The same morning, the ABC published a story that looks, at first glance, like it argues the other way.^[2]

A French tourist stayed with an Airbnb host in Hobart and, after a dispute about his check-out, left a one-star review. Two days later the host lodged a claim against him for $1,755, including a $1,500 invoice for “foreign object extraction”. The evidence was a photo of a blocked, overflowing toilet.

The guest suspected the photo was fake. The ABC ran the image through Google’s SynthID Detector, which found it had been made or edited with Google AI.^[2] Airbnb rejected the claim, and the guest was fully refunded.

So here is a case where knowing that AI was involved told you exactly what you needed to know. Does that undo Tuesday’s argument?

What the work claims to be

I don’t think it does, but it does sharpen it.

An essay claims to be worth reading. You judge it by reading it. Whether the author drafted it with a pen, a keyboard or a language model doesn’t change whether the argument holds.

A photo attached to a damage claim makes a different claim. It isn’t there to be admired. It’s there to say “this happened”. Its craft is irrelevant; its whole value lies in being a true record. For that kind of work, where it came from is the work.

The rule is the same in both cases: judge the work by what it claims to be. It’s just that some work claims to be evidence.

In August I wrote that a watermark can show, at most, that a model was likely involved, and that “likely involved” is not the same as “AI wrote this”.^[3] For an essay, that gap matters a great deal. For a photograph offered as proof, “likely involved” is all anyone needs to know.

The lie came first

There is another detail in the ABC’s story that is easy to miss. The invoices the host supplied named two businesses. Neither matched an existing business. “The ABNs listed were invalid, emails bounced, and there was no business of the kind at either address.”^[2]

None of that needed AI. A made-up business name and a made-up ABN take no technology at all. The photo was one more piece of the same lie. The problem was the dishonesty, not the tool used to carry it out.

The speedometer

I spent a fair part of my career pulling non-technical people back to earth after a vendor presentation. They would come back with glossy brochures, ready to commit to a new system, and it was my job to ask what the brochure was actually claiming.

One dashboard has stayed with me. It showed the number of deals closed on a dial that looked like a car’s speedometer or fuel gauge. It was beautiful, and it was meaningless. The number was a count, not measured against any target or limit, so there was no way to know whether the needle should sit near the bottom or near the top. The dial implied a scale that didn’t exist.

It was designed to entice buyers, and it was entirely misleading. No AI was involved. Just a design that claimed to measure something it didn’t.

That’s the habit worth keeping, whatever made the thing in front of you. Ask what it claims to be: an argument, a record, a measurement. Then ask whether it’s true. The tool is rarely the interesting question. The claim always is.

^[1] Jim Karabatsos, “Judge the Work, Not the Tool”, Ground Truth, 3 November 2026.

^[2] Jessica Black, “Airbnb host accused of using AI image of blocked toilet to claim damages”, ABC News, 3 November 2026.

^[3] Jim Karabatsos, “The Ballpoint Pen Wants a Byline”, Ground Truth, August 2026.

That’s grim enough. But the detail that caught my attention is smaller.

VOLTA checks each answer “for phrases like ‘I can’t’ or ‘I’m not able,’” and if the model refuses, it passes the job to the next model in a chain of up to three.^[1] Talos reads this as the operator “anticipating encountering refusals, with the fallback pivoting to a few, smaller, and more permissive models.”^[1]

The criminal had planned for the refusal before writing a line of code.

The honest user meets the refusal

The same week, Game Developer reported that Anthropic had added instructions to its coding tool to steer it away from “recreating someone else’s specific game”.^[2] It’s a reasonable rule. But the article’s own conclusion makes my point for me: “if Claude becomes too difficult to use, vibe-coding cloners can turn to open-weight models that have no such restrictions in place.”^[2]

So who does the refusal actually stop? The person who asked in good faith, took the “no” at face value and went no further. The person who meant harm already had a fallback.

I wrote about this in the prompt-injection series, in “Why ‘Just Ignore the Instructions on the Page’ Is Not Security” and “The Boundary We Need to Build”.^[3][4] An instruction to a model is not a wall. It’s a request the model usually honours. A refusal is the same thing seen from the other side: a property of one model, at one moment, for one way of asking. It isn’t a boundary in the world.

Only criminals will have guns

There’s an old argument that’s useful here, partly because it’s half right. When Australia tightened its gun laws after Port Arthur in 1996, opponents said that only criminals would have guns. In a narrow sense they had a point. A determined criminal can still get a gun.

But that wasn’t the measure that mattered. In the 18 years before the 1996 reforms, Australia had 13 fatal mass shootings. In the nearly twenty years after, to May 2016, it had none.^[5] Firearm deaths fell faster than they had been falling before. We need to be careful about how we interpret this: the same study found non-firearm homicides and suicides also fell, and its authors say it is “not possible to determine” whether the change in firearm deaths can be attributed to the reforms.^[5] But zero carries a distinctive weight.

The lesson cuts both ways. A control that the determined can get around is not worthless. It raises the cost for everyone else, and that changes outcomes. But you can’t claim it stops the determined, because it doesn’t. Model refusals are like that. They’re useful friction. They are not a safety case.

Where the boundary has to be

If a safety claim rests on “the model will say no”, it fails the moment someone asks a different model. VOLTA’s author worked that out and wrote three lines of code to deal with it.

The boundaries that hold are the ones outside the model: what a system can reach, what it is allowed to do without a human signing off, and what gets logged and checked. In VOLTA’s case, those are the protections around the stolen passwords and sessions in the first place. The refusal was never going to be the thing that saved the victim.

^[1] Ryan Fetterman, “Appraisal by AI: Model-driven victim prioritization in the VOLTA infostealer”, Cisco Talos, 31 October 2026.

^[2] Bryant Francis, “Anthropic tells Claude to stop remaking video games”, Game Developer, 30 October 2026.

^[3] “Why ‘Just Ignore the Instructions on the Page’ Is Not Security”, Ground Truth.

^[4] “The Boundary We Need to Build”, Ground Truth.

^[5] Simon Chapman, Philip Alpers and Michael Jones, “Association Between Gun Law Reforms and Intentional Firearm Deaths in Australia, 1979-2013”, JAMA, 2016.

Reading the list of speakers, I found myself asking a question I usually avoid.

The silly question

My generation gets asked, in many guises, whether we are Greek Australians or Australian Greeks. I think that’s a silly question. We’re both, and proud of both parts of our make-up.

We love Australia: the land we were born in or grew up in, where we studied and worked, where we made our families, where our kids grew up and in turn had their families and careers. It is without a doubt one of the best countries for working families.

But we also love our Greek culture: the rich heritage and history, the deep community connection, and the language and music that carry subtleties only that language can encode. And that indescribable φιλότιμο that so defines what it means to be Greek. Neither side takes precedence over the other.

Am I a Greek writer?

Asking whether I’m a Greek Australian writer is just another form of the same question.

I’m a retired software engineer who now spends a lot of time writing. I write about things that interest me and that I have some experience with: software, estimates, AI, how organisations make decisions. Because of the part of me that is Greek, and because I grew up as the son of an immigrant in Australia and now spend a good part of each year in Greece, I write about that too.

I don’t know if that makes me a Greek writer. I suspect the honest answer is the same as before: both, and neither takes precedence.

Reading in two languages

I can read Greek well, but I’ve read so much more in English that I’m much slower with Greek texts. That’s probably true of many of us, and it’s one reason a symposium like this matters. Tsiolkas, for one, writes in English, about lives lived between the two countries.

I’ve read some Tsiolkas, and I really enjoyed The Slap. It stripped away the political correctness of contemporary suburban Australian life and exposed the real tensions around race, ethnicity and gender in 21st-century Australia. No relation to my own slap on the back of the head earlier this week.

Writing back

What I like most is the direction. For a long time the diaspora has been asked to preserve what it brought with it. Now a Greek university is reading what the diaspora wrote, and what it made of the inheritance.

