nvidia publicly supporting the first trump administration while criticizing biden's policies for their impact on ai is a major vibe shift
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OpenAI announcing they’re teaming up with tech giants and dropping half a trillion dollars over four years to build a massive AGI/ASI
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Jevons Paradox Strikes Chip Stocks Overnight After DeepSeek Takes First Place in Stores
The release of DeepSeek sent ripples through the chip market, as engineers proved that optimizing code could maximize GPU efficiency without increasing hardware demand. The result? A major hit to chip stocks:
📉 Arm ($ARM): -5.5%
📉 Nvidia ($NVDA): -5.3%
📉 Broadcom ($AVGO): -4.9%
📉 Super Micro ($SMCI): -4.6%
📉 Taiwan Semi ($TSM): -4.5%
📉 Micron ($MU): -4.3%
📉 Qualcomm ($QCOM): -2.8%
📉 AMD ($AMD): -2.5%
📉 Intel ($INTC): -2.0%
Why the market panic?
Instead of relying on raw compute power, engineers behind DeepSeek focused on highly efficient code optimization, reducing dependency on high-end hardware.
FAQs About DeepSeek's Success:
Q: How did DeepSeek get around export restrictions?
A: They didn’t. Instead, they optimized their chips for maximum memory efficiency. With perfectly tuned low-level code, they avoided bottlenecks entirely.
Q: How did DeepSeek train so efficiently?
A: They used predictive formulas to determine which tokens the model would activate and trained only those tokens. This approach required 95% fewer GPUs than Meta by focusing training on just 5% of parameters for each token.
Q: Why is DeepSeek’s inference so much cheaper?
A: They innovated by compressing the KV cache, a breakthrough from earlier research that dramatically cut costs.
Q: How did they replicate o1?
A: Through reinforcement learning. They tested the model with verifiable, complex tasks (like math and code), updating it only when it got the answers correct.
The Bottom Line: DeepSeek’s success is a testament to software-driven innovation. Engineers are proving that efficiency can outpace brute force—and the market is feeling the impact.
OpenAI vacuumed the whole internet, while DeepSeek vacuumed the o1 models and karpathy warned us about this a month ago
The release of DeepSeek sent ripples through the chip market, as engineers proved that optimizing code could maximize GPU efficiency without increasing hardware demand. The result? A major hit to chip stocks:
📉 Arm ($ARM): -5.5%
📉 Nvidia ($NVDA): -5.3%
📉 Broadcom ($AVGO): -4.9%
📉 Super Micro ($SMCI): -4.6%
📉 Taiwan Semi ($TSM): -4.5%
📉 Micron ($MU): -4.3%
📉 Qualcomm ($QCOM): -2.8%
📉 AMD ($AMD): -2.5%
📉 Intel ($INTC): -2.0%
Why the market panic?
Instead of relying on raw compute power, engineers behind DeepSeek focused on highly efficient code optimization, reducing dependency on high-end hardware.
FAQs About DeepSeek's Success:
Q: How did DeepSeek get around export restrictions?
A: They didn’t. Instead, they optimized their chips for maximum memory efficiency. With perfectly tuned low-level code, they avoided bottlenecks entirely.
Q: How did DeepSeek train so efficiently?
A: They used predictive formulas to determine which tokens the model would activate and trained only those tokens. This approach required 95% fewer GPUs than Meta by focusing training on just 5% of parameters for each token.
Q: Why is DeepSeek’s inference so much cheaper?
A: They innovated by compressing the KV cache, a breakthrough from earlier research that dramatically cut costs.
Q: How did they replicate o1?
A: Through reinforcement learning. They tested the model with verifiable, complex tasks (like math and code), updating it only when it got the answers correct.
The Bottom Line: DeepSeek’s success is a testament to software-driven innovation. Engineers are proving that efficiency can outpace brute force—and the market is feeling the impact.
OpenAI vacuumed the whole internet, while DeepSeek vacuumed the o1 models and karpathy warned us about this a month ago
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semiconductor fund managers after seeing 4 memes about deepseek
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they deployed Chinese trade waifus to fight against our twinks
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