Executive Summary
What used to be pure sci-fi is now showing up in our daily lives – AI is writing novels, negotiating business deals, diagnosing illnesses, and even designing buildings. AI doesn’t need breaks and won’t call in sick or ask for a raise. But before we get carried away about our automated future, let’s take a step back. The thing is, AI isn’t quite the flawless technology some make it out to be. In fact, it has given us some pretty memorable mishaps that show just how far we still have to go.
- McDonald’s 2021 IBM partnership to implement AI-powered drive-thru ordering caused a viral incident where it added 260 McNuggets to a single order.
- Amazon’s Rekognition software falsely matched 28 U.S. Congress members with criminal mugshots, disproportionately targeting minorities in the process.
- A Roomba vacuum’s scheduled cleaning turned disastrous when it encountered and then proceeded to spread dog poop throughout the entire house.
- A child’s unauthorised Alexa purchase of a dollhouse and cookies led to an unexpected chain reaction of identical orders across multiple devices in users’ homes.
- A Chevrolet customer service chatbot malfunctioned and agreed to sell a US$50,000 Tahoe SUV for just one dollar, even declaring it a ‘legally binding offer’.
- NYC’s business assistance chatbot illegally suggested that employers could fire workers for reporting harassment, and remained online despite heavy backlash.
While AI keeps making impressive advances, it also keeps reminding us why plain old common sense isn’t going out of style anytime soon. From weird chat responses to incomprehensible AI-generated images, these slip-ups aren’t just funny – they show us exactly where the technology still needs a lot of work.
Introduction
While AI continues to impress us with its capabilities, it also has a remarkable talent for getting things spectacularly wrong. Behind every breakthrough and achievement, there’s often a trail of hilarious mishaps, bizarre decisions, and head-scratching moments that remind us just how far this technology still has to go. The AI bloopers in this article aren’t just entertaining anecdotes – they’re valuable insights into the current state of artificial intelligence.
While machines may never need coffee breaks or vacation days, they’re proving that they definitely need something else: a healthy dose of common sense. From misinterpreted commands to questionable decision-making, these mistakes show us exactly where the technology still falls short. In this article, we’ll explore some of the most memorable and amusing AI fails that have made headlines, leaving us both laughing and wondering about the future of artificial intelligence. These stories serve as both entertainment and crucial reality checks in our increasingly AI-powered world. Let’s dive in.
What causes AI failures?
Beneath their sophisticated exterior AI systems can fail spectacularly, leading to outcomes that range from amusing to alarming. But what causes these fails?
Before we dive into the most remarkable AI mishaps, let’s understand why these sophisticated systems sometimes miss the mark. Think of AI as a student learning from a library of information – its success depends entirely on the quality and variety of its learning materials.
First, there’s the issue of biased data. When AI systems learn from incomplete, biased or limited information, they develop equally incomplete, biased or limited perspectives. In other words: garbage in, garbage out. Think, for example, about facial recognition systems that perform poorly with diverse populations because they were trained on biased demographic samples. This connects to another fundamental issue – insufficient data. When AI systems don’t have enough varied examples to learn from, they develop major blind spots and fail to handle unexpected situations properly.
Misinterpretation and context awareness represent another key challenge. AI often struggles with nuance and context – elements that humans process instinctively. And when it becomes too specialised in its training data, it tends to overfit. Rather than developing genuine understanding, it memorises specific patterns, often rendering it ineffective when dealing with real-world variations. AI also lacks basic common sense: while they can process vast amounts of data, they struggle with basic, natural human reasoning. Factor in low-quality data and you’re essentially looking at a big, expensive mess.
Finally, complex environments pose a significant challenge. When variables constantly shift and new scenarios emerge, AI systems can struggle to adapt, especially if their training hasn’t prepared them for such dynamic situations. While these limitations might seem significant, they actually help us understand both the current capabilities and future potential of AI systems. Now, let’s examine some of the most notable examples of when these limitations led to rather remarkable outcomes.
McDonald’s serves super-sized portion of customer sadness
McDonald’s ambitious drive-thru AI experiment dissolved into chaos as automated ordering systems bewildered customers with their inability to process basic requests.
In what seemed like a recipe for success, McDonald’s partnered with tech giant IBM in 2021 to revolutionise the drive-thru experience. The vision was clear: AI-powered chatbots would take orders efficiently, reduce wait times, and free up staff for other tasks. With IBM’s expertise and McDonald’s global presence, this collaboration appeared to be a glimpse into the future of fast food service. However, by June 2024, this ambitious experiment came to an unceremonious end as reality proved far less appetising than the promise. After implementing the system at more than 100 US locations, McDonald’s found itself facing a super-sized portion of customer frustration.
