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

Still, the delta between this, which once upon a time would have been called ‘web design’ and ‘web application development’ is not great. In fact, the delta from this to distributed systems engineering for most non-technology companies is not that great.

The reality is that today those companies are probably making ‘build/buy’ decisions rather than ‘vibe code in-house/buy’ decisions, but even so, the point stands. However, soon—and sooner than I would have thought if you’d asked me two years ago—those companies are going to be turning to AI for the majority of their skilled work, both inside and outside their tech function.

Most of software engineering (especially in these types of companies, say a retailer or other large company whose main product isn’t software or services) is simply what I like to call ‘plumbing,’ that is, connection layers between different systems, different APIs, or even different layers within an application stack. Not all of these are created equal in terms of complexity of implementation for a given project, but generally you can see why AI agents could at least help substantially with the velocity of development, even if there’s no move to replace technical staff any time soon.

Now this isn’t ideal for me personally—I tend to say that my biggest skill is information synthesis, and AI tools can digest a far larger corpus of information, and infer things more succinctly and more quickly than I ever could. Bad news bears.

Hard Problems Have Novel Solutions

The quickest readers will have noticed though that there’s an absolute ocean of problems that don’t fit into the categories above. Off the top of my head, the broad ‘anything that touches real money,’ category, before we talk about large scale data-processing and engineering. Sure, there are plenty of low-hanging fruit in data science and ETL tooling, but the difference between a lazy iterator, an eager iterator, a parallel iterator et cetera in such situations (language depending, of course, and using a Clojure example from reflex) can be huge.

Sure, LLMs can identify this and write sensible code in many cases—but they need careful supervision. In the end, it’s likely to be (at best) a force multiplier than a complete game-changer. If you’ve worked with REPL-Driven Development (RDD) or the advanced compilers and formatters of languages like Rust or Haskell, you’re already used to advanced tools and hugely iterative workflows, and perhaps it is best to regard LLMs as falling mainly into this category of “spiking” tools.

Experienced engineers often “spike” ideas and “refactor” later, and in a sense, generating code via AI tooling is the purest, lowest-cost “spike” imaginable, as long as you can express your intent to the agent conscisely and get useful output. Two big ifs.

Still, many of the examples just given are areas that an AI would not have been able to tackle two years ago, but might just have a shot at today. Even if the prompt spat out unoptimized code, with an experienced engineer checking the output, a large task (writing the code and optimising the code) could be a much smaller one (optimising the code). As others have observed, this is a complete paradigm shift in the job description.2

Even so, there’s also a class of problems where it’s not so much that there needs to be a human in the loop for risk mitigation; it’s that it’s simply unclear whether a novel solution could be found without one. Don’t get me wrong, a human might not figure it out either—but here I’m thinking about long-lived research efforts such as the development of something like Malachite as a Rust rewrite of Tendermint (or even CometBFT as a continuation of the development of Tendermint).3

The ability to find novel approaches to a problem space is often key to such R&D efforts, and while there is a sense in which current AI tools are good at digesting context and offering adjacent solutions, it’s not clear that genuine novelty in implementing solutions is there yet.

Moreover, it’s also definitely not clear that for use-cases that are sensitive or complex, or can have serious negative consequences, the average user is willing to take on risk as a result of AI mistakes. They certainly wouldn’t tolerate a developer making mistakes that affect them, so it seems a reasonable expectation when considering either fully AI-generated code, or even AI assisted code (although, let’s face it, that’s just ‘code’ today). Even in non-regulated markets, the PR backlash from a project failure due to incorrect oversight is likely to be terminal.

Smarter people than me have written at length about project guardrails, governance, and humans in the loop better than I can, so I won’t regurgitate that here.

Hard Problems Are Business Edge

What all this means is that I have a strong feeling that there’s a class of hard problems in computer science, or indeed using computing to solve problems, that remain (a) completely, or relatively unexplored, and (b) are a bad fit for AI tooling due to novelty, user trust, or regulation. This is good news if you enjoy programming, and it’s great news if you want to start a business.

