ChatGPT can hold surprisingly human-like conversations, write essays, and even help with coding. It often feels like you’re chatting with a knowledgeable friend. But what’s really happening under the hood isn’t magic or genuine understanding – it’s math. Specifically, ChatGPT is powered by a type of AI that works on probabilities. In other words, it doesn’t think or understand in the way we do; it predicts. This article will demystify how ChatGPT works and why its “intelligence” is very different from human smarts. We’ll also explore the downsides of this probabilistic approach – especially when an AI tries to make decisions on its own – and why making AI truly smart (in a human sense) is so hard. Along the way, we’ll see how these challenges have tripped up even tech giants like Apple, leaving fans and famous YouTubers disappointed by delayed promises of smarter assistants.

Photo by Richard Bell on Unsplash

How ChatGPT Works: A Supercharged Autocomplete

One helpful way to imagine ChatGPT is as a supercharged autocomplete machine. You know how your phone suggests the next word when you’re texting? ChatGPT does that, but on a mind-boggling scale. It has been trained on vast amounts of text from the internet, books, and other sources. Using this training, it learned to statistically predict which word (or part of a word) likely comes next in any given sentence.

Stefan Auerbach posted on Linkedin that “Generative AI is basically a supercharged autocomplete machine — but instead of just guessing the next word in your text, it generates entire conversations, essays, images, and even music.”

At its core, ChatGPT is a large language model (often abbreviated as an LLM). The model doesn’t have a database of facts or a checklist of responses. Instead, it has a complex mathematical model (billions of internal parameters) that it uses to continue any text prompt you give it in a way that sounds coherent. When you ask ChatGPT a question, it starts by looking at your prompt and internally comes up with a probability distribution for what the next word should be. It picks the next word (or part of a word) based on those probabilities, then moves on to the next word, and so on. The result is a full answer composed word-by-word. Because it was trained on so much human-written text, the output often reads fluently and even shows elements of creativity or knowledge – but it’s all pattern-based. Think of it like a very clever parrot: it can mimic language impressively, but there’s no conscious thought behind the scenes.

No Real Understanding, Just Patterns

While ChatGPT can sound smart, it doesn’t truly understand the meanings of its words. It’s simulating understanding by statistically associating words that often appear together in human writing. AI researchers have a term for models like this: “stochastic parrots.” (Stochastic basically means randomly determined by probabilities.) The idea is that ChatGPT and similar AIs are great at parroting back patterns of language without any genuine grasp of the content’s meaning. One AI glossary explains that these systems “use statistics to convincingly generate human-like text, while lacking true semantic understanding behind the word patterns.” In other words, the AI can string sentences together that look meaningful, but it doesn’t truly know what it’s saying.

For example, if you ask, “What is the capital of France?” the model will likely answer “Paris” because in its training, the words “capital of France” are very often followed by “Paris.” It gets that right. But if you ask something requiring deeper reasoning or real-world experience, the model might falter. It has seen lots of text, but it doesn’t actually know facts or feel confidence or doubt. It just plays the odds with words. This is why ChatGPT sometimes states incorrect things with great confidence. It’s not lying on purpose – it has no concept of truth – it’s simply producing the most statistically likely answer, which might be wrong. The fancy term for this is “hallucination”: the AI effectively makes up a plausible-sounding answer when it doesn’t have a reliable pattern to draw on.

Because it lacks true understanding, ChatGPT also doesn’t have common sense or a moral compass unless those appear in the patterns of its training data. It can’t inherently distinguish fact from fiction, or good intentions from bad – it only knows what sounds like something a human might say. This limitation becomes more problematic when we rely on such models for anything critical.

The Downsides of Probabilistic “Intelligence”

Relying on probability and pattern-matching gives ChatGPT impressive linguistic fluency, but it comes with serious downsides:

• Confident Mistakes: As mentioned, ChatGPT can state incorrect or nonsensical information very confidently. Since it doesn’t truly understand, it has no built-in mechanism to know when it’s wrong. To the AI, an incorrect statement that frequently appeared in training texts is just as good as a correct one – both are patterns it can mimic. The result is that it may assert false information in a very convincing manner. This is fine if you’re chatting casually, but it’s risky if you’re using AI for, say, medical or legal advice without double-checking facts.
• Lack of True Reasoning: ChatGPT is not performing logical reasoning or careful planning; it’s following learned linguistic patterns. If a question or task requires multi-step reasoning or understanding cause and effect, the model often stumbles. It might follow the form of logical reasoning (because it’s seen similar reasoning in text), but it doesn’t truly know logic. This is why it might give you a detailed step-by-step solution to a math problem that looks right but actually has a mistake in the reasoning or result. It’s faking the reasoning process based on examples, not reasoning from first principles.
• Bias and Weird Quirks: Because ChatGPT learns from human-created text, it can pick up all sorts of human biases and quirks present in that data. It doesn’t have its own stance; it mirrors what it saw during training. If a large portion of the internet has a certain bias or harmful viewpoint, the model might reflect that in its outputs unless carefully filtered. Its “knowledge” is also only as up-to-date as its training data. Ask it about very recent events or niche topics it hasn’t seen, and it might give irrelevant answers or just make something up.
• Context and Consistency Issues: The model only “remembers” as much context as it was designed to (for ChatGPT, a few thousand words typically). It doesn’t have a long-term memory of past conversations or personal details unless those are included each time. It also doesn’t have any genuine personality or opinions – any style or persona it seems to have is part of the illusion created by patterns. It might sound friendly, or authoritative, or humorous, but that’s all mimicry. Underneath, there’s no stable identity or intent.

