Massive AI funding isn't solving the real bottleneck — companies need data, talent, and proven use cases that money alone can't create

The article argues that the $700 billion being poured into AI infrastructure and model training misses the actual constraint: companies don't know how to turn AI into working products that customers pay for, and there aren't enough people who know how to build them.

Money scales compute (the computing power to run AI models) but cannot scale expertise. Training data quality, skilled engineers who understand both AI and specific industries, and business models that work are all in short supply — throwing more funding at them doesn't automatically create them faster.

For business professionals and startups: the companies winning aren't those with the biggest model budgets, but those solving concrete problems (healthcare diagnoses, supply chain optimization, customer support automation) where they have domain experts who can shape AI to their industry's actual needs. Generic large language models are commoditizing; specialized, well-implemented solutions for specific industries are where real value now lives.