Distillation enables a new model to learn from the outputs of a stronger model, saving training time and computational resources. ByteDance internally acknowledges that without this approach, Seed's language model will struggle to catch up with DeepSeek, Kimi, and Qwen in the short term.

Author and source: 0x9999in1, ME News



TL;DR

• According to The Information, Zhang Yiming, founder of ByteDance, clearly stated at last month’s all-hands meeting of the Seed team: the company will not treat distillation as a shortcut to catch up with large models, even if it means falling temporarily behind domestic competitors.
• Distillation enables a new model to learn from the outputs of a stronger model, saving training time and computational resources. ByteDance internally acknowledges that without this approach, Seed's language model will struggle to catch up with DeepSeek, Kimi, and Qwen in the short term.
• What ByteDance truly fears is not a technological gap, but political risk. TikTok has long been under scrutiny by the U.S. government, and if accused of extensively extracting capabilities from American frontier models, Washington may renew its pressure.
• Previously, Anthropic named DeepSeek, Moonshot, MiniMax, Zhipu, and Alibaba for extensively extracting Claude's capabilities, but did not mention ByteDance.
• This isn't about moral high ground—it's about risk pricing. Zhang Yiming turned a technical decision into a matter of survival.

First, the conclusion: This isn't about being aloof; it's about doing the math.

Let me clarify this first.

Zhang Yiming rejected distillation not because he values his reputation more than others, nor because ByteDance’s engineers suddenly embraced a “faith in originality.”

Because he did the math.

The money saved through distillation in this case is far outweighed by the potential troubles it could bring. For other Chinese AI companies, distillation is an accelerator; for ByteDance, it’s a liability.

The same action carries completely different costs for different people. That’s the entire secret.

So don’t rush to crown Zhang Yiming with the title of "technological idealism." What he’s doing is calculated, even somewhat cold—he’s paying in advance for a risk others can’t see.

What exactly is distillation? Why is everyone using it?

Let's start with a quick lesson.

Distillation, in simple terms, is "AI apprenticeship." A smaller, more cost-effective model learns to mimic the outputs of a larger, more powerful model—just as a student copies the teacher’s answers. After training, the smaller model can achieve results nearly as good as the larger one, at a fraction of the cost.

How good is this technology? It’s so good that it’s become an industry standard.

Think about it. How much money, computing power, and high-quality data does it take to train a cutting-edge model from scratch? With distillation, you stand on the shoulders of giants and skip the most difficult part entirely.

Thus, distillation is nearly ubiquitous in China’s AI ecosystem. When DeepSeek’s R1 emerged and shook the entire industry, controversy surrounding distillation was ignited—many outside China questioned how Chinese labs leveraged outputs from cutting-edge U.S. models to accelerate their own development.

OpenAI has expressed concerns. Anthropic has as well. Both are considering tightening their terms of service to close this loophole for learning.

This is the context. When everyone is taking shortcuts, taking shortcuts itself no longer requires explanation.

The one who doesn't copy is the one who needs explanation.

Byte's "cleanliness obsession": Seed has never touched distillation since day one.

Here's the interesting part.

According to reports, ByteDance’s AI research team, Seed, has adhered since its founding to an unusual rule: never perform knowledge distillation.

It hasn't just been established recently; it has been this way from the beginning.

In an industry where mimicking stronger models is almost a rite of passage, TikTok’s parent company chose the harder path: building its model from first principles, brick by brick, using its own proprietary data.

What is the cost?

ByteDance internally also acknowledges: without pursuing distillation, Seed's language model will find it harder in the short term to catch up with domestic counterparts like DeepSeek, Kimi, and Qwen.

That’s a very honest statement—no sugarcoating.

Keeping up with large models is a race where every second counts. Using one less accelerator means you’ll need more time, more computing power, and more brute force to close the gap.

What does Byte stand to gain?

The real answer: ByteDance isn't afraid of falling behind—it's afraid of Washington.

The point is, not to hand Washington a knife.

This is the core of the entire issue. ByteDance has never been most concerned about temporarily falling half a step behind technologically—it’s worried about political risk.

Look back at the history, and you’ll understand. ByteDance has long been under scrutiny from the U.S. government due to TikTok. This isn’t new—it’s been a sword hanging over the company for years.

The current situation is this: TikTok’s U.S. data security business has brought in U.S. capital, but ByteDance still retains equity and continues to operate TikTok’s global commercial business.

In other words, the connection between ByteDance and TikTok has not been fully severed—it’s still at the table.

So the question arises: What would happen if ByteDance were accused one day of "massively extracting the capabilities of U.S. frontier models"?

The answer isn't hard to guess. In Washington's hands, a brand-new, seemingly legitimate reason will suddenly emerge to renew pressure on TikTok.

Look, this company even steals AI from the United States.

A single sentence can reignite the situation, pushing it back into turmoil.

So now, looking back at the rule "don't touch distilled spirits from day one," doesn't it feel completely different?

That’s not technical perfectionism—that’s an insurance policy you bought in advance.

Why isn't ByteDance on Anthropic's list?

There is a detail that is particularly intriguing.

