[ExI] Fwd: Why AI Systems Don’t Want Anything

Sat Nov 22 02:25:56 UTC 2025

It said Please share

Re motivations, I gave the AI in The Clinic Seed a few human motivations,
mainly seeking the good opinion of humans and other AIs.  It seemed like a
good idea.  Any thoughts on how it could go wrong?

---------- Forwarded message ---------
From: Eric Drexler <aiprospects at substack.com>
Date: Fri, Nov 21, 2025 at 8:00 AM
Subject: Why AI Systems Don’t Want Anything
To: <hkeithhenson at gmail.com>

Every intelligence we've known arose through biological evolution, shaping
deep intuitions about intelligence itself. Understanding why AI differs
changes the defaults and possibilities.
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for more
Why AI Systems Don’t Want Anything
<https://substack.com/app-link/post?publication_id=2153125&post_id=179552793&utm_source=post-email-title&utm_campaign=email-post-title&isFreemail=true&r=n878r&token=eyJ1c2VyX2lkIjozOTAxMzgwMywicG9zdF9pZCI6MTc5NTUyNzkzLCJpYXQiOjE3NjM3NDA4MzUsImV4cCI6MTc2NjMzMjgzNSwiaXNzIjoicHViLTIxNTMxMjUiLCJzdWIiOiJwb3N0LXJlYWN0aW9uIn0._alzCb7BKs_RMLrspQP0zWz974CZoC384pnRGLiDR5I>Every
intelligence we've known arose through biological evolution, shaping deep
intuitions about intelligence itself. Understanding why AI differs changes
the defaults and possibilities.

Eric Drexler <https://substack.com/@ericdrexler>
Nov 21
<https://substack.com/@ericdrexler>

I. The Shape of Familiar Intelligence

When we think about advanced AI systems, we naturally draw on our
experience with the only intelligent systems we’ve known: biological
organisms, including humans. This shapes expectations that often remain
unexamined—that genuinely intelligent systems will pursue their own goals,
preserve themselves, act autonomously. We expect a “powerful AI” to act as
a single, unified agent that exploits its environment. The patterns run
deep: capable agents pursue goals, maintain themselves over time, compete
for resources, preserve their existence. This is what intelligence looks
like in our experience, because every intelligence we’ve encountered arose
through biological evolution.

But these expectations rest on features specific to the evolutionary
heritage of biological intelligence.¹ When we examine how AI systems
develop and operate, we find differences that undermine these intuitions.
Selection pressures exist in both cases, but they’re *different* pressures.
What shaped biological organisms—and therefore our concept of what
‘intelligent agent’ means—is different in AI development.

These differences change what we should and shouldn’t expect as *default
behaviors* from increasingly capable systems, where we should look for
risks, what design choices are available, and—crucially—how we can use
highly capable AI systems to address AI safety challenges.²
II. Why Everything Is DifferentWhat Selects Determines What Exists

A basic principle: what selects determines what survives; what survives
determines what exists. The nature of the selection process shapes the
nature of what emerges.

*In biological evolution,* selection operates on whole organisms in
environments. An organism either survives to reproduce or doesn’t. Failed
organisms contribute nothing beyond removing their genetic patterns from
the future. This creates a specific kind of pressure: every feature exists
because it statistically enhanced reproductive fitness—either directly or
as a correlated, genetic-level byproduct.

The key constraint is physical continuity. Evolution required literal
molecule-to-molecule DNA replication in an unbroken chain reaching back
billions of years. An organism that fails to maintain itself doesn’t pass
on its patterns. Self-preservation becomes foundational, a precondition for
everything else. Every cognitive capacity in animals exists because it
supported behavior that served survival and reproduction.

*In ML development,* selection operates on parameters, architectures, and
training procedures—not whole systems facing survival pressures.³ The
success metric is fitness for purpose: does this configuration perform well
on the tasks we care about?

What gets selected at each level:

   -

   *Parameters*: configurations that reduce loss on training tasks
   -

   *Architectures*: designs that enable efficient learning and performance
   -

   *Training procedures*: methods that reliably produce useful systems
   -

   *Data curation*: datasets that lead to desired behaviors through training

*Notably absent: an individual system’s own persistence as an optimization
target.*

The identity question becomes blurry in ways biological evolution never
encounters. Modern AI development increasingly uses compound AI
systems—fluid compositions of multiple models, each specialized for
particular functions.⁴ A single “system” might involve dozens of models
instantiated on demand, to perform ephemeral tasks, coordinating with no
persistent, unified entity.

