Yi Wang

Make it happen before it happens.

[Yi Wang @wangyi8848]

@fuxianyi 少，而是当知识放弃良知，理性沦为工具，数字被用来裁决生命的价值。

[Yi Wang @wangyi8848]

@fuxianyi 辑漏洞百出的计划生育理论，却以“科学”“国策”的名义落地执行，最终伤害的不是抽象的“人口结构”，而是无数具体的人生——被剥夺的出生权、被扭曲的家庭、被提前透支的未来。人尚且不能决定自己的出生，又凭什么由他人、由学者、由权力，来替无数未出生的人做出生与否的决定？真正可怕的，从来不是人口多或

[易富贤Yi Fuxian《大国空巢》 @fuxianyi]

2012年我在《改革内参-高层报告》5万字报告建议果断停止计划生育；预测全面二孩，生育率将只暂升到1.4。但是后面朋友告诉我，大事不好，蔡昉、王丰等“二十多位顶级人口学家”出版报告，预测全面二孩生育率超过4.4，每年出生4700万；即便单独二孩，生育率也将达2.4。吓得领导人根本不敢全面二孩。

[Yi Wang @wangyi8848]

@LD_daofeichang 两千年时间，不知从哪朝哪代开始忘记祖宗了。王朝更替，记忆流失和重构，人们最终只记得被教会相信的版本。

[Yi Wang @wangyi8848]

@ClassicLearner @elonmusk That's true, but it's not the same all over the world. For example, in China, during the Song and Ming dynasties, the emperor was not allowed to imprison people unless he legally sentenced a judge, and the emperor could not build palaces on his own.

[Yi Wang @wangyi8848]

@Jiankui_He 无法无天 no hair no god

[Yi Wang @wangyi8848]

@dlw20202020 能和你比的牛逼人还不多，差生文具多。

[Yi Wang @wangyi8848]

@elonmusk American flag hides spaceship’s fire shines.

[Yi Wang @wangyi8848]

@AntiTrumpCanada really？

[Yi Wang @wangyi8848]

@Jiankui_He @CathyTie Quit your paws on human DNA modification.

[Yi Wang @wangyi8848]

@elonmusk Elon is a best communist ever!

[Elon Musk @elonmusk]

Best economist ever

[Yi Wang @wangyi8848]

@vvickym2 喝一点白酒 吃一段人参

[Yi Wang @wangyi8848]

@maoshen 的诗歌能让人浓郁的牛粪味道

[Yi Wang @wangyi8848]

@Baoliaogeming64 马斯克说辞比他的婚姻还令人呕

[Yi Wang @wangyi8848]

@Rumoreconomy 喜丧

[Yi Wang @wangyi8848]

@Himalaya_bear1 这是喜丧

[Andrew Ng @AndrewYNg]

The buzz over DeepSeek this week crystallized, for many people, a few important trends that have been happening in plain sight: (i) China is catching up to the U.S. in generative AI, with implications for the AI supply chain. (ii) Open weight models are commoditizing the foundation-model layer, which creates opportunities for application builders. (iii) Scaling up isn’t the only path to AI progress. Despite the massive focus on and hype around processing power, algorithmic innovations are rapidly pushing down training costs.

About a week ago, DeepSeek, a company based in China, released DeepSeek-R1, a remarkable model whose performance on benchmarks is comparable to OpenAI’s o1. Further, it was released as an open weight model with a permissive MIT license. At Davos last week, I got a lot of questions about it from non-technical business leaders. And on Monday, the stock market saw a “DeepSeek selloff”: The share prices of Nvidia and a number of other U.S. tech companies plunged. (As of the time of writing, some have recovered somewhat.)

Here’s what I think DeepSeek has caused many people to realize:

China is catching up to the U.S. in generative AI. When ChatGPT was launched in November 2022, the U.S. was significantly ahead of China in generative AI. Impressions change slowly, and so even recently I heard friends in both the U.S. and China say they thought China was behind. But in reality, this gap has rapidly eroded over the past two years. With models from China such as Qwen (which my teams have used for months), Kimi, InternVL, and DeepSeek, China had clearly been closing the gap, and in areas such as video generation there were already moments where China seemed to be in the lead.

I’m thrilled that DeepSeek-R1 was released as an open weight model, with a technical report that shares many details. In contrast, a number of U.S. companies have pushed for regulation to stifle open source by hyping up hypothetical AI dangers such as human extinction. It is now clear that open source/open weight models are a key part of the AI supply chain: Many companies will use them. If the U.S. continues to stymie open source, China will come to dominate this part of the supply chain and many businesses will end up using models that reflect China’s values much more than America’s.

Open weight models are commoditizing the foundation-model layer. As I wrote previously, LLM token prices have been falling rapidly, and open weights have contributed to this trend and given developers more choice. OpenAI’s o1 costs $60 per million output tokens; DeepSeek R1 costs $2.19. This nearly 30x difference brought the trend of falling prices to the attention of many people.

The business of training foundation models and selling API access is tough. Many companies in this area are still looking for a path to recouping the massive cost of model training. Sequoia’s article “AI’s $600B Question” lays out the challenge well (but, to be clear, I think the foundation model companies are doing great work, and I hope they succeed). In contrast, building applications on top of foundation models presents many great business opportunities. Now that others have spent billions training such models, you can access these models for mere dollars to build customer service chatbots, email summarizers, AI doctors, legal document assistants, and much more.

Scaling up isn’t the only path to AI progress. There’s been a lot of hype around scaling up models as a way to drive progress. To be fair, I was an early proponent of scaling up models. A number of companies raised billions of dollars by generating buzz around the narrative that, with more capital, they could (i) scale up and (ii) predictably drive improvements. Consequently, there has been a huge focus on scaling up, as opposed to a more nuanced view that gives due attention to the many different ways we can make progress. Driven in part by the U.S. AI chip embargo, the DeepSeek team had to innovate on many optimizations to run on less-capable H800 GPUs rather than H100s, leading ultimately to a model trained (omitting research costs) for under $6M of compute.

It remains to be seen if this will actually reduce demand for compute. Sometimes making each unit of a good cheaper can result in more dollars in total going to buy that good. I think the demand for intelligence and compute has practically no ceiling over the long term, so I remain bullish that humanity will use more intelligence even as it gets cheaper.

I saw many different interpretations of DeepSeek’s progress here in X, as if it was a Rorschach test that allowed many people to project their own meaning onto it. I think DeepSeek-R1 has geopolitical implications that are yet to be worked out. And it’s also great for AI application builders. My team has already been brainstorming ideas that are newly possible only because we have easy access to an open advanced reasoning model. This continues to be a great time to build!

[Original text: https://t.co/yiOHeGJgLZ ]