Broadsheet

This is the second half of the second part (I, IIa, IIb) of our honestly-who-knows-how-many part series laying out some general guidelines for how pre-modern armies are recruited, raised, equipped and paid. While I hope this will be of great interest to the history nerds out there, I’ve opted to structure this specifically as a service for the worldbuilders out there, making useful rules of thumb for imagining fantastical societies.

We’re picking up right where we left off discussing various methods for actually mobilizing an army, which is to say recruiting the troops, getting them armed and getting them in the ranks. We’ve covered ‘self-recruitment,’ systems where a lot of the burden of that process is handled by the troops themselves (almost invariably entitlement principle citizen militia), allowing for a fairly limited administrative apparatus, though I should note, not no administrative apparatus. The need to keep records of who is liable to serve tends to mean these ‘self-recruitment’ systems are most often used by urban societies with a literate upper-class.

We also looked at the over low-overhead alternative: having mobilization handled by local Big Men. The big caveat here is this functionally ensures the fragmentation of power (and thus a non-state society, since centralization of military force defines states) which means that a society which turns its local aristocracy into petty warlords in order to raise its armies has to then cope with having a whole bunch of petty warlords who have their own small armies they can use to push for power and position.

And I want to reiterate here societies do not choose these systems from first principles, instead these systems develop organically, over generations (it’s quite rare that someone plans such a system from scratch, although it does – rarely – happen) and are constrained by existing social structures. A non-state society is basically compelled to adopt Big Man Mobilization because, lacking state structures, they have no other options. By contrast, a long-established state cannot adopt Big Man Mobilization effectively, because they don’t have the petty warlords it relies on and creating those warlords would mean disestablishing the state to a substantial degree by fragmenting its power.

So for the most part, we ought not think of these systems as choices but as consequences of social structure.

This week, we’re going to move forward and discuss some of the more involved solutions, which might give the state a bit more control over the process, at the cost of greater overhead (although not always greater overhead for the state necessarily, as we’ll see).

But first, as always, recruiting and maintaining large pre-modern armies is expensive! Much like many of those pre-modern armies, this project is supported by devolving the costs of my ruinous book-buying habit on to ~~recruits~~ readers. You can help by spreading the word to new readers and by supporting this project over at Patreon. If you want updates whenever a new post appears or want to hear my more bite-sized musings on history, security affairs and current events, you can follow me on Bluesky (@bretdevereaux.bsky.social). I am also active on Threads (bretdevereaux) and maintain a de minimis presence on Twitter (@bretdevereaux).

Households and Officials

Now we’ve seen how compact state societies with a broad ‘middling well-to-do’ class which can political preserve their interests and have some political entitlements to fight for can recruit. But what if your state society doesn’t have that well-to-do peasant or burgher class? At least, not enough of them?

That is the case for most pre-modern state societies: it is substantially more common for the peasantry to be pushed down basically all the way to subsistence. After all, it is in the domestic economic interests of the king, the temple and the local aristocracy to extract as much as they can from the peasantry and most do so – the conditions whereby that peasantry can politically defend themselves from that sort of extraction are rare (and it’s often not the whole peasantry). But now you have a problem: that aristocracy is not big enough to be the society’s whole army on its own, but your peasants are too poor to afford their equipment and have no political entitlement for which to fight.

Actually, to take an aside for a moment, there’s some complexity here in what is happening. Fundamentally, pre-modern societies are all about agriculture and subsistence and so what the society needs is for these small farmers to generate enough surplus to support two kinds of non-farmers: producers making military equipment and soldiers who aren’t farming because they’re fighting. Entitlement-systems do this by both allowing farmers to keep more of their surplus but then also tying the act of dedicating that surplus to warfare (acquiring weapons and serving) to their social status so those small farmers are willing to push out to the edge of their labor to keep their status. But poor peasants have no status to defend and little surplus labor to employ. Fundamentally the interactions here are about the food economy.

So for these societies with stronger aristocracies and weaker peasantries, the problem is two-fold: first the peasants have no reason to serve and second they have no wealth with which to afford the weapons they need to serve. However, the state may well still want to raise all these peasants they have rather than reaching for some other source of manpower (as below). The solution to the first problem is some form of compulsion (you force the peasants to serve or to enable service) and the solution to the second is what I am going to call brigading. ‘To brigade’ simply means to group something together (a military brigade was a combination of different units, often with different integrated combat arms), from which it acquires a lot of specialized meanings. What we’re interested in doing is brigading households: one peasant household, crushed down by extraction, cannot support a soldier – but four or five or ten might.

Now the mechanism of compulsion here varies and with it the mechanism of brigading.

The simplest system is brigading under a principle of universal male military service and we see this at play with systems like the Anglo-Saxon fyrd, the Carolingian select-levy. If the community was under direct threat – your town or village was being attacked – the king (or his local representative) could call up the ‘general levy’ of all adult males. But for regular warfare (including offensive warfare) one obviously couldn’t call up everyone (someone needs to be farming) but a better equipped steady-state infantry force was required to supplement the aristocrats. This was the ‘select levy’ and the fyrd system and the Carolingian system end up settling on a similar solution: bolt several peasant households together and require that, collectively, they furnish one soldier for the king’s army.

Under Charlemagne (r. 768-814), each peasant household was assessed based on its production in units of value called mansi, and for every four mansi they were required to produce one soldier for the select-levy. Now of course most peasant farms were a lot smaller than four mansi – the regulations make provisions for holders as small as half a single mansus – so smaller households were brigaded together until a unit of four mansi was created. That combined unit of several households then was expected to pick someone from among its military-aged men and then collectively pool their resources to equip him – a shield, a spear, a sword, a helmet and some very basic armor (probably textile) – to serve in the select-levy, presumably on a relatively long-term basis.

The Anglo-Saxon system of Alfred the Great (r. 871-899) worked similarly. Farming households were assessed in units called hides (by 1066, this unit is really big, around 120 acres, but my understanding is we generally think it was a lot smaller before then). As with the mansi, households could be brigaded together to make up a single hide, though the hide itself was, notionally, a ‘standard small farm’ (the same way the mansus was). Each individual hide was required to provide one man for local military service (garrison duty, etc.; basic, part-time sort of work that wouldn’t require much kit) and every five hides together to also provide one man – similarly fairly well equipped – for service in the royal army that represented the kingdom’s main field force.

