The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents

1 THE SUPERINTELLIGENT WILL: MOTIVA TION AND
INSTRUMENT AL RATIONALITY IN ADVANCED
ARTIFICIAL AGENTS
(2012) Nick Bostrom
Future of Humanit y Institute
Facult y of Philosophy & Oxford Martin School
Oxford University
www.nickbostrom.com
[Minds and Machines, Vol. 22, Iss. 2, May 2012] [translation: Portuguese]
ABSTRACT
This paper discusses the relation between intelligence an d motivation in artificial agen ts,
developing and briefly arguing for tw o theses. The first, the orthogonality thesis , hol ds (with some
caveats) that intelligence and final goals (purposes) are orthogonal axes along which possible
artificial intellects can freely vary—more or less any level of intelligence could be combined with
more or less any final goal. The secon d, the instrumental conver gence thesis , holds that as long as
they possess a sufficient level of intelligence, agents having any of a wide range of final goals
will pursue simil ar intermediary goals becaus e the y have instrumental reasons to do so. In
combination , the two theses help us underst and the possibl e range of behavior of
superintelligent agents, and they point to some potential dangers i n building such an agent.
KEYWO RDS: superintelligence, artificial intelligence, AI, goal, instrumental reason , intelligent
agent
1. The orthogonali ty of motivati on and intelligence
1.1 Avoiding anthropomorphism
If we imagine a space in which all possible minds can be represented, we must imagine all human
minds as constituting a small and fairl y tight cluster wit hin that space. The personality
differences between Hannah Arendt and Benny Hill might seem vast to us, but this is because
the scale bar in our intuitive judgment is calibrat ed on the existing human distribution. In the
wider space of all logical possibilities , thes e tw o personalities are close neighb ors. I n terms of
neural architecture, at least, Ms. Arendt and Mr. Hill are nearly identical. Imagine their brains
laying si de by side in quiet repose. The differences would appear minor an d you would quite
readily recogni ze them as two of a kind; y ou mi ght even be unable to tell which brain was
whose. If you studied the morphology of the two brains more closely under a microsco pe, the
2
 impression of fundamental similarity would only be strengthened: you would then see the same
lamellar organization of the cortex, made up of the same types of neuron, soaking in the same
bath of neurotransmitter molecules.1
 It is well k nown th at naïve observers often anthropomorphize the capabilities of simple r
insensate systems. We might say, for example, “This vending machine is taking a long time to
think about my hot chocolate.” This might lead one either to underestimate the cognitive
complexity of capabilities which come naturally to human being s, such as motor control and
sensory perception, or, alternatively, to ascribe significant degrees of mindfulness and
intelligence to very dumb systems, such as chatterbo xes like Weizenbaum’s ELIZ A
(Weizenbaum 1976) . In a similar manner, there is a common tendency to anthropomorphize the
motivations of intelligent systems in which there is really no ground for expecting human -like
drives and passions (“My car really didn’t want to start this morning”) . Eliezer Yudkowsky
gives a nice illustration of this phenomenon :

Back in the era of pulp science fiction, magazine covers occasionally depicted a sentient
monstrous alien —colloquially known as a bug -eyed monster (BEM) —carrying off an
attractive human fem ale in a torn dress. It would seem the artist believed that a non -
humanoid alien, with a wholly different evolutionary history, woul d sexually desire
human females … Probably the artist did not ask whether a giant bug perceives human
females as attractiv e. Rather, a human female in a torn dress is sexy—inherently so, as an
intrinsic property. They who made this mistake did not think about the insectoid’s
mind: they focused on the woman’s torn dress. If the dress were not torn, the woman
would be less s exy; the BEM does not enter into it . (Yudkowsky 2008)

 An artificial intelligence can be far less human -like in its motivations than a space alien .
The extraterrestrial (let us assume) is a biological creature who has arisen through a process of
evolution and may theref ore b e expected to have the kinds of motivation typical of evolved
creatures . For example, it would not be hugely surprising to find that some random intelligent
alien would have motives related to the attaining or avoiding of food, air, temperature, ener gy
expenditure, the threat or occurrence of bodily injury, disease, predators, reproduction, or
protection of offspring. A member of an in telligent social species might also have motivations
related t o coope ration and competition: like us, it might show i n-group loyalty, a resentment of
free-riders, perhaps even a concern with r eputation and appearance.
 By contrast, an artificial mind need not care intrinsically about any of those things, not
even to the slightest degree. One can easily conceive of an artificial intelligence whose sole
fundamental goal is to count the grains of sand on Boracay , or to calculate decimal places of pi
indefinitely, or to maximize the total number of paperclips in its future lightcone. In fact, it
would be easier to create an AI with simple goals like these, than to build one that has a human -
like set of values and dispositions.

1 This is of course not to deny that differences that appear small visually can be functionally profound.
3

1.2 The orthogonality thesis
For our purposes, “intelligence” will be roughly taken to correspond to the capacity for
instrumental reasoning (more on this later) . Intelligent search for instrumentally optimal plans
and policies can be performed in the service of any goal. Intelligence and motivation can in this
sense be thought of as a pair of orthogonal axes on a graph whose points represent intelligent
agents of different paired specifications. Each point in the graph represents a logically possible
artificial agent , modulo some weak constraints —for instance, it might be impossi ble for a very
unintelligent system to have very complex motivations , since complex motivations would place
significant demands on memory. Furthermore, in order for an agent to “have” a set of
motivations , this set may need to be functionally integrated w ith the agent’s decision -processes,
which again would place demands on processing power and perhaps on intelligence. For minds
that can modify themselves, there may also be dynamical constra ints; for instance, an intelligent
mind with a n urgent desire to be stupid might not remain intelligent for very long. But these
qualifications should not obscure the main idea , which we can express as follows:

The Orthogonality Thesis
Intelligence and final goals are orthogonal axes along which possible agents can fr eely
vary . In other words, more or less any level of intelligence could in principle be
combined with more or less any final goal.

 A comparison may be made here with the Humean theory of motivation . David Hume
thought that beliefs alone (say, about wha t is a good thing to do) cannot motivate action: some
desire is required.2 This would support the orthogonality thesis by undercutting one possible
objection to it, namely, that sufficient intelligence might entail the acquisition of certain beliefs,
and that these beliefs would necessaril y produce certain motivations. Not so, according to David
Hume: belief and motive are separate.
 Although the orthogonality thesis can draw su pport from the Humean theory of
motivation, it does not presuppose it. In particular, one need not maintain that beliefs alone can
never motivate action. It would suffice to a ssume, for example, that an agent —be it ever so
intelligent —can be motivated to pursue any course of action if the agent happens to have certain
standing desires of some su fficient, overriding strength. Another way in which the
orthogonality thesis could be true even if the Humean theory of motivation is false is if
arbitrarily high intelligence does not entail the acquisition of any such beliefs as are (putatively)
motivating on their own . A third way in which it might be possible for the orthogonality thesis
to be true even if the Humean theory were false is if it is possible to build a cognitive system (or
more neutrally, an “optimization process”) with arbitrarily high intell igence but with
constitution so alien as to contain no clear functional analogues to what in humans we call

2 For some recent attempts to defend the Humean theory of motivation, see Smith (1987 ), Le wis (1988), and
Sinhababu (2009) .
4
 “beliefs” and “desires” . This would be the case if such a system could be constructed in a way
that would make it motivated to pursue any given final goal.
 The o rthogonality thesis , as formulated here, makes a claim about the relationship
between motivation and intelligence , rather than between motivat ion and rationality (or
motivation and reason ). This is because some philosophers use the word “rationality” to connote
a “normatively thicker ” concept than we seek to connote here with the word “intelligence”. For
instance , in Reasons and Persons Derek Parfit argues that certain ba sic preferences would be
irrational, such as that of an otherwise normal agent who has “Future -Tuesday -Indifference” :

A certain hedonist cares greatly about the quality of his future experiences. With one
exception, he cares equally about all the parts of his future. The exception is that he has
Future -Tuesday -Indifference. Throughout every Tuesday he cares in the normal way
about what is happening to him. But he never cares about possible pains or pleasures on
a future Tu esday... This indifference is a bare fact. When he is planning his future, it is
simply true that he always prefers the prospect of great suffering on a Tuesday to the
mildest pain on any other day. (Parfit 1984)3

Thus, the agent is now indifferent to his own future suffering if and only if it occurs on a future
Tuesday. For our purposes, we need take no stand on whether Parfit is right that this is
irrational, so long as we grant that it is not necessarily unintelligent . By “intelligence ” here we
mean something lik e instrumental rationality —skill at prediction, planning, and means -ends
reasoning in general . Parfit’s imaginary Future -Tuesday -Indifferent agent could have
impeccable instrumental rationality, and therefore great intelligence, even if he falls short on
some kind of sensitivity to “objective reason ” that might be required of a fully rational agent.
Consequently, this kind of example does not undermine the orthogonality t hesis.
 In a similar vein, even if there are objective moral facts that any fully rat ional agent
would comprehend, and even if these moral facts are somehow intrinsically motivating (such
that anybody who fully comprehends them is necessarily motivated to act in accord ance with
them) this need not undermine the orthogonality thesis. The t hesis could still be true if an agent
could have impeccable instrumental rationality even whilst lacking some other faculty
constitutive of rationality proper, or some faculty required for the full comprehension of the
objective moral facts . (An agent cou ld also be extremely intelligent, even superintelligent ,
without having full instrumental rational ity in every domain. )
 One reason for focusing on intelligence, that is, on instrumental rationality, is that this is
the most relevant concept if we are trying to figure out what different kinds of systems would
do. Normative questions , such as whether their behavior would count as being prudentially
rational or morally justifiable , can be important in various ways. However, such questions
should not bli nd us to the possibility of cognitive systems that fail to satisfy substantial

3 See also Parfit (2011) .
5
 normative criteria but which are nevertheless very powerful and able to exert strong influence
on the world.4