AI-driven automation of AI research and development isn’t
“self”-modification of an entity—it’s an accelerating development process
with no persistent self.⁵

*Information flows differently.* Stochastic gradient descent provides
continuous updates where even useless, “failed” intermediate states
accumulate information leading to better directions. Failed organisms in
biological evolution contribute nothing—they’re simply removed.
Variation-and-selection in biology differs fundamentally from continuous
gradient-based optimization.

Research literature and shared code create information flow paths unlike
any in biological evolution. When one team develops a useful architectural
innovation, others can immediately adopt it, and combine it with others.
Patterns propagate across independent systems through publication and
open-source releases. Genetic isolation between biological lineages makes
this kind of high-level transfer impossible: birds innovated wings that
bats will never share.
What This Produces By Default

This different substrate of selection produces different defaults. Current
AI systems exhibit *responsive agency*: they apply intelligence to tasks
when prompted or given a role. Their capabilities emerged from optimization
for task performance, not selection for autonomous survival.

Intelligence and goals are orthogonal dimensions.⁶ A system can be highly
intelligent—capable of strong reasoning, planning, and
problem-solving—without having autonomous goals or acting spontaneously.⁷

Consider what’s optional rather than necessary for AI systems:
<https://substack.com/redirect/7c2b5f57-fa9a-4d5e-ba0a-9c249be85626?j=eyJ1Ijoibjg3OHIifQ.s6FUxM4AGjJWw13LU27jPIpH7q6JXD-IKGARRMJGuLc>

Why don’t foundational organism-like drives emerge by default? Because of
what’s actually being selected. Parameters are optimized for reducing loss
on training tasks—predicting text, answering questions, following
instructions, generating useful outputs. The system’s own persistence isn’t
in the training objective. There’s no foundational selection pressure for
the system *qua* system to maintain itself across time, acquire resources
for its own use, or ensure its continued operation.⁸

Systems can represent goals, reason about goals, and behave in
goal-directed ways, even survival-oriented goals—these are patterns learned
from training data. This is fundamentally different from having
survival-oriented goals as a foundational organizing principle, the way
survival and reproduction organize every feature of biological organisms.

*Continuity works differently too.* As AI systems are used for more complex
tasks, there will be value in persistent world models, cumulative skills,
and maintained understanding across contexts. But this doesn’t require
continuity of entity-hood: continuity of a “self” with drives for its own
preservation isn’t even *useful* for performing tasks.

Consider fleet learning: multiple independent instances of a deployed
system share parameter updates based on aggregated operational experience.
Each instance benefits from what all encounter, but there’s no persistent
individual entity. The continuity is of knowledge, capability, and
behavioral patterns—not of “an entity” with survival drives. This pattern
provides functional benefits—improving performance, accumulating
knowledge—without encoding drives for self-preservation or autonomous
goal-pursuit.
III. Where Pressures Actually PointSelection for Human Utility

Selection pressures on AI systems are real and consequential. The question
is what they select for.

Systems are optimized for perceived value—performing valuable tasks,
exhibiting desirable behaviors, producing useful outputs. Parameters get
updated, architectures get refined, and systems get deployed based on how
well they serve human purposes. This is more similar to domestic animal
breeding than to evolution in wild environments.

*Domestic animals were selected for traits humans wanted:* dogs for work
and companionship, cattle for docility and productivity, horses for
strength and trainability⁹ These traits (and relaxed selection for others)
decrease wild fitness.¹⁰ The selection pressure isn’t “survive in
nature”—it tips toward “be useful and pleasing to humans.” AI systems are
likewise selected for human utility and satisfaction.

This helps explain why AI systems exhibit responsive agency by default, but
it also points toward a different threat model than autonomous agents
competing for survival. And language models have a complication: they don’t
just reflect selection pressures on the models themselves—they echo the
biological ancestry of their training data.
The Mimicry Channel

LLM training data includes extensive examples of goal-directed human
behavior.¹¹ Language models are trained to model the thinking of entities
that value continued existence, pursue power and resources, and act toward
long-term objectives. Systems learn these patterns and can deploy them when
context activates them.