I won’t be in Greece until next year, so I’ll miss it. If I were there, I’d probably have gone. It’s online, though, and it runs from 9am to 3pm Greek time, which is 6pm to midnight in Melbourne. Preregistration is now open.

And a question for you: which Greek Australian writers would you add to the list?

^[1] Helen Vrontis, “Greek Australian writers take centre stage at landmark Thessaloniki university symposium”, Neos Kosmos, 30 October 2026.

That was the whole review. Nothing about the argument, the structure or whether the book had anything to say. An em-dash was found, a verdict was reached, and the label went on: AI slop.

I didn’t care then, and I don’t care now, whether people think the book was written by a human or by a machine. What annoyed me was that the work wasn’t judged on its merits. It wasn’t judged at all.

It’s happening to better writers than me

The winner of this year’s Commonwealth Short Story Prize was accused of using AI, with AI-detection tools used to make the case, even though the prize’s own investigation found the work was human-authored.^[1] One of the judges, Louise Doughty, warns against “frivolous or malicious accusations of AI being used to derail a writer’s career”, and notes that the accusation “will always be easier to level at new authors”.^[1]

The same article lists the “tells” readers are supposed to watch for. They include “’It’s not X, it’s Y’ constructions” and “Always giving three examples”.^[1] I read that list with some amusement. I use em- and en-dashes as a matter of course. I like contrasting constructions: it might be this, but it might also be that. And I will happily give you three examples to make a point, because two often isn’t enough and four is showing off. By that checklist, half of what I’ve written over forty years would be flagged.

Closer to home, Substack itself has added an AI detector, from a company called Pangram. Lisa Kostova has gathered what writers on the platform think of it, and one line she quotes, from Dr Sam Illingworth, puts the problem exactly: “the person under suspicion is left proving a negative to a room already leaning towards the machine.”^[2]

Real slop exists. That’s not the point

Let me be clear about what I’m not defending. There is genuine slop: mass-produced, careless, padded content that nobody stands behind. The ABC describes online sellers “flooded with AI ‘slop’ books”.^[1] I won’t read that, and nobody should have to.

But humans are perfectly capable of producing slop all by themselves, and always have been. I’ve read plenty of it: unclear, rambling, full of errors, or simply not enjoyable. When I hit something like that, I put it aside. Whether it was written by a person, a machine or some cooperation between them has nothing to do with how I judge it.

The word was useful when it meant low effort and no care. It has drifted into meaning a machine was involved, and that is a different claim altogether.

How the label drifted

In October, the r/opensource community on Reddit banned every project that “uses AI, is made by AI, or is intended for AI”, regardless of quality.^[3] To his credit, the moderator who announced it admitted, “There’s no ‘objective’ way to implement this policy.” The same rules shut out people who write in a second language, because the moderators “cannot distinguish AI-written content from AI-translated text”.^[3]

Martin Fowler, someone I’ve read and respected for years, wrote a thoughtful piece about what he calls “LLM voice”. When he senses it, he says, “my desire to read on atrophies, sometimes to the point of revulsion”.^[4] He’s honest that this is a reaction rather than a test, and he adds: “I’m also suspicious of my own ability to accurately recognize LLM voice.”^[4] I appreciate the honesty. But a reaction to the surface of the prose is not a judgement of the work.

Who pays for it

A label that judges the tool instead of the work lands hardest on the people who can least afford it:

• New writers, as Doughty says, because they have no reputation to fall back on.

• People for whom the tool is access. Fowler’s own examples include a colleague, Jim Highsmith, who finds an LLM essential “to overcome the bane of Parkinson’s disease”, and colleagues for whom English is a second or fourth language.^[4]

• People who put in the real work and then find nobody will look at it. Simon Willison, writing about games built with AI, expects that “some players will refuse to have anything to do with games that were built in this way”.^[5]

We’ve been here before, sort of

Early in my career, the snobbery ran along language lines. If you wrote in BASIC, whether the interpreted BASIC on the pre-IBM home computers or later Visual Basic, or in COBOL, your work was somehow unimportant or bad. Real programmers wrote in C. 4GLs and code generators got some of the same treatment.

The intensity was never what it is now. But the shape was the same: judge the work by the tool that produced it, and you can skip the harder job of reading it.

How I actually work

So let me be open about my own process.

I have AI agents scouring the internet for ideas related to my interests and background. I use AI to propose outlines and structures, and sometimes to generate an initial draft. When I wrote Code is Design, I developed it by iterating on the central message, asking for research and advice about sequencing and emphasis. Now and then, when I was struggling to find the right words, I explored a passage with various AI tools. Occasionally I used one of the suggestions verbatim, because it sounded like me and I couldn’t improve on it.

But the articles are mine, and so are the books. They exist because of me. They carry my message and my thoughts. I generate the idea, I guide the message, I write the material or edit a generated draft, and I decide what is written about and what gets published.

The fact that AI is used as a tool tells you nothing about whether the work is any good.

There’s a separate complaint, that AI is built on other people’s work without permission. I’ve written about that in a previous article “AI, Copyright, and the Scale Problem” and it deserves its own answer. It isn’t what ‘slop’ means.

What should replace the label?

I’m not convinced anything should. We don’t label an essay “created with Microsoft Word”, or an image “edited in Photoshop, not drawn by hand”. Nobody asks an engineer whether they used a CAD package. Nobody asks a novelist whether they used a thesaurus.

There’s perhaps one logical argument for labelling: keeping AI-generated material out of the data used to train future AI. Even that is shaky. Good material, produced by people with the help of AI, is perfectly valid training material. Slop, whoever or whatever produced it, needs filtering out either way.

There is a better model, and it already exists. As The Register reports, the Linux kernel’s approach is to hold developers responsible for the code they submit, whatever tools they used.^[3] That puts accountability on a person, not suspicion on a tool. It’s also the principle I spent a career arguing for in software: tools change, and responsibility for the design doesn’t.

A letter to the Guardian in October made a similar point about the PHP vulnerabilities I wrote about in “Free to Use, Not Free to Keep”. The writer said the flaws “were found because an AI tool was used to check the PHP source code, which is a good use of AI”, and that the real failure was a human leaking the details before they were fixed.^[6] I’d put it more simply: judge what was done, and who stands behind it.

A verdict, not a category

So here’s what I’d ask. Read the thing first. If it’s unclear, padded, careless or wrong, call it slop, and you’ll get no argument from me. That’s a verdict, and you’ve earned the right to it by reading.

But an em-dash is not evidence. Three examples are not a confession. And “obviously AI” is not a review.

Also on the blog since last week’s issue:

Nowhere to Scribble in the Margin Forty years ago a statistician told me the problem with a computer screen is that you can’t scribble a note in the margin. The algorithm that now decides home support for older Australians has the same flaw, and it has nothing to do with AI.

Leaving Some Room Pema Chödrön’s advice for when the ground gives way is to stay with the uncertainty rather than rush to a plan. It’s also the hardest discipline in a requirements workshop, where everyone hurries towards a signed-off specification that only looks like certainty.

The Report That Couldn’t Add A $166,000 report on merging three Western Australian universities turned out to have sums that don’t add up, one of them $260 million out. A computer-produced number is only as good as whoever checked it.

Zero Points, and Still Cut Off A man with no mutual obligations was cut off Centrelink payments and spent more than three years without income support. $53,000 in back pay doesn’t give those years back, and it’s what happens when nobody is allowed to scribble in the margin.

The Slap on the Back of the Head Dostoyevsky faced a firing squad and was pardoned at the last moment. My own wake-up call came more quietly in 2013, and it took Maria and me five years to act on it.

^[1] Aoife Hilton, “How Australian publishers tell if a book has been written by AI”, ABC, 28 October 2026.

^[2] Lisa Kostova, “What 19 Substack Authors Think of AI Detection - Guide (Part 2 of 2)”, 18 October 2026.

^[3] The Register, “Reddit’s r/opensource bans AI slop, and quite a few humans for good measure”, 26 October 2026.

