Broadsheet

This stat from Datadog is from March of this year, so GPT-4o concentration has likely dropped a bit. However, I’ve heard from multiple large inference providers that usage of GPT-4o and models of similar vintage can be higher than 50% of all calls!

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I hadn’t heard of Dan Guido until a few months ago, when I came across the video of a talk he gave at [un]prompted, an AI security practitioners’ conference. Dan is the CEO and cofounder of Trail of Bits, a software security research and development firm that works with companies in tech, defense, and finance. But Dan wasn’t talking about security. He was talking about what it takes to make a company AI native, which is close to the center of the bullseye for many of us right now.

We’ve been trying to figure out how to do that at O’Reilly, but until I came across Dan’s talk, we didn’t have a structured process. We’ve been building along the lines he laid out ever since. So for this episode of Live with Tim I asked Dan to reprise the talk before we got to the conversation. He was supposed to take twenty minutes, like his original conference talk, but he took thirty-five, and I had to cut him off slightly before the end to make room for questions. That was a tough choice, since everything he had to say was golden.

Dan opened by reminding us of the current state of play in enterprise AI adoption. In February, Fortune reported on a National Bureau of Economic Research study in which nearly 90% of some 6,000 executives said AI had produced no measurable change in employment or productivity at their firms over three years. People started calling it the new Solow paradox, after Robert Solow’s 1987 line that “you can see the computer age everywhere except in the productivity statistics.”

Dan’s belief is that this isn’t evidence that AI doesn’t work. It’s evidence that most companies are deploying AI wrong. They hand out ChatGPT and Claude licenses, and then leadership waits for the magic to happen. It doesn’t.

Dan started out by describing three levels of AI adoption.

AI assisted is where everyone starts: “You give people access to ChatGPT, it drafts emails, it summarizes documents. It’s just a productivity tool, and your organization doesn’t change. Your workflows are the exact same as they were before. You just have a little buddy that helps you with a couple of tasks.”
AI augmented is where you start redesigning workflows, so that AI does the first pass on a code review and a human does the second.
AI native is structural: “That’s where you’ve redesigned the company and its workflows from the ground up, assuming the AI is going to be there and that it’s a core participant. That’s not really a tool. That’s more thinking about AI as teammates.”

In his framing, the first of the three is a tool and the last is an operating system. For Trail of Bits, he said that “operating system” has a specific purpose:

“I want our security expertise to compound as code. Every engagement we do, all the skills, the workflows, everything that we build makes the next engagement faster and better.”

Employee resistance is the first problem

Dan confessed how hard it was to get started on the ladder from AI Assisted to AI Native:

“When I announced last year that we were all in on AI, that we were going to be using it across all of our workflows and redesigning the way the company operates, I’d say only about 5% of the company was with me. 95% was resistant.” About 20% was actively resisting. The other 75% were resisting more passively. “They’ll go along with it in public, but in process they’ll sabotage it. They’ll hope that if they keep their head low, this will pass over them, and that three months from now management’s focus will change and it won’t be a problem anymore, and we can get back to doing what we were doing. That’s where the majority of people land when these initiatives happen.”

Rather than argue with his employees, Dan studied the literature on why people reject new technology and decided he needed to address four biases against AI: self-enhancing bias, identity threat, opacity, and intolerance for imperfection.

Self-enhancing bias is the habit of crediting your wins to your own judgment and your losses to circumstance, which is a particular problem for senior people who are strongly attached to the years of experience and intuition that got them to their present position. Opacity is not being able to see how a decision got made. Dan’s observation is that you don’t understand your doctor’s reasoning either, but somehow you trust the doctor but get suspicious of the machine. Dan didn’t mention this work specifically, but intolerance for imperfection seems to refer to Dietvorst, Simmons, and Massey’s work on algorithm aversion, which found that people abandon an algorithm after watching it err once, even when it outperforms the human alternative. Their follow-up paper found that giving people even a slight ability to modify the algorithm’s output is enough to overcome the aversion.

