An in-depth look at how Google's Transformer model, developed by eight researchers in 2017, radically sped up and augmented how computers understand language
Over the past few years, we have taken a gigantic leap forward in our decades-long quest to build intelligent machines: the advent of the large language model, or LLM. X: @madhumita29 , @benatipsos , @leokelion , @samjoiner , and @manjusrii . LinkedIn: Sam Joiner X: Madhumita Murgia / @madhumita29 : NEW: Our visual in-depth explainer of how a “large language model” works - & what makes it such a powerful, general cognitive engine. Months in the making from dream team @samjoiner @sam_learner @Dan_Clark5 @inari_ta et al https://ig.ft.com/... Ben Page / @benatipsos : One of the clearest analyses I have seen on “how” LLM works in detail - inside the “beauty” of the black box #generativeAI (and a touted 300 million white collar jobs at risk) h/t @axelheitmueller https://ig.ft.com/... Leo Kelion / @leokelion : Phenomenal, visual explanation of the role Transformers played in making #GPT4 and other generative AI models possible by @madhumita29 and the team at the @FT - works brilliantly on mobile too. Cracking journalism with a long shelf life . https://ig.ft.com/... Sam Joiner / @samjoiner : Generative AI exists because of the transformer. In our latest visual story, we explain how it works. With a crack team of @madhumita29 @Dan_Clark5 @sam_learner @inari_ta @olihawkins and @EadeMoon! https://ft.com/... [video] Belinda Barnet / @manjusrii : “While the text may seem plausible and coherent, it isn't always factually correct. LLMs are not search engines looking up facts; they are pattern-spotting engines that guess the next best option in a sequence. h/t @huseyinkishi https://ig.ft.com/... LinkedIn: Sam Joiner : How does generative AI really work? — Our latest visual story explains how large language models are underpinned by the transformer — and why this makes them such versatile cognitive engines. …
Context & Ripple Effects
Google’s neural-network upgrade to Translate was an early indication that language understanding could become a core computing interface; the Transformer extended that trajectory by changing how language models process context. Google’s earlier neural-network push in Translate provides the immediate precursor.
Related coverage has traced both the architecture’s broader application beyond language as transformers moved into computer vision and the later mechanics of systems such as ChatGPT. This explainer places those developments in the technical lineage of the 2017 research work.
First-order effects
- Transformer-based models gave Google and the wider research community a more capable foundation for handling language, enabling the LLM approach described here.
- The eight researchers’ 2017 work became a central reference point for subsequent language-model development, as later coverage of the paper’s co-authors underscores.
Second-order effects
- Companies building conversational products gained a common model architecture to adapt, helping shift competition from narrow language tasks toward general-purpose text interfaces.
- Because transformers could also be applied to vision, the advance broadened the commercial and research race from language systems to multimodal AI.
Third-order effects
- If model architectures continue to transfer across tasks, advantage will increasingly depend not only on inventing models but on distributing them through established products and services.
- The pattern points toward AI becoming a general computing layer, while leaving open how much durable power accrues to model creators versus the platforms that integrate models at scale.
The trend: The Transformer’s rise is one data point in the shift from task-specific AI toward reusable foundation models embedded across computing products.