AI: The Game-Changer We Never Saw Coming
Discover the Power of AI and How It's Impacting Our Society
Hello everyone,
I am excited to announce my first analysis; more will come every second Tuesday. If you have any questions or suggestions for further investigation, please comment, like and let's engage.
Let's dive into today's topic. For my first blog it must be the wave currently generating euphoria and fear:
AI and gAI (Generative AI)
The most intriguing aspect of the wave is that many didn't have it on their radar. Companies, media, and investors shared their thoughts about the Metaverse hype, which is at least a decade away. The missed attention rais the question if AI and gAI were a trend or something else. Many treated it like a trend, but for me, it was an "Attractor." Not a Black Swan that no one could foresee, nor a trend alone, but a "gravitational force." Here's my definition of attractors (please find more about the definitions I use in this blog):
"Attractors are a 'gravitational force' that describes the accumulation of changes around a Key Topic. This aggregated element can be a trend, vector, variable, or event. Attractors don't draw attention but slowly accumulate or move...but this can change abruptly. A stimulus that crosses a threshold is enough to transform an Attractor into a 'wildfire' or even a 'Gray Swan,' one of those 'small changes that can make a difference' or so-called 'tipping points.' As Attractors build up slowly, the organization might argue that the transformation is already integrated into their strategy or deny that the change exists. Then, all the connected variables suddenly shift together, creating a strong momentum and changing the game's rules." - Daniel Egger, 2015
AI has been gradually growing in integration into solutions, investments, and even the number of AI publications, with a notable increase starting in 2017. This growth results from the advancements in Machine Learning and Pattern Recognition (AI Index Report 2023). We can observe a steady development in AI. Nonetheless, we ignored its gravitational force, the examination of its influence, and the pace at which it could potentially assimilate into existing processes.
gAI is now here, and it's here to stay. Anticipating my future impact analysis, although it is technologically triggered, the primary impacts will be societal, individual, and organizational. This argumentation doesn't mean that technology will remain static; it will continue progressing, and societal implications will evolve.
To explain this reasoning, we can observe similar past technological movements that changed society through technology:
The Industrial Revolution introduced significant technological advancements, leading to societal shifts such as rapid urbanization, altered family structures, and the emergence of the working class
The Green Revolution increased food production, resulting in societal implications like transformed rural economies, environmental concerns, and debates on food security
The Internet revolution redefined society through altered social interactions, commerce, and politics
The widespread adoption of smartphones revolutionized communication, information access, and socialization, impacting political participation
gAI is a potent force that will continue to shape our future, just as previous technological revolutions have transformed society. As we move forward, it's crucial to remain open to questioning and exploring the possibilities and implications of these advancements. By doing so, we can better align ourselves with what's coming, seize opportunities, and create a more informed, innovative, and resilient society.
We can now include AI in this list of transformative technologies, indicating that we are witnessing the dawn of a new era. Reflecting on the past 40 years, we've experienced significant shifts brought about by the internet and mobile phones. This velocity of change demonstrates our inherent adaptability and resilience in the face of change. While embracing change may not always be easy, we shouldn't fear gAI and AI. Instead, let's focus on finding our place in this new reality, or better yet, collaborate to shape the future together. Ultimately, gAI and AI can be seen as a "synthetic collaboration," taking the co-creation of solutions and coexistence to new heights.
The Analysis:
At Trend Hacker, I employ various tools. For example, for the analysis of AI, I use the Wave Template, which represents the following:
The X-axis represents timeframes, generally in intervals of 2 years, looking at the future investigation in 2025, 2027, and 2029. In the chart, you can find how and what AI and gAI could impact us in the future concerning technology and social matters. I use reports from Deloitte, UNO, Accenture, Rackspace, AI Index Report, AI Now, Governmental Reports, Surf, Goldman Sachs, and my research as input.
Furthermore, I have already considered several factors in the analysis as “if the possibility can become a reality,” such as “financial benefit,” “production cycles,” “dependencies on another tech,” “social acceptance,” etc. This analysis is a snapshot in time and needs to be updated as we progress.
Financial Benefit and Engagement: Exploring the Impact of AI
To begin the exploration, I examined various areas such as healthcare, work automation, transportation, education, finance, art, family, religion, living, sex & intimacy, and home tech, to name a few. This initial reflection is crucial to identify the industries that drive AI investment. However, this does not mean these solutions will immediately reach market readiness.
Ranked topic
AI-driven healthcare
Work automation
Smart transportation
Personalized education
AI in finance
AI-driven healthcare, the top-ranked area, is also one of the most complicated impacts. It is complicated because it depends on regulatory approval, requires complete transparency in decision-making, demands a high confidence level in algorithms, strongly impacts ethics, and affects everyone. AI in healthcare has attracted already substantial investment, with the primary focus in recent years being the collection of quality data, a significant task.
The potential financial impact of AI-driven healthcare:
AI can reduce healthcare costs by minimizing errors and improving accuracy, leading to better patient outcomes as a "Checker" and "Companion" for diagnostics and treatment recommendations. It could save up to $24 billion annually (Dosage Error Reduction, Preliminary Diagnosis, Automated Image Diagnosis) in the US alone by 2026 (Accenture).
AI can lower healthcare costs by providing remote healthcare services to underserved communities, reducing the need for in-person visits, and fostering a more inclusive society that serves rural and lower-class patients. As a result, the global telemedicine market will reach $291.5 billion by 2028 (Exactitute).
According to Grand View Research, the global artificial intelligence in drug discovery market size will reach USD 1.1 billion in 2022. The research institute projects growth to USD 1.5 billion in 2023, with a Compound Annual Growth Rate (CAGR) of 29.6% until 2030. Additionally, AI holds the potential to significantly cut the time and cost associated with traditional drug development processes, leading to savings of hundreds of millions for each drug discovery.
Robotic surgery with AI could improve patient outcomes and reduce hospital stays, ultimately lowering healthcare costs with a potential annual benefit of $40 billion (Accenture).
AI-driven personalized medicine can optimize treatment plans, improving patient outcomes and reducing healthcare costs. Grand View Research expects the global personalized medicine market to reach $3.18 trillion by 2025, growing at a CAGR of 10.6%.
These examples highlight AI's significant impact on societal structures such as healthcare, transportation, education and industries like finance and production. