NEW REPORT #Coming #AI_Crash - 91% #Failure Rates and $600B in #Wasted Investment.
DAVIDV in a nutshell in layman terms..
LLM IS PREDICTIVE SEARCH SINCE MAJOR SEARCH ENGINE ICEPTION. A 30 YEAR OLD TECHNOLOGY REBRANDED.
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Chapters:
0:00 - Introduction
1:15 - The AI Reality Check
3:40 - The AI Failure Rate Exposed
5:50 - The Agent Washing Problem
6:50 - The $600 Billion Revenue Gap
17:00 - Conclusion & Call to Action
The AI industry just dropped some numbers that should terrify every executive who's betting their company's future on AI agents. Carnegie Mellon researchers put these systems through real workplace tasks, and the results are brutal. OpenAI's flagship GPT-4o? Failed 91% of the time. Amazon's Nova? A catastrophic 98% failure rate. Even Google's best-performing agent failed 7 out of 10 basic office tasks. While VCs poured $131 billion into AI this year alone, the dirty secret is that these systems can't even handle tasks your intern could complete. Are we witnessing the most expensive tech failure in history, or is there something deeper going on here?
The numbers don't lie, folks. While Silicon Valley has been screaming about AI agents replacing all of us, Carnegie Mellon just published the most comprehensive study yet on how these systems actually perform in real workplaces. The results should be a wake-up call for every business leader who's been drinking the AI Kool-Aid.
Carnegie Mellon researchers tested AI agents on 175 realistic workplace tasks and the results were absolutely devastating across every single model.
OpenAI's GPT-4o, the model everyone's been hyping as the future of work, managed to fail a staggering 91.4% of basic office tasks.
Amazon's Nova-Pro-v1 achieved the most spectacular failure rate of 98.3% - essentially making it worse than random chance on most problems.
Meta's Llama-3.1-405b crashed and burned with a 92.6% failure rate, proving that bigger models don't automatically mean better performance.
Even Google's best-performing Gemini 2.5 Pro, which led the pack, still failed 70% of tasks that any competent human worker could handle.
These weren't trick questions or edge cases - we're talking about responding to colleagues, basic web browsing, and simple coding tasks.
Gartner estimates that out of thousands of companies claiming to offer "AI agents," only about 130 are actually real - the rest is pure marketing fluff.
Companies are frantically rebranding existing automation, chatbots, and RPA tools as "AI agents" to ride the current hype wave.
Apple is facing a class action lawsuit over their "Intelligence" feature that promised AI capabilities but delivered disappointment instead.
Investment firm Delphia got slapped with a $225,000 SEC fine for their completely fake "AI financial analyst" that was just marketing smoke and mirrors.
This mirrors the dot-com madness of 1999 when every company slapped ".com" on their name without changing their actual business.
The pattern is identical to what I witnessed during the blockchain craze - lots of buzzwords, minimal substance, maximum investor confusion.
https://www.youtube.com/live/9ELXACQ6aMo
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DavidV: 25 YEARS 24/7 Experience:
* Software Architect (PhD) Supervisor -25 years 300K PMS (project management) hours
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