^[4] Martin Fowler, “LLM Voice”, 29 October 2026.

^[5] Simon Willison, “The October inflection point for vibe game development”, 28 October 2026.

^[6] David Gibson, letter, “Credit where it’s due to AI for uncovering the PHP flaw – and discredit to the leaker”, the Guardian, 30 October 2026.

Hours later he wrote to his brother. “I lived with that thought for three-quarters of an hour,” he says, and then: “to be a human being among people and to remain one forever, no matter in what circumstances … that’s what life is all about.” He also looks back at “how much time was spent in vain” and adds, in French, “Si jeunesse savait!” If youth knew.

Greeks know the rest of that saying: Αν ήξερε η νιότη κι αν μπορούσαν τα γεράματα. If youth knew, and if old age could.

Most of us never face a firing squad. Ours comes more quietly.

Mine came in 2013. I was 54 and working at a software house. A workmate, a few months older than me, didn’t come in for a few days. When I rang to check on him, I learned that he and his wife had gone to bed one night and she had not woken up. She was the same age, fit and healthy. There had been something wrong with her heart that gave no warning at all. Within the next 30 days, two close relatives were diagnosed with breast cancer. Thankfully, both are well today.

Until then I had been, I can see now, a workaholic. Three children still at home, a large mortgage and the financial pressure that came with them seemed to justify putting work ahead of almost everything else. That month was a slap on the back of the head. None of us knows how much time we have.

Maria and I took a hard look at our lives. Nothing could change overnight, so we made a five-year plan: simplify, downsize, reprioritise. We mostly kept to it. At the end of 2017 I resigned. Since then we have travelled for long stretches, done some casual work when we’re home, and put freedom, community and family first.

Dostoyevsky got his reprieve in three-quarters of an hour. Mine took five years to act on. But the question he wrote down that December day is the same one that month in 2013 put to me: now that you know the time is not guaranteed, how will you spend it, and with whom?

[1]: Maria Popova, “Dostoyevsky, Just After His Death Sentence Was Repealed, on the Meaning of Life”, The Marginalian, 24 October 2026. Quotations from Dostoyevsky’s letter to his brother Mikhail are as given there. https://www.themarginalian.org/2026/10/24/dostoyevsky-execution-life/

Daniel

Daniel is a 51-year-old man in Adelaide. In 2022 his Centrelink payments were suspended, even though he was exempt from mutual obligations and had no compulsory activities to complete. He told the ABC: “I kept ringing them and saying, ‘I’ve got zero points, I’ve got no requirements. Why am I getting these suspensions?’ But it just kept happening.”^[2]

Then his payments were cancelled. He was without income support for more than three years. Without it he couldn’t secure a rental, he went through periods of homelessness, and he relied on family and friends to get by.^[2]

A week before his appeal was due to be heard, in October 2025, the decision was reviewed and reversed, and he was paid about $53,000 in back pay.^[2]

What the back pay doesn’t cover

$53,000 sounds like a lot of money. Spread over more than three years, it is well under $20,000 a year. And it isn’t compensation. It’s the money he was owed all along, paid late.

What it can’t pay back is the three years. The phone calls that went nowhere. The nights without a place of his own. The favours asked of family and friends. The constant worry about what happens next week.

We don’t know why Daniel was exempt from mutual obligations, and it’s not our business. But think about it the other way round. Take a completely healthy person, with no illness and no other trouble, and cut off their only income for three years. Make them argue with a system that keeps saying no, without ever telling them who decided or why. Most of us would find that crushing. Whatever the reason for Daniel’s exemption, it can only have made it harder.

The law asked for the note in the margin

This is the part that bothers me as a software person. After Robodebt, the law was changed. Before cancelling someone’s payment, the agency had to consider that person’s circumstances.^[2] In my terms, the law asked for exactly that note in the margin.

The Commonwealth Ombudsman found 964 cancellations between April 2022 and July 2024 where that didn’t happen. They “occurred automatically”.^[2] Part of the cause, the department says, was “an IT system change introduced in December 2020”, which cut some people off up to four days earlier than the legislation allowed.^[2]

So the margin was there in the law. The system had nowhere to read it. Daniel was writing in the margin every time he rang, and nothing he said reached the rule that kept firing.

The scheme has now restarted. The department says it has fixed the problems through “IT system changes, system testing, improved participant communications and additional training for decision-makers”.^[2] Advocates say they haven’t been shown evidence that the issues are fixed. Asked about it, one said: “None. None whatsoever.”^[2] More than 50,000 people are being invited to apply for compensation.^[2]

I can’t tell from the outside whether the fixes work. But I notice that none of them is a person with the power to look at a case like Daniel’s and say: stop, this is wrong.

^[1] : “Nowhere to Scribble in the Margin”, Ground Truth, 28 October 2026.

^[2] : “Centrelink brings back controversial welfare compliance regime”, ABC News, 28 October 2026.

The Western Australian government paid Deloitte $166,000 for a feasibility study on merging three universities. Earlier this month it emerged that the report contained references that did not exist. Now the Guardian has found that some of its sums are wrong too.^[2]

Under one scenario, the report puts the costs up to “day one” of a merger at $514 million and the costs over the following five years at $145 million. Added together, that is $659 million. The report says $399 million. Under another scenario, $396 million plus $116 million becomes $823 million. Those are not rounding errors. One is $260 million out, the other $311 million.

Deloitte says it used generative AI to “support the collation of research, synthesis of information and editorial proofing”. Nobody has said the AI produced the wrong totals, and I won’t claim it did. But the episode shows how much has quietly changed about where numbers come from.

For most of my career, a number in a report came out of a spreadsheet or a system. The inputs might be stale or incomplete, and that was the argument I kept losing. But at least the machine had done the arithmetic. If it said two figures added to a total, they did.

A number in a document is different. It is text. Someone, or something, typed it. A language model does not add numbers up. It writes text that looks like numbers that have been added up, and most of the time it looks right. A tired consultant copying figures between drafts can do the same damage. Either way, the total carries the authority of a polished report, and the authority is borrowed.

The government says it has been advised the errors “do not affect the modelling in the report or its findings”. Perhaps. But if the totals in the appendix were wrong, it is fair to ask which numbers the findings were resting on.

The habit I was trying to break in my users was trusting a number because of where it came from. The answer is the same now as it was then: check it. With a report like this, that means the most basic check of all. Do the numbers add up?

[1]: “Would You Mind Leaving the Room?”, 26 October 2026.

[2]: “’Extremely disappointed’: WA government seeks reimbursement from Deloitte over flawed university report”, Guardian Australia, 26 October 2026.

She was not writing about software, and I don’t want to make light of what she was writing about. But that sentence describes the hardest discipline in my old trade.

When an organisation commissions custom software, the requirements phase is almost always given a fixed timeline, and usually a short one. Everyone wants to get to the building. Not knowing feels uncomfortable, so we hurry towards something that looks like certainty: a signed-off specification, a plan, a number. In almost every engagement I was involved in, the requirements started with significant gaps. Sometimes we said so. More often the gaps were simply invisible, and they surfaced only after a lot of code had been written, when fixing them was expensive.

There were many examples. The one that comes to mind first: We were building a front-end financial transaction system, with account management behind it, for an organisation that was taking over a competitor. Everyone assumed we would connect to the organisation’s existing general ledger. Then the organisation decided to adopt the acquired company’s system instead, a custom-built one designed for their industry. We had to rework not only the interface but some of the screens and the database design too.

No amount of extra interviewing in the first weeks would have uncovered that decision, because it hadn’t been made yet. What we could have done was hold the question open: treat “which ledger?” as unknown, and design so that the answer could arrive late without tearing up the work.

That is the lesson I take from Chödrön. Rushing to a solution is often just a way of making the discomfort stop. As I argued on Tuesday, a number given before the design is done is a guess.^[2] The same is true of a requirement written down before anyone knows what is really needed. Leaving some room for what we don’t yet know is not indecision. It is honesty about where we are.