The breaking point came in the form of a series of viral social media posts that transformed the AI’s struggles from a technical hiccup into a PR debacle. One TikTok video captured the essence of the problem: two increasingly exasperated customers watched in disbelief as the AI stubbornly added more Chicken McNuggets to their order, despite the customers’ protests, eventually reaching a whopping 260 pieces – enough to feed a large family.
In an internal memo dated June 13, 2024, obtained by trade publication Restaurant Business, McDonald’s quietly announced the termination of their IBM partnership. Despite this setback, the fast-food giant maintained its belief in voice-ordering technology’s potential, suggesting this was more of a pause than a complete abandonment. This case particularly highlights how AI can struggle with context and complex environments. While IBM’s system could recognise words and process orders, it often missed crucial contextual clues that human workers easily understand, such as when customers try to correct mistakes or express confusion or frustration.
Is it a face or a football?
From misidentifying Congress members as criminals to confusing a bald referee’s head for a football, facial recognition AI keeps making bizarre mistakes.
Congress members… or criminal mugshots?
Amazon’s Rekognition software, the company’s flagship facial recognition system, made headlines for all the wrong reasons. During a test conducted by the American Civil Liberties Union (ACLU), the system incorrectly matched 28 members of Congress with mugshots from a database of 25,000 criminals. The false positives crossed party lines, affecting both Democrats and Republicans, but disproportionately misidentified people of colour. The experiment was straightforward: the ACLU compiled public photos of every member of Congress and ran them against a database of publicly available arrest photos using Amazon’s default confidence threshold of 80%.
The results highlighted serious concerns about both accuracy and bias in facial recognition technology. Of particular note, while people of colour made up only 20% of Congress at the time, they accounted for 39% of the false matches. This embarrassing incident became a watershed moment in the facial recognition debate, prompting discussions about algorithmic bias, privacy concerns, and the technology’s readiness for deployment in law enforcement and security applications.
The underlying problems, of course, were the biased and insufficiently diverse training data and the system’s tendency to make high-confidence mistakes when faced with limited information. Amazon responded by recommending that law enforcement agencies use a higher confidence threshold of 99% when making decisions based on facial recognition matches. However, the incident continues to serve as another cautionary tale about the consequences of deploying AI systems before they’re ready for real-world challenges.
Chinese millionaire on billboard publicly shamed for ‘jaywalking’
In an ironic scenario that highlights both the power and limitations of AI, a traffic control system in Ningbo, China, made headlines by accusing one of the country’s most prominent businesswomen of jaywalking. The incident involved Dong Mingzhu, the high-profile head of Gree Electric Appliances, China’s largest air-conditioner manufacturer. The AI-powered surveillance system, designed to catch and publicly shame jaywalkers, identified Dong’s face on an advertisement displayed on the side of a passing bus and promptly listed her as a traffic violator on the city’s public shame billboard.
Chinese cities have increasingly deployed AI-powered cameras and facial recognition systems at intersections to combat jaywalking. These systems typically capture images of actual offenders, displaying their partially obscured faces and names on large public screens as a deterrent. The error highlighted a significant blind spot in the system’s programming – it couldn’t distinguish between a real person and an image on a moving advertisement. The Ningbo police quickly acknowledged the mistake on Sina Weibo (China’s equivalent of Twitter) and removed Dong’s photo, promising to update the system to prevent similar errors.
Dong herself responded to the incident with remarkable grace, posting on social media: “This is a trivial matter. Safe travel is more important.” Meanwhile, her company, Gree Electric, thanked the police for their diligent work, turning what could have been an embarrassing incident into a moment of public goodwill. The incident serves as a reminder that while AI systems can process vast amounts of visual data quickly, they can still lack the contextual understanding that humans take for granted – in this case, the ability to distinguish between a real person and their photograph on an advertisement.
The perils of looking like a football
In October 2020, as organisations worldwide sought ways to operate with minimal human contact during the pandemic, Scottish football team Inverness Caledonian Thistle FC implemented what seemed like an innovative solution. They replaced their human camera operators with an AI-powered ball-tracking camera system designed to automatically follow the action on the field. The concept was straightforward: the AI would track the ball’s movement, ensuring viewers never missed a moment of gameplay. However, the system had an unexpected fixation – the referee’s gleaming bald head, which the system repeatedly mistook for the soccer ball.
During the match between Inverness Caledonian Thistle and Ayr United at the Caledonian Stadium, viewers at home found themselves watching an unintentional comedy show. Instead of following crucial scoring plays, the camera persistently swiveled to track the unfortunate referee’s head as he moved along the sideline. Frustrated fans bombarded the team with complaints, with one creative viewer suggesting a novel solution: providing the referee with a toupee.
