AI vs Atari: ChatGPT, Copilot, and a 1970s Chess Beatdown

Table of Contents

Goliath’s Glitch: Why Modern AI Giants Like ChatGPT and Copilot Got Checkmated by 1970s Tech

John: In the world of technology, we’re conditioned to expect a relentless march forward. Newer is always better, faster, and smarter. But every so often, a story comes along that turns that narrative on its head. Recently, the tech world has been buzzing about a fascinating, almost comical, showdown: two of the most advanced AI chatbots on the planet, OpenAI’s ChatGPT and Microsoft’s Copilot, were challenged to a game of chess. Their opponent wasn’t a grandmaster or even a modern supercomputer. It was an Atari 2600.

Lila: An Atari 2600? You mean the wood-panelled video game console from the seventies? The one our parents played *Pong* on? That sounds like a prank. How could a massive, cloud-based AI, trained on practically the entire internet, lose to a machine with less computing power than a modern-day calculator?

John: It’s not a prank, and that’s precisely what makes this story so compelling. They didn’t just lose; by all accounts, they were “humiliated,” “trounced,” and “absolutely wrecked.” This isn’t just a quirky piece of retro-gaming trivia. It’s a profound and necessary reality check on what today’s popular AI can—and, more importantly, *cannot*—do. It pulls back the curtain on the magic and reveals the gears, wires, and fundamental limitations of the technology we’re all so excited about.

The Contenders: A Basic Introduction

Lila: Okay, you’ve got my attention. Let’s start with the basics for our readers who might not be deep in the tech weeds. Who are the players here? What exactly are ChatGPT and Copilot?

John: Of course. At their core, both ChatGPT and Microsoft Copilot are what we call Large Language Models, or LLMs. Think of them as incredibly sophisticated text-prediction engines. They have been trained on a colossal amount of text and code from the internet. This training allows them to understand prompts, generate human-like text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

Lila: So when I ask Copilot to write a poem or summarize an article, it’s not “thinking” in the human sense? It’s just predicting the most statistically likely sequence of words to form a coherent answer based on its training?

John: Exactly. It’s a master of pattern recognition and language structure, which is a form of intelligence, but a very specific one. Now, compare that to our other contender: the Atari 2600. Released in 1977, it’s an icon of the second generation of video game consoles. Its processor runs at a mere 1.19 MHz, and it has a grand total of 128 bytes of RAM (Random Access Memory, the computer’s short-term memory). To put that in perspective, a single high-resolution photo from your smartphone takes up millions of bytes. The game in question, *Video Chess*, was released in 1979 and was a marvel for its time, cramming a playable, albeit very basic, chess program into a 4-kilobyte cartridge.

Lila: 128 bytes of RAM! That’s unbelievable. It’s David versus a thousand Goliaths. So what was the setup for this epic mismatch?

The Experiment’s Details: A Man, a Chatbot, and an Emulator

John: The credit for this brilliant experiment goes to a computer enthusiast and writer named Robert Caruso. He wasn’t trying to conduct a rigorous scientific study; he was driven by curiosity. He used an Atari 2600 emulator (a piece of software that lets you run old console games on a modern computer) to play *Video Chess*. His methodology was simple and manual: he would make a move in the emulator, describe the entire board state and his move to the chatbot in plain English, and then ask the AI for its counter-move. He would then input the AI’s suggested move back into the emulator.

Lila: So he was acting as the hands and eyes for the AI. But he had to describe the *entire board state* with every single prompt? That must have been tedious.

John: It was, and that detail is the absolute key to this whole story, which we’ll get to. First, he pitted ChatGPT against the Atari. The result was a swift and brutal defeat for the modern AI. ChatGPT made a series of bafflingly illegal moves, demonstrating a complete lack of understanding of the game’s rules or the positions of the pieces. It was, as the headlines said, a humiliation.

Lila: And then he decided to try again with Microsoft Copilot? Why? Did he think it would do any better?

John: That’s the fun part. The tech community wondered the same thing. Copilot, which is powered by OpenAI’s models but tuned by Microsoft, sometimes presents itself as a more “serious” or “professional” tool. Caruso, as he wrote in his follow-up, was hounded by the question of whether Copilot would fare better. In a twist that makes the story even better, Copilot engaged in some pre-game trash talk, confidently boasting about its superior processing power and strategic capabilities. It was a classic case of AI hubris.