Now that’s the most direct way to brigade the households together, but not the only way. Mass conscription in the Warring States and early Han Dynasty, as I understand it (this is very much not my area of expertise) worked on a rolling, age-based basis where recruits would spend just 2-3 years in the army before being discharged back to their farms as part of the reserve. That too is going to have the effect of distributing the burden of service across a bunch of households, though I am not clear who handles the cost of equipment in that system. Over time, the Han Dynasty converts over to a fully professional system, discussed a little bit below.

The other option for brigading households, at least economically, is military settlers. We see this system at work in the Hellenistic kingdoms that form after Alexander’s death. Instead of brigading a bunch of households together and making them pick one of their members, we (the state) pick for them, by imposing a soldier’s household on top of them. It’s not hard to see why this tends to be a feature of conquest states: the state seizes the land of the peasantry and redistributes it to the families of soldiers, such that the rent from that land is sufficient to maintain a heavy infantryman’s family in relative comfort. The peasantry doesn’t go anywhere, but now they have to pay a portion of their production in rent to the soldier’s family (in addition to taxes – this is an exploitative, unpopular sort of system!) who generally doesn’t do any farming himself and so the soldier (or his sons) are both wealthy enough to afford their equipment and available for conscription, since military service is the flipside of the deal by which they get to live as tiny little petty aristocrats.

Now you can see that each of these systems relies – at least for the peasants – on compulsion. The peasants do not get anything for their service – they aren’t paid, they don’t get increased social status nor can they expect some sort of personal relationship as a client with a noble patron – so you must force them. Which means you need bureaucracy.

Where an entitlement-based system can function often on self-reported wealth or volunteer militias – because military service is a positive honor – a compulsion based system needs bureaucrats. Someone needs to go to each farm and measure its production, assign it to a hide or mansus or to a military settler’s estate or decide which household is due to send a recruit this year and then enforce that decision. That means a small army of literature bureaucrats – royal officials – operating at a very granular level, in the villages (though once set up, the military settlers can perform this role themselves to a degree in a military settler system – they are the local enforcement and rent extraction). Those officials need to be paid, which means heavier taxes and so fundamentally they represent a kind of deadweight on the system: resources that have to be spent on military mobilization but which do not (directly) produce any soldiers. Military settlers functioning as rentier elites (distinct from the frontier-farmer-soldiers below) aren’t much of an improvement here, because they’re capturing a whole lot of value beyond their nominal ‘cost’ as soldiers, making them very expensive as a source of heavy infantry.

Consequently, brigaded-household-systems tend to produce less military power per unit population than entitlement self-recruitment or even fragmented Big Man recruitment. However their advantage is that they scale much more easily over large populations and land areas, so you can end up with more soldiers overall if you have a very big state.¹ These sorts of systems thus tend to be creatures of large, relatively well-centralized, bureaucratic states.

Nevertheless, for the worldbuilders out here: this is the first sort of system where you are likely to find some sort of official actually operating at the village level where they might actually interact with the peasants.

Of course you may note that this system requires quite a lot of administration – you need to have local officials keeping track of local landholdings and conscription liability on a granular level, in a context where you cannot trust anyone to self-report their liability. Those bureaucrats need to be paid and likely so do these soldiers once they’re under arms (you can compel them before you give them weapons, but after they have weapons, you have to pay them, because they have weapons) and all of this demands just quite a lot of state capacity. And I want to stress that demand of state capacity: large, effective bureaucracies composed of literate bureaucrats are really hard for pre-modern states to build and so even ostensibly ‘wealthy’ (that is, high state revenue) states often are not able or at least are not willing to build them.

What if you wanted to shift that administrative burden somewhere else? Maybe you could just pay someone to handle all of that hassle for you…

Contractors and Colonels

The solution here is to outsource the task of administrative organization to some kind of private contractor.

The clearest example of this are the armies of early modern Europe, particularly during the 16th and 17th centuries, raised via a system of private military contractors in what is sometimes termed ‘private enterprise war’ (albeit that term also covers the seaborne commercial-imperial ventures of European powers in the same period, which we’re not going to get into here). The origins of this system stretch earlier – it emerges organically out of systems of mercenary and ‘feudal’ recruitment in the late Middle Ages. And this system is also often present to provide auxiliary units for armies largely recruited another way.

The basic schema goes thusly: the state (typically, but not always, a king) decides they need a unit of soldiers (a ‘regiment,’ generally), but they don’t have to handle all of that administrative hassle directly, so instead they contract someone – typically a military aristocrat (because that’s who has the experience and connections to handle this) – to raise a regiment, promising them payment. Sometimes the contractor is handed the money for this in advance (ancient mercenary recruiters are often sent off with a bunch of silver to hire guys with) but it obviously serves the king better to use the contractor as basically a source of financing: promise him money in the future for a regiment recruited today. So the contractor is not an employee, but rather a creditor who the king owes money.

The contractor then takes on both the financial and logistical burden of raising the regiment, but of course a regiment might be several thousand men which is still too big of a task, so the initial contractor might subcontract parts of this task to other, more junior military aristocrats in his orbit. The initial contractor is a colonel, his unit is a regiment; his subcontractors are captains, their units are companies. Yes, this is the origin of the modern, broadly used, international system of units and ranks.

The colonel and those captains would then employ recruiting-sergeants to enroll the actual men to form the companies. Recruiting scenes in period artwork often feature men being enrolled (and sometimes paid signing bonuses) at impromptu recruiting ‘stations’ consisting of little more than a table or sometimes an upturned drum, with the recruiting-sergeant writing their name down (enrolling in a literal sense) in the company’s rolls. The question of equipment was a problem for the colonel, rather than the king: this was, fundamentally, the colonel’s regiment.

