What is an AI Agent — Differences from Chatbots, Beneficiary Stocks, and Your Portfolio Now

You hear the term “AI agent” a lot these days, don’t you? From GTC presentations and podcasts to brokerage reports. However, it is difficult to find articles that clearly explain how they differ from chatbots. To start with the conclusion— chatbots answer questions, while agents handle tasks. Why this single difference drives computing demand by millions of times, and what does it mean for your portfolio? Aleph will explain it all in one go, for everyone from newcomers to investors.

1 What is an AI Agent — How is it different from a chatbot?

Let's start by clarifying the concepts. On the surface, AI agents and chatbots like ChatGPT both appear to be "conversing with AI," but they are completely different when you look inside.

The key lies in the frequency of computing consumption. Chatbots perform one inference per question and that's it. Agents are different. Like “reading files → writing code → testing → fixing errors → retrying,” they repeat inference dozens to hundreds of times until the job is done. The tokens used in this process amount to hundreds to thousands of times that of a chatbot. Now you suddenly understand why Jensen Huang said in his GTC 2026 keynote speech , “computing demand has grown a millionfold over the past two years,” right?

2 What Jensen Huang Said Himself — Check the Original Text

We have selected only verified statements made directly by Hwang at the GTC 2026 keynote speech (March 16) and the All-In podcast (March 19). These are the original texts, not summaries.

Regarding computing demand, he said: “Inference demand has grown about 10,000x since ChatGPT launched — and computing demand overall has grown a millionfold over the past two years.” This means that since the release of ChatGPT, inference demand alone has increased 10,000-fold, and overall computing demand has grown 1 million-fold. Of course, one must take into account that these remarks were made by the CEO of a company that sells NVIDIA GPUs.

The evolution of AI was summarized as follows: “AI has evolved from perception to generation, to reasoning, and now it can truly get things done.” Perception → Generation → Reasoning → Execution. The final “get things done” refers precisely to the era of agents.

He added that the scale of AI infrastructure investment in 2027 will be “at least a trillion dollars,” and that actual demand will be much larger than that.

3Why Agents “Explode” Computing Demand

It may not be immediately clear just by looking at the numbers. It becomes clearer why agents generate this level of computing demand when viewed through the structure. In his GTC keynote speech, Huang summarized the evolution of AI into four stages.

Here are three reasons why computing demand is exploding at the agent level.

① Iterative Inference. Agents do not stop reasoning until the task is finished. Even when writing a single piece of code, they repeat the process of “write → test → fix errors → retry” dozens of times. The difference in token consumption between a single chatbot conversation and a single agent task is hundreds to thousands of times.

② Multi-Agent Collaboration. In actual corporate settings, a single agent does not work alone. Research agents, coding agents, and verification agents collaborate simultaneously. It is essentially like an entire team taking over the space previously occupied by a single employee.

③ Always-On. In fact, this is the biggest change. The chatbot only turns on when I ask a question. The agent runs in the background 24 hours a day, monitoring and processing. It is like having a non-stop employee. Naturally, computing resources do not rest either.

4 Investor Perspective — Benefit Structure of the Agent Era

You have likely understood the concept. Now, the most important question from an investor's perspective is, "So, who makes money?"

The beneficiary structure consists of three main tiers. Tier 1 (chips and hardware) is the most direct. This includes NVIDIA, which supplies the GPUs used to create tokens; SK Hynix , which supplies the HBM memory attached to those GPUs; and Broadcom, which holds data center networking chips. The structure is such that demand in this tier increases as agents use tokens. Tier 2 (cloud infrastructure) provides a slightly more stable benefit. This is because the agents actually run in the cloud. Microsoft Azure and AWS fall into this category; however, it is important to note that both companies are currently developing their own chips, meaning they are moving toward reducing their dependence on NVIDIA in the long term.

5 How to Reflect the Agent Era in Existing Portfolios

If we re-examine the Balanced Portfolio (Overseas 55% / Domestic 35% / Hedge 10%) from Aleph's previously discussed 2026 AI Stock Portfolio Analysis from the perspective of the agent era, the overall framework is maintained, but two key points are added. If you haven't read it yet, it will be more helpful to read it along with this article.

6 2 Things to Do Right Now

Conclusion — Agents are not the next chapter of AI, but a completely different book.

Hwang’s “million-fold” figure is also a CEO statement that aligns with the company’s interests. Even taking that into account, it is difficult to deny the direction itself, as both OpenAI and Anthropic are moving toward agents. If generative AI was the era of chatbots, agents represent an era where corporate infrastructure is completely transformed.

There is only one thing that is certain. As agents work, tokens are consumed, and as tokens are consumed, chips are required. Check right now to see where your assets are located on this chain.

The speed of agent commercialization, the efficiency paradox, and the regulatory environment remain variables. Therefore, a structurally advantageous position takes precedence over short-term momentum.

Date: 2026.03.23 · Davar · Global Market Analysis & Investment Strategy 🌍
⚠ This article is for informational purposes only and does not constitute investment advice. Investment decisions and responsibilities rest with the individual. Source of Jensen Huang's remarks: GTC 2026 Keynote Speech (March 16, 2026) and All-In Podcast (March 19, 2026).