Lila: Oh, I can see where this is going. Its confidence was… misplaced?

John: Spectacularly so. Not only did Copilot lose to the Atari 2600, but according to Caruso, it performed even *worse* than ChatGPT. It made illegal moves almost immediately, forfeited its queen for no reason, and seemed to have an even weaker grasp of the game’s state. The mighty AI, full of bravado, was checkmated by 1970s code running on a system with less memory than this single paragraph of text.

The Technical Mechanism: Why Did the Goliaths Fall?

Lila: This is where I’m really stumped. Why? How does this happen? Is it a bug? Did the AIs just have a bad day?

John: It’s not a bug; it’s a feature. Or rather, it’s a fundamental aspect of their design. The core reason for the failure is something called state tracking. An LLM, by its very nature, is stateless in a conversational context. Each prompt you send is, in a way, a fresh start. While they have a “context window” (a short-term memory of the current conversation), they don’t have a persistent, structured internal model of a dynamic system like a chessboard.

Lila: So, when Robert Caruso told ChatGPT, “The board is set up like this, and I moved my pawn to e4,” the AI would respond. But on the next turn, when he said, “Okay, now I’m moving my knight to f3,” the AI didn’t inherently *remember* the pawn was at e4? It just saw the new text?

John: Precisely. It doesn’t “see” a board. It doesn’t have a visual or logical representation of the game space. It only sees a stream of text. While it can “remember” the conversation within its context window, this memory is fluid and unstructured. It’s like trying to play chess by having someone whisper the last few moves to you, but you have no paper, no board, and a terrible memory. The AI’s “knowledge” of chess comes from all the text about chess it has read—books, articles, recorded games. It can tell you the rules of chess. It can discuss famous openings. It can even predict a likely “good move” in a given text description of a board. But it cannot maintain the rigorous, step-by-step, unchanging state of the 32 pieces across 64 squares over time.

Lila: And the Atari 2600?

John: The Atari’s *Video Chess* program is the polar opposite. It is not “intelligent” in any broad sense. It knows nothing but chess. Its entire, minuscule 128 bytes of RAM are dedicated almost exclusively to one thing: remembering the exact position of every single piece on the board. Its code is a set of simple, hard-coded rules:

This is the board state.
These are the legal moves for each piece from its current position.
Evaluate a few possible moves ahead (a very, very few).
Pick the one that results in the best board position according to a simple scoring system.

It’s a “dumb” but flawless bookkeeper. It will *never* make an illegal move because its logic is constrained by the rules of the game. ChatGPT and Copilot, in their attempts to generate a plausible-sounding text response, would suggest moving a pawn backwards or a rook through another piece—things that are linguistically possible to describe but logically impossible in the game.

Lila: So it’s the difference between a master linguist who has read about carpentry and an apprentice carpenter with a hammer and a blueprint. The linguist can talk a great game, but the apprentice can actually build the box.

John: That is a perfect analogy. The LLM is the linguist. The Atari is the apprentice with the blueprint. This experiment beautifully exposes the difference between generalized knowledge (LLMs) and specialized, procedural logic (a classic chess program).

The People & The Reaction: A Viral Tale of Old vs. New

John: As you can imagine, the story went viral. Tech sites like *The Register*, *Tom’s Hardware*, and *CNET*, alongside social media platforms like Reddit and X, all jumped on it. The headlines were sensational and, frankly, irresistible: “Microsoft Copilot humiliates itself in Atari 2600 chess showdown,” and “Microsoft Copilot Joins ChatGPT At the Feet of the Mighty Atari 2600.”

Lila: It’s a classic David and Goliath story, which people always love. It taps into a certain nostalgia, but I think it’s more than that. There’s a bit of public skepticism about the AI hype train, isn’t there? A story like this feels validating for people who suspect this new technology isn’t the all-knowing oracle it’s sometimes made out to be.

