In June 2024, McDonald’s announced its decision to remove AI-powered automated ordering technology (AOT) from its drive-through venues. The fast-food giant is doing this because the technology proved unable to process customer orders with reliable accuracy. For example, in February 2023, a US customer posted a recording of a McDonald’s chatbot incorrectly placing an order for hundreds of dollars worth of chicken nuggets. In another mishap, bacon was added to a customer’s ice cream.

The launch of ChatGPT in November 2022 sparked a wave of AI hype in 2023. In June 2024, belief in AI’s potential caused Nvidia to (briefly) become the world’s most valuable company. However, doubts over AI’s limitations have been raised. Both OpenAI and DeepMind warned against AI overhype (in December 2023 and April 2024 respectively). How should we understand McDonald’s decision to scrap its AI project within this context?

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On the one hand, McDonald’s decision supports the argument that AI has become over-hyped in recent years. There is now a long list of examples of AI messing up within the consumer goods and retail sectors.

For example, in August 2023, New Zealand retailer Pak ‘N Save developed an AI application that generated meal plans based on the ingredients users inputted. ‘Bleach mocktails’ and ‘ant poison and glue sandwiches’ were among the meal concepts that the AI generated.

In December 2023, Uber Eats used AI to generate images of New York pizzeria Roma Pizza’s menu offerings. The generated images were mocked on social media for not looking real and for misrepresenting certain menu items. For instance, the AI was confused by New Yorkers’ tendency to refer to a pizza as a ‘pie’, resulting in a savoury ‘pie’ being depicted as a dessert item.

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These failures show that a gap exists between AI’s current capabilities and the transformative power that many investors and business leaders describe. On the other hand, recent instances of failure do not necessarily prove that AI will be all hype and no substance in the long term. According to Our World in Data, AI technologies are improving at a growing speed. Systems for which development started in the 1990s and 2000s (handwriting and speech recognition) took significantly longer to reach human-level performance than systems for which development started more recently. This has happened largely because the amount of computing power used to train AI systems has increased by a factor of 350 million since 2010.

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Hardware improvements (such as more powerful AI chips) and increased investments in AI have contributed to this change significantly. If available computing power continues to grow, AI will continue to improve at an accelerating rate. This means recent failure examples (including McDonald’s AI ordering systems) may be bumps in a road inescapably leading towards the god-like technology envisioned by AI enthusiasts.