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Subject: Coasean Bargaining at Scale
From: Cosmos Institute <cosmosinstitute@substack.com>
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View this post on the web at https://blog.cosmos-institute.org/p/coasean-ba=
rgaining-at-scale

Today=E2=80=99s guest post is a long read by Seb Krier, who leads the Front=
ier Policy Development team at Google DeepMind. He writes in a personal cap=
acity. If you want to pitch us an article, please send us a suggestion usin=
g this form [ https://substack.com/redirect/015375cd-c708-4e4c-a81f-00d405b=
193ed?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ]=
=2E
Much has been written about how AI can pose risks to society, particularly =
in aging Western countries where a sense of latent anxiety has taken over t=
he discourse on technology for the past decade. Sometimes this is legitimat=
e, and sometimes it feels like a continuation of existing Western pessimism=
=2E Few have been able to advance a pos=
itive vision of what we should be stri=
ving for at a socio-political level. Here I=E2=80=99d like to make an attem=
pt. This essay explores how, by providing cognition-and-agency on demand, A=
I agents could amplify human agency to the point where we can escape the ze=
ro-sum traps that have plagued political economy for centuries.
There is a timeless question at the heart of any (free) society: how do we =
allow individuals to pursue their own interests when one person's actions i=
nevitably affect the well-being of another in ways that are negative-sum? E=
conomists have a name for this: =E2=80=9Cexternalities,=E2=80=9D which can =
take either physical or financial forms. The sheer scale of this challenge =
was crystallized in a groundbreaking 1986 paper [ https://substack.com/redi=
rect/25d4f996-1d44-406f-a498-0216d52c620d?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj6=
4cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] by economists Bruce Greenwald and Jose=
ph Stiglitz. They demonstrated that because our world is rife with imperfec=
t information, moral hazards, and incomplete markets, externalities are not=
 the exception, but the rule. This pervasive market failure became the inte=
llectual bedrock for modern regulatory regimes. But the solution has always=
 been the same: the coercive hand of the state and a top-down micro-managem=
ent of society. We are told that only a central authority, a government boa=
rd or commission, can resolve these conflicts by dictating who can do what,=
 and where.
But as economists since Hayek have explained, the planner in Washington (or=
 in your state capital) simply cannot possess the dispersed, specific knowl=
edge of time and place known only to the individuals on the ground. This is=
n't the kind of theoretical knowledge you find in books, but the contextual=
, practical, intuitive, experiential and immediate knowledge that emerges f=
rom a particular situation in time. Writing about urban planning, Alain Ber=
taud argued that =E2=80=9Cplanners cannot possibly know the reasons househo=
lds may have for selecting a specific housing location,=E2=80=9D so mandate=
s often end up becoming blunt and arbitrary. Such information is tacit and =
is only revealed through the actions and choices of individuals within a ma=
rket. This blindness points to an alternative: letting people solve these c=
onflicts themselves.
This is the essence of the work of Nobel laureate Ronald Coase, who argued =
that if bargaining were cheap and easy, a polluter and their neighbor could=
 strike a private deal without any need for regulation. Of course sometimes=
 some pollution would still happen, but the payoff to the neighbor would en=
sure that both parties are better off than the zero pollution or no-limits =
pollution counterfactuals. The tragedy is not the existence of the conflict=
, but the transaction costs that prevent these mutually beneficial deals fr=
om being discovered and executed. It=E2=80=99s also the lesson from Elinor =
Ostrom, who documented how real-world communities successfully govern share=
d resources like fisheries and forests through their own intricate local ru=
les.
Their shared insight is that structures that encourage bottom-up order can =
work better than attempting to impose top down approximations for every con=
flict that requires a resolution. But their work also highlighted the formi=
dable barrier that has tends to stand in the way: transaction costs. Transa=
ction costs are not just legal fees; they are the friction of discovery, th=
e difficulty of negotiation, and the expense of enforcement. They are the c=
ognitive and logistical effort required to identify affected parties and st=
rike a deal.
Historically, because these transaction costs were insurmountable, societie=
s defaulted back to the planner. The inability to coordinate from the botto=
m up became the enduring justification for control from the top down. The r=
esult was always the same: clumsy, one-size-fits-all rules that stifle inno=
vation, distort incentives, and are inevitably captured by special interest=
s who learn to work the system for their own benefit. Today, we are repeati=
ng this same failure of imagination in the discussion around AGI. There is =
a rush to assume that the only way to manage its risks is through the same =
top-down control model, treating AGI as a centralizing technology by its ve=
ry nature. If AGI is analogous to a weapon of mass destruction, a genie in =
a bottle - then surely a central authority is the =E2=80=9Coptimal=E2=80=9D=
 answer?
I find this frame quite myopic. It fixates on the risks of a powerful new t=
echnology while completely overlooking its potential to strengthen the gove=
rnance mechanisms needed for a safe, coordinated society, which could well =
absolve the need for a centralized solution. As a general purpose technolog=
y, AGI is well placed to help us fix our decaying social and public institu=
tions. Better cognitive capabilities also means better coordination, better=
 governance, and better safeguards. Instead of empowering the central plann=
er, AGI could finally empower the individual bargainers of Coase and Ostrom=
 by arming them with the price system: what Michael Levin and Benjamin Lyon=
 call the =E2=80=9Ccognitive glue [ https://substack.com/redirect/26c5bf51-=
e0b4-4730-8c38-144b9f9aec46?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYW=
klSu8UKGoFv6eWS-HyoA ]=E2=80=9D of a free society.
Obliterating transaction costs
The difficulty of millions of people discovering one another's preferences,=
 negotiating, and enforcing agreements has always been the chief justificat=
ion for government intervention. It=E2=80=99s why your neighbor=E2=80=99s l=
eaf blower, my unwillingness to fund the local park, and a factory=E2=80=99=
s emissions all end up in blunt bans and political fights; we can=E2=80=99t=
 cheaply find each other, state exact terms, and lock in a deal. The =E2=80=
=9Ctransaction costs=E2=80=9D are simply too high. But this may no longer b=
e the case once we have AGI agents.
Before we begin, I think it=E2=80=99s important to avoid conceptualizing AG=
I as some sort of single omniscient brain-God - even though agents will eff=
ectively individually and collectively be =E2=80=98superintelligent=E2=80=
=99 and highly capable, and increasingly so over time. That is the central =
planner=E2=80=99s fallacy all over again. While we will continue to see eve=
r-larger training runs creating powerful foundation models, I think it's a =
mistake to assume this results in a singular AGI that carries all economica=
lly valuable tasks; economics ultimately favors efficiency at the point of =
delivery (inference).
Running a hyper-general model for every specialized task is incredibly expe=
nsive, and so this reality drives specialization: general models will be co=
mpressed, distilled, and optimized for specific uses. The future landscape =
will therefore be a hybrid: a vast ecology of personalized agents, services=
, applications, and robots with varying degrees of generality. While many m=
ay descend from a few common foundational ancestors, their deployment will =
be diverse and specialized. As such, imagining 'AGI' as a singular entity i=
s like talking about =E2=80=9CFinance=E2=80=9D as a singular thing.
I think it=E2=80=99s more helpful to imagine these agents through a differe=
nt lens: consider AGI deployed as a vast ecology of personalized agents and=
 systems. This emerging ecosystem is what Toma=C5=A1ev et al. (2025) charac=
terize as the =E2=80=9Cvirtual agent economy [ https://substack.com/redirec=
t/741f37a6-8464-4b41-b6fa-d35294d4b090?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cT=
NudluPwbMYWklSu8UKGoFv6eWS-HyoA ],=E2=80=9D a new economic layer where agen=
ts transact and coordinate at scales and speeds beyond direct human oversig=
ht. While this ecology will contain countless specialized agents, let's foc=
us on the one that matters most from an individual's perspective: your pers=
onal advocate. Think of it as a fiduciary extension of yourself: a tireless=
, extremely competent digital representative, closely tied to you, its prin=
cipal.
What could such an agent do? In principle, it can negotiate, calculate, com=
pare, coordinate, verify, monitor, and much more in a split second. Through=
 many multi-turn conversations, tweaking knobs and sliders, and continuous =
learning, it could also develop an increasingly sophisticated (though never=
 perfect) model of who you are, your preferences, personal circumstances, v=
alues, resources, and more. This should evolve over time - an agent=E2=80=
=99s alignment should follow the principal=E2=80=99s own evolution. Recent =
research [ https://substack.com/redirect/e1756d9f-3117-4ee7-8a97-2529c11cec=
2b?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] on=
 negotiation agents finds that =E2=80=9Chuman-agent alignment=E2=80=9D is p=
rofoundly personal. Users expect agents to not only execute goals but also =
embody their identity, requiring alignment on everything from preferred neg=
otiation tactics to personal ethical boundaries and the specific public rep=
utation they wanted to project. There are of course important privacy consi=
derations here, but none of these seem fundamentally intractable. For examp=
le these systems could be built on technologies like zero-knowledge proofs =
and differential privacy, ensuring that preferences are communicated and ag=
gregated without revealing sensitive underlying data.
Such an agent should also be able to communicate your preferences to millio=
ns of other agents in real-time, with a nuance and specificity that is curr=
ently impossible. It knows that you=E2=80=99ll tolerate loud music on a Sat=
urday, but not on a Sunday; that you=E2=80=99d be happy to carpool, but onl=
y if it adds less than ten minutes to your commute; that you=E2=80=99d will=
ingly pay a fraction of a cent more for clean electricity, but only during =
off-peak hours. All this in a split-second, at the right moment, for the ri=
ght purpose. In other words, AGI could enable hyper-granular contracting. T=
he friction that has always hindered us, the transaction costs that Coase a=
nd Ostrom identified as the great barrier to cooperation, could be massivel=
y reduced. So what can we now do in such a world that was otherwise not pos=
sible?
Pollution and road-traffic negotiations
Think of the agents as a built-in coordination device: instead of each acto=
r guessing everyone else=E2=80=99s move (what economists would call a Nash =
deadlock), they can condition their actions on shared signals and contracts=
, unlocking deals that were previously out of reach - a correlated equilibr=
ium.
Consider the implications. Your agent knows you have a child with asthma. A=
 blanket =E2=80=9Cjust ban the emissions=E2=80=9D rule sounds tidy, but it =
flattens everything into the same position: trivial harms and intolerable o=
nes, essential trips and frivolous ones. When a delivery truck=E2=80=99s ag=
ent plans its route, it doesn't need a government mandate to be considerate=
=2E It simply sees a higher =E2=80=9Cpr=
ice=E2=80=9D for entry onto your stree=
t, a signal broadcast by your agent, representing your strong preference to=
 avoid diesel fumes. The truck's agent can then calculate, instantly, wheth=
er it is cheaper to pay the =E2=80=9Cclean air fee=E2=80=9D to you and your=
 neighbors, or to take a different route. Conversely, if your neighbor=E2=
=80=99s agent flags an emergency, for example if she=E2=80=99s in labor and=
 needs the fastest route to the hospital, then everyone=E2=80=99s agents ca=
n auto-drop (or even invert) the price to clear a corridor, because they ac=
tually value her getting through fast. It's true that in some cases, enforc=
ement of these contracts might cost more than their value; but this could b=
e solved through automated escrows and reputation systems. Ideally the agen=
t system transforms enforcement from a costly legal battle into a near-inst=
antaneous computational verification.
In this scenario, the externality doesn't vanish, but it does get a price t=
ag. And once a cost is made clear, the marvel of the market can solve it. T=
he problem was never the pollution itself; it was the fact that the pollute=
r was allowed to impose a health and financial cost onto you for free. To b=
e clear, not all agent negotiations need to be purely financial. The system=
 I=E2=80=99m envisaging could enable two distinct modes: economic negotiati=
ons where willingness-to-pay determines outcomes (useful for commercial act=
ivities like delivery routes), and as I=E2=80=99ll outline later on in the =
essay, democratic negotiations where each person gets equal voting weight r=
egardless of wealth (essential for community values like neighborhood chara=
cter). Agents can seamlessly switch between these modes depending on the is=
sue at stake - using market mechanisms for efficiency where appropriate, wh=
ile preserving democratic legitimacy for fundamental community decisions.
What=E2=80=99s key though is that agents make that payment [ https://substa=
ck.com/redirect/150d42df-97cf-4531-b54c-42f826147c8f?j=3DeyJ1IjoiNXFxeXF4In=
0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] possible, managing a millio=
n micro-transactions in the background, all based on how your values genera=
lize across countless situations and contexts. When I lived in London, resi=
dents of my neighborhood were unhappy with congestion on roads so decided t=
o essentially prohibit cars from going through it at certain times; taxis a=
nd local merchants were naturally pretty annoyed. With the agent-bargaining=
 system, these low-traffic-neighbourhood detours stop being absolute: taxis=
 can pay a dynamically discovered =E2=80=9Ccut-through=E2=80=9D fee, while =
verified emergencies glide through at zero (or negative) price.
Neighborhood character negotiations
This mechanism clarifies plenty of other thorny disagreements too. Imagine =
a developer wants to build an ugly building in a residential neighborhood. =
Today, that is a political battle of influence: who can capture [ https://s=
ubstack.com/redirect/85d54108-7578-45d7-a4e0-0909f0ee2b5b?j=3DeyJ1IjoiNXFxe=
XF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] the local planning aut=
hority most effectively? In an agent-based world, it becomes a simple matte=
r of economics. The developer=E2=80=99s agent must discover the price at wh=
ich every single homeowner would agree. If the residents truly value the ch=
aracter of their neighborhood, that price may be very high. The project wil=
l only proceed if the developer values the location more than the residents=
 value the status quo. Conversely, if the residents=E2=80=99 asking price i=
s lower than the developer's willingness to pay, the project proceeds, and =
the residents are compensated. In either case, the true economic costs and =
benefits are accounted for. This mechanism forces the discovery of the most=
 valuable use of the resource, moving beyond the current system where proje=
cts are either blocked entirely (socializing the loss of potential gains) o=
r forced through politically (socializing the costs on the neighborhood).
But what if a resident decides to game the system and go for a really absur=
d price, holding everyone ransom? This is why you need a new secondary laye=
r of institutions on top of these agents. Crucially, these institutions can=
 be voluntary. In this neighborhood, homeowners can pool their agents into =
a simple bargaining club: each person privately inputs the minimum they=E2=
=80=99d accept; the software aggregates that into a single take=E2=80=91it=
=E2=80=91or=E2=80=91leave=E2=80=91it offer. This is essentially mechanism d=
esign [ https://substack.com/redirect/c64517f8-d549-4f21-a956-a670418f0753?=
j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] in ac=
tion: creating rules where being honest about your true minimum is the smar=
test move, not gaming the system. Overstating just risks killing the deal (=
you get zero), and if it clears, the payout is at the common clearing price=
 - so padding your number doesn=E2=80=99t boost your check. The group speak=
s with one voice without surrendering property rights, and the developer se=
es a single, fair number instead of a hundred ransom demands.
Skeptics might reasonably worry that NIMBYs can still name absurd buy-off p=
rices. This is a classic political economy dilemma. The benefits of blockin=
g a project are concentrated among a few motivated homeowners, while costs =
such as higher rents, longer commutes, and slower growth are diffused acros=
s a wide, unorganized public. As Janan Ganesh puts [ https://substack.com/r=
edirect/c73fe7c4-aabd-4bb6-9b72-bc52e2264c51?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDb=
Gj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] it, the potential losers are an =E2=
=80=9Cunconscious blob of people=E2=80=9D who don't even know what they're =
losing. Two guardrails fix this.
