Four possible scenarios for humans at the maturation of AI
And a fifth, unthinkable one
This article was originally written as my industry research paper for the Future Scenarios course at Hyper Island APAC. To fully appreciate the article, please consider its intention and flow. The article begins with a landscape survey of the industry to identify emerging trends through signals in political, economic, social, technological, legal, and environmental contexts. Using this information, four scenarios are mapped out, one of which is fleshed out as a full narrative story to illustrate a possible future. A high-level strategy is then laid out to address this scenario. The article is capped off with a personal reflection, sharing what I realized about myself and about work through the course of this exercise.
I. The Landscape
A. Race to high-bandwidth AI
Earlier this year, Sam Altman, CEO of OpenAI, was reported to be raising 7 trillion dollars to “reshape the business of chips and AI.”(Hagey & Fitch, 2024) OpenAI, the company behind ChatGPT, has been leading the way in recent years by finally putting artificial intelligence in the mainstream. Trust in AI systems is increasing as companies convince users of its value through strategic approaches (Batra et al., 2018), one of which is making free, user-facing GenAI tools like ChatGPT, Leonardo, Midjourney, and many more available to the public.
An AI entrepreneur and theoretical neuroscientist, Dr. Vivienne Ming, argues that a productive way to coexist and adapt to the exponential development of AI is by treating AI as “augmented intelligence,” or a means to enhance our capabilities and capacities. (PCMag, 2017). And this is what we are starting to discover as we actively experiment with the different publicly available AI tools to help solve problems at speeds and breadth that we otherwise cannot achieve humanly. (Morris, 2023) With the current state of AI, we are able to process more information and are becoming exponentially more productive. Yet, as described by Donald Lim during the Building People for the Unknown Philippine panel discussion, we are still at the “dial-up” stage of Artificial Intelligence, referring to the early stages of this technology, akin to the dial-up days of the modern Internet era.
This punches a small hole in the opaque walls of corporate strategies of tech companies, particularly Altman’s 7 trillion-dollar fundraising campaign. How far is he taking AI technology? And what wrecking ball will 7 trillion dollars buy that will destroy current barriers to the maturation of the technology?
B. The Digital Economy
Whether this development leads to a bright future or a bleak one might depend on whether one benefits from the digital economy and which side of the digital divide one is on. The digital economy is a new economic form, described by the G20 Digital Economy Development and Cooperation Initiative as having two main characteristics: platform-based and non-contact. Businesses classified under this type of economy operate on or provide digital platforms and services, with information and knowledge as critical production factors. Their contact with end consumers is minimal or not required, relying on information infrastructures and the Internet to facilitate business activities. (Xia & Pei, 2021) Workers in the digital economy are required to have a level of digital literacy to work effectively.
The wave of AI has been reshaping the digital economy in ways that Ernest Hemingway best describes: “gradually, then suddenly.” By lowering the “cost of cognition” (Ai won’t replace humans — but humans with AI will replace humans without AI 2023), the adoption curve of Generative AI tools is forecasted to be even steeper than that of smartphones. (Ben-Avie, 2024) But critically, this only speaks about those with access to the Internet.
C. The Digital Divide
The digital divide occurs when access to digital technologies is not distributed equitably. It happens when a group of people, whether geographically, racially, or culturally, do not have access to information technologies, particularly the Internet. And this divide might very well determine whether the AI future Altman is attempting to pioneer is a utopia or a dystopian one.
This divide is a global phenomenon, with one-third of the global population not having access to modern information and communication technologies. (Worman, 2023) Statistics show that 70% of the world’s geographic space, involving 3 billion people, cannot access the Internet. (Xia & Pei, 2021) In as recent as 2019, ninety percent of women in the world are behind men in having digital access, and groups not connected through digital means are socially sidelined as they do not share the benefits of being digitally connected. (Steele, 2019) Furthermore, individuals with disabilities, low education levels, low income levels, and those who have inadequate infrastructure due to geographical location fall victim to this divide.
