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Common factors leading to malpractice lawsuits in radiology
"Understanding the scope and characteristics of malpractice claims is essential for guiding policy decisions and risk mitigation efforts," experts write in RSNA's Radiology.
Is Tenet Healthcare Corporation (THC) A Solid Play on Expanding Ambulatory Business?
Tenet Healthcare Corporation (NYSE:THC) is increasingly relying on its ambulatory business as an important driver of growth, combining favorable long-term outpatient trends with attractive margins. According to BMO Capital Markets, Tenet’s ambulatory operations give the company exposure to an end market with favorable long-term growth characteristics. The firm expects continued investment in Tenet’s hospital operations […]
If Not AOC, Then Who?
What do we do if Alexandria Ocasio-Cortez decides not to run for president in 2028? It makes sense that many leftists are currently focused on calling on her to take the plunge. But the absence of a Plan B, or even much discussion about one, is worrisome. The US Left can’t afford to sit out the 2028 Democratic primary. Doing so in 2024 was a disaster: our abstention self-marginalized anti-billionaire politics from the national conversation and thereby gave space for Harris to pivot to the “center” (i.e. the policy preferences of corporate donors, establishment hacks, and AIPAC). Abstaining again in 2028 would be especially counterproductive since democratic socialists now have so much wind in our sails after our big electoral wins in NYC and across the country. It’s time to deepen our momentum, organization, and profile — not to retreat to the margins during America’s single most important political battle. Even more urgently, keeping Trump’s heir out of the White House may depend on whether leftists and the party’s unprecedentedly disenchanted base can force the Democrats in 2028 to take meaningful steps towards a very angry electorate and away from the corporate-consultant blob that has helped get us into this mess. If AOC (or Abdul El-Sayed) decides to run, we should enthusiastically go all in — DSA, fighting unions, progressive organizations, anybody who wants to see a better country and world. But, as far as I can tell from the outside, there’s a real chance that she doesn’t run. And we can’t just wait and see where she lands before discussing alternatives, since AOC’s decision might not come for quite a while, giving us little time to scramble at the last minute for an alternative. So we should start working now on getting a Plan B into motion, and recruiting a good candidate for the role. Here’s one idea. A Worker Candidate — Healthcare Not Warfare 2028 Run a union worker Leftists don’t need to always run experienced, well-known socialist politicians for high-profile contests. Lula, the current president of Brazil, began his political career as a metalworker militant who ran for governor of São Paulo in 1982. Especially in an era of institutional distrust, it’s possible to lean hard into making a virtue out of a working-class comrade’s distance from traditional politics. Experience abroad shows this clearly. Rachel Kéké — an immigrant hotel housekeeper — ran as the candidate of the leftist La France Insoumise for the National Assembly in 2022, defeating a former minister in the Macron government. The Belgian Workers’ Party in 2024 won its first Flemish seat in the European Parliament by running Rudi Kennes, a three-decade veteran of the Opel Antwerp auto factory. And the South Korean left in 2024 elected auto factory militant Yoon Jong-oh to the National Assembly. If a tiny French Trotskyist organization could make such a big splash in 2002 and 2007 by running a well-spoken postal worker cadre for office — the LCR’s Olivier Besancenot received about 1.5 million first-round votes — it’s not far-fetched to expect that a much larger organization like DSA, together with a broader coalition of the willing, could run this playbook to much greater effect. This is especially the case now that almost everything DSA does immediately becomes a national news story. We don’t need someone famous. We just need a charismatic, well-vetted comrade who works in a relatable working-class occupation — e.g. a nurse, or a worker in auto or construction, or at Amazon. Ideally, they have a compelling personal story or have been part of a significant strike or organizing campaign, but even those criteria don’t necessarily have to be deal-breakers. The main lesson of the Graham Platner fiasco isn’t that we should never run outsiders for office (the momentum he generated proves the exact opposite), but that they need proper vetting and roots in democratic membership organizations like DSA or a union. In any case, there are plenty of ways to signal working-class authenticity without resorting to Platner’s or Fetterman’s performative macho white dude affect. Better to have someone of any gender or race who is actually working class, with firm socialist politics, who can speak from experience about the day-to-day realities of trying to make it in America. There are surely at least a dozen such worker leaders in our movement nationwide; we just need to sit down and convince one that their personal sacrifice for the greater good (i.e. running for president) will be worth it. If we don’t have a frontrunner with a clear path to victory like AOC (or El-Sayed, were he to win Michigan), it’s better to embrace someone whose decision to run in itself tells an important story about US politics: workers have been iced out of our political system and are now fighting hard to break back in. The novelty factor, in itself, would generate press. And it would help democratic socialists signal to the American public that our movement’s defining characteristic is that we represent workers vs. bosses, not that we’re “the furthest left” force in American politics — a framework that suggests we’re extremists and that helps our opponents isolate us from non-college-educated voters by highlighting extremely minoritarian (and misguided) stances included in DSA’s platform like prison abolition. We don’t get many opportunities to speak to tens of millions of Americans; we need to use these opportunities to broaden our appeal beyond those already in our orbit. Run to polarize the 2028 primary and general election around “Medicare for All” and “End All Aid to Israel” Why should democratic socialists use our scarce time and volunteer energy to run in a race we’re unlikely to win? Running in the 2028 primary will not only recruit us scores of members (imagine having a DSA member intervening in eleven nationally televised primary debates!), but — more importantly — it has the potential to be a decisive leap forward towards an America that finally starts meeting the needs of working people instead of HMO and AIPAC billionaire donors. Polarizing litmus tests are good, actually — as long as they’re fought for around popular demands in a thoughtful and strategic way. Democratic socialists have an opportunity and responsibility to make the 2028 primary about two of the most widely and deeply felt issues of our moment: domestically, America’s unaffordable, family-breaking private healthcare system; internationally, America’s bankrolling of the genocidal Israeli government. A candidacy relentlessly demanding Medicare for All and No Aid to Israel would pose a sharp question to the American public, the Democratic Party, and other 2028 contenders: Why can’t working-class Americans afford their healthcare bills at the same time that our government is spending at least $3.8 billion yearly to fund an Israeli regime eager to keep dragging us and the world into more forever wars? Our candidate can agitate around a clear message: I’m running to give voice to all those working-class Americans who are struggling to get by. No matter what state you live in, who you voted for in 2024, or your background, it’s just plain common sense that we should make healthcare more affordable instead of spending billions to enable the Israeli government’s crimes against humanity. Every American who agrees with me when I say ‘Medicare for All — Not a Dime for Israel’ should make their voice heard through our campaign. Racking up a high number of votes for this agenda is a crucial way to show the Democratic Party and the country as a whole that we can’t keep supporting a life-destroying status quo in our healthcare system and in the Middle East. If we want to defeat Trump’s handpicked crony this coming November, we need to speak to ordinary people’s frustrations with the status quo — we can’t keep running the same tired establishment playbook and expecting a different result. For those highly engaged voters (or skeptical media pundits) who raise concerns about wasting votes, the candidate can take the time to explain why this isn’t the case: It's true I'm a long shot. But Zohran showed anything can happen — and voting for me is a pragmatic decision, not a wasted vote. In a heavily divided primary with a large number of candidates and a very discredited establishment, my campaign and the democratic socialist movement I’m part of are positioned to be the balance of power if we can get a large enough number of voters to loudly demand Healthcare Not Warfare. Since there’s a high chance no candidate will get to 50% in the 2028 primaries, the Democrats could very well be heading towards a brokered convention, where a disciplined, principled current committed to breaking from corporate donors can play an outsized role in making sure whoever ends up with the nomination raises the banner of single-payer healthcare and cutting aid to Israel. How exactly could running this sort of primary campaign be translated into actual leverage? The candidate replies: I’m aiming to win the nomination, but if I don’t achieve that goal, my delegates and I will pledge our support in the primary only for a candidate who agrees in the general election to call for ending US aid to Israel and establishing Medicare for All at home. These are both winning planks for November against the Republicans, and the only reason I can see that other candidates haven’t yet raised these is that they’re still more scared of the donor class than they are of the people. That needs to change. If Democratic leaders want young people, workers, and those sick of the status quo to show up in big numbers this November, they need to meet us halfway. In this way, whether or not we win the nomination, democratic socialists could still play a decisive role in 2028. The approach outlined here is similar to what radical left parties periodically do in parliamentary democracies like Denmark or Sweden when they find themselves as a junior partner electorally: they dictate to center-left allies what their terms would be for enabling, from the outside, the formation of a new government. Running a Healthcare Not Warfare candidate will make it significantly more likely that the 2028 Democratic candidate stands with ordinary people instead of donors on these two pivotal planks — a shift that will, in turn, make it easier to achieve the existentially important task of defeating MAGA’s heir in November. Look at how much popular traction the Uncommitted movement got with a similar strategy in 2024 with so little on-the-ground organization — surely we can go much further with a working-class candidate, a much bigger DSA, and a Democratic Party base that has turned against Israel, billionaires, and the hacks in the party who prop them up. Objections and Responses There are quite a few objections to this proposal, many of which are very reasonable. Let me briefly address each here. Socialists shouldn’t waste their scarce time and resources on a candidate unlikely to win. This is a good rule in general, but there are important exceptions, and presidential elections are the main one. There’s no other political contest that gets anything like this level of attention and engagement in the US, so if you want to be a serious force in nationwide politics, you basically can’t afford not to have a candidate. Far from being an overall drain on our resources, running a dynamic 2028 campaign would likely result in a major net increase in members and capacity. In that spirit, the very pragmatic Milwaukee sewer socialists — who mostly ran to win at home in Wisconsin — supported Eugene Debs’s propagandistic presidential campaigns, which they correctly saw as a crucial means of recruiting to the Socialist Party. And now that the (post-1972) Democratic Party gives more weight to voters than party operatives in determining its presidential nomination, it’s become feasible to run as a socialist in primaries without risking a split in the vote against the Republicans in November. We should just support Ro Khanna, who also supports M4A and ending aid to Israel. I like Ro Khanna and would enthusiastically vote and canvass for him for president were he to get the nomination. But there’s a real chance that Khanna’s bid for office doesn’t catch on; he lacks the grassroots organizational apparatus of DSA, and, because he’s not really of the Left or labor (despite his current very good positions), he’s unlikely to generate the type of buy-in and enthusiasm that a DSA candidate would. Moreover, Khanna would be running as an individual, not as someone emerging from and building an organized movement — his campaign would not likely focus on building sustained power from below, nor would it likely polarize the 2028 primary around M4A and Israel aid. Also, and not inconsequentially, the poor electoral results for Tom Steyer and Saikat Chakrabarti this spring suggest that there’s perhaps not much of a popular appetite in the Democrats’ base to support multi-millionaire class traitors. Much better, then, to run a democratic socialist worker. If by Super Tuesday it’s clear that our campaign has captured the main left lane in the primary, Khanna should drop out and endorse us; conversely, if he’s managed to capture this lane, DSA could at that point transform its Healthcare Not Warfare campaign into an independent campaign to elect Ro Khanna focused on winning (and holding him accountable to) those two critical planks. An openness to consolidating an anti-corporate candidate by Super Tuesday is a necessary component of the strategy I’m proposing, because otherwise we risk splitting the primary votes (per DNC rules, you need at least 15% to get any delegates to convention). We should convince Shawn Fain or Sara Nelson to run for president instead. I agree it’d be great if either ran for president, but my impression is that there’s basically no chance that will happen in 2028, because both are firmly focused on their union leadership duties (and, in the case of Fain, beating back a federal witch-hunt). This plan could have DSA end up endorsing a non-socialist, non-DSAer for president. That’s against our principles. It’s a myth, and bad politics, that socialists can’t ever support candidates who aren’t members of their organization and/or who don’t call themselves socialists. Bernie, after all, was not a DSAer. And socialists abroad regularly make tactical decisions about if and when supporting a non-socialist candidate makes sense from the standpoint of promoting working-class interests, defending democracy, and building a socialist current in the process. If your “principles” prevent you and DSA from helping achieve the momentous stride forward of having a Democratic candidate in 2028 run on Medicare for All and ending aid to Israel, those “principles” are not very good. In fact, they actively undermine the causes they’re aiming to support. We shouldn’t confuse principles (working-class independence) with tactics (the best way to advance this in a given context). In my recent article in Catalyst on Milwaukee sewer socialism (I’ve uploaded the full text here), I have a section showing how similarly rigid rules about endorsements had to be eventually discarded in the old Socialist Party nationwide after WWI because they tended to lead socialists towards a sectarian abstention from broader anti-corporate electoral insurgencies beyond their party’s control. For what it’s worth, Lenin’s current under Tsarism frequently backed bourgeois candidates in the second round of elections and Lenin himself advocated that British Communists support Labour Party candidates; for his part, Kautsky in 1912 defended the German Social Democracy’s electoral pact with the Progressives in the runoffs. If our predecessors could be that tactically flexible in an era before the limits of their underlying strategic assumptions about intransigent class politics in capitalist democracies were made clear, then there’s no need to insist on being more tactically rigid today, after a century’s worth of accumulated experience. As long as you maintain your own political profile and organization while engaging in broader coalitional efforts, there’s zero inherent contradiction with the principle of political independence. More specifically: DSA and allied organizations should enthusiastically back Michigan’s Abdul El-Sayed if AOC declines to run and he throws his hat in the presidential ring. (I have no idea whether he’s even considering this path, but there’ll surely be chatter about it if he wins his Senate race this November.) Though he doesn’t call himself a socialist, El-Sayed is a principled Berniecrat, a consistent fighter for Palestine, and would have a clear path to victory. Using the “s” word is far less important than whether a campaign fights for the interests of working people at home and abroad — and whether actively participating in it can grow the organized working class and the Left. And the same holds true if a less anti-corporate candidate were, under pressure, to adopt our two Healthcare Not Warfare planks. Why focus only on M4A and ending US aid to Israel? There are so many other important issues to fight for! Obviously there are many pivotal issues beyond healthcare and Israel, and our candidate — and especially our organizations involved in the campaign — should be prepared to speak on all of these. But one of the crucial lessons of the Zohran campaign is that you have to relentlessly stay on message if you want to cut through the noise. CNN and Fox News will want to talk about DSA’s views on Venezuela and abolishing the police; we need to polarize the debate on the widely and deeply felt issues that are most favorable for growing the Left, isolating our opponents, and winning urgently needed changes. One recent poll found 63% of Americans support M4A, even after respondents were told it would raise taxes and eliminate most private insurance. Another found that 74% of Democrats opposed “providing additional economic and military support to Israel.” And focusing on two demands sets the campaign up to be able to leverage its mandate to make clear demands upon other candidates (which we can’t do if we run with a laundry list of demands). Talking about a Plan B is a distraction from the more urgent work of building a groundswell of calls on AOC to run. It’s good for DSAers, fightback unionists, and progressive organizations to try to convince AOC to run. This proposal is meant as an addendum to pushes to get AOC to run and to line DSA up for that eventuality, not as a substitute for those efforts. But I’m worried that, like in 2024, we might eventually find ourselves with no time to find a viable alternative unless we start now. DSA, with its abundant deliberative processes, moves slowly. The approach you’re suggesting seems risky. If a DSA candidate runs but doesn’t receive a lot of votes, it’ll undercut our current national momentum. This is a reasonable worry. All big initiatives always carry risks, and the path I’m advocating is certainly not free of these. That said, if a Healthcare Not Warfare campaign doesn’t catch on like we envisioned, it’s a pretty simple pivot in March 2028 to explain that in the future we really need our standard-bearers like AOC, or our growing bench of national electeds, to step up in presidential primaries. Moreover, it’s worth noting that the risk of my Plan B proposal is on the whole significantly less than the risk entailed by an AOC run. We should all back her if she runs, since she’d fire up the base, provide huge openings for organizing, have a real shot at defeating a Republican (especially given MAGA’s unpopularity), be a great standard-bearer for our issues, particularly M4A and ending aid to Israel, and be less willing than mainstream candidates to capitulate to any anti-democratic machinations from Trump and co. But it’s also true that AOC could lose, which would be a devastating setback to the country and the Left. Or she could win and find herself unable to pass much, if any, of her (and our) program, a demoralizing prospect. So if we’re willing to take these risks with AOC (and we should), it follows that we should be able to take lower-level risks if she doesn’t throw her hat in the ring. What’s Your Plan B? The US Left is facing its biggest opening for growth and influence since the 1930s. Initiatives that would have been impossible a few months ago are now potentially feasible. A President AOC is now easier to imagine, but if she ultimately assesses that the risks aren’t worth the potential reward in 2028, we can’t be left flat-footed. So even if you’re not convinced by the specifics of my Plan B, I hope you can at least agree that it’d be a disaster for us not to have one — and not to start getting a Plan B into motion. Hopefully other organizers and political currents will put forward their responses and alternative ideas ASAP. The stakes are too high to just wait and see. More As always, please share this article widely. This newsletter depends on all you lovely people to get the word out. Thank you! I uploaded the full text of my Catalyst article on Milwaukee sewer socialism here (it’s updated and 3x as long as the version published earlier on this substack). If you haven’t yet, please make sure to subscribe to Catalyst here; I read every issue front to back, highly recommend. 🌹🎵 Ivan Lins (arranged by Arthur Verocai) — “General da Banda” 🌹🎵
Bulls-Heat postponed because of condensation on United Center court during humid, rainy winter night in Chicago
On an uncharacteristically warm January day in the Windy City, a nearly two-hour delay was followed by a postponement.
