Henry Li

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📜 Since there’s renewed interest in how AI could help with governance, here are 14 specific government processes where AI agents could make a measurable difference today: Impenetrable forms and applications: citizens face complex, jargon-filled forms that cause them to miss benefits or fail to comply with regulations. AI can replace forms with plain-language conversations that extract data from documents, calculate eligibility, and only ask relevant questions. In the US, citizens spend an estimated 6.5 billion hours per year on federal tax compliance. The IRS sends you no pre-filled return despite already having your W-2s and 1099s. An AI agent could pull all income data the government already holds, pre-populate a return, flag deductions you're likely eligible for based on your profile, and file - reducing the process from hours to a single review-and-confirm step. Regulatory bloat: guidance layered on regulations layered on statutes creates thousands of pages of rules that no person or caseworker can realistically navigate. Rules become too complex and get applied selectively by frontline workers. Agents can be used to map entire regulatory regimes, flag redundancies and conflicts, and let policymakers simulate how proposed rules would actually perform before enacting them. Stanford's RegLab built STARA, an AI system that surveyed San Francisco's municipal code and identified hundreds of outdated reporting mandates, which resulted in a 351-page ordinance to eliminate or consolidate more than a third of the city's 528 mandated reports. Obsolete code and IT systems: the US Social Security Administration runs on 60-million-line COBOL codebases from the 1980s; the IRS processes returns on systems from the 1960s; the World Bank's own internal review found its siloed divisions (IFC, IDA, IBRD) couldn't communicate across systems and its bureaucracy resisted modernisation. In each case, no one internally understands the code, so agencies can't fix a bug without months of waiting and enormous contractor fees. Agentic coding tools let internal teams point an AI at a legacy codebase and start making changes themselves. Fraud and improper payments: after Hurricanes Katrina and Rita, FEMA distributed $6bn in relief with $600m to $1.4bn in improper/fraudulent payments according to GAO. During COVID, the US lost an estimated $100-200bn to fraudulent unemployment insurance claims alone, many filed by bots. As bad actors adopt AI to generate synthetic identities, forge documents, and file claims at scale, that gap will widen fast. Agencies need their own AI agents doing real-time cross-referencing of claims against income data, identity records, and behavioural patterns. Siloed, department-centric service delivery: around 600,000 people leave US prisons each year. Each must separately navigate the Bureau of Prisons (release paperwork), SSA (Social Security card), state DMV (ID), Medicaid (healthcare), SNAP (food), HUD (housing), American Job Centers (employment), and a parole office; each with its own application, eligibility rules, and case system. These dependencies are sequential: without ID you can't get benefits, without benefits you can't stabilise housing, without housing you can't hold a job. An AI agent could intake one person's situation at release, determine eligibility across every level of government, and file applications in the right dependency order. Identity verification as a bottleneck to service access: 800m people worldwide can't legally prove their identity according to the World Bank, mostly in Sub-Saharan Africa and South Asia. Without ID you can't open a bank account, receive a cash transfer, or access most government services. India's Aadhaar is a nice positive example: 1.4bn biometric IDs, 523m new bank accounts, and a claimed $11bn saved by eliminating ghost beneficiaries; but this took a decade of state capacity to build and still fails often enough to lock out legitimate users. AI agents could compress this by cross-referencing whatever documents a person does have (a utility bill, a phone number history, a community attestation etc) against available records and flagging confidence levels for human review. Benefits eligibility screening: the US has over 80 federal means-tested programmes, each with its own application and documentation requirements. A single mother qualifying for SNAP, Medicaid, CHIP, WIC, EITC, Section 8, and childcare assistance faces what is effectively seven separate bureaucracies. An AI agent could intake one life-situation description, determine eligibility across every programme simultaneously, pre-fill and submit applications in parallel, and flag benefits cliffs (where a small income increase would trigger a sharp loss in support) before they hit. Building permit approvals: getting a construction permit in many US cities takes 3–12 months of back-and-forth between applicants and planning departments, often over PDF submissions reviewed manually against zoning codes. An AI agent could parse submitted plans against the local zoning and building code, flag non-compliant elements immediately, and return a preliminary approval or specific revision list within hours instead of months. A related case study: DeepMind recently helped the UK government translate mountains of old paper maps, PDFs, and scanned documents into usable data for modern planning systems with the Gemini-powered ‘Extract’ tool. Public records requests (FOIA): federal agencies have backlogs of hundreds of thousands of FOIA requests, with median response times stretching to months or years. Staff manually search filing systems and redact sensitive information page by page. An AI agent could search document repositories for responsive records, auto-redact exempt information (personal data, classified material, deliberative process content), and draft a release package for human sign-off. However this only works where records are digitised and searchable, and much of the government still runs on fragmented legacy systems where documents aren't centrally indexed… Court scheduling and case management: state courts lose enormous time to scheduling conflicts, continuances, and manual case tracking. In many jurisdictions, hearing dates are still set by phone or in-person. An AI agent could manage the full docket — auto-scheduling based on judge availability, attorney conflicts, and case priority, sending reminders, and rescheduling continuances without human clerk intervention. Over time you could also start exploring automating some low-value claims through novel arbitration pipelines, freeing up court capacity for more consequential cases. Business registration and licensing: starting a business in most jurisdictions requires navigating 5–15 separate registrations: state incorporation, EIN from the IRS, state tax registration, local business licence, zoning compliance, health permits, liquor licences, professional licences, etc. An AI agent could take "I want to open a restaurant serving alcohol at [address] in Brooklyn," query every relevant federal, state, and city database, produce the complete permit checklist in dependency order, pre-fill each application with the business details, and flag the long-lead items (e.g. liquor licence) that need to start immediately. Social worker caseload documentation: child protective services and adult social care workers spend the majority of their time on paperwork rather than with clients: writing visit notes, filing reports, updating case management systems. For every case, caseworkers complete roughly 400 forms totalling ~2,500 pages (multiplied across the 24–31 cases they typically carry simultaneously). An AI agent could listen to (or read transcripts of) a home visit, auto-generate the structured case note, update the system of record, and flag any safeguarding triggers, giving caseworkers their time back for actual care. Medicare/Medicaid claims adjudication: CMS processes over 1bn claims per year, with complex rules about covered services, bundling, medical necessity, and provider eligibility. Improper payments run to tens of billions annually, and 77% of these were due to insufficient documentation, not fraud. In parallel, Medicare Advantage denies 17% of initial claims, yet 57% of those denials are overturned on appeal. Agents could adjudicate straightforward claims automatically against the coverage rules, flag anomalous billing patterns in real time, and route only genuinely complex cases to human reviewers. Public comment synthesis for rulemaking: when a federal agency proposes a new rule, it often receives thousands to millions of public comments (the FCC received 22 million on net neutrality). Staff must read, categorise, and respond to each substantive comment. This may well get worse as people use agents to submit plausible-looking comments multiple times. Agents can help the government filter through these, cluster comments by theme, identify unique substantive arguments, flag form-letter campaigns, and draft the agency's response-to-comments document (a task that currently takes teams of lawyers months).

