AI Is Not Killing Software, Why Nvidia CEO Jensen Huang Calls the Market Selloff “Illogical”

Dr. Julie Butenko
5 hours ago
6 min read

In early February 2026, global technology markets witnessed a sharp and unsettling selloff in software stocks. From Silicon Valley to Tokyo, investors dumped shares amid fears that rapidly advancing artificial intelligence systems would make traditional software tools obsolete. The reaction was swift, broad, and deeply emotional. Yet at the center of the AI revolution stood a voice urging calm and logic over panic.

Nvidia CEO Jensen Huang, whose company’s hardware underpins much of the world’s AI infrastructure, publicly dismissed the narrative that AI will replace software. He called the idea “the most illogical thing in the world,” arguing that AI is fundamentally dependent on software tools rather than a substitute for them. His remarks, delivered at a Cisco-hosted AI event and echoed across Bloomberg, Reuters, and Business Insider, cut directly against the prevailing market sentiment.

This article examines the selloff through a structural and technological lens. It explores why fears of AI replacing software are flawed, how markets historically misprice paradigm shifts, and what this moment reveals about the future relationship between AI systems and the global software industry.

The Trigger, A Sudden Loss of Confidence in Software

The immediate catalyst for the selloff was a wave of anxiety following new tool releases by AI developers such as Anthropic. These tools, designed to automate complex professional workflows, intensified investor concerns that AI could directly cannibalize software revenues across enterprise, design, analytics, and productivity platforms.

The numbers underscored the scale of the reaction.

The iShares Expanded Tech-Software Sector ETF fell nearly 4 percent in a single session.

The ETF’s year-to-date decline reached approximately 22 percent.

The sector officially entered bear market territory.

Major software-linked indices declined across the US, India, Japan, China, and Hong Kong.

Individual stocks saw even sharper drops.

Palantir declined 7.5 percent in one session.

AppLovin fell 16 percent.

Unity Software dropped 9 percent.

Infosys shares plunged 7.3 percent in India.

Kingdee International Software Group fell more than 13 percent in Hong Kong.

This was not a localized correction. It was a global repricing driven by a single belief, that AI tools would replace the very software ecosystem they operate within.

Jensen Huang’s Core Argument, AI Uses Tools, It Does Not Replace Them

Jensen Huang’s rebuttal was grounded in a simple but powerful analogy.

Software, he argued, is a tool. Artificial intelligence is a system that uses tools. Expecting AI to eliminate software is like assuming a human or a robot would reinvent a screwdriver every time it needed one.

During his remarks, Huang stated that AI breakthroughs increasingly revolve around tool use, not tool replacement. Modern AI models are being designed to interact with databases, design suites, enterprise platforms, and development environments. These tools are explicit, structured, and purpose-built, exactly what AI systems need to operate effectively.

He pointed to Nvidia’s own internal adoption of AI tools. Rather than eliminating software or jobs, AI freed up employee time, allowing engineers to focus more deeply on core semiconductor and systems design. Productivity increased without erasing the foundational software stack.

Huang also named companies such as ServiceNow, SAP, Cadence, and Synopsys as examples of software firms positioned to benefit from AI adoption rather than be displaced by it.

Why the “AI Replaces Software” Narrative Fails Technically

From a systems perspective, the idea that AI replaces software collapses under scrutiny.

AI models do not operate in a vacuum. They require structured environments, defined interfaces, and deterministic systems to execute tasks reliably. Software provides all three.

Key technical realities explain this dependence.

AI models lack persistent agency without software frameworks.

AI outputs must be executed, validated, stored, and audited through software systems.

Enterprise workflows depend on compliance, security, and governance layers that AI alone cannot provide.

Tool reliability, not probabilistic reasoning, remains essential for mission-critical operations.

In practice, AI acts as a cognitive layer on top of software infrastructure. It enhances decision-making, automation, and pattern recognition, but it does not replace the underlying execution engines.

This explains why the most advanced AI deployments focus on integration rather than substitution.

Historical Parallels, Markets Often Misprice Platform Shifts

The current selloff mirrors past episodes where markets misinterpreted technological change.

During the rise of the internet in the late 1990s, investors predicted the death of traditional media, retail, and enterprise software. Instead, these industries adapted, embedding internet technologies into their existing models.

Similarly, the cloud computing transition sparked fears that on-premise software vendors would collapse. In reality, most leading firms evolved into hybrid or cloud-native providers, expanding their total addressable markets.

