Pepsi's Number Fever promotional campaign in the Philippines suffered a computer glitch that printed 800,000 winning bottle caps with number 349, leading to riots, violence, deaths, and massive financial liability when the company refused to honor all winning caps.
In 1992, Pepsi launched the Number Fever promotional campaign in the Philippines where consumers could win prizes by matching numbers under bottle caps to televised announcements. The campaign was managed by D.G. Consultores, a Mexican marketing firm that generated winning numbers via computer and stored them in a Manila safe deposit box. On May 25, 1992, number 349 was announced as the grand prize winner worth 1 million pesos ($37,000-$40,000). However, a computer glitch had caused 800,000 caps to be printed with this number, though only two had the proper security code. Approximately 486,170 people eventually claimed to hold winning caps. Pepsi declared it a computer error and offered only $18-$20 as a goodwill gesture instead of the full prize. This led to riots, bombings of Pepsi facilities, overturned delivery trucks, and violence that killed a schoolteacher and 5-year-old student when a homemade grenade missed its truck target. Between 32-37 Pepsi trucks were vandalized or destroyed. Organized protest groups like Coalition 349 formed, and thousands of civil and criminal complaints were filed. The crisis lasted into 1993, with Pepsi's market share dropping from 26% to 17% before recovering to 21% by 1994. The Philippines Supreme Court finally ruled in Pepsi's favor in 2006.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed