2026-04-13 17:04:19
I was recently reading the AI Futures' Q1 2026 timelines update and noted their quarterly updates (the last one being in December, with the release of the AI Futures Model) are struggling to keep pace with the thing they're trying to track.
The pace of AI development is incredibly fast and only hastening; Kokotajlo's shortened his timelines for an AC by 18 months (late 2029 to mid 2028) in a single update due to 4 specific parameter changes. Five days later, Anthropic announced Claude Mythos Preview, which arguably invalidated some of the said parameters before the ink had time to dry.
This isn't a criticism of the AI Futures Project; they do commendable work. To be clear, Kokotajlo and the AI Futures Project are arguably the best at what they do in the world. His track record is remarkable, and AI2027 has sparked immense conversation about the future of AI/timelines (it's what got me into LW), but when the field changes completely in its pacing every two months, the community more often than not is navigating with an outdated map. And the problem is getting worse. Mythos hasn't yet been evaluated by METR, Spud hasn't released, and by the time the Q2 update drops, the field will have again shifted to another focal point.
But the cadence itself is the surface issue; updates aren't nearly granular enough to be tied back to each "step". When Kokotajlo updates his priors for an AC, we don't see the causal chain leading to each decision shortening his timelines by X amount. His rationale for the AC median being 1 year of autonomous work was that Opus 4.6 "impressed" him. But the actual definition of what 1 year even means remains muddy; the original AI2027 scenario had the median set at 6 months for an SC before moving it back to 3 years. The SC definition shift of 3y-1y accounted for around half of the 18 month shift in his Q1 update; the stated justification is Opus "impressed" him. Impressed how? At what point between December and April did he change his priors? The entire causal chain here collapses to a single word in a blog post.
In software engineering, this would be the equivalent to someone pushing a commit to main with a message "fixed stuff because it now works". You'd never accept that for code, so why would you accept that for a justifiable reason for the most important technological revolution in human history?
There's no unified platform where forecasters can independently publish their timelines with substantial backing/integration with the platform itself. Sure, you can write a Substack article, spin up a short LessWrong post, perhaps post a Twitter thread, but these are strung all over and are discontinuous for someone trying to get a concrete perspective of what different forecasters think. One might say Metaculus is the solution; while this is a way of congregating forecasts, it's still less than optimum. Conversation and rationale is walled behind "forecast and pay" without a congregational space to discuss the reasoning behind those forecasts (yes there is a comment feature but it is scarcely used). There was an excellent post around Broad Timelines that highlighted this; Metaculus highlights "medians" and less of a full distribution that's more sought after in our space.
As neo noted in said post, we need to "design info-UI tools that facilitate that (the timeline formulation) process". Broad distributions need platforms that can track how they update over time. A quarterly blog post cannot do that. Forecasts updated granularly over time with reasoning and deliberation behind them can.
Why I'm using Git here as an analogy; SWEing fixed this class of problem years ago. You had commits (changes in timeline predictions) diffs showing what changed, comments showing why they changed, branches for code (in this analogy, scenario) forks, blame for accountability (we need to be less wrong after all), and merge conflicts that require resolution rather than dissolving into Twitter discourse.
The minimum viable version of this is frankly embarrassingly simple. A GitHub repo with each forecaster maintaining a YAML file with their distribution for an agreed upon definition (whether it be an AC, SC, ASI etc.). Commits are updates to said files/timelines with rationale in the commit message.
Claude Opus 4.6 had a 80% time horizon of 70 minutes. Assuming Mythos has an 80% TH of ~240 min, the doubling time is ~34-40 days. Even if we're pessimistic at a time horizon of 180 minutes, the doubling time is still 45 days. The thing we're forecasting is now shorter than our update cycle.
The rationalist community, of all communities, should find that unacceptable.
2026-04-13 15:04:06
I think the debate over whether AI risk should be addressed via regulation or treaties is often oversimplified, and confused. These are not substitutes. They rely on overlapping underlying capacities and address different classes of problems, and both van benefit from certain classes of research.
David Kreuger, to pick on someone whose work I largely agree with, recently posted that “Stopping AI is easier than regulating it.” I largely agree with what he says. Unfortunately, I also think it is an example[1] of advocates for a cause creating fights where they're not needed, and in this case making the discussions around AI unfortunately more rather than less contentious, and less rather than more effective.
And the reason the fights are not needed is that different risks live at different levels, and different tools are effective in different ways.