1.3 Predicting superintelligence motivation and behavior
The orth ogonality thesis implies that synthetic minds can have utterly non -anthropomorphic
goals—goals as bizarre by our lights as sand -grain -counting or paperclip -maximizing. T his
hold s even (indeed especially ) for artificial agents that are extremely intelligen t or
superintelligent. Yet it does not follow from the orthogonality thesis that it is impossible to make
predictions about what parti cular agents will do. P redictability is important if one seeks to
design a system to achieve particular outcomes, and th e issue becomes more important the more
powerful the artif icial agent in question is. S uperintelligent agent s could be extremely powerful,
so it is important to develop a way of analyzing and predicting their behavior. Yet despite the
independence of intelligence and final goals implied by the orthogonality thesis, the problem of
predicting an agent’s behavior need not be intractable —not even with regard to hypothetical
superintelligent agents whose cognitive complexity and performance characteristics might
render them in certain respects opaque to human analysis.
 There are at least three directions from which one can approach the problem of
predicting superintelligen t motivation :

(1) Predictability through design competence . If we can suppose that the designers of a
superintellig ent agent can successfully engineer the goal system of the agent so that it
stably pursues a particular goal set by the programmers, then one prediction we can
make is that the agent will pursue that g oal. The more intelligent t he agent is, the greater
the cognitive resourcefulness it will have to pursue that goal. So even before an agent
has been created we might be able to predict something about its behavior , if we know
something about who will build it and what goals they wi ll want it to have.

(2) Predictability th rough inheritance . If a digital intelligence is created directly from a human
template (as would be the case in a high -fidelity whole brain emulation), then the digital
intelligence might inherit the motivation s of the human template .5 The agent might
retain some of these motivations even if its cognitive capacities are subsequently
enhanced to make it superintell igent. This kind of inference requires caution . The
agent’s goals and values could easily become corrupted in the uploading process or
during its subse quent operation and enhancement, depending on how the procedure is
implemented.

4 The orthogonality thesis implies that most any combination of final goal and intelligence level is logically
possible; it does not imply that it would be practically easy to endow a superintelligent agent with some
arbitrary or human -respecting final goal —even if we knew how to construct the intelligence part. For
some preliminary notes on the value -loading problem, see , e.g., Dewey (2011 ) and Yudkowsky (2011) .
5 See Sandberg & Bostrom (2008 ).
6

(3) Predictability through convergent instrumental reasons . Even without detailed knowledge of
an agent’s final goals, we m ay be able to infer something about its more immediate
objectives by considering the instrumental reasons that would arise for any of a wide
range of possible final goals in a wide range of situations. This way of predicting
becomes more useful the greate r the intelligence of the agent , because a more intelligent
agent is more likely to recognize the true instrumental reasons for its actions, and so act
in ways that make it more likely to achieve its goals .

 The next section explores this third way of pre dictability and develops an “instrumental
convergence thesis” which complement s the orthogo nality thesis .

2. Instrumental convergence
According to the orthogonality thesis, artificial intelligent agents may have an enormous range
of possible final goals . Ne vertheless, according to what we may term the “instrumental
convergence ” thesis, there are some instrumental goals likely to be pursued by almost any
intelligent agent , because there are some objectives that are useful intermediaries to the
achievement of almost any final goal. We can formulate this thesis as follows:

The Instrumental Convergence Thesis
Several instrumental values can be identified which are convergent in the sense that their
attainment would increase the chances of the agent’s goal being realized for a wide range
of final goals and a wide range of situations, implying that these instrumental values are
likely to be pursued by many intelligent agents.

 In the following we will consider several categories where s uch convergent instrumental
values may be found.6 The likelihood that an agent will recognize the instrumental values it
conf ronts increases (ceteris paribus) with the agent’s intelligence . We will therefore focus mainly
on the case of a hypot hetical superintelligent agent whose instrumental reasoning capacities far

