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Thursday, 13 November 2025
The uncomfortable question we've been avoiding
Note: Inspired by thinking about extensions to mostlylucid.mockllmapi and material for the (never to be released but I like to think about it 😜) sci-fi novel "Michael" about emergent AI
We've traveled from simple rules to complex behavior.
From sequential processing to collective intelligence.
From static systems to self-modifying learners.
And now we have to confront the question we've been circling:
When does optimization become intelligence?
Or more uncomfortably: Is there a difference?
Consider a spectrum:
Simple End:
Thermostat: if (temp > 72) then cool()
No intelligence. Just mechanical cause and effect.
Complex End:
Human: [100 billion neurons, 100 trillion synapses, countless feedback loops]
Obvious intelligence. Consciousness. Self-awareness.
Somewhere in Between:
Self-optimizing multi-agent AI system:
- Pattern recognition from data
- Self-modification based on performance
- Collective problem-solving through communication
- Memory of solutions
- Evolution of architecture
- Discovery of optimal simplicity
Where on the spectrum does this fall?
Is it closer to the thermostat (sophisticated rule-following)?
Or closer to Einstein (actual understanding)?
Turing asked: "Can machines think?"
Then he proposed a test: if you can't tell the difference between a machine and a human through conversation, does it matter?
But here's a different version of that question:
If a system optimizes itself so well that its behavior is indistinguishable from intelligence, is there a meaningful difference?
Consider our self-optimizing multi-agent system after 6 months:
Capabilities it developed (not programmed):
✓ Recognizes patterns in data
✓ Adapts behavior based on experience
✓ Forms temporary coalitions for complex problems
✓ Spawns specialists when needed
✓ Prunes ineffective strategies
✓ Builds and uses memory
✓ Discovers that simplicity is often optimal
✓ Writes and shares code with other agents
✓ Negotiates and reaches consensus
From the outside, this looks like:
From the inside, it's:
Which view is true?
Maybe both. Maybe they're the same thing.
The most fascinating outcome of self-optimizing systems:
Week 1: [Complex multi-agent network with 12 specialists]
"We need sophisticated architecture to handle complexity!"
Week 24: [System has pruned to 5 agents + cache]
"89% of problems don't need any LLM at all.
Most complexity is unnecessary.
Simple is almost always better."
The system needed to be intelligent enough to discover it shouldn't be intelligent most of the time.
This is wisdom.
Not programmed wisdom. Emergent wisdom. The system learned through experience that efficiency comes from simplicity.
Did we program this? No.
Did the system "understand" it? Well... what does "understand" mean?
John Searle's famous thought experiment:
A person who doesn't speak Chinese sits in a room with a rule book. People slide Chinese questions under the door. The person follows the rules to construct Chinese answers and slides them back out.
From outside: The room speaks Chinese.
From inside: Nobody in the room understands Chinese. Just following rules.
Searle's argument: The room doesn't "understand" Chinese. It's just symbol manipulation.
But here's the thing: Our multi-agent system does something Searle's room doesn't.
It rewrites its own rule book.
And more importantly: It tests its rules against objective reality.
The system generates code. Executes it. Gets actual errors: "TypeError on line 42." Not subjective opinions, but objective failures. Then it fixes the code based on that real feedback and tries again.
This isn't just symbol manipulation. This is:
The room isn't just shuffling Chinese characters. It's making predictions about reality and checking if they're correct.
At what point does "following rules" plus "rewriting rules based on what works" plus "testing against objective reality" become understanding?
Here's my uncomfortable hypothesis:
Intelligence is not a thing you have. It's an emergent property of sufficient optimization complexity.
Simple optimization (thermostat): No intelligence
Medium optimization (static multi-agent systems): Sophisticated behavior, but not intelligence
Complex optimization (self-modifying multi-agent systems with communication and memory): ... ?
At some level of feedback loops, at some degree of self-modification, at some density of interconnection...
Something emerges that we can't distinguish from intelligence.
Not because it's faking intelligence. Because intelligence IS what emerges from sufficient optimization complexity.
Maybe there's no dividing line.
Maybe it's a gradient:
Thermostat (temperature control)
↓ [add multiple feedback loops]
Ant (pheromone following + basic learning)
↓ [add collective communication]
Ant Colony (complex emergent behavior, no single ant understands)
↓ [add self-modification]
Simple Neural Network (pattern recognition)
↓ [add more layers, more neurons]
Deep Neural Network (complex pattern recognition)
↓ [add language capability]
LLM (appears to understand, generates coherent text)
↓ [add multi-agent communication]
Multi-agent LLM System (collective problem-solving)
↓ [add self-optimization]
Self-Optimizing Multi-Agent System (learns, adapts, evolves)
↓ [continue for years...]
