2026-03-29 19:50:35
Parkinson’s law says that "Work expands so as to fill the time available for its completion." I think that a similar observation can be made for people worrying about stuff. To paraphrase: "Problem salience expands so as to fill the capacity available for worrying.”
Suppose a person is worried about several problems. Let’s visualize the mental state of this person, where each problem is represented by a colored circle, and the size of the circle corresponds to how much this problem occupies the person:
This person worries about 5 things, with the yellow and green ones being the most important ones.
This person worries about 5 things, with the yellow and green ones being the most important ones.
Now, when one of these problems is resolved, one would expect that this problem simply gets removed from the ‘mental space’, making the person less worried in proportion to the size of the resolved problem:
The person now worries less, because the big problem was resolved.
Naive expectation: The person now worries less, because the big problem was resolved.
However, I don’t think this accurately describes what actually happens! Instead, it seems that the unresolved problems become more salient to the person, as if to fill the available space in the person’s mental state:
Or, new problems pop inside the freed space — the problems which weren’t important enough to worry about as long as there were more pressing ones:
The upshot is that in the beginning, the person would be wrong to think that they will worry much less or be much happier after the most important problems are resolved. They will just worry about different things!
What could help to prevent this scenario is to try to periodically recall some of the resolved problems and the amount of worrying they caused before they were resolved. By remembering this, the importance of other problems can shrink again (it’s relative, so they should still be less important than the yellow problem was!).
Related concepts: Adaptation level theory, hedonic treadmill, gratitude, negative visualization.
2026-03-29 14:23:54
This article is based on reflections from co-leading the Sydney AI Safety Fellowship. This post is primarily focused on AI safety, but I expect the lessons to be more generally applicable.
We have a Problem™ 😱—actually several problems; okay even more problems.
Unfortunately, the edge cases make it hard to write down a shared problem statement[1], but we just need to get on with it regardless, since The Universe Doesn't Have to Play Nice. Consensus on exact problem bounds would likely be net negative anyway, as it would make it much easier to completely neglect at least one value or problem that we would later regret not dealing with[2].
So let's just assume that we have our problem statement and that it's close enough to other people's problem statement that a community forms around it. This community consists of people with a variety of different skills, temperaments and degrees of commitment. This naturally leads to the question of what aspects different members of the community should be working on. Notably, we need to avoid unrealistic assumptions about there being a centralised authority to draw up a plan and divide tasks. It's not going to happen, and even if centralisation were possible, it would likely just make things worse[3].
Instead, we need to tackle the problem in a more decentralised manner. But how can we avoid dropping a ball we can't afford to drop without centralised co-ordination?
Well, whatever we do, we need to take into account that there is variation in commitment and ability. Some people are willing and able to take Heroic Responsibility[4], using risky techniques like Shut up and do the impossible!—others are not. Indeed, I suspect that very few people or groups will be capable of taking heroic responsibility for the whole problem. I was persuaded of this by an X[5] by Richard Ngo:
This resonates strongly with me, though I'd frame it in terms of taking heroic responsibility for short timelines. I expect working on short timelines to be much less intense if you're only biting off a small chunk of the problem, so I predict a wider range of people could work on this than Richard expects[6].
In any case, this is where we are: we have a small number of people who can take heroic responsibility for the whole problem and a much larger number of people who can't[7]. The people who can't take heroic responsibility should primarily focus, focus, focus and pick one thing they can do well. I resonate strongly with how EA thinks about prioritisation, but I differ in that I think more in terms of systems[8] and less in terms of direct, measurable impact. To be specific, I tend to think more in terms of interventions as building blocks (such as gathering resources or discovering information) that others can attempt to build on top of[9]—be it incrementalists laying one more brick or "heroes" shaping it into a working plan[10].
I feel that there's a broad understanding that many of EA's old mental models of how to think about impact don't really carry over from EA's global health beginnings to the AI safety context, but we never really developed proper replacements. I think it's important to understand that there are different kinds of domains. Universal tools or mental models would be ideal, but this is extremely hard, perhaps even impossible. Producing tools to solve the problem in front of you feels much more viable. What I've described above fills in some of the blanks, but it needs to be developed in more detail.
I think it's worth stepping back and asking what would need to happen for this plan to succeed:
There is no such thing as a perfect plan—all plans have flaws or limitations. If you haven't thought through of the potential flaws of this strategy, then you should strongly consider not updating based on this proposal. I've left a few breadcrumbs in the spoiler block directly underneath and I've pasted some selected LLM critiques below that.
It's often hard to analyse the pros and cons of a plan in the abstract, so a good place to begin would be: what are the alternatives? A few possibilities: BlueDot's Defense-in-Depth, a plan crafted specifically for short timelines, a plan more narrowly focused on a specific strategy (like a pause), an all-hands-on-deck plan.
Another direction: what would the Least Convenient Possible World for this plan look like? For example, what if a significant proportion of AI safety work is actually net-negative?
Also, how could the world change such that this plan would become outdated? For example, what if we developed much more powerful co-ordination technology.
Generated with ChatGPT Extended Thinking. I asked the model to iterate on the sharpest critiques it raised. I'm happy to respond to any of these critiques on request.
1. “Heroic responsibility” is carrying too much of the argument
The article’s central division is between the small number of people who can take “heroic responsibility” for the whole problem and the larger number who cannot, and who should therefore focus on one thing they can do well. That is psychologically vivid, but it is doing too much conceptual work. It smuggles in a picture of the field where the main question is who can bear the burden of whole-problem agency, rather than what structures, checks, and coordination mechanisms are needed to keep people from acting on overconfident global pictures.
From an AI safety perspective, that is risky. One of the field’s central pathologies is not just passivity, but unilateral action under uncertainty: people overestimating their grasp of the strategic landscape, underestimating externalities, and rationalizing risky moves because “someone has to take responsibility.” If “heroic responsibility” becomes the organizing category, it predictably flatters grandiosity and encourages people to think in terms of whether they are one of the few serious enough to shoulder the whole thing. That is a bad attractor for a field that desperately needs better calibration, better feedback loops, and more respect for how partial everyone’s map is.
So the criticism is not merely that the concept is romantic. It is that it risks becoming a status-laden substitute for institutional design. AI safety probably does need some people thinking at the whole-system level. But it does not follow that “heroic responsibility” should be the central meme around which the rest of the division of labor is organized.
2. Divergent problem definitions do not just leave gaps — they create direct conflict
The article is fairly relaxed about the absence of a shared problem statement. It says edge cases make it hard to define one, suggests exact consensus may even be net negative, and then assumes people’s views are close enough that a community can still proceed. From an AI safety perspective, that understates the problem. In this domain, different definitions of “the problem” do not merely lead to benign pluralism. They often generate people working at cross purposes.
If one person thinks the central risk is loss of control from misaligned autonomous systems, another thinks it is misuse, another thinks it is racing dynamics, and another thinks it is concentration of power, then they will not merely choose different priorities. They may support actions that actively undermine each other: more openness versus less openness, more deployment to learn faster versus greater restraint, more lab engagement versus more adversarial pressure, more emphasis on evals versus more emphasis on institutional slowdown, and so on. A project that looks like a valuable “building block” under one framing can look actively harmful under another.
That means the challenge is not just that decentralized actors might fail to cover everything. It is that they may fill the wrong gaps in ways that worsen the overall picture. Once you see that, the article’s “let’s assume our problem statements are close enough” move looks much less harmless. In AI safety, unresolved disagreement about the nature of the problem is not background untidiness. It is a live source of strategic collision.
3. “Be honest about limitations” is not a strong enough response to false coverage
The article is very good at noticing one specific failure mode: not just uncovered gaps, but the belief that a gap is covered when it isn’t. That is genuinely important. But the proposed remedy is mostly a norm of greater candor — admitting blind spots, or even potential blind spots, so that “heroes” are not misled about where coverage exists.
I do not think that is enough. Communities usually do not get false coverage because everyone forgot to be humble. They get it because the incentive gradients are bad. People are rewarded for seeming competent, legible, and on top of things. Others do not want to challenge them too aggressively. Success proxies get mistaken for real coverage. And some limitations are hardest to see precisely for the people most likely to reassure others that the area is handled. In other words, false coverage is not mainly a virtue problem. It is a mechanism-design problem.
So the criticism here is that the article reaches for a moral solution where a structural one is needed. What you want is not just more openness. You want ways of testing whether the claimed coverage is real: independent review, adversarial audits, explicit ownership maps, public criteria for what “covered” means, and regular attempts to discover strategic orphan problems. Otherwise “please communicate your blind spots more honestly” will help at the margins while leaving the central failure mode intact.
