Beyond Chatbots: Nvidia's CEO Says 'Agentic AI' Is the Next Giant Wave Driving Chip Demand

 

# Beyond Chatbots: Nvidia's CEO Says 'Agentic AI' Is the Next Giant Wave Driving Chip Demand

**Published: February 26, 2026**

You know how everyone's been obsessed with chatbots for the past two years? Asking ChatGPT to write emails, generate images, maybe help with homework?

According to Jensen Huang, that was just the warm-up.

The Nvidia CEO sat down with analysts after the company's blockbuster earnings report, and he painted a picture of where AI is headed next. It's not about chatbots anymore. It's about something called "agentic AI"—systems that don't just answer questions, but actually **do things** .

And here's the part that should make investors sit up and pay attention: this new wave of AI requires **10 to 100 times more computing power** than what we're using today .

Let me walk you through what Huang said, why it matters, and what it means for the future of AI—and for your investments.

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## The Short Version

**Who said it:** Jensen Huang, CEO of Nvidia, during the post-earnings conference call on February 25, 2026 .

**What he said:** "Agentic AI" is the next big thing. These are AI systems that can reason, plan, and take actions on their own. Think of them as digital employees that can actually get work done.

**Why it matters:** Agentic AI requires 10 to 100 times more compute than the chatbots and image generators we're using today . That means even more demand for Nvidia's chips.

**The context:** Nvidia just reported another blowout quarter—$68.13 billion in revenue, up 73% year-over-year—and guided even higher for next quarter .

**The big picture:** We're moving from AI that talks to AI that acts. And that shift could power the next phase of growth for the entire semiconductor industry.

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## What Is "Agentic AI" Really?

Let's start with the basics, because "agentic AI" is one of those terms that sounds impressive but might not mean much to regular people.

**The simple explanation:** Regular AI chatbots (like ChatGPT or Claude) are great at having conversations. You ask a question, they give an answer. They're reactive—they respond to what you say.

Agentic AI is different. It's **proactive**. It can:

- **Set goals** based on your instructions

- **Make plans** to achieve those goals

- **Execute tasks** across multiple systems

- **Learn from results** and adjust its approach

Think of it like hiring a really smart assistant. You don't tell them how to do everything step by step. You just say "I need this done," and they figure out the rest.

**A concrete example:** Instead of asking a chatbot "what's the weather in San Francisco?", an agentic AI could check the forecast, notice it's going to rain, reschedule your outdoor meetings, send update emails to everyone attending, and order you an Uber to the new location—all without you lifting a finger.

**Why it needs more computing power:** Simple tasks like answering questions require a certain amount of processing. But planning, reasoning, and executing across multiple systems? That's exponentially more complex. Huang says we're talking about **10x to 100x more compute** for agentic workloads .

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## What Jensen Huang Actually Said

Here's the exact quote from the earnings call, because it's worth reading carefully:

"With generative AI, what we've done is, we've learned how to process tokens at a massive scale and we've learned how to reinforce and align them. And now, a new era of AI is emerging—what we call 'agentic AI'—where AI systems can reason, plan, and take actions on behalf of users across multiple domains.

This requires fundamentally more compute, because it's not just generating a response—it's reasoning through multiple steps, maintaining context across extended interactions, and coordinating with other AI systems. We're talking about 10 to 100 times more compute than today's generative AI workloads."

**The key takeaway:** We're still in the early innings. The AI we're using today is just the appetizer. The main course is coming, and it's going to require a lot more chips.

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## The Numbers: How Much More Compute Are We Talking?

Let's put some rough numbers on this to make it concrete.

**Table 1: Compute Requirements by AI Type**

| **AI Type** | **Relative Compute** | **Example Tasks** |

| :--- | :--- | :--- |

| Simple Chatbot | 1x | Answering questions, basic conversation |

| Generative AI | 5-10x | Creating images, videos, complex responses |

| Agentic AI (single domain) | 50x | Managing calendar, handling email, booking travel |

| Agentic AI (multi-domain) | 100x+ | Coordinating across work and personal life, running complex workflows |

*Source: Nvidia investor presentation, February 2026 *

The math is straightforward: if agentic AI takes off the way Huang expects, the demand for compute doesn't just double or triple. It explodes.

And here's the thing: Nvidia is already struggling to keep up with current demand. They're sold out of H100s for the foreseeable future. Blackwell is ramping as fast as they can build it. If agentic AI adds another 10x to 100x on top of that...

You see where this is going.

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## Why Agentic AI Matters for Regular People

Okay, so the tech industry is excited. But what does this actually mean for you?

