Gonzalo Laguna

Oxygen breathing carbon-based biped.

[Jonathan McDowell @planet4589]

OK for me this comment wins the internet for today

[Gonzalo Laguna @glaguna]

AI transformation is not a technology purchase, it’s an operating-model change.

Buying tools is easy, becoming AI-enabled is the real work.

[Vala Afshar @ValaAfshar]

Do not just teach your children how to read. Teach them how to question what they read.

—Professor Richard Feynman

[Gonzalo Laguna @glaguna]

"The lack is not in intelligence, which is in plentiful supply; rather, the scarce commodity is systematic training in critical thinking."

-Carl Sagan, Broca's Brain: Reflections on the Romance of Science

[Gonzalo Laguna @glaguna]

Apple slips up on the most basic rule: underpromise and overdeliver.

Arrogance? Naiveté?

a reminder of the complexity of creating personal, context-aware agents—when not even the most valuable company in the world can pull it off.

https://t.co/e8nWq9IcFQ

[🎸 Rock History 🎸 @historyrock_]

This guitar solo touches souls…

[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 ]

[Gonzalo Laguna @glaguna]

This reminded me of Feynman’s quote:

“I don't know what's the matter with people: they don't learn by understanding, they learn by some other way — by rote or something. Their knowledge is so fragile!”

[David Fowler @davidfowl]

I’ve been diving deep into various topics with ChatGPT this vacation. One major realization I’ve had is how important it is to build intuition about a subject when trying to truly understand it. Growing up, learning math was often focused on rote memorization—practicing concepts over and over—rather than developing an intuition for why or how a formula works.

[Gonzalo Laguna @glaguna]

This!!: “It cannot make leaps beyond is training set”.

[Gonzalo Laguna @glaguna]

Actually, my concern with AI is how much more boring everything will become if we take away the fun that natural stupidity provides us with daily.

[Gonzalo Laguna @glaguna]

A great reminder for our upcoming electoral times.

[Gonzalo Laguna @glaguna]

Creating autonomous driving cars implies answering moral questions. But the problem with ethical questions is that ethics is not universal. MIT studied global responses to a version of the “Trolley Problem”, asking “who would you save?”. Take a look: https://t.co/xJ57uPWUfQ

[Gonzalo Laguna @glaguna]

@svpino Here’s another interesting one. Lots of pictures too ;)

[Gonzalo Laguna @glaguna]

And the best quote of the week is: “$10B in Microsoft money, and they fired Sam via Google Meet.”

[Physics In History @PhysInHistory]

Richard Feynman was a brilliant physicist best known for his exceptional contributions to quantum mechanics and his captivating teaching style, among others.

Here are all of Feynman's Freely available Lectures at one place.

A Thread 🧵

[Gonzalo Laguna @glaguna]

@Grady_Booch Reminded me of Feynman’s lecture on the difference between knowing & understanding: https://t.co/oySqWWGVwF

[Gonzalo Laguna @glaguna]

@codinghorror David Gilmour- Comfortably Numb live in Pompeii. https://t.co/KnV3A5v5Bx

[Gonzalo Laguna @glaguna]

@davidfowl I spent the whole 4th season thinking: “I think I kinda like this show”, but not having the faintest idea of what’s really happening. And then, it was over 😂

[Gonzalo Laguna @glaguna]

@Damir Don’t go there 😂

[Gonzalo Laguna @glaguna]

30 years on…and I still believe this tour has never been topped. U2 - Zoo TV tour.