Interventional radiology training programs should return to in-person interviews, association says
“Interventional Radiology training inherently involves specialty-specific characteristics that make in-person interviews particularly valuable,” the APDIR contends.
Tuchel rues 'big mistakes' - did Man City charges have impact?
England's loss to Spain was decided by uncharacteristic mistakes, with two Man City players making errors - so did club news affect performances for country?
Interpretable AI in materials discovery: Uncovering how models make predictions
A method to interpret artificial intelligence (AI) models used in materials discovery by analyzing their learned features has been developed by researchers from Japan. The method extracts key features from an AI model trained on atomic structural data and optical absorption spectra, and then groups materials with similar structural and spectral characteristics. This approach can be extended to reveal how atomic arrangements influence other material properties, paving the way for more efficient materials design.
The Surprising Reason Some People Keep Dating Narcissists, According to Science
A new study suggests people with narcissistic or manipulative traits may be more attracted to partners who share those same characteristics.
Game Preview: Pittsburgh Penguins @ Calgary Flames 1/21/26
Who: Pittsburgh Penguins (23-14-11, 57 points, 3rd place Metropolitan Division) @ Calgary Flames (21-23-5, 47 points, 7th place Pacific Division) When: 9:30 p.m. eastern How to Watch: Locally broadcast on Sportsnet Pittsburgh, SN1 and TVAS in Canada, streaming on ESPN+ Pens’ Path Ahead: The road trip continues tomorrow night in Edmonton, then an uncharacteristic two-day break gives […]
Is SMIC N+3’s Metal Pitch Smaller than Intel 18A’s?
Almost four years ago, we published that SMIC had started shipping 7 nm (N+1) chips. Now, SMIC is shipping its third-generation 7 nm (N+3) process in Huawei’s Kirin 9030, with a minimum metal pitch of 32.5 nm, about 10% tighter than the 36 nm minimum metal pitch shipping in Intel’s latest Panther Lake CPUs on 18A. The headline is true, but incomplete cherry picked metric. N+3 reaches the density of TSMC N6 through aggressive DUV multi-patterning and design-technology co-optimization (DTCO), but it pays for that in complexity, efficiency and process control. We found this and more in our reverse engineering and teardown where we cover SMIC’s N3 process technology, Huawei’s packaging, memory, architecture, and more. SemiAnalysis has been building a state-of-the-art teardown lab in Oregon capable of analyzing the world’s most advanced and important chips over the last year and half. We have already generated revenue on advanced datacenter chip teardowns including our recent reverse engineering of a major TSMC customer’s COUPE CPO optical engine + EIC 3D stack. This is the first public report from the SemiAnalysis Teardown Engineering & Evaluation Lab, or STEEL for short. The lab is aggressively scaling up and out and we’re excited to announce it publicly. This is a bit of inconvenient timing for TechInsights as they are private equity owned and currently being sold while having enjoyed virtually no credible competition for decades. This has led to TechInsights underinvesting in CAPEX. SemiAnalysis exceeds TechInsights in revenue despite no venture or private equity ownership and being founded only 6 years ago. Because we have no external investors and are founder led, we move faster, build faster, and we can release client chip teardowns for free regularly, while focusing on datacenter for our major clients. Here’s the first public image from our lab, the HiSilicon Kirin 9030 Pro SoC: This report will detail our teardown of the Kirin 9030 and our findings on SMIC’s N+3 process, the most advanced in China. For comparison, we’ll show our teardown of the MediaTek Helio G99, made on TSMC N6. Through this comparison, we can look at the effect of export controls – SMIC N+3 and TSMC N6 are comparable nodes, but one is heavily export-controlled, the other free to use the West’s most advanced equipment. Here we see both China’s progress and constraints. SMIC N+3 reaches TSMC N6-class logic density, but it requires far more aggressive DUV multi-patterning, so it does not match N6 on process maturity or cost. The Kirin 9030 Pro performs similarly to three-year-old Android flagships, and trails far behind the current flagship SoCs from Apple, Qualcomm, MediaTek, and Samsung. The efficiency gap is even wider. Export controls have not stopped Huawei and SMIC from shipping advanced silicon, but they have forced a different path. Without EUV, SMIC is leaning harder on DUV multi-patterning, DTCO, and increasingly complex integration. The roadmap continues forward through tighter design rules and backside power, but each step adds cost and process risk. Huawei’s τ scaling and LogicFolding show another path: stacking active logic and recovering density through advanced packaging and system-technology co-optimization (STCO). To understand the Kirin 9030, we must first understand Huawei’s SoC history. HiSilicon is Huawei’s chip design arm, responsible for the Kirin smartphone SoCs, Kunpeng server CPUs, Ascend AI accelerators, and switch/router networking silicon. Before export controls, Huawei was TSMC’s largest customer – the only customer on TSMC’s first EUV node, N7+, and among the first on N5, alongside Apple. That ended in late 2020. Huawei switched to Qualcomm SoCs in its flagship smartphones, though export controls limited them to 4G-only variants. In late 2023, Huawei returned to in-house silicon with the Kirin 9000s, a successor to the Kirin 9000, fabricated on SMIC N+2 instead of TSMC N5. In the following years, they released the Kirin 9010 and 9020 on the same N+2 process. These chips used Huawei’s in-house TaiShan CPU cores and Maleoon GPU. We have not torn down a Kirin 9020 ourselves, so the predecessor die shot is from Kurnal. The die shots show how Huawei spent its silicon budget: which functional blocks are where, and how their areas compare to the predecessor. First, a quick guide to the major blocks on the die. The total die area is nearly identical, but the 9030 uses that area more aggressively. A denser process lets Huawei fit an extra middle CPU core, more GPU and NPU cores, and larger caches into the same footprint. In contrast, the Helio G99 is a much smaller, low-cost SoC, built for budget smartphones rather than a flagship device. While the Kirin 9030 is ~140 mm², the G99 is only ~29 mm², roughly one-fifth the area. The underlying TSMC process technology, however, is directly comparable as a baseline for analyzing SMIC’s. The Kirin 9030 is an evolutionary refresh, not a clean-sheet design. Its CPU, GPU and NPU cores carry over the 9020’s families, and the gains come from three levers: the SMIC N+2-to-N+3 process step, DTCO and floorplan work, and incremental microarchitecture. Area is where the first two show up, and the 9030 scales well here. Performance and efficiency are the harder test. Huawei’s design holds up better than its node would suggest, but the chip still trails, both because N+3 sits behind the leading-edge nodes and because its cores, while competent, remain a few generations behind the newest designs. The new prime core is an incremental update. The main changes are a 10% frequency increase from 2.5 GHz to 2.75 GHz and a doubling in the L2 cache from 1 MiB to 2 MiB. Despite the increased cache, the core size decreased by 7.6%. Excluding the private L2 cache, the core size decreased by 21%. This is a large reduction for an incremental node. Compared with the TaiShan New V120 core in the Kirin 9020, the Kirin 9030’s middle core is almost unchanged architecturally, yet each core shrinks by ~22%. Most of that comes from the move from N+2 to N+3, with layout likely accounting for the rest. Visually, the most noticeable change is the increase from 3 middle cores to 4. There is also a 20% increase in the shared L3 cache of the big cluster. This helps to improve multi-core performance without sacrificing much in terms of area. Even with each core shrinking, the big CPU cluster’s total area is essentially unchanged. The per-core savings went back into an additional middle core and larger caches. The tiny cores shrank less than the prime core (excluding its L2 cache) and less than the middle cores. This is likely because fixed overhead is a larger share on a small core. We cannot resolve any architectural changes from the die shot alone, but the per-clock and efficiency gains shown below point to more than pure process and layout scaling. The area reduction was offset by a doubling of the shared L2 cache from 2 MiB to 4 MiB, leaving the total tiny CPU cluster area slightly larger. Area is the easiest improvement to see from a die shot, but it is only one part of PPA (power, performance, area). For modern logic, power and performance matter just as much, and often more. Since Dennard scaling broke down in the mid-2000s, voltage and frequency have not scaled in step with transistor dimensions, so each node has had to fight harder for gains in performance and efficiency. The starkest comparison is not Kirin 9020 versus Kirin 9030 Pro. Apple’s efficiency cores run circles around Huawei’s prime core. Apple’s low-power core delivers 20% higher integer performance while drawing only 1 W, compared with 4.5 W for Huawei’s prime core. N+3 matches TSMC N6, but N6 is several generations old. Apple and Qualcomm build on N4 and N3P, which are denser and sit on a better voltage-frequency curve, giving them a larger transistor budget and more performance per watt. The 9030’s own cores did improve. The middle and tiny cores gained 17% and 14% in per-clock integer performance over the 9020, with floating-point flat on the middle core and up 11% on the tiny. The tiny core improves cleanly, with performance rising while power falls and efficiency increases by 45% in integer and 24% in floating point. The middle core is mixed: integer performance rises but power rises faster, cutting integer efficiency by 7%, while lower power lifts floating-point efficiency 16%. Per-clock gains at the same or lower frequency are microarchitectural, so the cores are tuned, not just shrunk. Both also failed to hold their rated maximum frequencies, pointing to thermal, power, or stability limits. Per clock, the middle core sits around Arm Cortex-A720 and the tiny core near the Cortex-A520; absolute performance trails because Huawei clocks them much lower. The prime core is roughly Cortex-X2 class per clock, a 2021 design. Apple’s 2020 M1 Firestorm core is still 35% higher per clock and 57% faster in absolute integer performance at a similar 4.5 W. The current leading edge is further ahead again: the Apple M5 P-core is 60% higher per clock and 2.7× faster, the Arm C1 Ultra 45% higher and 2× faster. Matching older high-end cores per clock is a genuine design achievement. What Huawei cannot match is the voltage-frequency curve and transistor budget of leading-edge nodes, which let Apple, Qualcomm and others spend more transistors in the same area on wider cores, larger caches and deeper buffers while running at lower voltage. Huawei’s LogicFolding roadmap is one answer, stacking active logic to recover density and shorten signal paths. We return to it later. The GPU compute units (CUs) changed more visibly than the CPU cores, moving to a more rectangular layout for both the arithmetic logic unit (ALU) clusters and the CU overall. Even with ray-tracing support added, a CU shrank ~28%. However, that shrink is offset by the increase from 4 to 6 CUs and the area outside the CUs grew 33%. Overall, the GPU cluster is larger by ~10%. The GPU is where Huawei makes its biggest gains. The Maleoon 935 is not competitive with current flagships, but it is a large step up from the 920 and reaches older-flagship territory. In 3DMark it is 70% faster in Wild Life Extreme (WLE) and 79% faster in Steel Nomad Light (SNL) than the 920; with 11% higher clocks and 50% more CUs, the ~67% theoretical uplift roughly matches WLE and is beaten by SNL. It edges ahead of the Snapdragon 8+ Gen 1 in WLE and SNL, and the Dimensity 9200 and Apple A16 in WLE, but stays far behind newer parts: the Snapdragon 8 Elite Gen 5 and Dimensity 9500 are ~2.4–2.6× faster in WLE and ~3.2× faster in SNL. The Maleoon 935 is Huawei’s first GPU with hardware-accelerated ray tracing; there it lands slightly ahead of the Exynos 2200, and on par with the Apple A16, with current flagships up to 3.7× faster. The Neural Processing Unit (NPU) saw the largest structural changes of any block, moving from a Lite and a Tiny core in the Kirin 9020 to a Lite and two Tiny cores in the Kirin 9030. Both core types also show significant layout changes. This is a reversal in Huawei’s NPU design. The Kirin 9000 5G, its last flagship chip on TSMC N5, used two Lite and one Tiny core. The series of SoCs on SMIC N+2 moved to one Lite and one Tiny core, likely for area savings. With the Kirin 9030, Huawei has shifted back toward a larger multi-core NPU cluster, but with the additional area going to a Tiny core rather than a Lite core. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Before diving into the process stack, the package and memory are worth separating from the SoC itself. The Pro variant of the Kirin 9030 carries 12 GB of Samsung DRAM, with two stacks of four dies each. The dies were identified as the K4L2E165YD, a 12 Gb LPDDR5X-9600 device fabricated on Samsung’s 1a node, the fourth generation of its 10 nm-class DRAM after 1x, 1y and 1z. 1a has shipped in volume since 2022, so this is current memory rather than older-node inventory. The 16 GB Pro Max variants we obtained were found with both CXMT and Samsung packages. The CXMT package is marked CXDD7JEDM, with two stacks of four dies, packaged in week 45 of 2025. The inferred die dimensions from X-ray computed tomography (CT) are consistent with a known density of ~0.3 Gib/mm² for the CXMT G4 process, roughly equivalent to other manufacturers’ 1z processes. The Kirin 9030 uses a typical integrated package-on-package (iPoP) stack: multiple DRAM dies in a memory package sit above an organic redistribution layer (RDL) interposer, which sits above the SoC and package substrate. The full package is then mounted to the printed circuit board (PCB) through ball-grid array (BGA) solder bumps. The memory package substrate is a thin bismaleimide-triazine (BT) laminate carrying the LPDDR5X stack. The organic RDL interposer over the SoC routes the PoP signals around the die and carries possible dummy thermal copper pillars. The package substrate, a thicker Ajinomoto Build-up Film (ABF) build-up over a BT core, fans the flip-chip bumps out to BGA pitch and embeds the power planes. The whole stack is organic. The only silicon is the SoC and the LPDDR5X dies; there is no silicon interposer. Keeping it all-organic brings the package’s coefficient of thermal expansion (CTE) close to the PCB’s, reducing board-level warpage, and avoids the cost of a silicon interposer the SoC’s bandwidth does not need. In an iPoP stack, the memory package connects to the organic RDL interposer through an array of solder bumps. Underfill fills the gap around those bumps, adding stiffness and protecting the joints from mechanical stress. The Pro and Pro Max variants differ here, which we cover behind the paywall. The die shot and architecture tell us how Huawei allocated its silicon budget. The process tells us what SMIC can manufacture. We use the Helio G99 as the process reference for TSMC N6. Both SMIC N+3 and TSMC N6 are evolutions of previous 7 nm-class nodes. We used targeted TEM cross-sections through logic and memory regions, imaged in both fin-cut and gate-cut directions. Each cross-section caption gives its horizontal field width (HFW), the real width of the imaged area. We start at the transistor fins, then move up through standard cells, local interconnect, and SRAM. SMIC has not overtaken Intel or TSMC. It uses aggressive DUV scaling and DTCO to reach N6-class density, but that density doesn’t translate into comparable performance and efficiency, for two reasons: the node gap to leading-edge nodes, and Huawei’s core designs. Fin Profile One of the most important knobs in a FinFET process is the fin profile: the shape of an individual fin and the channel where current passes from source to drain. The ideal fin is tall, narrow, and nearly vertical. A taller fin increases effective channel width, while a narrower fin improves electrostatic control by thinning the body the gate must control. Push either too far, and the process pays for it: weaker drive current, fragile fins, taper, footing and line-edge variation that hit yield and device variability. The Intel 22 nm, 14 nm, and 10 nm fin cross-sections show how FinFET nodes have improved over time. 22 nm fins were a first-generation structure, relatively short, wide and strongly tapered. The shape limits current density and reduces gate control uniformity across the height of the fin. At 14 nm and 10 nm, Intel pushed the fins taller and narrower while also making the sidewalls more vertical. Rather than shrinking the device, these changes increase the effective channel width per fin and improve electrostatic control. The trade-off is that taller fins at tighter pitches are much more difficult to manufacture. Now, let’s compare the Helio G99 on TSMC N6 with the Kirin 9030 on SMIC N+3. Both processes are in the same class, with fin pitch of 30-32 nm on N+3 and 34 nm in our N6 cross-section. The pitch for N6 is especially interesting as N7’s HD library is generally listed with a 33 nm fin pitch, and N6 did not shrink pitches directly. Its density gains came from DTCO instead of tighter pitches. The 34 nm pitch was stable across our sampled region and serves more as a comparison against the SMIC N+3 we have not investigated further. Pinning down N+3’s fin patterning scheme takes more than one core unit. The CPU cores show a dense ~32 nm pitch, with the pitch between N-P fin pairs alternating between 78 and 88 nm. Logic alone may be consistent with dual-pitch mandrels of 120 and 110 nm, but this is a complex and unusual approach. Combining the pitch from the the 8T SRAM, which has more complex repeat unit, with the CPU core sequence allows us to reverse engineer the patterning steps with more confidence. As both the logic and SRAM should share the same base grid, a single CD mandrel lithography pattern with 128 nm pitch undergoing SAQP produces a die-wide ~32 nm grid (128 nm/4), which supports the pitch sequencing seen in both logic and SRAM cells. In the sampled cross-sections, N+3 shows a taller, narrower, higher-aspect-ratio fin than N6. The measured fin aspect ratio is ~9.5:1 on N+3 versus 7.8:1 on N6. N+3 also shows less top rounding, with an estimated radius of ~2 nm, compared with 2.8 nm on N6. Even though the fin widths differ, the ratio of top rounding to fin width tells the same story, with N+3 at 0.37 and N6 at 0.44. In a geometric sense, lower is better; a perfectly rectangular fin would have no top-rounding penalty. These are single-digit-nanometer features measured from a handful of cuts, so treat the absolute numbers as approximate. The important result is the relative gap: N+3’s fins are consistently taller, narrower and less rounded than N6’s. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Standard Cell A standard cell is the basic building block of chip layout: a fixed-height row pairing one NMOS and one PMOS transistor that share a gate, tiled in a grid to build logic blocks. The key dimensions are contacted gate pitch (CGP), cell height (CH), fin count, and the lower-metal routing grid. To measure density, we use the Bohr metric: a weighted average of NAND2 gate area (60%) and scan flip-flop area (40%). This represents a realistic mix of combinational and sequential logic. This metric has its limitations, especially for complex cell layouts like TSMC’s FinFLEX, which alternates cells with different fin counts. Even so, it is the best metric for a pure process-level comparison. Another important measurement is the fin pitch; it refers to the distance between two fins of the same transistor. In a FinFET process, multiple fins are used in each transistor to increase the drive current and thus performance. TSMC N6 ships both a high-density (HD) library with 2 PMOS and 2 NMOS fins per cell, and a high-performance (HP) library with 3 of each. More fins under the shared gate mean more effective channel width. HP cells switch harder at the cost of area. Designers mix the two on a die, primarily spending HP cells on timing-critical paths, and matching their PPA targets. In the Cortex-A55 core of the Helio G99, we found a cell height of 240 nm for the HD cell. MediaTek has used HD cells in the G99 to minimize die size and thus cost. As an SoC for budget smartphones at ~$100, this is essential. By contrast, we found only one library in the Kirin 9030, with 2 NMOS and 2 PMOS fins. This suggests a narrower library strategy than TSMC N6, where both HD and HP libraries are widely used. This likely reflects the smaller customer base and the more constrained domestic design and electronic design automation (EDA) ecosystem. In all three CPU cores of the Kirin 9030, we found cell heights of 228 nm, 5% smaller than on N6. This is also a reduction of 9.5% over SMIC N+2’s cell height of 252 nm. SMIC N+3 and TSMC N6’s HD library both feature a CGP of 57 nm. For SMIC, this is a 9.5% shrink over N+2. In the past, CGP and cell height alone may have been enough to compare transistor density. Now, however, we must consider scaling boosters and DTCO as well. SMIC’s density gain does not come from EUV. It comes from using every available DTCO booster aggressively. First is fin depopulation: reducing the number of NMOS and PMOS fins in each cell. The first FinFET nodes started with 3 or 4 fins for each transistor. SMIC N+3 and TSMC N6 HD both use only 2 fins per transistor, trading drive strength for density. Next is contact over active gate (COAG). By landing the gate contact directly over the active gate, instead of out over the isolation region, the cell height drops. N+3 integrates COAG while N6 does not. Our N+3 gate-cut cross-sections indicate COAG, with the gate contact sitting over the active region, while N6 shows an off-gate contact. Last is single diffusion break (SDB). Diffusion breaks are inserted between cells in the same row to provide electrical isolation, but they also introduce local layout effects (LLE), layout-dependent shifts in electrical characteristics. In the past, a double diffusion break was used, consuming the space of two CGPs. SMIC N+3 and TSMC N6 instead use SDB, saving area but increasing LLE sensitivity. This must be controlled at the process level and accurately modeled in the process design kit (PDK) so that EDA tools can account for it. Overall, SMIC N+3 has a transistor density of 113.4 MTr/mm², slightly above TSMC N6 at 107.7 MTr/mm². Even without EUV, SMIC has achieved density beyond TSMC’s mature N6 node which utilizes EUV. Metal Stack The smallest critical dimension in the teardown is M0; SMIC N+3 uses a 32.5 nm local metal pitch. That is smaller than the 36 nm M0 pitch on Intel 18A in Panther Lake. However, this does not mean that SMIC has a better process than Intel 18A or TSMC N3P. M0 is a local intra-cell routing layer. Its usefulness depends on the full interconnect stack: M1 and M2 pitch, track count, via and line resistance, design rules, mask count, overlay control, and routing flexibility. The 32.5 nm M0 is consistent with self-aligned quadruple patterning (SAQP), whose four-population line-width loading we read coarsely as alternating widths of 21.5 to 24 nm; M1 and M2, at 38 and 40 nm, are consistent with self-aligned double patterning (SADP), a single A/B split. On TSMC N6, M0, M2, and M3 sit at a relaxed ~40 nm and are consistent with SADP-class double patterning, with no need for quadruple patterning. That said, we measure M2 for example at ~43 nm, likely inflated by sparse routing. We do not assign any specific layer to EUV from our cross-sections; the distinction we can draw is double versus quadruple patterning, not lithography wavelength. Transistor-level density in the front-end-of-line (FEOL) sets an upper bound, but the design is ultimately limited by what the interconnect stack can route. The lowest metals are the most important for standard-cell density, but the semi-global and global layers determine how usable that density is at the block and chip level. Two axes are commonly used for chip cross-sections: the fin-cut and the gate-cut. The micrograph above is a fin-cut and shows metals 0 through 3. This axis lets us see the even-numbered metals, with M0 right above the fins. There are two kinds of M0 lines. The first are the power rails; these are wide wires for the VDD and VSS running horizontally at the top and bottom edges of each standard cell. The wide wires measure 55 nm across, more than double the other M0 lines. Their width minimizes resistance and reduces IR drop. The second kind are intra-cell wires, short segments within the cell that connect terminals to M1. These have alternating widths between 21.5 and 24 nm. The M0 pitch is 32.5 nm, a 19% reduction versus N+2 and N6. At this pitch, DUV patterning requires more aggressive multi-patterning, increasing mask count, overlay sensitivity, process complexity and cost. M0 is below what a single DUV-defined spacer (SADP) can resolve, so SMIC cascades a second spacer step (SAQP). The cross-section reflects the cost: the M0 trenches are visibly more re-entrant (narrower at the bottom than the top) than M1 or M2 on the same chip and carry a bright barrier-rich foot where the trench meets the etch-stop layer. That shape is partly the intended damascene profile, as a slightly narrow bottom helps void-free copper fill, but its magnitude at M0 is driven by the tight pitch and the higher trench aspect ratio. Intel 18A supports an M0 pitch of 32 nm, although Panther Lake has only shipped with a looser 36 nm pitch. This is due to Intel’s heavy usage of HP libraries. Among leading-edge nodes, 18A has the loosest M0 pitches due to PowerVia. With power routing moving to the backside, congestion is reduced, and the entire front-side metal stack can be used for signal routing. M2 is the first true inter-cell routing layer. It runs horizontally like M0 but spans across multiple cells to carry block-level signals. The M2 pitch sets the cell’s track height – the number of M2 tracks that fit between the VDD and VSS rails, defining what the library calls a 6-track or 7.5-track cell. This layer is the most important, limiting the routing of entire blocks. SMIC N+3 features a 5.7-track cell. The M2 pitch is 40 nm, a 5% decrease over N+2 and the same as N6. This shrink keeps the pitch at the edge of what is possible with double patterning. Future nodes will need to increase the number of masks for M2 as reducing the number of tracks is much harder due to the limitations in routing. The micrograph above is in the perpendicular direction, the gate-cut, and shows metals 0 through 4. This allows us to see and measure the odd-numbered vertical metal layers. The M1 pitch is 38 nm, 9.5% less than N+2 and 33% less than N6. The M1-to-gate ratio matters because it sets local routing flexibility. N+2 and N+3 use a 3:2 ratio, while N6 uses a 1:1 ratio, explaining the huge differences in M1 pitch. The more M1 lines there are compared with the gates, the more flexibility there is for power and signal crossing within the cell. Routing flexibility enables more complex and better cells. Clean fractional ratios are also preferred as a grid is periodic and improves layouts. The 3:2 ratio gives SMIC more local routing flexibility than a strict 1:1 grid, but it also complicates layout and patterning. This is a DTCO choice, with SMIC increasing process complexity to recover density and routability without EUV. This 3:2 ratio is not very popular in the leading-edge nodes. TSMC has only used it on N7+, the N5 family, and the short-lived N3(B). They have switched back to a 1:1 ratio for N3E. Intel has only used it on the 10 nm/Intel 7 family, with Intel 4, 3 and 18A all using a 1:1 ratio. Samsung is the only one still using a 3:2 ratio at the leading edge, using it in the SF4 and SF3 families. It remains to be seen if SMIC will remain at a 3:2 ratio or move to a 1:1 ratio with its future nodes. The industry is still actively exploring these local-routing ratios. At VLSI 2026, imec will be presenting work on even higher ratios, including a 2:1 scheme that can reduce area by up to 14%. We will be covering the conference in a future newsletter article. Subscribe We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. The final local interconnect layer for N+3 is M3, with a pitch of 44 nm. The M3 pitch is the same as on N+2 and 10% larger than on N6. The semi-global layers carry the majority of block-level signal routing. They have a coarser pitch than the lower local layers. On leading-edge nodes, they are designed to sit at the limit of DUV single patterning. M4 through M11 pitches were found divided between 80–82 nm (M4–M6), 128 nm (M7–M10), and 148 nm (M11). Given limited sampling, it is possible these are divided further in dense routing areas. At the top are two giant metal layers, M12 and M13. These have kept the same pitches as N+2 at 1920 nm and 4600 nm respectively. While the lower layers’ pitches are generally fixed by the process and library, the upper layers vary much more in pitch and count, depending on the design. Even two smartphone SoCs on the same process can have wildly different metal stacks. The Helio G99 carries fewer routing layers, reaching coarse metal pitches of 850 nm by M9, while the larger and higher-performance Kirin 9030 keeps fine pitches until M11. SRAM At the leading edge, SRAM is much more difficult to scale than logic. TSMC’s latest nodes have seen little to no bitcell scaling, while logic still has more DTCO levers to pull. While looking for other logic libraries in the GPU compute units, we stumbled upon the SRAM. The most common type of SRAM has 6 transistors (6T), but this cell had 8 transistors (8T) instead. 8T SRAM adds two transistors to form a dedicated read port. Unlike a 6T cell, where reading disturbs the storage, the decoupled read port removes read-disturb, improving read stability and letting the cell be pushed harder for performance. At first glance, the cut looked like an unusual logic library, with each cell row having 3 fins of one polarity and 5 fins of another. The rows also alternated in orientation. Energy-dispersive X-ray spectroscopy (EDS) resolved our confusion. The cut had not landed on the GPU logic, but on the SRAM macro beside it. The unusual fin pattern was due to the SRAM library. We return to EDS in the process flow analysis behind the paywall. SRAM libraries are not like traditional logic libraries. Due to the unequal number of PMOS and NMOS transistors, they require specialized rules and layout libraries. They do not need the flexibility of logic libraries, so they are hyper-optimized for one purpose: dense, reliable memory. The SRAM cell we found is a 1:2:2-2:2 cell. This means there is 1 fin per pull-up (PU) PMOS transistor, and 2 fins per pull-down (PD) and pass-gate (PG) NMOS transistor. These 2 PU, 2 PD and 2 PG transistors would usually form a single 6T high-current cell (HCC). An 8T HCC adds a read-pull-down (RPD) and a read-pass-gate (RPG) NMOS transistor, each with two fins. We measured a cell height of 406 nm, which brings the bitcell size to 0.0463 µm². That is a theoretical peak density of 21.6 Mib/mm². We estimate that a 6T HCC would have a cell height of 292 nm and a size of 0.0337 µm². This is ~12% larger than a 6T HCC on Intel 3 and 4. We also estimate the 6T high-density cell (HDC) to have a cell height of 228 nm and a size of 0.0260 µm². This is coincidentally the same as the logic standard-cell height measured earlier. The estimate puts the cell near Samsung 7LPP/5LPP and slightly below TSMC N7/N6. That is a theoretical peak density of 38.5 Mib/mm². 6T HDC is arguably the most important cell as it is used for the largest caches in a chip, the L3 caches and system-level cache (SLC). Both the Kirin 9020 and 9030 have split the SLC into 4 banks to raise total SLC bandwidth. In the Kirin 9030, the SLC increased from 2 MiB to 3 MiB per bank. Correspondingly, the number of arrays within the bank also increased by 50%, from 16 to 24. Each array can store 128 KiB and forms an orderly pattern on the die shot. From the Kirin 9020 to the Kirin 9030, the area of a 128 KiB SLC array decreased from 0.0477 mm² to 0.0392 mm², an 18% shrink. The achieved density is 25.5 Mib/mm², 66% of the theoretical maximum. While the SLC was quite similar across both chips, the L3 has seen some major changes, particularly in terms of its layout. The total capacity also went up from 10 MiB to 12 MiB. Much like the SLC, the L3 is also split into 4 banks. In the Kirin 9020, an L3 bank consisted of 16× 128 KiB arrays and 16× 32 KiB arrays. However, an L3 bank in the Kirin 9030 instead consists of 48× 64 KiB arrays. In the Kirin 9020 L3, a 128 KiB array was 0.0513 mm² and a 32 KiB array was 0.0154 mm². The size of the 128 KiB array is different on the L3 and SLC as the assist circuitry for the two arrays differs depending on their purpose. In the Kirin 9030 L3, a 64 KiB array is 0.0210 mm². Although not a like-for-like comparison, normalized for capacity, it is 18% smaller than the 9020’s 128 KiB L3 array and 31% smaller than its 32 KiB L3 array. The achieved density is slightly lower than the SLC, at 23.8 Mib/mm², 62% of the theoretical maximum. Unlike the L3 and SLC, the prime cores’ private L2 cache uses a 2-bank design. As the prime cores’ L2 is latency-critical, it likely uses 6T HCC instead of 6T HDC. The 9020 has 16 arrays in each bank while the 9030 has 32. Each array has a capacity of 32 KiB. A 32 KiB array in the L2 shrank from 0.0171 mm² to 0.0142 mm², ~17% smaller. The density is 17.6 Mib/mm², ~59% of the theoretical maximum for 6T HCC. SRAM scaled well from N+2 to N+3, shrinking by ~19%, close to the theoretical logic shrink. The caveat is that N+2’s bitcells were unusually large, bigger than comparable 7 nm-class nodes, so part of the gain is catch-up rather than true scaling. With the insights from STEEL’s teardowns, we will be doing a deep dive into SRAM in a future newsletter article. Subscribe Everything above came out of a single STEEL teardown: die annotation, block-level area analysis and TEM cross-sections through logic and SRAM. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Future Roadmap The same cross-sections that pin down N+3 also show where SMIC can go next. Although N+3 is already close to the practical limits of DUV multi-patterning in several layers, SMIC still has a few scaling levers left. A theoretical N+4 would likely start with cell height. N+3 uses 5 M0 tracks between its power rails. Moving to 4 M0 tracks, as on SMIC N+2 and TSMC N6, could reduce cell height by roughly 15%. The routing grid is only one side of the shrink; the front end also must fit into the smaller cell. One possible FEOL lever is reducing the p-to-n isolation spacing from two diffusion grid units to one. Intel used this scaling booster on Intel 4, and TSMC did so on its N3 family. This path trades layout flexibility for density. Fewer M0 tracks reduce local routing resources, while tighter p-to-n spacing raises integration and design-rule difficulty. M2 is also constrained by the cell height shrink. For SMIC to maintain a ~5.7-track cell, M2 would need to move toward ~35 nm. That would move another layer into SAQP territory. SMIC could also reduce the CGP from 57 nm to 54 nm. Intel reached a similar CGP on Intel 10 nm/Intel 7 without EUV. The local interconnect is also tougher. If SMIC keeps the 3:2 M1-to-gate ratio, M1 would need to shrink to 36 nm and would likely require SAQP as well. If SMIC moves to a 1:1 ratio, M1 could relax to 54 nm, but it would give up routing flexibility. Under this theoretical path, we estimate that SMIC N+4 could reach a cell height of 198 nm and a CGP of 54 nm, implying a Bohr density of 137.8 MTr/mm², on par with TSMC N5 or Samsung SF4. However, the difficulty is cumulative. Each step is individually plausible, but together they make N+4 harder than the transition from N+2 to N+3. It will likely take longer, cost more, and carry less process margin. A theoretical N+5 would require a larger integration shift. One possible path is backside contacts (BSCon), moving power routing and source/drain contacts to the backside, which would reduce front-side routing pressure and enable another reduction in cell height. The front-side metal pitches could relax to reduce the process complexity. M0 would likely relax slightly to ~34 nm, and M2 and M4 pitches could relax further. CGP is unlikely to shrink much further. Even with EUV, 48 nm has been the practical limit for yield and process control. This approach would allow N+5’s cell height to fall to 170 nm and its CGP to 53 nm. This implies a Bohr density of 163.6 MTr/mm², on par with Intel 18A’s HP library. However, this would not make N+5 cost-competitive with the leading edge. It would reach a similar density through a much more expensive route. The integration difficulty rises sharply, with new process flows for backside alignment, wafer thinning, contact reveal, and backside metallization. Past this point, standard density and interconnect scaling become increasingly unattractive. That is where Huawei’s roadmap stops looking like a normal foundry roadmap and starts looking like a packaging roadmap. At ISCAS 2026, Huawei unveiled its tau (τ) scaling law, reframing process scaling in the time domain. τ is the time cost of data movement and processing: switching delays in transistors, RC signal propagation delays in circuits, compute, memory, and networking latency. Outside Huawei’s terminology, this is called system-technology co-optimization. This is Huawei’s answer to its lack of EUV lithography. Without EUV, planar density cannot keep pace with TSMC, Intel, or Samsung. If transistor density cannot shrink further, Huawei’s alternative is to shorten wires, reduce buffering, and stack logic vertically. “LogicFolding”, Huawei’s implementation of this new scaling idea, is, in practice, an aggressive 3D stacking approach. AMD V-Cache places SRAM above or below a CPU die. AMD’s MI350X places active interposer dies (AIDs) underneath accelerator and compute dies (XCDs), with the AIDs handling cache, IO interfaces, the network-on-chip (NoC) and embedded metal-insulator-metal (MIM) capacitors. With LogicFolding, parts of the same logic block are split across multiple active dies bonded face-to-face at ultra-fine pitch. This allows Huawei to shorten some critical paths and reduce buffer overhead, not merely add cache capacity or offload the IO and interconnect. Shortening wires is where the higher clocks come from. A large share of a modern core’s delay and energy budget goes into driving long interconnects and the repeater buffers along them. LogicFolding distributes a block’s critical-path gates across multiple stacked tiers bonded at a very fine pitch, so the bond interface behaves like an additional metal layer and the longest paths get shorter. That is how Huawei expects to recover frequency and efficiency it cannot get from the process alone. Huawei’s roadmap shows its intent. Prime core frequency is targeted to rise from 2.75 GHz in the Kirin 9030 to roughly 5 GHz by 2031, far beyond what planar scaling alone could deliver. Prime cores with 3.1 and 3.39 GHz clocks are being tested in its labs, although their power consumption is unknown. Beyond that, chips are in the design, simulation or pathfinding phase, meaning the frequencies are targets. However, the direction matters more: LogicFolding also helps with performance, not just density. The catch is that Huawei’s density claim is not directly comparable to foundry densities. A stacked design can report more transistors per package footprint by adding active layers, even if each patterned die remains well behind TSMC or Intel in front-end density. This is how Huawei can claim to reach foundry 14A-equivalent density by 2031. This is not a like-for-like foundry comparison, with Huawei using stacked logic and measuring density per package footprint. On a normalized Bohr-density basis, SMIC N+3 is ~114 MTr/mm², 38% less than Intel 18A’s HD library. Huawei’s 3D roadmap closes the gap by stacking active logic, reaching 215 MTr/mm² by 2030. In 2031, the roadmap density jumps to 295 MTr/mm², implying either a third active layer, partial EUV insertion or aggressive planar DUV scaling. Huawei’s methodology makes other foundries look much denser as well. Applying it to AMD’s MI450X with an N2 top die on an N3P base die yields a theoretical density of 460.2 MTr/mm² in 2026, compared with Huawei’s 295 MTr/mm² in 2031. This Kirin 9030 does not use LogicFolding, remaining in a conventional mobile SoC package. Instead, it forms the baseline for how far Huawei and SMIC can push planar scaling. Future teardowns of Kirin and Ascend chips will show both planar logic density and Huawei’s hybrid bonding solutions. Export controls changed China’s optimization problem rather than ending it. EUV restrictions raised the cost and complexity of leading-edge manufacturing without freezing it. SMIC reaches N6-class logic density through DUV immersion, SAQP and DTCO, while Huawei shifts more of the burden onto architecture, packaging and system-level integration. Future nodes will be tougher. N+3 still had room to tighten local metals and reduce cell height and CGP. Further scaling without EUV leaves fewer levers. More aggressive multi-patterning adds masks and overlay error. SMIC can keep pushing DUV, but each step will get more expensive and less forgiving. The design side is just as critical. Huawei had domestic EDA tools and flows before the Kirin 9030, with the Kirin 9000s, 9010 and 9020 making that clear. Huawei was able to ship multiple consumer SoCs on SMIC N+2 and N+3 while cut off from the Western EDA stack. US export controls restricted EDA tools for advanced chips in 2022 but did not target tools for more mature chips. In 2025, the US government briefly placed much broader restrictions on EDA software from Synopsys, Cadence, and others, before lifting them less than two months later as part of a trade deal tied to rare earths. Huawei has been unable to access those tools because it remains on a US trade blacklist. That forced Huawei, SMIC and Chinese academic institutions to build their own tools and flows. Researchers at Peking University recently announced a prototype EDA tool for Huawei’s LogicFolding architecture, which requires a new flow to handle the multilayer layout and floorplan. This is not the same as replacing the full Synopsys or Cadence stack, but it shows where domestic EDA is headed: toward tighter co-optimization between architecture, process and packaging. These advances are also diffusing into the Chinese ecosystem. SMIC is licensing its N+2 and N+3 processes to HLMC/Hua Hong at the government’s direction rather than by choice. If the same process learning feeds into Ascend accelerators for AI training and inference, the choke point shifts from one named fab to an ecosystem. Alibaba’s T-Head silicon arm and Cambricon, a Chinese AI chip designer that is expected to supply ByteDance, could also be major beneficiaries. Sanctions aimed at SMIC alone become less effective once the manufacturing knowledge has spread to other fabs and design houses. China is not closing the gap with Intel, Samsung and TSMC. The teardown shows the opposite in several places: no EUV, no backside power, higher process complexity, and visible trade-offs. But China is still advancing. If domestic chips become good enough for phones, inference, networking and security-sensitive workloads, they can matter strategically without matching TSMC at the leading edge. Behind the paywall, we show what else STEEL can do, with material and process flow analysis of SMIC N+3, and analysis of the Kirin 9030 package. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline, access the full Kirin 9030 and SMIC N+3 analysis or to commission a custom teardown, contact sales@semianalysis.com.