But the digital divide is an issue that is both systemic and personal. Systemic because the expansion of internet access moves slowly — decades, in fact, but have failed because “it’s not in their business model to do so.” (Ben-Avie, 2024) However, it is also a personal issue because general motivation and interest also factor in being on the wrong side of the digital divide. In this regard, education is a powerful influencing factor as it must address the three essential factors to make information accessible to more people, as established by the United Nations Committee on Economic, Social and Cultural Rights: acceptability and accessibility: acceptability, accessibility, and adaptability. (Orellana, 2021)
Acceptability is simply making the information appealing and relevant to those who need it. Education must make digital literacy an important-to-learn topic so that it becomes a public agenda, and may create a stronger market-oriented business case for service providers. Accessibility, the digital divide’s raison d’être, is both its cause and its effect. It’s about making information physically and economically accessible for everyone. (Orellana, 2021) The digital divide exists because of the lack of Accessibility, but its lack is also an effect of not having enough infrastructure, investments, and interest in the locations and people who might need it. And this cycle may result in a snowball effect that can continue to expand the digital divide.
In the US, 17.6 million students require minimum bandwidth to sustain digital learning, and globally 77% of jobs will require computer literacy. Furthermore, teachers who have no access to computers and the Internet cannot train on using these devices and facilitate digital learning to prepare the students for modern technologies, which are declared to be human rights in the Universal Declaration of Human Rights. (Orellana, 2021) These numbers are at grave risk of further increasing as developments in AI and automation get rolled out.
The third essential factor to information accessibility is Adaptability. (Orellana, 2021) This means requiring education to be flexible so it can adapt to the changing and varied needs of communities and cultures. This is why UN SDG 9: “Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation” is critical to address. (United Nations, 2016b) Without inclusive and sustainable industrialization, we cannot bridge the digital divide, and people on the wrong side of the divide will have difficulty accessing critical public goods and services, much more have quality and relevant education that are increasingly dependent on technology infrastructures to access. It is said that by 2030, if we’ve not made significant progress in bridging the gap, billions of people will fall further behind. (Worman, 2023) This goes to show that if we are not able to address the digital divide, lives and livelihoods will be at greater risk in the near future.
D. Structural Unemployment
Connect Humanity’s State of Dignity Inequity Report (2023) surveyed thousands of Civil Society Organisations around the world, and 95% of the respondents said that digital technology is critical to their work.
The same is being said about the digital economy. Enhancing collaboration between educational institutions and businesses has become imperative; establishing practical training facilities focused on big data, cloud computing, and artificial intelligence is now essential. (Xia & Pei, 2021) Some companies see this new business milieu as a boon, as industrial institutions will evolve to fit this new world, but this evolution also means shedding off the old skin of brick and mortar stores, offline transactions, and human sales agents to e-commerce sites, straight-through-processing, and online platforms, thereby causing large-scale unemployment. Unfortunately, this goes against UN SDG 8, which aims to promote full and productive employment for all. (United Nations, 2016a)
A main concern regarding AI and automation is its displacement of human workers. With the adoption of advanced technologies such as artificial intelligence and robotics by companies, some jobs may become redundant, resulting in job displacement and increased unemployment. For instance, the emergence of self-driving vehicles could potentially displace millions of truck and taxi drivers. This phenomenon is commonly called “Structural Unemployment.”
