What Happens When The Industry Runs Out Of Data?

InnovationWhat Happens When The Industry Runs Out Of Data?ByCharles Pensig,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)May 14, 2026, 06:45am EDTBy Charles Pensig, Founding Partner, Stratus Data. gettyFor the past few years, AI’s stratospheric growth has been driven by one main ingredient: more data. Feed models ever-larger amounts of text, images and audio, and they get smarter.But that approach is hitting a ceiling. Based on analysis of publicly available LLM data-consumption projections, these models have already been trained on roughly 20% of the world’s publicly available data.​​​So, what happens to AI when we run out of data? ​​New data is being created every day, but not at the current pace of AI. When you eliminate “junk” data or regurgitation, there simply isn’t enough new, high-quality information left (or being created fast enough) to fuel exponential growth. Add to that the number of web sources newly restricting their use of data, and it poses a real problem. Here’s another consideration: Training bigger models also demands massive amounts of computing power and electricity. Data centers are ballooning in size, and we're running out of energy capacity in the U.S.​​What we’re facing is "AI collapse": AI is trained on human data > AI produces information on the web > AI then starts training on its own output > AI becomes like a Ponzi scheme, ready to collapse under its own strain. ​​The answer is to stop chasing LLMs and start rethinking how we’re building AI and what we really want to get out of it. ​It isn’t a lost cause, and this isn’t the first time we’ve faced a tech dilemma like this. In the '90s and early 2000s, there was a similar race to increase computer clock speed (megahertz to gigahertz). But once we reached physical limitations (the fastest clock speeds for compute...