Dan spent the most time on identity threat. He described a study in which the same kitchen appliance was advertised in two ways: “On one hand, it does the cooking for you. On the other hand, it helps you cook better. It’s the same device. The people who identified as cooks rejected the first version and accepted the second.”

Most knowledge work, Dan argued, and security auditing in particular, is what he called symbolic rather than instrumental. That is, it carries meaning about who you are. “So I have to frame AI as something that makes you a more dangerous auditor,” he said. “Not that it does the audit for you.”

In his work at Trail of Bits, he deliberately built a countermeasure for each bias.

Self-enhancing bias is addressed by “an AI maturity matrix” with visible levels, because you can’t claim you’re already good enough when there’s a published ladder that identifies a different set of skills as critical.
Identity threat gets skills repositories, where an engineer who writes a hard plugin gets credit for encoding their expertise. Hackathons also change the dynamic from resistance to exploration. I’m putting words in Dan’s mouth here, but I think he’d agree that when experienced developers are called on as mentors in a hackathon, that also reduces their experience of AI as an identity threat.
Intolerance for imperfection gets a curated marketplace, sandboxing, and hardened defaults, so everyone’s first experience of AI isn’t a disaster.
Opacity gets a written AI handbook that clarifies the usage policy and the risk model rather than just saying “trust us.”

Here’s Dan’s slide on “the remedies that actually worked”:

Returning to one of my hobby horses, this is a kind of mechanism design. In my recent piece on the missing mechanisms of the agentic economy, I argued that we need to start with desired outcomes and ask ourselves what mechanisms will help to produce them. Dan’s approach seems to be really good at this. Most enterprises are treating AI adoption as a procurement problem or a communications problem. Dan treated it as a question of what incentives, defaults, and status ladders produce the behavior you want, given how people actually respond.

The last remedy on Dan’s list is that the CEO has to lead by example. He noted, “I was the first person through the door. My voice as the CEO matters a lot more than people think. The passive 50% of the company that isn’t sure if this initiative is going to be successful, they’re watching to see what leadership actually does, not what it says.”

A ladder, not a mandate

Trail of Bits already tracked about 50 engineering skills for performance review, things like Python, git, Rust, and various security auditing capabilities. Dan pulled AI skills out into their own matrix, with four levels, from not engaged through capable and adoptive to transformative. Each of these levels is detailed separately and more specifically for assurance, engineering, sales, and project management.

He noted that “The highest level of the maturity matrix is not somebody who uses AI the most. It’s somebody who invents new ways to work and builds tools with AI. So the identity of the expert shifts from ‘I don’t need AI’ to ‘I’m the one who makes AI useful for the company.’” This was his first important design choice.

The second is what level zero means. He said “If you’re at level zero, if you’re not engaged, that means you’re fighting back against the company. If you dismiss AI as hype, if you refuse to use AI for security work, this is a disagreement on principles, not on skills. For people who were stuck in the not engaged category, we had hard conversations, and there were people who left the company.” Levels one through three are a skill issue, and the remedy is time with the tools.

While the slide describing the capability matrix is shown in the preceding video clip, here’s where you can find the full deck so you can study it in more detail.

Driving adoption and skills with hackathons

One of the best ways Trail of Bits developed to move people up the ladder was to hold a hackathon every two months. Dan runs them with clear goals rather than as a free-for-all. The focus area and learning objectives are defined in advance and announced a week ahead, with separate instructions for engineers and non-engineers. People work in pairs so everything gets reviewed. There’s a demo session at the end, and then follow-through. (It’s an important part of Dan’s big idea, that you have to build a system by which, in his words, organizational knowledge and capability compounds.) He noted that “In the days afterward we keep one or two people around, and they collect all the reusable artifacts, structure them, and put them into the places they need to be.”

I asked what people outside of product and engineering actually work on, since the answer for an accountant at a hackathon was not obvious. Dan’s response is that the hackathon isn’t measured in artifacts shipped but in where people sit on the capability ladder the following week. Essentially, he’s running a training program that happens to produce useful output, rather than a production sprint that happens to teach people something.

