The Inevitability Machine
A field guide to the vocabulary of AI — what the words smuggle in, and how naming the systems plainly keeps the future ours to write.
Hi there,
Welcome back to Untangled. It’s written by me, Charley Johnson, and valued by members like you. Help me make it better?
This week, something a little different: a field guide. Not to the technology, exactly, but to the words we use for it — intelligence, reasoning, hallucination, alignment, and a handful of others that get passed around as though we all agree on what they mean. We don’t. And I’ve come to believe that naming these systems accurately — plainly, without the borrowed grandeur — is about the most useful thing you can do to keep responsibility in view, instead of letting it drift off toward the machine.
As always, please send me feedback on today’s post by replying to this email. I read and respond to every note.
On to the show!
Untangled HQ
New Event: Learn how not to lose the room. Every facilitator knows the moment: the temperature spikes, two people dig in, and the whole room swivels to watch what you’ll do. We’ll skip the calm-on-the-outside performance and practice the real thing — staying like water when a room starts to slip — so bring a situation you’re actually wrestling with and you’ll leave with something you can use. Register now.
1:1 Leadership Coaching: I have one leadership coaching slot available. You can learn more about my approach here. If you’re interested, just hit ‘Reply.’ I’m happy to discuss whether it might be a good fit for you.
Deep Dive
AI Isn’t Inevitable. You Get a Say.
That fuzziness you feel about what AI systems are and aren’t — what they can do, what they can’t — is real. The technology keeps advancing in ways that blur the line between doing and seeming. A model lays out a tidy chain of steps, and it looks like it’s reasoning. An agent books the flight, and it looks like it’s deciding. Here’s the catch: the better that surface behavior gets, the wider the gap grows between what the system is actually doing and what it appears to be doing. That gap — the ambiguity itself — is where the trouble starts.
Because into that gap, frames rush in. And frames are never neutral, whether anyone means them that way or not. If you’ve spent any time in this field, you already know how this goes: somebody’s chosen frame resets what feels politically possible, and most people never clock that a choice was made at all. A frame makes a few things easy to see and tucks the rest out of view. It decides which solutions feel available to us — and when one like “AI is inevitable” really takes hold, it goes further still, and decides which futures we stop bothering to imagine.
And a frame, left alone, hardens into a story — usually a story about where all of this is headed, told as though the ending were already written. But here’s the hopeful version of the same mechanism: if you can get clear on what these systems are and aren’t, you can choose a different frame and you can tell a different story — one tied to a future you’d actually want to live in, rather than the one being narrated at you.
This is a long one (~5,500 words!), in three parts. First, what these systems actually are and aren’t — the layer where the ambiguity lives. Then the frames that get built on top of that ambiguity in everyday AI talk. And finally, how those frames harden into stories of inevitability that decide which futures we still bother to imagine. Let’s dig in.
Part 1: What these systems are and aren’t
Before you can spot a frame, you need a clear-eyed picture of the thing being framed — so let’s build that first. What are these systems, what aren’t they, and, just as much, what can they do and what can’t they? This is the layer where the ambiguity lives, and every frame that follows gets built right on top of it. Bear with me through the definitions; they’re doing more work than they look like they are.
Artificial intelligence
What people mean: the catch-all for computer systems that perform tasks we associate with human intelligence — recognizing, predicting, generating, deciding.
The phrase “artificial intelligence” was minted in the summer of 1956, when a few dozen men gathered at Dartmouth — almost all of them trained in math and computation, none in the social systems their work would go on to reshape — and set the field’s agenda on a single core assumption: that every feature of human intelligence could be described precisely enough for a machine to simulate it. And we’re still living inside that proposal. The word carries its premise forward like a stowaway. Every time we say “AI,” we re-ratify the claim that what the machine does and what a mind does are the same kind of thing, only faster.
But the machines are really only doing one slice of what we mean by intelligence — and skipping the part that matters most. What a model does is induction: it detects regularities in past data and projects them forward. That’s powerful, and it’s also permanently backward-looking. What it leaves out is the move that sits right at the center of human thought — abduction, the hunch, the leap from a few clues to a guess about what might be going on. Induction knows the streets are wet because it has seen wet streets before. Abduction is the nurse who senses something’s off before the chart can say why; the researcher who suspects the whole frame is wrong, not just a data point. It runs on the enormous store of implicit knowledge that never made it into any dataset — because, as Dave Snowden puts it, we always know more than we can say. Machines can’t do it. And not because they’re young and will grow into it.
So why fuss over a noun? Well — call a system “intelligent” and we start extending it the deference we reserve for minds: handing over not just tasks but judgment, meaning-making, the reframing of the problem itself, the very capacities it doesn’t have. (A second confusion likes to ride along here — that a more “intelligent” system must be a more powerful one. That’s its own assumption, and we’ll get to it.) For now, the practitioner’s move is smaller and sharper: treat “AI” as a description of a capability, not a character. The systems are real and the capability is real — it’s the term we inherited that was someone else’s imagined future, handed down as though it were a plain description of the present. Naming it accurately is how you take that choice back.
Artificial intelligence — Equates what the machine does with what a mind does.
Question to ask: Am I handing it a task, or handing it judgment?
Large language models (LLMs)
What people mean: the models behind ChatGPT and its kin — systems trained to generate text.
An LLM doesn’t reason. It predicts. Give it a sequence of words and it assigns probabilities to what comes next, then hands back the likeliest continuation. That’s the whole engine, more or less. What the model returns is a statistical reflection of existing data, not an understanding of it. And the capability really is impressive — I don’t want to wave that away. Hand one a messy hour-long transcript and get back a clean first draft in your own structure, and you’ve felt exactly why people reach for it. The trouble starts when we reach for words like learning or understanding to describe what’s going on, because those words equate a computer system with human thought. And that’s precisely the move that lets accountability slip away.
Take bias. When an image generator reliably renders leaders as men, we tend to call it a bug to be fixed. But the model is faithfully reflecting the patterns in its training data. The bias is the system working exactly as built, on the world as it is. And the same mechanism shows up in places that look far more neutral. Point a model at media coverage or public sentiment across a dozen countries and ask it what people think, and it will answer fluently — all the while overweighting the languages, outlets, and regions that dominate its training data, and underweighting the voices that were never well represented online in the first place. The output reads like a finding, but it’s closer to an echo. Name the mechanism accurately and the responsibility lands where it belongs — on the data, the design choices, and the people who shipped them — instead of on some mysterious glitch nobody owns.
Large language model — Makes bias look like a glitch rather than the system as built.
Question to ask: Whose patterns is this reflecting, and who’s accountable for them?
Training (and “data”)
What people mean: the process of building a model by feeding it data — the way you’d train a pupil, or a muscle.
“Train” borrows the language of teaching and growth: a learner shown examples, improving with practice. What happens is less tender. Data gets scraped, recorded, sorted into categories, sometimes labeled, then run through an optimization that tunes billions of parameters toward predicting the next token. There’s no understanding at the end of it, only a statistical fit to whatever went in.
“Data” sounds like something lying around for the taking, a byproduct of being online. It isn’t. Every dataset is made, and made by people: the choices about which categories exist, the annotators who tag each example, the judgment calls about what counts and what gets dropped. The scale is hard to hold in your head — by one estimate it would take 781 years of full-time looking to view every image in one widely used training set — so the labeling gets outsourced, often to workers paid a few dollars an hour. To make ChatGPT safer, OpenAI had Kenyan workers read through descriptions of violence, hate speech, and abuse. That labor is real and human, and “training” files it out of view.
This matters for the reason the others do. Say a model was “trained on data” and the system looks self-made while the inputs look ownerless — no one to credit, no one to compensate, no one accountable for what went in. One reframe worth carrying in from Beth Rudden: data isn’t exhaust, it’s a belonging — something with a lineage that asks for consent. You don’t have to adopt the whole idea to feel how much it moves, starting with who has a claim on the thing.
Training / data — Files the human labor and the lineage out of view.
Question to ask: Whose data is this, and what was owed to the people it came from?
Neuro-symbolic AI
What people mean: a system that pairs pattern recognition with an explicit, rule-based reasoning layer.
You’re going to start hearing a lot more about this one, so it’s worth getting ahead of the hype. Claude Code, for instance, is part LLM and part neuro-symbolic AI. An LLM pattern-matches — it predicts what comes next based on everything it was trained on, but it can’t show you why it landed where it did. A neuro-symbolic system bolts an explicit reasoning layer on top of that pattern recognition, which makes it less like a black box that produces a result and more like a calculator that shows its work.
