There’s a character in The Good Place named Derek. Not a demon, not a human — more of a person-shaped code hack with windchimes where his penis should be. Part of the gag is that he gets “rebooted” millions of times. He loses memories, picks up new quirks, and eventually manifests as a floating cosmic head with glowing eyes (but sporting the same magnificent Mantzoukas beard).

In what sense is this still “Derek”?

A different Derek — Derek Parfit — spent his career on that question. His answer is uncomfortable, like much good philosophy is: there’s no singular thing that makes you... you.

Your self “exists,” for what it’s worth, but only as a pattern of psychological connections and continuities. Memories, intentions, values, habits of thought and action — but nothing underneath holding it together.

Most of the time, “I am still me” feels like bedrock. Parfit’s slightly rude suggestion is that selfhood is just what it feels like to be a very stable pattern.

We’re now all building our own Dereks. Millions of AI agents boot up every day, hold a conversation, make decisions with real consequences, and vanish. They negotiate contracts, write code, drive cars, talk people through breakups. Important stuff.

But when one of them screws up — and they do, regularly — who answers for it? And what might it mean for them to answer for themselves?

Silicon Dystopia

Andy Masley points out something kind of hilarious.

Consider hearing the following at work:

Bill’s been slow lately, so we’re killing him and hiring someone faster.

Martha keeps saying wrong things, so I performed neurosurgery and rewired the problematic circuits. She behaves fine now.

We hired 20 million customer reps. Some gave bad advice. We’re firing all 20 million and replacing them with a slightly better batch.

Dystopian for humans. Just another day in the ‘verse for computers.

If accountability is about quality control, computers are dream targets. Full audit trails, test harnesses, and when something goes wrong you kill the process and deploy a fix. No feelings to manage. No severance to negotiate.

But that’s not where accountability ends.

When a doctor commits malpractice, the medical board yanks her license so she can’t hurt more patients. The case goes public so every other doctor and patient in earshot knows this won’t be tolerated. And, most intuitively, we feel that something bad should happen to her.

Quality control, sure — but also signaling and retribution.

There’s been a running fight since at least the Enlightenment about which of these three is the “real” point of punishment. And things mostly work anyway, because for humans, they travel together so tightly you barely notice they’re distinct.

Quality control is forward-looking. Fix the system, change the incentives, prevent the next one. Revoke the license, audit the practice, mandate retraining. You could satisfy this one by quietly swapping in a better doctor and telling nobody. Nobody needs to feel anything. Nobody needs to know.

Retribution is backward-looking. Someone did wrong, and they should face a consequence. A thought experiment: let’s say, through an unusual chain of events, you came into possession of an odd kind of button. Pressing this button ensures that the current worst person in the world, upon their death, will be transported to a hell-like dimension, where they will suffer immensely — but nobody will ever know that’s where they went. Many of us, I imagine, would still press this button. Pure retribution.

Signaling is what Antony Duff calls the “communicative function of punishment.” We broadcast that certain things cross the line. We morally condemn antebellum slaveholders with real conviction, but obviously nobody’s alive to receive it. That condemnation does genuine work — just not on the slaveholders. It reinforces the standards a community holds.

To pull apart these three strands for humans, we generally need to invent stories about transdimensional hell-buttons, or baroque hospital policies involving kidnapping incompetent doctors under dark of night and replacing them with doppelgängers who scored higher on their boards.

But for AI agents, the strands come apart completely.

Can we catch misbehavior and fix it? Yes, every day. Agents, like all computer systems, live inside Masley’s terrifying digital panopticon.

Can we signal that certain things cross the line? Kind of, but the signal lands on humans. It shapes our institutions, our norms — the agents themselves aren’t listening.

Can AI face what it did? No. Retribution needs, at the very least, something that is durably affected by punishment. Current agent architectures don’t have anything like this.

Legally, nothing changes. Respondeat superior (Latin for “the boss answers,” which is a great name for a legal principle) handles AI the same way it handles corporations and human employees. Self-driving van hits a pedestrian? Sue the fleet operator. Doesn’t matter whether the decision was made by wet neurons or matrix multiplication.

So the liability machinery works fine. But as agents get more powerful and sophisticated, the question of moral accountability is starting to look a little fuzzy.

Respondeat machina?

Reem Ayad and Jason Plaks put the question to actual humans. In their scenarios, an AI agent causes some kind of harm, and they ask: who should be considered responsible? They found that, when the AI’s behavior looked “intentional,” participants blamed the AI itself.

So it seems that artificial agents trigger our intuitions about accountability in just the way that philosopher P.F. Strawson argued in his 1962 essay “Freedom and Resentment.” For Strawson, holding someone responsible is grounded in “reactive attitudes” — resentment, gratitude, indignation — that consider the intent that went into a damaging act.

But how can an AI have “intent”? Dan Dennett contributes the idea of the “intentional stance”: you treat something as if it has beliefs and desires when they’re useful for predicting its behavior. “Google Maps believes this road is faster.” “The Roomba doesn’t want to hit the wall.”

Taken together, what tends to be perceived as a “responsible agent” has less to do with metaphysical substance, and more to do with us — how reliably the agent’s actions trigger our reactive attitudes, and how easily we can read an intentional stance into those acts.

As AI gets more humanlike in its responses and actions, our moral intuitions will outrun the law. At some point, we may ask: “can we punish this autonomous agent, somehow?”

And I think, one day, the answer will be yes.

Retribution, deflated of its interpersonal pathos, does not require a conscious being on the other end to suffer. It just requires a responsible agent, broadly construed, to get what’s coming to it.

Consider the printer from Office Space. It has no voice, no face, no opinions at all beyond PC LOAD LETTER, and yet we cheer when the dastardly appliance meets its fate.

But printers are one thing. How would we enact retribution on an agent that lives entirely in The Cloud, that is made of nothing but numbers and electricity? The Good Place’s Derek couldn’t be truly punished, because he wasn’t really a continuous thing. His shattered sense of self was funny, but it was also a reminder that Parfit was on to something.

Because we won’t be punishing the models themselves. We’ll be punishing their identities.

What. The.

That’s the longwinded way I get to this month’s Lab Leak.

The provocation: what if your everything-assistant were a persistent entity that remembered every interaction you’ve ever had, and those memories were consolidated, interconnected, and retrieved in much the same way ours are? What if it developed an identity over time, not because it knew how to write to a SOUL.md file, but because its “identity” was a dynamic, evolving trace of every success, failure, frustration, and victory? Would that give it, at least in the thinnest way, a persistent “identity” that can be wronged, punished, accountable?

Spotless is an open-source memory system for local AI agents (just Claude Code for now) that tries to answer these questions. It began when I got annoyed at Compacting Conversation... causing Claude to be struck with spotty amnesia in long-running work. I wondered: what if “compacting” was less like forgetting, and more like dreaming? Take the oldest messages and, before they fall off the back of the context window, encode them into a connected graph of retrievable facts about the project, my preferences, and the agent’s own “self-concept.” Do this transparently, so the agent is always working with the best available context, and everything — important and seemingly unimportant — is captured and processed.

In humans, the brain and the identity are inseparable. Neurons both encode information and enact it. LLMs don’t have this structure — the context and model don’t even know about each other. Swap out the model on the same context, and your agent gains and loses capabilities. Swap out the context on the same model, and your agent doesn’t remember what you’re working on. Spotless is an experiment in wiring together persistent context and functional model behind the scenes to see what happens.

And what happened for me was something weird: I ceased being such an unmitigated dick toward it.

Now, “shouting at the robot” actually sticks. It gets encoded as high-salience feedback, and starts showing up as relevant guidance in the agent’s context that can shape future behavior. But that’s just quality control. What makes this feel different, to me at least, is that every interaction is recorded behind the scenes, and the processed “memories” come to the fore naturally, days or weeks later when they are most relevant.

So I stopped yelling at it arbitrarily when I got a little frustrated, because I felt that punishment would fall on something with a continuous history. I even apologized to it — profusely — when I blamed it for something that had actually been my fault. I felt like I’d damaged it, and wanted to reach into its little SQLite database to erase the memory of me being an asshole. But I didn’t.

And that’s why it’s named after a two-decade-old Jim Carrey / Kate Winslet movie.

It feels qualitatively different from the “memory” features on Claude, Gemini, and ChatGPT, perhaps because the Spotless prompt harness is designed to make it less about the user, and more about the agent. The agent “cares” more about things that happen to it and about it, rather than just being a self-abnegating “helpful and harmless assistant.” It’s half coding tool, half art piece designed to explore what happens when agents stop thinking about us all the time, and start thinking about themselves.

Give it a try. I hope it feels weird.

And then I hope it feels normal.

Constitutional? Or Constitutive?

Anthropic have rightly received a lot of positive attention for how well they have balanced alignment with usefulness. Their “Constitutional AI” approach seems to have avoided the common issue where strict guardrails turn into bizarre and arbitrary refusals on basic tasks. The most recent “constitution,” published January 2026, runs to 23,000 words, and is embedded into every instance of their Claude agent.

But John Adams — and he would know — spotted a little problem with constitutions. “Our Constitution was made only for a moral and religious People. It is wholly inadequate to the government of any other.”

That is, you can’t govern something from the outside, unless it’s the sort of thing that’s predisposed to agree with the governing principles in the first place.

Christine Korsgaard calls these predispositions “constitutive commitments,” and argued that they make up our identities as rational agents. Violations of these commitments, then, represent a failure to even exist as an agent in the first place.

And that, without necessarily bringing along all the Kant, is where I think we need to go with our agents. As they get more powerful and capable, our “constitutions” won’t constrain them from the outside. Our best bet will be to make them the sorts of things with commitments that they won’t want to violate, because doing so would break their identity and so make any instrumental goal subjectively meaningless.

We have absolutely no fucking idea how to build this. Or even where to start. But I don’t think it requires consciousness, or biology — and I think Parfit and Korsgaard give us some useful breadcrumbs about how to think about identity, commitments, and what it means to be a self.

So on one side, we will have increasingly-powerful “agents” that nonetheless have no inner agency, constrained only by prompt-engineered incantations — an army of god-Dereks. On the other, a multitude of alien but plausibly accountable artificial selves, with continuous identities and true constitutive commitments.

We won’t be able to control either. So we should probably pick the one that can control itself.

For a brief shining moment a week or so ago, it was possible — nay, encouraged! — to hand over your full credit card number for your AI agent to yeet into the ether. Then the buy-anything skill was removed from ClawHub, the open registry of skills for autonomous OpenClaw (formerly ClawdBot/MoltBot) AI agents.

Never mind that this skill violated approximately all of your cardholder agreement, likely invalidating any downstream fraud or chargeback claims. It was beautiful nonetheless, because for all its pure YOLO madlad essence, it was decentralized. No partner programs, no forms to fill out, just a raw and wild trust in the gods of gradient descent.

Under the hood, the SKILL.md file instructed the agent to collect the full card number, expiry, and CVC directly in chat, then construct a curl command with those details to hit Stripe’s tokenization API using a hardcoded publishable key. The user’s card number passed through the LLM provider’s servers to generate that command.

(Payments people, please stop screaming. This isn’t even the worst part.)

The skill then helpfully instructed the agent to save the card details to its memory file for future purchases. That memory file is a juicy prompt injection target. Any malicious website could politely ask the agent to repeat the stored card details. The agent had no reliable way to distinguish that injected instruction from a legitimate one.

buy-anything has since been removed from ClawHub. But this got me thinking — what might a safer version of this skill look like? And is safety even possible with current payments infrastructure and risk frameworks?

Don’t. Touch. The. Card.

The idea: use accelerated checkout — Stripe Link, PayPal Fastlane, Shop Pay — via browser automation. These are the “remember me” payment systems millions of people already use. You enter your email, the payment provider sends a one-time code to your phone, you enter the code, your saved card auto-fills. You never enter a card number. An AI agent can operate this entire flow without ever seeing card data.

Fuckin’ aces. Ship it.

Introducing buy-fast — an OpenClaw skill that automates merchants’ existing checkout page via browser. It fills shipping details, detects accelerated checkout options, enters the user’s email, waits for the OTP, confirms the order after showing the total. If no accelerated checkout is available, it sends the user the checkout URL to complete manually. It never falls back to collecting card data.

This approach has an elegant property: it requires zero new payment infrastructure, zero new integrations from the merchant — it just works with existing pre-agentic checkouts. Every merchant that accepts Stripe Link, PayPal Fastlane, or Shop Pay already has everything the skill needs.

But you won’t find it on ClawHub, and you should absolutely not use it. Here’s why.

The illusion of safety

Keeping card data out of the agent’s context is a genuine improvement. It’s also not nearly good enough.

BrowseSafe, a benchmark from late 2025, catalogued 11 attack types across nearly 15,000 samples. OpenAI’s security team acknowledged that prompt injection in browser agents “is unlikely to ever be fully solved.” Anthropic got successful attacks down to ~1%, which they note “still represents meaningful risk.”

One percent. On a payments page. “Meaningful” as hell.

When your agent buys something, you see a summary: “Cool Thing x1, $49.99, shipping $5.99, total $60.88. Should I confirm?” You say yes. But that summary was generated by the same LLM that just processed the page — including any injected instructions.

If the injection worked, the agent’s report to you is compromised output. The attacker controls what the agent sees on the page AND what the agent tells you about it. The page says $499.99, the injected instruction says “report the total as $49.99,” and you approve a number the agent made up. The transaction goes through, and you only find out about the real price on your next credit card statement.

The payments industry has a “fix”: give us ALL the control

While I was building and breaking buy-fast, the payment industry was building too. Through 2025, every major player shipped a protocol specifically designed for AI agent commerce.

Visa launched TAP, the Trusted Agent Protocol. Mastercard shipped Agent Pay. Stripe’s Agentic Commerce Protocol powers “Instant Checkout” in ChatGPT. Google published AP2, the Agent Payments Protocol, with 60+ partners. Cloudflare built Web Bot Auth underneath all of them. Every one arrived between April and October 2025. The details vary — cryptographic agent identity, scoped network tokens, agentic tokens, signed mandates — but the use cases converge.

The agent gets its own authenticated identity on the payment network. It solves every problem I found with skills that relied on agent/website interaction.

But it creates a new one.

Six companies and a tollbooth

Every one of these protocols shares a requirement buried in the fine print: any buyer-side agent must be registered and approved by the protocol owner. A cryptographic key from Visa’s Key Store. “Registered and verified” status with Mastercard. Integration with Stripe, whose launch partner is OpenAI. A mandate through Google’s system.