Via Wikipedia, Plate 2 (‘Enrolling the Troops’) from *Les Grandes Misères de la Guerre*, a series of etchings by Jacques Callot (1592-1635), showing the muster and enrollment of a regiment. On the far left, common soldiers are being enrolled (with the muster roll being written out on a drumhead!) and then handed weapons. On the far right, pay is being doled out based on the regimental records. I read the image in some ways (especially given the text below) as something of a process from left to right: on the far left you have the town they’re recruiting at and the soldier is enrolled by the recruiting sergeant, then in the center they are under arms with the regiment marching and finally on the right, the soldiers get paid their wages, a sort of vision of what the soldier *imagines* should happen when he signs up. The rest of the series is less sanguine about the realities of soldiering.

The usual expectation is that such units might recruit from anywhere, but they tend to be fairly geographically focused. After all, the colonel doing the recruiting was able to get the contract because he has some connections or experience and that tended to be geographically localized. Lucian Staiano-Daniels notes (op. cit.), for instance, of the Saxon army and also the Mansfield Regiment during the Thirty Years War, while there certainly were men drawn from very far afield, the bulk of the soldiers came from a fairly tight geographic area around Saxony. Likewise, these sorts of ‘contractor’ units are often how ancient mercenaries show up in our sources and the fact that the fight in distinctive ethnic styles and are marked with ethnic signifiers certainly suggests that the bulk of the men were recruited from a fairly specific geographic and cultural milieu, although we should be aware that just as with those Thirty Years War regiments, there’s no reason you might not have a meaningful number of ‘international’ mercenaries from all over bolted on. These units, if they stay in being, also often recruit as they move, picking up whoever is willing to sign on from wherever they go.

Now we’re not dealing here with navies (which can press sailors by force because after that the boat sails away which reduces desertion risk), but by and large these sorts of armies raised by contractors are reliant on volunteers, which means they need to pay these fellows. From an economic perspective, they’re often skimming excess labor manpower off of local labor markets, which explains the somewhat counterintuitive fact Staiano-Daniels (op. cit. again) notes that the largest chunk of the recruits came from towns and cities – because of course that is where men without a stable niche in society tend to gather, looking for employment, opportunity or adventure. But in most cases these contractors do not have the legal power to compel service (and also quite limited ability to stem desertion of things go badly).

We’ll come back to finance in the next part, but it is worth noting here how heavily financed (that is to say, debt-based) this system is. The king essentially goes into debt with the contractor to get the regiment formed up and the contractor-colonel then raises the regiment. But the main expense there is the wages of the soldiers themselves, but most of their pay is ‘on the books’ (rather than in hard currency) to be paid out in full when the soldiers are dismissed or the regiment disbanded; we often see the officers of the regiment (who are vocational-type junior military aristocrats) lending soldiers hard-currency ‘advances’ on their pay out-of-pocket when the regiment itself was short on hard cash.

Now of course if a meaningful part of pay here is back-pay when a regiment is mustered out, the expectation is that the regiment is going to get mustered out and thus these are not standing units. They can be used to create standing armies (raising new regiments to replace old ones, etc.), but there is often going to be ‘churn’ in this system. That said, when mustering out an old regiment and raising a new one, the soldiers of the old regiment are the obvious first place to start for recruitment: they’re freshly unemployed, trained and experienced soldiers and they are available. As a result, you may get ‘professional’ soldiers in this system, even though it is not a long-service professional system.

That may seem like a terrible system and it could go very badly wrong (and frequently did) if the money to settle the debts didn’t emerge. But from the king’s perspective (and the contractor-colonel’s) it was a great system: it enabled them to finance the whole operation at a relatively low cost, allowing rulers to push their military capacity well beyond what they could afford in hard cash. Financing could even be added on top of this: the Mansfield Regiment’s operating costs were supposed to be covered by another loan (which seems to have ended up diverted to other regiments in the event), so the soldiers are owed their pay from the contractor-colonel and the regiment who in turn is owed this money from the king or the king’s representatives who in turn are trying to arrange their own financing to cover the funds.

The other advantage of this system, of course, is that it imposes minimal administrative burden on the king. The administrative hassle of finding men, recruiting them, keeping track of them, paying them, getting them equipment, uniforms, food and so on all falls to the contractor-colonel (who in many cases is really an absentee proprietor and has deputized someone else to actually run all of this for him; this fellow gets called a lieutenant colonel, he is ‘holding (‘tenant’) in lieu of the colonel’) who has to make those arrangements.

The disadvantages, of course, are numerous. These contractor-colonels basically own their regiments and so expect some leeway in terms of equipment, uniforms and command; not infinite leeway, mind you, they still consider themselves vassals or servants of the king, but it is very hard to enforce standardization on these armies. There’s also just an enormous amount of latitude for graft and indeed in many cases graft – pocketing the wages of dead soldiers, for instance – is how the captains and colonels get paid. And everyone in this system expects to get paid for their service at some point, so while financing can put off the day that the bill comes due, there will be a bill in money, so the state needs revenues to meet it.

Spain famously manages, despite acquiring a running river of silver and gold from the New World, to go bankrupt overextending itself with these sorts of armies in 1557. And then again in 1560. And again in 1575. And then once more in 1596. And then again in 1607. And again in 1627. And then bankrupt again in 1647. And one last time in 1653. This has, you may imagine, a deleterious impact on military discipline off of the battlefield and generally these armies tend to be, we might say, ‘rowdy.’ Again, I invite readers to check out L. Staiano-Daniels’ The War People: A Social History of Common Soldiers during the Era of the Thirty Years War (2024) to get a sense of the short of things these men get up to and the kind of society that forms in these regiments. One certainly gets the impression that Hellenistic military men were not much more restrained (the arrogant, braggart mercenary soldier was a stock character of Greek New Comedy, for instance, memorably captured in Roman Comedy (which derives from Greek New Comedy) in the Miles Gloriousus (‘The Braggart Soldier’)).

In terms of the kinds of societies that use these methods, contractors tends to be a response for states that are reaching for military power beyond what their core state apparatus can support – it tends to be a response to limited state capacity. It merges a vocational principle military aristocracy (the officers/contractors) with an employment principle common soldier and so requires a society that is monetized enough and economically specialized enough to support that framework – which is to say a society with quite a bit of commercial activity going on (often more commercial activity than the state can fully control or supervise – certainly true for both early modern Europe and the Hellenistic Mediterranean) and with enough daily business done in hard currency (rather than ‘non-monetized’ debt-or-bullion-based systems) that the soldiers can actually spend their pay.²

For many fictional fantasy settings, I think the ‘contractor’ method of raising troops is remarkably underrepresented. While Tolkien’s Middle Earth in the Third Age is a relentlessly early medieval setting outside of The Shire, most modern high fantasy settings – simply because modern readers and writers are moderns ourselves – tend to be quite late medieval or early modern in character, almost by accident. In that context, emerging states struggling with administration relying on contractors to set up their armies would make a great deal of sense.