Structural unemployment pertains to individuals who are jobless as a result of permanent changes in the industry and in the economy, typically influenced by factors such as technological advancements. (Kumar et al., 2021)
While new jobs are opening as industries shift to new models, in as late as 2016, 80% of jobs, at least in the United States, are posted online only. (Closing the Digital Divide A Framework for Meeting CRA Obligations 2016) And if we infer how much it is for other less economically- developed countries, making the argument that technology is reducing frictional unemployment becomes less convincing, and the phenomenon of the digital divide contributing to structural unemployment more apparent. As long as science and technology continuously progress, economic structures will adjust accordingly, and structural unemployment will continue to happen and even grow. (Xia & Pei, 2021)
E: Breaking silos for the massive task
Bridging the divide, addressing structural unemployment, and breaking their cycle is a massive task. It will cost hundreds of billions of dollars (Ben-Avie, 2024), but it is possible if ‘we all do our part’. (Worman, 2023) A World Economic Forum White Paper (2023) argues that civil society is a crucial player in bridging the interests of the public and private sectors so that they can successfully advance shared interests. Philanthropies must recognise that digital equity is foundational to achieving wider and more significant goals. Governments, having the most financial power and policy muscle, must make policies and regulations to invest in hard-to-reach communities. (Worman, 2023) This cooperative approach needs new sources of capital: public funds, philanthropic donations, commercial capital, and impact investments, working together with public policy to achieve this goal (Ben-Avie, 2024) and to ensure that macroeconomic changes do not leave anyone behind. (Kumar et al., 2021)
II. Conceptual Framework
Given everything previously mentioned, what might happen in the next 10 years? My framework, which I call the “Why Compass”, illustrates the reasons behind my process of understanding the factors that have occurred in the past and what might happen in the future. By placing the “why” question at the centre, I have tried to make sense of what has happened and studied their patterns and relationships in order to envisage possible progressions of scenarios, and even imagine the unimaginable ones.
To briefly explain the framework, the left side, labelled “Because”, breaks down the relationships of drivers and trends by referencing past cause-and-effect relationships in the form of a problematique map. (Librero, 1993) The right side of the compass, labelled “So That / Then”, uses the variation of the futures mapping (Phillips, 1996) process to extrapolate and imagine possible scenarios, which can then be used to build strategies.
Summarising the previous section, Because maps out how the digital economy has highlighted the digital divide. As companies reorganise to adapt to new technologies and the new economic paradigm, many individuals around the world are being let go and become structurally unemployed. The diagram below illustrates this.
Having worked on the drivers, I extrapolated what might happen and imagined what might be unimaginable to form four scenarios in the form of a 2x2 matrix. The Futures Map below illustrates the process of coming up with the scenarios:
III. Four Scenarios
The four scenarios in the Futures Map are fleshed out below.
Adding additional depth and substance to these scenarios, I referred to the work of Kai-Fu Lee. In his TED talk in 2018 (Lee, 2018), he presented ways in which humans can adapt to a Human-AI hybrid workforce. In his talk, he presented a 2x2 matrix that demonstrates how optimisation, creativity or strategy, and compassion might create four human-AI work conditions. The diagram below illustrates these intersections.
- Creativity or Strategy — Compassion Needed (Human + AI): These refer to jobs that require problem-solving and discovery of new ideas but also awareness of human idiosyncrasies and nuances like like CEOs, Social Workers, Concierges, PR and Marketing Directors.These jobs can be led by humans with AI as tools to augment their abilities:.
- Optimisation — Compassion Needed (Human (AI)): These are jobs that require an understanding of human behaviours but with practices and processes that can be standardised and optimised. These jobs, which include Wedding Planners, Caregivers, Remote Tutors, Tour Guides, and more, can be undertaken by AI, but managed and moderated by humans.
- Creativity or Strategy — Compassion not Needed (AI / Human + AI): These jobs require problem-solving and discovery but are systematic, objective, and do not necessitate care and compassion, such as columnists, economists, scientists, and research analysts. These roles can involve a “partnership” between humans and AI, with AI leading the automated work and generating suggestions, while humans focus on creative or novel problem-solving and supplementary decision-making.
- Optimisation — Compassion not Needed (Full AI): Examples of such jobs that can be automated and do not require compassion and emotional warmth include customer support, security guards, telesales, dishwashers, haematologists, and others. These roles can be fully automated and replaced by AI systems.
Scenario 1: Dreary Drag
“The new world will remember that we depend on each other when they slow down.”