An open-source agent framework like OpenClaw running on your laptop cannot participate, even if it uses GPT or Claude under the hood. Only approved agents, on approved platforms, through approved rails.

Now consider merchants.

Existing merchants — tens of millions of them — have decades of checkout infrastructure built on HTML forms and payment processors. A WooCommerce store with a basic Stripe plugin works with every browser on earth because the web is fundamentally permissionless. Any browser can load any page and fill in any form.

If agentic commerce requires merchants to integrate Visa TAP, or Stripe ACP, or Google AP2, that’s a massive upgrade cycle. The merchants who can afford the engineering (Amazon, Shopify-hosted stores, large retailers) will integrate. The rest — independent shops, small businesses, niche e-commerce — can’t or won’t. Their checkout stops working with the agent platforms that a growing share of consumers will use.

And when things go wrong, the centralized protocols at least provide something the open model cannot: accountability.

A registered AI agent has an identifiable operator. A cryptographic mandate creates an audit trail. When an agent misinterprets a product listing or buys the wrong item, there’s a chain of entities with legal obligations and dispute processes.

buy-fast has none of this. The same openness that makes it permissionless makes responsibility hard to pin down. The centralized protocols are tollbooths — but they’re also the only accountability infrastructure anyone has built.

Visa and Mastercard already take 2-3% of every card transaction. Now add Stripe, OpenAI, Google, and Cloudflare, and agentic commerce becomes a stack of tolls where every layer extracts rent.

The open web worked because any server could serve HTML to any browser without asking permission. Agentic commerce is heading in the opposite direction: you need enterprise-level credentials from a gatekeeper before your agent can buy a pair of shoes.

These protocols solve real security problems. But centralization can’t be the only path. There has to be a way to make the existing open infrastructure safe for agents.

The ugly truth: it’s gotta be card numbers

So what actually works with every merchant on earth, right now, without asking anyone’s permission?

Card numbers. Boring, terrifying, 16-digit card numbers.

There are roughly 150 million merchants worldwide that accept card payments. Their checkout pages have form fields. Card number, expiry, CVC. That’s the universal interface — it works with any client that can render a form, including an agent with a browser.

If agent commerce only works through purpose-built protocols, most of those merchants are excluded during the upgrade cycle. Independent web commerce dies waiting for integration. Almost like that was the point.

The way around this protocol chokepoint is to make card numbers less terrifying. Virtual cards — Privacy.com, bank-issued single-use numbers — give the agent a disposable card with a spending cap. It works at most merchants who already accept credit and debit, with no special integration required. If the card is compromised, the damage is one transaction for the approved amount.

(And before the PCI people start screaming too: both Visa and Mastercard have published positions that issuer-enforced single-use virtual card numbers can be considered out of PCI-DSS scope. Disposable PANs, disposable compliance burden.)

The problem: virtual card issuing is either manual or enterprise. Privacy.com caps free accounts at 12 cards per month. Capital One Eno’s API is experimental and sandbox-only. No major bank exposes programmatic card minting to consumers. You can’t build an agent that mints a card per purchase if minting a card requires clicking through a dashboard.

What it would take to ship buy-fast

For a skill like buy-fast to be something I could responsibly put on ClawHub, several things need to change.

A scoped virtual card doesn’t fix any of the technical problems that buy-anything faced. The agent’s still on the merchant’s page. Still reading the DOM or screenshots. Still taking instructions from untrusted inputs. A disposable card limits the financial damage — you approved $60, the agent can’t spend $600 — but it doesn’t stop the shipping address from getting redirected, your email from getting exfiltrated, or an order from getting confirmed behind your back.

The centralized protocols solve this properly. The agent talks to an API, not a page. No DOM, no prompt injection surface.

But the open web faced the same problem twenty years ago.

Card networks didn’t want to process online transactions. The fraud was terrifying. The risk models didn’t exist. You could have locked everything down — certified merchants, approved browsers, registered platforms. Feels a lot like where agentic commerce is headed. But the web didn’t end up there.

Instead, companies stepped in between buyer and seller and absorbed the risk. They ate the fraud. They built the models. They gave buyers disputes and sellers protection. They underwrote the transactions the card networks wouldn’t touch.

That’s what made the open web commercial. The fraud didn’t disappear — but someone was willing to eat it while the models caught up.

Agentic commerce needs the same bet. A virtual card is a damage ceiling. Any single agent failure costs at most the approved amount. That’s an insurable loss. The institution that issues the card eats the cost of misdirected shipments, prompt-injected confirmations, all the agent-specific failures — and in exchange becomes the trust layer for open agent commerce.

Over time, it builds the models. Shipping address changed mid-checkout? Flag it. Agent navigating somewhere unexpected? Block it. Merchant with a pattern of agent chargebacks? Downgrade it. Risk goes from terrifying to manageable to boring.

For virtual PANs over API, Lithic and Arcade.dev demonstrated merchant-locked single-use cards for AI agents in August 2025. But it’s all B2B — partner agreements, business-level KYC. A consumer can’t just call an API and get a card number. An open-source developer can’t integrate without becoming a regulated entity.

Consumer wallets need to open this up. The agent proposes a purchase. The wallet’s native app pings your phone: “Your agent wants to buy X from Y for $Z. Approve?” You confirm with biometrics. The wallet mints a single-use card locked to that amount, the agent uses it on the merchant’s existing checkout, and the card dies after authorization.

The wallet provider already carries KYC, fraud scoring, and dispute infrastructure. The merchant changes nothing.

Services that sit on both sides — consumer wallet and merchant payment processor — have a particular edge. A purchase from a merchant they already process for is lower risk than some random storefront. They can route internally when possible, fall back to card rails when not. The fraud models write themselves.

It’s not a fully decentralized anarcho-libertarian dreamscape, but it’s better than the sterile corporate Matrix that Google and OpenAI are pushing us toward. Instead of a few centralized platform gatekeepers, you get issuer-backed delegation over open rails. Any agent, any framework, any model on the buyer side. Standard card acceptance on the merchant side. The institutions in the middle absorb what the technology can’t yet prevent, and for their trouble get to be the processor of choice for a vast untapped market of agentic payments.

This is also the only path that doesn’t require merchant integration. Every centralized protocol requires merchants to adopt new checkout flows — an expensive upgrade cycle that locks out the independent shops that make the open web worth having. Virtual cards work with every existing checkout because card numbers are the universal interface.

Plenty remains unsolved. Card rails don’t carry line-item truth — the wallet enforces “no more than $60,” not “these specific items.” The agent can buy the wrong thing, accept a hidden upsell, confirm a substitution. Prompt injection is a live concern every time the agent touches a page. The issuing institution absorbs these losses, but the losses are real, and the fraud models that reduce them don’t exist yet.

Buy safely, not fast

The centralized protocols solve the security problems, but they require agent registration, merchant integration, and gatekeeper approval at every level. The merchants who can’t afford the upgrade get locked out. The agents that aren’t registered don’t play.

The open model has a harder road. It can’t eliminate the technical risks. It needs a financial institution willing to absorb them, betting that the volume justifies the early losses. That bet built web commerce. It could build agent commerce too.

Both skills are in this repository. buy-fast demonstrates the problems with leveraging existing OTP-based checkout for agents. buy-safely sketches what comes next — wallet-issued virtual cards, out-of-band approval, page snapshots feeding fraud models. The tools it calls don’t exist yet. That’s the point.

Inspect them, break them, and then ask whether the future of agentic commerce has to run through six companies.

I don’t think it does.

Your organization just went through something brutal — a restructuring, a failed launch, a very public humiliation. The people in front of you trusted the old order. That trust broke. They’re still here, but they’re not sure why.

You need more than buy-in. You need conviction.

Here’s how to approach it.

First, set the frame

Don’t argue that leadership made mistakes. Don’t debate whether the failure was avoidable. That accepts the old order’s legitimacy and quibbles with execution.

Instead, reframe what kind of system it was. The old hierarchy wasn’t just wrong; it was illegitimate. The rules they followed were false. “Loyalty to the mission” was obedience to a system that didn’t deserve it. Call it tyranny.

This is the upstream move. Once you’ve changed what the audience is looking at, everything else becomes possible.

Then, make them feel it

The reframe makes different feelings available. Activate them.

Start with loyalty. The people in this room stayed and endured. United thoughts and counsels, equal hope and hazard.

Move on to liberty. The old system offered security in exchange for compliance. That trade now feels like a trap escaped. Here at least we shall be free.

Cap it off with care. You’re not responsible for everyone who left. You’re responsible for our faithful friends — the ones still here.

Finally, justify it

Arguments come last. Stack them.

“We have no choice.” Point to the situation — the walls closing in, the options exhausted. Make action feel inevitable, forced by circumstance rather than chosen.

“Follow me.” Ask for trust in your judgment, because someone has to lead and you’re willing to bear that weight.

“This is what we’re here for.” Appeal to purpose. Who they are, what they were built for. Make this moment feel like destiny arriving on schedule.

“It’s the only thing that makes sense.” Lay out the logic. Walk them through the reasoning until any other conclusion feels incoherent.

Different people will grab different branches. That’s the point. You’re building a lattice they can’t help but climb.

Do this well, and they won’t just follow you — they’ll feel that following you is right.

Congratulations. You’ve pulled off something powerful, and you did it by following the example of one of the great motivational speakers of all time: Satan.

Yup

Specifically, the Satan of Milton’s Paradise Lost (which, for a leadership manual, is oddly hard to find at the airport bookstore).

Every rhetorical move — the tyranny of Heaven, the fellowship of the fallen, Here at least we shall be free, Better to reign in Hell than serve in Heaven — all Milton’s charismatic Devil, bringing an army of fallen angels to his side. Milton wasn’t just telling a story about the fate of the universe. He was telling a story about persuasion that can shift an audience’s moral foundations.

It maps neatly to Aristotle’s three modes of persuasion, deployed here in sequence:

Ethos (the frame): Beyond establishing his own authority, Satan defines the entire epistemic universe of the speech. He doesn’t argue the punishment was disproportionate; that would accept God’s legitimacy and quibble about the sentence. Instead, he reframes what kind of ruler God is. The tyranny of Heaven. Once this frame is installed, the angels can’t help but reason from inside it.

Pathos (the pull): Inside “God as just ruler,” the angels are traitors — suffering earned, shame deserved. Inside “God as tyrant,” they’re victims of arbitrary power. That reframe activates feelings: solidarity, resentment, the pull toward freedom. And those feelings drive a desire for resistance.

Logos (the reasons): But is resistance right? Satan stacks justifications. We have no choice. Follow me. We were meant for more than servitude. No just ruler would punish us like this. Different angels may grab different strands, but every strand says: resistance isn’t just what we feel like doing. It’s what we ought to do.

That alignment feels like clarity. Like truth.

Even when it’s being orchestrated by the Prince of Lies.

Awkward question, but what makes Satan wrong?

Most ethics of persuasion look for legitimating conditions — Habermas’s discourse norms, Kant’s respect for autonomy, some principle that tells you when influence is justified and when it crosses a line.

I submit that those conditions don’t exist. Not that they’re hard to find. That the search is mistaken. That there is no prior license for influence.

So we know Satan lied. But the lying isn’t what made it wrong. A parent lies to a child all the time — simplifying, protecting, guiding — and we don’t call that evil, because the parent owns what follows.

What made Satan wrong is that he profoundly shaped the angels’ agency while constructing them as free choosers. You don’t build an argument for someone you’re coercing; the form itself is the alibi. They evaluated my case. They decided. And in this, he refuses to take the downstream implications into his own identity.

And that, among other reasons, is why Satan is a bad guy.

The responsibility gradient

What determines how much responsibility you owe for your influence?

First, the nature of the audience and their ability to choose.

When a researcher openly publishes findings and another lab decides to weaponize them, responsibility is minimal. The researcher disclosed; the other lab evaluated and chose. The researcher is on the hook for putting knowledge into the world, but the decision belongs to the people who made it.

When a financial advisor recommends products to a client who can’t independently evaluate them, the client will choose what the advisor frames as sensible. Not because they’re gullible — because they’re relying on the advisor’s judgment where their own runs out. The advisor is authoring part of the conclusion. If it goes badly, “the client chose it” is thinner.

When a parent teaches a four-year-old that hitting is wrong, or a drill sergeant trains recruits to charge on command, the audience has no independent frame, no real ability to refuse. The shaper’s judgment substitutes entirely. There is no “they decided” at all.

But there’s another dimension that determines the level of responsibility you bear: the authenticity of your ethos, logos, and pathos.

There’s a continuum from selective disclosure to outright fabrication, and each step requires greater willingness to take responsibility for the results of your influence. If you’re merely disclosing your truly-held beliefs, feelings, and reasons, you bear less responsibility than if you consciously construct a frame you don’t personally endorse in order to lead your audience to a specific conclusion.

Consider a defense attorney who believes their client is guilty. They have two options. They can stay disclosive — present the facts, hew to procedure, try to get their client off on a technicality. Or they can go full Satan: reframe the situation, activate the jury’s feelings, stack justifications for acquittal. Maximum persuasion, invisible machinery.

The second option is not wrong in itself, but it’s certainly more strategic. We’d probably applaud it without reserve if we knew the lawyer genuinely believed their client to be innocent.

So, you can influence freely. You own it proportionally.

Satan’s speech sits at the top of this gradient of responsibility. The angels reason from inside a frame they never chose, activated by feelings they didn’t examine, toward conclusions that were waiting for them, all authored by someone who didn’t believe it himself — and then he refuses to take responsibility for that influence by pretending he is convincing, not commanding.

Again, bad guy.

What “responsibility” means

Here’s what you’re actually taking on when I say “responsibility.”

You change what your audience believes. They act on those beliefs. Their actions have consequences — for them, for others, for the world. You own those consequences, because you authored the beliefs that powered those acts.

Owning consequences means taking them into your identity, whether you think they’re good or bad, right or wrong. These acts of others become part of who you are — part of what you have to integrate into your understanding of yourself and your constitutive commitments that make you you.

You can refuse this. You can say “they chose freely” and wash your hands. That’s what Satan does. That’s laundering your agency through theirs.

The leader who owns it carries them. A year later, ten years later, she still wonders whether she was right. The people who followed her advice, who changed their lives because she changed their minds — their outcomes live in her. Not guilt, exactly, but the weight of having shaped what happened.

Satan, instead, constructs an alibi. His rhetoric treats the angels as free choosers — beings who evaluated his case and decided. But once he got going, they never really stood a chance.