Why Model Flows Are the Key for Reproducibility in AI for Science
Reproducibility is absolutely critical in science, but it’s a troublesome characteristic when it comes to AI. Frontier models developed by Big AI may deliver superior accuracy and reasoning capabilities, but they do so largely as black boxes with little regard for reproducibility. If AI is going to turbo-charge scientific productivity, it must do so without […]
The World Cup gives America a unified look. The rest is complicated
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The core anti-Pangram constituency is phony writers
Welcome to The Closing Argument, our verdict on the news, plus everything The Argument published and appeared in this week. The Verdict, by Kelsey Piper A lot of people hate AI for drowning the internet in slop and displacing human creatives. You might think that these people would be thrilled by a program that can identify and flag AI-generated work. But many people who hate AI also hate Pangram, an impressively accurate AI detector that produces very few false positives, according to high-quality independent research. This loathing found its most recent expression in a bizarre Wired article that amplifies a wide range of false arguments from Pangram skeptics: that AI detectors are racist (they’re not) or ableist (they’re not)1 or don’t work (this one does)2. Rather than starting from the premise that it would be really valuable to have a working AI detector and scrutinizing Pangram to make sure it lives up to that promise, the article starts from the premise that Pangram obviously has bad vibes and sets out to discover why. (One answer: The CEO microwaved lunch during an interview!) Why aren’t writers who resent Pangram more eager to distinguish themselves from posters who are having ChatGPT ghostwrite all their work? There are a few different things at play, but I think one key dynamic is this: These are often the same people. A core constituency for arguments like Wired’s is “writers” who are using AI to write their posts but resent this fact being made public. They stand to lose from AI detectors because AI detectors call them out. Share When Substack announced a partnership with Pangram, a sizable fraction of the platform’s authors rose up in outrage. Some people had experience with other AI detectors that didn’t work, and some felt that AI writing ought to be encouraged — both of which are fair enough. But the fury was fueled by authors passionately insisting that Pangram kept wrongly accusing them on almost every post they published. Pangram can be wrong, especially on short passages. But in the overwhelming majority of cases where Pangram looked at a whole blog post rather than a single paragraph, the program correctly detected work as AI. Even if we can’t be 100% sure in any individual case, we can notice the trend in the aggregate. Wired quotes Sam Illingworth, a professor of critical AI literacy at Edinburgh Napier University, who said, “I don’t think [AI detectors] work. … I think that detectors are prejudiced against certain people.” This spring, he said that he used AI for research and editing; this summer he acknowledged that in addition to research and editing, he also uses it “now and then for a turn of phrase I keep.” Pangram informs us that most of his posts are 100% AI-written (some are mixed AI-and-human) and that 100% AI-written posts started appearing last year. The favorable interpretation is that, as someone whose recent work Pangram consistently mistakes for AI, Illingworth sees Pangram’s flaws firsthand. The unfavorable interpretation is that the tool’s credibility trades off against his own, because its account of who writes his posts is not consistent with his. Illingworth is not alone in insisting the detector does not work. “It’s a Monday night in late July, and I’m pasting my own Substack draft into an AI detector for the first time. Not because I’m worried. Because I’m curious. I wrote every word of it myself, the way I write everything, out loud in my head first, in a rhythm that still carries the accent of a language I learned to think in before I learned to write in this one,” begins one Substack post by an author indignant that Pangram flags her writing as AI. “That’s the part I had to sit with before I could write this.” If you, like me, have worked with AIs a lot, you won’t even need Pangram there. The author of that piece is AI. It is obviously AI! I checked 10 other posts on the blog, and they were also obviously AI! “I have spent 22 years as an award-winning author and essayist. And now this bullshit tool that learned from MY writing is going to accuse me of plagiarism,” popular Substack writer Carlyn Beccia wrote in an anti-Pangram post. “For the last and final time, AI DETECTORS DO NOT WORK.” Here’s what her writing sounded like in 2020: “Most success stories revolve around one person who believed enough in you to say — give it a try. We call these people muses, but they are so much more than that. They are the strong man giants whose shoulders we stand on so we can see the horizon. Throughout the editing process, I am sure my editor felt less like a giant of greatness and more like Atlas with the world on her shoulders.” Pangram says it was 100% human-written. (So were other articles of hers in 2020.) And here’s what it sounds like now: “And then there is the reshuffling, which sounds like paperwork until you remember that empires have always loved paperwork. The Trump regime recently moved special education oversight from the Education Department to HHS. That is not a minor bureaucratic transfer. It changes the frame.”3 Pangram says this is mostly AI. I am not convinced that Pangram is wrong. In my own experience, Pangram is very effective on long texts — if it marks a long essay as AI, the essay was either written by AI or deliberately written to sound like AI. On tweet-length or paragraph-length texts, it’s not perfectly reliable. It seems to work just as well for non-native English speakers as for native speakers. While I have seen dozens of claims of pre-2022 text returning a positive result on Pangram, I have tried repeatedly to corroborate those and have never once succeeded. To be clear, I’m absolutely not saying that complaining about Pangram suggests a writer is a cheat. There are plenty of other concerns. Most are mistakenly premised, such as the false claim that Pangram trains on work you put into it, but others are reasonable: It’s unreliable on sufficiently short text segments; it creates an internet witch-hunt mentality when there are defensible uses of AI; people who are open about their AI use are sometimes blamed, when the only people really doing something wrong are the ones who hide it; and it isn’t perfect, so a single positive result shouldn’t be taken as decisive proof of misconduct. I’m happy to concede that Pangram is imperfect and possible to over-rely on. If an author’s posts are mostly non-AI and one scans as partially AI, this could be a mistake. It’s also possible to defeat Pangram with intentional effort. If you think we need something more robust and more reliable, fine! But if you don’t like AI and don’t like Pangram, your attitude really ought to be that it’s a shame that our existing tools for AI detection are imperfect, and it’s important to invent better ones. If your stance is instead that AI is evil and so is looking for AI, then ultimately you’re contributing to the sloppification of the internet. You’re making it harder for people who write their own text to distinguish themselves, and providing cover for authors who are definitely not writing their own text. Subscribe Top stories this week, by Kobe Yank-Jacobs Commentators often worry that the decline of reading could mean the decline of liberal democracy — but deliberative governance actually predated widespread literacy. This week, Maibritt Henkel pointed out that many liberal cultures thrive on a culture of raucous, interactive debate, which can happen in-person as easily, if not more so, than in text. Read on, dear reader, to learn more. It may actually cheer you up: [ ](https://www.theargumentmag.com/p/reading-wont-save-liberalism) [ Reading won’t save liberalism ](https://www.theargumentmag.com/p/reading-wont-save-liberalism) Maibritt Henkel · Sep 1 [ Read full story ](https://www.theargumentmag.com/p/reading-wont-save-liberalism) It used to be bad to downplay climate change (if we mean denying its reality). Today, it’s actually good to downplay climate change (if we mean keeping the issue out of political fights so we can make real progress). This counterintuitive view is called “climate hushing,” and contributor Alex Trembath argued that it’s the key to enacting good policy going forward. Get up to speed on the latest in climate politics: [ ](https://www.theargumentmag.com/p/we-wont-stop-climate-change-by-talking) [ We won’t stop climate change by talking about it ](https://www.theargumentmag.com/p/we-wont-stop-climate-change-by-talking) Alex Trembath · Sep 2 [ Read full story ](https://www.theargumentmag.com/p/we-wont-stop-climate-change-by-talking) Canada’s Prime Minister Mark Carney is standing up for the forces of liberalism against Donald Trump’s assault on our closest ally. So, why is one prominent left-wing commenter attacking Carney? Columnist Matthew Yglesias explains: [ ](https://www.theargumentmag.com/p/when-economic-liberty-means-central) [ When "economic liberty" means central planning ](https://www.theargumentmag.com/p/when-economic-liberty-means-central) Matthew Yglesias · Aug 31 [ Read full story ](https://www.theargumentmag.com/p/when-economic-liberty-means-central) 🌟Abundance Wins of the Week🌟 Shout out to my home state of California, whose legislature passed two housing bills this week. One requires a housing density minimum in transit-oriented areas of the state’s most transit-rich cities (Los Angeles, Sacramento, San Francisco, Oakland, San Jose, San Diego, and Long Beach). Another bill closes two common loopholes used by third parties to delay housing projects, including an opportunity to challenge maps of a given lot created by the developer after a project has been approved. Shout out to our big-state rival, Texas, where pro-housing SB 785 took effect this week. The new state law requires municipalities to have at least one district permitted for manufactured housing without a special permit. This will promote zoning changes in 44% of municipalities. The company Fervo Energy signed a deal with Google for what is set to be the world’s largest enhanced geothermal facility, proving a technology can work at a scale that could be crucial to a decarbonized, energy-abundant economy. Share Worth watching… This week, forecasting legend Nate Silver joined Jerusalem, Matt, and Jeremiah on a crowded episode of the pod. It was a party. And speaking of parties, our round table debated the factional lines within the Democratic Party: Is it possible to be center-left and also anti-establishment? Watch or listen to find out: The Rio Grande Valley in Texas is one of the areas that swung hardest toward Trump in 2024, but musician Bobby Pulido is running to flip it back. Right now, The Argument/Split Ticket’s model has him running 1.1 points behind his Republican opponent in a district Trump won by 18 points. Tune in to find out how he differs from national Democrats and what he thinks he can to do win this seat. On Thursday, Bernie Sanders was among the first politicians to call for a ban on superintelligence. My guest this week, Andrea Miotti, has been lobbying to ban superintelligence for several years, most recently in the U.K., U.S., and Canada. I asked him how he gets legislators to take speculative risks seriously, and we dug into what plans might look like to actually pass a ban. Listen to learn more: What’s News with The Argument The Argument recommends, by Kobe Yank-Jacobs If we ever lose track of Justin Zuckerman, I’ll have the team split up and check all the movie theaters in the city. I’m shocked (and a little jealous) at how often he gets out to see good cinema. This week, he brings us It Ends, a “horror-ish movie,” which he described as a “Gen Z Waiting for Godot.” I was intrigued before he added that it was “funny, dark and existential” — at which point I was fully locked in. I guess I also had existential themes on the mind this week. One morning, a single line from a poem I read long ago just came to me: “Is death a stretch of time in which a life is just a flash?” The poem is called “From 20,000 Feet” by Heather McHugh. I suggest the version in The Vintage Book of Contemporary American Poetry, which, by tradition, my best friend and I always bring backpacking, extra weight be damned. We’ll stay thematically dark for just one more rec from senior editor John Robert Thomason, whose upcoming trip to Baltimore reminded him to suggest Michael Clune’s memoir White Out (about being addicted to heroin in Baltimore in the 1990s). Ben Lerner’s blurb called it _“_Disturbing, brilliant, hilarious—it’s as if Proust had written Jesus’ Son.” John told me this “seems accurate, with the caveat that I’ve never read Proust.” I, personally, have also shelved Proust until retirement. At this point, I was a little concerned for the team since all of us tossed out such psychologically grim material — maybe we’re all down about the end of summer — but thankfully, at least a few of us were into more upbeat selections. Milan Singh recommended Made In’s 5-quart stainless clad saucier, suggesting he had some fun in the kitchen. Jeremiah Johnson asked if he could recommend yet more Carly Rae Jepsen. Once denied, he offered up Game Changer instead_,_ calling it “the most wildly creative game show, maybe in the entire history of the genre.” Eli Richman mentioned _Lucky, “_a B-tier show with an A-tier cast and an S-tier theme song.” I suppose he was recommending the song, Horns of a Bull. Still, despite these cheerier recs, I couldn’t help but be concerned that we were subconsciously worried about racing toward summer’s end — the end of swim trunks and splashing around. Then a message landed reminding me of the three-day weekend to come. A whole extra day for sunshine. One more swim in the lake. I was grateful this called to mind yet another line from a poem, one by Mary Oliver, the patron saint of every gorgeous summer day (and about whom a documentary is now out). Speaking to God in the poem “Thirst,” she wrote: grant me, in your mercy,a little more time. Love for the earthand love for you are having such a longconversation in my heart. We have merch! We have quarter-zips, keychains, hats, and stickers. Each one is a great conversation starter in its own way. Buy them here. Subscribe More to read: [ The Opening Argument ](https://www.theargumentmag.com/p/seeing-like-an-elite)[ Seeing like an elite ](https://www.theargumentmag.com/p/seeing-like-an-elite) Kobe Yank-Jacobs · Sep 4 What do Americans hear when they watch Sen. Bernie Sanders or Vice President JD Vance rail against the elite? Are they thinking of right-wing tech billionaires? Imperious college professors? Liberal magazine writers? [ Read full story ](https://www.theargumentmag.com/p/seeing-like-an-elite) [ The Weekly Scroll ](https://www.theargumentmag.com/p/god-is-back-on-the-timeline)[ God is back on the timeline ](https://www.theargumentmag.com/p/god-is-back-on-the-timeline) Jeremiah Johnson · Sep 5 This week, we’re talking about Substack’s new religious debates, the continuing MAGA influencer scandal, how to retire from YouTube, and aura battles. [ Read full story ](https://www.theargumentmag.com/p/god-is-back-on-the-timeline) 1 This was a legitimate thing to worry about, but doesn’t seem to be a problem given the actual technology: Marijke Van Vlasselaer, Filip Van Droogenbroeck, and Bram Spruyt’s 2026 paper looks at whether Pangram is less accurate when evaluating the work of non-native speakers of English. It isn’t. That’s because Pangram isn’t really looking at how “unusual” a piece of writing is but whether it has the characteristic patterns of LLMs. The Wired article makes an odd gesture at a different Pangram-is-racist argument. There were several controversies this summer during which upcoming books were canceled after being discovered to be AI-written: “The three novels mentioned earlier — Shy Girl, Daggermouth, and Call Me, easily publishing’s biggest AI-detection scandals — were all written by writers of color.” But come on! As Zvi Mowshowitz put it: “They were not primarily written by people of color. They were primarily written by AIs.” That’s the whole problem! And as my colleague Jeremiah Johnson recently observed with respect to Cambridge’s Arday scandal, a community being unwilling to call out fraud if it’s perpetuated by people of color is not an anti-racist tendency; it is ultimately quite racist. 2 Many non-Pangram detectors don’t work, but many possible airplane designs also don’t work, and no one considers this a general argument against aviation; the thing that matters is whether any detection works. 3 The phrase “empires have always loved” mysteriously became all the rage in the last few years. See here, here, here, here and here. Before 2025, the only use of that phrase I have been able to find is a single Tweet. The logic of the sentence, too, is incoherent in an AI-characteristic way. “...sounds like paperwork, until you remember that empires have always loved paperwork”? Does remembering that make it sound less like paperwork? It’s a craft-ful sentence, but what is it actually saying??
2026 March Madness: What Makes an NCAA Tournament Champion?
March Madness is here, and FOX Sports Research has done a historical deep-dive into the characteristics that make up a tournament champion.
New York attorney general files lawsuit against makers of PFAS chemicals
The lawsuit alleges that the companies that make PFAS chemicals knew about their toxic characteristics and continued to make and sell them.