First, chronic hyper-bidders see their voting weight fade or must pay an =
=E2=80=9Coption fee=E2=80=9D: a Harberger-style tax [ https://substack.com/=
redirect/35dbba4d-803c-4ba8-9ada-372dcd06e732?j=3DeyJ1IjoiNXFxeXF4In0.h9dED=
bGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] in which you periodically pay a pe=
rcentage of the price you claim; overstate, and it soon hurts. For example,=
 if you claim your property is worth $10 million to block a development, yo=
u must be prepared to pay taxes on that valuation too! Second, and more imp=
ortantly, AGI agents can give that =E2=80=9Cunconscious blob=E2=80=9D a pow=
erful voice. Any coalition that vetoes must then reimburse that quantified =
loss, with agents handling the transfers automatically. The diffuse cost be=
comes a concentrated, explicit price. Stonewalling remains possible, but it=
 now carries a real, rising cost. Moreover, with this setup, bargaining isn=
't just between NIMBYs and developers; other residents, now aware of the po=
tential gains, can bargain directly with the holdouts.
Sugar and healthcare externalities
Consider another example: sugar/junk food consumption and public health. Pr=
oponents of a sugar tax correctly identify an externality: poor diet choice=
s impose costs on the shared healthcare system. Their solution, however, is=
 (shock!) a clumsy, top-down tax. This harms food producers, is regressive =
(as it affects the poor more than the rich), and ultimately imposes a cost =
on many people who would not in fact be =E2=80=9Cguilty=E2=80=9D of imposin=
g costs on the healthcare system. An agent-based market addresses the same =
problem with bottom-up precision.
Instead of lobbying the government, your health insurer's agent communicate=
s with your advocate agent. It looks at your eating habits, calculates the =
projected future cost of your diet and makes a simple offer: a significant,=
 immediate discount on your monthly premium if you empower your agent to di=
sincentivize high-sugar purchases. At that very moment of decision, the mar=
ket responds. Acting like a hyper-alert Kirznerian entrepreneur spotting a =
profit opportunity, a soft drink company's agent, to retain your business, =
might instantly propose a deep discount on a healthier drink.
Now consider smoking bans in public places. A simple free-market approach w=
ould let every restaurant or bar owner decide their own policy. But non-smo=
kers value having a broad range of options for a night out; if smoking beco=
mes the default, their social world narrows significantly. This loss of cho=
ice is a cost that a full-on ban tries to crudely handle. AI agent negotiat=
ion, however, allows for a more precise, Millian solution. Instead of banni=
ng the externality, we can finally price it in through voluntary, real-time=
 negotiation. Once again, we=E2=80=99re not banning the externality, but pr=
icing it in. This price wasn't imposed by a committee of very smart policym=
akers sitting in a grey room in Westminster, but discovered through volunta=
ry, real-time negotiation. The choice remains with the individual, but it i=
s now a truly informed choice, where the full costs and alternatives are tr=
ansparent.
Another example can be seen in the rules we have on airplanes and the air w=
e share with fellow passengers in this private space. Even when we don=E2=
=80=99t use government rules, airlines generally have to come up with a gen=
eric rule that works okay for who it expects to be on a typical airplane ri=
de. During the COVID pandemic, even many people who wanted mask mandates fo=
r airplanes did not wear masks on flights themselves, as they considered th=
e value of wearing a mask while others would not to be minimal.
Similarly, airlines generally do not make accommodations for people sensiti=
ve to airborne allergens. Virgin Airlines can't tell if your peanut allergy=
 is life-threatening or just a mild inconvenience. To avoid opening the flo=
odgates to thousands of hard-to-verify requests (=E2=80=9CI'm allergic to p=
erfume,=E2=80=9D =E2=80=9CI'm sensitive to blue lights,=E2=80=9D) they just=
 make a simple, inflexible rule, like =E2=80=9Cwe will serve nuts.=E2=80=9D=
 Much of this is, of course, due to an aversion to what seems like inevitab=
le lobbying for accommodations that would come from conceding the principle=
=2E However, if flight policies are n=
egotiated over by AI agents, we don=E2=
=80=99t have to choose between all or nothing on masking. We don=E2=80=99t =
have to rule out accommodations to people with allergen sensitivities for f=
ear of frivolous requests; instead we move from all-or-nothing mandates to =
nuanced, negotiated outcomes, where the intensity of a person's need is acc=
urately represented and compensated.
This agent-negotiated world delivers three principles essential to a free a=
nd effective society.
First, accountability. A billionaire who wants to close a public beach for =
a private party faces a new constraint: his agent must make a public, audit=
able offer to every single person who would be deprived of access. The cost=
 of his desire becomes explicit and traceable. Of course, he might still tr=
y the old route of bribing a bureaucrat in secret - but this parallel trans=
parent market creates pressure and comparison points. When combined with AG=
I-enhanced governance (automated auditing, pattern detection for corruption=
 etc.), the corrupt path becomes even more risky and costly.
Second, the power of voluntary coalitions. Today, diffuse interests are oft=
en ignored because the transaction costs of organizing are too high. A sing=
le person in a low-income neighborhood has little bargaining power. A multi=
national polluter is more likely to get away with building a monstrosity in=
 a Brazilian favela than in the Hamptons, even if the true social cost is h=
igher. But what if the agents of 10,000 residents, seeing a factory=E2=80=
=99s proposal to increase emissions, can form a bargaining coalition in a n=
anosecond? They can spontaneously band together and declare, =E2=80=9COur c=
ollective price to accept this pollution is X million dollars, non-negotiab=
le.=E2=80=9D They solve the collective action problem instantly, creating w=
hat is effectively a powerful digital union to counterbalance concentrated =
wealth.
Third, continuous self-calibration. Because every agent streams its user=E2=
=80=99s context-rich preferences into live markets, the rules themselves fl=
ex in real time. Noise caps, curb uses, even peak-hour electricity rates sl=
ide automatically as new bids and conditions roll in, rather than waiting f=
or a city-council vote five years from now. Tacit desires, like how much qu=
iet you need for a newborn=E2=80=99s nap or what premium you=E2=80=99d pay =
for a car-free street, become explicit, machine-readable prices. The system=
 therefore functions as a permanent feedback loop. It detects mismatches be=
tween policy and lived reality, reprices the externality within seconds, an=
d nudges behavior accordingly. Governance shifts from statute to thermostat=
: sometimes through formal institutions built on top of these agents, such =
as professional guilds, and sometimes through instantaneous ad-hoc =E2=80=
=9Cflash coalitions=E2=80=9D - emergent order.
So what=E2=80=99s the catch?
The Coasean vision amplified by AGI agents is powerful, but it's not a pana=
cea. Ronald Coase himself was no naive utopian, emphasizing that his theore=
m was a theoretical benchmark for a world without transaction costs, not a =
description of reality. In practice, the theorem has faced decades of rigor=
ous critique from economists, legal scholars, and behavioral scientists, wh=
o argue that its assumptions crumble under real-world frictions. These limi=
tations explain why Coasean bargaining rarely materializes today, leading s=
ocieties to default to clumsy government interventions or inaction.
My response to this is twofold. First, the Coase theorem and its instantiat=
ion forces us to identify and analyze frictions, like imperfect information=
, legal costs, or strategic holdouts, that prevent efficient private soluti=
ons. This is not to say it solves everything, but it=E2=80=99s a powerful t=
oolkit to prompt us to look for creative, private, and market-based solutio=
ns to problems where we might have only considered government regulation or=
 violence before. Second, many of these critiques ignore what governance te=
chnologies and institutional arrangements AGI can enable in the first place=
 - and I think there are good reasons to think that this technology can hel=
p us bypass limitations that would otherwise block progress on cooperation.
It=E2=80=99s true that even with perfect agent coordination, there remains =
what Acemoglu calls the 'political Coase theorem [ https://substack.com/red=
irect/5d050456-a7f5-4796-bd93-035592f9a7e3?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj=
64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ]' problem: those with political power =
cannot credibly commit to not exploiting that power tomorrow, since no exte=
rnal enforcer exists for contracts with the sovereign itself. The sovereign=
 can always renege. This is a tricky challenge, but the agent system offers=
 countermeasures. First, the transparency created by agent negotiations rai=
ses the political cost of expropriation or bribery: it=E2=80=99s harder to =
steal what is clearly priced and publicly recorded. Second, AGI must be dep=
loyed not just for market transactions, but to enhance institutional accoun=
tability. In other words, we should automate many aspects of how we govern:=
 automated auditing, real-time monitoring of regulatory capture, automated =
dispute resolution, automated public spending monitoring, and agent-based a=
nti-corruption measures can harden the governance mechanisms that constrain=
 the arbitrary use of power. Institutions matter!
Just as agents can aggregate citizen preferences for market negotiations, t=
hey can also transform how the =E2=80=9Cmachinery of government=E2=80=9D it=
self operates. The information asymmetries and coordination failures that J=
ames C. Scott describes in Seeing Like a State, where central authorities o=
perate with crude categories that miss local knowledge, can finally be reso=
lved as well. On the =E2=80=9Cexecutive=E2=80=9D side, agent networks can p=
rovide governments with high-resolution, real-time feedback about policy im=
pacts, citizen preferences, and emerging problems. On the =E2=80=9Ccivil=E2=
=80=9D side, the automation of key protections against executive corruption=
, overreach, and misalignment protects people against the erosion of libera=
l democracy.
Here, I'll explore some of the most salient critiques, preempt common objec=
tions to applying them in an AGI-agent context, and propose some countermea=
sures. The goal isn't to dismiss the critiques but to show how agents can s=
ubstantially mitigate them. This strengthens the case for a hybrid system: =
agents handling the micro-coordination, with carefully designed institution=
s (including the state) addressing the rest.
Zero transaction costs, really? And what about inequality?
Skeptics might say agents don't eliminate costs entirely; they just shift t=
hem to compute overhead, data privacy setups, agent configurations etc. Thi=
s is true! But it also underestimates the scale of reduction. Agents aren't=
 burdened by human limitations like fatigue, bias in communication, logisti=
cal hurdles, social awkwardness, irrational decision making and so on. What=
 costs $10,000 in legal fees today might cost pennies to compute tomorrow. =
Consider how a billionaire=E2=80=99s phone today is no more powerful or eff=
ective than yours.
Even then though, you might reasonably think that this still creates inequi=
ty in the short run. To prevent cost barriers for low-income users, governm=
ents or philanthropies could provide baseline agent services (similar to pu=
blic defenders) and guarantees. This is a low cost to pay for the efficienc=
ies gained by a system that otherwise promises to save society orders of ma=
gnitude more by slashing legal overhead, unlocking stalled projects, and tu=
rning countless externalities into win-win trades. In other words, underwri=
ting entry-level agents for the poorest citizens is like funding public roa=
ds: a modest civic outlay that makes the whole market run faster, fairer, a=
nd vastly more productively.
In some contexts, for low-income users, governments or philanthropies could=
 provide baseline agent services (similar to public defenders) - or more li=
kely, the necessary compute to level things up and ensure equitable partici=
pation. This is unlikely to be a huge growing cost over time, as agent tech=
 commoditizes, these costs approach zero asymptotically. The model here cou=
ld mirror school voucher systems like Sweden's, where the government provid=
es credits that ensure universal access to essential services while allowin=
g choice and competition. Just as educational vouchers guarantee every chil=
d can attend school regardless of family income, =E2=80=9Cagent vouchers=E2=
=80=9D or compute credits could ensure everyone can participate in democrat=
ic deliberation, access legal representation, or navigate essential governm=
ent services. The key is targeting subsidies where they matter most for civ=
ic participation and fundamental rights - you'd want generous credits for d=
emocratic decision-making, healthcare choices, or educational planning, but=
 not for negotiating garage parking disputes or lawn ornament preferences.
Alternatively, or complementarily, the system could employ direct redistrib=
ution in highly sensitive areas - providing everyone with a base allocation=
 of compute credits or =E2=80=9Cagent wealth=E2=80=9D to spend as they see =
fit. This approach avoids the paternalism of defining the above =E2=80=9Ces=
sential services=E2=80=9D centrally, which would recreate the very social p=
lanner problem we=E2=80=99re trying to avoid. Individuals could allocate th=
eir resources according to their own priorities rather than predetermined c=
ategories. A hybrid might work best: a universal basic compute allocation f=
or personal use, plus additional targeted support for specific democratic a=
nd legal functions where equal participation is constitutionally guaranteed=
=2E
This tiered approach ensures equity where it counts without creating an uns=
ustainable fiscal burden, while still allowing market dynamics to operate i=
n less critical domains. In practice however, this does mean a lot of infra=
structure will be required. For example, built-in protocols for multi-party=
 discovery. For high-volume scenarios, hierarchical agents could aggregate =
at neighborhood or city levels. But much of this will need to be designed a=
s part of a wider push for improving institutional decision making.
Which rights are the =E2=80=98default=E2=80=99?
Another important consideration here is that you still need an agreed =E2=
=80=9Cdefault position=E2=80=9D - do people have a right to make noise, or =
a right to quiet? What's the basic right that is being negotiated - the rig=
ht to pollute, or the right to be free from pollution? The machinery runs e=
ither way. What does change is who ends up richer, which is why the initial=
 allocation of these rights is a constitutional choice, not a technicality.=
 Even if bargaining is cheap, outcomes aren't invariant to initial property=
 rights because wealth influences willingness to pay. A poor farmer might s=
ell pollution rights cheaply to a rich factory not because it's efficient, =
but because they need cash now. Conversely, changing who starts with the ri=
ghts changes the wealth distribution, which affects what people can afford =
to bid and therefore changes which 'efficient' outcome the market settles o=
n. Beyond wealth effects, behavioral factors like the endowment effect [ ht=
tps://substack.com/redirect/f3d239bd-c22a-42d9-9c42-61ab8703dfd9?j=3DeyJ1Ij=
oiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] - people demand=
ing far more to give up a right than they'd pay to acquire it - make initia=
l allocations stick even with perfect bargaining. Agents might correct for =
such biases, though whether we want them to 'debias' negotiations or faithf=
ully represent our psychological quirks remains an open design question.
So how do we ensure fairness without reverting to top-down control? What is=
 the =E2=80=9Cdefault position=E2=80=9D to start with? Well, that baseline =
of who starts with which entitlement is a normative, collective choice. Age=
nts don=E2=80=99t magic it away; they only make it explicit, contestable, a=
nd cheap to renegotiate. My view here is that we already have many of these=
 rights set up by centuries of jurisprudence, and this is the right startin=
g point. To the extent that these need to change or adapt, our democratic p=
olitical systems are the right mechanism to do so. The bad news is that the=
se systems are now pretty ossified, slow, captured, and dysfunctional. The =
good news is that agents can improve them materially.
Beyond periodic voting on baseline entitlements, agents could fundamentally=
 transform how citizens deliberate and exercise their democratic rights. Re=
cent papers show that AI systems can effectively learn and represent human =
preferences with remarkable efficiency. Studies like ConstitutionMaker [ ht=
tps://substack.com/redirect/7682a5f1-c234-4016-97ae-bbe5807274a2?j=3DeyJ1Ij=
oiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] show how natura=
l language principles can be extracted from preference data, while Inverse =
Constitutional AI [ https://substack.com/redirect/d4072a87-9a3a-4be3-887a-7=
16bc1df8517?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-=
HyoA ] proves that just a handful of preferences can be compressed into int=
erpretable principles that accurately reconstruct individual and group valu=
es. This suggests agents could continuously learn citizens' nuanced policy =
preferences through ongoing interactions, creating rich, privacy-protected =
preference profiles.
Currently, we delegate representation to biological agents - mayors, counci=
lors, representatives - who operate within opaque, underfunded institutions=
 plagued by accountability problems, information asymmetries, and the impos=
sibility of truly representing thousands of diverse constituents. With agen=
t infrastructure, we could significantly improve these systems. Imagine eve=
ry citizen having a personal agent that deeply understands their values, ca=
n engage in sophisticated policy deliberation on their behalf, and coordina=
te with millions of other agents to find optimal compromises in real-time.
