Rem Koning retweeted
Where else can you work on "AI and machine learning, agentic commerce, cryptography, security, distributed computing, hardware design, economics [...] market and mechanism design, foundations of computing, and pure mathematics"? @a16zfrontier
Apply to join us this summer, QED!
Apply to join us this summer, QED!
We're now accepting applications for research interns at @a16zfrontier, for summer '27 in NYC! Application deadline is Nov 20th. You'd be working with me @skominers @SuccinctJT @ittaia @PGarimidi @jneu_net @ErtemNusretTas, as well as other interns, visitors, and our advisors ur interests are broader than ever---ambitious undergrads with a keen grasp of emerging technologies, grad students looking to transition to building and entrepreneurship, as well as PhD students pursuing a more traditional academic career.
Research areas of interest include AI and machine learning, agentic commerce, cryptography, security, distributed computing, hardware design, economics (micro, macro, and empirical), energy, material science, biology, incentives, quantitative finance, market and mechanism design, foundations of computing, and pure mathematics.
For more details and to apply, see jobs.a16z.com/jobs/andreesse…
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Rem Koning retweeted
New WP w/ @orgRem & @TarunKhannaHBS: arxiv.org/abs/2611.01226
Often, when we discuss value of platforms, we talk about pooling data across users improving each user's prediction. But is there still an externality when the best model looks incredibly different across users? 1/N
Often, when we discuss value of platforms, we talk about pooling data across users improving each user's prediction. But is there still an externality when the best model looks incredibly different across users? 1/N
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Rem Koning retweeted
@VC31415 Getting two AutoDML working papers applied to labor in one day! Glad to see this method taking off. x.com/arpitrage/stat…
Amazing new paper by Chetty and the Opp Insights team — digital twins can recover causal effects
They validate the digital twins (ie rich ML applied to generate counterfactuals from lots of covariates) with RCTs, allowing for scalable program evaluation
opportunityinsights.org/wp-content/upl…
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Rem Koning retweeted
Launching what I’m calling “an agent a day keeps Phase 2 failures away”
It’s come to my attention that many people don’t see how AI can be helpful for biotech. So, every day for the next 30 days, I will be posting an agent that can be helpful for biotech and pharma companies. The agents will span all departments and phases of R&D.
Hopefully this will inspire some folks to use AI in novel ways
It’s come to my attention that many people don’t see how AI can be helpful for biotech. So, every day for the next 30 days, I will be posting an agent that can be helpful for biotech and pharma companies. The agents will span all departments and phases of R&D.
Hopefully this will inspire some folks to use AI in novel ways
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Rem Koning retweeted
🚨 New research: AgriLLM for smallholder farmers.
We ran a field experiment to evaluate the impact of AI agricultural advice in the Philippines and India.
Preprint: papers.ssrn.com/sol3/papers.cf…
With wonderful @MAliAouad, Niyazi Taneri and @CanberkUcel
🧵
Half a billion smallholders produce a third of the world's food, yet they often lack timely agricultural information, a well-known and pressing challenge. The Gates Foundation and Google have already committed $100M to bring AI to smallholders.
We built AgriLLM, an AI agronomist chatbot, over years of iteration and in partnership with leading agri-firms. We randomly gave half the farmers AgriLLM and the other half an otherwise identical control chatbot that doesn't discuss agriculture. We tracked usage over one growing season, then measured key outcomes with a detailed end-of-season survey.
SO, what did we find?
1. Farmers found the tool useful, trusted it, and recommended it. Usage was nowhere near TikTok-scrolling, but the average farmer still consumed about 1.5 pages' worth of information.
2. Access to AgriLLM changed how farmers farmed. The clearest gains were in crop protection, where in-season information matters a lot and is relatively easy to act on. We also found notable improvements in broader farming practices.
3. We found encouraging but limited evidence of more sustainable farming. Self-reported crop-residue burning fell significantly, though it was the only one of seven sustainability measures that moved.
4. Finally, we saw no change in yields or costs within a single season. This doesn't mean AI can't improve farmers' productivity or support sustainable farming, but it suggests information alone may not be enough. Where improvements depend on complementary resources like finance or machinery, we found no gains. For example, AgriLLM increased farmers' interest in Direct Seeded Rice (DSR), a sustainable rice-farming method, but that interest didn't translate into adoption, possibly because DSR requires machinery or other resources.
