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NN, Inc. Provides Update on Business Growth in the Data Center, AI Reasoning, and Cloud Computing Markets
CHARLOTTE, N.C., March 18, 2026 (GLOBE NEWSWIRE) -- NN, Inc. (“NN” or the “Company”) (NASDAQ: NNBR), a global diversified industrial company that engineers and manufactures high-precision components and assemblies with six sigma quality, today provided an update on its growing presence in the data center market, a key targeted growth market for the Company. NN has secured multiple new awards with a leading global provider of AI infrastructure and data center computing equipment. As this industry
Phillies’ Don Mattingly reveals simple reasoning behind recent success
Philadelphia Phillies manager Don Mattingly discussed the club’s recent turnaround Monday before a three-game set vs. the Cincinnati Reds.
WV Superintendent of Schools Launches First AI and Digital Science Framework in the Nation
Charleston, W.Va. - West Virginia Superintendent of Schools Michele L. Blatt will share the state’s Artificial Intelligence (AI) and Digital Science Framework with educators during professional development sessions at the Charleston Coliseum and Convention Center, Monday through Wednesday, July 27-29, 2026. The Framework, the first of its kind in the country, outlines an approach for digital science and AI literacy education that equips students with the knowledge, skills and credentials to succeed in this rapidly evolving workforce. It also offers educators a way to increase efficiencies in their work and better personalize learning for students.Earlier this year, Superintendent Blatt appeared before a U.S. House of Representatives Subcommittee to share the state’s strategy on addressing the use of AI while also protecting students, their data and educational integrity. The Framework is the result of years of planning, collaboration and development. Digital science encompasses the evolving fields of computer science, AI literacy, data reasoning, cybersecurity awareness, media literacy and the practical application of digital technologies. The framework equips educators to prepare students with the AI and digital science knowledge and skills needed to thrive in the workforce of the future.The West Virginia AI and Digital Science Framework establishes a statewide structure for sustained success. From policy to educator development and student learning, it defines how the state will implement digital science and AI readiness at scale. It serves two priority goals:Respond to the rapidly changing technology landscapeEstablish best practices as a leader for rural models of AI readiness, workforce development and digital science educationWith three statewide commitments, educators will be equipped with understanding and the tools to support implementation. These commitments are:Student Access - Every student will have access to AI literacy learning experiences and digital science instruction before graduationEducator Capacity - All counties will sustain local AI training capacity through a train-the-trainer modelWorkforce Alignment - Every approved pathway will connect to employer demand, postsecondary alignment, and portable or verified credentialsNext week, the West Virginia Department of Education (WVDE) will launch the framework during three educator and staff development sessions. These sessions include unique opportunities for new county central office hires, the Support, Appreciate, Invest, and Learn (SAIL) Conference, and the West Virginia Middle Schools: Bridging the Future Initiative launch. Each session is designed to meet the needs of K-12 educators, professionals, paraprofessionals, service personnel and others who serve in the education community.West Virginia is a national leader in developing a framework for AI and digital science education. We are determined to elevate our model because it prepares students and educators for the future. Our partners at Prodigy Learning and Microsoft understand this commitment and believe, as do we, that our work has national implications for accessibility and scalability because we are building systems that address learning at all levels that lead to post-graduation success.Michele L. Blatt, State Superintendent of SchoolsAs part of the partnership, Prodigy Learning is providing West Virginia educators, at no additional cost, their in-person and virtual training on all products and the materials required for license renewal.
NFL Scout Rips Bucs' QB Mayfield
An anonymous evaluator slams the quarterback’s contract disappointment as a "crock," but fails miserably in his reasoning.
New 'AI scientists' are improving—but reveal their fundamental limits
Many of the most exciting discoveries in science involve highly specialized knowledge and making connections between far-flung facts. Scientists must combine deep analysis with broad reasoning strategies.
“Cognitive Spoofing”: How AI Fakes Expertise, And Why It’s A Huge Problem In Healthcare
AI systems have to be pressure-tested in real-world clinical settings to ensure correct reasoning, accuracy and reliability.
Is AI Reasoning Right for the Wrong Reasons?
The idea that artificial intelligence can “reason” is more intuitive than ever. But intuitions can be wrong, and the science is far from settled.
Scientists must be trained to work without AI, as well as working ...
As AI and autonomous labs become more capable, human reasoning must be preserved
IoT gadget maker AcuRite shares reasoning for killing customers’ favorite app
The move to AcuRite NOW has not been as smooth as some customers expected ..."
Galileo Galilei, scientist: “In questions of science, the authority of a thousand is not worth the humble reasoning of a single individual”
Galileo’s warning about scientific consensus still resonates centuries later as modern discoveries continue overturning accepted wisdom.
"Watch Dogs" Franchise Is 'Completely Dead'?
Ubisoft announced the other day a corporate restructure, one that led to multiple projects being cancelled, including “Prince of Persia: The Sands of Time” remake. In addition, two of its studios (Ubisoft Halifax and Ubisoft Stockholm) were being shut down, while others would be hit by smaller impacts. The reasoning was put down to a […]
ChatGPT’s new ‘Thinking’ mode just hit a 94% reasoning score — 7 prompts it can solve that standard AI can’t
OpenAI's latest flagship model, GPT-5.4, introduces an "Extended Thinking" toggle — here is how to use its 94% reasoning power to solve "impossible" tasks
Washington's tribes boost state economic activity
The scope of Washington's tribal economies is expanding its reach — and the tribes' reasoning is that's to the aid of all.
Broncos' 2026 offense considered average under poor reasoning
The Denver Broncos have a chance to be much better on offense in 2026 for exactly the opposite reason that one analyst feels they won't be.
On complex political issues, most Americans take shortcuts to simplicity
A novel study by UC Berkeley scholars finds that when Americans are presented with complex decisions on immigration, climate change or political candidates, they often avoid difficult reasoning.
Why Netflix’s ‘Maternal Instinct’ left out Taylor Parker’s mental health diagnosis
Director Jessica Dimmock explained her reasoning to Netflix's Tudum.
AI breakthrough cuts energy use by 100x while boosting accuracy
AI is consuming staggering amounts of energy—already over 10% of U.S. electricity—and the demand is only accelerating. Now, researchers have unveiled a radically more efficient approach that could slash AI energy use by up to 100× while actually improving accuracy. By combining neural networks with human-like symbolic reasoning, their system helps robots think more logically instead of relying on brute-force trial and error.
Spotify’s new Playlist Notes let users and editors explain their song picks
Spotify launches a new feature that gives users a chance to explain the stories and reasoning behind their favorite music. Editors will be using the feature, too, on top playlists like RapCaviar and others.
Anthropic's new "J-lens" reveals a silent workspace inside Claude that mirrors a leading theory of consciousness
Anthropic’s new Claude research reveals a hidden internal “global workspace” that resembles human conscious processing, raising major questions about AI reasoning, interpretability, safety, and machine consciousness.
Truth and Politics in the Age of Post-Truth
Get E-International Relations delivered to your inbox, free of charge. As you sign up, consider becoming a paid subscriber, or make a donation, to support our work. Subscribe This is an excerpt from Global Politics in a Post-Truth Era. You can download the book free of charge from E-International Relations. The term post-truth is by now so ubiquitous in describing the state of politics in the Western world and even beyond that the most pressing issue of the day appears to be about making democracy work in the era of post-truth rather than questioning the normalisation of the term itself in the first place. The critical turning point appeared to have occurred in 2016 when the term was chosen by the Oxford English Dictionary as ‘The Word of the Year.’ On its website, the Dictionary notes the transformation of post-truth from ‘being a peripheral term to being a mainstay in political commentary and connects ‘the spike in frequency’ directly with the Brexit referendum in the United Kingdom and the election of Donald J. Trump in the United States – both occurring in 2016. Paired with the noun ‘politics,’ post-truth is defined as an adjective ‘relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief.’ The Oxford English Dictionary traces the first use of the term to a 1992 essay in The Nation by the playwright Steve Tesich as he reflected on the 1980s Iran-Contra scandal and the 1990–91 Gulf War: We are rapidly becoming prototypes of a people that totalitarian monsters could only drool about in their dreams. All the dictators up to now have had to work hard at suppressing the truth. We, by our actions, are saying that this is no longer necessary, that we have acquired a spiritual mechanism that can denude truth of any significance. In a very fundamental way we, as a free people, have freely decided that we want to live in some post-truth world (Tesich 1992, 13; emphasis added). He then continued, The Gulf War is over but the war at home goes on. The gulf between rich and destitute widens – between those of us who live in a modern post-industrial nation and those of us who live in the Third World countries of our inner cities. The present administration’s response to this internal crisis has shifted from benign to malignant neglect. The current levels of misery and decomposition of our cities and the economic gulags of our ghettos are acceptable. Since there is only so much hope to go around, there is a freeze on hope. The have-nots have now been reclassified as never-will-haves (Tesich 1992, 13). In the context of Tesich’s essay, truth includes both facts, as in what happens, and ‘moral absolutes,’ as in the ‘self-evident truths mentioned in our Constitution’ (Tesich 1992, 14). Together, these two notions of truth entail the obligation to both act and react. The two passages cited above show that even in the early post-Cold War years, Tesich was already deeply concerned that the American people are neither prepared to act on truth nor react to truth that is disconcerting, or what he calls ‘bad news’ (Tesich 1992, 12). Accordingly, Tesich’s insight as captured by the term post-truth is not that truth is irrelevant to what we know, but rather it is irrelevant to how we act. In contrast, ‘post-truth politics,’ as defined by Oxford as a term that pitches ‘objective facts’ against ‘emotion and personal belief’ such that the latter appears to have overtaken the former in shaping ‘public opinion’ and thereby, democratic politics. When Tesich’s post-truth politics and its current usage are juxtaposed, the two are in fact not identical in terms of what truth is and how it relates to politics. Against such consideration, this chapter provides a critical survey of selected historical events and theoretical considerations for the purpose of gaining a more nuanced understanding of the relation between truth and politics. Truth and Language One notable impact of post-truth politics is that language is being overtly deployed as a political weapon not only by politicians and those in power, but among the general population. It seems that once facts are irrelevant to the validity of what is being said, language becomes the platform for an all-out political contest in which what is said is no more than an act of will expressed as an assertion of power and/or an exercise in defiance. As early as in the writings of Thucydides (460–400 BCE), the vulnerability of logos – which is the human capacity for rational discourse in tumultuous times – was already noted. Thucydides belonged to a school of Greek thought which maintained that language is conventional and that the association between name and things is a result of ‘human use and habit’ rather than ‘nature or divine dispensation’ (Connor 1984, 99). The conventional nature of language means that it is incapable of providing its users with any independent criteria for judgement. In Thucydides’ study of the Peloponnesian War (431–405 BCE), the subversion of logos as seen in the manipulation of language was introduced through his account of the civil war in Corcyra (Thucydides 1972, 236-245). The war first broke out in 427 BCE between the pro-Athenian democratic faction and the pro-Spartan oligarchic faction before it spread to the rest of the Greek world. There was a general breakdown of law and order, resulting in extreme violence and death. Language underwent changes that were reflective of the events of the time. The English philosopher Thomas Hobbes (1588–1679) translated Thucydides’ description of the situation as follows: The received value of names imposed for signification of things, was changed into arbitrary. For inconsiderate boldness, was counted true-hearted manliness: provident deliberation, a handsome fear: modesty, the cloak of cowardice: to be wise in everything, to be lazy in everything... he that had been so provident as not to need to do the one or the other, was said to be a dissolver of society, and one that stood in fear of his adversary. In brief, he that could outstrip another in the doing of an evil act, or that could persuade another thereto that never meant it, was commended (Hobbes 1839–1845, 8: 348; Thucydides 1972, 242-243). What concerned Thucydides most was that the meanings of words are no more than linguistic conventions that require logos to support them. The Corcyrean civil war was a reminder that such capacity cannot be assumed. This is why history understood as an authentic account of what happens matters. Guided by logos, the historian’s task is ‘the search after truth’, which is established through ‘the absence of the fabulous,’ including ‘the rejection of myth and unverifiable stories’ (de Jonge 2017, 2). Thus, history plays an important role in facilitating the stability of language – without which distinctions and judgements cannot be rendered communicable. Just like Thucydides, Hobbes was a nominalist who subscribed to the view that there is no inherent meaning to words. Hobbes’ state of nature, where there is no common authority, is precisely one in which distinctions cannot be made. As Hobbes put it, there is ‘no Mine and Thine distinct’ nor is there distinction between ‘the notions of Right and Wrong, Justice and Injustice’ (1968, 188; emphasis in original). This is clearly an unsustainable condition for everyone. Behind Hobbes’ famous words that life in the state of nature is ‘solitary, poor, nasty, brutish, and short’ is his uncompromising argument for the ‘Leviathan’, which is described by the late American political theorist, Sheldon Wolin (1922–2015), as the ‘sovereign definer’ (Hobbes 1968, 186; Wolin 2004, 238–243). Accordingly, the social contract is importantly a ‘linguistic contract’ that binds all parties to ‘general names agreed upon’ (Peters 1979, 125; Hobbes 1968, 111). Through Thucydides and Hobbes, we can identify a line of thinking that sees politics as a contestation over the use of language, which is based on shared conventions and norms. Thus understood, politics is not a collective quest for the true nature of our being or an epistemological exercise in pursuit of true knowledge. Rather, politics is about humans living with one another by finding a shared language to communicate with one another on matters that affect them all. Truth is in that sense contingent on what is being said as opposed to setting the standard for it. In other words, truth is the embodiment of shareability among us. While truth as such ensures the viability of a political entity, truth by no means represents a normative standard to adjudicate among contesting actors in what they say, let alone in what they do. During Thucydides’ times, ancient China was also experiencing a tumultuous time as the ruling Zhou dynasty deteriorated into the period of the Warring States (481–221 BCE). This was when the rulers of Zhou no longer presided over the territories brought under its control through conquest. Instead of professing allegiance to the Zhou royalty, the lords of these territories became kings themselves to rule over their own land. The period was one marked by endless military campaigns as each of the seven major states aimed to conquer the rest (Lewis 1999). Yet, the period was also known for its intellectual vibrancy as different thinkers challenged the restoring of order among these warring states – including offering ideas and advice to rulers on good governance. Among these thinkers was Confucius (551–479 BCE). His thought, which came to be known as Confucianism, went on to become the state ideology of China from the Han dynasty (206 BCE–222 CE) until the end of the Qing dynasty in 1911 when China became a republic. The founding text of Confucianism was the Analects, which is a collection of dialogues between Confucius and his students as recorded by them. One key concept Confucius advanced is the ‘rectification of names’. Though the term only appears in one exchange in the Analects, it is considered a major aspect of Confucius’ thought. Generations of Confucian scholars from past and present, including Western academics, have debated over its interpretation (Makeham 2003). The passage in question reads as follows: Zi Lu said: ‘The monarch of the state of Wei wants you to govern the country, what is the first thing you plan on doing?’ Confucius said: ‘First it is necessary to rectify the names.’Zi Lu said: ‘Is that really what has to be done? You are being too pedantic, aren’t you now? How will you rectify these names?’Confucius said: ‘Zhong You, you are too unrefined. A gentleman, faced with a matter that he does not understand, takes a skeptical attitude. If names are not correct, one cannot speak smoothly and reasonably, and if one cannot speak smoothly and reasonably, affairs cannot be managed successfully. If affairs cannot be managed successfully, rites and music will not be conducted. If rites and music are not conducted, punishments will not be suitable. And if punishments are not suitable, the common people will not know what to do. So, when the gentleman uses names, it is necessary to be able to speak so that people understand. If one can say it, one can definitely do it. A gentleman should not be careless with words’ (Analects 13:3 in Cao 2016, 148). This translation is offered by the contemporary philosopher Cao Feng. As Cao notes, the passage is what started the whole debate over ‘exactly what kind of names did Confucius... wish to rectify’ (Cao 2016, 148). Of the three major groups of interpretation examined by Cao, beginning with classical Confucian scholarship, one is arguably the most influential and certainly most pertinent to the issue of language and politics. This is the interpretation that was first associated with the Neo-Confucian master, Zhu Xi (1130–1200), who argued that the ‘rectification of names is simply the rectification of politics’ (Cao 2016, 149). Within this interpretive framework, rectification has the dual functions of correction and prescription by ensuring that predetermined hierarchical roles are strictly adhered to through following ‘the system of rites and propriety’ (Cao 2016, 151). In addition, the rectification of names entails ‘using names to rectify actuality’ (Cao 2016, 151). Yet Cao rejects this whole body of interpretive works by arguing that these were ideas developed later. Instead, Cao suggests the following: The original meaning of Confucius’ rectification of names may, then, be quite simple. Confucius did not mean to establish a concrete, normative system of “names”; rather, Confucius was simply the first person in history to realize or remark upon the importance of language in politics. As a politician, he noticed and foresaw the impact that the indeterminacy, ambiguity, and arbitrariness of names would have on politics. Confucius recognized the political consequences of language’s not accurately expressing meaning or not being accurately received by people. He realized the great role that names, as a means of distinguishing right and wrong and establishing norms, could play in society and politics (Cao 2016, 168). Scholarly debates aside, Cao’s view has the advantage of identifying an interpretation of this controversial exchange in the Analects that enables its readers to extrapolate from it the observation that the meaning of words depends as much on their users as on their interpreters; neither of which can be predetermined. Accordingly, a good ruler is someone who can speak without ambiguity to his subjects and make good his words through actions. Stated differently, Cao’s view on what concerned Confucius then, who was living through times of protracted political upheaval, is that language is an indispensable political tool for those in power and can be deployed by both good and bad rulers alike. To sum up, this historical survey shows that politics as a war of words is by no means a distinctive marker of contemporary politics nor the Western world. Be it ancient or current, east or west, language is subject to manipulation to serve political purposes and when there is an outright war of words that defies existing norms, it is indicative of a political situation in which people no longer share a language and truth becomes irrelevant. Accordingly, political order entails stability in language use. Truth and Politics As previously noted, current use of the term post-truth as an adjective to the term ‘politics’ suggests that once there was a time when politics aligned with truth. Indeed, if we start with Plato’s Republic as the key founding text of Western political thought, we see that the ideal polis (city-state of ancient Greece) is where truth and politics converge, and justice is achieved. Written around 380 BCE when Athens was still coping with its defeat by the Spartans in the Peloponnesian War, the Republic is a defence of the rule of the ‘philosopher king’, defined as someone who is ‘guided by the truth’ and is ‘always and in every way’ in pursuit of the truth (Plato 1974, 490a). For Plato, truth converges with virtues and hence, letting the philosopher take charge of politics is to let the wisest and the most virtuous lead the less wise and the less virtuous even if it requires compulsion (Plato 1974, 499b). In Plato’s words, ‘…until the philosophers attain power in a city there will be no respite from evil for either city or citizens…’ (Plato 1974, 501e). The uncompromising rule of philosopher-kings is made palatable by the claim that we are all ‘earth- born brothers’ with random mixtures of gold, silver, bronze and iron, but only the gold can rule (Plato 1974, 414–415). Understandably, academic debates over the interpretation of Plato’s Republic revolve around the question of whether truth as attained in philosophy can be realised in politics as action and whether such a connection is even desirable. Without getting into the details of these scholarly exchanges, I suggest that they all share the concern that truth acquired through the rigorous process of philosophical reasoning is not without problem when applied to the real world of living humans. This is because not all humans are philosophers – and indeed, in Plato’s view, not everyone is capable of becoming a philosopher given the protracted and arduous training that one needs to endure to become one. Hannah Arendt (1906–1975), a highly influential political philosopher of the twentieth century, notes that ‘Plato was the first to introduce the division between those who know and do not act and those who act and do not know’ and that such a separation has remained ‘at the root of all theories of domination’ (Arendt 1958, 223, 225). What Arendt points out in this remark is not the gap between theory and practice, but rather that Plato provides the normative ground for those who know the truth to tell those who do not know what to do. Arendt herself took on the challenge of understanding the relationship between truth and politics through her analysis of Nazism and Stalinism. Arendt’s insight, which was controversial when The Origins of Totalitarianism was published in 1951, was to identify Nazism and Stalinism as representative of a new form of political rule – totalitarianism – making it distinct from ‘other forms of political oppression... such as despotism, tyranny and dictatorship’ (Arendt 1973, 460). Arendt defines totalitarianism as a self- contained system of ideas driven by their ‘logic’ and marked by a mode of reasoning called ‘ideological thinking.’ Ideological thinking as such is a distinctive form of political reasoning defined by three characteristics. First, ideological thinking claims to provide a ‘total explanation’ of all historical happenings, encompassing the past, the present, and the future. Second, while ideological thinking is an attempt to account for ‘factual reality’ in its totality, this mode of thinking is paradoxically divorced from reality and experience. This is because experience, being rooted in specificity, is clearly limited in its claim to totality. It follows that as its third characteristic, ideological thinking aims to emancipate thought from the limitation imposed by experience through a ‘specific method of demonstration.’ The method involves arranging facts into an ‘absolutely logical procedure’ that starts from an ‘axiomatically accepted premise’. It is, in short, a ‘process of deduction from a premise’ – which is the only possible movement in the realm of logic that leads to one definitive conclusion. This is how the ‘logicality of ideological thinking’ provides the basis for total explanation and the ‘principle of action’ for totalitarian movement (Arendt 1973, 468–474). Stated differently, totalitarianism is a form of uncompromising idealism that exists in one’s head irrespective of shared reality as experienced. It is the perverse form of what Arendt later refers to as ‘rational truth’, which is the product of the ‘speculative mind’ that belongs to philosophy, mathematics and the sciences. The ‘speculative mind’ is the mind working on its way to generate axioms and theories by relying on the cognitive capacity of the human brain (Arendt 1977, 231). The presence of others has no bearing whatsoever on rational truth. In contrast, ‘factual truth’ is what happens when humans are ‘living and acting together’ (Arendt 1977, 231). Factual truth is therefore particular and temporal by definition. But more importantly, factual truth is ‘political by nature’ because it can only be validated in the context of the human community (Arendt 1977, 238). In other words, factual truth is about humans in their plurality rather than in their singularity. For factual truth to be sustainable, we need what Arendt calls ‘common sense’ – understood as ‘community sense’ (Arendt 1982, 72). There is also another distinction about truth that Arendt makes which is pertinent to understanding how totalitarianism works. The issue is whether the so-called totalitarian ideology is simply an unnecessarily cumbersome way of saying that it is an all-round and polished lie. Here, Arendt calls totalitarian ideology the ‘modern lie’ – in contrast to the ‘traditional lie’. The latter is restricted by two conditions: that it is a lie about the ‘particulars’ and that it deceives only the ‘enemy’ – which means that the liar is not engaged in self- deception. The modern lie, however, involves a ‘complete rearrangement of the whole factual texture’. It also intends to deceive everyone alike, to the extent that those who initiate the lie eventually ‘fall victims to their own falsehood’ (Arendt 1977, 249–254). This distinction between the traditional and modern lie is important. It indicates that not only is totalitarianism a lie, but the lie is in fact too big to fit into the standard meaning of the word. To tell a lie from truth, we need a common standard. In the case of a traditional lie, the common standard is precisely ‘the fabric of factuality’. As such, a lie always appears as a ‘tear’ to that fabric. Yet, modern lies require making a surrogate reality with a completely different context for facts to fit ‘without seam, crack, or fissure, exactly as the facts fitted into their original context’. Arendt notes that as long as those who concoct the surrogate reality are prepared to engage in self- deception to ‘create a semblance of truthfulness’, there is nothing to prevent ‘these new stories, images, and non-facts from becoming an adequate substitute for reality and factuality’ (Arendt 1977, 253–254). Totalitarianism therefore requires a person to stand outside of the system itself in order to make sense of it. Arendt points out that it is futile for us to argue with (for example) a Nazi or a Stalinist on race or class. To confront totalitarianism requires more than just a confrontation with specific facts. It is a more fundamental confrontation between reality as experienced and its total rejection. This means that Nazism and Stalinism are more than radical ways of conducting racial conflict or class struggle. Rather: Totalitarian politics – far from being simply antisemitic or racist or imperialist or communist – use and abuse their own ideological and political elements until the basis of factual reality, from which the ideologies originally derived their propaganda value– the value of struggle, for instance, or the interest conflicts between Jews and their neighbours – have all but disappeared (Arendt 1973, xv). Accordingly: The ideal subject of totalitarian rule is not the convinced Nazi or the convinced Communist, but people for whom the distinction between fact and fiction (that is, the reality of experience) and the distinction between true and false (that is, the standards of thought) no longer exist (Arendt 1973, 474). Who then is the ‘ideal subject’ of totalitarianism? Although there is a revival of interest in Arendt’s observation on truth and politics since the onset of post- truth politics, not enough has been said about her reply to the question (Hyvönen 2018; Klinkler 2018; Lee 2019; Zerilli 2020). Yet if we take a look at Arendt’s portrait of this ideal subject, her view is even more pertinent to our current challenge. As a self-contained system of ideas that is detached from experience, totalitarian thinking offers consistency in ways that reality cannot. Arendt notes that this kind of consistency is especially appealing to those who have lost a sense of bearing in the world (Arendt 1973, 353). They constitute what she calls the ‘masses’. The ‘mass man’ is thus a highly isolated and atomised individual, who is ‘obsessed by a desire to escape from reality’ (Arendt 1973, 318, 352). In his ‘essential homelessness’ the mass man can ‘no longer bear its accidental, incomprehensible aspects’ (Arendt 1973, 352). This longing for escape from reality is a ‘verdict against the world’ in which one is ‘forced to live’ but ‘cannot exist’ (Arendt 1973, 352). Such a situation is ripe for the ‘revolt of the masses’ against ‘common sense,’ which is ‘the result of their atomisation, of their loss of social status’ and with it, the ‘whole sector of communal relationships in whose framework common sense makes sense’ (Arendt 1973, 352). In short, the masses are people who are considered to be ‘superfluous or can be spared … [who] cannot be integrated into any organisation based on common interest’ (Arendt 1973, 311). Whether one agrees with Arendt’s analysis of the social conditions in the interwar years that led to the formation of the ‘masses’, her portrait of the ‘mass man’ is a powerful account of what can happen to an individual who has become disposable to the society that they used to belong to. Hand in hand with this phenomenon is indeed the collapse of ‘common sense’ that leaves the dislodged individual scrambling for reconnection. By offering the masses a substitute reality validated by the ‘logic of an idea’ rather than by experience, totalitarianism makes it possible for them to live in a world where there is no condition of plurality. Between ‘facing the anarchic growth and total arbitrariness of decay’ and ‘bowing down before the most rigid, fantastically fictitious consistency of an ideology’, the masses will likely ‘choose the latter’ (Arendt 1973, 352). A world created by ideology, then, is more attractive to the masses not because ‘they are stupid and wicked, but because in the general disaster this escape grants them a minimum of self- respect’ by conjuring up ‘a lying world of consistency which is more adequate to the needs of the human mind than reality itself’ (Arendt 1973, 353). Moreover, it is precisely this ability to guarantee consistency that makes it possible for the leaders of totalitarian movements to demand ‘total, unrestricted, unconditional and unalterable loyalty of the individual member’ (Arendt 1973, 323). While this chapter is not a study of the social factors that led to specific contemporary developments such as the result of the Brexit referendum or the election of Donald Trump in 2016, Arendt’s ‘mass man’ seem to have found their counterparts among the supporters of Brexit and Trumpism. Studies of voters’ profiles in both events point to a disproportionate share of supporters from white male lower income backgrounds without university educations. These are individuals who have either already lost their jobs to globalisation or who are at high risk of losing their job with very little capacity to be retrained for the twenty-first century market economy of advanced industrialised states (BBC 2016; Pew Research Center 2018). In short, these displaced workers are part of the tapestry forming the modern equivalent of Arendt’s ‘mass man’ – ready to be the recruits of totalitarianism. Conclusion If we step back to take a more comprehensive approach to the relationship between truth and politics, both historically and theoretically, it is not so obvious that 2016 is a defining moment. From ancient China and ancient Greece to our so-called post-truth age, the indeterminacy of language appears to be a persistent political challenge. Language is always open to manipulation by its users to serve power. Nonetheless, the tendency these days is to single out the ubiquity of social media and the proliferation of Internet platforms as the incubator for the countless cyberwars of words that in turn fuel the real world of politics. In this context, language is no longer the conveyor of objective facts and accurate information, but rather the medium for expressing subjective opinions and feelings, as captured by the Oxford Dictionary’s definition of the term ‘post-truth politics’. In contrast, the historical approach used in this chapter shows that it is in fact too easy to point the figure at the Internet and digital communication for the political challenge that we face today. What is being suggested here is that with or without the Internet, language, be it used to convey facts or emotions, is simply a tool for communication that humans have invented. While language per se cannot adjudicate what is true or not true, what is right or wrong, it embodies such capacity when language is a shared norm. That shared norm cannot be sustained without some semblance of cohesion in any given community. Any war of words, irrespective of its platform, is indicative of the breakdown of such cohesiveness. Returning to the original context in which the term post-truth politics was first used by Steve Tesich helps to identify the important distinction between truth as the standard for knowledge and truth as the standard for action. The two do not necessarily coincide. Placed in the context of the history of political thought, this distinction is in fact at the heart of Plato’s Republic. Yet when the philosopher as the quintessential bearer of truth becomes king, the polis that he rules over is far from the ideal of a just state that it purports to be. Instead, it is one that is held together by lies and coercion. What Arendt reveals through her study of Nazism and Stalinism is that it is dangerous for the kind of truth that philosophy generates – ‘rational truth’ – to become the guide to politics. Politics guided by rational truth can only be uncompromising and follows a logic of its own that cannot afford the ambiguity and indeterminacy of reality as lived experiences of humans. A world of post-truth politics can be an opportunity for a new kind of politics if it is indeed about dismantling the idea that truth is the ideal that politics should strive for. But as it stands, the post-truth politics of today is more about invoking a past that never exists to judge the presence as a crisis in naming when the real crisis is about rebuilding a shared space that has room for all – be they of the left, right or centre; cosmopolitan or parochial; civic-minded or self-centred; liberal-minded or bigoted. Politics is and always will be about striving for that space, without which no human can thrive. References Arendt, Hannah. 1958. The Human Condition. Chicago: The University of Chicago Press. Arendt, Hannah. 1973. The Origins of Totalitarianism. Rev. ed. New York: Harcourt Brace Jovanovich. Arendt, Hannah. 1977. “Truth and Politics.” In Between Past and Future, 227–264. New York: Penguin Books. Arendt, Hannah. 1982. Lectures on Kant’s Political Philosophy, edited by Ronald Beiner. Chicago: University of Chicago Press. British Broadcasting Corporation. 2016. “Brexit vote: The breakdown.” BBC News, December 7, 2016. https://www.bbc.com/news/uk-politics-38227674 Cao, Feng. 2016. “A New Examination of Confucius’ Rectification of Names.” Journal of Chinese Humanities 2: 147–171. https://doi-org.subzero.lib. uoguelph.ca/ doi/ 10.1163/23521341-12340032. Connor, W. Robert. 1984. Thucydides. Princeton: Princeton University Press. de Jonge, Casper C. 2017. “Dionysius of Halicarnassus on Thucydides.” in The Oxford Handbook of Thucydides, edited by Sara Forsdyke, Edith Foster, and Ryan Balot. Oxford University Press. DOI: 10.1093/ oxfordhb/9780199340385.013.17 https://www-oxfordhandbooks-com.subzero. lib.uoguelph.ca/view/10.1093/oxfordhb/9780199340385.001.0001/oxfordhb-9780199340385 Hyvönen, Ari-Elmeri. 2018. “Careless Speech: Conceptualizing Post-Truth Politics.” New Perspectives 26 (3): 31–55. https://www.proquest.com/ scholarly-journals/careless-speech-conceptualizing-post-truth/ docview/2204515132/se-2?accountid=11233. Hobbes, Thomas. 1839–1845. The English Works of Thomas Hobbes, edited by Sir William Molesworth. London: John Bohn. Vol. 8. Klinkner, Melanie. 2018. “Reasonable Truth.” International Journal of Media & Cultural Politics 14 (3): 393–99. doi:10.1386/macp.14.3.393_7. Lee, Haiyan. 