Box118: Complicated, complicated, lemon, complicated.

04.12.24

DS106. Look I don’t mind telling you that DS105 was a trash fire. I set out to do a complex fluid simulation which a) took a long time and b) came out like total shit. And this is the truth of creativity. 90% of it is wasted but as long as you learn from your mistakes it was never truly a waste. Except for the computing and hard drive space that was wasted. That is a waste.

It’s dark and cold which has the paradoxical effect of creating warmth in the things you know. Like a heat-map where the hotspots are more concentrated because the space between is markedly more bleak. From the noise of heat, the shape of warmth as it were.

It’s not complex

I’m in the midst of selling a flat and buying a house. Today or tomorrow, in fact, we might finally have the final bits of information we need to head into the final strait. We’re sort of on tenterhooks because we know all of the pieces we need but they’re not coming in the right order or are coming frustratingly slowly. Anyway, occasionally people will ask if (or tell me that) moving house is stressful but I’ve been finding this vexing because quite simply, I’m not finding it stressful at all. It’s hard work and occasionally frustrating, yes, but it’s not actually stressful. Not in the way I understand stressful things which are things that are both irresolvable and unignorable. With moving house, it’s quite easy to pick up and put down and not dwell on it. I don’t lose sleep or think myself in knots. And so I was thinking about why it is that I’m finding what is apparently one of the most stressful things a person can do, is not at all stressful.

Well here’s the thing. Because life demands a lot, my decisions are usually based around little aphorisms and algorithms I’ve collected to aid reasoning. Like, I have this thing about trying any food; if it’s an enjoyable food to someone then it must fit along a bell curve of ‘nice.’ Now, it may be more or less nice but if it has been eaten before and enjoyed then it’s perfectly possible – more than likely given bell curves, in fact – that I’ll enjoy it and so far, that has always worked and, as a result, I haven’t really ever encountered a food I don’t like*. What does this utter garbage mean for house moving, Revell, I hear you wail in agony? Well, people have moved house literally millions of times. So it’s perfectly possible to do and in fact it’s so remarkably possible-to-do that whole entire jobs and institutions are there to make it happen. So no one in this process is doing anything that never has been tried before. Therefore it fits along a curve of ‘doable’ and relative to ‘holiday on the moon,’ profoundly near the top of that curve.

(This is where I’m going to lurch into gonzo systems thinking; forgive me Mother Meadows.) Moving house is complicated, but it’s not complex. There are lots of moving parts, and some of them are conflicting and that can be frustrating. But, the process is entirely coherent, profoundly well understood and furthermore, it’s well known and understood what that process looks like when it’s running well. Another example: A bicycle is a relatively straightforward but complicated thing. [editors note; this is the second time I have edited out a motorist’s metaphor to undo the tyranny of combustion-centrism] If something goes wrong with your bicycle it can be frustrating to figure out what and repair it (unless, like myself, you enjoy solving complicated problems) but it’s been done before, the component pieces and overall system are coherent, well known and understood and you know what it looks like when it works well.

Now, restoring soil health is complex. Reducing car use in cities is complex. They are not coherent problems; they have massive amounts of externalities like weather, wealth inequality and class, tax regimes and technological investments that shape them. They are not well known and understood; there’s no one-to-one mapping of how to restore soil health or reduce car use, both are markedly contextual problems in different places. So, while working bicycles look the same everywhere and require the same kind of fixes no matter where you are, the same can’t be said of cities or grassland between South America and the Urals.

No problem

The other thing. (And I’ve known the complicated/complex divide for a while but really never thought about how it shapes the way I respond to different situations.) The other thing is that repairing bicycles, or moving houses are challenges. In a challenge (like ‘get to the top of that mountain’) you know what success is before you’ve solved it (being at the top of the mountain.) In a problem (like ‘find the two prime divisors of 149,614,657’) you don’t know the solution or outcome until after you’ve solved it (?).

So, moving house or repairing a bicycle are challenges because they have known solutions; e.g. you’ve moved house or repaired your bicycle. And both are complicated challenges because their component parts are coherent and well known. On the other hand, finding the prime divisors of a large prime is a complicated problem because the process of solving it is well known (iteratively try every combination of primes (even if it takes a loooooooooooooooooooooooooooooooooooooooooooooooooong time)) but you have no idea what the answer is until you find it.

