At a Feb. 26, 2026 interview with CNBC, Nvidia CEO Jensen Huang pushed back against widespread investor fears that generative AI will displace enterprise software firms. Speaking after Nvidia reported fiscal fourth-quarter revenue that jumped 73% year-over-year to $68.13 billion and issued a bullish fiscal‑first‑quarter guide of $78 billion ±2%, Huang argued agentic AI will act as a user of existing software tools rather than a wholesale replacement. He cited familiar examples — from web browsers to Excel — to illustrate agents using, not eliminating, the platforms enterprises rely on. The remarks arrived amid a sharp selloff in many software stocks and renewed debate over how AI will reshape enterprise economics.
Key Takeaways
- Nvidia reported fiscal Q4 revenue of $68.13 billion, up 73% from a year earlier, beating analysts’ estimate of $66.21 billion.
- The company guided fiscal Q1 revenue to $78 billion ±2%, well above the consensus forecast of $72.6 billion.
- Jensen Huang told CNBC investors “got it wrong,” arguing agentic AI will use established enterprise tools (examples: Cadence, Synopsys, ServiceNow, SAP) rather than replace them.
- After-hours moves: Synopsys fell about 3.6%, Cadence slipped 0.9%, ServiceNow held roughly flat and SAP rose ~0.3% following the comments.
- The S&P 500 software and services index had lost nearly 23% year-to-date as of the market close on Feb. 26, 2026, reflecting investor worries about AI-driven disruption.
- Some market participants warn that AI could automate workflows and compress margins, raising survival risk for weaker software firms, while others emphasize adaptation and consolidation as countervailing forces.
Background
Investor concern about AI’s impact on software providers has been growing through 2025 and into 2026, driven by rapid adoption of large language models and specialized accelerators that promise to automate routine tasks. Analysts have argued that agentic systems—AI programs that take multi-step actions on behalf of users—could reduce demand for some legacy software functions and create pricing pressure. At the same time, hyperscaler and enterprise spending on AI hardware and chips has surged, benefiting firms such as Nvidia and stoking debate about whether that demand is sustainable.
Historically, major technology transitions (railroads, canals, the internet) created winners and losers rather than uniformly eliminating entire industries; investors and managers are trying to pinpoint which software companies will prosper, which will be disrupted, and which will consolidate or exit. Enterprise vendors such as ServiceNow and SAP supply workflow orchestration and application backbones; design automation vendors like Cadence and Synopsys serve specialized engineering needs. Each vendor’s product depth, customer relationships, and ability to embed AI into workflows will shape its resilience.
Main Event
Huang made his comments during a televised interview following Nvidia’s earnings release on Feb. 26, 2026. The company’s quarterly results and forward guidance outperformed Street estimates, reinforcing Nvidia’s central role in the current AI investment cycle. In that context, he dismissed the narrative that agentic AI will “eat” enterprise software, saying instead that agents will invoke and orchestrate existing applications to carry out tasks.
He offered concrete analogies—pointing to browsers and spreadsheet software as platforms agents will call upon—and named enterprise vendors such as Cadence, Synopsys, ServiceNow and SAP as examples of tools that agents would use. Huang emphasized that the work these platforms perform is specialized and not easily replaced by a general-purpose agent; rather, agents will enhance productivity by leveraging the established capabilities of those systems.
The market reaction was mixed. Nvidia shares rose modestly in extended trading after the report, while several software stocks continued to trade lower, reflecting investor differentiation across the software sector. Some portfolio managers cautioned that, despite Huang’s optimism, automation risks remain real for firms with thin moats or commoditized offerings.
Analysis & Implications
If Huang’s framing proves accurate, agentic AI could expand total addressable markets for many software vendors by increasing usage and embedding new automation-driven workflows that rely on back-end systems. Vendors that provide APIs, integrations, data governance and domain-specific logic may find demand rising as agents require structured, reliable tools to complete tasks and return interpretable results to users.
Conversely, the economics will shift. Automation can compress service revenues and lower switching costs in some segments, particularly where human-intensive workflows dominate today. Companies that fail to productize AI-enabled features, secure their data pipelines, or maintain strong customer relationships risk margin pressure and customer churn. Investors will likely increasingly differentiate between vendors with defensible platforms and those exposed to rapid commoditization.
Macro effects matter too. Sustained, outsized spending on AI infrastructure could reallocate corporate IT budgets and influence M&A patterns: acquisitive incumbents may buy to fill AI gaps, while struggling suppliers could face consolidation. Regulatory and privacy constraints will also shape how quickly agents can automate cross-application workflows, especially where sensitive data is involved.
Comparison & Data
| Metric | Value | Consensus/Note |
|---|---|---|
| Fiscal Q4 revenue (Nvidia) | $68.13 billion | YoY +73%; analysts est $66.21B |
| Fiscal Q1 guidance (Nvidia) | $78.0 billion ±2% | Street forecast ~$72.6B |
| S&P 500 Software & Services index (YTD) | ≈ -23% | As of market close Feb. 26, 2026 |
The table highlights the tension that drove the day’s headlines: Nvidia’s outsized revenue growth and aggressive guidance contrasted with sizable YTD declines in software indexes. That divergence reflects differing market views on where near-term profits will accrue in the AI value chain.
Reactions & Quotes
Market veterans and commentators offered divergent takes after Huang’s interview and Nvidia’s results.
“I think the markets got it wrong,” Jensen Huang said, arguing agents will use enterprise software as tools rather than replace them.
Jensen Huang, Nvidia (CEO)
Context: Huang used the comment to counter narratives predicting broad software obsolescence, stressing that specialized platforms will still be needed to finish tasks and present results in understandable ways.
“There’s some real companies that are going to go to zero in the software space,” Dan Niles warned, urging caution about automation and margin compression.
Dan Niles, Founder & Portfolio Manager, Niles Investment Management
Context: Niles emphasized that while some vendors will adapt, others with weak moats or exposed business models could be severely damaged by automation and new entrants lowering barriers.
“The software companies are survivors. They can merge. They can adapt,” Jim Cramer observed, suggesting consolidation and adaptation are likely outcomes rather than outright extinction.
Jim Cramer, Host, Mad Money
Context: Cramer highlighted historical resilience in the software sector and argued that firms priced for perfection may simply undergo restructuring or M&A instead of disappearing wholesale.
Unconfirmed
- Which specific software vendors will fail or go to zero as a direct result of AI remains speculative; outcomes depend on execution, positioning and market dynamics.
- The long‑term sustainability of the current pace of corporate AI hardware spending is uncertain and could change with macro conditions or shifting priorities.
- The degree to which agentic AI will be able to perform deep, domain‑specific tasks without human oversight is an open question and will vary by industry and application.
Bottom Line
Jensen Huang’s central contention is that agentic AI will act as an orchestrator that leverages existing enterprise tools rather than a force that simply erases them. Nvidia’s strong Q4 results and aggressive Q1 guidance reinforce the company’s central role in powering current AI deployments, but they do not settle the broader debate about software winners and losers.
Investors and managers should expect a mixed outcome: some vendors will strengthen their positions by embedding AI and offering reliable integrations, others will face margin pressure and potential consolidation, and some business models will need to be reimagined. Monitoring product differentiation, customer stickiness, data governance, and partnership strategies will be essential to separate transient market fear from enduring competitive shifts.