What with my recent adventures in non-clinical psychopathy (here’s the first post, the second is linked below), I thought it was worth going back to finish a book that’s been on the shelf for years—journalist1 Jon Ronson’s 2011 book The Psychopath Test.

I’ll foreground the biggest issues with the book—it is structurally muddled, and if you take a step back from the alternately shocking and humourous vignettes, you might find yourself thinking, “so what?”2

At various points, it’s not clear what conclusions the reader is supposed to draw, and by the end all I could discern was that the PCL-R is perhaps overused, and the DSM is being harmfully used to medicate children.3

The first section of the book covers the definition of criminal (or clinical) psychopaths. These are the kind of individuals that you’d think of as movie villians—incorrigible and malevolent—as well as the checklist, the PCL-R, that was developed by Bob Hare to diagnose them. Bob Hare returns throughout the book, with Ronson taking his training course, and then catching up with him periodically to discuss the individuals that he meets.

To add a ‘whodunnit’ element, Ronson introduces a Broadmoor inmate—Tony—that claims he is wrongfully detained as a psychopath. Despite the stakes for the individual, the story is amusing and illustrates the broader themes pretty well.

The rest of this section is pretty fascinating, and the other people he interviews—death squad leader Emmanuel Constant and corporate restructurer Al Dunlap, are pretty good examples of the personality type.

The interview with Constant is particularly shocking and disturbing, not least because he appears to have enough self-control to not murder a passer-by in the street,4 but was happy to plan and execute crimes against humanity.

Dunlap, on the other hand, strikes me—from an armchair diagnosis—as one of the more malignant but non-criminal personalities discussed in my posts, or by many other authors on disordered personalities in leadership positions.5

However, after that point, the book loses its way a bit. The subtitle of The Psychopath Test is ‘A Journey Through the Madness Industry,’ and it is while trying to deliver on that promise that the wheels come off.

Ronson takes a detour to talk about representation of mental illness in the media, conspiracy theorists (his favourite subject) and over-medicalisation of mental disorders. It’s all interesting enough, but feels like ‘a series of articles by Jon Ronson’ rather than part of a coherent narrative.

Luckily, the most interesting section—at least according to my biases—comes right at the end.

Without giving away too much, the conclusion of the story of the Broadmoor inmate Tony allows for a nuanced discussion both with Bob Hare and another professional over the PCL-R. During that discussion, Hare points out that it’s just a checklist, and he colloquially means “very high scorers” when he says “psychopath”—that is, those that score close to the maximum.

At least in Ronson’s telling, he has a moment of pause about those potentially detained indefinitely for scoring, say, a 30 on the test. He also offers a dark observation about risk and incentives:

“Sure, Bob says, over-labelling occurs. But it’s being perpetrated by the drug companies. ‘Just wait and see what happens when they develop a drug for psychopathy. The threshold’s going to go down, to twenty-five, twenty…”6

Finally, discussion of the non-clinical psychopathic personality crops up, but it’s late, never fully defined, and Ronson doesn’t explore any other disordered personality types—naturally, I’m thinking of the Narcissitic and Machiavellian types of the Dark Triad.7

Although, as noted by the original Dark Triad paper, these other personalities share characteristics of the Psychopath, they are different personality types, and I can’t help but feel that Ronson’s missing middle chapters were probably in not being able to mine material from a discussion of these types and the relationship between them. That would have made a lot more sense, both narratively and in terms of content.

Oh well!

My summary would be that it’s a good book, though it has some real structural issues. It feels like a Guardian Long Read-type piece8 that wasn’t quite long enough for a book, and was too long for a single article.

Still, Ronson’s an engaging writer, and you can probably finish it in a couple of evenings or a single long train trip.

M’father often told me, when I was just a lad,
A coder’s life was very hard, the code was always bad,
But now I’ve joined a startup, with a sub to latest Claude,
And now I find a coder ain’t a coder any more!

Chorus:
Don’t think on the code, don’t think on the arch,
If you do a refactoring it might be your last,
Get your CV ready for another run-ashore,
A coder ain’t a coder, ain’t a coder any more!

The ‘Killick’ of our team, he says we’ve had it soft,
It wasn’t like this in his day, when he was writing lisp,
We like our bunks and sleeping-bags, what’s Hickey’s hammock for?
Thinking on design ideas or lying on the floor?

Two slices of pizza a day, that’s your bleeding lot!
But now we gets an extra two when they lay us off,
So, we’ll put on our civvy-clothes and find a hackathon,
A coder’s still a coder, just like he was before!

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The other day, I saw an article in the I paper on the newsstand—apparently amid a spiralling budget, there’s a fresh round of calls to axe HS2. As somebody that uses the West Coast Main Line every week, this makes me cringe. We already, at best, will wait until 2035 to use it, and it is only going to go as far as Birmingham.1

The British Railway system has a capacity problem, a reliability problem, and an attitude problem. All of those issues need tackling if public infrastructure is going to improve, and HS2 is the test-case that defines the whole.

But speed is not the main point of the new line. The objective is capacity, and not just capacity for fast intercity services, either, but for those local regional and commuter services between small towns that have been so neglected. The complicated bit is explaining why that is.

Britain’s railways were largely built in the Victorian era, for a different kind of travel. Today, the same lines carry a mix of express intercity trains – the kind which HS2 will take – and stopping local and commuter services, the kind people use to get to work, or pop to a neighbouring town.

This mix is a very inefficient way to run a railway, for a reason that is quite obvious if you think about it: trains cannot overtake each other on the same set of tracks. They would bang into the back of one another if they tried. Not good.

- Jon Stone, The Independent Online, 2019

So, can we do something as basic as build a high-speed rail line in the 21st century?

If the answer is no, then we may as well give up, because we have become sketch-show conservative and parochial, and we deserve what we get as a result.2

This conservatism has taken root at all levels of our society and across the political spectrum. It blinds us to the art of the possible using the banality of our endless now. We can’t have new railways, new infrastructure, because some habitat might be disturbed, some sleepy rural town hear the occasional train.

At least in the former case, this comes from a fear of total loss in the face of environmental collapse, but turning over huge swathes of the English landscape to ecosystem-destroying sheep farms on marginal land is a political choice. Allowing grouse moors to be the site of millions of non native pheasant to be released every year is a political choice. Letting those same moors, denuded of their natural vegetation, flood the towns that lie below them is a political choice.3

Back to HS2; if we didn’t have the most depleted natural ecosystem in Europe, if people my age couldn’t remember bird numbers drastically declining in their own lifetime, would we be as concerned about this trade-off? No we wouldn’t. So it’s bizarre that it’s beyond the realm of possibility to just say “okay, let’s build the railway, but we’re going to create a national park over here.”

We’re so busy trying in vain to wrap our hands around what is left, we’ve lost sight of the obvious fact that we have the ability to create new things. We can create something from nothing, of course, but we can also reshape the old into something new. As the Bomb the Music Industry! song goes, “nothing’s forever, dude.”

How did we lose sight of that fact?

I’m not the first to rebut this piece, but yolo, I’m doing it anyway.

Obviously, it’s a bad faith article by The Economist, and Norman Solomon’s rebuttal points out that it’s very much in line with the political goals of the billionaires that fund the magazine.1

It’s also nothing new. In 2019 I remember The Economist writing of the need to ‘urgently resist’ Bernie Sanders and Jeremy Corbyn in a cover piece called ‘The Rise of Milennial Socialism.’

The editorial staff of The Economist don’t seem to see the connection that if the intellectual and political class repeatedly, one generation at a time, dismiss calls for a more equitable society, then all that will happen is a descent into extremism. Maybe they just don’t care.

After all, I strongly suspect that the billionaire class would rather have fascist governments that they can influence or pay off, than centrist or left-wing governments that they have to change their behaviour around, or—God forbid!—hand over some tax to. Look at who funds the reactionaries, and you’ll see a pattern.2

Okay, back to the matter at hand. There are five claims made in the article that I don’t like:

• The first is that green parties like GPEW3 aren’t interested in the environment, and that these policies are “peripheral concerns.”

• The second is that the left’s is a “zero-sum mindset, where a better outcome comes from not from creating, but from taking.”

• Third, that wealth concentration due to AI is not a valid concern.

• Fourth, that “once the left wanted higher taxation for everyone.”

• Fifth, that there is even anything wrong with the reductive statement, “Now Gen-Z socialists demand handouts funded by billionaires.”

So let’s go in order.

1. The Environment Matters

The Green Party is still principally concerned with people and planet at the core of its political programme. In fact, tacking left on many peripheral issues is not, as the article suggests, because it is necessarily a “socialist” party (something I think many ex-Labour members also mistake) but because it is an outcome-focussed platform based on these two pillars.4

If people do not have the means to prosper and have a nice life then obviously that is a moral failing, but also, pragmatically, you will not be able to convince people to make any hard choices to protect the natural world for themselves, others, and their children.

The idea that the party most responsible for pushing a green transition is not interested in value creation is frankly ridiculous, doubly so when parties on the right are tripping over themselves to abolish net zero targets for purely ideological reasons.

52.5% of the UK’s energy was produced by renewables in 2025, and it is storage, capacity and distribution that are currently pulling down further gains. In an economic and geopolitical environment where natural gas supply is already both constricted and problematic, and oil is, well, both constricted and problematic, this isn’t even a fluffy green issue.

This is a national defence issue, and as probably the most hawkish Green I’ve ever met, I don’t understand why energy security as defence necessity in the long run isn’t tied more frequently to renewables and more decentralized energy production.

Before I hear another “but Greens something something NATO!” remember that (a) Trump is doing his best to dismantle the alliance, (b) there is in fact no current Green policy to leave NATO, and (c) that Greens don’t like Russian T-72s parked on their lawn any more than the next guy.5

Combine those and you’ll see why I just don’t think the criticism matters in any world where the Greens get anywhere near power. For all of the dismissive right-wing talking points, we’re actually a pretty pragmatic lot.

2. The Left’s Mindset is Not Zero-Sum

That our mindset is zero-sum is possibly the dumbest single claim.

Duh, we want to create a green economy. That’s value creating, it creates jobs, R&D, innovation. Zero-sum requires a closed system, so they’re just wrong. On a very basic level.

We, in simple terms, know that ya gotta spend money, to make money.

Greening the grid, greening transport, is going to require a huge infrastructural effort. Perhaps not on the scale of China building 50,000km of high-speed rail in 17 years, but the fact that we can’t even finish HS2 and link the Northern cities with a high-speed link (that is, an over 250km/h service) is a joke.6

Moreover, the Greens specifically are also interested in community assets, education, and opportunity. Let’s talk about that a second. Prominent economists like Mariana Mazzucato7 have spoken more eloquently than I can on how under investment in education and opportunity can have a disproportionate impact on productivity, creativity, and growth. It’s not a controversial opinion that if you spend money as public investment then you can expect a return down the line.

Subsidies and paying consumer bills to an energy supplier—now those are (potentially) non-productive uses of government money, especially if there is no new infrastructure component (as there is with e.g. cavity wall insulation, solar panels, or heat pump subsidies, etc). As a parent that benefits from free nursery hours for working parents, I hate to admit it, but this is probably an example of just giving government money away with little anticipated return.8 It’s plugging market failure rather than proactively investing in the future. Yes it means parents can work, but it’s not like free school meals for all children.

Investment in people’s futures is always a sound bet.9

On free school meals: I’m not the sharpest tool in the shed, but I’m going to put it out there that I reckon children not being able to afford food might just have an impact on productivity.10

In case The Economist need some education on the scale of the scandal here, 4m children in the UK live in poverty, with the majority (7 out of 10) having a working parent, according to the Child Poverty Action Group. Last year the Trussell Trust distributed 2.6m emergency food parcels via its community of food banks, with 900k parcels provided for children. Food bank usage is up 45% on pre-pandemic levels. Around 4% of UK individuals used a food bank in the 2023/4 year.

To give you an idea of the increase, in 2010/11, the Trussell Trust “distributed around 60,000 emergency food parcels.” That’s a 43-fold increase. I can’t even begin to describe how angry this makes me as a parent of young children. It is obscene.

Does The Economist need me to explain how investment works?

The whole point of investment in public services, infrastructure, young people, healthcare, education and other initiatives is not only the moral imperative to improve the lives of people in this country, but also the slightly less altruistic knowledge that doing so will result in a healthier economy in the long run.

But yes, whatever, we’re zero-sum, take take take.

No. You know who is take take take? The right. Their billionaire backers. The management consultants. The spineless execs of companies that are always seeking “to do more with less.” Remember that under austerity, a cowardly, ideologically-motivated policy, the UK underinvested to the tune of hundreds of billions of pounds:11

“The cost of failing to maintain [the existing] rate of public spending growth, and instead imposing spending cuts, came to £91bn of lost public expenditure in the final year of the austerity programme, or £540bn over 2010-19.”

Remember also that the private sector underinvested too. So a lack of vision, and conservatism about the future is not just limited to the political class. It’s why British deep tech startups such as Deepmind and ARM eventually seek investment from elsewhere, often being acquired by US tech giants.

Left-of-centre think-tanks naturally have criticised austerity as counterproductive; but the approach taken in the UK was even somewhat at odds with that well-known hotbed of radical thinkers, the IMF, a fact that was seized on by John McDonnell at the time.12 The reason will be familiar to anybody that’s done GCSE Economics, that the distribution of wealth and income is important, because people spend unevenly; thus, “[i]ncreased inequality in turn hurts the level and sustainability of growth.”

Even as these private sector ‘leaders’ continue to add no vision and no guts, and low-to-no investment to our economy, they are content to give themselves record bonuses and talk at conferences about how innovative they are.13

So here we are, hundreds of billions of investment short in the public sector, and even more short in the private sector. Many of our pressing issues share the same shape.

The 6.5m homes housing gap? Look at a chart of housing in the UK and France and note where austerity comes in. It might not quite be a case of, “it’s the economy, stupid,” but it’s probably a case of, “it’s under investment, stupid.”

If, however, by “take take take” what The Economist means is that we favour redistributive policies, then yes we do. I’ll talk about that in Section 4.

3. Concerns About AI Are Valid, Actually

I’ve written a three part series on AI, and you can read many other much better writers on the subject (links in those posts). This is well-worn ground both for the commentariat, and also in terms of historical context.

