Interesting to see that DeepSeek is owned by a hedgefund …
Did they short nvidia before announcing the world - through a paper authored by their lab - that the DeepSeek-R1 model outperforms cutting-edge models such as OpenAI’s o1 and Meta’s Llama AI models across multiple benchmarks?
That's an easy one...
Source: The Future Investors / @ftr_investors
The point of making money is not so you can buy everything.
Source: Seek Wiser
Everyone knows that Japan is getting old, but look what is happening in major European countries.
Who is supposed to pay for pensions and healthcare entitlements in Germany, Italy and Spain in few years? Source: Michael A. Arouet
Bitcoin ETFs bought $4.2 BILLION bitcoin in the last 7 trading days.
$150b annual run rate... That's almost 4x the 2024 inflows. Source: Bitcoin Archive
Chinese debt pile is GIGANTIC: China's total debt to GDP rose to a RECORD of ~370%.
The share has DOUBLED over the last 14 years. This does not include shadow banking (outside traditional banking sector). China has a massive debt problem and it will not go away soon. Source: Apollo, Bloomberg, Global Markets Investor
CBOE Volatility Index $VIX dropped to its lowest level this year, even with stocks declining on Friday
No fear left in the market except by Bears. Today might be another story… Source: Barchart
About DeepSeek founder Liang Wenfeng
>> Studies machine vision at Zhejiang University >> At 30 in 2015, launches High-Flyer quant hedge fund >> Makes a fortune (now $8B AUM) >> Wants to build “human” level AI as side hustle and pitches partners but they initially sceptical >> Buys 10,000 H800 chips in 2021 and brings over his top hedge fund employees (all have tons of experience squeezing juice out of Nvidia GPUs for the fund) >> Launched DeepSeek in 2023 and hires dozens of PhDs from top Chinese universities (Peking, Tsinghua and Beihang) >> Pays top top top salary for tech talent only matched by Bytedance in China…wants DeepSeek to be leading “local” company >> US export restrictions force DeepSeek team to get creative and they do, finding new training methods to make LLM models (V3, r1) competitive with OpenAI, Anthropic, Gemini, Grok, LLama etc at ~1/20th the cost >> Training costs not exactly apples-to-apples but novel methods and clear improvements in efficiency (also questions around copying other models, larger H-100 clusters they maybe can’t talk about and/or CCP support) >> Open sources and publishes methods (r1 reasoning paper has 200+ authors) >> DeepSeek just hit top of App Store *** FT: https://lnkd.in/e96ffxmU Source: Trung Phan @TrungTPhan on X, FT