In The Millionaire Fastlane, MJ DeMarco offers five commandments to follow when starting a business, and two are particularly relevant here:4

1. The commandment of control—be in control of your business, pricing and operations (an ironic one considering this blog is hosted on Substack, but it’s not our, or my, core business)

2. The commandment of entry—the lower the barrier to entry to a market, the higher the competition will be, and the lower the margins. Try to find a market with a higher barrier to entry (or, implicitly, find a market where you have the ability to either create the market or create the barrier to entry in the market)5

The barrier to entry on the easy problems, with AI, is now effectively zero. It’s a double-edged sword for those chasing them too—by addressing them using AI tooling or commodity solutions, a would-be business owner is also violating the commandment of control, as they have no edge or USP.

Chase hard problems, and you will have an edge over everybody else that is using the powerful new tooling that’s available to chase the easy problems. After all, the tooling will also help you lower your time to market and iterate on hard problems too. It just won’t help by the same proportional amount versus project size, even if it helps the same absolute amount.

Again—if you have a unique idea (bonus points for it obeying the commandments above) that is easy and you can bootstrap your product to market now by yourself where you couldn’t have in the past, then go and do it now!

If you have any other kind of stake in tech and/or have an entrepreneurial mindset, you need to be chasing only one type of business idea—things that are (a) hard for AI to do (currently), and (b) have a high barrier to entry (either because they’re hard, because of regulation, or because of required specialist knowledge, et cetera).

Of course, for all I know, the tech might leap forward in a few weeks with a new release and render this post fully obsolete, in which case I will probably give up on this programming lark and become a carpenter. Time will tell.

There’s a by-election in my constituency and it looks like it will be two-way between the Greens and Reform. Suddenly I have a lot of skin in the game, much sooner than I had anticipated. I’ve also realised that on some level it’s patriotism (cringe) that makes me want to defeat Reform so much.

The Battle of Britain was S-Tier Antifascist Action

I’ve been interested for some time in a patriotic left-wing movement against the Far Right. I’m unashamedly left-wing, except for a slight hawkish streak on defence—which perhaps the current state of things has helped to validate—but grew up, like many people in England, on stories of the blitz, the Battle of Britain and films like A Bridge Too Far, 633 Squadron,1 and Where Eagles Dare.2

Now, perhaps it took me too long to have this thought, but my entire adult life has been taken up by the right claiming exclusive rights to the imagery and legacy of British involvement in World War Two. Last time I checked, World War Two was the greatest struggle in history against the extreme right, so at what point did it become weaponized by those that want to foist authoritarianism upon these isles?3

Though I’m no nationalist, and I’m a bit shy of ‘devoted’ to my country, I’m definitely a pragmatic patriot. I’m proud to be English, British, European—in spite of the bad parts—but feel that admitting this is somehow problematic due to the behaviour of many of the most performative, far-right “patriots.” That’s a bit weird, isn’t it?4

It’s in this context that I genuinely believe that we have a patriotic duty to defend England against Reform. Against Trump. Against Putin.5 I’m being deadly serious.

Identity and the Social Contract

There’s a chance this thing doesn’t get too out of hand and it remains a spiritual struggle, confined to the ballot-box, but that requires acting together and acting for the good of the country.6 You wanted blitz spirit? That’s what I’m selling. Let’s chuck the chancers out and rebuild a country of agency, opportunity and decency, without having to go through the bloodshed required to form a social consensus last time around.

The question is, what’s the smallest thing we could put to paper and all agree on? That we want to build a better country? That we want to build a better tomorrow? Less? Just that we want to build anything that is real? We need to find that shared hope. That’s the kernel. Then we can ask the question: where can we go from there? It’s definitely a huge task.

If I had to guess, I think Maggie Holland might have an answer in her song A Place Called England.7 At the moment, the country is “Run by men who think that England’s just a place to park their car.”

