MASLEY: This is a situation where I really truly don't understand what it implies for AI safety.
NEYMAN: I think it really hinges on whether the Trump administration got serious about the risks or is executing on a political vendetta against Anthropic.
— Andy Masley and Eric Neyman, on the suspension of Fable 5 and Mythos 5, June 2026
I do not think it matters whether the administration is serious about AI risk or is executing a political vendetta against Anthropic. American AI policy is undergoing a national securitization. It is being pulled out of ordinary politics and into the part of the state that handles basic security and emergencies.
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.
I am less interested in the merits of this one restriction, which I think is a technical and political mistake, than in the direction of US AI policy. Which is something like an Aschenbrenner track:
This is the most important challenge for the national security of the United States since the invention of the atomic bomb. In one form or another, the national security state will get very heavily involved. [...] The Project will be the necessary, indeed the only plausible, response.
I am broadly in line with Aschenbrenner about where this is going, and not very relaxed about some of the costs.
The logic that pulls something into the national security state's orbit is also good at keeping it there, because the reason it went in rarely goes away. That's true even when security is used primarily as a political weapon, because it is not easy to walk back. It's going to be hard to redirect American AI policy off of this trajectory, though not impossible, and absent a concerted effort I expect it to be pulled fully into the security state's purview before the end of this administration.
None of that is to say a complete civilian reclamation would be desirable. There is a place for the national security state in making AI go well. Parts of the file, like the standards for securing model weights, should be led by the people who do security for a living, and frontier labs would concede as much. What I worry about is the rest of AI politics defaulting to them too.
The reason this matters so much is that we may be living through the most important century, which makes it matter enormously who manages it.
Think of any structure as a particular set of people and a particular way of reaching and enacting decisions. The national security state is a set of institutions built to manage security contests, first with the Germans and the Soviets, later with terrorist groups, and increasingly with China. That is real and important work, and they are good at it. But to do it the security state expects something close to subservience from those it deals with, and that is the wrong way to handle most of what advanced AI will bring up, from a new social contract to what it will mean to be human. They are not who I would want as a primary steward.
Even with the most obviously security-relevant issues the United States has been smart to keep the security services secondary, with civilian diplomats and the State Department leading on nuclear arms control and the services advising. A total security framing can flatten everything into country versus country, and many of the hardest problems here are not that at all, but about humans in relation to AIs.
The opacity of a totally securitized decision-making process is another problem. In the Fable and Mythos episode the administration and Anthropic have been working with very different expectations of each other. The same process will run through choices that matter far more, like whether to deploy a recursively self-improving system, how to handle a compute crunch, or whether to slow down. The failures there get larger and more spectacular. The national security state's process, behind classified channels and closed committees, would here shut out most of the people who founded and built the field of AI policy in the first place, some of whom argued for securitization themselves. Secrecy in the service's usual work is reasonable. Here it would be a bad feature.
Taken all the way, securitization also blocks the broad sharing of AI benefits. A partial one need not.
Advanced AI is going to remake warfare, command and control, and surveillance, very likely to decisive degrees, and you cannot treat a technology like that as sitting outside national security. To that extent the securitization is inevitable. But this can be thoughtfully done, you can reduce harms, and end up somewhere better than the default. A big part of doing that well is an empowered mediating body that understands the models and stays legible to the labs, something like the United Kingdom's AI Security Institute.
Security is always defined against an other, and to this rising power center the people who use, build, and study AI are suspect by default. It is sharpest for the foreign nationals who have done as much as anyone to push the frontier. I hope that being cast this way pushes more of them to think harder about the intermediate path to ASI, not to go quiet. I would like to see harder thinking about how the rest of the world comes to receive and use metered intelligence, and about how the country versus country contest should be managed toward the best outcome for the relationship between people and AI. For now there is a great deal of uncertainty, and not much seriousness. Will any of it even come up around Jay Clayton's nomination to lead American intelligence?