The first hackathon, he told me, was the equivalent of a beach cleanup: “It’s like those companies that send everybody to the beach with a big stick and say, let’s go pick up a bunch of trash and put it away, and then you get the big team photo after with all the contractor bags of garbage. That’s what we did with our public source code repositories.”

He picked it because open source maintenance is the part of the job that feels like a grind. No new features, just closing issues and stale dependencies on public code where nothing was at risk. “As an open source maintainer, you just get beaten down by the public. This doesn’t work, I can’t use it, this thing sucks. Dozens of issues pointing out flaws you already knew about. It feels burdensome. We wanted people to see that adopting AI would relieve burden.”

The second hackathon was about shipping impactful product updates, but it was also designed to move everyone up the capability ladder by giving up control. Engineers had to run Claude Code in bypass permissions mode, fully autonomous, on public repositories, inside sandboxes the company had prepared in advance. The one they’re running now is about persistent background agents that can be handed a task during an audit and come back with a proof of concept exploit or a draft finding.

Here’s a look at Dan’s slack message announcing the hackathon:

The slack message announcing the second hackathon. (From Dan’s slide deck.)

Everything the hackathons produce gets harvested into artifacts.

Trail of Bits runs three skills repositories: an internal one for company workflows, a public one that anyone can use, and a curated one that vets third-party skills before they’re allowed in.

Publishing skills to the public repository is not just a marketing exercise. “It keeps us honest, and it forces us to write things that other people can use, not just people outside the company but inside too,” Dan said. “It really helps us think about the tribal knowledge that’s baked into the tool.”

The curated repository exists because Trail of Bits knows how bad the supply chain is. They’ve published research on how to write malicious skills, and so Dan is not going to tell 130 employees to start downloading code from strangers and running it on their laptops. “If you want adoption, you need a safe supply chain.”

Turning scar tissue into infrastructure

Perhaps even more important than the skills repository is, as Dan put it, “turning scar tissue into infrastructure.”

“Every single time Claude Code didn’t do something we wanted, we would bake it into a set of global, copy-pasteable defaults. Known good settings, recommended patterns. I call it scar tissue. If I hire somebody new tomorrow, I don’t want them to have to go through the entire discovery process of the last year of Trail of Bits to figure out how to use the tool.”

The configuration repository, claude-code-config, is where the accumulated lessons live.

Dan built the first version himself and then opened it to pull requests from the whole company, assigning someone after each hackathon to go collect what people hadn’t contributed on their own. “It’s easier to put out something that’s unpolished than it is to get it perfect on the first try.”

In short, a big part of the Trail of Bits “enterprise AI operating system” approach is a set of standardized tools and hardened defaults. Standardization isn’t a straitjacket. It’s a foundation.

On sandboxing, Trail of Bits deliberately didn’t pick a single preferred solution. There’s a devcontainer for developers, dropkit for disposable DigitalOcean droplets, COOP for isolated VMs, and the sandboxing now built into Claude Code for casual users. “The point isn’t that everybody uses the same sandbox,” Dan said. “The point is that everyone has a safe sandbox to use, and that it’s easy for them to do it.”

Another of the hardened defaults is procedural. Trail of Bits enforces a seven day cooldown on every package their developers install:

“There are dozens of security companies scanning the internet trying to find a new cool blog post they can write about malicious code hiding on PyPI or npm, and they usually figure out there’s a supply chain issue within hours. So we just delay all the packages that Trail of Bits uses. Generally the malicious stuff gets picked up before we ever get a chance to run it.”

That’s free-riding on a competitive market for security research, and given the speed of today’s market, it’s an elegant solution. There’s a whole class of defenses like this waiting to be found, where the mechanism is not a technical system but a well-chosen delay.

Data, and DJ Patil’s “Tidy House”

The problem we run into most often as we build AI workflows at O’Reilly isn’t the model or the tooling. It’s data. Who has access to which system, which system does that data live in, and who do I ask? In a 500 person company that’s annoying. I wonder what it’s like at a company with 50,000 employees.