Clearly, many of the risks and harms of AI should not be addressed internationally. There is little reason or ability to harmonize domestic laws on fraud, discrimination, or liability, which would be a distraction from either reducing the harms or addressing other risks. Existing laws should be adapted and applied, and new regulations should be formulated where needed. International oversight would be unwieldy and ineffective for even most treaty compliance efforts - as other treaties show, there is a mix of national and international oversight. But domestic regulation can create liability incentives, require or standardize audits, clarify rules, and provide enforcement mechanisms and resources. All of those are at least sometimes useful for treaties as well. When Kreuger says “the way I imagine stopping AI is actually a particular form of regulating AI,” he is not talking about the harms and risks regulation could address - though given what he has said elsewhere, he agrees that many of them are worth mitigating, even if they are not his highest priority. So it should be clear that treaties will not, cannot, and should not address most prosaic risks of AI systems and misuse.
By the converse argument, which he and others have made convincingly in the past, some harms of AI systems come from racing towards capability rather than prioritizing safety. These types of risk emerge from the dynamics of international markets and from great power competition. Obviously, these dynamics aren’t well addressed by domestic regulation on the part of any single actor. It is incomprehensible to talk about regulation alone to address those risks, just like it is tendentious to talk about using international treaties to mitigate other classes of risks and harms of AI systems.
Unfortunately, many discussions put “we need a global treaty to stop AI risks” in opposition to “domestic regulation is the only realistic path.” Not only do I think this is backwards, but I’ll argue that so is the related false dichotomy of industry self-regulation versus government rules. Industries that embrace safety welcome well-built regulation. Even in areas where they don’t have strict rules, airlines have national bodies that manage risk and accident reporting. (And the AI industry leaders often claim to be the same way, wanting national or international rules - just not any specific ones.)
So, to come to my unsurprising conclusion, we actually have several different plausibly positive and at least partially complementary approaches.
So we end up with a sort of triad, where research can enable measurement and definitions, and provide tools, regulation can force adoption and enforce usage of tools, and treaties can align incentives around defection dilemmas and provide common aims.
This doesn’t imply that most safety research is net risk-reducing, that most regulation is useful, or that most possible treaties will reduce risks. But it does say that they can be complementary. Some disagreements are substantive. But others are treating complementary approaches as mutually exclusive - and I think we should instead figure out common ground, which can make the fights about these issues both more concrete, and narrower.
yet another example
2026-04-13 14:54:19
Written extremely quickly for the InkHaven Residency.
Like humans, AI models do worse on tasks that take longer to do. Unlike humans, they seem to do worse on longer tasks than humans do.
This is a big part of why the METR time horizon results make sense: because longer tasks are also “harder” for models, and more capable models can do longer tasks, we can use the length of tasks that the models can perform as a metric of model capability.
There’s a clear etiological or causal-historical explanation of why models do worse at long tasks: they’re probably trained on more short tasks and fewer long tasks. This is both because it’s easier to make shorter tasks, and because you can train models on more short tasks than longer tasks with a fixed compute budget.
But from the perspective of AI evaluations, it’s also worth considering mechanistic explanations that make reference only to how properties of long tasks interact with the AI system in deployment. Whatever the training story may be, the AI models as they currently exist have some property that makes long tasks genuinely harder for them in a way that tracks capability. Understanding what this property is could matter a lot for interpreting the METR time horizon and even for forecasting AI capabilities over time.
So here are five such possible hypotheses that explain why longer tasks seem consistently harder for current models, based in large part on my experience at METR.
Long tasks are less well defined, and require judgment or taste (which models are bad at). For a software engineer, a 1-minute coding task might involve composing a single 10 line function or running a relatively simple SQL query. By their very nature, these tasks tend to be easy to define and easy to score, with relatively objective success criteria and little human judgment involved. A 15 minute task may be implementing a relatively simple data processing script or fixing a simple bug: more complicated, but still relatively easy to score. In contrast, an 8 hour task likely involves substantial amounts of design taste (in ways that are harder to score), and month long tasks likely involve communicating with a stakeholder or building code with properties that are hard to algorithmically verify (e.g. maintainability). (This is also related to why algorithmically scorable longer tasks are harder to make.)
While the longer METR tasks are still algorithmically scored, they tend to require models to build sophisticated software artifacts or iteratively improve on experiment design, where taste plays a larger role in success. Since models seem to lack ‘taste’ of some sort, relative to humans of comparable execution ability (hence the complaints about AI Slop), this could explain why they do worse on longer tasks.