These agents wouldn't just vote every few years but could participate in co=
ntinuous liquid democracy, dynamically delegating expertise to trusted enti=
ties for specific domains, instantly aggregating or constructing [ https://=
substack.com/redirect/c9821b95-6350-43df-a53e-5848b60bd485?j=3DeyJ1IjoiNXFx=
eXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ] preferences on emergi=
ng issues, and ensuring that policy truly reflects the evolving will of the=
 people rather than the frozen snapshot captured at the last election. Of c=
ourse, this risks enabling digital NIMBYism at unprecedented scale, and we =
certainly don't want everyone's agents micromanaging nuclear safety protoco=
ls or monetary policy - but these are mechanism design and governance chall=
enges, not fundamental obstacles.
Today, citizens already don't vote on every financial regulation or technic=
al standard; agent-mediated democracy needn't change that. To the extent th=
at enhanced coordination could enable minorities to hold majorities hostage=
, we'll need clever mechanisms to prevent such digital paralysis. There's p=
lenty of work ahead for policymakers, economists, evaluation designers, soc=
iologists, and game theorists to get these institutional designs right!
Lastly, the system would also need to balance dynamism with stability. Mark=
ets require predictable rules; indeed, the constant renegotiation of proper=
ty rights would destroy investment incentives. But just as options markets =
price volatility, an agent-mediated system could explicitly price the value=
 of stability versus flexibility, letting some rules ossify by mutual agree=
ment (basic property rights, contract enforcement) while others remain perp=
etually negotiable (noise ordinances, parking rules). The agents themselves=
 would likely converge on stable equilibria for most issues simply to reduc=
e computational overhead - constant renegotiation is expensive even for AGI=
=2E
But what about catastrophic risks?
A lot of people working in AI governance are interested in catastrophic ris=
ks where a few actors can impose great harm on others at scale; many will r=
ightly say =E2=80=9Cthis all sounds great but doesn=E2=80=99t address CBRN =
risks.=E2=80=9D They=E2=80=99re not wrong.
A malicious actor intending to release a pathogen is not a market participa=
nt to be bargained with, and admittedly, the agent system can do little to =
stop them. Instead this is the state's first and most important job: to enf=
orce law and order and protect citizens from violence, whether from a forei=
gn army or a domestic bioterrorist. The Coasean multi-agent framework relie=
s on this protection to even exist in the first place: the state needs to e=
nforce contracts. If the delivery truck=E2=80=99s agent agrees to the =E2=
=80=9Cclean air fee=E2=80=9D but the company refuses to pay, there must be =
a court system: a neutral arbiter with the power to enforce the agreement. =
This is a non-negotiable role for the state.
In AI governance discussions, the aversion to the totalising, centralising =
proposals espoused by some communities has been met with the inverse prescr=
iption: various flavours of free for all e/acc libertarianism or anarchy. T=
his falls into the opposite trap, and wrongly assumes you can do away with =
the state entirely. The Coasean framework does not eliminate the state, but=
 it transitions its role from =E2=80=9Ccentral planner=E2=80=9D to =E2=80=
=9Cframework guarantor=E2=80=99=E2=80=9D focusing its power on what it alon=
e can do. It allows the market, supercharged by agents, to handle the compl=
ex work of coordinating preferences and pricing externalities, a job the st=
ate has always done poorly. This should in principle appeal to conservative=
s wary of big government and liberals wary of power abuses. But it doesn=E2=
=80=99t do away with the state, and nor should it - it just makes it a lot =
leaner. The (gradually automated) state continues to define and enforce bas=
ic property rights, contract law, criminal justice, and constitutional righ=
ts - the bedrock rules without which agent negotiations would be meaningles=
s.
So the Coasean multi-agent system, for all its genius, has a critical limit=
: it is designed to price trade-offs. It can put a price on diesel fumes, n=
oise, or a blocked view. It cannot, however, price the non-negotiable. What=
 happens if technology unlocks a true =E2=80=9Crecipe for ruin=E2=80=9D? A =
discovery, like =E2=80=9Ceasy nukes=E2=80=9D or a simple method for creatin=
g a devastating pathogen, that allows a single actor to threaten civilizati=
on itself? This is not an externality to be bargained over!
Such a risk is a form of ultimate coercion, and its prevention falls square=
ly within the most fundamental duty of the state: protecting its citizens f=
rom violence. Therefore, the state=E2=80=99s role is not just to enforce th=
e contracts that agents make, but to define the absolute boundaries of what=
 they are permitted to do in the first place like prohibiting actions that =
create catastrophic, un-priceable risks like man-made pandemics. While the =
Coasean framework itself does not price these existential risks, the underl=
ying cognitive infrastructure it creates is part of what a modern state may=
 need to manage them: enabling the high-speed coordination and automated go=
vernance required to make the state a more effective protector.
Matryoshkan Alignment=20
So what does this mean for how we think about the normative/sociopolitical =
question of who agents should be aligned to? To whom, or what, is an agent =
ultimately loyal? Is it fully and solely aligned to the user, like a comput=
er that will execute any command? Or is it aligned to an amorphous set of c=
ollective values decided by some citizen jury? Or a global institution that=
 sets top down directives? Or is it all up to the model developer? As with =
humans, I think the answer is not a single master. The meta-framework is a =
series of nested layers of governance, like a set of Matryoshka dolls. This=
 nested structure mirrors what Levin calls [ https://substack.com/redirect/=
d9b00f1a-a3b0-4428-96f5-ed58ea355a0c?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNu=
dluPwbMYWklSu8UKGoFv6eWS-HyoA ] =E2=80=9Cscale-free cognition=E2=80=9D wher=
e each level of organization (from cells to tissues to organisms) maintains=
 its own goals and decision-making capacity, with larger-scale goals emergi=
ng from but not replacing smaller-scale ones. There are many possible layer=
s, but for the sake of simplicity I=E2=80=99ll outline three here.
The outermost, and largest, doll is the law. This is the non-negotiable bou=
ndary enforced by the state. An agent, no matter how personalized, cannot b=
e a tool for committing crimes. Your agent cannot help you orchestrate frau=
d, DDOS a hospital, hire a hitman, or procure materials for a bioweapon, an=
y more than your word processor can grant you immunity for writing a fraudu=
lent cheque. To a large extent, existing laws basically criminalize all of =
the above; although some ways need some updating to account for agentic cap=
abilities, difficulty in establishing a mens rea, delineation of responsibi=
lities across the =E2=80=9Cvalue chain,=E2=80=9D and so on. There=E2=80=99s=
 plenty of interesting work going on in legal circles trying to work this o=
ut, and legitimate arguments for why certain gaps may need to be filled.
Within that legal boundary operates the second layer: the free market of di=
fferent services, deployers, products, and providers. A company offering an=
 agent service is not the government; it is a voluntary association with it=
s own rules. One social media site can cultivate a different environment fr=
om another, and users are free to choose. If a provider=E2=80=99s agent ref=
uses to engage with topics the company deems harmful to its brand or commun=
ity, that is their right. A user who finds these policies too restrictive i=
s not a captive; they are a customer who can, and will, take their business=
 to a competitor offering more customization or utility. This competition w=
ill be the primary force pushing agents to become powerful and loyal advoca=
tes for their users. Today, we arguably have an increasing number of develo=
pers of general purpose models of all kinds, costs are going down, and impo=
rtantly many more actors are able to customize, fine-tune, and modify model=
s deployed through cloud infrastructure. Unlike social media, network effec=
ts are also far weaker. And in a competitive market with switching costs ap=
proaching zero, parasitic agents get quickly identified and abandoned.
Finally, at the core, is the individual. Within the bounds of law and the t=
erms of service you voluntarily accept, the agent=E2=80=99s purpose is to b=
e your tireless, personal advocate. This is where the power of user-level c=
ustomization and alignment is unleashed, where a private =E2=80=9Ccognitive=
 DNA=E2=80=9D can be grown. The user should have immense freedom to tune th=
eir agent to their unique preferences and values. They should also have com=
plete privacy and control over their =E2=80=9Ccognitive profile=E2=80=9D de=
veloped by the agent, for obvious reasons. Practically speaking though, thi=
s is the hardest part: how do you design and evidence an agent (mostly) ali=
gned to a user? How do you evaluate this and the continuous learning? We do=
n't need the perfect answer to these questions - alignment is not something=
 to be =E2=80=9Csolved.=E2=80=9D
There are of course important technical [ https://substack.com/redirect/d93=
5d176-a727-4db5-9eb7-662a96f81fea?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudlu=
PwbMYWklSu8UKGoFv6eWS-HyoA ] questions that are not fully addressed - the r=
ight norms, the right level of agreeableness, the right level of deference =
and corrigibility, fully addressing reward hacking, ensuring agents aren=E2=
=80=99t deceptive, the right evaluations to test for user alignment, and mo=
re. Few of these have a single right answer however, and markets are genera=
lly fairly well incentivised to solve them - no company or person wants a r=
eward hacking agent. My intention here is not to dismiss them away - rather=
, I think the way the =E2=80=9Calignment problem=E2=80=9D is often conceptu=
alized is out of date and comparable to asking =E2=80=9Chow do we ensure wh=
at is written always leads to truth? How do we solve the =E2=80=98truth pro=
blem=E2=80=99?=E2=80=9D after the invention of writing or typewriters. Ther=
e isn=E2=80=99t and cannot be any guarantee. In fact, the starting point sh=
ould be reversed; as Dan Williams notes [ https://substack.com/redirect/5cf=
d0858-2cd9-428b-bc42-41385af1e690?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudlu=
PwbMYWklSu8UKGoFv6eWS-HyoA ], the real question should be =E2=80=9Cwhy do w=
e even have truth at all?=E2=80=9D This is a question of institutions and g=
overnance, and not one solved by software engineering. It=E2=80=99s an unsa=
tisfactory answer only for those seeking centralized guarantees. You mean w=
e=E2=80=99re going to have to muddle through things? Yes. As put by Leibo e=
t al [ https://substack.com/redirect/da7f7eaa-a091-4f41-b6f6-4632f84c18b8?j=
=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ], we sh=
ould model societal and technological progress as sewing an =E2=80=9Cever-g=
rowing, ever-changing, patchy, and polychrome quilt.=E2=80=9D
What we need to ensure is that agents that genuinely serve their users' int=
erests will outcompete those that don't and build the right governance mech=
anisms. From a commercial point of view, these agents won't just be adopted=
 and used by everyone out of the box. They need to actually produce value t=
o their principals too. People will want AIs for financial planning, solvin=
g scheduling and calendars, helping with negotiations with roommates, findi=
ng romantic partners, etc. I expect this adoption to begin in the immediate=
 sphere: agents negotiating the office thermostat, allocating shared resour=
ces in an apartment block, or fairly dividing household chores. As these sy=
stems prove their worth in reducing daily friction, people will trust them =
more. The same mechanisms used to settle a parking dispute can be adapted a=
nd scaled up to manage urban planning conflicts or discover the true cost o=
f local externalities in other contexts. Eventually, this bottom-up archite=
cture provides a credible pathway to solving the grand challenges, from fun=
ding national public goods to perhaps one day even navigating the complexit=
ies of interstate disputes.
In some cases, you may need more than market forces. Just as public defende=
rs ensure legal representation regardless of ability to pay, we may need gu=
aranteed access to advocacy agents through voucher systems, market intermed=
iaries [ https://substack.com/redirect/641c4f8b-071e-4bb4-bbe9-8fb3ee94ce47=
?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA ], =E2=
=80=9Cright to an agent=E2=80=9D provisions, oversight mechanisms for autom=
ated governance, and so on. For the most part though, users will gravitate =
toward agents that actually help them achieve their goals. Providers whose =
agents consistently deliver value will gain market share. Markets remain on=
e of the most powerful forces discovered to date; and with agents, we can s=
urely improve both the rules governing them (e.g. through political agents =
and automated governance) and the mechanisms that ensure their efficiency (=
e.g. through Coasean bargaining agents).
The vision presented here shifts the locus of governance from centralized c=
oercion to decentralized negotiation. AGI agents can help us create a vastl=
y more efficient, accountable, and adaptable society. There is no need to c=
entralize all the labs into a government monopoly, nor should we just accel=
erate aimlessly and do away with the state. And unlike solving alignment fo=
r a singular, centralized AGI (where failure is catastrophic), the distribu=
ted model of millions of user-agent relationships creates a massive paralle=
l experiment. It is a system that learns, adapts, and continuously aligns i=
tself over time, allowing us to build a society that is both more free and =
more coordinated than anything that has come before.
***
Thanks to Nathaniel Bechhofer, Roberto-Rafael Maura-Rivero, Lee B. Cyrano, =
Andrew Cordington, Max Nadau, Ivan Vendrov, Conor Griffin, Alex Obadia, Ben=
oit Lepine, Ryan Murphy, Harry Law, Conor Griffin, and Benjamin Lyons for c=
omments.
Cosmos Institute [ https://substack.com/redirect/e35859e5-1b37-4f6d-8c6a-0f=
18c0942891?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-H=
yoA ] is the Academy for Philosopher-Builders, technologists building AI fo=
r human flourishing. We run fellowships, fund fast prototypes, and host sem=
inars with institutions like Oxford, Aspen Institute, and Liberty Fund.

