99% of AI Startups Will Fail by 2026?! скачать в хорошем качестве

99% of AI Startups Will Fail by 2026?!

So little is currently understood about how to leverage AI for real world value. As a result, you get wild articles like the one I talk about this video. Buckle up for a crazy ride! So many misconceptions in one place, its fun. AI wrappers to the max. "99% of AI start ups will be Dead by 2026" on YouTube by Primeagen:    • 99% of AI start ups will be Dead by 2026   Original Article:   / 99-of-ai-startups-will-be-dead-by-2026-her...   Critique of "Wrappers" Argument: The article defines most AI startups as "wrappers" (simply repackaging existing AI APIs with a nice interface) and claims they will fail. The video argues this is an oversimplification, stating that many companies add significant value beyond just an API call, and that defining all AI companies this way is incorrect and unhelpful. Contradictory Nature of Arguments: The speaker points out that some arguments presented in the article are contradictory or based on flawed assumptions. For example, while it suggests most "wrappers" will fail, the video questions the impact of the surviving 1% of those companies, and the emergence of new AI companies. Misinterpretation of Moats: The article claims companies like OpenAI lack a moat. The video disagrees, suggesting that OpenAI's moat is its vast user base, particularly those paying for ChatGPT Pro, and the data they generate. It highlights that the core AI models themselves are becoming commoditized, which is a different issue than lacking a moat. NVIDIA's Role: The video agrees that NVIDIA is a big winner in the current AI landscape due to its control over GPUs and CUDA. However, it cautions against assuming their dominance will last forever, as commoditization of GPUs and new model types could shift the landscape. Critique of Benchmarks: The video, echoing "The Primagen," argues that current benchmarks for AI models are not reliable indicators of real-world performance or value. They focus on narrow aspects and can be misleading. A good example of this is Claude 3.7 vs. Claude 3.5, where Claude 3.5 performed better, even if it had worse benchmarks. Underestimation of AI's Potential: The speaker emphasizes that AI, especially Large Language Models (LLMs), offers new capabilities for solving problems that were previously impossible with software. They believe that while there are challenges (like hallucinations and reliability), a lot of work is being done to overcome these, and human creativity in utilizing these tools is still in its infancy. The "Human Nature" of Startups: The video suggests that the failure of many startups is a common phenomenon in any industry, not unique to AI, often due to a focus on quick wins rather than building sustainable foundations. #aistartups #aiwrappers #ai #42robotsai #theprimetime #theprimeagen