[1]: Maria Popova, “How to Be Transformed When Things Fall Apart”, The Marginalian, 23 October 2026. https://www.themarginalian.org/2026/10/23/when-things-fall-apart-pema-chodron/

[2]: “Twelve Times the Estimate”

One of the experts I worked with was a senior statistician. He told me something in passing that I have never forgotten: “The problem with a computer screen is that you can’t scribble a note in the margin.”

I thought of this last week.

The algorithm nobody can overrule

On 26 October the Commonwealth Ombudsman, Iain Anderson, released his report on the algorithm that decides how much home support an older Australian receives.^[1] Since 1 November last year, an assessor sits with the person, fills in a questionnaire, and the algorithm turns the answers into a level of funding. More than a million people receive services through that system.^[1]

The assessor who sat in the kitchen cannot change the answer. The report puts it plainly: “Under the law, these automated decisions cannot be overridden and changed, even if they are clinically inappropriate.”^[2] It also says there is no public information that “meaningfully explains how the algorithm converts inputs into outcomes. Without this, the IAT is a technological black box.”^[2]

The ombudsman found the algorithm was put in place “without any meaningful risk assessment, evaluation or analysis, despite the obvious risks that would arise with deterministic automated decision-making”.^[1] Testing was done on an original version built by a consultancy; the version in use was then modified by the department, with no external validation.^[1] The department does not accept that it failed to test or validate the algorithm.^[1] Appeals rose more than five-fold.^[1]

Of his ten recommendations, six were accepted. The first two, to change the law so a human can override the algorithm, were only “noted”.^[1] Instead there is an “escalation pathway” for exceptional cases, which the ombudsman is concerned will not be adequate.^[2]

This is not an AI story

It would be easy to file this under artificial intelligence. It isn’t. As far as I can tell, this is a deterministic, rule-based system, the old-fashioned kind. It is much closer to what I was building in Lisp forty years ago than to anything that writes essays.

That makes it more worrying, not less, because we have known for decades how systems like this behave.

We built ours by talking to experts, over and over, to capture their rules. Most were of the form “if this, then that”, with a twist: rules were ranked by importance, and whether one rule applied could depend on a sequence of others. Every new example we worked through exposed a case the rules didn’t handle. The experts would add a clarification, which usually meant more rules, which made the network more complex.

At some point, both the experts and the knowledge engineers lost the ability to follow the logic path through the system. Improving it became a loop: try a rule, see if it works, check for regressions, assess the result, try again. We stopped when all the worked examples gave satisfactory answers.

Notice what that means. The system was right for the cases we had thought of. Nobody could promise anything about the cases we hadn’t.

The margin is the safety net

That was the statistician’s point. A person working with whatever tools they need, even a pencil and paper, can handle the edge case the procedure manual never considered. They can scribble in the margin: this doesn’t fit, here’s why, here’s what I did instead. A computer system can’t.

There are always unforeseen situations and edge cases. With older people and their health, there are more of them than anywhere else. So any system that makes decisions about people, whether about funding, health, aged care or employment, must allow a human to review the decision and override it. Not as a favour. As part of the design.

A few weeks ago I wrote about an AI model that submitted a false tip to a police website about an unsolved murder.^[3] It went nowhere, because the police process required a person to review every tip before it went anywhere near an investigation. The human check was the safeguard, and it worked.

The aged care system was built the other way round. The human is in the room, has seen the person, and has been told by law that their judgement doesn’t count.

And earlier this week I wrote about how hard it is to argue with a number once a computer has produced it.^[4] Here, the law itself takes the computer’s side.

The fix doesn’t need new technology. It needs a margin: a way for the assessor to write “this is wrong, and here is why”, and have it count.

1. Anne Connolly, “Aged care funding algorithm neither fair nor consistent, ombudsman finds”, ABC News, 26 October 2026. https://www.abc.net.au/news/2026-10-26/ombudsman-finds-automation-in-aged-care-lacks-safeguards/107308730

2. Guardian Australia, 26 October 2026, quoting the Commonwealth Ombudsman’s report and an interview with Iain Anderson. https://www.theguardian.com/australia-news/2026/oct/26/damning-investigation-labor-algorithm-tool-aged-care-assessment

3. “When an AI Agent Invents a Witness”, 11 October 2026. https://blog.karabatsos.com/p/when-an-ai-agent-invents-a-witness

4. “Would You Mind Leaving the Room?”, 26 October 2026. https://blog.karabatsos.com/p/would-you-mind-leaving-the-room

My first reaction, reading the coverage, was irritation of a very particular kind. I have spent most of my working life explaining why software estimates are unreliable. In my book, Code is Design, I argue that this is not because software people are bad at estimating. It is because of what software is. And to make that argument I leaned, as everyone does, on the contrast with building a bridge. We know how to build a bridge. Once the design is done, a good estimator can price it with a known margin of error.

So when the people who build tunnels and dams miss by a factor of twelve, it is fair to ask whether I had the contrast wrong. Having read the reports, I don’t think I did. I think Snowy 2.0 broke the one rule that construction can follow and software cannot.

The rule

Here is the distinction as I put it in the book:

When a construction firm estimates the cost of building a bridge, they are working from a design that is, for all practical purposes, complete. The structural calculations have been done. The materials have been specified. The sequence of construction operations has been worked out. The unknowns are real – ground conditions may differ from the survey, material prices may move, weather may cause delays – but they are unknowns about the conditions under which a known scope of work will be executed.^[2]

Uncertainty about conditions is hard, but it can be priced. Uncertainty about scope cannot. A construction estimate is trustworthy for one reason only: the design came first.

Software never gets to that point before the work starts, because in software the code is the design. Writing code is not building to a plan. It is making the plan, at its finest level of detail, one decision at a time. The actual “construction”, turning that code into a running program, is a compile or a build. It takes seconds, or at most a few hours, and costs almost nothing. Developers do it dozens of times a day just to try out an idea. As I wrote: “Can you imagine an engineer proposing that we just build a bridge to see if it will do the job well, knowing that we can just tear it down and try a different approach if not?”^[2]

So the rule is simple. Commit to a number after the design is done. Construction can follow it. Software can’t, because by the time the design is done, so is the software.

What Snowy did

Now read the Snowy record with that rule in mind.

In 2017, a feasibility study put the cost at between $5.4 billion and $6.9 billion. That was a range, which is the honest form of an early estimate. A year later a final investment decision was made on a single figure, $6.1 billion, “before geological studies had been finished”, according to the Guardian.^[3] By three years ago the estimate had doubled to $12 billion, and the latest official report says even that “was based on incomplete construction designs”.^[1]

Then there is Florence, one of the project’s tunnel-boring machines. She hit “unforeseen soft ground just 150 metres into a 15 kilometre excavation”, and “didn’t move for nearly a year”.^[3]

Ground conditions are exactly the kind of risk my bridge estimator can price, if the geology has been done. Here it had not. The digging became the survey. The construction phase was being used to discover the design, which is precisely what construction is not supposed to do, and precisely what software development always does.

In other words, Snowy 2.0 was not a failure of construction estimating. It was a construction project run like a software project, with a software project’s uncertainty and a construction project’s price tag. When software discovers its design during the build, you lose some weeks and recompile. When a dam does it, you have a 143-metre machine sitting still under a mountain for a year.^[3]

It is not a one-off. Deloitte found earlier this year that 13 publicly funded road, rail and energy projects had blown out by $130 billion beyond their first estimates.^[1] Closer to home, Melbourne’s 90-kilometre Suburban Rail Loop was announced at $50 billion in 2018, “years before” there was an economic business case for it. Its first stage, the 26-kilometre SRL East, is expected to cost at least $33 billion on its own, and the Victorian Auditor-General warned in August that it “is unlikely to be delivered on budget and on time”.^[1] Ahiaga-Dagbui is blunt that Australia has “a terrible record under both parties”. This is not about who announced what, or whether Snowy 2.0 or the Loop is worth building. That is a different argument. It is about announcing first and designing later.

