#model-efficiency

#model-efficiency

We're living in a token economy. Each piece of content -- words, images, sounds, etc. -- is treated by an AI model as an atomic unit of work called a token. When you type into a prompt in ChatGPT, and you receive a paragraph in response, or you call an API to do the same thing inside an app you've built, both the input and the output data are counted as tokens. As a result, the meter is always running when you use AI, racking up costs per token, and the total bill is set to go higher in aggregate.

In the race to deploy large language models and generative AI across global markets, many companies assume that "English model → translate it" is sufficient. But if you're an American executive preparing for expansion into Asia, Europe, the Middle East, or Africa, that assumption could be your biggest blind spot. In those regions, language isn't just a packaging detail: it's culture, norms, values, and business logic all wrapped into one. If your AI doesn't code-switch, it won't just underperform; it may misinterpret, misalign,

At its core, EmbeddingGemma serves as a text embedding model. It translates text, such as notes, emails, or documents, into specialized numerical codes called vectors. These vectors represent the meaning of the text in a high-dimensional space, allowing devices to grasp context rather than just matching keywords. This fundamental capability enables much more intelligent and helpful search, organization, and other AI functionalities, powering generative AI experiences directly on user hardware.

Andrej Karpathy, a former OpenAI researcher and Tesla's former director of AI, calls his latest project the "best ChatGPT $100 can buy." Called "nanochat," the open-source project, released yesterday for his AI education startup EurekaAI, shows how anyone with a single GPU server and about $100 can build their own mini-ChatGPT that can answer simple questions and write stories and poems.

While OpenAI and Anthropic continue begging for more and more investor cash in the face of consistently lackluster earnings, some vendors delivering advanced AI to the legal industry dropped hints about growing interest in small models. It's not that large language models don't work - though they often don't - but they're overbloated science experiments that, as Goldman Sachs observed, require exponentially increased resources to achieve tiny linear gains.