Why GPT-5 Gets Facts Wrong: AI's Knowledge Cutoff Explained

8/12/2025

So, You Asked GPT-5 Who's President... & It Got It Wrong. Here's Why.

Alright, let's talk about something that’s probably confusing a bunch of people right now. You just got access to GPT-5, this INSANELY powerful new AI from OpenAI that was officially released in August 2025. You’ve seen the headlines, you know it’s a huge leap forward in reasoning, coding, & even creative writing. So, you decide to ask it a simple, straightforward question: "Who is the current President of the United States?"

And it tells you it's Joe Biden.

You stare at the screen for a second. Wait, what? You know that Donald Trump was sworn in as the 47th president on January 20, 2025. So why is the most advanced AI on the planet giving you outdated information? Did it break? Is it biased?

Honestly, the answer is a lot simpler & way more interesting. It gets to the very heart of how these large language models (LLMs) actually work. It’s not a bug; it’s a feature. A fundamental aspect of their design.

The short version is: GPT-5, for all its power, isn't browsing the internet in real-time. It's not "aware" of current events in the way a person is. It's more like a super-intelligent, hyper-knowledgeable librarian who has read every book in the world up to a certain date... & then was locked in the library with no new books, newspapers, or internet access.

Let's dive deep into what’s really going on here. Because once you get it, you'll understand the entire field of AI a whole lot better.

The "Knowledge Cutoff": AI's Expiration Date

Every major AI model, from GPT-3 to GPT-4o & now GPT-5, has what's called a "knowledge cutoff date." Think of it as the last day of school for the AI before its final exam.

During its training phase, which is an incredibly long & expensive process, OpenAI fed GPT-5 a colossal amount of text & code from the internet. We're talking about a significant chunk of publicly available websites, books, articles, scientific papers, & more. This is how it "learns" about the world, how it understands language, context, facts, ideas, & the relationships between them.

But here's the critical part: this training process is not continuous. It has a distinct end point. Once the training is complete, the model's knowledge is essentially frozen in time at that cutoff date. For GPT-5, while the exact date isn't always publicly broadcasted down to the day, its training data clearly precedes the events of late 2024 & early 2025.

So, when you ask it who the president is, it's not querying a live database or checking the latest news. It's simply recalling the information that was most prominent & factually correct within its vast training dataset. Up until its knowledge cutoff, Joe Biden was the president. That fact is referenced in millions of articles, news reports, & official documents that the AI learned from. The event of the 2024 election & the inauguration in 2025 happened after it finished its training.

It's not "thinking" Biden is still president. It's "remembering" that, based on the library of information it was built from, Biden was the last person to hold that title. It's a subtle but crucial distinction. One article put it perfectly, noting that even with all its advancements, "GPT-5 cannot operate outside the boundaries of its training data."

Why Can't It Just Google the Answer?

This is the next logical question, right? If it's connected to the internet to give you an answer, why can't it just perform a quick search?

The answer touches on the core architecture of these models. Primarily, for safety, consistency, & control.

Safety & Predictability: If an AI model could browse the web freely in real-time to answer any query, it could lead to some SERIOUS problems. It could stumble upon & recite misinformation, hateful content, or dangerous instructions that were just published seconds ago. By training it on a vetted (though still imperfect) dataset, OpenAI has a much better handle on its behavior & can implement safeguards more effectively.
Computational Cost: A live, continuous learning process is computationally mind-boggling. The training for a model like GPT-5 costs millions of dollars & requires immense processing power. It's not feasible to have it constantly updating its entire knowledge base. The architecture is designed for recall & reasoning based on a fixed dataset, not for live learning.
Consistency: Imagine asking the AI a question & getting a different answer every five minutes as news breaks. While that sounds good for current events, it would be a nightmare for tasks that require stability & reproducibility, like writing code, analyzing a legal document, or performing a complex reasoning task. The frozen knowledge base ensures that the model is a stable tool.

Now, this is where it gets interesting. While the core model is static, we are seeing new systems built around the model. For instance, some applications might use a feature where the AI can perform a search, retrieve new information, & then use its powerful language capabilities to summarize or analyze that fresh data. But in those cases, the AI isn't learning from the new data; it's just being given a temporary document to read. It won't remember that information for the next conversation.

This Is Where Business & AI Get REALLY Interesting

Understanding this limitation is HUGE for businesses. You can't just plug a generic AI into your website & expect it to know your company's latest product updates, your current shipping policies, or that you just hired a new CEO.

This is precisely the problem that platforms like Arsturn are built to solve. It's one thing for GPT-5 to not know who the president is, but it's another thing entirely when a chatbot on your website doesn't know your own company's information. It creates a terrible customer experience.

Here’s the thing: businesses need an AI that is an expert in their world, not just the world of 2023. This is where the idea of a no-code, custom AI chatbot becomes a game-changer. With Arsturn, a business can take all of its current data—product catalogs, FAQs, support articles, policy documents, even transcripts of past customer service chats—& use it to train a specialized AI chatbot.