The social concerns are valid. The economic concerns are valid. The artistic concerns are valid.

Look, I suspect the tooling is pretty useful, as in, gives you a 3-10% increase in productivity (that isn’t nothing!), and I wrote in Part 3 of that series about how I and others leverage it. I’ve also discussed how small companies may disproportionately benefit, and that this presents an opportunity, because the maths of scaling it all may not add up for big or legacy organisations. That’s competitive advantage, folks!

However. I’ll just say that it looks really out of touch to mock concerns about AI when young people are facing a collapse in jobs and opportunity on the one hand, and being told by billionaire AI bros on the other hand that all their problems will be solved by the thing that conveniently, those same AI bros are selling.

Hey, look! It’s an article in The Economist about the problems of rising AI usage bills.

The kids aren’t stupid. The kids are alright.

4. Let’s Talk Taxes

Do I have to explain progressive taxation to The Economist?14

Let’s be charitable and assume that’s what they mean by this talking point. To which we then say... er, yes? If you create inflation (growth) but don’t control how that is distributed, the common result is wealth concentration.

The point of wealth taxes is to tax wealth, not work. It doesn’t mean that high earners couldn’t pay a bit more than they currently do. Taxes aren’t mutually exclusive as policy levers. Higher income tax in the top band is a reasonable deal, in fact, in the context of an economy that could (and should) bring back in free university and vocational education.

Personally, I think wealth taxes are a bit of a red herring—they don’t bring in enough to be transformational, but they bring in something.15 You’re going to have to also change other progressive taxes (income tax) and reform council tax in order to get enough money to have credible, transformational, spending plans.

I remember when, as a student in 2006 the UK finally paid off its World War Two debts. As a result, I was delighted to see these interviews with Mariana Mazzucato where she points out—correctly—that we can always find money to fight a war, and those kinds of long-term debts are not factored in to whether financial markets will lend money to an economy in the Global North.

Austerity was justified using an analogy (that the national budget functions like a household) that is incorrect—and the structure of long-term debt for things like war loans bears this out. Some kinds of spending are always possible, if the justification is deemed “good enough.”

The same is true of a programme to fix the UK economy. If it is credible, the money will be there.

This is why we don’t need to worry too much about the bond markets so long as we can articulate a long-term economic plan that sounds credible. Truss’s failure was being less convincing than a lettuce, and using uncosted, vibes-based tax cuts. We can do better. Much better.

If The Economist is also talking about initiatives like LVT16 (which I am not sure the Greens currently have a position on), then there is evidence that this would be a net positive for most households in the UK, representing a good way of not only making Council Tax fairer across the UK, but also working towards redistribution.

Finally, let’s talk again about wealth concentration. As another Progressive Economy Forum working paper notes, in the UK since 1980, “the market value of privately held wealth (property, physical and financial assets net of debt) has risen at more than twice the pace of the economy, a pattern repeated across the globe.” No surprise that this broadly has coincided with successive tax cuts for the rich.

Trickle-down economics, the Reagan-era idea that making the rich richer17 benefits all participants in an economy, has been pretty roundly debunked at this point. Moreover, it’s part of the reason that Liz “the lettuce” Truss’s mini budget was rejected by the markets—it’s as if somebody read Naomi Klein’s The Shock Doctrine and went, “oh yeah, great idea!”

Anyway, the paper debunking trickle-down economics that I referenced just now was the most downloaded LSE-hosted research paper ever, and is worth a read. There’s some cracking lines in the cover article, however:

In explaining why cutting taxes for the rich did nothing to boost economies, Dr Hope referred to the economist Thomas Piketty, who argues that unless capitalism is reformed, it will threaten the democratic order.

“Our results align pretty closely with some work from Thomas Piketty, that would suggest that what happens if you cut taxes on the rich is that they then bargain more aggressively for their own compensation at the direct expense of workers lower down the income distribution. So, the story of the paper then is really to do with rent-seeking among CEOs and top executives—and that increasing when you have lower taxes on the rich.”

Rent-seeking is the effort to increase one’s share of existing wealth without creating new wealth—rather like a greedy child demanding a bigger slice of the pie so that there’s less left on the plate for everyone else.

I like this quote because it also neatly explains rent-seeking. Rent-seeking crops up all over the modern economy, but not just in housing, which is a common misconception.

Those of us who were in crypto for reasons other than pure gambling18 were likely in it because the economic model of web2 encourages the development of rent-seeking platform capitalism (Varoufakis) or vector capitalism (McKenzie Wark), two different terms for monopolistic, rent-seeking behaviour. Think the business model of Amazon, Uber, et cetera—that’s classic rent-seeking.

Perhaps the most disappointing thing about web3 is that many blockchain Foundations essentially re-created the platform or vector capitalist model of coercion and centralization in their blockchain economies, something I’ve written about in conference papers presented at FCiR25 and SDLT9.

Anyway, back to the point: in an economy struggling with flat productivity, flat growth, and rising inequality, rents are problematic, non-productive capital is problematic, and it’s not just radicals that think or have thought so. Imagine my suprise that Winston Churchill of all people agrees with me:

Land Monopoly is not the only monopoly, but it is by far the greatest of monopolies—it is a perpetual monopoly, and it is the mother of all other forms of monopoly.

Unearned increments in land are not the only form of unearned or undeserved profit, but they are the principal form of unearned increment, and they are derived from processes which are not merely not beneficial, but positively detrimental to the general public.

Wealth taxes are an attempt to curtail the impact of rent-seeking. Frame it that way and they are a class of taxes addressing a very broad problem.

5. Siding With Billionaires is Pathetic

Billionaires have the money to spare.19

“But they’ll leave!” They already live extra-juristictional lives, for the most part. In so far as we have any ability to force the matter, we should say, pay your share or GTFO.

These people aren’t afraid of us, they aren’t afraid of the state, and they should be.20 A journalist friend of mine memorably recounted a discussion where a politician had said, “how do you deal with a man as powerful as Elon Musk?” To me, in the retelling, he quipped, “the SAS.”

The landlord class have always been economically distorting and non productive and this crop are no different. That’s the most charitable read I have.

The argument against wealth accumulation in the renter class is not a new one. I hope I made that case adequately in the last section.

Adam Smith, John Stuart Mill, Lloyd George and Winston Churchill have all argued that rents are non-productive for an economy, and in an environment where investment (both private and public) is acutely needed, taxing and discouraging private renting in favour of investment in other areas of the economy is hardly a crackpot idea.

Plus, if you side with billionaires over your family, friends and community, you’re a bootlicker. Sorry, but there it is.

Let’s Wrap This Up

I’m a long-time subscriber to Private Eye, and there’s a tradition in that magazine to end furious letters with, “I will not be cancelling my subscription.”

The same is true here. The Economist remains one of the few news outlets that seem to bother with fact-checking and basic journalistic standards,21 even if I generally don’t care for their worldview.

However, this is a dismal, dismissive effort that can’t go unchallenged.

In exchange for hard work, people want a nice life. They want dignity. And boy, that is a hill I will happily die on.

On May 20th, 2022 the British art pop band Everything Everything released their sixth album, Raw Data Feel. In some ways, it was a return to form. The band (unfortunately) had had more misses than hits in their two preceding records.1

There’s a track on it that hit me like a truck, Track 11, My Computer.

Some of the lyrics on the album are nonsensical even by EE’s oblique standards, and that’s because their lyricist, Jonathan Higgs, was experimenting with generatively creating them:

“With assistance from Mark Hanslip, a musician and researcher at the University of York's Contemporary Music Research Centre, Higgs developed an AI bot dubbed ‘Kevin,’ named after a recurring character in the album, to compose song lyrics generatively. Higgs fed it four different sources of information—LinkedIn's terms and conditions, the epic poem Beowulf, 400,000 posts from the message board 4chan, and the sayings of Confucius—before compiling and tweaking the results into usable material. Ultimately, the bot contributed roughly 5% of the album's lyrics and a song title (‘Software Greatman’), receiving a songwriting credit in the process” (Wikipedia)

So eccentric is Higgs’s lyrical style that I’m not actually sure whether either of the chorus’ two variations is purely his mind, or the generative model:

‘Cause you can bend, you can slide
You can push me to my limit
You can run, you can crush
Every blue boy on the planet
You’re in love with the future, I don’t know why
You’re in love with the future, I don’t know why

And you can hide, you can seek
With that ultraviolet ammo
You can sing, you can play
My ribcage like piano
You’re in love with the future, I don’t know why
You’re in love with the future, I don’t know why

The line, “you’re in love with the future, I don’t know why,” came to embody everything about the chaos that had overtaken my working life.

At the time, the Juno blockchain was navigating the tail end of the Proposal 16 insanity,2 when a governance process had attempted to seize $120 million worth of tokens, and I had spent something like 2 months 24/7 firefighting, trying to broker a compromise, talking to lawyers, dealing with a zero-day cyberattack that halted the chain, and somehow running my business.

The perils of being close to the bleeding edge and saying, “damn the consequences” had never been more real, and this song was a reminder that most of the time at the frontier, you’re surrounded by gamblers—and are probably one yourself. There’s no safety in numbers, and just because you’re where the action is3 doesn’t mean you’re collectively right.

I heard this song and realised that we had collectively believed our own hype. In uncritically believing our technology was the future, we’d committed that cardinal sin that always leads to destruction: hubris.

In 2021 we’d needed that uncritical, enthusiastic energy to launch projects, often working for free, collaborating with idealists, chancers, gamblers, founders, degens and everybody in between. The blockchain ecosystem was always more varied in its personalities, ideologies, politics and opinions than its critics gave it credit for, and everybody needed to—and largely did—pull in the same direction.

However, by late spring 2022 the cracks were showing. Projects had launched, rivalries had surfaced, money had been made (unevenly) and it was time to pony up the use-cases. At just the time when the mood needed to shift a little to be more self-critical, it had become even more hysterical.4

At that time, the Cosmos experiment with DAOs was hitting the buffers. It wasn’t Juno that poisoned the well of Cosmos—depending on how you look at it, that was either the Terra/UST collapse, or the effect of continued high interest rates,5 but in hindsight, the writing was on the wall. The summer that followed was probably the last time that most validators in that ecosystem were consistently in the green.

What this experience left me with was an apprecation of two things:

1. To get an outsized return on the energy you expend, you have to be where the action is; however

2. People where the action is are by definition gamblers, so be extra skeptical of their claims, and bear in mind their risk tolerance might not be the same as yours, or as society’s.

This is extra relevant in the current AI maximalist world we live in—to be where the action is, you’ve got to pick up your shovel,6 and head in that direction. However, I’ve seen first-hand what happens when skepticism is abandoned and wishful thinking takes over.

If that wasn’t the lesson of Proposal 16 itself, then it certainly was the lesson of everything that followed. There’s nothing wrong with being where the action is, but when things are exploding is when you need to be at your most critical, honest, and level-headed.

Perhaps that tension is obvious in the posts on this blog, I don’t know—but the chorus lyric is as relevant as ever.

Building on several posts in the last two months, I realised there was a connection between our work on decentralized governance (accounting for coordination costs in an organisation or network) and the way that AI tooling changes organisation dynamics.

So, continuing that conversation, I present an AI Law:1

The ability to benefit from non-human workers is directly proportional to your human employees’ ability to account for the costs created by the non-human workers.

The general case of this can be thought of in terms of transaction cost economics:

The value of a network can only be realised if the relationship between value in the network created by adding nodes versus the cost they incur does not mean that communication within the network becomes cost inefficient.

So let’s dive in.

Agent Sets and Cost

In my last post, I discussed the cost of adding nodes (‘agents’ in the network sense, not the ‘AI agents’ sense) to a network in the context of blockchains.

In a sense, it can be thought of as sort of an inverse Metcalfe’s Law; the idea that the value of the network scales with the number of pairwise connections.

In other words, it’s what in tech we often refer to as ‘the network effect.’

That is to say the usual formalization of the simple version is,2

Which describes the break-even point. Strictly, it’s the following, where there is linear cost growth, Cn, and A, a “non-constant proportionality factor affinity”—or, value per connection.

The issue is that this assumes value outstrips costs accrued past that point, and the break even point is only related to fixed and variable costs.3 While the original simplification was exponential, Metcalfe has vociferously qualified in later writing that it should be viewed as quadratic.

We argue there is a cost of coordination that applies to these networks, as we are in the process of formalizing for blockchain governance. This cost is accrued on a marginal basis as we add nodes.

However, this also applies to any agents in a social or organisational graph. As we saw in the last post, we can cost a pairwise connection based on the type of agent that is connected. That is, we assign agents membership of a set, and assign connections between two sets a cost.

How does this relate to the cost of AI within an organisation? Simple. Let’s rebuild the logic of our prior post but using only engineers and AI agents as our agent sets.

Let’s say our organisation is made up of nodes that are agents (A), interacting with one another. Let S be the set of engineers or staff, and L the set of LLM agents in an organisation chart. S and L are both subsets of agents.

They are disjoint sets, so a given agent or node (let’s call them n) in the org chart is in either one set or the other:

We can then see that the normal costs of communication increases (such as Brooks’s Law) are simply the description of an increase of edges (that is, connections between nodes, or lines, in a visual representation) in the graph.

The distinction we make here is simply the related cost and the value accrual.

So here is the big hypothesis or question: do AI agents generate more value than the costs they create?

This is important, as it is the intersection of Brooks’ Law and Metcalfe’s Law.4

It’s important for your organisation because at present, it is only employees (the staff set S) that can account for (pay down, or offset) these costs. Employees can generate value and account for costs (as well as create cost, of course). AI Agents (L) can only create value and create cost.5

Who Accounts for Cost?

In practice this simplified ex-ante analysis is, of course, insufficient. What happens as the system operates is that it is dynamic. Obviously cost is not uniform as in the simple model. Indeed, one Agentic LLM (L) might be best thought of as intersecting with Tasks (T), a set of background agentic tasks that each have a cost.

The intuition here is simple; that adding additional sets not only introduces granularity for the purposes of analysis, but shows how rapidly cost can scale with the addition of extra agents, given that only one set (the employees or staff, S) can offset the cost creating activities of the non-human nodes in the organisational graph.

