$700 Billion Bet Behind AGI is Completely Wrong скачать в хорошем качестве

$700 Billion Bet Behind AGI is Completely Wrong

👉 Free training — How to make money with YouTube: https://ytincomeaccelerator.com/get-f... I've generated 100M+ views & helped businesses generate millions with YouTube. Follow me on Twitter -   / davidcarbutt_   Contact me here for business or sponsorship opportunities - dcsocialmediadc@gmail.com This accountant saved me a fortune. If you're in the UK, book a call with Ben for accounting or bookkeeping. https://calendly.com/donnellanbouchie... Want to join a private stocks group? Click here -https://www.fejeremy.com/2024-app-plt... 📖 Recommended Palantir Substacks Arny Trezzi - https://arnytrezzi.substack.com/ Either Square - https://eithersquare.substack.com/ Sam Altman says AGI could arrive within a few years, and most attention goes to GPUs and model benchmarks. This video explains why the real bottleneck is shifting below the model layer to memory, interconnects, and new chip architectures. If you want to understand where AI value will concentrate next, this breaks down the hardware layers that could matter most. • Why AI is moving beyond raw GPU scaling • The growing importance of high-bandwidth memory • How silicon photonics may unlock cluster performance • Why neuromorphic chips could reshape AGI efficiency As Nvidia powers today’s AI boom, constraints are emerging in high bandwidth memory, silicon photonics, and energy-efficient architectures. Jensen Huang’s five-layer AI framework highlights how chips, cloud, and infrastructure interconnect, while research from Intel and IBM into neuromorphic computing points to limits in current transformer models. The next phase of AI may be defined less by who has the most GPUs and more by who controls the underlying hardware stack. Subscribe for more insights on AGI, semiconductors, and AI infrastructure. #AGI #SamAltman #Nvidia #AIInfrastructure #HighBandwidthMemory #SiliconPhotonics #NeuromorphicComputing #Semiconductors #AIInvesting #JensenHuang