As the AI boom continues, a growing disconnect has emerged. On one side is where the hype and money are flowing: toward generative AI and large language models.
On the other is where companies are actually seeing results.
While billions pour into ChatGPT-style technologies, traditional, less splashy, non-generative techniques continue to quietly power everything from Meta's advertising profits to personalized medical diagnostics to rocket engine design.
This divide is creating both overlooked opportunities for investors and a mismatch between the AI that's getting funded and the AI that's actually working.
The numbers from Meta's second-quarter earnings call tell a stark story. Meta CFO Susan Li was blunt: "[W]e don’t expect that the GenAI work is going to be a meaningful driver of revenue this year or next year,” she said.
Meanwhile, the company's traditional machine learning systems delivered a roughly 5% increase in ad conversions across Instagram and 3% across Facebook.
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