The Capability Curve and Outsourced Thinking
We usually think about the complexity of human thinking as increasing over time: as we’ve become better at expressing and sharing ideas we are able to “think” better thoughts that more accurately describe reality. This is partially true, it is also a triumph of outsourcing. The trajectory of intelligence is likely a long, sloping curve downwards from a theoretical apex of "pure" independent thought toward an accelerating reliance on collective intelligence.
This transition, from local and biologically processed thought to a distributed external network has defined civilization and progress. AI is a discontinuity in this process, however, because it is the first communicative, synthesized, intelligence that is generated outside of the human mind. There is no better place to see all this happening than in the early-stage world! This is why we’ve been so fired up at Character Capital recently.
In the past, say 100,000 years ago, the cognitive burden of modeling reality, predicting outcomes, and making decisions of actions to take was overwhelmingly determined by an individual. This is what I mean when I say the “compute” was almost completely local. Communication was rudimentary and the bandwidth for transferring abstract concepts or complex worldviews was severely rate limited. In addition, the fidelity of the knowledge transfer was low and the accumulation of collective wisdom was very slow, limited to the fragility of human memory. This human memory expired fast! Life was short and rough for our ancestors, but they were primarily thinking for themselves.
Things have changed dramatically since then. A lot of our capability has been driven by our success in offloading memory and computation to the environment and collective of humanity. This concept, of course, has been studied and is referred to as the “extended mind” by Clark and Chalmers (The Extended Mind Hypothesis, 1998). Several distinct epochs have arisen:
Spoken language: the first major transition of thought from immediate experience. This is one of the key things that allowed for the compression, and transmission, of complex information as a step towards a more networked collective intelligence. One of the major benefits was that it allowed each person to gain knowledge without having to relearn everything from the start.
Writing: knowledge became further decoupled from the individual human experience when it exceed the duration of the human lifespan. This is one of the first major shifts for memory being located outside of, and more expansively, than in a single human brain.
Printing press: one of the first distributed ways to replicate external memory. This allowed the cost of information to plummet. Some beneficial second-order effects included a trend toward the standardization of knowledge, and concurrently, the reliance on the collective mass of learned knowledge increased.
Computers: the calculator and the computer marked the outsourcing of algorithmic processing. This allowed people to offload procedural tasks and freed us to think in higher-level abstraction.
While monumental, each of these innovations were fundamentally derivative because they did not introduce external synthesis. To wit, a book is an unconversable artifact of human thought, a database executes human-defined queries, and a calculator performs human-defined algorithms. Each of these tools were multipliers, but the information originated from human minds.
A simple way to think about this concept of change in workload between the independent and collective mind is to develop a simple model to talk about it. Let’s call the Ratio of Independent Computation R(t). This is the proportion of cognitive processing that an individual completes internally relative to the total cognitive processing available.
Now let C_ind represent the biologically constrained processing capability of a single person. For now we will assume this has remained relatively stable since behavioral modernity. In addition, let K_ext(t) represent the accessible externalized knowledge and computational capacity at time t. This concept of collective intelligence was described by Hutchins in 1995 in Distributed Cognition.
Then a basic model the Ratio of Independent Computation is:
R(t) = C_ind / (C_ind + K_ext(t))
A long time ago K_ext(t) was close to zero and so R(t) ≈ 1. As civilization has grown, K_ext(t) has increased, and is autocatalytic: knowledge accelerates the creation of further knowledge. This means that it is not a linear accumulation of accessible knowledge, but an exponential increase. The rate of this exponential growth follows major communication breakthroughs (language, writing, print, digital networks.) So as K_ext(t) becomes much larger than C_ind then R(t) approaches zero.
This graphic is neat, but it is also characterized by the accumulation of knowledge from humans. AI makes a qualitative shift in the nature of K_ext(t) because these systems operate through processes (like high-dimensional vector navigation, emergent pattern recognition) that are distinct from human cognition. They are able to synthesize and generate outputs that are not derived or explicitly programmed. Recall that in the past human minds were required to operate technologies and interpret their output into actionable intelligence.
AI represents active external cognition that models reality and generates information that mediates our interaction with the world based on a person's high-level intent (the prompt.) It is different from an inert dusty book on the shelf or an old-school TI-83. Amazingly, AI turns K_ext(t) into an engine of cognition rather than just a repository.
This shift has major economic and social implications (as has been frequently and sometimes vociferously debated over the past few months.) Previously people were limited by their biologically constrained rate C_ind to generate and share knowledge, but active external cognition removes this bottleneck.
My co-founder JZ and I have been thinking a lot about the inverse of this declining slope of independent computation which we call the “capability curve”, the measure of what civilization can achieve, because it has been rapidly steepening. The acceleration is due to the outsourcing of the innovation process itself! We see AI being deployed to solve difficult problems (and some not so difficult logistical chokepoints of thought and language) at a pace that human cognition can not match.
Startups are well suited to use this new form of K_ext because they are not shackled by the legacy processes that were designed around the limitations of C_ind. The unprecedented change in the capability curve is directly tied to the harnessing of an intelligence that scale algorithmically rather than biologically. We’ve decoupled capability from human cognitive limitations, that is an interesting development!
The world as we know it is an exodus from the solitary mind. We’ve outsourced more and more cognitive burden to the collective and gained unprecedented power in exchange for independence of thought. For thousands of years our collective intelligence was a reflection of ourselves, an architecture built by and utilized exclusively from human thought.
AI has closed the book on this anthropocentric cognitive era. It is the introduction of an intelligence that is truly different. Our relationship with reality is now mediated not just by our own worldview, or the accumulated knowledge of our ancestors, but by an evolving non-human cognitive process. The declining slope of independent computation is approaching its asymptote, and it signals a new hybridized cognitive age.