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    /* Disable offset on mobile/tablet */
  }
  .typography .captioned-image-container figure:has(> a.image2-align-left),
  .typography.editor .captioned-image-container figure:has(> a.image2-align=
-left),
  .typography .captioned-image-container figure:has(> a.image2-align-right)=
,
  .typography.editor .captioned-image-container figure:has(> a.image2-align=
-right) {
    float: none;
    margin: 1em auto;
    max-width: 100%;
    width: auto;
    padding: 0;
  }
  .typography .captioned-image-container figure:has(> a.image2-align-left.t=
hefp),
  .typography.editor .captioned-image-container figure:has(> a.image2-align=
-left.thefp),
  .typography .captioned-image-container figure:has(> a.image2-align-right.=
thefp),
  .typography.editor .captioned-image-container figure:has(> a.image2-align=
-right.thefp) {
    margin: 1em auto;
  }
  .typography .captioned-image-container figure:has(> a.image2-offset-left)=
,
  .typography.editor .captioned-image-container figure:has(> a.image2-offse=
t-left),
  .typography .captioned-image-container figure:has(> a.image2-offset-right=
),
  .typography.editor .captioned-image-container figure:has(> a.image2-offse=
t-right) {
    margin: 1em auto;
  }
  .typography .captioned-image-container figure:has(> a.image2-align-left) =
=2Eimage2-inset,
  .typography.editor .captioned-image-container figure:has(> a.image2-align=
-left) .image2-inset,
  .typography .captioned-image-container figure:has(> a.image2-align-right)=
 .image2-inset,
  .typography.editor .captioned-image-container figure:has(> a.image2-align=
-right) .image2-inset {
    display: block;
    justify-content: initial;
  }
}
@media (max-width: 768px) {
  .typography .markup div.sponsorship-campaign-embed,
  .typography.editor .markup div.sponsorship-campaign-embed {
    margin-top: 24px;
    margin-bottom: 24px;
  }
  .typography .markup div.sponsorship-campaign-embed:first-child,
  .typography.editor .markup div.sponsorship-campaign-embed:first-child {
    margin-top: 0px;
  }
}
@media screen and (max-width: 650px) {
  .typography .markup div.youtube-overlay,
  .typography.editor .markup div.youtube-overlay,
  .typography .markup div.vimeo-overlay,
  .typography.editor .markup div.vimeo-overlay {
    display: none !important;
  }
}
@media screen and (max-width: 370px) {
  .typography .markup div.tiktok-wrap,
  .typography.editor .markup div.tiktok-wrap {
    width: calc(95vw - 32px);
    height: calc((95vw - 32px - 2px) / 0.485714);
  }
}
@media screen and (max-width: 650px) {
  .typography .markup div.embedded-publication-wrap .embedded-publication.s=
how-subscribe,
  .typography.editor .markup div.embedded-publication-wrap .embedded-public=
ation.show-subscribe {
    padding: 24px;
  }
}
@media screen and (max-width: 650px) {
  .typography .markup div.subscription-widget-wrap .subscription-widget.sho=
w-subscribe,
  .typography.editor .markup div.subscription-widget-wrap .subscription-wid=
get.show-subscribe,
  .typography .markup div.subscription-widget-wrap-editor .subscription-wid=
get.show-subscribe,
  .typography.editor .markup div.subscription-widget-wrap-editor .subscript=
ion-widget.show-subscribe,
  .typography .markup div.captioned-button-wrap .subscription-widget.show-s=
ubscribe,
  .typography.editor .markup div.captioned-button-wrap .subscription-widget=
=2Eshow-subscribe {
    padding: 0px 24px;
  }
}
@media screen and (max-width: 650px) {
  .typography .markup div.subscription-widget-wrap .subscription-widget.sho=
w-subscribe .subscription-widget-subscribe .button,
  .typography.editor .markup div.subscription-widget-wrap .subscription-wid=
get.show-subscribe .subscription-widget-subscribe .button,
  .typography .markup div.subscription-widget-wrap-editor .subscription-wid=
get.show-subscribe .subscription-widget-subscribe .button,
  .typography.editor .markup div.subscription-widget-wrap-editor .subscript=
ion-widget.show-subscribe .subscription-widget-subscribe .button,
  .typography .markup div.captioned-button-wrap .subscription-widget.show-s=
ubscribe .subscription-widget-subscribe .button,
  .typography.editor .markup div.captioned-button-wrap .subscription-widget=
=2Eshow-subscribe .subscription-widget-subscribe .button {
    padding: 10px 12px;
    min-width: 110px;
  }
}
@media (max-width: 650px) {
  .typography .markup .tweet,
  .typography.editor .markup .tweet {
    padding: 12px;
  }
}
@media (max-width: 650px) {
  .typography .markup .tweet .tweet-text,
  .typography.editor .markup .tweet .tweet-text {
    font-size: 14px;
    line-height: 20px;
  }
}
@media (max-width: 650px) {
  .typography .markup .tweet .tweet-photos-container.two,
  .typography.editor .markup .tweet .tweet-photos-container.two,
  .typography .markup .tweet .tweet-photos-container.three,
  .typography.editor .markup .tweet .tweet-photos-container.three,
  .typography .markup .tweet .tweet-photos-container.four,
  .typography.editor .markup .tweet .tweet-photos-container.four {
    height: 200px;
  }
}
@media (max-width: 650px) {
  .typography .markup .tweet a.expanded-link .expanded-link-img,
  .typography.editor .markup .tweet a.expanded-link .expanded-link-img {
    max-height: 180px;
  }
}
@media (max-width: 650px) {
  .typography .markup .tweet a.expanded-link .expanded-link-description,
  .typography.editor .markup .tweet a.expanded-link .expanded-link-descript=
ion {
    display: none;
  }
}
@media screen and (max-width: 650px) {
  .typography .markup .apple-podcast-container,
  .typography.editor .markup .apple-podcast-container {
    width: unset;
  }
}
@media (max-width: 420px) {
  .typography .markup .install-substack-app-embed img.install-substack-app-=
embed-img,
  .typography.editor .markup .install-substack-app-embed img.install-substa=
ck-app-embed-img {
    margin: 0 auto 16px auto;
  }
}
@media (max-width: 420px) {
  .typography .markup .install-substack-app-embed .install-substack-app-emb=
ed-text,
  .typography.editor .markup .install-substack-app-embed .install-substack-=
app-embed-text {
    margin: 0 0 12px 0;
    max-width: 100%;
    width: auto;
    text-align: center;
  }
}
@media (max-width: 420px) {
  .typography .markup .install-substack-app-embed .install-substack-app-emb=
ed-link,
  .typography.editor .markup .install-substack-app-embed .install-substack-=
app-embed-link {
    display: flex;
    justify-content: center;
  }
}
@media screen and (min-width: 481px) {
  .share-button-container {
    height: 38px;
  }
}
@media screen and (min-width: 481px) {
  .share-button-container a.comment {
    height: 38px;
    line-height: 38px;
    padding-right: 10px;
  }
}
@media screen and (max-width: 480px) {
  .share-button-container .separator {
    display: block;
    margin: 0;
    height: 8px;
    border-left: none;
  }
}
@media screen and (max-width: 480px) {
  .share-button-container a.share.first img {
    padding-left: 0;
  }
}
@media screen and (min-width: 481px) {
  .share-button-container a.mobile {
    display: none !important;
  }
}
@media screen and (min-width: 541px) {
  .settings-add-pub-modal-wrapper .container .add-recommending-pub-modal-co=
ntainer {
    padding: 36px;
    height: 680px;
  }
}
@media screen and (min-width: 541px) {
  .settings-add-pub-modal-wrapper .container .add-recommending-pub-modal-co=
ntainer .footer {
    position: absolute;
    bottom: 36px;
    margin: 0px;
  }
}
@media screen and (max-width: 650px) {
  .header-anchor-parent {
    display: none;
  }
}
@media screen and (max-width: 768px) {
  .post {
    padding: 16px 0 0 0;
  }
}
@media screen and (max-width: 650px) {
  .post .post-header .post-label {
    margin-top: 8px;
  }
}
@media screen and (max-width: 650px) {
  .post .post-header .meta-author-wrap.alternative-meta .meta-right-column =
=2Epost-meta {
    margin-top: 6px;
  }
}
@media screen and (max-width: 650px) {
  .post .footer-facepile-container {
    height: 64px;
    padding: 0 16px;
    display: flex;
    align-items: center;
    justify-content: flex-start;
    width: 100%;
  }
}
@media screen and (max-width: 650px) {
  .post .post-footer.use-separators {
    justify-content: center;
  }
}
@media screen and (max-width: 650px) {
  .post .post-footer.next-prev {
    height: 64px;
    justify-content: space-between;
    box-sizing: border-box;
  }
}
@media screen and (max-width: 650px) {
  .post-contributor-footer .post-contributor-bio-table {
    display: block;
  }
  .post-contributor-footer .post-contributor-bio-table-row {
    display: flex;
    flex-direction: row;
  }
  .post-contributor-footer .post-contributor-bio-userhead-cell,
  .post-contributor-footer .post-contributor-bio-body-cell {
    display: block;
  }
  .post-contributor-footer .post-contributor-bio-body-cell {
    flex-grow: 1;
  }
  .post-contributor-footer .post-contributor-bio-body-table {
    display: block;
  }
  .post-contributor-footer .post-contributor-bio-body-table-row {
    display: block;
  }
  .post-contributor-footer .post-contributor-bio-copy-cell,
  .post-contributor-footer .post-contributor-bio-controls-cell {
    display: block;
  }
  .post-contributor-footer .post-contributor-bio-copy-cell {
    margin: 0 0 16px 0;
  }
  .post-contributor-footer .post-contributor-bio-controls-cell {
    width: auto;
  }
  .post-contributor-footer .post-contributor-bio-controls {
    margin: auto;
  }
  .post-contributor-footer .post-contributor-bio-controls .button.primary {
    width: 100%;
  }
  .post-contributor-footer .post-contributor-bio-text {
    font-size: 14px;
  }
}
@media screen and (min-width: 768px) {
  .post-silhouette {
    padding: 32px 0;
  }
}
@media screen and (max-width: 650px) {
  .post-silhouette .post-silhouette-title {
    margin-top: 10.44225025px;
    height: 120px;
  }
}
@media screen and (max-width: 650px) {
  .post-silhouette .post-silhouette-meta {
    width: 75%;
  }
}
@media screen and (max-width: 650px) {
  .post-silhouette .post-silhouette-meta.with-byline-image {
    margin: 20px 0;
  }
}
@media screen and (max-width: 650px) {
  .use-theme-bg .post-meta.alternative-meta .post-meta-item,
  .post-meta.alternative-meta .post-meta-item {
    padding-right: 16px;
  }
}
@media screen and (max-width: 370px) {
  .use-theme-bg .post-meta.alternative-meta .post-meta-item,
  .post-meta.alternative-meta .post-meta-item {
    font-size: 14px;
  }
}
@media screen and (max-width: 650px) {
  .use-theme-bg .post-meta.alternative-meta .post-meta-item.guest-author-pu=
blication,
  .post-meta.alternative-meta .post-meta-item.guest-author-publication {
    display: none;
  }
}
@media screen and (max-width: 370px) {
  .post-meta .post-meta-item .post-meta-button {
    height: 36px !important;
    /* important to override in-line height style on emails */
  }
  .post-meta .post-meta-item .post-meta-button .meta-button-label {
    display: none;
  }
  .post-meta .post-meta-item .post-meta-button > svg {
    margin-right: 0;
  }
}
@media screen and (max-width: 370px) {
  .post-meta .post-meta-item {
    font-size: 12px;
  }
}
@media screen and (max-width: 650px) {
  .post .floating-subscribe-button {
    bottom: 20px;
    right: 20px;
  }
}
@media (max-width: 1024px) {
  body .pullquote-align-left,
  body .pullquote-align-right,
  body .pullquote-align-wide,
  body .pullquote-align-center {
    float: none;
    margin: 0 auto;
    width: 100%;
    max-width: 100%;
  }
}
@media all and (-ms-high-contrast: none), (-ms-high-contrast: active) {
  body .markup table.image-wrapper img,
  body .markup table.kindle-wrapper img {
    max-width: 550px;
  }
}
@media (min-width: 1024px) {
  body:not(:has(#toc)) .captioned-image-container figure:has(> a.image2-off=
set-left) {
    margin-left: var(--image-offset-margin);
  }
  body:not(:has(#toc)) .captioned-image-container figure:has(> a.image2-off=
set-right) {
    margin-right: var(--image-offset-margin);
  }
}
@media (min-width: 1300px) {
  body .captioned-image-container figure:has(> a.image2-offset-left) {
    margin-left: var(--image-offset-margin);
  }
  body .captioned-image-container figure:has(> a.image2-offset-right) {
    margin-right: var(--image-offset-margin);
  }
}
@media (max-width: 1024px) {
  body {
    /* Disable offset on mobile/tablet */
  }
  body .captioned-image-container figure:has(> a.image2-align-left),
  body .captioned-image-container figure:has(> a.image2-align-right) {
    float: none;
    margin: 1em auto;
    max-width: 100%;
    width: auto;
    padding: 0;
  }
  body .captioned-image-container figure:has(> a.image2-align-left.thefp),
  body .captioned-image-container figure:has(> a.image2-align-right.thefp) =
{
    margin: 1em auto;
  }
  body .captioned-image-container figure:has(> a.image2-offset-left),
  body .captioned-image-container figure:has(> a.image2-offset-right) {
    margin: 1em auto;
  }
  body .captioned-image-container figure:has(> a.image2-align-left) .image2=
-inset,
  body .captioned-image-container figure:has(> a.image2-align-right) .image=
2-inset {
    display: block;
    justify-content: initial;
  }
}
@media (max-width: 768px) {
  body .markup div.sponsorship-campaign-embed {
    margin-top: 24px;
    margin-bottom: 24px;
  }
  body .markup div.sponsorship-campaign-embed:first-child {
    margin-top: 0px;
  }
}
@media screen and (max-width: 650px) {
  body .markup div.youtube-overlay,
  body .markup div.vimeo-overlay {
    display: none !important;
  }
}
@media screen and (max-width: 370px) {
  body .markup div.tiktok-wrap {
    width: calc(95vw - 32px);
    height: calc((95vw - 32px - 2px) / 0.485714);
  }
}
@media screen and (max-width: 650px) {
  body .markup div.embedded-publication-wrap .embedded-publication.show-sub=
scribe {
    padding: 24px;
  }
}
@media screen and (max-width: 650px) {
  body .markup div.subscription-widget-wrap .subscription-widget.show-subsc=
ribe,
  body .markup div.subscription-widget-wrap-editor .subscription-widget.sho=
w-subscribe,
  body .markup div.captioned-button-wrap .subscription-widget.show-subscrib=
e {
    padding: 0px 24px;
  }
}
@media screen and (max-width: 650px) {
  body .markup div.subscription-widget-wrap .subscription-widget.show-subsc=
ribe .subscription-widget-subscribe .button,
  body .markup div.subscription-widget-wrap-editor .subscription-widget.sho=
w-subscribe .subscription-widget-subscribe .button,
  body .markup div.captioned-button-wrap .subscription-widget.show-subscrib=
e .subscription-widget-subscribe .button {
    padding: 10px 12px;
    min-width: 110px;
  }
}
@media (max-width: 650px) {
  body .markup .tweet {
    padding: 12px;
  }
}
@media (max-width: 650px) {
  body .markup .tweet .tweet-text {
    font-size: 14px;
    line-height: 20px;
  }
}
@media (max-width: 650px) {
  body .markup .tweet .tweet-photos-container.two,
  body .markup .tweet .tweet-photos-container.three,
  body .markup .tweet .tweet-photos-container.four {
    height: 200px;
  }
}
@media (max-width: 650px) {
  body .markup .tweet a.expanded-link .expanded-link-img {
    max-height: 180px;
  }
}
@media (max-width: 650px) {
  body .markup .tweet a.expanded-link .expanded-link-description {
    display: none;
  }
}
@media screen and (max-width: 650px) {
  body .markup .apple-podcast-container {
    width: unset;
  }
}
@media (max-width: 420px) {
  body .markup .install-substack-app-embed img.install-substack-app-embed-i=
mg {
    margin: 0 auto 16px auto;
  }
}
@media (max-width: 420px) {
  body .markup .install-substack-app-embed .install-substack-app-embed-text=
 {
    margin: 0 0 12px 0;
    max-width: 100%;
    width: auto;
    text-align: center;
  }
}
@media (max-width: 420px) {
  body .markup .install-substack-app-embed .install-substack-app-embed-link=
 {
    display: flex;
    justify-content: center;
  }
}
@media screen and (min-width: 500px) {
  body .header a.logo {
    width: 42px;
    height: 42px;
    border-radius: 12px;
  }
}
@media screen and (max-width: 420px) {
  body .subscription-receipt table:first-of-type .subscription-amount .subs=
cription-discount {
    width: 72px !important;
  }
}
@media screen and (min-width: 481px) {
  body .share-button-container {
    height: auto;
  }
}
@media screen and (max-width: 480px) {
  body .share-button-container .separator {
    display: block !important;
    margin: 0 !important;
    height: 8px !important;
    border-left: none !important;
  }
}
@media screen and (max-width: 650px) {
  .digest .item .post-meta-item.audience {
    display: none;
  }
}
@media screen and (min-width: 500px) {
  .digest-publication .logo img {
    width: 42px;
    height: 42px;
    border-radius: 8px;
  }
}
@media screen and (max-width: 650px) {
  .comments-page .container .comment-list .collapsed-reply {
    margin-left: calc(10 + 32px - 24px);
  }
}
@media screen and (max-width: 650px) {
  .comment > .comment-list {
    padding-left: 24px;
  }
}
@media screen and (max-width: 650px) {
  .finish-magic-login-modal .modal-content .container {
    padding: 24px 0;
  }
}
@media (max-width: 650px) {
  .reader2-text-b3 {
    line-height: 24px;
  }
}
@media screen and (max-width: 650px) {
  .reader2-text-h4 {
    line-height: 24px;
  }
}
@media screen and (min-width: 541px) {
  .user-profile-modal {
    padding-left: 12px;
    padding-right: 12px;
  }
}
@media screen and (max-width: 650px) {
  .subscribe-widget form.form .sideBySideWrap button.rightButton {
    padding: 10px 12px;
  }
}
@media screen and (min-width: 541px) {
  .pub-icon:hover .logo-hover,
  .feed-item-icon:hover .logo-hover {
    display: block;
  }
}
@media screen and (max-width: 650px) {
  .post-ufi.single-full-width-button .post-ufi-button-wrapper {
    width: 100%;
    padding: 16px;
  }