	Challenge – Good outcome is known	Problem – Good outcome is not known
Complicated – known components and constraints and variables	Repairing a bicycle or moving house. – Constraints and variables are all well-known. – What good outcome is is well known.	Prime divisors of large prime numbers. – Constraints, variables and process of solving are well-known. – Outcome is not known at beginning.
Complex – poorly understood components, constraints and variables	Reducing car use in cities – Constraints and variables are highly contextual, changing and poorly understood. – Outcome (lower car use) is well known.	Survival of life on Earth – Constraints and variables are riven with externalities and contestation – Uncertainty in the veracity of any solution.

So we could draw up a map that looks a bit like this based on how much understanding of the pieces of the puzzle we have and how much understanding of what a good outcome looks like we have. Ok wait tactually he puzzle thing is even more useful metaphor. Hold on a second.

	Challenge – Good outcome is known	Problem – Good outcome is not known
Complicated – known components and constraints and variables	The puzzle has eight pieces and the box has a picture on it.	The puzzle has eight pieces but there’s no picture on the box.
Complex – poorly understood components, constraints and variables	Only some of the puzzle pieces are in the box. There is a note with a promise to return an unknown number of missing pieces and another saying that the puzzle has not finished being made yet. The pieces that are in the box appear different sizes and may not in fact belong to the same puzzle There are some loose bits of newspaper as well but there’s a picture of the puzzle on the box.	You are given 4 large sheets of MDF and an old man shrugs at you and gruffly mutters; ‘We must make our own fun this Christmas.’

Yes. Something like that. So moving house is maybe unignorable in the sense that you know, if you’re committed to it, you can’t then just hope it goes away; you have to do stuff. But all the stuff you have to do is resolvable because it’s a complicated challenge. So you know, it will resolve with effort and time, therefore not stressful. Anyway, if this house move doesn’t happen soon, we must make our own fun this Christmas and it’ll be great anyway.

*There are exceptions. The famous fake vegan meet of 2013 was awful and I struggle with aubergines because I don’t like cooking them but totally fine to eat them.

PhD

It’s really interesting tricky at the moment (speaking of complex challenges). I’m increasingly trying to tease out the subtleties of different ideas. Sometimes I worry it’s wasting time but it’s super engaging intellectually and therefore I have to believe is contributing to the overall thesis. I’m increasingly finding and developing what I keep calling concepts which are grouped by type. So, you have concepts that are myths, effects or flaws, concepts that underline idea of intelligence, futures and applications and so on and so on. But this means getting deeper and deeper into super tricky and fun nuance. For example I shared on instagram that I had managed to tease apart ‘reducibility,’ ‘reductionism’ and ‘recasting as computable’ as three different concepts of the same idea; that everything is a technical problem.

For this example, ‘reducibility’ is the rhetorical insistence that social phenomena are reducible to computational components and in my notes I’ve framed that as a rhetorical concept. ‘Reductionism’ is a ‘flaw’ of machine learning when actually applied to real-world social situations where continuous phenomena have to be captured as discrete and externalities are removed in order for machine learning to work (amongst many other things.) While ‘recasting as computable’ is an imaginary effect in society where the first two things reinforce the commonly held belief that social problems are in fact technical problems. And that’s just three of about 50 different, inter-related and nuanced concepts! Throw in exemplification (myth), standardisation (flaw), normative reproduction (effect) and a few dozen other nuanced spins on what AI does to how we think about, engage in and imagine social problems! Phoo-eeee.

Concept map as of today of my PhD. Red dots are concept groups and orange are concepts themselves. A lot of work at the moment is getting super precise on the nuances between them to better understand how they act together to create imaginaries or are deployed by designers.

I wonder if this ever ends; is there. a zero-sum number of concepts. At the moment it gernally works that a concept note gets bigger and bigger as I put more content, quotes and references in until I think ‘this is actually three things’ and split it out (as in the reductionism example above which was originally one concept.) Anyway, I’m committed to and, thus far, sticking to one hour a day at least since my binge a few weeks back and words and ideas are flowing so whether or not they make the cut, it feels like we’re moving forward and are in the cut and thrust of it.

