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Resource-efficient ML for Edge and Endpoint IoT Devices✨

The New Normal: Rethinking Gen AI Strategies The hype surrounding gen AI application startups has begun to unravel, as many fail to meet the lofty expectations. The long-tail weakness of the gen AI models has proven to be a significant stumbling block for many use cases that seemed promising at the PoC stage. Nvidia is likely to face a significant decline in valuation due to the cooling AI startup landscape. However, the cost of using GPUs on cloud platforms may decrease as demand falls, potentially benefiting surviving startups. For investors and job seekers, consider startups that have found ways to mitigate the limitations of gen AI models. Those leveraging hybrid AI, a combination of gen AI and other AI techniques, are particularly promising.

the use cases for GenAI are legit and address both topline and bottom opportunities. The correction in course is around what you've stated i.e. combining genAI with traditional AI/DS techniques. That would require more skilled manpower, more data and more compute. So the course correction may actually end up boosting Nvidia and the likes even more. Companies have invested heavily in new leadership positions Director of AI, VP Data science, Head of AI and their charter will continue to deliver albeit slowly. The skills shortage will be a major stumbling block as 1000s of graduates with Data Science and AI degrees hitting the market lack the experience in combining traditional and genAI techniques while focusing on the use cases

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