I’m taking a stab in the dark, but I think that’s a statement that both disaffected Reform and Green voters could agree on. I’m going to self-indulgently quote two whole verses of the song:8

And come all you at home with freedom, whatever the land that gave you birth
There’s room for you, both root and branch, as long as you love English earth
Room for vole and room for orchid, room for all to grow and thrive
Just less room for the rich landowner, he can stay in the Virgin Isles

For England is not flag or empire, it’s not money, it’s not blood
It’s limestone gorge and granite fell, it’s Wealden clay and Severn mud
Blackbirds singing from the May-tree, lark ascending through the scales
Robin watching from your spade and English earth beneath your nails

So here’s two cheers for a place called England, sore abused but not yet dead

We need ideas and action, not the neoliberal sound-bites and stasis of Labour and the Conservatives, and not the reactionary, millionaire-funded, self-interested dross of Reform.9 In fact, even the Left doesn’t really have any answers for this one, because it’s not about the political spectrum, and I’m not even sure it’s about a specific policy,10 it’s about who we are when we are together. England. Britain.

The flip of this lack of narrative control over patriotism by the left is the state of the Conservatives—somewhere I’ve drafted a post about the social contract in Jack Aubrey’s navy, which is really I suppose about one-nation conservatism, or old-fashioned patriarchal Tories. Maybe you can’t have a coherent answer to the far-right unless you have a coherent right-wing.11

Both the left and the right have to rebuild the social contract, and give people the space to decide who we are together when we face challenges together. There’s no shortage of challenges, even if there is a shortage of political will to face up to them.

Returning to the most immediate challenge, if the current right is Reform, then after the bizarre penguin episode in London12 I think we have to start asking the question, not only whether they are just dangerous,13 but whether they are in touch with reality at all.14

Regardless of how this all pans out, I think at our best, we’re braver, bolder, and more resourceful than the political class that fear making real decisions about the future believe we are. I also think we’re more human, more prepared to sacrifice, and more capable of cooperation than the cynical opportunists allow for either. Most people are just waiting for an opportunity to show it.

I’m a bit less constructive by nature than the Young’uns—too many old war movies, I guess—so I’ll end with a snippet of a rousing song:

Our ship being cleared, the foe we neared, with expectations high
That we should show the murd'rous foe
That British courage still will flow
To make them strikе or die!

—The First of June

1

The ending of this film (image above) always reminded me of this moment from Blackadder. I think it’s telling that the arch neoliberals like Michael Gove seem to hate it.

Nate Silver’s 2024 book On The Edge is the best non-fiction book I’ve read this year.1 Obviously I’d recommend giving it a read. It’s a great study of risk-taking behaviour, gambling and technology companies, and having worked in and around blockchains for five years, most of it rang uncomfortably true.

There’s one aspect of it that I think is worth responding to, however, and that’s the fact that narcissism, psychopathy and the Dark Triad personality type aren’t mentioned in the text, other than in one passing quote.

Silver builds a case for two communities, Riverians (risk takers) and Villagers (risk avoiders).2 Throughout the book, there’s attempts to pin down what these personalities might have in common—particuarly on the Riverian side—and ultimately despite a well-written section on neurodivergence and asperger’s, he doesn’t draw any firm conclusions, or examine other conditions.3

“narcissist” or “Narcissist”?

Part way through the chapter Acceleration, after the nominal half-way point of the book, Silver quotes veteran tech journalist Kara Swisher:

I asked Swisher why tech leaders like Thiel and Musk are so obsessed with their media coverage. She didn’t need much time to consider her answer. “It’s because they’re narcissists. They’re all malignant narcissists,” she said.

Similarly to Silver, I’m not going to make any hard and fast conclusions. That’s because as is (already) common for my blog posts, I’m a little out of my lane. Even so, I do wonder if she’s actually being quite specific with the use of medical jargon here, because it tranforms the meaning of the quote.