2019. “When Nothing Is True, Everything Is Possible: On Truth and Power by Way of Socialist Realism.” PMLA/Publications of the Modern Language Association of America 134 (5). Cambridge University Press: 1157–64. https://doi.org/10.1632/pmla.2019.134.5.1157 Lewis, Mark E. 1999. “Warring States Political History.” In The Cambridge History of Ancient China: From the Origins of Civilization to 221 BC, edited by Michael Loewe and Edward Shaughnessy, 587–650. Cambridge: Cambridge University Press. Makeham, John. 2003. “Zheng ming (Rectification of names).” In RoutledgeCurzon Encyclopedia of Confucianism, edited by Xinzhong Yao. Vol. 2: 813-814. New York: Routledge. Oxford Languages, n. d. “Word of the Year 2016.” Accessed 19 August 2017. https://languages.oup.com/word-of-the-year/2016/ Peters, Richard. 1979. Hobbes. Westport: Greenwood Press. Pew Research Center. 2018. “An examination of the 2016 electorate, based on validated voters.” Report, August 9, 2018. https://www.pewresearch.org/ politics/2018/08/09/an-examination-of-the-2016-electorate-based-on- validated-voters/ Plato. 1974. Republic. Translated by G. M. A. Grube. Indianapolis: Hackett. Tesich, Steve. 1992. “Watergate Syndrome: A Government of Lies.” The Nation: 12-14. January 6/13, 1992. Thucydides. 1972. The Peloponnesian War. Translated by Rex Warner. Harmondsworth, England: Penguin Books. Wolin, Sheldon. 2004. Politics and Vision: Continuity and Innovation in Westen Political Thought. Expanded Edition. Princeton: Princeton University Press. Zerilli, Linda M. G. 2020. ‘Fact-Checking and Truth-Telling in an Age of Alternative Facts.’ Le foucaldien 6, no. 1: 2, 1–22. Theresa Man Ling Lee is an Associate Professor of Political Science at the University of Guelph (Canada). Lee is a political theorist who specializes in contemporary political thought. Her research and teaching areas include continental philosophy, Marxism and critical theory, feminism, postmodernism, philosophy of social science, psychoanalysis, multiculturalism, human rights, disability studies, modern Chinese political thought and comparative political theory. Lee has published on a wide range of subjects in these areas, including intercultural pedagogy and the challenges of teaching as a person with multiple minority designations in a Canadian university classroom.
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New OMB rule could break science in the United States
The drug that saved your mother. The treatment your doctor recommended. The clinical trial that bought someone you love more time. None of it was inevitable. It required a system built to operate outside politics and sustained over decades, because science doesn’t operate on the timeline of a politician. That system is now under direct threat. The Office of Management and Budget (OMB) has proposed a new rule to regulate all federal grants, not just science, but housing, education, defense, NASA and everything in between, that would fundamentally change how research is conducted in the United States. There have been a lot of fires in the past 16 months, and if everything is an emergency, nothing is an emergency. But this is a very big deal. These rules would strip away the independence that has kept science from becoming a political tool. The good news is there’s still something we can all do. How the grant system was designed to work When I was a new faculty member, I was full of millions of ideas, so I sat down and wrote an NIH proposal. I worked on it for months and thought it was so good. Then I sent it to my mentor. It came back covered in comments. Places where my reasoning wasn’t as airtight as I’d thought, methods that could be stronger, gaps in my thinking that I had been too close to the work to see. It stung, as honest feedback always does, but peer review almost always makes the work better. In fact, it’s the backbone of how American science has functioned for generations. At NIH, submitted proposals go to a panel of scientists who have experience in the field. These are the people who can read research methods and spot their weaknesses, who understand when an experiment is designed well and when it isn’t. Applications are scored rigorously, and often only the top 3-20% of proposals receive funding. The people making funding decisions are required to justify them based on these reviews. To be clear: this is not a perfect system, and good science still goes unfunded more often than any of us would like due to limited resources. There is also criticism that peer review may undervalue bold ideas that are risky but could have high payoffs. But the peer review system is nonetheless a meaningful part of the grant selection process. While thoughtful and careful changes could improve the system, the changes being proposed by OMB will not make things better. The rule that could change everything Through this OMB proposal, the system that helps decide what gets funded is being dismantled. There are many troubling elements buried in this proposal, but three provisions stand out in particular. Each would be damaging alone. Together, they would change what kind of science gets done in America, and who it serves. 1. Political appointees would decide which science gets funded, and peer review would be explicitly sidelined or ignored. The guidelines your doctor follows, the standards that determine what’s in your air and water, the drugs that get approved for your family: all of it starts with a funding decision. Right now, that decision is made by scientists who can evaluate whether the work is actually sound and whether it could move the needle forward. Under this proposal, funding decisions could be made by political appointees. The careful, expertise-driven review process that shapes grant funding would be effectively sidelined. That means that a poorly designed study could get funded simply because someone in a political office liked the sound of it, while rigorous, important work doesn’t get funded because it no longer aligns with the administration’s priorities. In other words, critical work that benefits all of us could now be missed just because it has become politically inconvenient. 2. Grants could be canceled at any point, without warning, in the middle of ongoing work. Say your tax dollars have been paying for a cancer study for two years. The grant was awarded for five years, so the team is mid-experiment. There are three additional years’ worth of answers that could change how the disease is treated for hundreds of thousands of people. With this new rule, the federal government could cancel the remaining funding tomorrow, walk away from the commitment it made with your money, and have no legal obligation to finish what it started. This is a huge waste of money. There are rarely other sources of funding for this work, which means the team, the graduate students, postdocs, and research staff who have dedicated years of their careers to this project could lose their jobs. Patients who have contributed their time to studies may not see it turn into useful learnings for others. Every hour of work already completed could result in nothing useful. Science can take decades to get to tangible outcomes. It needs a stable funding system to flourish. 3. Researchers would have a harder time communicating their findings without pre-approval from the federal government. There is a saying from Sir Mark Walport: “Science isn’t finished until it’s communicated.” Science is no use to anyone if it is kept locked away in the labs that produced the data. The proposed rule could make communicating research findings harder in a number of ways: Make it harder for scientists to widely share their findings in scientific journals. Publication costs, including journal fees that allow for broad public access, could not be paid for by federal funds which is often how scientists pay these costs. Restrict researchers from engaging publicly about how their findings should inform policy or public health guidelines. This would restrict scientists from doing the work that transforms years of lab work into decisions that actually affect people’s lives. Make it harder for scientists to attend conferences. Conferences would now need to be approved by the federal agency at the time of grant award. This disadvantages conferences or publications that are not politically aligned. Conferences are a key way in which scientists learn about work happening in their field so they can collaborate and build on each other’s findings. What good is science with a muzzle? The long-term impact These changes could (and hopefully would) be reversed under the next administration. But by then, damage has been done. Once projects are halted or denied funding by political appointees, people leave their science careers for other paths or even leave the country to do their amazing science elsewhere. You cannot pause a scientific study the way you pause a subscription you decided isn’t useful anymore. You cannot freeze a research team that depends on salaries to feed their families, causing a scramble for replacement funding that, in most cases, simply does not exist. When the money stops, everything stops, and all the work in progress and its potential are lost. Despite all of the language about reducing waste, fraud, and abuse, this new proposed rule increases the administrative burden tremendously. Every researcher watching this unfold will make a rational decision: don’t start anything that will take a decade to finish. This includes not building a team around funding that could vanish, or asking the big questions that need patience and stability to answer. The most consequential science, the kind that produces the treatments and the breakthroughs that reach your doctor’s office twenty years from now, is exactly the science that would be abandoned first. This is still a proposal, so we can all do something Some worry this rule is already a done deal. But by law, OMB is required to open a public comment period. Those comments matter because they inform members of Congress, they can be used in litigation, and they create a public record. So here’s what you can do: Submit a public comment. The public comment period on this proposed change is open until July 13. Anyone can submit a comment, and OMB is required to respond to substantial ones. Each comment must be different to count as distinct; identical comments will be counted only once. Comment as an individual, not on behalf of your organization. Here’s a full resource to help you, with useful examples, but in general: Share a story about how science or federal funding has benefitted you or is involved in your work. Are you or someone you loved a cancer survivor? Do you know someone with Alzheimer’s? Have you received NIH/NSF or other federal funding? All of these can be stories you use to highlight why you care. Explain why you’re worried about these proposed changes. You can use the wording found here or in other resources to help you. Ask them to withdraw this proposed rule. Contact your elected representatives. Congress allocates money to federal agencies to spend, and it has oversight of how that process works. You can let your representatives know this proposed change concerns you and ask them to intervene. There are levers they can push that can help at this moment. Let others know why federal funding matters. This isn’t just about science, but ALL federal grants, including for education systems, defense projects, and more. The process, including the importance of federal funding, is invisible to many. We can all make it more visible by sharing how science benefits us and why we value it. You can also share this article, or any of the resources linked here, to help spread awareness! Bottom line We are quickly losing ground in scientific research in the U.S. More changes, like the proposed rule by OMB, would decimate our ability to conduct research here, leaving us all worse off: fewer treatments for pancreatic cancer, less research on pollution impacts, fewer answers for patients with rare disease, and more unaddressed health needs. The impact will be very real for all of us, regardless of where we stand politically. Using our voices for science is more important than ever. Love, LM Big thanks to Dr. Megan Ranney for reading this over and providing brilliant insights, as always. Dr. Liz Marnik, PhD, is Executive Director of The Evidence Collective at YLE and a science communicator and immunologist working at the intersection of public trust, science, and public health. You can find her on Substack, LinkedIn, Instagram and Threads. Your Local Epidemiologist (YLE) comprises a team of experts, ranging from physicians to immunologists to epidemiologists to nutritionists, working together with one goal: to “Translate” ever-evolving public health science so that people are well-equipped to make evidence-based decisions. The YLE suite of newsletters reaches over 475,000 people across more than 132 countries. This newsletter is free to everyone, thanks to the generous support of fellow YLE community members. To support the effort, subscribe or upgrade below: Subscribe
Good science requires much more than evidence
Empiricists believe that scientific theories are true only if they make correct empirical predictions: if we can't verify these predictions, then there is no reason to expect that they tell us anything about reality. However, Professor of Philosophy at King's College David Kyle Johnson argues that this is mistaken. In response to Bas van Fraassen, who has written on this subject for the IAI, he argues that we have many reasons for believing a theory to be true, such as it being parsimonious and having large scope — qualities which have been used throughout the history of science as criteria for judging theories. In 2024, Princeton philosopher Bas van Fraassen wrote a short article for IAI News entitled “Science Does not Describe Reality: The Limits and Benefits of Explanation.” At the time, I was working on two projects (a science book and a Great Course) that, when completed, would (among other things) defend science’s ability to describe reality. I wanted to write a response to van Fraassen, but the projects demanded too much of my attention. But now that those projects are complete, I am in a position to explain the objections to van Fraassen’s thesis. Summarizing van Fraassen’s argumentIn a nutshell, his argument is this: What scientists do is make predictions based on theories and then invite others to test to see whether those predictions are borne out. “If theory T is right, result R should happen in experimental condition C.” Many think that running an experiment that shows that R happens in condition C is proof that theory T is true (and should be believed). But, van Fraassen argues, that R happened in condition C is only a reason to think that R happens in condition C. That’s the only observable, or empirical, result we can actually see, so that is all that we are really justified in believing. (The underlying theory that made that prediction still may or may not be true.) That is why he calls those who accept his position “empiricists.”Before I explain what is wrong with this position, let me explain what it gets right. What van Fraassen Gets RightFirst, a theory correctly predicting the results of an experiment is not, strictly speaking, “proof” that the theory is correct. The basic line of reasoning is this:If the theory T is true, result R should happen. Result R did happen. Thus, theory T is true.In other words:“If T then R. R. Thus, T.”This is a deductively invalid, fallacious mode of reasoning, called “affirming the consequent.” Those premises do not guarantee that the conclusion follows. “If I am in Pennsylvania, then I am in the USA. I am in the USA. Thus, I am in Pennsylvania.” Clearly that argument doesn’t guarantee its conclusion; neither does the above argument.However, science is not a deductive method of reasoning; it is an inductive one. And inductive arguments merely try to raise the probability of their conclusion, not “prove” them for certain. Thus, the fact that an experimental result doesn’t prove a theory is true is irrelevant; it’s not supposed to. That is not what scientific reasoning is about.___If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational.___Second, it’s also true that a single experimental result doesn’t provide enough justification to believe that a theory is true. Scientists do not come to a consensus about whether a theory is true by looking at a single experiment; they look at all the evidence to determine where the preponderance of evidence points. The mere fact that I am in the USA raises the probability that I am in Pennsylvania, but not nearly enough to conclude that I am in Pennsylvania. However, if I am also regularly encountering people wearing Eagles and Phillies hats and seeing advertisements for Founding Father tours, I probably am. In the same way, a theory successfully predicting the outcome of one experiment is not enough to conclude that it is true; but if a theory makes a wide variety of predictions that are all vindicated by multiple experiments, performed by many different groups and people, all actively trying to prove the theory false (which is what good scientific experiments are designed to do)—that is a good reason to conclude that the theory is true. In other words, if you repeatedly try to prove something false but can’t, that is a very good indication that it isn’t. The problem of underdeterminationIn reply, those whom van Fraassen calls “empiricists” often point to the “problem of underdetermination.” For any given theory that makes successful predictions about what happens in experimental conditions, there are countless other possible theories that make exactly the same predictions—and since they do, the experimental evidence in question is just as much reason to accept any of those theories as it is to accept our original one. Thus, the argument goes, we have no way of knowing whether our original theory is true, or one of the other “predictively equivalent” theories is true. SUGGESTED VIEWING How Occam's Razor changed the world With Johnjoe McFadden As an example of underdetermination, philosophers often offer this example. The evidence of fossils seems to provide reason to conclude that the universe is quite old; ancient fossils are what the “ancient universe” hypothesis predicts. But it’s also possible that God created an ancient-looking universe 200 years ago, complete with a “fake” fossil record for us to find, to make it look old. The existence of fossils is what that theory predicts too. So, the argument goes, the fossil record can’t be used as evidence that the universe is ancient; the fossil record supports the “200-year-old ancient-looking universe” theory just as much. The problem of underdetermination points out that, for any theory with supporting evidence, we could invent another completely different theory that predicts exactly the same evidence, and thus undermine that evidence’s ability to support the original theory.But this “argument from underdetermination” betrays a fundamental misunderstanding of what scientific reasoning is and how it works. To explain, let me use another rather strange (but very real and commonly discussed) philosophical thought experiment called “The Grue Problem.” The “Grue” ProblemSuppose I want to know what color emeralds are, so I observe a few, see that they all appear green, and form the theory that “all emeralds are green.” I thus predict that the next 100 emeralds I find will also appear to be green. And let’s say that happens, and so I thus conclude that my theory that all emeralds are green is true. Now, one might rightly point out that I don’t know for sure that all emeralds are green—I have not seen them all. Maybe some I haven’t found yet are blue. Fair enough. After all, we used to think that all swans were white, until we found some black ones. But let’s say that I go on a quest and collect every emerald in the universe—and, for the sake of the argument, let’s say I can do this, and can even know that I have every one of them. So, I collect them all, look at them all, and verify that they all appear green. Can’t I now rightly conclude that all emeralds are green?Inspired by the problem of underdetermination, a friend might argue that I cannot.Sure, they all appear green now—but there could be another kind of color called “grue.” Something is grue if it is green up to a certain point in time, but then spontaneously turns blue. And there could be an infinite number of “shades” of grue. Something that is one shade of grue will turn blue at noon today, but something that is another shade of grue will turn blue at noon tomorrow, etc. So yes, the emeralds appear green now; and since they haven’t turned blue yet, they are not some “past” shade of grue. But they might be some “future” shade of grue (e.g., they may turn blue at noon tomorrow). And since all future shades of grue are consistent with “being green up to this point in time,” the evidence you have is completely consistent with the notion that all emeralds are some future shade of grue, rather than green. Indeed, that they would appear green until now is what the “some future shade of grue” hypothesis predicts. So, we have just as much reason to conclude that all emeralds are some shade of grue as we do to conclude that they are green.You’re probably thinking “It’s not equally possible that the emeralds are grue; they are obviously green.” You are right! But explaining why your instinctive reaction to my friend’s argument is correct will help inform why van Fraassen’s “empiricist” is wrong and science actually does describe reality.The main mistake my friend makes is in thinking that the “empirical evidence” (of all the emeralds appearing green) is (or even should be) all I am using to draw the conclusion that all emeralds are green. It is not. I am also considering known facts about how colors typically work; things don’t usually change color, and when they do, they don’t do so suddenly, without cause, and all at once. So, even though the simple evidence of the emeralds all appearing green up to this point is consistent with them being some future shade of grue—that is even what the grue hypothesis “predicts”—it is still exceedingly unlikely that they are. The hypothesis that they are green is much more likely. Understanding science refutes the problem of underdetermination The misunderstanding about science that the “empiricist” pointing to “the problem of underdetermination” makes is similar to the one that my friend made. Science is not just about considering experimental evidence; in fact, it’s not even just about considering empirical evidence. As Ernan McMullin and many other philosophers of science have pointed out, the inference that makes science is “inference to the best explanation” (sometimes called “abduction” or “retroduction” by McMullin). While, depending on the circumstances, scientists use many different kinds of reasoning, science as a whole is, simply put, the process of considering multiple competing explanations and comparing them to certain criteria—explanatory criteria (the kinds of things explanations must be and do, by definition, to be a good explanation)—to see which explanation is the best.And these criteria do not deal only with empirical results. Yes, successful predictions raise the probability of a hypothesis, but it’s also the case that the more new assumptions a theory makes, the less likely it is to be true; every new assumption presents an opportunity for the theory to be false. (The fewer assumptions a theory makes, the simpler or “more parsimonious” it is said to be.) Likewise, the more existing evidence a theory contradicts, the less likely it is, because in order for a theory to be true, every piece of evidence it contradicts has to be somehow faulty. (The fewer established theories a new theory contradicts, the more “conservative” it is said to be.) And, obviously, the more something explains, the better explanation it is. (This is called having “scope.”) If a theory raises unanswerable questions, it’s not expanding our understanding, and thus is not wide-scoping. Scientists consider all these things when deciding which theory to accept, because the theory that adheres best to these explanatory criteria is most likely to be true.___the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth.___The reason the grue hypothesis is no good is because it invents an entirely new kind of color—not just a new shade, but an entirely different category of color (a different way colors work)—and thus is not parsimonious. It raises unanswerable questions about how and why emeralds would just spontaneously change from green to blue (and thus does not have wide scope). And it contradicts what is already well established about how color works (and thus is unconservative).The theories that the problem of underdetermination points to do the same thing. Yes, the theory that God created an “ancient-looking universe” (complete with an ancient-looking fossil record) two hundred years ago is also consistent with the empirical evidence (of a fossil record) that presently exists; but that theory is monumentally unparsimonious (it requires many extra assumptions about infinite entities) and not wide-scoping (it raises unanswerable questions about why God would do such a thing). So, I can rationally conclude that it is false.Indeed, the history of science includes many examples of theories that were rejected because they lacked parsimony and/or scope, even before their falsity (and the truth of their competing theory) was confirmed by observation. As I point out in my book, geocentrism was rejected by the scientific community because it lacked parsimony, almost 200 years before the observation of parallax confirmed that the Earth moves around the sun. And the continuum theory of matter was completely rejected by 1860, because it couldn’t explain chemical reactions or organize matter into a periodic table, even though the experiments that eventually confirmed atomic theory weren’t even proposed until the 1870s. Solving the best of a bad lot problemIn response, the “empiricists” are likely to object, this time pointing to the “best of a bad lot” problem. “We have no reason to conclude that an explanation that has been shown to be better than others is true because we have no guarantee that the true theory was among those that we were considering.” If you have a “lot” (a collection) of poor explanations, even the best one is not true.The responses to this problem, however, are numerous. Peter Lipton argues that the set of hypotheses that scientists consider are not just randomly selected and thus unlikely to contain a theory that is true; they use existing knowledge to home in on what is most likely to be true. A doctor trying to explain abdominal pain is not going to bother wondering whether demons are causing it; they are going to stick with things like appendicitis and kidney stones, because they are most likely. Further, despite its name, inference to the best explanation is not just the process of comparing hypotheses and accepting the best one; we also want one that is a good explanation in its own right. If it’s not, we are going to keep looking until we find an explanation that is. And while that process does not guarantee that we will come across the exact truth, it’s very likely that the theory that emerges as the best will be at least approximately true, and thus we will be justified in believing it. And lastly, as I point out in my book, although many theories not based in scientific evidence (like geocentrism and continuum theory) have been completely overturned, science almost always progresses by the refinement of scientifically established theories—not their replacement. (Although our sun is not the center of the universe, heliocentrism was our first step in understanding the Earth’s true place in the universe.) So even if a theory we accept today may not be completely right, accepting it is a step on the journey toward the complete truth. The example of atomic theoryNow, it’s worth pointing out that most empiricists are not skeptical of all scientific theories; they are only skeptical of the ones that hypothesize the existence of “unobservables.” It’s not like they think that round Earth, heliocentrism, and germ theory are just useful (but untrue) “instruments” for making predictions about how to most efficiently navigate the seas, predict planetary motion, and treat diseases. They admit that the Earth actually is round, that the planets revolve around the sun, and that germs exist and cause disease. These facts and entities can be observed; the theories that suggest they exist are true. It’s only when theories start hypothesizing the existence of unobservables—like electrons, or quantum fields—that can’t be observed directly, that we should start doubting the theories’ truth. And to be fair to van Fraassen, in this published work, he doesn’t really say that we should doubt the truth of such theories. Only that scientific reasoning does not rationally force us to accept them as true. Skepticism, or agnosticism, he argues, is warranted. SUGGESTED READING Science is based in metaphor By Andrew Reynolds But the history of atomic theory throws a monkey wrench into that argument. Atoms used to be unobservable; again, scientists first accepted the theory that they exist because it had such grandiose explanatory power. But then atoms were seen with electron microscopes. They thus moved into the category of the observable. But this didn’t substantially change anything about what scientists were justified in believing; it certainly didn’t change any “atomic skeptics’” minds. It just confirmed what everyone already knew! If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational. The fact that the thing itself is not technically observable does not make doubting its existence rational.What’s more, the atoms were seen with an electron microscope. But electrons are themselves unobservable. How can an electron microscope reveal an atom if electrons are not real? This leads us to what is famously known as Hilary Putnam’s “miracle argument.” It’s (not) a miraclePhilosopher Hilary Putnam’s miracle argument is somewhat famous. Basically, he argued that a scientific theory being able to repeatedly make successful predictions—even about unobservables—without being at least approximately true would require a “miracle.” In other words, it would be such an improbable occurrence that it could not rationally be believed—the better explanation for why scientific theories about unobservables make the right predictions is because the unobservable entities and structures they hypothesize exist.Van Fraassen has addressed this argument. Indeed, in his original article, where he references Alison Gopnik’s point about orgasms not being “necessary for procreation” even though the desire for orgasm “drives the creation of progeny,” he is pointing to his own argument about how organisms are not naturally selected by evolution to get true beliefs; to the extent that belief-forming processes are selected for, the ones that are selected are the ones that are useful for survival—not the ones that produce true beliefs. In the same way, van Fraassen argues, scientific theories are accepted, not because they are true, but because they aren’t falsified; they make successful predictions; that’s what makes them “stick around.” So, the fact that they make successful predictions (and thus became accepted) is not a reason to think they are true.This argument is similar to C. S. Lewis’s “argument from reason,” which he used to argue against evolution: “The truth of evolution undercuts our ability to know that it is true.” Lewis’ argument famously failed; Elizabeth Anscombe, by Lewis’s own admission, demolished Lewis in a debate on the topic, and I have explained elsewhere why modified versions of the argument fail as well. In short, it’s because it fails to recognize that a belief-forming process can make an organism more likely to survive because it reliably produces true beliefs. It is therefore not the case that beliefs generated by naturally selected belief-forming processes are automatically false or unjustified.In the same way, the reason a theory “sticks around” could be because it’s not falsified, because it “makes correct predictions.” But the reason it makes correct predictions is most likely because it is true. (In pointing this out, I am echoing the arguments of Musgrave, Lipton, Psillos, and Kitcher, who refuted van Fraassen’s argument and analogy long before his 2024 article.) It would be a huge coincidence that it could do so without being true. And that is the point of Putnam’s miracle argument. Thus, contrary to van Fraassen’s suggestion, the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth. ConclusionLet me close by saying something about the fact that empiricists don’t doubt the truth of all scientific theories, just the ones that hypothesize unobservables. In today’s society, this theory is dangerous to espouse publicly. The nuance between being skeptical about all of science, and just the highly technical theories about unobservables, is guaranteed to be lost to the average reader. And there is so much unjustified skepticism and doubt about the findings of science and experts that a headline like “Science Does Not Describe Reality” could easily fuel the anti-science movement, worsen climate change denial and cause the next pandemic.This is why I believe it was important to still respond to van Fraassen’s article, a full two years after he wrote it. Not only, as I have argued, does it represent a philosophically inaccurate view of how scientific reasoning works and what scientific reasoning can do, but it is a dangerous view to boot. It should thus be rejected.
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Merchants of Doubt and the attacks on Extreme Event Attribution science
Last week, I attended a meeting at Columbia University on attribution science and climate law, hosted by the Sabin Center. It was a fantastic event, bringing together scientists and legal experts working at the intersection of extreme event attribution and climate law. Subscribe For those unfamiliar with it, extreme event attribution attempts to quantify the contribution of climate change to an extreme event. For example, several groups analyzed the impact of climate change on Hurricane Harvey’s enormous rainfall totals over Houston, Texas and they found that climate change increased rainfall by 15 to 38%. One thing that came up again and again was how terrified fossil-fuel interests are of extreme event attribution science. They are acutely aware that this research could land them in court. And losing those cases would leave them legally liable for billions of dollars in climate damages. Because the legal stakes are so high, the blowback has turned ugly. I spoke with several scientists at the meeting who are facing ongoing harassment over their work. This blowback is a coordinated campaign to make the entire field look suspect. The goal is to create the impression that attribution science is too uncertain, too political, or too conflicted to be useful in court or in public policy. The strategy is not based on actual science or evidence of misconduct, but on the generation of doubt. The new Merchants of Doubt We’ve seen this before. In fact, not that long ago: We only have to go back a year to the Department of Energy (DOE) Climate Working Group (CWG) report to see an example of using doubt as the tool to push back against well-established science. This strategy is laid out in an email from a member of the CWG, Dr. Roy Spencer, that was released during litigation over the Climate Working Group process. The key quote is: About all I can hope is that what we write will provide sufficient “reasonable scientific doubt” regarding the science claims in the 2009 TSD [technical support document], based upon almost 2 decades of new science, to call into question the original reasoning for the EPA Administrator’s decision that CO2 presents a threat to human health and welfare. This statement is strong evidence that at least some members of the committee were working to support a particular policy outcome: revoking the Endangerment Finding. The email also explains how they planned to do it: by attempting to generate “reasonable doubt”. This is going to be hard, Spencer implies. Despite falsely claiming that “2 decades of new science” weakens the case, Spencer explicitly acknowledges that the actual peer-reviewed science of climate change overwhelmingly rejects his position: But if the science argument is decided upon by a vote, or by the number of published citations, we lose the science argument. We can go back even further: This CWG email shares unmistakable DNA with the infamous 1969 tobacco memo that declared: “Doubt is our product, since it is the best means of competing with the ‘body of fact’ that exists in the mind of the general public. It is also the means of establishing a controversy.” The tobacco memo also acknowledges the limit of this strategy: Like the CWG, they knew the science was not on their side. Share The new new Merchants of Doubt The people attacking the IPCC chapter on extreme event attribution are the newest iteration of the Merchants of Doubt. Their goal, like all Merchants before them, is to introduce doubt into the process. Because the report is not even out yet, they cannot attack its conclusions. So they are attacking the authors instead. Here is a press release from the House Science, Space, and Technology Committee: In the letter, the Chairmen express concerns about potential conflicts of interest involving members of the Attribution Committee, stating that “publicly available information suggests a troubling pattern” in which committee members are affiliated with nonprofits that support climate accountability lawsuits, “raising the appearance of impropriety and member bias.” To be clear, this is just innuendo. There is no actual evidence of bias. And given the robust process that these reports go through, including multiple lines of peer review, it seems very unlikely that significant bias can survive into the report. When the report comes out, critics will have the opportunity to make legitimate criticisms of the report — if any exist. If none do, however, they’ll still make criticisms, but they’ll be bogus, simply designed to generate doubt. We’ll see. A note to the press: Fix your frame To any journalists reading this: The public debate over extreme event attribution science is not going away. The science is simply too dangerous to fossil-fuel interests for them to stop fighting it. You very well might be assigned to write an article about this area of research in the future. When you do, do not automatically adopt the framing that climate misinformers want you to use. They want you to frame the story around questions like: Are climate scientists trying to put their thumb on the scale to achieve a predetermined, politically motivated result? Are climate scientists improperly letting their politics invade the science of the IPCC? That frame is a trap. Instead, you need to view this through the historical lens of the Merchants of Doubt. How does the ecosystem of doubt operate? Who funds it? What methods do they use to misrepresent science and slime researchers? What scientific results are they trying to keep people from understanding are legitimate? Ultimately, you instead need to focus your article on the generation of doubt as a way to maintain the fossil fuel industry’s social and legal license to keep burning oil, gas, and coal. If you treat the misinformers’ frame as a legitimate, good-faith scientific critique, you are helping them produce doubt. Don’t do it. Don’t be a Merchant of Doubt. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe other stuff The Staying Curious Substack has an interesting post about the different flavors of climate sensitivity. This is crucial information if you want to understand how scientists think about how much warming the Earth will experience. I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.
