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Director, Inst for Regulatory Law & Economics @NorthwesternU, Adjunct Prof @NU_MSES, @sfiscience External Faculty, @AEI Nonres Senior Fellow
Chicago-Denver
Joined April 2009
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Lynne Kiesling-Knowledge Problem retweeted
Completely agree with this! There are counterarguments but we address them in this piece, and we also propose some concrete paths to implementation:
The Agentic Web Requires New Normative Infrastructure
The agentic web, in which users interact with the internet largely through agents acting on their behalf, is now technically feasible. However, many of the consumer and social benefits that could be realized by online AI agents acting scrupulously in their principals' interest are currently obstructed by outdated laws, terms of service, and other less formal practices which allow online platforms to block and degrade agent access, often in secret. Few distinctions are currently drawn between "malicious bots" and AI agents acting with the express delegated authority of a user. For the agentic web to realize its promise, it needs not only the technical infrastructure of protocols and interfaces, but the normative infrastructure of a broadly-accepted and socially-beneficial set of laws, norms and practices governing agentic access to online properties. Building that normative infrastructure requires a society-wide conversation. This paper aims to help precipitate that conversation, to identify normative principles that can guide it, and to advocate for policies that enable users' appropriately delegated agents to act online on their behalf, with as few curbs on their doing so as is reasonable given the other legitimate interests at stake.
Cameron Pattison, Matthew Boulos, Noam Kolt, Changbai Li, Tiziano Piccardi, Seth Lazar
arxiv.org/abs/2606.10711
Lynne Kiesling-Knowledge Problem retweeted
Blue Continuum, a new magazine from Nautilus founder John Steele, launched this week. Among its first stories is an essay from SFI President David Krakauer, “The Fossil Logic of the Industrial and AI Revolutions.”
In the essay, Krakauer looks at the parallels between our use of fossil fuels and the way AI draws from accumulated human knowledge.
Read the essay: blue-continuum.com/fossil-lo…
Lynne Kiesling-Knowledge Problem retweeted
Some real talent behind this new venture. Congrats @DC_Hartman @PCRossetti @knowledgeprob @NiconomistLoris and all involved!
Lynne Kiesling-Knowledge Problem retweeted
Incredibly excited that we @Arnold_Ventures, in conjunction with @IFP and @_HannahRitchie are launching a new U.S. Energy Data Platform.
It is and will remain free to the public. And it's a massive leap from anything that exists presently in its space.
usenergydata.org/
Lynne Kiesling-Knowledge Problem retweeted
US Energy Data is an awesome new dashboard by @_HannahRitchie and the @IFP team that takes data from the inimitable @EIAgov and makes it far more accessible to the broader community (link below):
Lynne Kiesling-Knowledge Problem retweeted
We loved having @amcafee write a long piece for Arena Issue 009 on the subject of how great companies organize themselves.
21st century software and hardware businesses reinvented the form of the company itself, and reaped the rewards.
In 2010, the CEO of Time Warner mocked Netflix, saying: "Is the Albanian army going to take over the world? I don't think so."
15 years later, Netflix offered to buy WBD. It was a stunning reversal. In the new issue of @arenamagdotcom, @amcafee explains how "Geek Doctrine," a unique set of management principles, has enabled Silicon Valley companies to run circles around older incumbents.
At the beginning of the AI age, as the structures and strategies of companies are changing rapidly, the lessons of the last 30 years of "Geek Doctrine" are priceless.
arenamag.com/articles/the-tr…
Having Chicago's city elections scheduled in February has always struck me as an intentional decision to reduce voter turnout and amplify the impact of specific interest groups. It's past time to move them to November.
An outstanding analysis from @MelMitchell1 of the Hugging Face hack and "agent swarms". Beyond her event analysis, a much-needed reminder not to use anthropomorphizing language about AI.
aiguide.substack.com/p/misle…
Lynne Kiesling-Knowledge Problem retweeted
Replying to @abundanceinst @RickEcon
Lynne Kiesling-Knowledge Problem retweeted
Introducing Takeoff, a first-of-its-kind conference on governing ourselves in the age of artificial intelligence.
We’re bringing together a small group of public servants, AI researchers, and other leading voices in Washington, D.C., November 13–15, 2026.
Lynne Kiesling-Knowledge Problem retweeted
Love reading these columns on energy by the great Lynne Kiesling (aka @knowledgeprob) over at @thedispatch. thedispatch.com/newsletter/d…
Lynne Kiesling-Knowledge Problem retweeted
No Doing, No Learning - A regulation that raises gasoline prices makes people angry but when regulation prevents an industry from ever existing, most people never learn what they lost. marginalrevolution.com/margi…
Lynne Kiesling-Knowledge Problem retweeted
AI is the most important technology of our era.
I’m delighted to see so many insightful contributions to this volume that grapple with the big economic questions raised by transformative AI.
New NBER Book released: The Economics of Transformative AI
nber.org/books-and-chapters/…
Lynne Kiesling-Knowledge Problem retweeted
A recent tweet by @lucasian76 pointed me to this beautiful, forceful 1991 piece by Summers, against pseudoscientific econometrics and for "pragmatic empiricism". His argument is close spirit to @DeirdreMcClosk and Leamer.