In everyday use, “narcissist” means “self-centred”; but it has a concrete definition, which we will explore below. Moreover, Narcissism forms part of a triad of behaviours—along with Machiavellianism and Psychopathy4—that together form what is known as the Dark Triad (Paulhus and Williams, 2002).

There’s quite a bit of literature out there on the Dark Triad, and some debate about a central core behaviour, currently hypothesized as aggression.5

The Dark Triad, Narcissism and Psychopathy

So what is the Dark Triad? I’m going to be as concise as possible, quoting the original Paulhus and Williams paper,6 before quoting some definitions from a later paper (since those authors incorporate work post-Paulhus and Williams) on the constituent parts.

Here’s Paulhus and Williams on the Dark Triad:

Despite their diverse origins, the personalities composing this ‘Dark Triad’ share a number of features. To varying degrees, all three entail a socially malevolent character with behavior tendencies toward self-promotion, emotional coldness, duplicity, and aggressiveness. In the clinical literature, the links among the triad have been noted for some time (e.g., Hart & Hare, 1998). The recent development of non-clinical measures of all three constructs has permitted the evaluation of empirical associations in normal populations. As a result, there is now empirical evidence for the overlap of (a) Machiavellianism with psychopathy (Fehr, Samsom, & Paulhus, 1992; McHoskey, Worzel, & Szyarto, 1998), (b) narcissism with psychopathy (Gustafson & Ritzer, 1995), and (c) Machiavellianism with narcissism (McHoskey, 1995). Given such associations, the possibility arises that, in normal samples, the Dark Triad of constructs may be equivalent.

Although they also describe Machiavellianism, Psychopathy and Narcissism (at the time of writing, non-clinical psychopathy was the newest of the three facets to be recognised; “SRP scores predict anti-social behavior in forensic and non-forensic populations”),7 A longer description of each is helpful.

Walker et al. (2022) in a paper on faking good and bad on tests have several useful definitions. Importantly, they make it clear that the three constituent behaviours are not equivalent. First, the triad:

The ‘dark triad’ is an umbrella term for a set of three socially aversive personality traits comprised of narcissism, Machiavellianism, and psychopathy (Paulhus & Williams, 2002). While all three traits are associated with ethical, moral, and socially deviant behavior, among other shared characteristics, they are considered independent of each other. Recent debate relating to a shared common core among the dark triad traits continues, but there is some consensus on the role of antagonism connecting narcissism, Machiavellianism, and psychopathy (Jones & Figueredo, 2013; Jones & Neria, 2015; Paulhus & Williams, 2002). Furthermore, each of the three traits shares exploitative characteristics with goal-focused manipulation of others’ emotions to get what they want.

Then, Narcissism:

While narcissism has traditionally been conceptualized as a personality disorder, mild to extreme sub-clinical characteristics of narcissism are found in non-clinical populations (Raskin & Hall, 1979; Samuel & Widiger, 2008). Accordingly, narcissism is commonly regarded and investigated as a personality trait (Miller & Campbell, 2008) and comprises two facets: grandiose and vulnerable narcissism. As the name suggests, individuals high in grandiose (or “overt”) narcissism are characterized by grandiosity, self-confidence, and exploitation of others and tend to rely on self-validation to maintain self-esteem. When threatened, individuals high in grandiose narcissism tend to blame and devalue others while denying their own weaknesses (Dickinson & Pincus, 2003; Zhang, Luo, Zhao, Zhang, & Wang, 2017).

Vulnerable (or ‘covert’) narcissism tends to rely on external validation. While grandiose fantasies also characterize vulnerable narcissism, people high on vulnerable narcissism tend to oscillate between self-love and self-loathing, thereby exhibiting fragile self-esteem, defensiveness, and resentment (Weiss, Campbell, Lynam, & Miller, 2019). 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).

