Google Bard AI chatbot factual error in launch promo
Google's own promotional video for its new Bard AI chatbot showed the bot confidently giving a wrong answer, and the resulting embarrassment contributed to a roughly 9% one-day drop in Alphabet's share price, wiping out about $100 billion in market value.
The damage was public. The root cause was preventable.
Google's own promotional video for its new Bard AI chatbot showed the bot confidently giving a wrong answer, and the resulting embarrassment contributed to a roughly 9% one-day drop in Alphabet's share price, wiping out about $100 billion in market value.
What the software actually got wrong
Asked what a 9-year-old could be told about discoveries from the James Webb Space Telescope, Bard incorrectly claimed the telescope took the first-ever photographs of a planet outside our solar system, a feat actually achieved years earlier by a different telescope; the error was not caught before Google published the video, exposing how confidently large language models can state incorrect information.
Asked what a 9-year-old could be told about discoveries...
Asked what a 9-year-old could be told about discoveries from the James Webb Space Telescope, Bard incorrectly claimed the telescope took the first-ever photographs of a planet outside our solar system, a feat actually achieved years earlier by a different telescope
Requs AI Edge Case flags this exact pattern at the requirements and architecture stage — before a single line of code implementing it exists — so the assumption behind it gets challenged while it is still cheap to fix.
The error was not caught before Google published the...
the error was not caught before Google published the video, exposing how confidently large language models can state incorrect information
Requs AI Software FMEA traces this failure mode back to the system-level hazard it feeds, tagging it against the Common Defect Enumeration so it surfaces in review instead of in the field.
Beyond code coverage and "shall" testing
Root causes like this one rarely show up in code coverage or requirements-compliance testing, because nobody wrote a requirement anticipating the specific edge case that broke. Requs AI Edge Case and Requs AI Software FMEA are built to surface exactly this class of overlooked failure mode — before the software is written.
Surfaces this before code exists
Identifies edge cases like this one at requirements and architecture time, using the Common Defect Enumeration to catalog failure patterns seen across hundreds of real-world software failures — including this one.
Connects the failure mode to the hazard
Traces this class of root cause directly to the system-level hazard it can produce, so a defect pattern like this one gets flagged during design review instead of after it ships.
Find the overlooked root causes before they ship.
Schedule a demonstration, or explore how Requs AI Edge Case and Software FMEA use the Common Defect Enumeration.