Long tasks require more narrow expertise (which models may not have). An important property of the METR task suite is that longer tasks should not be trivially decomposable into shorter tasks. That is, a 10 hour-task should not trivially be decomposable into 10 1-hour tasks, and 10 short math problems do not become a single longer math problem. Perhaps as an artifact of the property, many of METR’s longer tasks (and perhaps longer tasks in people’s day-to-day work in general) rely on more specialized procedural knowledge that is hard to easily acquire via Google. For example, many of METR’s long tasks are cryptographic or machine learning challenges that require some amount of procedural knowledge in the relevant fields to approach. Insofar as the long tasks are more likely to require procedural knowledge outside the AI models’ area of expertise, they may struggle.
Personally, I find this relatively unlikely as an explanation for the METR time horizon tasks (since AI models seem to have a lot of expertise in the relevant areas), but it might be a large explanation for the inability of AIs to autonomously complete large tasks in general.
Long tasks take models longer, leading to more stochastic failures (which models exhibit). A popular explanation that people cite is that tasks that take humans longer also take AI agents more steps to complete, and AI are not fully reliable, and fail with some small probability on each step. For example, Toby Ord raises this as a hypothesis in a response to our Time Horizon paper.
I think this is definitely part of the explanation (and why longer tasks are harder for humans as well), with some caveats: first, I caution against naively interpreting human time as proportional to AI steps and applying a constant hazard model. For example, it turns out that if you fit the failure rate model for AI agents over time, the failure rate goes down as the task goes on! Second, AI models seem to have different time horizons across different domains, and simple versions of this hypothesis cannot explain that phenomenon.
Long tasks take models longer, causing failures due to distribution shift or self conditioning (which models may suffer from). A related explanation is that longer tasks take models more off-distribution: base models (at least earlier on) were not trained to predict long sequences of model-generated outputs, and even RLVR’ed models were probably trained with short tasks, far shorter than the 16 hour, tens of millions of token tasks that we might ask them to do. This increases both the chance that the models are simply off distribution (and thus may be less competent in general), and the chance that they accumulate errors by chance and start conditioning on being the type of agent that makes such mistakes (and thus becoming more prone to make such mistakes). In the same way that naive versions of the constant hazard model seem contradicted by evidence, I suspect that naive versions of this hypothesis are also likely to fail. But it’s possible that more sophisticated versions may play a key role in explaining the phenomenon.
Long tasks require better time and resource management (which models struggle with). Finally, an explanation that I often think is neglected is that longer tasks tend to require meta-cognition and explicit strategy, which current models seem to struggle with. A 5-minute task such as writing a simple function or script can be done in one go, without much planning, but getting the best score in a machine learning experiment over 8 hours requires allocating scarce resources including remaining time and compute. It’s been observed that models understandably struggle a lot with understanding how much (wall clock) time they take to do particular tasks, or often double down on failing approaches instead of switching strategies.
I welcome more thinking on this topic, as well as more empirical work to distinguish between these hypotheses.
2026-04-13 14:04:26
I get sick a lot. Getting sick sucks. Maybe there are cheap and easy ways to get sick less?
I asked LLMs[1] to read all the relevant literature reviews and figure out what supplements or medicine I should be taking to get sick less and make it suck less. I looked through the recommendations and did a little additional research to make sure the AIs weren’t making egregious mistakes, but I am not an expert—this should not be viewed as credible medical advice.
Here is the quick list of steps I am currently taking or think might be useful to others.
If you work on important problems then getting your coworkers sick is bad for the world (in addition to bad for them). If you are going to work while sick, consider doing it from home. If you work from the office, you should wear a mask, wash your hands frequently (and especially before touching a bunch of communal stuff), and cover your cough/sneeze (not with your hand).
Zinc acetate Lozenges.
What: When you start feeling any symptoms at all, or when you’ve been exposed, start sucking on zinc lozenges. Your goal is to coat your mouth and throat in zinc for basically as long as possible. So you should be sucking each lozenge for 20-30 minutes (don’t chew), and then don’t drink or eat anything for another 20 minutes. Aim for 5-7 lozenges in a day, once every two hours or so.
What to buy: The particular lozenge probably matters a lot! The lozenges you want are big and slow to dissolve, Amazon link, manufacturer’s link (note that Amazon is frequently out of stock, and the manufacturer gives discounts for larger orders, I might buy 4 bottles at a time).
Evidence basis: Literature review pointing to the fact that they might reduce cold duration some. The main counter evidence, an RCT finding either similar or worse recovery than placebo.
Notes:
Here’s a ChatGPT chat with an initial research report.
2026-04-13 13:22:01
Epistemic status: This is an intuition I've had for a while that feels obviously correct to me from an inside view perspective. Note however that I am not a doctor and have no training in the medical field. I also do not have experience losing weight. You should caveat this information appropriately. I will note that I am capable of running mountain marathons and have a six pack (despite not working out for the past 6 months) as evidence that this mode of thought works well for me.
With apologies to anyone I offend with my ranting and rhetoric, this was the only way I was able to write the article authentically.
I've spent just over a year now immersed in various aspects of the rationalist community. It's a weird and wonderful place and I am glad that I'm here. It is also home to the inkhaven residency, where I have recently been getting to know some of the local belief systems.
I shall attempt to break one of them, at least in part, today. I will start by linking you to the article "The Blueberries of Wrath" by my friend MLL[1]. It's a long, challenging article, and I understand approximately half of the words. Here's an extract:
I’m not going to review the entire cursed realm of internet users claiming to be sensitive to dietary salicylates, polyphenols, and whatever other Trojan berry adversaries that might be captivating their paranoia. But here’s a mugshot. In it we see people:
- Attributing all kinds of symptoms to dietary salicylates including dark undereye circles and adrenal fatigue.
- Fixating on a handful of studies from the 1990s (mostly in autistic children) suggesting that phenol sulfotransferase deficiency is responsible for the accumulation of dietary phenols in the body.
- Failing to rigorously distinguish between polyphenols, salicylates, and phenols in general, let alone different polyphenols, instead lumping everything into “high-phenol” foods. Likewise the recommended treatments for salicylate sensitivity, phenol sensitivity, and methylation disorders more or less overlap.
- Self-diagnosing with conditions in the absence of established diagnostic tests; while in principle elimination/challenge dieting can reveal things, we should expect it to be vulnerable to placebo and confirmation bias.
- Following modern variants or extensions of the Feingold diet. The most structured is FAILSAFE (Free of Additives, Low in Salicylates, Amines, and Flavour Enhancers), which has a large Facebook following but no modern evidence to back up the salicylate claim
OK, so by my reading of the article, people on the internet have looked at some studies, decided that "phenol sulfotransferase deficiency" is responsible for the accumulation of "dietary phenols" and therefore decided that berries are bad for them. MLL goes through and points out a variety of errors they're making. Apparently, one of these is "failing to rigorously distinguish between polyphenols, salicylates, and phenols in general, let alone different phenols".
I do not know if this is true or false. I do not know what this means. I do know that it is very possible to be healthy without the slightest hint of knowledge about phenols. I know it because I've done it. I also know because I've met a large number of wonderfully healthy and fit individuals who haven't touched a biology textbook in their lives. I also think that the fact that people can come to the conclusion that blueberries are bad for them via this sort of interrogation is suspect.
To be clear, the human body is, on a fundamental level, physics. It can be understood through the laws of chemistry and biology, and I hold huge respect for the researchers looking into it. However, if we want to talk about personal health, here is a map of the known biochemical pathways in the body:
If you wish to try to claim that understanding this is the fastest way to get healthier, I'll be waiting for you in the gym. If, in more reasonable fashion, your claim is that understanding parts of the diagram can help you optimise your nutrition, I'll still be waiting for you in the gym, but note the following first:
Basically, it's really hard to understand, if you do understand it that doesn't mean you can control it, and if you can both understand and control one aspect of it you're still likely to break whatever else is connected to it. Of course, we have the caveats that if you're only using it to make minor adjustments, you're unlikely to take your body out of distribution so you'll be fine[3]. But a broader question emerges.
Does this seem like the most effective way to go about life to you? Do you want your personal wellbeing to depend on whether or not you've thought about your phenol intake correctly? No? Good. I have another path.
If you can't use white-box thinking, use black-box instead. You were designed to grow up in the hunter-gatherer environment, so your body will take whatever actions it thinks necessary to ensure your survival within that environment. Rather than argue for this line of thought, which I expect people to understand in principle, I'll demonstrate it on an example. In heretical fashion, I will be picking on Eliezer Yudkowsky.
A couple of months ago, I spent a bit of time messing around with my scraped version of LessWrong, and, while going through the lowest karma posts, happened upon the wonderfully titled "Genuine question: If Eliezer is so rational, why is he fat?".
He replies in a comment with some content copied over from X. A summary:
For the benefit of latecomers and CICO bros, my current equilibrium is "spend 1 month fasting / starving on 700 cal/day keto; spend 2 months eating enough to work during the day, going to bed hungry, and therefore gaining 1-2 lb/wk".
