Why Google’s AI can’t spell Google (or anything else) - TechCrunch

2026-05-28 · Source: artifical intelligence via Google News · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, short

Summary

Google's AI Overview, integrated into its Search product, continues to exhibit fundamental spelling and counting errors, such as incorrectly stating "Google" has two 'P's or misspelling "journalism" as "journadiam." This issue is not new; previous iterations of AI Overviews provided dangerous advice, like eating rocks, and cited satirical sources. Google acknowledges that "counting within words has been a known challenge for LLMs" and is actively working on a fix. The core problem stems from the transformer architecture of Large Language Models (LLMs), which process text by breaking it into numerical "tokens" rather than understanding individual letters or words as humans do. Researchers explain that LLMs translate prompts into encodings, meaning they recognize "the" as a single unit, not as 'T', 'H', 'E'. This token-based limitation makes researchers pessimistic about a complete solution, highlighting that AI outputs require careful verification.

Key takeaway

For AI Product Managers evaluating generative AI integration into user-facing products, recognize that current LLMs have inherent architectural limitations regarding character-level accuracy and spelling. You should implement robust verification layers for AI-generated content, especially for factual or numerical outputs, to prevent public-facing errors. Do not assume advanced models overcome these fundamental challenges, as even Google's AI struggles with basic word structure. Prioritize user trust by clearly communicating AI capabilities and limitations.

Key insights

Large Language Models inherently struggle with character-level tasks like spelling due to their token-based processing.

Principles

• LLMs encode words as single units, not letter sequences.
• Tokenization creates inherent limits for character-level accuracy.
• AI outputs demand human verification for factual correctness.

In practice

• Verify AI-generated content for spelling and factual errors.
• Understand LLM limitations for precise character manipulation.

Topics

• Google AI Overview
• Large Language Models
• AI Limitations
• Tokenization
• Generative AI
• AI Accuracy

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Tech Journalist, AI Product Manager, General Interest

Related on AIssential

• Google’s AI Can’t Spell Google. That’s Not a Joke. — LLMs fundamentally struggle with character-level tasks like spelling due to tokenization, impacting their reliability in factual search.
• Google AI Overviews draws criticism after failing to spell basic words - Business Standard — Large language models struggle with precise letter-level tasks due to token-based text processing, not character-by-character reading.
• How Large Language Models Actually Work (Explained Simply) — LLMs are statistical prediction engines, not reasoning entities, whose capabilities emerge from massive scale and sophisticated attention mechanisms.
• I don't understand AI. How does it work? — LLMs predict text by mapping words to semantic vectors and learning statistical relationships from vast training data.
• When a chatbot runs your store — Large language models struggle with reality adherence, making them unreliable for autonomous transactional roles.
• Inside the AI Industry's Most Expensive Mistake — AI's reliance on tokens for "thinking" is an architectural limitation, contrasting with human pre-linguistic thought.

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Editorial summary, takeaway, and curation by AIssential. Original article published by artifical intelligence via Google News.