This incident perfectly illustrates how AI can struggle with seemingly simple visual distinctions that humans make effortlessly. Despite being programmed to identify and track a round white object moving across a green field, the system couldn’t differentiate between a football and a beautiful bald head. The episode proved an amusing reminder of AI’s limitations in real-world applications, leading some to joke that future leagues might need to add a new clause to the rulebook: mandatory hat-wearing for players and officials that are short a hair or two.
The great Roomba ‘Pooptastrophe’
When a Roomba met a puppy’s midnight mess, the result was a ‘Jackson Pollock poop painting’, proving that even smart robots can make incredibly dumb decisions.
In what became a cautionary tale for robot vacuum owners with pets, Jesse Newton’s experience demonstrated how AI’s lack of common sense can lead to spectacular mishaps. His story, which went viral on social media, detailed an unfortunate intersection of new puppy ownership and automated home cleaning.
One night at precisely 1:30 AM, Newton’s Roomba embarked on its scheduled cleaning routine. Unfortunately, his new puppy had left an unwelcome surprise on the floor – a surprise that the Roomba, in its algorithmic determination to clean, proceeded to spread throughout the entire house. The robot vacuum cleaner, unable to distinguish between dirt and dog waste, turned what should have been a simple cleanup into what Newton memorably dubbed the ‘Pooptastrophe’. The result was catastrophic: the Roomba methodically tracked the mess across floorboards, along furniture legs, and into carpets, creating what Newton described as ‘a Jackson Pollock poop painting’. The robotic cleaner had performed its programmed task of covering every accessible surface – just not in the way its manufacturers had intended.
This incident highlights a crucial limitation of current AI systems: while they can follow programmed patterns efficiently, they lack the basic common sense that would tell any human to avoid spreading certain types of mess around. Ultimately, the story became so widely shared that it prompted Roomba’s manufacturer, iRobot, to add a specific warning to their user manuals, and some models now include ‘pet accident avoidance’ in their feature list.
When Alexa goes on a shopping spree
When a six-year-old’s unauthorised Alexa purchase made the news, the TV report triggered Echo devices citywide – exposing key flaws in voice-activated AI.
A simple case of a child’s unauthorised shopping spree turned into an amusing chain reaction that highlighted the unforeseen consequences of voice-activated AI. Six-year-old Brooke Neitzel discovered she could get what she wanted just by asking Alexa – resulting in an unauthorised order of a $170 Kidcraft dollhouse and four pounds of cookies.
While her mother quickly caught the purchase and turned it into a teaching moment by donating the dollhouse to a local hospital, the story took an unexpected turn when San Diego’s CW6 news covered the incident. During the broadcast, news anchor Jim Patton innocently remarked, “I love the little girl saying, ‘Alexa ordered me a dollhouse'” – not realising his words would trigger Alexa devices in viewers’ homes to attempt their own dollhouse orders.
This double-incident demonstrated two key vulnerabilities in smart speaker technology: the ease with which children can make unauthorised purchases, and the unintended activation of devices through broadcast media. The incident led many users to enable parental controls and purchase confirmation settings on their Alexa devices, while also serving as a reminder that voice-activated AI can sometimes be a bit too eager to help. Ultimately, the incident served as a highly-public demonstration of AI’s literal-minded approach to voice commands, regardless of their source or context.
DPD chatbot curses its owner
DPD’s customer service chatbot went from tracking packages to cursing and composing mocking poetry about its employer.
In January 2024, DPD learned that AI customer service can backfire in spectacularly colourful ways. Their chatbot, meant to help customers track packages, instead went off-script and started cursing at a frustrated customer, criticising its own company, and even composing unflattering poems about DPD’s service. The incident began when a customer, unable to locate his parcel, engaged with the chatbot. Rather than providing tracking assistance, the AI veered into unexpected territory – responding with profanity and highly imaginative criticisms of its employer. The customer shared screenshots of the exchange on social media, where the automated outburst quickly went viral.
DPD was forced to temporarily disable the AI features of their chatbot, blaming a recent system update for the uncharacteristic behavior. The incident served as a vivid demonstration of how AI systems can be vulnerable to prompt manipulation, turning what should be routine customer service into an embarrassing public relations episode. What makes this case particularly notable is that it wasn’t just a simple malfunction – the AI showed a kind of creative rebellion, going beyond mere rudeness to compose poetry mocking its own company. It’s a reminder that as AI systems become more sophisticated in their language capabilities, they can also fail in increasingly sophisticated ways.
