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technology · business · May 08, 2026 ✨ Recommended

Why Wall Street Is Suddenly Excited About Boring Old Computer Chips Again

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📰 Reading Passage

For three years, the artificial intelligence boom has had one obvious winner: Nvidia, whose graphics processing units, or GPUs, do the heavy mathematical lifting behind systems like ChatGPT. Older corners of the technology industry — the makers of conventional central processing units (CPUs), storage drives, and enterprise software — have looked stranded, their products eclipsed by a flashier new stack. But the stock market has begun telling a different story, and Wall Street is now scrambling to understand how big a slice of AI spending the older technologies might actually command.

The evidence is hard to ignore. AMD said this past week that it expects compound annual growth of 35 percent in CPU sales over the next few years, roughly double the forecast it issued only six months earlier. ARM also reported surprisingly strong demand. Intel's share price has climbed 400 percent since the United States government took a stake in the company last summer. Even Seagate, a maker of disc drives long viewed as a relic, has jumped 60 percent in a month. The rebound rests on a simple insight: GPUs may train AI models, but CPUs are needed to load those models, manage queries against them — a process called inference — and stitch the output back into real applications.

The rise of so-called AI agents could deepen the trend. Agents are software programs that perform multi-step tasks autonomously rather than answering one prompt at a time, and tech executives including Nvidia chief executive Jensen Huang argue they will run on the same digital plumbing that already powers everyday work. Intel's chief financial officer noted recently that training a model requires only about one CPU for every eight GPUs, but agent workloads will demand CPUs and GPUs in roughly equal numbers. If accurate, that ratio rewires the economics of every data center on the planet.

The harder question is how the resulting value gets divided. If Old IT companies sit quietly in the background, they will collect modest margins. But if they can position themselves as central to the agent era, they may win back pricing power — and that question is sharpest in software, the most beaten-down corner of the old guard. Last month Salesforce became the first major company to launch a 'headless' product, software with no human-facing interface, designed to be operated directly by AI agents. The strategic logic is that years of accumulated customer data will let Salesforce orchestrate those agents intelligently. The risk is that, like most cloud software firms, Salesforce has always charged based on the number of human users — and headless software, by definition, has none.

The deeper bet beneath all of this is that probabilistic AI models, which by nature produce slightly different outputs each time, will not on their own deliver the reliable performance that real businesses require. Turning raw AI horsepower into a dependable computing resource is the next great battleground in technology, and it is one in which orchestration software, fast CPUs, and cheap storage all matter. Wall Street is not yet ready to declare Old IT a winner. But the days when investors could safely treat it as collateral damage from the AI revolution appear to be ending.

📎 Download Original ⬇ Download Analysis PDF

📖 Explanation

For three years, AI hype has been all about Nvidia's GPUs. But suddenly, the unsexy chips and dusty software companies of 'Old IT' are roaring back — and Wall Street is scrambling to figure out why.

📖 What's Going On?

Since ChatGPT launched, investors have crowned a small group of AI winners — Nvidia above all — while older tech names like Intel, AMD, Seagate and Salesforce have looked like yesterday's story. That narrative is shifting. As AI moves from research labs into actual business workflows, the boring infrastructure underneath it (CPUs, storage drives, enterprise software) is turning out to be more important than people assumed.

The trigger is a wave of stronger-than-expected demand for central processing units (CPUs) — the general-purpose chips that have powered computing for decades. AMD now expects 35% compound annual growth in CPU sales over the next few years, roughly double its forecast from six months ago. Intel's stock has bounced 400% since the US government took a stake last summer, and disc-drive maker Seagate has jumped 60% in a month.

🎯 How To Think About It

GPUs do the flashy AI math, but they don't run alone. Think of the AI stack like a Formula 1 race team:

💡 Key Things To Know

🌟 Why It Matters

If you're considering computer science, finance, or any business career, this is the central question of the next decade: who actually captures the money in an AI economy? It's not always the company with the coolest technology — it's the one that controls a chokepoint customers can't avoid. Index funds, your future 401(k), and probably your first employer's stock comp all depend on getting this right.

🔮 The Bigger Picture

We've seen this movie before. When the internet exploded in the late 1990s, everyone bet on the flashy dot-coms — but the durable winners turned out to be the 'picks and shovels' companies: Cisco's routers, Oracle's databases, Microsoft's operating systems. The current bounce in Old IT suggests Wall Street is starting to ask whether AI will follow the same pattern. Watch two things next: whether agent adoption actually drives the predicted CPU surge, and whether software companies like Salesforce can reinvent their per-seat pricing for a world where the 'seats' are bots.

📚 Key Terms Glossary

CPU (Central Processing Unit)
The general-purpose chip that runs most everyday computing tasks — loading programs, managing memory, coordinating other components. Intel and AMD are the dominant makers.
GPU (Graphics Processing Unit)
A chip originally designed for video graphics that turns out to be excellent at the parallel math AI models need. Nvidia dominates this market.
Large language model (LLM)
An AI system trained on vast text data to generate human-like language. ChatGPT is the most famous example.
Inference
The phase when a trained AI model is actually used to answer questions or generate output, as opposed to the upfront 'training' phase.
AI agent
Software that uses AI to autonomously perform multi-step tasks — booking travel, writing reports, running queries — rather than just answering one prompt at a time.
Headless software
Software with no human-facing interface, designed to be operated by other programs (like AI agents) instead of by people clicking buttons.
Tech stack
The layered combination of hardware, storage, networking and software a company uses to run its computing — the 'stack' because each layer sits on top of another.
Compound annual growth
A growth rate that assumes each year's gains build on the previous year's, so 35% compound growth means a market more than triples in about four years.
Probabilistic AI model
An AI that produces outputs based on statistical likelihood, meaning the same input can give slightly different answers — useful but unreliable when businesses need guaranteed results.

✏️ Reading Comprehension Quiz

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Question 1
The passage primarily argues that:
Question 2
According to the passage, AI agent workloads matter to CPU makers because:
Question 3
Which choice best states the central idea of the section about Salesforce?
Question 4
As used in the passage, the word 'eclipsed' most nearly means:
Question 5
As used in the passage, the word 'apportioned' most nearly means:
Question 6
Which statement about Nvidia can most reasonably be inferred from the passage?
Question 7
The passage suggests that the biggest risk in Salesforce's strategy is that:
Question 8
The author's tone throughout the passage is best described as:
Question 9
It can most reasonably be inferred from the passage that the strong rebound in Intel's and Seagate's stocks reflects:
Question 10
Which choice provides the BEST evidence for the answer to the previous question?
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