Diets like the potato diet fail, not because they don't succeed in forcing me to eat less -- I do, indeed, end up with not enough room in my stomach to eat enough potatoes to work and not feel tired. The potato diet fails because it doesn't protect me from the consequences of starvation, the brainfog and the trembling hands. If I'm going to be too sick and exhausted to work, I might as well go full keto on 700cal/day and actually lose weight, rather than hanging around indefinitely in potato purgatory.
Semaglutide failed, tirzepatide failed, paleo diet failed, potato diet failed, honey diet failed, volume eating with huge salads failed, whipped cream diet failed, aerobic exercise failed, weight lifting with a personal trainer failed, thyroid medication failed, T3 thyroid medication failed, illegal drugs like clenbuterol have failed, phentermine failed (but can help make it easier to endure a bad day when I'm in my 600cal/day phase), mitochondrial renewal diets and medications failed, Shangri-La diet worked for me twice to effortlessly lose 25lb per session and then never worked for me again.
Wow. That's a long list of things to have fail on you. Let's see if we can gain any insight in our new black box frame.
The first thing we note is that we evolved to live in a range of different environments. Humans range geographically from America to Australia, from Africa to Asia. Over the millions of years of our evolution we have lived on top of mountains, by the sea and in the desert. Many of these environments, especially in temperate zones, will vary enormously in their conditions throughout the year. One of the most important evolutionary adaptations, we would therefore expect, would be to have a body which can itself adapt to whichever environment it finds itself in.
Let's think about the implied environment surrounding Eliezer then.
Now we ask what the ideal body type is for that environment. I would argue that it's a body which:
His body is acting perfectly rationally for the environment he's told it he's in! As far as I can tell, he's in an inadequate equilibrium where he wants his body to become thinner, but his body desperately wants more calories.
So what does this new way of seeing things mean for how he should act in practice?
I should first remind you that this approach is still entirely theoretical. It has not been battle tested, although it seems to me to suggest reasonable courses of action. In this particular case, it seems to me like the priority is for Eliezer to convince his body that he is in an environment more amenable to his preferred body type. What does this environment look like?
If I was to recommend a course of action in this particular case, I think it would be something like "Eat enough to satisfy your hunger. You will gain weight, but this is to be expected when moving out of a local minimum. Do long distance. Build up your physical endurance, this should have additional benefits in other areas of your life. I don't know how long this will take. Given how long you've spent convincing your body of the environment it's in, I expect it to take a while to convince it of its new surroundings. Use physical endurance as your metric for progress, not weight."
To be clear, I know he's tried a bunch of things, including exercise and an extreme diversity of diets and drugs. I do not have access to more detailed specifics of what he's done, and I expect he's had advice from a wide variety of people far more knowledgeable than me. It could be that my armchair help is just one more on the pile of failed attempts. It does however seem to me to provide an explanation for why many of the past attempts have failed, and to provide a way out which would (possibly?) previously have ended up being rejected due to weight gain. I don't know.
I hope this is useful to someone.
There are a few additional points relevant to the main thesis here, which I haven't been able to fit into the main post.
In no particular order:
He's checked through this article, so I hopefully haven't made any massive blunders where this is concerned.
Yes, evolution is the blind, idiot god, but creating an organism is also a hard problem, which means that progress can be continuously made for long periods of time. The paper "Long-term dynamics of adaptation in asexual populations" showed that e.coli fitness increases were better fit by a power-law model than a hyperbolic model (which asymptotes). This is evidence towards the theory of there being no practical upper bound to the progress that can be made by evolution.
There is the additional caveat that mechanistic information is generally much more useful for fixing broken things – it doesn't take a genius to figure out that if your shinbone is in two pieces, that needs to be fixed.
2026-04-13 13:14:26
tl;dr: you can subdivide Mounjaro pens to get less than the stated dose from them. This lets you e.g. buy a 15mg pen and instead get 5mg at a time out of it (so you’d get 12 doses instead of 4). This works out to be much cheaper than buying a pen of the correct dose, and cheaper than using grey market peptides. Use this calculator to figure out how much to use.
[This is not medical advice I am not your doctor etc etc]
(See also: “You’re not sick enough for this medicine.”)
Miracle weight-loss drug Mounjaro comes in fixed-dose pens and a fixed dose escalation schedule. You can 1) choose to ignore this schedule and just stay on a low dose, and 2) subdivide the pens to make them last longer and save a lot of money.