Rather than even costing edges, for now let’s use the simple intuition that we can assign a value to the node itself (maybe this is the sum of all edge costs that are added by its inclusion in the graph, I’m hand-waving a bit to simplify things).

The logic for something like that would give us the following for calculating a break-even point, where V is network value, C is total cost, and Cn is known network costs, and we have a function f that takes a new node and returns its cost. Attempt at a formalization is in the footnote, if you’re that way inclined.6

The question then becomes, what is the cost of adding nodes? Is it linear? Quadratic? Log? Exponential? is there ever a point at which, after adding a node, C is greater than V?

Bringing this back to AI agents, is there a point at which growth in C outpaces V?

Intuitively, of course there is.

So, the question then becomes: how soon do you hit it? More nebulously, in your current distribution of S to L,7 do you hit it earlier than you would if you relied on only humans, or a different distribution of S to L?

At that point, depending on who feels the decrease in value first, and how it is expressed, we might expect to see systemic problems arise. Anecdotally, we hear about bottlenecks and burnout daily in the context of expanded AI usage, and I’m not sure this is strictly a governance problem—after all, a key part of my thesis is simply this: in many cases, governance does not scale.

My guess is that for many scaling situations, adding new nodes to a network graph follows the sigmoid function described by Metcalfe. However, in cases such as blockchain validators, and AI agents, it might be skew normal (a lopsided bell curve, left chart below). Even if the value of adding agents is sigmoid (i.e. like the governance example, right chart below), eventually cost will outpace it, depending on what that curve looks like.

To quote Jamie Hurst,

The cost of building has collapsed, but the cost of aligning organisationally has not. If anything, it's gone up. When three different teams can each produce a working solution to the same problem in the time it used to take to write a proposal, the bottleneck moves from engineering to coordination. The MR review situation is a good example: it's now easier to build a new bot than to adopt someone else's, which means cohesion gets harder to achieve, not easier. We're solving more problems, faster, and the org-level alignment work is paying the price.

Metcalfe’s Law has been used to justify the valuation of Facebook (now Meta) and other tech stocks. However, whether or not the hyperscalers’ stock price is in any way related to value is debatable. It could be (and likely is) more about animal spirits than value.

While I think the intuition that network effects are part of any scaling story is correct, it’s notable that past a point these huge companies scale as much by acquisitions as by developing their core product. In fact, with some of the main products experiencing user decline, perhaps the argument that some external costs build up and make even these systems fundamentally inefficient in terms of their transaction economics holds water.

If network scaling issues in other domains hold true—and it is a big if—then likewise we should be wary of scaling our organisations and teams.

Going Further

Except, it’s not quite as simple as outlined above. As the graphs show, there’s actually a network topology (technical graph, containing developers and agents) and governance topology (containing the employees and agents described previously).

With this in mind, we need to adapt the bold statement from the introduction to make it clear that, as in blockchains, costs generated from the techncical operations of the organisation are expected to be accounted for in the governance of the organisation. Similarly to a blockchain, in other words. Thus:

Conclusion

Hidden costs are nothing new to technical strategists and technical leaders. We’ve known about, debated and discussed these things endlessly. We usually use the jargon TCO, or Total Cost of Ownership, rather than the more economics-y terms8 I’ve used here, but the principle is the same. AI has blown traditional ways of reasoning about, and accounting for, costs out of the water, and TCO is back to the top of the list of burning issues.

The state of the art in academia seems to show a task-contingent cost curve, referred to as A Jagged Frontier (Dell’Acqua et al., 2026), which means that the benefits of AI usage are felt unevenly.9 On average, AI use, whether supervised or not, was found to always improve output when “within the frontier,” and result in worse results outside of it.10

Taking the ideas from our last post, we could build this cost-curve based on sets of tasks, where each have different costs. This would show uneven cost of AI use, and how it contributes to total organisational cost. Figure out value per task (easy, right?)11 and you could overlay the two.

The question not addressed in the study is a longer time horizon—whether deskilling, or other factors would change this result over time, shifting the cost and value curves. Within the bounds of the study however, the result appears clear—some amount of AI use, on average, will result in better outcomes on within-frontier tasks. Any negative effects might simply be a cost of doing business in the AI age, where a black swan cost or risk generated by an out-of-frontier task could sink your project or business at any time.12

Variations on such risks have always existed.13

The good news, then, is that understanding and managing TCO was already most of the ballgame, just as things like TDD and good engineering standards were. So nothing changes, right? Because we all had everything figured out and running smoothly before the AI wrecking ball came along…

NB: An earlier version of this post did not reference Jamie Hurst’s blog post. The post was updated to include a quote.

Apologies for anybody that was after a ‘light’ piece this week—instead it’s a work-in-progress piece of academic work. For a while I’ve been trying to formalize some way of structuring the agent types in a blockchain network to show how risk, cost and security are felt, and for now, I’ve settled on pursuing an angle that involves weighted sets.

In this article I’ll talk about two ways of looking at agent sets for analytical purposes: weighting the nodes in the set, and weighting the edges between nodes.

First however, we need to talk about how these networks are structured. Based on qualitative research, industry experience and an extensive review of the literature, we advanced a model that operates on two axes—the technical and the social.1

1. Network Topology and Governance Topology

In our work, we model a network as split across two axes:

• Network topology, the physical and technical structure of the network.

• Governance topology, the structure of decision making power in the network.

We summarise this in our papers as a split between the ‘protocol’ and the ‘governance of the protocol.’ It’s a useful way of looking at socio-technical systems, especially when it comes to how externalities are felt.

We hypothesise that many of the external costs generated by the network topology are assumed to be accounted for in the governance topology—however, in practice, they usually are not.

Finally, these axes—or perhaps ‘layers’ would be a better term—are stratified, in the sense that the governance of the network always takes precedence.

2. A Weighted Sets Model of Decentralization

Let’s attempt a formalisation that shows the interaction of agents as they account for the cost of security2 in a network. Following the reasoning of Budish (2025) we assume:

1. Adding a single node to the network always has a linear cost as the best-case

   scenario; thus, the weighting factor is always at least 1.

2. The combined cumulative value of all nodes multiplied by their weights adds up to the combined fixed cost of security for the network.

This would not be the total cost, due to the presence of externalities3—we hypothesise that there is a negative externality associated with the cost of co-ordination in both the case of a network and a governance topology that is not accounted for on most existing ledgers.4

Excluding exogenous externalities as beyond the scope of this piece, from the perspective of a single agent submitting data to the consensus and finality process, we can model the structure of the cost of finality in terms of its locus in the subsystems and agent personas in a system.

Let’s assume, for this simplified model, that agents can be in only one subset. Thus, we might imagine the network topology of a network as a set of subsystems or nodes, S. However, different nodes or subsystems in this entity graph, for example, validators V, software implementations I, a Foundation, F, or data centres D, have different weightings for their centrality. Another way of thinking of this is in terms of their centrality to the integrity guarantees of the system in operation.

We can imagine a formalization of weighted centrality that is as follows:

1. A weighting function:

2. A function (sigma) that maps subsystems (S) to their subset or partition:

Where P is a subset, or partition of S.

This means we can use a weighting function (w) for a single subsystem (n):

To confirm the total weight sums as expected, it is possible to sum the weighted sets:

This same exercise can be completed for the governance topology of a network. As stated before, with ‘real’ values, we would expect this sum to add up to the (fixed) cost of securing a network at a given point in time.

The questions then are:

1. What are we weighting? There are two options:

   1. The nodes themselves

   2. Their relationships (which would mean we actually need to uniquely weight the edges between distinct sets, e.g. Validators (V) and Data Centres (D))

2. How do we represent externalities?

Asking the first question raises some questions about what exactly weighting the nodes tells us.

3. Visualising Weighted Sets as Cost Curves

The goal here is to look at all the potential places in the network that affect the integrity of our assets,5 and apply a weighting to them, which we can later express as a cost.

This shows our risk exposure as a transacting agent within a network to other agent types, as well as the relationship to the cost of security. In the next section, we will describe how this might be calculated such that it would represent a cost curve.

To model a public, permissionless network, we simplify the topology down to only three components: client implementations (1), validator set (100), and then all other subsystems, which we assume add up to a further 200 nodes—and to whom we apply an equal weighting.

The single client implementation is the first point of centrality, followed by the hundred validators, followed by the remaining subsystems (in this case, we allow for only one type—we could easily say it simply represents stakers).

Currently these charts use only arbitrary numbers, but if we exempt client implementation in the case of an example system such as a Cosmos blockchain or Sark network,6 we could model the effect of a ledger as single point of failure, versus a system where a higher level of trust in a single agent (a Porter) was required.7

‘Porters’ are simply the in-system name for relays, to which an asset is ‘tethered’ for its integrity. The transactable asset in Sark is the USO, an Unforgeable, Stateful, Oblivious asset8 and can only be updated by a single relay, which is identified in the asset’s data payload by the identity of the Porter which is allowed to update it.

4. Weighted Nodes vs Weighted Edges

This weighting is all very well, but it’s still not telling us much. My first intuition was the weighting could result in a gravity-weighted graph that would show the relative centrality of a Foundation or controlling entity.9

However, I think the place to take this next is to maintain the partitioned sets model, but abandon weighting the node itself, and add an additional group of sets: the edges between nodes (i.e. agent sets). By doing this, we can later use the edges to weight the node based on their relationships.

Then you can start to model value of adding an agent, and cost of adding an agent. Think of it in terms similar to Metcalfe’s Law—that adding an agent has a value, related to its set, as well as a cost.10 Of course, this is unlikely to be linear. In a BFT system, the 4th node is the most valuable systemically, and we would assume a long tail of diminishing value accrual thereafter.

For simplicity, you would weight the edges between nodes in the same sets equally (even though concerns such as voting power, jurisdiction etc might change the weighting). In practice, a Foundation might delegate a hundred USD to one validator, and a million to another, but here, we would assume equal weighting of edges (pairwise, symmetrical connections).

In an example, the subset of edges, E, should be the same whether a V (Validator) or F (Foundation) is the source. Thus, given a function that can return a weighting for an edge, given its source and target, f, the value of a node is the sum of this set of edges.

This would allow you to simply sum the value of all the edges to a given node, giving the node a concrete value.

In this simplification, it’s bi-directional, but that naive weighting might not work. At the end of this section, I’ll add some thoughts about how this relates to cost, but the intuition should be that it operates similarly to Metcalfe’s Law, and the visualisations in the previous section are simply the cost curves.

Let’s think of a visual example. Users can hold one unit of an asset—a USO in the case of Sark, and a whole token of some nominal cryptocurrency.

How would we put a number to it? For a dummy example, the intuition would be to weight the edges between two sets as 1/(number of nodes that have responsibility for integrity) in the case of integrity.

Consider the case of a Porter from the point of view of a USO asset holder—to update that asset, they (dark blue) need to talk to only the Porter (light blue) specified in the asset by either the issuer or the last holder. Therefore, the weighting of their edge with that Porter is 1 (1/1).

Now consider the case where we have a cryptocurrency typical of a public, permissionless network. It can be updated by sending a request to any Validator (in fact, any RPC, but let’s ignore RPCs for now in this super-simple example). We omit RPCs because we care about the subsystem responsible for integrity—the valset. RPCs are only for reads. Assuming, as in the prior examples, a valset of 100, the user’s dependence on that validator is 0.01 (1/100). The edge could be weighted by that value.

Here’s a simplified version, with 20 Validators (light blue) and a single user (blue), with a weighting of 0.05 per edge. The Validators are all assumed to be 100% reliant on the Foundation (green) for their existence/integrity (profitability). In reality, they’re somewhat dependent on users, but we base this on our fieldwork, where Validators rely on Foundations and generally don’t find gas, MEV or other incentives to be key in the long-run. However, this isn’t quite right. They’re dependent on one another too. Here they’re in a full P2P mesh.

To bring in cost, and weight the set in the context of the whole system, we need to change that value of 1 (even though in the above examples, it wouldn’t tell us much, since only validators and users are represented). Then, assuming the weighting value for each set of edges between agent sets was scaled appropriately to the total fixed cost in the network, the edges would be weighted according to cost.

In the case of adding an agent, there is a marginal cost to adding the agent, but from the perspective of a marginal transaction, this is likely to be felt as a fixed cost. The intuition here is simple: if the edges’ weighting is, in a sense, the fixed cost, these visual representations form a basic hypothesis as to whether these systems can scale, or not.

Even the existence of an edge is significant for describing the cost of system-level coordination. What it shows is that the system’s topology in terms of connections matters—in both the network topology and governance topology.

It’s easy to see that naïve decentralization (or performative decentralization, also known as ‘decentralization theatre’) along the lines of “more nodes is better” simply leads to an increase in cost. The question is—the fixed cost can be seen clearly, but if there is an external cost, then it could be exponential, depending on the node graph. How do we represent it?

Conclusion

As I suggested in the section before, it seems likely that modelling edges might be more informative. The key reason is this: adding another edge always increases the total cost, from the perspective of the user, even if the weighting is small. As you’ve probably guessed, if this logic holds, it will be coming in a paper soon.

Acknowledgements: although this work is at a very early draft stage, I think it’s valuable to try and generate some discussion, so I am putting it out there. It builds upon many discussions at UCL and specific sessions on modelling externalities and scaling permissionless networks with Prof Tomaso Aste and Dr. Geoff Goodell.

I should note that Tomaso and Geoff would likely characterise the current state of my work as not far above the ‘shower thought’ level, so no endorsement is implied or indended—merely that I value the conversations I’ve had.

Anyway, at the time I tried to distil what I learned about agile into a post that was a 1-minute read. It wasn’t bad.

Since, I’ve worked for a startup that was later acquired, contracted as a fractional CTO, done numerous digital transformation and due diligence projects, been the technical lead1 for a blockchain, started a company,2 and launched two blockchain protocols.

So what have I learned since?

Funnily enough, the original post mostly stands up. Probably the main thing I’d change is making the importance of trust3 even more central to both facets of delivery. I’d also emphasise feedback loops. To some degree, what you’re really trying to do with agile is:

1. Identify the desired outcome

2. Develop systems around feedback loops to iterate to that outcome

In a sense, this terser definition is even better than my past one, as it doesn’t rely on specific tools, approaches or conditions; it is generic advice (in the good way!).

As sharper readers will have spotted, as this is a generic approach, it won’t have any real impact unless the right outcome can be identified. That’s a very deep rabbit-hole, and outside of the scope of this post.4

However, in the spirit of the original post, I’ll have another crack at it:

Tech side

What?

• Reduce cost of change

• Reduce cost of failure

• Fail fast

• Identify and solve the right problems

How?

• Trust

• Staff agency

• Experimentation

• Automation

• Iteration and building feedback loops

Why?

• Deliver faster

• Be able to scale solutions

• Make money

• Save money (either absolute terms or TCO)

• Address correct outcomes

People side

What?

• Meet user needs

• Reduce staff churn

• Improve ability of staff to address relevant business problems

How?

• Diversity of thought and experience5

• User research

• Building trust

• Building agency

• Iteration and building feedback loops

Why?

• Deliver smarter

• Solve the problems people care about

• Make money

• Save money

• Address correct outcomes

In both the ‘why’ sections, I’ve made a clearer link to business outcomes; in both the ‘how’ sections I’ve foregrounded trust and agency.6 I’ve also dealt with staff retention, hiring, churn and output in various guises in my consulting work, and so that has to be added to the ‘what.’

In both cases, I’ve seen a lack of agency expresses itself in both an inability to identify the most important problems, and then a lack of agency to actually tackle them.7

Finally, I’ve brought in the points about outcomes and systematizing feedback loops. Even within a business, you need to think about scaling an approach, and that’s what systematization does—allows you to take a playbook and apply it to different systems, processes or problems within a business to drive an outcome.

Indeed, it’s why, during my time in a transformation team, I worked on projects that were digital-first, in-store, in funeral care (both physical and digital) in data, and in hardware. The same set of analytical tools and system thinking can lead to problem solving in heterogenous domains. It’s basically what I’ve been doing since, both in my day job and my academic work.8

So with this week’s theme of “Can you spot a disordered personality?” in mind, let’s revisit some definitions and then talk about them the context of workplaces.

Obviously:

1. As in previous posts, I’m talking about the non-criminal/non-clinical end of the disordered personality spectrum here.

2. I’m not encouraging vigilanteeism—I’m mainly writing this so you can spot the signs and minimise these peoples’ impact on you.

The Field Guide

I’m adapting some content here from my review of Nate Silver’s book On the Edge, since I quoted a several academic definitions of non clinical/criminal Dark personalities (Narcissists, Machiavellians, and Psychopaths).

That post also covers the crossover (or not) with the Dark Triad, as originally explored by Paulhus and Williams (2002). We’ll reference that paper a lot in the next section(s).

A later (2024) article by Walker and McCann has some nice, terse definitions of the disordered personality types:

Narcissism

As a subclinical personality domain, narcissism covers a spectrum from mild to extreme (Miller & Campbell, 2008) and comprises two facets: grandiose and vulnerable narcissism. Grandiose narcissism is characterized by grandiosity, self-confidence, and exploitation of others with a tendency to rely on self-internal validation (Dickinson & Pincus, 2003; Zhang et al., 2017). When threatened, people with high grandiose narcissism may blame and devalue others while refusing to acknowledge their own weaknesses. Vulnerable narcissism is characterized by grandiose fantasies, oscillations between self-love and self-loathing, a fragile sense of self, a reliance on external validation for self-esteem maintenance, and hypersensitivity to negative feedback (Wink, 1991). When threatened, people with high vulnerable narcissism become defensive and resentful, and may show aggressive outbursts toward others (Wink, 1991).

Walker et al. (2022) in a different paper on faking good and bad on tests have several useful definitions, which I quoted in my previous post. For Narcissism, I just want to re-quote one point on what happens in the case of criticism or conflict (my emphasis):

Individuals high in vulnerable narcissism are characteristically hypersensitive to negative feedback and tend to act aggressively when their sense of self is threatened (Wink, 1991). Though grandiose and vulnerable narcissism share several core characteristics such as an antagonistic interpersonal style, self-importance, entitlement, and hypersensitivity to criticism (Dickinson & Pincus, 2003; Krizan & Herlache, 2018; Weiss et al., 2019), there is clear evidence that these two facets are distinct (Krizan & Herlache, 2018; Miller, Vize, Crowe, & Lynam, 2019; Walker et al., 2021).

The entitlement point is something that I think comes across a lot when you listen to these individuals talk—particularly in the political sphere.

Machiavellianism

Subclinical Machiavellianism is derived from the philosophical writings of Niccolo Machiavelli, a political advisor to the Medici family in 16th century Firenze (Christie & Geis, 1970). Machiavellianism is characterized by goal-focused manipulative and callous social interactions, including the use of long-term strategic planning to delay gratification for better rewards in the future, questionable morals, and a cold, cynical world-view (Christie & Geis, 1970; Furnham et al., 2013).

Psychopathy

Subclinical psychopathy is characterized by superficial charm, pathological lying, and lack of empathy, conscience, and remorse (Cleckley, 1951; Hare, 2003), existing on a continuum in the wider population (Berg et al., 2013). Psychopathy measures, such as the Levenson’s Self-report Psychopathy Scale (Levenson et al., 1995), were developed to measure the two-factor structure of psychopathy proposed by Karpman (1941). This two-factor structure comprises two related but distinct factors differing in their etiology and symptomology: primary and secondary psychopathy (Hare, 2003).

Both facets are typified by indifference to one’s own and others’ emotions, underpinned by an antagonistic interpersonal style (Miller & Lynam, 2012)…Egocentricity relates to interpersonally manipulative and antagonistic characteristics associated with perceived low social responsibility (Christian & Sellbom, 2016; Sellbom, 2011), callousness relates to lacking empathy and remorse and is associated with cold-heartedness, lack of remorse, and low empathy (Anderson et al., 2013; Sellbom, 2011), whereas antisocial is related to impulsivity, and antisocial behavior (Brinkley et al., 2008; Sellbom, 2011).

As in my previous post, I think it’s worth discussing the difference between Primary and Secondary psychopathy, so I’ll again quote Walker (2022):

Primary psychopathy is characterized by: (a) a lack of guilt and remorse, with elevated levels of callousness, manipulation, and socially desirable behavior (Hare, 2003), (b) having superficial affect (Casey, Rogers, Burns, & Yiend, 2013), and (c) deficits in affective empathy (Wai & Tiliopoulos, 2012). Primary psychopathy is also associated with lower levels of fear (Patrick, Bradley, & Lang, 1993) and lower indications of repentance (Hare, 2003; Lee & Salekin, 2010).

Secondary psychopathy is characterized by higher levels of antisocial behaviors such as aggressiveness, impulsivity, and anxiety (Levenson, Kiehl, & Fitzpatrick, 1995). These types of characteristics are potentially a result of experiencing strong emotions which are unable to be effectively regulated (Hare, 2003). Additionally, individuals high on secondary psychopathy have been shown to possess guilt and fear responses not typically observed in individuals high in primary psychopathy (Lykken, 1995; Wallace, Malterer, & Newman, 2009).

A Narcissist and a Psychopath Walk into a Bar Their Place of Work

To discuss some of the heuristics you can use to validate a hunch, the study contained in Paulhus and Williams (2002) has some useful data and discussion, so I’ll quote from there. It contains a lot of references to prior studies on the different personality types, which are interesting if you really want to go down the rabbit hole!

First up, I’ll note that all three Dark Triad personalities tend to lie, a lot. So if you have somebody that often says things where you think “that can’t be true,” or “that must be an exaggeration,” then perhaps your gut is telling you something.1

The other things they have in common are a lack of empathy, callousness, and a core of aggression (either passive or straightforwardly aggressive).

I’ve noticed weird “fight or flight” impulses tied to meeting people like this, including a time when I was introduced to an older gentleman at a wedding. I couldn’t tell you why—but I felt he was squaring up to me, for no discernible reason. I’m certain he was a disordered personality.2 Why he gave me that feeling, I’ve never been sure.

Before we go on—a pop quiz: What are Mrs Bennet from Pride and Prejudice, Homer Simpson, and Wallace from Wallace and Gromit? (Answers in the footnote)3

Narcissists

Finding Narcissists is usually pretty simple, especially grandiose ones. They want to be the centre of attention and at the centre of gravity for any social network.

By definition I’m talking about non-clinical cases (both for Psychopaths and Narcissists). These are, after all, people that can hold down a job and go unnoticed as problem personalities by many people.

In my experience, drama seems to follow these people, but they often also manage to stand above it. At the very least, I’ve noticed with Narcissists, they don’t so much stand above it, as people will go out of their way to excuse their behaviour, usually (I think) out of fear.

A more charitable read could be that somebody’s behaviour is only going to improve if you cut them some slack. Maybe they’re going through some stuff. Disordered personalities drive a truck through this loophole in human social norms.

If there’s somebody in your workplace that is at the centre of everything, that everybody speaks well of, but also everybody seems to tiptoe around—bingo! You’ve found your candidate.4

A heuristic that is useful is the degree of delusion for the three personality types. Paulhus and Williams (2002) describe that Narcissists are the most self-involved, and least grounded in reality, “narcissists have a strong self-deceptive (i.e., low insight) component to their personality (Raskin et al., 1991; Paulhus, 1998),” followed by Psychopaths, especially “clinical range” ones—Machiavellians, by contrast, are more “reality-based in their sense of self.”

What this meant in studies that surveyed student populations was that a good (but not perfect) predictor of Narcissism was where respondents over-estimated their own intelligence, “narcissists and, to a lesser extent, psychopaths tended to overestimate their intelligence… whereas Machiavellians did not.” (Paulhus and Williams, 2002)5

Back to Mrs Bennet—her own desire to be at the centre of her children’s matchmaking results not only in delusional behaviour but also from a goal-oriented perspective, near catastrophe in actually reaching her desired outcome.6 Entitlement, lack of empathy and social awareness are her characteristic traits.

Another heuristic I’ve seen is the following, that only Narcissists can say: “You hurt me! It’s your fault!” as well as “I hurt me! It’s your fault!” Especially with the victim complex inherent in vulnerable Narcissism, there’s little room for them taking responsibility for their actions, and lashing out or looking around for someone else to blame is the norm (look at Trump over Iran).

Grandiosity and drama resulting in sub-dominant outcomes for Narcissists is something I’ve seen over and over again, and was recently played out in perfect fashion with Fiona (a Narcissist) on the BBC series The Traitors, or when Andrew Tate revealed his location while arguing with Greta Thunberg, leading to his arrest.

Final point—be a little bit wary of anybody that’s a self-diagnosed autist (I say this carefully, with diagnosed autists as friends and family). They might be a Narcissist using self-diagnosis to shift the blame onto a different condition with some superficially similar behaviours. The strategy makes sense—even diagnosed autism potentially has a limited comorbidity with vulnerable narcissism. Thus, it’s important to stress that they are different.

In my experience, my autistic friends occasionally display a superficially similar but distinct pattern of behaviour—things like slight selfishness in certain situations where they miss social cues, or a tendency to do most of the talking when a subject comes up they’re interested in (pot kettle black, I know). I can think of examples like a friend whose legs I had to lift up to clean around (more than once) as he lay on the sofa, but jumped up to help the second he was directly asked. Think of Wallace from Wallace and Gromit.

Simply put, in the words of this post, autists aren’t a—holes:

“The distinction here is between the person who’s often rude and the one who’s often, or at least occasionally, nice…Narcissists crave admiration, whereas autism desperately maneuvers for acceptance…The difference between autism and narcissism, [is] that one implies a high degree of non-sensitivity, whereas the other implies one of insensitivity; the former doesn’t know and the latter doesn’t care. The narcissist displays patterns of indifference and cruelty, whereas the autistic individual may feel remorse for some social blunder.”

For your friends and family, it’s easy to identify if they mean well. The difficulty of distinguishing patterns of behaviour for people you don’t know well, or sorting the claims of the self-diagnosed, is perhaps at the heart of the reason I think Nate Silver spent so much time musing on autism in his book—when he should (in my opinion) have been investigating disordered personalities.

When Musk did a ‘Heil Hitler’ at Trump’s inauguration, some clowns were attempting to excuse him based on his ‘autism.’ Being autistic doesn’t mean you can’t be a Narcissist, Psychopath or Dark Triad, just as being autistic doesn’t mean you are a Narcissist, Psychopath or Dark Triad. I don’t understand why this is hard to get.

Psychopaths

For psychopath-hunting, as documented by Babiak and Hare7 and others, if you’re in a large corporate, it’s a good bet to look at your boss and start going up the org chart. You’ll find one soon.

I remember being in an all-hands to be introduced to our new exec member and watching in disbelief as he stood at the front and said, while presenting a slide deck about himself (I paraphrase here due to brevity and length of time since then), “my PA said I should put a picture of my family here, before the dogs, but…” he then advanced to a slide of his dogs.8

A good heuristic for finding Psychopaths is that childhood delinquency predicts higher non-verbal IQ and SRP scores. Again, it’s not perfect, but it turns out, antisocial behaviour is a pattern, even if confined to relatively harmless things.

Psychopaths also lack anxiety. Think about the stories of that boss that had a major relationship issue, then appeared at work fine the next day, or gave out cake just before announcing redundancies.9

Although it doesn’t help you spot one, as mentioned above, Psychopaths are typically better at non-verbal communication cues, which is at least a way to distinguish them from Narcissists.10 Both are “associated with extraversion and openness.” (Paulhus and Williams, 2002)

If you need a personification of the (non-criminal) Psychopathic personality, then read almost any Richard Morgan novel—Takeshi Kovacs (Altered Carbon novels and TV series) and Hakan Veil (Thin Air) are psychopaths with a side of bipolar disorder, and Market Forces and Thirteen are just portraits of a psychopath.11

If you can’t spot a psychopath, unfortunately some criminal psychopaths can spot their victims just by walking gait. A 2013 study showed psychopaths were adept at spotting people vulnerable to victimization in this way.12

In the Paulhus and Williams paper, they find men display more of the Dark Triad traits, and that the strongest inter-correlation is between Psychopathy and Narcissism—which makes sense, since grandiosity is also to some extent a Psychopathic trait.

On which final point, it is possible to distinguish the three Dark Triad disordered personalities by their degree of self-aggrandisement:

On two objective measures, narcissists exhibited the most self-enhancement, followed by the psychopaths…In contrast, machiavellians showed no sign of self-enhancement. This difference is consistent with previous evidence that Machiavellians are more grounded, or reality-based, in their sense of self (Christie & Geis, 1970), whereas narcissists have a strong self-deceptive (i.e., low insight) component to their personality (Raskin et al., 1991; Paulhus, 1998).

Although I don’t have to line manage any more (and haven’t for some time), I’m still in the habit of trying to distill key points here and there from books that I’ve read. Partly (as I’m sure others will sympathise with) this is so that I don’t forget them, or can refer to them later, and partly it’s because I still do a little bit of mentoring and career advice and these notes can be useful in those cases.

One of the most interesting chapters in Nate Silver’s 2024 book On The Edge is the middle chapter on risk-taking behaviours. Something that I’ve observed a lot when looking at the careers of others is that they don’t take enough risks. I’ve never particularly thought of myself as a risk-taker, but with the benefit of fifteen years of hindsight, it appears that, compared to the average, my perception might not be correct.

While reading the book, in fact, I realised that on some level, much of my advice to folks (particularly those at the start of their careers) could be summarised as, “take on more risk,” with a side of “but make sure that it’s calculated.”

To make sure that you’re making a good calculation here, a good rule of thumb is to figure out what the upside of a given choice is—the terms I tend to think of here are simple: money or learning—and optimise for what matters to you most at the time.

I’ve taken jobs that were more about the money, some that were more about the learning,1 and some, like my second engineering job at the data consultancy Swirrl,2 which rather fortunately offered both.

As Erik Deitrich points out, when you take a job, you tend to get more benefit than the company that hired you, especially if you’ve chosen a new workplace where you will upskill, but that over time, this learning levels out, and the relationship becomes that your employer benefits more than you do. This is why it’s important to have clear success and exit criteria in mind to govern when you will move on to your next challenge, a mentality he describes as “Always Be Leaving.”

I’ve always encouraged folks to have a rough five year plan3 and to have some kind of heuristic for making decisions. In Scott Hanselman’s Scaling Yourself talk, he references a quote from Christopher Hawkins, which I paraphrase: “if it isn’t making me money, I drop it.” He then flips that to put his own primary goal in its place, “if it isn’t helping me spend more time with my family, I drop it.”

This kind of heuristic is continually useful, for evaluating both decisions in the large (do I take this job) and the small (do I take a meeting in person rather than online).

But anyway, back to risk. Silver’s 13 habits aren’t all strictly relevant to thinking about risk in your career, but some of them are (I’d say 1-3, 5, 8-10, 12, and 7 is crucial—these are all in bold):

1. Successful risk-takers are cool under pressure.

2. Successful risk-takers have courage.

3. Successful risk-takers have strategic empathy.

4. Successful risk-takers are process oriented, not results oriented.4

5. Successful risk-takers take shots.5

6. Successful risk-takers take a raise-or-fold attitude toward life.6

7. Successful risk-takers are prepared.7

8. Successful risk-takers have selectively high attention to detail.

9. Successful risk-takers are adaptable.

10. Successful risk-takers are good estimators.

11. Successful risk-takers try to stand out, not fit in.

12. Successful risk-takers are conscientiously contrarian.8

13. Successful risk-takers are not driven by money.

If you want to read his full reasoning, pick up the book—obviously this is just a tiny summary for the purposes of this article. I’d also note—although this is a subject covered elsewhere in my review of the book—that many of these behaviours are also present in non-clinical psychopaths. Oh well.

I realised that I’ve never written about one of my favourite books. Skunk Works is the story of the Lockheed Martin Skunk Works, the top-secret engineering shop that produced (in secret, and sometimes under budget) some of the Cold War’s most incredible pieces of aerospace innovation—the U2, the SR-71 Blackbird, and the F-117 Nighthawk.1

If you’re interested in an insight into that period of defence history it’s a pretty fascinating read, but it’s equally instructive as a book about management and organisational design.

The team could be characterised as having high agency, but high responsibility, and the amount of initiative that designers, engineers and assembly staff used is continually surprising to a reader used to the micromanagement inherent in the modern corporate workplace.

Managers were consistently able to provide what I’ve seen described as ‘air cover’—insulation from interference by stakeholders and bureaucracy—while delivering against the real objectives of a project and the outcomes that mattered.

The book covers what (necessarily for the narrative to work) is framed as a golden age, from shortly before the development of the U2, to the operational deployment of the F-117 during Operation Desert Storm.

Anyway, you should absolutely read the book, it’s great, but there’s a passage in it which I go back to a lot, have described to other people, and I gather is valuable.2 So here goes.

In it, the author Ben Rich speaks to his boss, the legendary aeronautical engineer Kelly Johnson, and says he’d like to attend a course at Harvard Business School. Kelly writes him a “glowing recommendation” and gets Lockheed to pick up the bill, even as he declares it “a waste of time,” giving Rich instead three concise rules for leadership:3

1. “You don’t need Harvard to teach you it’s more important to listen than talk.”

2. “You’ll never make the grade unless you are decisive: even a timely wrong decision is better than no decision.”

3. “Don’t half-heartedly wound problems - kill them dead.”

“That’s all there is to it,” Kelly concludes. Of course, Rich goes to business school anyway, finds it somewhat useful, but ultimately values Kelly’s argument enough to include it in the book.4

Hopefully you find these rules useful.

Over the last few years I’ve worked on, and managed the development of, a lot of open source code. As you might expect, it’s primarily been hosted on cloud platforms (mostly GitHub, occasionally GitLab).

I had a realisation last month—that my relationship with open-source code was changing. My internal cost/benefit calculus was shifting. At a minimum, if you’re iterating towards an MVP, it should no longer be in a public repository.1

For most organisations, this would be a given anyway—just because a competitor might not understand what they’re looking at, why take the risk? Still, the chance of that code being made comprehensible by an AI assistant is pretty strong, so this scenario has gone from a poor strategy to suicidal strategy.

Okay, in all likelihood, you’d have built in a private repo anyway, but is that safe?

Although GitHub say that they don’t train their AI models on organisation private repositories, they’ve made no such claims on individual accounts. Indeed, any attempts to gain clarity on the matter using their issue tracker et cetera have been met with crickets. It’s been the subject of threads on Reddit and Hacker News, with some users openly not trusting the public statements given by the company. This comprehensive post by Simon Williamson digs into the matter in depth and still can’t reach a definitive answer.2

This does ultimately mean you’re putting your faith in a large organisation (Microsoft) with hugely asymmetric power over you to both a) not act maliciously and b) not act incompetently. Quite a big ask.

After all, from Microsoft’s point of view, they’re not in the business of stealing the IP of nascent ideas—they’re happy to buy them—so you’ve got to ask the question, how does it even get back to them if a) or b) above is violated, and your IP is leaked via Copilot?

This may be paranoia, but it’s worth considering. In my previous post on Hard Problems, I pointed out that in the age of AI coding agents you need hard problems with barriers to entry. A good way of ensuring the barrier to entry you thought you had (your unique idea, GTM and IP) remains unique is to apply some adversarial thinking to your development stack.

This is all rather bizarre for somebody that’s worked in crypto for the last 5 years—where your DMs blow up if you release anything that isn’t fully open-source, and your competitors brazenly just fork your code and deploy it—but I can see myself in the future taking not only steps back from open-sourcing system components that are part of products we are building, but even going so far as to host them on company Git servers instead of cloud platforms.

In my life as a consultant I talk a lot about TCO—it’s the reason most businesses should be using these cloud tools, rather than rolling their own—but by the same logic, I now find many of my own projects falling on the other side of that fence. There’s a cost to risk, and I think it would be naïve to not factor that in.

One changed line in a cloud platform’s Terms and Conditions that gets missed, a malicious or incompetent move, and you’re toast. It’s a Dark Forest now.

Watching Inside the Manosphere the other night,1 I was struck by the cognitive dissonance of some of the personalities interviewed.2 They still saw themselves as victims, plucky rebels fighting The Culture, even as their avatar sat in the Oval Office. Somehow, despite having won by any reasonable metric, they still saw themselves as the victims, because everybody else still does not agree with them.

This single-minded fixation on being right and being the centre of the universe is a classic Narcisstic trait.

It’s also how the politics of self-interest can rapidly become the politics of oppression, authoritarianism, and silencing dissent.

However, and here is where I might make myself a little unpopular in my own political camp—there’s also a grain of truth to their original complaint about being the victims of The Culture. Ironically enough, the reason is simply this: the Left (however we define it) has its fair share of narcissists too. Of course it does.

Like narcissists on the right, they are too busy thinking of themselves to start looking for win/win solutions.3

This has always been the danger of identity politics. Unlike the politics of community and collective vision and effort, it is atomised, and on some fundamental level, essentially narcissistic. At its worst, it enforces a zero-sum view of the world that sees everything in win/lose terms rather than allowing for the possibility of win/win.

No surprise then, that the far right has decided to fight fire with fire,4 and as events have shown, are much better at playing the game than the Left.

As others have noted, a plurality of opinions are required in order to sustain a win/win equilibrium. However, there’s also a flip side to this in order to avoid the politics of narcissism. Taking responsibility for their actions is something narcissists and psychopaths are both bad at, but in order to have your opinion, it also needs to be hedged against the consequences of using it.

Being ‘cancelled’ is one of those—a social enforcement of a behaviour norm. Good manners cost nothing, as the saying goes, and the people that think they can have their opinion without also respecting others shouldn’t be surprised if what goes around, comes around.5

Proof of Ownership, or PoO is a new consensus paradigm for a stablecoin-first future. It comprises 3 main components, with the goal being to ensure an endogenous token (a so-called ‘gas token’, to many) retains value even as a stablecoin is the main transactable asset on a network.

1. Only wallets with a valid soulbound or wallet-bound (and potentially even time-bound) NFT can transact. If your stablecoin is private-by-design (it should be) then the creation of this should be a zero-knowledge event where only a proof is written to the ledger. The existence of this proof is locally required at the time of issuing a transaction by a valid client, but it not audited centrally.1

2. All transaction fees (as well as gas) are paid to the network in the endogenous token. Transaction fees should likely be stable (but one would envisage greatly cheaper than the equivalent fees when e.g. using the Visa or Mastercard rails).

3. Optionally, some amount of endogenous token is staked or bonded to a validator before a wallet can transact.2 An antehandler3 filters all transactions that would increase a validator’s share of VP above a fixed sensible point (e.g. 5%, if you assume a valset of 20). Another option that would reduce the governance overhead would be to have all vals be within +/- a couple of percent of the average VP in order to be eligible for stake bonding.

This system requires an oracle, since the value of the NFT should be stable relative to the exogenous price of a real-world currency (let’s say the dollar, if you envisage a dollar stablecoin, but you should probably denominate it in something else unless you are (a) American, (b) an American company).

Thus, at least in principle, the value of the NFT is stable.4 You might additionally consider making them bound to wallets and non-transferable. Assuming an HD type wallet, you would need one for each sub wallet.

Although this is not the goal of the system, you could imagine AML/CFT or KYC measures occurring at the point of NFT sale.

In fact, it is possible to use this general scheme on top of say, a Tendermint style blockchain and make it work with a Sark payments layer.5

Relays (Porters) would have to take additional metadata and verify NFT ownership on-chain before saving transactions to their local transaction trie, but apart from that, the principle should work.6

If assets needed to be transacted more than once (for example if they represented an RWA and not a payments primitive, like a stock holding), then you could in principle continue to use a system like Sark with multi-hop updates and saturate the full provenance of an asset to determine its history in the case of regulatory need. Let’s call this extended protocol PoOP, or Proof of Ownership and Provenance.

Having typed this whole thing, and despite the date, I’m still totally unsure if I’m serious or joking.7

💩

I came across Evans F. Carlson a few years ago and was fascinated by his story. During WWII, the US military established what was essentially the first US special forces unit,1 the Marine Raiders. They created two Battalions, and gave their commanders extensive leeway in their organisation and tactics. Merritt Edson was given command of the 1st, organising it along the lines of existing unit doctrine, and Carlson the 2nd.

Carlson structured his unit more unconventionally than Edson. As an observer, he had seen the Chinese Communists fighting during the Chinese Civil War, and had been surprised by their effectiveness. He took from this two lessons—the importance of an éspirit de corps based on co-operation, and their size of unit organisation, which was typically 3 fighters:

Carlson used egalitarian and team-building methods: he treated officers and enlisted men with minimum regard to rank as leaders and fighters, gave his men “ethical indoctrination,” describing for each man what he was fighting for and why, and used the Chinese phrase “Gung-ho!” as a motivational slogan which he learned from the Communist forces during his years in China. He also eschewed standard Marine Corps organization, forming six rifle companies of two platoons each, and innovating 3-man “fire teams” as its basic unit. (Wikipedia)

The best thing about this story is the etymology of “gung ho,” which became a war cry for the US Marines as a whole after a propaganda film of the same name about the Makin Raid.

Carlson explained in a 1943 interview: “I was trying to build up the same sort of working spirit I had seen in China where all the soldiers [of the Eighth Route Army] dedicated themselves to one idea and worked together to put that idea over. I told the boys about it again and again. I told them of the motto of the Chinese Cooperatives, Gung Ho. It means Work Together — Work in Harmony.” (Wikipedia)

However, Carlson was mistaken. The phrase is simply an abbreviation for ‘industrial co-operative.’ Still, the fact that he adapted what he thought the phrase meant for a fighting unit is interesting.

After assembling two more Raider battalions, the force was consolidated into the 1st Marine Raider Regiment, with Carlson as executive officer. His 2nd Battalion was returned to ‘normal’ structure, but his ideas were adopted first by the Regiment, and then the whole Corps.

Carlson's 3-man fire team and 10-man squad organizations were adopted, first by the Raiders and then by the entire Marine Corps. Edson contributed the concept of a highly trained, lightly equipped force using conventional tactics to accomplish special missions or to fill in for a line battalion. (Wikipedia)

It’s the last point of that paragraph which hints at the ultimate fate of the Raiders experiment.

