You don't need Sam Altman or his big, beautiful LLM

"At so many pivotal moments in our adoption of digital technology, people and businesses mistake a company's walled garden for the broader, more powerful network underneath. In the 1990s, many people genuinely believed AOL was the internet. When I left Facebook in 2013, hundreds of people asked how I would function "without the web." Over and over, packaged products-operating systems, app stores, streaming services-eclipse quieter, less expensive, bottom-up alternatives like Linux or torrents. We forget they exist."

"To many of us, " AI" now means choosing among a handful of commercial LLMs such as ChatGPT, Claude, Gemini, or Grok -and perhaps even choosing the one that matches our cultural or political sensibilities. But these systems share important structural limitations: they are centralized, expensive, energy-intensive operations that depend on massive data centers, rare chips, and proprietary data stores. Because they're trained on roughly the same public internet, they also tend to generate the same generalized, flattened results."

"Companies using them wholesale often end up substituting their own expertise with recombinations of whatever is already out there. This is how AI will do to businesses what social media did to publications, and what the early web did to retailers who went online without a strategy. Using the same generic tools as everyone else produces the same generic results."

Commercial AI is commonly equated with selecting among proprietary large language models such as ChatGPT, Claude, Gemini, or Grok. Those models are centralized, expensive, and energy-intensive, relying on massive data centers, rare chips, and proprietary data stores. Shared training on the same public internet produces generalized, flattened results. Organizations that adopt generic models wholesale often substitute internal expertise with recombinations of public content and outsource core knowledge processes to opaque services. That outsourcing undermines long-term capability-building and diminishes on-the-job learning for junior staff. Decentralized, bottom-up, and open alternatives remain cheaper and often overlooked.

Read at Fast Company