The second half of the chessboard and what it means for organizations
The second half of the chessboard
is a phrase, from a seminal essay by Ray Kurzweil about the rate of change in digital tech that contained stunning observations about the rice and chessboard problem, to illuminate the future power of the Moore’s law process.
This is a beautiful narration of the rice and chess parable by Andrew McAffee:
It should be pointed out that as the emperor and the inventor went through the first half of the chess board, things were fairly uneventful. The inventor was given spoonfuls of rice, then bowls of rice, then barrels. By the end of the first half of the chess board, the inventor had accumulated one large field’s worth (4 billion grains), and the emperor did start to take notice. It was as they progressed through the second half of the chessboard that the situation quickly deteriorated.
One version of the story has the emperor going bankrupt as the 63 doublings ultimately totalled 18 million trillion grains of rice. At ten grains of rice per square inch, this requires rice fields covering twice the surface area of the Earth, oceans included. Another version of the story has the inventor losing his head.
Ray Kurzweil from “The Law of Accelerating Returns”
Even still during or hopefully soon after the covid-19 pandemic, the rice corn legend — or often referred to as the wheat and chessboard problem — is still highly valuabe to explain the disability of humans to anticipate exponential developments.
It is in the later phases of exponential growth that the effects become extraordinary, and beyond all common-sense (of human sense) models or bluntly put: in an era of exponential increase in the power of technology, things get only get really interesting in the second half of the chessboard.
Kurzweil also uses this as part of building the case for the singularity — a point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. This point in time was initially hypothetical and there is a wonderful discussion with updates going on whether we will have a fully working working AGI (general artificial intelligence) around 2030, just before 2045 or more around 2060 or wether the next release of openAI’s GPT will end this discussion much sooner. But that is a different post.
Why do organizations need to care?
An organizational strategy is a plan that specifies how a business will allocate resources (i.e. money, labor, inventory, etc.) to support infrastructure, production, marketing, inventory, and other business activities. It is usually based on a set of research backed assumptions derived from a set of formal planning procedures that ensures examination of major strategic issues faced by their organization and valid for a defined period of one to ten years.
But in the recent years corporate strategies have reached their best before date sooner than ever before and the strategy maps look more like rutted fields and fail miserably at serving as a north star for managers and employees.
“In times of turbulence the biggest danger is to act with yesterday’s logic.”
Peter Drucker
A couple of things to consider
- Real disruptions (exponential growth) seem to appear over night, but actually take about ten years to develop (i.e. grow from the first half of the chessboard).
- No Powerpoint scaling hockey stick scenario can fool the slow growth on the first half of the board.
- Companies don‘t fail because they do the wrong things. They fail because they keep on doing things, that were right in the past.
- The tenure in a management position in corporates is usually shorter than it takes for a new capability to resurrect from the trough of disillusionment.
- Patience and diligence with developing capabilities that will eventually reach the second half is a skill and motivation that has not enabled careers in corporates. If at all people found companies or join start-ups. Consider that a loss.
- Weak signals, multi-lateral business and value cases help separating slow linear growth from early phase exponential growth.
Field 33
Field 33 helps large companies connect and better manage the increasingly complex interactions of strategic goals and external influencing factors. In contrast to previous approaches based on combinations of specific consulting methods and broad data science, Field 33 delivers predefined profiles of domain knowledge that are selectively populated and networked with data at critical points. Field 33 customers thus receive a holistic foundation of knowledge and data that is iterated into an increasingly complete and accurate digital twin of the organization. This approach is used primarily in areas where interactions across system boundaries are important and organizational change is difficult. For example, Field 33 quantifies the effects of digital products on business goals and finds the right organizing principle for a business unit. For managers without a technical background and in real time, i.e., at the push of a button.
The 33rd field is the first field on the second half of the chessboard.