(Click here watch the broadcast)
The first scenario envisions the possibility of unemployment rates causing a recession, leading to a slowdown in technology advancement and adoption. Humanity gradually adjusts to the new way of life shaped by artificial intelligence’s massive socio-economic impact. The digital divide will hinder people on the other side of the gap from quickly adopting and even finding jobs, exacerbating their challenges. Meanwhile, metropolitan areas will experience exponential innovation. However, this progress will stall as the global economy slows down due to the vicious cycle of the divide and rising unemployment. The economic downturn will persist until more technologies trickle down, whether through policy or philanthropy, to the late adopters and tech laggards on the other side of the divide. The ability to afford new technologies will eventually enable people to find new jobs.
Scenario 2: Human Ballast Bags
“Infinite growth comes at an infinite cost.”
In this future, humans become redundant in pursuit of growth goals, and procreative AI systems are developed. AI’s pervasive influence on the global economy turns it into a significant investment sinkhole. The majority of multinational companies adopt AI systems that automate almost all “Compassion not needed” jobs and significantly reduce “Compassion not needed — Creativity” roles. As shareholder expectations soar, the development of AI systems must accelerate. However, human capabilities have limits. “Procreative” AI systems — systems capable of creating their own systems — are developed, rendering even the role of AI systems developers obsolete. This unleashes a cycle in the artificial intelligence field, with AI systems replacing jobs at an accelerated rate, outpacing humans’ ability to adapt. The digital divide escalates into a global existential crisis.
Scenario 3: AI-shaped Abyss
“Digital will create a modern caste system.”
This scenario envisages the emergence of a new social hierarchy based on digital literacy. The widespread adoption of AI exacerbates the digital divide to such an extent that bridging the gap becomes nearly impossible. Society becomes stratified into “digital classes,” with individuals classified based on their level of digital literacy. The digitally literate can navigate across various industries and job roles, while those on the other side of the gap are relegated to manual labour that digital-class citizens shun. These “digital pariahs” will be significantly disconnected from new technologies, requiring several steps up the proverbial digital literacy ladder to cross over.
Scenario 4: Digital Posthumanism
“If robots are exactly like humans but better, then we don’t need humans.”
In this scenario, AI and automation become indistinguishable from humans. Artificial Intelligence technologies reach a critical point where they can accurately mimic human behaviour, particularly in affective fields, making them seem like organic humans. This leads to widespread acceptance of posthumanism, eroding the old worldview of anthropomorphic dominance and ushering in the age of artificial beings or robots. “Compassion not needed” jobs are rapidly replaced or cost-reduced by AI systems, resulting in significant unemployment. It will take a few more years before the laid-off talents can reintegrate into the professional class, but only after companies adapt to new AI technologies, level the playing field of competition, and rediscover the value of the human touch in the workforce.
Futures Cone
I positioned the four scenarios on the Futures Cone. “Digital Posthumanism” is a possible scenario, but it is not as probable or plausible because it challenges our own sense of existence. “AI-shaped Abyss” and “Human Ballast Bags” are likely scenarios, but they would require not only further technological revolutions but also political decisions to materialise. Based on the evidence I gathered, “Dreary Drag” emerges as the most plausible and therefore the most desirable scenario among the four.
IV. Scenario story
“Antenang Bato” tells the story of a person who lost her job to AI and was unable to find a new job due to the digital divide until the government intervened to mitigate rising global unemployment rates. This story serves as an example of the scenario “Dreary Drag.”
Antenang Bato
Maria had dedicated years of her life to her job as a security guard at the Mabuhay Printing Press, but when technological advancements swept through the industry like a tidal wave, her role became obsolete overnight. Alongside her, many of her colleagues and friends found themselves out of work, casualties of a shifting landscape that prioritised automation over manpower.
With no prospects in the city, Maria made the difficult decision to return to her hometown nestled in the mountains of Nueva Vizcaya. Enduring an arduous 16-hour journey of bus rides and jeepney trips, she finally arrived, only to find herself faced with another challenge: the lack of reliable internet access in her remote village.
