate revenue by building, maintaining, and optimizing software for global clients, the concern is not replacement but margin compression. Investors worry that AI could reduce billable hours or automate portions of work traditionally done by large teams.
In China and Japan, declines in software and research firms reflected similar anxieties, layered with domestic economic uncertainty and policy considerations. Yet across regions, the underlying question is the same: will AI collapse demand, or reshape it?
Huang’s argument suggests reshaping is far more likely. AI changes how work is done, but it does not eliminate the need for structured systems. In many cases, it raises expectations for speed, accuracy, and customization—requirements that reinforce the value of specialized software rather than negate it.
Stock Volatility as a Symptom of Valuation Reset, Not Industry Collapse
The depth of the selloff also reflects how richly valued software stocks had become. Years of stable growth, recurring revenue, and low interest rates encouraged investors to treat the sector as a defensive growth play. AI disrupted that comfort, forcing markets to reconsider long-term margins and competitive moats.
What followed was a classic valuation reset. Stocks priced for certainty were suddenly repriced for uncertainty. That process can be violent, but it does not necessarily signal structural decline. Historically, similar resets have occurred during previous technological transitions—from cloud computing to mobile platforms—before new leaders and business models emerged.
The distinction between cyclical repricing and structural erosion is critical. Huang’s comments implicitly argue that the latter is being overstated. AI changes the economics of software development and usage, but it does not invalidate the industry’s core role.
Nvidia’s Position Reflects Ecosystem Thinking
Huang’s confidence is also informed by Nvidia’s vantage point within the AI ecosystem. As a supplier of computing infrastructure rather than application software, Nvidia sees firsthand how AI systems are built and deployed. Training and inference workloads rely on vast software layers: operating systems, compilers, frameworks, libraries, and orchestration tools.
From this perspective, AI is not a monolith but a stack. Hardware accelerates computation, models provide reasoning, and software binds everything together into usable systems. Remove any layer, and the system fails. This ecosystem view contrasts sharply with the market’s tendency to isolate AI as a singular, replacement force.
It also explains why Huang frames the debate in terms of logic rather than optimism. His argument is not that software companies are immune to disruption, but that the idea of wholesale replacement misunderstands how intelligence—human or artificial—actually operates.
As markets digest these dynamics, the focus is likely to shift from fear of displacement to scrutiny of adaptation. Investors will increasingly differentiate between companies that treat AI as an add-on and those that redesign products, workflows, and pricing models around it.
The recent selloff, while painful, may ultimately accelerate that differentiation. By forcing valuations lower and expectations higher, it raises the bar for execution. Companies that demonstrate credible AI integration strategies are likely to regain investor confidence, while those that fail to adapt may continue to lag.
In that sense, the volatility surrounding Huang’s remarks is less about defending the past and more about clarifying the future. AI is not erasing software. It is redefining how software creates value—and markets, after an initial shock, are beginning to price that distinction more carefully.
(Source:www.businesstimes.com.sg)
The market turbulence has been amplified by a broader re-rating of technology risk. Software stocks, long treated as predictable growth engines, suddenly found themselves priced as if core demand could evaporate. Yet Huang’s argument cuts across that fear, insisting that AI is not a replacement layer but an accelerant—one that increases the importance of tools, platforms, and structured software rather than erasing them.
A Market Selloff Rooted in Misread Disruption
The recent decline in software stocks has been driven less by earnings revisions and more by narrative shock. New AI releases, particularly those demonstrating advanced reasoning and task execution, have intensified the belief that large segments of professional and enterprise software could be bypassed. Investors extrapolated early demonstrations into sweeping conclusions: if AI can write code, analyze data, and generate reports, why would companies continue to pay for specialized software?
This line of thinking spread rapidly across global markets. IT services firms, enterprise software providers, and professional data companies were sold aggressively as investors priced in accelerated disruption. The speed of the selloff reflected how crowded software exposure had become in global portfolios, especially in markets where technology services represent a large share of equity benchmarks.
Yet what markets often do in such moments is compress complex technological change into a binary outcome—replacement or survival. That compression creates volatility, but it also opens space for correction once the underlying mechanics are examined more closely.
Huang’s Core Argument: AI Is a Tool User, Not a Tool Destroyer
At the heart of Jensen Huang’s pushback is a functional view of artificial intelligence. AI systems, no matter how advanced, do not operate in abstraction. They require structured environments, defined interfaces, databases, APIs, development frameworks, and security layers. In other words, they require software.