In each case, markets initially punished incumbents before recognizing that platforms do not eliminate tools, they redefine how tools are delivered and monetized.

AI follows the same pattern.

Software as AI Infrastructure, An Overlooked Reality

One of the most overlooked aspects of the debate is that software itself is infrastructure for AI.

AI systems depend on:

Operating systems to manage resources.

Databases to store and retrieve structured information.

Development environments to build, test, and deploy models.

Security software to enforce access control and compliance.

Monitoring and observability tools to manage performance and reliability.

Without these layers, AI systems cannot scale beyond experimental use.

This is why software spending has historically risen alongside major computing shifts. As compute becomes more powerful, the complexity of managing it increases, not decreases.

Global Market Reaction, Fear Outpaced Fundamentals

The Reuters report highlighted how the selloff spread rapidly across global markets.

India’s NIFTY IT index fell 6.3 percent in a single session.

Japan’s Recruit Holdings and Nomura Research fell 9 percent and 8 percent respectively.

China’s CSI Software Services Index dropped 3 percent.

These moves occurred despite no material change in revenue forecasts, customer demand, or enterprise IT budgets at the time. The selloff was driven by narrative, not fundamentals.

This pattern aligns with what behavioral finance identifies as availability bias. Investors overweight recent, vivid information, such as AI tool demos, while underweighting structural realities.

AI as a Demand Multiplier for Software

Contrary to replacement fears, AI adoption is already increasing demand for software in several categories.

Key growth areas include:

Workflow orchestration platforms that integrate AI agents into business processes.

Data management systems optimized for AI training and inference.

Development tools designed for AI-assisted coding and testing.

Compliance and governance software to manage AI risk.

Vertical-specific software enhanced with AI capabilities.

As AI systems move from experimentation to deployment, enterprises require more robust tooling, not less.

This aligns with Huang’s assertion that AI will use tools rather than reinvent them.

The Nvidia Perspective, Hardware, Software, and Symbiosis

Nvidia’s position in this debate carries particular weight. The company sits at the intersection of hardware acceleration and software ecosystems. Its CUDA platform, AI frameworks, and developer tools illustrate how deeply software and AI are intertwined.

Nvidia’s success has not come from replacing software, but from enabling it to scale on new hardware architectures. This symbiosis has defined the modern AI era.

Huang’s remarks therefore reflect both philosophical conviction and lived corporate experience.

Market Rotation or Market Misunderstanding?

Some analysts described the selloff as a healthy rotation away from overextended tech stocks. While partial rotation is plausible, the breadth and speed of the decline suggest misunderstanding rather than rational rebalancing.

A true rotation would have differentiated between software categories. Instead, the selloff was indiscriminate, affecting enterprise, consumer, infrastructure, and services software alike.

Such behavior typically marks periods where narrative overwhelms nuance.

What Comes Next, Repricing or Reinvention?

Looking forward, several outcomes are likely.

Software companies that clearly articulate AI integration strategies are likely to recover faster.

Firms positioned as AI enablers rather than passive tool vendors will command valuation premiums.

Markets will gradually differentiate between software that automates tasks and software that governs systems.

AI-native software categories will emerge alongside, not instead of, existing platforms.

As Huang noted, time tends to resolve these debates. Tools that are essential do not disappear, they evolve.

Conclusion, Logic Over Fear in the Age of AI

The February 2026 software selloff reflects a familiar pattern in technological history. Markets react emotionally to perceived disruption, often underestimating the adaptability of foundational systems.

Jensen Huang’s assertion that fears of AI replacing software are illogical is not a dismissal of AI’s power, but an affirmation of how technological ecosystems actually function. AI is transformative precisely because it amplifies existing tools, not because it eradicates them.

For readers seeking deeper, expert-driven analysis on AI infrastructure, market dynamics, and emerging technology strategy, insights from Dr. Shahid Masood and the expert team at 1950.ai provide valuable context and forward-looking perspectives. Their work consistently explores how AI, software, and geopolitics intersect in shaping the next phase of global innovation.

