The Global Automaton Fallacy
Is the Tech World Becoming Stupid?
Automation is a task or process that operates without human control.
An automaton is an automated mechanism/process or machine/robot.
A global automaton is a robot that i) can do things that humans can do, ii) can create processes that control other robots, iii) can create other robots.
The global automaton fallacy is a belief that there exists, in the near future, a global automaton that can yield positive economic results for its creator.
What’s the Demand?
Assume a global automaton that can i) provide expert tutoring to architecture students, ii) create full architectural drawings for architects, designers or hobbyists using some inputs about land-size, preferences, location, etc, iii) can be connected to a massive 3D printer that requires only the measured construction material as ‘ingredients’ to complete the job.
The creator of this automaton has to ask himself/herself the following : what is he or she in the business of? Is it in ‘knowing’/teaching everything there is to know about architecture ; or is it in creating drawings ; or building construction?
The first problem (of demand) with trying to create the ‘global automaton’ of architecture (in this example) is that each of these ‘tasks’ are complex enough to require a lot of ‘training’, ‘testing’, ‘assessment’ and ‘proof’ before they can be trusted by potential customers.
The second problem (of demand) is that pricing will necessarily be complex since the ‘jack of all trades’ automaton is not just a master of a singular task that could be easily quantified or compared with market rates.
The third problem (of demand) is that the creator will be faced with the unenviable challenge of marketing or rationalising the value or utility of the ‘global automaton’ to both potential investors and customers. To investors, how will the creator justify the need being addressed or the problem being solved? To customers, how will the creator justify the benefits of the product/service or the switching costs from using human substitutes?
What’s the Supply?
The reality that any ‘global automaton’ potential creator has to face is one of motivation, costing, intent, rationalisation and opportunity costs.
The first problem (of supply) is that of motivation. Motivation is very much like a contagious disease. Both the lack or the possession of it can spread around the company very quickly; especially for a startup that doesn’t have ample funds to ‘motivate’ people to switch jobs or career paths. But without a clear goal as to whether one is building ‘the best tutor automaton’ for architecture students, or ‘the best designer automaton’ for architects, or the ‘the best construction automaton’ for builders, motivation is bound to be hard to come by. In the rare case that it is found, it is bound to be short-lived as the task at hand involves the creation of something that is better than average (for it to be economically viable) at three extremely complex tasks (and that might still not fulfil a real need or want).
The second problem (of supply) is that costing will very likely be extremely prohibitive vis-a-vis either focusing on one local automaton or just hiring human substitutes.
The third problem (of supply) is that of intent or vision. Imagine if the founder-creator was to realise that all he ever really cared about was faster, cheaper, better and elegant building constructions. In which case, not only are the first two local automatons irrelevant, but there possibly exists a way to achieve the same goal without creating an automaton to do so.
The fourth problem (of supply) is that of (internal) rationalisation. Setting aside the possible ‘ethical’ questions as to whether such a robot should exist, the question could be whether it is actually faster, cheaper, better and elegant than human substitutes. Why spend time ‘creating’ a (possible) Frankenstein when one could be performing the actual tasks of (architecture) tutoring, designing and building construction? Is this time, money and talent effectively spent?
The other related problem of rationalisation is that expertise or (i hate the word) specialisation. Is one an expert at creating automatons or at all things concerning architecture and construction? How and who assesses ‘a job well done’ let alone ‘better than average’? Aren’t both of these complex and valuable enough to require focus and optimisation over a sustained period of time? Besides if you wanted to improve the products or offer compelling benefits to the customer for the customer to switch or change behaviour, is it really economically (or psychologically) optimal to strive to do both monstrous tasks?
The fifth problem (of supply) is that of opportunity costs. Assuming that there exists a market for both (architecture) automatons and for faster, cheaper, better and elegant buildings/architecture, why not assess which market is bigger and the least costly? What is the lead time to market between the two options? What happens years after the automaton has been built, when the market factors, tastes, laws, and competitors have changed? How customizable or anti-fragile is such an automaton to unforeseen black swan events? Meanwhile, the costs and complexity of building the automaton may not even bring the benefits envisioned to justify the endeavour.
All those resources, all that time, all that money, and all that talent, could have been spent on things that are more profitable, more effective, more ground-breaking, more fruitful and more exciting for everyone involved.