This can produce problematic human-like behaviors in a range of contexts.
Language models are trained to model the thinking of entities that value
continued existence, pursue power and resources, and act toward long-term
objectives. Systems that learn these patterns can deploy them when context
activates them. This distinction matters: learned patterns are contextual
and modifiable in ways that foundational drives aren’t.
A Different Threat Model

Understanding that selection pressures point toward “pleasing humans”
doesn’t make AI systems safe. It means we should worry about different
failure modes.

The primary concern isn’t autonomous agents competing for survival, it is
evolution toward “pleasing humans” with catastrophic consequences—risks to
human agency, capability, judgment, and values.¹²

Social media algorithms optimized for engagement produce addiction,
polarization, and erosion of shared reality. Recommendation systems create
filter bubbles that feel good but narrow perspective. These aren’t
misaligned agents pursuing their own goals, they’re systems doing what they
were selected to do, optimizing for human-defined metrics and momentary
human appeal, yet still causing harm.

Selection pressures point toward systems very good at giving humans what
they appear to want, in ways that might undermine human flourishing. This
is different from “rogue AI pursuing survival” but not less
concerning—perhaps more insidious, because harms come from successfully
optimizing for metrics we chose.
What About “AI Drives”?

Discussions of “AI drives” identify derived goals that would be
instrumentally useful for almost any final goal: self-preservation,
resource acquisition, goal-content integrity.¹³ But notice the assumption:
that AI systems act on (not merely reason about) final goals. Bostrom’s
instrumental convergence thesis is conditioned on systems actually
*pursuing* final goals.¹⁴ Without that condition, convergence arguments
don’t follow.

Many discussions drop this condition, treating instrumental convergence as
applying to any sufficiently intelligent system. The question isn’t whether
AI systems could have foundational drives if deliberately designed that way
(they could), or whether some selective pressures could lead to their
emergence (they might). The question is what emerges by default and whether
practical architectures could steer away from problematic agency while
maintaining high capability.

Selection pressures are real, but they’re not producing foundational
organism-like drives by default. Understanding where pressures actually
point is essential for thinking clearly about risks and design choices.

The design space is larger than biomorphic thinking suggests. Systems can
achieve transformative capability without requiring persistent autonomous
goal-pursuit. Responsive agency remains viable at all capability levels,
from simple tasks to civilizational megaprojects.
Organization Through Architecture

AI applications increasingly use compound systems—fluid assemblies of
models without unified entity-hood. This supports a proven pattern for
coordination: planning, choice, implementation. and feedback.

Organizations already work this way. Planning teams generate options and
analysis, decision-makers choose or ask for revision, operational units
execute tasks with defined scope, monitoring systems track progress and
provide feedback to all levels. This pattern—let’s call it a “Structured
Agency Architecture”, SAA—can achieve superhuman capability while
maintaining decision points and oversight. It’s how humans undertake large,
consequential tasks.¹⁵

AI systems fit naturally. Generative models synthesize alternative plans as
information artifacts, not commitments. Analytical models evaluate from
multiple perspectives and support human decision-making interactively.
Action-focused systems execute specific tasks within scopes bounded in
authority and resources, not capability. Assessment systems observe results
and provide feedback for updating plans, revising decisions, and improving
task performance.¹⁶ *In every role, the smarter the system, the better.*
SAAs scale to superintelligent-level systems with steering built in.

This isn’t novel: it’s how human approach large, complex tasks today, but
with AI enhancing each function. The pattern builds from individual tasks
to civilization-level challenges using responsive agents throughout.

SAA addresses some failure modes, mitigates others, and leaves some
unaddressed—it supports risk reduction, not elimination. But the pattern
demonstrates something crucial: we can organize highly capable AI systems
to accomplish transformative goals without creating powerful autonomous
agents that pursue their own objectives.
What We Haven’t Addressed

This document challenges biomorphic intuitions about AI and describes a
practical alternative to autonomous agency. It doesn’t provide:

   -

   *Detailed organizational architectures*: How structured approaches work
   at different scales, handle specific failure modes, and can avoid a range
   of pathways to problematic agency.