Recents

The chapter I wrote with Julian for Practices of Future-casting has been published. The text explores ways of making imagination serious in a business setting and the value it brings. We’re going to be recording a podcast about it at some point. There’s other things honestly but I need to go to bed now.

Reading

Not many. As above, bed time. As above, thinking, writing, nuancing. Reading on pause.

Another one I’ve re-read in my PhD work recently; From Rules to Examples from Schwerzmann and Campolo explores how the move from rules-based computation and calculation (things are sure and such a way) to machine learning’s reliance on exemplars (thing ought to be such and such a way) create or reinforce normative orders for the world. Also, The Nooscope Manifested from Pasquellini and Joler is another one I’ve revisited recently which was super formative in my PhD thinking.
FOMO is Not a Strategy. Rachel Coldicutt’s sober analysis of the state of AI, getting ahead of management consultant and tech firm end of year hype cycles: “the absolute nonstop cavalcade of Big Tech hype means there is a lot to cut through just now. In particular, there is a barrel-load of shamelessly over-optimistic sales and marketing that is both bending economic policy.”

Ok, I have to go to bed, I write these things the night before so I have time to check in the morning (yes, I miss typos but this is a blog; it’s a work in progress with a 15 year record and little to show for it). And obviously I imagine you picked up the sub-sub-gag in this week’s title. But just in case. Ok, I love you and speak soon.

Box117: Obsidian

21.11.24

DS104: Major sunk cost, deeply unhappy with this one even though I invested four or five hours in it. Rubbish.

The temperature has suddenly dropped. Only four weeks ago we were on a beach in Menorca and now it’s four layers and neoprene gloves. It came out of nowhere. I had my first commute yesterday where my fingers and nose were numb. I don’t mind it while cycling but it’s the fumbling with keys and cards when you can’t feel anything that drives me crazy. I miss the anticyclonic gloom. When it was just gloomy and mid.

Most of this blog is about Obsidian and my implementation of it for PhD research. So stop right now if that’s not interesting, or skip down to the recents/reading sections at the bottom. I loosely documented my journey over on Instagram but let me rattle through what happened and what I did and why and whether I think it was worth it or not. This is your last chance to get off the ride. Ok? Here we go.

What is Obsidian?

There’s a good chance that if you’re the type of person who is chronically online and therefore one of the few hundred people who read this blog that you already know what Obsidian is and are likely using it. Obsidian is an open source ‘second-brain’ note taking software. As I understand it, there’s a handful of these about which you give you a bunch more malleability than Apple Notes, OneNote or Evernote which are seen as ‘first generation note apps.’ Obsidian is a bit more like building your own personal wiki and as such has what I’d call a ‘cult of methodology’ around it where people debate the most effective knowledge management methods and how to retain and structure information. There’s Zettelkasten, which is loosely what I do or things like PARA.

Zettelekasten (which means like ‘slipbox’) is basically a method where you note things that are interesting and then over time file them away until collections of things that are interesting become more fully-formed ideas. I don’t stick to this dogmatically (it’s a bit too Linux-bro for me) but the spirit of it is sort of how I think: For example in futures work bunches of signals might become trends or in the PhD work, recurring ideas become more solid and connected as I read broader across stuff. The important thing is that it’s a wiki, not an essay so links are what drives it. It’s all about dropping the blessed double square bracket which opens up a menu of things you can connect to; to other notes, to headings within notes, to images, to external references and the more you do it, the richer the project becomes.

Now, the enormous benefit of Obsidian is it’s highly customisable. Pretty much any time I’ve thought ‘I wish I could do that,‘ I’ve been able to (with a little reading of forums and trial and error). I can set up hotkeys, workflows and templates with all the information I want and arguably cut out a bunch of manual work searching, moving and duplicating things. The downside of Obsidian is it’s highly customisable which means, like any open source, customisable software (cue Blender) it requires investment of your own time and skill to get it to a place you want it to be.

This is loosely my Obsidian setup (showing blogs). On the left are files and folders of notes. (More on these in the context of the PhD shortly.) Then I usually have two editing windows open just because I’m used to this from Scrivener. Most of my time is spent cross-referencing and moving blocks of text from one note to another so having two windows makes that easier. I also have the graph view up. I’ll be honest, it’s not *useful* per se. But it is pleasing.