  .post-ufi.single-full-width-button .post-ufi-button-wrapper:empty {
    display: none;
  }
  .post-ufi.single-full-width-button .post-ufi-button {
    width: 100%;
    justify-content: center;
  }
}
@media screen and (max-width: 768px) {
  .file-embed-wrapper {
    padding: 0;
  }
}
@media screen and (max-width: 768px) {
  .file-embed-wrapper-editor {
    padding: 0;
  }
}
@media screen and (max-width: 768px) {
  .file-embed-wrapper-editor:active {
    padding: 0;
  }
}
@media only screen and (max-width: 650px) {
  .file-embed-button.wide,
  .file-embed-error-button.wide {
    display: none;
  }
}
@media only screen and (min-width: 630px) {
  .file-embed-button.narrow,
  .file-embed-error-button.narrow {
    display: none;
  }
}
@media screen and (min-width: 541px) {
  .audio-player-wrapper .audio-player {
    min-width: 500px;
  }
}
@media screen and (max-width: 650px) {
  .audio-player-wrapper .audio-player .audio-player-progress {
    border-left-width: 16px;
    border-right-width: 16px;
  }
}
@media screen and (max-width: 650px) {
  .audio-player-wrapper .audio-player .audio-player-progress .audio-player-=
progress-bar .audio-player-progress-bar-popup {
    top: -54px;
  }
}
@media screen and (max-width: 650px) {
  .audio-player-wrapper-fancy .audio-player .audio-player-progress {
    border-left-width: 16px;
    border-right-width: 16px;
  }
}
@media screen and (max-width: 650px) {
  .audio-player-wrapper-fancy .audio-player .audio-player-progress .audio-p=
layer-progress-bar .audio-player-progress-bar-popup {
    top: -54px;
  }
}
@media (min-width: 250px) {
  .audio-player-wrapper-fancy .audio-player {
    padding: 32px;
  }
  .audio-player-wrapper-fancy .audio-player .btn-group {
    display: flex;
  }
  .audio-player-wrapper-fancy .audio-player .btn-group .button:last-of-type=
 {
    display: block;
  }
}
@media (min-width: 300px) {
  .audio-player-wrapper-fancy .audio-player .btn-group {
    display: block;
  }
  .audio-player-wrapper-fancy .audio-player .btn-group .button:first-of-typ=
e {
    display: block;
  }
}
@media (min-width: 350px) {
  .audio-player-wrapper-fancy .audio-player .audio-player-substack-logo {
    display: block;
  }
  .audio-player-wrapper-fancy .audio-player .audio-player-title {
    margin-top: 16px;
  }
  .audio-player-wrapper-fancy .audio-player .audio-player-hero-image-contai=
ner {
    padding-top: 15%;
    width: 15%;
    display: block;
  }
  .audio-player-wrapper-fancy .audio-player .btn-group .button:first-of-typ=
e {
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token=3DeyJtIjoiPDIwMjUwOTI2MTQwMTU3LjMuZWNmYjhiZjRkOTZmZmIwOUBtZzIuc3Vic3R=
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ht=3D"16"><td height=3D"16" style=3D"font-size:0px;line-height:0;">&nbsp;</=
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uto" style=3D"--image-offset-margin: -120px;padding: 32px 0 0 0;font-size: =
16px;line-height: 26px;"><div class=3D"body markup" dir=3D"auto" style=3D"t=
ext-align: initial;font-size: 16px;line-height: 26px;width: 100%;word-break=
: break-word;margin-bottom: 16px;font-family: Lora,sans-serif;font-weight: =
400;"><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px=
;font-size: 16px;margin-top: 0;"><em><span>Today&#8217;s guest post is a lo=
ng read by Seb Krier, who leads the Frontier Policy Development team at Goo=
gle DeepMind. He writes in a personal capacity. If you want to pitch us an =
article, please send us a suggestion using </span><a href=3D"https://substa=
ck.com/redirect/015375cd-c708-4e4c-a81f-00d405b193ed?j=3DeyJ1IjoiNXFxeXF4In=
0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb=
(54,55,55);text-decoration: underline;">this form</a><span>.</span></em></p=
><div style=3D"font-size: 16px;line-height: 26px;"><hr style=3D"margin: 32p=
x 0;padding: 0;height: 1px;background: #e6e6e6;border: none;"></div><div cl=
ass=3D"captioned-image-container-static" style=3D"font-size: 16px;line-heig=
ht: 26px;margin: 32px auto;"><figure style=3D"width: 100%;margin: 0 auto;">=
<table class=3D"image-wrapper" width=3D"100%" border=3D"0" cellspacing=3D"0=
" cellpadding=3D"0" data-component-name=3D"Image2ToDOMStatic" style=3D"mso-=
padding-alt: 1em 0 1.6em;"><tbody><tr><td style=3D"text-align: center;"></t=
d><td class=3D"content" align=3D"left" width=3D"1456" style=3D"text-align: =
center;"><a class=3D"image-link" target=3D"_blank" href=3D"https://substack=
=2Ecom/redirect/7e589422-c89e-4fd4-906=
2-99277ebaf42b?j=3DeyJ1IjoiNXFxeXF4In0.=
h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"position: re=
lative;flex-direction: column;align-items: center;padding: 0;width: auto;he=
ight: auto;border: none;text-decoration: none;display: block;margin: 0;"><i=
mg class=3D"wide-image" data-attrs=3D"{&quot;src&quot;:&quot;https://substa=
ck-post-media.s3.amazonaws.com/public/images/43b92b0d-c8dc-4930-bb07-033a3e=
4bb555_1594x956.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&=
quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:873,&quot;width&qu=
ot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:3032819,&quot;alt&q=
uot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&qu=
ot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true=
,&quot;internalRedirect&quot;:&quot;https://blog.cosmos-institute.org/i/174=
340269?img=3Dhttps%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2F=
images%2F43b92b0d-c8dc-4930-bb07-033a3e4bb555_1594x956.png&quot;,&quot;isPr=
ocessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt=
=3D"" width=3D"550" height=3D"329.77335164835165" src=3D"https://substackcd=
n.com/image/fetch/$s_!1cH8!,w_1100,c_limit,f_auto,q_auto:good,fl_progressiv=
e:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimage=
s%2F43b92b0d-c8dc-4930-bb07-033a3e4bb555_1594x956.png" style=3D"border: non=
e !important;vertical-align: middle;display: block;-ms-interpolation-mode: =
bicubic;height: auto;margin-bottom: 0;width: auto !important;max-width: 100=
% !important;margin: 0 auto;"></a></td><td style=3D"text-align: center;"></=
td></tr></tbody></table><figcaption class=3D"image-caption" style=3D"box-si=
zing: content-box;color: #777777;font-size: 14px;line-height: 20px;font-wei=
ght: 400;letter-spacing: -.15px;margin-top: 8px;width: 70%;padding-left: 15=
%;padding-right: 15%;text-align: center;">Sergius, the Builder by Nicholas =
Roerich (1925)</figcaption></figure></div><p style=3D"margin: 0 0 20px 0;co=
lor: rgb(54,55,55);line-height: 26px;font-size: 16px;">Much has been writte=
n about how AI can pose risks to society, particularly in aging Western cou=
ntries where a sense of latent anxiety has taken over the discourse on tech=
nology for the past decade. Sometimes this is legitimate, and sometimes it =
feels like a continuation of existing Western pessimism. Few have been able=
 to advance a positive vision of what we should be striving for at a socio-=
political level. Here I&#8217;d like to make an attempt. This essay explore=
s how, by providing cognition-and-agency on demand, AI agents could amplify=
 human agency to the point where we can escape the zero-sum traps that have=
 plagued political economy for centuries.</p><p style=3D"margin: 0 0 20px 0=
;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>There is a =
timeless question at the heart of any (free) society: how do we allow indiv=
iduals to pursue their own interests when one person's actions inevitably a=
ffect the well-being of another in ways that are negative-sum? Economists h=
ave a name for this: &#8220;</span><em>externalities</em><span>,&#8221; whi=
ch can take either physical or financial forms. The sheer scale of this cha=
llenge was crystallized in a groundbreaking 1986 </span><a href=3D"https://=
substack.com/redirect/25d4f996-1d44-406f-a498-0216d52c620d?j=3DeyJ1IjoiNXFx=
eXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"colo=
r: rgb(54,55,55);text-decoration: underline;">paper</a><span> by economists=
 Bruce Greenwald and Joseph Stiglitz. They demonstrated that because our wo=
rld is rife with imperfect information, moral hazards, and incomplete marke=
ts, externalities are not the exception, but the rule. This pervasive marke=
t failure became the intellectual bedrock for modern regulatory regimes. Bu=
t the solution has always been the same: the coercive hand of the state and=
 a top-down micro-management of society. We are told that only a central au=
thority, a government board or commission, can resolve these conflicts by d=
ictating who can do what, and where.</span></p><p style=3D"margin: 0 0 20px=
 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>But as ec=
onomists since Hayek have explained, the planner in Washington (or in your =
state capital) simply cannot possess the dispersed, specific knowledge of t=
ime and place known only to the individuals on the ground. This isn't the k=
ind of theoretical knowledge you find in books, but the contextual, practic=
al, intuitive, experiential and immediate knowledge that emerges from a par=
ticular situation in time. Writing about urban planning, Alain Bertaud argu=
ed that &#8220;</span><em>planners cannot possibly know the reasons househo=
lds may have for selecting a specific housing location</em><span>,&#8221; s=
o mandates often end up becoming blunt and arbitrary. Such information is <=
/span><em>tacit </em><span>and is only revealed through the actions and cho=
ices of individuals within a market. This blindness points to an alternativ=
e: letting people solve these conflicts themselves.</span></p><p style=3D"m=
argin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;">=
This is the essence of the work of Nobel laureate Ronald Coase, who argued =
that if bargaining were cheap and easy, a polluter and their neighbor could=
 strike a private deal without any need for regulation. Of course sometimes=
 some pollution would still happen, but the payoff to the neighbor would en=
sure that both parties are better off than the zero pollution or no-limits =
pollution counterfactuals. The tragedy is not the existence of the conflict=
, but the transaction costs that prevent these mutually beneficial deals fr=
om being discovered and executed. It&#8217;s also the lesson from Elinor Os=
trom, who documented how real-world communities successfully govern shared =
resources like fisheries and forests through their own intricate local rule=
s.</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px=
;font-size: 16px;">Their shared insight is that structures that encourage b=
ottom-up order can work better than attempting to impose top down approxima=
tions for every conflict that requires a resolution. But their work also hi=
ghlighted the formidable barrier that has tends to stand in the way: transa=
ction costs. Transaction costs are not just legal fees; they are the fricti=
on of discovery, the difficulty of negotiation, and the expense of enforcem=
ent. They are the cognitive and logistical effort required to identify affe=
cted parties and strike a deal.</p><p style=3D"margin: 0 0 20px 0;color: rg=
b(54,55,55);line-height: 26px;font-size: 16px;">Historically, because these=
 transaction costs were insurmountable, societies defaulted back to the pla=
nner. The inability to coordinate from the bottom up became the enduring ju=
stification for control from the top down. The result was always the same: =
clumsy, one-size-fits-all rules that stifle innovation, distort incentives,=
 and are inevitably captured by special interests who learn to work the sys=
tem for their own benefit. Today, we are repeating this same failure of ima=
gination in the discussion around AGI. There is a rush to assume that the o=
nly way to manage its risks is through the same top-down control model, tre=
ating AGI as a centralizing technology by its very nature. If AGI is analog=
ous to a weapon of mass destruction, a genie in a bottle - then surely a ce=
ntral authority is the &#8220;optimal&#8221; answer?</p><p style=3D"margin:=
 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>=
I find this frame quite myopic. It fixates on the risks of a powerful new t=
echnology while completely overlooking its potential to strengthen the gove=
rnance mechanisms needed for a safe, coordinated society, which could well =
absolve the need for a centralized solution. As a general purpose technolog=
y, AGI is well placed to help us fix our decaying social and public institu=
tions. Better cognitive capabilities also means better coordination, better=
 governance, and better safeguards. Instead of empowering the central plann=
er, AGI could finally empower the individual bargainers of Coase and Ostrom=
 by arming them with the price system: what Michael Levin and Benjamin Lyon=
 call the &#8220;</span><a href=3D"https://substack.com/redirect/26c5bf51-e=
0b4-4730-8c38-144b9f9aec46?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWk=
lSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);text-decoration=
: underline;">cognitive glue</a><span>&#8221; of a free society.</span></p>=
<h2 class=3D"header-anchor-post" style=3D"position: relative;font-family: '=
SF Pro Display',-apple-system-headline,system-ui,-apple-system,BlinkMacSyst=
emFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif,'Apple Color Emoji','Se=
goe UI Emoji','Segoe UI Symbol';font-weight: bold;-webkit-font-smoothing: a=
ntialiased;-moz-osx-font-smoothing: antialiased;-webkit-appearance: optimiz=
elegibility;-moz-appearance: optimizelegibility;appearance: optimizelegibil=
ity;margin: 1em 0 0.625em 0;color: rgb(54,55,55);line-height: 1.16em;font-s=
ize: 1.625em;"><strong>Obliterating transaction costs</strong></h2><p style=
=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16=
px;">The difficulty of millions of people discovering one another's prefere=
nces, negotiating, and enforcing agreements has always been the chief justi=
fication for government intervention. It&#8217;s why your neighbor&#8217;s =
leaf blower, my unwillingness to fund the local park, and a factory&#8217;s=
 emissions all end up in blunt bans and political fights; we can&#8217;t ch=
eaply find each other, state exact terms, and lock in a deal. The &#8220;tr=
ansaction costs&#8221; are simply too high. But this may no longer be the c=
ase once we have AGI agents.</p><p style=3D"margin: 0 0 20px 0;color: rgb(5=
4,55,55);line-height: 26px;font-size: 16px;">Before we begin, I think it&#8=
217;s important to avoid conceptualizing AGI as some sort of single omnisci=
ent brain-God - even though agents will effectively individually and collec=
tively be &#8216;superintelligent&#8217; and highly capable, and increasing=
ly so over time. That is the central planner&#8217;s fallacy all over again=
=2E While we will continue to see ever-=
larger training runs creating powerful=
 foundation models, I think it's a mistake to assume this results in a sing=
ular AGI that carries all economically valuable tasks; economics ultimately=
 favors efficiency at the point of delivery (inference).</p><p style=3D"mar=
gin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;">Ru=
nning a hyper-general model for every specialized task is incredibly expens=
ive, and so this reality drives specialization: general models will be comp=
ressed, distilled, and optimized for specific uses. The future landscape wi=
ll therefore be a hybrid: a vast ecology of personalized agents, services, =
applications, and robots with varying degrees of generality. While many may=
 descend from a few common foundational ancestors, their deployment will be=
 diverse and specialized. As such, imagining 'AGI' as a singular entity is =
like talking about &#8220;Finance&#8221; as a singular thing.</p><p style=
=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16=
px;"><span>I think it&#8217;s more helpful to imagine these agents through =
a different lens: consider AGI deployed as a vast ecology of personalized a=
gents and systems. This emerging ecosystem is what Toma&#353;ev et al. (202=
5) characterize as the &#8220;</span><a href=3D"https://substack.com/redire=
ct/741f37a6-8464-4b41-b6fa-d35294d4b090?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64c=
TNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);te=
xt-decoration: underline;">virtual agent economy</a><span>,&#8221; a new ec=
onomic layer where agents transact and coordinate at scales and speeds beyo=
nd direct human oversight. While this ecology will contain countless specia=
lized agents, let's focus on the one that matters most from an individual's=
 perspective: your personal advocate. Think of it as a fiduciary extension =
of yourself: a tireless, extremely competent digital representative, closel=
y tied to you, its principal.</span></p><p style=3D"margin: 0 0 20px 0;colo=
r: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>What could such =
an agent do? In principle, it can negotiate, calculate, compare, coordinate=
, verify, monitor, and much more in a split second. Through many multi-turn=
 conversations, tweaking knobs and sliders, and continuous learning, it cou=
ld also develop an increasingly sophisticated (though never perfect) model =
of who you are, your preferences, personal circumstances, values, resources=
, and more. This should evolve over time - an agent&#8217;s alignment shoul=
d follow the principal&#8217;s own evolution. Recent </span><a href=3D"http=
s://substack.com/redirect/e1756d9f-3117-4ee7-8a97-2529c11cec2b?j=3DeyJ1Ijoi=
NXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"=
color: rgb(54,55,55);text-decoration: underline;">research</a><span> on neg=
otiation agents finds that &#8220;human-agent alignment&#8221; is profoundl=
