Yudkowsky is a recurring person in the Astral Codex Ten archive, appearing 9 times across 9 issues between June 11, 2021 and February 12, 2026. The archive places it in contexts such as "Holt, like Graham and Yudkowsky, sees school as instilling permanent cognitive biases"; "It still feels like there’s something that Pinker and Yudkowsky are more in favor of"; "Robin Hanson debate Eliezer Yudkowsky on the future of AI". It most often appears alongside Eliezer Yudkowsky, Ajeya Cotra, Biological Anchors.
- Article page
- Yudkowsky
- Mention count
- 9
- Issue count
- 9
- First seen
- June 11, 2021
- Last seen
- February 12, 2026
Outbound links
- https://astralcodexten.substack.com/p/biological-anchors-a-trick-that-might?s=w
- https://astralcodexten.substack.com/p/practically-a-book-review-yudkowsky
- https://astralcodexten.substack.com/p/practically-a-book-review-yudkowsky?s=w
- https://astralcodexten.substack.com/p/yudkowsky-contra-christiano-on-ai?s=w
- https://intelligence.org/2018/10/03/rocket-alignment/
- https://spec.commonmark.org/dingus/”https://www.lesswrong.com/posts/48WeP7oTec3kBEada/two-more-things-to-unlearn-from-school”
- https://time.com/6266923/ai-eliezer-yudkowsky-open-letter-not-enough/
- https://wiki.lesswrong.com/wiki/Yudkowsky%27s_Law_of_Ultrafinite_Recursion
- https://www.lesswrong.com/posts/7im8at9PmhbT4JHsW/ngo-and-yudkowsky-on-alignment-difficulty
- https://www.lesswrong.com/posts/ax695frGJEzGxFBK4/biology-inspired-agi-timelines-the-trick-that-never-works
- https://www.lesswrong.com/tag/original-sequences
- https://www.lesswrong.com/tag/the-hanson-yudkowsky-ai-foom-debate
Issue trail
- Your Book Review: How Children Fail
- What Are We Arguing About When We Argue About Rationality?
- Yudkowsky Contra Christiano On AI Takeoff Speeds
- Davidson On Takeoff Speeds
- Tales Of Takeover In CCF-World
- Sakana, Strawberry, and Scary AI
- P-Zombies Would Report Qualia
- Book Review: If Anyone Builds It, Everyone Dies
- What Happened With Bio Anchors?
Related pages
Source context
And he makes similar claims, similarly argued, to those of Paul Graham and Eliezer Yudkowsky, that the strategies that lead to nominal success in school are often the ones that stop at superficial understanding of the subject--hacks to be able to get to the correct answer quickly, without ever really looking at the problem.
Inline links: Paul Graham, Eliezer Yudkowsky
Per the last demographic survey of the readership of this blog, you are most likely not nine years old. However, you are almost certainly a former nine-year-old, and that’s another excellent audience for this book. Holt, like Graham and Yudkowsky, sees school as instilling permanent cognitive biases--habits that are best unlearned whenever you can.
This is Eliezer Yudkowsky’s standing-on-one-foot definition of rationality.
Inline links: standing-on-one-foot definition of rationality
A few weeks ago, when I posted my predictions for 2022, a commenter mentioned that various “rationalist” “celebrities” - Eliezer Yudkowsky, Julia Galef, maybe even Steven Pinker - should join in, and then we would find out who is most rational of all. I hope this post explains why I don’t think this would work. You can’t find the best economist by asking Keynes, Hayek, and Marx to all found companies and see which makes the most profit - that’s confusing money-making with the study of money-making. These two things might be correlated - I assume knowing things about supply and demand helps when starting a company, and Keynes did in fact make bank - but they’re not exactly the same. Likewise, I don’t think the best superforecasters are always the people with the most insight into rationality - they might be best at truth-seeking, but not necessarily at studying truth-seeking.
Inline links: Keynes did in fact make bank
Yudkowsky: Rationality Is Systematized Winning?
In 2008, thousands of blog readers - including yours truly, who had discovered the rationality community just a few months before - watched Robin Hanson debate Eliezer Yudkowsky on the future of AI.
