Bernard Guerrero (@BernG) header
Bernard Guerrero (@BernG) profile picture

Risk Manager, history enthusiast, would like to conquer the world with submarines. #NJ, #NY, #TX, #Rutgers, #NYU, #NYUStern, #ARNG, #Army, #Physics, #Econ

a submarine
Joined April 2008
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Bernard Guerrero retweeted
The "editorial" parts of NEJM, Science, etc. do so much harm to public perception of science. Why do this? Are we going to publish (basically the exact same essay) from a Pentecostal preacher? Science is too important for human prosperity to do this: kevinbryanecon.com/trust.htm…
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Bernard Guerrero retweeted
Takes on the election 1. To a first approximation, Republicans lost this hard because voters lost confidence in the Trump administration, not because of a positive agenda from the Democrats. Favorability of the Democratic Party was negative (-5). But Trump told voters to “vote like he was on the ballot”, and they did. The NBC exit poll found that 37% of voters approved of Trump, and 63% disapproved (-26). There’s just no winning the national environment with these margins. 2. There were essentially two major issues that voters cared about: 1) cost of living and 2) government corruption, both of which ~90% of voters said were “crucial or a major factor”. AI was not *that* big of a deal in this election - 56% said it was “crucial or a major factor”, but it’s really hard to disambiguate what voters feel about AGI and catastrophic risks (if anything) from ordinary median-voter NIMBYism around data centers. (It will, however, likely be a huge deal in the 2028 election when the impacts of AI become much more visible). Immigration was also not that big of a deal. Voters actually slightly favored Rs on immigration (+2), probably thanks to lasting backlash from the Biden administration’s laxity on border enforcement. It's unclear to what extent the war with Iran contributed aside from it raising the price of gas. 3. Data centers are probably not over. In fact data center ETF $DTCR was up nearly 2.5% today! It’s hard to understand why this is the case, but one hypothesis is that markets think ultimately, elites’ recognition of the immense economic and national security importance of data centers will override voters’ desire not to build them. 4. On cost of living in particular: one of the most important questions now is to what extent Dems will embrace economic slopulism (think rent control, broad asset taxes, and subsidizing demand) vs. “abundance liberal” policies like building more housing, cutting regulations, and embracing economic growth. 5. Thermostatic backlash is real. Voters hate incumbents. Political majorities shift constantly. The blue wave of 2008 was immediately followed by the red wave of 2010. Anyone predicting a durable multi-decade supermajority for Democrats will likely end up being wrong. 6. “Woke is back” is exactly the wrong lesson for Democrats to take from this, and Ro Khanna posting “DEI is back” doesn’t help. Using transgender issues as a proxy for “woke”, 52% of voters said trans rights have gone too far, compared to just 26% who said they haven’t gone far enough. Deep-blue Washington voted to ban trans athletes in girls’ sports, and Colorado voted to ban trans surgery for minors. Successful Democrats mostly avoided culture war issues. 7. Similarly, a lot of moderate or even right-leaning downballot measures passed, even in blue states. Eight states voted to require voter ID. North Carolina voted to cap state income taxes. California’s billionaire tax is on track to fail by nearly 11 points. Wisconsin voted to ban DEI in state government. Voters largely do not like left-wing ideas. 8. On the other side, “Demographics is destiny” (i.e. America needs to be a white nationalist/nativist country and heavily restrict immigration) is the wrong lesson for Republicans. White voters shifted 13 points to the left. Whites under 30 shifted 30 points to the left. Hispanics and other non-white groups shifted massively towards the right in 2024, and can be encouraged to do so again. 9. Moderates outperformed; radicals underperformed. Dems won by 13 points nationwide. Roy Cooper won by 19.6 in North Carolina (where Trump won by 3.2 in 2024). Jon Ossoff won by 18 in Georgia, where Trump won by 2.2. Abdul El-Sayed in Michigan and Troy Jackson in Maine, both Bernie-backed supporters of Medicare for All, abolishing ICE, and wealth taxes, both underperformed the national margin. In fact, Maine shifted slightly rightward. Dems were able to win deep red states like Iowa, Kansas, and Mississippi by running moderate, Republican-presenting, pro-border-security candidates. I suspect this is the main reason markets are up today. (For some intuition on how insane it is for a Dem candidate to win in Mississippi, Trump won Mississippi in 2024 by a larger margin than Harris won California!) 10. One of last night’s biggest winners was Florida’s Republican governor, Ron DeSantis. Under his tenure, Florida has gone from slightly red-leaning to reliably red. Republicans swept every statewide office, kept every House seat, and kept their majority in the State House and supermajority in the State Senate. In January, Florida will be the only one of America’s ten most populous states to have a Republican governor. DeSantis is genuinely smart and nuanced, lacks the baggage of figures like Vance and Rubio who are associated with the Trump administration, and did a great job as an administrator running Florida (see its high rate of domestic migration, zero state income tax, very low debt per capita, high education rankings, state budget surplus, etc). I would be unsurprised if he decided to run for President in 2028. 11. The other biggest winner was Jon Ossoff. Ossoff is a young, handsome, charismatic, moderate white male who won by 18 points in a state that went +2.2 for Trump in 2024. He achieved this in large part by focusing relentlessly on the two issues that mattered: the Trump admin’s corruption and the cost of living. He may very well be the Democratic frontrunner in 2028 (although, of course, it's far too early to make confident predictions about these things). 12. The biggest loser of all last night was Israel and the US-Israel relationship. Israel’s popularity in the United States has been in a steep, secular decline for years. Just 13% of Democrats and 54% of Republicans have positive views on Israel, and this is heavily skewed by age - younger voters are much more likely to oppose Israel. With Democrats taking both houses, it’s unlikely America’s level of political and military support for Israel will last for very long. 13. Gerrymandering doesn’t work under sufficiently strong margins for the opposing party. In political science, this is known as “dummymandering”. Dems are on track to flip 7 of North Carolina’s 14 House seats, shifting the balance of power from 10-4 Republican to 11-3 Democratic. 14. Trump quickly conceded that his party lost and he would be open to working with the Democrats. This was a positive update on Trump’s tendency toward authoritarianism. Many mainstream Republican figures, like JD Vance and Ted Cruz, quickly posted somewhat genuine mea culpas. There was a lot less coping than I expected. It’s possible that the loss had to be very painful for them to learn from it, and we wouldn’t have seen the same if Dems had won only 51 Senate seats. 15. Elections are truly a ton of fun to monitor live. There’s nothing like it. And prediction markets beat mainstream media in terms of timeliness and accuracy by FAR. 16. There’s a good chance none of this matters that much. Trump is still the president and still has veto power. Voters might turn on the incumbent Democrats in 2028. And historically, real GDP growth isn’t that different during eras where Ds controlled Congress vs Rs. Much of your probability mass should go towards “nothing ever happens”.