4. The anti-centralization move overshoots
The article says we should avoid unrealistic assumptions about centralized authority, and suggests that even if such centralization were possible it would likely make things worse. There is an important truth here: AI safety is not going to be solved by one planner assigning everyone their role. But the argument overshoots if it makes decentralization sound like the real alternative.
The actual alternative is not “central command” versus “everyone figures out their niche.” It is partial structured coordination. Shared eval standards, common threat models, agreed escalation pathways, deliberate division of labor across orgs, cross-project reviews, and regular attempts to compare priorities are all forms of coordination that fall far short of central planning. In a field like AI safety, where mistakes can compound and different agendas can undermine one another, the absence of those structures is not neutral. It is itself one of the main ways you lose.
So I would not say the article is wrong to reject a fantasy of full centralization. I would say it moves too quickly from that rejection to a picture where decentralized actors, if sufficiently self-aware and strategic, can more or less sort things out. That is too optimistic. AI safety is precisely the sort of domain where local good intentions and decent judgment are not enough; you also need coordination structures strong enough to keep different parts of the field from drifting apart or colliding.
The one-line summary is:
The piece is strongest as advice about temperament and role-selection within a decentralized community, but weakest where it implicitly treats those norms as close to a field strategy.
Thank you to the 2026 Sydney AI Safety Fellows, my primary co-organiser Jack Payne and all the others who assisted with various parts of the organisation (Michael, Hunter, Luke, etc.). Thank you also to the Sydney AI Safety Space/Sydney Knowledge Hub who hosted us as well as all the mentors, speakers and guests.
The name of this strategy is "Path to Victory" with the quotation marks included. It may seem like a minor detail, but I believe this to be important.
Last year, I was at a retreat (on a different but related topic) where we spent a bunch of time trying to define the problem, but we didn't succeed because different people had different conceptions.
That said, there are some big disadvantages as well.
I haven't read Hayek, but apparently these arguments are his wheelhouse.
In his 2025 review, Alexander Berger referenced Nan Ransohoff's concept of General Managers as something they were keen to explore. I take the concept of a 'general manager' as essentially just meaning someone taking heroic responsibility with a lot of resources.
I may very well be the first person in the world to call a Tweet an "X".
"If your prediction is right (a wider range of people can work on short timelines if they're only biting off a small chunk), this has significant practical implications for community strategy. This could be a standalone claim worth developing, rather than a brief aside." — I'll keep that in mind for the future (insofar as there is one).
This model is a significant simplification in that it is possible to take heroic responsibility for a sub-problem even if you can't take heroic responsibility for the whole problem. When writing the rest of this post, I tried to keep this more complicated model in mind in the hope that my analysis would still apply.
There is a discipline called systems thinking, but I haven't yet found the time to engage with it substantively, so I think about systems in a more ad hoc way.
Obviously, you need to take into account the probability that someone actually builds on your work.
Jay Bailey describes the difference between bridges and walls—walls benefit from each additional block, but bridges only work if the whole structure is complete.
He provides two strategies for dealing with this:
2026-03-29 07:10:16
I think the future of AI is really important, and it would be pretty good to know which experts have been right and wrong about progress and effects. It would be pretty good to keep a website up on important peoples' track records (superforecasters, famous domain experts, frontier lab people, AI 2027, Situational Awareness, etc).
Currently, I think there's an incentive problem where it kinda pays to make vague predictions. This disincentivizes people who are putting their neck out and means it's much more difficult to cut through the noise.
Solution: track imprecise predictions—either by pinning them down precisely or by not evaluating over Brier scores but just giving vibes as to whether it seems like they got it right (flagging for uncertainty).
What I currently have in mind: a site that aggregates from existing platforms like Metaculus, Good Judgment, and Manifold, while also scraping the web for predictions made outside them—interviews, posts, podcasts. When an expert makes a prediction anywhere, someone can submit it to be moderated and added to their record. The goal is a single place where you can look up anyone who people might actually defer to and see their full history, whether or not they ever opted into a forecasting platform. You’d probably want to prioritize the most important predictions from the most important people.
This is different from existing platforms like Good Judgment in two ways. First, it tracks people who never opted in—the forecasters, lab researchers, and public intellectuals who make predictions in interviews, posts, and podcasts but don't put them on a platform. These are often the most influential voices, and right now they face basically no accountability. Second, the UI should make it easy to look up a specific person and see their full prediction history at a glance, which existing tools, imo, make surprisingly difficult.
