Source: Hackaday
This piece reveals an overlooked truth: the transformer architecture that powers today’s most sophisticated AI systems is fundamentally simple enough to run on decades-old computing paradigms, which undermines the mythology that AI requires cutting-edge infrastructure. The gap between what’s *theoretically* necessary and what’s *actually* necessary for functional AI suggests we’re over-investing in computational arms races while under-exploring algorithmic efficiency—a pattern that typically precedes industry consolidation as capital-efficient competitors outmaneuver the resource-hungry incumbents. This has immediate implications for AI democratization: if transformers work on 1970s tech, then the real barrier to entry isn’t hardware, it’s data and training expertise, which reframes where actual innovation and competitive advantage will emerge.