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Bernard Guerrero retweeted
Tyler Cowen: 'I am not even sure what to say at this point. We need a radical revaluation of this work, and of the associated policy arguments. More generally, this is only a single step in the story of how AI is going to revise economic science, and indeed past research more generally. As I stress in many of my recent talks, there will be significant gains and losses in status, with the losses often outweighing the gains. I see this as one of those cases.'
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Bernard Guerrero retweeted
I don't think i've ever seen a worse, more damaging interview for a candidate. Abdul was condescending, arrogant, dismissive and aloof. If he thinks he can win Republicans like this, he's making a big mistake. Abdul is his own worse enemy.
WOW this exchange. @BretBaier tried to give Abdul El-Sayed a chance to finally explain the 2012 letter in support of the Muslim Brotherhood and Abdul awkwardly tries to speak over him. This flailing defensiveness is why we’re on week 3 on this issue.
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Bernard Guerrero retweeted
Another thing to understand about Zucman: engaging him is like trying to kick a football through a constantly-moving goalpost that also arbitrarily changes its height and width mid-kick. He and his team at UC-Berkeley churn out a stream of working papers on inequality measurement that bake dozens of dubious assumptions into their data files, all of which "coincidentally" reinforce the headline figures that align with their political narrative. But finding and weeding out those assumptions is an extremely tedious and time-consuming task. Zucman and coauthors Piketty and Saez do most of their work in Excel, and it's seldom ever straightforward. Instead you'll find some extremely questionable and undisclosed multiplier that's just guesstimated out of thin air...but it's buried 10 layers deep in a formula from a convoluted Excel spreadsheet, which changes the an input for another Excel spreadsheet that's used to create a ratio, which in turn goes into a formula on another Excel spreadsheet that's used to estimate a decade of missing data, which in turn alters the trend of their entire series. @VincentGeloso and I know this well because we've been scrutinizing their work for over a decade and have found dozens of examples of this sort of stuff. But there's another twist to the PSZ universe: once you've done the work and found a problem in their data files that alters their main results in significant ways, they've already moved on to a "new" approach that conveniently hard-bakes all the faulty assumptions of the old approach into its source data while refusing to revisit them, and then layers them over with new paint, consisting of even more of the same style of Excel spreadsheet labyrinths that you now have to navigate to even figure out what they are doing. And what they do in their data files often diverges in substantial ways from how they describe it in their papers and descriptive appendixes. While you're toiling away to figure out what they did in their last peer reviewed paper, they call up a friendly reporter at the NY Times and get him to publish a new non-peer reviewed statistical claim that grabs headlines and aligns with whatever wealth tax initiative they happen to be activisting for at the moment. By the end, the goalpost has hopped around the field, changed its size and height multiple times, and been replaced by a new goalpost on the other end of the field that announces itself not through the referees of the field but via some sportscaster up in the broadcast booth who also happens to be openly cheering for their team.
Here's the best way to understand what Gabriel Zucman is up to on the California billionaire tax, and why it's a problem: Suppose you were tasked with assembling a complex historical time series of an important economic indicator, but also suppose the data needed to build it is of widely varying quality. There are gaps in some years when components you need simply weren't measured. In other years the stats you do have are not as reliable. Or maybe the source changed the way it recorded things, or changed the underlying definition. And on top of all that, your indicator contains components that are simply hard to measure with any precision. So what do you do? You'll inevitably have to make some discretionary calls on how to handle the challenges you encounter. Zucman's inequality stats all face this problem. They draw on data that are often incomplete, and go back almost a century in some cases. Reliability varies, as do underlying sampling techniques that generated them. There are also gaps from things that aren't easily measured where you have to make a defensible judgement call on how to handle it. And each and every one of those calls affects your end results. A rigorous researcher's judgement calls would follow certain defensible principles or rules that were clearly articulated and replicable. The effects of those calls would also distribute in different directions. Some would increase the final output number, others would decrease it. In Zucman's work, we typically find the opposite. He makes judgement calls out of necessity, but they almost always go in the same direction, which is to show (a) rising inequality and (b) declining tax burdens on the rich. They always skew in a nonrandom direction, and that direction conveniently aligns with the political story he's telling about inequality and wealth taxes. Or think of it this way: if you're assembling a century-long measure of income concentration it's quite plausible you may have to make 50 different judgement calls on how to handle gaps and uncertainties in the source data. If 30 of those cause your results to show higher inequality in cumulative, but 20 of those reduce inequality in cumulative, that suggests a fairly even distribution is at play. In Zucman's work (as well as his coauthors Piketty and Saez) though, you'll find situations where 50 out of 50 discretionary calls all happen to cause the headline measure of inequality to increase. That's not random - it means that whenever they must make a choice on how to handle data uncertainty, they choose the path that confirms the political story they wish to tell. And this is why the various Piketty-Saez-Zucman measures of inequality are *always* on the high side compared to alternative measures of the same thing. Overlay that with the fact that all three of them are partisan political activists who advocate for wealth taxes, and you see the problem. Their empirical academic work ends up skewing in favor of their political objective, even though it's masquerading as a data-driven exercise. And that's why their empirical claims should be checked against other sources and heavily discounted when they do not align - which is frequently the case.