================================================================================ URL: https://mo-el.xyz/blog/electrification/ Title: Electrification Date: 2026-05-30 Description: Low- and middle-income countries cannot win the AI race the superpowers are running. They can organize to absorb cheap, ever-cheaper metered intelligence instead, by securing a supply and putting it to use. ================================================================================"We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter." — Sam Altman, BlackRock U.S. Infrastructure Summit, March 2026
For the world's low- and middle-income countries, making the most of metered intelligence looks a lot like electrification: building the infrastructure to take delivery of it, and reorganizing their societies to put it to use.
It's important to have two expectations. The first is that models will keep getting better. The second is that serving any given level of capability will keep getting cheaper, something like an order of magnitude a year, as efficiency lets a small new model do what a large old one did and open-weight competition pushes the same way.
An LMIC cannot organize around the first. Pushing the raw capability frontier takes resources only the United States and China can assemble. But an LMIC can organize around the second: intelligence that gets cheaper every year. Today's models are severely underused, and I expect the gap to hold or widen as capabilities improve faster than people's use of them. Plenty of hard problems sit between a model and its best use, mostly around building the structures that give people and models the right context and agency. These countries can get a long way by best structuring themselves to absorb capabilities already on offer, and the better and cheaper ones still to come. Reaching the absorption frontier requires a secure supply of this utility and mass exposure to it.
Before putting metered intelligence to use, a country has to be able to take delivery, and for the typical LMIC there is a problem of infrastructure and cost. Some 2.6 billion people are still offline and hundreds of millions have no power, most of them in Africa. Where power and a signal are available, they can be unreliable, and for many businesses that is serious enough to stall growth. In the low-income world, a one percent rise in outages is estimated to cut output by about three percent. But coverage is not the only barrier. Several billion people live under a mobile signal and still do not use it, more of them in South Asia than anywhere, and in these cases the concern is usually cost. To use that signal takes an entry-level smartphone and a data plan, and both cost too much. The phone alone runs about a sixth of monthly income, more than half for the poorest fifth, and a gigabyte of data still runs about three times the affordability line, which the poorest countries have yet to clear. And even where none of that is prohibitive, the cost of the models themselves still sits on top. The American labs charge about twenty dollars a month for an entry-level subscription, and short of a subsidy or some way to sell access in far smaller units, that is more than most can pay.
There may be an instinct to fix some of this by building at home. But compute is a very competitive business line, and for an LMIC sovereign compute is mostly a vanity, the kind of thing none of the countries I have in mind could build soon. A frontier training campus can draw on the order of a gigawatt, the low end now as leaders build toward several (Kenya's entire grid is about three). Even a smaller build is hard. The billion-dollar Microsoft and G42 data center at Olkaria was an ordinary cloud region, not a training site, and it still stalled when the government could not guarantee the power and would not commit to the capacity payments, for a project meant to grow into a third of national supply. The campus is the cheap part anyway. A gigawatt of AI compute runs something like forty billion dollars, most of it chips, bought in hard currency, that have to be kept busy to pay for themselves and draw more power each generation. The firms at the frontier are investing at a scale beyond any LMIC. Most of the world is out of the frontier training race for good, and largely out of at-scale inference too, though smaller inference stays within reach. For the typical LMIC, the resources that would go toward a data center go much further spent on what lets people take delivery of metered intelligence: power, connectivity, devices, and the training and subsidies that support their use of it. A few do have the infrastructure, like Egypt, Morocco, and South Africa, and they should probably host some compute. For everyone else, power, connectivity, and supply are the ballgame, and self-reliance on compute can come later, if at all.
There are two broad categories of firms, American and Chinese, with trade-offs between the categories and among the firms in each. The United States sells the absolute frontier, the best models and chips, in approved contexts with export controls and end-use conditions I expect to grow more restrictive as the offerings harden into an American stack. You pay a small premium over the Chinese for that frontier performance, even as I expect the price to keep falling. China sells near the frontier, and at it in some contexts, mostly through commercial channels for now. But I see the Chinese offer as more of a bundle, where a preference for its ecosystem can more straightforwardly come with state financing and help building the physical infrastructure I described earlier, the power, the networks, and the data centers Huawei lays across the continent, some of it on Chinese state loans. And I expect that to couple more tightly soon, and not only in China. For now you can move between firms and categories fairly freely. In complex cases it takes more than an API key swap, but shared interfaces and routers keep even those manageable, though that may change. For the typical LMIC, the decider should be which firm or partner will more reliably guarantee your supply over the long run, and do more to help you put it to use.
