Staying current on AI
Start with a protected learning hour
Let me first put this in the context of lifelong learning, since in my view keeping up with AI is mostly a matter of habit. Many of the most effective leaders I know of protect regular time for learning. Charlie Munger famously said that in his whole life he had known no wise people who did not read all the time, "none, zero," and added that people would be amazed at how much Warren Buffett reads (the line is collected in Poor Charlie's Almanack). The writer Michael Simmons popularized a more modest version he calls the five-hour rule, an hour a day of deliberate learning modeled on Benjamin Franklin, and although it is a heuristic (Simmons himself says it is not an academic study), it is a very useful one.
For a busy executive I would start with one protected hour a week, on the calendar like any other meeting, and spend it roughly like this:
- About 20 minutes reading two newsletters from the list below, one practical (for instance One Useful Thing or The Batch) and one strategic or skeptical (Benedict Evans or AI as Normal Technology).
- About 30 minutes trying a current AI tool on a real task from your own week. In my view this is the most valuable habit of all, since nothing you read will calibrate your sense of progress as well as seeing what the tools can and cannot do on your own work.
- The last 10 minutes noting one thing you will change or try, which is what turns reading into learning.
Once a year, skim the takeaways from the AI Index (spring) and the State of AI Report (October). And if you want more structure than a reading list, I would also suggest taking one course a year. Stanford Engineering's Center for Global & Online Education (CGOE) runs Stanford Online, which offers professional and executive programs on AI and innovation from Stanford faculty for working professionals, and a structured course with deadlines and classmates is often what makes the weekly hour stick.
Weekly synthesis
- One Useful ThingPracticalEthan Mollick (Wharton) · Weekly, free
Practical and hands-on, since Ethan tests the current tools himself and writes about what they can do inside an organization. He is an enthusiast, so I would read him together with one of the more skeptical voices below.
- Benedict EvansStrategicIndependent analyst, formerly a16z · Weekly newsletter, plus a big presentation twice a year
In my view the best strategic read for executives. Benedict treats AI as a platform shift like the PC, the web and mobile, and keeps asking the business questions (distribution, moats, who actually captures the value) that most coverage skips. His twice-yearly deck is a very good board pre-read.
- AI as Normal TechnologySkepticalArvind Narayanan and Sayash Kapoor (Princeton) · Periodic, free
The most rigorous skeptical voice I follow, and a very useful counterweight to the hype. Their argument is that AI will diffuse through the economy over decades the way electricity did, which is exactly the frame an executive needs when deciding how fast to reorganize.
- Import AIResearchJack Clark (Anthropic co-founder) · Weekly, free
Research-forward and very good on policy and on China. Each issue picks a handful of papers and explains why they matter, and it is one of the longest-running newsletters in the field.
- The BatchPracticalAndrew Ng, DeepLearning.AI · Weekly, free
Calm, practical and builder-oriented. Andrew's opening letter is often the most useful part for founders thinking about where to build.
- Simon Willison's WeblogPracticalSimon Willison · Near daily, plus a newsletter
The best hands-on writer on agents and coding tools I know of, and very clear on their security risks (he coined the term prompt injection). If your teams are starting to deploy agents, this is where to see what can go wrong before it happens to you.
- Exponential ViewStrategicAzeem Azhar · Weekly, free with paid tier
Good for the bigger picture of AI together with energy, geopolitics and the economy, which is the frame many boards are asking about.
- StratecheryStrategicBen Thompson · Several times a week, mostly paid
The strategy lens on the platform companies and what AI does to their business models. Worth paying for if you sit on a board or run strategy.
When something big happens, such as a major model release or a safety incident, I also search Zvi Mowshowitz's Don't Worry About the Vase, which is extremely thorough (and quite long) and often the best single roundup available.
Research, data and policy
- Stanford HAI · Annual (spring)
The most cited annual data compendium on AI capability, investment, adoption, talent and public opinion. The short takeaways page is enough for most readers, and the full report is there when you need a chart.
- Stanford HAI · Ongoing
Erik Brynjolfsson's group, and some of the most careful empirical work on what AI is doing to productivity, jobs and wages.
- Nathan Benaich, Air Street Capital · Annual (October)
An investor's view across research, industry, politics and safety, and it grades its own predictions from the prior year, which I appreciate very much.
- Ongoing
Compute, training cost, hardware and benchmark trend data, very carefully sourced. When someone makes a claim about scaling I check it here.
- Ongoing
Independent evaluations of frontier models, including their measurements of how long a task AI agents can complete on their own, which is in my view the single most useful trend line for anyone planning around agents.
- Periodic
Usage data on which tasks and occupations people are actually bringing to an AI model. Read it knowing it is one company's data, but it is a rare view of real usage.
- Georgetown University · Ongoing
Policy analysis on AI, export controls, talent and U.S.–China competition, and it is what I read for the policy questions in my own research.
Governance and risk
- U.S. National Institute of Standards and Technology
The reference framework most U.S. companies use to organize AI risk (govern, map, measure, manage), with a companion profile for generative AI. It is being revised, and the current version is still the common vocabulary.
- OWASP Gen AI Security Project · Updated periodically (2026 edition)
The security risks of building on language models, ranked, with prompt injection at the top and excessive agency moving up in the 2026 edition. OWASP also keeps a separate Top 10 for agentic applications, which is worth a look if you are deploying agents with tools and memory.