The reality was that in the Pacific war, there were relatively few opportunities for true Commando-style raids. Increasingly, landings were done in force, using amphibious tractors, and what was needed were more line Marine units. Moreover, the lightly-armed, specialized Raiders were disliked as an ‘elite within an elite’2 by some senior officers, and the 1st was eventually redesignated the 4th Marine Regiment and brought back in line with standard force organisation.3

Enter Robin Dunbar

Though the Raiders themselves were relatively short lived, their experimentation had a lasting effect, as well as being correlated in other areas. The 3-person structure crops up over and over—in terrorist cells4 and, subsequently, in the standard force organisation of most NATO militaries, where the smallest atomic unit is typically a fire team of 3-4 personnel.

This perhaps makes sense for a reason—Dunbar’s number. Though the methodology and conclusions have been disputed, I think the theory behind it has become so widespread because people can look around them and see some correlation between real life and theory.

The reason you will probably have heard of it is the idea that you can at most maintain about 150 meaningful relationships.

However, the model is stratified, and was developed by further research. Although most people will think of the smallest ‘circle’ of relationships as 5, there’s actually an inner one, 1.5.5 If we round this up to 2, we can see that one person with two close relationships adds to 3.6

For completeness, here are the layers:7

1. 1.5: Intimates/Inner sanctum

2. 5: Loved ones

3. 15: Good friends

4. 50: Friends

5. 150: Meaninful contacts

6. 500: Acquaintances

The takeaway is this: you don’t have to deliberately always structure around these numbers,8 but it maybe pays to be aware of them. When your teams break, consider if it’s because of crossing a threshold, or due to other external factors.

Finally, always remember that if in doubt, 3-4 motivated people can almost always get the job done.

In my mammoth series on AI,1 I discussed at length the economics, benefits, and potential improvements to ways of working and outcomes offered by AI tooling.

However, I also argued that in most cases organisations would not be nimble enough to take full—or possibly any—advantage of these opportunities, due to organisational inertia, team skill, exec capability, or what my former boss Craig would have called ‘hysterical raisins.’2

To quote Part 2 of that series,

The problem here isn’t that these gains exist—it’s that they are localised to teams that are able to capitalise on them. Most software teams are not bottlenecked by outputs, they’re bottlenecked by a lack of clarity on what to build, why they should build it, or even the permission to build it at all.

In Parts 2 and 3 of that series, I noted that although engineers inside large organisations were using the tooling and able to find gains, the majority of outsize impact appeared to be in small teams with agency, small organisations and individual domain experts using their own initiative.

Move Fast and Make Things

As I talked to people, I firmed up the idea that small, agile (small-a), motivated teams were the ones that seemed to benefit the most. I concluded that the “most effective users of AI tooling in my network either work at start-ups or scale-ups.”

All of this leads me to a prediction. We’re headed for a golden age of micro-consultancies.3

My experience in enterprise-scale companies leads me to think there are two main reasons that a consultancy is brought in:

1. An incumbent team lacks one of: headcount, skills, buy-in, trust, or agency to execute a given programme of work (sometimes all of the above).

2. An exec team bringing in a consultancy looks good to their stakeholders and shareholders, board et cetera.

I’ll note that in point one, generally headcount is not the key driver. Almost always it’s the organisational factors. Those pressures are likely to be more acute in the AI case, where smaller teams able to iterate and learn how to deploy tooling and best practice rapidly will outpace their less nimble competitors—both other consultancies and internal teams in larger organisations.

The cost of coordination within teams and between teams is often above-linear,4 hence the advantage that smaller teams sometimes demonstrate. Additionally, agents represent, well, extra agents, as the name suggests, in your organisational graph, which means they de facto add to your organisational complexity in the same manner as members of staff.

The question here is whether adding AI agents is above-linear, or sub-linear. In the latter case, they probably introduce less cost than adding headcount. Which is good, but anybody who’s ever encountered the ‘mythical man-month’ knows that simply adding resource to a project doesn’t necessarily speed it up.5

So you have a perfect storm where organisational factors will act as a sea-anchor for internal teams, introducing drag on their ability to up-skill and iterate with tooling. Meanwhile, other teams will find themselves with a powerful lever. Even if it’s a 20% or 30% force multiplier6 then that’s going to be a decent margin to build a business on top of.

For these teams, headcount is going to be aesthetically difficult to sell, so I would guess you’d be looking at teams of 3 product-minded engineers forming micro-consultancies to bid together on project work.

Large Orgs Should Take Advantage of This

For enterprise organisations that have the ability to trust (and procure from) organisations that small, it’s an easy bet to make—even at contractor’s rates, small teams like that are a small cost (relatively), and delivering a deadlocked or unrealistic project is potentially a very large upside.7

As a result, I’d argue that larger organisations that want to benefit from AI gains, or at least explore them, should attempt to streamline their procurement process to accomodate smaller players with a strong track record, and manage many small external teams delivering software.8

Inside many large organisations, staff augmentation contracts are already common. Many of these became fully remote during COVID-19, and stayed that way. For organisations already looking at the pre-AI trend of ‘near shore’ offshoring, it’s a much more palatable sell to meet in the middle between staff augmentation and offshoring.

These companies can then instead assign project-based work to small teams that may even adopt the full governance lifecycle of the client company and join the company Slack. I think that’s a decent compromise between internal governance and risk in exchange for the potential of a better result.

Obviously I’m biased, because this is basically what Envoy Labs has been doing for half a decade in the blockchain space, but hey, it’s an optimistic take if you’re an engineer that is:

• (a) entrepreneurial, and

• (b) product-minded.

Find some colleagues you trust and get started.

I, for one, hope I’m right—and to that end, this is a direction we as a company are going to explore pivoting to in the coming months. If you want to talk, then get in touch via our website or LinkedIn.

As promised, I’ve put in the (virtual) shoe leather cost to offer some thoughts over how to use the tools, the pros and cons, some of the most obvious pitfalls, and what’s left for creative work.

Hopefully this is the most positive section of the post series—I certainly meant it that way. Remember, that even when it looks like you might not, you always have a choice. Whether to participate, how to participate, and what your lines are.

5. A Tolerable Engineering Workflow

A provocative title, but hopefully this section will prove relatively optimistic.

As part of the research for this post, I reached out to most of my friends and former colleagues to get a sense of who was using the tools in what way. The idea that human computer interfaces have fundamentally shifted is interesting, but it seems too maxi, and isn’t reflected in day-to-day of the people I talked to.

Few-to-no experienced engineers are actually “vibe coding” production code. Instead, there’s a huge spread of heuristics for developing confidence1 in the AI output.

A common response was what one friend summarized as, “use the ideas, not the output.” That is, potentially don’t even have the tools integrated directly into your workflow, but have them near-to-hand, to prompt, pair with, bounce ideas, and gain context.

However, some take it much further. As part of this exploration, I asked the best engineer I’ve ever worked with (a former CTO of mine) whether they were using AI tools, and what their workflow was. It turns out the answer is yes, and the workflow they described to me is what I’d call very sensible.

They first generate and then iterate a spec document that multiple agents can read, using beads. They observe that a clear plan is crucial, “LLMs are mostly great for helping with stuff you already understand…but they do surprising stuff all the time…”

Then they iterate.

“An iterative process of

• describe the next step, maybe ask the LLM to research similar things in other languages

• Have the LLM implement it

• Review and iterate

• On a couple of occasions, decide it was

  • Unsalvageable, or

  • A bad experiment

• Then throw it away to either start again or go in a different direction

It wasn’t a quick process, but it was a lot quicker than doing it the old fashioned way… the LLM interaction process actually led to me finding and exploring things I didn’t set out to do.”

What’s most interesting is that they are solving what I would call Hard Problems—in this case writing an Algebraic Effects library in Elixir. Moreover, they say that the output is better than they could have achieved by themselves. One of the main benefits they identify is not only the speed of development, but also an ability to discard work, in light of a lower emotional investment in the code generated. This is also something my former colleague Paul mentioned.

In other words, the cost of iteration has been reduced.

Something to note about this person that seems to be really succeeding with the tooling is that they are a domain expert. They’ve been iterating solutions in this space for a decade and know what questions to ask, in addition to having a strong mental model of the problem space and grokking the key desiderata.

If you don’t have that, you might be in trouble. As they say,

“Mixed in with all the great, often it’s just stupid, wrong, or both—it doesn’t actually understand anything and it has no aesthetic sense beyond reversion to the mean, which makes it a powerful tool in the hands of an expert, and a chainsaw in a kindergarten for many other users.”

The idea of using the agent just to research for you is already a pretty valuable task—and verifying its suggestions aren’t crazy is relatively fast compared to say, parsing code. Gergely Orosz says HashiCorp founder Mitchell Hashimoto, “always [has] an agent running in the background doing something. He kicks off tasks before leaving the house — research, edge-case analysis, library comparisons — so work progresses while he drives or is away.” There’s a good quote from Mitchell on that episode of the Pragmatic Engineer podcast:

“There’s a lot of people like, ‘I don’t want it to write code for me.’ But just delegate some of the research part.” He uses agents for library comparisons, edge-case analysis, and deep research—not just code generation. “You don’t need to pick up on the ‘it must replace you as a person’ kind of propaganda.”

For what it’s worth, this is kind of where I am currently. So naturally, confirmation bias kicks in and I think this is a Great Take.

Returning to the cost of iteration, Will Faithfull, another Northern technologist—whose blog you should read—argues this is why a 50:50 split of features to refactoring is needed when using these tools. It’s easy to get carried away, and the tools have no aesthetic taste, beyond “whatever patterns they encounter; from the codebase, from the current session, from their own prior output.”

Why 50/50? We’ve anecdotally observed that AI productivity decays over the lifetime of a project, and that the rate of decay is strongly influenced by codebase quality and complexity. Complexity grows unavoidably as a project matures; there’s not lots you can do about that. What you can control is quality, and that determines how steep the decline is. What drives this decay? Pattern amplification.

It’s worth saying that some working in AI claim that the agents do now have aesthetic taste. However, one of the models mentioned there is the one my former boss is using. I trust their judgement more. Moreover, that statement is couched in a piece that is (a) pure ‘Criti-hype’ fearmongering, and (b) by somebody that works in AI. Caveat emptor.2

Finally, back to Will’s point on diminishing returns: I think there’s likely an inverse relationship to TCO, depending on how good your decisions are about when to ‘do the work’ and accrue domain knowledge, versus when to offload that work to an AI tool. Lack of understanding of the domain, or codebase, represents a hidden cost that isn’t accounted for in the cost of production or sale—something in economics we’d call an externality.3

At a meta-level, there’s a cross-project version of this externality—deskilling. If the argument that users of AI tools gradually deskill holds true, then you would expect this externality related cost to grow across all your projects.4 The more your team uses the tools, the worse they perform, over time, across all work—unless you have a way of maintaining their skill base. Needless to say, this is a hard problem.

As discussed in Part 2 in the context of organisational design, what Stuart Winter-Tear calls Speeding One Cog Breaks the Machine5 is a serious risk in software projects. If the process improvement isn’t holistic, all that happens is you move the bottleneck point within a meta system or process and flood somewhere else.

Anecdotally, that point seems to be code review, and I’ve personally seen not only code review, but often additional post-hoc sense checks of code have become necessary. That’s a big, time-consuming piece of work.

Also key to this all working in the long-term is a missing part of the current picture—completely open-source AI models, governed in a way we can trust.6 If you optimise a workflow on top of technology that will eat our societies then you’re still rearranging the proverbial deck chairs on the Titanic.

This is a super hard problem and I’m not sure I have any answers at all, let alone any easy ones.

6. You’ll Own Nothing, and You’ll Be An Start-Up

This is pretty short, because the argument is simple. Integrating AI at the heart of your business venture is likely not a good idea unless you’re a large business, an incubent, or operate in an area with high barriers to entry.

If you build your business on somebody else’s model, then you are accruing wealth, power and money to their platform. If your business fundamentally requires their model, then you’re in even tougher straights. You have no USP, you have no real barrier to entry, and perhaps you have no business.

Think of the Commandment of Control and Commandment of Entry, which I’ve discussed before, and the point should become clear. You’re not a SaaS product or a platform product, if your start-up depends on an AI model, as other writers have noted; you’re a margins player, and your business can be disrupted by a new competitor at any time, for a low cost.

7. A Manifesto for Non-Participation

As should probably be pretty clear by this point, I think there’s a moral, political and creative imperative to resist, so far as is practical, the use of AI tools in the creative process.

I’m more of a pragmatist when it comes to the world of work, but I’m also not jazzed about the level of asymmetric power at play. I’m happy to use the tools to a point, but by doing so feel complicit in a political and ideological programme that is toxic. I don’t know how to square that circle.

To take it back to a reference from the start of this piece, this asymmetric power recalls the plight of weavers at the advent of the Industrial Revolution. I believe my name is French Huguenot, so maybe I have a bone-deep latent sympathy with the weavers and Luddites for that reason, I don’t know.

You might be in the situation where if you completely reject AI tooling, you might lose your job or face sanctions—I’m, to be honest, in a similar situation in my profession.7 So don’t get fired, but don’t blindly accept, either.8 Remember, you always have a choice.

At the very least, non-participation is an act of resistance. Non-participation is also an optimistic act. As far as I can tell, many people kind of hate AI, but go along with using it out of fear.9

AI enthusiasts and the e/acc crowd can beat you over the head with non-participation and can you a Luddite.10 However, as I’ve said, this is an optimistic act. It’s doubly so when you combine it with a call to arms for creativity.

Nobody can tell you not to create. Every scribble is art. In Understanding Comics,11 Scott McCloud advances possibly the best definition for art I’ve ever seen:12

“Any human activity which doesn't grow out of either of our species' two basic instincts: survival and reproduction.”

Even then, this might be too narrow!

Don’t ask for permission. Create. Your work is valid. Your thoughts are valid, your aesthetic is valid.

Don’t replace yourself. Don’t deskill yourself. Don’t empoverish yourself. Do the hard thing because that is all there is to life.

Life, like creative suffering, is what you make of it.