Inline links: Robin Hanson debate Eliezer Yudkowsky
Previously in series: Yudkowsky Contra Ngo On Agents, Yudkowsky Contra Cotra On Biological Anchors
Previously in series: Yudkowsky Contra Ngo On Agents, Yudkowsky Contra Cotra On Biological Anchors Prelude: Yudkowsky Contra Hanson In 2008, thousands of blog readers - including yours truly, who had discovered the rationality community just a few months before - watched Robin Hanson debate Eliezer Yudkowsky on the future of AI.
You get what futurists call a “takeoff”. The first graph shows a world with no takeoff. Past AI progress doesn’t speed up future AI progress. The field moves forward at some constant rate. The second graph shows a world with a gradual “slow” takeoff. Early AIs (eg Codex) speed up AI progress a little. Intermediate AIs (eg an AI that can help predict optimal parameter values) might speed up AI research more. Later AIs (eg autonomous near-human level AIs) could do the vast majority of AI research work, speeding it up many times. We would expect the early stages of this process to take slightly less time than we would naively expect, and the latter stages to take much less time, since AIs are doing most of the work. The third graph shows a world with a sudden “fast” takeoff. Maybe there’s some single key insight that takes AIs from “mere brute-force pattern matchers” to “true intelligence”. Whenever you get this insight, AIs go from far-below-human-level to human-level or beyond, no gradual progress necessary. Before, I mentioned one reason Davidson doesn’t like these terms - “slow takeoff” can be fast. It’s actually worse than this; in some sense, a “slow takeoff” will necessarily be faster than a “fast takeoff” - if you superimpose the red and blue graphs above, the red line will be higher at every point1. CCF departs from this terminology in favor of trying to predict a particular length of takeoff in real time units. Specifically, it asks: how long will it take to go from the kind of early-to-intermediate AI that can automate 20% of jobs, to the truly-human-level AI that can automate 100% of jobs? (“Can automate” here means “is theoretically smart enough to automate” - actual automation will depend on companies fine-tuning it for specific tasks and providing it with the necessary machinery; for example, even a very smart AI can’t do plumbing until someone connects it to a robot body to do the dirty work. CCF will talk more about these kinds of considerations later.) In order to figure this out, it needs to figure out the interplay of a lot of different factors. I’m going to focus on the three I find most interesting: How much more compute does it take to train the AI that can automate 100% of the economy, compared to the one that can automate 20%?
The OP school expect the rise of AI to be gradual, multipolar, and potentially survivable. The MIRI school expect it to be sudden, singular, and catastrophic. Yudkowsky vs. Christiano on Takeoff Speeds is a good intro here, with Yudkowsky representing MIRI and Christiano OP.
Inline links: Yudkowsky vs. Christiano on Takeoff Speeds
I mentioned earlier that the CCF report comes out of Open Philanthropy’s school of futurism, which differs from the Yudkowsky school where a superintelligent AI quickly takes over. Open Philanthropy is less explicitly apocalyptic than Yudkowsky, but they have concerns of their own about the future of humanity.
A Twitter discussion between Ajeya Cotra and Eliezer Yudkowsky:
Inline links: Twitter discussion
But nobody finds this scary. Nobody thinks “oh, yeah, Bostrom and Yudkowsky were right, this is that AI safety thing”. It’s just another problem for the cybersecurity people. Sometimes Excel inappropriately converts things to dates; sometimes GPT-6 tries to upload itself into an F-16 and bomb stuff. That specific example might be kind of a joke. But thirty years ago, it also would have sounded pretty funny to speculate about a time when “everyone knows” AIs can write poetry and develop novel mathematics and beat humans at chess, yet nobody thinks they’re intelligent.
There’s a long-running philosophical argument about the conceivability of otherwise-normal people who are not conscious, aka “philosophical zombies”. This has spawned a shorter-running (only fifteen years!) rationalist sub-argument on the topic. The last time I checked its status was this post, which says:
Inline links: “philosophical zombies”, this post
1. Both Yudkowsky and Chalmers agree that humans possess “qualia”.
3. Yudkowsky argues that such a being would notice that humans discuss at length the fact that they possess qualia, and their internal narratives also represent this fact. It is extraordinarily improbable that beings would behave in this manner if they did not actually possess qualia. Therefore an omniscient being would conclude that it is extremely likely that humans possess qualia. Therefore, qualia are not extra-physical.
