Public Market- Where are the biggest institutional opportunities?- Digital Assets: Realised https://
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market-where-are-the-biggest-institutional-opportunities-digital-assets-realised/
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ECONOMY
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Institutional Opportunities in Digital Assets Public Markets
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Temu Sensation, Nvidia Dominates S&P500 Semiconductors
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Sell-side most interesting: What makes Temu Tick, Nvidia the most owned Pinduoduo’s Temu is a sensation and a mystery, but Bernstein breaks down some of the math, CrowdStrike surges on a “stellar” report, the most broadly owned S&P500 semiconductor name is Nvidia, and ASML
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Broadcom CEO Endorses AMD’s Bold AI Revenue Projection
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Broadcom CEO endorses that crazy AI revenue projection from AMD AMD CEO Lisa Su’s stunning remark that AI chips will be almost the size of the entire chip industry in 2027 has been a source of much chatter in the past few months. Thursday evening, Broadcom CEO Hock Tan endorsed
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Midjourney’s Profitability Stands Out Among AI Image Tools
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I think Midjourney is hugely profitable. I can't name a second one.
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Institutional opportunities in digital assets and traditional finance bridge
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Spoke at the Digital Assets: Realised in #HongKong on “Where are the biggest institutional opportunities?” #etf #stocks #bonds #MutualFunds Anndy Lian: “As Senior advisor to Peach Income fund, we bridge tradfi and digital markets globally.”
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Generative AI Revenue Market Size and OpenAI’s Market Share
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My tweet says 4B (2B consumer + 2B enterprise). OpenAI is half of that. Note that the relevant quantity is gen AI derived revenue from companies that train their own models and directly monetize them. Not all revenue made by anyone using gen AI (e.g. some company using a 3rd
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AI Chip Economics: VC Loss, Big Tech Profits, Uncertain Returns
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~1/3rd of the chips are bought via venture capital that won't see a return. 2/3rds are big tech. That portion is mostly to provision cloud services (which are very profitable). In turn, these cloud services are sold to companies which may or may not see a return from their models
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LoRA Land’s Mistral-7B Models Cut Inference Costs Significantly
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LoRA Land's 27 fine-tuned #opensource #mistral-7b #LLMs no only rival #GPT-4, but they're also very cost-effective! Assuming 2M tokens (90% input; 10% output) per adapter per day, you can cut inference costs by 45%-65% vs. GPT-3.5 Turbo and 97%-98% vs. GPT-4 Turbo
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AI Value Addition vs Provider Investment in Model Training
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This is true — gen AI will add value (and thus revenue) to a wide range of products, but… this is irrelevant to how much a chatbot/API provider should invest in model training. What they should invest on depends on how much money *they* will make via their subscriptions and
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Models as Fast-Depreciating Assets with Limited Lifespan
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Also — models are fast-depreciating assets. Lifespan is 2 years or less.