长久以来，政府通过数据收集让社会变得“可读”，而公民对政府的监督则受限于信息处理的巨大成本。如今，AI正在打破这一不对称。但这究竟是民主的福音，还是打开了新的潘多拉魔盒？当每个人都能成为“调查记者”，权力运行的逻辑将发生何种根本性改变？

核心观点：AI的真正革命性不在于赋能政府，而在于颠覆性地赋能公民，将“国家看清社会”的单向权力结构，扭转为“社会看清国家”的双向透明，这不仅是技术升级，更是民主问责机制的一次底层重构。

安德烈·卡帕西关于“AI赋能公民监督政府”的思考，触及了一个远比“技术工具论”更深刻的命题。它指向的并非简单的效率提升，而是权力结构中一个古老悖论的消解可能：理论上，现代民主政府产生海量数据，理应高度透明；实际上，这些数据如同天书，构成了事实上的信息壁垒。监督的瓶颈从来不是“访问”，而是“理解”。四千页的综合法案在法理上是透明的，但对99%的公民而言，它与一堵密不透风的墙无异。这种“透明的不可读性”，长期以来将深度监督权垄断在极少数专业精英——调查记者、政策分析师、专业游说观察机构——手中。AI的介入，正在撼动这一垄断的根基。

这种撼动首先体现在监督的“粒度”和“广度”上。传统监督如同探照灯，只能聚焦于少数引发公众愤怒的焦点事件。而AI驱动的分析，则可能将探照灯变为无影灯，照亮预算开支的每一行代码、立法修订的每一次细微变动、议员投票记录与公开表态的每一次背离、游说网络与政策产出之间千丝万缕的关联。地方政治尤其将成为这场变革的前沿。全国性媒体无暇顾及每一个市议会关于 zoning、警务拨款或学校资源的辩论，但这些决策却最直接地塑造着社区的日常生活。当本地居民能够用自然语言查询、分析和可视化这些会议记录、合同与决策流程时，“天高皇帝远”带来的监督真空将被极大填补。这不仅仅是让政府更“负责”，更是重新定义了“负责”的标准——从“不犯大错”到“每一笔开销、每一次投票都可被追溯、可被质询”。

然而，将希望完全寄托于技术赋能公民，是一种危险的简化论。乐观的叙事背后，至少潜藏着三重不确定性。首先，是“赋能的非对称性”风险。AI工具并非均匀分布。财力雄厚的大型企业、组织严密的利益集团，在利用AI分析政府数据、寻找监管漏洞、优化游说策略方面，可能远比分散的公民个体更高效、更成体系。这可能导致监督能力的“马太效应”：强者更强，弱者名义上被赋能，实则差距拉大。原本旨在制衡权力的技术，可能反而巩固了既有强势者的优势。其次，是“解读的战争”问题。AI能提供关联和模式，但无法提供唯一的“真相”。同一组游说资金与立法投票的数据，可以被构建成“资本腐蚀民主”的叙事，也可以被解读为“行业专家提供必要信息输入”。当分析工具普及，争夺数据解读框架的话语权斗争将空前激烈，公民可能陷入更复杂的信息迷雾。最后，是公民参与的“可持续性”困境。技术降低了单次分析的门槛，但持续的、系统的监督依然需要时间、专注和社区组织——这些是技术无法直接生成的稀缺资源。一时的热情过后，监督的重担是否会重新落回少数积极分子肩上？

更根本的挑战在于，这场变革可能触及政府运行本身的“反脆弱性”。詹姆斯·斯科特在《国家的视角》中警示，国家试图让社会变得清晰、可读、可管理的过程，常常会简化复杂的社会现实，导致灾难性的规划失败。如今，社会试图用同样的“清晰化”工具反向审视国家，是否也会陷入类似的陷阱？政府运作中存在大量必要的模糊性、妥协、非正式沟通和基于具体情境的判断，这些都无法被完全编码进数据。过度追求“全透明”和“绝对可读性”，是否可能僵化行政过程，扼杀必要的政治弹性，甚至催生一种新型的、数据驱动的民粹主义问责，对公共官员形成寒蝉效应，使得无人愿意承担任何带有风险但必要的决策？

