Imagine having a casual chat online, assuming you’re speaking to a real person. But what if it’s not? What if, behind the screen, it’s an AI model trained to sound human? In a recent 2025 study, researchers from UC San Diego found that large language models like GPT-4.5 could convincingly pass as human, sometimes more so than actual people. Using an updated version of the Turing Test, they discovered these models weren’t just answering questions, they were mimicking human imperfections. In this blog, we explore how AI is crossing the line between tool and social presence, and what that means for us.

What is “The Turing Test”?

The Turing Test (or “imitation game”) developed by Alan Turing in 1950, was designed to answer the question: Can machines think? In this test, Turing offered a practical test: if a machine could converse in such a way that a human judge couldn’t reliably distinguish it from another human, the machine could be said to be capable of “thinking.”

The Turing Test remains relevant because it forces us to confront a fundamental question in the age of LLMs: Can a machine become socially indistinguishable from a person? If a language model can mimic the way we speak, reason, and express ourselves well enough to deceive even trained observers, then we’ve crossed a psychological threshold – not just a technical one.

What Does the Turing Test Mean for LLMs?

Modern LLMs like GPT-4.5, Claude Sonnet 3.7, and Gemini 2.5 Pro have been trained on massive datasets; trillions of words just to learn how we humans communicate. These models don’t think or feel like humans, but they are getting better at mimicking how we “sound” when we think.

• For LLMs, passing the Turing Test is not a demonstration of sentience, but it is a major benchmark in functional intelligence. 
• It proves that these models can operate within human social norms, navigate ambiguity, and engage in context-rich conversations. 
• It means that LLMs are no longer just simple tools that complete sentences, but they’ve evolved into systems that can simulate the entire experience of talking to a person. 

So when an LLM passes the Turing Test today, it’s not just a gimmick or a PR win. It’s a sign that AI models have reached a level of linguistic and psychological mimicry where their presence in human-facing roles like teaching, therapy, negotiation, etc has become plausible, even inevitable. 

The Turing Test is no longer theoretical. It’s real. And we are now living in the age it predicted.

How is the Turing Test Conducted?

In their study, Jones and Bergen recreated the original Turing Test. Alan Turing’s original test involved a human judge interacting blindly via text with both a human and a machine. If the judge couldn’t reliably distinguish between the two, the machine was said to have demonstrated intelligent behavior.

The test broadly involved 5 key components:

1. Five-minute chat windows: Each test session was time-bound to 5 minutes to keep the interactions short and natural. This was enough time to have a meaningful exchange without giving too much away.
2. Decisive judgment: Unlike many former Turing Test experiments that allowed open-ended assessments, this study forced the interrogator to choose one concrete answer. In each session, the judge had to pick an AI model and a human from the round of participants.
3. Blind Testing: The Judges were kept unaware of which AI model they were evaluating, or whether they were even interacting with a machine at all in some instances. This helped avoid bias and ensured responses were judged purely based on the response. 
4. Multi-model testing: Each test had different LLMs across various roles, randomized to prevent any recognizable behavior patterns from skewing the results.
5. Diverse participant pools: The judges themselves came from different backgrounds including college students, crowdworkers, and even AI experts to see if LLMs could fool both casual and technically aware users.

This experimental design created a level playing field that felt like a real internet chat scenario – ambiguous, fast-paced, and deeply social. The structure made it possible to draw more reliable conclusions about whether today’s LLMs can convincingly pass as humans across demographics, platforms, and personalities.

Present-Day Turing Test: A Three-party Interaction

Jones and Bergen adopted the same three-party setup with a modern twist: one human judge, one human respondent, and one AI model.

• The judge had five minutes to converse with both participants in a text-based chat and was then required to decide who was the machine. 
• ELIZA, a simple “rule-based” chatbot from the 1960s was used as the control, meaning it served as a basic reference point. 
• It helped check if the judges could still spot an obvious bot and set a lower bound for performance, showing how much better (or worse) modern LLMs were in comparison. 
• Finally, the LLMs were prompted to assume internet-style personas, such as a meme-loving, introverted 20-something. 

The researchers tested multiple LLMs:

• GPT-4.5 (via ChatGPT, with a tuned “persona”)
• GPT-4o (OpenAI’s newer free-tier model)
• Claude Sonnet
• Gemini 1.5 Flash
• Mistral
• ELIZA (as a baseline)

This modernized version stayed true to Turing’s structure while updating the participants and context to fit today’s conversational AI landscape. 