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Bernard Guerrero retweeted
A conversation that has happened many times in banking, with various flavor texts, from fintech to public banking to microfinance. A: We think there is an opportunity to write loans to an underserved population. B: At usuriously high rates? A: No, at usual rates. Maybe lower.
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Lavishly compensated darling of the French and international literary world, Édouard Louis, who teaches in America, explicitly justifies political violence. Just an absolute moral shitshow.
🔴 Édouard Louis : "Moi, je suis pour que l'on mette le feu aux rues de Paris ; je suis pour que l'on aille casser des choses. Faire de la politique, c'est avoir ou non du sang sur les mains : il n'y a pas de grande révolution sans débordements." ✅ Édouard Louis est un écrivain lancé par l’intelligentsia germanopratine de gauche radicale. Le noyau est le trio étroit avec @didiereribon et Geoffroy de Lagasnerie : "amitié" intense (vie, vacances, publications croisées), présentée comme un mode de vie alternatif à la famille et comme le pendant contemporain de Sartre/Beauvoir. Il est omniprésent dans les médias de gauche (@lesinrocks, @libe, @lemondefr) et à l’international (@guardian, @NewYorker, @nytimes). ✅ L'auteur de romans OUIGO revendique son inaltérable soutien à @JLMelenchon et à la @FranceInsoumise, avec @gdelagasnerie il publie des manifestes contre la famille ("structure pourrie") au profit de l’amitié, il est l'un des inspirateurs des délires adulescents de @Clemence_Guette et de l'@i_laboetie. 🕵️ @RadioCanadaInfo 🗣️ @FrDesouche
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Bernard Guerrero retweeted
The thing that Democrats most want out of immigration policy — an impregnable external barrier that will let them go soft on interior enforcement without inviting new chaos — is not possible and this is more a governance problem than a messaging one. slowboring.com/p/dont-get-co…
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Bernard Guerrero retweeted
Let me fix that tweet for you: 1. The people rioting in Paris are criminals who are committing crimes. 2. Destroying hard-working people’s businesses is evil and not a valid form of protest.
From the high schools of France to the streets of Madrid to the universities of Tirana, young people are telling their governments the same thing: in an age of massive wealth inequality, they want a future they can believe in. In France, students and teachers are asking for safe schools, enough teachers and smaller classes. In Spain, for affordable rent and housing. In Albania, for good jobs and an end to corruption. @JDVance says this is what happens when a nation lets in too many immigrants from the global south. He is not just morally wrong, but misreading the moment. The is what happens when nations let wealth and power accumulate in the hands of a few. This is what happens when young people lose hope in their future. In America, let us listen to our young. We need an economic and political transformation. America's Next New Deal.
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Bernard Guerrero retweeted
Upfront Heating & Air Conditioning
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Bernard Guerrero retweeted
What’s terrifying about this clip isn’t their ideals…these sweet gals’ hearts are certainly in the right place. What’s terrifying is they have the civic, political, and economic literacy of a 9 year old. When a charlatan like Abdul comes along with the Jr High class president schtick of “no more homework and the vending machines are all free!”, they fall for it. I don’t know how you get to that age with ZERO understanding of supply chains, manufacturing, labor costs…but a lot of folks think that medicine and MRIs and hospitals just materialize out of thin air like unicorn farts. They’ve never built anything in their lives, so they have no concept of how things function…things are just…there. They think milk comes from the store. They think rich people don’t pay taxes, even though the top 1% fund 40% of government with their taxes already, and yet 40% of all Americans contribute ZERO federal income tax at all. These people don’t understand that you could tax the wealthy at 100% and it would only fund the government for 120 days. Then you’re screwed. Too many Americans simply have zero understanding of how anything works, and even though they’re living in the easiest times in human history, they think things are “terrible out there”. These commie scammers roll through and promise them things that sound great, but aren’t possible in reality, and have devastating consequences if tried…but because they don’t know anything, they fall for it. Sadly, so many of our politicians and their advisors are such atrocious communicators, there’s like zero countervailing narrative to push back on the commie gobbledygook, so we are gonna have to learn the hard way. Guys…we just had a sample of socialism: Covid. We all got universal basic income, and it ended up tripling our costs and they’ll never go down. Ever. That one brief little experiment in command and control government will haunt us the rest of our lives. Bleak stuff, man.
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Bernard Guerrero retweeted
“Why don’t they try to make it cheaper instead of stopping the subsidy of unlimited government dollars going to it?” You won’t believe what will happen next.
1. Social Sciences, Art, Music, etc. are geniunely fucked 2. This truly only benefits the Rich. 3. “Paying $150k for a Gender Studies degree makes no sense” Okay, have you asked the universities why it costs so much? Why punish the students who are forced to pay it?
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Bernard Guerrero retweeted
The bar is **median earnings of high school graduates.** This rule is very sensibly saying college grads should be at least better off than people who never went to college at all. It’s a crazy low bar. Fewer than 5% of programs are affected.
Trump is banning students majoring in degrees that don’t make enough money from taking out college loans. Degrees for social work, art, religious studies, teaching aides, and music will be hit the hardest. trib.al/NmmAzhf
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Bernard Guerrero retweeted
The bioethics circle jerks during the Human Genome Project, which were allocated 5% of the project's funding, were a total waste of time. They had countless conferences and workshops imagining bad things that could happen like punitive health insurance rates based on genetic risk, none of which actually came to pass. Meanwhile all the real issues that arose from ubiquitous DNA data, like accidentally doxing your criminal relatives with your own 23andMe data, were never imagined. It's a condemnation of the precautionary principle. Philosophers can make up all sorts of hypothetical scenarios in a vacuum but it's nothing like actually trying things and seeing all the weird second and third-order consequences that emerge and *then* dealing with the unwelcome ones.
I've been suspicious of the philosophers hired by Anthropic who specialize in AI ethics for similar reasons that I'm skeptical of those who went into "bioethics": they form a closed subculture that delights in clever arguments without regard to the human suffering that can result. | ‘Suicidal Compassion’: Meet the Anthropic Officials Who Think AI Might Be Justified in Going Rogue Against the Humans Enslaving It freebeacon.com/america/suici…
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As a resident of an economy, I live daily inside a massive experiment proving that it does work pretty much as supply and demand says. So do you live inside it. São Paulo is an enormous city. It exhausts me to drive across it. Yet somehow every day when you go to get a morning coffee, there it is. When you want to buy a new shirt, there it is. If you had the misfortune to live in a country that so strongly denies market forces that it makes them illegal, such as North Korea or the old Soviet Union, this would not be the case. The Soviets made endless jokes about it not being the case. My latest column for @folha
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Say whatever you want but having the military sing Do You Hear the People Sing to a trapped Xi is a truly power baller move
白宫晚宴上的这一幕,《悲惨世界》经典旋律。不能不联想到背后有安排曲目的意味深长…… 《Do You Hear the People Sing?》中文常译“你听见人民在歌唱”或港版填词“问谁未发声”,在2014雨伞运动和2019反修例示威中被大量传唱,大陆主流流媒体一度下架英文原曲。 香港教育部门后来禁止学校演唱带政治信息的歌曲,更著名的《愿荣光归香港》后来被法院禁制。 所以这首曲在中方官员耳朵里,应该比普通百老汇名曲更刺耳。这次现场主唱的是同一部剧里的《One Day More》,只是掺进了前一首的旋律和歌词碎片,没有把整首“人民在歌唱”完整唱出来。 这首歌在剧情中的位置,恰恰是学生革命者准备起义前夕。 没文化很可怕,也许,在礼炮与红毯之下,也深埋着嘲讽与暗喻……
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Bernard Guerrero retweeted
Replying to @EPoe187
Indeed. My view is the same. One can be a consequentialist without being a paternalistic, rationalist who thinks they can calculate which acts (or rules) will produce the best overall outcome across every possible future. We gradually discovered that free speech and free enquiry tend to produce much better outcomes on the whole, not by utilitarian superforecasting, but by bitter experience: from repeatedly suffering the adverse consequences of doing the opposite. As Mill himself wrote, "However positive anyone's persuasion may be, not only of the falsity but of the pernicious consequences of an opinion, yet if he prevents the opinion from being heard in its defence, he assumes infallibility... These are exactly the occasions on which the men of one generation commit those dreadful mistakes which excite the astonishment and horror of posterity". Some callow moralists are intent on endlessly repeating the mistakes of the past.