When AIs Try to Act as Agents

One particularly tricky downside appears when people try to use models like ChatGPT not just for answering questions, but for making decisions or taking autonomous actions (what some call agentic behavior). Since the AI doesn’t truly understand goals or consequences, giving it free rein can lead to comically ineffective or unpredictable behavior. For instance, an experimental system called “Auto-GPT” tried to let ChatGPT-style AI pursue goals by itself – essentially telling the AI to figure out a task and take steps to complete it. What happened? Users found it often got stuck in loops or made pointless plans. One report noted that Auto-GPT “looks amazing at first glance, but then completely fails because it creates elaborate plans that are completely unnecessary,” sometimes looping endlessly without finishing the task. In other words, without human guidance at each step, the AI wandered off-track.

This highlights a key point: without true understanding or common sense, an autonomous AI can’t reliably make sound decisions. It might take an action that seems statistically appropriate based on its training (or its prompts), but that action could be irrelevant or even harmful in real life. Imagine an AI assistant that, upon being told to “save my computer some memory,” decides the statistically likely action is to start deleting files to free up space – possibly deleting important data. A human knows to ask or consider the consequences; a purely probabilistic AI agent might not, unless such caution was explicitly part of its training data patterns. This is why AI developers are cautious about letting these models operate autonomously in the real world. We currently use them as assistants that suggest or draft things, with a human in the loop to check or make the final decisions.

Why It’s Hard to Make AI “Super Smart”

Given these limitations, you might wonder: can’t we just make the AI smarter to avoid these problems? The challenge is that we don’t yet have a clear recipe for true intelligence or understanding in machines. Scaling up language models (making them larger, training on more data) has certainly made them better at sounding intelligent. The jump from earlier chatbots to ChatGPT is huge in terms of fluency and versatility. However, simply being larger doesn’t automatically grant common sense or genuine comprehension. The model might know more patterns, but it’s still bound by the pattern-matching nature of its design.

AIs also lack a body and experiences – things that form a huge part of human understanding. We learn from touching, seeing, and interacting with the world, not just reading about it. A pure language model only learns from text. Without grounding in the real world, it’s hard for it to develop the kind of robust understanding a human has (like knowing that if you drop a glass, it might break, or that “John” in a story refers to the same person throughout). Researchers are exploring ways to give AI more grounded understanding (through images, robotics, etc.), but it’s a tough problem.

Moreover, the more “intelligent” we try to make AI, the more unpredictable or inscrutable it can become. With billions of parameters, even the creators of ChatGPT can’t exactly explain why it gave a particular answer. This black-box nature makes it hard to trust AI with critical autonomous decision-making. If we don’t fully know how it works, we can’t easily guarantee it will behave as expected in all cases. There’s also the risk that a super smart AI (if we ever get there) could make decisions or form strategies that are too complex for us to follow – which is a bit of a sci-fi nightmare scenario. For now, we’re not at that point; today’s AI is both impressive and limited – great at mimicking intelligence, but not truly groundbreaking in understanding.

Big Tech’s AI Reality Check (Apple’s Siri and Friends)

The gap between seeming smarts and real understanding isn’t just a theoretical concern – it’s playing out in the tech industry right now. Even big tech companies are struggling with the limits of AI’s probabilistic approach. A prime example is Apple and its quest for a smarter Siri. Apple previewed an “Apple Intelligence” upgrade in 2024, promising a more context-aware Siri that could understand your personal context and perform complex tasks across apps. This was Apple’s answer to the wave of AI assistants like ChatGPT (and Google’s upcoming model Gemini). Many Apple fans were excited; after all, Siri has lagged behind rivals in conversation skills and flexibility. Tech YouTubers hyped the potential of a Siri that could finally feel as smart as, say, ChatGPT.