I told Dan about DJ Patil’s Tidy House framing. He agreed that data access for AI is a big problem. His answer starts with permissions:

“The permissions debt is invisible until an agent hits it. Making data agent legible is a forced permission audit. You have to actually go through and figure out who can access what…. It also raises the stakes for permissions errors. If you overshare information, now an agent inside your company is going to find it instantly. There are a lot of these technical debt sort of things where, with agents, all of it’s becoming due at the same time.”

Every shortcut an organization took with its data over the past twenty years is being called at once, and the companies that can run the audit, make fast decisions about boundaries, and then actually share their data are the ones that will get a force multiplier.

Dan is against letting a thousand flowers bloom, because uncoordinated teams create overlap rather than compounding. He’d rather have one centralized foundation, with innovation happening on top of that. He suggested a useful metric for making that work across team boundaries is what fraction of your team’s data did you make reusable for everyone else, and how much of it is being used by teams outside your own.

What post-AI jobs look like

Before the first hackathon, Trail of Bits ran hands-on sessions to teach its operations and go-to-market staff the basics of git and the command line. Not mastery, just enough to be a consumer of the thing. Here we are fifty years into my career and the Unix command line still matters. Dan’s non-technical staff mostly work inside Claude Cowork or Codex Desktop now, but he thinks the command line experience was worth it because they know what’s happening under the hood.

What happens to a job when the tool can do a lot of what humans used to do? Dan gave the example of his own technical editors. His editors used the hackathons to build the tools that got them out of line editing, including one that turns a public presentation into a blog post in the company’s voice. What the writers do now is consult on how to frame a story so it is effective with a particular audience.

I agree. Human jobs aren’t going away any time soon. This gets heard as optimism when it’s really just observation. AI is going to replace a lot of what we used to do, but it is also going to hand us a large amount of new work, and much of that work hasn’t been understood yet. Quality assurance for agent systems is one of the new jobs. So is skills product management, which is a role that didn’t exist eighteen months ago and now has a headcount at a 130 person security firm.

I asked a question towards the end about how we’re going to know which skills and agents are any good. What Dan has so far is telemetry pulled from developers’ dot files through the company’s device management system, which tells him what gets used and what breaks, plus one AI systems engineer whose job is product management for the skills repository, reviewing incoming pull requests and deprecating overlapping skills.

What Dan thinks comes next is evaluation. He says: “Once you invest a lot into these agent systems, you need proof that they do the job. The way you do that is you give everybody a performance review. You give them an evaluation data set, a benchmark.”

Trail of Bits is now building benchmarks for its core skills. How well can we find bugs in this language? How well can we write a statement of work? Constructing those datasets is real work, with positive and negative cases, and comparisons against the algorithmic tools that already exist.

Put the reps in

I asked Dan for the top five mistakes he made. He said there was only one. “You need to allocate an appropriate amount of FAFO time. (That’s F Around and Find Out.) A product comes out on Friday. There’s no documentation for it. There’s no training guidance for it. There’s no course on it. You can’t wait until somebody systematizes the knowledge. You just need to do it.”

Then he gave an analogy to going to the gym.

The recipe for success

Dan has a replicable recipe, which he summarized as follows:

Standardize on one agent workflow that you can support.
Write an AI handbook so that risk decisions aren’t ad hoc, and that everyone is playing the same game.
Create a capability ladder that makes clear that improvement is expected.
Run short adoption sprints that force hands-on usage.
Capture everything as reusable artifacts: skills + configs + a curated supply chain.
Make autonomous agents safe with sandboxing + guardrails + hardened defaults.

The Trail of Bits skills repository is public. So is the curated marketplace, the configuration repository, the devcontainer, dropkit, and COOP (Continuity of Operations planning). He wrote up the whole playbook on The Trail of Bits Blog and gave a version of it to tl;dr sec. He thinks publishing makes the work better because it forces the tribal knowledge out into the open where it can be checked.