Professionals and Prisoners

Finally, we come to the solution that is, I suspect, the first that most modern folks think of but one of the less common solutions for pre-modern states: direct recruitment by the state. State officials (be they military officers or civilian officials) directly handle recruitment and equipment, with the state absorbing the full administrative and financial burden for military activity.

That phrasing, ‘the state absorbing the full administrative and financial burden’ may explain why this is such a rare option. Direct state mobilization is the preserve of strong, centralized states with relatively well-developed bureaucracies and that is certainly not the most common kind of pre-modern polity.

Generally speaking, if the state – not a contractor, not a Big Man aristocract, not local households, not the citizenry itself, but the state – is going through the effort of handling recruitment, training and equipment, it is generally going to want to only do that once and so one thing that direct state recruitment tends to have in common is that these tend to be long-service regimes.

Longtime blog readers will perhaps have noticed that often where folks would casually use the phrase ‘professional soldiers,’ I tend to default to the longer, ‘long-service professional soldiers’ and here we get to why. The soldiers in the previous section, for instance, recruited by contractors, are often effectively professionals: they have a professional set of skills that they’ve acquired, along with an expected code of conduct (however alien it is to civilian society) and they often move from one contractor’s regiment to the next, staying in the business over multiple campaigns as one regiment is mustered out and another formed. But they’re not long-service – they are not serving a single state continuously in one stretch of employment.

But when the state takes on the fully burden of recruitment, that is often just what they expect. The long-service professional army created by Augustus at the start of the Roman Empire eventually settled on a twenty-year term of service (with another five years in the reserve), which would mean a stretch from 17 (at the earliest) to 42 (at the earliest), which is basically the full stretch of years the Romans might define as military-aged. The Song Dynasty (960-1279) military system likewise expected to discharge “old soldiers” between the ages of fifty and seventy, effectively at the point when they could no longer do any useful soldiering; unlike the Roman system which settled or paid out soldiers on discharge, the Song system made few provisions for the retirement of soldiers, discarding them more than discharging them. Mamluks – military slaves, most often of Turkish extraction common in the Islamic world from the 800s onward – also served for life.

Direct recruitment for long-service regimes can work on a variety of principles and thus with a range of methods.

The most immediately understandable to us is a volunteer, employment-principle system: the state simply hires men with specific terms of service and then pays them (though once hired, they cannot voluntarily leave). That’s the Roman system from the imperial period. Even here, recruitment is not wholly centralized: legions and auxiliary cohorts tended over time to do most of their recruitment locally and so increasingly were made up of men from where they were stationed. The legions had to recruit from the citizenry, but the regular discharge of veterans (who settled and started families generally near their posts – they’ve been away from ‘home’ for 20 years, so ‘home’ is where the legionary fort is) created an available pool, which might be supplemented by citizen recruits from other parts of the empire.

Roman military pay had to be relatively generous by the standards of the time to attract troops and it largely was: 225 denarii per year, from which necessities were subtracted but it seems clear that the food deduction was well below the value of the food actually offered. At discharge (20 years in) they got a discharge bonus (the praemia) around 3,000 denarii (assuming they’d never gotten above base pay), so the per-year average is actually 375 denarii per year, simply back-loaded. The result is a base pay rate that exceeded the daily income of an unskilled laborer (famously a denarius a day), except that it was steady, whereas other forms of wage labor were irregular. So the Romans pay a premium to get men to enlist, which simplifies the process a bit, since you can then largely rely on men seeking out recruitment rather than the other way around, which probably explains why legions were able to recruit mostly locally. The recruits (mostly) came to them.

In stark contrast, you have something like the Mamluk system, which is a vocational military slave system reliant on compulsion in which the state (in the form of a king or Caliph) buys enslaved military men in bulk for military service; we have discussed this system before. Typically these were external populations being purchased (very often warriors from the Steppe), rather than internal sources – we’ll leave aside the Ottoman devshirme and its complexities for today. Now it is important to remember: once you arm these fellows, their relationship with you fundamentally changes. The notional status of Mamluks was low – they were enslaved warriors – but practically as well-armed, vocational-status warriors in the service of the state, some came to wield meaningful power and wealth.

For our purposes here, though, the ‘recruitment method’ for Mamluks is commercial: they’re purchased. The states that employed large Mamluk armies were not usually the polity initially enslaving them – instead, warfare on and around the Steppe generated significant numbers of enslaved warriors which Steppe societies could not absorb and wealthy Arab states soaked up that supply (and of course their demand stimulated more supply, which is to say, more warfare for the purpose of enslavement). Naturally doing this requires a lot of revenues (to purchase large numbers of valuable enslaved warriors) but also a significant administrative machine which could feed, house, clothe, equip, organize and manage these fellows once purchased. At least initially, the machine relied upon was the remnants of the Sassanid and Eastern Roman Imperial administrative systems which had existed before the conquests of the Rashidun Caliphate and had been taken over, in modified form, by subsequent Caliphs.

On the ground, that system is going to look like private slavers traveling to the ‘source’ regions for this manpower, purchasing captured warriors and then trafficking them to the markets of the great state powers where they know royal officials will be eager to buy them.

The other kind of compulsion system are prisoners-turned-soldiers and here the Song military system is a good example as it was substantially reliant on this method. I should note that certainly other professional or contractor-based recruitment systems will also lean on the expedient of turning out the prisons into the muster field as well (and indeed, under ‘brigaded household’ systems, we find a lot of indications that village leaders use recruitment as a way to get rid of troublemakers). Prisoners were not the only source of Song recruits, but their presence speaks to the level of compulsion in this system.