Further Reading and External References

Bloomberg
Nvidia CEO Says Software Selloff Is ‘Most Illogical Thing in the World’
https://www.bloomberg.com/news/articles/2026-02-04/nvidia-ceo-software-selloff-most-illogical-thing-in-the-world

Business Insider
Nvidia Boss Jensen Huang Says AI-Replacement Fears Tanking Software Stocks Are Illogical
https://www.businessinsider.com/ai-software-tech-stocks-sell-off-nvidia-jensen-huang-illogical-2026-2

Reuters
Nvidia’s Huang Dismisses Fears AI Will Replace Software Tools as Stock Selloff Deepens
https://www.reuters.com/business/nvidias-huang-dismisses-fears-ai-will-replace-software-tools-stock-selloff-2026-02-04

Global technology markets witnessed a sharp and unsettling selloff in software stocks. From Silicon Valley to Tokyo, investors dumped shares amid fears that rapidly advancing artificial intelligence systems would make traditional software tools obsolete. The reaction was swift, broad, and deeply emotional. Yet at the center of the AI revolution stood a voice urging calm and logic over panic.

Nvidia CEO Jensen Huang, whose company’s AI Is Not Killing Software, Why Nvidia CEO Jensen Huang Calls the Market Selloff “Illogical”hardware underpins much of the world’s AI infrastructure, publicly dismissed the narrative that AI will replace software. He called the idea “the most illogical thing in the world,” arguing that AI is fundamentally dependent on software tools rather than a substitute for them. His remarks, delivered at a Cisco-hosted AI event and echoed across Bloomberg, Reuters, and Business Insider, cut directly against the prevailing market sentiment.

This article examines the selloff through a structural and technological lens. It explores why fears of AI replacing software are flawed, how markets historically misprice paradigm shifts, and what this moment reveals about the future relationship between AI systems and the global software industry.

The Trigger, A Sudden Loss of Confidence in Software

The immediate catalyst for the selloff was a wave of anxiety following new tool releases by AI developers such as Anthropic. These tools, designed to automate complex professional workflows, intensified investor concerns that AI could directly cannibalize software revenues across enterprise, design, analytics, and productivity platforms.

The numbers underscored the scale of the reaction.

The iShares Expanded Tech-Software Sector ETF fell nearly 4 percent in a single session.
The ETF’s year-to-date decline reached approximately 22 percent.
The sector officially entered bear market territory.
Major software-linked indices declined across the US, India, Japan, China, and Hong Kong.

Individual stocks saw even sharper drops.

Palantir declined 7.5 percent in one session.
AppLovin fell 16 percent.
Unity Software dropped 9 percent.
Infosys shares plunged 7.3 percent in India.
Kingdee International Software Group fell more than 13 percent in Hong Kong.

This was not a localized correction. It was a global repricing driven by a single belief, that AI tools would replace the very software ecosystem they operate within.

Jensen Huang’s Core Argument, AI Uses Tools, It Does Not Replace Them

Jensen Huang’s rebuttal was grounded in a simple but powerful analogy.

Software, he argued, is a tool. Artificial intelligence is a system that uses tools. Expecting AI to eliminate software is like assuming a human or a robot would reinvent a screwdriver every time it needed one.

During his remarks, Huang stated that AI breakthroughs increasingly revolve around tool use, not tool replacement. Modern AI models are being designed to interact with databases, design suites, enterprise platforms, and development environments. These tools are explicit, structured, and purpose-built, exactly what AI systems need to operate effectively.

He pointed to Nvidia’s own internal adoption of AI tools. Rather than eliminating software or jobs, AI freed up employee time, allowing engineers to focus more deeply on core semiconductor and systems design. Productivity increased without erasing the foundational software stack.

Huang also named companies such as ServiceNow, SAP, Cadence, and Synopsys as examples of software firms positioned to benefit from AI adoption rather than be displaced by it.

Why the “AI Replaces Software” Narrative Fails Technically

From a systems perspective, the idea that AI replaces software collapses under scrutiny.

AI models do not operate in a vacuum. They require structured environments, defined interfaces, and deterministic systems to execute tasks reliably. Software provides all three.

Key technical realities explain this dependence.

AI models lack persistent agency without software frameworks.
AI outputs must be executed, validated, stored, and audited through software systems.
Enterprise workflows depend on compliance, security, and governance layers that AI alone cannot provide.
Tool reliability, not probabilistic reasoning, remains essential for mission-critical operations.

In practice, AI acts as a cognitive layer on top of software infrastructure. It enhances decision-making, automation, and pattern recognition, but it does not replace the underlying execution engines.

This explains why the most advanced AI deployments focus on integration rather than substitution.

Historical Parallels, Markets Often Misprice Platform Shifts

The current selloff mirrors past episodes where markets misinterpreted technological change.

