GenAI for the rest of us

Generative AI has been in every headline for a few years now, which is exactly why it's worth stopping to ask a plain question: what is the thing, actually? Not the hype, not the doom. The mechanism.

It's a prediction engine, not a database

A large language model, the technology behind ChatGPT, Claude, Gemini, and the rest, doesn't look things up. It doesn't have a filing cabinet of facts it consults. What it does, at its core, is predict the next word (technically, the next token, a chunk of a word) given everything written before it. It learned to do this by reading an enormous amount of text and adjusting billions of internal numbers until its predictions got good.

That's the whole trick. Ask it a question, and it's not retrieving an answer. It's generating the most statistically plausible continuation of your question, one token at a time, informed by patterns it absorbed during training. Most of the time, for most questions, "statistically plausible" and "actually true" line up closely enough that the distinction doesn't matter. The trouble starts when they don't.

Why it hallucinates

"Hallucination" is the field's word for when a model states something false with the same fluent confidence it uses for something true. It's not a bug that occasionally slips through. It's a predictable consequence of how these models are built and graded.

OpenAI put out a clear explanation of this in 2025: during training, models are effectively rewarded for guessing. If a model answers "I don't know" to a question it's unsure about, it scores worse on most benchmarks than a model that guesses and sometimes gets lucky. So the training process quietly favors confident bluffing over honest uncertainty, because that's what the scoring rewards. The model isn't lying in the human sense. It has no concept of what it doesn't know. It's producing the most plausible-sounding answer available to it, and plausible is not the same thing as correct.

Practically, this means the more obscure or specific your question, the less you should trust an unverified answer, no matter how confident the tone. Ask it for sources. Check them. This is also why the same model can be brilliant at summarizing a well-documented topic and quietly wrong about a small, under-documented one: the fluency of the answer doesn't change, only the odds that it's true.

What an "agent" actually is

For years, "using AI" meant typing a question into a chat box and reading the reply. An AI agent is different. It's a model given the ability to take actions and check its own work in a loop: searching the web, running code, editing files, calling other software, rather than just producing text for a person to act on.

Anthropic's engineering team drew a useful line here: a workflow is an AI model plugged into a fixed sequence of steps that a person designed in advance. An agent is a system where the model itself decides what to do next, based on what happened after its last action, until the task is done. A workflow follows a recipe. An agent decides, at each step, what the next step should be.

This matters because it changes what can go wrong. A chatbot that gives you a bad answer wastes your time. An agent that takes the wrong action, sends the wrong email, deletes the wrong file, buys the wrong thing, has already acted before anyone reviewed it. The more autonomy you hand an agent, the more that oversight gap matters. It's also why the sensible version of "using an agent" usually still involves a person checking its work at the boundaries, like before it sends something, spends something, or deletes something, even if it runs unsupervised in between.

What actually changed in the last year

It's easy to be numb to "AI moves fast" as a sentence. Here's what that's meant concretely.

Stanford's 2026 AI Index Report tracks a benchmark called OSWorld, which measures how well an AI agent can complete real computer tasks: the kind involving a mouse, a browser, multiple apps, and no script written in advance. A year earlier, top agents succeeded at roughly 12% of these tasks. By early 2026, that number had climbed to around 66%. That's not a marginal improvement. It's the difference between an interesting demo and a usable tool, for a large category of tedious computer work.

At the same time, adoption moved faster than the underlying technology curve alone would predict. The report puts global generative-AI adoption at roughly half of people within about three years of the technology reaching the public, a faster climb than the personal computer or the internet managed. The frontier stayed uneven along the way, too: the same models that now clear graduate-level math problems still trip on tasks a person would consider simple, a pattern researchers have taken to calling the "jagged frontier." Capability didn't arrive as a smooth, uniform upgrade. It arrived unevenly, fast in some places and stalled in others, which is part of why it's hard to form one clean opinion about how good AI is right now. The honest answer depends entirely on which task you mean.

Two recent, concrete examples of the trend line: in the last two weeks of June and early July 2026 alone, both Anthropic and OpenAI shipped new model families built explicitly around cheaper, more autonomous agent use rather than better chat answers, with pricing tiers aimed at running agents constantly instead of occasionally. That's a market decision, not just a research one, and it's a sign of where the labs think the actual demand is heading.

None of this requires believing the more excitable claims about what's coming next. The prediction-engine mechanism hasn't changed. What changed is how far that mechanism now reaches when it's wired up to tools, given room to take multiple steps, and left to work with less supervision per step. That's exactly why understanding the mechanism, plainly, matters more now than it did a year ago.

Sources

OpenAI: Why language models hallucinate

Anthropic: Building Effective AI Agents

Stanford HAI: The 2026 AI Index Report