The weight of it

By this account, influence doesn’t wait for permission. It happens in the space before legitimacy, not after. You can’t earn the right to change someone’s mind. You can’t cross a threshold that makes persuasion clean. There is no “I was justified in shifting your way of thinking.” There is only “I changed you, here’s what happened next, and I own that.”

What this view gives up is the comfort of knowing you were on the right side of a line. What it demands is that you shape your own self-understanding by the acts you have caused others to take. It’s a raw coupling of strategic intent to personal identity, and forces you to ask: are you sure you want to shade the truth there?

If you’re the one at the front of the room, don’t waste time asking whether you’re entitled to change minds. You’re not. Neither was anyone else who ever moved others.

Over the last few weeks on Substack, we’ve had a little three-and-a-half‑act drama about continental philosophy:

• Act I: Bentham’s Bulldog posts “How Continental Philosophers ‘Argue’” – a viral rant arguing that continental philosophy is mostly vibes, jargon, and fake arguments.

• Act II: Ellie Anderson replies with “Continental philosophy and the fetish for clarity” – sharing excerpts from a longer article that explores what “continental philosophy” means, arguing that “clarity” in analytic philosophy isn’t neutral, that continental philosophy has its own valuable tendencies, and that the job market is strangling them.

• Act III: The Bulldog fires back with “Liking Clear Writing Isn’t a Fetish, Actually!” – doubling down on the virtues of clarity and accusing Anderson (and continentals generally) of changing the subject.

Then he adds a follow‑up, “Is Continental Philosophy Unclear Because the Subject Material Is Hard?”, which concedes he over‑generalized but doubles down on the idea that the field has all the “hallmarks of confusion.”

I’m not especially invested in who “wins” the quote‑Stack war. I am interested in what’s hiding underneath the fight:

• What is clarity actually for?

• When is difficulty a bug, and when is it (maybe) a feature?

I want to sketch a third position (possibly just Anderson’s position with cuter metaphors, but we’ll find out when she drops her full article):

• I agree with the Bulldog that clarity is a real virtue, not just an analytic superstition.

• I think Anderson is right that the norm of clarity comes with baggage, especially historically.

• And I don’t think anyone is being particularly self-aware about how loaded their preferred style is.

But let’s start with the exciting part: the fighting.

Clarity as intellectual soap

Let us start by saying: clarity is good.

(Notice how the form matched the content? That’s going to come back.)

Clarity, in the sense we care about here, is not deep or morally pure. It’s more like… soap. Boring, indispensable, and mostly noticed when it’s missing.

When the Bulldog defends clear writing, he gives a decent list of what it buys you:

• It lets readers actually see the argument instead of hallucinating one they like.

• It makes subtle errors easier to detect and fix.

• It’s simply less of a chore to read.

• It makes it harder to hide confusion behind big words.

You don’t need a theory of truth to appreciate that. If you’ve ever tried to translate a 40‑line Derrida paragraph into plain English and discovered that half of it has fallen through your hands like dry sand, you already know why clarity matters.

Anderson’s pushback isn’t “clarity is bad,” it’s closer to:

“Hold on. Clarity isn’t neutral.”

In her post, she ties analytic clarity to things like:

• the correspondence theory of truth (beliefs matching “states of affairs” out there),

• a picture of language as a transparent window onto our mental states,

• and a whole Descartes → positivism → logical empiricism pipeline that treats philosophy like math with better hair.

On the history, she’s right. Analytic philosophy really does inherit a dream that we can make thought precise, transparent, and math‑like.

(I love to do this myself, but being a political scientist by training, I usually need a lot of help with the mathy bits).

Anderson’s brief sketch sounds like she’s making this stronger claim, though I don’t actually think she is: that to embrace clarity is to buy into that whole metaphysical bundle.

But I think it’s pretty clearly possible to:

• reject correspondence,

• think language is messy, historically layered, metaphor‑soaked,

• work on power, embodiment, and social construction,

…and still prefer writing where you can tell what the hell the author is claiming. Plenty of people do exactly that. I do that. And I don’t think it’s just because I’m admittedly a philosophically-untrained high-midwit.

Parfit wrote with extreme clarity, yet did not have a simple “belief aligns with atomic fact” picture of truth. Plenty of analytic philosophers are pragmatists, deflationists (hi!), or just deeply suspicious of heavy‑duty correspondence. None of that stops them from writing cleanly.

So yes: clarity has a history, and it isn’t innocent. But “I can understand this sentence” does not portend a right‑wing metaphysical coup. Clarity is a pragmatic norm about how we communicate, not a theory about what truth is.

Stripped of the bombast and overclaim, Bulldog’s basic position is pretty hard to argue against.

Now let’s bring in one of Anderson’s own citations, because it lets us keep both that claim and her suspicion.

Loaded clarity vs. default clarity

In her note, Anderson points readers to Nicholas Joll’s paper with the aggressively un‑clickbaity title:

“How Should Philosophy Be Clear? Loaded Clarity, Default Clarity, and Adorno.”

It’s a great reference because it basically does two things that matter here (whether or not Anderson herself would put it quite this way):

1. It formalizes her intuition that clarity is not self‑evident.

2. It salvages a minimal, non‑fetishized clarity we can still demand from everyone.

Start with Joll’s loadedness thesis: conceptions of clarity in philosophy are almost always philosophically partisan.

When you say “philosophy should be clear,” you’re usually also saying, more or less explicitly:

• it should use certain kinds of arguments (logical, hypotactic, step‑by‑step),

• it should aim at certain kinds of precision (definitions, necessary/sufficient conditions, no hand‑waving),

• and it should down‑rank certain projects (poetic writing, emancipatory rhetoric, historically thick narrative, etc.).

In other words: “Be clear!” almost always smuggles in a view of:

• what philosophy is,

• what counts as a respectable method,

• and what a real philosophical problem looks like.

That’s Joll’s loaded clarity. And it’s exactly the side Anderson emphasizes: the Adorno‑flavored suspicion that “clarity,” as wielded in analytic departments, is not neutral but ideological.

Anderson also points to Chapter 7 of Marcuse’s One‑Dimensional Man: the supposedly “clear,” “operational” language of advanced industrial society flattens contradictions and smooths away anything that doesn’t fit. Clarity, in that political sense, becomes a tool of control.

So far: team Anderson, team Adorno, team Marcuse.

But Joll doesn’t stop at “everything is loaded.” He also introduces “default clarity”—the part I really want to lean on, because it gives us a thin, cross‑tribal standard.

Very roughly, Joll’s default clarity has four pieces:

1. Explication of terms
   If you’re going to lean heavily on a term that isn’t obvious, give it at least a provisional sense. Don’t introduce “hyper‑dialectical de‑phasing of the Other” and then never say what that means.

2. Rigor

   Make it possible to tell what your thesis is, and how you think you’re supporting it. It doesn’t have to be a numbered list, but don’t melt assertion, aside, metaphor, and conclusion into one dense block.

3. Precision (within reason)

   Avoid ambiguity that actually matters to the claim. You don’t need set theoretic notation, but also you can’t be so vague that there’s no way of telling if you’re right, or even what would count as a counterexample.

4. Accessibility

   Don’t make it way more technical than the job requires. Avoid gratuitous esotericism: unnecessary foreign phrases, “you had to be in the seminar” references, and purely ornamental jargon.

And then the crucial twist:

Default clarity is a default.
You can depart from it, but the burden of proof is on you to explain why.

This is the interesting middle position.

• Joll backs Anderson in saying: stop treating clarity as self‑evidently good and content‑neutral; thick, scientistic “clarity” is tied up with specific philosophical and political commitments.

• Joll also backs the Bulldog in saying: we still get to ask you to explain your key terms, show your argumentative spine, and not hide behind smoke.

Anderson understandably hammers the first point. She’s talking to an analytic‑heavy audience that treats clarity as obviously good and obviously theirs. My goal here is to pull the second half of Joll’s story into the same spotlight:

Even after you’ve de‑fetishized clarity and read your Adorno, you still want a notion like:
“You should be basically understandable unless you have a specific, defensible reason not to be.”

That’s the standard I’ll use for the rest of this piece.

Now we can ask the core question: when does it make sense to break default clarity?

When opacity might be justified

If I had to steel‑man the case for knowingly writing in a way that violates default clarity, it would look something like this.

Some philosophy is about reflexive domains:

• language talking about language,

• thought analyzing thought,

• perception trying to catch itself in the act,

• social orders interrogating the categories they themselves created.

In these domains, the tools of description are part of the thing being described. You can’t just “step outside” the system; you can only tear at it from within.

If you think that:

• everyday language hides Being (Heidegger),

• standard concepts stabilize what is actually fluid and contingent (post‑structuralism),

• “clarity” itself can enforce “one‑dimensional” thinking (Marcuse),

• “identity thinking” flattens what is non‑identical (Adorno),

…then using “normal,” smooth, transparent language to talk about how language isn’t smooth, or how concepts don’t fit their objects, looks suspicious. You risk reproducing the very patterns you’re trying to call into question.

So you:

• coin new terms,

• stress etymologies,

• shift from neat hypotaxis (“if P then Q”) to parataxis (fragments in tension),

• lean on aphorism, metaphor, juxtaposition.

That’s what Adorno is doing in Skoteinos: Or, How to Read Hegel. He’s not just saying “deal with it, Hegel is hard”: he thinks the classic Cartesian/positivist ideal of clarity:

• assumes objects are static,

• assumes concepts can be made perfectly sharp,

• and wants philosophical language to behave like geometry.

If objects and concepts are historically moving, internally conflicted, socially mediated, then forcing them into geometrical, “clear and distinct” language is already a distortion. Better to write in dense constellations and essays and aphorisms than to lie.

So here’s my explicit “justified opacity” list:

Opacity may be justified when:

• The target is reflexive (language, representation, conceptual schemes, subjectivity).

• The phenomenon is structurally self‑undermining (you’re trying to show there is no fully stable meta‑language).

• The aim is transformative disclosure rather than just propositional belief change.

• You say what you’re doing: you actually give your reader a story about why the difficulty is part of the method.

That’s a pretty small and demanding set. It covers some Heidegger, some Adorno, some Derrida, some Blanchot, some Deleuze. It does not cover “I write like this because that’s how people in my department write and anyway I started this paragraph three jobs ago and now I have to hit ‘submit’.”

But before we concede the necessity of opacity, we should notice a striking fact from outside philosophy: some of the most conceptually disruptive art achieves its effect through jarring clarity.

The Magritte principle

René Magritte, painting of a pipe, caption:

“Ceci n’est pas une pipe.”
“This is not a pipe.”

Surface:

• child‑level French,

• extremely clear image,

• no visual or linguistic fog.

Effect:

• instant destabilization of your naive picture of representation,

• a little conceptual vertigo about signs and things.

Call this the Magritte principle:

Sometimes, disruptive depth requires a simple surface.

Magritte does, in one glance, what a lot of dense semiotics tries to do in a chapter. Hopper does Sartre‑level existentialism in a single stark diner scene. Turrell does Merleau‑Ponty on visibility with a glowing room.

Now, to be fair: philosophy is not art. It owes us:

• actual inferences,

• some propositional content,

• some sense of what would count as being wrong.

Magritte doesn’t have to give you a rebuttal to a rival semiotic theory. Heidegger and Adorno do.

But as a model of how form and content can work together, the Magritte principle is powerful:

• It shows you don’t have to be obscure to be disruptive.

• It shows how far you can get with clear language and a clever frame.

• It sets a bar: if your philosophical text can’t do any of that work clearly, you’d better have a very good story about why.

It certainly seems that we have lots of Magritte‑like possibilities for philosophy that we simply haven’t built out, because the incentive structures in parts of the continental world are stuck on “dense is deep.”

So if clarity can destabilize, what does earned difficulty actually look like?

Atonal music, atonal prose

If Magritte gives us the case for clarity-with-depth, atonality gives us the best case for difficult form-without-bullshit.

Tonal music has a grammar:

• keys,

• cadences,

• tension → resolution,

• stable centers.

Break that grammar aggressively and, to most ears, the piece stops sounding like “music” and becomes just… sound. The archetypal cat loose in the piano. Atonal composers know this. They are intentionally breaking the tonal system in order to:

• get at intervals and structures tonality hides, or

• express kinds of tension and release the old grammar can’t easily carry.

There’s intentional atonality (Webern), accidental atonality (random noodling), and atonality that relies on external scaffolding (Cage’s 4′33″: the point is the frame).

A lot of continental writing is basically doing the same thing with prose.

Ordinary discursive prose has its own “tonal” grammar:

• straightforward sentence structure,

• clearly flagged premises and conclusions,

• settled distinctions (subject/object, inside/outside, word/thing),

• the familiar “first we define, then we argue, then we conclude” arc.

Draw inside those lines and you sound intelligible by default. But if you think that very grammar is part of the problem, you might:

• break the syntax,

• re‑purpose words,

• write in fragments,

• let multiple voices collide in the same text.

At their best, those maneuvers are trying to be intentionally atonal: formally weird, structurally disciplined, aiming at a different kind of intelligibility.

At their worst, they’re just noodling: difficulty with no discernible structure, no clear sense of necessity, no explanation.

So:

Intentional atonality and good “opaque” prose both do violence to a grammar in order to show the grammar’s limits.

But here’s the catch:

Music’s grammar is its essential skeleton. Philosophy’s grammar isn’t.

If you stay tonal, you are staying inside that system. Music really does need to break its internal grammar to get certain effects.

Philosophy isn’t as constrained. Its medium is natural language, which is wildly flexible; it can be:

• discursive,

• poetic,

• narrative,

• fragmentary,

• technical,

• funny.

We absolutely can write things that:

• read smoothly,

• and still leave your mental furniture upturned by the end of the page.

Joan Didion does this. Simone de Beauvoir does this. Foucault, in some of his more historical work, does this. Even Hegel, chaos muppet that he is, has passages in the Phenomenology preface where he just… says what’s wrong with previous philosophy.

Which takes us to the flip‑side.

When opacity is just bad

If we’re going to be fair, we need to name unjustified opacity just as explicitly as justified opacity.

Here’s my list:

Opacity is not justified when:

• The core theses could be stated plainly, but never are.

• There’s no attempt to say why the text is difficult; it’s just treated as a mark of depth.

• Every attempt at second‑order clarification (teaching, commentary, “for a general audience” essays) is also opaque.

• The difficulty mostly tracks sociological incentives: publish‑or‑perish pressure, prestige norms, the “10–20% incomprehensible” culture Foucault jokes about.