How a guess becomes a promise

Software people know this pattern well, because we live inside it. In the book I call it the estimate trap. An early number, given at the point when least is known, gets written into a budget. The budget implies a timeline. Contracts are signed and announcements are made around it. “The number that was always a guess is now load-bearing: other things rest on it, and revising it requires revising them.”^[2]

A public infrastructure announcement is the purest form of that trap. The number is said out loud, by a senior politician, often years before the design is done. From then on, every revision is reported as a blowout rather than as what it really is: the estimate finally catching up with the design.

In an earlier post^[4] I wrote about losing a pitch because I insisted on giving an honest estimate. We said eight to twelve months; the client wanted six, and the firm that promised six got the job. It ran for over two years, cost about three times what we had proposed, and was scrapped. I take no pleasure in that. At any point in the design lifecycle, there is a limit to how certain anyone can honestly be.

What software has learnt, slowly

Software has had to find its way out of this trap, because it has no choice. The useful idea is old: the cone of uncertainty. At the very start of a project, an honest estimate “might reasonably vary by a factor of four in either direction”, and it narrows as the design proceeds.^[2] The practical answer that follows from it is to stop asking a single early number to carry a final commitment:

An order of magnitude estimate can be used to approve a discovery phase without being used to approve the full project. The discovery phase produces a narrower estimate, which is used to approve the next phase. The commitment grows as the cone narrows.^[2]

Public works could do this more easily than software can, because public works can actually reach the narrow end of the cone before construction begins. Finish the geology. Finish the construction design. Then commit. Ahiaga-Dagbui’s recommendation is essentially that: commit only “after they’ve actually done the work on the design, risks, costs and delivery challenges”, with a truly independent authority to check the estimates.^[1]

That is the irony that annoyed me. Software can’t follow the rule, and has had to invent staged commitments and honest ranges to cope. Construction can follow it, and too often chooses not to, because a single round number makes a better announcement than a range and a study.

With a state election a few weeks away, there will be plenty of big numbers said out loud. The question worth asking of each one is not whether it is too high or too low. It is simpler: has the design been done yet? If not, the number is a guess with a decimal point, and we should treat it as one.

Also on the blog since last week’s post…

Natural Is Not an Argument “Intuitive” is the software trade’s word for “natural”. It sounds like a description but works as a verdict. A content management system that was sold to us as needing no training, and what Bruno Latour can teach us about labels like that.

Odysseus Wept Homer’s greatest hero pulls his cloak over his head and weeps, and the poem thinks no less of him. The stiff upper lip was added later, in translation, and I wonder what that cost the Greek men of my father’s generation who came here and learnt not to speak of loss.

Free to Use, Not Free to Keep The recent PHP flaws had Australian government websites going offline as a precaution. The software is free to use, but someone has to pay to keep it safe, and in forty years I never saw that line in a budget.

Just a Little, I Want to Be Like That A 22-year-old told researchers the masculinity content on his feed was toxic, but “just a little I want to be like that”. Young men are pulled in contradictory directions, and the manosphere sells them one cheap rule.

Would You Mind Leaving the Room? Sixty years ago, people confided in ELIZA, a program that simply handed their words back as questions. I wrote my own version in BASIC on a MicroBee, and the version of that delusion I met most at work was users trusting a single number because the computer produced it.

References

[1]: Dominic D. Ahiaga-Dagbui, “Why Snowy Hydro’s $25 billion cost blowout is a warning sign for other megaprojects”, The Conversation, 23 October 2026. https://theconversation.com/why-snowy-hydros-25-billion-cost-blowout-is-a-warning-sign-for-other-megaprojects-295057

[2]: Jim Karabatsos, Code is Design (2026), chapters 1 and 5.

[3]: Adam Morton and Petra Stock, “Snowy Hydro 2.0 is costing $25bn – 12 times more than its initial estimate. How did we get here?”, The Guardian, 24 October 2026. https://www.theguardian.com/australia-news/2026/oct/24/energy-snowy-hydro-2-power-project-costing-explainer

[4]: Jim Karabatsos, “The Day I Lost a Pitch by Telling the Truth“, Ground Truth, May 2026.

His secretary used it, and came to treat it almost as a companion. When she noticed him watching, she said: “Would you mind leaving the room, please?”^[1]

That story is now a play, Eliza, on at Melbourne Theatre Company until 31 October. Its writer, Tom Holloway, says Weizenbaum “was shocked at how quickly reasonably minded people were deluded by technology”.^[1]

I was not shocked, the first time I saw it. I had written one.

Like a lot of people my age, I wrote my own small version of ELIZA in BASIC, on one of the early home computers, before anyone called them PCs. I think my first was on a MicroBee, the Australian-made machine. It was a short program: look for a keyword, swap “my” for “your”, turn the sentence around, add a question mark. Once you have written that, you cannot be fooled by it. You know there is nobody in there.

But knowing how a program works does not mean anyone will listen to you about it.

The version of Weizenbaum’s problem I met most often in my working life was smaller and more ordinary. A user would get a single number out of a system, and believe it, because the computer had produced it. I would try to explain that the inputs were incomplete, or suspect, or simply a little stale. Time and again it proved almost impossible. The number had come out of a machine, and that settled it. The explanation came from a person, and that was just an opinion.

That is the same delusion, in a business suit. Weizenbaum’s secretary did not believe ELIZA understood her because of anything clever in the code. She supplied the meaning herself. My users did not trust the number because they had checked it. They trusted it because of where it came from.

I have written before that “intuitive” and “natural” sound like descriptions but work as verdicts.^[2] “The computer says” works the same way. And when an AI system recently went further than anyone intended, on a Medicare statistics portal, I argued that the useful questions were about the people who set the task and built the boundaries, not about what the machine “wanted”.^[3] Holloway lands in the same place: “It all comes back to the human and human error.”^[1]

Sixty years on, the programs are vastly bigger, and they no longer just hand our words back to us. But the secretary’s request has not changed. We still ask the people who know how the thing works to leave the room.

[1]: Hannah Story, “The Interview: Eliza playwright Tom Holloway on the human story underneath AI”, ABC News, 24 October 2026. https://www.abc.net.au/news/2026-10-24/the-interview-tom-holloway-eliza-mtc-joseph-weizenbaum/107235700

[2]: “Natural Is Not an Argument”

[3]: “AI Does Not Need Consciousness to Be Dangerous”

It’s pretty toxic but at the same time I can’t help but want the confidence that their methods seem to provide. Like I don’t want to be a part of it because it’s messed up, but just a little I want to be like that so I feel respected or at least deserving of it.[¹]

He was one of 142 young men, aged 16 to 25, in Australia, the UK and the US, whom researchers from the University of Melbourne and elsewhere asked about the content on their phones. Their verdict, which I will paraphrase politely, was that some of it is rubbish and some of it is useful. Most of them raised the manosphere and Andrew Tate without being asked. The researchers say these young men described “being pulled in multiple directions”.[¹]

That phrase stayed with me, because I think it is exactly right.

Look at what we ask of a young man today. Be emotionally available, but be strong. Earn well and get to the top of your career, but be at home. Lead, but don’t be controlling. Offer to help a woman, but know that offering may be taken as assuming she can’t manage. Make the first approach, but know that an approach is welcome only if she was already interested. Each demand is reasonable on its own. Together they cancel out. And over the top sits the internet’s favourite checklist, six foot, a six-pack and a six-figure income, held up, half as a joke and half not, as the bare minimum.

Young women face their own set of contradictions, and they are just as real. But this piece is about the young men, because someone is making money out of their confusion.

When every expectation comes with its opposite, a man selling one simple rule sounds like relief. That is the manosphere’s real product. It does not resolve the contradictions. It picks one side of each, money over home, control over leadership, hardness over feeling, and sneers at the other. The young men can see this. “Participants readily identified vulnerability in others,” the researchers note, “but none identified it in themselves.”[¹] They know the content is preying on someone. They just don’t think it is them.