Reuniting with her family brought a sense of comfort amidst the uncertainty. She reunited with her parents, nephew, and her brother who works as a mobile phone repairman in the next town. Despite the sweet reunions, Maria knew she needed to find a way to support herself and contribute to her family’s well-being. Determined, she walked around her town and sought out a spot atop a rocky outcrop where she finally found intermittent access to the internet. She dubbed the spot “Antenang Bato,” which means “stone antenna.”
Maria visited Antenang Bato every afternoon between 2–5 pm when there were fewer clouds to block the signal coming from beyond the valley. For three hours every day, she would try to get news from the city and message her friends about possible job openings. But despite her efforts, Maria struggled to find employment in a world increasingly reliant on AI security systems.
In a desperate effort, she goes to the local government centre to ask about livelihood programmes or training that might be available, particularly on digital. However, the town captain regretfully said that they can’t provide those kinds of training as they don’t have the necessary equipment nor trainers.
Frustrated by the lack of resources and support in her hometown, she persisted, continuing to visit Antenang Bato every day to learn what she can learn. A few months pass, Maria, still unable to find a job in the city, decides to learn physical trades like doing manicures and pedicures, and sometimes helping out in the local bakery in the city. She notices that the population in her town is increasing as more and more people come home, most after having lost their jobs in the city.
One day, while she’s on Antenang Bato, Maria sees on Facebook that governments around the world and the United Nations will be investing heavily in digital literacy and upskilling on digital skills to increase global employment rates. Excited by the prospect of global initiatives aimed at empowering individuals like her with essential digital skills, Maria felt a renewed sense of hope and determination.
Maria returns to the town hall and asks about training. She was able to reserve a spot for training as a Large Language Model trainer. She undergoes training and eventually gets hired as a trainer for LLM systems, equipped with a laptop, portable WIFI, and a sign-on bonus.
V. Strategy
“Antenang Bato” represents a low-impact, high-risk story that is more likely to occur. Transnational groups like the United Nations’ Sustainable Development Goals and governments’ funding policies can be seen as signals of the direction they are taking. Among the four scenarios I presented, “Dreary Drag” is my preferred future. While there are certainly more optimistic scenarios imaginable, historical precedents such as the industrial revolutions and, more recently, the COVID-19 pandemic have demonstrated that leaving some behind slows down progress for everyone. Therefore, in the event that “Dreary Drag” materialises, how might we gradually pull everyone back to catch up and thrive in a mature AI-enabled digital economy?
Utilising the strategy framework by Howell Malham Jr. (Howell, 2013), I have crafted a high-level strategy below to address the aforementioned challenge.
Goal
The goal is to reduce structural unemployment caused by industry shifts, exacerbated by the lack of digital literacy and inadequate access to quality digital services.
Plan of action
The Theory of Change template below by Stories of Impact (2022) maps out the inputs, activities, and outputs (Connel & Kubisch, 1998) to achieve the goal.
In the event that the Dreary Drag scenario unfolds, individuals who lose their jobs due to industry shifts will struggle to find new employment opportunities because of the lack of digital access and literacy. They will require stable employment that aligns with modern industry needs, along with continuous upskilling opportunities to avoid future job losses. This is significant on a global scale because reducing structural unemployment improves the overall status of the global economy. However, achieving this goal necessitates three main actions: firstly, improving global digital literacy rates; secondly, reducing the digital divide by expanding access to digital services; and thirdly, addressing unemployment stemming from insufficient digital skills. This is where the education industry plays a very important role.
The successful delivery of these outcomes depends on several crucial preconditions. Firstly, technological and economic superpowers such as the US, China, Taiwan, Israel, and Russia, among others, must agree to collectively address this issue and cooperate on a concerted effort. In addition to nations, major tech companies like Google, Apple, Meta, and Sam Altman’s OpenAI, as mentioned earlier in this paper, must also be willing to cooperate and share necessary information. And lastly, the Training and Education sector must be given adequate financial support in order to “train the trainers” and multiply the effects. These are monumental tasks, hence the third precondition is the necessity of political will among those who will drive and implement the required changes.