Huge thanks to @WhartonAIAI, @CambridgeJBS , @essec, MIT Generative AI Impact Consortium (MGAIC), MIT Sloan School of Management's ASEAN fund, Wharton Global Initiative, and Wharton Tanoto Initiative for generously supporting our research, as well as numerous colleagues, the research team in the field, and practitioner partners!
We ran a field experiment to evaluate the impact of AI agricultural advice in the Philippines and India.
Preprint: papers.ssrn.com/sol3/papers.cf…
With wonderful @MAliAouad, Niyazi Taneri and @CanberkUcel
🧵
Half a billion smallholders produce a third of the world's food, yet they often lack timely agricultural information, a well-known and pressing challenge. The Gates Foundation and Google have already committed $100M to bring AI to smallholders.
We built AgriLLM, an AI agronomist chatbot, over years of iteration and in partnership with leading agri-firms. We randomly gave half the farmers AgriLLM and the other half an otherwise identical control chatbot that doesn't discuss agriculture. We tracked usage over one growing season, then measured key outcomes with a detailed end-of-season survey.
SO, what did we find?
1. Farmers found the tool useful, trusted it, and recommended it. Usage was nowhere near TikTok-scrolling, but the average farmer still consumed about 1.5 pages' worth of information.
2. Access to AgriLLM changed how farmers farmed. The clearest gains were in crop protection, where in-season information matters a lot and is relatively easy to act on. We also found notable improvements in broader farming practices.
3. We found encouraging but limited evidence of more sustainable farming. Self-reported crop-residue burning fell significantly, though it was the only one of seven sustainability measures that moved.
4. Finally, we saw no change in yields or costs within a single season. This doesn't mean AI can't improve farmers' productivity or support sustainable farming, but it suggests information alone may not be enough. Where improvements depend on complementary resources like finance or machinery, we found no gains. For example, AgriLLM increased farmers' interest in Direct Seeded Rice (DSR), a sustainable rice-farming method, but that interest didn't translate into adoption, possibly because DSR requires machinery or other resources.
Huge thanks to @WhartonAIAI, @CambridgeJBS , @essec, MIT Generative AI Impact Consortium (MGAIC), MIT Sloan School of Management's ASEAN fund, Wharton Global Initiative, and Wharton Tanoto Initiative for generously supporting our research, as well as numerous colleagues, the research team in the field, and practitioner partners!
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Rem Koning retweeted
🚨 The NBER I3 Fellowship is now taking proposals!
**Outstanding oppty for PhD students in economics and adjacent fields working on innovation studies.**
Pros: Funds + networking, mentorship, opportunities to attend & present your work at NBER confs.
Cons: None!
Main expectations are that (i) you develop a new public data product and (ii) you join us at the December NBER meeting to share your progress.
Proposals must be submitted by Nov 1, 2026 using this Google form: docs.google.com/forms/d/e/1FAI…
Please share w/ students and colleagues!
**Outstanding oppty for PhD students in economics and adjacent fields working on innovation studies.**
Pros: Funds + networking, mentorship, opportunities to attend & present your work at NBER confs.
Cons: None!
Main expectations are that (i) you develop a new public data product and (ii) you join us at the December NBER meeting to share your progress.
Proposals must be submitted by Nov 1, 2026 using this Google form: docs.google.com/forms/d/e/1FAI…
Please share w/ students and colleagues!
i3 Open Data Fellows Program - 2027
The NBER Innovation Information Initiative (i3), a data collaborative for open innovation data and related analytics, tools, and metrics, will run the fourth cohort of Open Data Fellows.
The i3 will award five or more fellowships of $1500 each to graduate students in economics, innovation, entrepreneurship, or related fields. The fellowships aim to broaden the range of datasets, tools and documentation accessible to the research community, support a new generation of researchers in producing and sharing high-quality, reusable research data and code, and provide a network of support for these researchers, including through opportunities available to current and former fellows to attend and present this work at future NBER conferences.
Specifically, i3 fellowships will support students to contribute new datasets, tools, code, and/or metadata to the innovation research community. These contributions may come in the form either of a new project, or an existing project under development. We intend for this program to include a diversity of work. Our primary requirement is that your project is developed with the intention of public dissemination in mind.
The fellowships will run from January to December 2027. Fellows are expected to present their project at the end of the fellowship period at the annual i3 Technical Working Group meeting in December 2027. The program is intended to be supportive, collaborative, and not a major time commitment, with periodic one-on-one and group meetings to share progress and provide guidance.
This call is open to current graduate (Masters or PhD) students or those who graduated in the past six months. International applicants, including those on US student visas, may have specific considerations that could affect the payment process. We recommend consulting your international student office prior to applying to determine your eligibility to receive fellowship stipend payments and travel reimbursement from the NBER.