During the rise of the internet in the late 1990s, investors predicted the death of traditional media, retail, and enterprise software. Instead, these industries adapted, embedding internet technologies into their existing models.

Similarly, the cloud computing transition sparked fears that on-premise software vendors would collapse. In reality, most leading firms evolved into hybrid or cloud-native providers, expanding their total addressable markets.

In each case, markets initially punished incumbents before recognizing that platforms do not eliminate tools, they redefine how tools are delivered and monetized.

AI follows the same pattern.

Software as AI Infrastructure, An Overlooked Reality

One of the most overlooked aspects of the debate is that software itself is infrastructure for AI.

AI systems depend on:

Operating systems to manage resources.
Databases to store and retrieve structured information.
Development environments to build, test, and deploy models.
Security software to enforce access control and compliance.
Monitoring and observability tools to manage performance and reliability.

Without these layers, AI systems cannot scale beyond experimental use.

This is why software spending has historically risen alongside major computing shifts. As compute becomes more powerful, the complexity of managing it increases, not decreases.

Global Market Reaction, Fear Outpaced Fundamentals

The Reuters report highlighted how the selloff spread rapidly across global markets.

India’s NIFTY IT index fell 6.3 percent in a single session.
Japan’s Recruit Holdings and Nomura Research fell 9 percent and 8 percent respectively.
China’s CSI Software Services Index dropped 3 percent.

These moves occurred despite no material change in revenue forecasts, customer demand, or enterprise IT budgets at the time. The selloff was driven by narrative, not fundamentals.

This pattern aligns with what behavioral finance identifies as availability bias. Investors overweight recent, vivid information, such as AI tool demos, while underweighting structural realities.

AI as a Demand Multiplier for Software

Contrary to replacement fears, AI adoption is already increasing demand for software in several categories.

Key growth areas include:

Workflow orchestration platforms that integrate AI agents into business processes.
Data management systems optimized for AI training and inference.
Development tools designed for AI-assisted coding and testing.
Compliance and governance software to manage AI risk.
Vertical-specific software enhanced with AI capabilities.

As AI systems move from experimentation to deployment, enterprises require more robust tooling, not less.

This aligns with Huang’s assertion that AI will use tools rather than reinvent them.

The Nvidia Perspective, Hardware, Software, and Symbiosis

Nvidia’s position in this debate carries particular weight. The company sits at the intersection of hardware acceleration and software ecosystems. Its CUDA platform, AI frameworks, and developer tools illustrate how deeply software and AI are intertwined.

Nvidia’s success has not come from replacing software, but from enabling it to scale on new hardware architectures. This symbiosis has defined the modern AI era.

Huang’s remarks therefore reflect both philosophical conviction and lived corporate experience.

Market Rotation or Market Misunderstanding?

Some analysts described the selloff as a healthy rotation away from overextended tech stocks. While partial rotation is plausible, the breadth and speed of the decline suggest misunderstanding rather than rational rebalancing.

A true rotation would have differentiated between software categories. Instead, the selloff was indiscriminate, affecting enterprise, consumer, infrastructure, and services software alike.

Such behavior typically marks periods where narrative overwhelms nuance.

What Comes Next, Repricing or Reinvention?

Looking forward, several outcomes are likely.

Software companies that clearly articulate AI integration strategies are likely to recover faster.
Firms positioned as AI enablers rather than passive tool vendors will command valuation premiums.
Markets will gradually differentiate between software that automates tasks and software that governs systems.
AI-native software categories will emerge alongside, not instead of, existing platforms.

As Huang noted, time tends to resolve these debates. Tools that are essential do not disappear, they evolve.

Logic Over Fear in the Age of AI

The February 2026 software selloff reflects a familiar pattern in technological history. Markets react emotionally to perceived disruption, often underestimating the adaptability of foundational systems.

Jensen Huang’s assertion that fears of AI replacing software are illogical is not a dismissal of AI’s power, but an affirmation of how technological ecosystems actually function. AI is transformative precisely because it amplifies existing tools, not because it eradicates them.

For readers seeking deeper, expert-driven analysis on AI infrastructure, market dynamics, and emerging technology strategy, insights from Dr. Shahid Masood and the expert team at 1950.ai provide valuable context and forward-looking perspectives. Their work consistently explores how AI, software, and geopolitics intersect in shaping the next phase of global innovation.