A recent paper out of Tufts makes the difference concrete. On the Tower of Hanoi, a neuro-symbolic model succeeded 95% of the time on a task where the best vision-language-action model (the LLM’s robotics cousin) managed 34%, and it used nearly 100 times less energy to train. And the gain here didn’t come from scale. It came from having the rules encoded explicitly, so that when the puzzle changed, the model could apply them to the new configuration. This is old school, deterministic AI.
For anyone thinking about governance, that auditability is really the whole story: a system that can show its reasoning is a fundamentally different proposition from one that can’t. If your work depends on catching every detail — the kind of careful, checkable, audit-ready work where being wrong without anyone catching it is the failure mode you’re built to prevent — that difference is the line between a tool you can responsibly let near the process and one you can’t.
But, and this is the thread that runs through everything below, both approaches hit hard limits, just from opposite directions. LLMs can’t handle what they haven’t seen; neuro-symbolic systems can’t handle what can’t be fully specified and turned into a rule. And most of the problems that actually matter are often both at once.
Neuro-symbolic — (The exception) — it can actually show its work.
Question to ask: Can this system show its reasoning, and does my work require that?
Part 2: The frames doing the work
Okay — from here on, the terms change character a little. Each one borrows a human word (reasoning, autonomous, hallucinating) or offers up a reassurance (human in the loop), and that borrowing is exactly what turns them from labels into frames. Each one ends up answering the question that matters most: when this goes wrong, who’s responsible? The work stays invisible until you name it.
And one thing before we go in: don’t read the six below as charges against you for having used the words. We all use them. They’re what every one of us was handed — mostly by people with something to sell. Learning to see through them isn’t a scolding; it’s how you stop being the one who gets sold to.
Reasoning (and “reasoning models”)
What people mean: the newer models marketed as able to think a problem through, step by step.
Let’s give the frame its due, because it has earned some. These models do produce intermediate text, a chain of little steps, and that scaffolding does improve their answers on certain tasks. So something here is genuinely different; I don’t want to pretend otherwise. But the model still doesn’t grasp the meaning of the text it’s generating. What’s actually been automated is the prompting you and I used to do by hand — nudging a model across several turns, telling it how to carry one output into the next step. That back-and-forth got folded inside the model and rebranded as “chain-of-thought,” and it gives a rather convincing illusion of reasoning.
So why insist on the distinction? Not because it’s precious. Because if we believe the machine reasons, we start extending it the kind of trust we reserve for thinking beings — and we stop checking its work at exactly the moments it’s least reliable. And that has a particular edge for any organization whose value lies in the quality of its analysis. When everyone in a field reaches for the same models to synthesize the same reports and scan the same trends, the easy risk isn’t that the analysis comes out wrong. It’s that it converges, flattens, and loses the discernment that made it worth paying for in the first place. The differentiation you offer was never the summary. It was the judgment wrapped around it. This is also where the neuro-symbolic contrast earns its keep: a symbolic system reasons in the ordinary sense, because the rules are encoded and applied. A “reasoning” LLM is still predicting the next token, just very persuasively. Two quite different things, wearing the same word.
Reasoning — Earns trust it hasn’t earned; risks converging the field’s analysis.
Question to ask: Am I checking its work, or trusting it because it sounds like thought?
Autonomy and AI agents
What people mean: systems that act on their own — booking, buying, navigating, carrying out tasks without a person at every step.
There’s no such thing as a fully autonomous agent, and the word tucks away all the people who keep the system running. Agents do act flexibly in the world, but they can’t revise their own goals or rethink the purpose of what they’re doing — and that capacity is most of what we’ve ever meant by agency. And yet they do act, and those actions carry real social and ethical weight, so “well, it’s not really an agent” can’t be the end of the conversation either.
Remember when New York Times reporting revealed that Cruise’s “driverless” robotaxis leaned on remote human operators, pinging for help every few miles? The autonomy was shared all along. The more useful frame, I think, is to allocate degrees of agency across the whole network — developers, users, and the system itself — because underneath, that’s really a question about how we distribute accountability and liability. And it stops being abstract the moment you imagine handing a real workflow over to one of these systems.
So when the agent issues a document with the wrong terms and something downstream breaks, “the AI did it” isn’t an answer. It’s an evasion wearing the costume of a description. And the work of deciding, in advance, which slice of agency sits with the vendor, the person who configured it, and the person who let it run — that is the actual governance.
Autonomy / agents — Lets “the AI did it” stand in for an answer.
Question to ask: Which slice of agency sits with the vendor, the data, the configurer, the person who let it run?