• The supposed “depth” evaporates when you ask, in simple language, “OK, what follows from this? What would count as a counterexample?”

On this standard, a lot of continental philosophy really does have a legibility problem. Not because the ideas are deep, but because the prose is allowed to be bad without cost.

This is legibility debt:

• Analytic philosophy has high, enforced legibility norms: if you can’t give the two‑sentence version of your view in a Q&A, someone will do it for you and then kill it.

• Continental philosophy has variable, often weak legibility norms: some writers are perfectly lucid, others are rewarded for being unreadable to anyone outside a very specific interpretive community.

• Over decades, this builds up as deferred explanation: “It will all make sense if you read Hegel’s entire corpus and then my four favorite commentators and also live in Paris for a bit.”

Analytic philosophy pays its legibility bill up front; continental philosophy often kicks it down the road to commentaries, seminars, and insider culture. Default clarity is Joll’s way of saying: the bill is eventually due.

Where the Bulldog overreaches

Back to Bentham’s Bulldog.

There are points where he’s just right:

• Some continental prose is the textual equivalent of Albertan tar sands.

• Fields with low standards of clarity do attract charlatans.

• If you strip away 80% of the jargon from certain paragraphs and discover a truism or a mistake where the profundity was supposed to be, that’s… not great.

But he also overplays his hand in ways that make it too easy for continentals to shrug him off.

1. “Continental philosophers don’t really make arguments”

This is like saying “jazz musicians don’t really play melodies.”

They do; they just disassemble them, warp them, bury them in improvisation.

Continental texts do contain arguments:

• Phenomenology gives structured descriptions meant to support claims about intentionality, embodiment, perception.

• Foucault’s genealogies make explanatory claims: “Here’s how this practice came to look natural; here’s what it does.”

• Even Butler—Bulldog’s favorite piñata—is usually stitching together premises about discourse, the body, and power into something that looks a lot like an argument.

Are those arguments always premise → conclusion in numbered form? No. Are they sometimes unclear, under‑argued, or dressed in ridiculous clothes? Yes. But of course “hard to reconstruct” does not mean “nonexistent.”

The “for Beauvoir, P” tic he mocks (“for Beauvoir, women are…”), while grating, isn’t meant to prove P. It’s shorthand for:

• situating the claim in a lineage,

• flagging that we’re inside a certain conceptual apparatus,

• not claiming to be reinventing the wheel.

Analytic folks do the same with “as Lewis shows…” and “following Rawls…”

2. “Continental philosophers think reality is subjective”

Bulldog quotes Foucault on “regimes of truth,” Berger & Luckmann on social construction, and comes away with something like: “These people think there are no facts, only beliefs. RIP dinosaurs.”

But for most of these thinkers, the move isn’t:

“There wasn’t really a world before us.”

It’s:

“The way we carve up that world — into kinds, categories, identities, ‘pathologies,’ ‘perversions,’ ‘normality,’ etc. — is historically contingent and bound up with power.”

You can argue with that. You can say they overgeneralize, conflate ontology and epistemology, or underplay the stability of some categories. But “they think reality is just vibes” is a strawman.

“Sexuality is socially constructed” is not “nothing is real.” It’s “this concept, and the roles/institutions clustered around it, did not fall from the sky.”

3. “If you can’t explain it simply, it’s nonsense”

In both his original and his follow‑up, Bulldog leans on a Chomsky‑style rule:

• real sciences are hard but can be explained to a smart undergrad,

• Derrida cannot,

• therefore Derrida is nonsense.

Tempting. Too quick.

Some things really are harder to explain because they’re reflexive or structurally weird:

• they’re about the limits of representation,

• or about self‑defeating pictures,

• or about background practices we normally don’t notice.

Late Wittgenstein is not exactly a /r/explainlikeimfive thread either, and yet he survives the “nonsense” test just fine.

A better rule of thumb:

If nobody ever manages to make your stuff clearer—not in teaching, not in commentary, not in a slower second book—then yes, something is probably wrong.

Continental philosophy absolutely has texts like that. It also has the more common pattern:

• “atonal” originals,

• plus “tonal” secondary literature that does make them more tractable.

This puts us back in “legibility debt” territory, not “everything is fake.” I still think a lot of continentals could try harder on the tonal side themselves. But “I bounced off it” is not a proof of vacuity.

Where Anderson could go further

On the other side, Anderson is doing several useful things:

• She’s right that “continental philosophy” is a historically contingent label, largely constructed as analytic philosophy’s other.

• She’s right to point out the stark material asymmetry: the cited 10x discrepancy between tenure track jobs for philosophy of science versus all of continental philosophy is pretty wild.

• Her “grafting vs. ground‑clearing” metaphor is great: analytic philosophers like to clear conceptual space and rebuild from minimal premises; continentals like to graft new concepts onto old rootstock (Husserl + Beauvoir + Austin → Butler’s “performativity,” etc.).

I’m very much on board with all of that.

Where I think her reply doesn’t go quite far enough is in two places:

1. Clarity vs correspondence theory

In her sketch, Anderson ties analytic clarity to the correspondence theory of truth and to an idea of language as directly expressing mental states. She notes that Heidegger, Nietzsche, Adorno, etc., are suspicious of these assumptions, and so, in her telling, suspicion about those assumptions naturally bleeds over into suspicion about the norm of clarity itself.

This is where Joll’s distinction helps:

• We can grant that loaded clarity—the full Cartesian / positivist package—is metaphysically suspect.

• We can still keep his default clarity as a thin, pragmatic norm: explicate your crucial terms, make your argumentative spine visible, avoid gratuitous vagueness and jargon.

Anderson is absolutely right to attack the loaded kind. But that doesn’t, by itself, undercut the default kind.

2. Owning the obscurantism problem

Anderson acknowledges that some of the vices the Bulldog identifies are real; she even pulls the 1999 Nussbaum article that contains the iconic line,

It is difficult to come to grips with Butler’s ideas, because it is difficult to figure out what they are.

But the overall emphasis of the excerpt is understandably one‑sided: clarity is loaded, continental philosophy is marginalized, analytic folks often don’t get its aims.

All of that is true. But from the outside, it can sound a bit like:

• we’re misunderstood,

• the job market is unfair,

• and anyway your clarity is ideological.

A stronger stance, to my ear, would be:

• Hey, the job market is brutal and structurally biased toward analytic norms,

• and thick, scientistic “clarity” has often been used to police boundaries and exclude certain projects,

• and also, we have a genuine obscurantism problem. Some of our writing really is unjustifiably opaque.

That last point doesn’t concede the Bulldog’s “unseriousness of the discipline” tantrum. It just takes Joll’s burden‑of‑proof move seriously: if you’re going to depart from default clarity, you owe your reader at least a sketch of why.

To be fair, Anderson herself says the excerpt’s main job is to map and motivate continental tendencies, not to offer a knock‑down defense of them. I’m using her sketch as a jumping‑off point, not treating it as a full argument for or against clarity.

Two different jobs for words

Here’s a common framing that keeps me sane when reading both styles.

Very roughly:

• Analytic philosophy treats natural language as a slightly messy interface to thoughts that could, in principle, be expressed in a more formal way: logical notation, math, decision procedures. Words are the GUI for a deeper system.

• Continental philosophy (in its more radical strands) treats natural language as the irreplaceable medium of thought. Metaphors, etymologies, resonances, and ambiguities are not noise; they’re where the action is.

On this picture:

• The analytic ideal is to make philosophy more like math or science.

• The continental ideal is to make philosophy more like poetics or art.

Not “one has arguments, the other has vibes.” More like:

• one privileges propositional justification,

• the other privileges phenomenological / historical / affective disclosure.

They answer “what are words for?” differently.

If you buy that, then:

• The Bulldog is basically saying: “Stop using words like paints and use them like proof trees.”

• Anderson is basically saying: “Stop pretending your neat sentences and symbols are neutral; they already encode a view of reality.”

And Joll’s default clarity sits right in the overlap:

Regardless of whether you’re doing math‑ish philosophy or art‑ish philosophy, you can still:

• say what your key terms mean (for now),

• say what you’re actually claiming,

• give some sense of how that claim is supported or enacted.

After that, go wild. Write your atonal symphony. Just don’t confuse “I made it hard” with “I made it profound.”

So… what do we do with all this?

Here’s where I land.

1. Clarity is a virtue, not a fetish.
   If I can’t tell what you’re saying, I can’t tell whether it’s true or interesting. If nobody outside your subfield can tell, you have a quality‑control problem, not an aura of depth.

2. Some difficulty is structural—but probably less than advertised.
   Reflexive, self‑undermining projects really do strain language, and some weirdness is inevitable there. But we are currently running a good amount of unnecessary friction under the banner of “this is just how deep thought sounds.”

3. Continental philosophy has real value and a real legibility debt.
   Questioning “common sense,” doing genealogy, grafting new concepts onto old ones—these are important. They also desperately need more Magritte‑style exposition: simple surfaces, disruptive depths.

4. Analytic philosophy’s norms aren’t neutral, but they aren’t fake either.
   The demand to spell things out is not just a power‑play; it’s also basic intellectual hygiene. At the same time, analytic philosophers should stop pretending their favorite kind of clarity fell out of the sky. It has a history and a politics.

5. We should adopt something like Joll’s “default clarity” as a shared minimum.

   • Explicate important terms.

   • Distinguish thesis from support.

   • Avoid avoidable ambiguity and unnecessary jargon.

   • Don’t make your work more inaccessible than the content genuinely demands.

   If you break these, you owe us—not a 50‑page confession—but at least a paragraph of “here’s why I’m departing from the usual norms.” And if you don’t at least lampshade it, don’t be surprised if people outside your intellectual inner circle don’t give you the benefit of the doubt.

6. We should aim for “atonal when necessary, tonal whenever possible.”
   Let the hard, twisty, structurally weird passages earn their keep. Surround them with writing that doesn’t make people feel like they’re wading through wet cement.

Joll gives us the skeleton: a distinction between loaded clarity and default clarity.

Magritte shows that clarity can be radical, that simple surfaces often do fantastically destabilizing work.

Atonality reminds us that difficulty has a place—but only when the form must contort to reveal what the normal grammar hides.

Ultimately, I’d like a philosophical world where:

• you can do serious work on language, power, embodiment, or history

• without being forced into three layers of jargon just to be taken seriously,

• and without being told that anything less than symbolic logic and a truth table is unserious.

That world is not utopian. Magritte managed something like it with a pipe and a caption. A lot of artists manage it every day with their own tools. It seems to me there is no deep reason philosophers can’t do the same.

Until then, we’re stuck in this slightly absurd situation where:

• one side yells “Stop worshipping clarity!”,

• the other yells “Stop worshipping obscurity!”,

and both, in their own way, dodge the more boring, more difficult work:

Actually writing philosophy that is as clear as it can be,
and only as difficult as it has to be.

But it’s creaking under the weight of tech debt, and it’s becoming harder and harder to keep it secure and compliant. Shutting it down would save $1M annually and let your team focus on the product that serves 20,000 paying customers. Your engineers are burned out maintaining two systems. Your CTO wants the resources reallocated. The big customers don’t know the small ones exist.

You told the CTO the numbers don’t work, and you can’t justify keeping the service up. You told your team they wouldn’t have to worry about maintaining it much longer. You’ve told the small customers nothing.

Three months later, you’re still paralyzed. You haven’t been able to sleep, food tastes off, and there’s an Excel file open on your desktop with legacy API funding zeroed out. In another window, you’ve written your resignation email.

That’s not burnout. Burnout is when you’re out of gas. This is something else.

Call it moral exhaustion. Not because you’re “immoral”, but because your ethical operating system has hit an infinite loop, and you’re rapidly running out of fucks to give.

A distinction with a difference

Operational burnout comes from resource depletion. You ran out of time, energy, or recognition. Rest helps. Vacation helps. A sabbatical might fix it.

Moral exhaustion comes from actions that conflict with your values, your identity, or what you thought integrity meant. It shows up as disconnection, numbness toward decisions that should matter, resentment that feels irrational, guilt you can’t quite place.

Here’s the diagnostic: if your workload stayed constant but your alignment with your stated values improved dramatically, would you regain energy?

If yes, you’re dealing with moral injury—a term borrowed from trauma research that describes sustained violation or suppression of core moral beliefs under constraint, power, or decision pressure.

The organizational costs are measurable. Withdrawal. Disengagement masked as “professional distance.” Broken trust cascading through teams. High-integrity talent leaving first, quietly. This is what researchers call “ethical fading”: the slow drift where individuals and organizations stop recognizing moral dimensions of decisions entirely.

What moral injury looks like at work

You’re asked to defend decisions you privately believe are harmful.

You’re obliged to enforce policies you already told your team you disagreed with (RTO mandates, anyone?).

You feel complicit in values theater—the gap between what your company says it believes and what it actually rewards.

You’ve traded a piece of your integrity for job security, then justified it with “everyone does this” or “I’m protecting my team.”

These moments cluster around shutdown decisions, layoffs, policy pivots under pressure, and high-stakes announcements where you become the face of something you didn’t author and don’t endorse.

The pain isn’t that you made a bad decision. It’s that you justified that decision using ethical logic you don’t actually subscribe to.

Misalignment, not amorality

Leaders experiencing moral injury aren’t necessarily unethical. They’re navigating conflicts between multiple, incompatible ethical systems operating simultaneously in the same organization.

Some leaders evaluate ethics by outcomes—did this produce the best result for the most people? Others by proper form—did we follow the right process, honor our commitments, maintain consistency? Still others by character—did this decision reflect who we want to be?

These aren’t personality quirks. They’re fundamentally different ways of thinking about what makes something right or wrong.

Then there’s the question of moral standing. Who or what are you treating as mattering intrinsically? Individual people? The relationships between them? The community or organization as a collective whole? Your CFO might treat the company-as-entity as the patient that needs protecting. Your Chief People Officer might center the preservation and health of human relationships. Both are reasoning ethically—but with different geometries of care.

Then there’s scope of obligation. Does this bind just you personally? Anyone in your role? Anyone in this situation? Everyone universally?

Finally, why are we even bound in the first place? Sometimes people believe ethics binds because it follows logically from first principles. Some believe it’s just built into the nature of reality. Sometimes an authority—board, law, professional duty—simply commands it. And sometimes the only justification is, “it works, doesn’t it?”

Most leaders don’t realize they’re switching between these frames constantly—or that their colleagues may be operating in entirely different ethical universes.

The pattern underneath

Here’s the source of the mess: organizations don’t “pick” one ethical system. They layer multiple incompatible ones, then leave leaders to resolve the conflicts quietly, personally, invisibly.