Yep. Quote tweeted article in The Atlantic imo is largely right, aside from comical attempt to shift blame onto the current US admin. IMO China now effectively equal/ahead of even the US in most relevant domains of science (and trend lines still holding). Westminster sleeps.

If Jose Mourinho made a comment like that he’d get banned

Long overdue discussion about missions and science

@veronica_humble zoological constructivism

A mild fail

Photography killed the painter-as-copyist job and gave us visionaries like Monet, Picasso, and Rothko who saw the world differently. Lean + AI will kill the mathematician-as-lemma-grinder job and give us… what? That’s the exciting part. We literally don’t know yet.

If AI scientists are writing millions of papers, many of which are slop, and some of which are incremental progress, how would we identify the one or two which come up with an extremely productive new idea? In 1948, Shannon was one of hundreds of engineers at Bell Labs working on how to cleanly send voice signals over noisy copper wires. His paper sat in the same technical journal as reports on reducing static and building better filters. How would you recognize that he has come up with this very general framework for thinking about information and communication channels, which over the coming decades would have enormous use from domains as far apart as cryptography to genetics to quantum mechanics? It seems like it can take fields multiple decades to recognize the significance of unifying new concepts. Because it is on that time scale that the fruits of such general concepts lead to new discoveries across many different fields. We’ve managed to solve this peer review problem for human scientists (at least somewhat). Now we’ll need to do it at a much greater scale for the mass of AI science that will be thrown at us.

a new phase in global health nytimes.com/2026/03/16/hea…

What does “sovereignty” mean when the means of production become programmable? I’m coining Generative Sovereignty to describe a shift in where leverages live: less in owning outputs, more in shaping the systems that make production possible. linkedin.com/pulse/what-hap…

We’re still standing! @TangramTx UK biotech keeps selling itself to America giftarticle.ft.com/giftarticle/ac…

On a related note, global health folks should also look into this. Supply-side approaches drive innovation & access to cutting-edge drugs. China’s NRDL ignited a 94% trial surge since 2018. It revolutionised biotech innovation. Smart market designs trump endless equity debates.

My realisation from recent years: 1) These factual insights about the nature of China’s competitive dynamics and edge aren’t widely understood in Western contexts; 2) they really should be; 3) overlooking them has likely hindered economic progress in the West.

I love the diversity of EVs in China but what exactly is this backlight animation in aid of

Great article from @finrow in @statnews on China biotech's "Cambrian explosion". Key: the speed w/ which they are getting first in human data as key. I hope it will wake us up to the opportunity costs of our bureaucratic inertia. statnews.com/2025/12/23/chi…

@Lyan82 Good luck with his annual review

he has his moments

A long time in the works . What can the 19th century German economist Friedrich List, the arch-intellectual counterpoint to Adam Smith, tell us about national prosperity and power in an age of technological interdependence ? open.substack.com/pub/guywj/p/fr…

AI is impressive, yes. But most people don’t care if AI is magically good at coding. They’re asking whether it helps with the things they actually care about. And right now, it mostly doesn’t, in two different ways. First, AI doesn’t meaningfully improve the physical conditions of life. I think to the extent that technology makes people happier, it does so when it trickles down into the physical realm: healthcare, infrastructure and housing are good examples. These are domains where often the bottleneck is not idea generation, but lies downstream of that, in implementation. I am somewhat optimistic that AI will improve medicine, but I also think it won't revolutionize it unless other issues are solved, too. Second -- people feel pessimistic about the consequences of AI in the spiritual domain. Status, belonging, love, meaning -- these are all things people care about where AI does not seem merely neutral, but an active threat. We live in the shadows of the first tech revolution, one that seems to have done some serious damage to our social fabric. Why would people be excited about the spiritual consequences of "Tech Revolution, this time turbocharged"? This is compounded by the lack of AI leaders (or political leaders or some leaders?) that are attuned at all to what people need or care about. The least bad are those who repeat utilitarian platitudes along the lines of YAY UBI. The worst are those who seem to be actively cheering at the birth of a neo-feudalist society. Note that as a nerd, I am pretty excited about AI as such. But it's easy for me to see why people do not welcome it with excitement.

I & @JackScannell13 wrote a manifesto on reviving pharma productivity for @IFP. Public debates focus on improving science or loosening approval. We argue there's real leverage in optimizing the middle part of the drug discovery funnel: Clinical Trials. macroscience.org/p/to-get-more-…

💯 @RuxandraTeslo is right