If these preconditions are met, then transnational policies supporting the development of information technology infrastructure, infrastructure projects, and training and placement programmes can be collaboratively created. This can be achieved through the formulation of transnational strategies and policies, discussions involving governments, industries, and the public sector, and the development of a global transformation roadmap. Inputs such as macro and microeconomic studies, policy proposals, state of technology reports from technology companies driving AI and automation, and state of humanity reports, including risk-benefit analyses, are crucial to kickstart the change process.
Metrics
The primary objective of this programme is to reduce, if not eradicate, structural unemployment. However, given that technological advancement is an ongoing process, this issue is expected to persist. Nevertheless, to accurately assess the effectiveness of programmes, we can examine reductions in “Cyclical Unemployment” (Nickolas, 2024). Additionally, we can evaluate digital technology adoption rates and the reduction of “digital deserts” — areas where there is little to no Internet infrastructure (Wang, 2020).
Ethical implications
Despite the anticipated changes resulting from this strategy, there will be consequences and implications, both foreseeable and unforeseen. One potential ethical dilemma that may arise is the impact of standardised programmes on cultural diversity. While technology is expected to enhance the use of cultural resources in communities, the standardisation and digitalisation of training and education may lead to the narrowing down of curricula to subjects, approaches, and agendas dictated by technology companies. While this could result in globally-aligned curricula and skills, along with lower dissemination costs and greater accessibility, materials may lack cultural relevance and context, and may even be unacceptable to communities. Furthermore, as information from the Internet forms the basis of machine learning models, they may contain inherent biases, stereotypes, and historical discrimination, which could seep into learning materials and perpetuate through the learners.
Another ethical implication to consider is the potential reduction of the roles of teachers or trainers. As this strategy aims to produce standardised results as quickly and effectively as possible, it may involve supervising and limiting teachers through automated decisions, thereby preventing them from crafting teaching approaches tailored to learners’ needs, interests, and realities. This could also contribute to worsening working conditions and deprofessionalisation (Barry, 2022).
To address or mitigate these ethical challenges, governments need to develop teachers’ digital competencies and place more trust in their abilities and decision-making. Achieving such a monumental task begins at the grassroots level, with people working together to address one small issue at a time.
VI. Personal Reflection
Among all the papers I’ve written at Hyper Island, this was the paper that intimidated me the most because I realised halfway through (as I was writing the Why Compass) that I am not as imaginative as I once thought I was. What I’ve been doing in recent years is not imagining, but extrapolating. When I was younger, I remember being very imaginative. I would come up with weird stories, draw unusual figures and objects, and think of outlandish ideas. But in the past few years, as I transitioned into more management roles, I found myself having a harder time imagining scenarios. Since then, I found frameworks to be my crutches.
I’ve shared with friends, crewmates, and even officemates that information design and frameworks are my strong suits. And that may well be true, but now thinking about it, I used them as tools to scaffold what I thought was my imagination or creative process. However, when faced with the task to imagine the unimaginable and to speculate on the future, I realised that my frameworks only allowed me to extrapolate but not imagine. This was a major, even humbling realisation: to see that I have no clothes on. I don’t imagine. I extrapolate. And this is why I titled this IRA “The Solitaire Strategy.”
“The Solitaire Strategy” is not the strategy I fleshed out in the previous chapter but the strategy I personally apply to my life. Some call it being a lone wolf, some see it as arrogance, but I see it as a coping mechanism, no, more than that, a survival mechanism. And it is flawed.
I’ve always lived inside my head since I was a young child. Upon discovering that I was part of an “accelerated class” in first grade, I was informed that if I remained in the class until sixth grade, I would skip a level to First Year High School. For six years, as a child, this goal was set for me, and it became a weight I carried; fortunately, I succeeded. However, the burden of maintaining a focus on a target years ahead persisted into college when I had to apply for an academic scholarship so that my brother could benefit from my parents’ educational plan instead of me. This baggage continues to accompany me into my professional life, even today. Since childhood, I’ve played solitaire, attempting to impose order on a shuffled deck of cards. I didn’t have control over how the cards were shuffled, but I could control how they were sorted out.