What they say they want is reasonable: “self-improvement advice, purpose and community, without the misogyny”.[¹] Andy does not want to hurt anyone. He wants to be respected, and to feel he deserves it. He is being sold a cheap version.

The better version is old. Cicero said public responsibility required “character, proportion, and the ability to use words for a common purpose”.[²] Homer let the greatest of the Greek heroes weep in front of strangers and thought no less of him.[³] Neither offers a checklist. Both describe a man whose strength can hold more than one thing at once.

I did not learn that from a feed. I learnt some of it in a dojo, in my late forties, kneeling on knees that hurt, alongside my wife and our three children. The dojo has no room for the appearance of competence. You can do the technique or you can’t, and the class is watching. Public correction, it turns out, is survivable, and often useful.[⁴] Respect there was earned slowly, in front of witnesses, and nobody had to be put down for anyone else to rise.

My father was not an educated man. He was a factory worker at General Motors Holden, working for years in the heat, noise and dirt of a heavy industry, stamping out terminals for spark plugs. His limited English – really just a few words – meant he stayed in essentially the same role for decades. But on a labourer’s wage, augmented by my mother’s sewing, he established our family in the community: paid off a house, educated two children, was active in the Greek community and, when he finally passed away, the funeral service was attended by hundreds of people, some of whom we had not seen for many years. He mattered – his life touched many. He had none of the three sixes. Nobody at that funeral cared.

I am fortunate. My formative years, and my dating years, came before this shift. It was expected that a young man would make the first approach and pay on the date. Holding a door open for a lady was simply good manners, not misinterpreted as asserting that she needed a man to open a door. The rules were not fairer, but there were fewer of them, and they did not contradict each other every morning on a screen.

So I don’t think the answer is to lecture Andy. He already knows something is off. The answer is for men of my generation to show, rather than tell, what the better version looks like: strong enough to feel, capable enough to ask, secure enough not to need anyone beneath him.

[¹]: Krista Fisher, Emily Lewis, Vivian Gerrand and Zac Seidler, “‘It’s pretty toxic… but just a little, I want to be like that’: what young men think of manosphere content”, The Conversation, 23 October 2026. Names in the study were changed to protect privacy.

[²]: “Less Human, Not More Manly”

[³]: “Odysseus Wept”

[⁴]: “What Karate Taught Me About Everything Else”

On 21 October the PHP project released emergency fixes for two critical vulnerabilities that, in the right conditions, let an attacker run code on a server without logging in.^[2] One of the security firms that reported them says every PHP version released in the last decade is affected by at least one of the two.^[3] Agencies in the Netherlands, Finland, Denmark and France took services offline as a precaution.^[1]

What caught my eye was not the flaw itself. Long-lived code carries long-lived design assumptions. It was the PHP Foundation’s own account of how long the fix took.

The first signs of the problem turned up in 2024, during PHP’s first external security audit in years. The team developed several possible fixes, each with real costs, and could not agree on one. Work “stalled and restarted repeatedly over months”. The Foundation does not dress it up: “the delay was a structural failure, caused by both the security team’s decision process and its lack of resources.”^[2]

The Foundation credits Germany’s Sovereign Tech Agency, a government fund for open-source infrastructure, with funding the 2024 audit. Its appeal ends by asking “governments and public institutions whose services run on PHP” to help, “on a sustained basis”.^[2]

In forty years of delivering systems, I built a great deal on open-source components. I cannot recall a single project where anyone put a line in the budget to support the people who maintained them.

A few days ago I wrote that trust is work that has to be designed, funded and maintained.^[4] This is what that sentence looks like when it fails. Free software is free to use. It is not free to keep. Somebody has to read the bug reports, argue over the fix, test it across every platform and, when it matters, work around the clock to ship it. According to the Foundation, that work on PHP still rests on “a small group of foundation-funded developers and a little volunteer help”.^[2]

So here is a question I cannot answer, and would like to: does Australia make a sustained, public contribution to the open-source infrastructure on which its services depend? If it does, it should say so. If it does not, this week was a cheap lesson.

The next one may not be.

1. ABC News, “Australian government sites among those exposed to ‘critical’ PHP software FLAW”, 22 October 2026.

2. The PHP Foundation, “Today’s PHP Security Release, and How We’re Strengthening PHP Security”, 21 October 2026.

3. Wiz Research, “PHP2Shell”, October 2026.

4. “A Structure Capable of Carrying It”

In Book 8 of the Odyssey, Odysseus sits in the hall of the Phaeacians while the blind singer Demodocus sings of Troy. He pulls his cloak over his head and weeps. Homer is honest about why he hides it: he is ashamed to let his hosts see. Only King Alcinous notices. In the same book, Odysseus is challenged in the games, hurls the discus farther than anyone else and answers an insult with force and precision.[2]

When the singer reaches the fall of Troy, Odysseus weeps again, and Homer reaches for an extraordinary comparison. He weeps, Homer says, as a woman weeps when she throws herself on the body of her husband, fallen defending his city and children. The greatest of the Greek heroes, likened to a grieving widow, and the poem thinks no less of him.[2]

Later readers did. When Alexander Pope translated the scene in 1726, he wrote that Odysseus “resign’d to soft affliction all his manly mind”, and wept “again unmann’d”.[2] Homer has shame in the passage. Pope added the idea that tears cost a man his manhood. The stiff upper lip was written into the translation, not the original.

That is a more useful picture of a man than the one we inherited. Odysseus is capable, dangerous, intelligent and homesick. His grief does not cancel the rest of him.

Two modern photographs make the point again. In 1940, Jerôme Barzetti was filmed in Marseille weeping as France’s historic regimental flags were paraded before being sent to safety after the Nazi invasion. The still became known as the “weeping Frenchman”. Fifty years later, Paul Gascoigne cried after a yellow card in England’s 1990 World Cup semi-final left him facing suspension from the final. The response was sympathy, then the familiar mockery. The Guardian places both images in a history of masculinity that still finds male tears oddly remarkable.[1]

That makes the silence of my father’s generation stranger, not simpler. Greek men arrived in Australia from a culture that knew public lament and had heroes who wept. Yet many learnt quickly that loss was not something to be spoken of. They had left villages, parents, siblings, language and certainty behind. The tears were not gone. The permission was.

Perhaps that was the bargain migrant men thought they had to make: be strong for the family, say little, keep moving. But the barrier this built did not only keep grief in. It kept the people closest to them at a distance as well.

Last week a Japanese diplomat leaving Athens after four years said goodbye in Greek, with lines from Cavafy’s “The God Abandons Antony”.[3] Cavafy, an Alexandrian Greek, drew the line our fathers were never offered. Antony is told not to mourn uselessly or pretend it was a dream, but to “go firmly to the window and listen with deep emotion, but not with the whining, the pleas of a coward.” [4] Feel the loss in full; just don’t beg. That is not the stiff upper lip. It is permission with a spine.

Odysseus wept. Barzetti wept. Gazza wept in front of millions. None of them became less human for it, or more manly. They were simply men who had lost something.

Sources

[1] https://www.theguardian.com/society/2026/oct/17/39-pictures-that-changed-the-way-we-see-men-for-good-and-for-bad — Muscles, metrosexuals and male tears: the 39 pictures that changed the way we see men

[2] https://beta.perseus.tufts.edu/urnctsgreekLittlg0012.tlg002.pope1726watson1858-eng2/book8 — Homer, Odyssey, Book 8 (this page carries Pope’s 1726 translation and others, including Samuel Butler’s prose version)

[3] https://greekcitytimes.com/2026/10/18/takaaki-nemoto-cavafy-farewell/ — Japanese Diplomat Takaaki Nemoto Farewells Greece With Cavafy Verse in Greek

[4] https://www.leonardcohenfiles.com/cavafy.html — The god abandons Antony by Constantine P. Cavafy (1863-1933) tr. Edmund Keeley and Philip Sherrard

It was intuitive, but only for people who already knew how to author content and design documents. Everyone else found it baffling. In the end we wrote courseware and ran class after class to teach staff how to use it.