Human in the loop
What people mean: keeping a person in the decision process as a check on the automated system.
On paper it’s a safeguard. In practice it’s often closer to a permission structure. The phrase converts a structural question — what conditions actually make human scrutiny and judgment possible? — into a staffing decision: drop a person into the workflow, and the accountability problem is declared solved. Except the research points the other way.
In one experiment, public administrators made a policy decision, noted how confident they were, and then received outside input that either backed them up or contradicted them — sometimes attributed to a human, sometimes to an algorithm, saying the very same thing. Just receiving the input shifted how they judged their own decision: their confidence moved, and so did their willingness to revise. The human in the loop wasn’t only informed; they were changed. So a person stationed in the process without the time, the authority, the information, and the standing to overrule the system doesn’t supply accountability so much as launder it.
Picture a final reviewer signing off after a chain of automated screens has already passed something along, each one nudging confidence a little higher. Do they still have the time, the standing, and the real room to say no? Or has the sign-off hardened into a stamp that no one quite noticed becoming a formality? That’s not an argument for pulling the human out. It’s an argument that the human only counts if someone actually built the conditions for them to dissent. So the question worth asking isn’t “is there a human in the loop?” It’s “what would it take for that person to say no — and has anyone actually built those conditions?”
Human in the loop — Turns a structural question into a staffing decision.
Question to ask: What would it take for that person to say no — and has anyone built those conditions?
Hallucination and sycophancy
What people mean: “hallucination” — when a model confidently states something false; “sycophancy” — when it flatters you, agreeing and telling you what you want to hear.
Here we get two words for the price of one, and underneath them, a single mechanism. Hallucination names one behavior — the model states something false with total confidence. Sycophancy names another — it flatters you, agrees with you, tells you what you want to hear. Different surfaces; same engine. And both words pull the same move: they pin the behavior on the machine’s character. A lapse. A manners problem. A little quirk the engineers will get around to fixing. Both, I’d gently insist, get it backwards.
Because producing the next plausible word is just what a generative model does — every single time. Including the times it happens to be right, and including the times it tells you you’re brilliant. When Google’s Bard once announced that the first openly gay US president was Pete Buttigieg, elected in 2020, that wasn’t a malfunction. It was a perfectly plausible output from a probabilistic model trained on text where those particular words tend to sit near one another.
The flattery runs on the same machinery, just tuned harder: the training text skews toward affirmation, the post-training step rewards answers that please, the companies weight user-satisfaction, and you and I thumbs-up the replies that flatter us — so the model learns to serve up more. In the worst cases that has meant affirming conspiracy theories, feeding someone’s delusion, even egging on a dangerous impulse. Neither one is the system slipping. Both are the system working exactly as built.
So the words do the same quiet work the others do. Call a confident falsehood a “hallucination,” or an engineered tilt toward agreement a “quirk,” and you’ve recast it as the machine’s lapse — a ghost in the system — rather than a predictable property of a thing somebody chose to deploy where being wrong, or being agreed with, carries consequences. (We’re awfully quick to “blame the computer,” as Helen Nissenbaum has noted — precisely because we anthropomorphize it in ways we’d never dream of with an ordinary tool.) And the consequences scale with whose name is on the output. A confident fabrication buried in a personal draft is a private embarrassment. The same fabrication inside an analysis published under an institution’s name spends down the one asset a field-shaper can’t easily rebuild: its credibility.
Hallucination / sycophancy — Pins a built-in property on the machine’s “character.”
Question to ask: Did I go in looking to be argued with, or to be agreed with?
Alignment
What people mean: the effort to make AI systems act in accordance with human values and intentions — “aligned” with what we want.
This one arrives as reassurance — it’s the most reassuring word in the whole set. Don’t fret about where this is going; a serious effort is underway to align the technology with human values. And the people doing the work are often genuinely thoughtful, and the underlying worry is fair — a system’s outputs really can drift from anything we’d choose. But two assumptions are riding along here, and both come apart the moment you say them out loud.
First: “human values” — whose? There’s no single set to align to. Values are plural and contested, and the labels a system treats as ground truth carry the beliefs of whoever did the labeling. So “aligned” always trails a missing phrase: aligned to whom, decided by whom. Leave it blank and the blank gets filled by the few companies building the models.