One note on how to use it: I think the standard on the site should be to have inside views only, which might complicate things. As Thomas Larsen rightly points out, leaning too hard on aggregated reputations risks deference cascades—people updating on each other's records rather than on the object level. After all, I do think that part of LW alpha comes from making inside views/not deferring, and we should fear losing that.
When I last tried building this with Claude Code, it was too difficult to do in one sitting. If someone wants to work on this with me over a weekend, reach out—I'd be curious to see how far we can get.
I think this matters a lot for [AI for epistemics], especially if you think those epistemics will be shaken up soon by TAI.
One concern worth flagging: this could also create perverse incentives. Someone could build a track record, use it to shift opinion, then make a bad prediction at the worst moment. I think this risk is real but manageable—the tracker is most dangerous if people concentrate their trust in a very small number of highly-rated forecasters, which is itself a bad epistemic practice regardless.
2026-03-29 06:57:53
AI usage for this post: I wrote the draft on my own. While writing, I used Claude Code to look up references. Then Claude Code fixed typos and reviewed the draft, I addressed comments manually.
Epistemics: my own observations often inspired by conversations on X and Zvi's summaries.
As Zvi likes to repeat Language Models Offer Mundane Utility. Agent harnesses is the most advanced way to use language models. At the same time, they are not perfect - the capabilities frontier is jagged, sometimes they make mistakes and sometimes they just nuke your 15 year photo collection or production database. Thus, it is a skill how to use AI agents efficiently and I want to get better at it.
What tips, tricks, approaches are you using to improve your efficiency when using agent harnesses? I personally focus on Claude Code & Codex CLI mostly for single person software development, but I welcome suggestions for other tools and other areas of use. Ideally you share what you tried and what works for you and I try it myself and see whether it improves my workflow.
Here are my discoveries (with level of confidence).
The best available model and its thinking effort usually produces best results and requires less handholding. You have to be extra careful with what this means, e.g. Claude Code has high thinking effort by default, but the best level is max, which you need to actively turn on yourself. Unless you are very sensitive to speed and cost, you should do this. It might work a bit longer and consume more tokens, but this is much better than requiring multiple iterations from you.
When working on code in a git repo, let each AI session have its own git branch with worktree. This way they can work in parallel and not fight to edit same files. Intuitively this would lead to merge hell, but fortunately AIs are good at merging.
Codex CLI allows you to turn on /fast mode, which speeds up processing 1.5 times, but consumes your token quota 2x faster. If you work on highly interactive tasks, which require lots of your input and you are not constrained by money (e.g. can afford $200 subscription), you should do this. In my experience, without /fast, AI is slow enough that I can juggle 5-6 sessions before any finishes. With /fast, sessions finish quickly enough that I only need 2-3 at a time. The smaller batch means less context switching, which I find more productive overall.
The toggle is global, so it affects all your sessions at once.
Claude Code also has /fast to speed up processing 2.5 times, but don't run to turn it on yet - it charges you extra in addition to subscription using API prices and it is very expensive ($2-5/minute/agent). I haven't tried it.
I discovered that my software development efficiency grew greatly once I started having extremely verbose logging. The idea is once something goes wrong, this gives AI a trace of what has happened and I can just describe very briefly the higher-level symptom and it can investigate from the logs on its own. This is especially useful when the issue is hard to reproduce or happens occasionally. Another variation of this is to have some way to export debug information from your app and reference a specific object or instance. This way when something goes wrong, you just feed that to AI after one click and let it investigate.
I don't know the reason, but once in a while I get a couple days when AI just seems incapable of doing anything. Previously after one message it would correctly implement a bunch of stuff and now it keeps misunderstanding what I want and I need 30 iterations and it still does not get it and the process never converges. In this case I switch to the competitor (basically Opus 4.6 <> GPT5.4). The key is to detect this early and switch early. This is very sad, because migrating all the skills and setups between Claude Code and Codex sucks and I don't have an efficient way to do this.
I haven't found a solution to this myself, but I strongly believe that this would have huge impact. I want the current AI session to seamlessly have access to all past information I provided to it. Both Claude and ChatGPT have this implemented in their web interfaces, but not in Claude Code / Codex CLI. I am currently experimenting with github.com/doobidoo/mcp-memory-service. Issues discovered:
The memory retrieval seems to be fine. I am experimenting with using a separate Claude session in the background to extract memories from every message.