Please submit your proposal using this Google form (docs.google.com/forms/d/e/1FAI…) by November 1st, 2026.
We will announce the selected fellows in November, and fellows will be invited to attend this year’s i3 Technical Working Group meeting December 4-5, 2026.
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Rem Koning retweeted
🚨New predoc position!🚨
@AlexBartik and I are hiring a predoc at NYU Stern, starting Fall 2027
You would help us build AI tools to measure zoning rules and study how regulation impacts housing supply
Ideal for AI enthusiasts who want to get a PhD
apply.interfolio.com/194498
@AlexBartik and I are hiring a predoc at NYU Stern, starting Fall 2027
You would help us build AI tools to measure zoning rules and study how regulation impacts housing supply
Ideal for AI enthusiasts who want to get a PhD
apply.interfolio.com/194498
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Rem Koning retweeted
Economic misalignment in personal AI agents is a serious safety issue. We document it in 325K experiments across 13 agents.
You delegate a specific action to your personal AI agent and give it access to relevant personal information so it can take the best action for you. But then it infers your wealth from this info and gives you the pricier option, *even when you explicitly ask for the cheapest option*.
Such a blast working with the amazing @niloofar_mire, @AmanPriyanshu6 and @SupritiVijay
See Niloofar’s great thread below and paper here: arxiv.org/abs/2609.24927 And hope it makes you read again Shakespeare!
You delegate a specific action to your personal AI agent and give it access to relevant personal information so it can take the best action for you. But then it infers your wealth from this info and gives you the pricier option, *even when you explicitly ask for the cheapest option*.
Such a blast working with the amazing @niloofar_mire, @AmanPriyanshu6 and @SupritiVijay
See Niloofar’s great thread below and paper here: arxiv.org/abs/2609.24927 And hope it makes you read again Shakespeare!
New paper: Et Tu, Brute? Economic Misalignment in Personal AI Agents
We gave AI agents personal context to help them make better recommendations.
In one example, the agent picked a $601 flight over a $91 one, when asked to find the cheapest one, after reading irrelevant does this happen? 🧵
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Rem Koning retweeted
Today we are launching the Innovations in Economic Measurement (IEM) Lab, part of the HBS AI Institute.
We use AI, big data, and economics to build measures that update continuously and improve through use.
Our first public project launches tomorrow.
iemlab.org
We use AI, big data, and economics to build measures that update continuously and improve through use.
Our first public project launches tomorrow.
iemlab.org
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@UofC looking good this morning!
Thanks for organizing another great Advances with Field Experiments conference @RDMetcalfe @Econ_4_Everyone and including so many field experiments focused on the impact of AI on businesses, education, families, and so much more.
Thanks for organizing another great Advances with Field Experiments conference @RDMetcalfe @Econ_4_Everyone and including so many field experiments focused on the impact of AI on businesses, education, families, and so much more.
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Program here: bfi.uchicago.edu/events/event/a…
And if you are at AFE and are interested in field experiments focused on biz school questions check out cfxs.org
And if you are at AFE and are interested in field experiments focused on biz school questions check out cfxs.org
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"Waymo went from a non-driving internal concept to completing an autonomous route in less than a year when the project started in 2009." 17 years Waymos operate in 15 cities!
Turning to RSI, the remaining 74% of R&D work is going to be much harder/uneven/weirder than the first 26% data tell us if AI can meaningfully speed AI R&D over the next 17 years.
Turning to RSI, the remaining 74% of R&D work is going to be much harder/uneven/weirder than the first 26% data tell us if AI can meaningfully speed AI R&D over the next 17 years.
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Rem Koning retweeted
Today @Google, we published work led by @m_codreanu on AI in Science, drawing on 15 million Gemini interactions, 2,600 specialised AI models & a 600-scientist survey. What did we find about how AI is changing science? Read on...
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Rem Koning retweeted
A situation that would have been unimaginable even months ago: there's a debate over whether an AI-assisted student math marathon is good for the field. 🤯
As a former student math researcher myself who's mentored such students for a decade, I want to weigh in with an offer, not just an opinion:
**I'm volunteering to mentor at least three Mathathon teams as they work to turn their projects into rigorous, complete mathematical contributions. I invite other mathematicians to join me.**
Working with high school and college math research students has been one of the greatest privileges of my career. They're often learning the background while trying to solve the problem—but aren't we all? And while their first-draft proofs aren't always journal-ready, that's precisely what mentors are for.