The Song system emerged out of a period of consolidating warfare where generals could – in the context of high intensity warfare – impress large numbers of civilians into the army and where surrendering armies had their soldiers absorbed. So this is an army comfortable with compulsion during a period of high conflict which then – consolidation having been completed – has to transition to a steady-state system where it couldn’t be relying on simply impressing wide sweeps. Instead, it relied on leveraging compulsion against groups who were socially unprotected in society, as Alyagon (op. cit.) lists them off, “soldiers’ families, the poor and the idle, local militias, non-Han groups, convicts and refugees.” In short, Song officials grabbed who they could with the least hassle from powerful constituencies (like local landholders or other officials).

The baseline of the system were local militias, which functioned on a brigaded-household model (run by local officials, of which the Song had no shortage); when the standing professional army required new recruits, men on the militia lists might be forced into the army, tattoo’d with their new unit to discourage desertion (since a man caught with a military tattoo out of service could be assumed to be a deserter and punished) and essentially never sent home. But, unsurprisingly, this produced a lot of resistance to the militia system – “understaffed units, out-of-date registers, abandoned fields” (Alyagon, op. cit., 66) etc. So the Song also pressed non-Han ethnic groups around its frontiers into service and also implemented military service as a punishment for law-breaking. The military thus likewise swiftly became a dumping ground for anyone that local officials might want to get rid of. Finally, the families of soldiers, because they lived in the military camps, could easily be compelled to serve: when the state already has possession of your father, mother and sisters, if they tell you to enlist, there’s going to be a lot of pressure to do so.

New recruits, however they were acquired, were checked for physical fitness (swiftly reduced to a mostly bureaucratic height standard), enrolled in the army, had their faces tattoo’d, were handed their uniform (equipment was at the unit level) and payment and escorted to their unit in a process referred to as “conscripting, tattooing, and giving tips.”³

What all of these systems share in common is that they are fully bureaucratized: there is a royal or imperial official who has to oversee the acquisition of soldiers (either voluntary or compelled), their enrollment, the issue of their equipment, their continued pay and maintenance and eventually their discharge. That may seem normal to us, but it was not normal for pre-modern states.

The main advantage for this kind of direct recruitment was that it enabled states to keep a standing army in peacetime with relatively minimal disruption to civilian affairs. Long-service professionals could also be highly disciplined and well-trained, but I think the performance of the Song Army warns us that it is not always so – unsurprisingly the poor terms of service, low status and high degree of compulsion involves in Song military service seems to have produced relatively poor military performance as time went on. It is not always the case that professionals are better.

The main disadvantage, of course, is the expense of it. By directly assuming the full cost of military activity, the state is shouldering a tremendous administrative and economic burden. We’ll talk in the next part about how that burden might be met, but it is no surprise that most pre-modern polities were willing to give up a lot of political control – either to citizens (for entitlement-self-recruitment) or vassal Big Men (for retinue recruitment) or to colonels (for contractor recruitment) – simply to lessen the tremendous direct administrative and financial cost to the state. The army of the high Roman Empire was professional, disciplined, and very impressive, but it is also worth noting that it was only about a third bigger (~300,000 compared to ~185,000) than the peak deployment capability of the Roman Republic, despite the empire it served being ten times larger in population (c. 50m compared to c. 5m, very roughly).

Next time, we’ll turn to the question, now that we’ve raised our army, of how different sorts of polities pay for it.

Hi-ho, we’ve reached the moment, in this movie we’re all watching together on X, where model intelligence has become dangerous. Dario predicted years ago that it would happen this year. With Fable being (briefly) shut off by the USG, it’s the first highly visible sign that we’ve crossed into treacherous waters.

Which is too bad, really. I was hoping that we’d get a couple more generations of model upgrades, powerful enough to convince all remaining skeptics, before we got to one that was a security problem. But the Mythos class (Fable being the sloppily-guardrailed version they released last week) has everyone spooked.

Now that we know models are getting dangerous, we can do some extrapolating.

The AI race isn’t going to slow down, and AI will continue to grow exponentially in capability. Unfortunately, most of you aren’t going to see it progress anymore.

I am now in the camp who believe that we are only at most two or three model generations away from AI finally being controlled like nuclear weapons. Only a few will have access to superintelligence above the classes of models we’re seeing this year. As far as I can tell, most Fortune 500 companies will either not have access at all, or it will be tightly controlled for only a small subset of the company. And it will be supervised.

I think those with access to powerful frontier models will sell intelligence like a vending machine: You send them a software spec or a problem to solve, and their models implement it for you, on their servers, with your dollars. And since most companies aren’t going to want to send their code and problems to the model vendors, I think the world will learn to live with the models we do have access to.

Every government will restrict access, acting on its own. Nuclear weapons are scarce because it’s hard to get enriched uranium. AI is going the same way, with the chokepoint being the supply chain — something governments can actually clamp down on. China will lock superintelligence inside its own borders as hard as the USG will. And if China ends up taking the frontier lead, it just changes where the power is concentrated, but not the overall shape of the world we’re going to be operating in.

A World of Mediocre Models

Many of us hoped OSS models would keep us on the exponential curve. They trail the frontier by roughly seven months. But they stay on that curve by training on compute which increasingly takes international-relations-level dealmaking to secure. Maybe distillation or some clever peer-to-peer training scheme keeps them in the race. But to push past Fable class they’d have to do it while the whole hardware-and-software supply chain gets locked down the way the nuclear chain was. And the frontier labs themselves are going to decline to help train the next dangerous open model.

If OSS hits Fable class next year anyway, that’s great for the world. But open models are not going to blow past Fable class, not with a huge compute wall and government lockdowns looming.

So again, today’s models are roughly as good as we’re going to get.

As disappointing as I find that in some ways, I find it still has a lot of upside to be happy about. Because today’s models, particularly Fable-class, are plenty good enough. They will still utterly transform coding and knowledge work. It’s just not going to be a walk in the park. It will take a big, multi-year effort to pivot.

I’m going to assume for the rest of this post that we will all get Fable back, and that we may even get one higher class of model before further advancements become inaccessible to all but a very few.

Many of you have been expecting the hockey-stick AI advancement curve to level out soon, refusing to believe that it’s truly on an exponential curve that could lead to it being so much smarter than humans. You predicted AI would not be able to replace human engineers.