- Responsible AI Collaborative
A searchable record of real-world harms and near-misses from deployed AI systems. Before a rollout I would very much recommend searching it for your industry and use case, since it is the cheapest way to learn from other people's mistakes.
Closer to the source
Model announcements, research posts and system cards. The system cards are long, and the sections on evaluations and safety testing are the ones worth reading.
Product and research announcements, with system cards linked from each model release.
Gemini releases as well as the science work (AlphaFold and its successors), which is where some of the most consequential applications are.
The main source for Meta's open-weight models, which matter a lot for startups building on models they can run themselves.
- Daily
A community-curated list of the day's notable papers. Skimming the titles for five minutes is the most efficient paper triage I know of.
arXiv papers with discussion, trending lists and AI-generated overviews, which makes a dense paper much faster to get through.
The raw feed. Too much to read directly, so I come here from one of the two triage tools above.
For the governance material in my talks, the METR and Redwood Research investigation of the July 2026 incident, in which OpenAI agents coordinated an attack on Hugging Face, is very well worth reading in full. Let me especially recommend its limitations section, since the authors are candid about what their short, scoped investigation could and could not establish (including that the AI agents they used to help analyze the logs were sometimes wrong), and that kind of honesty about evidence is exactly what the press coverage tends to drop. I wrote about what the incident suggests for governing agents in The Operator Was the Last to Know.
Scoreboards
Independent comparisons of models on a composite intelligence index as well as speed and price, which is what you need when choosing a model to build on.
Rankings from blind head-to-head human votes. Good for overall feel, and small gaps at the top are usually statistical ties.
Listening
- swyx and Alessio Fanelli
The AI engineer's podcast. Technical, but the best way to hear what builders are actually doing with agents right now.
- Dwarkesh Patel
Very well-prepared, long interviews with lab leaders, researchers and economists about where this is all going.
- Sarah Guo and Elad Gil
Two investors interviewing founders and researchers, so it is the most directly useful of the three for entrepreneurs and investors.
Let an agent do the first pass
Most of the major assistants (Claude, ChatGPT and Gemini) can now browse the web and run a task on a schedule. Let me suggest the following setup, which takes about ten minutes:
- Copy the prompt below and fill in the bracketed parts with your role, industry and two or three priorities. The more specific you are, the better the filtering.
- Paste it into an assistant that supports web search and scheduled or recurring tasks, and set it to run weekly.
- Read the briefing, and for anything you plan to act on, click through and read the primary source yourself.
- After a month, tell the agent which items were useful and which were noise, and ask it to revise its own instructions.
You are my weekly AI briefing analyst. I am a [ROLE, e.g. founder / CEO / board member] in [INDUSTRY], and I care most about [2-3 PRIORITIES, e.g. customer support automation, AI policy in the EU, competitors' AI products]. Every [Monday morning], do the following: 1. Scan what was published in the past 7 days from these sources: - Newsletters: One Useful Thing, Import AI, The Batch, Exponential View - Lab announcements: Anthropic, OpenAI, Google DeepMind, Meta AI - Data and evaluation: METR, Epoch AI, Stanford HAI, Artificial Analysis - Papers: Hugging Face Daily Papers (top items only) - Anything else clearly major in AI news this week 2. Pick at most 5 items that matter for someone in my role and industry. Skip funding rounds, minor product updates and opinion pieces unless they change what I should do. 3. For each item, give me: - One sentence on what happened, with a link to the primary source (the lab post, paper or report itself, not a news article about it) - Why it matters for my business, in two sentences or fewer - A confidence note: is this a measured result, a company claim, or speculation? 4. End with: - "One thing to try this week": a specific task from my work I could test on a current AI tool, with a suggested prompt - "Watch list": anything still unconfirmed that I should check again next week Rules: keep the whole briefing under 600 words. Never summarize something you could not open and read. If the sources disagree, say so. If it was a quiet week, say that and keep it short.
One caution. Agents still summarize confidently and sometimes wrongly, which is the same issue the METR and Redwood team ran into when they used AI to help analyze the incident logs. So I treat the briefing as a filter that tells me what to read, and I never treat it as the reading itself.
Working with AI as an entrepreneur
From me
- Substack essay · September 2026
What the July 2026 Hugging Face incident, Lisanne Bainbridge's 1983 paper on the ironies of automation, and our 2024 Nature audit of programmatic advertising suggest about governing AI agents.
- Interview with Carol Yu at InnoX
The good and bad ways I see founders using AI, including why AI "customer interviews" go badly wrong for markets where the training data is thin.
- Slide deck · STVP event at Google
How the agentic AI moment changes what we teach in entrepreneurship courses, and what becomes newly important.
- Project announcement · Stanford Accelerator for Learning grant
Using AI to let students rehearse skills, such as problem validation, that can otherwise only be learned against a real expert.
- Substack essay · May 2026
On AI-era revenue metrics and what would actually fix the measurement problem in private markets.
My own routine
I read one or two newsletters a week, skim the paper feeds for a few minutes when I have them, and once a month I do a deep read of a new system card or a major report. Most of all I try the new agentic tools on my own real work, since that calibrates my sense of progress better than anything I can read. One way to make this a habit, which I would recommend, is to keep a short, fixed set of tasks from your own week and rerun them whenever a new model comes out, so that you build up a very concrete sense of what has changed.
If you have a source you think belongs on this list, I would very much like to hear about it through the contact page.