CES 2024 was all about “AI”, with many companies rebranding anything involving algorithms as AI and running the risk of overpromising to consumers
This year at CES was the year AI took over. … It was a little too much. — It may be the year of AI at CES, but many of these …
Context & Ripple Effects
Pre-show coverage had already cast CES as a launchpad for AI-branded laptops, TV processing and connected-home products, as reflected in the expected wave of AI features across consumer devices. The show’s messaging therefore matters because it shapes how consumers compare products before those claims are tested in everyday use.
The concern also fits a longer record of AI coverage creating unrealistic expectations, with promotional language potentially obscuring the difference between established automation and genuinely new capabilities.
First-order effects
- Device makers can use “AI” as a common marketing label for algorithmic features, making immediate product differentiation less clear for consumers.
- Companies that imply more than a feature can deliver risk disappointing buyers when real-world use fails to match the AI framing.
Second-order effects
- Brands with more concrete AI functionality face pressure to explain what their features do, rather than relying on the label alone, while retailers and reviewers must translate vague claims into usable comparisons.
- A crowded field of loosely defined AI products can reduce the signaling value of the term, raising the value of demonstrable performance and clear product messaging.
Third-order effects
- If AI remains a catch-all consumer-electronics label, differentiation may shift from announcing AI to proving outcomes, reliability and ease of use.
- The pattern is an early test of whether consumer AI commercializes as a durable product category or becomes another broad feature-marketing layer; the answer depends on whether products meet the expectations their branding creates.
The trend: CES illustrates the consumerization of AI, as firms race to distribute AI-branded features across existing hardware categories before a shared standard for meaningful capability emerges.