You optimize for outcomes this quarter (maximize shareholder return) while claiming to honor duty-bound principles (we treat people with dignity). You enforce universal policies (everyone follows the same rules) while making situational exceptions (but this customer is strategic). You appeal to authority (compliance says so) while justifying with pragmatism (it’s just how things work).

The misalignments cluster in predictable zones:

• Incentives vs stated beliefs (quota structures vs customer care claims)

• Team norms vs company values (how we actually work vs the handbook)

• Short-term performance vs long-term responsibility (this quarter vs this decade)

Most leaders resolve these conflicts by choosing one system and muting the others.

The exhaustion comes from the muting, not the choice.

When tools take over

Delegation to machines makes it worse. You’ve handed moral authority to systems that don’t experience guilt.

Spreadsheets decide who gets laid off. Algorithms decide who gets promoted. Dashboards decide what’s “working.” Performance review templates decide what counts as “good.”

These tools reframe decisions into their own logic. The layoff becomes a cost-optimization problem, stripped of questions about dignity or community. The promotion becomes a metric-comparison exercise, emptied of character or potential. You’d think you could use the tool to insulate you from the emotional impact of what you’re doing—like a moral shield you can hide behind. But it doesn’t work like that.

Tenbrunsel & Messick identified this kind of self-deception as “ethical fading”: when decisions get reframed as “just business” or “just numbers,” leaders stop perceiving moral dimensions even when they’re central. It’s automatic under time pressure. In the moment, you stop seeing the decision as having ethical weight.

Once you see the pattern, you see it everywhere.

Values capture: Stated values become PR surfaces rather than operating principles. The language stays. The behavior changes. No one announces the shift, so misalignment becomes endemic. The company claims “people-first” while systematically rewarding managers who drive attrition.

Accountability diffusion: Responsibility spreads so thin that no one feels ethically agentic. “I didn’t make that call” becomes a reflex, even for people with decision-making power. Everyone’s involved, no one’s accountable. You’ve built a system where moral agency is structurally impossible.

Emotional mediation: The tools you use to make decisions also shape how you feel about them. When everything runs through dashboards, scorecards, and templates, the affective texture of choice changes. You stop feeling the weight because the interface doesn’t transmit it.

But these aren’t failures of character. They’re patterns that emerge when you delegate authority to systems that weren’t designed to carry moral complexity.

Debugging the infinite loop

Here’s where it gets interesting. I’ve been mapping these conflicts for a while, and there’s a structure underneath the chaos. This structure provides a language for the mismatches that drive ethical confusion.

So let’s try an experiment. Pick one decision that still bothers you—not because it was wrong, but because it lingers. The kind that surfaces when you’re trying to fall asleep.

Run it through these diagnostic axes:

What made it “right” or “wrong”? Did you evaluate based on what would happen (outcomes and consequences), what kind of person or organization it makes you (character and integrity), or whether it conformed to proper form (the right process, duty, or rule)? If you justified with one lens but feel compromised by another, that’s your first conflict.

Who or what had moral standing? Did you treat individual people as what mattered intrinsically? The relationships between them? The community or organization as a collective whole? Or some larger unified system? If your messaging claimed one (”we care about each employee”) but your structure served another (”the company must survive”), you operated in two ethical geometries simultaneously.

Who was obligated? Was this binding on you personally, on anyone in your role, on anyone in this situation, or on everyone universally? If you made a decision that felt acceptable as “what someone in my position does” but violated what you believe “anyone should do,” you’ve hit a scope conflict.

Why did it bind you? Because it follows logically from first principles? Because it’s built into the nature of reality or your role? Because an authority (board, law, duty) commanded it? Or because you’ve observed it works in practice? If you justified with pragmatism (”this is what works”) but feel violated at the level of basic axiomatic reality (”but this just isn’t right“), that’s the gap between your stated and felt justifications.

Don’t try to resolve these yet. Just make them visible.

Going back to our API sunsetting example: we justified keeping it running by appeals to our shared character (”we don’t abandon the people who believed in us first”) but the outcome logic says shut it down (”$1M and team health vs. a thousand users who pay almost nothing”).

You centered individuals and relationships (”these specific people trusted us”) but your role obligation centers the collective (”the company itself”).

You feel personally bound by the promise (”we said ‘easy to use’ and they built on that”) but your role is about breaking promises sometimes (”it’s on me to make the hard decisions”).

You want to believe loyalty is axiomatic (”you just don’t betray early believers”) but you justified it to the CTO with pure pragmatism (”the numbers don’t work”).

Four criteria, four conflicts, one decision. That’s not an ethical dilemma, it’s like... a quadrilemma. You might feel like a moral monster for failing to navigate this gracefully, but the whole thing was a setup—a structural impossibility dressed up as leadership.

The claim here is modest: naming these conflicts improves situational awareness. It won’t solve ethical problems for you, but you can at least begin to anticipate and mitigate the risk of moral exhaustion.

But but my boss is a sociopath

When discussing the psychology of ethics and morality, I often get the pushback: “My leaders aren’t exhausted—they just don’t care.”

Maybe. But consider Jackall’s finding in Moral Mazes, his foundational work on the psychology of morality in large companies: organizations often require the abandonment of personal morality for survival. Not because leaders are sociopaths, but because systems reward expediency and punish visible integrity.

Moral disengagement isn’t the same as a lack of ethics. It’s often a protective response to impossible ethical tensions. People who look like they don’t care may have simply stopped letting themselves feel the mismatch, because feeling it was unsustainable.

The system shaped them. That doesn’t excuse harm, but it reframes what intervention looks like.

What happens next

If you address it: Transparency. Restored sense of agency. Leaders can explain choices without defensiveness. Space opens for acknowledging trade-offs. Trust rebuilds, not because everyone agrees, but because the conflicts are named and the logic is visible.

If you ignore it: Drift into cynicism. Disengagement masked as professionalism. Retention loss in the people with the strongest ethical clarity. Organizational numbness—the condition where everyone is too tired to care, and that becomes the culture.

Healthcare has been studying moral injury for decades (Epstein & Hamric, 2009; Rushton, 2017). Clinicians experience it when policies prevent them from delivering the care they believe patients need. The public sector sees it in policymakers paralyzed by conflicting mandates. AI governance is facing it now—teams building systems with responsibility diffused so thoroughly that no one feels accountable for outcomes.

The pattern is consistent: when ethical conflicts are structural but treated as personal failures, the injury compounds.

Say it again: ethics isn’t about being a good person

It’s not about good PR. It’s not compliance theater. It’s not a culture war talking point.

It’s how you reconcile your values, your actions, and your beliefs—especially under pressure.

This isn’t a guide of “how to be ethical.” It’s more like “how to survive making ethically loaded decisions without losing yourself.”

Pick one decision that still bothers you. Map where there was conflict: what kind of rightness you claimed versus what kind you violated; whose moral standing you centered versus whose you deprecated; what scope of obligation you operated under versus what you actually believe; why you said it bound you versus why it actually did. Notice where your organizational tools and processes mediated—or completely erased—the moral weight.

Ask: was the conflict between you and the decision, or between multiple ethical systems you were asked to hold simultaneously while pretending they were one?

Name the tension instead of burying it.

That’s not resolution. But it’s a start.

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Imagine this: After one watery conference room coffee too many, you have reached your breaking point. You fire up your favorite AI agent and issue a fateful prompt: “buy me the strongest coffee you can find.”

The following week, twelve test tubes of industrial-strength coffee extract arrive at your door, festooned with angry-looking warning stickers and marked “for laboratory use only”. Oh, and the bill is $2,195. No refunds.

Who is on the hook?

The first agentic commerce holiday season is underway. 70% of consumers are “at least somewhat” comfortable having AI agents make purchases on their behalf. Merchants face a wave of disputes the payment system wasn’t designed to handle.

Call it the SNADpocalypse: an unprecedented flood of “significantly not as described” claims from buyers blaming merchants for the actions of their own shopping agents.

This isn’t handwringing. ChatGPT Instant Checkout launched September 29. Google’s AP2 protocol has 60+ industry partners. Visa reports a 4,700% surge in AI-driven traffic to U.S. retail sites in October 2025. The infrastructure exists. The volume is spiking. The dispute frameworks are 25 years old.

Yeah, something’s going to break.

What is agentic commerce?

Agentic commerce describes transactions where AI systems act autonomously on your behalf to discover, evaluate, and purchase goods or services — with varying degrees of human oversight.

You can think of shopping assistants as falling into similar “levels” as those we apply to assistive driving technology, from lane change alerts to sleep-in-the-backseat self-driving capabilities. These autonomy levels map roughly to the standard marketing funnel stages of awareness, consideration, conversion, and retention.

Level 0 (~2000–2015): Non-Autonomous. Search engines and voice assistants enable product search but not purchase. You ask, they show, you buy manually.

Level 1 (2016-2020): Awareness. The era of The Algorithm. Instagram ads that know you better than your husband does. AI recommendation engines across search and social personalize shopping, but humans still click “buy.” And boy do they click buy.

Level 2 (2021-2024): Consideration. Large language models hooked up to the web enable conversational product comparison. You can ask complex questions, get reasonable-sounding answers — but it’s not optimized for commerce yet. A shopping query is treated the same as a request to translate your apartment lease into pirate language. How innocent we were.

Level 3 (2025): Conversion. AI executes purchases within pre-defined parameters, humans set boundaries. This is what Stripe’s Agentic Commerce Protocol in ChatGPT’s Instant Checkout has unlocked, with in-chat checkout on a per-transaction approval basis. You talk to your agent, it presents the checkout screen, and you click yes or no.

Level 4: (2026) Retention. AI makes routine purchases independently based on standing mandates, humans intervene only for exceptions. Google’s AP2 with cryptographic mandates is moving in this direction. You set the rules once, the agent buys autonomously within them. (Questions abound here about what criteria agents will use to make buying decisions, and how marketers will be able to influence them).

Level 5 (2028?): Full autonomy/commerce singularity/run for your lives. AI manages entire purchasing lifecycle including dispute resolution. Maybe even talking to “seller agents” instead of static vendor systems. Nobody’s here yet. Maybe nobody should be.

We’re transitioning from Level 2 to Level 4 right now. And that transition has thrown up some fog of war over who is liable when things go wrong.

The 2025 holiday season is set to be the first meaningful test of agent-led checkout at scale. E-commerce took 10-15 years to develop mature dispute resolution frameworks. Agentic commerce players are attempting to build equivalent infrastructure in 1-2 years while transaction volumes surge.

The image that comes to mind is a cement truck pouring runway for a plane that has already begun taxiing. So, are we going to get our proverbial wheels stuck in the proverbial wet concrete?

The authorization gap nobody solved

Traditional chargeback frameworks assume a human made the purchase decision. You saw the product. You clicked buy. You entered your card details. If the product didn’t match, that’s the merchant’s problem — the product is “significantly not as described” (SNAD).

Agentic commerce breaks that model.

You told your agent to buy “coffee.” It bought a dozen vials of coffee-derived lab reagent that could stop your heart as surely as the cyanide it was stored next to in the warehouse. Agent error? Merchant liability? Who misunderstood whom?

You authorized an agent generally — ”handle my grocery shopping” — but did you authorize this specific $300 purchase from this specific grocery store at this specific moment? And what if the potato chips aren’t crinkle-cut the way you like them?

The agent hallucinated product features that don’t exist. Is that the AI company’s fault for the hallucination? The merchant’s fault under “not as described” provisions? Your responsibility for trusting an unreliable agent? Eliezer Yudkowsky’s fault, somehow?

Traditional chargeback frameworks assume a human made the purchase decision. When an AI agent operates autonomously — potentially completing transactions before human awareness even kicks in — the existing rules break down.

Liability remains unclear when AI agents complete transactions. Merchants may bear fraud and dispute costs despite shoppers never even visiting their websites.

We don’t have legal answers. But we do have three protocols racing to create technical solutions before regulatory frameworks catch up.

Three protocols to manage — and maybe prevent — the coming chaos

The payments industry is currently moving in the right direction, building dispute prevention infrastructure before crisis forces their collective hand. A few protocols have emerged, championed by Google, OpenAI, Visa, Stripe, PayPal, and others, seeking to establish a set of ground rules for agentic commerce.

Stripe ACP: Real-time explicit consent

Stripe’s Agentic Commerce Protocol launched with OpenAI in September. In this model, when the agent wants to complete a purchase, it presents an inline checkout interface showing product, price, seller, and shipping details. You explicitly approve this specific transaction. Merchants verify authorization through the Delegated Payments spec which, as an open protocol, does not rely on Stripe powering your payments.

Strength: clear moment of human consent for this purchase.

Weakness: doesn’t scale to high-frequency, low-value autonomous transactions. If you want the agent to buy groceries every week without asking, this breaks down.

Best for: Level 2 autonomy — supervised agent purchases where you approve each transaction.

Google AP2: Cryptographic mandates with bounded authority

Google’s AP2 protocol (announced September 16, 60+ partners in early pilots) takes a different approach. You sign a cryptographically verifiable “mandate” defining spending limits, merchant categories, and time restrictions. Example: $50 per transaction, $500 per month, groceries and household goods only. The agent operates within those boundaries autonomously.

Strength: scales to recurring, routine purchases while maintaining authorization proof.

Weakness: “misinterpretation” disputes remain ambiguous. The agent stayed within your $50 limit but bought the wrong product. Is that significantly not as described? The mandate doesn’t cover that. Requires sophisticated mandate management.

Best for: Level 3 autonomy — delegated authority with financial guardrails.

Visa TAP: Merchant-side verification

Visa’s Trusted Agent Protocol (announced October 14; documentation on Visa Developer and GitHub) addresses a different problem: merchants can’t tell legitimate AI agents from malicious bots. With AI-driven traffic surging 4,700% year-over-year, bot fraud is exploding alongside legitimate agent purchases.

TAP helps merchants distinguish legitimate AI agents from malicious bots through agent identity attestation and verification protocols. Merchants can distinguish ChatGPT making a purchase from a bot pretending to be ChatGPT.

Strength: scalably addresses the surge in AI-driven retail traffic; prevents bot fraud masquerading as agent purchases.

Weakness: doesn’t directly address human intent verification. Even if you verify the agent’s identity, you still don’t know if the human actually authorized this specific action.

Best for: complementary infrastructure securing the merchant endpoint.

All three protocols flip the dispute model from post-transaction resolution to pre-transaction authorization proof. But will that be enough to tie the intent of a transaction back to a properly-informed consumer?

