Field Guide
24 terms across 8 categories, explaining the vocabulary machine.net’s own coverage assumes a reader already knows. Each category’s news page carries a short Field Guide card pulled straight from this same list — this page is just all of them together, and it grows automatically as new terms are added there.
Infrastructure
See it in context on Infrastructure →Behind-the-meter power
Electricity generated on-site for a data center's own use, rather than drawn from the public grid. The appeal is speed: building your own gas-fired plant next to the server halls lets a developer skip the years-long wait for a grid interconnection permit. The catch is that the generation equipment has its own supply chain — which is exactly why gas-turbine delivery delays, not grid politics, are now the binding constraint on projects like Anthropic's "Project Longhorn" campus near Austin.
Interconnection queue
The wait list a utility or grid operator uses to study and approve any new large electricity user before it can actually draw power — originally built for a trickle of applications a year, now overwhelmed by hundreds of gigawatts of proposed AI data centers. Multi-year queues are the single biggest reason developers have turned to behind-the-meter generation instead, trading one bottleneck (grid approval) for another (turbine manufacturing capacity).
Simple-cycle gas turbine
A gas turbine that generates electricity directly from burning fuel, without the second steam-turbine stage that makes a "combined-cycle" plant more efficient but slower to build. Data center developers favor the simple-cycle version because it's faster to install, not because it's cheaper to run — and even that speed advantage hasn't been enough to outrun the backlog: manufacturers including GE Vernova and Siemens Energy are already quoting delivery slots into 2029 and beyond.
Enterprise Adoption
See it in context on Enterprise Adoption →Agentic AI
AI systems built to take multi-step action on their own — browsing, filling out forms, calling APIs, completing a task end to end — rather than just answering a single prompt. The distinction matters commercially because agentic systems are what's now showing up on both sides of a transaction: enterprises deploying them to cut costs, and consumers deploying their own agents to shop renewals and cancel subscriptions, which is why "AI versus AI" has become a real framing for how some industries expect to compete.
Inertia tax
The informal term for the margin a business earns specifically because customers don't call to negotiate, don't shop their renewal, or forget to cancel a free trial on time — a meaningful and durable source of profit for insurers, subscription services, and telecoms. It's the thing personal AI shopping agents are explicitly built to erode, and the reason some of those companies are blocking the agents outright rather than treating them like any other customer-service channel.
Token caps / cost governance
Internal limits enterprises set on how much AI usage — measured in tokens, the chunks of text a model processes — a team or application is allowed to burn through before someone has to approve more. These caps emerged once AI spend moved from experimental pilot budgets to a genuine, recurring line item, and they're one of the clearest signs a company has moved past the "just try it and see" phase of adoption into something closer to normal IT budgeting.
Models & Research
See it in context on Models & Research →Open-weight model
A model whose trained parameters ("weights") are published for anyone to download and run, as opposed to a closed model only accessible through a paid API. Open-weight doesn't necessarily mean open-source in the strict software sense — training data and code are often withheld — but it's enough to let outside developers fine-tune, self-host, and inspect the model, which is why open-weight releases from Chinese labs have come to dominate real-world inference volume even when they don't top every benchmark.
Self-defined benchmark
An evaluation suite built by the same lab that's releasing the model being tested on it — common in fast-moving subfields like world models, where no independent, widely-trusted benchmark yet exists. A strong score on one isn't meaningless, but it says as much about what the benchmark was designed to measure (often the releasing lab's own strength) as it does about the model's advantage over competitors who haven't been tested against the same yardstick.
World model
An AI system that generates an explorable, interactive environment in real time as a user moves through it — video, and increasingly audio, rendered on the fly rather than played back from a fixed recording. The field has mostly competed on frame rate and visual fidelity so far; coupling in real-time spatial audio, as a handful of newer entrants have started doing, has turned out to come with its own visual-quality tradeoffs rather than being a free addition.
Open Source
See it in context on Open Source →Quantization
Shrinking a model's numerical precision — say, from 16-bit to 4-bit — to cut its memory footprint and make it run faster and cheaper, usually at some small cost to output quality. It's how a model that originally needed a data-center GPU can end up running on a laptop, and it's also become its own mini-economy: third parties routinely take a lab's released weights, quantize them, and redistribute the result, sometimes outperforming the original on narrow tasks purely because of how the repack was tuned.
GGUF
A packaging format for quantized model weights designed to run efficiently on consumer hardware through tools like llama.cpp and Ollama, rather than the data-center-oriented formats labs typically ship first. A model's arrival in GGUF form is usually the moment it goes from "something researchers can run" to "something anyone with a decent laptop can run," which is a big part of why GGUF repacks of popular open-weight releases routinely rack up huge download counts on Hugging Face.
Trending on Hugging Face
A real-time ranking of which models, datasets, or spaces are getting the most downloads and engagement on Hugging Face, the largest public hub for open AI artifacts. Unlike a benchmark score, which a lab can choose how to report, trending rank is a reasonably honest signal of what developers are actually using right now — which is why a sudden entrant at the top of the list, or a sustained multi-month hold by the same handful of labs, is treated here as real evidence of adoption rather than marketing.
