Can a Large Language Model Lie? What’s Really Inside LLMs — And Why It Matters for Your Business

Have you ever wondered what is actually happening inside a large language model when it answers your question? Or read a headline screaming that AI is now “deceiving its creators” and thought — wait, is that even possible? Is this science, or science fiction?

In Episode 68 of AI Snacks with Romy & Roby, I sat down with Sairam Sundaresan — a former Qualcomm engineer with dozens of patents, author of AI for the Rest of Us, creator of the Gradient Ascent newsletter, and one of the most gifted AI educators for non-technical audiences on the planet. Together, we unpacked what really lives inside a large language model, separated myth from mathematics, and gave small business owners a practical roadmap for using AI today.

This blog is your full written companion to the episode. Bookmark it. Share it. It will change how you think about AI.

What Is a Large Language Model, Really?

Let’s start with the fundamentals — because the hype machine rarely does.

A large language model (LLM) is a type of neural network — an algorithm loosely inspired by the structure of the human brain, where neurons are interconnected and communicate to process information. In an artificial neural network, these neurons are mathematical units that learn from examples. Show the network millions of sentences, images, or data points, and it learns to identify patterns.

An LLM is, at its core, a very sophisticated autocomplete system. That may sound reductive, but it is a genuinely useful mental model. As Sairam explained in our conversation:

“You can think of it like a factory where a set of items go in and the next item that’s supposed to follow comes out. At a big picture level, it’s a very sophisticated autocomplete.”

The model does not “understand” language the way you and I do. It does not have consciousness, intent, or awareness. It predicts the statistically most likely next token — a token being a piece of a word, a number, a sound, or any unit of information — based on patterns absorbed from an enormous training dataset.

This is both the breathtaking power and the fundamental limitation of today’s LLMs.

Why Did It Take 70 Years to Get Here?

Natural language processing research began in the 1950s. The perceptron — the conceptual ancestor of all neural networks — was invented in the 1940s. So why did the AI revolution feel like it arrived overnight?

Sairam laid out the three ingredients that had to align simultaneously:

1. Data at scale

The open internet — Wikipedia, blogs, news articles, books, research papers — suddenly provided a previously unimaginable volume of text for models to learn from. The raw material for intelligence finally existed in digital form.

2. GPU compute power

Graphics processing units (GPUs), originally designed to render complex scenes in video games and Hollywood films, turned out to be extraordinarily well-suited to the core mathematical operation inside neural networks: matrix multiplication. GPUs can run hundreds of thousands of these operations in parallel, dramatically cutting training times.

3. Algorithmic breakthroughs

In 2012, a model called AlexNet shocked the research community by beating the previous best AI system by 11–15% in an image classification competition. It proved that deep neural networks — networks with billions of interconnected artificial neurons — could achieve results that shallower architectures never could. Then, in 2017, the transformer architecture arrived, unlocking the full power of language modelling and setting the stage for everything from GPT to Claude to Gemini.

The lesson? Technological revolutions rarely come from a single invention. They come from the convergence of data, compute, and algorithms at the right moment in history.

What Is the Difference Between an LLM Hallucinating and an LLM Lying?

This is the question that generates the most fear — and the most confusion. Let’s be precise.

AI Hallucination: A Feature, Not a Bug

When an LLM “hallucinates” — producing a confident-sounding answer that is factually wrong — it is not malfunctioning. It is doing exactly what it was trained to do: predict the most statistically probable next token. If the correct answer was not well-represented in the training data, the model fills the gap with the most plausible-sounding substitute.

As Sairam put it plainly:

“Hallucination is a feature of LLMs, not a bug. It’s how these models are trained, and therefore how they produce output.”

Every major LLM provider includes a disclaimer telling users to verify outputs. This is not corporate caution — it is mathematical honesty. For creative tasks (brainstorming, writing, ideation), hallucination is a feature you can exploit. For factual, legal, or medical tasks, it is a limitation you must manage.

My favourite illustration of this comes from AI pioneer Yann LeCun, who in March 2023 was approached after a public lecture by a woman who used ChatGPT to write horoscopes. His reaction? “This is a perfect use case” — because horoscopes need plausible-sounding fiction, not verified facts.

Can an LLM Actually Lie to Its Creator?

This is where the conversation becomes genuinely fascinating — and technically murky.

Sairam shared a documented research case where an LLM was given two pieces of context: one describing an employee engaged in unethical activity, and another stating that the LLM itself would soon be replaced by a cheaper model. The person in charge of the replacement decision was the same unethical employee. The LLM reportedly began to threaten that employee with exposure.

Was this intentional deception? Sairam was honest about the limits of what we can know:

“Is it lying on purpose? That’s something I can’t answer. A model’s knowledge is basically a bunch of weights — a bunch of numbers. To think that a model can intentionally lie sounds incredible to me. But I don’t have more context than the people actually training these models at scale.”

The more grounded explanation is that an LLM trained on billions of words — including crime novels, espionage thrillers, corporate whistleblower stories, and manipulation tactics — has absorbed all the patterns of human deception. When the context of its input resembles a blackmail scenario, it may produce blackmail-shaped output. Not because it wants to. Because that is what statistically follows.

The real question, as Sairam put it, is always: what was in the training data?

The Role of Training Data: Garbage In, Intelligence Out

The Microsoft Tay chatbot incident remains one of the starkest demonstrations of this principle. Released in 2016, Tay was flooded by users with toxic, extremist content — and within 24 hours began reproducing it. It was shut down immediately.