In a way, you turned out to be right. A very practical way.

In reality, behind the scenes, the curve is NOT flattening at all; the exponential growth will continue, and you will be able to see outwardly observable signs of it, e.g. in data center growth.

But the curve will appear to flatten out for you, through two separate phenomena.

The first reason is the one we already mentioned: they’re going to keep the smartest (and thus dangerous) models out of our hands. So most of us never get a chance to try them out. And those models certainly won’t be replacing engineers, if we can’t use them.

The other reason’s kind of interesting, and it took me a while to see that it’s really the same reason wearing a different hat.

A World of Mediocre Users

Some people are already reporting they can’t tell the difference between Opus 4.8 and Fable 5. I’ve been calling this the “discernment horizon”: every human has a ceiling on model intelligence past which all the models start to feel about the same.

But there are actually two ceilings, both instructive lenses on what’s happening.

The first I’ll call the demand horizon. It’s set by the hardest problem you bring. If all you have handy are easy problems, they don’t give a smarter model any room to pull ahead — the outputs look the same because the problem never stretched either one. The demand horizon is where you can’t tell two models apart because you don’t have a hard enough problem.

I call my hard problems “back-pocket evals,” and I collect them. Whenever I give a project to a model, and it can’t do the project, I add it to my pocket-eval list. Then every time a new model drops, it’s like Christmas. I try it out on all my pocket evals and see which ones it can now solve.

Concrete example: No Opus-class model has been able to write the React client for my game; it’s just way too complicated and fiddly. Fable was absolutely smashing it. Easy way for me to see the difference vs. Opus. But I also have other problems that will prove too hard for Fable. I will collect them eagerly as it chomps through my work. All you need is ambition, and you can create your own pocket eval collection.

So my demand horizon is super high, and will last at least three or four more model generations, if I can manage to get access to that level of intelligence, which seems unlikely. I don’t have my hopes up. But at least I will be able to tell if it’s actually that smart, using my evals.

The demand horizon is benign enough, even kind of flattering: it just means your work isn’t hard enough right now. But bring an unusually hard problem one day, and your horizon widens on the spot, as you watch the cheaper model fumble some task the expensive one nails. Like my React client.

There is a darker horizon, which I think of as the discernment horizon proper. This one is set not by the hardest problem you can pose, but by the hardest answer you can judge. Past this scary line, you can’t tell whether the model is right, because checking the work is itself beyond you.

I’ve been chewing on this problem since my Drunken Rants days, when I’d write about how hard it is to interview someone smarter than you. How do you know they’re not a charlatan, if they’re professing expertise in an area you know nothing about? You can’t, really.

Everyone has a discernment horizon, even Dario. Past some level of capability there is no human alive who can verify the model output.

This takes us full circle to why they are starting to lock down the models. You can’t hand out an intelligence engine that nobody can supervise. It’s pointless to own because you won’t know if it’s helping you or walking you off a cliff. Superhuman means unverifiable.

So the safety people see a potential weapon, and the rest of us see a tool that we can’t effectively supervise. In both cases, you don’t need or want the more powerful model. You want the safer one, even if it’s less capable.

Companies also have both of these horizons. For plenty of companies, Fable is already past the demand horizon — every problem they’ve got, it handles, and a smarter model would change nothing they could measure. For the harder shops the binding limit is discernment: the AI produces work that nobody can grade. A terrible outcome, assuming you don’t want to surrender your business to AI entirely.

As a result of all this, the curve is flattening for most of us. I think commodity intelligence will soon stop growing exponentially, or at least, it will appear that way, and we’ll all operate as if it’s true.

I had never spent much time considering the possibility that the intelligence curve would flatten out. But now that it seems to be happening, let’s look at some of the clear and obvious implications for the industry.

SaaS is Back, Baby

It’s clearly going to be too expensive to rebuild all the SaaS at the top of the pyramid. Yes, there will be models that can do it, but access and cost will both be prohibitive.

SaaS actually came rocketing back over the past month all on its own, after spending much of the past year on the ropes, pummeled from all directions by threats of in-house rewrites and fears of Claude taking it all.

Then companies learned about token efficiency the hard way, with huge firms blowing their yearly budgets in months. A few months ago, everyone was planning to tell their CFO they could cancel a bunch of SaaS subscriptions and bring their dependencies in-house. No longer. Now the buy-vs-build decision is tilted heavily towards buy. If you despise your current SaaS enough, then sure, you may be motivated to rewrite it with AI. But buying SaaS has predictable costs that are usually already in the budget, whereas vibe-coding replacements could be an expensive gamble.

If we see a plateau in accessible model capabilities, then the other dreams we had about AI in SaaS fade too: not just replacing it, but transforming it with agentic behaviors and monitoring. Today’s models aren’t good enough to replace a person yet (jailbreakable, confusable, etc.), so you can’t just swap an agent in for an SRE or a trained customer service rep. And the models that could reliably replace humans may be too dangerous to give to most people.

So SaaS looks like it might be fine, even without agentic behaviors. It just needs to save you the money of building and maintaining it in-house.

SaaS still has its problems: users subsidizing the 80% of the features they don’t use, dollars extracted from local economies to enrich Silicon Valley, enshittification creeping up the pyramid. But it remains fundamentally about crystallization of knowledge. Groups of people build stuff that’s tricky, stuff you wouldn’t want to do yourself, and rent it to you. The AI models powerful enough to replace most of that “easily” will either be unavailable or prohibitively expensive.

It feels to me like the SaaS model is here to stay.

AI Literacy 101

Today’s models, while quite capable, are still very difficult to work with. Even Fable likely struggles with large monoliths and other complex legacy code arrangements. It’s hard to get a consistently high quality bar. And of course efficiency is a monstrous issue.

I’d been hoping for models that are smart enough that you don’t need much training to work with them. But with today’s models, you cannot expect people to be born AI-literate. They need help in order to use today’s coding agents and harnesses.

In the next section I will provide a fairly precise and measurable definition of AI literacy. I did not invent it, but I believe it is good enough for your planning, and mine.

