Curvonomics (and the rise of AGI)
\"World-changing ideas generally evolve over time as slow hunches rather than sudden breakthroughs\"
In Where Good Ideas Come From, Steven Johnson tried to answer questions like: What sparks the flash of brilliance? How does groundbreaking innovation happen?
His counterintuitive conclusion? That world-changing ideas generally evolve over time as 'slow hunches' rather than sudden breakthroughs. Breakthrough ideas don't just appear fully formed like in the movies (arc reactors, flux capacitors, double-decker couches, shrink rays); in reality they evolve slowly, sometimes over years or even decades. They start as vague notions or partial insights that linger in the back of one's mind. Over time, these hunches connect with other ideas, experiences, and information, gradually taking shape and maturing.
Slow hunches take patience (good ideas often need time to develop fully), open mindedness (new contributions to the state of the art usually come from multiple sources in multiple disciplines), connectivity (letting new ideas interconnect, overlap, and dissect one another), and persistence.
There are lots of examples of this: Johnson cites the theory of evolution, the invention of the telephone and the web, discovery of penicillin, and more. On my recent appearance on the Big Technology podcast, the host Alex and I were joshing that the search for extraterrestrial life is probably another one.
I suspect the path to artificial general intelligence - AGI - will play out this way, too.
The Path to AGI is S-Shaped
It takes time for all the pieces of any invention to fully form: the language by which they are described, the layers of abstraction, the core capabilities and supporting structures required for success, the compounding factors which contribute more than the sum of their parts to its broad applicability.
I've talked before about how the arc of technology capability tends to follow an S-curve over time.
In the early stages of a technology's development, progress is often slow and incremental. This phase is marked by experimentation, prototype development, and the establishment of foundational knowledge.
As the technology matures, key capabilities compound on one another, resulting in a period of rapid improvement (and adoption). This phase is characterized by exponential growth - a \"hockey stick\" on the path toward a steep upward trajectory.
Eventually, the rate of improvement slows as the technology approaches its theoretical limits or market saturation. This results in a flattening of the curve at the top of the \"S\".
Retrospective Inevitability
AGI is not a singular, monolithic endeavor, but rather a complex interplay of advancements across multiple technological and scientific domains. And so, while AGI is likely to follow its own combined S-curve over time - the multi-dimensional nature of AGI progress suggests that its emergence will likely be the result of numerous S-curve advancements in various fields, each progressing at slightly different rates and phases.
Various components contributing to AGI development— natural language processing, machine learning algorithms, hardware capabilities, and knowledge representation—will each follow their own S-curve trajectories. These curves are likely to be slightly out of sync with one another, creating a tapestry of progress rather than a single, uniform advancement.
As these technological components advance along their respective S-curves, the markers of progress towards AGI are likely to become increasingly apparent, but separate: a new demo one week; a press release another; a new pre-print article a month later. Over time, we'll see sufficient advancement in enough places, that AGI - instead of arriving in a thunderclap instant - appears more and more inevitable. The aggregate process required to realize the technology will lessen. The gaps will narrow.
By the time there is an achievement of AGI, it may appear rather obvious and unsurprising, as the groundwork will have been laid visibly over an extended period. It may even be perceived as boring. A retrospective inevitability. This is a healthy and positive thing overall (except maybe for the breathless commentators who wonder aloud, 'what did Illya see?').
Whither, AGI?
Environments that foster slow hunches - by providing time, resources, and diverse inputs - are likely to be more conducive to innovation than those focused on any single immediate breakthrough.
If you agree, this perspective has implications for research strategies and resource allocation. Rather than focusing solely on breakthrough moments, it is more likely that the field would benefit from sustained, parallel efforts across multiple domains, with an emphasis on integration and synergy between advancements.
This lens of 'curvonomics' - multi-dimensional, asynchronous progress - suggests that while the journey to AGI may be long and complex, it is likely to be marked by visible, incremental advancements that collectively pave the way for this transformative technology. Counterintuitively, a single-minded focus on AGI may actually turn out to be a disadvantage rather than an advantage, to finding the steady path which delivers it, whatever 'it' turns out to be. I am deliberately leaving 'it' undefined here as today, your definition is at least as good as mine (if not better), and while for any given definition the specifics may be different, the general S-shaped curve to our path remains.