y personal. Users expect agents to not only execute goals but also embody t=
heir identity, requiring alignment on everything from preferred negotiation=
 tactics to personal ethical boundaries and the specific public reputation =
they wanted to project. There are of course important privacy consideration=
s here, but none of these seem fundamentally intractable. For example these=
 systems could be built on technologies like zero-knowledge proofs and diff=
erential privacy, ensuring that preferences are communicated and aggregated=
 without revealing sensitive underlying data.</span></p><p style=3D"margin:=
 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>=
Such an agent should also be able to communicate your preferences to millio=
ns of other agents in real-time, with a nuance and specificity that is curr=
ently impossible. It knows that you&#8217;ll tolerate loud music on a Satur=
day, but not on a Sunday; that you&#8217;d be happy to carpool, but only if=
 it adds less than ten minutes to your commute; that you&#8217;d willingly =
pay a fraction of a cent more for clean electricity, but only during off-pe=
ak hours. All this in a split-second, at the right moment, for the right pu=
rpose. In other words, AGI could enable hyper-granular contracting. The fri=
ction that has always hindered us, the transaction costs that Coase and Ost=
rom identified as the great barrier to cooperation, could be massively redu=
ced. </span><em>So what can we now do in such a world that was otherwise no=
t possible?</em></p><h3 class=3D"header-anchor-post" style=3D"position: rel=
ative;font-family: 'SF Pro Display',-apple-system-headline,system-ui,-apple=
-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif,'Ap=
ple Color Emoji','Segoe UI Emoji','Segoe UI Symbol';font-weight: bold;-webk=
it-font-smoothing: antialiased;-moz-osx-font-smoothing: antialiased;-webkit=
-appearance: optimizelegibility;-moz-appearance: optimizelegibility;appeara=
nce: optimizelegibility;margin: 1em 0 0.625em 0;color: rgb(54,55,55);line-h=
eight: 1.16em;font-size: 1.375em;">Pollution and road-traffic negotiations<=
/h3><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;f=
ont-size: 16px;">Think of the agents as a built-in coordination device: ins=
tead of each actor guessing everyone else&#8217;s move (what economists wou=
ld call a Nash deadlock), they can condition their actions on shared signal=
s and contracts, unlocking deals that were previously out of reach - a corr=
elated equilibrium.</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);=
line-height: 26px;font-size: 16px;"><span>Consider the implications. Your a=
gent knows you have a child with asthma. A blanket &#8220;just ban the emis=
sions&#8221; rule sounds tidy, but it flattens everything into the same pos=
ition: trivial harms and intolerable ones, essential trips and frivolous on=
es. When a delivery truck&#8217;s agent plans its route, it doesn't need a =
government mandate to be considerate. It simply sees a higher &#8220;price&=
#8221; for entry onto your street, a signal broadcast by your agent, repres=
enting your strong preference to avoid diesel fumes. The truck's agent can =
then calculate, instantly, whether it is cheaper to pay the &#8220;clean ai=
r fee&#8221; to you and your neighbors, or to take a different route. Conve=
rsely, if your neighbor&#8217;s agent flags an emergency, for example if sh=
e&#8217;s in labor and needs the fastest route to the hospital, then everyo=
ne&#8217;s agents can auto-drop (or even invert) the price to clear a corri=
dor, because they actually </span><em>value </em><span>her getting through =
fast. It's true that in some cases, enforcement of these contracts might co=
st more than their value; but this could be solved through automated escrow=
s and reputation systems. Ideally the agent system transforms enforcement f=
rom a costly legal battle into a near-instantaneous computational verificat=
ion.</span></p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-hei=
ght: 26px;font-size: 16px;">In this scenario, the externality doesn't vanis=
h, but it does get a price tag. And once a cost is made clear, the marvel o=
f the market can solve it. The problem was never the pollution itself; it w=
as the fact that the polluter was allowed to impose a health and financial =
cost onto you for free. To be clear, not all agent negotiations need to be =
purely financial. The system I&#8217;m envisaging could enable two distinct=
 modes: economic negotiations where willingness-to-pay determines outcomes =
(useful for commercial activities like delivery routes), and as I&#8217;ll =
outline later on in the essay, democratic negotiations where each person ge=
ts equal voting weight regardless of wealth (essential for community values=
 like neighborhood character). Agents can seamlessly switch between these m=
odes depending on the issue at stake - using market mechanisms for efficien=
cy where appropriate, while preserving democratic legitimacy for fundamenta=
l community decisions.</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,5=
5);line-height: 26px;font-size: 16px;"><span>What&#8217;s key though is tha=
t agents make that </span><a href=3D"https://substack.com/redirect/150d42df=
-97cf-4531-b54c-42f826147c8f?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMY=
WklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);text-decorati=
on: underline;">payment</a><span> possible, managing a million micro-transa=
ctions in the background, all based on how your values generalize across co=
untless situations and contexts. When I lived in London, residents of my ne=
ighborhood were unhappy with congestion on roads so decided to essentially =
prohibit cars from going through it at certain times; taxis and local merch=
ants were naturally pretty annoyed. With the agent-bargaining system, these=
 low-traffic-neighbourhood detours stop being absolute: taxis can pay a dyn=
amically discovered &#8220;cut-through&#8221; fee, while verified emergenci=
es glide through at zero (or negative) price.</span></p><h3 class=3D"header=
-anchor-post" style=3D"position: relative;font-family: 'SF Pro Display',-ap=
ple-system-headline,system-ui,-apple-system,BlinkMacSystemFont,'Segoe UI',R=
oboto,Helvetica,Arial,sans-serif,'Apple Color Emoji','Segoe UI Emoji','Sego=
e UI Symbol';font-weight: bold;-webkit-font-smoothing: antialiased;-moz-osx=
-font-smoothing: antialiased;-webkit-appearance: optimizelegibility;-moz-ap=
pearance: optimizelegibility;appearance: optimizelegibility;margin: 1em 0 0=
=2E625em 0;color: rgb(54,55,55);line-he=
ight: 1.16em;font-size: 1.375em;">Neig=
hborhood character negotiations</h3><p style=3D"margin: 0 0 20px 0;color: r=
gb(54,55,55);line-height: 26px;font-size: 16px;"><span>This mechanism clari=
fies plenty of other thorny disagreements too. Imagine a developer wants to=
 build an ugly building in a residential neighborhood. Today, that is a pol=
itical battle of influence: who can </span><a href=3D"https://substack.com/=
redirect/85d54108-7578-45d7-a4e0-0909f0ee2b5b?j=3DeyJ1IjoiNXFxeXF4In0.h9dED=
bGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,=
55);text-decoration: underline;">capture</a><span> the local planning autho=
rity most effectively? In an agent-based world, it becomes a simple matter =
of economics. The developer&#8217;s agent must discover the price at which =
every single homeowner would agree. If the residents truly value the charac=
ter of their neighborhood, that price may be very high. The project will on=
ly proceed if the developer values the location more than the residents val=
ue the status quo. Conversely, if the residents&#8217; asking price is lowe=
r than the developer's willingness to pay, the project proceeds, and the re=
sidents are compensated. In either case, the true economic costs and benefi=
ts are accounted for. This mechanism forces the discovery of the most valua=
ble use of the resource, moving beyond the current system where projects ar=
e either blocked entirely (socializing the loss of potential gains) or forc=
ed through politically (socializing the costs on the neighborhood).</span><=
/p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;fo=
nt-size: 16px;"><span>But what if a resident decides to game the system and=
 go for a really absurd price, holding everyone ransom? This is why you nee=
d a new secondary layer of institutions </span><em>on top</em><span> of the=
se agents. Crucially, these institutions can be voluntary. In this neighbor=
hood, homeowners can pool their agents into a simple bargaining club: each =
person privately inputs the minimum they&#8217;d accept; the software aggre=
gates that into a single take&#8209;it&#8209;or&#8209;leave&#8209;it offer.=
 This is essentially </span><a href=3D"https://substack.com/redirect/c64517=
f8-d549-4f21-a956-a670418f0753?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwb=
MYWklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);text-decora=
tion: underline;">mechanism design</a><span> in action: creating rules wher=
e being honest about your true minimum is the smartest move, not gaming the=
 system. Overstating just risks killing the deal (you get zero), and if it =
clears, the payout is at the common clearing price - so padding your number=
 doesn&#8217;t boost your check. The group speaks with one voice without su=
rrendering property rights, and the developer sees a single, fair number in=
stead of a hundred ransom demands.</span></p><p style=3D"margin: 0 0 20px 0=
;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>Skeptics mi=
ght reasonably worry that NIMBYs can still name absurd buy-off prices. This=
 is a classic political economy dilemma. The benefits of blocking a project=
 are concentrated among a few motivated homeowners, while costs such as hig=
her rents, longer commutes, and slower growth are diffused across a wide, u=
norganized public. As Janan Ganesh </span><a href=3D"https://substack.com/r=
edirect/c73fe7c4-aabd-4bb6-9b72-bc52e2264c51?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDb=
Gj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,5=
5);text-decoration: underline;">puts</a><span> it, the potential losers are=
 an &#8220;unconscious blob of people&#8221; who don't even know what they'=
re losing. Two guardrails fix this.</span></p><p style=3D"margin: 0 0 20px =
0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>First, chr=
onic hyper-bidders see their voting weight fade or must pay an &#8220;optio=
n fee&#8221;: a </span><a href=3D"https://substack.com/redirect/35dbba4d-80=
3c-4ba8-9ada-372dcd06e732?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWkl=
Su8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);text-decoration:=
 underline;">Harberger-style tax</a><span> in which you periodically pay a =
percentage of the price you claim; overstate, and it soon hurts. For exampl=
e, if you claim your property is worth $10 million to block a development, =
you must be prepared to pay taxes on that valuation too! Second, and more i=
mportantly, AGI agents can give that &#8220;unconscious blob&#8221; a power=
ful voice. Any coalition that vetoes must then reimburse that quantified lo=
ss, with agents handling the transfers automatically. The diffuse cost beco=
mes a concentrated, explicit price. Stonewalling remains possible, but it n=
ow carries a real, rising cost. Moreover, with this setup, bargaining isn't=
 just between NIMBYs and developers; other residents, now aware of the pote=
ntial gains, can bargain directly with the holdouts.</span></p><h3 class=3D=
"header-anchor-post" style=3D"position: relative;font-family: 'SF Pro Displ=
ay',-apple-system-headline,system-ui,-apple-system,BlinkMacSystemFont,'Sego=
e UI',Roboto,Helvetica,Arial,sans-serif,'Apple Color Emoji','Segoe UI Emoji=
','Segoe UI Symbol';font-weight: bold;-webkit-font-smoothing: antialiased;-=
moz-osx-font-smoothing: antialiased;-webkit-appearance: optimizelegibility;=
-moz-appearance: optimizelegibility;appearance: optimizelegibility;margin: =
1em 0 0.625em 0;color: rgb(54,55,55);line-height: 1.16em;font-size: 1.375em=
;">Sugar and healthcare externalities</h3><p style=3D"margin: 0 0 20px 0;co=
lor: rgb(54,55,55);line-height: 26px;font-size: 16px;">Consider another exa=
mple: sugar/junk food consumption and public health. Proponents of a sugar =
tax correctly identify an externality: poor diet choices impose costs on th=
e shared healthcare system. Their solution, however, is (shock!) a clumsy, =
top-down tax. This harms food producers, is regressive (as it affects the p=
oor more than the rich), and ultimately imposes a cost on many people who w=
ould not in fact be &#8220;guilty&#8221; of imposing costs on the healthcar=
e system. An agent-based market addresses the same problem with bottom-up p=
recision.</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-heigh=
t: 26px;font-size: 16px;">Instead of lobbying the government, your health i=
nsurer's agent communicates with your advocate agent. It looks at your eati=
ng habits, calculates the projected future cost of your diet and makes a si=
mple offer: a significant, immediate discount on your monthly premium if yo=
u empower your agent to disincentivize high-sugar purchases. At that very m=
oment of decision, the market responds. Acting like a hyper-alert Kirzneria=
n entrepreneur spotting a profit opportunity, a soft drink company's agent,=
 to retain your business, might instantly propose a deep discount on a heal=
thier drink.</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-he=
ight: 26px;font-size: 16px;"><span>Now consider smoking bans in public plac=
es. A simple free-market approach would let every restaurant or bar owner d=
ecide their own policy. But non-smokers value having a broad range of optio=
ns for a night out; if smoking becomes the default, their social world narr=
ows significantly. This loss of choice is a cost that a full-on ban tries t=
o crudely handle. AI agent negotiation, however, allows for a more precise,=
 Millian solution. Instead of banning the externality, we can finally price=
 it in through voluntary, real-time negotiation. Once again, we&#8217;re no=
t banning the externality, but </span><em>pricing </em><span>it in. This pr=
ice wasn't imposed by a committee of very smart policymakers sitting in a g=
rey room in Westminster, but discovered through voluntary, real-time negoti=
ation. The choice remains with the individual, but it is now a truly inform=
ed choice, where the full costs and alternatives are transparent.</span></p=
><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font=
-size: 16px;">Another example can be seen in the rules we have on airplanes=
 and the air we share with fellow passengers in this private space. Even wh=
en we don&#8217;t use government rules, airlines generally have to come up =
with a generic rule that works okay for who it expects to be on a typical a=
irplane ride. During the COVID pandemic, even many people who wanted mask m=
andates for airplanes did not wear masks on flights themselves, as they con=
sidered the value of wearing a mask while others would not to be minimal.</=
p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;fon=
t-size: 16px;">Similarly, airlines generally do not make accommodations for=
 people sensitive to airborne allergens. Virgin Airlines can't tell if your=
 peanut allergy is life-threatening or just a mild inconvenience. To avoid =
opening the floodgates to thousands of hard-to-verify requests (&#8220;I'm =
allergic to perfume,&#8221; &#8220;I'm sensitive to blue lights,&#8221;) th=
ey just make a simple, inflexible rule, like &#8220;we will serve nuts.&#82=
21; Much of this is, of course, due to an aversion to what seems like inevi=
table lobbying for accommodations that would come from conceding the princi=
ple. However, if flight policies are negotiated over by AI agents, we don&#=
8217;t have to choose between all or nothing on masking. We don&#8217;t hav=
e to rule out accommodations to people with allergen sensitivities for fear=
 of frivolous requests; instead we move from all-or-nothing mandates to nua=
nced, negotiated outcomes, where the intensity of a person's need is accura=
tely represented and compensated.</p><p style=3D"margin: 0 0 20px 0;color: =
rgb(54,55,55);line-height: 26px;font-size: 16px;"><strong>This agent-negoti=
ated world delivers three principles essential to a free and effective soci=
ety.</strong></p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-h=
eight: 26px;font-size: 16px;"><span>First, </span><em>accountability</em><s=
pan>. A billionaire who wants to close a public beach for a private party f=
aces a new constraint: his agent must make a public, auditable offer to eve=
ry single person who would be deprived of access. The cost of his desire be=
comes explicit and traceable. Of course, he might still try the old route o=
f bribing a bureaucrat in secret - but this parallel transparent market cre=
ates pressure and comparison points. When combined with AGI-enhanced govern=
ance (automated auditing, pattern detection for corruption etc.), the corru=
pt path becomes even more risky and costly.</span></p><p style=3D"margin: 0=
 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>Se=
cond, </span><em>the power of voluntary coalitions</em><span>. Today, diffu=
se interests are often ignored because the transaction costs of organizing =
are too high. A single person in a low-income neighborhood has little barga=
ining power. A multinational polluter is more likely to get away with build=
ing a monstrosity in a Brazilian favela than in the Hamptons, even if the t=