6 Stephen Omohundro has written two pioneering pape rs on t his topic (Omohundro 2008 a, 2008b) .
Omohundro argues that all advanced AI systems are likely to exhibit a number of “basic drives ”, by which
he means “ tendencies which will be present unless explicitly countera cted.” The term “AI drive” has the
advantage of being short and evocative, but it has the disadvantage of suggesting that the instrumental
goals to which it refers influence the AI’s decision -making in the same way as psychological drives
influence human decision -making, i.e. via a kind of phenomenological tug on our ego which our willpower
may occasionall y succeed in resisting. That connotation is unhelpful. One would not normally say that a
typical human being has a “drive” to fill out their tax return, even though filing taxes may be a fairly
convergent instrumental goal for humans in contemporary soci eties (a goal whose realization averts
trouble that would prevent us from realizing many of our final goals). Our treatment here also differs from
that of Omohundro in some other more substantial ways, although the underlying idea is the same. (See
also Chalmers (2010) and Omohundro (2012) .
7 exceed those of any human. We will also comment on how the instrumental convergen ce thesis
applies t o the case of human beings, as this gives us occasion to elaborate some essential
qualifications concernin g how the instrumental convergence thesis should b e interpreted and
applied. Whe re there are convergent instrumental values, w e may be able t o predict some
aspects of a superintel ligence’s behavior even if we know virtually nothing about that
superintelligence’s final goals.
2.1 Self-preservation
Suppose that an agent has some final goal that extends some way into the future. There are
many scenarios in which the agent, if it is still around in the future, is then able to perform
actions that increase the probability of achieving the goal . This creates an instrumental reason
for the agent to try to b e around i n the future—to hel p achieve its present future-oriented goal.
Agents with human-li ke motivational structures often seem to place som e final val ue on
their own survival . This is not a necessary feature of artificial agen ts: som e may b e designed to
place no final value whatever on thei r own survival. Nevertheless, even agents that do not care
intrinsicall y about their own survival would, under a fairly wide range of conditions, care
instrumentally to some degree about their own survival i n order to accomplish the final goals
they do value.
2.2 Goal-conten t integrity
An age nt is more likel y to act in the future to maximize the realization of its present final goals if
it still has thos e goals in the future. This gives the agent a present instrumental reason to prevent
alterations of its final goals. (This argument applies onl y to fi nal goals. In order t o attain its final
goals, an intelligent agen t will of course routinely want to chan ge its subgoals in light of new
information and insight.)
Goal-content integrit y for final goals is in a sense even mo re fundamental than survival
as a convergent instrumental motivation. Among humans , the opposite may seem to be the case,
but that is because survival is usually part of our final goals . For software agents, which can
easily switch bodies or create exact duplicates of themselves, preservation of self as a particular
implementation or a particular physical object nee d not be an important instrumental value.
Advanced software agents might als o be able to swap memories, download skills, an d radically
modif y their cognitive architecture and personalities. A population of such agents might operate
more like a “functional soup” than a society composed of distinct semi-permanent persons.7 For
some purposes, processes i n such a system mi ght b e bett er individuated as teleological threads ,
based on their final values, rath er than on the basis of bodies , personalities , memories, or
abilities. In such scenarios, goal-continuity might be said to constitute a key aspect of survival.
Even so, there are situations in which an agent may intentionally chan ge its ow n final
goals. Such situations can arise when any of the following factors is significant:
7 See Chislenko ( 1997) .
8

 Social signaling . When others can perceive an agent’s goals and use that information to
infer instrumentally relevant dispositions or other correlated attributes, it can be in the
agent’s interest to modify its goals to make whatever desired impression. For example,
an age nt might miss out on beneficial deals if potential partners cannot trust it to fulfill
its side of the bargain. In order to make credible commitments, an agent might therefore
wish to adopt as a final goal the honoring of its earlier commitments, and to a llow others
to verify that it has indeed adopted this goal. Agents that could flexibly and
transparently modify their own goals could use this ability to enforce deals among one
another.8
 Social preferences . Others may also have preferences about an agen t’s goals. The agent
could then have r eason to modify its goal s, either to satisfy or to frustrate those
preferences.
 Preferences concerning own goal content . An agent might have some final goal concerned
with the agent’s own goal content. For example, the a gent might have a final goal to
become the type of agent that is motivated by certain values, such as compassion .
 Storage costs . If the cost of storing or processing some part of an agent’s utility function is
large compared to the chance that a situ ation will arise in which applying that part of the
utility function will make a difference, then the agent has an instrumental reason to
simplify its goal content, and it may trash that part of the utility function.9 10

 We h uman s often seem happy to let our final goals and values drift . This might often be
because we do not know precisely what they are . We obviously want our beliefs about o ur final
goals and values to be able to change in light of continuing self -discovery or changing self -
presentat ion needs. However, there are cases in which we willingly change the goals and values
themselves, n ot just our beliefs or interpretations of them . For example, somebody deciding to
have a child might predict that they will come to value the child for its own sake, even though at
the time of the decision they may not particularly value their future child or even like children in
general.

8 See also Shulman (2010) .
9 An agent might also change its goal representation if it changes its ontology, in order to transpose its old
representation into the new ontology. Cf. de Blanc (2011) .
10 Another type of factor that mig ht make an evidential decision theorist undertake various actions, including
changing its final goals, is the evidential import of deciding to do so. For example, an agent that follows
evidential decision theory might believe that there exist other agents like it in the universe, and that its own
actions will provide some evidence about how those other agents will act. The agent might therefore
choose to adopt a final goal that is altruistic towards those other evidentially -linked agents, on grounds
that this will give the agent evidence that those other agents will have chosen to act in like manner. An
equivalent outcome might be obtained, however, without changing one’s final goals, by choosing in each
instant to act as if one had those final goals.
9
 Humans are complicated, and many factors might be at play in a situation like this.11 For
instance, one might have a fi nal value that involves becoming the kind of person who cares
about some other individual for his or her own sake (here one places a final value on having a
certain final value ). Alternatively, one might have a final value that involves having certain
experiences and occupying a certain soci al role ; and becoming a parent —and undergoing an
associated goal s hift—might be a necessary part of that. Human goals can also have inconsistent
content, goal content; and so some people might want to modify some of th eir final goals to
reduce the inconsistencies.