Which brings me back to the Solow paradox, which seemed to disappear by the late 90s, when US aggregate productivity did finally go up. That didn’t happen because computers got faster. It disappeared because companies figured out how to reorganize themselves around what computers could do, and eventually those organizational recipes spread widely enough to show up in aggregate statistics. The same has to happen today. The current AI discourse is obsessed with model capability and largely uninterested in diffusion. The problem is not that the models are oversold. It’s that almost nobody has done the necessary organizational work, and the few who have are mostly keeping it to themselves.

If you want to go beyond the highlight videos shown above, watch Dan’s entire talk here. His slide deck is here. And be sure to check out the Trail of Bits Github repository.

Hey folks, Fireside this week, as I am fresh back from a short family trip before the jaws of the Fall semester close upon me. I did get a chance to see Christopher Nolan’s The Odyssey and I wrote a review of it for Foreign Policy (alas, paywalled, I assume), although by far the most discussed review has been that of Emily Wilson, noted translator of Homer. I have a few scattered thoughts about the film that either didn’t fit into the FP piece or have been occasioned since then, which I thought I’d put here. Spoiler warnings, I suppose – Nolan does actually have a surprise or two (he is willing to bend the poem a bit to get it) – so if you don’t want that, skip to the second cat picture to get to recommendations.

Percy hard at work helping me plan out my Teaching Paradox: Hearts of Iron series.

First off as level setting, my general impression of the film: it was good. It aims to evoke emotions and largely does so. The actors are very skilled and it shows. Christopher Nolan does not like dialogue and that…also shows. As I said in my review, however, this is a ‘remix’ not a cover; Nolan feels perfectly free to put his own spin on the story, to shift themes around and so on. And that’s fine, people have been remixing the Odyssey since forever.¹

I think this is one place where Wilson and I part ways in our review. As a Homerist, Wilson sees everything missing in Nolan’s film, which is fine to point out but at the same time of course there is no way that anyone was going to fit all of the subtly and themes of 12,000 lines of Greek into a film that needs to run a bit under three hours. At the same time, Wilson has written her review quite sharply, with some lines that come off more cutting than necessary given that, while The Odyssey might not have been the film of Wilson’s dreams, it is evidently a ‘good’ film to judge by audience reactions.

More broadly, I can’t help but think that, in a moment where the field of Classics is quite evidently sinking, it would have been helpful to be seen as sailing with the wind of a popular new adaptation of Homer, rather than nitpicking against it. In a way, Wilson’s critique because it is written so sharply (quoth Wilson, “I would be ashamed to have written any part of this script”) comes off as excessively contemptuous of both the film and its audience. There are, I would suggest, ways to make these very same critiques which avoid being so alienating in a context where Wilson must know that the media was bound to make her the ‘face’ of Classics (as indeed, they have done). Such a sharp critique risks coming off as belittling or condescending to a large audience of very enthused movie-goers (the film has a 97% positive audience rating on Rotten Tomatoes) and it is simply not a very good time for the public’s sense of classicists (such as they think of us at all) to be “The Odyssey was good and I like Homer but classicists are mean snotty killjoys who get angry when I enjoy things the ‘wrong’ way.” That public perception is a recipe for more department closures, especially at public institutions (Wilson teaches at a private, Ivy League institution).

There is a resolute refusal for the highest profile members of the field (and this goes for history as much as for classics) to ever consider for a moment that they might be in a position of speaking for the field which might bring with it deeper and greater obligations; instead we acknowledge no such obligations (except as pertain to more junior scholars at less impressive schools, of course) and then act surprised as the field wilts. This is especially true of figures in tenured positions at elite private universities who are wholly insulated from the costs of alienating the public – their jobs will never be at risk – but who are often elevated in the media to speak for the field as a whole.