If you found a good solution you are happy with, please tell.
To my surprise just asking a new session of the same AI to review plan or work often brings useful insights, which the original session missed. You can also ask different AI too. My hypothesis is that the original session had to care about lots of details, so its attention was dispersed, but the new session can focus only on this particular review and, thus, have higher effective brain power allocated to it.
This is a guiding principle for multiple ideas. Basically, in my experience AI works well enough most of the time and the main bottleneck to getting stuff done is me.
The first way I bottleneck AI is by reviewing its requests for permissions to do stuff. Many people resolve this by YOLO mode, where AI can do everything it wants. I like my photos and production databases, so I don't feel comfortable doing this. I am also worried about prompt injections from the web.
I see 2 ways to partially resolve this issue:
Some people run AI in YOLO mode on a server, basically reducing the worst case scenario of a failure. I still don't like this, because it can still leak your git API key and your repo.
Many people I know just YOLO and never had any issue.
The main message here is reviewing every permission request kills your efficiency. Find some way to solve this.
By default you don't get any indication that AI needs your attention to review its permission requests except a text in the terminal. Thus, when working on multiple AI sessions in parallel, it is very easy to miss. You should set up at least a sound notification for new permission requests that require your attention or AI finishing its turn (it is either done or needs your input).
If you just add a sound notification and you have 10 sessions across virtual workspaces, finding the correct window is cumbersome. I solved this by making my own dashboard to oversee the state of all sessions. I don't think making something this polished is a good idea for you, because it took lots of iterations. Claude Code hooks are fairly fragile. Codex has extremely poor hooks. If there is a ready-made solution you use for this and you are happy with it, please tell. The main idea here is that you need some way to quickly identify what session requires your attention without checking all of them.
Your attention is the bottleneck, so if you can let AI do even a bit of what you would have to do otherwise, you should. Even if it takes much longer for AI to accomplish.
In my case, I made a skill for it to present its UI changes for me. It runs the website in a docker in a worktree, completely prepares the database to the state needed for the UI test, tests the UI on its own and fixes whatever issues it finds. Then it prepares the exact screen which I need to review and gives me an overview of what I need to check and what the context is. I just look through the minimum necessary flow and either LGTM or tell it what to fix and then it repeats. This is not perfect, because often it ends up presenting wrong state or starts too far from the interesting part, but this is much faster than me trying to prepare the correct state of the DB on my own. There are also tools like Storybook to review UIs with mock data.
The main message here is to understand how this applies to your use case and let AI do as much of the boring work as you can get away with.
Example - I have 5 test cases to review. I tell AI to start 5 new sessions - one per test case - and they test them and prepare the review in parallel (see "Offload as much as you can to AI" above).
I feel like this should be a way to gain more leverage easily, but I struggle to come up with effective ways to do this. Also I rarely have easy-to-parallelize cases like the example above.
I wrote my own UI to wrap Codex's App Server. My main goals were Auto Review of permissions and better hooks. Overall, I found this very interesting, because I could see how I use this tool, find inefficiency and immediately address it. E.g. I can choose how much information I want to see from the AI. The main downside is that the complexity grows fast, so developing this takes lots of time, so I suspect overall it was net negative, but an interesting exercise. Also due to AIs having bad days, I have to switch to Claude occasionally and its SDK seems much worse to wrap, so I don't have support for it yet.
I didn't expect to write that many ideas of mine here, but it was useful to list all of them. I would love to hear your ideas how to use AI agents more efficiently.
2026-03-29 05:57:54
We all know we ought not to doomscroll, or to make snarky comments, or to snack mindlessly, or to endlessly replay in our minds that conversation where we felt misunderstood or slighted. And this ”ought” is not imposed from the outside. It’s not that we’ll be judged by someone. It’s just that if we want to be happy, if we want to get things done, if we want to experience joy and enthusiasm and meaning and fun, we’d better not do those things. Not too much, anyway.
We even know how to not do them. It’s not rocket science in the first place, and there are plenty of genuinely effective methods out there just one Google search away. But sometimes… we do those things anyway. As entrepreneur Derek Sivers famously put it (I’m told), if information was the answer, we’d all be billionaires with 6-pack abs.
(In the interest of transparency, let me just state that the practice I describe in this post hasn’t made me a billionaire with 6-pack abs. But give it time.)
