安德烈·卡帕西关于AI赋能公民监督政府的乐观推文，揭示了一个更深层的趋势：技术正在消解信息处理能力的垄断。但这并非一条坦途，它同时带来了信息操纵、算法偏见和公民参与疲劳等新挑战。我们正站在一个十字路口，技术民主化可能带来更健康的民主，也可能加剧社会的撕裂与不信任。

核心观点：AI的真正颠覆性不在于创造了新的监督工具，而在于它正在将政府监督从少数精英的专业能力，转变为一种可大规模普及的公民素养，从而可能重塑权力制衡的底层逻辑。

安德烈·卡帕西在社交媒体上表达了他对AI赋能公民监督政府的乐观看法，其核心论点是：历史上，是政府通过普查、档案、法律等手段让社会变得“可读”，而AI将赋予社会反向“阅读”政府的强大能力。这个观点看似是关于技术工具的应用，实则触及了一个更根本的问题：民主制度中权力制衡的瓶颈，究竟在于信息获取的渠道，还是信息处理的能力？长期以来，答案似乎是后者。政府公开了海量数据——从数千页的综合法案、联邦预算明细、游说披露记录到地方议会的会议纪要——但只有极少数具备专业训练和资源的调查记者、学者或活动家，才有能力从这些数据泥潭中提炼出有意义的洞见，并形成有效的监督压力。这个“处理能力瓶颈”构成了事实上的信息垄断，使得政府问责在很大程度上依赖于一个脆弱且规模有限的精英阶层。

AI的介入，承诺打破这一瓶颈。它并非简单地提供更多数据，而是大幅降低了从数据到洞察的认知门槛。想象一下，一个普通公民可以要求AI助手：“分析过去五年我所在选区议员的投票记录，与其竞选承诺进行对比，并找出其主要竞选捐款来源与投票倾向之间的关联。”或者，“追踪本市年度预算中公共安全支出的变化，并与犯罪率统计数据、社区投诉记录进行交叉分析。”这些在过去需要团队数月工作的任务，未来可能只需一个清晰的指令。卡帕西列举的领域——支出审计、立法差异追踪、游说网络图谱、监管俘获预警——都将从高度专业化的领域，转变为具备一定技术素养的公民可触及的领域。这种转变的本质，是监督权的“技术民主化”。

这种民主化带来的潜在收益是巨大的。首先，它可能显著提高不当行为的发现概率和成本。当监督者从几百人变成潜在的数百万人时，掩盖丑闻、进行利益输送的难度将呈指数级上升。其次，它可能促进更精细、更基于证据的公共讨论，减少空泛的口号和对立。公民可以就具体的政策条款、预算条目进行辩论，而非仅仅停留在意识形态层面。再者，它对地方政治的激活可能尤为显著。全国性媒体往往忽视地方事务，而AI工具可以帮助本地居民深入理解 zoning（分区）法规变更的影响、学校董事会决策的依据或市政合同中的可疑条款，从而加强基层民主的活力。

然而，通往技术赋能监督的乌托邦之路布满荆棘。第一个悖论在于，赋能公民的同一套工具，同样可以赋能当权者进行更复杂、更隐蔽的信息操纵与叙事控制。政府或政治行动委员会可以利用更强大的数据分析能力来精准定位脆弱选民、设计更具迷惑性的宣传话术、甚至制造海量的、以假乱真的“数据烟雾弹”来混淆视听。当双方都拥有强大的信息处理武器时，公共领域可能不会变得更清晰，反而会陷入一场更高级别的“信息军备竞赛”，普通人在其中可能更加无所适从。

第二个挑战关乎算法本身的“黑箱”与偏见。用于分析政府数据的AI模型，其训练数据、目标函数和内在假设都可能包含设计者（无论是科技公司、非营利组织还是政府自身）无意识的偏见。一个旨在“识别浪费性支出”的算法，可能会系统性地将某些类型的公共服务（如社会福利、艺术资助）标记为低效，而强化既有的新自由主义政策偏好。如果公民过度依赖某个单一平台或工具提供的“洞察”，他们可能在不自知的情况下，被导入一个带有特定意识形态的监督框架中，这反而损害了批判性思维的多元性。

第三个，或许也是最现实的障碍，是公民参与的“注意力瓶颈”和“疲劳阈值”。技术降低了处理信息的认知负荷，但并未消除理解复杂政策议题所需的时间投入和心智努力。面对AI生成的、关于无数议题的详尽分析报告，大多数公民可能依然会选择性地关注与自身利益直接相关、或具有高度戏剧冲突的少数事件。监督的“民主化”未必带来监督的“普遍化”，更可能的结果是形成新的、基于兴趣和技能的数字公民精英阶层，而大多数人则停留在偶尔的、碎片化的参与层面。此外，持续暴露于政府运作的复杂性和不完美之中，可能加剧公众的幻灭感和犬儒主义，而非激发建设性的参与。

更深层地看，卡帕西的乐观主义建立在一种技术中立和理性公民的假设之上。但政治的本质远非纯粹的信息处理游戏。权力、情感、身份认同、历史恩怨、部落主义，这些因素在政治行为中扮演着至少与技术理性同等重要的角色。一个公民即使拥有AI提供的、确凿无疑的关于某位政治家腐败的证据，他可能依然会出于党派忠诚、族群认同或对“另一方”的更大恐惧，而选择继续支持该政治家。技术可以改变信息的供给，但难以轻易改变人们消化和运用信息的情感与心理机制。在高度极化的社会里，更强大的事实核查工具有时反而会强化“回音室”效应，因为人们会用它们来更高效地攻击对方阵营，并加固己方的信念堡垒。

因此，AI赋能公民监督的真正前景，不在于它能否创造一个全民皆监察官的时代，而在于它能否在现有的民主制衡体系中，增加一个更灵敏、更去中心化的“传感器网络”。这个网络的价值不在于取代传统的监督机构（如媒体、审计部门、司法系统），而在于为它们提供更丰富的线索、更及时的情报和更广泛的公众压力基础。成功的模式可能是“公民-技术-专业机构”的协同：公民利用工具发现异常、提出质疑；专业记者和调查机构进行深度核实与叙事构建；法律和制度机构则据此采取行动。在这个过程中，技术的角色是“赋能”和“连接”，而非“取代”人类的判断与制度的作用。

最终，我们面临的考验是双重的：在技术层面，我们能否开发出透明、可审计、抵制滥用且易于普及的公民监督工具？在社会政治层面，我们能否培养出足以驾驭这些工具的数字公民素养，并构建相应的社会规范与制度框架，以确保这种新型监督力量被用于促进公共利益，而非加剧社会分裂？AI让我们看到了重塑政府透明度和问责制古老命题的新可能，但它交给我们的，是一个比以往任何时候都更需要智慧、审慎和集体责任的新工具。如何使用它，将决定我们走向一个更明亮的民主未来，还是一个更复杂的数字迷宫。

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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