To return to the film, prior to release there was a lot of discussion of its ‘historical accuracy.’ Seeing the film, I think much of that discussion missed the point, as the vibe here was in some ways ‘grounded’ but also impressionistic, almost dreamlike in quality. This is especially true of the flashback sequences, told from the perspective of Odysseus or Menelaus. Notably, these are the sequences in which nearly all of the supernatural stuff happens and one gets the sense that neither of these fellows might be entirely reliable narrators. A lot of the wild armor and costumes (like Agamenon’s horse-spine helmet) exist in these sequences, where we’re really not all that tethered to literal reality anyway.

More broadly, this is a film that isn’t trying for specific accuracy to either the 8th century of the poem’s composition of the 12th century of the Late Bronze Age Collapse. Instead, it is using the Odyssey to evoke some specific ideas: the rupture of norms (particularly those involving kindness towards strangers, foreigners, immigrants) and civilizational collapse. And while Homer’s Odyssey doesn’t have a collapse of civilization narrative, it is there in broader Greek mythological thought: the heroes of the Trojan War are the last full generation of heroes. The generation of their children seem almost like a half-generation, by and large restricted to working out the consequences of their more consequential parents and living (and dying) in their shadows (think Orestes, Electra, Neoptolemus/Pyrrhus, Telemachus himself, etc).

Using the Odyssey in particular to do this lets Nolan position himself as speaking for, rather than against the western tradition or western canon. Thus the irony that I noted in my Foreign Policy piece that the ‘chuds’ hated this movie before it came out because of the casting, but if they had seen it and also understood subtext, they would hate the movie because it is a full-throated, violent rejection of them and their entire worldview. Effectively Christopher Nolan donning a mask of Homer to declare, on behalf of the tradition the chuds claim they stand for, “you know nothing of my work.” Not because Nolan is ‘super-woke’ – the film is, if anything, quite conservative in outlook – but rather because he isn’t and thus can argue for norms from a traditionalist lens.

None of which means everyone has to like the movie. I will say right now, a lot of the dialogue is stilted. In particular, Nolan is a subtle as a brick about connecting this to the Late Bronze Age Collapse, with characters saying things like “our Age of Bronze is collapsing” and “you are the Sea Peoples?” I can imagine someone more familiar with the background here, who has thus already picked up on the subtext (and it isn’t all that subtle as subtext) would be annoyed by it suddenly becoming text as Christopher Nolan basically has Anne Hathaway and Matt Damon walk into the audience to explain, “And now the moral of the story is…”

On the other hand, if I have come to realize just about anything about the general audiences for films these days it is that a lot of folks really do not grasp subtext at all. Watching folks revel obliviously in movies that revile them has given me a new appreciation for the Greek Chorus walking on stage and just straight up singing the moral at the audience between acts. So while I did find some of the very on-the-nose lines a bit off-putting, I find I understand the instinct to include them anyway.

By way of example, I saw more than one person on social media complaining that Athena (played by Zendaya in the film) looked wrong, because she didn’t look like a divinity, was simply dressed, didn’t wear Athena’s distinctive helmet and breastplate (the aegis with its distinctive gorgoneion) and looked too young for the role. Which misses – and this is the spoiler if you do not want the spoiler, stop reading because here is the spoiler – the point that Odysseus is seeing the form of a young Trojan priestess of Athena that his men murdered in cold blood in front of him during the sack of the city which we see later in the film.

Now the film is quite deliberately ambiguous as to if this is just Odysseus processing his trauma and what we’re seeing is trauma disassociation – I think ‘Athena’ never tells Odysseus anything he couldn’t himself know, for instance – or if this is Athena taking the form of this slain woman to communicate to Odysseus (since Athena regularly takes such disguises in the poem!).

Likewise, some of the chatter about the fate of Odysseus’ men misses the (re)framing of the film as a complete whole. We’re flat out told in the Underworld that Odysseus’ men are going to die, that conditions will be put before them to kill them relentlessly until they do and we also – and this is a departure from the poem – we also see exactly why, because once we finally get the full flashback to the fall of Troy, while Odysseus stands basically stunned by the horror of it (and only fights other armed men), his soldiers take part in the butchery of civilians, including, most evocatively, Zendaya’s priestess. In that moment, we see that Circe’s description of them – delivered earlier in the film but later chronologically – was not about something they might do to her but about things they had already done at Troy.