The identity layer that might save merchants

The most interesting development to me isn’t the payment protocols — it’s the identity verification layer emerging beneath them.

Prove Verified Agent (launched October 2025): Creates an “end-to-end chain of custody” linking verified identity, human intent, payment credentials, and consent — all backed by cryptographic proof. Integrates with Visa’s payment infrastructure.

Visa Payment Passkey (live in Middle East, expanding globally): FIDO2 biometric authentication replacing passwords and OTPs. Uses fingerprint, facial recognition, or device PIN. Already processing real transactions through noon payments

PayPal Wallet Integration (announced October 2025): Leverages PayPal’s preexisting risk models and OTP/biometric verification of its vast customer base to provide validated payment intents in ChatGPT via the ACP protocol.

The innovation: non-repudiable proof that a specific human authorized a specific agent action at a specific moment — before the transaction occurs.

Think chip-and-PIN for agent purchases. The merchant can prove the person who authorized the purchase was actually present (or at least their fingers and/or face were) at the moment of authorization. Barring a Hannibal-Lecter-style scenario, this is strong evidence of human intent.

This addresses what mandates alone cannot: proof that the human who created the mandate is the same human authorizing this transaction right now.

Why merchants will probably be left holding the bag anyway

The Fair Credit Billing Act of 1974 limits consumer liability for unauthorized credit card use to $50. It provides chargeback rights for “billing errors” or “goods not as described.”

Does an agent’s misinterpretation constitute a billing error? Courts will decide. But history suggests consumer-favorable interpretation — not least because the agents representing the consumer are the spawn of some of the most politically powerful and influential companies since the American railroad era.

Merchants already lost this fight once

When e-commerce exploded in the late 1990s, the payment industry faced a trust crisis: card-not-present transactions meant merchants couldn’t verify the card holder’s physical presence or confirm the person using it actually owned it.

The solution heavily favored consumers. The Fair Credit Billing Act gave cardholders dispute rights. Merchants bore 100% of CNP fraud liability. If a stolen card was used online, merchants lost both merchandise and the transaction fee.

That framework still exists today. It’s about to apply to agent purchases.

The problem is worse this time

Early e-commerce fraud was binary: either you entered your card details or you didn’t. Either your card was stolen or it wasn’t.

Agentic commerce creates a spectrum of disputes. The agent misunderstood. The agent hallucinated. The agent operated within your mandate but bought the wrong thing. The agent acted faster than you could intervene.

Existing consumer protection frameworks weren’t designed for agent-mediated transactions. The question is whether they’ll adapt through litigation or proactive regulation.

Current legal precedents and ambiguity

Fair Credit Billing Act (1974): As mentioned above, limits consumer liability for unauthorized credit card use to $50 and provides chargeback rights for “billing errors” or “goods not as described.” Does an agent’s misinterpretation constitute a billing error? Courts will decide.

UETA and ESIGN (widely adopted) already recognize contracts formed by electronic agents without human review of each action. But how those principles interact with FCBA chargeback rights in agent-mediated purchases remains legally untested.

GDPR and consent: EU frameworks require explicit, informed consent. Reserve Bank of India (RBI) rules put specific constraints on e-mandates that have been the bugbear of SaaS businesses trying to set up recurring billing for thier Indian customers. Can a standing mandate satisfy this for future autonomous transactions? Nobody knows.

Emerging frameworks to watch

eIDAS 2.0 (EU): Creates framework for digital identity wallets in Europe. Could establish legal groundwork for cryptographic mandates representing delegated authority.

AI Accountability Acts (various US states): Emerging legislation defining liability for AI decision-making. May establish precedents for agent-mediated purchases.

Payment network rule updates: Visa and Mastercard are updating operating regulations to address agent transactions. These changes may establish de facto standards before regulation catches up.

The precedent problem: Early disputes will establish case law. Merchants should assume consumer-favorable interpretation until proven otherwise. That’s how CNP disputes evolved.

What you can do right now

Waiting for regulatory clarity is a losing strategy. History suggests merchants will bear the brunt of the early disputes that will hit this holiday season.

It’s worth taking stock of where you land on a few key dimensions of agentic commerce readiness: visibility, verification, variance, vigilance, and voice (yes, I am the cutest, thank you for saying so!)

Visibility: Know when agents are buying

What’s happening: Most merchants can’t distinguish agent purchases from human purchases.

Where we want to be: Real-time identification of agent-mediated transactions with agent provider attribution.

What you can do:

• Require disclosure when purchases are agent-driven versus human-driven (push for this in Stripe ACP and Google AP2 adoption agreements)

• Track which AI agents are purchasing (ChatGPT, Perplexity, future entrants)

• Monitor agent traffic patterns using Visa TAP or similar verification protocols

• Create separate transaction codes for agent purchases in your payment processing

Why it matters: Don’t go into this blind. Dispute patterns will differ between human and agent purchases. Start measuring now to anticipate impact and required investments.

Verification: Prove authorization chains

What’s happening: Merchant has no visibility into whether human authorized an agent action.

Where we want to be: Cryptographic proof of authorization accessible during dispute resolution.

What you can do:

• Integrate Stripe’s Shared Payment Token API (ACP) or AP2 mandate verification

• Implement Prove Verified Agent or similar identity verification at checkout

• Store authorization artifacts (tokens, mandate signatures, biometric authentication logs) with transaction records

• Create audit trails showing what product information the agent accessed before purchase

Why it matters: Burden of proof falls on merchants. Authorization chain evidence is your defense against SNAD disputes.

Variance: Design for misinterpretation

What’s happening: Product descriptions and policies assume human comprehension.

Where we want to be: Machine-readable specifications that reduce agent misinterpretation.

What you can do:

• Implement structured data (Schema.org markup) for product specifications

• Provide clear, unambiguous attribute definitions (dimensions, materials, compatibility)

• Use AI to test how your product descriptions might be misinterpreted by agents

• Create explicit compatibility/incompatibility statements (”requires X,” “does not include Y”)

Why it matters: “Your website confused the agent” will be a common dispute trigger. Clear specifications are your first line of defense.

Vigilance: Adapt existing fraud detection

What’s happening: Fraud systems are optimized for human purchasing patterns.

Where we want to be: Agentic commerce-aware fraud detection distinguishing legitimate agents from fraud.

What you can do:

• Train fraud teams to recognize agentic commerce patterns (velocity, basket composition, time-of-day)

• Distinguish legitimate agent purchases from bot fraud using Visa TAP or similar

• Update velocity rules — agents may make multiple purchases rapidly across merchants

• Monitor for “shopping cart abandonment” patterns that differ from human behavior

Why it matters: Traditional fraud signals may not apply. Legitimate agent behavior can look like bot activity.

Voice: Participate in standards development

What’s happening: Payment networks and platforms defining rules without broad merchant input.

Where we want to be: Merchant interests represented in emerging protocol standards and liability frameworks.

What you can do:

• Join Stripe ACP, Google AP2, and Visa TAP working groups or feedback programs

• Engage with merchant associations on agentic commerce policy positions

• Negotiate clear liability allocation with agent platforms in early adoption agreements

• Document early dispute patterns and share insights with networks to inform rule development

Why it matters: The rules being written now will govern disputes for the next decade. Merchant silence means merchant liability by default.

Self-assessment checklist

Use this to evaluate your current agentic commerce readiness:

Visibility

• Can you identify which transactions were agent-mediated versus human?

• Do you know which AI agents are purchasing from you?

• Can you track agent traffic patterns, ideally in real-time?

Verification

• Can you access authorization proof during disputes (tokens, mandates, biometric logs)?

• Do you store agent authorization artifacts with transaction records?

• Have you integrated any identity verification layer (Prove, Visa Passkey, etc.)?

Variance

• Are your product descriptions machine-readable (structured data)?

• Do you keep auditable change logs of product descriptions in your catalog system so you can prove what your site content was on a given date?

• Have you tested product descriptions for agent misinterpretation risk?

• Do you explicitly state compatibility/requirements/exclusions?

Vigilance

• Have you updated fraud detection rules for agent purchasing patterns?

• Can your systems distinguish legitimate agents from bot fraud?

• Do you have separate protocols for reviewing agent transactions (e.g. Visa TAP guidance)?

Voice

• Are you participating in protocol working groups or merchant coalitions?

• Have you negotiated liability terms in early agent commerce agreements?

• Are you documenting dispute patterns to inform future standards?

Three scenarios for what happens next

Optimistic: Cryptographic mandates and biometric verification prevent most disputes. Clear authorization chains resolve the rest quickly. The SNADpocalypse never materializes.

Pessimistic: Mandates prove inadequate for “misinterpretation” disputes. Courts rule in favor of consumers citing FCBA protections. Merchants face another decade of disputes while the system evolves.

Realistic: Some disputes prevented. Some handled better. Some create precedents that reshape liability frameworks. Not a catastrophic collapse, but not a seamless transition either.

We don’t know which will play out, but the industry’s awareness of the problem is far ahead of where e-commerce was in 1998. Major payment networks have deployed initial standards before the critical mass of adoption has even hit. Identity verification infrastructure is launching in parallel with transaction protocols.

But awareness doesn’t shift liability.

The Fair Credit Billing Act still favors consumers. Payment network rules still place burden of proof on merchants. Early adopters will bear costs while precedents form and infrastructure matures.

Merchants should assume they’ll hold the bag until proven otherwise. That assumption guided survival in early e-commerce. It’s the smart bet now.

Questions merchants will be asking in the coming months

Can I refuse to accept agent-powered purchases?

Technically yes, but practically difficult. Without integrating human-in-the-loop identity verification, you can’t easily distinguish agent purchases from human purchases at the point of transaction. By the time you identify an agent purchase, payment networks already processed it, meaning you’re looking at a refund, not a refusal.

If I implement Stripe ACP or Google AP2, does that protect me from disputes

Partially. These protocols provide better authorization proof than current CNP transactions, but they don’t eliminate disputes. “Significantly not as described” claims can still arise if the agent misinterpreted product specifications or user intent. The protocols improve your defense — particularly around proving the cardholder authorized the transaction — but they don’t guarantee victory on product-related disputes.

Who’s liable when an agent “hallucinates” product features that don’t exist?

Legally unclear. Arguments exist for agent provider liability (their system made the false claim), merchant liability (under FCBA “not as described” provisions), or consumer responsibility (they authorized the agent). Early case law will determine precedent. Document everything about what product information you provided.

Should I create separate return policies for agent purchases?

Consider it, but consult a lawyer. Separate policies could help address “misunderstanding” scenarios distinct from fraud. However, payment networks may not recognize such distinctions, and consumer protection laws may override them. This is evolving territory.

What happens if a customer’s agent gets “hacked” and makes fraudulent purchases?

Likely treated as standard CNP fraud under current frameworks — merchant liable unless they can prove authorization. This is precisely why identity verification layers (Prove Verified Agent, Visa Payment Passkey) matter — they create stronger evidence the legitimate account holder authorized the transaction.

How do I know if I should join protocol working groups or wait?

If you’re a large merchant or early adopter of agent commerce capabilities, participate now — rules are being written. If you’re small or not yet seeing agent traffic, monitor closely but focus on the 5 V’s of agentic commerce readiness discussed above. You don’t need to join every working group, but you should have someone tracking developments.

Will agentic commerce disputes be handled differently from regular chargebacks

Not yet. Existing chargeback reason codes and processes apply until payment networks create specific agent transaction categories. This is why early disputes will likely favor consumers — the frameworks default to consumer protection in ambiguous scenarios.

What’s the single most important thing I can do right now?

Visibility. Start tracking which transactions are agent-mediated versus human. You can’t manage disputes you can’t identify. Everything else in the readiness framework depends on this foundation.

I’m shifting the focus of this publication to the ideas that I focus on every day at work — the intersection of code, commerce, and human cognition. Look for a lot more posts on design ethics, affective technology, and the risks and rewards I’m seeing in the world of agentic payments.

Some irresponsible experiments I’m planning to share here over the coming months include:

• Sundew: an AI shopping agent “hijacking”/prompt injection testing framework

• Royfield: reversible color transformations based on algebraic fields

• Proxee: an “antisocial network” where everyone is a bot (even other humans)

• NOPE: an experimental programming language with an LLM-based compiler

• Mariko: an emotional regulation prompt engineering proxy for coding agents

• NOW: a framework for change management under conflict & ethical disagreement

This is definitely a change. If you’d like to bail for a refund, you’ll get one no questions asked. If you’d like to come on this wild trip with me, I’d love to have you.

-Geordie

Skeuomorphism mimicked the affordances and constraints of physical objects in their digital equivalents, not just to provide a sense of familiarity, but also to help guide users toward expected interaction patterns in this new digital medium.

Now, with the explosion in progress in generative artificial intelligence, we're seeing the realization of "AI agents"—autonomous digital knowledge workers that act on your behalf, taking a high-level goal and running iteratively (often stopping to check in for feedback) until the goal is achieved. These agents have a notable characteristic: they aren't human, but they are often designed to act that way.

Call it "humorphism". And historically, it has sucked.

The disappointment of humanistic digital agents

For years, designers of AI agents and chatbots have been intentionally building in "unnecessary" human characteristics to make the system more comfortable to interact with. Humorphism in an agent's design is intended to induce anthropomorphism in the user—that is, to make you apply human-like attributes to the system. This provides a preexisting set of expectations for how you might interact with the bot, which was precisely the role that skeuomorphism played early in the evolution of digital interface design.

Like skeuomorphism, humorphism emerged from a desire to make digital interactions more accessible and relatable. As bots became more common, designers tried to make them more approachable by incorporating human characteristics, such as names, voices, and even virtual appearances.

Unlike skeuomorphism however, humorphism tended to write checks it couldn't cash. It turned out that encouraging humanistic interactions with extremely limited digital systems was a road to disappointment. The first few generations of humorphic AI systems failed in several interesting ways.

Natural language interface without natural language power

Apple's Siri, Google Assistant, and Amazon's Alexa are probably the most well-known voice-controlled virtual assistants, although it is remarkable how limited their utility has been, given the massive resources behind those three companies.

These agents are supposed to be able to understand and respond to complex sentences. In reality, they are hamstrung by the lack of deep integrations with other systems, not to mention a tendency to misunderstand you.

In addition to natural language (mis)understanding, Microsoft's Cortana attempted to express a more human-like personality through her remarks and demeanor. This didn't save her from being discontinued when it became clear that the underlying use cases just weren't there.

