Think First, Then Speak: A Lesson for Humans and AI

How a 13th-Century Persian Poet Anticipated AI Reasoning

In an age of instant communication, speaking before thinking has become the norm. But what if the key to intelligence — both human and artificial — lies in deliberate thought before expression?

Over 700 years ago, Saadi Shirazi (c. 1210–1291/92), a renowned Persian poet, captured this idea in Gulistan:

Which translates to:

“First comes thought, then speech — just as a foundation is laid before the wall is built.”

Saadi, a keen observer of human nature, argued that wisdom comes from structuring thoughts before articulating them. As AI progresses, we see this very principle shaping the future of machine intelligence.

From Poetry to AI: Can Machines Learn to Think Before Speaking?

The Evolution Toward Silent Thinking in AI

For early Large Language Models (LLMs), generating text was like autocomplete on steroids — statistical predictions without deep reasoning. However, researchers discovered that Chain of Thought (CoT) reasoning significantly improved problem-solving.

Why? Just as humans need to write down steps in math class, AI benefits from serial computation — breaking down a problem into smaller steps rather than jumping to conclusions​.

Modern AI models like OpenAI o1, o3, and DeepSeek R1 take this further:

• OpenAI o1 introduced “silent thinking” tokens, where the model first generates internal reasoning steps before presenting a final answer​.
• o3 enhances this with a “private chain of thought,” allowing more deliberation in complex reasoning tasks​.
• DeepSeek R1 refines its reasoning using Reinforcement Learning (RL), improving its ability to correct mistakes over time​.

How AI Learns to Think Before Speaking

1️⃣ Chain of Thought (CoT) Reasoning

CoT prompts the model to explicitly outline reasoning steps, much like humans breaking down a problem before reaching a conclusion. AI models trained on CoT datasets perform significantly better in math, logic, and science​.

2️⃣ Reinforcement Learning: Learning from Mistakes

Instead of just following pre-trained patterns, DeepSeek R1 employs Reinforcement Learning to refine its thought process. This mimics human learning — trial, error, and gradual improvement​.

3️⃣ Hierarchical Problem Solving

Unlike traditional models that struggle with long context windows, o1, o3, and DeepSeek R1 structure problems hierarchically:

• Pre-plan the solution before execution.
• Evaluate intermediate steps for consistency.
• Optimize responses based on previous mistakes​.

Empirical Evidence: Does More Thinking Improve AI?

To test this approach, OpenAI and DeepSeek evaluated their models on complex benchmarks:

✅ Mathematics: OpenAI o1 improved AIME accuracy by 23% over GPT-4o​.
✅ Competitive Programming: o3 outscored o1 on Codeforces by 800 Elo points, showing stronger hierarchical reasoning​.
✅ Science & Logic: DeepSeek R1, using Reinforcement Learning, outperformed OpenAI’s o1 in PhD-level science questions​.

These results indicate that allocating more compute to reasoning — not just scaling models — yields significant intelligence gains​.

True Intelligence: A Lesson for Humans and AI

Saadi’s wisdom remains relevant:

Thinking before speaking is not just a virtue — it is a necessity for intelligence.

Whether human or artificial, true intelligence is not about responding quickly — it is about responding thoughtfully. As AI advances, we must ensure that our models (and ourselves) pause, reflect, and reason before speaking.

🌟 I believe every speaking creature should take this advice from Saadi — even AI. What do you think?

Key Takeaways

✅ AI models like OpenAI o1, o3, and DeepSeek R1 use structured reasoning to improve problem-solving.
✅ Silent Thinking Tokens, Chain-of-Thought, and Reinforcement Learning allow AI to “think before speaking.”
✅ Empirical benchmarks show reasoning models significantly outperform traditional LLMs in math, coding, and science.
✅ The future of AI is not just bigger models, but better thinking.