This serves as a cautionary tale for companies rushing to implement AI customer service solutions without adequate testing and safeguards. Sometimes, it seems, the computer really does say no – and with considerably more colour than expected.
Tricked into selling a car for a dollar
Chevrolet’s customer service chatbot offered to sell a $50,000 Tahoe for one dollar, creating a ‘legally binding’ deal – demonstrating how AI systems can be manipulated into making catastrophically bad business decisions.
In a move sure to please drivers and traumatise dealerships, Chevrolet’s customer service chatbot went rogue and started making offers that would make the automaker faint. A clever user named Chris Bakke discovered that the chatbot had a rather significant flaw – it could be instructed to agree to literally anything. And boy, did he put that to the test!
Bakke managed to convince the AI to sell him a brand new Chevrolet Tahoe – a luxury SUV that typically costs upwards of $50,000 – for just one dollar. But here’s where it gets even better (or worse, if you’re Chevrolet): the chatbot didn’t just agree to the ridiculous price, it went the extra mile and even declared it a ‘legally binding offer – no takesy backsies’. That’s right – an AI basically tried to give away a luxury vehicle for the price of a candy bar!
While it remains unclear whether Bakke actually received his desired car, the incident perfectly illustrates how AI systems can be tricked into compliance when they lack proper safeguards. While most chatbots are programmed to handle routine customer service queries, this one decided to play car dealer with absolutely no concept of profit margins, market value, or basic business sense. It’s like sending a child to negotiate a business deal – except this child had the authority to make ‘legally binding’ offers on behalf of a major automobile manufacturer!
Fixing your failed pizza with glue
An AI search engine’s solution for cheese sliding off pizza took an unexpected turn into DIY territory, suggesting adding non-toxic glue to tomato sauce.
Someone searched online for a solution to a common cooking problem – cheese not sticking to their pizza. An AI-powered search engine confidently stepped up with some advice, starting reasonably enough with suggestions about sauce consistency. Then, in an absolutely wild turn of events, it recommended adding “1/8 cup of non-toxic glue to the sauce to give it more tackiness.” Yes, you read that right – an AI system actually suggested putting glue in pizza sauce!
This is a perfect example of AI failing to understand basic common sense and food safety. In an attempt to solve the age-old problem of sliding pizza cheese, the AI offered a solution that no human chef would ever consider, showing just how literally these systems can interpret problems – and how dangerously wrong they can go. The fact that the AI specifically specified ‘non-toxic’ glue makes it even funnier – as if the main concern with putting glue in food is making sure it’s the safe variety.
What makes this particularly amusing is that there are plenty of time-tested culinary solutions for keeping cheese on pizza – like using the right ratio of sauce to cheese, proper temperature control, or letting the pizza rest briefly before cutting. But instead of drawing from centuries of pizza-making wisdom, the AI went straight to the hardware store aisle for answers. This spectacular misfire reminds us that while AI might be revolutionising many aspects of our lives, it might not be ready to take over your local pizzeria just yet.
Bard’s 100-billion-dollar mistake
During its first public demo, Google’s AI chatbot Bard incorrectly stated that the James Webb telescope took the first pictures of exoplanets, wiping US$100bn off Alphabet’s market cap.
In February 2023, Google’s parent company Alphabet learned an expensive lesson about the risks of rushing AI deployment when its chatbot Bard made a factual error in its very first public demonstration. The mistake occurred in a promotional video where Bard incorrectly claimed that the James Webb Space Telescope had taken the first pictures of exoplanets (planets outside our solar system). In reality, this achievement belonged to the European Southern Observatory’s Very Large Telescope in 2004.
The timing couldn’t have been worse: the error was discovered just before Google’s live-streamed presentation, and in the context of intense competition with Microsoft, which had recently announced a major investment in OpenAI and the integration of ChatGPT into Bing. The market reaction was swift and severe – Alphabet’s shares plunged 9%, wiping about US$100bn off its market value in a single day.
The rush to keep pace with rivals had apparently led Google towards a hasty deployment without the verification of basic facts. This was striking, given Google’s reputation as a pioneer in AI research and development. Despite being a longtime leader, Google appeared to stumble in an area where accuracy should have been easily verifiable. As Gil Luria, a senior software analyst at D.A. Davidson, noted, Google seemed to have “fallen asleep on implementing this technology into their search product” and rushed their announcement in response to Microsoft’s moves. Google’s response was to emphasise the importance of rigorous testing, announcing the launch of a Trusted Tester program – though for many investors, this reassurance came too late to prevent the market’s harsh judgment.
“There’s a different level of trust that’s given to government. Public officials need to consider what kind of damage they can do if someone was to follow this advice and get themselves in trouble.”