因此，AI赋能公民监督的愿景，其成功与否不取决于算法精度，而取决于我们能否构建与之匹配的新社会契约与制度设计。这需要超越工具层面的思考：第一，在数据开放上，必须从“可机读”走向“可理解”，建立统一、实时、高颗粒度的公共数据标准，并立法保障其可持续性。第二，需要培育中立的“ Civic Tech ”生态和数字素养教育，防止分析工具和解读框架被完全私有化或政治化。第三，或许也是最重要的，是重新思考媒体、公民社会与新技术之间的角色分工。AI不会取代调查记者，而是将其角色从“信息的原始挖掘者”部分转向“复杂叙事的构建者、分析框架的提供者与社区监督的组织者”。

最终，卡帕西的乐观其来有自，但我们需清醒地认识到，技术提供的是一把威力巨大的锤子，它既可以敲碎信息垄断的枷锁，也可能砸碎有效治理所依赖的某些微妙平衡。从“国家看清社会”到“社会看清国家”的范式转移，其意义不亚于从君主专制到代议制民主的转变。它承诺了一个更贴近“主权在民”理想的未来，但通往这个未来的道路上布满了新的权力陷阱与治理难题。我们正在步入一个监督民主的新时代，其形态并非由代码预先决定，而是取决于我们如何有智慧地使用这些代码，并在使用过程中，不断重新协商权力、责任与透明度的边界。这场实验刚刚开始，其结局将定义下一个世纪的民主质量。

如果把这个判断再往前推一步，真正重要的不是 Something I've been…、RT by @paulg: A new…、I was recommended @… 本身，而是它们共同暴露出的分配逻辑。 x 在同一轮里把注意力推向同一问题，通常意味着这个主题正在从圈层内部经验，转向更可共享的公共议题。 这也是为什么这种内容值得写成长文：短帖只负责提醒你“这里有事发生”，但只有长文才能把背景、代价、误判空间和后续影响放到同一张桌面上。 换句话说，AI的真正革命性不在于赋能政府，而在于颠覆性地赋能公民，将“国家看清社会”的单向权力结构，扭转为“社会看清国家”的双向透明，这不仅是技术升级，更是民主问责机制的一次底层重构。 之所以重要，不是因为它看上去新，而是因为它会重新定义用户接下来应该如何理解这一类内容。

参考来源：

Something I've been thinking about - I am bullish on people (empowered by AI) increasing the visibility, legibility and accountability of their governments.

Historically, it is the governments that act to make society legible (e.g. "Seeing like a state" is the common reference), but with AI, society can dramatically improve its ability to do this in reverse. Government accountability has not been constrained by access (the various branches of government publish an enormous amount of data), it has been constrained by intelligence - the ability to process a lot of raw data, combine it with domain expertise and derive insights. As an example, the 4000-page omnibus bill is "transparent" in principle and in a legal sense, but certainly not in a practical sense for most people. There's a lot more like it: laws, spending bills, federal budgets, freedom of information act responses, lobbying disclosures... Only a few highly trained professionals (investigative journalists) could historically process this information. This bottleneck might dissolve - not only are the professionals further empowered, but a lot more people can participate.

Some examples to be precise: Detailed accounting of spending and budgets, diff tracking of legislation, individual voting trends w.r.t. stated positions or speeches, lobbying and influence (e.g. graph of lobbyist -> firm -> client -> legislator -> committee -> vote -> regulation), procurement and contracting, regulatory capture warning lights, judicial and legal patterns, campaign finance... Local governments might be even more interesting because the governed population is smaller so there is less national coverage: city council meetings, decisions around zoning, policing, schools, utilities...

Certainly, the same tools can easily cut the other way and it's worth being very mindful of that, but I lean optimistic overall that added participation, transparency and accountability will improve democratic, free societies.