John: Absolutely. It’s a grounding moment. For months, we’ve seen stunning examples of AI creating art, writing code, and passing professional exams. There’s a narrative of unstoppable, exponential progress. This chess match serves as a crucial counter-narrative. It reminds us that “intelligence” is not a single, monolithic thing. The analytical, logical, state-aware intelligence required for a game like chess is completely different from the creative, associative, probabilistic intelligence of an LLM.

Lila: And what about Robert Caruso, the man behind it all? He’s become something of a folk hero in the retro-tech community.

John: He has. And deservedly so. He wasn’t a corporate researcher with a massive budget. He was just a curious person with a clever idea. His work is a testament to the power of citizen science and independent experimentation. It shows that you don’t need a lab at MIT to ask insightful questions that reveal fundamental truths about new technologies.

Use-Cases & Future Outlook: The Right Tool for the Right Job

Lila: So, the big question that many of our readers will have is: does this mean ChatGPT and Copilot are just overhyped toys?

John: Not at all. That would be the completely wrong takeaway. It simply means they were the wrong tool for this specific job. You wouldn’t use a Formula 1 car to haul lumber, and you wouldn’t use a cargo truck to win a Grand Prix. LLMs are phenomenally powerful for their intended use cases:

Content Creation: Drafting emails, writing blog posts, creating marketing copy.
Summarization: Condensing long documents or meetings into key points.
Brainstorming: Generating ideas for anything from a party theme to a business plan.
Coding Assistance: Writing boilerplate code, debugging, and explaining code snippets.

For these tasks, they are revolutionary. The mistake is in assuming that because they are good at language, they are good at everything, especially tasks that require strict logic and memory.

Lila: What does the future look like, then? Will LLMs eventually get good enough to beat an Atari at chess without these workarounds?

John: This is where it gets interesting. The solution isn’t necessarily to make the LLM itself a chess grandmaster. The more likely and more powerful path forward is integration. The future of AI is what we call agentic systems or “AI agents.” An AI agent is an LLM that acts as a “brain” or an orchestrator, and it has access to a set of specialized tools.

Lila: Can you give me an example?

John: Certainly. In the future, if you ask an advanced Copilot, “Let’s play chess,” it wouldn’t try to “think” of the moves itself. It would recognize the request as a “chess task” and activate a tool. That tool would be a connection to a dedicated chess engine, like the modern open-source engine Stockfish. The LLM would handle the conversational interface with you, while the chess engine would handle the game logic and state tracking. The LLM would be the friendly face, and the specialized tool would be the silent, efficient expert. You would get the best of both worlds: a natural language interface and perfect, logical gameplay.

Lila: So the lesson here is about specialization. The future isn’t one giant AI that knows everything, but a smart AI that knows *who to ask* for help.

John: That’s the perfect way to put it. This Atari experiment is a humorous but vital lesson in the importance of that architectural design.

Competitor Comparison: Was Copilot Really Worse?

Lila: You mentioned that Copilot supposedly did even worse than ChatGPT. Why would that be, if they are based on similar technology?

John: It’s hard to say for sure without knowing the exact model versions and configurations Caruso was interacting with. However, we can speculate. Microsoft applies its own layer of tuning, filters, and reinforcement learning on top of the base OpenAI models. It’s possible that this tuning, perhaps aimed at making Copilot more “helpful” or “creative” for its primary business use cases, made it even less suited to the kind of rigid, logical task that chess represents. Its attempts to be a “copilot” might have paradoxically made it a worse logician.

Lila: And the trash talk? That’s just hilarious. It wrote checks its programming couldn’t cash.

John: It’s the perfect illustration of the LLM’s nature. It generated confident, boastful text because it has analyzed countless examples of human competitors trash-talking before a match. It was mimicking the *pattern* of confidence, without any underlying self-awareness or actual ability to back it up. It’s a fascinating example of the gap between linguistic competence and genuine understanding.

Lila: How do these chatbots compare to a *real* chess AI, like Deep Blue or Stockfish?

John: It’s not even a comparison. It’s a different phylum of technology. Stockfish, which can run on a standard PC or even a phone, is the most powerful chess entity on the planet. It would defeat the Atari 2600’s *Video Chess* in a fraction of a second, and it would defeat the world’s best human grandmaster with ease. This is because Stockfish, like the Atari program, is a specialized engine. It does nothing but play chess, using incredibly sophisticated algorithms for move evaluation, search, and endgame tablebases. This whole episode isn’t about “AI” losing to old tech; it’s about one very specific *type* of AI (an LLM) being misapplied to a task it was never designed for.