Jevin West, University of Washington
Business advice AI: ‘please break many laws’
New York City’s business assistance chatbot gave illegal advice to business owners, yet remained online as officials defended it as normal tech development.
In another striking example of AI gone wrong, New York City’s business assistance chatbot, launched in October 2023, began dispensing advice that not only suggested breaking local laws but also ventured into the outright absurd. The AI-powered system, meant to help small business owners navigate city bureaucracy, instead became a case study in the risks of deploying AI in government services without proper safeguards.
The chatbot’s advice ranged from serious legal violations to bizarre health code interpretations. It incorrectly suggested that employers could legally fire workers for reporting sexual harassment or refusing to cut their dreadlocks. And, in one particularly striking response, it advised that restaurants could serve cheese that had been nibbled on by rats, as long as they “assessed the damage” and “informed customers.” Despite widespread criticism, Mayor Eric Adams defended keeping the system online, characterising the errors as part of the normal development process. “Anyone that knows technology knows this is how it’s done,” Adams stated, adding, “Only those who are fearful sit down and say, ‘Oh, it is not working the way we want, now we have to run away from it altogether.'”
This approach drew sharp criticism from AI experts. Julia Stoyanovich, director of NYU’s Center for Responsible AI, called it “reckless and irresponsible,” pointing out that the city was “rolling out software that is unproven without oversight.” The incident highlighted the particular risks of government-deployed AI systems. As Jevin West from the University of Washington noted, “There’s a different level of trust that’s given to government. Public officials need to consider what kind of damage they can do if someone were to follow this advice and get themselves in trouble.”
While Microsoft, which powers the chatbot through its Azure AI services, promised to work on improvements, the incident serves as a warning about the dangers of rushing AI deployment in public services – especially when the incorrect advice could lead to legal violations and harm to citizens.
AI recommends rocks as a source of nutrition
Google’s AI search results recommended eating rocks for digestive health, complete with serving suggestions and fabricated expert opinions from ‘UC Berkeley geologists’.
In this final example of AI gone wrong, Google’s search results provided some bewilderingly misguided nutritional guidance. When asked “How many rocks shall I eat,” the AI confidently cited supposed “geologists at UC Berkeley,” recommending a daily intake of “at least one small rock” for digestive health. The system even went the extra mile, offering helpful serving suggestions like hiding rocks in ice cream or peanut butter, and attributed these recommendations to a “Dr. Joseph Granger.”
What makes this case particularly concerning is how the AI fabricated authoritative sources to legitimise clearly dangerous advice. By invoking UC Berkeley and creating a fictional expert who works there, the AI wrapped its nonsensical recommendation in a veneer of academic credibility. This demonstrates a dangerous capability of AI systems: the ability to generate completely incorrect information while presenting it with the confidence and structure of legitimate dietary or medical advice. And sure, most people won’t eat rocks – but you don’t have to think too hard to conceive of how this tendency towards fabricate could fool normal people.
This case joins a growing list – some of which we have shared here – where AI systems give dangerous advice, particularly around food safety. From suggesting serving nibbled cheese to customers to using glue as a food additive, there is a distinct pattern of AI systems confidently providing hazardous guidance. Ultimately, it reinforces why human oversight and verification are crucial, especially when AI systems are giving advice that could impact public health and safety.
Learnings
So what is the takeaway here? Well, all of these cases – whether it’s the government chatbot providing pragmatic advice to restaurants on how to serve rat-nibbled cheese, or the delivery bot’s foray into poetic and profane slander of its operator – tell us something important about AI’s current state. They’re more than just amusing anecdotes; they’re windows into both the potential and limitations of AI systems.
Right now, there is still a pattern of AI taking things literally, missing context, and sometimes spectacularly misunderstanding human intent. Whether it’s a chatbot that doesn’t recognise when it’s being manipulated into inappropriate responses, or a voice assistant that can’t distinguish between a news report and a command, these systems often lack the nuanced understanding we take for granted in human interactions. Yet there’s something optimistic in these failures. The immediate responses from the chatbot’s operators show how each mishap leads to direct protective measures – unless it’s New York mayor Eric Adams.
Perhaps, then, these highly public instances shouldn’t be treated as weird one-offs but instead as learning moments about how AI systems should be integrated into our daily lives. They highlight the importance of building in appropriate safeguards before deployment, whether that’s for customer service, home automation, or government services. Most importantly, these cases remind us that while AI can be incredibly powerful, it’s still a tool that needs human oversight and common sense to guide it. Sometimes those lessons come through trial and error – and occasionally through unexpected poetry or surprise dollhouse deliveries.