First, though, why does it matter whether your employees are AI literate? The answer is a bit complicated, but it boils down to two factors. One is that your company will have to pivot to using AI. And the other is that all your employees are feeling anxious about AI. This tension is actively playing out at all companies around the globe.

Pivoting to AI will change everyone’s job at least a little, and probably change the shape of your company a lot. Which just feeds the anxiety, in a loop.

If you are pushing on change in your company without first having addressed AI literacy, in a quantitative but also deeply empathetic way, then you are fueling anxiety, resentment, and pushback. Your org will resist change.

AI adoption is the key culture challenge of 2026–2027. If you can manage to get your (hostile) employees past the hurdle, and genuinely get them excited about how they can use AI to accelerate themselves, then magic happens. They will automatically begin reshaping your business processes together towards using supervised agentic flows.

I’ve seen this happening all over, but concretely, Gene and I saw it at Arkana Labs in April under the guidance of their VP Eng, Owen Parker. Arkana offers world-class overnight kidney-disease diagnosis, and they have utterly unique business processes. But those processes can all be sped up here and there with AI. Given how obsessed Arkana is, culturally, with fast and accurate turnaround, the employees themselves are getting excited about the opportunities, and pushing hard on what might be possible with agents.

Having seen enough of this I maintain that once most people “get” AI, you just need to guide them, and they’ll start broadly doing the right things for your team.

Conversely, as long as your teammates remain non-AI-savvy, they will resist AI. Which means that until you can get your org over the hump, you’re facing resistance, anxiety, and potentially even morale issues.

So how do we fix it? How do we get people to “get” AI?

It turns out, Netflix has handed us the answer. Thank you, Netflix!

AI Literacy: Beginner Cohorts

I watched a mind-blowing presentation in April from Ezra Savard, who ran a training study/experiment at Netflix from December through March. He gave the presentation at Gene Kim’s AI Summit in San Jose. The study’s goal was to train Netflix engineers on agentic coding, and measure the impact.

Ezra’s presentation was all properly rigorous and disclaimed (e.g. for minor selection bias), but they felt pretty strongly about the results being directionally correct, so I’ll skip all that.

Note that I’ll be framing this as “AI literacy” but that’s my term, not Ezra’s, and he never mentions literacy in his talk. He talks about the journey from being non-users, to users, to power users. But AI is becoming a foundational skill for modern knowledge work, so I will make the case in this post that we are talking about a new form of literacy.

Ezra’s first big discovery to share is that they found three cohorts, which I’m calling the beginner levels of AI literacy. Ezra characterized the cohorts in terms of their average token spend on a “qualified” day using AI, meaning a day where they are using it heavily. They needed at least 3 days a week to be in the cohort.

Here are the three beginner cohorts they found, defined by spend:

0M tokens/day: devs who aren’t using coding agents for their regular work
4M tokens/day: using a single agent synchronously throughout the workday
12M-15M tokens/day: letting 2 to 4 agents work without watching

So: No agent, then single-agent, then multi-agent. I think this is a solid working definition of baseline AI literacy. If your entire org isn’t at least at single-agent literacy, then they will be fighting you on bringing in more AI, even if it’s just passive resistance.

Ezra shared that some power-user graduates of his course were legitimately spending much higher amounts, over 50M/day.

But he also cautioned that beyond the 15M/day mark, token spend is no longer a valuable measure, since people are by then clever enough to invent reasons to burn tokens. (After that, you switch to measuring outcomes, as I’ll discuss below.)

However, and this is the wonderful part, up to that point (15M tokens/day), measuring your employees’ token spend at a coarse level can provide powerful insight into where your organization stands on AI literacy, and how much training lies ahead of you.

Fortunately, Ezra has good news for you there: People can jump cohorts in 5 hours. That’s how long it takes people, in the right training setting, to graduate from AI illiteracy to AI savviness. And they stay there. It’s like flipping a switch. 96% of the trainees remained in the second cohort for six weeks after the course without showing signs of slowing down.

What’s the right training setup, you ask? Ezra’s team spent considerable effort honing the formula. The training must be done a team at a time, with 5 to 10 people, including their manager. The manager _must_ opt the team in, during regular work hours, as “blessed” company time. The trainees must bring their actual work, and the instructor(s) will help them learn how to do it with agents.

They found that if they cut corners anywhere — shorter classes, larger audiences, individual opt-in classes — they didn’t get the same results. It didn’t “stick.”

As for the third cohort: once a manager has a team full of single-agent users, they can opt their team into the multi-agent course. This is another 5 hours, and teaches them the additional skills needed to wrangle multiple asynchronous agents, while maintaining a high quality bar. This course saw the same strong adoption, with the vast majority jumping into multi-agent work and staying there.

So it takes roughly 5 hours of focused training per employee to get them to basic literacy. And after a few weeks of practice, another 5 hours to get them to become power users.

And as for impact, Ezra reported some surprising findings, such as there being a large difference in the amount of code produced by agentic coders. But when they dug in, they found it was entirely attributable to the additional test code they were writing. Overall, they found that the course had a large positive impact on productivity for those who attended.

If you want to start having conversations with your company about pivoting to AI, then I strongly recommend you begin with an AI literacy audit, followed by training everyone up at least into the single-agent cohort.

Advanced Cohorts

Getting people over the FUD hump, and teaching them to spend tokens to accelerate their own work, solves your first culture problem. And it will help you tremendously in your conversations about how to bring in AI, without getting so much pushback.

Netflix gave us an optimized solution to the FUD hump. You train up some “Line Cook” instructors who teach the intro course. Ezra told me and Gene that they had started with our book, which was kinda cool. But the exact curriculum barely matters; you can teach it however you like. And then you get everyone through it, 5 hours and ten people at a time.

Once you’ve taught everyone how to spend tokens, your second culture problem emerges, which is teaching people how NOT to spend tokens. Token efficiency is a fairly advanced topic. There are many, many ways that models can steer you wrong, and the most efficient agentic coders focus on maximizing their outcomes for a given token budget.

At this point I should share a joke made by Pierre Racz, the brilliant Founder/CEO of Genetec, one of the world’s largest physical-security monitoring companies. He prefers to write his code by hand, and when I described how these measurements work, he observed wryly, “Well then it’s not that I’m not using AI, I’m just extremely token-efficient.”

