The durable ideas beneath the headlines — how modern AI works, why scale changed everything, and the alignment problem at its heart.
Artificial intelligence is the effort to build machines that perform tasks we associate with human intelligence — perceiving, reasoning, deciding, and creating. For most of its history it advanced in fits and starts, but the 2020s brought a phase change: large models trained on vast data crossed a threshold of usefulness that reshaped the whole field.
As of early 2026, the frontier is defined by a handful of labs — OpenAI, Anthropic, and Google DeepMind chief among them, with xAI, Meta and others close behind — racing on a shared set of capabilities: reasoning, tool use, and autonomous 'agents' that carry out multi-step work. The bottleneck has shifted from raw cognition toward governance, safety, and energy.
This hub explains the durable ideas beneath the headlines — how these systems actually work, why scale changed everything, what an AI agent is, and the alignment problem that determines whether the technology stays under human control. The specific models will change monthly; these concepts will not.
The systems behind the AI boom — models that learn language by predicting the next token.
The empirical finding that more data, compute, and parameters reliably make models better.
The 2017 architecture, built on 'attention', that made modern AI possible.
AI that doesn't just answer — it plans, uses tools, and carries out multi-step tasks.
The problem of ensuring powerful AI systems reliably do what humans intend.