Huang’s dismissal of replacement fears rests on a simple logic: intelligence, whether human or artificial, is most effective when it uses tools designed for specific purposes. Software encodes decades of domain knowledge—rules, workflows, compliance requirements, and optimization logic—that AI models do not spontaneously recreate. Rebuilding those layers from scratch would be inefficient, risky, and commercially irrational.
Instead, AI systems are increasingly designed to sit on top of existing software stacks, calling tools when needed, automating sequences, and enhancing productivity within established frameworks. The most advanced AI breakthroughs have moved in this direction, emphasizing orchestration and tool use rather than end-to-end replacement.
Why Software Becomes More Valuable as AI Spreads
Paradoxically, wider AI adoption can increase demand for software rather than reduce it. As organizations deploy AI across functions, the complexity of managing data flows, permissions, auditing, and integration grows. This complexity is not solved by models alone. It requires robust platforms that define how AI interacts with real-world systems.
Enterprise software vendors that control data pipelines, identity management, compliance frameworks, and industry-specific workflows remain critical. AI does not eliminate the need for customer relationship systems, financial platforms, legal databases, or enterprise resource planning tools. It increases the need for them to be interoperable, extensible, and secure.
This distinction matters for investors. The risk is not that software disappears, but that pricing power shifts toward vendors that successfully embed AI into their offerings. Markets initially sold the sector as a whole, but longer-term performance will hinge on execution rather than existential threat.
Global Markets Reflect the Same Anxiety, With Local Nuances
The selloff’s spread across Asia highlights how universal these fears have become. In markets such as India, where IT services firms generate revenue by building, maintaining, and optimizing software for global clients, the concern is not replacement but margin compression. Investors worry that AI could reduce billable hours or automate portions of work traditionally done by large teams.
In China and Japan, declines in software and research firms reflected similar anxieties, layered with domestic economic uncertainty and policy considerations. Yet across regions, the underlying question is the same: will AI collapse demand, or reshape it?
Huang’s argument suggests reshaping is far more likely. AI changes how work is done, but it does not eliminate the need for structured systems. In many cases, it raises expectations for speed, accuracy, and customization—requirements that reinforce the value of specialized software rather than negate it.
Stock Volatility as a Symptom of Valuation Reset, Not Industry Collapse
The depth of the selloff also reflects how richly valued software stocks had become. Years of stable growth, recurring revenue, and low interest rates encouraged investors to treat the sector as a defensive growth play. AI disrupted that comfort, forcing markets to reconsider long-term margins and competitive moats.
What followed was a classic valuation reset. Stocks priced for certainty were suddenly repriced for uncertainty. That process can be violent, but it does not necessarily signal structural decline. Historically, similar resets have occurred during previous technological transitions—from cloud computing to mobile platforms—before new leaders and business models emerged.
The distinction between cyclical repricing and structural erosion is critical. Huang’s comments implicitly argue that the latter is being overstated. AI changes the economics of software development and usage, but it does not invalidate the industry’s core role.
Nvidia’s Position Reflects Ecosystem Thinking
Huang’s confidence is also informed by Nvidia’s vantage point within the AI ecosystem. As a supplier of computing infrastructure rather than application software, Nvidia sees firsthand how AI systems are built and deployed. Training and inference workloads rely on vast software layers: operating systems, compilers, frameworks, libraries, and orchestration tools.
From this perspective, AI is not a monolith but a stack. Hardware accelerates computation, models provide reasoning, and software binds everything together into usable systems. Remove any layer, and the system fails. This ecosystem view contrasts sharply with the market’s tendency to isolate AI as a singular, replacement force.
It also explains why Huang frames the debate in terms of logic rather than optimism. His argument is not that software companies are immune to disruption, but that the idea of wholesale replacement misunderstands how intelligence—human or artificial—actually operates.
As markets digest these dynamics, the focus is likely to shift from fear of displacement to scrutiny of adaptation. Investors will increasingly differentiate between companies that treat AI as an add-on and those that redesign products, workflows, and pricing models around it.
The recent selloff, while painful, may ultimately accelerate that differentiation. By forcing valuations lower and expectations higher, it raises the bar for execution. Companies that demonstrate credible AI integration strategies are likely to regain investor confidence, while those that fail to adapt may continue to lag.
In that sense, the volatility surrounding Huang’s remarks is less about defending the past and more about clarifying the future. AI is not erasing software. It is redefining how software creates value—and markets, after an initial shock, are beginning to price that distinction more carefully.
(Source:www.businesstimes.com.sg)