2.3 Cognitive enhancement
Improvements in rationality and intelligence will tend to improve an agent’s decision -making,
making the agent more likely to achieve her final goals . One would therefore expect co gnitive
enhancement to emerge as an instrumental goal for many types of intelligent agent. For similar
reasons, agents will tend to instrumentally value many kinds of information.12
 Not all kinds of rationality, intelligence, and knowledge need be instrum entally useful in
the attainment of an agent’s final goals. “Dutch book arguments” can be used to show that an
agent whose credence function does not obey the rules of probability theory is susceptible to
“money pump” procedures, in which a savvy bookie a rranges a set of bets, each of which
appears favorable according to the agent’s beliefs, but which in combination are guaranteed to
result in a loss to the agent, and a corresponding gain for the bookie. However, this fact fails to
provide any strong gene ral instrumental reasons to seek to iron out all probabilistic incoherency .
Agents who do not expect to encounter savvy bookies, or who adopt a general policy against
betting, do not stand to lose much from having some incoherent beliefs —and they may gain
important benefits of the types mentioned: reduced cognitive effort, social signaling, etc. There
is no general reason to expect an agent to seek instrumentally useless forms of cognitive
enhancement , as an agent might not value knowledge and understandi ng for their own sakes.
 Which cognitive abilities are instrumentally useful depends both on the agent’s f inal
goals and its situation . An agent that has access to reliable expert advice may have little need for
its own intelligence and knowledge , and it may therefore be indifferent to these resources . If
intelligen ce and knowledge come at a cost, such as time and effort expended in acquisition, or in
increased sto rage or processing requirements, then an agent might prefer less knowledge and

11 An extensive psychological literature explores adaptive preference fo rmation. See, e.g., Forgas et al.
(2009 ).
12 In formal models, the value of information is quantified a s the difference between the expected value
realized by optimal decisions made with that information and the expected value realized by optimal
decision s made without it. (See, e.g., Russell & Norvig 2010 .) It follows that the value of information is
never negative. It also follows that any information you know will never affect any decision you will ever
make has zero value for you. However, this kind of m odel assumes several idealizations which are often
invalid in the real world —such as that knowledge has no final value (meaning that knowledge has only
instrumental value and is not valuable for its own sake), and that agents are not transparent to other
agents.
10
 less intellig ence.13 The same can hold if the agent has final goals that involve bei ng ignorant of
certain facts: likewise if an agent faces incentives arising from strategic commitments, signaling,
or social preferences, as noted above.14
 Each of these countervailing reasons often comes into play for human beings . Much
information is irrelevant to our goals; we can often rely on others’ skill and expertise; acquiring
knowledge takes time and effort; we might intrinsically value certain kinds of ignorance; and we
opera te in an environment in which the ability to make strategic commitments, socially signal,
and satisfy other people’s direct preferences over our own epistemic states , is often important to
us than simple cognitive gains .
 There are special situations in wh ich cognitive enhancement may result in an enormous
increase in an agent’s ability to achieve its final goals —in particular, if the agent’s final goals are
fairly unbounded and the agent is in a position to become the first superintelligence and thereby
potentially obtain a decisive advantage enabling the agent to shape the future of Earth -
originating life and accessible cosmic resources according to its preferences . At least in this
special case, a rational intelligent agent would place a very high instru mental value on cognitive
enhancement.

2.4 Technolog ical perfection
An agent may often have instrumental reasons to seek better technology, which at its simplest
means seeking more efficient ways of transforming some given set of inputs into valued outputs.
Thus , a software agent might place an instrumental value on more efficient algorithms that
enable its mental functions to run faster on given hardware. Similarly, agents whose goals
require some form of physical construction might instrumentally value improved engineering
technology which enable s them to create a wider range of structures more quickly and reliably ,
using fewer or cheaper materials and less energy. Of course , there is a tradeoff: the p otential
benefits of better technology mus t be weighed against its costs, including not only the cost of
obtaining the technology but also the costs of learning how to use it, integrating it with other
technologies already in us e, and so forth.
 Proponents of some new technology, confident in its superiority to existing alternatives ,
are often dismayed when other people do not share their enthusiasm, but peoples’ resistance to
novel and nominally superior technology need not be b ased on ignorance or irrationality . A
technology’s valence or normative character depends not only on the context in which it is
deployed, but also the vantage point from which its impacts are evaluated: what is a boon from
one person’s perspective can be a liability from another’s. Thus, although mechanized looms
increased the economic efficie ncy of textile production, the Luddite handloom weavers who