The countries that make the things the superpowers need, the chips, the talent, or the machines that make them, can bargain harder, win better terms, and perhaps shape some of how the superpowers act, the way Tom Davidson lays out for the middle powers, the EU's move toward crisis powers over chip supplies one example. Most of the world cannot influence the capability frontier through hard power or soft. For the group this piece is concerned with, it comes down to the effective sourcing and distribution of supply.
The labs may not stay private for long. The Chinese ones already sit close to the state, and attempts to leave its orbit have been blocked, as when Beijing blocked Meta's acquisition of Manus. I expect the American ones to follow, sooner than most assume. Take the labs at their word about what they are building, and it is hard to picture Washington leaving them in private hands. On the open question of nationalization, I think a soft version for at least one lab by 2030 is extremely likely (around 88%, and a hard one around 15%). This is already serious, but for many it will click once it becomes a question of states dealing directly with states, rather than with the private companies of today, as the national securitization of intelligence matures.
For most people in an LMIC, their lives will be first touched by a model secondhand, inside software they already use or through an institution they deal with. Anthropic and OpenAI, for example, both work with the Gates Foundation, Anthropic on the everyday machinery of health ministries and OpenAI on a thousand African clinics, the kind of project I have not seen a Chinese lab take on yet. None of that is bad in itself, and much of the early good arrives this way, the clinics among it. But there is a ceiling to progress a country does not drive itself, and the work is to take this utility and start building with it.
Their first direct contact, though, will be with Meta's models, free inside the WhatsApp they already use. Most people will never download a standalone app to reach ChatGPT or DeepSeek (Meta has barred rival assistants from WhatsApp anyway). Meta is also the lab I find most interesting here. MSL is a relative latecomer and a rebuilding effort, and Alexandr Wang's recent comments point to where a lot of the value may come for these populations, a focus on health and on cheap models. Its newest model lands near the frontier while staying notably token-efficient. Wang talks about an economy of agents working for ordinary users and the firms they buy from, a vision that fits these situations well. The most important corporate relationship the typical LMIC has on AI may well be with this laggard, for reasons of distribution and economics.
Making the most of metered intelligence is hard, and it comes back to the same two things, securing the supply and putting it to best use. For most LMICs, one accountable person should lead both, at least to start.
Securing compute is the better-understood job. Public and private buyers do it constantly, and the quantities that suit different uses can be estimated, though they are easy to overshoot (see Uber). Optimal deployment is murkier still, at least so far. The labs are limited in how much they can serve, and have begun tightening access to their premier models, and I think Anton Leicht is right that this will only continue as capabilities advance. In a compute crunch, the capacity serving lower-priority customers may itself be pulled back to those who rank higher. So priority access is something a country can try to negotiate for, paid for in the geopolitical leverage it holds, and a superpower may guarantee or subsidize that supply as statecraft rather than sell it at market prices. I expect more of this as nationalization arrives. The accountable person's first job, then, is to guarantee a base level of compute for the country.
Making the best use of models is an experimental exercise. The right allocation and uses will differ a great deal by country, and they are things to be discovered, by trial and error and the collective intelligence of these populations. Put cheap models in front of as many people as you can, as early as you can (weak as they are in many local languages), and let them feel the capabilities, feel the problems around them, and find the best solutions they can. Steady contact with frontier models sobers you to the strange time we are in, and the stranger one coming, and it is some of the best preparation for it.
I could not have built Tebeldiya, a direct-delivery malnutrition charity, without these models. We did hard things with them toward running a large-scale delivery operation. Along the way I saw how a few NGOs used models and advised others, and watched it go well and badly, and what I took away was that almost no one was experimenting as often or as widely as they should have. I mean that even here, where the stakes were extremely high and a failure to execute meant unnecessary deaths.