Why tho?

I’ve been Obsidian-curious for like a decade but never managed to find the time to get into it. As above, like all big open source software there’s a skill and time investment involved, but I had a very distinct and motivating problem I wanted to solve when I took a week off to focus on PhD the other week: The problem I have is that I get about an our a day to do PhD work and I really need to make the most of that time and hit the ground running. Unlike my previous workflow which involves reading and highlighting things in Zotero then manually copying them into big, open ‘notes’ documents in Scrivener, Obsidian is a middle step which allows me to break my research and thinking down into small, manageable chunks that I can navigate around like a wiki. I also want to use Obsidian more for day-to-day work, newsletter reading and so on so that I can bring together all the things I read about and think about into one space.

Scrivener, which I still use for prose is just long, linear lists of text, intimidating to navigate or move across.

For example, I already write blogs in here because it allows me to connect the reading with any reflections on the reading. I can see where I come across an article while moving about Obsidian that I wrote something about it on the blog. Because I’m always switching between different brains, I put things in Obsidian for Future Tobias to be able to trace my steps and thinking.

How the hell are you invoking this tumult, Revell?

I’m not going to spend ages going into the exact setup I’m using because I don’t think that’s necessarily useful. Obsidian relies on lots of plugins and snippets of code to get it to do exactly what you want (though I have discovered there is a purist community who consider modifications sacrilege.) I’ve been evolving my Obsidian over the last few weeks as I’ve been figuring out what I need it to do and am in a place where I’m pretty confident with it now, as far as the PhD goes, at least. So here’s my workflow for how I’m doing PhD thinking with it:

I come across articles, papers, projects and examples of real things, usually through all the newsletters I follow or from references in other papers. I save al these to Zotero using the browser plugin.*
On import, Zotero does a bunch on auto-filling fields but I use the Bibtex plugin to generate a reference (LastnameDateTitle) that is attached to each Zotero item.
I read the thing in Zotero and:
- Throw in a bunch of thematic tags (e.g. ‘personificaiton,’ ‘humans,’ ‘data’ – anything that feels relevant to the item).
- (and this is annoying and feels like someone should have automated by now) tag any ‘Related’ items from Zotero. So for instance, if a paper references another paper or projects. Honestly, should be able to scan the bibliography and suggest papers in my library that are already there to automate this step but I can’t find anything that does this.
- As I read, I drop a bunch of highlights in. This is all intuitive, anything that feels novel, important or part of a more general consensus. I use yellow in the main but red for anything that’s a ‘holy shit’ moment.
Then I use the Zotero Integration plugin in Obsidian. I’ve got this set up to Command+Shift+R and it will automatically drag whatever I have open in Zotero in to Obsidian as a new note in my ‘Unsorted’ folder and apply a template I’ve built to it.

That template has been setup to do a bunch of clever things.
- First of all it autofills all the title, date and author metadata and brings in the abstract and a link to the original source.
- Then it brings in all the tags and adds an ‘s/’ before so it’s separate from internal Obsidian tags.
- It also grabs and forms all the highlighted references into the List Callouts plugin. This is because I really like working with nested bulletpoint lists and Obsidian allows you to expand collapse them. So basically a ‘-‘ and a symbol (!, £, @, &) after it will highlight the imported quote in a different colour but that is done automatically based on the colour of the quote. (So ‘If Zotero highlight is red add ‘- @’ before highlighted text’ basically and it’ll show up yellow with the quote symbol in Obsidian.) You can see, for example where I made a note on a highlight and it’s automatically put the pencil symbol in front of it (‘- £’).
- Finally it uses those generated Bibtex references to automatically link to anything already in Obsidian that references it or to create ‘orphan’ links for things not yet in Obsidian but are in Zotero waiting to come across.
- I then write a bit about idea of the thing or why it was interesting in the ‘Notes’ section of the imported note. This is important because it is very much true that you learn better when you write or make something from what you’ve learned and I like to do it while it’s fresh.
- Then I shoot off all the highglighted references to be filed away in different places using the Text Transporter plugin. This allows me to embed the block quotes in ‘concept’ notes using the Command+Shift+P to select a ‘concept’ note to send it to the ‘Unsorted’ section of while keeping the original quote inside the source note. Those little sequences of numbers and letters after each quote are the internal aliasing system that allows obsidian to back-track to the source of each embedded block.
- Then I open up all the concept notes I’ve just added to with whatever I’ve just imported.