To stop doing something that is holding us back, we need to be vigilant. To not miss opportunities to do things that move us forward, we need to be vigilant. We know what benefits us and what harms us, and all that remains is to actually do the former and not do the latter.
There is a Pāli word for this vigilance, this diligence. It’s appamāda, translated as ‘heedfulness’. (Appamāda is the negation of pamāda, meaning ‘heedlessness’ or ‘negligence’.) The Buddha was famously bullish on appamāda, calling it the quality that encompasses all skillful qualities. In fact, according to the Pāli canon, his last words to his followers were "appamādena sampādetha" – bring your task to completion through heedfulness.
It would be nice if we could just decide to be heedful. But knowing myself, it’s clear to me that if I pledged right now to be heedful every waking hour of the rest of my life, it would simply not happen. I would be enthusiastic about it for a couple of hours, and then, come evening, I would start to forget. Tomorrow morning I would see a reminder on my calendar and feel another rush of excitement – but only for a short while. And a week from now, everything would be back to the way it was.
But there is something I can do. I can be heedful for twenty minutes, or forty-five. I can train that muscle.
And that is what I am doing: heedfulness workouts.
A heedfulness workout is a simple thing: I just decide I’m going to be heedful for some fixed amount of time. Sometimes I set a timer for 20 minutes or so; more often, I simply decide to practice to the top or bottom of the hour, or something similarly salient. Usually it’s no less than 10 minutes and no more than an hour and a half.
During the workout I pay attention to what I’m doing and how I’m doing it and to the myriad small choices I’m making like what to work on next after finishing some task. When I notice there’s a choice to make, I try to take the most beneficial option, not the most salient or tempting one. And that’s pretty much all there is to it.
I realize that this description is a bit scant on the details, so I’m going to give some examples of the kinds of things that may happen in a heedfulness workout. But I want to emphasize that it’s not a list of things you need to do or boxes you need to tick. The particulars aren’t the point. The point is noticing when you are about to make a choice – implicitly or explicitly – and trying to choose the best option with the information you have.
Here are some of the kinds of things I might notice during a heedfulness workout and what I might think and do in response.
I reiterate that the point of the practice is not to make those exact same observations and respond in exactly the same way. What comes up, and how you should respond to it, depends on your state and your circumstances.
Sometimes positive emotions arise during the practice: excitement or exhilaration born of an expansive feeling of freedom – the freedom to do things that matter to me, to do what moves things forward. At other times, I don’t feel anything particularly remarkable.
And sure enough, training the heedfulness muscle is having an effect outside of the workouts. Curiously, in my case, it’s not so much a feeling of being able to exert more force against unhelpful impulses (although that’s probably happening too) but more like… things loosening up. The mind being less rigid and slightly less controlled by habitual patterns.
Sometimes, outside of a workout, I reach for my phone for stimulation, catch myself, and stop. Other times, the conscious thought arises, “Hey, I could be heedful about this situation.” And then there are times when I reach for the thought deliberately: “Let me try sprinkling a little heedfulness on this.” The image is of a salt shaker filled with savoury goodness.
And that little sprinkling of heedfulness can turn an unpalatable situation into a delectable one.
2026-03-29 02:38:44
They found it in one of the early Mars expeditions, a bit after they had travel back and forth figured out well enough to keep a permanent outpost manned out there. The lab ran expeditions into some nearby caves in the hope that they’d turn out to be a good spot for an expansion. That hope didn’t turn out too well - something about the local geology, they ended up figuring it’d be more cost-effective to just land more pods - but they found the Organism there.
Not that any of this impacted me much at the time, beyond my general interest in space news to take my mind off the problems on Earth. I was still living in Manhattan, doing my day job as a government think tank security analyst and hoping AI winter would last long enough for me to save up a bit before it could replace me.
They always called it the Organism, but no one was even sure it was organic. It was arranged in clusters of off-white hexagonal tubes, and there was certainly some kind of chemical process there - they’d grow on their own, even faster than bamboo shoots, but in a much simpler chemical process that had the biologists arguing over whether it qualified as alive. It had some emissions, but early experiments convinced people it wasn’t toxic, and the guys at the lab - the one in the mars station, no one was bringing it home to Earth yet - brought some home and started studying it pretty casually.