Does ‘vulnerable’ narcissism sound at all like a billionaire angry at negative media coverage? I won’t over-egg it. Their discussion of Machiavellianism is more terse:

Machiavellianism is commonly understood to be a unidimensional personality construct characterized by goal-focused manipulative and callous social interactions (Christie & Geis, 1970). Individuals high in Machiavellianism tend to be viewed as strategic, capable of delaying gratification for bigger and better rewards in the future, possessing low moral commitment, and engaging in long-term strategic planning with a cold and cynical world-view (Christie & Geis, 1970; Furnham, Richards, & Paulhus, 2013).

Finally, psychopathy is described:

Psychopathy has been typified by interpersonal, affective, and behavioral characteristics, including superficial charm, pathological lying, and lack of empathy, conscience, and remorse (Cleckley, 1951; Hare, 2003). Although clinically identified with antisocial personality disorder, the sub-clinical characteristics of psychopathy, like narcissism, exist on a continuum in the wider population (Berg et al., 2013). Based on Karpman’s (1941) work, enduring classifications of psychopathy make a distinction between two subtypes differing in their etiology and symptomology. Expanding on earlier models, Hare (2003) made a distinction between primary and secondary psychopathy. Both psychopathy facets involve affective elements suggesting some indifference to their own and others’ emotions, each underpinned by an antagonistic interpersonal style (Miller & Lynam, 2012).

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).

Note the difference between Primary and Secondary psychopathy, and bear that in mind when we talk about Elon Musk later.

So as to not throw out the baby with the bathwater, it’s worth saying that in the original article, the authors don’t advance a strong opinion on how destructive the individual traits might be:

Indeed, Machiavellians and narcissists may be more of an interpersonal irritant than a threat: Data suggest that such characters are a mixed blessing in personal life (Robins & Beer, 2001), interpersonal life (Paulhus, 1998), and some organizational contexts (Hogan, Raskin, & Fazzini, 1990; Robins & Paulhus, 2001). Adaptive interpersonal correlates of subclinical psychopathy may be more difficult to find. Their positive self-view and lack of anxiety, however, can be viewed as adaptive in an intrapsychic sense (Taylor & Armor, 1996).

Indeed, Walker et al.’s “faking good” and “faking bad” paper (using exercises such as a simulated job interview) built on work on the Big Five personality traits which showed people answer according to how they think makes them appear best—meaning such deceit is not the preserve of disordered personalities:8

People are highly motivated to be viewed in a favorable light by others. There is evidence that people distort their responses on personality scales under high-stakes conditions (Sjöberg, 2015). In fact, meta-analytic findings show people can and do substantially change their personality scores when motivated to do so (Birkeland, Manson, Kisamore, Brannick, & Smith, 2006; Viswesvaran & Ones, 1999). Much of this research has focused on the broad personality domains of the Big Five/Five-factor model (extraversion, agreeableness, conscientiousness, neuroticism, and openness; MacCann, 2013).

Elon Musk and SBF

Of particular interest in Silver’s book is the case where he talks about Elon Musk and Sam Bankman-Fried (the first being one of the “malignant narcissists” mentioned before). In the first case, Musk is portrayed as being without fear, playing poker according to what is referred to as the “maniac” strategy—go all in, every hand—while SBF is portrayed as a utilitarian prepared to extend potentially flawed logic to a degree that frankly beggars belief in the telling.9

Indeed, Musk’s fearlessness extends beyond business to his physical well-being. His first appearance in Silver’s book is an account from Thiel about how—ostensibly as a a result of both impulsiveness and fearlessness—he crashed his MacLaren F1, and amazingly was unscathed. As Silver relates, “It’s not that Musk had made some rational calclation—that if you’re worth $22 million, you can just afford to buy a new one. Rather Thiel said, Musk hadn’t bothered to consider the possibility of a crash.”

The question here is, had Musk not considered the possiblility, or was he consitutionally unable to?

Crucially, besides their impulsiveness and fearlessness, both SBF and Musk seem to not really understand the implications of the prisoner’s dilemma—i.e. that many games are not zero sum.10 These things combined are a heady cocktail, when we’re talking about personalities that have Bond-villain levels of power.