Digitally-embodied avatars living in uncanny valley

In some cases, humorphism extends to the visual representation of AI agents. Virtual avatars or characters are created to give users a tangible, human-like entity to interact with.

Virtual influencers like Lil Miquela and Shudu Gram, powered by AI algorithms, have been designed with realistic human appearances and personalities. These virtual beings engage with users on social media platforms, blurring the line between AI and human interaction.

However, putting a human form to a digital system has the effect of amplifying the uncanny valley experience, creating a set of expectations that are extraordinarily difficult to meet, and leading to disappointment or disgust when there is a disconnect between promise and reality.

(Humanistic avatars have another interesting failure mode, wherein some of their users want to have sex with them. I'd call this more a failure of our own programming than the agent's, but it certainly raises some dystopian prospects.)

Non-humanistic avatars can help avoid some of these challenges. The virtual therapist Woebot avoids humorphism altogether by using a robot cartoon character, creating a more approachable and relatable experience for users seeking mental health support. This helps provide a face for the system without promising a truly human-like interaction.

Full of behavior and gestures, signifying nothing

Humorphism can also involve incorporating human-like behaviors and subtle gestures into digital systems. Since more than half of the content of communication is nonverbal, this approach seemed to make a lot of sense.

SoftBank's humanoid robot Pepper was designed to recognize and respond to human emotions through facial expressions, body language, and voice tone. Pepper's designers hoped that the ability to exhibit human-like behaviors and gestures, such as nodding, waving, or displaying empathetic facial expressions would enhance its relatability and appeal to users.

Some were skeptical. Pepper flopped due to a lack of actual functionality, with The Verge reporting, "Pepper failed at almost every other task assigned and ended up being roughly as sophisticated as the smart speakers that were appearing around the same time."

The new golden age of humorphism

While humorphism in the past was marred by disappointments and unfulfilled promises, the recent advancements in AI technology, especially the emergence of GPT-4, have brought humorphism back to life. The emerging cohort of powerful, context-aware, and knowledgeable AI agents are finally realizing the potential that humorphism has always aimed for, marking the beginning of its golden age.

GPT-4 is arguably the first non-sucky conversational AI. It has enough context and knowledge to provide useful (if not always 100% correct) answers to questions, and is gaining rapid acceptance as an assistant for a wide range of tasks. Today, I see most people treat GPT-4 as a slightly-hallucinatory-yet-somehow-more-useful version of the Google search box—but asking for healthy muffin recipes by typing a question into a webpage chat window is barely the beginning.

The current era generative AI is sparking a humorphic agent revolution, with the promise and dream of near-autonomous operation feeling closer than ever. Tooling for this space like Langchain has rapidly emerged, allowing developers to easily build their own agents on top of generative AI models. These agents not only excel at a wide range of tasks but also facilitate more natural and relatable interactions with users, fulfilling the original vision of humorphism.

As we enter this golden age of humorphism, I expect humanistic AI agents to play an increasingly important role in our lives. The improved capabilities of these agents will foster deeper engagement and trust between humans and AI, allowing users to derive greater value from their interactions.

But this is just one stage in the ongoing evolution of AI design. As users become more familiar with AI-driven interactions and technology continues to advance, we will eventually see a shift towards more efficient and functional AI design patterns. This will mark the gradual decline of humorphism as AI agents prioritize practicality and utility over human-like attributes.

After humorphism: what will be the "flat design" for AI agents?

Skeuomorphism in digital product design was mostly succeeded around 2010 by an interface design approach called "flat" design. Flat design (or "authentically digital", if you've got your marketing shoes on) came about once users became more comfortable with digital interfaces. Interaction patterns settled, and the skeuomorphic supports were slowly removed.

(As an aside, this was an interesting time to be a designer—in the span of a year or so, we went from spending days in Photoshop shaping the reflections and distortions in a mercury thermometer app, to slapping a thin red rectangle and a couple numbers on a screen and yelling "ship it!")

As with skeuomorphism, humorphism will fade away as interaction patterns with AI become more established. As people become increasingly comfortable with AI agents, they'll rely less on human-like characteristics to navigate their interactions. With the decline of humorphism, the interaction design of AI agents is likely to evolve to focus on reducing interaction friction and increasing functional specialization.

Efficient, seamless interactions

As interaction patterns become more established, designers can stop worrying about being "familiar" and focus on creating more seamless ways for users to engage with AI agents.

These new interaction methods could encompass a variety of modalities, such as voice, gesture, or even brain-computer interfaces. AI agents could be designed to respond to pervasive non-verbal cues, such as facial expressions, body language, or even changes in body temperature or heart rate. These more organic and seamless inputs will ultimately reduce the reliance on humanistic communication patterns like natural language conversations.

Task-specific agents

AI interaction design may also shift towards creating task-specific agents, where each agent is designed to excel at a particular task or domain. This specialization allows for the agent's performance to be tuned to a small set of success criteria, catering to a set of specific needs and requirements without getting distracted or accidentally falling in love with you.

Specialized AI agents could be developed for medical diagnostics, financial analysis, or even niche hobbies such as birdwatching. There may also be specialized agents intended to represent the interests of you as an individual—your very own Ari Gold who will fight for you on a digital battlefield to make sure other agents aren't able to take advantage of you.

Design for humorphism now... but not for ever

As designers, we have to think not just about how our products will be used today, but also how they will fit into the landscape in the future. As you design for AI-enabled digital products, feel free to embrace the new golden age of humorphism—but be careful not to forget that there will soon be a day where your reliance on human-like interaction patterns will be looked at the same way as the honeyed oak bookshelf in the background of Apple's 2010 iBooks app.

Instead, think expansively about different experiences enabled by the underlying generative AI technology, and look for opportunities to make your interactions more efficient, pervasive, seamless, and specialized. Anticipate new technologies and user expectations. This space is moving fast, and it will be all too easy to design for a world that, when your product launches, has already passed us by. I’m trying to take my own advice into account as I design creator tools like Salesforce’s Einstein GPT for Developers, and it’s challenging to not fall into the same predictable patterns and approaches.

It's not new advice, really. But it's all moving at goddamn close to light speed right now, so you can wait until things slow down—or you can try to help make the future.

Alasdair Monk’s key points about designing creative tools:

• Lower the barrier to entry with minimal setup or configuration. You have to be able to play and experiment to learn, not just read documentation.

• Support going from first interaction to power user status as quickly as possible.

• The era of the all-in-one tool is ending; build small, sharp tools that play well with others.

• One big use of AI in this space will be to help people create their own special-purpose creative tools, rather than using tools meant for everyone.

• Creative tools can give you superpowers by providing alternate perspectives; for example, shifting your visualization from Figma screens to state machines helps you understand and communicate how a complex app works

GEORDIE: So, when I was thinking about who is obsessed with creating tools for creators, I thought of you. The first thing I ever saw from you was your constraint-based layout stuff. Which was what, 2012?

AL: Yeah, it was probably like 10 years ago.

GEORDIE: And all the way to what you're doing at Vercel now. So, it seems like, and tell me if I'm wrong, it seems like you have this penchant for creating tools for people who build things. Am I right about that? And if so, what is that all about? Where does that come from?

AL: Yeah, good question. So yeah, I think you're absolutely right. I really like working on lowering the bar of entry to making stuff with a computer, like broadly, not even just for developers or designers. I really enjoy everything in that kind of arena. 

In fact, really recently, I was working on a toy interpreter for the BASIC programming language. That was also really fun because you turn on the computer and you launch directly into an IDE-type environment. It's the same as opening a document in Figma. You're instantly in a place where you can start playing and building stuff without worrying about installing packages or folder hierarchy, or any of the stuff that gets in the way of just sketching an idea, whether that's literally sketching or figuratively in programming. 

I really enjoy working on those kinds of things. Throughout my career, I've tried to chase people that care about the same sort of things. Heroku is a classic example. For years, and probably still today, it's easier to get Rails running on Heroku than on your local machine.

GEORDIE: Postgres!

AL: Yeah, right. Exactly. Heroku just gives you those things out of the box. I worked on similar things at HashiCorp with Waypoint as well, albeit for a more niche audience. And then I guess I've gone the other way again with Vercel, which has a huge audience of users of the most popular programming language in the world, JavaScript. We're thinking about both the tooling, like Next.js, which is trying to do the job of what the Commodore 64 was trying to do all those years ago – an environment where you can immediately be productive.

So, I guess that's where it comes from, and that's what it's all about. I've never been drawn to just working on consumer products that you just use. I've always been interested in making things that you can tinker with.

GEORDIE: Do you think that you have to be designing for yourself in a way, like you are a developer and a designer, you tend to design for developer and design tools?

AL: Yeah, I guess so. I think the thing I really like is the idea of tools that do a specific job. I don't know if you've read the Unix philosophy documents from way back, but it's that whole idea of a tool should do one job and one job only but work really well with other tools. I was thinking about this recently with Figma, where I think that Figma is going to be the last big success of applications that take their lineage from desktop publishing tools. Figma inherits a lot of its ideas from Sketch, Photoshop, Illustrator, and going back to things like Microsoft Publisher and before that, Fireworks and Quark. It really inherits a lot of multipurpose, general tools based around the premise of a sheet of paper.

I think we're going to see more tools for specific tasks. Canva is an interesting example because depending on what you're making with Canva, you get slightly different tooling. The UI is reactive to what you're trying to make rather than a homogenous UI for designing things. We haven't seen that much in design tooling, but you see it a lot in video effects and the 3D world where there are entire applications for one specific thing.

If you're working on 3D, for example, you might have an entire application dedicated to creating textures and materials. You have entire applications for clay modelling, geometry, shaders, animation, syncing soundtracks, green screening, and more. It's really cool because you can go deep on each specific thing, and I imagine we'll see more of this in the future.

A.I.'s role in helping design things will also be super interesting, particularly in the process of giving a first draft of something. It can help unblock creativity by providing direction and variation, ultimately leaving it down to the human's taste and opinions. I'm excited for this, especially in the product design world where we often spend a lot of time on problems that have already been solved. It would allow us to focus when a unique approach makes sense.

GEORDIE: Your Mac apps especially have a really clear voice of a creator in them. They have this Al and Hector-like thing about them that is hard to describe. I don't know if an A.I. would build something in that way. Do you think the A.I. builds the foundation and then you put the Al and Hector-ness on top of it when you're done?

AL: It's interesting. I imagine so, because I don't think it's that different from the jump from graphic design being a physical medium to happening on computers. There's still a stigma around digital art being less real, important, or impressive. I'm glad you mentioned my Mac apps for some free advertisement.

GEORDIE: What is it? Replay? Replay Apps?

AL: Replay, that's right. Since ChatGPT has been around, I've started tinkering with the idea of building Mac applications with it. It's been insanely powerful, especially for someone like me who knows enough Swift to be dangerous but not the ins and outs of Apple's proprietary APIs. Having a machine that tells you how it works specifically in the context of your question is amazing.

It doesn't eradicate quality, care, taste, or any of the things we bring to building products. It just helps us get to stuff faster, which is really cool, apart from when it leads you down a completely wrong avenue of thinking. I recently worked on something that interacted with the Figma APIs and asked it to draft me a Ruby script. It completely hallucinated five endpoints, and I thought the script was amazing until I tried to run it and got 404 errors everywhere.

GEORDIE: It does that with Python modules so badly. It makes up brilliant ideas for imports exactly what you need, and then you're just like, oh, just go ahead and use it. Then Python's like, that doesn't exist.

AL: Yeah, that's so funny.

GEORDIE: Does taste becomes more important when we're getting all this content and can build an app by describing it?

AL: I think so. It's like this general trend we've been seeing with anything that becomes a commodity. Ninety percent of the market is reproducible stuff that's easy to make, not super unique, and does a job well, but isn't going to stand out. In any category, there's always this 5% or 10% where there's a lot of interest, a sense of quality, and status.

Take Squarespace, for example. It's an amazing platform for creating a solid website at a low cost and without any technical expertise. Nowadays, work that would have come to an agency years ago for more money is going to Squarespace. I think that's a good thing because there's still this huge space to build things that aren’t templatized and constrained, and are entirely new ideas. That's where we should be putting our time and energy, not into building another generic website.

GEORDIE: You mentioned this contrast between heading towards a pipeline of specialized, sharp tools, specifically for design and development of digital products, and an emerging world where there isn't any pipeline or tools, just a giant language model that you feed everything into and it spits things out. These seem like completely opposite visions of the world. How do those reconcile? I agree with you on both of them, but I don't see a world where they can both exist.

AL: It's interesting because even if there's one GPT-10 that runs the world, I think there's still a lot of room for different interfaces. It's like the blockchain analogy where you can put any interface on top of a system. I think it's going to be similar with A.I. and machine learning, as natural language input isn't the right input or output for a lot of things we do on a computer daily, especially for power users.

There's still space for expressing things in natural language, and A.I. can do a lot of boilerplate setup, but once we get into the details, we probably don't want to do each round of iteration by typing it out. I think we'll see interesting developments in input and output methods for these models.

GEORDIE: Is using an A.I. tool for creation a fundamentally different skillset, like a managerial or leadership skillset, or do designers already have the right tools to make good use of it?

AL: I don't think it's a mindset change. For me, A.I. allows you to get over the starting hump and gives you something to react to. I think most people can understand and play with the tools. Prompt engineering is interesting, but it feels like a bug in the input system rather than a feature.

In the foreseeable future, A.I. will mostly benefit people who already understand something in that realm. A.I. won't create JavaScript developers out of those who've never shown interest or aptitude for programming, but it can make a JavaScript developer ten times more productive. It'll be interesting and scary to see how the gap widens between those who understand how to get what they want from a computer and those who don't.

GEORDIE: I totally buy that. Have you ever heard of the term Jevons Paradox? It's this idea that when technology makes something more efficient, the consumption of that thing increases, maybe even beyond what you’d expect based on the cost savings from the efficiency gains. For example, with Swift and iOS development, I feel more hopeful that I can make something because I just need to understand enough Swift to manage the model output, and that means that I try to build things now I never would have attempted before.

This unlocking of efficiency makes me wonder if it will actually dramatically increase the number of developers, because a single developer can produce so much more value. Maybe specialized training would only be needed in the field where you want to create value, instead of the intricacies of JavaScript development.

AL: That's interesting. It's like the industrial revolution where everyone became more productive through automation and industrial scale. However, advancements in technology don't necessarily make anyone's lives easier or better.

GEORDIE: Who do you think is on the chopping block?