Eliezer Yudkowsky’s Machine Intelligence Research Institute is the original AI safety org. But the original isn’t always the best - how is Mesopotamia doing these days? As money, brainpower, and prestige pour into the field, MIRI remains what it always was - a group of loosely-organized weird people, one of whom cannot be convinced to stop wearing a sparkly top hat in public. So when I was doing AI grantmaking last year, I asked them - why should I fund you, instead of the guys with the army of bright-eyed Harvard grads, or the guys who just got Geoffrey Hinton as their celebrity spokesperson? What do you have that they don’t?
Inline links: Machine Intelligence Research Institute, sparkly top hat
Despite my gripes above, this is an impressive book. Eliezer Yudkowsky is a divisive writer, with plenty of diehard fans and equally committed enemies. At his best, he has leaps of genius nobody else can match; at his worst, he’s prone to long digressions about how stupid everyone who disagrees with him is. Nate Soares is equally thoughtful but more measured and lower-profile (at least before he started dating e-celebrity Aella). His influence tempers Yudkowsky’s and turns the book into a presentable whole that respects its readers’ time and intelligence. The end result is something which I would feel comfortable recommending to ordinary people as a good introduction to its subject matter.
Eliezer Yudkowsky, at his best, has leaps of genius nobody else can match. Fifteen years ago, he decided that the best way to something something AI safety was to write a Harry Potter fanfiction. Many people at the time (including me) gingerly suggested that maybe this was not optimal time management for someone who was approximately the only person working full-time on humanity’s most pressing problem. He totally demolished us and proved us wronger than anyone has ever been wrong before. Hundreds of thousands of people read Harry Potter and the Methods of Rationality, it got lavish positive reviews in Syfy, Vice, and The Atlantic, and it basically one-shotted a substantial percent of the world’s smartest STEM undergrads. Fifteen years later, I still meet bright young MIT students who tell me they’re working on AI safety, and when I ask them why in public they say something about their advisor, and then later in private they admit it was the fanfic. Valuing the time of the average AI genius at the rate set by Sam Altman (let alone Mark Zuckerberg), HPMOR probably bought Eliezer a few billion dollars in free labor. Just a totally inconceivable level of victory.
Epoch/Croxton are current best estimates, and can probably fairly be read as the “real” answer against which Cotra and Davidson’s earlier guesses should be judged. All numbers are yearly multiples, so 1.4 means that willingness to spend grows 1.4x per year, ie 40%. Willingness To Spend: How much money are companies willing to spend on AI, in the form of chips and data centers? $/FLOP: How quickly do Moore’s Law, economies of scale, and other factors bring down the price of AI compute? Training Run Length: How long are companies spending on AI training runs for frontier models (instead of using those chips for smaller models, experiments, or consumer services)? Real Compute: The product of the three parameters above. Algorithmic Progress: How effectively do researchers discover new algorithms that makes training AIs cheaper and more efficient? Total Effective Compute: The product of real compute and algorithmic progress. So for example, the Epoch column’s 10.7x means that in any given year, you can train an AI 10.7x better than the last year, because you have 3.6x more compute available, and that compute is 3.0x more efficient. Cotra and Davidson were pretty close on willingness to spend and on FLOPs/$. This is an impressive achievement; they more or less predicted the giant data center buildout of the past few years. They ignored training run length, which probably seemed like a reasonable simplification at the time. But they got killed on algorithmic progress, which was 200% per year instead of 30%. How did they get this one so wrong? Here’s Cotra’s section on algorithmic progress: Algorithmic progress forecasts Note: I have done very little research into algorithmic progress trends. Of the four main components of my model (2020 compute requirements, algorithmic progress, compute price trends, and spending on computation) I have spent the least time thinking about algorithmic progress. I consider two types of algorithmic progress: relatively incremental and steady progress from iteratively improving architectures and learning algorithms, and the chance of “breakthrough” progress which brings the technical difficulty of training a transformative model down from “astronomically large” / “impossible” to “broadly feasible.” For incremental progress, the main source I used was Hernandez and Brown 2020, ”Measuring the Algorithmic Efficiency of Neural Networks”. The authors reimplemented open source state-of-the-art (SOTA) ImageNet models between 2012 and 2019 (six models in total). They trained each model up to the point that it achieved the same performance as AlexNet achieved in 2012, and recorded the total FLOP that required. They found that the SOTA model in 2019, EfficientNet B0, required ~44 times fewer training FLOP to achieve AlexNet performance than AlexNet did; the six data points fit a power law curve with the amount of computation required to match AlexNet halving every ~16 months over the seven years in the dataset.