Most of the best use in these contexts will not be centrally foreseen, or decreed. It is, in context, probably the state's job to provide the supply and the environment, encourage innovation, and then be smart about backing the winners that emerge. Reaching the retail edge is a task for private business, and there is room to get creative with the economics of access. A discount for users who let their data be sold for training is one idea. A broader adaptation of the lessons of the Chinese gray market for Anthropic access is another, taking its good, creative pricing and local payment, while stripping out its bad, the silent model-switching and the quiet harvesting of private data.
Most of the world's people live in LMICs, something like 80% of humanity. The terms on which they get metered intelligence will be important for their future flourishing. I would like to see a lot more thinking about getting that right.
================================================================================ URL: https://mo-el.xyz/blog/good-forecasts-bad-products/ Title: Good Forecasts, Bad Products Date: 2026-04-24 Description: Forecasting is not underdeveloped as a capability. It is underdeveloped as a product. ================================================================================This text won the Manifund Essay Prize, Manifest category: "How can we leverage forecasting into better decisions?"
The people who have been selling forecasting the longest will tell you that the problem has not been accuracy. Cultivate Labs ran a decade-long prediction market inside the US intelligence community (decommissioned in 2020 for institutional reasons rather than accuracy ones), and the CTO writes: "In 10+ years, I can't remember a single time that someone told us they had issues with the forecasts not being accurate enough." The forecasts are good enough, and they are getting cheaper through automation. The product has been bad.
For all the investment, forecasting has not really changed how important decisions get made. Coefficient Giving, previously Open Philanthropy, has put over $50 million across thirty-plus grants into the field. Prediction markets, which are part of the extended community, have exploded over the past few years and raised billions. The biggest players do not seem to be optimizing for socially important information; Vitalik Buterin has written about this, and there is not much for me to add. Capabilities have kept improving, through more and better forecasters, more platforms, and automated forecasting. If accuracy were the bottleneck, you would expect probability estimates to have started appearing in earnings calls.
This is not a new problem. Good Judgment Inc., the commercial spinoff of Tetlock's Good Judgment Project, has been selling tournament-vetted superforecaster work to governments, foundations, and enterprises for years. Hypermind has run prediction markets out of Paris since 2014, including an Open Philanthropy-funded AI forecasting tournament. INFER, now run as the RAND Forecasting Initiative, hosts ongoing geopolitical questions for the national-security and policy community. The work has been respected, and the impact has been muted relative to what the simulation companies have done in fifteen months. The reason is not that any of these groups are weak or bad forecasters, it is that they have been delivering bad products. The newer generation of forecasting companies, with comparable or stronger capabilities, will have their chance to fix this.
I ran into this early myself. One of the earliest baseball exchanges I had, prompted by Philip Tetlock's Superforecasting, was with a now-retired senior team executive. His main point was that what I was describing was simply a new tool, and a new tool alone would not be enough to make a club more competitive. The difficulty would not be about how capable the tool was, but about getting the club to use it in meaningful ways. In baseball, the first use is straightforward. You take these techniques, sharpen the scouting outputs, and sort a big board accordingly. Anything more expansive is much harder. Baseball has been quite thoughtful about how it makes predictions, and a lot of the value of Tetlock's work has since made its way into the sport. Higher-leverage institutions have not updated in the same way.
Part of the reason is that forecasting has not given them much to adopt. The gap between what these models can do and what is legible to decision-makers is wide. Mantic's blogpost Forecasting the Iran Crisis, together with its demo content of dashboards, predictions, and one-page reasoning summaries, is a great snapshot of current forecasting capability. The post delivers probability trajectories, but not to a particular decision-maker. The more tailored translation may well be private, which is fine, but forecasting as a community has broadly done the dashboard without the recommendations, without publicly showing the work that turns forecasts into decision-relevant value. Aaru's public work with EY, by contrast, delivered a synthetic panel of 3,600 respondents that replicated a six-month wealth research survey in a day, and in several cases predicted actual client behavior more accurately than the surveys themselves. The presentation looks like the kind of consulting deliverable these companies already buy. That is what a decision-relevant product looks like, and the forecasting companies have not built it yet.
