A part of the following Re -humanize: How to create human -centric organizations in the era of algorithms Written by Fanish Puranam.
Engineers talk about the “design time” of a project. This is the time where the formulat design for a project must be effective. The design of the idea of this book is not measured in months or years, but as long as we continue the bionic companies (or vice versa, until we reach the zero-human organization). However, the rapid speed of development in AI, you can well ask, why is it reasonable to assume why the bionic age of the organization will be lasting enough to plan? In the long run, can people have any benefits (more than AI) that companies still make it necessary to include them?
To answer this question, I need to ask you one of my own. Do you think that the human mind does more than processing information? In other words, do you believe that what our brains do is more than the most sophisticated manufacturer of data and information? If you answer ‘yes’ you may probably see the difference between AI and humans – a one that can never be bridged and which refers to the period of our design quite long.
As it happens, my question is my own answer ‘no’. In the long run, I am not just confident that we can deny technologies that can replicate everything that people do now. If it is processing all the data, there is no reason to believe that creating a better information processing system is physically impossible than what natural selection has created from us. However, I believe that the duration of our design for the bionic organization is still at least a decade long, if not. This is because the time is on the side of Homo sapiens. I both mean both separate lifetime, as well as evolutionary times that have brought us to where our species is.
Throughout our distinctive lifetime, each of our data is revealed in the form of volume, philosophy, taste, touch and smell – and only after a lot, the text – is so large that the largest largest language model looks like toys. Computer scientist Ian Lakun, who led AI in Meta, recently observed that human children absorbed about fifty times more visual data in the four -year -old period than the text data sent to LLM training like GPT 3.5. A man will take multiple lifetime to read all those text data so that our intellect (initially) is not clearly not clear. Furthermore, it is probably the order that in any of these one accepts and processes these large amounts of data matters, only if it is possible (not at present) is not even able to get a single -time data dump.
This comparison of human data access benefits compared to machines has clearly assumed that the quality of processing architecture is comparable to humans and machines.
But even this is not true. At the evolutionary time, we exist as a distinct species for at least 200,000 years. I guess which gives us more than 100 billion individuals. Every child born on this earth comes with some different neuronal wiring and will receive very different data during its life. Natural selection works in these variations and selects for fitness. This is why human engineers are competing when they conduct tests in various model architecture to find the kind of improvement that natural selection has been found through blind variations, elections and holding. As intelligent as an engineer, at this point, there is a big ‘head’ of natural selection (if you forgive the rhythm).
How is the future of the workplace turning into artificial intelligence
It is still after the maximum cutting edge of our minds that are published on a wide range of effectiveness (we are all original-natural-general intellectuals!). We only remember and do not argue, we do in a way that involves influence, sympathy, abstraction, logic and analogy. These powers are above all newborns in AI Technologies. It is not surprising that these are very powerful among people that are predicted to have high demand soon.
Our advantage is also expressed in our brain strength skills. At the age of twenty -five, I assume that our brain consumes about 2,500 kilowatts of hours; GPT is thought to have used about 1 million kilowatts for 3 training. AI engineers have a long Ways to make energy consumption in training And their models are deployed before they start approaching the level of human skills. Even if the machines exceed the human capabilities through the extraordinary growth of data and processing power (and quantum computing magic, some enthusiastic arguments), it may not be economic for a long time. In Re -humanize, I am giving more reasons why people can be effective in bionic agencies, even if they lower the algorithm, they are different from the algorithm until they know. I see that the variation is protected because of the unique data we have as logic above.
Keep in mind that I did not feel the most important reason I could think of the most important reason for the continued people involved in the organization. Researchers studying guaranteed basic income schemes are looking for people who do not need money, but want to be included in companies and work. Rather, I say that the only pure target-centered factors are enough to expect the future of the bionic (nearest) future for us.
It was said that none of them are in the event of employment opportunities for people (problems for policy makers), or about human work situations (what I focus) in agencies. In our organizational life, we do not need AI technology to match or overcome their human ability to play an important role for them. We already live in bionic agencies and we can create a greater and widening interval between the target and the human center or can help to bridge that gap between the human center. Technologies for observation, control, hyper-specialization and work atmization do not need to be as intelligent as we are to make our lives sad. Just deployed to them – other people – DO.
We have already begun to see serious questions about the organizational context that digital technologies create in the bionic agencies. For example, what does our performance mean continuously and even predicting? To be managed by the algorithm, shaped and naked for our behavior with or without our awareness? What does it mean to work as well as AI to you about its internal works? It can see complex patterns in data that you can’t? Can it learn a lot more faster than you can learn it from you? It controls your employer in such a way that cannot be a colleague?
Quoted from Re -humanize: How to create human -centric organizations in the era of algorithms Written by Fanish Puranam. Copyright 2025 penguin business. All rights are reserved.
[publish_date