² They also show that linear programming displayed a similar trend over a longer period of time: when hardware is held fixed, the time in seconds taken to solve a standard basket of mixed integer programs by SOTA commercial software packages halved every ~13 months over the 21 years from 1996 to 2017.³ Grace 2013 (”Algorithmic Progress in Six Domains”) is the only other paper attempting to systematically quantify algorithmic progress that I am currently aware of, although I have not done a systematic literature review and may be missing others. I have chosen not to examine it in detail because a) it was written largely before the deep learning boom and mostly does not focus on ML tasks, and b) it is less straightforward to translate Grace’s results into the format that I am most interested in (”How has the amount of computation required to solve a fixed task decreased over time?”). Paul is familiar with the results, and he believes that algorithmic progress across the six domains studied in Grace 2013⁴ is consistent with a similar but slightly slower rate of progress, ranging from 13 to 36 months to halve the computation required to reach a fixed level of performance. Additionally, it seems plausible to me that both sets of results would overestimate the pace of algorithmic progress on a transformative task, because they are both focusing on relatively narrow problems with simple, well-defined benchmarks that large groups of researchers could directly optimize.⁵ Because no one has trained a transformative model yet, to the extent that the computation required to train one is falling over time, it would have to happen via proxies rather than researchers directly optimizing that metric (e.g. perhaps architectural innovations that improve training efficiency for image classifiers or language models would translate to a transformative model). Additionally, it may be that halving the amount of computation required to train a transformative model would require making progress on multiple partially-independent sub-problems (e.g. vision and language and motor control). I have attempted to take the Hernandez and Brown 2020 halving times (and Paul’s summary of the Grace 2013 halving times) as anchoring points and shade them upward to account for the considerations raised above. There is massive room for judgment in whether and how much to shade upward; I expect many readers will want to change my assumptions here, and some will believe it is more reasonable to shade downward. Cotra’s estimate comes primarily from one paper, Hernandez & Brown, which looks at algorithmic progress on a task called AlexNet. But later research demonstrated that the apparent speed of algorithmic progress varies by an order of magnitude based on whether you’re looking at an easy task (low-hanging fruit already picked) or a hard task (still lots of room to improve). AlexNet was an easy task, but pushing the frontier of AI is a hard task, so algorithmic progress in frontier AI has been faster than the AlexNet paper estimated. In Cotra’s defense, she admitted that this was the area where she was least certain, and that she had rounded the progress rate down based on various considerations when other people might round it up based on various other considerations. But the sheer extent of the error here, compounded with a few smaller errors that unfortunately all shared the same direction, was enough to throw off the estimate entirely. Since Cotra and Davidson were expecting AI to get 3.6x more effective compute each year, but it actually got 10.7x more, it’s no mystery why their timelines were off. When John recalculates Davidson’s model with Epoch’s numbers, he finds that it estimates AGI in 2030, which matches the current vibes. IV. With this information in place, it’s worth looking at some prominent contemporaneous critiques of Bio Anchors. Various people criticized Bio Anchors’ many strange anchors for how much compute it would take to produce AGI. For example, one anchor estimated that it would take 10^45 FLOPs, because that was how many calculations happened in all the brains of all animals throughout the evolutionary history (which eventually produced the human brain that AIs are trying to imitate). To make things even weirder, this anchor assumed away all animals other than nematodes as a rounding error (fact check: true!) All of these seemed to detract from the main show, an attempt to estimate the compute involved in the human brain. But even this more sober anchor was complicated by time horizons - it’s not enough to imitate the human brain for one second; AIs need to be able to imitate the human brain’s capacity for long-term planning. Cotra calculated how much compute AGI would require if it needed a planning horizon of seconds, weeks, or years. Thanks to METR, we now know that existing AIs have already passed a point where they can do most tasks that take humans seconds, are moving through the hour range, and are just about to touch one day. So the “seconds” anchor is ruled out. But it also seems unlikely that AGI will require years, because most human projects don’t take years, or at least can be split into tasks that take less than one year each (intuition pump: are we sure the average employee stays at an AI lab for more than a year? If not, that proves that a chain of people with sub-one-year time horizons can do valuable work). The AI Futures team guessed that the time horizon necessary for AIs to really start serious recursive self-improvement was between a few weeks and a few months (though this might look like a totally different number on the METR graph, which doesn’t translate perfectly into real life). If this is true, then all three anchors (seconds, hours, years) were off by at least an order of magnitude. But it turns out that none of this matters very much. The highest and lowest anchors cancel out, so that the most plausible anchor - human brain with time horizon of hours to days - is around the average. If you remove all the other anchors and just keep that one, the model’s estimates barely change. But also, we’re talking about crossing twelve orders of magnitude here. The difference between the different time horizon anchors doesn’t register much on that level, compared to things like algorithmic progress which have exponential effects. Maybe this is the model basically working as intended. You try lots of different anchors, put more weight on the more plausible ones, take a weighted average of each of them, and hopefully get something close to the real value. Bio Anchors did. Or maybe it was just good luck. Still hard to tell. Eliezer Yudkowsky argued that the whole methodology was fundamentally flawed. Partly because of the argument above - he didn’t trust the anchors - but also partly because he expected the calculations to be obviated by some sort of paradigm shift that couldn’t be shoehorned into “algorithmic progress” (like how you couldn’t build an airplane in 1900 but you could in 1920). As of 2026 - still before AGI has been invented and we get a good historical perspective - no such shift has occurred. The scaling laws have mostly held; whatever artificial space you try to measure models in, the measurement has mostly worked in a predictable way. There have really only been two kinks in the history of AI so far. First, a kink in training run size around 2010: Second, a kink in time horizons around 2024 and the invention of test-time compute: The 2010 kink was before Cotra’s forecast and priced in. The 2024 kink is interesting and relevant - but since it was on a parameter Cotra wasn’t measuring, and probably too small to show up on the orders-of-magnitude scale we’re talking about, it’s probably not a major cause of the model’s inaccuracy. Other things have been even more predictable: So Cotra’s bet on progress being smooth and measurable has mostly paid off so far. But Yudkowsky further explained that his timelines were shorter than Bio Anchors because people would be working hard to discover new paradigms, and if the current paradigm would only pay off in the 2050s, then probably they would discover one before then. You could think of this as a disjunction: timelines will be shorter than Cotra thinks, either because deep learning pays off quickly, or because a new paradigm gets invented in the interim. It turned out to be the first one. So although Yudkowsky’s new paradigm has yet to materialize, his disjunctive reasoning in favor of shorter-than-2050 timelines was basically on the mark. Nostalgebraist argued that Cotra’s whole model was a wrapper for an assumption that Moore’s Law will continue indefinitely. If it does, obviously you get enough compute for AI at some point, even if it requires some absurd process like simulating all 500 million years of multicellular evolution. I never entirely understood this objection, because - although Bio Anchors does depend on a story where Moore’s Law doesn’t break before we get the relevant amount of compute - this is only one of many background assumptions (like that a meteor doesn’t hit Earth before we get the relevant amount of compute). Given those assumptions, it does a useful not-just-assumption-repeating job of calculating when transformative AI will happen. As Cotra implicitly predicted, we seem on track to get AGI before Moore’s Law breaks down, and so Moore’s Law didn’t end up mattering very much. And if all of Cotra’s non-Moore’s-Law parameter estimates had been correct, her model would have given about the same timelines we have now, and surprised everyone with a revolutionary claim about the AI future. But Nostalgebraist added, almost as an aside: Cotra has a whole other forecast I didn’t mention for “algorithmic progress,” and the last number is what you get from just algorithmic progress and no Moore’s Law. So depending on how much you trust that forecast, you might want to take all these numbers with an even bigger grain of salt than you’d expected from everything else we’ve seen. How much should you trust Cotra’s algorithmic progress forecast? She writes: “I have done very little research into algorithmic progress trends. Of the four main components of my model (2020 compute requirements, algorithmic progress, compute price trends, and spending on computation) I have spent the least time thinking about algorithmic progress.” ...and bases the forecast on one paper about ImageNet classifiers. I want to be clear that when I quote these parts about Cotra not spending much time on something, I’m not trying to make fun of her. It’s good to be transparent about this kind of thing! I wish more people would do that. My complaint is not that she tells us what she spent time on, it’s that she spent time on the wrong things. Like Cotra herself, I think Nostalgebraist was spiritually correct even if his bottom line (about Moore’s Law) was wrong. His meta-level point was that a seemingly complicated model could actually hinge on one or two parameters, and that many of Cotra’s parameter values were vague hand-wavey best guess estimates. He gave algorithmic progress as a secondary example of this to shore up his Moore’s Law case, but in fact it turned out to be where all the action was. V. Those were the rare good critiques. The bad critiques were the same ones everyone in this space gets: You’re just trying to build hype.