This is a very short one on ‘foreclosue’ but it was the only one I could get a full screengrab of.

So ‘concept’ notes are the core of my thinking space. They are loose ideas related to the PhD. I have some categories for them but they are meant to be emergent. So, for instance there is a subfolder of ‘Trope’ concepts which are common AI myths or gimmicks used to build AI imaginaries and then there’s ‘Critical Practice;’ concepts which are strategies and techniques used by designers connecting to those projects. But most are just ‘vibes’ let’s say.

Over time I keep adding quotes, references and links to each of the concept notes, every time I open one to add things I also go in and ‘garden’ them a bit. This might mean bringing connected ideas together under a heading or even rewriting an idea based on new information from a new source. When I feel like a concept note is too big I might split it out into smaller notes because, again, this needs to be manageable and not another huge list of text. For instance, this morning I separated ‘History of AGI’ and ‘Fictions of AGI’ from the ‘AGI’ note because they felt weighty enough to be their own concepts.
Importantly I always try and write ‘why’ I’ve added a quote, reference or idea to a concept note; what is it telling me or revealing or connecting. It’s not enough to simply list things, it’s also about rationalising them. This is similar to always writing on each source while it’s fresh. Also, because this is my notes, it doesn’t have to be ‘good’ as long as it makes sense to Future Tobias.
And all the time I’m linking. Where for example in the note above, part of the imperative of future-making is ‘reproducing the norm’ (a different concept) I point to it. At first I was very careful about this, because I’d seen some great images of orderly knowledge graphs on the internet and wanted to tidy my thinking but over the last few weeks I’ve found there’s much more use in linking as much as possible so that I can quickly remember connections and overlapping ideas.

Even then, the end result as you can start to see here, is a graphical representation of important or recurring ideas and concepts that are ‘well linked’ or ‘orphaned.’ The tidying really is a big part of it. If something is ‘orphaned’ for too long – e.g. nothing links to it, I will try and see if it fits under another concept. If something has too many links I will see if It needs to be broken up. This constant iteration, revisiting, re-reading reinforces my understanding a little bit every time and reveals new ideas and connections.

Another snippet of code and plugins colours it. Red is core stuff (or Maps of Content – MOCs), orange are concept notes, green are sources like papers or articles, blue are real-world examples and fuchsia are blogs.

*I also use Raindrop to save bookmarks that are less academic and I don’t have a great system for not having to save things twice; once to Zotero, once to Raindrop. Obviously where it’s an academic paper I don’t need it in Raindrop but things like design projects I want in both.

Is it worth it?

I took me almost a week to get Obsidian setup. A week where I could have been just reading and writing which raises the obvious question; was it worth doing it?

Well for a start, I set it up while reading and writing, so I spent the week reading papers in my backlog and writing notes on them and building my Obsidian system as I went and in response to what I needed at the time, so I wouldn’t say it was a waste, even if I didn’t get as much done as I wanted.

Here’s the thing, I have to work on my PhD around everything else so it can only happen in little bursts. I get an hour or two in the morning and maybe some time on the weekend while the kid is asleep. The problem I’ve had is that throwing notes and thoughts into Scrivener just results in a really intimidating mountain of stuff. I open up a 35,000 word file that I know is a chapter and have no idea how to approach it. I will have forgotten where I last was or any notional connections while as with Obsidian, everything is in bitesize, trackable pieces. What’s more, as a designer, the spatial approach to knowledge, whether through the graph or file trees makes sense; things are where they should be, connections are visual and so on.

There are distinct moments where I’ve seen a connection, had an idea deepened or enriched or identified something recurring that I need to understand better because I’ve been using Obsidian. It’s impossible to know if I would have had the same insights sticking to my old Zotero-to-Scrivener workflow but I’m reasonably confident I wouldn’t. As I go about writing the next chapter it will be interesting to see if the process is markedly different from the old one without Obsidian in it.