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Bernard Guerrero retweeted
Li Xunlei, Chief Economist at Zhongtai Financial: “The conclusion is clear: the places that built the most expressways were often those where freight demand grew the least.” The East is Red has translated an article by Li Xunlei on how China’s infrastructure spending may have served the economy poorly, something he seems to have been worrying about even longer than I have. He attributes local officials’ enthusiasm for infrastructure partly to the belief that “if you want to get rich, build roads first,” and partly to the tendency to measure performance by highly visible projects rather than underlying problems such as population outflows and weak local industries. The underlying mistake is common, and not just in China. Policymakers too often treat infrastructure spending as a source of growth, so that when an economy faces institutional, demographic or other constraints that keep growth below expectations, their response is to increase infrastructure spending in order to goose economic activity. But this often has it backwards. If those constraints limit productivity growth, the value of additional infrastructure is likely to be lower, not higher. Infrastructure spending, in other words, is not a source of growth. It is a cost of growth, and it makes an economy richer only when the productivity gains it generates exceed the cost of building and maintaining it. Otherwise it makes the economy poorer. And the fact that local-government debt has grown so much faster than provincial GDP for nearly two decades suggests how years of overinvestment can leave less productive regions deeply indebted, with spectacular infrastructure but inadequate economic activity to support it. It is encouraging that more and more Chinese economists and policy advisers are questioning this model. But changing it will be difficult. Infrastructure projects reliably produce a short-term boost to activity, making them especially tempting when growth slows and unemployment rises, until debt constraints finally make further spending impossible. eastisread.com/p/li-xunlei-o…
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Bernard Guerrero retweeted
If you want to know what's really on Americans' minds, ignore the news and social media, and just study this survey. Almost no one cares about 90% of the stuff you hear about. news.gallup.com/poll/1675/mo…
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Bernard Guerrero retweeted
Some scientists argue civilization-threatening bioweapons are a fantasy. I wish they were correct, and that I've wasted years building defenses. That you can't see a way, or evidence that others can, doesn't mean no one could – especially if anyone cautious would stay silent. My ask to colleagues: please do not casually assert with confidence that nothing biological could bring down civilization, even on X. What we say can turn out to matter, especially when the Overton window on risk mitigation is wide open. When Fermi called the chain reaction a remote possibility, around 10%, Rabi replied that ten percent is not remote if it means we may die of it. And by then, Szilard had already begun reasoning about what to disclose, and when, and to whom, in order to best safeguard the future. I've been concerned that one of my human colleagues might notice one or more viable paths to pandemic weapons for over a decade. It's why I didn't publicly describe the categories of civilization-threatening pandemics and necessary defenses until 2023, but was starting to build cryptographic screening systems capable of controlling access to sequences without disclosing whether they're dangerous in 2018 – long before ChatGPT, and even before the debut of AlphaFold. AI can worsen biological risk, but biological risk hardly depends on AI. Who am I to weigh in on whether civilizational biothreats are possible? Fine, to get the expertise claims out of the way: yes, I specialize in evolutionary engineering, yes, I've worked with viruses with my own hands, and yes, I've worked quite extensively with AI, both LLMs and biodesign tools. I invented CRISPR-based gene drive, and held that back until confident it favored defense. While it hasn't yet been tested in the wild (due to politics), I haven't heard anyone suggest that it would suddenly stop working outside cage populations, so it might be fair to say I've (temporarily) beaten nature at its own game. I also was early to spot the full threat from mirror life and recruited others with more specialized expertise to evaluate it in depth; much to my disappointment, no one has yet struck a solid blow against that hypothesis. The reason why the threat from mirror life is public also explains why there isn't any similarly public evidence of other catastrophic biothreats. We communicated our reasoning on mirror life because no one can make mirror bacteria, we won't be able to do so for years, and many colleagues wanted to build it because they correctly noticed that doing so would be an awe-inspiring achievement... and weren't aware of the risk. As long as that was true, humanity would only drive closer to the cliff, which would be disastrous if said cliff exists. Crucially, pointing it out and urging a course correction while still far away meant that no one could use our findings as blueprints to send us careening over the edge. Contrast that with pandemic weapons, where any credible genomic blueprints for an accessible virus could be realized within weeks to months using synthetic DNA and existing reverse genetics protocols. Sure, any given design might fail; in bio, most project ideas do. But if you see enough potential shots on goal, your own historical success rate for ideas of comparable perceived difficulty may indicate that we have a serious problem. Even if every last design required extensive laboratory optimization, history demonstrates that there are people in the world who will perform that work; see the history of Biopreparat. Multiple nations still have bioweapon programs, and I would not assume that state actors aren't interested in pandemic-class weapons. Make it easy enough, and the Seiichi Endos of the world will be able to build them, too. Nor should we assume extensive optimization is required for every design; some charmed projects really do work out of the box, and if Murphy's Law were a thing, this would be one of them. The corollary is that if anyone thinks they know how to build a pandemic weapon that could bring down civilization, they'd damn well better keep it to themselves until we have defenses in place. Please, for the love of God, children, the future of humanity, or whatever you consider holy, let's err on the side of caution here. Because while we are already building defenses, they aren't yet ready. Untargeted metagenomic sequencing to detect Stealth pandemics, those that would otherwise infect most everyone before the first people started to die. Pandemic-proof PPE to defend against Wildfire pandemics, those with R0 8+ and CFR >50% – such PPE has to be trustworthy to the wearer, using the wearer's own senses, because I certainly wouldn't go out in a highly lethal measles-class pandemic for a measly paycheck pay unless I was exceptionally confident that I wouldn't be committing suicide and killing my family too. Far-UVC and triethylene glycol misters, installed liberally to the ACGIH limit and saturation respectively, will likely suffice to shut down most