This chatbot doesn't have a knowledge cutoff from a year ago. Its knowledge cutoff is whenever you last updated its data source. It becomes a true digital expert on your business.

So, when a customer lands on your site at 3 AM with a question about a product you launched yesterday, they don't get a generic "I don't have that information." They get an instant, accurate answer. That's how you use AI to actually build meaningful connections with your audience. The AI can provide instant customer support, answer questions, & engage with website visitors 24/7 because it’s trained on your specific, up-to-date information.

"But I've Seen It Talk About Recent Stuff Before!"

You're not wrong. Sometimes it might seem like the AI is aware of recent events. This usually happens in one of two ways:

"Fuzzy" Knowledge: Sometimes, information about future events is present in the training data. For example, the training data might contain millions of articles from 2023 talking about the upcoming 2024 election, who the candidates were, & when the inauguration would be. So, the AI might be able to "infer" that a change was scheduled to happen, even if it can't confirm that it did happen. It knows the "story" of the election, but not the final chapter.
Retrieval-Augmented Generation (RAG): This is a fancy term for a simple concept that I touched on earlier. Some applications, like the latest versions of search engines integrated with AI, will first do a live search for your query. Then, they feed the search results to the AI as context along with your question. The AI then uses this fresh, just-retrieved information to formulate its answer. It's a powerful hybrid approach, but it's important to remember that the core model isn't being updated. It's more like giving the librarian a newspaper from today & asking them to summarize the front page. They can do it, but once you take the newspaper away, their core knowledge remains the same.

This is, again, super relevant for businesses. A simple chatbot might just give canned responses. A more advanced system, maybe one you build with Arsturn, can be designed to pull from a live database. For example, a customer asks, "Is the Blue Widget Pro in stock?" The Arsturn-powered chatbot can be set up to check your inventory database in real-time, get the answer, & then use its conversational AI to tell the customer, "Yes, it looks like we have 17 left in stock! Would you like me to help you add it to your cart?"

That's the difference between a generic tool & a true business solution. You're building a no-code AI chatbot trained on your own data to boost conversions & provide these kinds of personalized customer experiences. It's not just about answering questions; it's about driving business goals.

The Human Element: Why AI Hallucinations Happen

This brings us to another critical concept: "hallucinations." This is what happens when an AI, faced with a gap in its knowledge, essentially makes something up.

Because these models are designed to be helpful & to provide answers, their programming pushes them to generate a response. If it doesn't know the definitive answer to "Who is president in August 2025?", it will fall back on the last known, heavily weighted fact in its training data: Joe Biden was president. It’s not trying to lie or deceive. It's just trying to complete the pattern based on the overwhelming evidence it was trained on.

It's a bit like if you asked a person who won the World Series in 2028. They don't know, because it hasn't happened. But they might say, "Well, the Yankees have a strong team, so maybe them?" The AI does a more sophisticated version of this, stating the most probable answer based on its historical data with an air of confidence that can be misleading.

The new GPT-5 model has made significant strides in reducing these hallucinations, which is a huge step forward. It's much better at admitting when it doesn't know something. But the fundamental limitation of the knowledge cutoff means that for very recent events, the risk is always there.

What This Means for the Future of AI & Us

So, here we are in August 2025. We have this incredible tool, GPT-5, that can write code, draft legal documents, explain complex scientific theories, & even help with medical information. It’s smarter, faster, & more capable than any of its predecessors. Yet, it can’t tell you the most basic piece of civic information about the country it was created in.

This isn't a failure; it's a necessary boundary that we need to understand to use this technology responsibly & effectively.

Always Be Critical: Treat AI-generated information, especially about recent events, as a starting point, not a definitive final answer. Verify with a trusted, primary source.
Understand the Tool's Purpose: GPT-5 is a reasoning & generation engine, not a live news ticker. Use it for what it's great at: brainstorming, summarizing, creating, coding, & explaining concepts based on its vast library of knowledge.
For Businesses, Specificity is Key: If you're going to use AI for your business, you can't rely on a generic model's outdated public knowledge. You need a platform that lets you build and train an AI on your private, current data. This is where Arsturn comes in, allowing businesses to create these custom-trained AI chatbots that become genuine extensions of their brand & knowledge base, helping with everything from customer engagement to lead generation & website optimization.

The fact that GPT-5 thinks Biden is still president isn't a sign of its stupidity. It's a reminder of its nature. It’s an artifact of incredible intelligence, not a person. It’s a snapshot of human knowledge, not a live feed. And understanding that difference is the key to unlocking its true potential while avoiding the pitfalls.

Hope this was helpful & cleared things up. It's a pretty fascinating topic, & it’s only going to get more relevant as these models become more integrated into our daily lives. Let me know what you think