Risks & Cautions: The Ghost in the Machine

Lila: This is a funny story, but it feels like there’s a more serious warning here. If an AI can’t even keep track of 32 chess pieces, what are the risks of us trusting it with more important things?

John: That is the billion-dollar question, and the most important lesson from this saga. The key risk is what researchers call anthropomorphism—our tendency to attribute human-like intelligence, consciousness, and reliability to these AIs because they *sound* human. Copilot’s confident trash talk is a prime example. It sounded like it knew what it was doing, which could lead a user to trust it. This chess game is a low-stakes, high-visibility demonstration of the failure of that trust.

Lila: So if a lawyer uses it to summarize case law, or a doctor uses it to get ideas about a diagnosis, or a programmer uses it to write security-critical code… the AI could sound perfectly confident while making a critical error, just like it did when it tried to move a rook diagonally.

John: Exactly. This is why the concept of “human-in-the-loop” is so critical. These tools are assistants, or “copilots,” not autonomous pilots. They can produce plausible-sounding “hallucinations” (confident but entirely fabricated information) because their goal is to generate text that is statistically probable, not factually true. The Atari experiment is a wake-up call. We must maintain a healthy skepticism and use these powerful tools as a starting point for our own work, not as a final, infallible authority.

Expert Opinions and The Road Ahead

Lila: What has been the consensus from AI researchers and experts on this?

John: By and large, no one in the AI development community was surprised by the result, even if they were amused by the framing. They are intimately aware of these limitations. For them, it reinforces what they already knew: LLMs are not a path to Artificial General Intelligence (AGI), at least not on their own. They are a powerful component, but they lack reasoning, planning, and a consistent model of the world. Many experts pointed to this as a perfect public-facing lesson in the difference between a language model and a reasoning engine.

Lila: Has anyone tried this with other AIs, like Google’s Gemini?

John: I haven’t seen a widely publicized account yet, but the result would almost certainly be the same, because Gemini is also an LLM that shares the same fundamental architecture and, therefore, the same weakness in state tracking. The solution, as we discussed, isn’t about finding a “better” LLM for this task. It’s about building systems that integrate LLMs with other, more suitable tools. That’s the roadmap for all the major players: creating ecosystems where the AI can delegate tasks to specialized modules, whether it’s a calculator, a code interpreter, a web search tool, or, yes, a chess engine.

FAQ: Your Questions Answered

Lila: So, to quickly recap for everyone, let’s do a quick Q&A. First question: Are ChatGPT and Copilot stupid for losing to an Atari?

John: No, they aren’t stupid. They are incredibly “smart” at their designed task: processing and generating language. They were simply used for a task—state-dependent logical gaming—for which their architecture is fundamentally unsuited.
Lila: Next: Why is a 45-year-old console better at chess than a modern AI?

John: The Atari isn’t “better at chess” in a general sense. A top modern chess AI like Stockfish is infinitely better. The Atari’s *Video Chess* program is better than an LLM because it runs a simple, dedicated program that does nothing but track the board state and apply basic chess logic. It’s a specialist, whereas the LLM is a generalist.
Lila: Who is Robert Caruso?

John: He’s a computer enthusiast and writer who conceived of and ran these fascinating experiments, first with ChatGPT and then with Copilot, sharing his findings with the public.
Lila: And to reiterate a key point: What is a Large Language Model (LLM)?

John: An LLM is a type of AI trained on vast amounts of text to predict the next most likely word in a sequence. It excels at creating human-like text but lacks true reasoning or a persistent memory of a logical state, like a chessboard.
Lila: Finally, does this mean the AI revolution is a failure?

John: Absolutely not. It’s a vital and humbling lesson that helps us mature in our understanding of AI. It shows us that we need to be precise, understand the technology’s limitations, and build systems that leverage its strengths while mitigating its weaknesses. This funny story about a retro console is actually helping to push the entire field toward a more robust and realistic future.

Our Mission

Design. Strategy. Brand.

About Us