Mike's Notes
Great reflection and open questions from Ksenia Se in Turing Post.
Microduck
And just for fun. We all need one or three. đ
Resources
- https://www.turingpost.com/p/permanent-dawn
- https://arxiv.org/abs/2608.17271
- https://archive.org/details/tacitdimension0000pola
- https://www.turingpost.com/p/fod42
- https://www.cs.ucdavis.edu/~koehl/Teaching/ECS188/PDF_files/Machine_stops.pdf
- https://rtraba.com/wp-content/uploads/2017/01/golem-xiv.pdf
- https://en.wikipedia.org/wiki/Her_(2013_film)
- https://andrewmbailey.com/money/readings/keynes.pdf
- https://archive.org/details/dli.ernet.528547
- https://philpapers.org/rec/SUITGGcom
- https://efi.ed.ac.uk/people/shannon-vallor/
- https://philpapers.org/s/John%20Danaher
- https://renewal.org.uk/articles/the-progressive-work-ethic-for-labour-an-interview-with-elizabeth-anderson/
- https://news.harvard.edu/gazette/story/2021/01/the-myth-of-meritocracy-according-to-michael-sandel/
- https://philosophybreak.com/articles/three-philosophers-three-ways-to-rethink-the-new-year/erral
- https://youtu.be/RAtzEyGBGFU?si=JQeS5_YMo7dGNtlD
References
- ASI-Bench: At the Dawn of Artificial Superintelligence.
- The Tacit Dimension, by Polanyi, Michael, 1891-1976.
Repository
- Home > Ajabbi Research > Library > Subscriptions > Turing Post
- Home > Handbook >
Last Updated
29/08/2026
Permanent Dawn
Mom of 5.
Ksenia is a writer, analyst, and editor covering machine learning and AI for more than seven years. At Turing Post, she shapes the editorial direction, leads the Inference interview series, and produces Attention Span, a video series explaining major shifts in AI with technical clarity, historical context, and a healthy suspicion of hype.
She is the co-founder of TheSequence.ai and a speaker and moderator at industry conferences, including AIE, HumanX, Ai4, and others. She also serves on the board of Track Two: An Institute for Citizen Diplomacy.
Before founding Turing Post, Ksenia held editor-in-chief roles in media and contributed to publications including Stratfor and Towards Data Science.
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Four years into an announced era, and I still cannot picture the thing we are running at.
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Today’s editorial: The superintelligence we are racing toward – and how to record our path to it.
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Permanent Dawn
I want to stop. I want to take a few steps back actually, because I need to see the whole picture and I have not been able to.
We are racing – through the spasms and the fever of social media, through the launches and the leaderboards and the week's argument about timelines – toward something that none of us has managed to describe. What surprises me most is that even science fiction, which used to be reliable for a glimpse of what was coming, is no help here.
What is this superintelligence we are at the dawn of? The disagreement about when it arrives seems to me the smaller trouble. What bothers me is that we are reorganizing our lives around a thing we have not yet put into words.
There is an old way of finding out what a person is made of, and it is always the same method: take the help away and see what remains. It is how we test a student, how a craft decides an apprentice is finished, how a parent notices a child has grown. You withdraw the instructions, and whatever is still standing afterward is the person.
A benchmark ASI-Bench: At the Dawn of Artificial Superintelligence published last week – the thing that started me thinking about the dawn of superintelligence in the first place – runs that experiment on machines. Sixty research projects across eleven sciences, served at four levels of help: full procedure, then only the name of the method, then only the goal and the data. The scores fall off a cliff, and they fall at a particular place, the moment the written procedure is removed. Take away the steps and most of the capability goes with them. Take away everything else and little more is lost, because there was not much else there to lose.
That is the pretext, and it is only a pretext. The question underneath it is a great deal older than the technology.
The part that cannot be written down
Michael Polanyi, the Hungarian-British polymath, gave this a name in 1966 (in The Tacit Dimension): we know more than we can tell. The surgeon's hands. The editor's ear for a sentence that has gone false. The scientist's suspicion that a result is too clean. Little of it survives transcription. It passes by standing next to someone for years, and it tends to die with people who never had an apprentice.
I want to be that apprentice. Standing next to a thing for long enough to catch what it cannot say about itself strikes me as a reasonable job description, for a person and for a publication both.
We rehearsed for a different arrival
We have not managed to describe this thing, and yet we spent a century describing it in advance.
We have "seen" the robot with a body, countable and discrete, standing in a doorway. We were persuaded it would be a hostile singular mind with a plan of its own. There was an android asking to be recognized as a person, and others besides – most of those stories assumed the machine would want something.
What arrived has no body and no edges, and wants nothing of its own. It is not singular and not continuous, and it does not persist between conversations. It is not hostile, and its characteristic failure is not rebellion but a fluent, untroubled wrongness that few novelists thought to invent. It came through a text box, priced like a streaming service. Or even for free.
It also took the wrong things first. The tradition assumed arithmetic and heavy lifting would fall early, and that poetry, argument, and drawing would be the last human ground. The so-called Moravec paradox, that we debunked a couple of years ago.