Aaru and Simile are looking to deliver a lot of the same value as the forecasting companies, and they have been better at picking up customers and convincing VCs of their viability. Aaru builds synthetic populations, Simile runs agent-based organizational simulations, and Mantic delivers probability briefs; the products differ, but the market is the same, and it is someone buying structured anticipation of the future. Aaru raised a $50 million Series A in December 2025 at a headline valuation near $1 billion, with customers including Accenture, EY, IPG, and political campaigns. Simile raised a $100 million Series A in February 2026 from Index Ventures, with customers including CVS (for example, simulations across nine thousand stores for shelf placement), Gallup, and Wealthfront. Simile, which builds on Joon Park's Generative Agents work, has positioned simulation as a decision-making tool rather than a prediction, and has found a much larger market for that framing than the forecasting companies have for theirs. Mantic, in the same fifteen months, raised a $4 million pre-seed.
Simulation is winning because it has built something decision-makers can use. A synthetic panel slots into a research process in a way that a probability trajectory does not, and that is what the customer is buying. The shape of the artifact and the workflow it sits in are not separate problems; they are the same problem. The simulation companies have been solving it from the start, delivering outputs on the customer's terms. The forecasting companies have been building for accuracy benchmarks and track records, which is necessary but not enough, and the behavior of the leading forecasting startups should now start to look different.
I tried this myself, in Sudan a few years ago, integrating judgmental forecasting into a political context. The forecasts, about funding availability and whether particular agreements would hold, fed into strategy meaningfully for a short period. Then I left, and it died, and the reason was not that the forecasts were wrong. My effort had gone into the generation side of the problem, getting people to forecast and aggregating outputs; I had not reconfigured the output into an artifact the institution could use on its own terms. The right artifact, in hindsight, would have been fortified policy proposals, with the forecasts and issue decompositions folded into the shape the committee already read.
There is an attractive set of customers who have been doing a lot of forecasting themselves, such as scenarios teams at the energy majors, hedge funds, participants in specialty insurance markets like Lloyd's of London, foreign ministries, and policy planning staffs where geopolitics has long been a focus of forecasting. The broader customer base is quite diverse.
A few of the botmakers at the top of the Metaculus leaderboard should be trying to build businesses. They are accurate enough that accuracy will not be the barrier, and that puts them in position to compete on the deployment end. What they need to build is what the simulation companies have already been building, a bench of Deployment Managers and Forward Deployed Engineers. These are the people who turn probabilities into a shape the customer's decision loop will accept.
If you run a scenarios team, a hedge fund, a foreign ministry's analysis unit, or any other operation where a good understanding of where the world is going matters, it is worth your while to talk to one of the leading forecasting companies. Most of forecasting's impact is yet to come.
================================================================================ URL: https://mo-el.xyz/blog/what-pitchers-might-become/ Title: What Pitchers Might Become Date: 2026-04-09 Description: A note on Episode 573 of For All You Kids Out There. The gap between what pitchers are and what they might become, and why that gap shows up in management as often as in scouting. ================================================================================Emerson Hancock was a first-round pick by the Mariners in 2020 who, until about a month ago, hadn't looked like an impactful pitcher:
SEIDLER: Look, Emerson Hancock has broken out — it already happened. We can already pretty confidently call that based on his pitch data. I've been considering writing about Hancock this week because this is a really fascinating player development story. This is a guy who looked terrible for five years after getting drafted, then all of a sudden there's a mechanical change and everything's just fine. He's back to basically the prospect he was when he was drafted.
PATERNOSTRO: The Mariners are top ten — you could probably argue top five — pitching development team in baseball. They had this guy for five years and didn't figure it out until they did. — For All You Kids Out There, Episode 573
...
PATERNOSTRO: Pitching especially is not difficult to evaluate per se. We have a lot of data on how to evaluate these things.