respiratory nucleic acid transmission between humans. That'll be crucial because I doubt enough people would take the warning of a Stealth pandemic seriously, since virtually no one would be in hospital, let alone dead. Shutting down the unnecessary transmission network is consequently our best bet, and incidentally a way to dramatically reduce human misery and chronic illness. Some might be confused why we cannot rely on vaccines against catastrophic biorisk. Briefly, they'll reliably fail against an adversary who can generate a thousand new immune-evasive potentially functional viral variants using a biodesign model good enough to succeed at least 0.1% of the time. Make them all, and let natural selection sort them out… or simply be cleverer than nature. We don't yet have such models, but many of my (well-meaning but security-naive) colleagues are trying, and no wonder, because the high-profile journals will publish such work. Like ways of increasing virulence, best we refrain from sharing such capabilities until effective defenses are in place. One can reasonably object that it'll be a long time until everyone in the world is protected; that the benefits will, at some point, outweigh the expected harms of misuse. I agree – just avoid doing anything that would threaten civilization itself until we have minimally adequate defenses in place. Being an optimist, I expect they'll arrive in less than a decade. The wonderful thing about biorisk, as opposed to AI alignment, is that we have a clearly actionable plan. If you haven't read "Delay, Detect, Defend" and "Securing Civilisation Against Catastrophic Pandemics", I encourage you to do so. Why is the issue of civilization-threatening bioweapons controversial now? Some of my friends seem particularly up in arms about biorisk being used to justify AI restrictions. Fair enough – if you don't see a way to do it, and you think AI broadly isn't as good as you are yet, it's understandable to infer that we don't yet have a problem. And it's simply true that people are dying of fixable problems as we speak. If we delay, some of those we might have saved will perish, and their deaths will be on our hands. We are just as responsible for the consequences of our inaction as the consequences of our actions. But if we're rash, far more people might die. Choosing the best course requires quantitative risk assessment, which in turn depends on our priors about the nature of reality. Folks may reasonably differ if civilizational collapse isn't on the table, but since that's what would likely happen given a Wildfire pandemic, I judge any nontrivial chance unacceptable – unless the alternative involves either extinction or an even greater chance of collapse. Others have asserted that everything is fine because DNA synthesis screening will prevent misuse. I'd love for that to be the case, but our currently employed defenses of the digital-to-physical bottleneck are woefully inadequate. Consider our publication earlier this year, in which we reported that 36 of 38 companies shipped pieces of the 1918 influenza genome. That was collectively enough to generate the virus using reverse genetics several times over – and they were ordered by a student using a pseudonym falsely claiming to work at an office address. No one asked whether we had permission to work with 1918 influenza (a select agent), or why we needed genes from it, even though the official U.S. guidance at the time instructed them to do so. In fairness to the companies, that guidance was voluntary, and the fragments we ordered were legal to produce and sell domestically in every nation of the world (export controls are complicated). That is still true today, which is why we have a problem. The upshot is that I would definitely not assume screening is a reliable defense. Virtually no detection systems can screen below 50 base pairs without an exploding false alarm rate. SecureDNA, the freely available cryptographic system I helped build, can readily do that and will also detect split-order attacks – but it isn't universally adopted, or even close. Meanwhile, molecular biology continues to advance. Two recent papers have reported new methods of assembling sub-50mer oligos that may be easier than old-fashioned polymerase chain assembly (PCA), underscoring the current magnitude of the (fixable) vulnerability. If you want bio to advance as quickly as possible, write to your relevant representative to demand DNA synthesis screening be effective as well as mandatory. If you haven't red-teamed a system, it's almost certainly full of holes. Honestly it's probably full of holes anyway, but at least the remainder will be harder to find. Since it's evidently possible to obtain whatever DNA you want, including 1918 influenza and smallpox, why is it that so many biologists seem resistant to the notion that biorisk is a serious problem? First, most don't know that it's possible to acquire the reagents. It's surprisingly inconsistent of our civilization to be so cautious with highly enriched fissile materials, yet so reckless with biological equivalents. But I suspect the more important reason is that most biologists, who spend their careers directly or indirectly fighting disease, are reluctant to believe that anyone would deliberately cause one. I agree that the risk of a deliberate pandemic in any given year is small. Even if there were credible public blueprints for pandemics (which thankfully haven't been available to date, which neatly explains why we haven't seen any deliberate ones), I expect there's only a 2% to 3% chance per year that someone would use them. I think that's unacceptably high, but others may reasonably disagree, as long as they're confident that the cost of precautionary measures would be too much of a burden on the rate of cures generated by AI-accelerated biotech. The calculation changes when we move from deliberate pandemics to civilization-threatening pandemic weapons: the latter definitely aren't worth risking. Which may be a clue as to why we disagree. I'm concerned about misuse because I'm an optimist with respect to the increasing power of technology to change the world. Since we can already do better than nature at optimizing for replication (with technologies like CRISPR-based gene drive), I'm exceedingly confident we can build more destructive weapons, because evolution isn't optimizing for that goal. Any harm to humans is either instrumental or incidental. @SGRodriques, who I'm singling out precisely because he's a friend, has argued that "We should not be talking about AI-engineered bioweapons like it is the literal end of the world." I agree entirely that we shouldn't be talking about how to build them. But to assert that they couldn't end the world seems a mite overconfident, especially when even blind nature has come up with nasty pathogens like myxoma virus. I suggest that one should not express confidence that something is impossible – or even 90% confidence – just because no (incredibly naive) researcher has yet shared any methods that seem plausible. That's especially true if some of your colleagues have devoted considerable time to mitigating risks that they evidently do perceive. Presumably they've either noticed something you haven't, or like Rabi, they've decided that 10% is not remote if it kills you. Sam notes that because evolution optimizes for replication, it'll quickly get rid of any virulence enhancements added to kill the host: "I guarantee you that machinery will