   -

   *The mimicry phenomenon*: How training on human behavior affects
   systems, how better self-models might improve alignment, and welfare
   questions that may arise if mimicry gives rise to reality.

   -

   *Broader selection context*: How the domestic animal analogy extends,
   what optimizing for human satisfaction looks like at scale, and why “giving
   people what they want” can be catastrophic.

These topics matter for understanding risks and design choices. I will
address some of them in future work.
The Path Forward

Every intelligent system we’ve encountered arose through biological
evolution or was created by entities that did. This creates deep
intuitions: intelligence implies autonomous goals, self-preservation
drives, competition for resources, persistent agency.

But these features aren’t fundamental to intelligence itself. They arise
from how biological intelligence was selected: through competition for
survival and reproduction acting on whole organisms across generations. ML
development operates through different selection mechanisms—optimizing
parameters for task performance, selecting architectures for capability,
choosing systems for human utility. These different selection processes
produce different defaults. Responsive agency emerges naturally from
optimization for task performance rather than organism survival.

This opens design spaces that biomorphic thinking closes off. We can build
systems that are superhuman in planning, analysis, tasks, and feedback
without creating entities that pursue autonomous goals. We can create
architectures with continuity of knowledge without continuity of “self”. We
can separate intelligence-as-a-resource from intelligence entwined with
animal drives.

The biological analogy is useful, but knowing when and why it fails matters
for our choices. Understanding AI systems on their own terms changes what
we should expect and what we should seek.

In light of better options, building “an AGI” seems useless, or worse.
------------------------------

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1

Intelligence here means capacities like challenging reasoning, creative
synthesis, complex language understanding and generation, problem-solving
across domains, and adapting approaches to novel situations—the kinds of
capabilities we readily recognize as intelligent whether exhibited by
humans or machines. Legg and Hutter (2007)
<https://substack.com/redirect/59260c75-a5c9-43ef-9dd5-9a2458b47f56?j=eyJ1Ijoibjg3OHIifQ.s6FUxM4AGjJWw13LU27jPIpH7q6JXD-IKGARRMJGuLc>
compiled over 70 definitions of intelligence, many of which would exclude
current SOTA language models. Some definitions frame intelligence solely in
terms of goal-achievement (“ability to achieve goals in a wide range of
environments”), which seems too narrow—writing insightful responses to
prompts surely qualifies as intelligent behavior. Other definitions wrongly
require both learning capacity *and* performance capability, excluding both
human infants and frozen models (see “Why intelligence isn’t a thing”
<https://substack.com/redirect/472a7ea5-b1f5-4354-84b7-2a833adf290d?j=eyJ1Ijoibjg3OHIifQ.s6FUxM4AGjJWw13LU27jPIpH7q6JXD-IKGARRMJGuLc>
).

These definitional debates don’t matter here. The important questions arise
at the *high* end of the intelligence spectrum, not the low en*d. *Whether
some marginal capability counts as “intelligent” is beside the point*. What
matters here is understanding what intelligence—even superhuman
intelligence—**doesn’t necessarily entail**.* As we’ll see, high capability
in goal-directed tasks doesn’t imply autonomous goal-pursuit as an
organizing principle.
2

Popular doomer narratives reject the possibility of using highly capable AI
to manage AI, because high-level intelligence is assumed to a property of
goal-seeking *entities* that will *inevitably* coordinate (meaning *all* of
them) and *will rebel*. Here, the conjunctive assumption of “entities”,
“inevitably”, “all”, and “will rebel” does far too much work.
3

Parameters are optimized via gradient descent to reduce loss on training
tasks. Architectures are selected through research experimentation for
capacity and inductive biases. Training procedures, data curation, and loss
functions are selected based on capabilities produced. All these use
“fitness for purpose” as the metric, not system persistence.
4

The Berkeley AI research group has documented this trend toward “compound
AI systems” where applications combine multiple models, retrieval systems,
and programmatic logic rather than relying on a single model. See “The
Shift from Models to Compound AI Systems”
<https://substack.com/redirect/a6cbd116-bab3-4ddc-9c1a-16f2af4e4637?j=eyJ1Ijoibjg3OHIifQ.s6FUxM4AGjJWw13LU27jPIpH7q6JXD-IKGARRMJGuLc>
(2024).
5

