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Source document· July 24, 2026

Why Most AI Projects Will Fail — And How to Find the Companies That Won't

View original at nasdaq.com
Why 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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  • Steve Lucas previously turned Marketo into a $4.75 billion acquisition by Adobe

    60% confidence
  • The four major U.S. hyperscalers spent around $400-410 billion on AI capex last year, rising to over $700 billion this year

    60% confidence
  • A $1,000 investment in Netflix at the time of its Stock Advisor recommendation on December 17, 2004 would be worth $369,577

    60% confidence
  • Stock Advisor's total average return is 908%, compared to 208% for the S&P 500

    60% confidence
  • AI is meaningless without data, and unique proprietary data is what investors should look for

    60% confidence
  • ROI now supersedes AI as the priority for boards and executives evaluating AI investments

    60% confidence
  • Companies citing AI efficiency as the reason for layoffs are often engaging in spin without real data to back up productivity claims

    60% confidence
  • New customer/logo acquisition is the number one indicator of a sufficiently transformative technology

    60% confidence
  • Claims that AI will take away human jobs are largely nonsense and fraud designed to scare people into buying a product

    60% confidence
  • Nvidia 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% confidence
  • Gartner 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% confidence
  • A $1,000 investment in Nvidia at the time of its Stock Advisor recommendation on April 15, 2005 would be worth $1,301,557

    60% confidence
  • The cost to train GPT-2 was just shy of $50,000

    60% confidence
  • The cost to train frontier AI models in 2026 exceeds $1 billion

    60% confidence
  • OpenAI is burning $3 billion a month, which is a reported and reliable figure

    60% confidence
  • Elon Musk put a cap on what his employees can spend at Tesla and SpaceX, per a recently reported news item

    60% confidence
  • If 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% confidence
  • As many as 40% of enterprise AI projects could ultimately be abandoned

    60% confidence
  • Within the next two decades, AI will largely manage or solve the major health challenges humans currently face, including curing type 1 diabetes

    60% confidence

Data points we hold from this source

OpenAI · cash burn rate3 billion_USD_per_month
What we know · the intelligence behind this page
Live from the substrate
What we're seeing
AI Capital Surge Meets Investor Caution: Record Funding Rounds and Government Contracts Amid Valuation Skepticism
A single-week cluster of large AI/fintech funding rounds (Socure, Stability AI, Emerald AI, Generalist AI, Instinct, Gatik, Regent Craft) shows venture capital still pouring into AI infrastructure, identity, and autonomy plays, while Palantir's Army TITAN contract win coincided with a 6% stock drop — signaling that even flagship AI-defense revenue isn't immune to market reassessment of AI valuations. Efficiency-focused innovations like Multiverse Computing's model compression suggest the sector is also pivoting toward cost/inference economics as capital intensity draws scrutiny.
Our read on the data ›
Signals we're tracking
EPKINLY Regulatory-Clinical Success Cascade
High probability of expanded label indications, additional combination approvals, and competitive positioning strength in follicular lymphoma market. Predicts positive commercial uptake and potential accelerated review for related indications.
Patterns we're watching ›
Where sources disagree
Morgan Stanley & Co. LLC
The same metric (eps) for the same entity (Morgan Stanley & Co. LLC) reported for the identical fiscal period (Q1 2026) and observation date (2026-03-31) has two conflicting values: 3.43 USD_per_share vs 3.08 USD. This is not a temporal change — both observations claim to measure the same point in time. The ~10% discrepancy (0.35 USD difference) is material for a financial metric.
We flag conflicts openly ›
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