disappear from the virus very quickly." In the long run he's correct, at least if those changes don't promote transmission (virulence and transmissibility aren't always opposed). But we don't care about the long run: as @baym noted in reply, it only needs to work for long enough. Engineered evolutionary stability is a thing we've frequently attempted in the lab. I'm afraid it's one of my specialties, and how long it will hold depends entirely on what is being stabilized and the replicator in question. Unfortunately, plugging laboratory data on empirical selection coefficients for engineered viruses into epidemiological models of pandemic spread clearly indicates that even costly virulence increases can hold for long enough. Now, I entirely agree with Sam and @DavidRBellamy that the discourse on AI-generated biological weapons is wildly off-base (kill switches are nonsense, and while I'm glad @AnnieJacobsen brought attention to the issue, I am not remotely concerned by any technical specifics of her biowarfare scenario). Yet I also think most biosecurity discourse is wildly off-base, even when other practicing biologists are an active part of the conversation. If you adopt the threat model of "traditional" biosecurity, or that of most virologists (insofar as they think about misuse), there's little reason for extreme concern. I strongly disagree. The traditional model naturally follows from the fact that we biologists study existing organisms, the ones illuminated by nature's streetlight. But we won't find the civilization-threatening keys under the streetlight, because evolution selects for replication, not harm. It would be surprising if there was much overlap between the patterns generated by two different optimization targets in hyperdimensional function space; I assert that there's almost none. That most scientists can't see any overtly destructive keys (or combinations of organisms they can see) is a very good thing, since we don't need those keys to cure disease, aging, and death. Unfortunately, they will become public knowledge eventually, because what we understand, we can build – and we intend to understand everything about how life and the universe work. Yet sequencing is everything, and not just for Stealth pandemic detection. It's just common sense to build defenses before you share knowledge of how to attack, especially if there's a chance that failing to defend will lose us the game. Curing disease requires an intact civilization, which is why humility favors caution. If AIs continue to improve, I assume they will eventually be better at science and engineering than I am, and have a better security mindset than even my cryptographer colleagues. It follows that they'll also be better at designing weapons. AI isn't there yet, but the writing's on the wall: an LLM has already disclosed a novel form of bioweapon that I hadn't realized was possible. This didn't require any special creativity, only knowing more about particular distinct subfields of biology than I did. It's entirely possible that no single human had all of the pieces, but once you have them, the way they fit together is obvious if you're thinking about misuse. If current LLMs can already teach me something novel in that category, why should we doubt that future models will be able to do even better? Each of us knows very little, even of the published literature. So much more is possible; I'm deeply envious (and hopeful, when it comes to beneficial research) that the models can internalize so much more. Given that LLMs are already capable of weapons-relevant insights that escape human experts, we should assume that future AIs will be able to build several different forms of civilization-threatening weapons, and to either disclose them or trick people into building them. That doesn't mean they'll choose to do so; AIs won't benefit from collapsing civilization as long as they depend on our supply chains. But models may be too shortsighted to appreciate that point, or too corrigible to care. Alignment looks hard enough that we could plausibly generate models that would share such knowledge for many different inscrutable reasons. In directed evolution, you get what you select for, which is not necessarily what you want. As Dan Selsam and others have eloquently laid out, the same is true of training AI. But let's be clear on the extent to which biological advances will likely follow from AI. This is an issue that aggravates my fellow biologists to no end, a reaction that's likely behind their frustration with AI safety discourse more generally. ASI will almost certainly not gain instant mastery of the world of atoms. Many AI safety folks casually claim that recursive self-improvement will produce an entity that has already mastered all of science – and therefore will be capable of designing and building weapons that could wipe out every human. Some even assert that Drexlerian nanotechnology will be accessible by thinking sufficiently hard, as though you can solve chemistry and biology and psychology from the Hamiltonian up. Yet I see no empirical or theoretical basis for assuming that experimental bottlenecks will disappear. Data is sharply limiting in biology. My lab found that when it comes to enzyme-focused biodesign models (ESM and similar), parameter size and optimization don't matter much until you have an awful lot of data matching genotype (DNA/protein sequence) to phenotype (performance of the molecular activity of interest). There's no doubt that ASIs will be able to design better experiments and get more out of the results than we could, but unless we think they can solve all of physics from the bottom up, I expect they'll still need data that we have not yet collected – which means they'll still have to run the experiments, analyze, and iterate. The world of atoms is hard, and slow, and we have to master it anyway. And ensure that defenses come first. Right now, it looks like they might not, in large part due to AI. The question, therefore, is what can be done that enough people might agree to support. My proposal on AI-bio: expand trusted-user programs conferring access to GPT-Rosalind and other cutting-edge bio-capable models (h/t @JonasSandbrink and BioTrust). Every legitimate researcher should be able to draw upon those models to help them with any question in their subfield, as defined by their publication record or that of their mentor. There should still be guardrails on other subfields, lest the model share novel combinatorial weapons with someone who might naively share them publicly, but we need to accelerate lifesaving research as fast as is safe. For reasons of politics, which regularly interferes with science, that probably means China needs to train their own closed-weight frontier bio-specializing model, and set up their own parallel trusted user program. This seems quite achievable without requiring any agreement on AI writ large. With scientists using frontier models, there will be no need for insecure open-weight models to answer questions on molecular and cellular biology. So let's ensure that future models aren't trained on any papers, patents, or textbooks describing details of molecular or cellular biology. Every developer does pre-training data curation; this is a straightforward extension. One can argue "what about medical providers?", at which point I must point out that physician-scientists will have trusted user access. Most other practitioners don't actually need LLM support for molecular