Acknowledgements: Thanks to the many people that fed back on earlier or draft versions of this series, including but not limited to: Jon Stone, Craig McMillan, and Rob Bowley. Cheers for the conversations while I worked out shower thoughts to Andy Gray, Geoff Goodell, James Morgan, David Scott, David Alesch and Jack Gray. Thanks also to all my network that I have bugged about AI tooling, workflows, best practice in their places of employment and for opinions. I hope I’ve done your thoughts and feedback justice.

In Part 1, we talked about how chasing productivity as the cure of all ills was a category error. In Part 3, we will talk about the potential positives of AI tooling, as well as what agency you, as a citizen and user, can exercise on our precarious Now.

In the next two sections, we’ll discuss why the current political programme behind AI is feudalism at best, and fascism at worst, and why for most organisations, the potential gains are unlikely to be realized.

A better future is possible—but we have to call out that the present way this programme is structured sucks.

Let’s start by discussing the best thing I’ve seen on the subject—a video essay by the musician Adam Neely (embed below).

3. AI Accelerationism and Fascism

In the essay,1 Neely starts off by simply addressing the ethics and creative dissonance involved in AI’s incursion into art, but then goes much deeper. He connects the intellectual lineage of the silicon valley VCs and e/acc enthusiasts2 to Italian futurism, and specifically the poet and writer Filippo Tommaso Marinetti.

Neely points out that in that case, the logical conclusion of such a reductionist techno-optimist manifesto of ‘speed at all costs’ was Italian fascism—Marinetti was the co-author of the Fascist manifesto. The fetishisation of speed and progress implicitly results in a suppression of critique, something that we see with AI skepticism.

Bringing things back to the present, something I had never noticed until Neely pointed it out was that Mark “conehead” Andreessen has a nod to Italian Futurism on his wall, and repeatedly paraphrases Marinetti in his own writing. This should be concerning when considering incredibly powerful, wealthy people that switched to supporting Donald Trump when potential AI regulation was on the cards at the tail end of the Biden administration.3

As Neely points out, for every argument like democratization or removing shame that holds up to some scrutiny,4 the end result is the same. Though these are arguments applied to AI art, twist the perspective just a little, and you see the mental contortions applied to most areas of creative work in order to justify AI maximalism. If you’re on LinkedIn, look at your feed and you’ll see it.

These AI capitalists aren’t looking to solve the root cause—lowering barriers to technical or musical education, or working on making young people more resilient to failure, or to feel less ashamed of emotional cultural expression. They’re not looking at structural access to skills or opportunity, such as Universities, apprenticeships, or art schools.

No, they want to sell you a product. A product trained on everybody else’s intellectual property, and walled-off in their platform for them to rent-seek in perpetuity. Great.

Policy programming music critical thinking art essay writing academic work learning being a creatively fulfilled human doing stuff is hard and requires collaboration, interaction, struggle and self-reflection. It’s much easier to just solve problems by lowering the bar.

Then we can repeat the present forever. Which suits these guys, because they’re control freaks. They control the present, and for all their bluster about progress, they’d rather no future happen, than a future they can’t control.

Or maybe it’s simpler than that. A lot of the companies wrapped up in this are seeking to maintain stock valuations as ‘growth’ companies, with potential valuations far in excess of their actual P/E ratio. In such a context, their incentive with AI, as with blockchain before it, is to claim that not only are they on the verge of changing the world, but they’re the only ones that can do it.5

Some of the most powerful people and institutions on the planet want you to believe that the changes they promise are coming are a foregone conclusion, that they are inevitable. This is not the case—what they fear most is that you, we, will realise that in fact we have agency and a choice. No surprise that those who stand to gain most are those that say these changes are part of an inevitable ahistorical process of ‘progress.’6

It’s also no surprise that these same people are generally the narcissists and psychopaths interviewed and profiled by Nate Silver in his book On the Edge. I discuss these behaviours at length in my post reviewing his book.

You’ll have read the endless blog posts and think-pieces about how AI is already taking entry-level positions, even as the FT suggests that the underlying cause is simply normal employment cycles, or the effect of interest rate changes on hiring:

“Linking job losses to increased AI usage rather than other negative factors like weak demand or excessive hiring in the past conveys a more positive message to investors,” points out Ben May, director of global macro research at Oxford Economics.

An interesting data set is a National Bureau of Economic Research working paper, surveying 6000 executives. In it, the respondents predicted AI use would impact employment by either cutting headcount by up to 0.7%, or by increasing it by up to 0.5%. Though obviously the data will have been primarily on older models, they report a marginal impact, or no impact, on productivity.

This isn’t real, the AI isn’t coming for your job—at least not tomorrow—you’re being coerced.

Will your job change? Yes, it looks like it. Will it be only the boring parts that are left? Yes, it looks like it. Will it pay less? Yes, it looks like it.

All of those are bad things, but it’s not yet a doomsday scenario. For one, as I describe in the next section, put the middle class out of work, and maybe the whole gameshow ends anyway, and for another, we already have a political crisis caused by stagnant real wages, let alone falling ones. This is the same political and economic problem we were already facing, just accelerated.

Moreover, the AI can’t take your hobbies and your skills. Some things are worth doing for the sake of it. I talk more about that in the final section, Section 7.

Neely concludes by advocating for the virtues of Service, Patience, Craft and Beauty in creative pursuits, and that’s not a bad North Star as we navigate an increasingly uncertain world.

4. These AI Gains Aren’t Possible Anyway

Let’s take a moment to consider the AI productivity gains again.

Even in light of David’s post, what I’ve seen the tools do, and the workflow outlined in Section 5, I’m still not sure I buy it.

Why? Two big reasons. First, organisations can’t even take advantage of 20 year old technology, let alone frontier tech, and second, that our economies would collapse if they could.

Let’s talk economics first.

At the beginning of this hype cycle, there was a much-paraphased report by McKinsey on AI productivity gains that today, honestly, might pale in comparison to some of the most frothy predictions. Quoting Lee Vinsel’s piece on ‘Criti-Hype,’7 which you should read, we see a typical labour market prediction.

For example, in its 2017 report, the AI Now Institute, which is associated with New York University, paraphrased another report from the consulting firm McKinsey claiming that 60 percent of occupations would have 1/3 of their activities automated.

The reasoning is very simple here.

If white collar workers were laid off, middle-career, in the numbers that are (a) required for these AI companies to pay back their investments, (b) with enough revenue back to the AI companies to pay for the trouble, and (c) without any job to go to, our societies will collapse.

Let’s break this down.

• First, in order to pay back the scale of investment, the level of adoption has to be on the order described, or not far off.

• Second, the cost model of AI tools needs to change in order to cover its full cost (at the moment many companies are making a loss). I don’t imagine they will price in their externalities—what capitalists ever have?—but this increase in cost will also mean that the marginal saving between the employee and the AI will go down. It’ll likely still be a large enough gap, however.

• Third, these workers become unemployed. Potentially for good. They go from productive members of society with social capital and a good standard of living to not being able to pay their mortgages.8 Let’s put aside the social unrest that would cause (I cover that below, and yes, it involves guillotines), and focus on what happens within an economy. What do you suppose happens to sales of IKEA furniture when 19.8% of young professionals cease to exist?9

A version of this demand-side liquidity crunch is what cryptoeconomies have speed run in bear markets for the last few years. First, products have to compete for a reduced pool of spending power (liquidity), then some go bust and others consolidate. However, the pool of available money continues to decline, and eventually the Foundation typically has to step in and either boost demand or subsidise the projects and validator set required to keep the chain alive.

Real-world economies are no different, and this kind of sustained, demand-side shock is already a issue in developed economies. In many, real wage growth has been stagnant for thirty years, and credit has taken its place. Obtaining credit relies on a job, on security, and on predictability. Offering credit when it cannot be repaid is what led to the Subprime Mortgage Crisis and the 2008 Great Financial Crash.

See the problem?

Demand can’t be stimulated by simply printing money, or creating more credit, because people will be unable to pay it back. Second, a Universal Basic Income (a good idea in principle) can’t plug the gap, because tax receipts for the state will decline. They have fewer people working on the one hand, and less sales tax from businesses on the other. Simply—how can states afford to pay for it?10

Our populations will continue to age, driving up the cost of social security for old age pensioners onto those that continue to work. They will conclude, as many already do in countries like the UK, that work does not pay.

However, in a situation where many will be unemployed and in worse material circumstances, people will continue to work, but with increasing resentment. Look, this isn’t that different to our existing paradigm—the trends, after all, are the same—it is just far more stark.

This is a negative cycle.

A social upheaval I think about a lot isn’t just economic. After World War One, soldiers returned home to find economic chaos, and in many cases—especially in the defeated powers—punitive sanctions in place. They found themselves at peace but unable to survive.11 I will always remember the words of my lecturer in describing the result, “every capital East of Paris fell.”

That’s what happens when people really think the social contract has been broken.

When people are laid off, while those that do work do so in increasing misery,12 all the while seeing billionaires and platform capitalists doing just fine, planning colonies on Mars to keep them safe from rogue AI, what do you suppose their conclusion might be?

Or, as Jack Clark from Anthropic says in Nate Silver’s On The Edge,

“People don’t take guillotines seriously. But historically, when a tiny group gains a huge amount of power and makes life-altering decisions for a vast number of people, the minority gets actually, for real, killed.”

Your Mars colony is only a decent escape plan if you don’t get caught and guillotined on the launchpad, Elon.

All of this is before we consider what organisations are actually in a position to capitalize on.

As we saw in the last section, it’s likely that so-called “AI job losses” may just be normal cyclical economic behaviour from firms, with a bit of PR spin on top.

No doubt that as models improve, entry level positions will take a hit, but maybe once the macro position improves, the impact will be less than what we’re seeing right now. I graduated into the fallout of the Great Financial Crash in 2008 and I remember how lean the first few years of my career were in terms of finding any work I could.13

I’ve spent a long time working and consulting in both start-ups and enterprise companies, and I can tell you that for every start-up (or scale up, for that matter) able to pick up the latest and greatest tooling and run with it, there’s an enterprise organisation that would fail to realise substantial gains whatever happened.

This isn’t something I’ve made up. When I was speaking to former colleagues for the next section, I found many inside large organisations that used new tooling, with frontier models for their day-to-day work. Some said the use of tools was encouraged, some said it was a soft mandate. Others said that it wasn’t required, but the best engineers were getting incredible work done with the tools.

The problem here isn’t that these gains exist—it’s that they are localised to teams that are able to capitalise on them. Most software teams are not bottlenecked by outputs, they’re bottlenecked by a lack of clarity on what to build, why they should build it, or even the permission to build it at all. None of these things are strictly an engineering problem, and solving this complicated interrelation of impulses is part of the reason that agile software originally came about.

Still, what we’ve seen in large organisations is that agile adoption—in the sense of “work iteratively and systematize your successes”—has been slow, difficult, and often has failed. I don’t think these organisations are likely to see any huge benefits soon, simply because the average rate of productivity in the company is the sum total of all teams, well managed or not, and the ability to deliver the right thing, rather than just deliver full-stop.

Not only will the average be dragged down, but delivering the right thing, so far as I can tell, is not something that AI can necessarily help with—other than perhaps information synthesis after you’ve talked to your users, or by triaging help tickets and identifying themes of problems with your product.14 My former boss Rob has written several posts on this subject, and I think he’s on the money when he says, “typing was never the bottleneck.”

Moreover, the maintenance of these new projects still works the same as any software project—it’s typically expensive, and dominated by the cost of change and the number of staff that understand it. TCO (Total Cost of Ownership) has to be factored in when using AI tooling, and although the tooling can digest and answer questions about a corpus of code, ultimately a human needs to grok it before it can be reasoned about, or safely changed. That part hasn’t changed, and the cost is still high.

The current pricing model of AI models obviously doesn’t take into account externalities—environmental, social, et cetera—but it also doesn’t reflect the full cost of production. As soon as these models dominate, they will have to increase their prices, and at that point you’re in a classic vendor lock-in conundrum as a business. In that situation, TCO of using the AI tools might be only marginally less than doing things the old-fashioned way. It might even be higher—that remains to be seen.

Start-ups, and many scale-ups often find themselves working in a pseudo-agile way by default, simply out of necessity (most would not describe it that way). Put simply, they only eat what they kill, so they’re often applying the principle of trying ideas and abandoning them fast, trying to find product market fit and make the company work. This attitude makes me think that only small companies are likely to reap the real benefits of AI tooling—and then only with experienced staff who can execute well within a framework of building the right things.

The apparently most effective users of AI tooling in my network, either work at start-ups or scale-ups, which certainly influences me in this hunch. Every post like this lengthy post on how the Software Development Lifecycle is Dead strengthens that suspicion. It’s a reasonable post—if you work in an enterprise company—but much of it was never relevant in the kind of small, nimble companies I’m thinking of anyway. However, it’s a small sample set and maybe I’m wrong.

It’s also possible I’m wrong that the improvements in LLMs will hit a plateau and the curve will flatten, with each new generation only showing marginal gains. We will see.

In the final part of this series, we’ll talk about how you as an engineer (or creative) can use the tools, manage the risk of deskilling, and what is left for creative work.

Acknowledgements: Thanks to the many people that fed back on earlier or draft versions of this series, including but not limited to: Jon Stone, Craig McMillan, and Rob Bowley. Cheers for the conversations while I worked out shower thoughts to Andy Gray, Geoff Goodell, James Morgan, David Scott, David Alesch and Jack Gray. Thanks also to all my network that I have bugged about AI tooling, workflows, best practice in their places of employment and for opinions. I hope I’ve done your thoughts and feedback justice.

This post is part of a series: Part 1 | Part 2 | Part 3

I’ve come to much the same conclusion as David Whitney in his devastating post, Existential Dread and the End of Programming, which I read while copy-editing this one. The AI tools are here, they’re Good Enough, programming as we know it is over, and, taking a wider view, we’re not institutionally or individually ready for what comes next.

Which sucks. But we’re programmers. We solve problems, so let’s try and put the dread aside and work the problem.

This essay is an attempt at that. Maybe I’m being too optimistic. Maybe Hard Problems are Not Still Hard. Maybe they’re easy now. Who knows.

David says,

“While so much of what we do in software is remixing existing concepts, innovation isn’t going to come from an existing corpus of information, but business innovation might. You’ll still need those experts if you want to do something actually unique.”

Which I suspect might be true. That’s my thesis in Section 2, and my previous post.