13 This strategy is exemplified by the sea squirt larva, which swims about until it finds a suitable rock, to
which it then permanently affixes itself. Cemented in place, the larva has less need for complex
information processing, whence it proceeds to digest part of its own brain (its cerebral ganglion).
Academics can sometimes observe a similar phenomenon in colleagues who are granted tenure.
14 Cf. Bostrom (2012 ).
11
 anticipated that the innovation would render their artisan skills obsolete may have had good
instrumenta l reason s to oppose it. The point here is that if “technolog ical perfection” is to name
a widely convergent instrumental goal for intelligent agents, then the term must be understood
in a spec ial sense—technology must be construed as embedded in a particu lar social context, and
its costs and benefits must be evaluated with reference to some specified agents’ final values.
 It seems that a superintelligent singleton—a superintelligent agent that faces no
significant intelligent rivals or opposition , and is thus in a position to determ ine global policy
unilaterally —would have instrumental reason to perfect the technologies that would make it
better able to shape the world according to its preferred designs.15 This would probably include
space colonization tec hnology, such as von Neumann probes —automatic, self -mending and self -
replicating spaceships that can extend its reach beyond the Solar System. Molecular
nanotechnology, or some alternative still more capable physical manufacturing technology , also
seem s potentially very useful in the service of an extremely wide range of final goals.16

2.5 Resource acquisition
Finally, resource acquisition is a nother common emergent instrumental goal, for muc h the same
reasons as technological perfection: both technology and resources facilitate physical
construction projects.
 Human beings tend to seek to acquire resources sufficient to meet their basic biological
needs. But people usually seek to acquire resources far beyond this minim um level. In doing so,
they may b e partially driven by lesser physical desiderata, such as in creased comfort and
convenience. A great deal of resource accumulation is motivated by social concerns —gaining
status, mates, friends and influence, through wealth accumulation and conspicuous
consumption. Perhaps l ess commonly, some people seek additional resources to achieve
altruistic or expensive non -social aims.

15 Cf. Bostrom (2006 ).
16 One could reverse the question and look instead at possible reasons for a superintelligent singleton not to
develop some technolo gical capabilities. These include: (a) The singleton foreseeing that it will have no
use of some technolo gical capability ; (b) The development c ost being too large relative to its anticipated
utility . This would be the case if, for instance , the technology will never be suitable for achieving any of the
singleton’s ends, or if the singleton has a very high discount rate that s trongly discourages investment; (c)
The singleton having some final value that requires abstention from particular avenues of technology
development; (d) If the singleton is not certain it will remain stable, it might prefer to refrain from
developing tech nologies that could threaten its internal stability or that would make the consequences of
dissolution worse (e.g., a world government may not wish to develop technologies that would facilitate
rebellion, even if they had some good uses, nor develop t echno logies for the easy production of weapons of
mass destruction which could wreak havoc i f the world government were to dissolve); (e) Similarly, the
singleton might have made some kind of binding strategic commitment not to develop some technology, a
commit ment that remains operative even if it would now be convenient to develop it. (Note, however, that
some current reasons for technology -development would not apply to a singleton: e.g., reasons arising from
unwanted arms races.)
12
 On the basis of such observations it might be tempting to suppose that a
superintelligence not facing a competitive social world wo uld see no instrumental reason to
accumulate resources beyond some modest level, for instance whatever computational resources
needed to run its mind along with some virtual reality. Yet such a supposition would be entirely
unwarranted. First, the value of resources depends on the uses to which they can be put, which
in turn depends on the available technology. With mature technology, basi c resources such as
time, space, and matter , and other forms of free energy , could be processed to serve almost any
goal. For instance, such basic resources could be converted into life. Increased computational
resources could be used to run the super intelligence at a greater speed and for a longer duration,
or to create additional physical or simulated (virtual) lives and civilizations. Extra physical
resources could also be used to create backup systems or perimeter defenses, enhancing security.
Such projects could easily c onsume far more than one planet’s worth of resources.
 Furthermore, the cost of acquiring additional extraterrestrial resources will decline
radically as the technology matures . Once von Neumann probes can be built, a large portion of
the observable universe (assuming it is uninhabited by intelligent lif e) could be gradually
colonized —for the one -off cost of building and launching a single successful self -reproducing
probe. This low cost of celestial resource acquisition would mean that such expansion could be
worthwhile even if the value of the additional resources gained were somewhat marginal. For
example, even if a superintelligence cared non -instrumentally only about what happens within
some pa rticular small volume of space, such as the space occupied by its origina l home planet, it
would still have in strumental reasons to harvest the resources of the cosmos beyond. It could
use those surplus resources to build computers to calculate more optimal way s of using
resources within the small spatial region of primary concern. It could also use the extra
resources to build ever -more robust defenses to safeguard the privileged real estate. Since the
cost of acquiring additional resources would keep declining, this process of optimizing and
increasing safeguards might well continue indefinitely even if it were subject to steeply declining
returns.17 18