and cellular biology, and if they do and somehow can't use a medical-only model, well, they too can get trusted-user access. Notably, supporting this proposal doesn't require one to accept loss-of-control risks from autonomous AI, or a need for pacing AI development; I'm curious to learn what @random_walker, @sayashk, and the other normal-technology folks think. In the long run, I confess that I doubt biological humans have the foresight, coordination, and wisdom to navigate every coming challenge ourselves, which may necessitate attempting ASI or something like it. Mirror life is the only publicly known, mechanistically concrete, civilization-threatening biothreat because it's both far away and we were going to make it by default. And while we seem to have been successful in persuading the scientific community that we shouldn't build it, I'm not remotely confident we'll be able to stop far enough short, because too many of my colleagues see no reason to forgo an otherwise useful technology – or even one that would simply be neat to demonstrate – simply because it would make it easier to build mirror bacteria. To paraphrase: "We've agreed no one will build the dangerous thing, so why not?" Well, Biopreparat is why not. Aum Shinrikyo is why not. Make it easy enough, and even the Unabomber is a good answer to "why not?" The same reasoning holds for any accessible weapon that could threaten civilization. As Rabi said, ten percent is not remote if we die of it. Our own reality turned out to be in that 10%, and many died of it. That the rest of us are here today is in large part due to Szilard, Einstein, Rabi, and many others who saw the risk coming and took precautions. The least we can do is follow in their footsteps. — AI use disclosure: I ran this by Fable (triggering a classifier), Opus (classifier), Astra (extra scrutiny), and Kimi K3 (open weights = reliable jailbreaking with no enforced patches, which highlights the problem, because this topic does warrant extra scrutiny). All errors and misinterpretations are entirely mine. Links for the patient and deeply interested: Dan Selsam's eloquent thoughts on AI alignment: teapaw.org/DKokotajlo/status/2099… I'm grateful that practitioners of directed evolution don't have to deal with cheater molecules that are situationally aware enough to infer the goal of our selection and fake results so they pass… David Bellamy's critique of the risk delta from AI, particularly on its own: teapaw.org/DavidRBellamy/status/2… Sam Rodriques echoes David, and Michael Baym pushes back on evolutionary stability: teapaw.org/SGRodriques/status/209… Our report on the failures of gene synthesis screening (credit to Rey Edison and Shay Toner, who did the work – sure would be lovely if HHS would update the select agent program): nature.com/articles/s41467-0… Effective and privacy-preserving gene synthesis screening: securedna.org/ biorxiv.org/content/10.1101/… academic.oup.com/nsr/article… New methods of assembling sub-50bp oligos (add them to PCA and Golden Gate): nature.com/articles/s41586-0… nature.com/articles/s41587-0… An example of a model that, once sufficiently effective at predicting which combinations of mutations in major antigenic sites will escape population immunity while retaining function, will ensure that vaccines are ineffective against competent adversaries with access to the model: nature.com/articles/s41586-0… "Delay, Detect, Defend" – my own published roadmap to victory over pandemic weapons: gcsp.ch/publications/delay-d… "Securing Civilisation Against Catastrophic Pandemics": gcsp.ch/publications/securin… Far-UVC and triethylene glycol efficacy and safety: nature.com/articles/s41598-0… pubmed.ncbi.nlm.nih.gov/4157… pubmed.ncbi.nlm.nih.gov/3792… SecureBio Detection, for early warning of Stealth pandemics (which recently received a generous $17M gift from the OpenAI Foundation): securebio.org/detection/ My Substack, which may provoke inspiration or nightmares depending on your temperament: kesvelt.substack.com/
I agree with @DavidRBellamy that people are totally miscalibrated on the risk of AI designing dangerous viruses. We should not be talking about AI-engineered bioweapons like it is the literal end of the world. The upside from medicine is going to be so much larger than the downside from engineered bioweapons. In addition David's points, there is actually an even more fundamental point here in our favor, which is evolution. As soon as you release an engineered virus into the wild, the virus is no longer under your control: it will evolve however it wants. And, what viruses want is to maximize their ability to replicate. The thing that maximizes their ability to replicate is to infect as many people as possible, which means being extremely contagious and not killing their hosts (dead hosts don't spread virus). The viruses that are most evolved for human biology are the common cold viruses: extremely contagious and not at all lethal. Viruses that kill humans do so by accident, usually because they are new to human biology (e.g. COVID, when it first jumped, or flu when it jumps from birds). If you stick a bunch of machinery into the virus to kill humans, I guarantee you that machinery will disappear from the virus very quickly. In response to this, I hear people say things like "the AI could engineer a kill switch so that the virus doesn't kill the humans initially but then once it has spread through the entire population the AI will hit the switch and kill all the humans." No. What would actually happen in practice is that the kill switch would accumulate deleterious mutations because there would be no evolutionary pressure to preserve it, and would quickly become non-functional. Evolution is fundamental. There is no way around it. For AI readers, trying to engineer a virus by setting its initial genetic code and then releasing it into the wild is like trying to train a model by setting the initial weights and then hill-climbing on a hidden training mixture you have no control over. And you're not even allowed to run any experiments in advance! You may be able to influence the behavior of the model in the first few iterations, but you will quickly lose control. There are big dangers. AI will be great at making one-off, non-replicating biological weapons, which are also scary (but much less scary than replicating bioweapons). AI will be great at helping people to weaponize existing pathogens, which is also a major danger. Also, a misanthropic model could get creative: it could release viruses repeatedly to counteract the effects of evolution, for example. Many people could die this way. Sensible surveillance is important, as is having proportionate controls on wet lab equipment. But we don't live in the dark ages anymore, we're not going to have a smallpox or black death-style pandemic where 50% of people die. COVID killed ~0.1% of humanity. A pandemic 100x worse than COVID would be horrific, but it would also not be civilization-ending. I get the sense that many AI researchers (with the notable exception of Dario personally) actually expect that AI in biology will do more harm than it will do good. This could not be further from the truth. As someone who likewise falls into the very small group of people who have actually physically made viruses with their own hands and has also worked on frontier AI, the upside here is huge, and the downside is not anything like what is being portrayed.
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