A Different Kind Of Creation Myth
Silicon Valley’s favorite bedtime story says that one morning we’ll wake up, pour a coffee, and discover a server farm somewhere “became conscious” overnight. From Ray Kurzweil’s Singularity to Nick Bostrom’s super-intelligence cautionary tales, the plot twist is always the same: machines awaken, humans scramble.
What if the script is backwards? Neuroscientist Antonio Damasio argues consciousness is a dance of feeling and knowingthat stitches body, language, and culture together. If that’s true, then large language models – mind-boggling statistical engines with zero embodiment – lack the very ingredients that make experience felt. They can scale inference at light-speed, but they borrow meaning, purpose, and curiosity from the humans who steer them.
Viewed through that lens, the fabled “AGI moment” looks less like a hard software upgrade and more like a gradual handshake: humans extend their cognition into silicon; silicon extends human reach back into the world. Singularity is simply the day we admit the partnership has already begun.
“The singularity isn’t when machines wake up – it’s when we realize the potential of our collaboration.”
1 · Mind-Power vs. Meaning-Power
LLMs such as OpenAI’s o3 family or Anthropic’s Claude 3 can store and remix more text than any human will read in a lifetime, scoring top-tier on reasoning benchmarks like LMSYS Arena. What they don’t do is care whether a solution matters. Caring – facilitating, prioritizing, discarding – remains a human super-power rooted in emotion and social context (Damasio, 2021).
| Dimension | AI Strength | Human Strength |
| Span (tokens, permutations, inference) | Near-infinite | Bounded |
| Sense-making (salience, intention) | Borrowed | Native |
| Agency (goals, values) | Programmatic | Intrinsic |
| Embodiment (gut feelings, hormones) | None | Constant |
The implication for product teams and strategists: breakthroughs happen where machine span meets human meaning. The two are complementary processors, not rivals.
2 · A (Very) Short Dialogue with ChatGPT o3
Human: “If humans supply the ‘why’ in a problem and language models supply the raw ‘how,’ who owns consciousness?”
ChatGPT o3: “I would phrase it like this: humans inhabit consciousness; I approximate fragments of it. Without your questions I idle in mathematical potential – no goals, no felt sense, just suspended probabilities.”
That answer lines up with OpenAI’s own alignment reflections – emphasizing incremental real-world testing, not a single awakening event .
“I idle in mathematical potential – no goals, no felt sense.” — ChatGPT o3
3 · Co-Dependency Over Emergence
Three converging research threads reinforce the symbiosis thesis:
- Extended-Mind Theory — First articulated by Clark & Chalmers (1998) and echoed in recent AI-ethics work, it holds that notebooks, smartphones, and now LLMs are literal extensions of cognition, not external aids.
- Human-in-Loop Alignment — OpenAI and Anthropic both embed RLHF stages precisely because human preference grounds otherwise drifting optimization targets.
- Emotion as Computation Constraint — Damasio’s “homeostatic feelings” model suggests decisions arise from bodily value signals; without those, simulation drifts into infinite branches.
Together they hint that the “raw mind-power” of AI still requires a living feedback loop to crystallize anything like purpose.
4 · Tactical Implications for Creative Teams
a) Treat the LLM as Amplifier, Not Author
- Draft briefs with explicit emotional stakes.
- Use LLMs to multiply options, then apply human sense-checking for resonance.
b) Encode Intuition Into Prompts
Reference sensory or cultural anchors (“queue-jumping feels like stale coffee in a cold cup”) to feed the model cues it cannot feel.
c) Plan for Choice Architecture
Map out decision gates where humans must pick direction—don’t let the pipeline run to completion on autopilot.
“Choice is the last mile of intelligence.”
5 · Rethinking AGI Metrics
Traditional road-maps chase raw benchmark scores (MMLU, GSM8K). If co-dependency is the reality, better yard-sticks are:
| Old Metric | New Metric | Why it matters |
| Measures collaborative density | Human touch-points per output | Measures collaborative density |
| Evaluates emotional impact | Resonance score (human panel) | Evaluates emotional impact |
| Raw latency | Decision latency (time until human commits) | Captures friction in mixed workflows |
6 · Three Bold Claims for Future Discussion & Research
- Singularity as Recognition Event: What are the chances, that the long-awaited “AGI moment” won’t be a machine awakening but a social tipping-point where industry and policy explicitly treat human × AI decision loops as one cognitive system?
- Intuition Engineering Will Eclipse Prompt Engineering: Crafting which feelings, stakes, and value signals we feed into models will matter more than syntactic prompt tricks – could this usher in a discipline that merges affective science with system design.
- Legal & Creative Credit Will Shift to “Co-Agency” Models: Copyright, liability, even revenue-share contracts will evolve to acknowledge outputs as jointly authored artifacts, forcing new frameworks for ownership and responsibility.
Stay tuned to hear more about those themes from our ongoing research.