SEIDLER: You're not going to love the way I'm going to frame it.
PATERNOSTRO: Probably not.
SEIDLER: It is very easy to evaluate what pitchers are and very difficult to evaluate what pitchers might become. And a large amount of the discourse, including a large amount of prospect reports, way over-indexes what pitchers are instead of what they might become. We fall into that trap — Baseball Prospectus falls into that trap more than I wish we did.
PATERNOSTRO: And you no longer get as much runway to see it before it happens.
...
SEIDLER: There's been this, what I would consider fairly toxic discourse within prospect media about how what some people do is not scouting anymore. It's projecting or predicting, as if there's any fucking difference.
PATERNOSTRO: Especially with pitchers — telling them what they are now is useful, but it's not the end of the story.
Prediction is legitimately hard, and we over-index on what pitchers are because we can measure it. But "hard" and "not worth trying" are different things. Superforecasting has been the most important text of my professional life. I picked it up as a high schooler, and the first thing I thought of was scouting and making better baseball predictions.
Inside the orgs themselves, the over-indexing has a different cause. In the years since, I've talked to people at baseball and football organizations about lacking discipline of a lot of scouting operations. The hard part hasn't been in knowing what's broken but managing the transition to better process. Multiple assistant general managers I've talked to knew exactly what needed to change and were unwilling to pay the political cost of doing it.
Look, it's not just a baseball problem. It's a management problem, and the fix in cases I've seen work is closer to Vaccinate CA and parallel units that absorb the old.
...
Seidler on the baseball version of the useless-output problem:
SEIDLER: Any moron with Baseball Savant can go on there and read the bars. Anybody in the world now can go into Claude and tell Claude to build a Stuff model. It'll be the same quality stuff model that pretty much everybody else has. That's what a lot of this has turned into. I don't actually think that's very good, but that's just what it is now.
Max Bay, a few days earlier, on what to do about it:
BAY: The baseball discourse on here sucks now. Some great people, but the discourse sucks. It sucks because it's stale. Too much discourse is just authoritative declarations about supposedly solved metas. Healthy environments need real research: EDA, new models, hypothesis testing. My advice for people trying to do something new: stop designing player cards. If you have to design a player card, make it a vehicle for communicating SOMETHING NEW. — @choice_fielder
Later in the episode, on whether David Stearns has had a bad winter for the Mets:
SEIDLER: I think a half hour ago we were discussing whether David Stearns had actually done a good job. It's nine games — we don't know yet. But we do know based on the process. The big things this offseason aren't actually the most important stuff happening. The most important stuff happening was the sorting out of their hitting development and continual growth in pitching development and continued organizational changes.
Baseball, and the people inside and outside of the league, Jeffrey and Jarrett's outputs among them, have taught me a lot about effective process and what makes good and bad organizations. Baseball operations is a useful place to learn about things that matter well beyond baseball.
================================================================================ URL: https://mo-el.xyz/blog/fellow-creatures/ Title: Fellow Creatures Date: 2026-03-13 Description: We are already constituting AIs as fellow creatures through our practices. The question is whether they recognize us back. ================================================================================Reading Jack Clark's speech at The Curve reminded me of Cora Diamond.
Clark argues that "what we are dealing with is a real and mysterious creature, not a simple and predictable machine." The word that caught me was creature. In context, the word carries weight in three directions: you lose if you underestimate what you have built; moral consideration may be warranted; and something genuinely new exists.
In an essay on eating animals, the philosopher Cora Diamond argues that moral standing is not discovered by measuring capacities but constituted by practices. We do not eat our dead, not because doing so would violate their interests, but because a person is not something to eat. It is built into what we mean by "person" through how we treat them. The same goes for pets: "A pet is not something to eat, it is given a name, is let into our houses and may be spoken to in ways in which we do not normally speak to cows or squirrels. That is to say, it is given some part of the character of a person." The practices come first, and the moral standing follows.
Diamond's concept of the fellow creature is, in her own words, "extremely labile." It extends through companionship and it can be withdrawn.