Which is really the final hurdle. At the moment I’m trawling through that massive page of notes in Scrivener and back-casting into Obsidian. I’m adding new stuff as well but probably only 1/4 of what I’m putting into Obsidian which is mostly things I’ve read over the last 12 months and carelessly noted in Scrivener without any thought to Future Revell. The next bit will be taking Obsidian notes into Scrivener to turn into the prose of a chapter and I still don’t know how that process will work. It might be ‘stop/start:’ At some point just stop using Obsidian and swtich to Scrivener in which case; how will I capture the insights and learning that come with writing up notes into prose back into scrivener? Or it might be first-drafting in Obsidian and then just tightening up in Scrivener. Either way, I feel there has to be some loss, just as there was with the last chapter. A general lesson of creative work is you will lose things and you have to come to peace with that.

PhD

Well I just wrote about this a bunch. But I’m gravitating around the next case study now, thanks to all the work above. I spent a bit of time at the weekend trying to tease out some ideas I’ve maybe talked about before that there’s a sort of nihilism underlying AI that drives AI forward; that people and society aren’t good enough and need to be ‘perfected’ in, at best, techno-solutionistic terms, at worst eugenicist terms. Here’s my sort of thorny problem: I’ll go along with problem + technology usually means some things solved but a bunch of new and often more difficult problems and harms being added. But, also, there are already problems. So, like healthcare: Adding a bunch of AI to healthcare will likely cause a bunch of new risks and harms. But also healthcare (in a lot of places) isn’t great. And if you can save lives, you should?

If the choice is between fancy AI gizmo now that can save lives tomorrow versus slow, diligent and thoughtful policy and design work that can save lives and ensure resilience over a longer-term, I’m completely sympathetic as to why you’d go for AI now.

I suppose in the context of the PhD writing I’m not on such practical considerations, more trying to critique the idea promulgated through AI rhetorics that a perfect healthcare systems speculatively exists in the first place and that technology is the way to get there.

Recents

Last chance to get tickets for the Design Declares event tomorrow. It’s looking like it’s going to be a really fun afternoon.

Reading

I remember watching Powerpoint Programming from the Great Impractical Ideas in Computer Science lot ages ago but came across it again just now and man is it good.
The Ethico Politics of Design Tookits from Tomasz Hollanek is a good review of the normative assumptions in ethics toolkits for AI. Including the fact that so few address the idea that you might not do AI at all. This is one of the most aggravating things in AI work; the idea that actually you shouldn’t be doing it. It’s like road deaths, as Louise Haigh said; we’ve completely normalised and accepted that thousands of people are killed each year (in the UK alone) by cars.
I’ve been reading a lot about scaling at the moment.
I re-read Sun Ha Hong’s Predictions Without Futures which really is a phenomenal thing. It completely shifted the direction of my PhD when I started. The idea that technology itself is a sort of performance of futurity that never really has to work.

Listening

This set is amazing.

Box116: Slipping a Disk for Squirrels

13.11.24

The anticyclonic gloom has passed! I really loved the anticyclonic gloom. I loved the words and I loved the ways it evoked the idea of being in Silent Hill (the original). It may not surprise you to know that the fog in Silent Hill was a technical convenience to reduce render distance so the PlayStation could handle the map. I will miss the anticyclonic gloom.

Slipping a Disk

And if you do play games, (or do renderings) have you ever had that thing where a character or object just glitches and starts spinning around wildly and incomprehensibly? I feel like that’s what hype does to the futures cone. When big, blustery and implausible claims about how a technology is going to solve climate change or cure all social ills is made, you’e briefly forced to shake up the cone in order to even consider and dispel them.

Promissory rhetorics and big speculative assertions about future benefits are, after all possible. It’s possible that we might one day live in a socioeconomic utopia brought about by generative AI but it’s not plausible. But when we’re told that this technology will usher in an age of amazing prosperity, that speculative cone is sort of shaken up and for a brief moment the possible but implausible slips into the frame of the plausible.

You’re forced to imagine this future if only to discredit it but in doing so temporarily move it into the domain of credibility so that you can expose it.

Squirrels

Ok, here’s an analogy I might run with the kid: So let’s say we both know about squirrels but I basically only know things from cursory observation and general understanding of rodents.