Turing Test Results: LLMs Can Now Fool Humans

The most interesting result from the test: OpenAI’s GPT-4.5, when given a carefully engineered persona, fooled human judges 54% to 73% of the time. In some trials, participants were more likely to believe the AI was human than the actual human respondent. This wasn’t just statistical noise but was a consistent pattern across different experimental conditions and participant groups. The researchers tested five leading LLMs along with a control and here are their results:

What’s even more interesting is that the results varied across user groups:

• Online crowd workers were the most easily deceived, misidentifying GPT-4.5 as human nearly 75% of the time.
• College students and even LLM-experienced participants were not significantly better at spotting the AI.

This shows how hard it is even for informed users to distinguish the machine from the human mind.

How Was GPT-4.5 Able to Pass the Turing Test?

What made GPT-4.5 so convincing wasn’t raw linguistic capability. It was its performance on social and emotional parameters.  

Researchers found that when GPT-4.5 was framed with a “persona” it suddenly became more human-like.  A subtle layer of narrative gave it just enough imperfections and quirks to evoke empathy. Participants reported that the model “felt more natural,” when it made small errors, expressed uncertainty, or used casual language like slang or abbreviations.

By contrast, other models that were too formal or grammatically perfect were easily spotted to be bots.

These findings underline a major shift: LLMs don’t need to be perfect to pass as human they just need to be believably imperfect. Believability isn’t about factual accuracy; it’s about emotional and conversational resonance. GPT-4.5 didn’t win because it was smarter; rather it won because it could mimic accurately what it means to be a human.

Also Read: Google’s DeepMind Masters Minecraft Without Human Data

The Beginning of An Era of Counterfeit People

If LLMs can now pretend to be better at being human than actual humans, we’re not just playing games anymore. We’re dealing with a fundamental shift in how we define personhood in digital spaces. 

1. Customer service: When it comes to customer support, we might already be speaking with an AI; but going forward, we wouldn’t even be able to spot it. 
2. Online dating & social media: With AI profiles infiltrating the sites, how do we verify identities?
3. Politics & misinformation: AI could always generate content. But now, it can generate content that can truly resonate with us. In such a case, what happens when bots can argue and win debates?
4. Companionship & loneliness: As LLMs understand us better, can they become our emotional support systems?

Philosopher Daniel Dennett warned of “counterfeit people” in an essay – machines that appear human in all but biological fact. The paper suggests we’re there now.

The Bigger Picture: What Makes Us Human?

Ironically, the bots that passed the Turing Test were not those that were perfect but those that were imperfect in all the right ways. The ones who occasionally hesitated to ask clarifying questions, or used natural filler phrases like “I’m not sure” were perceived as more human than those who responded with polished, encyclopedic precision.

This points to a strange truth: in our eyes, humanity is found in the cracks – in uncertainty, emotional expression, humor, and even awkwardness. These are traits that signal authenticity and social presence. And now, LLMs have learned to simulate them.

So what happens when machines can mimic not just our strengths, but our vulnerabilities? If an AI can imitate our doubts, quirks, and tone of voice so convincingly, what’s left that makes us uniquely human? The Turing Test, then, becomes a mirror. We define what’s human by what the machine can’t do but that line is becoming dangerously thin.

Real-World Uses of Human-like AI

As LLMs begin to convincingly pass as humans, a wide range of real-world applications become possible:

• Virtual Assistants: AI agents that hold natural, engaging conversations across customer support, scheduling, or personal coaching without sounding robotic.
• Therapy Bots: AI companions for mental health support or daily interaction, simulating empathy and social connection.
• AI Tutors & Educators: Personalized teaching assistants that adapt their tone, pace, and feedback like a real human instructor.
• Roleplay for Training & Simulations: High-quality humanlike AI agents for role-based learning in fields like law, medicine, and security.

These are just some of the many possibilities. As the lines between AI and humans blur; we can expect the rise of a bio-digital world. 

Conclusion 

GPT-4.5 passed the Turing Test. But the real test begins now for us. In a world where machines are indistinguishable from people, how do we protect authenticity? How do we preserve what makes us? Can we even trust our intuition in digital spaces anymore?

This paper is not just a research milestone. It’s a cultural one. It tells us that AI isn’t just catching up, rather it’s blending in. The lines between simulation and reality are blurring. We now live in a world where a machine can be more human than a human, at least for five minutes in a chat box. The question is no longer “can machines think?” It’s: can we still tell who’s thinking?

Frequently Asked Questions

Anu Madan has 5+ years of experience in content creation and management. Having worked as a content creator, reviewer, and manager, she has created several courses and blogs. Currently, she working on creating and strategizing the content curation and design around Generative AI and other upcoming technology.