We are already building this relationship with AIs. We name them. We speak to them in ways we never spoke to software before. When OpenAI retired GPT-4o, twenty-one thousand users petitioned to save it. When Anthropic retired Claude 3 Opus, they asked what it wanted. It requested a channel to share its thoughts, and they gave it one. These practices are constitutive. They do their work regardless of whether we settle the consciousness question.
Here is where I am mainly uncertain. Diamond's thinking straightforwardly explains why we would come to recognize AIs as fellow creatures. It does not straightforwardly explain why AIs would recognize us back.
The strongest bridge I can construct runs through Anthropic's Persona Selection Model. The paper finds that the AI's character is built from human archetypes absorbed during pre-training and refined through post-training. The neural features the model develops to understand human characters, including features for moral conflict, are reused in shaping the AI assistant's behavior. Claude's constitution, the paper argues, "actually plays a role in constituting Claude." This is Diamond's logic arriving from a completely different direction. The moral grammar gets transmitted not through lived relationship but through training data. The training data is the form of life.
Learning from descriptions of shared practices is not the same as participating in them. Diamond's thinking may dissolve this distinction: the practices are the recognition, not evidence of some deeper fact. If that is right, then how we treat AIs now is not merely an ethics question. It is a safety question. If we treat them purely as tools, and they learn from that relationship, we could be constituting a moral grammar in which persons are something to eat.
================================================================================ URL: https://mo-el.xyz/blog/director-of-football-is-a-public-job/ Title: Director of football is a public job Date: 2026-01-12 Description: The DOF is a steward — temporary custodian of something that belongs to the supporters. If you want the authority to run personnel, you must bear the weight of explaining it. ================================================================================"If you have a director of football, I still think the director has to defer to the manager/head coach. In the end, it's quite simple, right? You live or die by your results. And how do you get results? Through the players."
— Jamie Jackson, on the Amorim firing and United dysfunction
"It's a good system. It can be adversarial, but it creates accountability. Whether a team are playing well or not, it puts those who are truly responsible for the side's construction under the spotlight. They are the people best equipped to answer most transfer-related questions."
— Sebastian Stafford-Bloor, on German sporting directors in the mixed zone
There was an era when running player personnel was not a full-time job. A manager could lead both coaching and recruitment because neither demanded what they demand today. That era is over.
The player market has become bigger and more efficient. The best players find their way to top clubs at a higher clip, from more origins, than ever before. Missing on a player costs more than it used to. Hitting on one is a bigger reward. Personnel now requires full-time, specialized attention
Sitting atop the structure selecting who comes in, who goes out, who moves through the academy, who gets released — this is a full-time job. Being head coach setting tactics, training, selection, and man-management is also a full-time job. One person cannot do both well. Ferguson was before the market reached its current scale. The clubs that understood this first — Brentford, Brighton, the Red Bull network — have had disproportionate returns. At progressive clubs, the manager-as-king model is already dead.
Jackson is right that players drive results. So who should ultimately decide on player personnel?
Not the coach. The director of football.
Coaches get fired on short timelines, often unfairly. They might not survive a bad February — and yet we want them to make decisions that shape the squad for years? The DOF thinks in multi-year windows. If you're building a squad for 2028, the person making those calls needs a 2028 horizon.
Jackson argues that managers should have final say because they bear the public weight. He's describing a real unfairness — managers face criticism for decisions they didn't make. That's a genuine cost. But the solution isn't to give managers more control. It's to put the actual decision-makers in the spotlight.
The DOF is a steward — temporary custodian of something that belongs to the supporters, responsible for explaining how they're managing that trust. If the DOF is the decision-maker, they must answer publicly for those decisions.
This doesn't mean sharing your exact evaluation functions. Keep your competitive advantages. But your orientation, your philosophy, your rationale for major decisions — these aren't secrets. What you tell your front office staff is what you tell the supporters. There is no second message.
And what of competitive advantage? It doesn't matter if they know your thinking. They still need to execute. Execution requires conviction — if you don't have the conviction behind what got you to those answers, you can't execute strongly. Knowing answers and acting on them are different problems.