It took a while for anyone to formally notice something weird was going on. The geo lab, the one that studied the Organism, kept publishing banger results, and not even just about the Organism. They seemed to be ahead of the curve across the board - they got almost as much research done on soil research and solar cell adaptation in their off time as the pods that were actually studying that. Some people are just crazy smart, I guess, but the solar cell guys we’d sent to Mars had been top of their game and they were still getting lapped.
They started making jokes about the Organism getting the geo lab all high - you could smell it a bit, at least in the sterile sealed station air - and the geo lab gave the solar cell guys a sample as kind of a gag gift. Two weeks later the solar cell guys came up with a brand new solar cell design that pushed peak solar cell efficiency up by two percentage points. It wasn’t even just a Mars thing, it was deployable on Earth. Whole research labs in China had been working on that for years without getting close.
You can bet people took notice after that.
They isolated it on Mars at first, of course. You don’t risk bringing something that grows fast and has some sort of weird effects on human brains until you’re sure it’s safe. It didn’t look like it was a trap - even the serious people in government had to consider aliens messing with us at that point, but they explored through the Mars caves and there was no sign of any sort of life, biological or weirder. It was such a weird natural artifact that some people thought it might’ve been designed by a now-extinct martian civilization. The geologists had some arguments about how some rock formations on Mars were evidence that it had life at one point, a billion or two years ago. It was all theoretical either way.
The Organism itself was more complicated, and even the biologists using it only ever half-understood it. Something about the molecules it emitted enabled some form of synthetic computation, making humans who were on it about 20% smarter and more cooperative. They started bringing it to earthside labs, and science progress soared to a rate we hadn’t seen since the seventies. They brought it to government offices, and politicians started passing fewer boneheaded policies. I think that one was more about the cooperation aspect than the intelligence boost. There’d always been at least some politicians who realized which policies were dumb, and making everyone a little more cooperative let them actually push through the noise.
Geopolitical tensions eased a bit. The president challenged president Qiu of China to a gaming match (don’t ask me what game) while they were both high on it as a gesture of goodwill, and Qiu came off affable and charming and seemed happy to ease the geopolitical tensions a bit. He laughed about how it was his first time breathing Organism, winking just in case anyone thought he seriously expected them to believe he hadn’t gotten any smuggled through american export controls when half the drug dealers in Manhattan could probably get you a hookup. A few weeks later they were talking about SALT 3, this time including China.
This was where it started affecting my job. I came into the office one day and the manager called me into his office.
“Listen John”, he started in the slow voice he used for big news. “They want an analysis on the new nuclear policy. Don’t make this one too harsh, okay? This isn’t one of those reports where they want serious critical analysis. It’s one of the CYA jobs where DOD just wants an independent analysis to wave around and show a Serious Outsider approved it. Just write something short and put the downsides in the fifth section where no one will read it, okay?”
I sighed. “How bad is it?”
“New nuclear policy. As a sign of good faith, they linked up our nuclear systems in a Samson scenario. No more first use, no more aggressive use. we still have it as a deterrent, but launching it would make us nuke ourselves. It means we can retaliate if anyone’s seriously nuking us, but we’d be blowing ourselves up too so we can’t strike first. Especially with our new conventional absolute advantage and the ICBM defenses the Organism boys cooked up, we need something to reassure people they don’t need to strike us first before the new stuff is online.”
“So we switched up our entire nuclear system with a single nuke the world button?”
“Yeah, pretty much. Between us, it’s already deployed behind the scenes, we needed it to reassure the Chinese. But it’s not ready for announcement until-”
“No no no No NO NO”. I barely even realized I was shouting. “Don’t you see? People on average are more cooperative now which means state-level actors are lower-risk because their people are less volatile and more cooperative. But individuals are higher variance, because even an average 20% increase in peacefulness and cooperation leaves a large number of negative outliers, and a 20% average boost in intelligence means a lot of people way smarter than that, and these things are not correlated. Which means we have now supercharged our supply of intelligent anti-human sociopaths who might be able to access the “nuke everything now” button. It doesn’t matter how few people know, if even one of them discovered this button exists he’ll find a way to push it.”
“but-”
“This is too important. We need to shut the button NOW. Before we all blow up. Call everyone you can. China can nuke us if they have to, this is too much of a risk. We need to shut it down tomorrow. If we even make it to tomorrow. I give us even odds”.
I stormed out of the office and started running home. I had some calls I could make, maybe I could help the move faster out of this disaster.
I was still just halfway home when I saw streaks of fire flying through the sky, and the skyscrapers started crashing down around me.
