Article Complete
    AIM

    It’s Disturbing How OpenAI’s DALL·E, FLUX.1, and Other Image Generators Can’t Grasp Time, Always Stuck at 10:10

    It’s Disturbing How OpenAI’s DALL·E, FLUX.1, and Other Image Generators Can’t Grasp Time, Always Stuck at 10:10

    Discover why DALL·E and FLUX.1 struggle with time accuracy. Learn more about AI image generation limitations today!

    It’s Disturbing How OpenAI’s DALL·E, FLUX.1, and Other Image Generators Can’t Grasp Time, Always Stuck at 10:10
    Sagar Sharma·
    5 min

    No matter how hard you try, your text-to-image models including your favorite OpenAI’s DALL·E 3 won’t be able to generate correct picture of an Analog watch showing the correct time. When AIM tried generating image of an analog clock showing 6:30 on DALL·E, it gave us the following output:

    (Output from DALL·E)

    AIM tried doing the same with FLUX.1 AI pro but results were pretty much the same:

    (Output from FLUX.1 Pro)

    AIM also tried the same prompt with Ideogram, which recently launched Ideogram 2.0, but it also struggled to show the correct time and was stuck at 10:10.

    (Output from Ideogram)

    Sure, if you prompt further, the time will change to something else but in our testing, it never showed a correct time which was given in the prompt.

    Super scary, right? 

    This phenomenon is likely due to the training data used by these models. Many stock photos and product images of analog clocks are set to 10:10, as this configuration is considered aesthetically pleasing. As a result, the AI models learn to associate the concept of an analog clock with the 10:10 arrangement.

    Furthermore, the issue highlights a fundamental limitation of current AI systems: the inability to reason or think critically. Rather than understanding the components and hierarchy of a clock (e.g., hour, minute, and second hands), the models simply reproduce patterns observed in their training data.

    The success of AI applications heavily relies on the similarity between the training data and the expected test data. Without the ability to reason or generalize to novel scenarios, AI models may struggle to perform reliably in real-world situations where the input data differs from the training examples.

    Your reaction

    Discussion

    What's your take on this story?

    0/1000

    No comments yet — be the first to share your view.

    Explore our newsletters

    Build your routine with some of our top newsletters or view them all here.

    Wake up informed

    Make sense of the day's AI news and breakthroughs with our morning briefing.

    Belamy
    WeeklyBelamySee the latest

    Industry intelligence

    Receive a roundup of AI adoption stories by industry vertical, curated for professionals.

    3x WeeklySector6See the latest

    By signing up, you agree to our Privacy Policy.

    Membership

    Stay ahead with AI insights.

    Unlimited access to premium articles, exclusive analysis, and the stories shaping India's AI economy.

    Subscribe now

    AIM Americas

    1190 Miraloma Way Suite P, Sunnyvale, CA 94085

    AIM India

    1st Floor, Sakti Statesman, Bellandur, Bengaluru – 560103

    © 2026 AIM. All rights reserved.