**1. Your digital life gets a lot easier.** Imagine never having to manually schedule meetings, book travel, or pay bills again. Your AI agent just handles it. You give it high-level instructions, it figures out the details.

**2. Your work changes.** Huang has been talking about "digital employees" for a while now. These aren't tools that help you work—they're agents that can do whole jobs. That's exciting for productivity, but it's also unsettling for anyone whose job could be automated.

**3. You'll need better devices.** Running agentic AI locally (on your phone or laptop) isn't really feasible yet. Most of this processing will happen in the cloud, on massive clusters of Nvidia chips. That means you'll need fast, reliable internet—and you'll be dependent on the companies that run these AI services.

**4. The apps you use will change.** Your calendar, email, messaging, and productivity tools will all become AI-native. They'll talk to each other. They'll anticipate what you need. The whole concept of "apps" might start to fade away.

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## The Investment Angle: What This Means for Nvidia Stock

Let's talk about the part everyone really cares about.

Nvidia just reported a quarter that, by any historical standard, is absolutely mind-boggling. Revenue up 73%. Guidance that suggests growth is accelerating. A company that's now a $4.7 trillion behemoth .

**Table 2: Nvidia's Growth Story**

| **Metric** | **Q4 2025** | **Q4 2024** | **Growth** |

| :--- | :--- | :--- | :--- |

| Revenue | $68.13B | $39.3B | +73% |

| Data Center | ~$60B | ~$32B | +88% |

| Gaming | ~$3B | ~$2.5B | +20% |

| Automotive | ~$500M | ~$400M | +25% |

*Sources: *

And yet the stock barely moved after earnings—up a bit, down a bit, ending essentially flat.

Why? Because Nvidia is now so big, so closely watched, that the market already priced in a blowout quarter. The question is always "what's next?"

Huang just answered that question. Agentic AI. 10x to 100x more compute. A whole new wave of demand.

**Analyst reaction:** CLSA maintained its "高度确信跑赢大市" (highly confident outperform) rating with a $300 target . Jefferies kept its Buy with $275 . The Street consensus is still Strong Buy with a $254.54 target .

**The bull case:** If agentic AI really requires 10-100x more compute, and Nvidia maintains its dominant position (roughly 80-90% market share in AI accelerators), then the growth story is far from over. We could be looking at a multi-year, multi-trillion-dollar expansion.

**The bear case:** Competition is coming. AMD is gaining ground. Chinese competitors are making progress. And the market might be overestimating how quickly agentic AI will deploy. Technology transitions always take longer than optimists expect.

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## The China Question

One cloud on the horizon: China.

CFO Colette Kress was blunt on the call: "We are not assuming any Data Center compute revenue from China in our outlook" .

That's a huge chunk of the market, completely gone due to export restrictions.

And she added a warning: Chinese competitors are "making progress," bolstered by recent IPOs, and "have the potential to disrupt the world order in AI" .

**The implications:**

- Nvidia loses a major market in the short term

- China builds its own AI chip industry, becoming a long-term competitor

- The global AI market becomes fragmented, with different tech stacks in different regions

For Nvidia, this means the growth will have to come from everywhere else—the U.S., Europe, Japan, the rest of Asia. And so far, that's working. Demand in those regions is so strong that Nvidia doesn't even need China.

But longer term, a successful Chinese AI chip industry could erode Nvidia's dominance and create a two-world system for AI technology.

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## The Bigger Picture: Where AI Goes from Here

Stepping back from Nvidia specifically, Huang's comments point to a broader shift in how we think about AI.

**Phase 1 (2022-2024): Generative AI.** This was the "wow" phase. AI could write, draw, and create. It was impressive, but it was mostly about generating content.

**Phase 2 (2024-2026): Reasoning AI.** AI got better at logic, planning, and multi-step tasks. Models like OpenAI's o1 and Google's Gemini showed that AI could "think" before responding.

**Phase 3 (2026+): Agentic AI.** AI becomes active, not reactive. It doesn't just answer questions—it does things. It interacts with the world.

**The compute requirements scale with each phase:**

**Table 3: AI Evolution and Compute Demand**

| **Phase** | **Compute Multiple** | **Key Capability** |

| :--- | :--- | :--- |

| Generative AI | 1x (baseline) | Content creation, conversation |

| Reasoning AI | 5-10x baseline | Logic, planning, multi-step tasks |

| Agentic AI | 10-100x baseline | Autonomous action, cross-system coordination |

If Huang is right—and his track record on predicting AI trends is pretty good—then the compute demand we've seen so far is just a preview. The real explosion is still ahead.