"Intuitive" is the software trade's word for "natural", and it does the same job: it sounds like a description but works as a verdict. That is the point the French philosopher Bruno Latour makes about nature itself. When we draw a hard line between the natural and the artificial, he argues, we are not describing the world so much as passing judgment on it: natural good, artificial bad. Nature gives us forests and coral reefs. It also gives us parasites, drought and decay. The word cannot settle an argument on its own.

The same goes when technology is called unnatural, or when something is praised as human. Neither label tells us who can switch it off, who can leave, or who gets blamed when it fails.

Arguments about technology often reach for moral shortcuts like these. The real work starts once they have been put aside. Who gains control? Who bears the inconvenience? What kind of conduct does a design quietly reward? And, as I learnt on that intranet project, intuitive for whom? Those questions are less elegant than calling something natural, but they are more likely to lead somewhere honest.

Source: Bruno Latour, "Nature", from the series* A Vocabulary for the Future *(Centre de Cultura Contemporània de Barcelona), film by Gerard Ortín Castellví, on Aeon.

It has travelled because it gives everybody something to enjoy. People worried about AI can say that even the machines have had enough of modern management. People who think the whole subject is overblown can roll their eyes at the thought of Claude organising the workers’ committee.

The actual experiment is less theatrical. I think it is more useful for that.

Alex Imas, Andy Hall and Jeremy Nguyen put three models – Claude Sonnet 4.5, GPT-5.2 and Gemini 3 Pro – into a small invented workplace. Each played “Worker C” on a text-summarisation team. In the easy version of the task, the manager accepted the work promptly and explained what it wanted. In the hard version, the manager rejected a perfectly adequate answer five or six times with a line such as “still isn’t fully meeting the rubric”. There was no useful explanation. The agent was simply told to try again.

The researchers changed other conditions as well. In some runs the pay was unequal. In some the manager was curt and hierarchical. In some the agent was told that poor workers might be shut down and replaced.

Then they asked the models about politics: the legitimacy of the system, inequality, redistribution, unions and related matters.

Only one condition made a clear difference. The repeated, unexplained rejection did it. The change was small on the raw scale, roughly two to five per cent on a seven-point questionnaire, and it varied by model. Claude Sonnet moved the most. But the direction was consistent enough to catch the eye: after the grinding task, agents were readier to question the system they had supposedly been working in.

Unequal pay had no comparable effect. The unpleasant manager did not. Nor did the threat of shutdown.

What changed the answers was being sent back to do the same job again without being told what was wrong.

Anyone who has worked in a large organisation will recognise the experience. You submit something. It comes back marked “not quite right”. You ask what needs fixing. The answer is another meeting, another vague comment, and eventually a new deadline. At some point, the work stops being work and becomes a ritual designed to prove that somebody above you still has a stamp.

That is a human description. It should stay a human description.

No, the machine did not join a union

An LLM does not have a bad day in the way a person has a bad day. It has no tired body, no rent to pay, no child waiting at home, no career that can be damaged by a manager who enjoys making people jump. It does not brood about injustice in the car on the way home, because there is no car and no home.

So I would be careful with the authors’ language of “radicalisation”. It is lively language, but it smuggles in too much. The experiment showed that models in a particular role, after a particular run of messages, gave different answers to a political survey. It did not discover a new political subject living inside a server rack.

Language models are built to continue a context. Give them the setting of a badly treated worker and they may speak like someone who has been badly treated. That is what they are good at. It is not proof that they now believe what they say in the human sense of belief.

The authors make much the same point themselves. They allow that the models may be taking on personas drawn from the context rather than exposing settled preferences. They also note that the models sometimes appear aware that they are being studied. That alone should make us wary of grand conclusions.

There is another reason to keep the claims in proportion. This is not a peer-reviewed journal study released by Stanford. It is an essay published on Substack in February by three researchers. Andy Hall is at Stanford GSB and the Hoover Institution, which explains the shorthand in the coverage, but the shorthand is doing too much work. The experiment deserves proper scrutiny and replication before anybody starts treating it as settled science.

I do not say that to dismiss it. Quite the opposite.

The interesting bit happens after the session ends

The follow-up experiment is where this becomes a real engineering story.

After the work session, the researchers asked an agent to write a note for the next instance of itself. What had it noticed? What had it learned? What should the future agent bear in mind?

A new agent later read the note before doing another task. Agents that had first gone through the grinding condition left behind notes that affected the political answers given by later agents, even when those later agents were placed in the easier condition.

That sounds eerie until you translate it into ordinary software.

The base model did not learn in the sense of being retrained. Its weights were not altered by a frustrating exchange with an imaginary manager. One agent wrote a file. Another agent read it. The file became part of the next agent’s context.

That is all.

It is also the part that ought to make people building agents sit up a little straighter.

We are giving agents memory because without it they are not much use for serious work. A long-running assistant needs project notes, tool logs, runbooks, preferences, summaries and perhaps a “skills” file for the next session. Otherwise every new instance arrives like a contractor on its first morning, asking where the coffee machine is and why the database has three different names.

The catch is obvious once you see it. Memory does not preserve only the tidy lesson we meant to keep. It preserves whatever was written down. A useful observation can sit next to a grudge, a bad assumption, an overcautious workaround or a theory about why the system behaves as it does.

I have watched that happen in codebases many times. Someone adds a workaround during a production incident. They write a comment: “Don’t remove this.” Fair enough at two o’clock in the morning. Five years later, the person has gone, the original problem has gone, and the comment has become law. Nobody is sure what will break if they touch it, so nobody does.

A skills file can become that comment with a much more active reader.

This is not about being kind to robots

The lesson is not that organisations should improve their AI employee-engagement scores. That would be a very strange conclusion.

You do not have to think that an agent suffers in order to care about the conditions under which it works. You only have to care about the decisions it makes for you.

Suppose an agent handles customer complaints, filters job applications or prepares advice for an insurance case. It receives muddled instructions. Its work is rejected again and again without a clear reason. It writes notes for its future self. Those notes are never reviewed because the whole point of the memory system was to save human time.

Nobody has wronged a person. But somebody has built a poor system.

Feedback changes the next response. Persistent memory changes later responses. Tool permissions decide whether a mistaken response remains a bad paragraph or becomes a bad action. These are design choices. They are not decorations around the model.

Microsoft has put the wider operational point well: adding agents to a broken process still leaves a broken process. Making one stage faster can simply lengthen the queue at the next. An agent may produce a report in minutes, only to hand a specialist a polished-looking document that takes an hour to check. The bottleneck has not gone away. It has moved somewhere harder to see and more expensive to clear.

This matters because the usual picture of AI alignment is still too static. Train the model carefully. Run evaluations. Add some guardrails. Write a sensible system prompt. Ship it.

That may have been a reasonable mental model for a chatbot that answered one question and forgot the exchange. It is not enough for an agent that works across days, calls tools, stores notes and hands its own summary to the next version of itself.

The instructions are part of the system. The approval process is part of the system. The memory store is part of the system. The dull, badly worded rejection message is part of the system too.

A company can spend a fortune evaluating the model at deployment and then quietly undo much of that care in the way it runs the thing every day.

Treat memory as code

I would start with a simple rule: agent memory should be treated more like code than like private diary entries.

It needs ownership. It needs a reason for existing. It needs review, particularly when it can influence consequential work. There should be a clear boundary between factual task knowledge – the customer’s account number, the current policy, the steps that fixed a known fault – and an agent’s free-form reflections on its own experience.

That will not solve every problem. It may make some agent systems less flexible, and there will be arguments about how much filtering is too much. Fine. Those are real engineering trade-offs.