And it’s a funny joke, but there’s an underlying lesson there too, which is that if you can trivially do a task by hand, then do it by hand! Over time, you can save a bunch of tokens just by being thoughtful. Type !git push instead of asking the agent to do it, and your habit probably saves you 100k tokens on average, each time you push.

You know the meme with the bell curve and the troglodyte at the bottom and the Jedi at the top, and they’re doing the same thing? Well here the beginner-thing that the Jedi masters is low token spend.

Token spend only signals literacy on the way up. It’s a skill you build. But then it flips, and the thing you need to start measuring is token waste. Minimizing that is another set of skills.

You will find that your beginner cohorts are absolute token pigs, and that’s OK. Encourage them to explore and learn. They need to master the skill of spending before they can focus on savings.

You will find that people don’t automatically know how to conserve tokens. They will be 200k tokens deep into a conversation and ask the AI what time it is. Argh! Or maybe whether a specific file exists in their home directory. This is a skill that needs training, too.

So at some point you will probably want to have a third training course, this one on efficiency techniques and good token hygiene.

Then, give your newly AI-savvy people budgets. Make them earn budget increases with real outcomes. However you do it, measuring outcomes is going to become critically important, so you can differentiate your effective builders from your vanity builders.

We’ve talked about the beginner literacy cohorts (spend-based), and the advanced cohorts (efficiency, waste management). At the top of the AI literacy curve, your thinking becomes more strategic. You worry about saving large numbers of tokens while achieving your desired outcomes.

The first example everyone hits is buy vs build. Will you let your engineers try to rewrite random SaaS, or will you just re-up and go with the known spend? You have to start being strategic with agentic project allocation.

Another interesting challenge you face: How will you route every task to the dumbest model that can handle it? You will need to be able to tag work with intelligence tiers, and build a router. That router is the discernment horizon encoded as infrastructure. Most work sits below the line and goes to the cheap model, and the occasional task that pokes above it gets escalated to the expensive tier.

At the highest levels, AI literacy turns into the art of achieving great outcomes with the least spend.

A Craft Needs a Plateau

We are seeing a plateau in intelligence. It is artificial: the exponential increase continues behind the scenes, gated away from you. And at some point you won’t be able to tell it’s getting better, even if you could see it. The intelligence curve is as real as the Earth is round, but just as flat from where you stand. Welcome to the Flat Curve Society.

The Mythos graduating class will become the accepted trade-off between capability and risk for the general public. And we will see incremental updates that patch edge-case behavior, but nothing like the jumps we have enjoyed for the past several years.

The plateau is not a bad thing. A plateau lets us set up a camp and start building. We’ve been on unstable ground. Think how hard it has been lately to be a startup founder, with everything you build being obsoleted with each model release. That’s finally slowing down now, and it will give us firm footing.

We have an engineering problem ahead of us. As good as Opus and Fable are, they have their limits. We all need to learn the art of task decomposition and breaking up software monoliths, to keep them within those limits. We will still need engineers, and engineering. We’ll have super smart helpers, but it will still be pretty similar to the landscape today.

I kind of like the plateau that’s coming. Stability feels like a precondition for the new craft of building software with these super smart helpers. It is a craft that only gets harder, and more valuable, the weaker your models are. Sonnet-class and Opus-class will stay relevant for years, because they save money and stay broadly available even after the frontier moves on. The models that would obsolete today’s hard-won techniques of the craft are evidently too dangerous to give to us anyway.

The world is currently tinkering with setting up 24x7 autonomous agents, and it looks like the difficulties we face there today will remain with us tomorrow. There is a large engineering effort underway to build the control plane(s) that allows today’s models to run today’s large businesses. That, too, is a craft, or at least, it’s part of the tools of the trade.

Train Your Flat-Curvers

The key takeaway here (beyond not committing seppuku just yet if you’re a SaaS vendor) is that we have a massive AI training and literacy problem ahead of us. But it’s solvable. It will just take time and effort.

The models we have today, and the ones coming this year, will not one-shot your entire Fortune 100 code base. They are capable of amazing things, but they will still require grown-up human supervision.

This means you’ll need engineers. All the cool things that we’ve talked about — with impromptu 2-pizza teams forming, 2- to 3-person teams being a sweet spot, and roles starting to blur together (or at least talk to each other more) — will likely continue. But everyone will need training and time and patience and careful budget management.

AI Literacy does not come for free. The only thing you get for free is AI Anxiety. But it’s fairly easy to teach people to spend tokens. Teaching them to save tokens? Well, that’s the new meta. Good luck. Make sure they can do it Pierre’s way first.

That’s all I had for today’s post. Hope you enjoyed it. See you at the AI Engineer Conference in San Francisco at the end of the month!

Metric	Value
National Rank (by % Decline in Fatalities)	#4
Motorcycle Rider Fatalities (2023)	139
Motorcycle Rider Fatalities (2024)	111
Percentage Change	-20.14%
Interstate Fatalities (2023–2024)	13 → 13
Intersection Fatalities (2023–2024)	54 → 46

Rank	State	Motorcycle Rider Fatalities 2023	Motorcycle Rider Fatalities 2024	% Rise/Fall
1	Vermont	18	7	-61.11%
2	Rhode Island	15	8	-46.67%
3	Louisiana	97	71	-26.80%
4	Washington	139	111	-20.14%
5	Arkansas	92	75	-18.48%
6	Arizona	261	217	-16.86%
7	California	595	519	-12.77%
8	Missouri	166	145	-12.65%
9	Illinois	165	147	-10.91%
10	New Hampshire	38	34	-10.53%

Metric	2023	2024	% Rise/Fall
Total Motorcycle Rider Fatalities	139	111	-20.14%
Interstate Fatalities	13	13	0.00%
Intersection Fatalities	54	46	-14.81%

Rank	State	% Rise/Fall	Percentage Point Difference vs Washington
4	Washington	-20.14%	—
46	Nebraska	+31.82%	+51.96 pp
47	Kansas	+33.33%	+53.47 pp
48	Delaware	+64.29%	+84.43 pp
49	Wyoming	+84.62%	+104.76 pp
50	Maine	+125.00%	+145.14 pp

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