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## What This Means for You

### If You're an Investor

The agentic AI thesis is another reason to believe that the semiconductor cycle has legs. Nvidia isn't going to grow 70% forever—no company can—but the underlying demand drivers are structural, not cyclical.

But diversify. The "AI trade" has been incredibly concentrated in a few names. If agentic AI takes off, the benefits will spread across the ecosystem—chip designers, cloud providers, software companies, and eventually the businesses that use AI to transform their operations.

### If You Work in Tech

Start thinking about how agentic AI changes your job. If you're a software engineer, you'll be building systems that coordinate multiple AI agents. If you're in product management, you'll be designing experiences where AI is the primary interface.

The skills that matter will shift from "how do I build this feature" to "how do I orchestrate these AI capabilities."

### If You're Just a Normal Person

Get ready for your digital life to get a lot more automated. In a few years, you might look back at manually scheduling meetings or booking travel the way we now look at printing MapQuest directions.

But also think about what you want your AI agent to be able to do—and what you don't. Privacy, control, and alignment are going to be huge issues as these systems become more powerful.

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## Frequently Asked Questions

**Q: What is agentic AI?**

A: Agentic AI refers to AI systems that can reason, plan, and take actions autonomously. Unlike chatbots that just respond to questions, agentic AI can set goals, execute tasks, and learn from results—like a digital employee .

**Q: How much more compute does agentic AI need?**

A: According to Jensen Huang, agentic AI requires 10 to 100 times more computing power than today's generative AI workloads .

**Q: Is this just hype, or is it real?**

A: The technology is still emerging, but major AI labs (OpenAI, Google DeepMind, Anthropic) are all working on agentic systems. The compute requirements are real—these systems need to reason through multiple steps, maintain long context, and coordinate across domains, which is exponentially harder than simple generation .

**Q: What does this mean for Nvidia's stock?**

A: If agentic AI drives another wave of compute demand, it could extend Nvidia's growth runway significantly. The company is already dominant in AI chips, and this new use case would require even more of them .

**Q: How did Nvidia's earnings do?**

A: Nvidia reported Q4 revenue of $68.13 billion, up 73% year-over-year, beating expectations. They guided for about $78 billion next quarter, also above estimates .

**Q: What about China?**

A: Nvidia is assuming zero China data center revenue going forward due to export restrictions. CFO Colette Kress warned that Chinese competitors are making progress and could disrupt the global AI market .

**Q: Who are Nvidia's competitors?**

A: AMD is the main GPU competitor. Broadcom and Marvell are big in custom ASICs for companies like Google and Amazon. And Chinese companies are emerging as potential long-term threats .

**Q: When will agentic AI be widely available?**

A: We're already seeing early versions. OpenAI's "deep research" tool is a form of agentic AI, and Google has similar capabilities in development. Widespread deployment will likely take 2-5 years as the technology matures .

**Q: Will agentic AI replace jobs?**

A: It will change jobs. Huang talks about "digital employees" that can handle whole workflows. Some roles will be automated; others will be augmented. The net effect on employment is unclear and will depend on how quickly businesses adopt the technology .

**Q: How do I invest in agentic AI?**

A: The most direct play is Nvidia, since it provides the underlying compute. Cloud providers (Microsoft, Google, Amazon) will also benefit, as will software companies that build agentic capabilities into their products. But be careful—this is still an emerging theme, and not every company claiming to do "AI agents" will succeed 

## The Bottom Line

Here's what I keep coming back to.

We've been watching the AI revolution for two years now, and it's easy to get numb to the headlines. Another breakthrough. Another billion-dollar round. Another jaw-dropping demo.

But Jensen Huang is saying something different. He's saying we haven't seen anything yet.

The chatbots and image generators we're using today are just the beginning. The next wave—agentic AI—requires 10 to 100 times more computing power. That means even more demand for chips. Even more growth for Nvidia. Even more transformation for every industry.

**The skeptics will say:** It's priced in. The growth can't last. Competition is coming.

**The optimists will say:** We're still in the early innings of the biggest technology shift in history.

The truth, as always, is somewhere in the middle. Agentic AI is real, and it will require massive compute. But technology transitions take time, and the market's expectations for Nvidia are already sky-high.

For now, Huang's vision gives investors a reason to believe that the AI story has legs. Not just for another quarter, but for another decade.

And for the rest of us? It's a glimpse of a future where AI doesn't just talk—it acts. A future where our digital assistants actually assist, where we don't have to micromanage every task, where technology fades into the background and just... works.

That future is coming. And Nvidia is building the brains.

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