Anthropic previously accused DeepSeek, Moonshot AI, MiniMax, Zhipu, and Alibaba of extensively extracting Claude's capabilities.

The list is clearly laid out.

But ByteDance is not on this list.

I don’t want to overinterpret a piece of information that “did not appear.” But when viewed alongside ByteDance’s own claim of “never distilling,” it at least paints a logically consistent picture:

When others were publicly named for distillation, ByteDance happened to be in a place untouched by the spotlight.

This is the reward for paying in advance.

The sweat you shed today is to ensure you won’t be caught off guard tomorrow. While the entire industry faces public pressure from accusations of copying, ByteDance can calmly say: This has nothing to do with us.

In an increasingly geopolitically conscious environment that values "pure origins," the statement "My model never drank from American models' milk" is itself an asset.

But the cost is real money: there's no free answer to this question.

Then again, don't make this sound too romantic.

Refusing to distill, the bill ByteDance must pay is real and tangible.

The first is time. Training from scratch is inherently slower than distillation. In a field where iterations occur on a weekly or monthly basis, being slow is itself a form of falling behind.

The second point is about computing power and cost. The savings from distillation must be offset by ByteDance themselves. Building from scratch means higher data costs, longer training cycles, and more trial and error.

The third point is the short-term competitive position. ByteDance itself has acknowledged that, in the short term, it will be harder to catch up with domestic peers in language models. Behind this statement are rankings on leaderboards, media attention at product launches, and the patience of capital markets.

So Zhang Yiming made it clear to his team: be willing to sacrifice short-term gains for long-term goals.

It’s easy to say, but doing it means enduring the pressure of every quarter and the anxiety of “someone else just launched a new model,” over and over reminding yourself—don’t rush, don’t copy, just keep going.

There’s no such thing as free clarity—clarity is always bought at a price.

Zhang Yiming's long-termism has finally hit the mark.

Anyone familiar with Zhang Yiming knows that he has been talking about "long-termism" for many years.

But often, long-term thinking is just a pretty phrase—spoken without action. When it comes time to put money behind it, to admit you’re wrong, or to watch your competitors pull ahead, very few can hold strong.

This time, long-term thinking paid off.

It’s no longer a slogan on a PowerPoint slide—it’s a concrete, painful choice: there’s a faster path right in front of you, and everyone is taking it, yet you refuse to.

Why?

Zhang Yiming isn’t focused on the ranking in this particular race. He’s focused on whether ByteDance can securely survive and thrive in the global market over the next five to ten years, rather than being toppled overnight by sudden political accusations.

TikTok taught him one thing: for a company spanning China and the U.S., the greatest risk has never been insufficient technology, but rather giving others leverage over you.

Distillation can make the model stronger. But distillation could also become that vulnerability.

Between "stronger" and "more secure," Zhang Yiming chose the latter.

Is this smart, or just conservative?

Some might say this is too conservative. In AI competitions, every second counts—is it worth slowing down voluntarily for a risk that hasn’t even materialized? What if your competitor pulls ahead decisively using distillation?

This concern is not unfounded.

But I prefer to view it as a "risk pricing" capability.

Most companies only calculate the immediate costs: how much money distillation can save, how many days it can speed up. Zhang Yiming, however, is calculating a different equation: Could this action become the final straw that breaks TikTok at some moment I cannot control?

The previous entry is an accounting record. The latter entry is a matter of survival.

No matter how impressive the financial statements, they can't outweigh the balance sheet of survival.

Of course, I'm not saying Zhang Yiming is definitely right. Perhaps, looking back in a few years, this decision caused ByteDance to miss a critical window. Perhaps the competitor's distillation path did not trigger the anticipated political storm. History never guarantees that caution will always prevail.

But I appreciate this "think it through before acting" approach.

In an industry where everyone is shouting “hurry, hurry, hurry,” there’s a company willing to pause and ask itself: Can I afford to take this shortcut?

In conclusion

Ultimately, distillation is just a technical term.

But Zhang Yiming’s decision has long transcended the realm of technology. It was a risk assessment made by a company caught between China and the U.S., and a statement by a founder who values longevity over speed.

Others take shortcuts because they can afford to. ByteDance takes the long road because it cannot.

Once you understand this level, you’ll realize: Zhang Yiming’s “slowness” has never been about ability—it’s about clarity.

And staying clear-headed in this volatile industry is itself a rare luxury.

It’s fine if it’s slow. Some debts have to be paid eventually. Zhang Yiming simply chose to settle them ahead of time.

Source citation

• The Information, "ByteDance's Zhang Yiming Won't Use Distillation to Advance AI Models" (August 2026)
• Cryptopolitan, "ByteDance founder rejects distillation as US targets Chinese labs" (August 2026)
• Cryptobriefing, "ByteDance bans distillation from its AI playbook, betting everything on homegrown data" (August 5, 2026)
• American Bazaar, "ByteDance founder Zhang Yiming says company won't rely on AI distillation" (August 5, 2026)
• TechFlow Post, "ByteDance Founder Denies Using Distillation Technology for AI Models" (August 5, 2026)
• AI Weekly, "ByteDance's Zhang Yiming Rules Out Distillation for AI Models" (August 2026)