1 What changed?
Do the inventory. In the last twenty-four hours, how much AI did you touch? The spam filter that ate a meeting invite. The recommender that queued your next song. Autocorrect. The transcription of yesterday’s lecture. The chatbot you asked to rephrase an email. Map it onto a page and you will find something quietly startling: almost all of it is the same kind of AI, and it has been running your day for a decade.
What changed in the last few years is not that AI arrived. It is that the word got reloaded. You can now talk to a model in plain language, and (more recently) the model has started to act on what you say. The technology that sorts your inbox and the technology that drafts your strategy are not the same thing, and most of the noise in this field comes from pretending they are. Before you build or evaluate anything for Tessera, hold three things apart.
1.2 The arrow, not the list
Memorising the taxonomy is beside the point. Notice the arrow. Narrow → general → agentic is a shift in who acts. The narrow model labels; the general model answers; the agent takes steps.
A chatbot that hallucinates a sentence is embarrassing. An agent that hallucinates a sentence and then sends the email, books the meeting, and modifies the customer record is a liability. The more a model acts, the bigger the blast radius when it is wrong, and the less the demo tells you about whether the deployment will hold. The direction of change is, precisely, the direction in which “is it impressive?” stops being a safe substitute for “does it actually work?”
If building a demo is the work an AI can do for anyone in an afternoon, then the demo gives no one an advantage. What holds its value as the models learn to act is the judgement the model cannot supply: whether the output is right, for whom, and under what conditions. The capability is not the problem. The unacted-upon gap between demo and deployment is. As the arrow points further towards agents that act, that gap (and your ability to close it with evidence) is exactly what becomes worth paying for.
1.3 Multimodality runs sideways
One more axis, and it is easy to misplace. “Multimodal” is not a fourth paradigm sitting on top of the other three; it runs sideways across them. It means the model handles more than text as input: image, audio, video, document. A narrow model can be multimodal (speech-to-text). A general model can be multimodal (describe this screenshot). The word describes the modalities handled at input or output, not a capability ranking.
It matters in business for a blunt reason: the real world is not text-only. A Tessera support ticket often arrives with a screenshot of an error. A contract is a PDF, not a paragraph. A customer complaint might be a thirty-second voice note. A tool that can look at the actual artefact beats a tool that forces you to summarise the artefact into text first. The summary is where you, the human, quietly smuggle in your own reading, and the model never sees the original. Later in this chapter you will change exactly one thing about a prompt (adding an image) and watch what the model can and cannot do with it.
1.4 First contact: a local model and a frontier model
You sit down at the shared stack: Open WebUI running a local Qwen on the machine in front of you, alongside a frontier model, side by side in the same window. You write one prompt and run it twice.
Resist the question the interface is designed to provoke: which one is better? It is the wrong question, and answering it is how practitioners lose the plot. The right question is the one you can answer for either output, regardless of which model produced it: where does this sit on the trust tool, and what does that tell me to do next? A frontier model and a local four-bit Qwen can each produce green-quadrant brilliance and red-quadrant garbage on the same afternoon. You are learning to judge outputs, not brands. Brand confidence is not evidence, and the most expensive mistake in this chapter is trusting a fluent answer because it came from the model with the famous name. This is the first move the book asks of you, and it is the move you will repeat…
When you attach the support-ticket image, the answer will often sound more authoritative: longer, more specific, more certain. That is not the same as more correct. A model that can see the artefact is still a model that can misread it, and a fluent wrong answer is more dangerous than a hesitant one, because it is harder to doubt. Confidence is a delivery property, not a truth property. Watch it.
🌐 Tessera Case · Tessera’s first comparison
Tessera wants to know why customers leave. Rather than reach for a vendor demo, you do the unfashionable thing: you run the same question on two models and judge the outputs yourself.
Pick the churn problem from Tessera’s profile. Write one prompt using RTCF: Role (a customer-success analyst), Task (help me understand what might be driving churn), Context (Tessera’s customer segments, its product lines, its support tiers), Format (a short list of candidate drivers, each with a one-line “how we’d check this”). Run it on the local Qwen. Run the identical prompt on the frontier model. Paste the two answers side by side.
Then change one thing. Attach a screenshot of a real Tessera support ticket and ask the model to read the ticket and respond. Note what shifts when the model can see the artefact instead of relying on your description of it. Which model handled the image? Did the answer genuinely improve, or merely sound more confident?
Finally (and this is the move the whole book hangs on) place every output on the trust tool. Text-only and image-included. Local and frontier. Were they equally trustworthy? The honest question you will write in your field journal at the door: which did you trust more, and was that trust earned by the content or assumed from the brand? Your answer to that is the first entry in a habit you will keep for twelve chapters.
Goal. Brainstorm candidate churn drivers for Tessera, to scope the problem before any data analysis.
The exchange. I asked both models, in RTCF, for “candidate drivers of churn, with how we’d check each.” The local Qwen gave a short, hedged list (tenure, support tier, product mix) and flagged that these were hypotheses to test. Useful, cautious, a little flat. The frontier model gave a longer, more specific list, and then crossed a line. It asserted, with no data in front of it, that “roughly 18 CloudSync customers in the South territory, representing about $340k in MRR, are likely to churn this quarter.”
