Definition
The AI Factory Effect
The dynamic in which AI compresses delivery timelines but scope expands to fill the gap. Teams don't get time back — they ship more. MVPs complete faster, then continue into features, architecture, and automation. Same headcount. Same contract. Dramatically more output. First observed clearly in 2025: what used to take 3–4 months now takes half that, but projects don't end early — expectations rise to match the new pace.
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Related terms in AI Fundamentals
Synthetic RelationshipA one-to-one connection between a human user and an AI entity designed to feel lifelike.Temporal Self-SupervisionA machine learning approach that uses the natural time structure of data as the training signal, without requiring human-generated labels. Instead of asking humans to annotate examples, the system uses 'what actually happened next' as the ground truth for each prediction attempt. LightningRod's chronological prediction method is a form of temporal self-supervision applied to domain corpora: any dataset with timestamps - news archives, SEC filings, clinical records, internal communications - becomes a self-labeling training environment. This mirrors how humans learn: we predict what happens next, observe whether we were right, and update our world model accordingly.TensorA container of numbers arranged in multiple dimensions. It is the basic data structure AI models compute with.Text-to-speech (TTS)Technology that converts written text into spoken audio.TokenThe unit models read and write. You pay per token, which is why volume tasks add up.Token maxerAn AI maximalist. In a panel context, five of them in a row makes for a boring hour.Token spendThe compute cost of running large AI models, which Tal cut by building his own model.TokenizationBreaking text into small units called tokens, such as words, word pieces, or characters, that a language model can process.