Inline links: ”Measuring the Algorithmic Efficiency of Neural Networks”, ”Algorithmic Progress in Six Domains”, later research, fact check: true!, argued that, https://epoch.ai/blog/the-longest-training-run, https://www.lesswrong.com/posts/GAJbegsvnd85hX3eS/thoughts-on-extrapolating-time-horizons, https://epoch.ai/benchmarks/eci, argued that
So Cotra’s bet on progress being smooth and measurable has mostly paid off so far. But Yudkowsky further explained that his timelines were shorter than Bio Anchors because people would be working hard to discover new paradigms, and if the current paradigm would only pay off in the 2050s, then probably they would discover one before then. You could think of this as a disjunction: timelines will be shorter than Cotra thinks, either because deep learning pays off quickly, or because a new paradigm gets invented in the interim. It turned out to be the first one. So although Yudkowsky’s new paradigm has yet to materialize, his disjunctive reasoning in favor of shorter-than-2050 timelines was basically on the mark. Nostalgebraist argued that Cotra’s whole model was a wrapper for an assumption that Moore’s Law will continue indefinitely. If it does, obviously you get enough compute for AI at some point, even if it requires some absurd process like simulating all 500 million years of multicellular evolution. I never entirely understood this objection, because - although Bio Anchors does depend on a story where Moore’s Law doesn’t break before we get the relevant amount of compute - this is only one of many background assumptions (like that a meteor doesn’t hit Earth before we get the relevant amount of compute). Given those assumptions, it does a useful not-just-assumption-repeating job of calculating when transformative AI will happen. As Cotra implicitly predicted, we seem on track to get AGI before Moore’s Law breaks down, and so Moore’s Law didn’t end up mattering very much. And if all of Cotra’s non-Moore’s-Law parameter estimates had been correct, her model would have given about the same timelines we have now, and surprised everyone with a revolutionary claim about the AI future. But Nostalgebraist added, almost as an aside: Cotra has a whole other forecast I didn’t mention for “algorithmic progress,” and the last number is what you get from just algorithmic progress and no Moore’s Law. So depending on how much you trust that forecast, you might want to take all these numbers with an even bigger grain of salt than you’d expected from everything else we’ve seen. How much should you trust Cotra’s algorithmic progress forecast? She writes: “I have done very little research into algorithmic progress trends. Of the four main components of my model (2020 compute requirements, algorithmic progress, compute price trends, and spending on computation) I have spent the least time thinking about algorithmic progress.” ...and bases the forecast on one paper about ImageNet classifiers. I want to be clear that when I quote these parts about Cotra not spending much time on something, I’m not trying to make fun of her. It’s good to be transparent about this kind of thing! I wish more people would do that. My complaint is not that she tells us what she spent time on, it’s that she spent time on the wrong things. Like Cotra herself, I think Nostalgebraist was spiritually correct even if his bottom line (about Moore’s Law) was wrong. His meta-level point was that a seemingly complicated model could actually hinge on one or two parameters, and that many of Cotra’s parameter values were vague hand-wavey best guess estimates. He gave algorithmic progress as a secondary example of this to shore up his Moore’s Law case, but in fact it turned out to be where all the action was. V. Those were the rare good critiques. The bad critiques were the same ones everyone in this space gets: You’re just trying to build hype.
Inline links: argued that
These questions have no right answer, but one conclusion does seem pretty firm. Most of the bad-faith critics, having identified that Ajeya’s model was imperfect and could fail, defaulted to the Safe Uncertainty Fallacy - since we can never be sure a model is exactly right, things are uncertain, which means we can continue to believe everything is fine and normal and timelines are wrong and we don’t have to worry. But as Yudkowsky pointed out, there’s uncertainty on both sides! Sometimes the fact that a forecast is imperfect and you can never be certain means things are more dangerous than you thought!
Inline links: Safe Uncertainty Fallacy