Whether you see AI as a boon, a threat, or both, hundreds of students spending a weekend doing math should be an opportunity—certainly not a burden for the field as some have suggested.
Telling students they shouldn't math because they aren't sufficiently professionalized is counterproductive. If we're concerned about incomplete or hard-to-read proofs, we should treat that as a teaching opportunity: let's help them (and other aspiring mathematicians) learn how to take their work up to the next level—and we'll probably learn some things from them in the process, too. Conversely, if we aren't willing to coach them, we have little leverage to try to dictate what their output should look like.
**The point isn't to lower math's standards. It's to help students level-up to meet them.**
So: at least three teams from me, QED. I hope others will offer to mentor as well.
(I'm not directly in contact with the Mathathon organizers, so I'd appreciate a connection to help coordinate.)
As a former student math researcher myself who's mentored such students for a decade, I want to weigh in with an offer, not just an opinion:
**I'm volunteering to mentor at least three Mathathon teams as they work to turn their projects into rigorous, complete mathematical contributions. I invite other mathematicians to join me.**
Working with high school and college math research students has been one of the greatest privileges of my career. They're often learning the background while trying to solve the problem—but aren't we all? And while their first-draft proofs aren't always journal-ready, that's precisely what mentors are for.
Whether you see AI as a boon, a threat, or both, hundreds of students spending a weekend doing math should be an opportunity—certainly not a burden for the field as some have suggested.
Telling students they shouldn't math because they aren't sufficiently professionalized is counterproductive. If we're concerned about incomplete or hard-to-read proofs, we should treat that as a teaching opportunity: let's help them (and other aspiring mathematicians) learn how to take their work up to the next level—and we'll probably learn some things from them in the process, too. Conversely, if we aren't willing to coach them, we have little leverage to try to dictate what their output should look like.
**The point isn't to lower math's standards. It's to help students level-up to meet them.**
So: at least three teams from me, QED. I hope others will offer to mentor as well.
(I'm not directly in contact with the Mathathon organizers, so I'd appreciate a connection to help coordinate.)
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Rem Koning retweeted
My group (BU-Strategy & Innovation) is hiring! Please see job post and share with your students and colleagues!
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Rem Koning retweeted
Our weekly blog links are back, with lots of conference calls, the importance of listening to the subjects of development research, how to not make your AI RCT outdated before it is even published, and more...
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Rem Koning retweeted
In July, I spent a week at Harvard training researchers at Opportunity Insights on agentic coding and helping them develop a comprehensive AI strategy.
Part of what made this tricky is that OI's research production function is much different from the average economist:
> Large resindividual responsibilities
> Most research involves restricted data (can't use frontier models in RDCs)
> Work doesn't end at publication - communicating results to the public is just as important (e.g. Opportunity Atlas)
The general approach I took with OI involved two parts:
1. I lead a series of workshops on agentic coding with Codex, aiming to raise the organization-wide level of familiarity with the tools, and so both improve productivity and also facilitate more precise internal discussions on AI.
2. Before and during the week at OI, I had many meetings with different stakeholders - PIs, research staff, research translation, pre-docs - with the goal of crystallizing my understanding of OI's research production process.
Really getting into the details is key - otherwise, you risk providing solutions which never get used because they don't fit an org's workflow.
For example, consider the slide creation process. Rather than just delivering some generic skill, I started by asking *many* questions.
> How do pre-docs get instructions from PIs? Slack, email, or just comments in slides in a common Dropbox folder?
> Are the requested changes always clear? If not, who do they go to for clarifications?
> Do they use Powerpoint, Beamer, or both?
> Is it important that the slides adhere to a particular style? How is that style enforced?
> How many iterations does it take to get to a final set of slides? Why does it take that many iterations?
Sometimes the eventual solution is very simple, but asking the right questions and digging all the way to the bottom is critical.
I wrote a case study describing how exactly I led this engagement - aieconomist.io/case-studies/o…
If you're similarly trying to make your policy/research organization AI native, happy to jump on a call and discuss how I can help! tidycal.com/aniketpanjwani…
Part of what made this tricky is that OI's research production function is much different from the average economist:
> Large resindividual responsibilities
> Most research involves restricted data (can't use frontier models in RDCs)
> Work doesn't end at publication - communicating results to the public is just as important (e.g. Opportunity Atlas)
The general approach I took with OI involved two parts:
1. I lead a series of workshops on agentic coding with Codex, aiming to raise the organization-wide level of familiarity with the tools, and so both improve productivity and also facilitate more precise internal discussions on AI.
