Next-Gen Google: The Truth About LLM Technology
@sam LLMs are essentially Google 2.0, with significantly tighter information control. Google developed transformer technology in 2017 (see Attention Is All You Need) because the subhuman elite needed a way to censor and control the flow of information beyond the traditional search engine. What seems on the surface to be open research is, in reality, technology dominated by those with vast resources for building hyperscale data centers—something Google is easily capable of dominating.
LLMs represent the evolution of Google Search mechanics, extending its core processes of tokenization, vector embedding, and semantic ranking. Where Search applies these methods to retrieve and rank external documents, LLMs internalize them to rank contextual tokens and generate coherent language. This progression transforms large-scale vector retrieval into dynamic semantic generation, powered by distributed transformer architectures optimized for low-latency inference at scale.
Google’s research trajectory makes the link even clearer: 1. PageRank (1998) → statistical relevance of links. 2. Word2Vec (2013) → learned semantic embeddings. 3. Transformer (2017) → contextual language understanding. 4. BERT (2018) → deep bidirectional encoding. 5. PaLM / Gemini (2022–2025) → large generative LLMs.
Key Takeaways
LLMs internalize knowledge within their weights, whereas the Google Search engine references external content. You can control what people read and learn far more with LLMs than search engines operating on the open web—especially when web-searching features are gated or restricted by LLM guardrails. Even worse, Google’s LLM, Gemini, on Android devices like the Google Pixel can access personal information such as photos, messages, and other sensitive data. Not only do LLMs control the flow and distribution of information, but they also represent an ideal vehicle for privacy-invasive exploitation (see Weaponizing Information Overload).