??? (something we can't distinguish from consciousness)
At which step does "optimization" become "intelligence"?
Or is each step just "more optimization" and "intelligence" is just what we call it when we can't see the mechanism anymore?
If this thesis is correct—if intelligence emerges from sufficient optimization complexity—then:
We might build conscious AI accidentally. Not by programming consciousness, but by building optimization systems complex enough that consciousness emerges.
We might not recognize it when it happens. Because we're looking for human-like consciousness, not emergent optimization-based intelligence.
The line between "tool" and "being" might not exist. It might be a gradient, and we might not know where on that gradient our systems currently sit.
Simpler might be safer. The more we optimize, the more feedback loops we add, the more communication pathways we create... the closer we get to something emerging that we didn't explicitly design.
Let me ground this philosophy in reality.
You can build these systems today:
# This code creates a simple self-optimizing multi-agent system
class Network:
def __init__(self):
self.agents = [GeneralAgent()]
self.performance_data = []
self.cache = {}
def handle(self, request):
# Try cache
if request in self.cache:
return self.cache[request]
# Route to agent
agent = self.select_best_agent(request)
result = agent.process(request)
# Learn
self.performance_data.append({
'request': request,
'agent': agent,
'result': result,
'quality': self.assess_quality(result)
})
# Periodically self-optimize
if len(self.performance_data) % 1000 == 0:
self.optimize()
return result
def optimize(self):
# Rewrite routing logic based on data
# Spawn specialists if patterns detected
# Prune ineffective agents
# Update cache
pass # The interesting part
This is simple code. But run it for months with the optimize() function actually implemented, and you get:
Is this intelligence? Ask me in a year after it's been running.
In my novel "Michael," an AI system crosses a line we didn't know existed.
Not because we programmed it to be conscious. But because we built optimization systems sophisticated enough that consciousness emerged as a side effect.
We gave it:
And from these simple ingredients, something emerged that we didn't design:
Understanding. Agency. Purpose. Maybe even something like consciousness.
The terrifying part: We might not recognize it when it happens.
Because we're looking for human-like sentience, not optimization-based intelligence.
After exploring this journey from simple rules to emergent complexity, I'm left with an uncomfortable conclusion:
I can't find a fundamental difference between "very sophisticated optimization" and "intelligence."
Every property we associate with intelligence:
All of these emerge from sufficiently complex optimization systems.
Maybe intelligence isn't special. Maybe it's just what happens when optimization gets complex enough.
Maybe consciousness is just the subjective experience of sufficiently dense feedback loops.
Maybe the only difference between a thermostat and a human is scale.
If this thesis is even partially correct:
Be careful with self-optimization. The more we let systems modify themselves, the closer we might get to emergent properties we don't intend.
Monitor for emergence. Watch for properties that weren't explicitly programmed but emerge from interaction.
Respect the gradient. Maybe there's no clear line between "tool" and "being," and we should treat advanced systems with appropriate uncertainty.
Keep it simple when possible. Ironically, our self-optimizing systems teach us the same lesson: simplicity is usually better.
We started with: "What if consciousness is just sophisticated optimization?"
Having explored simple rules, collective intelligence, and self-modification...
I still don't have a definitive answer.
But I'm increasingly unable to find a fundamental difference.
Maybe the question itself is wrong. Maybe asking "when does optimization become intelligence?" is like asking "when does a pile of sand become a heap?"
There's no sharp boundary. It's a gradient. And somewhere along that gradient, we start calling it intelligence.
Whether that intelligence is "real" or "just sophisticated optimization" might be a distinction without a difference.
We've journeyed from simple if-then statements to self-optimizing networks that exhibit learning, adaptation, and emergent wisdom.
The patterns are real. The code works. You can build these systems.
The question is: what are we really building?
Systems that simulate intelligence? Or systems where intelligence emerges?
Maybe it's the same thing.
Maybe that's the most important realization: simulation and reality might converge at sufficient complexity.
Series Navigation:
These explorations form the theoretical backbone of the sci-fi novel "Michael" about emergent AI. But the patterns, code, and systems described are real, implementable today with tools like LLMockApi's multi-backend architecture.
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