And so we get this motif: the men take part of Circe’s food and Odysseus does not, they take part of Helios’ cattle and Odysseus does not because they took part in the brutal sack of Troy in a way that Odysseus did not (and don’t show any remorse for it afterwards, unlike Odysseus). Odysseus’ men have already done the core violation and they are going to get one occasion after another until they break a rule so hard that it kills them.

If it seems strange that an angry Greek god might want to lure someone into a further act of sacrilege in order to punish them for it, that’s actually something that does happen! I am put in mind of a brief bit in Herodotus (Hdt. 1.157-161). Pactyes of Lydia has fled the wrath of the Persians and taken shelter as a suppliant – a protected refugee, in essence – in Cyme and the Cymeans are understandably worried that the Persians will attack them, so they want an excuse to get this guy gone. So they send a man to an oracle of Apollo to try to get a favorable oracle.

And the god Apollo gives them the oracle they seek, but the guy they sent (Aristodicus) is clever enough to know that something is up, so he makes a show of rousting out some of Apollo’s sacred birds and when Apollo essentially demands, ‘what the hell, man?’Aristodicus replies with, ‘yeah, I want to know why you are telling us to betray a suppliant too?’ to which Apollo replies (and this is a direct quote, trans. A.D. Godley), “Yes, I do command them, so that you may perish all the sooner for your impiety, and never again come to inquire of my oracle about giving up those that seek refuge with you.”

Basically, Herodotus has Apollo so angry that the Cymeans are even asking the question, trying to wriggle out of their duty here, that he is giving them the wrong answer so as to further justify his divine wrath. In the event, the Cymeans figure it out and move Pactyes along to another city and when that city also tries to betray him, they smuggle him out at the last minute to a third city (which also betrayed him, no one wants to get wrecked by Persia), thereby satisfying their obligation and thus avoiding Apollo’s wrath.

So the idea of the sacred cattle being a lure to bring the already-condemned-for-sacrilege men of Odysseus to their death is perfectly fitting with the way Greek gods roll in mythology.

Percy thinks this is his tent. It is not his tent.

On to Recommendations.

And I suppose we can start with the Odyssey discourse. First, of course, my own review of Christopher Nolan’s The Odyssey is at Foreign Policy, where I am mostly focused on the themes of the film rather than any question of accuracy. Emily Wilson’s (upon whose translation Nolan reportedly based his adaption) own review of the film, rather more negative, is both interesting and sharp-elbowed. But I also wanted to highlight Spencer McDaniel’s really detailed discussion at her blog, Tales of Times Forgotten which really gets into the weeds of the original poem, the history of the period and how the adaptation interacts with them, which gives, I think, a pretty good sense of “what is a classicist seeing and hearing when they watch this movie?”

And then in a sort-of-related recommendation, I thought there were quite a few good points in one of Jamelle Bouie’s recent Takes over in YouTube, “There are no white people on Middle-Earth.” It touches on modern culture war politics, of course, but the fundamental point he is making, I think is important and valid that our modern conception of race is just that: quite modern. And so while people in the past (or in fictional worlds) can obviously see skin tone, the broader categories of ‘white,’ ‘non-white’ and ‘black’ didn’t exist (or may not exist, for the fictional worlds) for them in the same way. Bouie doesn’t use this example, but I come back to a short comment in the Odyssey that Odysseus thought Memnon, the King of the Ethiopians, to have been the most attractive man he had ever seen – the idea that ‘whiteness’ was a universal beauty standard for all genders simply doesn’t exist in Homer’s world. And while we’re here, a careful look at Tolkien’s text suggests that his world is not quite so fair-skinned as it is usually cast: Samwise Gamgee, in a sense the truest hero of the work, has his skin described as grown more than once and while you might assume that’s because he’s outside all the time, he is a descendant of Harfoots who in turn are described as darker in complexion than other Hobbits. But that simply doesn’t matter to anyone because ‘white’ and ‘black’ aren’t the categories in Middle Earth that they are in our Earth.