Why I Don’t Think Ride Or Die Should Have Been Cancelled (And What Company Should Save It...)
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) So…I recently came across a Reddit thread, talking about films on streaming, wondering why there are no reliable sources out there for streaming ratings data. Sigh. Honestly, as annoyed as I am that so many still don’t realize that we have streaming ratings, I somewhat get it. Until now, there was such a long delay between when a show or film came to streaming and when we got its viewership data; I understand why people were confused. (But, as you know, this just changed.) Also, even back in the day, how many people knew how well films did on television? Luckily, as a reader, you’re in the know. Okay, on to this week’s issue. I want to take a look at Prime Video cancelling Ride or Die and whether I agree with that decision. Overall, we didn’t have any breakout hits (no TV shows landed over 20 million hours according to Nielsen) but we have a lot of steady performers on the streaming charts. Plus, we’ll look at where $50 million in missing box office dollars went. (Spoiler: straight-to-streaming.) All that, plus new episodes of The Secret Lives of Mormon Wives, Supergirl heads to streaming, a new YA show does well on Netflix, the viewership for the Daily Wire’s $50 million fantasy show, what TV show hit-maker can’t make streaming hits, CD sales, all the flops, bombs and misses, and a whole lot more. Let’s dive right in! (Reminder: The streaming ratings report focuses on the U.S. market and compiles data from Nielsen’s weekly top ten viewership ranks, Luminate’s Top Ten Data, JustWatch and Reelgood interest data, Samba TV household viewership, company datecdotes, Netflix hours viewed data, Google Trends, and IMDb to determine the most popular content. While most data points are current, Nielsen’s data covers the weeks of September 7th to September 13th, 2026. You can find a link to my terminology here.) Subscribe Television - Ride or Die Is a Hit…But It Got Cancelled? What The Data “Says” I hate the phrase “here’s what the data says”, even though sometimes I find myself using it. If data just told us everything, we data folks would be out of a job! Data is often messy, complicated and nuanced. When it comes to strategy, many of the best decisions can’t actually use “data”, since it’s vastly too complicated to model. Anyways, I’ve been singing the praises of Prime Video’s Ride Or Die this summer, a show that did very well for them. And yet…it got cancelled. Now, usually when a show gets cancelled, a bunch of websites say “a hit show got cancelled”... Here’s the thing, though: in this case, they’re right! The data “says” that this show was a hit. Specifically, out of 522 first seasons to make a week on the Nielsen charts since 2020, it’s the 75th biggest. On Amazon, Ride or Die is eighth out of 41 first seasons: That’s top 15th percentile all time, almost exactly, which is my definition of a “hit”! I mean, outside of Reacher, Amazon needed a hit this summer! (Admittedly, Off Campus did well for Amazon in the spring, but Ride or Die performed even better.) So what happened? Why did Amazon cancel one of their rare 2026 bright spots? Well, Deadline reported a reason—which I’ll get to—but I’d break it down into three relevant questions: What is a given streamer’s reasoning? Is that reasoning sound? Could the stated reason not be the real reason? According to Deadline, Amazon felt the show “over-indexed” in middle-aged women. (This was based on leaks from within Amazon.) The logic is this: Amazon already reaches this demographic because of unlimited two-day shipping bundled with Prime, so they don’t really need more shows like this. Now, it’s my job to ask if that’s a good reason, and I’ll be honest, in this case, I don’t think it is. Sure, this show may “over-index” in middle-aged women—I’ll just assume that’s the case here—but I think folks often oversell how important over-indexing actually is. When a show is a “hit”, it isn’t a bit bigger than its rivals; it’s usually multiples bigger. For example, Elle only made the charts for two weeks at 8.3 million hours each. So Ride or Die was 80% bigger in its first two weeks, and likely had a much stronger hold, with its terrific 13 million hours in week three. Ride or Die was nearly three times bigger than Sterling Point through three weeks too. And that means that _Elle, Sterling Point a_nd other Amazon YA shows need to not just over-index a little with younger women (assuming that’s who Amazon prefers over middle-aged women), but massively over-index. Otherwise, more people in that demo (and a bunch more besides) likely watched Ride or Die. (I’ll try to explore this concept more in a future article.) And don’t get me started on how Ride or Die did compared to a certain (very, very popular, possibly the world’s most popular) YouTuber’s reality show…which didn’t make the charts after its first week and likely cost much, much more than Ride or Die. All to say, Amazon-MGM Studios/Prime Video can tell reporters that this show didn’t reach the right audience, but that excuse feels weak to me. So this leads to the third question: could other reasons have come into play? Yes! Renewal or cancellation decisions rarely (I’m tempted to say “never”) boil down to one variable. At its simplest, it’s two things: budget versus viewership. But often factors like critical acclaim, ownership and, yes, personal opinion come into play. In this case, Amazon has a new executive running things, and this show isn’t owned by Amazon-MGM Studios (unlike Elle). And yes, it may not have indexed with the right target viewers, too. Likely all of those factors came into play.1 My guess is ownership ended up mattering most. Amazon doesn’t own Ride or Die. It’s actually produced by another major studio. Fortunately/allegedly, this show is being shopped around. I can think of one brand new CEO who should consider it for one of his two major streamers…especially if he wants to rebuild goodwill in Hollywood. His name is on this list of exec producers of Ride or Die… If David Ellison needs some goodwill—and he does—rescue this female-led show tomorrow and grab some good headlines for a day. The data justifies it. Quick Notes on TV We’re just getting started with this issue, but the rest is for paid subscribers of the Entertainment Strategy Guy, so if you’d like to find out… How The Secret Lives of Mormon Wives latest season did on Hulu, post-controversy... Whether Supergirl soared on HBO Max... Why the box office is losing money due to the streamers... The viewership for the Daily Wire’s $50 million fantasy show... Updates on The Gentlemen, Lanterns, Outer Banks, King of the Hill and more... What former hit-maker has three streaming flops in a row... Whether The Mandalorian and Grogu popped in week two... All the flops, bombs and misses... And a whole lot more... ...please subscribe! We can only keep doing this great work with your support. If you want an idea of just how much content you’d get in a full issue, check out this older issue. Coming Soon! Oh man, we’re almost caught up! We just have two more issues, then the streaming Ratings Report will be coming out just two weeks after a TV show premieres! Next issue, I’ll be looking at the first two weeks of the NFL Thursday night games on streaming, a huge new spinoff, Reacher’s Neagly on Prime Video, and the latest edition of Monster: The Lizzie Borden Story on Netflix, the return of Slow Horses on Apple and MobLand (quietly one of their biggest shows) on Paramount+, and Dancing with the Stars on both ABC and Disney+. Plus I’ll take a look at animation for adults in 2026. The week after, a ton of movies are coming to streaming: Toy Story 5 on Disney+, Backrooms on HBO Max, Jackass: Best and Last on Paramount+ and The Breadwinner on Netflix, plus a Unabomber film on Netflix and a romcom on Prime Video. Woody Harrelson and Matthew McConaughey have a show on Apple, along with a game show inspired by Willy Wonka that everyone seems to hate. Long term, some crazy (but hopefully crazy like a fox) IP news. FX ordered a new Sons of Anarchy show from Charlie Hunnam, but it’s not about the biker gang. No, it features the show’s cast playing themselves in a new “meta-thriller”. Huh. And a lot of the cast is coming back. Next, Ryan Gosling is producing a Flintstones movie about a “grown-up Bamm-Bamm Rubble”. Also, huh. This one is in “early development” so we’ll see what happens. Both projects are based on IP, but also seem to have crazy takes on the IP, which I like.
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How Dangerous Is Anthropic’s New AI Model? Its Chief Science Officer Explains.
Just how fast is the future coming at us, and how worried should we be? Last week, Anthropic announced Claude Mythos, the latest in what has become the leading family of AI models. In a very short time, AI models from Anthropic and other companies have become shockingly powerful at coding. But Mythos stands out because it is not just able to write code that can hack into critical systems, like those that run financial companies, but also to analyze systems and find and string together vulnerabilities in them, allowing Mythos to evade even sophisticated defenses. Anthropic chose to release Mythos to only about 40 leading corporations, so they could analyze the model’s capabilities and prepare countermeasures, in an effort it calls Project Glasswing. The release of Mythos—and the prospect of similarly powerful AIs to follow—has caused tremors at the top levels of Washington and corporate America. Jared Kaplan, co-founder and chief science officer at Anthropic, is at the center of this breakthrough. We sat down with Kaplan so he could walk us through just how powerful Mythos is, what the implications are for issues like our personal privacy, and what comes next. This conversation has been edited for length and clarity. Sean Fischer: For the layman, how is Claude Mythos different—more powerful, more dangerous—from previous models released by Anthropic? Jared Kaplan: I think most people have experienced the fact that our computers get faster and faster over time and over many decades. There’s this very smooth trend where every 18 months, maybe, computer processing power doubles. I think AI is on a trend in which it’s improving maybe 10 times faster than that. A lot of the earliest work that I did in AI was around identifying this scaling trend in AI. Claude Mythos is the latest model from Anthropic, but it’s not different because it’s qualitatively different. It’s really just kind of the culmination of the trend of AI models getting smarter and smarter at all kinds of general capabilities, from reasoning to software engineering to scientific research to knowledge work. For us, it was the first model to demonstrate very elite-level cybersecurity capabilities. It wasn’t because we trained Claude Mythos to focus on cybersecurity, but it was because, as a byproduct of its general intelligence and its general ability with software, it’s particularly good at identifying vulnerabilities in software and how to exploit them.