Calling all current and future scientists: Kiel is the place to be this week
This week, Kiel has become a meeting place for science in Europe. Researchers, school pupils, young scientists and members of the public are coming together through a series of major events led or co-organised by Kiel University.At the centre of it all are around 150 of Europe’s most talented young researchers, aged 14 to 20, who have travelled to Kiel from nearly 40 countries for the 37th European Union Contest for Young Scientists (EUCYS). Running from 22 to 27 September, the European Union’s flagship competition for young scientists brings together winners of national science competitions to present around 100 original research projects across fields ranging from engineering and computer science to medicine, environmental research and the social sciences. The winners will be announced at the Award Ceremony on Saturday.For Kiel University, hosting EUCYS is both an honour and a natural extension of a much longer story. Germany’s northernmost comprehensive university has invested for many years in supporting young scientific talent and bringing research into dialogue with society. The Kiel Science Factory gives school pupils opportunities to experience research first-hand, while programmes such as Rent-a-Scientist send researchers directly into local classrooms. Major annual public events including Kiel Week and the European Researchers’ Night take science beyond the campus through talks, exhibitions and hands-on activities.That long-standing commitment has increasingly taken on a European dimension – and this week, many of those efforts are coming together.One of the foundations for this success is the European Researchers’ Night (ERN), which celebrates its tenth edition in the Kiel region this year. The initiative is funded through the Marie Skłodowska-Curie Actions, with Kiel University developing the successful EU proposals and securing the funding, while regional partner KielRegion coordinates the many activities on the ground. Together with research institutions, schools, companies, local authorities and civil society, they have built ERN into a major platform for public engagement.Over the years, Kiel’s ERN programme has grown far beyond a single evening into a whole “Science Summer”, beginning with “Science Shopping” in spring and culminating tomorrow, 25 September, in Science Day.“We have developed a wide range of public engagement formats, often together with the other research institutions in the region, the city and regional authorities, schools, companies and civil society,” says Eckhard Quandt, Kiel University’s vice president for research, academic infrastructure and transfer. “Over the past decade, we have increasingly taken this commitment to the European level.”That combination of European funding expertise and strong regional partnerships also helped Kiel University take the next step. Drawing on its experience with ERN and its European networks, including the SEA-EU University Alliance, the university brought together Brest in France and Split in Croatia for a successful bid to the EU’s new Science Comes to Town initiative. Throughout 2026, the three cities are serving as Europe’s first Science Ambassadors, with more than one thousand activities bringing science into schools, public spaces, cultural venues and everyday life.EUCYS is perhaps the clearest example of how Kiel University is using Science Comes to Town to create new connections between major European initiatives. From the outset, the university deliberately planned the contest for the week of European Researchers’ Night and opened the EUCYS exhibition to the wider Science Day audience. Tomorrow, that means thousands of visitors can encounter the next generation of scientific talent first-hand, while the young researchers themselves gain a much bigger public stage.“Because we opened EUCYS up for European Researchers’ Night, there will be a lot of people actually visiting the exhibition,” says organiser Stefanie Altemark of Kiel University.“We wanted Europe’s most talented young scientists not only to compete with one another, but also to become visible to the wider public. At the same time, people in Kiel will have the opportunity to see what the next generation of scientists are already capable of,” says Quandt.“We’ve shown over the last years that science is a big part of Kiel, and it’s always been the goal to not only show that to people in Germany, but also to show it to people all over Europe. Thanks to Kiel University’s expertise in European funding programmes, we have been able to turn that ambition into reality,” says Sünje Fock, who is coordinating the Science Summer at KielRegion.The model is characteristic of Kiel University’s approach: combining European funding expertise and international networks with strong regional partnerships to give major initiatives greater reach and visibility.This year also marks the 30th anniversary of the Marie Skłodowska-Curie Actions, giving Kiel University the special opportunity to welcome Vanessa Debiais-Sainton, Head of Department for the Marie Skłodowska-Curie Actions at the European Research Executive Agency, to Kiel tomorrow. Her visit brings together two priorities that have shaped Kiel University’s work over the past decade: supporting researchers and bringing science closer to society. Debiais-Sainton emphasises how closely the two are connected: "For thirty years, the Marie Skłodowska-Curie Actions (MSCA) have given more than 150,000 researchers the chance to pursue their dreams and open up new fields. Doctoral Networks and Postdoctoral Fellowships give them the freedom to move, to connect, to train and to follow their curiosity across borders, sectors and disciplines. But excellence cannot live only in laboratories. Researchers’ Night takes science out of the lab and into the city, to the families, the schoolchildren and the curious minds who will become the next generation. Kiel shows what that looks like at its best. That is the MSCA promise: open science, open borders, open doors."For Kiel University, these activities are not separate endeavours. They are part of a broader understanding of academic excellence in which outstanding research, talent development, international cooperation, knowledge transfer and engagement with society strengthen one another.“For me, one of the most important lessons from the past decade is that scientific excellence, knowledge transfer, talent development and engagement with society cannot be treated as separate tasks,” stresses vice president Quandt. “They reinforce one another.”That philosophy is also reflected in Kiel University’s current bid for Germany’s Excellence University status. The designation is part of Germany’s national Excellence Strategy, which provides substantial long-term funding to selected universities to strengthen internationally competitive research and their overall institutional development. A successful outcome would provide new opportunities to build on what has already been created: deepening international cooperation, creating more opportunities for knowledge exchange and public engagement, and extending the university’s reach across the region and beyond. “If we are successful, it would allow us to take what we have built in Kiel considerably further – including expanding our networks and transfer activities beyond the region and increasingly across the Baltic area,” says Quandt.This week’s events offer a glimpse of what that could mean in practice: a university using its research strength, European expertise and regional partnerships to create opportunities for scientists at every stage of their careers while making science more visible, accessible and exciting to the wider public.
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.
Sunlight Boosts Fertility? What Scientists Have Discovered Will Surprise You - Futura-Sciences
Vitamin D, Sunlight, and Sperm: Digging Into the Science A meta-analysis published in the journal Biomedicines reviewed several observational studies that all pointed to a significant link between vitamin D levels—which are mainly produced in humans through sunlight exposure—and sperm characteristics. These studies also suggested a connection between higher blood...
The State of Rural Hospitals in the U.S, 2012-2023
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When regulation is good for business
By now, I think I have established both my view that I am a fan of ESG investing and the benefits of regulating what is and isn’t a sustainable investment, and my view that in Europe, ESG regulation has gone too far and become too costly. It almost feels like the old Aristotelian maxim of doing things in moderation that leads to the best outcomes is valid in regulatory efforts as well. To give you an example, let’s look at a study by the National Bank of Belgium on the impact of the EU’s Sustainable Finance Disclosure Regulation (SFDR) on fund flows of conventional and ESG funds. The SFDR was announced on 27 November 2019 and came into effect on 10 March 2021. It was implemented in several stages and split funds marketed in the EU into three broad categories: (i) Art. 6 funds that are basically conventional funds without any specific ESG integration, (ii) Art. 8 funds that ‘promote environmental or social characteristics’, which must provide additional reporting how they do that, and (iii) Art. 9 funds that are impact funds. The gaps between the announcement and the phased implementation give us an idea of how fund flows were affected by the regulation. It is clearly visible in the figure below that once the new regulation was announced and when it was initially implemented, fund flows in Art. 8 and Art. 9 funds increased significantly. Conventional Art. 6 funds also saw more inflows, but the increase was not statistically significant. I should add that the data below is pure fund flow, cleaned of performance effects and of any reclassified funds that moved from one classification to another. Note also that once the initial rush in ESG funds was over, additional, stricter regulatory requirements didn’t create that many new flows. This implies that with additional regulation, the funds incurred higher costs to comply but didn’t benefit in terms of new inflows and higher assets under management. Fund flows as the SFDR regulation was implemented in the EU Source: Emiris et al. (2026) When examining the drivers of these flows, the authors of the study found that institutional investors were more likely to switch to ESG funds and particularly so in countries where concerns about sustainability are culturally more accepted. In a society where buy-in into environmental and social risks is higher, private investors were shifting more of their assets into ESG funds as the regulation made it clear which funds are really green and which ones are not. And institutional investors in these countries did so as well, given the societal pressures and reputational risks of sticking with conventional funds. Meanwhile, fund managers reacted to the changing regulatory environment as well. Many funds that were mildly green before the regulation was announced became greener and more sustainable as the regulation was introduced to attract new assets and new investors. Cleverly, and unsurprisingly, these ESG funds also charge on average higher fees, which means that not only did fund managers gather fresh assets, but their profitability increased as well.
Scientists discover a major brain shift between ages 50 and 75
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The disunity of science is a feature, not a bug
Mathematical models wield miraculous power, but mistaking them for reality has trapped science in a destructive quest for a “Theory of Everything.” Physicist and philosopher Ragner Fjelland argues that this leads to bloated, Frankenstein theories kept alive by clumsy, ad-hoc amendments, which reduce rather than boost science’s power. Fjelland calls for a radical shift in our attitude: we must reorientate towards creating entirely new theories, celebrating rather than lamenting disunity and diversity in science. Science, from the scientific revolution in the seventeenth century to the present day, has more or less developed in a “reductionist” way. The fundamental assumption is that all sciences may be ordered in a hierarchy that can be represented as an upside-down pyramid. At the top we find the human sciences, and at the bottom physics. Strong (or “ontological”) reductionism maintains that all theories at one level can be completely reduced to the lower level, all the way down to theories of atoms and elementary particles, or whatever is “at the bottom.” In the end, all diversity in the world can be unified in one fundamental theory. Hence it is called a final theory, or a Theory of Everything.One of the central figures in developing the idea of a Theory of Everything is the Dutch theoretical physicist Gerard 't Hooft, who received a Nobel Prize in physics in 1999 for his work. He emphasizes the non-empirical status of the theory:There exists no closely resembling alternative theory. This means that any slight change brought about in the rules would make the theory unlikely or inelegant. The theory will be a "package deal": take it, or leave it. This should hold both for the local laws and for the boundary conditions.This endeavor is not new. On the contrary, it goes all the way back to the birth of Western philosophy 2,500 years ago. The Babylonians made regular astronomical observations during a period of 600 years, from 747 BCE until 150 ACE. They developed mathematical techniques to predict astronomical phenomena, but they made no attempts at explaining them. However, the Greeks developed models, both material and theoretical, to explain the observed phenomena.The best example is Plato’s late dialogue Timaeus. Here he introduces a divine craftsman, the Demiurge, who has constructed the universe according to an original plan. Plato seems to have thought that the original plan is what is actually real, while the material world is merely an imperfect realization of it. He therefore does not, in the Timaeus, reconstruct the material universe, but the original plan, which can then be used to explain the material universe.Plato’s aim in producing a theoretical model is to reconstruct the original plan. That is what he carries out in Timaeus—and some modern physicists have embarked on the same project. For example, Einstein’s basic attitude is reflected in the title of Abraham Pais’ biography: “Subtle is the Lord...,” echoing Einstein’s comment, “Subtle is the Lord, but malicious He is not.” In an interview Einstein compared our situation to a child who enters a huge library. The child knows that someone has written the books, but he does not know the languages in which they are written. He suspects that there is a mysterious order, but he does not know what it is.___“We have all been working in what Husserl called the Galilean style; that is, we have all been making abstract mathematical models of the universe to which at least the physicists give a higher degree of reality than they accord the ordinary world of sensation.”Steven Weinberg, Nobel Prize in Physics (1979)___This Platonist view is contrary to a widespread impression of science, symbolized by the picture of the “father” of modern science, Galileo Galilei, with his telescope. According to this impression, Galileo liberated himself from the dominating metaphysics of his day—basically Aristotelian philosophy—and constructed a telescope that he directed towards the sky, showing that Jupiter has four moons. He also performed experiments with bronze balls rolling down inclined planes and found the law of freely falling bodies.However, in other cases Galileo emphasized the unreliability of our senses. It is easy to see why. He defended Copernicus’ heliocentric hypothesis. According to this hypothesis, we are not at rest at the center of the universe, but moving at a tremendous speed through space. Why don’t we observe this motion? Galileo praises Copernicus because “with reason as his guide he resolutely continued to affirm what sensible experience seemed to contradict.” In other places he directly refers to Plato. SUGGESTED VIEWING The quest for a final theory With Michio Kaku Galileo’s Platonism goes back to his early writings. In The Assayer, published as early as 1623, Galileo compared the universe to a grand book that is written in the language of mathematics, and “its characters are triangles, circles, and other geometric figures without which it is humanly impossible to understand a single word of it.” Therefore, things that cannot be described by mathematics—for example, colors, smells and odors—are not real. They are merely subjective.This division into subjective and objective, advocated by Galileo and his younger contemporary René Descartes, is normally associated with a mechanistic world view, which is regarded as the opposite of Platonism. When we reduce something to, for example, molecules and atoms, we move “downwards.” In contrast, the Platonist moves “upwards,” to abstract entities. However, it only shows that when we address fundamental questions, we should be careful in applying everyday spatial metaphors like “up” and “down.”___Nature is complex, and we cannot in general construct mathematical models that adequately describe the world.___The endeavor of finding a Theory of Everything has been heavily criticized. In 2006, the theoretical physicist Lee Smolin published “The Crisis in Fundamental Physics.” He argued that one of the most fundamental principles of science is that we should only regard theories as scientific if we have the possibility to show that they are false by experiments. Although he did not refer to Karl Popper, other critics have referred to Popper and argued that a theory is not scientific if it cannot be falsified (see for example Ellis & Silk 2014).These were not the first physicists to be anti-reductionists. In 1972, the journal Science published an article by the physicist Philip Anderson with the title “More Is Different.” This is the kind of article a scientist would normally write only after he has won a Nobel Prize. However, Anderson won the Nobel Prize for physics five years later, in 1977. The article is amazing, not primarily because it argues in favor of anti-reductionism in general, but because it argues for anti-reductionism within physics itself. Anderson’s conclusion was:At each stage entirely new laws, concepts, and generalizations are necessary, requiring inspiration and creativity to just as great a degree as in the previous one. Psychology is not applied biology, nor is biology applied chemistry.We may say that Plato started the “unity of science movement,” based on Euclidean geometry as the model of all knowledge. A distinguishing mark of geometry is that all possible constructions can be reduced to a few fundamental principles. We have to acknowledge that this approach has limitations. In general, we can easily model technological systems. In addition, we may construct simplified and idealized models that capture important aspects of systems, from various perspectives and for various purposes. But nature is complex, and we cannot in general construct mathematical models that adequately describe the world. In a complex system, each level has its distinct, emergent, properties that are characteristic of this level. Therefore, a complex system should be investigated at different levels and from different perspectives. Instead of exclusively pursuing a scientific ideal based on the unity of science, we should acknowledge the diversity—or even the disunity—of science.
