Tencent launched its first reasoning LLM, T1, and the headlines focused on the tech:
Enterprise-grade. Logic-first. Built for decision-making.

But at AI Thinking, what caught our attention wasn’t the architecture, it was the intention.
And more importantly, what that intention says about culture.

🤖 AI Reflects the Culture That Builds It

We often hear the word bias in AI, usually described as a problem we must eliminate.
But here’s what most conversations miss:

Bias is just another word for culture.

Bias is our tendency to prefer or favour one idea, perspective, or outcome over another—often without realising it.
It’s how we’re trained to think as a collective within our society.

The way we write prompts, train models, and interpret results?
It’s shaped by the assumptions, values, and goals of the people building the system in other words, their culture.

What looks like “bias” to one person might look like “structure” or “common sense” to someone else.
It’s not wrong, it's human.

This becomes especially important with reasoning LLMs, which are designed to follow chains of thought.
If those chains don’t align with our way of thinking, we can’t connect with them.
For a model like T1 to feel useful, its logic has to make cultural sense to us.

🇨🇳 What T1 Tells Us About Tencent (and China)

T1 isn’t here to chat, create, or entertain.
It’s here to reason. To support institutional decision-making.

Finance. Healthcare. Education. Government.
This isn’t built for play, it's built for structure, logic, and order.
And it was trained on local enterprise data.

That’s not just product strategy.
That’s cultural design thinking.

T1 was trained to reflect Chinese thinking, and it will replicate those cultural reasoning patterns.
While ChatGPT focuses on conversation and creativity, and Gemini quietly supports your productivity,
Tencent is building an AI that takes up space in the boardroom.

This isn’t just about tech.
It’s about how different cultures imagine the role of AI in society.

🔍 Why This Matters

When we talk about using AI in our organisations, we usually ask:

• Is the model accurate?

• Is it safe?

• Where is the data stored?

• Is it aligned with our business goals?

But we rarely ask:
👉 Is it culturally aligned with how we think, work, and decide?
👉 Does its purpose match how we want AI to function in our world?
👉 And if not—will the cultural mismatch make it harder for people to understand and cooperate with it?

If we skip that step, we risk adopting tools that clash with who we are—and how we believe decisions should be made.

🧊 My Thoughts

Bias isn’t always a flaw.
Sometimes it’s a representation of our culture showing up inside the system.

What we believe, how we think, and what we value—
All of it shows up in our personal and organisational data.

Even simple actions, like saying “sorry” or “thank you,” reflect cultural patterns.
They carry meaning. They signal who we are.
And when it comes to LLMs built for reasoning, that cultural layer becomes essential—because these models are built to support decisions.

But decisions aren’t made out of thin air.
We want to believe there’s one universal logic, one shared idea of common sense.
But there isn’t.

What we define as a “good decision” is shaped by how we see the world,
how we define success, and how we reason through challenges.

If your team can’t follow the LLM’s logic, they won’t trust its output.
And that logic—the decision framework comes from the culture behind the model.

Want to think more clearly about the AI you’re using? Let’s talk.

At AI Thinking, we believe every LLM carries the mindset of its makers.
Understanding that doesn’t just help you choose the right tools, it helps you understand yourself in the process.

So before bringing AI into your business, pause and ask:
What kind of intelligence do we want to work with?
Not just what it can do—but what it was built to do, and which culture it was built in.

Because in the end, AI doesn’t just reflect data—
It reflects us.

Want to think more clearly about the AI you’re using?