There were people who saw some angles of what we have. E.M. Forster wrote "The Machine Stops" in 1909, about people who live alone in cells and consult a disembodied system through a screen for everything, including company (but we do not live in cells). StanisĆaw Lem's Golem XIV is a superintelligence that lectures its audience and has no particular interest in them (but it has interests of its own). In 2013, Her gave us a voice-first, bodiless, emotionally competent system sold as a consumer product (but Her still felt too human-like, and LLMs are not).
Image Credit: Warner Bros
None of them works as the image for what we have now, or for what is coming. Fiction was never forecasting. It was rehearsal – the advance picture that tells people where to stand when the thing walks in. We rehearsed for the uprising and the rights hearing. We did not rehearse for a colleague with no self, who writes better than we do and is sometimes confidently wrong about exactly the things we are least able to check. And we certainly have not rehearsed for abundance that somehow became associated with that very text box.
We have too many images of machine intelligence, most of them wrong, and now that wrongness is fogging the real picture.
Who has an audience
The people who asked the larger question well were not the ones imagining machines, and I think that is why they lasted.
Keynes asked it in 1930. He guessed the economic problem would be solved within a century, and then, instead of celebrating, he worried. He thought we would be delivered into our permanent problem – how to occupy a life that necessity no longer occupies – and he expected something like a collective nervous breakdown, judging by the wealthy women of his own time who had been released from need and found little on the other side.
Arendt asked it in 1958. The opening pages of The Human Condition describe a society of laborers about to be freed from labor, which she thought close to the worst thing that could happen, because such a society knows of nothing better and has nothing else it knows how to do. Her separation of labor from work from action remains, for me, the most useful equipment anyone has built for this moment.
Bernard Suits asked it in 1978 and gave the strangest answer. If every instrumental activity became unnecessary, what would be left is games – voluntary attempts to overcome unnecessary obstacles – not as a consolation prize, but as the highest form of existence available to a being with nothing it has to do.
They lasted because each described the human condition without necessity, and that description does not depend on what the machine turns out to be. The novelists specified the hardware, and the hardware is what rotted. Abstraction outlived imagination, which is not the usual result.
People are working on this now – Shannon Vallor on what these systems reflect back at us, John Danaher on automation and utopia, Elizabeth Anderson on work and freedom, Michael Sandel on merit and dignity, Kieran Setiya and Susan Wolf on meaning. The problem is that the asking has no audience where the decisions are made. It is not flashy, it does not sell an LLM or a world model, and it will not be reposted by Elon Musk. And if you think about it, the most important topics are currently discussed on X, which is essentially a living feed. It is a remarkable way to stay inside the Silicon Valley bubble and read every mover in the industry at once, but even their words turn elusive there, because they dissolve into the noise of everyone else's.
What I want to do, and what I want to ask you about
Here is the thing I have been circling for months, and I would like your advice before I commit to it. (That was the topic I wanted to discuss with you last Friday, but I couldn’t formulate it yet.)
I do not think we need to predict the future, which is what so many reports spend their pages doing. I think we need to register it carefully – write down what was claimed, by whom, and when – and then go back and check at each stage. Kept up for long enough, that unfolds the future for us without anyone having to forecast anything.
So I want Turing Post to keep a register. Not forecasts, and not another feed of takes, but a running record. What was claimed. Who claimed it. What would have to be true for the claim to hold. And then, at intervals, what happened. The claims themselves are easy to find and easy to forget, which is the whole trouble, because they are made in a format designed to be forgotten. A register – a ledger, am almanac? – would hold them still long enough to be checked.
Part of that belongs online, where it can be corrected and extended. But I have come to want a material version as well: something printed, arriving a few times a year, something extremely beautiful that you can put on a shelf and take down in 2030 to see what we believed in 2026 and how much of it survived. Or even read it to your children. They often see things we miss.
And yes, I just need to hold it in my hands and flip the pages, don’t you?
Alongside the record I want the reflection, which is where the philosophers and the economists come in. Not commentary on the week, but people willing to say what a claim would mean for a life rather than for a valuation. I have been building toward this with the economists already. The philosophers are the next step.
What I do not know is where the line sits, and this is the part I would like help with. Whether a register is something you want at all, or whether the weekly explanation is enough. Whether print reads as serious or as nostalgia. What are we missing in this whirlpool of news and changes?
I am not confident in any of this. I am more confident that we are describing the wrong problem. We are doing it very enthusiastically and very loudly, but I keep thinking we are missing the bigger picture.
So I would like to know what you see. Send me your thoughts. I do not trust a poll on that.
P.S. Turing Post has always tried to connect the development of AI with the humans building it and living with it. But some questions are too large for the daily news cycle. They need time, history, disagreement and repeated examination.
Each quarterly Almanac could take one such question and follow it across technology, economics, institutions, history and human life. Not to manufacture a final answer, but to understand the choices being made while those choices are still ours.
If this really is the dawn of superintelligence, we should document more than how intelligent the machines become.
We should ask what kind of humans we intend to be beside them.
