95%

Photo by Igor Omilaev on Unsplash

AI slop, the Ouroboros swallowing it's tail equivalent, is now becoming a crisis because scaling LLMs without understanding what the scaling is supposed to do, limits the AI's ability to do it's intended job, deal with the inherent vagaries of business in the real world. 

One must know, in depth, the problems the AI's supposed to solve, AKA the prime directive of
The Mythical Man Month

MIT report: 95% of generative AI pilots at companies are failing

The GenAI Divide: State of AI in Business 2025, a new report published by MIT’s NANDA initiative, reveals that while generative AI holds promise for enterprises, most initiatives to drive rapid revenue growth are falling flat.

Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L. The research—based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments—paints a clear divide between success stories and stalled projects.

“Some large companies’ pilots and younger startups are really excelling with generative AI,” Challapally said. Startups led by 19- or 20-year-olds, for example, “have seen revenues jump from zero to $20 million in a year,” he said. “It’s because they pick one pain point, execute well, and partner smartly with companies who use their tools,” he added.

How companies adopt AI is crucial. Purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds succeed only one-third as often.

This finding is particularly relevant in financial services and other highly regulated sectors, where many firms are building their own proprietary generative AI systems in 2025. Yet, MIT’s research suggests companies see far more failures when going solo.

Companies surveyed were often hesitant to share failure rates, Challapally noted. “Almost everywhere we went, enterprises were trying to build their own tool,” he said, but the data showed purchased solutions delivered more reliable results.

The report also highlights the widespread use of “shadow AI”—unsanctioned tools like ChatGPT—and the ongoing challenge of measuring AI’s impact on productivity and profit.

An ouroboros in a 1478 drawing in an alchemical tract[1]