Penn State vs. Northwestern: Four Keys to Victory
Penn State vs. Northwestern faces off in a rare primetime matchup on Friday night. The Nittany Lions will look to bounce back after last week's 24-20 defeat to Wisconsin. The loss was ugly. The offense struggled to move the ball, and the defense collapsed in the fourth quarter. It was uncharacteristic of a Penn State …
A Political Pulpit for Me but Not for Thee
A final book from Old Testament scholar Walter Brueggemann is characteristically convicting and vivid—but myopic about his progressive politics.
Do black holes have hidden hair? Scientists are searching
If black holes have extra matter around them – aka hidden hair – astronomers could find it by measuring the different characteristics of these ripples.
Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
Oklahoma soars past North Carolina in Game 3 to win MCWS
Oklahoma capitalized on North Carolina's uncharacteristic pitching struggles and got another clutch performance from LJ Mercurius out of the bullpen on its way to victory in the winner-take-all Game 3 of the Men's College World Series finals Monday night.
Scientists Identify Atomic Trick That Keeps Gold Shiny
Gold’s characteristic glow famously doesn’t fade for thousands of years—and scientists have finally found the molecular trick that makes it so.
What Makes Your Cannabis Business Buyable Right Now?
It’s a fraught question for many operators: How attractive is my business to potential buyers in the current market? M&A experts offer 5 characteristics buyers are seeking for entrepreneurs considering or curious about their businesses’ potential in an exit. Plus, 5 ways to make your business more attractive to buyers.
Populism is plain to see all around us
Letters: The characteristics include attachments to authoritarian regimes including the one based in the Kremlin, writes Kevin Lloyd; plus a letter from Peter Gray
What Is a Person, and How Do We Know?
The characteristics invoked in the abortion debate — consciousness, rationality, and intrinsic human dignity — function as recalcitrant facts for naturalism.
Unlocking Forest Diversity from Above: Fieldwork at Smithsonian’s BiodiversiTREE
Laying groundwork for using airborne remote sensing to better understand the characteristics of forest species diversity.
New adenomyosis atlas reveals lesion-specific signals that may spare healthy uterine tissue
Researchers at the University of Liverpool have identified distinctive biological characteristics within adenomyosis lesions that could help pave the way for more targeted, less invasive treatments. The ...
Characteristics of Leadership: Micromanaging
Knowledgeable Alaskans share methods of effective leadership. This month: when micromanagers carry too much of the load.
Characteristics of Leadership: Corruption
Knowledgeable Alaskans share methods of effective leadership. This month: when corruption spoils the whole organization.
Scientists discover recent tectonic activity on the moon
Scientists have produced the first global map and analysis of small mare ridges (SMRs) on the moon, a characteristic geological feature of tectonic activity. Published in The Planetary Science Journal ...
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
Scientists grow iron-rich meat protein in lettuce, a step toward animal-free burgers
Scientists may eventually be able to introduce meat-like characteristics into crops people can eat directly.
Studio Display XDR White Paper Reveals New Color System and Future Calibration Feature
Apple's newly published Studio Display XDR Technology Overview white paper reveals two notable display technologies: a forthcoming Full Calibration feature and a new color measurement model called Apple CMF 2026. According to the document, a future macOS update will introduce Full Calibration, a feature that allows users to recalibrate key display characteristics using professional measurement equipment.
Oddball 'platypus galaxies' spotted by James Webb telescope may challenge our understanding of galaxy formation
Astronomers spotted nine galaxies with characteristics that have never been seen as a collection before. It's possible this is a newly found type of star-forming galaxy.
What Makes an Iron Lady Iron?
‘Thatcher…accepted political damage as the cost of policies she believed necessary for Britain’s recovery. It was these sorts of pressures that refined Thatcher and forged her into the Iron Lady we know today...The question for Japan today is whether Sanae Takaichi demonstrates similar political characteristics within the context of modern Japanese politics.’
More data, more answers? A 1.9 million-person health study shows why scale has limits
Researchers analyzed phenotypic and linked health-record data from more than 1.9 million adults in Our Future Health, comparing demographic characteristics, disease patterns, clinical associations, and rare-condition case counts with national data and other major cohorts. The cohort's size provides unusually large samples for many common and less common conditions, although its demographic composition and current data sources require careful consideration when interpreting population-level estimates.
Letter: Hawaii lawmaker puts politics over women
U.S. Rep. Jill Tokuda’s revisionist history of Title IX suggests the late U.S. Rep. Patsy Mink intended it also apply to transgender groups. Title IX addressed sex and related immutable characteristics, not gender, a social construct.
Bladen Community College to host “So You Want to Start a Small Business” class and preview Pitch It Bladen 2027
The Small Business Center at Bladen Community College invites aspiring entrepreneurs to attend “So You Want to Start a Small Business” on Tuesday, Oct. 13, 2026, from 6:00 p.m. to 8:00 p.m. in Building 14, Room 104. This class will help participants explore what it takes to become an entrepreneur and take the first steps toward turning a business idea into reality.Participants will explore the entrepreneurial mindset and consider whether they have the interests, characteristics, motivation and willingness to take on the challenges of business ownership. The class will encourage attendees to examine their personal goals, expectations, strengths and readiness to pursue entrepreneurship.Attendees will also gain insight into the realities of starting and operating a small business, including the commitment, planning, decision-making and perseverance required to build a successful venture. Whether participants are just beginning to consider business ownership or already have an idea they want to pursue, this class will provide a foundation for determining their next steps.In addition to the class instruction, attendees will receive advance information about the upcoming Pitch It Bladen 2027 Business Competition, including the application form and details about the program. Individuals who are ready to move forward may submit their applications early and have the opportunity to receive advance acceptance into the program.Pitch It Bladen helps aspiring and emerging entrepreneurs develop their business ideas through structured instruction in business planning, financial management, cash-flow projections, marketing and operations. Participants work toward completing a business plan and preparing a business pitch, with the opportunity to compete for cash prizes.Last year's Pitch It Bladen program demonstrated the value of investing in local entrepreneurs by helping participants strengthen their business knowledge, develop their business plans and prepare to present their ideas. The competition showcased the creativity and potential of Bladen County's small-business community and provided participants with practical tools to move their business goals forward.Building on that success, Pitch It Bladen 2027 will provide another opportunity for local entrepreneurs to gain knowledge, build confidence and take meaningful steps toward launching or growing a business.“The Small Business Center wants to help individuals make informed decisions about entrepreneurship before they take the leap into business ownership,” said Theresa Dennison, Small Business Center director, in a press release. “This class gives participants an opportunity to evaluate whether entrepreneurship is right for them while also introducing them to Pitch It Bladen 2027 and the resources available to help them succeed.”Registration: Visit www.ncsbc.net and search for the class by name, or contact the Small Business Center at 910-879-5572 for additional information.
Jiang Feng: The Unravelling of German & European Politics
A state election in eastern Germany has become a revealing test of the changing political landscapes of Germany and Europe, writes Jiang Feng, Professor and Chairman of the Council of the Shanghai Academy of Global Governance and Area Studies (SAGGAS) at Shanghai International Studies University. His analysis follows the decisive victory of the Alternative for Germany (AfD) (which has been classified as right-wing extremist by German authorities)in the state of Saxony-Anhalt on 6 September, in which it finished far ahead of its mainstream rivals. Jiang previously served as Minister-Counsellor at the Chinese Embassy in Berlin. The following article was published on 8 September 2026 on huanqiu.com, the online platform of the Global Times. On 6 September, the results of the state election in Saxony-Anhalt, Germany, were announced. The Alternative for Germany (AfD), classified as far-right by German authorities, made history by becoming the largest party in the state parliament, winning 43.8 per cent of the vote — twice its share in the previous election. The Christian Democratic Union (CDU), which had governed the state continuously for 24 years, won just 17.2 per cent, its worst result ever in Saxony-Anhalt. On the night the results were announced, the atmosphere at the CDU gathering was unusually calm. There was neither shock nor argument, as if the defeat had long been expected. Several veteran party members who had spent years working in the state government said: “Defeat is not a bad thing. It is time to make changes.” This state election has not only upended Saxony-Anhalt’s political landscape, but also laid bare the deep tensions between Germany’s traditional party structure and model of governance, on the one hand, and an increasingly polarised society on the other. Its repercussions are already being felt at the federal level, making Saxony-Anhalt a useful case study of the broader trend towards political polarisation and realignment in Europe. Governing Difficulties after Electoral Victory The most immediate consequence of the election is a historic setback for the mainstream party order that took shape in post-war Germany. In Saxony-Anhalt, the AfD has leapt from a peripheral party to the largest political force in the state. Its electoral base has expanded beyond the traditional category of “marginal protest voters” to take on the characteristics of a cross-class coalition. The party has not only consolidated its support in rural areas, but also attracted many middle-class voters and even members of the social elite with stable jobs, their own homes, and substantial incomes. It has thus evolved from a “protest party” into one with the potential to govern. Despite its commanding lead in the vote, however, the AfD faces an institutional obstacle to governing the state: the mainstream parties’ long-standing “firewall” against it. Neither of the two potential routes to forming a government appears likely to break the current political deadlock. The first would be a coalition of establishment parties, led by the CDU and bringing together the Social Democratic Party (SPD), the Greens, the Left Party (Die Linke) and the Sahra Wagenknecht Alliance (BSW) in a cross-camp government. In terms of parliamentary arithmetic, such a coalition could prevent the AfD from forming a government and preserve the outward stability of the traditional political order. Yet these parties are fundamentally divided over key issues including immigration, the energy transition, economic management, and cultural and education policy. Coalition negotiations would therefore be extremely difficult, and even if a government could be formed, it would most likely prove short-lived. More importantly, an alliance of establishment parties aimed at keeping the largest party out of government would play directly into the AfD’s narrative that “political elites are suppressing public opinion”. It could deepen perceptions of political unfairness, further strengthening the AfD’s political capital, and creating a dynamic in which “the more it is suppressed, the more people vote for it”. Ulrich Siegmund, the AfD’s candidate for state premier, has said that if efforts to form a government fail because other parties refuse to cooperate, the AfD will push for a fresh election, which he believes could deliver the party an absolute majority of seats. This points to more intense political battles ahead. The second option would be a coalition between the AfD and the BSW. Together, the two parties would command a simple majority in parliament, making this, on paper, the quickest route to forming a government. The BSW is the only elected party that has not ruled out cooperation with the AfD, and the two parties share some common ground, including on policy towards Russia. There is therefore a degree of political basis for cooperation. Were such a coalition to materialise, Germany would see its first state government jointly formed by a party defined as far-right and one with a far-left background, creating what might be called a “bipolar anti-establishment alliance”. Such an outcome would fundamentally break with the ideological boundaries of post-war German party politics. It is worth noting that even if the AfD ultimately fails to enter the state government, its roughly 47 per cent share of parliamentary seats would give it a powerful blocking minority. Without its consent, major decisions requiring a two-thirds majority — including constitutional amendments, significant budgetary adjustments, and state-level institutional reforms — would be extremely difficult to pass. Therefore, whoever ultimately forms the government, the AfD will be an unavoidable force in Saxony-Anhalt politics, with a direct influence on the state’s legislative process. Systemic Failures in Germany’s Governance Model The Saxony-Anhalt election result reflects a systemic failure of Germany’s post-war mainstream-party model of governance to adapt to rapidly changing socioeconomic conditions. It stems from multiple, mutually reinforcing structural tensions and highlights the existing political system’s difficulty in adapting to “an increasingly polarised society First, collective anxiety has replaced individual material hardship as a central driver of political mobilisation. The rise of the AfD can no longer be explained simply by poverty or social marginalisation. Its increasingly cross-class electoral base suggests that what is driving voters towards the party is, above all, concern about the broader direction of society. This anxiety about the future of the political community goes beyond individual gains and losses. In political discourse, economic anxiety has become closely intertwined with immigration, while immigration and refugee issues are, in essence, questions of social fairness. This perception turns conflicts over the distribution of social resources into negative assessments of governing parties, providing a broad social basis for far-right political narratives. Second, regional disparities and East German identity have undermined the strategy of addressing eastern Germany’s structural challenges at the regional level. More than three decades after German reunification, eastern states such as Saxony-Anhalt have experienced periods of economic growth, but their underlying economic structures remain weak, and household incomes and wealth remain significantly below those in western Germany. At the same time, they continue to face population outflow, an ageing population, labour shortages, and other structural challenges. At a deeper level, historical experiences — including the collapse of the former East German economic system, mass unemployment during the transition, and the westward shift of decision-making power over major issues — have left some residents with a sense of being “second-class citizens” and of being excluded. For years, the German government treated the “syndrome” of reunification as a regional problem to be gradually addressed at the local level. That approach is becoming increasingly unsustainable, as regional development disparities emerge as a core structural factor shaping national politics. Third, the erosion of institutional trust is perhaps the most decisive factor. AfD supporters tend to distrust establishment institutions, including parliament, government, and the mainstream media. Their concerns about economic security, pensions, and social fairness are significantly greater than the social average. Increasingly, their support for the AfD reflects not an expectation that the existing system can be improved, but a search for an alternative to the current political order. Such a systemic deficit of trust will be extremely difficult for the traditional parties to repair in the short term. Fourth, a generational shift in political mobilisation has amplified political polarisation. Unlike traditional parties, which rely heavily on offline rallies and mainstream media coverage, the AfD has developed a social-media mobilisation strategy built around “raising an issue — identifying who is responsible — offering a simplified solution — building a shared identity”. It turns complex social tensions into concise political narratives, rapidly channelling public sentiment into political support. The contrast on the eve of the election was particularly telling. The establishment parties, together with trade unions and churches, organised an offline march attended by more than 10,000 people, but its reach was largely confined to those present and to traditional media coverage. The AfD held a much smaller indoor event, yet livestreaming on TikTok, YouTube, and other platforms, combined with the involvement of online influencers, gave it a reach and mobilising capacity far beyond that of the march. The contrast illustrates how the traditional parties’ methods of political mobilisation are increasingly struggling to keep pace with a political environment transformed by changes in media technology. A Microcosm of Structural Political Change in Europe The shockwaves from the Saxony-Anhalt election quickly travelled beyond the state to the federal level. Calls for political accountability followed immediately after the results were announced. The governing CDU and SPD are now under enormous pressure, while Chancellor Friedrich Merz and Vice Chancellor Lars Klingbeil face an unprecedented test. Political polarisation in Germany is no longer simply a regional phenomenon. The post-war model of multiparty government by establishment parties is breaking down and being reshaped. Viewed in a broader European context, the Saxony-Anhalt election is part of a continent-wide wave of party polarisation, mainstream-party decline, and rising right-wing forces. Far from an isolated phenomenon, it is a microcosm of the structural political changes unfolding across Europe. Weak economic growth, cultural tensions over immigration, and declining trust in institutions are fuelling the rise of right-wing forces in several countries, challenging the liberal internationalist order established in post-war Europe. As a core member state of the European Union, Germany’s ability to adapt its political system and the direction of its politics will have profound implications for both European integration and the evolution of the international order. Subscribe [ Pekingnology China’s Germany watchers take stock of Berlin’s uneasy turn On 7 December 2025, nearly 50 Chinese scholars and policy researchers gathered in Beijing for the 2025年德国形势年终研讨会 “2025 Year-End Seminar on the Situation in Germany… Read more 9 months ago · 31 likes · 1 comment · YIRUI LI and Yuxuan JIA ](https://www.pekingnology.com/p/chinas-germany-watchers-take-stock?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=1151841) [ Jiang Feng on Germany's 2025 and beyond: no easy exit ](https://www.eastisread.com/p/jiang-feng-on-germanys-2025-and-beyond) Yirui LI and Yuxuan JIA · Jan 24 The following is an op-ed by Jiang Feng, published on 29 December 2025 at Shanghai Observer 上观新闻, the digital platform of Jiefang Daily, the official newspaper of the Shanghai Municipal Committee of the Communist Party of China (CPC). [ Read full story ](https://www.eastisread.com/p/jiang-feng-on-germanys-2025-and-beyond)
Opening the future of science
The U.S. NSF LCCF Overview and Characteristic Science Applications virtual panel featured remarks from NSF and TACC leaders, followed by a moderated panel of researchers using TACC's Frontera and Vista supercomputers to tackle some of science's most complex challenges.