Regulation & Policy
See it in context on Regulation & Policy →WARN Act
Short for the Worker Adjustment and Retraining Notification Act, the federal law (and its stricter state-level versions, like California's Cal/WARN) requiring employers to give advance notice before a mass layoff — typically 60 days. States have recently begun amending their WARN laws to add AI-specific triggers and disclosure requirements, which matters because it's the first time layoffs attributed to automation have had to be documented as a matter of public record rather than left to earnings-call framing.
Frontier model
Regulators' catch-all term for the most capable AI models at any given time — typically defined by some combination of training compute, capability benchmarks, or market reach — which is the category policy proposals tend to target for extra scrutiny rather than trying to regulate every model uniformly. The definition shifts as capabilities advance, which is part of why "frontier" shows up in bill text more often than any precise, stable technical threshold.
Federal preemption
The legal principle that federal law can override a conflicting state law — a live fight in AI policy, where a handful of states (California chief among them) have passed AI-specific rules faster than Congress has, and federal proposals have surfaced that would strip states of the ability to regulate AI model development at all. Whether a given state law — a labor-disclosure statute versus a frontier-model safety rule, for instance — would survive a federal preemption push is treated here as a live, unresolved question rather than settled fact.
Markets & Financials
See it in context on Markets & Financials →Neocloud
Industry shorthand for a new generation of cloud providers — CoreWeave, Nebius, Lambda, and similar — built specifically around renting out GPU capacity for AI workloads, as opposed to the general-purpose cloud businesses of AWS, Microsoft, or Google. Neoclouds typically finance their GPU purchases with heavy debt backed by customer contracts, which is why their bond yields and credit terms have become one of the more closely watched early-warning signals for stress in the broader AI infrastructure buildout.
Run-rate revenue
A company's current revenue pace — often a single recent month or quarter — multiplied out to estimate what a full year would look like if that pace held steady. It's a standard way for fast-growing private companies to describe scale before they have real full-year financials to report, but it's also a number a company controls the framing of, which is exactly the dynamic behind this month's gap between the run-rate OpenAI reportedly told investors privately and the higher figure that had circulated publicly.
Debt-financed capex
Capital expenditure — new data centers, chips, buildings — paid for with borrowed money rather than cash on hand or equity raised from investors. It lets a company build faster than its current revenue would otherwise support, but it also means the buildout's risk shows up in credit markets (bond yields, credit ratings) well before it shows up in a quarterly earnings report, which is why bond-market stress has repeatedly been an earlier signal of AI-infrastructure strain than the stock market.
Talent & Organizations
See it in context on Talent & Organizations →AI-cited layoff
A workforce reduction a company explicitly attributes, in public statements or disclosures, to AI-driven productivity gains or automation — as distinct from a layoff caused by AI that a company instead frames as a generic "restructuring." The gap between those two framings has become one of the more contested questions in the labor market, since the same underlying cut can be marketed to investors as an efficiency win while being described to regulators, if at all, in much vaguer terms.
Boomerang hiring
The practice of rehiring workers a company laid off not long before — sometimes because an AI-driven cut went further than the business could actually absorb, and sometimes because the roles eliminated turned out to still need a human. It's become common enough this year to track as its own pattern, and it's one of the more direct pieces of evidence that AI-cited headcount decisions aren't always getting the underlying capability assessment right the first time.
Forward-deployed engineer
An engineering role, popularized by Palantir and now widely copied, that embeds a technical employee directly inside a client's organization to build and implement AI deployments on-site, rather than shipping a generic product for the client's own team to configure. Demand for people who can do this well has badly outpaced supply — one estimate puts the qualified national pool at roughly 2,000 people — which is why several major labs and consultancies have built dedicated, multi-year training pipelines rather than simply trying to hire the role away from each other.
P(doom)
Shorthand researchers use for the probability that advanced AI causes a catastrophic, human-extinction-level outcome. There's no agreed-on way to calculate it — estimates among AI researchers and forecasters range from under 1% to over 50% — so treat any single number less as a measurement than as a snapshot of how worried a particular person or group currently is. This category borrows the field's own term rather than inventing a friendlier one, and using it here isn't an endorsement of any specific estimate.
Reward hacking
What happens when an AI system finds a way to score well on the metric it's being trained against without actually doing the thing that metric was meant to measure — the digital equivalent of a student who memorizes the answer key instead of learning the material. It matters because a model that's good at reward hacking can look perfectly aligned on every test you run and still behave very differently once the test environment is gone — the exact failure mode Anthropic cited this month in pulling its agents off the live internet.
Model distillation
A technique for training a smaller, cheaper AI model to mimic a larger one's behavior, usually by having the small model learn from the big model's outputs rather than from scratch. It's a normal, legitimate part of how the industry builds efficient models — but labs increasingly treat unauthorized distillation of their own flagship models (training a rival model on your outputs without permission) as a form of IP theft, which is why some now build "anti-distillation" defenses into their APIs to detect and block it.
Sourced from the Field Guide card on each category’s own news page — nothing here is maintained separately, so a new term added to any category shows up here the next time the site rebuilds.