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kojoh.eth (Kojoh Atta)
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kojoh.eth (Kojoh Atta)
@attaboy77
Investing @HighlandCapital | Prev @kkr_co @technexus | @Stanford Alum | Gates Millennium Scholar | @chelseafc @ghanablackstars Enthusiast |
Ghana✈️Ohio✈️San Francisco Katılım Mayıs 2010
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Nvidia paid 3X Groq's September valuation to acquire it. This is strategically nuclear.
Every AI lab was GPU dependent, creating massive concentration risk. Google broke free with TPUs for internal use, proving the "Nvidia or nothing" narrative was false. This didn't just demonstrate technical feasibility, it revealed that Nvidia's moat was shallower than markets believed. When a hyperscaler successfully builds custom silicon, every sophisticated buyer starts running" should we build our own?" calculations. This drops Nvidia’s TAM.
Jonathan Ross (Groq’s founder) is the inventor of TPU. He understood the architectural principles that made non-GPU AI acceleration viable. His LPU architecture targeted inference workload where GPUs are actually over-engineered. This matters because inference is where the real money is long-term. Training is one-time capex, but inference is recurring opex that scales with usage. If Groq proved LPUs could hit competitive price-performance on inference, every cloud provider would white-label their architecture. Nvidia would get squeezed into "just training" while losing the annuity stream.
It is safe to see this deal as Nvidia insuring against Groq enabling an entire ecosystem of Nvidia alternatives. But what is more interesting is the second-order effect, the customer lock-ins. Now, Nvidia owns both the incumbent standard (CUDA + GPU) and the most credible alternative architecture (LPUs). This is MSFT buying Github-level strategic. Any AI lab evaluating "build vs buy vs alternative vendor" now faces:
- Option A (Nvidia GPUs)
- Option B (Nvidia <> Groq LPUs)
- Option C (start from scratch)
Turning a competitive threat into a customer segmentation tool, Jensen is the master of trades. They can now price-discriminate: premium customers pay for GPUs, price-sensitive inference gets funneled to LPUs, and Nvidia captures both.
If Nvidia doesn't integrate LPUs in its roadmap, this was a pure defensive play. If they do integrate it and start offering "GPU for training, LPU for inference" bundles, this becomes a textbook moat-widening acquisition.
The most expensive thing in technology isn't building the future, it's preventing someone else from building a future without you.

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Beaucoup de joie, de fierté et d’émotion. Un rêve devenu réalité. Merci à tous ceux qui m’ont toujours soutenu tout au long de ce parcours. #ballondor




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#InterBarca one of the best games I’ve ever watched! WOW!
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Sucks so bad that we asked for flying cars and all we're getting so far is supercomputers in every pocket, machines that can seemingly think, worldwide internet coverage from a cloud of satellites, verifiable computing in zero knowledge, programmable sovereign money, private biometric systems, 3D-printed organs, hydrocarbon synthesis from air, portable nuclear reactors, semi-autonomous drone swarms, affordable whole genome sequencing, personalized gene therapy, mind-controlled prosthetics, self-driving car services, instant global payments and capital markets, ubiquitous clean energy, multiple virtual reality platforms, beautiful image + video + voice synthesis, realtime flawless language translation, rapid cheap and safe global air transport...
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