rue social cost is higher. But what if the agents of 10,000 residents, seei=
ng a factory&#8217;s proposal to increase emissions, can form a bargaining =
coalition in a nanosecond? They can spontaneously band together and declare=
, &#8220;</span><em>Our collective price to accept this pollution is X mill=
ion dollars, non-negotiable.</em><span>&#8221; They solve the collective ac=
tion problem instantly, creating what is effectively a powerful digital uni=
on to counterbalance concentrated wealth.</span></p><p style=3D"margin: 0 0=
 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>Thir=
d, </span><em>continuous self-calibration</em><span>. Because every agent s=
treams its user&#8217;s context-rich preferences into live markets, the rul=
es themselves flex in real time. Noise caps, curb uses, even peak-hour elec=
tricity rates slide automatically as new bids and conditions roll in, rathe=
r than waiting for a city-council vote five years from now. Tacit desires, =
like how much quiet you need for a newborn&#8217;s nap or what premium you&=
#8217;d pay for a car-free street, become explicit, machine-readable prices=
=2E The system therefore functions as a=
 permanent feedback loop. It detects m=
ismatches between policy and lived reality, reprices the externality within=
 seconds, and nudges behavior accordingly. Governance shifts from statute t=
o thermostat: sometimes through formal institutions built on top of these a=
gents, such as professional guilds, and sometimes through instantaneous ad-=
hoc &#8220;</span><em>flash coalitions</em><span>&#8221; - emergent order.<=
/span></p><h2 class=3D"header-anchor-post" style=3D"position: relative;font=
-family: 'SF Pro Display',-apple-system-headline,system-ui,-apple-system,Bl=
inkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif,'Apple Color =
Emoji','Segoe UI Emoji','Segoe UI Symbol';font-weight: bold;-webkit-font-sm=
oothing: antialiased;-moz-osx-font-smoothing: antialiased;-webkit-appearanc=
e: optimizelegibility;-moz-appearance: optimizelegibility;appearance: optim=
izelegibility;margin: 1em 0 0.625em 0;color: rgb(54,55,55);line-height: 1.1=
6em;font-size: 1.625em;"><strong>So what&#8217;s the catch?</strong></h2><p=
 style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-si=
ze: 16px;"><span>The Coasean vision amplified by AGI agents is powerful, bu=
t it's not a panacea. Ronald Coase himself was no naive utopian, emphasizin=
g that his theorem was a theoretical benchmark for a world without transact=
ion costs, not a description of reality. In practice, the theorem has faced=
 decades of rigorous critique from economists, legal scholars, and behavior=
al scientists, who argue that its assumptions crumble under real-world fric=
tions. These limitations explain why Coasean bargaining rarely materializes=
 </span><em>today</em><span>, leading societies to default to clumsy govern=
ment interventions or inaction.</span></p><p style=3D"margin: 0 0 20px 0;co=
lor: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>My response to=
 this is twofold. First, the Coase theorem and its instantiation forces us =
to</span><em> identify and analyze</em><span> frictions, like imperfect inf=
ormation, legal costs, or strategic holdouts, that prevent efficient privat=
e solutions. This is not to say it solves everything, but it&#8217;s a powe=
rful toolkit to prompt us to look for creative, private, and market-based s=
olutions to problems where we might have only considered government regulat=
ion or violence before. Second, many of these critiques ignore what governa=
nce technologies and institutional arrangements AGI can enable in the first=
 place - and I think there are good reasons to think that this technology c=
an help us bypass limitations that would otherwise block progress on cooper=
ation.</span></p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-h=
eight: 26px;font-size: 16px;"><span>It&#8217;s true that even with perfect =
agent coordination, there remains what Acemoglu calls the '</span><a href=
=3D"https://substack.com/redirect/5d050456-a7f5-4796-bd93-035592f9a7e3?j=3D=
eyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" s=
tyle=3D"color: rgb(54,55,55);text-decoration: underline;">political Coase t=
heorem</a><span>' problem: those with political power cannot credibly commi=
t to not exploiting that power tomorrow, since no external enforcer exists =
for contracts with the sovereign itself. </span><em>The sovereign can alway=
s renege</em><span>. This is a tricky challenge, but the agent system offer=
s countermeasures. First, the transparency created by agent negotiations ra=
ises the political cost of expropriation or bribery: it&#8217;s harder to s=
teal what is clearly priced and publicly recorded. Second, AGI must be depl=
oyed not just for market transactions, but to enhance institutional account=
ability. In other words, we should automate many aspects of how we govern: =
automated auditing, real-time monitoring of regulatory capture, automated d=
ispute resolution, automated public spending monitoring, and agent-based an=
ti-corruption measures can harden the governance mechanisms that constrain =
the arbitrary use of power. Institutions matter!</span></p><p style=3D"marg=
in: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><sp=
an>Just as agents can aggregate citizen preferences for market negotiations=
, they can also transform how the &#8220;machinery of government&#8221; its=
elf operates. The information asymmetries and coordination failures that Ja=
mes C. Scott describes in </span><em>Seeing Like a State</em><span>, where =
central authorities operate with crude categories that miss local knowledge=
, can finally be resolved as well. On the &#8220;executive&#8221; side, age=
nt networks can provide governments with high-resolution, real-time feedbac=
k about policy impacts, citizen preferences, and emerging problems. On the =
&#8220;civil&#8221; side, the automation of key protections against executi=
ve corruption, overreach, and misalignment protects people against the eros=
ion of liberal democracy.</span></p><p style=3D"margin: 0 0 20px 0;color: r=
gb(54,55,55);line-height: 26px;font-size: 16px;">Here, I'll explore some of=
 the most salient critiques, preempt common objections to applying them in =
an AGI-agent context, and propose some countermeasures. The goal isn't to d=
ismiss the critiques but to show how agents can substantially mitigate them=
=2E This strengthens the case for a hyb=
rid system: agents handling the micro-=
coordination, with carefully designed institutions (including the state) ad=
dressing the rest.</p><h3 class=3D"header-anchor-post" style=3D"position: r=
elative;font-family: 'SF Pro Display',-apple-system-headline,system-ui,-app=
le-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif,'=
Apple Color Emoji','Segoe UI Emoji','Segoe UI Symbol';font-weight: bold;-we=
bkit-font-smoothing: antialiased;-moz-osx-font-smoothing: antialiased;-webk=
it-appearance: optimizelegibility;-moz-appearance: optimizelegibility;appea=
rance: optimizelegibility;margin: 1em 0 0.625em 0;color: rgb(54,55,55);line=
-height: 1.16em;font-size: 1.375em;">Zero transaction costs, really? And wh=
at about inequality?</h3><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55=
);line-height: 26px;font-size: 16px;">Skeptics might say agents don't elimi=
nate costs entirely; they just shift them to compute overhead, data privacy=
 setups, agent configurations etc. This is true! But it also underestimates=
 the scale of reduction. Agents aren't burdened by human limitations like f=
atigue, bias in communication, logistical hurdles, social awkwardness, irra=
tional decision making and so on. What costs $10,000 in legal fees today mi=
ght cost pennies to compute tomorrow. Consider how a billionaire&#8217;s ph=
one today is no more powerful or effective than yours.</p><p style=3D"margi=
n: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><spa=
n>Even then though, you might reasonably think that this still creates ineq=
uity</span><em> in the short run</em><span>. To prevent cost barriers for l=
ow-income users, governments or philanthropies could provide baseline agent=
 services (similar to public defenders) and guarantees. This is a low cost =
to pay for the efficiencies gained by a system that otherwise promises to s=
ave society orders of magnitude more by slashing legal overhead, unlocking =
stalled projects, and turning countless externalities into win-win trades. =
In other words, underwriting entry-level agents for the poorest citizens is=
 like funding public roads: a modest civic outlay that makes the whole mark=
et run faster, fairer, and vastly more productively.</span></p><p style=3D"=
margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"=
>In some contexts, for low-income users, governments or philanthropies coul=
d provide baseline agent services (similar to public defenders) - or more l=
ikely, the necessary compute to level things up and ensure equitable partic=
ipation. This is unlikely to be a huge growing cost over time, as agent tec=
h commoditizes, these costs approach zero asymptotically. The model here co=
uld mirror school voucher systems like Sweden's, where the government provi=
des credits that ensure universal access to essential services while allowi=
ng choice and competition. Just as educational vouchers guarantee every chi=
ld can attend school regardless of family income, &#8220;agent vouchers&#82=
21; or compute credits could ensure everyone can participate in democratic =
deliberation, access legal representation, or navigate essential government=
 services. The key is targeting subsidies where they matter most for civic =
participation and fundamental rights - you'd want generous credits for demo=
cratic decision-making, healthcare choices, or educational planning, but no=
t for negotiating garage parking disputes or lawn ornament preferences.</p>=
<p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-=
size: 16px;">Alternatively, or complementarily, the system could employ dir=
ect redistribution in highly sensitive areas - providing everyone with a ba=
se allocation of compute credits or &#8220;agent wealth&#8221; to spend as =
they see fit. This approach avoids the paternalism of defining the above &#=
8220;essential services&#8221; centrally, which would recreate the very soc=
ial planner problem we&#8217;re trying to avoid. Individuals could allocate=
 their resources according to their own priorities rather than predetermine=
d categories. A hybrid might work best: a universal basic compute allocatio=
n for personal use, plus additional targeted support for specific democrati=
c and legal functions where equal participation is constitutionally guarant=
eed.</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26=
px;font-size: 16px;">This tiered approach ensures equity where it counts wi=
thout creating an unsustainable fiscal burden, while still allowing market =
dynamics to operate in less critical domains. In practice however, this doe=
s mean a lot of infrastructure will be required. For example, built-in prot=
ocols for multi-party discovery. For high-volume scenarios, hierarchical ag=
ents could aggregate at neighborhood or city levels. But much of this will =
need to be designed as part of a wider push for improving institutional dec=
ision making.</p><h3 class=3D"header-anchor-post" style=3D"position: relati=
ve;font-family: 'SF Pro Display',-apple-system-headline,system-ui,-apple-sy=
stem,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif,'Apple=
 Color Emoji','Segoe UI Emoji','Segoe UI Symbol';font-weight: bold;-webkit-=
font-smoothing: antialiased;-moz-osx-font-smoothing: antialiased;-webkit-ap=
pearance: optimizelegibility;-moz-appearance: optimizelegibility;appearance=
: optimizelegibility;margin: 1em 0 0.625em 0;color: rgb(54,55,55);line-heig=
ht: 1.16em;font-size: 1.375em;">Which rights are the &#8216;default&#8217;?=
</h3><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;=
font-size: 16px;"><span>Another important consideration here is that you st=
ill need an agreed &#8220;default position&#8221; - do people have a right =
to make noise, or a right to quiet? What's the basic right that is being ne=
gotiated - the right to pollute, or the right to be free from pollution? Th=
e machinery runs either way. What does change is who ends up richer, which =
is why the initial allocation of these rights is a constitutional choice, n=
ot a technicality. Even if bargaining is cheap, outcomes aren't invariant t=
o initial property rights because wealth influences willingness to pay. A p=
oor farmer might sell pollution rights cheaply to a rich factory not becaus=
e it's efficient, but because they need cash now. Conversely, changing who =
starts with the rights changes the wealth distribution, which affects what =
people can afford to bid and therefore changes which 'efficient' outcome th=
e market settles on. Beyond wealth effects, behavioral factors like the </s=
pan><a href=3D"https://substack.com/redirect/f3d239bd-c22a-42d9-9c42-61ab87=
03dfd9?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA"=
 rel=3D"" style=3D"color: rgb(54,55,55);text-decoration: underline;">endowm=
ent effect</a><span> - people demanding far more to give up a right than th=
ey'd pay to acquire it - make initial allocations stick even with perfect b=
argaining. Agents might correct for such biases, though whether we want the=
m to 'debias' negotiations or faithfully represent our psychological quirks=
 remains an open design question.</span></p><p style=3D"margin: 0 0 20px 0;=
color: rgb(54,55,55);line-height: 26px;font-size: 16px;">So how do we ensur=
e fairness without reverting to top-down control? What is the &#8220;defaul=
t position&#8221; to start with? Well, that baseline of who starts with whi=
ch entitlement is a normative, collective choice. Agents don&#8217;t magic =
it away; they only make it explicit, contestable, and cheap to renegotiate.=
 My view here is that we already have many of these rights set up by centur=
ies of jurisprudence, and this is the right starting point. To the extent t=
hat these need to change or adapt, our democratic political systems are the=
 right mechanism to do so. The bad news is that these systems are now prett=
y ossified, slow, captured, and dysfunctional. The good news is that agents=
 can improve them materially.</p><p style=3D"margin: 0 0 20px 0;color: rgb(=
54,55,55);line-height: 26px;font-size: 16px;"><span>Beyond periodic voting =
on baseline entitlements, agents could fundamentally transform how citizens=
 deliberate and exercise their democratic rights. Recent papers show that A=
I systems can effectively learn and represent human preferences with remark=
able efficiency. Studies like </span><a href=3D"https://substack.com/redire=
ct/7682a5f1-c234-4016-97ae-bbe5807274a2?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64c=
TNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);te=
xt-decoration: underline;">ConstitutionMaker</a><span> show how natural lan=
guage principles can be extracted from preference data, while </span><a hre=
f=3D"https://substack.com/redirect/d4072a87-9a3a-4be3-887a-716bc1df8517?j=
=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"=
" style=3D"color: rgb(54,55,55);text-decoration: underline;">Inverse Consti=
tutional AI</a><span> proves that just a handful of preferences can be comp=
ressed into interpretable principles that accurately reconstruct individual=
 and group values. This suggests agents could continuously learn citizens' =
nuanced policy preferences through ongoing interactions, creating rich, pri=
vacy-protected preference profiles.</span></p><p style=3D"margin: 0 0 20px =
0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;">Currently, we de=
legate representation to biological agents - mayors, councilors, representa=
tives - who operate within opaque, underfunded institutions plagued by acco=
untability problems, information asymmetries, and the impossibility of trul=
y representing thousands of diverse constituents. With agent infrastructure=
, we could significantly improve these systems. Imagine every citizen havin=
g a personal agent that deeply understands their values, can engage in soph=
isticated policy deliberation on their behalf, and coordinate with millions=
 of other agents to find optimal compromises in real-time.</p><p style=3D"m=
argin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;">=
<span>These agents wouldn't just vote every few years but could participate=
 in continuous liquid democracy, dynamically delegating expertise to truste=
d entities for specific domains, instantly aggregating or </span><a href=3D=
"https://substack.com/redirect/c9821b95-6350-43df-a53e-5848b60bd485?j=3DeyJ=
1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" styl=
e=3D"color: rgb(54,55,55);text-decoration: underline;">constructing</a><spa=
n> preferences on emerging issues, and ensuring that policy truly reflects =
the evolving will of the people rather than the frozen snapshot captured at=
 the last election. Of course, this risks enabling digital NIMBYism at unpr=
ecedented scale, and we certainly don't want everyone's agents micromanagin=
g nuclear safety protocols or monetary policy - but these are mechanism des=
ign and governance challenges, not fundamental obstacles.</span></p><p styl=
e=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 1=
6px;">Today, citizens already don't vote on every financial regulation or t=