Though they may not share every behaviour, the inability to see the world as anything but zero-sum is something they share with Donald Trump. Yes, it is concerning that there’s a Dark Triad narcissist who sees the world in fully zero-sum terms with their finger on the nuclear button, but what can you do?

To highlight how insane the zero-sum worldview is, even the most meme-level (and arguably cringe) business resources tend to have the need for collaboration built-in.11 One of the 7 Habits of Highly Effective People is to seek “win-win” situations, so it’s not as if this insight isn’t an informal Schelling point as well as one more formally described by (among other things) the logic of mutually assured destruction.

Even if you flip this argument on its head and still look to win in a zero-sum (or close to zero sum game), it’s likely that it is a more effective strategy to not think of the game as zero sum, or even if not seeking a compromise or asummetrical compromise in your favour, to at the very least put yourself in the shoes of your opponents.

Nate Silver quotes American general HR McMaster pointing out the need for strategic empathy, not for the compromise case, but to defeat your opponents, by understanding their success and exit conditions.

Other than on an instinctual level, the narcissist—as opposed to the psychopath—is perhaps less able to do this in order to act in their own self-interest. I’m on shakier ground here in terms of my reading, so I’ll just give an example and then wrap up.

To me, a good example of how these personalities might interact would be (with an armchair diagnosis, I know) in a recent episode of the UK edition of the Traitors, where the contestant Fiona (a Traitor, in the game) completely sabotaged her own game as the result of having her ego bruised, all the while proudly saying to the camera that she was executing a master plan (see Walker et al., quoted previously, “[w]hen threatened, individuals high in grandiose narcissism tend to blame and devalue others while denying their own weaknesses.”).

Her opponent (who was also a Traitor, and almost certainly also a disordered personality along the Machiavellian or Psychopath axes), calmly briefed against her and got her voted off the show while receiving no votes in return.12 The situation has more than a little of the Musk/Trump spat about it; no doubt if Fiona could have fired her opponent via tweet, she would have.

Just another day in the Corps

I’ve mentioned the books of Richard K Morgan in the footnotes, so indulge me while I use them to conclude my argument.

In the Takeshi Kovacs novels (as opposed to the Altered Carbon TV series)13 it would be hard to describe the Envoys as anything other than psychopaths—and in fact, not even subclinical ones, since the first page the Envoy Corps is mentioned, there’s a time jump to Kovacs watching a fellow member of the Corps being incarcerated on a future meeting, “She was going down for eighty to a century; excessively armed robbery and organic damage.”

To some extent the ability of Envoys (psychopaths) to use their lack of anxiety and remorse to reshape the world around them mirrors the main themes of Morgan’s other novels Market Forces and Thirteen—the latter a very explicit study of psychopathy. Perhaps Silver’s book is similarly a story of psychopaths, not risk takers, being able to reshape a world that can no longer govern their behaviour.

Schwarzkopf? A lightweight. Montgomery? Overrated. Patton? A blowhard. Napoleon? A loser. Wellington? Just lucky. Nelson? Didn’t even survive his own battles. Sun Tzu? Probably ghostwritten.

No. I am speaking of course of Denethor, Son of Ecthelion, Steward of Gondor. Clearly in Number 10, they’ve gotten out the popcorn and have had a rewatch of Peter Jackson’s Lord of the Rings Trilogy.1

From this they have decided to follow Denethor’s cunning plan2 (I will leave the reader to figure out the metaphor here):

1. Retake Osgiliath, a ruined city of little strategic value

2. Double down on defending this site of little strategic value while getting outflanked

3. Get outflanked and lose site of little strategic value with heavy losses

4. Double down again on retaking the site of little strategic value, resulting in your son being mortally wounded and the loss of a significant portion of your forces

5. Get attacked in your heartlands by an emboldened and unchecked enemy

6. Have your capital city sacked by rampaging Orcs

7. Try to set son on fire rather than accept medical help, even though you have no relevant expertise

8. Set self on fire

9. Run through the streets on fire

10. Plunge from the prow of Minas Tirith

11. Die