Fast forward to now, and that promise feels underwhelming. Apple quietly admitted that some of the most anticipated Siri improvements have been delayed until 2026. In an official statement, the company said it needs more time to deliver features like a personalized, context-savvy Siri that can take actions for you. This delay has not gone unnoticed. Popular tech reviewers on YouTube – from Marques Brownlee (MKBHD) to Quinn Nelson (Snazzy Labs) – have voiced disappointment. In fact, MKBHD even released a video titled “Apple’s AI Crisis: Explained” expressing his frustration with the slow rollout of Apple’s AI features. “Apple’s AI ambitions have hit a wall,” one tech writer observed, noting the repeated delays and the lackluster progress on Siri’s promised overhaul.

Why is Apple struggling here? Part of it is the inherent difficulty of the AI problems we discussed. Siri’s current architecture was built on fairly rigid commands and limited understanding – bolting on true conversational intelligence is hard. Apple is also famously cautious, especially when it comes to user privacy. Unlike some competitors, Apple is reluctant to hoover up unlimited user data to train AI models (which could help the AI learn faster) because that might violate its privacy values. The company is trying to find a way to make Siri smarter without compromising privacy, such as doing more AI processing on the device itself. That’s a commendable approach, but it’s technically challenging. In the meantime, competitors are moving ahead. Google has been integrating its advanced AI (like the Gemini model) into Google Assistant, and Amazon recently gave Alexa a hefty AI upgrade that lets it handle more complex, conversational queries. Those systems aren’t perfect either, but they’re evolving visibly. By contrast, Siri’s improvement has been so slow that some jokingly say Siri feels “stuck in 2016”.

This illustrates a broader point: today’s AI is powerful, but not a magic switch you can flip to instantly revolutionize a product. Companies as resourceful as Apple still face delays and setbacks making these systems truly useful and reliable in everyday devices. The probabilistic AI might excel in a controlled demo, but turning it into a dependable feature for millions of users (where mistakes could be costly or frustrating) is an enormous task.

Wrapping Up: Cool Tech, But Not a Brain

ChatGPT and its AI cousins are an amazing leap forward in technology. They show that machines can generate human-like language and be genuinely helpful for many tasks. However, it’s important to remember that behind the fluid conversation is a complex math engine playing the odds with words, not a thinking mind. This “intelligence” is alien to our idea of understanding – it’s pattern recognition, not comprehension. That’s why these AIs can both amaze us and mess up in baffling ways.

As we’ve discussed, this has real implications: we should be cautious about trusting AI outputs blindly, and even more cautious about letting AI systems make autonomous decisions in the real world. Making AI smarter in a truly general, human-like way remains an unsolved puzzle. It’s not just about more data or bigger models; it might require fundamentally new approaches (or maybe a lot more time and refinement of current ones). In the meantime, it’s wise to appreciate what ChatGPT can do – use it for brainstorming, quick information, drafting writing, and so on – while also understanding its limits.

The excitement around AI is justified, but so is the skepticism. If you’ve felt that today’s “AI revolution” hasn’t yet delivered a Jarvis-like super assistant, you’re not alone. Even the biggest tech companies are grappling with this reality. The good news is that the tech we do have is still quite useful when used right. ChatGPT may not truly think, but it’s a darn good wordsmith backed by probability and patterns. Knowing that, we can enjoy the magic it seems to offer, without losing sight of the math that actually makes it tick – and without expecting our AI tools to be more than the clever (but limited) parrots that they are.

👋 Connect with Me

I’m Javian Ng, an aspiring Full-Stack Infrastructure Architect & LLM Solutions Engineer based in Singapore. I love building scalable infrastructure and AI systems.

• 🌐 Website: www.javianng.com
• 💼 LinkedIn: linkedin.com/in/javianngzh
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Feel free to reach out or explore more about my projects and experiences.

Sources:

1. MKBHD on Apple Intelligence – https://www.youtube.com/watch?v=5hG9UvwLpTg
2. Snazzy Labs on Siri and AI – https://www.youtube.com/watch?v=kGM-53zxqUo
3. What is a Stochastic Parrot? – https://ai.stanford.edu/blog/stochastic-parrots/
4. Auto-GPT and Agentic AI Issues – https://venturebeat.com/ai/auto-gpt-why-we-need-to-talk-about-autonomous-agents/
5. Apple Delays Smarter Siri Until 2026 – https://www.macrumors.com/2024/07/10/apple-delays-ai-features/
6. Apple Intelligence Announcement Overview – https://www.apple.com/newsroom/2024/06/introducing-apple-intelligence/
7. Google's Gemini and AI Assistant – https://blog.google/products/assistant/google-assistant-ai-updates/
8. Amazon Alexa's AI Upgrade – https://www.theverge.com/2024/05/25/amazon-alexa-ai-upgrades