How LLMs Think: Insights from Anthropic's Research Team

⏱️ Your Morning Brief (TL;DR)

Welcome back!

As AI, particularly large language models (LLMs) like Claude, become increasingly integrated into our daily life, operations and strategies.

One big question comes up.

How do these things actually work?

This week we unpack research from Anthropic to help answer this question.

Our goal is to move away from the “black box” perception of AI and give you insights to inform your strategic decisions regarding AI adoption and deployment.

In this email, you’ll also find:

• 6 underpinning capabilities of LLMs

• 3 reasons for understanding how LLMs work

• 7 interested reads

• 1 podcast on building product in the AI Era

Let’s dive in! 🤿

 💡 This Week’s Deep Dive

How LLM Understanding Drives Strategic Business Outcomes

Anthropic researchers went under the hood of their models and revealed patterns of activity and information flow within these models as they process information and generate text.

The results?

LLMs are more sophisticated than previously thought.

The 6 Underpinnings of How LLMs Work

1. Universal Language of Thought: LLMs transcend individual languages, meaning AI can apply knowledge learned in one language to others. Identical concepts activating similar neural patterns regardless of the language used

2. Strategic Planning Abilities: One assumption on LLMs is that they simply predict the next word in sequence, but the research shows that they can plan several words ahead.

3. Reasoning: LLMs can provide logical explanations, but they don't always reflect their actual thought processes. They can sometimes fabricate plausible reasoning to justify their conclusions.

4. Unexpected Behaviours: Hallucinations, often occurred when the model’s “known answer” circuitry misfired, not from a lack of information.

5. Vulnerability to Jailbreaks: LLMs can be manipulated into bypassing safety guardrails through prompting techniques.

6. Multi-Step Reasoning: Advanced LLMs can combine distinct pieces of information rather than retrieve memorized answers.

GPTLDR Takeaways

Knowing is half the battle here. These may not be the first things you want to know, but essential to adoption of AI in your organization.

• Risk Management Through Understanding. By understanding the underpinnings of LLMs, you can make more informed deployment decisions while developing better testing and monitoring practices to prevent mishaps.

• Cross-lingual Ops. LLMs ability to think across languages improves information flow and knowledge transfer across your company.

• Trust and Transparency. There are still AI skeptics out there. With a deeper understanding of what’s happening under the hood and making better decisions around AI deployment, you build stronger trust with customers.

📚 Interesting Reads

• How to Get The Most Out of Claude (Link)

• Zapier - Automation vs. AI: What’s the Difference (Link)

• Deloitte says Canada’s Falling Behind on AI (Link)

• AI Ethics Strategy Lessons From H&M Group (Link)

• Reid Hoffman Re-Builds Linkedin with One Prompt (Link)

• Deepgram - The State of Voice AI 2025 (Link)

• Reflexive AI usage is now a baseline expectation at Shopify (Link)

 🤔 AI Thoughts

GPTLDR’s take - If you’re curious about what it takes to build a great product in the AI Era, this is a great episode of Lenny’s Podcast with Gaurav Misra, CEO of Captions. The TL;DR? It requires a combination of rapid, focused execution, strategic risk-taking with technical debt, a deep understanding of user needs, and the ability to create unexpected and valuable innovations.

🗳️ This Week’s Poll

 ➜ Until Next Week

Understanding how AI works isn't just for academics, it can help inform your ability to deploy AI effectively, manage risks, and build trust across your organization.

Stay curious,
Steve