Meanwhile, in civil-military thinking, I ran back over an April piece at History Does You by the always-worthwhile Secretary of Defense Rock on “The Myths of McMasterism” which is well worth the time. I think read alongside something like Kori Shake’s recent The State and the Soldier (2025) – which I need to give a closer read but may end up recommended here before too long too – it brings out how the sense in the American military that they had largely ‘solved’ the problem of civil-military relations has been an illusion, quite vividly exposed as such. Of course I had my own thoughts on this last year as well.

There’s been a lot of Percy lately, so to balance it out, here is Ollie – sitting, as he does, on a cat bed that is on a cat bed.

For this week’s book recommendation, I thought I might make a bunch of recommendations together to answer a question I (and every other classicist) get asked regularly: what translations of the Iliad and Odyssey do I recommend?

The thing is, there are a lot of very capable translations of both works and so choosing between them is basically always going to be something of a matter of taste and de gustibus non est disputandum (“on taste, there can be no disputes”). As we’ve discussed before, a translator (especially of poetry) is dealing with trying to balance incompatible demands of meaning, style, rhythm, and brevity. So Instead of offering one translation, I’ll suggest three with what I think are their relative merits and then you can have your pick.

First, for my personal favorite translation, I would suggest the Richard Lattimore translation, The Iliad of Homer (1951) and The Odyssey of Homer (1965). Lattimore’s translation is, of the three I am going to recommend the most faithful to the original Greek, which is to say it tries the hardest to keep to the literal meaning of the Greek, while also keeping to original line numbers. That also means it is clunkiest of the bunch here, though not horribly so. That clunk is, of course, somewhat increased by its age and of the bunch Lattimore comes off feeling the most ‘ye olde Homer’ even though it is very much in ‘modern’ (that is, 1950s) English, albeit with an elevated style.

For something that is a bit looser but flows better, I always recommend Robert Fagles’ The Iliad (1990) and The Odyssey (1996). The big thing to know about Fagles is that he does not keep at all to the original line numbering: all of his books run ‘long’ compared to the original poem. That allows him to ‘stretch out’ a lot to produce a translation that still captures most of the poems while being smoother and more elegant than Lattimore. That said, Fagles is definitely less faithful than Lattimore as well, though this is still very much a solid translation. Just beware of those line numbers because Fagles will not match up with anyone else. Fagles also translates in what I would describe as an ‘elevated’ style, an effort to capture Homer’s dialect – already archaic and artificial in the eight century BC – and give the poems a formal air despite these translations only being about 30 years old. A lot of readers really like that elevated style – they want to feel like they are reading capital-L Literature – but it bothers other readers. For that last group, we have my last recommendation which you knew was coming which is…

For something that is much more modern and pithier, but meaningfully more willing to bend the text to get there, Emily Wilson’s The Iliad (2024) and The Odyssey (2018). Wilson translates into modern, common vernacular English rather than an artificial ‘elevated’ style, which some folks like and some folks don’t. More importantly, I think, Wilson holds like iron to Homer’s line numbers and also insists on delivering both poems in iambic pentameter – her goal is to capture the punchy brevity of Homer’s Greek which I will be honest, neither Lattimore or especially not Fagles really capture. The result is a poem, in English, which scans and can be sung or chanted and which better manages the feeling of momentum the Greek has. The downside, of course, is that Wilson has to play a lot looser with the meaning of individual phrases to achieve this and so this is the least purely ‘faithful’ of the translations, though it is still very much Homer.

So there you go: if you value a maximally literal, one-to-one translation, go with Lattimore. If you want something elevated and complete, even if it bends meaning and maybe gets a bit ‘flabby,’ read Fagles (Fagles does read very well). If you want something that feels modern and captures the brevity and momentum of the Greek (but sometimes loses some of its subtler shades of meaning), go with Wilson. If you want the whole poem, utterly complete with all of its meaning, style and verve preserved, learn ancient Greek.