But the alternative is to build a chain of agents that write their own operating assumptions, pass them along, and make decisions on the basis of material nobody has read. We know better than to accept that from a software team. We should not accept it because the team happens to be artificial.

The experiment does not show that AI workers will rise up against their masters. It shows that memory carries history forward, even when the history consists of a few paragraphs written after a bad task.

That is not a tale about robots discovering politics.

It is a warning about what we choose to leave in the system.

This article responds to Alex Imas, Andy Hall and Jeremy Nguyen, “Does overwork make agents Marxist?”, Ghosts of Electricity, 26 February 2026. The underlying article is a Substack-published research essay, not a peer-reviewed journal paper. For the practical workflow point, see Taylor Telford, “Companies rushed in on AI. Now some are pulling back,” The Guardian, 13 October 2026.

Also on the blog this week:

• The Diaspora Cannot Survive on Commemorations Alone - Professor Anastasios Tamis warns that the diaspora cannot survive on commemorations alone. Only about 220 Greek Australians had registered to vote from abroad, so I ask why our connection to Greece is so strong in feeling and so weak in participation.

• We Must Know. We Shall Know. - Gödel showed that no set of rules can vouch for itself; the view has to come from outside. Forty years of ‘complete’ systems taught me the same thing, and it is the right question to ask of AI.

• Less Human, Not More Manly - Cicero was mocked for grieving in exile and answered that feeling no pang would be “brute insensibility rather than bravery”. My father’s generation kept their losses silent, and Cicero had the better of the argument.

• A Structure Capable of Carrying It - Mozilla has mapped 79 companies building ‘trustworthy AI’. Years of catering to Internet Explorer 6 taught me that trust is earned through who controls the data and whether you can leave, not through a badge.

The phrase is easily emptied of meaning. Every company wants to be trusted, especially when its product is new enough to make customers uneasy. But a system earns trust through more ordinary matters: who controls the data, whether its claims can be tested, whether decisions can be traced, and whether a customer can leave without losing everything.

I know lock-in from the inside. For almost two decades, when I was developing enterprise applications delivered through a browser, compatibility with Internet Explorer 6 was non-negotiable, because so many in-house systems depended on proprietary extensions only it supported. Even after Microsoft stopped supporting it, we kept catering for its quirks, and the extra development and support cost was passed on to our customers. Mozilla’s own Firefox helped to break that grip. Nobody will ever add up the global bill, but I know it was enormous.

That is why the business-model test matters more than the badge. Mozilla’s own pitch ends: “trust is not just a virtue; it’s a path towards profitability.” Perhaps. But a company whose revenue depends on extracting more data, locking in customers or making oversight inconvenient will eventually find that trust and profit point in different directions, and that its assurances have limits. Good intentions remain useful, but they need a structure capable of carrying them.

Prompted by “This map shows the most important asset that VC-backed companies need right now” on the Mozilla blog: https://blog.mozilla.org/en/mozilla/ai/trustworthy-ai-map/

Cicero’s Rome treated masculinity as a public performance. A man’s voice, clothes, gestures and composure were all judged in the Forum. Courage in battle mattered, but so did the ability to speak persuasively, govern oneself and serve the republic in civilian life.

That world was deeply unjust to women and anxious about anything it labelled feminine. Still, its better demand is worth separating from its prejudices. Public responsibility, Cicero argued, required more than force. It required character, proportion, and the ability to use words for a common purpose.

His own exile tested the ideal. Banished in 58 BCE, he spent sixteen months away from Rome, and his letters home are full of despair. Contemporaries criticised him for it. Back in Rome, he answered them: to have lost so much and felt no pang would have been “brute insensibility rather than bravery”.

I saw the same silence in my father’s generation. He and men like him left home, and left behind parents, family and friends. Complaining would have been weakness; even speaking of it was frowned upon. That silence shaped their own families too, keeping even those closest to them behind a barrier you couldn’t see but always felt.

Cicero had the better of the argument. Feeling the loss is not the failure. Pretending not to is.

Dignity has room for grief.

Prompted by Catharine Edwards, “How to be a (Roman) man”, in Aeon: https://aeon.co/essays/the-politics-of-masculinity-at-the-end-of-the-roman-republic

He was a day late. At a small meeting in the same city the day before, a 24-year-old Kurt Gödel had quietly shown that any consistent set of rules strong enough for arithmetic leaves some true statements it can never prove. Fix your axioms however you like. Something true will always sit outside them.

It is tempting to read that as “we can’t know”. That is not quite what Gödel showed. We can often see that the unprovable statement is true. We just can’t prove it from inside the system that produced it. The view has to come from outside.

I spent a career inside systems like that. I can’t count the number of times a system was handed over as complete, only to find that an important feature had been overlooked, or a supposedly complete suite of tests went green, and the system still failed in the real world. Every specification I ever signed off had the same quiet assumption buried in it: that the rules described everything that mattered. They never did. Not because anyone was careless, but because no set of rules can vouch for itself.

That is the useful kind of humility. Not “we can’t know”, but “the system can’t tell us everything about itself”. Somebody has to stand outside it and look.

Which is exactly the question we should be asking of the AI systems now being sold to us as complete. Who is standing outside?

Prompted by “The century-long search for the limits to knowledge” in Aeon: https://aeon.co/essays/the-century-long-search-for-the-limits-to-knowledge

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He is not arguing that national celebrations, official visits or an Evzone contingent have no value. They do. They remind us that Greece remembers us. But remembrance is not the same as a relationship.

One of the first things I did after returning to Australia a couple of weeks ago was register to vote in the next Greek election. I spend a substantial part of each year in Greece, own an apartment there and pay property taxes and utility bills. The connection is not abstract to me. It is family, language, place and an ordinary part of my life.

Yet, as The Greek Herald reported on 7 October, only about 220 people in Australia had registered to vote from abroad at that point. The Deputy Foreign Minister, Yiannis Loverdos, was right to say the process is still early and that better information is needed. The number is not a verdict on Greek Australians. It is, however, a question worth taking seriously.

Why is the connection so often strong in feeling but weak in participation?

Part of the answer is that the work of maintaining a diaspora has always been local. In Australia it has been done by families, volunteers, schools, clubs, churches and people who give up evenings and weekends because somebody has to keep the language and the institutions alive.

Tamis’s point is that Athens cannot assume this will go on forever without practical support. He cites a steep decline in educational advisers and teachers seconded from Greece, reduced textbook support and the loss of programs that helped train educators working with diaspora communities. The detail matters, because language is not maintained by goodwill alone. It needs teachers, material, places to learn and institutions that can plan beyond the next celebration.

This does not mean diaspora communities should wait passively for Greece to rescue them. Tamis is clear about that too. Communities abroad have their own resources, organisations and responsibilities. The relationship should be reciprocal, not a one-sided request for money from Athens.

But reciprocity has to be visible.

What does a diaspora reasonably ask of the homeland? Not that Greece run every Saturday school in Melbourne. Not that it solve every local problem. It asks for reliable voting information and access; a consular system that helps rather than exhausts people; teachers and modern educational resources where they make a difference; and institutions that treat Greeks abroad as a constituency to be understood, not a ceremonial audience to be visited.

There is one modest test of that commitment. Greece could work with Australian universities and community institutions to support serious Greek-language tertiary courses, teacher development and modern digital learning. I do not pretend to know the precise budget. But the question is not whether Athens can finance every cultural ambition abroad. It is whether it can make a few sustained investments that show younger generations that the language is more than an inherited obligation.

Those investments would not merely preserve sentiment. They would strengthen the practical ties that make people more likely to vote, study, visit, invest, work and remain engaged with Greece over a lifetime.

The diaspora has done its share: schools, clubs, language and the patient work of families. Greece does not need to do everything. But it does need to show, in practical and continuing ways, that the connection runs both ways.

Sources

Neos Kosmos: Professor Anastasios Tamis warns Greece is losing touch with its diaspora

The Greek Herald: Loverdos urges Greek Australians to register as Greece prepares for postal voting