echnical standard; agent-mediated democracy needn't change that. To the ext=
ent that enhanced coordination could enable minorities to hold majorities h=
ostage, we'll need clever mechanisms to prevent such digital paralysis. The=
re's plenty of work ahead for policymakers, economists, evaluation designer=
s, sociologists, and game theorists to get these institutional designs righ=
t!</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px=
;font-size: 16px;">Lastly, the system would also need to balance dynamism w=
ith stability. Markets require predictable rules; indeed, the constant rene=
gotiation of property rights would destroy investment incentives. But just =
as options markets price volatility, an agent-mediated system could explici=
tly price the value of stability versus flexibility, letting some rules oss=
ify by mutual agreement (basic property rights, contract enforcement) while=
 others remain perpetually negotiable (noise ordinances, parking rules). Th=
e agents themselves would likely converge on stable equilibria for most iss=
ues simply to reduce computational overhead - constant renegotiation is exp=
ensive even for AGI.</p><h3 class=3D"header-anchor-post" style=3D"position:=
 relative;font-family: 'SF Pro Display',-apple-system-headline,system-ui,-a=
pple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif=
,'Apple Color Emoji','Segoe UI Emoji','Segoe UI Symbol';font-weight: bold;-=
webkit-font-smoothing: antialiased;-moz-osx-font-smoothing: antialiased;-we=
bkit-appearance: optimizelegibility;-moz-appearance: optimizelegibility;app=
earance: optimizelegibility;margin: 1em 0 0.625em 0;color: rgb(54,55,55);li=
ne-height: 1.16em;font-size: 1.375em;">But what about catastrophic risks?</=
h3><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: 26px;fo=
nt-size: 16px;">A lot of people working in AI governance are interested in =
catastrophic risks where a few actors can impose great harm on others at sc=
ale; many will rightly say &#8220;this all sounds great but doesn&#8217;t a=
ddress CBRN risks.&#8221; They&#8217;re not wrong.</p><p style=3D"margin: 0=
 0 20px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;">A malici=
ous actor intending to release a pathogen is not a market participant to be=
 bargained with, and admittedly, the agent system can do little to stop the=
m. Instead this is the state's first and most important job: to enforce law=
 and order and protect citizens from violence, whether from a foreign army =
or a domestic bioterrorist. The Coasean multi-agent framework relies on thi=
s protection to even exist in the first place: the state needs to enforce c=
ontracts. If the delivery truck&#8217;s agent agrees to the &#8220;clean ai=
r fee&#8221; but the company refuses to pay, there must be a court system: =
a neutral arbiter with the power to enforce the agreement. This is a non-ne=
gotiable role for the state.</p><p style=3D"margin: 0 0 20px 0;color: rgb(5=
4,55,55);line-height: 26px;font-size: 16px;">In AI governance discussions, =
the aversion to the totalising, centralising proposals espoused by some com=
munities has been met with the inverse prescription: various flavours of fr=
ee for all e/acc libertarianism or anarchy. This falls into the opposite tr=
ap, and wrongly assumes you can do away with the state entirely. The Coasea=
n framework does not eliminate the state, but it transitions its role from =
&#8220;central planner&#8221; to &#8220;framework guarantor&#8217;&#8221; f=
ocusing its power on what it alone can do. It allows the market, supercharg=
ed by agents, to handle the complex work of coordinating preferences and pr=
icing externalities, a job the state has always done poorly. This should in=
 principle appeal to conservatives wary of big government and liberals wary=
 of power abuses. But it doesn&#8217;t do away with the state, and nor shou=
ld it - it just makes it a lot leaner. The (gradually automated) state cont=
inues to define and enforce basic property rights, contract law, criminal j=
ustice, and constitutional rights - the bedrock rules without which agent n=
egotiations would be meaningless.</p><p style=3D"margin: 0 0 20px 0;color: =
rgb(54,55,55);line-height: 26px;font-size: 16px;">So the Coasean multi-agen=
t system, for all its genius, has a critical limit: it is designed to price=
 trade-offs. It can put a price on diesel fumes, noise, or a blocked view. =
It cannot, however, price the non-negotiable. What happens if technology un=
locks a true &#8220;recipe for ruin&#8221;? A discovery, like &#8220;easy n=
ukes&#8221; or a simple method for creating a devastating pathogen, that al=
lows a single actor to threaten civilization itself? This is not an externa=
lity to be bargained over!</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,=
55,55);line-height: 26px;font-size: 16px;">Such a risk is a form of ultimat=
e coercion, and its prevention falls squarely within the most fundamental d=
uty of the state: protecting its citizens from violence. Therefore, the sta=
te&#8217;s role is not just to enforce the contracts that agents make, but =
to define the absolute boundaries of what they are permitted to do in the f=
irst place like prohibiting actions that create catastrophic, un-priceable =
risks like man-made pandemics. While the Coasean framework itself does not =
price these existential risks, the underlying cognitive infrastructure it c=
reates is part of what a modern state may need to manage them: enabling the=
 high-speed coordination and automated governance required to make the stat=
e a more effective protector.</p><h2 class=3D"header-anchor-post" style=3D"=
position: relative;font-family: 'SF Pro Display',-apple-system-headline,sys=
tem-ui,-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,s=
ans-serif,'Apple Color Emoji','Segoe UI Emoji','Segoe UI Symbol';font-weigh=
t: bold;-webkit-font-smoothing: antialiased;-moz-osx-font-smoothing: antial=
iased;-webkit-appearance: optimizelegibility;-moz-appearance: optimizelegib=
ility;appearance: optimizelegibility;margin: 1em 0 0.625em 0;color: rgb(54,=
55,55);line-height: 1.16em;font-size: 1.625em;"><strong>Matryoshkan Alignme=
nt </strong></h2><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-h=
eight: 26px;font-size: 16px;"><span>So what does this mean for how we think=
 about the normative/sociopolitical question of who agents should be aligne=
d to? To whom, or what, is an agent ultimately loyal? Is it fully and solel=
y aligned to the user, like a computer that will execute any command? Or is=
 it aligned to an amorphous set of collective values decided by some citize=
n jury? Or a global institution that sets top down directives? Or is it all=
 up to the model developer? As with humans, I think the answer is not a sin=
gle master. The meta-framework is a series of nested layers of governance, =
like a set of Matryoshka dolls. This nested structure mirrors what Levin </=
span><a href=3D"https://substack.com/redirect/d9b00f1a-a3b0-4428-96f5-ed58e=
a355a0c?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA=
" rel=3D"" style=3D"color: rgb(54,55,55);text-decoration: underline;">calls=
</a><span> &#8220;scale-free cognition&#8221; where each level of organizat=
ion (from cells to tissues to organisms) maintains its own goals and decisi=
on-making capacity, with larger-scale goals emerging from but not replacing=
 smaller-scale ones. There are many possible layers, but for the sake of si=
mplicity I&#8217;ll outline three here.</span></p><p style=3D"margin: 0 0 2=
0px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;"><span>The ou=
termost, and largest, doll is the law. This is the non-negotiable boundary =
enforced by the state. An agent, no matter how personalized, cannot be a to=
ol for committing crimes. Your agent cannot help you orchestrate fraud, DDO=
S a hospital, hire a hitman, or procure materials for a bioweapon, any more=
 than your word processor can grant you immunity for writing a fraudulent c=
heque. To a large extent, existing laws basically criminalize all of the ab=
ove; although some ways need some updating to account for agentic capabilit=
ies, difficulty in establishing a </span><em>mens rea</em><span>, delineati=
on of responsibilities across the &#8220;value chain,&#8221; and so on. The=
re&#8217;s plenty of interesting work going on in legal circles trying to w=
ork this out, and legitimate arguments for why certain gaps may need to be =
filled.</span></p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-=
height: 26px;font-size: 16px;">Within that legal boundary operates the seco=
nd layer: the free market of different services, deployers, products, and p=
roviders. A company offering an agent service is not the government; it is =
a voluntary association with its own rules. One social media site can culti=
vate a different environment from another, and users are free to choose. If=
 a provider&#8217;s agent refuses to engage with topics the company deems h=
armful to its brand or community, that is their right. A user who finds the=
se policies too restrictive is not a captive; they are a customer who can, =
and will, take their business to a competitor offering more customization o=
r utility. This competition will be the primary force pushing agents to bec=
ome powerful and loyal advocates for their users. Today, we arguably have a=
n increasing number of developers of general purpose models of all kinds, c=
osts are going down, and importantly many more actors are able to customize=
, fine-tune, and modify models deployed through cloud infrastructure. Unlik=
e social media, network effects are also far weaker. And in a competitive m=
arket with switching costs approaching zero, parasitic agents get quickly i=
dentified and abandoned.</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55=
,55);line-height: 26px;font-size: 16px;"><span>Finally, at the core, is the=
 individual. Within the bounds of law and the terms of service you voluntar=
ily accept, the agent&#8217;s purpose is to be your tireless, personal advo=
cate. This is where the power of user-level customization and alignment is =
unleashed, where a private &#8220;cognitive DNA&#8221; can be grown. The us=
er should have immense freedom to tune their agent to their unique preferen=
ces and values. They should also have complete privacy and control over the=
ir &#8220;cognitive profile&#8221; developed by the agent, for obvious reas=
ons. Practically speaking though, this is the hardest part: how do you desi=
gn and evidence an agent (mostly) aligned to a user? How do you evaluate th=
is and the continuous learning? We don't need the perfect answer to these q=
uestions - </span><em>alignment is not something to be &#8220;solved.&#8221=
;</em></p><p style=3D"margin: 0 0 20px 0;color: rgb(54,55,55);line-height: =
26px;font-size: 16px;"><span>There are of course important </span><a href=
=3D"https://substack.com/redirect/d935d176-a727-4db5-9eb7-662a96f81fea?j=3D=
eyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6eWS-HyoA" rel=3D"" s=
tyle=3D"color: rgb(54,55,55);text-decoration: underline;">technical</a><spa=
n> questions that are not fully addressed - the right norms, the right leve=
l of agreeableness, the right level of deference and corrigibility, fully a=
ddressing reward hacking, ensuring agents aren&#8217;t deceptive, the right=
 evaluations to test for user alignment, and more. Few of these have a sing=
le right answer however, and markets are </span><em>generally </em><span>fa=
irly well incentivised to solve them - no company or person wants a reward =
hacking agent. My intention here is not to dismiss them away - rather, I th=
ink the way the &#8220;alignment problem&#8221; is often conceptualized is =
out of date and comparable to asking &#8220;</span><em>how do we ensure wha=
t is written always leads to truth? How do we solve the &#8216;truth proble=
m&#8217;?</em><span>&#8221; after the invention of writing or typewriters. =
There </span><em>isn&#8217;t </em><span>and </span><em>cannot </em><span>be=
 any guarantee. In fact, the starting point should be reversed; as Dan Will=
iams </span><a href=3D"https://substack.com/redirect/5cfd0858-2cd9-428b-bc4=
2-41385af1e690?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGoFv6e=
WS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);text-decoration: underline;=
">notes</a><span>, the real question should be &#8220;</span><em>why do we =
even have truth at all?</em><span>&#8221; This is a question of institution=
s and governance, and not one solved by software engineering. It&#8217;s an=
 unsatisfactory answer only for those seeking centralized guarantees. </spa=
n><em>You mean we&#8217;re going to have to muddle through things? </em><sp=
an>Yes. As put by </span><a href=3D"https://substack.com/redirect/da7f7eaa-=
a091-4f41-b6f6-4632f84c18b8?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYW=
klSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);text-decoratio=
n: underline;">Leibo et al</a><span>, we should model societal and technolo=
gical progress as sewing an &#8220;ever-growing, ever-changing, patchy, and=
 polychrome quilt.&#8221;</span></p><p style=3D"margin: 0 0 20px 0;color: r=
gb(54,55,55);line-height: 26px;font-size: 16px;">What we need to ensure is =
that agents that genuinely serve their users' interests will outcompete tho=
se that don't and build the right governance mechanisms. From a commercial =
point of view, these agents won't just be adopted and used by everyone out =
of the box. They need to actually produce value to their principals too. Pe=
ople will want AIs for financial planning, solving scheduling and calendars=
, helping with negotiations with roommates, finding romantic partners, etc.=
 I expect this adoption to begin in the immediate sphere: agents negotiatin=
g the office thermostat, allocating shared resources in an apartment block,=
 or fairly dividing household chores. As these systems prove their worth in=
 reducing daily friction, people will trust them more. The same mechanisms =
used to settle a parking dispute can be adapted and scaled up to manage urb=
an planning conflicts or discover the true cost of local externalities in o=
ther contexts. Eventually, this bottom-up architecture provides a credible =
pathway to solving the grand challenges, from funding national public goods=
 to perhaps one day even navigating the complexities of interstate disputes=
=2E</p><p style=3D"margin: 0 0 20px 0;co=
lor: rgb(54,55,55);line-height: 26px;=
font-size: 16px;"><span>In some cases, you may need more than market forces=
=2E Just as public defenders ensure leg=
al representation regardless of abilit=
y to pay, we may need guaranteed access to advocacy agents through voucher =
systems, </span><a href=3D"https://substack.com/redirect/641c4f8b-071e-4bb4=
-bbe9-8fb3ee94ce47?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMYWklSu8UKGo=
Fv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);text-decoration: underl=
ine;">market intermediaries</a><span>, &#8220;right to an agent&#8221; prov=
isions, oversight mechanisms for automated governance, and so on. For the m=
ost part though, users will gravitate toward agents that actually help them=
 achieve their goals. Providers whose agents consistently deliver </span><e=
m>value </em><span>will gain market share. Markets remain one of the most p=
owerful forces discovered to date; and with agents, we can surely improve b=
oth the rules governing them (e.g. through political agents and automated g=
overnance) and the mechanisms that ensure their efficiency (e.g. through Co=
asean bargaining agents).</span></p><p style=3D"margin: 0 0 20px 0;color: r=
gb(54,55,55);line-height: 26px;font-size: 16px;">The vision presented here =
shifts the locus of governance from centralized coercion to decentralized n=
egotiation. AGI agents can help us create a vastly more efficient, accounta=
ble, and adaptable society. There is no need to centralize all the labs int=
o a government monopoly, nor should we just accelerate aimlessly and do awa=
y with the state. And unlike solving alignment for a singular, centralized =
AGI (where failure is catastrophic), the distributed model of millions of u=
ser-agent relationships creates a massive parallel experiment. It is a syst=
em that learns, adapts, and continuously aligns itself over time, allowing =
us to build a society that is both more free and more coordinated than anyt=
hing that has come before.</p><p style=3D"margin: 0 0 20px 0;color: rgb(54,=
55,55);line-height: 26px;font-size: 16px;">***</p><p style=3D"margin: 0 0 2=
0px 0;color: rgb(54,55,55);line-height: 26px;font-size: 16px;">Thanks to Na=
thaniel Bechhofer, Roberto-Rafael Maura-Rivero, Lee B. Cyrano, Andrew Cordi=
ngton, Max Nadau, Ivan Vendrov, Conor Griffin, Alex Obadia, Benoit Lepine, =
Ryan Murphy, Harry Law, Conor Griffin, and Benjamin Lyons for comments.</p>=
<div style=3D"font-size: 16px;line-height: 26px;"><hr style=3D"margin: 32px=
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px;margin-bottom: 0;"><em><a href=3D"https://substack.com/redirect/e35859e5=
-1b37-4f6d-8c6a-0f18c0942891?j=3DeyJ1IjoiNXFxeXF4In0.h9dEDbGj64cTNudluPwbMY=
WklSu8UKGoFv6eWS-HyoA" rel=3D"" style=3D"color: rgb(54,55,55);text-decorati=
on: underline;">Cosmos Institute</a><span> is the Academy for Philosopher-B=
uilders, technologists building AI for human flourishing. We run fellowship=
s, fund fast prototypes, and host seminars with institutions like Oxford, A=
spen Institute, and Liberty Fund.</span></em></p></div></div><div class=3D"=
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