Category: Investing strategies

IBM just lost $68.8 billion in a single day! Here’s what it’s really telling you…

On Tuesday 14 July 2026, IBM had the worst trading day in its 115-year history!
The company’s shares fell 25.21%, and in a single session, $68.8 billion of market value evaporated.
Before markets opened the following morning, the question in every investment conversation was the same: is this a buying opportunity or a warning sign?

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AI-Flation: The hidden cost of the AI boom that just arrived on your doorstep

The promise of artificial intelligence has always carried an implicit economic assumption: that more computing power, applied intelligently, would drive costs down. Cheaper drug discovery. Cheaper logistics. Cheaper software development. A more productive economy with lower prices for everyone. That assumption just ran into reality. And reality, on 25 June, took the form of two back-to-back price announcements from two of the world’s most valuable companies.Giving rise to AI-flation.

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Why AI company, Broadcom crashed 14%…

After markets closed, Broadcom – the seventh-largest company in the world by market cap – posted what it described as “record revenue, record operating profit, and record free cash flow.” And then its stock fell 14%. That reaction says more about investor psychology in the current AI market than it does about Broadcom’s business. Understanding the gap between those two things is one of the most important skills an investor can develop right now.

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Nvidia’s results confirmed a much bigger story for AI

Nvidia quarterly results were exceptional. Revenue for the first quarter of fiscal year 2027 surged 85% year-on-year to $81.6 billion, comfortably ahead of market expectations. Adjusted earnings per share also beat forecasts, while Data Centre revenue, the core engine of the AI boom, climbed 92% to $75.2 billion.

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Three AI stocks to watch in 2026: The companies getting rich from AI – without building a single model

Almost every AI conversation eventually gets to the models – ChatGPT, Gemini, Claude, Grok. That’s where the public attention goes. That’s what gets the headlines.
But behind every one of those models is a layer of infrastructure that most investors never think about: the networks that move the data, the pipes connecting the processors, and the teams cleaning and labelling the training data that makes the intelligence possible in the first place.
These are the picks-and-shovels of the AI gold rush. And right now, three companies operating in this space are posting numbers that are genuinely hard to ignore.

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