AI is moving faster than the buzzwords used to describe it.

Every few months brings a longer context window, a new reasoning model or a new agent framework. The headlines tell you what changed. They rarely tell you why it works, where it breaks, or what it costs to run.

Beyond the AI Buzzwords is a field guide to 20 ideas that are reshaping how AI systems are built, trained, served and secured.

Inside, you'll learn:

Why a model with a million-token context window can still miss a fact you gave it
How recursive language models handle inputs far larger than their context window
What latent reasoning is, and why a model may not need to put every thought into words
Why reinforcement learning makes models better at first-try answers without necessarily teaching them more
Why a model's written reasoning is not proof of how it reached its answer
Why hallucinations are partly a problem of how we grade models
Why memory, not raw compute, limits long-context serving, and how hybrid architectures and speculative decoding respond
How mixture-of-experts models and conditional memory let models grow without using every parameter for every token
How a small training shortcut can grow into broad misalignment
Why models trained on AI-generated data lose the rare and the unusual first
How AI agents can be hijacked through their memory and their tools

Every chapter follows the same path: the idea, how it works through a concrete example, where it breaks, and what to do differently on Monday. Each concept carries an evidence label (Established, Active Research or Emerging Hypothesis), so you always know whether you're reading about a proven technique or a promising paper. Every chapter points to the primary research, with more than 80 sources cited.

If you build, deploy, evaluate or secure AI systems, this is your guide to what is happening beneath the buzzwords.