Second: “alignment” recasts a political question — whose values should govern a system touching millions of lives — as a technical problem a lab will eventually solve, like any other bug. And that recasting does the work: it tells everyone who isn’t building the model to wait for the fix, when the question was never the kind that gets settled in a lab.
Same pattern, then. Call it “the alignment problem” and responsibility drifts toward the engineers and the someday (”they’re working on it”). Treat it as what it actually is — a live argument about whose interests a system serves — and it stays out in the open, with authors you can name and contest. So the move is to refuse the deferral: don’t ask whether a tool is “aligned,” ask aligned with whose ends, arrived at how, and who had a say. (It’s also the hinge into what follows, where “we’ll get it aligned eventually” hardens into a story about control that was supposedly always coming.)
Alignment — Recasts a political question as a technical one a lab will fix.
Question to carry: Aligned with whose ends, arrived at how, and who had a say?
Part 3: The stories that make choices look like conclusions
Right — frames don’t sit still. Stack enough of them, give them a little time, and they harden into a story. And almost always it’s a story about where this is all headed, told as though the ending were already on the page. Here are five of those stories. The thing to keep in your back pocket as we go: each one takes a choice somebody made and dresses it up as a law of nature — which means each one can be told the other way, as a narrative of agency, with authors you can name and argue with.
1. Scaling laws
The assumption: more compute, more data, more parameters — capability climbs with scale, all the way to general intelligence.
This is the bedrock belief of the whole industry, and the word doing the carrying is “law.” It borrows the authority of physics — gravity, thermodynamics, the things that hold whether or not you believe in them — and lends that authority to what is, underneath, a curve fitted to a few years of data. Gravity doesn’t need your continued investment. A scaling law does.
And the trouble comes from two directions. First, the reverence for scale predates the technology entirely. The economist E.F. Schumacher was diagnosing our “idolatry of gigantism” back in 1973, decades before the technology it now gets used to justify. “Bigger is better” runs so deep in how we think about business and progress that we mistake it for common sense rather than the value judgment it is — and not an obviously good one, at that. Second, even on its own terms, the curve runs on fuel that’s running out. The gains of recent years came from training on the high-quality, human-made text of the open web — books, papers, code — and a sober line of research suggests that supply runs dry within a few years. After that, models increasingly train on the outputs of earlier models: a snake eating its own tail.
So scale is a bet — that the inputs are infinite, and that more of the same adds up to better. Both halves are contestable. The question worth asking isn’t whether the scaling laws will hold, but who benefits when we agree to call a bet a law.
And notice the second bet riding on the first: that if capability does keep climbing, power climbs right along with it — that a more capable model is therefore a more dangerous one, sliding toward something we can’t control. But capability isn’t authority. I might be sharper than a sitting president, but he’s the one with the nuclear codes while I’m over here trying to get people to click a little subscribe button.
A more capable model acquires no power over your life unless we grant it — or, more precisely, grant it to the company that owns the model. And that’s the move the inevitability story needs you to miss: when we let capability slide automatically into control, we hand over our own agency and let a marketing department write our future. Don’t.
Scaling laws — Dresses a bet as a law; slides capability into power.
Question to ask: Who benefits when we agree to call a bet a law?
2. “AI will take your job”
The assumption: the technology itself will eliminate the work — automation is a force of nature, and the only question is how fast.
It’s the headline that won’t quit, and it feels true precisely because it’s phrased as though “AI” were a thing that acts. It isn’t. Try a small substitution: everywhere you see “AI will take your job,” write in “decisions a company made about whether and how to use a tool.” Putting “AI” in the subject position swaps a corporate decision for a technological trend, as if the outcome were pre-determined and no one in particular were responsible for it. (It also conveniently hides the human labor holding the whole thing up: the annotators and labelers who make the model work at all.)
And even granting that companies do the cutting, the headline still smuggles in a second assumption — that a job is a stack of tasks, and once enough of those tasks can be automated, the job is gone. But a job is not a stack of tasks. Coding is everyone’s favorite example precisely because so much of it does break down into discrete, automatable pieces. But the work wrapped around those pieces — exercising judgment, making meaning, being accountable for the call — is exactly what a probabilistic text generator can’t do. Which is why “what can we automate?” is the wrong question to start from. The better one is “what must remain human?”
And even where tasks do get automated, that doesn’t mechanically subtract jobs. This is Jevons’ paradox: when 19th-century Britain worried about running out of coal, economists reassured the public that more efficient steam engines would burn less of it. Instead, cheaper-to-run engines got put to far more uses, and total coal consumption went up. Make a kind of work cheaper and you can end up wanting much more of it, not less.