In the US these are often autoinjector pens; there’s no way to customise the dose as it just gives you what’s printed on the side. In other countries you get a KwikPen, which is more like an insulin injector.
You take off the lid, screw on a single-use needle, twist the dial on the end until the window shows “1”, jab yourself with the needle and slowly push down the plunger.
Technically you can “count clicks”. The dial clicks as you rotate it; a full dose is 60 clicks, so 30 clicks gives you half a dose, 15 a quarter, etc. But the pen will only deliver four doses regardless of size and then it locks you out!
There’s a trivial and safe way to get around this: use an insulin needle to draw the amount of liquid that you want, and then inject it directly. You can buy the highest-dose pen, which contains four 15mg doses (60mg total) and get e.g. 24 × 2.5mg doses from it. This means you can stretch out a single pen to last many months.
The cost savings are substantial – a 2.5mg pen costs £37.24 per dose, but a 15mg pen is only slightly more expensive. So if you’re using a 15mg pen but only taking 2.5mg at a time, it works out at £12.46 per dose. This is less even than grey market Chinese peptides, which often run $100 for 10mg.[1]
Yes, and this is where this trick runs counter to the manufacturer’s advice.
Once you start using the pen, it’s been exposed to the air and to any bacteria on the needle. There is a risk of bacterial growth in the liquid in the pen – the typical guidance is to not use an opened multidose vial beyond 30 days – but this is probably overstated.
The tirzepatide solution in the pen contains benzoyl alcohol which inhibits bacterial growth, although not forever. In the case of e.g. multi-dose insulin vials, using them well beyond 28 days shows negligible contamination in practice – one study found only trace skin flora after 53 days of use, another found the preservatives actively killed deliberately introduced bacteria through day 50, and a third found zero contamination across six months of twice-daily use[2]. Anecdotally people have used the pens for many months after they’re first used without issue.
But the medical guideline is to use the pens for no longer than 30 days after opening. It’s up to you to decide your risk appetite here. Aaron Kaufman wrote an excellent post on how long you can use peptides after reconstitution (which in our case is just after the first time you’ve used the pen):
I’ve concluded that the 28-day limit appears to be conservative regulatory boilerplate mostly divorced from any specific scientific reasoning. … Based on the considerations above, I personally throw out refrigerated reconstituted peptide vials at about the 4-month mark, which is almost totally arbitrary. I make no judgement on what anyone else should do.
The peptide itself is stable in the fridge for many months, up to the expiry date on the pen.
The typical dose escalation schedule is to start on 2.5mg weekly for four doses, and then increase stepwise every four weeks up to 15mg. But plenty of users don’t need to go to the higher doses, with many staying at 2.5mg. There’s no good reason to increase your dose if you’re seeing satisfactory results at lower doses.
You can even take 1.25mg, or half the starting dose, if you’re concerned about side-effects when you start.
You’ll need an insulin needle and a Mounjaro KwikPen. You can essentially follow any guide to injecting from a multi-dose vial, you’re just using the pen itself as a vial.
Based on prices listed at ASDA Online Doctor as at publication. Example per milligram prices:
2.5 mg: £148.97 ÷ 10 mg = £14.90/mg
5 mg: £188.97 ÷ 20 mg = £9.45/mg
7.5 mg: £248.97 ÷ 30 mg = £8.30/mg
10 mg: £278.97 ÷ 40 mg = £6.97/mg
12.5 mg: £288.97 ÷ 50 mg = £5.78/mg
15 mg: £298.97 ÷ 60 mg = £4.98/mg
So even using a 5mg pen for 2.5mg doses is a substantial cost savings, especially added up over a long period of time – most patients will take Mounjaro for months or years to get to their goal weight, and often will stay on a 2.5mg a week maintenance dose indefinitely.
In a study of 69 multi-dose insulin vials used by patients for an average of 53 days, only 8 showed any bacterial contamination at all — just 1 colony-forming unit per millilitre of common skin flora (S. epidermidis and P. acnes), with no endotoxin detected. Critically, when vials were deliberately inoculated with S. aureus and P. aeruginosa and kept at room temperature, they were sterile within 48 hours — and this antibacterial effect was maintained through serial re-contamination at days 17, 30, and 50.
A more recent study went further: refrigerated multi-dose insulin vials aspirated twice daily with a new syringe for six months showed no microbial contamination at any point during the study period.
A human retrospective study of insulin glargine used up to 74 days beyond the recommended duration found no injection site infections.