(the quoted tweet is half-ish related, but inspired me to post some recent thoughts) - https://nitter.net/karpathy/status/2040549459193704852#m

RT by @paulg: A new paper in @Nature from David Reich, @aliakbari23 and colleagues breaks the conventional understanding of recent human evolution. The field believed that strong selection in the recent past (~10,000 years) was rare, with few exceptions like the lactase persistence locus. In this paper, the authors challenge that belief, showing that we weren't looking at the problem right.

Previous studies that looked for evidence of selection using ancient DNA addressed the problem cross-sectionally, asking if allele frequencies differed across populations more than what one would expect based on genetic drift and migration. Most arrived at the conclusion that population structure primarily explained the observed differences. Here, the authors addressed the problem longitudinally, accounting for when ancient individuals lived by explicitly modeling time as a variable in the analysis. It turns out doing it this way dramatically increases power, increasing the number of genome-wide significant selection signals by 20-fold!

Looking at why accounting for the time variable led to such dramatic changes in results, the authors find that previous studies missed so much because selection often happened not on new variants leading to dramatic sweeps (the conventional model: new variant -> selection -> increase in frequency) but on already existing variants driven by transient environmental pressures. Many of these variants underwent reversals, selected up when a pressure existed, then purged when it disappeared or the trade-off cost became dominant. A great example is the TYK2 variant, where an allele boosting immunity was selected for thousands of years because it protected against TB, then got purged as TB endemicity declined and the autoimmune cost took over.

The scale of what they found is striking: hundreds of loci showing strong selection in the past 10,000 years with a median selection coefficient of ~0.86%. This number is pretty big in evolutionary terms, meaning allele frequencies have been shifting by ~1% per generation in a consistent direction. Previous selection scans found a maximum of 20 loci, and this one finds hundreds. That isn't an incremental change. It fundamentally reframes our understanding of how common strong selection has been in recent human history.

Some of the most striking findings come from polygenic selection, where hundreds of small-effect alleles were pushed in the same direction simultaneously. Polygenic scores based on large-scale GWAS of today predict recent negative selection for traits like body fat, waist circumference and schizophrenia, and positive selection for others like cognitive traits. One important caveat is that GWAS phenotypes are measured in industrialized societies today, and how well they capture what was actually being selected in ancient environments is debatable.

For me personally, these findings have direct implications for drug discovery. When using human genetics to find drug targets, we often fixate on the benefit and risk profiles of variants visible today. But we need to be aware that a variant's benefit:harm ratio might be environmentally contingent, and could reverse when the wrong environment manifests. An evolutionary understanding of a variant's association with traits is therefore essential.

The same logic applies, perhaps even more urgently, to embryo selection. Selecting embryos based on polygenic traits is humans making permanent, heritable decisions for their offspring with a narrow view of today's environment. The ancient DNA record now shows that cost-benefit landscapes flip over time. So, an embryo carrying man-made selections is carrying those changes into an unpredictable future environment.

The broader takeaway is that human evolution didn't freeze in the last 10,000 years. We just lacked the tools and datasets to see its movement. The current findings are based on European populations. I am curious to see these analyses extended to other populations too, like South Asian, East Asian and African populations, which might be holding more surprises to blow our minds.

Akbari et al. Nature 2026

https://www.nature.com/articles/s41586-026-10358-1 - https://nitter.net/doctorveera/status/2044679999450664967#m

I was recommended @sonofatailor by all of you

Custom fitted t-shirts based on your own body's measurements

I love them, 100% cotton, great quality

But I guess as is the problem with all clothing brands, they always change stuff every season (to keep selling new stuff) so for ~2 years now they've switched to the most boring uninteresting colors imaginable

It's all some gray pastel depressing shit

There's no happy fun colors anymore

This is why guys when they finally find some good clothes they like, they buy all the colors because you know a month or year later, it's forever gone! Sad! - https://nitter.net/levelsio/status/2044719493705040008#m