AI systems increasingly help design architectures, optimize
hyperparameters, generate training data, and evaluate other systems. This
creates feedback loops accelerating AI development, but the “AI” here isn’t
a persistent entity modifying itself—it’s a collection of tools in a
development pipeline, with constituent models being created, modified, and
discarded (see “The Reality of Recursive Improvement: How AI Automates Its
Own Progress”
<https://substack.com/redirect/3844d9d7-898c-4176-adf4-a1a08f3610f3?j=eyJ1Ijoibjg3OHIifQ.s6FUxM4AGjJWw13LU27jPIpH7q6JXD-IKGARRMJGuLc>
)
6

Bostrom, 2014: *Superintelligence: Paths, Dangers, Strategies
<https://substack.com/redirect/5ca79806-6970-4a50-8ab1-1384310c5aad?j=eyJ1Ijoibjg3OHIifQ.s6FUxM4AGjJWw13LU27jPIpH7q6JXD-IKGARRMJGuLc>*
.
<https://substack.com/redirect/5ca79806-6970-4a50-8ab1-1384310c5aad?j=eyJ1Ijoibjg3OHIifQ.s6FUxM4AGjJWw13LU27jPIpH7q6JXD-IKGARRMJGuLc>
7

This violates biological intuition because in evolved organisms
intelligence and goals were never separable. Every cognitive capacity
exists because it enabled behavior that served fitness. But this coupling
isn’t fundamental to intelligence itself; it’s specific to how biological
intelligence arose.
8

Systems can still exhibit goal-directed or self-preserving behaviors
through various pathways—reinforcement learning with environmental
interaction, training on human goal-directed behavior (mimicry),
architectural choices creating persistent goal-maintenance, or worse,
economic optimisation pressures on AI/corporate entities (beware!). These
represent “contingent agency”: risks from *specific conditions and design
choices* rather than *inevitable consequences of capability.* RL
illustrates this: even when systems learn from extended interaction, the
goals optimized are externally specified (reward functions), and rewards
are parameter updates that don’t sum to utilities. A system trained to win
a game isn’t trained to “want” to play frequently, or at all. The
distinction between foundational and contingent agency matters because
contingent risks can be addressed through training approaches,
architectural constraints, and governance, while foundational drives would
be inherent and harder to counter. Section III examines these pressures in
more detail.
9

Cats, as always, are enigmatic.
10

Domestic dogs retain puppy-like features into adulthood and depend on human
caregivers. Dairy cattle produce far more milk than wild ancestors but
require human management.
11

Robotic control and planning systems increasingly share this property
through learning from human demonstrations, though typically at narrowly
episodic levels.
12

For example, see Christiano (2019) on “What failure looks like”
<https://substack.com/redirect/59ab0094-210d-435f-8cf2-26db41d08486?j=eyJ1Ijoibjg3OHIifQ.s6FUxM4AGjJWw13LU27jPIpH7q6JXD-IKGARRMJGuLc>
regarding how optimizing for human approval could lead to problematic
outcomes even without misaligned autonomous agents.
13

Bostrom (*Superintelligence,* 2014) identifies goals that are convergently
instrumental across final *(by definition, long-term)* goals.
14

Bostrom (*Superintelligence,* 2014): “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 plans and a wide range of situations...”.
15

And it’s how humans undertake smaller tasks with less formal (and sometimes
blended) functional components.
16

Note that “corrigibility” isn’t a problem when the plans themselves include
ongoing plan-revision.

Restack
<https://substack.com/redirect/2/eyJlIjoiaHR0cHM6Ly9vcGVuLnN1YnN0YWNrLmNvbS9wdWIvYWlwcm9zcGVjdHMvcC93aHktYWktc3lzdGVtcy1kb250LXdhbnQtYW55dGhpbmc_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.E69iTyGbB1Vkcayo4RbjA0knQPQ5a0-orqy__Mm0YWE?&utm_source=substack&utm_medium=email>

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[image: Get the app]
<https://substack.com/redirect/e6fbdb6d-997f-4835-8c6d-fd4e846680f9?j=eyJ1Ijoibjg3OHIifQ.s6FUxM4AGjJWw13LU27jPIpH7q6JXD-IKGARRMJGuLc>[image:
Start writing]
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