In this series I’m going to argue three main things:

1. AI is going to change the world of work, but probably won’t take your job. In fact, many organisations might see little benefit, for organisational and TCO reasons.

2. Identifying productivity as the key problem we face is a category error—it’s creativity that’s the problem, and AI doesn’t solve this.

3. You can and should reject the political programme behind AI in its current form, doubly so in your creative pursuits and hobbies.

This article is split into three posts (coming this week—I will update these links as they go live):

• Part 1 is about how AI addresses the wrong thing: a productivity deficit as opposed to a creativity deficit.

• Part 2 is about the political programme behind AI, and why it isn’t great—as well as why we probably won’t see any huge changes because of inertia, particularly in large organisations.

• Part 3 is about how you, as an technologist, can use these tools, why that’s still a hard problem, and what is left. I try to end on a hopeful note.

Let’s go.

1. AI Is Boring, But That Won’t Save You

The more I work with AI1 tools, the more I find myself becoming apathetic. I’ve managed to avoid them entirely for my writing, and perhaps this is why I find my interest for it so reinvigorated of late. However, programming with too much AI in the loop (I’ll tolerate ‘turbo-autocomplete’, having spent some years used to Rust and Haskell compiler hints) is quite joyless.

Before I continue, if you’re already typing “luddite” in the comments2 then you might want to skip to section 5 in Part 3, where I argue for what a tolerable workflow might look like. It’s not dissimilar to David’s, outlined in his post.

In a future post (not part of this series), I’ll make the optimistic argument that being able to iterate quickly and make outsize gains may usher in a new era of small, high-impact software shops. It’s certainly the direction I see my consultancy, Envoy Labs, going.

Still, like David, I find the process exhausting, perhaps more so. I will also note, that for me, while such a workflow is fine for work, it is still (a) kind of joyless, and (b) not sustainable in the long-run unless we train engineers the old-fashioned way, probably without, or with minimal AI tooling. It’s not something I have much interest in adopting for my own personal projects, even if it may present a significant business opportunity (that we will be seeking to capitalize upon).

I began programming as a creative pursuit, an extension of writing that allowed me to build abstract, yet concrete things out of words and symbols. If cyberspace is real, then programming is a form of magic, conjuring real things from the ether by giving them their real names. The writer Alan Moore argues that prose is already a form of magic—programming then is an order of magnitude more powerful.

Don’t get me wrong, AI has some uses—off the top of my head, making adjacent similar things, like lesson plans if you’re a teacher, or speeding up making low-effort semi-creative work, such as shitposts or memes for your friends.

In the programming world, I’m happy to get an agent to spit out additional tests for code that I’ve already worked out the hard parts of. My friend Paul—who is a very good engineer—has a really good use case. He uses them to synthesise information, so he can ask questions as he thinks about design.

That’s a pretty good, time-saving use. It’s not world changing, however.

The key is that simply automating low-hanging drudgery that in many cases we simply would not have bothered doing if it wasn’t for the AI assistance isn’t going to juice the valuations of the AI companies.

Or, as another writer described their usage of AI,

“On the other hand, if I had needed to pay someone proper money to do it, I probably would not have done it at all.”

The investors in these AI companies, as well as the incumbent tech giants rushing to put forward their own AI solutions, all have a colossal vested interest in arguing that AI can 10x, 100x productivity, or do jobs that otherwise wouldn’t have been done. Until recently, their argument was still pretty thin.

However, something changed in the last generation of models, and that’s that they got reasonably good at spitting out decent, usable code. They went from reliably knocking out short scripts to reliably knocking out plumbing code for backend applications. Cue another round of hype over productivity gains.

The thing is, even if AI does deliver those gains (it doesn’t in most cases—organisations need to be set up to realise them, see Section 4), it wouldn’t matter, because we are optimising for the wrong thing. Again, for the counter-argument, scroll down to A Tolerable Engineering Workflow (Section 5).

Moreover, there’s the question whether you can prompt to something novel. Weak Sapier-Worff suggests the answer is, possibly not. Still, this might not matter. Most of creative output is a remix or repurpose of something that exists, after all.

Thus we arrive at our subject for today. Why creativity isn’t the same as productivity, and why the political programme of productivity and speed at all costs is not only toxic, but misses the mark of what we actually need.

2. Creativity versus Productivity

On some level, AI productivity increases are just the story of doing more, with less. It’s sort of like austerity, except applied to putting in effort and using imagination. But, as the old saying goes,3 “if less is more, just think of how much more ‘more’ will be!” Creativity is the answer to the scarcity mindset implied by AI maximalism.

AI is the presumptive owner of the narrative of progress, obsessed with the aesthetic of the future. This isn’t new; the crypto scene was obsessed with 80s retrofuturism and the cyberpunk aesthetic. The internet as jurisdiction and peer-to-peer networking are kind of both edgy and cool. Moreover, if the aesthetic, or the performance of crypto, actually matched the reality, then perhaps it would have formed part of a creative answer to our stalled present.

Agency is one of the things we lack. Alienation is one of the things we feel. It may be dorky to say it, but peer-to-peer networking implicitly has a promise of both agency and community. Ask anybody that file-shared using BitTorrent in the old days and they’ll tell you—it wasn’t just about getting that new record first, it was also about something less tangible, and more vibes-based.4

Of course the reality of crypto was mostly just gambling, but I can tell you, when a network genesis happened, you felt something irrational, something—dare I say it—emotional, or hopeful, about the peering process starting to spit out blocks. Even if there was no reality to back the performance, well, we were still engaging in a shared performance. Maybe, after all, what we were doing in crypto was at least part performance art.5

As a result, there’s a bit of me that has a tiny bit of sympathy for the AI maxis who genuinely believe the hype.6 It’s quite a natural human feeling to look on something that has the appearance of novelty with optimism.

The main selling-point of AI (and of blockchain before it)7 is the idea that we can boost productivity, and create a new margin of growth that will unstick semi-stalled developed economies. This is the inevitable logic of ageing populations that have to support more retired people with fewer working-age people, but it’s also an often-unchallenged attitude in the political and business class as much as it is a function of demographics.

The more I think about it, however, the less I think we’re looking at a productivity crisis—we’re looking at a creativity crisis. Perhaps the need for ‘more creativity’ is simply perspective, and I’m dismissing the ‘wrong’ creativity.

After all, much as it might be hugely overstated, the creation of AI tooling, marketing and proliferation represents a huge creative endeavour. It’s not just ekeing out additional efficiency from existing paradigms. Even if the AI programme fails, it has, as a doctrine, at least been a novel intellectual and narrative one.

Still, that’s just one novel doctrine or movement. I think we can do better. Much better. I think moreover, we can and should demand a plurality of novel movements, and seek to bring them into being.

What if we more effectively oriented around creativity such that we could imagine the new, both in our day-to-day work and at a higher level, in our institutions, businesses, communities and politics? Sort of a radical optimism made doctrine, if you like. On the face of it, this isn’t that different to e/acc—it’s just less pedantic, as it doesn’t make the arrogant assumption that capitalist technology is the only game in town for the transformation of our world.

Even if we confined ourselves to the narrow sphere of economics, I could illustrate the difference thus: AI means that barriers to entry are lower, but retained knowledge and context is kept by the AI model owner, as they iterate on their model. They operate, essentially as a feudal landlord.

In a sense, they’re the end state of what McKenzie Wark called ‘Vector Capitalism.’ They own the platform and the means to connect the dots in the economy. More straightforwardly, the landlord analogy is one that’s been made by former Greek finance minister and economist Yannis Varoufakis, among others. As a result, the tendency is for incumbents to benefit more, even if there is the illusion of greater agency for ‘creative destruction’ and challenging incumbents with new ideas.

What if instead of this, in the most extreme example, we ignored the AI tooling and just tried hard to come up with new ideas for businesses? What if we aggressively subsidized that principle as a government, accepting the huge failure rate of new businesses? What if we essentially took the VC model for risk and made it a part of the social contract?

What if we said, “we, society, will take a risk on your novel idea, citizen, if you at least try your hardest to make it a reality.” That’s opportunity and agency in action.8

Then, instead of the performance of disruption, we might get the reality of disruption. Then, instead of the performance of agency, we might get the reality of agency. Then, instead of the performance of productivity, we might get the reality of creation.

AI claims ownership of the aesthetic of progress, but that’s all it is. it is not a reality. It can only be an endless repetition of the present, with each repetition endlessly accumulating capital in the hands of the feudal AI landlords.

Only creativity can bring forth the novel, and this is not something that AI tooling in its current form can offer. It’s not clear that it is something AI tooling will ever be able to offer.

Even if it is, I suspect the reality, viewed through the lens of tech, will be something like what David Whitney describes,

“These engineers will need to have taste, and they’ll probably be involved in early hand writing of some categories of code to establish patterns for the machines to follow in the first instance, but likely will accelerate to the point where traditional workflows of pull-requests and reviews don’t make sense when faced with the pace change can be made.”

These engineers will need to be experts, and fully understand their domain (see Section 5); from whence will they come unless we (or a business) subsidise their traning and apprenticeship? In fact, David’s discussion of how a novel software project might be run sounds very like the research projects we’ve been working on in the Future of Money group.

Most of the work thus far has been exploratory code to understand the domain. Many younger contributors have used a lot of AI tooling. I’ve mostly worked by hand, though I expect my usage to flip in the next project stage. This seems a natural progression if we are to benefit from the tooling, but the key thing is that the early, exploratory, novel phase of the project had to be paid for first. It’s not actually as fast or cheap as it looks.9

Thomas Kuhn’s argument on the structure of scientific revolutions is often referenced by tech types, usually with the assumption that paradigm shifts must necessarily happen in the large, due to technology, but I don’t believe that.10 I believe that creativity is the spark for the paradigm shift, and this can happen in the small and the large.

Still, it is incumbent on us as societies, if we buy the AI argument (progress)11 to try and make it manifest by actually catalysing the creativity that brings forth the novel. By definition, even if we limit ourselves to start-up enterprise and not novel ideas in art, culture,12 politics, et cetera, we cannot know ahead of time what these new businesses and ideas will look like. We must simply write the cheque and give people the space to create.

In case I haven’t been clear enough—only the novel can solve the problems of our societies. Thus AI—in its current form, at least—is a bust.

Furthermore, in case I haven’t been clear enough on another point—the novel doesn’t have to be technological. It can be cultural, artistic, or political. Subsidize a new generation of polytechnics and art schools—we do not know from where the ideas will come.

Much has been written on the AI-inevitability narrative of late—there’s great posts by Cory Doctorow13 and others that go into the subject in great depth. However, the best commentary I’ve seen so far is a feature-length video essay by the musician Adam Neely.

We’ll talk about that in Part 2.

Acknowledgements: Thanks to the many people that fed back on earlier or draft versions of this post series, including but not limited to: Jon Stone, Craig McMillan, and Rob Bowley. Cheers for the conversations while I worked out shower thoughts to Andy Gray, Geoff Goodell, James Morgan, David Scott, David Alesch and Jack Gray. Thanks also to all my network that I have bugged about AI tooling, workflows, best practice in their places of employment and for opinions. I hope I’ve done your thoughts and feedback justice.

A broken clock is right twice a day, and some of my old blog posts come around every few years. I’m in various technical leader chats, forums and face-to-face meetups, and one idea seems to come up over and over again. Whether it’s said over a pint or from a stage by a speaker, there’s this perennially popular idea that to succeed in tech leadership, you don’t need to be technical.1

Let me be absolutely clear: this is only ever true if, and only if2 the leader in question has independently developed the same gut feeling and reasoning that the apprenticeship path of software engineering tends to develop in most competent engineers.

And what is that reasoning? The goal of technical leadership is systematization.3

I wrote in several posts about the apprenticeship pattern of ‘software engineering’ described by Adewale Oshineye and Dave Hoover in Apprenticeship Patterns,4 and I think that an important result of taking this approach to a technical career is necessarily that your analytical and problem-solving skill, as well as your simple gut instinct become highly trained.5

That training (in my experience) tends to express itself as pattern recognition. Knowing the detail, it is possible to then interrogate whether a pattern you have identified (or intuited) has any basis in reality. This is where the ability to switch back into the detail matters.6

In essence, you have to be very senior before having no grasp of the detail is describable as anything other than a weakness. I’d use the analogy of me trying to explain the benefit of functional-core, imperative-shell (pick your code organisation habit of choice here) to an entry-level developer.7

Without experience of at least one large, production project experiencing growing or maintenance pains, not only does that developer not have any context for understanding the deeper implications of the argument (or perhaps even the foundational need for change or improvement at all), they also have no experience of it on either a hands-on level, or perhaps, more importantly, on an emotional level.8

There’s always a risk of over-priviledging your painful failures like the character on the Five of Cups,9 but it is through systematizing these lessons that truly exceptional individuals self-actualize (to use a dreadful term).

The counter-argument is of course clear at this point. The goal of technical leadership is systematization, and that is independent of deep technical knowledge.

This means, surely, that tech leaders don’t need to be technical.

Obviously, I can’t disagree with this point—I can merely restate that yes, of course not all engineers will be able to contextualise and systematize their successes and failures, but they empirically have much more opportunity to do so than those that are non-technical. They will encounter these situations daily, monthly, yearly over the course of a ten or fifteen-year track into management. That is an observable, testable, quantative fact.

The fallacy, I think, being played into is that because systematization is independent of deep technical knowledge, it is somehow mututally exclusive. This is not the case.

Moreover, technical leaders do not exist in a vacuum, and they will have talented non-technical colleagues, likely in product, design, delivery, and other disciplines, to collaborate with. Again, ability to collaborate is independent of deep technical knowledge, but it is not mututally exclusive.

So if you’re an effective technical leader from a non-technical path—celebrate yourself, and know that I’m not getting at you in this post. I’ve worked with many excellent non-technical CIOs and CTOs. No doubt I will work with many more in future.

However, if you want to be a technical leader, doing it the easy way definitely means doing the long yards in a technical role and reflecting on your work as you go. The really harmful thing about this debate is the same thing I identified in my old post (nearly eight years ago!)—that the advice that they did not need to pursue a technical track was being given to folks in their early career. In the general case, this is objectively not correct.10