Why Most AI Projects Will Fail — And How to Find the Companies That Won't
View original at nasdaq.comWhy Most AI Projects Will Fail — And How to Find the Companies That Won't In this episode of Motley Fool Hidden Gems Investing, Motley Fool contributor Rachel Warren sits down with Steve Lucas, chairman and CEO of Boomi, to unpack what Wall Street is missing: Why the next wave of AI winners won't be the flashy model ma…
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Companies citing AI efficiency as the reason for layoffs are often engaging in spin without real data to back up productivity claims
60% confidenceGartner forecasts that a number of agentic AI projects will fail or fail to return results and will be shut down by the end of 2027
60% confidenceNew customer/logo acquisition is the number one indicator of a sufficiently transformative technology
60% confidenceClaims that AI will take away human jobs are largely nonsense and fraud designed to scare people into buying a product
60% confidenceNvidia is the one company unequivocally making a ton of money from AI, while many other companies build amazing models but lose extraordinary amounts of money
60% confidenceSteve Lucas previously turned Marketo into a $4.75 billion acquisition by Adobe
60% confidenceThe cost to train frontier AI models in 2026 exceeds $1 billion
60% confidenceA $1,000 investment in Netflix at the time of its Stock Advisor recommendation on December 17, 2004 would be worth $369,577
60% confidenceAI is meaningless without data, and unique proprietary data is what investors should look for
60% confidenceROI now supersedes AI as the priority for boards and executives evaluating AI investments
60% confidenceWithin the next two decades, AI will largely manage or solve the major health challenges humans currently face, including curing type 1 diabetes
60% confidenceA $1,000 investment in Nvidia at the time of its Stock Advisor recommendation on April 15, 2005 would be worth $1,301,557
60% confidenceIf humans don't trust something, it will never be used, and this applies to AI just as it did to prior data and analytics projects
60% confidenceAs many as 40% of enterprise AI projects could ultimately be abandoned
60% confidenceThe cost to train GPT-2 was just shy of $50,000
60% confidenceThe four major U.S. hyperscalers spent around $400-410 billion on AI capex last year, rising to over $700 billion this year
60% confidenceStock Advisor's total average return is 908%, compared to 208% for the S&P 500
60% confidenceOpenAI is burning $3 billion a month, which is a reported and reliable figure
60% confidenceElon Musk put a cap on what his employees can spend at Tesla and SpaceX, per a recently reported news item
60% confidence
Data points we hold from this source
| OpenAI · cash burn rate | 3 billion_USD_per_month |