Landlord-tenant spat at center of Highland Park's retail losses
Uncharacteristic store closures in popular Highland Park linked to landlord-tenant dispute that's headed to court.
‘Uncharacteristic' Wheeler searches for answers as Phillies' win streak ends
Zack Wheeler’s late-season concern grew in Seattle, where he allowed eight runs as the Phillies’ nine-game win streak ended.
Shaky political science: using "simulated" elections over real-world elections
[Dear DSOS readers – this article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in early December, we examined over 40 studies focused on ranked choice voting. We found that many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data, and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first three articles in our series: the first one summarizing the overall results of our report, the second one analyzing a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
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Studying Science in an Anti-Scientific Time
This Chronicle Review Deep Cut was first published in 2025. By Julianne Werlin Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe The last two decades have not been kind to science studies. Already bruised and battered by the “science wars” of the 1990s, by the 2000s sociologists of science — who had long argued that science had to be understood in its human context — were encountering the funhouse-mirror version of their own views in right-wing critiques of climate scientists. It was enough to give some prominent scholars pause. “Was I wrong to participate in the invention of this field known as science studies?” asked Bruno Latour, the most famous sociologist of science in the world, in 2003. “Should we apologize for having been wrong all along? Or should we rather bring the sword of criticism to criticism itself and do a bit of soul-searching here: What were we really after when we were so intent on showing the social construction of scientific facts?” In response, Latour proposed a change of method and attitude. Science studies, now chastened and contrite, would no longer seek to strip facts of their authority, but to enrich and enhance them. What this meant in practice was never fully clear. But his diagnosis of problems resonated, even if his solutions did not. Latour’s exercise in public hand-wringing signaled a shift in mood. By 2016, with the first election of Donald Trump, the idea that skepticism of science was a right-wing position, not a left-wing one, had become a commonplace, discussed eagerly in The New York Times. For liberals, vaccine denial during the Covid era and Trump’s second-term attacks on the funding of scientific research, covered extensively in these pages, added a new urgency to the defense of science on both practical and principled grounds. There was little appetite for a sociology or philosophy of science that emphasized the messiness and missteps of the scientific process rather than its integrity and triumphs. The distinguished historian of science Peter Dear’s new book, The World as We Know It: From Natural Philosophy to Modern Science (Princeton University Press, 2025), which seeks to tell the story of 18th- and 19th-century science for a general public, may seem comfortably remote from these bitter contests. In its 16 short chapters, Dear draws swift, sure sketches of major discoveries from Newton to Einstein, ably guiding the reader through debates on stellar nebulae, animal taxonomy, atomic chemistry, evolution, and other flashpoints in the history of science. But though a work of history, Dear’s book must still negotiate our polarized present. In a blurb on its back cover, the historian of biology Lynn K. Nyhart alludes to the “present moment, when scientific knowledge is being swamped by misinformation and its institutions are under siege.” Dear himself, in his conclusion, refers to the “yard signs” and “bumper stickers” that proclaim “Science Is Real.” One antidote to both anti-science propaganda and to reductive slogans in science’s defense is a robust history and philosophy of science: exactly what Dear’s scholarship has long offered. But his new book illustrates how difficult such work has become. Dear, an emeritus professor of history at Cornell University, is best known as a creative and philosophically minded historian of the Scientific Revolution. His edited volume The Literary Structure of Scientific Argument (1991) analyzed the rhetoric of Galileo’s thought experiments to brilliant effect. Discipline and Experience: The Mathematical Way in the Scientific Revolution (1995) was a major contribution to the analysis of scientific experiments in early modern Europe and beyond, while Revolutionizing the Sciences: European Knowledge in Transition, 1500-1700 (2001), now in its third edition, remains one of the best overviews of the profound epistemological upheavals of 16th- and 17th-century science. But Dear has also ventured into later periods. Nearly two decades ago, in The Intelligibility of Nature: How Science Makes Sense of the World (2006), he turned to the 18th and 19th centuries to sketch a history and theory of scientific knowledge. Aimed at a generalist audience, Dear’s simple prose belied serious philosophical ambitions. Science, Dear argued, has two aspects: “natural philosophy,” the quest to understand the true nature of the universe, and “instrumentality,” the applied skills and technologies that allow us to manipulate the world for human advantage. Both strands are ancient, but the innovation of the phenomenon we call “science” was to yoke them together. As Francis Bacon wrote, “Human knowledge and human power meet in one; for where the cause is not known, the effect cannot be produced,” an insight typically abridged to “knowledge is power.” Or, in Bertrand Russell’s characteristically blunt phrase, science has two functions, “1. to enable us to know things, and 2., to enable us to do things.” So far, so conventional. But Dear took issue with this familiar story in one respect. Bacon’s formula implied that theory and practice could be fused in a single method. In fact, Dear argued, the quest to understand nature and the desire to manipulate it were coupled only loosely. They could come together, but they could also pull apart. Focusing on natural philosophy, the “knowing” half of the scientific circle, he used a series of case studies from cosmology, taxonomy, chemistry, electromagnetism, and quantum theory to show how theories changed over time, in dialogue with practice, but never reducible to it. Take the case of matter, the basic substrate of the physical world. In the 18th and 19th centuries, scientists not only disagreed about what it was, but also about how well it could be known. In 1789, the chemist Antoine Lavoisier dismissed speculation about the atomic composition of chemicals as “discussions entirely of a metaphysical nature,” as he sought to orient chemistry away from philosophical abstraction and toward laboratory results. But in the 19th century, atoms returned with a vengeance in the atomic chemistry of John Dalton, who wanted to explain the underlying structure of nature. It was not just that Dalton and Lavoisier had a different set of experimental results, or even a different theory, Dear showed. What counted as an explanation had changed. In arguing that scientific theories depended on their intelligibility to human beings with complex arrays of beliefs and commitments, Dear was in sympathy with science studies. Key figures such as the sociologist Steven Shapin emphasized the importance of psychological mechanisms like trust in the formation and acceptance of scientific ideas. But in focusing on the scientific pursuit of knowledge, not its politics or economics, Dear resisted the more cynical perspective of some of his colleagues. The Intelligibility of Nature strikes a very different note than Shapin’s collection of a few years later, Never Pure: Historical Studies of Science as If It Was Produced by People With Bodies, Situated in Time, Space, Culture, and Society, and Struggling for Credibility and Authority (2010), to name just one example. Walking a very fine line, Dear depicted science as driven at once by complex social dynamics and by the intellectual power of real discoveries. It was a testament not only to his own acumen but also to the vitality of the field that he was able to do so. Nearly two decades later, The World as We Know It returns to the questions broached in The Intelligibility of Nature. Like Dear’s earlier study, it begins with the Newtonian cosmos and ends with debates between Einstein and Bohr on quantum physics. Beyond its terminal figures, many of the same characters appear in both studies, including the naturalists John Ray and the Comte de Buffon; the chemists Étienne François Geoffroy, Antoine Lavoisier, and John Dalton; as well as Charles Darwin and Michael Faraday, among others. Its subject is nearly identical: As in The Intelligibility of Nature, The World as We Know It considers natural philosophy as opposed to instrumentality, here defined as the “desire to create a picture of what the world is really like — the world as God knows it — rather than simply having instrumental or operational control over it.” The two books also overlap in insights — and sometimes even in language. The reader who has learned in The Intelligibility of Nature that Buffon’s method of classifying animals “reflected his belief in the importance of the senses and empiricism in learning about nature (a doctrine associated with Newton and the philosopher John Locke)” will experience a sense of déjà vu on reading, in The World as We Know It, that the classification “reflects Buffon’s commitment to the epistemological stance of Newton and his philosophical underlaborer John Locke, who stressed the role of the senses in creating natural knowledge.” Later in the passage, we read that, “because of its stress on understanding the ways of life of animals in their environments, Buffon’s approach could almost be labeled (anachronistically) as ‘ecological.’” And again, “an anachronistic way of putting it would be to say that Buffon recommends an ecological mode of understanding.” Likewise, in The Intelligibility of Nature we read of Dalton that he was initially drawn to meteorology, and that: He did a bit of experimental work on these things, especially having to do with the water-holding capacity of gases, but his real interest lay in understanding what, at an underlying natural-philosophical level, was really going on physically.” Whereas, in The World as We Know It: He did experimental work on these things, especially concerning the water-holding capacity of gases — although not at a very refined level quantitatively — but he was continually trying to develop a theoretical conception of what was happening physically. The final full chapter of each book closes on the same resigned note. Intelligibility: These days, most serious work on the natural philosophical underpinnings and implications of quantum mechanics is performed not by physicists but by philosophers of science. Among scientific practitioners themselves, Bohr’s attempt to use instrumentality as sufficient grounds for a respectable science has met with a high degree of success. And again, in The World as We Know It: Bohr’s and Einstein’s concerns are now questions that are mostly discussed by philosophers of science rather than by physicists themselves. The marginalization of these sorts of questions amounts to the demise of natural philosophy, at least in physics, in the 20th century. Quantum mechanics “works,” and that’s good enough for most scientists who encounter it. Examples could be multiplied. When handling the same subject matter, some echoes are inevitable. Scholars should not feel that because they have written “Isaac Newton died in 1727” in one book, in the next they must describe him as perishing, expiring, or meeting his maker. But the repetitions of language and arguments (not to mention six reproduced images) threaded throughout The World as We Know It are more extensive than that. They should have been caught by Princeton University Press or its reviewers, and revised. Because the two books have so much in common, it is easy to see where they diverge. The World as We Know It is a less theoretical work than its predecessor. The sociology of knowledge that organized The Intelligibility of Nature has been reduced in scale and folded into readings and examples. The taut interplay between history and theory, which gave the earlier study so much interest, is there, but it is subtle. In the earlier book, the case studies demonstrated theoretical claims about the character and development of science. In the later work, each narrative of discovery is an end in itself. Insights emerge from the history, but they do not control the organization of the study. With the theory muted, it can be hard to understand the logic uniting Dear’s sprawling history of more than two centuries of scientific knowledge. The 16 brisk chapters cover an enormous amount of material. But the closer the volume edges to comprehensiveness, the more obvious the missing elements become. A chapter on “Institutions and Pedagogy,” describing the birth of the research university, illustrates the problem. Inserted roughly halfway through the book, the unnumbered chapter is designed as an “entr’acte,” in acknowledgement that it does not fit the study’s organization around scientific discoveries. Yet as Dear is well aware, no general history of the development of modern science can bracket its 19th-century institutionalization and professionalization. It was the 19th-century university, after all, that gave us the “scientist,” a term coined by the Cambridge philosopher William Whewell. Dear’s history of scientific discoveries implies a wider social and institutional history, but it remains largely subterranean, only occasionally extruded under the pressure of the narrative. It must be said that the balance is not all on the side of the earlier study. There are some advantages to the more historical, less theoretical organization of The World as We Know It. Dear’s accounts of paradigm shifts are remarkably lucid, no small accomplishment given that they span fields and centuries. He makes excellent use of the wealth of new scholarship on Darwin and his influences, to which he has himself contributed. It is satisfying to see puzzle pieces from Buffon’s natural history, Georges Cuvier’s animal taxonomy, Thomas Robert Malthus’s demography, and Charles Lyell’s geology recombined seamlessly in the Darwinian jigsaw. Likewise, Dear’s discussion of stellar nebulae, divided between chapters two and 15, shows vividly how Newton, Kant, Herschel, and Hubble reimagined insights and observations as they sought to discover whether the diffuse light that filtered through early telescopes was a glowing, gaseous substance or discrete but distant stars. Yet despite such pleasures, the book remains a more modest work than The Intelligibility of Nature. Dear has clearly not changed his views about the history of science. But in the two decades that have elapsed, the discipline and the world around it have changed, making it harder to write a sociologically and philosophically ambitious history of science for the general reader. That is a shame, because in the age of Silicon Valley and biotechnology, both the reciprocity and the tensions Dear identified between the epistemological and instrumental ambitions of science are more evident than ever. In recent years, some of the most exciting new books on the history of science in early modernity have focused precisely on this pressure point. Important studies such as Pamela H. Smith’s From Lived Experience to the Written Word (2022), James Poskett’s Horizons: The Global Origins of Modern Science (2022), and Vera Keller’s The Interlopers: Early Stuart Projects and the Undisciplining of Knowledge (2023) all shed new light on how early modern science negotiated the relationship between its instrumental and epistemological ambitions, or in Russell’s phrase, knowing and doing. There is every opportunity for a philosophically informed approach to draw new insights from this wealth of material. Dear, unfortunately, has not done so in The World as We Know It. But for future scholars of the history of science, his body of work over nearly four decades may provide just the model. Julianne Werlin is an associate professor of English at Duke University. 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