17 Suppose that an agent dis counts resources obtained in the future at an exponential rate, and that because
of the light speed limitation the agent can only increase its resource e ndowment at a polynomial rate.
Would this mean that there will be some time after which the agent woul d not find it worthwhile to
continue acquisitive expansion? No, because although the present value of the resources obtained at future
times would asymptote to zero the further into the future we look, so would the present cost of obtaining
them. The pre sent cost of sending out one more von Neumann probe a 100 million years from now
(possibly using some resource acquired some short time earlier) would be diminished by the same
discount factor that would diminish the present value of the future resources t he extra probe would
acquire (modulo a constant factor).
18 Even an agent that has an apparently very limited final goal, such as “to make 32 paperclips”, could
pursue unlimited resource acquisition if there were no relevant cost to the agent of doing so. For example,
even after an expected -utility -maximizing agent had built 32 paperclips, it could use some extra resources
to verify that it had indeed successfully built 32 paperclips meeting all the specifications (and, if necessary,
to take corrective acti on). After it had done so, it could run another batch of tests to make doubly sure that
no mistake had been made. And then it could run another test, and another. The benefits of subsequent
tests would be subject to steeply diminishing returns; however, so long as there were no alternative action
13
 Thus, there is an extremely wide range of possible final goals a superintelligent singleton
could have that would generate the instrumental goal of unlimited resource acquisition . The
likely manifestation of thi s would be the superintelligence’s initiation of a colonization process
that would expand in all directions using von Neumann probes . This would roughly result in a
sphere of expanding infrastructure centered on the originating planet and growing in radiu s at
some fraction of the speed of light; and the colonization of the universe would continue in this
manner until the accelerating speed of cosmic expansion ( a consequence of the positive
cosmological constant) makes further material acquisition physicall y impossible as remoter
regions drift permanently out of reach .19 By contrast, agents lacking the technology required for
inexpensive resource acquisition , or for the conversion of generic physical resources into useful
infrastructure , may often find it not cost-effective to invest any present resources in increa sing
their material endowment. The same may hold for agents operating in competition with other
agents of similar powers. For instance, if competing agents have already secured accessible
cosmic resources, a late -starting agent may have no colonization opportunities. The conver gent
instrumental reasons for superintelligences un certain of the non-existence of other powerful
superintelligent agents are complicated by strategic considerations in wa ys that we do not
currently fully comprehend but which may constitute important qualifications to the examples
of convergent instrumental reasons we have looked at here.20
 It should be emphasized that the existence of convergent instrumental reasons, even if
they apply to and are recognized by a particular agent, does not imply that the agent’s behavior
is easily predictable. An agent might well think of ways of pursuing the relevant instrumental
values that do not readily occur to us. This is especially true for a superintelligence, which could
devi se extremely clever but counterintuitive plans to realize its goals, possibly even exploiting
as-yet undiscovered physical phenomena. What is predictable is that the convergent

with a higher expected utility, the agent would keep testing and re -testing (and keep acquiring more
resources to enable these tests).
19 While the volume reached by colonization probes at a given time might be roughly spherical and
expanding with a rate proportional to the square of time elapsed since the first probe was launched (~t2),
the amount of resources contained within this volume will follow a less regular growth pattern, since the
distribution of resou rces is inhomogeneous and varies over several scales. Initially, the growth rate might
be ~t2 as the home planet is colonized; then the growth rate might become spiky as nearby planets and
solar systems are colonized; then, as the roughly disc -shaped volu me of the Milky Way gets filled out, the
growth rate might even out, to be approximately proportional to t; then the growth rate might again
become spiky as nearby galaxies are colonized; then the growth rate might again approximate ~t2 as
expansion procee ds on a scale over which the distribution of galaxies is roughly homogeneous; then
another period of spiky growth followed by smooth ~t2 growth as galactic superclusters are colonized; until
ultimately the growth rate starts a final decline, eventually rea ching zero as the expansion speed of the
universe accelerates to such an extent as to make further colonization impossible.
20 The simulation argument may be of particular importance in this context. A superintelligent agent may
assign a significant probab ility to hypotheses according to which it lives in a computer simulation and its
percept sequence is generated by another superintelligence, and this might various generate convergent
instrumental reasons depending on the agent’s guesses about what types o f simulations it is most likely to
be in. Cf. Bostrom (2003 ).
14
 instrumental values would be pur sued and used to realize the agent’s final goals, not the specific
actions that the agent would take to achieve this.

Conclusions
The orthogonality thesis suggests that we cannot blithely assume that a superintelligence will
necessarily share any of the f inal values stereotypically associated with wisdom and in tellectual
development in humans —scientific curiosity, benevolent concern for others, spiritual
enlightenment and contemplation , renunciation of material acquisitiveness , a taste for refined
culture or for the simple pleasures in life , humility and selflessness, and so forth. It might be
possible through deliberate effort to construct a superintelligence that values such things , or to
build one that values human welfare, moral goodness, or any other complex purpose that its
designers might want it to ser ve. But it is no less possible—and probably technically easie r—to
build a superintelligence that places final value on nothing but calculating the decimals of pi .
 The instrumental convergence thesis suggests that we cannot blithely assume that a
superintelligence with the final goal of calculating the decimals of pi (or making paperclips, or
counting grains of sand) would limit its activities in such a way as to not materially infringe on
human intere sts. An agent with such a final goal would have a convergent instrumental reason ,
in many situations, to acquire an unlimited amount of physical resources and, if possible, to
eliminate potential threats to itself and its goal system .21 It might be possib le to set up a situation
in which the optimal way for the agent to pursue these instrumental values (and thereby its final
goals) is by promoting human welfare, acting morally, or serving some beneficial purpose as
intended by its creators. However, if and when such an agent finds itself in a different situation,
one in which it expects a greater number of decimals of pi to be calculated if it destroys the
human species than if it continues to act cooperatively, its behavior would instantly take a
sinister turn. This indicates a danger in relying on instrumental values as a guarantor of safe
conduct in future artificial agents that are intended to become superintelligent and that might be
able to leverage their superi ntelligence into extreme levels power a nd influence .22