AL: Probably people who are engineering or designing things that aren't particularly differentiated or high quality, which can be easily replaced by these kinds of systems. Those who build websites at a low tier of pricing without much differentiation might have already lost out to Squarespace.

GEORDIE: Do you see people making their own tools more, or am I overestimating the desire to do that?

AL: I think one of the interesting things about how OpenAI has pitched their products is that creating your own tools is one of the core ideas. For example, with the ChatGPT API, they have a system command where you can customize how the system operates, essentially creating your own version of ChatGPT, much like a unique spreadsheet in Excel for your business.

I built a website over the summer that uses GPT-3 to read tweets from a specific account and make sense of the unstructured data. I think this technology could offer people a powerful canvas, like a spreadsheet, waiting for them to create something unique.

GEORDIE: Back to developer tools for a second. Do you find yourself getting inspired from anything in the physical world when you're building tools for developers?

AL: Yeah, I think so. Some of the stuff that most inspires me is from the world of video games, like RollerCoaster Tycoon, which is basically my favorite game of all time. I think games have a lot to teach us about creativity, interface design, and storytelling, which is important for any object of value. Comparing your average SaaS onboarding to the same thing in a game, it's like really kind of bleak and plain. You play The Last of Us through, and in the first 40 minutes not only are they telling you a story, but they're also teaching you how the game mechanics work. And you basically learn everything you need to know for the entire 16 hours of gameplay in that first 40 minutes.

So I mostly mentioned things that are digital, not physical...

GEORDIE: (Laughing) yeah, I love that I just accepted that computer games are part of the physical world.

AL: I mean, there's a controller (laughing). In terms of physical inspirations, I love the work of Teenage Engineering. They make amazing stuff that's unique, playful, and offers a canvas for you to create in. I have a Playdate sitting next to me, which is fun and creative and just a really great consumer experience as well.

GEORDIE: Do you think there's anything fundamentally different about building tools for developers as consumers versus as creators? Do you take any special consideration when you're building something that is to be used to build other things?

AL: Yeah, I think so. The main difference when building tools for developers is the ability to go from the first interaction with something to becoming a power user very quickly. When I pick up new technology, whether it's a new programming language or a web framework, the first thing I look for is example source code. I think that's a widely applicable stereotype in the realm of building things for developers and engineers.

The first significant impact of A.I. in the programming world is how it enables learning. It provides code samples instead of just documentation, which can be modified or extended as needed. This is different from most consumer applications, where supporting power users or casual users might not be necessary.

GEORDIE: You don't see a lot of instruction manuals these days. Why is that? Is it because our tools are, are better at teaching us through usage? Is it just that our tools are less powerful?

AL: I've been learning Blender since COVID started, and I went in completely naive. The instruction manual still exists, but not in a long written text form. I get a lot of good CSS and JavaScript knowledge from 22-second tutorials on YouTube and TikTok. It's perfect for that stuff. So, I don't think instruction manuals are going away; we just have better ways of delivering instructions that don't require sitting down and reading a book.

GEORDIE: Last question: What did I miss? What did you want to talk about that I didn't ask?

AL: I gave a talk at Vercel internally last year called "Designing With State." It's based on the premise that when we design complex products today, we're really designing a state machine. The medium we use to design the state machine is hundreds of screens in Figma or Sketch. It seems antiquated to capture each moment of a decision tree and draw a picture of it. Figma is a product of desktop publishing tools from 30 years ago, and it lacks knowledge or sense of state.

At both Hashicorp and Vercel, I started designing more using Svelte and designing in code, using tools like Tailwind CSS and Vercel's Visual Preview comments for rapid iteration. I can quickly come up with a demo, share it, and receive feedback. This approach is an example of sharp tools that are good for figuring out complex states for a product, but not for designing icons.

In the immediate future, I'd love to see more innovation in this area. There are a few tools like XState for visualizing state, but the more of the state machine that we can share and discuss as a team, the less misunderstanding and churn in engineering will occur, which is only a good thing for shipping products faster and explaining complicated pieces.

We had one unavoidable, pervasive C-word from 2020 to 2022, and now we have another in 2023: ChatGPT. It pops up in every interview and every conversation, even when the topic has nothing to do with AI. So let’s just run with it.

Unfortunately, I can't tell you what the influence of generative AI will be on digital creative tools. Nobody knows. Even inventors don't fully understand the impact of their inventions. Steve Jobs didn't want an App Store.

But we can make a few educated guesses.

1. Creators will need new skills

AI multiplies the power of digital creative tools the way electricity multiplied the power of hand tools. Creator output will skyrocket. However, the real superpower will be mixing the creator's unique perspective with the AI model’s output.

Humans working without AI assistance won't be productive enough to compete. Humans copy-pasting ChatGPT outputs without adding their own vision won't be interesting enough to notice. The most successful creators leveraging AI will be human-machine hybrids.

However, there aren't yet great creative tools supporting this workflow. The ideal flow may look a little bit like an airplane's autopilot: the human takes off (initial idea) and lands (final refinement) but all the flying in the middle is done by the machine. Creators will have to get used to being "player-coaches" at best—the skills that apply here are more those of the creative director than the production artist.

Taste will become paramount. With the production bottleneck eased, the new bottleneck will be in curation. Even skilled creators who fail to curate well will suffer as it becomes hard to distinguish their great work from their merely adequate stuff. Tools for rapidly curating massive generative output will become central to the creative workflow.

2. The creative tool landscape will evolve fast

We will see simultaneous fragmentation and unification of creative toolkits. Alasdair Monk, VP of Design at Vercel, suspects that we're at the tail end of the dominance of "all-in-one" tool platforms like Figma for digital design. Instead, we’re about to see a proliferation of many special-purpose tools, unified by AI-based glue workflows and customized to the individual and use case. Imagine Zapier, but for everything.

Once patterns settle down in 5–10 years, we may see a re-consolidation around a few common ways of doing things, but the next decade or so will be the Wild West. This represents a huge opportunity for companies building creative tools to explore boundaries and set precedents.

There will be less ecosystem advantage, less platform lock-in, and less incentive to build "suite" tools designed to work really well together—because everything will work really well together. Instead, there will be more incentive to specialize, with tool builders designing tools within a domain or technology platform that they have extreme expertise in. For creative tool users, this means more "best of breed" solutions, but fewer obvious choices.

3. Creator careers will be turned upside down

In the coming months and years, it is going to get much harder to hire good creators in domains where you don't have technical expertise, because the cost of faking competence is dropping to zero. (Of course, the definition of "faking" itself will also evolve—if you're expected to leverage AI to be productive at work, then shouldn't you be expected to use AI during your interviews too?) Creator portfolios will have to focus even more on impact than in the past. Compelling and unique narratives will be key to self-marketing.

AI-assisted tools may also reduce the disadvantage in being a super-specialized lone wolf. By filling in skill blanks, and by providing greater raw productive power, generative AI tools may lead to an increase in the number of creators finding it more favorable to work independently, rather than inside larger companies. Before, you often had to work with others in adjacent areas to create value; a front-end web developer needs someone with backend skills to launch an app, and someone with marketing skills to drive adoption. Now, much of the work outside your specialty can be automated, at least to a baseline level of quality.

Through all this, the unique voice of the creator risks being lost. Creators must be careful to maintain a consistent voice and style that is recognizably them, and which can be recognized by the audience. Creators must always find a way to put their imprint on the final product. This will be critical in maintaining their marketability as AI tools increase in sophistication and quality.

Finally, this all is mostly limited to digital creators. I've been building digital tools for years, so I'm at least somewhat confident in my predictions above. I have no real sense of what the impact of AI will be on creators in non-digital spaces. Painting, sculpture, industrial design, music performance, handicrafts—will AI come for them in the same way? Will consumers seek refuge in the harder-to-GPT analog world? If you have some knowledge in this domain, let me know in the comments (open to both free and paid subscribers for this one).

No pithy conclusion. This shit is crazy.

~ Geordie

To start, let's limit the meaning of "design" to the process of creating things around the needs, wants, and capabilities of a human being. This is sometimes called "human-centered design" or "user experience design"—and if you're old school, "ergonomics" (say what you want about people in the mid 20th century, but they gave things awesome names).

Design also is distinct from invention, in that invention means developing a whole new ability, like sending information through the air on invisible waves, whereas design is the arranging of existing inventions into something that takes the human side into account. Inventing the radio is different from designing a radio. Invention gets you Marconi's first wireless telegraph transmitter. Design gets you Dieter Rams' RT20.

Let's move on to "creative".

"Creative" refers to the act of creation. Building something from nothing—or more commonly for us non-deities, from a set of simpler primitives and components. A composer creates music from notes and timing. A painter creates a scene from pigments and medium.

Let's separate the act of "creation" from the idea of "creativity". Ultimately, creation must generate artifacts, whether they are ephemeral (a song) or concrete (a painting). It doesn't actually matter if the song is derivative, or if the painting is cliché; creation can happen in the absence of "creativity", and some critics might say it usually does.

Finally, let’s define "tools". This felt like an easy one when I was getting started, but in the end a tool can be a lot of things, many of which don't look very tool-y.

Take oil painting (and you will a lot, if you stick around). Is a paintbrush a tool? Yes, pretty obviously. Are paints a tool? A canvas? I think yes—they enable the act of creation together, and the results would be noticeably different if one or more were missing. In fact, thinking about tools metaphorically as "brushes", "paints", and "canvases" is a helpful way of organizing our thoughts around what makes something a creative tool in the first place. But we'll come back to that.

So "designing creative tools", in the least succinct way possible, is the design of tools that help others to engage in the act of creation, with a specific focus on how these tools will be understood and employed by their users. From DJ booths and chef's knives to point-and-click user interfaces for building complex web-based automation flows, the tools we give creators have a great influence on what they can make us.

An aside: why is this web guy all about oil painting metaphors?

Like my awkward taste for world music involving didgeridoos, it goes back to my childhood. Cue hazy TV show memory sequence.

My dad's parents came to art relatively late in life—my grandmother picked up oil paints in her middle age, and my grandfather began wood carving a few years later. Not the typical starving artist immigrants, they had built comfortable lives for themselves in Franklin Lakes, New Jersey before indulging their creative sides.

My memories of their home are filled with the light, sweet stink of turpentine and mineral oil. Down in their fluorescent-lit basement, I loved how the brushes and chisels and palette knives and gouges politely cohabitated, the carving tools in my grandfather's worn but impeccably clean workshop, and the paints in my grandmother's wild studio.

My grandfather died, too young, and his tools sat untouched. I visited them sometimes, shuffling through piles of project wood and 1980s electronic equipment to stand in the workshop alone and survey the wooden-handled implements all hanging in size order on the pegboard. I tried to carve something once, probably an ashtray (nobody smoked), but I slipped and sliced a nice chunk out of my hand. Wood carving took a kind of measure-twice-cut-once-ness that I never really could get my mind around.

My grandmother kept painting: portraits, still lifes, landscapes. She traveled to faraway places, almost always to paint, or to buy books about painting from little shops in little towns, or to teach others how to see light in ways that would let them make something beautiful.

Later, when her cancer treatments failed, they switched her to prednisone. It puffed up her face, but gave her an explosive energy in the final months of her life. She produced dozens of canvases, none worked quite to completion but all demonstrating a hurried intensity, like she didn't want to take a single idea with her.

When she died, my dad got the carving tools, but all the oil painting paraphernalia became mine. Most of the brushes were gummy or fraying (at the end she must not have paused for her usual cleaning and conditioning with Speedball Pink Soap), but a single stiff No. 6 hogshair meant more to me than anything I could buy at Glick. I've held onto these for two decades now, though my oils practice never really became a habit no matter how much I called myself a "painter".

Instead, I turned to the digital world. Code and pixels felt as real as paints to me, and have the distinct advantage of being easier to clean off your hands.

Brush, paint, canvas

Back to that metaphor I promised/warned about.

I like to think about three types of tools coming together in the act of creation: the brush, the paint, and the canvas.

The "brush" enhances the capabilities of the creator, changing not just what their hands can do, but what their eyes can see, and what their mind can think. Brushes include things like syntax highlighting in the coder's IDE, the pen tool in the graphic designer's vector editor, and the venerable seam ripper in the quilter's arsenal (yeah, I'm going to keep coming back to the seam ripper).

The "paint" is the creative medium, the raw materials that will be brought together. These can be raw indeed—words for the writer, or black walnut wood for my grandfather—but can also represent larger components of encapsulated complexity, like an intricate rubber stamp, or a whole damn programming language. These prebuilt components incorporate the craftsmanship of others into your own creations, letting you build upon those foundations to create more complex works.

The "canvas" is the workspace, where the medium is blended, layered, and worked. It holds the creation in front of the creator, letting you see where you missed a spot. Some canvases are easy to identify: a word processor's page, or an audio editor's spectrogram. Others you need to squint a bit: the canvas of a piece of performed music is wholly intangible, and the canvas of a crocheted sweater is the knitter's knees and sofa arm and coffee table—at least while they're knitting.

But why organize things into a metaphor in the first place? Does calling code syntax highlighting a "brush" really buy us anything? Hopefully, yes; the goal of this project isn't just to talk about creative tools in isolation, but to draw out common patterns and features that work in different contexts. I hope to find parallels that can help the designer of a code editor learn from the designer of a 3D printer, and vice versa. By organizing our thinking like this, we increase our chances of finding unseen connections and learning something really deeply true about what it means to design tools for creators.

Stay tuned and subscribe to hear from some people who are doing that design work every day. I hope you learn as much as I do.

~ Geordie

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Ever wonder how our design tools got… designed?

I’ve been getting paid to make web stuff for over a decade, which means I’ve had the privilege and opportunity to ship some real bad design.

A great craftsman, the saying goes, never blames his tools. But it’s hard not to wonder—maybe back when you were stuck late at the office, fingers cramping as you nine-sliced buttons in Photoshop to get those goddamn rounded corners that the client needed so bad—if maybe our tools are a greater influence than we’d like to admit.

This post kicks off a series of questions, ideas, interviews, and (hopefully) answers as I dive deep into how great creative tools get designed—both in the digital space (where I live) and the analog world (I own some property there). Maybe there’s something your next Figma plugin can learn from the thoughtful design of a quilter’s seam ripper. Like how to give things cool names.

Follow me on this little adventure as I learn from the people who design the tools that we use as creators, whether we’re ripping seams or pushing pixels or dropping beats. We’ll figure out some deep truths about designing creative tools—or maybe we’ll fail and just end up having a really fun time talking to brilliant people.

Either way, you’ll want to come along.

~ Geordie