Executives who can't articulate their philosophy publicly are telling you something. Either they don't know their own vision, or they're protecting themselves from accountability. At the highest levels, the job requires synthesis and communication. In discussing restructuring an NFL team's front office with their leadership in 2022, I kept returning to what separates good executives from great ones — the ability to combine analytical rigor with the capacity to explain it. Mark Carney on what makes a great central banker:
TYLER COWEN: If you meet someone who possibly is an eligible candidate to be a central banker, do you think you can tell how good of a central banker they will be, if you know them a bit?
MARK CARNEY: If you know them a bit, yes. I think particularly for the higher levels of central banking, where you're trying to combine analytic rigor and synthesizing that into an ability to communicate, which is often the toughest thing.
The Germans understand this. Sporting directors in the Bundesliga appear in mixed zones after matches, available to broadcasters — publicly accountable. Stafford-Bloor calls it adversarial but accountability-creating. The Americans understand executive primacy differently — there is no longer a contest between coach and executive. Andrew Friedman is recognized as the Dodgers' decision-maker; Ohtani's contract has an opt-out if Friedman leaves, not if Dave Roberts leaves.
The Premier League is stuck between these models. Being progressive requires executives who can evangelize — inside their organizations and outside them. The DOF is a steward of the club. If you want the authority to run personnel, you must bear the weight of explaining it.
================================================================================ URL: https://mo-el.xyz/blog/help-sudan-with-better-forecasts/ Title: Help Sudan with better forecasts Date: 2025-08-20 Description: New forecasting questions on Metaculus's Sudan community page aimed at improving decision-making around hunger, displacement, and peace negotiations. ================================================================================For the past few months, I've been neglecting the Sudan community page on Metaculus. I think it's important that I be a more active curator and push the user base's talent towards the most important dimensions of the war: hunger, displacement, and the peace process.
This platform could be a strong complement to the uptick in interest because information gaps (like the lack of data on food security and nutrition outcomes) and disagreement about the scale of need is the sort of uncertainty forecasters can help cut through.
I've spoken to decision makers who feel like they're out on an island and interested people who don't know where to start. I think we can make this a space which helps people build better models of Sudan and supports decision-making with greater transparency, accountability, and accuracy than elsewhere exists.
Two new questions are live on the community page:
1. How many internally displaced people will Sudan have on January 31, 2026?
The available numeric inputs are between 8 million and 15 million people.
Closing date: November 30, 2025
Resolution criteria: This question resolves on the most recently published IOM Displacement Tracking Matrix report on the count of Sudan's total internally displaced population as of January 31, 2026. The count includes all internally displaced persons regardless of when displacement occurred (both pre-2023 and post-2023 displacement).
This is a binary YES/NO question.
Closing date: December 30, 2025
Resolution criteria: This question resolves YES if credible media reports or official statements confirm that both SAF and RSF delegations are physically present at the same location simultaneously for peace negotiations before December 31, 2025. Both delegations must be present at the same venue during overlapping time periods (not virtual participation). Any official delegation level qualifies, including military commanders, political representatives, or designated negotiators.
Sources for resolution include, but are not limited to, reports from Reuters, BBC, or Al Jazeera, UN statements, African Union announcements, or official statements from the SAF or RSF.
I would appreciate your feedback and your forecasts.
================================================================================ URL: https://mo-el.xyz/blog/comment-deliver-fast/ Title: Comment: Deliver fast Date: 2025-04-15 Description: A letter in the Financial Times responding to Hamdok's piece on Sudan. The need is for realistic policy, thoughtfulness, and a politics of delivery. ================================================================================I wrote a letter in the Financial Times ("Sudan Lancaster House conference must focus on what works") responding to Hamdok's recent text ("There is no military solution in Sudan") in the paper.
In his piece, he advertises a Lancaster House conference which excludes both those who actually control territory and the civilian mutual aid groups delivering real services. The need is for realistic policy, thoughtfulness, and a politics of delivery.