References
Bostrom, N. (2003). Are You Living in a Computer Simulation? Philosophical Quarterly , 53(211),
243-255.

Bostrom. N. (2006). What is a Singleton? Linguistic and Philosophical Investigations , 5(2), 48 -54.

21 Human beings might constitute potential threats; they certainly c onstitute physical resources.
22 For co mments and discussion I am grateful to Stuart Armstrong, Grant Bartley, Owain Evans, Lisa
Makros, Luke Muehlhauser, Toby Ord, Brian Rabkin, Rebecca Roache, Anders Sandberg, and three
anonymous referees.
15
 Bostrom, N. (2012 ). Information Hazards: A Typology of Potentia l Harms from Knowledge.
Review of Contemporary Phil osophy , 10, 44 -79. [www.nickbostrom.com/information -hazards.pdf ]

Chalmers, D. (2010): The Singularity: A Philosophi cal Analysis. Journal of Consciousness Studies ,
17, 7 -65.

Chislenko, A. (1997). Technology as Extension of Human Functional Architecture. Extropy
Online . [project.cyberpunk.ru/idb/technology\_ as\_extension.html ]

de Blanc, P. (2011). Ontological Crises in Artificial Agent’s Value Systems. Manuscript . The
Singularity Institute for Artificial In telligence. [arxiv.org/pdf/1105.3821v1.pdf ]

Dewey, D. (2011). Learning What to V alue. In Schmidhuber, J., Thori sson, K. R., Looks, M. (eds.).
Proceedings of the 4th Conference on Artificial General Intelligence , AGI 2011 (pp. 309 -314),
Heidelberg: Springer.

Forgas, J. et al. (eds.) (2009). The Psychology of A ttitude s and Attitude Change . London: Psychology
Press.

Lewis, D. (1988). Desire as belief. Mind , 97(387), 323 -332.

Omohundro, S. (2008a). The Basic AI Drives. In P. Wang, B. Go ertzel, and S. Franklin (eds.).
Proceedings of the First AGI Conference , 171 , Frontiers in Artificial Intelligence and Appli cations.
Amsterdam: IOS Press.

Omohundro, S. (2008b). The Nature of Self -Improving Artificial Intel ligence. Manuscript .
[selfawaresystems.files.wordpress.com/2008/01/nature\_of\_self\_improving\_ai.pdf ]

Omohundro, S. (forthcoming 2012). Rationally -Shaped Artificial Intelligence. In Eden, A. et al.
 (eds.). The Singularity Hypothesis: A Scient ific and Philosophical Assessment (Springer, forth coming).

Parfit, D. (1984). Reasons and Persons . (pp. 123 -4). Reprinted and c orrected edition, 1987. Oxford:
Oxford University Press.

Parfit, D. (2011). On What Matters . Oxford: Oxford University Press.

Russell, S. and Norvig, P. ( 2010). Artificial Intelligence: A Modern Approach . (3rd ed. ). New Jersey:
Prentice Hall.

Sandberg, A. and Bostrom, N. (2008). Whole Brain Emulation: A Roadmap . Technical Report 2008–
3. Oxford: Future of Humanity Institute, O xford Univer sity.
16
 [www.fhi.ox.ac.uk/Reports/200 8-3.pdf ]

Shulman, C. (2010). Omohundro’s “Basic AI Drives” and Cata strophic Risks. Manuscript .
[singinst.org /upload/ai -resource -drives.pdf ]

Sinhababu, N. (2009). The Humean Theory of Motiva tion Reformulated and Defended.
Philosophical Review , 118(4), 465 -500.

Smith, M. (1987). The Humean Theory of Motivation. Mind , 46 (381): 36 -61.

Weizenbaum, J. (1976). Computer Power and Human Reason: Fro m Judgment to Calculation . San
Francisco: W. H. Freeman.

Yudkowsky, E. (2008). Artificial Intelligence as a Positive and Negative Factor i n Global Ri sk. In
Bostrom, N. and Cirkovic, M. (eds.). Global Catastrophic R isks. (pp. 308 -345; quote from p. 310).
Oxford: Oxford University P ress.

Yudkowsky, E. (2011). Complex Value Systems Are Required to Realize Valuable Futures. In
Schmidhuber , J., Thorisson, K. R., Looks, M. (eds.). Procee dings of the 4th Conference on Artificial
General Intelligence , AGI 2011 (pp. 388 -393). Heidelberg: Springer.