Sebei T Nchabeleng

[Sebei T Nchabeleng @TimNape]

I think a new form of coding is emerging:

Intent Compilation.

You start with an idea, then make the intent explicit through specifications, architecture, constraints, domain knowledge and acceptance criteria.

[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 ]

[Rahul @sairahul1]

This is wild.

This tool can replace anyone from any video, with just a photo 🤯

10 wild examples (+link to create for FREE at the end)

[Greg Brockman @gdb]

Sora for artists and filmmakers:

[Miguel | AP @angrypenguinPNG]

SORA can animate images pretty amazingly.

Prompt: "In an ornate, historical hall, a massive tidal wave peaks and begins to crash. Two surfers, seizing the moment, skillfully navigate the face of the wave."

[Graham Love @glove931]

This video was generated using OpenAI’s powerful new Sora text-to-video model.

Prompt: Scotland winger destroys entire England rugby team

[Rowan Cheung @rowancheung]

🚨 BREAKING: Nvidia just released Chat with RTX, an AI chatbot that runs locally on your PC.

It can summarize or search documents across your PC's files and even YouTube videos and playlists.

The chatbot runs locally, meaning results are fast, you can use it without the internet, and the user's data stays private.

New day, new chatbot. Let's go.

[Stelfie the Time Traveller @StelfieTT]

Wtf.
A new code has been released, and it´s open to everyone.

https://t.co/nzSMcvxDGS

[Dr. Eli David @DrEliDavid]

Those who said “Palestinians never invented anything” were wrong

[Angry Tom @AngryTomtweets]

RIP TikTokers 💀

[AI at Meta @AIatMeta]

Today we’re sharing two new advances in our generative AI research: Emu Video & Emu Edit.

Details ➡️ https://t.co/qm8aejgNtd

These new models deliver exciting results in high quality, diffusion-based text-to-video generation & controlled image editing w/ text instructions.

🧵

[Sebei T Nchabeleng @TimNape]

@everymovieplug The creature is horrible at flying

[Javi Lopez ⛩️ @javilopen]

Left = Original video
Right = Generated with AI

Generative AI still struggles with simulating complex facial expressions, but we are getting closer bit by bit.

💡 AnimateDiff + ControlNet x 3

[Ian Curtis @XRarchitect]

Why not use the real-world as a game level?

This is a real-time augmented reality prototype using a physics based character controller, all running on the web 👀

[Bilawal Sidhu @bilawalsidhu]

Corridor Crew refined their video2anime workflow even more, ethically sourced training data, and made yet another a piece of internet history:

[Evan Kirstel #B2B #TechFluencer @EvanKirstel]

😂 Human powered automation!

[Ad Professor @The_AdProfessor]

The 9 most creative social media ads I've collected this week:

1. World Wildlife Foundation

[OpenAI @OpenAI]

Introducing Custom instructions! This feature lets you give ChatGPT any custom requests or context which you’d like applied to every conversation. Custom instructions are currently available to Plus users, and we plan to roll out to all users soon!  https://t.co/3aFa1q6LyI

Here are some use cases we’ve heard from our early testers:
- Expertise calibration: Sharing your level of expertise in a specific field to avoid unnecessary explanations.
- Language learning: Seeking ongoing conversation practice with grammar correction.
- Localization: Establishing an ongoing context as a lawyer governed by their specific country’s laws.
- Novel writing: Using character sheets to help ChatGPT maintain consistent understanding of story characters in ongoing interactions.
- Response format: Instructing ChatGPT to consistently output code updates in a unified format.
- Writing style personalization: Applying the same voice and style as provided emails to all future email writing requests.

[Cristóbal Valenzuela @c_valenzuelab]

There’s a new way to make videos. A new way to tell stories.

h/t https://t.co/pofv6Z3R9e

[Elon Musk @elonmusk]

Transparency builds trust …