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The Surprising Reason Some Children Struggle With Math, According to Science
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Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
Are your memories real? Physicists revisit the Boltzmann brain paradox
A new analysis of the “Boltzmann brain” paradox suggests our memories and sense of reality could, in theory, be random illusions born from cosmic chaos. By uncovering circular reasoning in how physicists think about time and entropy, the study raises fresh doubts about what we can truly know about the past.
Sandia is automating Bayesian reasoning for science
Sandia's Agent Bayes project faces a nine-month test that asks whether AI can help scientists perform Bayesian reasoning.
The Math Singularity Is Here, and Science May Be Next
OpenAI has released an extraordinary body of mathematical work produced by an unreleased frontier model: 722 manuscripts spanning 372 research families across fields including number theory, complexity theory, and mathematical physics. Each result required, on average, the equivalent of roughly three hours of ChatGPT Pro reasoning. A New Era of Discovery We may be witnessing the beginnings of a “math singularity”: a point at which machine-generated discovery accelerates beyond humanity’s ability to independently reproduce, verify, or fully absorb it. If frontier models can generate hundreds of meaningful results in the time it takes a human researcher to investigate one, the constraint will no longer be producing new mathematics. It will be our capacity to validate, interpret, and integrate it. This does not make mathematicians obsolete. It changes their role. They may increasingly serve as guides, critics, and meaning-makers. Increasingly, they will decide which questions matter, determining which results can be trusted, and explaining why they are important. Mathematical insight may become abundant, while human judgment becomes more valuable than ever. Mathematics May Be Only the Beginning Every scientific discipline could eventually experience a similar acceleration. AI systems could generate and test hypotheses, connect findings across specialized fields, and explore possibilities at a scale no human research community could match. In medicine, imagine models identifying overlooked disease mechanisms, designing drugs and clinical trials, personalizing treatments, or discovering cures in months rather than decades. In materials science, AI could help create better batteries and cleaner industrial processes. In climate science, it could accelerate advances in energy, forecasting, and carbon removal. In biology, it could uncover relationships hidden across enormous genomic and molecular datasets. The result would not simply be faster research. It could expand the frontier of what is scientifically possible. The New Bottleneck Scientific discovery, however, is not the same as scientific progress. A medical breakthrough must still survive laboratory testing, clinical trials, regulatory scrutiny, manufacturing constraints, and real-world deployment. As machine-generated ideas multiply, the bottleneck may shift from producing breakthroughs to validating them safely and translating them into practice. We may soon have more hypotheses than laboratories can test, more drugs than clinical trials can evaluate, and more mathematical results than experts can review. That imbalance will require better systems for automated verification, reproducible research, prioritization, peer review, and collaboration between AI and domain experts. The importance of OpenAI’s work is not merely the number of manuscripts produced. It offers a glimpse of a world in which intelligence can explore thousands of intellectual paths in parallel. If ideas cease to be scarce, humanity’s defining challenge will be deciding which are true, which matter, and how to turn them into progress. The math singularity is not the end of human inquiry. It is the beginning of a new kind—one in which machines expand the frontier of knowledge and humans decide where it should lead. Subscribe Share Leave a comment
AI needs science’s search history
“Actually, that’s where the gold is,” said Alasdair Russell, my graduate school friend who leads a pre-clinical genome editing group at Cambridge. He was talking about the winding road of science that is omitted from published papers. “When you’re discussing how to do the experiment, and why this way is better than another way, and what does the data really mean? I know what it shows, but what does it mean?” At RAAIS 2026, he described how his group has begun logging the twisting path of discovery as it happens, recording the verbal and written exchanges that normally disappear. Ideas become nodes in a living graph: they branch when a meeting produces two plausible experiments, merge when separate lines of evidence converge, and go dark when someone quietly stops pursuing them. In his implementation, one agent scores novelty, while another tries to learn “how scientists think and how they navigate through a complex world of data,” so that high-potential nodes can trigger deeper investigation. As frontier AI labs deploy agents toward scientific discovery, giving AI authentic scientific taste remains a trillion-dollar dilemma. The bottleneck is that the record we have kept for centuries of science might be insufficient to get us there. The experiments that never make the paper Peter Medawar, 1960 Nobel laureate and “father of transplantation,” asked in 1963 whether the scientific paper was a fraud, and answered yes: the form of the paper misrepresents the thinking that produced it. Sixty years on, the diagnosis is unchanged. A paper presents a clean progression from hypothesis to result to conclusion. Lost along the way are the unconventional theories, the abandoned or unaffordable methods, and the underwhelming and inconclusive data. It reads like a browser history with every dead end deleted: the ten open tabs, the four rephrased queries, the wrong turn down a subforum. All scrubbed, leaving one clean path from question to answer as the canonical path. What has changed since then is that the discarded material is now worth something. No high-impact journal wants these artifacts, but raw trial and error is the most likely source of the training data required to develop scientific intuition, or what researchers call taste. Earlier attempts to capture the discards were motivated by scientific integrity. The Journal of Negative Results in Biomedicine launched in 2002 to publish rigorous studies that disputed established models or exposed ineffective treatments. Its archive preserved negative conclusions after they had become papers, rather than the live alternatives and arguments that produced them. BioMed Central closed it in 2017, saying the mission had been served now that other journals publish null results. A less generous reading is that in fifteen years it published around 200 papers because almost nobody wants to read a negative result, let alone write one up when it will not count toward academic tenure. AI models are different. Even an uninteresting negative result can be useful, provided it is labeled. Earlier this year, Anthropic put a version of this to the test. The company pulled 129 decision points from real Claude Code sessions between January and March 2026, showed models the work up to a human detour, and asked what should happen next. Claude Mythos Preview beat the human choice 64% of the time, while Opus 4.5 managed 51%. The comparison was tilted toward the models because Anthropic deliberately chose moments where the human decision had room for improvement. On 127 further scenarios where the human action was already strong, the models improved on it only about 20% of the time. The study was possible because Anthropic’s researchers work inside a tool that logs by default. The reasoning, detours and outcome are produced in the same working environment. Biology has no equivalent. Its reasoning happens in hallways, at benches, in Slack threads and on whiteboards, while the outcome arrives weeks later somewhere else. The record has to emerge from the work itself. Ask scientists to reconstruct it afterwards and we’ll create another polished account. Subscribe A log is not a label Before any of this becomes training data, a negative result has to say what failed. The first paper published in the Journal of Negative Results in Biomedicine examined 234 negative studies across five leading medical journals. Only 30% discussed statistical power, and half clearly defined a primary outcome. Tell a model an experiment failed, without telling it whether the assay was underpowered, a reagent had degraded, or the hypothesis was simply wrong, and it will learn noise with confidence. Decision histories have a second missing label too. When a lab considers five experiments and runs one, only the chosen branch returns an outcome. The other four are experimental counterfactuals. Researchers call this the selective labels problem: the data reveal results only for the actions someone chose to take. Such a record can teach a model to imitate a lab’s taste, but it cannot establish that the taste was good. It also smuggles local constraints into general lessons. A lab that never ran cryo-EM because it did not own a microscope teaches a model that cryo-EM is rarely the right call. Alasdair’s nodes preserve the candidate set, which is the necessary first step. To be useful, each decision record needs six fields: The evidence available at that moment, and nothing that arrived later. The candidates considered, including the ones dismissed in a sentence. For each candidate: the expected result, the confidence attached to it, and the cost in time and money. The route chosen, and the reason. What happened next. How the evidence changed the scientist’s view. These must be timestamped before the outcome, because hindsight turns uncertainty into inevitability. Disagreement also has to survive. Averaging three scientists into one clean rationale reproduces exactly the information loss we are trying to fix. But there is an obvious failure mode here. Once decisions are logged and scored, people log for the record post-facto. Anyone who has watched an electronic lab notebook fill up with retrospective tidying knows how quickly a research tool becomes a compliance exercise. A record that costs a scientist ten minutes of honest reflection per decision is worth far more than one that costs an hour of performance. A publication initiative called Registered Reports offers a useful starting point. Here, researchers submit their rationale, methods and analysis plan for peer review before the data exist, and the journal commits in principle to publish if they follow the approved plan. Nature has now expanded the format across every field it covers. This approach to paper writing timestamps intent before the outcome, but it freezes one plan. A useful search history must also preserve how the plan changed, which alternatives were rejected, when, and on what evidence. Subscribe Run the runner-up Autonomous labs show what happens when decisions and outcomes are connected in a loop. Liverpool’s mobile robotic chemist ran 688 experiments over eight days in a ten-variable formulation space. A batched Bayesian search used each result to choose the next experiments and found photocatalyst mixtures six times more active than the starting formulations. Every completed experiment changed what the system did next. The robot optimized within a goal and search space that humans had already chosen. It did not decide which scientific question mattered. So what this work demonstrates is narrower, but still useful: a search history pays off when the options return standardized outcomes quickly. Open-ended biology is harder because branches can take weeks and the discarded alternatives may never be run. Some exploration therefore has to be bought. Where two branches are plausible and the stakes justify the cost, a lab should sometimes run the runner-up. Otherwise the record captures what today’s scientists usually chose and stays silent on what they systematically overlooked. A funder could ring-fence a small fraction of a grant for the branch not taken, provided that the decision record and the outcome are both deposited. That costs money, but so does having every lab rediscover the same abandoned path. Test whether taste transfers When it comes time to evaluate if our new AI system exhibits taste, the comparison should run across laboratories and fields. At selected decision points in a live research campaign, we should freeze the six fields: the evidence, the candidates, expected results, confidence, costs and the choice made. Experts then rank the options before the outcomes are known, and those outcomes are checked independently later. We could give models one of two diets - papers alone, or papers plus decision histories - and ask them to rank the next experiment on unfamiliar projects. Then, we score information gained per dollar and week, calibration, and how quickly weak branches are abandoned. If histories improve choices only inside the lab that produced them, they amount to useful organizational memory. If they improve choices on unfamiliar problems elsewhere, Alasdair’s group will have captured something science has never managed to write down: a transferable record of taste. Until that happens, a scientific search history is a promising record, not yet a training set.
Argonne: Building Scientific Computing Ecosystems for AI-Enabled Discovery
Trustworthy AI-enabled scientific discovery depends on software, people, institutions and community action. Oct. 1, 2026 — Artificial intelligence (AI) is changing scientific computing rapidly. AI systems can now generate and analyze code, synthesize information, assist with hypothesis generation and carry out complex reasoning tasks within scientific workflows. Alongside advances in computing architectures and data-intensive research, […]