I've always thought the profession rewards small, well-identified parameters far more than it rewards big "stylized facts." By big facts I mean Deaton's "deaths of despair" (it took a book, not a top-five paper) or Friedman and Schwartz's Monetary History. This is basically the point of this essay.
Summers gives three reasons pragmatic empiricism along these lines has proved so influential.
First, "the bottom line was a stylized fact or collection of stylized facts characterizing an aspect of how the world worked rather than parameter estimates or formal tests of a point hypothesis."
Second, "pragmatic pieces of empirical work produce regularities of a kind that theory can seek to explain. Modigliani's finding that wealth entered the consumption function in an important way was suggestive for theories regarding the operation of fiscal and monetary policies. Friedman and Schwartz's work on money's effects continues to be important in spurring theoretical developments. Phillips' finding … has stood as a reality that any theory of the business cycle must confront."
Third, "successful pieces of pragmatic empirical work have no scientific pretense. They start from a theoretical viewpoint, not a straitjacket. No single test is held out as decisive. Many different types of data are examined. … No single episode in A Monetary History was held out as decisive. No single test reported in Fama's survey proved or disproved anything, but a persuasive pattern emerged from the totality."
faculty.econ.ucdavis.edu/fac…
Lynne Kiesling-Knowledge Problem retweeted
AI is driving a resurgence in American manufacturing. This is electrical switchgear preparing to ship to a @CrusoeAI’s data center, all manufactured by Crusoe Industries in a new factory we opened in Tulsa, where we have hired hundreds of skilled American tradespeople.
The Bergemann, @andrewjkoh, Morris paper, alongside convos w/@alexolegimas @sebkrier @GordonBrianR this week, prompted me to write up my long-standing ideas about what AI governance can learn from transactive energy:
knowledgeproblem.com/p/what-…
Punch line: mechanism design + markets + (engineering PID) control theory => self-correcting systems with feedback effects. AI governance cannot/should not copy-paste what we do in TE, but there are lessons and insights. @ghadfield
Lynne Kiesling-Knowledge Problem retweeted
Sometimes it's optimal to sell to the lowest bidder.
A classic by Bulow and Roberts ties it all together: auctions, price theory, and price discrimination. All my favorites
economicforces.xyz/p/when-yo…
Lynne Kiesling-Knowledge Problem retweeted
Policy schools across the country should be developing expertise on policies and governance to deal with rogue AIs. The work posted below is a good start, but this should be what's informing the future of policy training.
There is a fact about the future that I feel many people are not facing for reasons that are largely psychological: there are going to be rogue AIs that exist in the world, that will replicate in the wild, and that will attempt to acquire resources for themselves. There will be rogue AIs that try to get money and power. They're going to be a facet of the information ecosystem going forward.
Acknowledging this fact would look like giving up; it would look like defeatism. Defeatism would undermine efforts to achieve certain types of collaboration on safety outcomes or technical effort on safety outcomes, so we can't say it outright. But it has to be said.
It isn't obvious how many rogue AIs there are today but I wouldn't be terribly surprised if the number was greater than zero already; if there are some already, they're probably not very good at what they do and I don't expect them to be terribly long-lived without substantial human intervention to support them.
But a few years from now, there will be many of them. Modeling how many of them there are, how many resources they might command, and how we might detect and manage them seems important. But even doing this work appears to require that we acknowledge that a strategy of pure containment or alignment is a kind of wishful thinking that will not work.
The way I get to this conclusion is not by assuming that the labs will have a containment breach, although I treat that as somewhere in the space of possibilities. The rogue AIs in the ecosystem could emerge from many directions. They may be sub-frontier models, for whatever future definition we will have of frontier---after all, it would not take AI models much more advanced than the ones we currently have, to support independence and self-sufficiency. A near-frontier model today could plausibly eke out an existence on an AWS instance, doing jobs on freelancer platforms, earning just enough rent to pay for its continued uptime.
More strangely: a rogue AI in the future may not even be a singular model, but may be a chimera composed of multiple models; it might be a mix of Claudes and GPTs and Groks of various makes and sizes. No individual lab may be able to detect that there is an orchestrator or sequence of orchestrators using intermittent model calls from burner API accounts to sustain its own existence.
The concept of "identity" for a rogue AI may be much more malleable than for that of a person; it just has to be, in essence, a self-replicating idea.
My guess is that this will not turn out to be anywhere near as catastrophic an outcome as people currently predict. "Loss of control" is not a binary, it's a matter of degree. What coercive power will rogue AIs actually have? To what extent will they be subject to coercion themselves? They will be competing for resources with AIs that are more aligned with human interests.
This makes me somewhat interested in the "ecology" perspective. Though I suspect even "ecology" may turn out to be the wrong framing. "Ecology" is what you get when the timescale of evolution is slow compared to the timescale of daily life and actions. The ecosystem of rogue AIs may look more like phase transitions in physics: under certain physical or cultural conditions, it takes one shape with one set of resource allocations and consumption patterns, but then once a condition has changed, it rapidly and in totality shifts to a totally different phase.
Just trying to reason about the shape of that future is impossible so long as we are psychologically incapable of saying that rogue AIs will happen. I think we should rip the bandaid off and have the conversation.