What this exercise has taught me is that “The Solitaire Strategy” is the wrong strategy. Much like the concerted efforts required to address the Dreary Drag scenario, breaking out of my imagination freezer is not supposed to be a solitary task. “Team is everything”, as the Hyper Island slogans say; and in imagining, having people around you to help is as important as it will ever be. This is why in the process of writing this IRA, I sought the help of a few of my friends and family to break out of the extrapolation trap and begin thawing my frozen imagination.
And this is why I came up with an unimaginable, fifth future scenario: “Tech Reclusion.”
Upon reflection, I realised that the four initial scenarios I created seem to come from an urban-centric perspective. They focused on the potential effects of AI on society with an implicit belief that the technologically-updated urban way of life is the right life to live, and that the digital divide is a challenge to overcome.
The Iceberg illustration below shows the causal layers contributing to the problems of job loss due to the digital divide, technological advancements, and structural unemployment.
What this model revealed to me, prompting the writing of the fifth scenario, is that the notion of modern technology improving human life is simply a myth. Last week, my wife and I visited her home province of Oriental Mindoro, where we spent time at our house and small farm. As I conversed with our farm caretaker, Santo, I found myself pondering: “Does he even know what AI is? And does he even care?” Would people living in rural areas, who grow their own food and sew their own clothes, be concerned about losing a desk job to artificial intelligence? And in the event of another disaster, whose job is truly important? Is it the farmer who grows food or the AI prompt engineer? This is the central idea of the fifth, unimaginable scenario: What if what we are doing is not important at all?
Scenario 5: Tech Reclusion
“We don’t need your technology but you need our goods.”
(Click here to listen to the transmission)
Artificial intelligence will advance to the extent that technological laggards are unable to integrate into the new tech ecosystem, while late adopters struggle even more to catch up. Consequently, the digital divide will widen and deepen, sparking a new kind of revolution: a retreat from modern technologies. These technology refuseniks will break free from the dominant influence of the digital economy and revert to simpler, more analogue, agrarian lifestyles. Individuals will rediscover traditional trades such as agriculture, carpentry, weaving, and cookery, establishing self-sustaining communities. Although these communities may still utilise forms of communication technology to interact and trade with modern-device-using traders, they will increasingly rely less on such technologies themselves.
VII. In closing
I previously likened much of my life and work to playing solitaire. As a creative thrust into management roles without proper training, I initially believed that management simply involved monitoring and maintaining order — a Complicated problem. However, each day I am reminded that management varies greatly from one company to another; at Hyper Island, it is Complex, and at times even Chaotic, as it revolves around people, not just processes.
Managing, especially at Hyper Island, is not akin to playing solitaire, but rather to participating in a dragonboat race. These are entirely different endeavours that require a complete mental shift. It is not solitary entertainment enjoyed from the comfort of a desk, but rather a dynamic event where individuals must learn to move in sync with others while fulfilling their specialised tasks based on their position. It is a game of efficiency, cooperation, and speed aimed at reaching the team’s goal as swiftly as possible.
That is, until Sam Altman develops the AI chip and replaces us all.
VII. References
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Connect Humanity (2023) ‘State of Digital Inequity: Civil Society Perspectives on Barriers to Progress in our Digitizing World’. Connect Humanity.
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Librero, F. (1993) ‘Towards a methodology for Problematique Analysis: A Philippine experience’, Asian Journal of Communication, 3(1), pp. 84–102. doi:10.1080/01292989309359574.
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Nickolas, S. (2024) Structural vs. cyclical unemployment: What’s the difference?, Investopedia. Available at: https://www.investopedia.com/ask/answers/050715/what-difference-between-structural-unemployment-and-cyclical-unemployment.asp#:~:text=Cyclical%20unemployment%20happens%20when%20the,a%20drop%20in%20their%20profits. (Accessed: 24 March 2024).
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