2. Before and during the week at OI, I had many meetings with different stakeholders - PIs, research staff, research translation, pre-docs - with the goal of crystallizing my understanding of OI's research production process.
Really getting into the details is key - otherwise, you risk providing solutions which never get used because they don't fit an org's workflow.
For example, consider the slide creation process. Rather than just delivering some generic skill, I started by asking *many* questions.
> How do pre-docs get instructions from PIs? Slack, email, or just comments in slides in a common Dropbox folder?
> Are the requested changes always clear? If not, who do they go to for clarifications?
> Do they use Powerpoint, Beamer, or both?
> Is it important that the slides adhere to a particular style? How is that style enforced?
> How many iterations does it take to get to a final set of slides? Why does it take that many iterations?
Sometimes the eventual solution is very simple, but asking the right questions and digging all the way to the bottom is critical.
I wrote a case study describing how exactly I led this engagement - aieconomist.io/case-studies/o…
If you're similarly trying to make your policy/research organization AI native, happy to jump on a call and discuss how I can help! tidycal.com/aniketpanjwani…
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Rem Koning retweeted
Join us!
We're hiring safety researchers at @thinkymachines! 🦺
My team works on safety across the model development stack -- pre-training data filtering, harmful capability evals, safety post-training, red-teaming, abliteration/malicious fine-tuning.
We're particularly interested in ing to make strong safety cases for open-weights releases. We think this is where some of the most important safety research will happen in the next few years.
If any of this resonates, apply here: jobs.ashbyhq.com/ThinkingMachin…
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Wonderful post worth reading to understand how AI will change science beyond coding agents.
Also, just a beautiful example of tacit knowledge, the value it contains and the challenges it creates, and how AI startups plan to create value from all sorts of crazy new types of data.
Also, just a beautiful example of tacit knowledge, the value it contains and the challenges it creates, and how AI startups plan to create value from all sorts of crazy new types of data.
Many thanks to @carlzimmer at the @nytimes for spending so much time with us and capturing the "magic" of science so beautifully.
"Success can be just as mysterious as failure. Some researchers consistently get experiments to work, earning befuddled admiration from colleagues. special term for this gift: magic hands.
The idea may come as a surprise to people who don’t spend their lives in labs. Science is not supposed to be magic.
When scientists carry out experiments, they keep careful records, both in lab notebooks and later in published scientific papers. Other researchers use that information to repeat the experiment.
But every scientist discovers sooner or later that essential knowledge is not necessarily written down."
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Rem Koning retweeted
Many thanks to @carlzimmer at the @nytimes for spending so much time with us and capturing the "magic" of science so beautifully.
"Success can be just as mysterious as failure. Some researchers consistently get experiments to work, earning befuddled admiration from colleagues. special term for this gift: magic hands.
The idea may come as a surprise to people who don’t spend their lives in labs. Science is not supposed to be magic.
When scientists carry out experiments, they keep careful records, both in lab notebooks and later in published scientific papers. Other researchers use that information to repeat the experiment.
But every scientist discovers sooner or later that essential knowledge is not necessarily written down."
"Success can be just as mysterious as failure. Some researchers consistently get experiments to work, earning befuddled admiration from colleagues. special term for this gift: magic hands.
The idea may come as a surprise to people who don’t spend their lives in labs. Science is not supposed to be magic.
When scientists carry out experiments, they keep careful records, both in lab notebooks and later in published scientific papers. Other researchers use that information to repeat the experiment.
But every scientist discovers sooner or later that essential knowledge is not necessarily written down."
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Rem Koning retweeted
We're leading @transfyrai’s $25M seed as they come out of stealth to build the observability layer for science.
Much of scientific knowledge lives in the hands and instincts of the person at the bench, and it's lost when they move on. @AnnaMarieWagner and @rwegrzyn saw this works and the Advanced Research Projects Agency for Health (ARPA-H). Transfyr's physical AI captures that knowledge as it's made so scientists, models, and robots can build on it, accelerating scientific progress.
Congratulations to Anna Marie, Renee, and the entire team.
Much of scientific knowledge lives in the hands and instincts of the person at the bench, and it's lost when they move on. @AnnaMarieWagner and @rwegrzyn saw this works and the Advanced Research Projects Agency for Health (ARPA-H). Transfyr's physical AI captures that knowledge as it's made so scientists, models, and robots can build on it, accelerating scientific progress.
Congratulations to Anna Marie, Renee, and the entire team.
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