Silicon Valley promised AI would democratize creativity. New research tracking 442 participants found the opposite: people who were more creative without AI produced better work with it. The gap didn't close. It may have widened.
Enterprises are spending billions on AI pilots, but MIT's research shows most deliver no return. It's not the technology failing. The gap between impressive demos and working systems comes down to data quality, technical debt, and organizational readiness.
Good Morning from San Francisco, Anthropic crowned its new model the coding champion. The White House invoked the Manhattan Project
Enterprises are spending billions on AI pilots, but MIT's research shows most deliver no return. It's not the technology failing. The gap between impressive demos and working systems comes down to data quality, technical debt, and organizational readiness.
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This Week: The $40 Billion Mirage
Today we're starting with a question that should make every executive uncomfortable: why are 95% of enterprise AI pilots delivering nothing?
MIT released research in July 2025 that put a number on what boardrooms already knew but wouldn't say out loud. Companies have spent $30 to $40 billion on enterprise AI. Only 5% of those deployments created measurable business value. The rest? Perpetual pilots or quietly shelved projects.
The technology works. In labs, in demos, in controlled environments, AI performs exactly as advertised. The problem isn't the models. It's everything else. Data infrastructure that can't handle the load. Organizations that resist change. Technical debt piling up faster than teams can pay it down. And most fundamentally, a failure to understand the difference between showing AI works and making it work.
We're going to look at why this divide exists, who's actually succeeding, and what separates that 5% from everyone else. The IBM Watson disaster, the regulatory reckoning, the shadow AI economy thriving inside your company right now. The infrastructure spending that enriches hyperscalers regardless of whether customers see returns. The back-office automation wins that nobody talks about because they're not sexy enough for press releases.
This is the full picture.
Marcus Schuler
Six months ago, a global insurance carrier unveiled what executives called a transformational claims assistant at an industry conference in Orlando. The generative AI system could analyze accident photos, cross-reference policy terms, and draft settlement recommendations in seconds. The demo drew gasps. The press releases followed. Then the project quietly died.
The AI couldn't integrate with the firm's legacy claims database. Adjusters didn't trust outputs they couldn't explain. Compliance raised flags about regulatory exposure. By August, the "revolutionary" tool was shelved indefinitely, joining a graveyard of enterprise AI experiments that looked brilliant in PowerPoint and failed in production.
This isn't an isolated story. It's the dominant pattern.
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