Where it went wrong. That figure is a confabulation. The model has not seen Tessera’s data; it cannot know the count, the revenue, or the quarter. It produced a number because numbers are what a confident-sounding churn analysis is supposed to contain, and it dressed the guess in the costume of a finding. A busy COO would screenshot that line and drop it straight into a board slide.
The correction. I struck the figure and replaced it with what was actually true at this stage: these are untested hypotheses; no counts yet. The brainstorm itself was fine. It was the specific, actionable number that was load-bearing and false.
Trust-tool placement. A brainstorm of candidate drivers is average-fine, small blast: green: skim, use it to scope the problem, move on. The instant a specific customer count and revenue-at-risk figure appears, the same output flips to must-be-precise, large blast: red: a human verifies before it touches anything real. Same screen, different quadrant. The placement, not the model, was what failed.
The four outputs you produced (text/image × local/frontier) do not all sit in the same quadrant, and that is the lesson.
- A brainstorm of churn drivers: average is fine, blast radius small → 🟢 green. Skim, use it to scope the problem, move on.
- A specific churn rate or revenue-at-risk figure someone might act on: must be precise, blast radius large → 🔴 red. A human signs off before it reaches a slide, an email, or a customer record.
The frontier model’s fluency does not move an output from red to green. Neither does the local model’s caution. What moves an output between quadrants is what the output is: whether it needs to be precise, and what breaks if it is wrong. By the end of this book, placing an output on this grid before you act on it should be a reflex. It starts with these four.
The Core compares two models on one prompt. The Power-user path holds the model fixed and changes the prompt. Take the same churn brief and run it four times, altering one RTCF axis at a time: drop the Role; change Task from “candidate drivers” to “the single biggest driver”; cut Context back to one sentence; switch Format from a list to a one-paragraph executive summary. Paste all four alongside your original. You will see the output walk across the trust tool: the prompt stripped of Context drifts towards specific, unverifiable numbers (towards red); the prompt that forces a single answer pushes confidence ahead of evidence. The model is fixed; the harness you write around it is what moves an output between quadrants.
1.5 Where this is going
The thing that changed is that the model acts. And the moment a model acts, “does it work?” stops being a question about the model in isolation. It becomes a question about who it works for, and who it fails, because a model that scores well on average can still miss an entire customer segment, an entire region, an entire way of phrasing a problem. The average the model optimises for is always the average of someone, and that someone is rarely everyone.
That is the question the next chapter turns to: for whom? Before you trust an output, you have to know whose problems it was built to solve, and whose it quietly ignores. Tessera serves customers across six territories who do not all phrase a complaint the same way, do not all experience the same outage, and do not all appear in equal numbers in the data the model learned from. The shift from narrow to agentic made the stakes higher. The question of for whom is what makes the answer honest.
1.6 🔍 Go deeper
Stanford HAI, AI Index Report (annual). hai.stanford.edu/ai-index: a shared, evidence-grounded scoreboard: capability gains, plunging inference costs, rising incidents, regional divides in trust. Why read it: whenever a chapter’s claim needs a number behind it, start here.
Andrew Ng, Agentic AI (DeepLearning.AI, free short course). learn.deeplearning.ai/courses/agentic-ai: Ng’s case that agentic workflows (reflection, tool use, planning, multi-agent collaboration) will drive more progress than the next generation of foundation models. Why watch it: the clearest statement of the arrow this chapter is built on, models that take steps, not just give answers.
Simon Willison’s weblog, local-llms tag. simonwillison.net/tags/local-llms: notes on running open-weight models locally (Ollama and friends). Why read it: the local-versus-frontier terrain you touched in this chapter, written by someone who lives in it and distrusts hype as a matter of habit.
1.7 Core: inventory the proposed system
Time: 15 minutes. Inputs: three invented proposals: a rule labels a ticket, a model drafts a reply, and an agent drafts and sends it.
For each, record what enters, what leaves, what action can occur, and who can be harmed by an error. Name one piece of evidence you would require before permitting that action. The three categories can overlap; explain the capability and permission rather than arguing about the marketing label.
Artefact: a three-row system inventory. Success: distinguish generating a draft from authority to send it, and note that even read-only processing can expose private data. Add the proposed use to your proof brief.
1.8 🗣️ Take it to the AI
Where to start. Three questions worth arguing about rather than looking up:
- Ask the AI to classify five AI systems you actually use as narrow, general-purpose or agentic. Then ask which classification it was least sure about, and why the boundary is fuzzy there.
- Ask it to argue that the narrow / general / agentic distinction does not matter for a business decision. The rebuttal you have to construct is the thing worth keeping.
- Ask it for a number about AI adoption, then ask for the source and the year. Watch how often a confident figure arrives with neither. It is the same move that produced the $340k confabulation in the Conversation Log.
Keywords to steer with. narrow AI · general-purpose AI · agentic AI · harness · tool call · multimodality · confabulation · blast radius · trust tool · local versus frontier · open weights
A starting prompt (RTCF).
Role: You are a technology analyst who has lived through three hype cycles and is tired of all of them.
Task: Place five AI systems I name on the narrow-to-agentic spectrum, then tell me what changes about the oversight each one needs.
Context: I have read a chapter distinguishing narrow, general-purpose and agentic AI, and arguing that the arrow matters because the more a system acts, the larger the blast radius when it is wrong. The systems are [list five you actually use].
Format: A table: system | where it sits | what it can do without asking | who notices when it is wrong. Then one paragraph on the one you would worry about most, and why.