# Open Source Wins, AGI Is Here, and Scorsese's AI Toolkit with CEOs of Cerebras & Black Forest Labs > Source: > Published: 2026-07-10 01:26:00+00:00 - Inference - The computational process of running a trained AI model to generate outputs; distinct from training. In the context of reasoning models, inference is extremely compute-intensive. - Moore's Law - The historical observation that transistor density on chips doubles roughly every 18 months, leading to proportional performance gains. Cerebras claims to have exceeded this curve with its new architecture. - Wafer-scale chip - A processor that uses an entire silicon wafer as a single chip rather than cutting it into many smaller chips; Cerebras' approach, enabling massively more on-chip memory and bandwidth. - Latent diffusion - An algorithm that compresses data (images, video, audio) into a compact latent representation and trains a generative model on that compressed space; the foundational method behind Stable Diffusion and most modern generative AI. - Reasoning model - An AI model that generates many intermediate 'thinking' tokens internally before producing a final answer, allowing it to tackle complex multi-step problems — at significantly higher compute cost than standard models. - Token - The basic unit of text processed by a language model (roughly a word or word-fragment). Token consumption is the primary measure of AI compute usage and cost. - Token maxing - Colloquial term for consuming AI tokens inefficiently or without strategic intent — like wandering every Costco aisle rather than going straight to what you need. - AGI (Artificial General Intelligence) - AI that matches or exceeds human-level performance across a broad range of tasks, not just narrow domains. The episode debates whether this threshold has already been crossed. - Prompt whisperer - Colloquial term for someone who has mastered the craft of writing precise prompts to elicit good outputs from AI models — a skill becoming less necessary as models learn to interpret intent. - Red teaming - A security practice in which a group attempts to find vulnerabilities or failure modes in a system before it is deployed publicly; in AI, it means stress-testing a model for dangerous capabilities. - Sovereignty (AI context) - The ability of a country, organization, or individual to control their own AI infrastructure and models rather than depending on foreign or third-party systems. - Multimodal model - An AI model that can process and generate multiple types of data — such as text, images, video, and audio — within a single unified architecture. - Action prediction - A capability in AI models that predicts the next physical action to take based on visual and contextual inputs; the bridge between generative video models and robotics. - P-doom - Short for 'probability of doom' — the estimated likelihood that advanced AI leads to catastrophic or existential outcomes. Used colloquially to describe AI pessimism or doomism. - Hyperscaler - A company that operates massive-scale cloud computing infrastructure — specifically AWS, Microsoft Azure, and Google Cloud — known for buying enormous quantities of chips and data center capacity. - MFU (Model FLOP Utilization) - A metric measuring how efficiently a training or inference run uses the theoretical peak compute of the hardware — higher MFU means less wasted processing power. - Loop maxing - Emerging term for the practice of chaining AI reasoning loops iteratively — each output feeding the next — to produce exponentially better results than a single-pass query. - Paradigm shift (Kuhn) - Thomas Kuhn's concept that scientific worldviews don't change gradually but through sudden revolutionary breaks, typically only after old guard thinkers die or retire. - Drosophila - The common fruit fly, used extensively in genetics research because it reproduces rapidly (two generations per day), allowing scientists to study many generations quickly. Used here as a metaphor for AI's accelerated learning cycles. - Flux - Black Forest Labs' flagship open source image generation model, widely adopted for its quality and flexibility across text-to-image and image-editing tasks.