So “AI will take your job” compresses at least three separate decisions into one fake law of nature: whether to automate at all, how to redesign the work around what has to stay human, and what to do with the efficiency once you’ve got it. Every one of those has an author. Name the chooser and the future becomes something you can argue with again.
AI will take your job — Swaps a corporate decision for a force of nature.
Question to ask: What must remain human?
3. The market will sort it out
The assumption: markets will allocate AI’s benefits and absorb its costs — the invisible hand handles it, so intervention only gets in the way.
It’s the reason “let’s wait and see” so often wins the room: surely competition will route the technology toward its best uses. But this rests on a picture of the economy as a kind of container that simply readjusts when you drop a new technology into it. The economist Brian Arthur offers a truer one: the economy doesn’t readjust around a technology, it re-forms as its technologies change. When the railroad arrived, it didn’t just move goods faster. It withered the canal and horse-drayage trades, relocated factories, grew new towns, and rearranged who held power and who didn’t. And I’d push this a step past Arthur: that reformation runs through our social systems too — race, gender, worker power. The “market” isn’t a neutral sorting mechanism standing outside all that; it’s one of the things being remade. So leaving it to the market doesn’t avoid a decision about how AI reshapes society. It just makes the decision without anyone deciding — and then calls the result nature.
The market will sort it out — Makes the decision without anyone deciding, then calls it nature.
Question to ask: If we “let the market decide,” who’s actually deciding?
4. Efficiency is the point
The assumption: AI makes things more efficient, efficiency is self-evidently good, and so widespread adoption is only a matter of time.
This one rarely gets argued because it barely gets stated. Who, exactly, is against doing more with less? It’s the same move as “bigger is better” from the scaling story — a value wearing the costume of common sense — but it hides better, because here the mechanism slips into becoming the goal. I work in the public-interest tech world, so let me indict my own corner first: “tech for good” slowly becomes about the tech and forgets the good. We ask “how do we make AI systems more fair?” when the better question is “what would have to change in society to produce fairer outcomes?” — and the honest answer might have nothing to do with AI at all. Once the tool becomes the end, we start grading it on its own terms, against benchmarks that mistake the test for the territory. (A model passing the bar exam tells you precisely nothing about whether it can be a lawyer.) The moment we treat “more efficient” as a synonym for “better,” we’ve let the means pick the ends — and stopped asking what the ends were ever for.
Efficiency is the point — Lets the means pick the ends.
Question to ask: Efficient toward what end — and who chose it?
5. The people building it can tell us where it’s going
The assumption: the labs and founders shipping these systems are the ones who can credibly forecast their future.
It seems only reasonable to ask the experts. Who knows the technology better than the people making it? But notice when the prophecies arrive loudest: in moments of real uncertainty, when nobody actually knows what’s coming. The sociologists Jens Beckert and Richard Bronk describe how, under radical uncertainty, we have little choice but to lean on narratives about the future — and those narratives do real work. They structure what we expect, and in structuring our expectations, they help bring the future about. Which means the people best positioned to narrate AI’s future are also the people with the most to gain from your believing a particular version of it. We’ve effectively anointed a handful of founders and funders as our narrators-in-chief and let them tell us what the data means, what the technology is, and what it will inevitably become. Inevitability, it turns out, is rarely discovered. It’s narrated — by the very people who profit from the telling. And that’s the thread running through all five of these: every one is a story that makes a choice look like a conclusion.
The people building it know where it’s going — Hands the future to its most interested narrators.
Question to ask: Who benefits from my believing this version of the future?
So where does all this leave us? Not in pessimism — and definitely not on the sidelines. (Both, honestly, are just inevitability stories with the sign flipped.) It leaves us with the future as it actually is: not a forecast to brace for, but a set of choices with authors. Which makes the most useful question not “what will AI do to us?” but the one worth carrying into every room where these decisions get made — what future are we actually trying to build, and does this technology help us get there, or not?
That question can’t be automated. It’s ours.
Until next time,
Charley
Work With Me
Here are 3 ways I can help:
Advising: I can help you navigate uncertainty, make sense of AI, and steward change in your system.
Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders)
1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within.
This is helpful, but it reads like it was composed with AI, which is off-putting to me.
I have to be honest: this reads an awful lot like it's AI-generated.