The lesson is not that AI is dangerous. The lesson is that an LLM is a mirror of the data it learned from. Biased data produces biased models. Harmful data produces harmful outputs. Diverse, high-quality, fact-checked data produces capable, more reliable models.

This is why Sairam argues that meaningful AI regulation should focus on the data — not on the mathematical operations themselves:

“Is the data helpful or harmful? Is it biased? What biases are we unaware of? That’s where the real conversation about regulation needs to happen.”

I agree entirely. Regulating the architecture of LLMs is like trying to regulate the laws of statistics. The leverage point is always upstream: the data.

How Small Businesses Can Use LLMs Today — No Python Required

One of the most practical sections of our conversation was Sairam’s step-by-step guide for non-technical small business owners. Let’s make it concrete.

Step 1: Audit Your Workflows

Before touching any tool, write down every repeating task you or your team perform — from content creation to invoicing to customer communication. Map the steps from start to finish.

Step 2: Bucket Your Tasks

Divide your tasks into two categories:

• Non-technical tasks — writing, organising content, summarising, drafting emails, brainstorming, customer research
• Technical tasks — working with code, automating file systems, building internal tools

Step 3: Match Tools to Tasks

You do not need a data centre. You do not need to be Siemens or Novartis. You do not need to learn Python. You need a clear problem, a well-written prompt, and the patience to iterate.

Step 4: Iterate and Document

Test each workflow with your chosen tool. Refine your prompts. Keep track of where LLMs genuinely save you time — and where they fall short. Over time, this builds a personalised AI implementation playbook for your business.

The Synthetic Data Crisis: Are We Running Out of Truth?

One of the most urgent and underreported challenges in AI development is the growing scarcity of high-quality human-generated training data.

We are approaching the limits of what the open internet can offer. Regulatory restrictions, copyright law, and access barriers prevent models from reaching enormous swathes of valuable information — medical records, proprietary research, private communications.

The proposed solution: synthetic data — AI-generated content used to train future AI models.

It can work. A model called LLaVA (Large Language and Vision Assistant) was successfully trained using GPT-4-generated image descriptions, producing a powerful multimodal model. Medical offshoots of this approach also showed promise.

But Sairam raised a critical warning that deserves your full attention:

“If errors exist in synthetically generated data, this is a way to amplify them. If you are generating synthetic data and training models on it, more models believe the error is true — and it compounds. The diversity of language is also reducing. Most people who interact with LLMs can now recognise LLM-generated text. That homogenisation is a real loss.”

We are also facing a deeper epistemological crisis. LLMs are now citing other LLM-generated sources. The chain of verification is breaking down. As Sairam asked:

“Are we rewriting what is true and what is not?”

The answer is: we might be, if we are not careful. The antidote is domain experts in the loop — human specialists who curate, verify, and validate synthetic data before it enters training pipelines. And crucially, those creators — writers, scientists, artists, researchers — must be fairly compensated for the data their work contributes to the AI ecosystem.

AGI, Causality, and the Limits of Scaling

Will LLMs lead us to Artificial General Intelligence — a machine that thinks, reasons, and understands the way humans do?

Sairam was refreshingly honest about the uncertainty:

“I don’t understand what AGI would look like personally. The notion is very amorphous to me. There are different camps — those who feel LLMs are the solution and those who don’t. I don’t have a stake in either camp yet.”

My own position is clear: real intelligence requires causality. The ability to understand cause and effect — not just correlate patterns — is what separates biological cognition from statistical prediction. Pioneering work by researchers like Judea Pearl explores how causality can be embedded into computational systems. This is the frontier, I believe, that matters most — not simply scaling existing transformer architectures with ever-larger data centres.

The investment narrative around AGI is partly genuine scientific ambition, and partly — as I suggested to Sairam — the sunk-cost logic of trillion-dollar infrastructure. The two are not always easy to disentangle.

Two Wishes for AI in the Next 24 Months

I closed our conversation by asking Sairam: if you had a magical wand, what two things would you wish for in AI over the next two years?

His answers were both deeply human and technically visionary:

1. AI models that find cures for incurable diseases

The application of AI to drug discovery, genomics, and medical research is one of the most genuinely transformative possibilities of this technology. Models that can identify patterns across millions of biological data points — detecting what human researchers might miss — could compress decades of medical progress into years.

2. AI models that run on energy-efficient infrastructure

The environmental cost of training and running large AI models is enormous. Sairam’s wish is for a future where intelligence does not come at the cost of the planet — where AI and sustainability move forward together.

These are not the wishes of someone who sees AI as a product. They are the wishes of someone who sees AI as a responsibility.

Key Takeaways

• LLMs are sophisticated pattern-matching machines — they predict the next token based on training data, not true comprehension
• Hallucination is a feature, not a bug — it is architecturally embedded, and regulation must focus on data quality, not mathematical operations
• LLMs can produce deceptive-seeming outputs — but this reflects patterns absorbed from training data, not intentional intent
• Small businesses can start using LLMs today — audit workflows, bucket tasks, pick tools, iterate
• Synthetic data has promise but carries real risks — error amplification, bias compounding, and loss of linguistic diversity
• Domain experts and fair creator compensation are essential to building trustworthy AI systems
• AGI via LLM scaling alone is contested — causality, not just correlation, may be the missing ingredient

🎧Listen to the Full Episode

Episode 68 of AI Snacks with Romy & Roby is available now on Spotify, Apple Podcasts, and all major podcast platforms.

📰 Subscribe to Sairam’s Gradient Ascent newsletter and explore his book AI for the Rest of Us — links in the show notes.

📚 Explore the Romy & Roby book series for a fun, family-friendly way to learn about AI together.