And let’s say she says to me one day; ‘Squirrels have six legs.’ and I say, ‘No they don’t’ and she says; ‘Yes they do’ and I say, ‘Based on my experience so far with squirrels and my understanding of mammals, it’s just implausible that squirrels have six legs. There might be one with six legs but squirrels, as a rule, do not have six legs.’

‘But they do,’ she insists ‘the ones you’ve seen just happen to have four legs.’ So I go out and spend a day looking at squirrels, hunting for even one example of an elusive six-legged squirrel.

Of course I don’t find one, but by engaging in this activity I’ve given credibility to the idea that there might be one or even that I’m just extraordinarily lucky in only having seen the incredibly rare four-legged squirrel my entire life and what’s more have just spent a bunch of time thinking and talking about squirrels. So just replace squirrels with AI, the four-legged one being the usual annoying, disappointing but sort of charming squirrel/AI and the six-legged squirrel being this childish fantasy that the kid (multi billion dollar technology company) is absolutely adamant is real and what’s more, you’re the only one who doesn’t see it.

The effect of this all is incredibly disorienting. If you’re constantly having to flex and reorient just to comprehend ludicrous claims how can anyone have time or clarity to focus on the actual? So hype, as much as a mechanism to inflate expectations and drive attention is also a sort of psyop that can undermine more general credibility of futures. It makes the whole structure of the cone (if it’s still the appropriate metaphor) unstable and unreliable, not because it’s been provably broken (e.g. the implausible turns out to actually be possible) but because you’re constantly having to refresh it to entertain and integrate ludicrous claims.

I guess it’s a similar phenomena to the idea that as the world is flooded with more and more misinformation and synthetic data, the credibility of everything is undermined and people start to question things even labelled as ‘real.‘ If enough VCs have told you that they’re going to end homelessness using their app, when one comes along that might actually do that, you’re disinclined to believe it. You’ve increasingly tightened your cone and ossified it around four-legged squirrels so much that when a six-legged one does come across your view, it must be a trick.

PhD

We’re motoring along again. Today, obviously, as I’m writing this I am not doing it because I am also trying to get back to blogging since approximately five or six people have asked ‘what’s going on there?’ Next week I might write up how I’m using Obsidian. I’m a few weeks off having a definitive sense of the next chapter (enough to share with you anyway.) I’m currently back-filling; so taking the (now) 20,000 words of unstructured notes in a massive document in scrivener and filing and shooting them away into different corners of Obsidian and watching, quite beautifully and remarkably, a structure and narrative naturally emerge.

Recents

Julian and I wrote a chapter for the Practice of Futurecasting which is the product of a few days spent in the mountains of Austria last spring. We wrote a chapter about taking imagination seriously and how that can be done in a ‘business’ setting.
If you’re in London next Friday I’m taking part in a panel with some incredibly luminary folks called for ‘Design Declares.‘ I think the focus is a little more on the overlap between futures and sustainability and how we can bring them all together.

Reading

Now I’m back in PhD flow, it is all just PhD stuff I’m afraid. Not as much random articles. The newsletter backlist is piling up again (I’d got it down to about 140 week before last, it’s back up at 250 ish today.)

Why is Retraction Watch so good? Like rubber necking a bad science car crash.
A lot of the assumptions about scaling in generative AI are off according to AI scaling myths. The AI Snake Oil folks don’t so much questions the efficacy of scaling (in this piece) but the fact of it. The assumption that data and power are just out there ready to be used (for instance, yes, there’s a gajillion terabytes of YouTube but a lot of it has no words in it.)
The best critical mass ever?
Absolutely wonderful, wonderful, gorgeous, gorgeous writing from Tim Maughan in Not My Problem. It has the same sort of feeling as The Deluge (which I’m still shook by) in that things don’t just go dystopia, it’s just a future of piling on inconvenience and hypocrisy in the name of speculative utopias. Dispondo-futuro? Something like that. What would a cynics (in the classical sense) futurism be called? Cynicofuturism maybe. Anyway, it’s gorgeous and i love him.

Listening

Sometimes I do this dance walking into the office to feel good. Because I love you. Speak to you later.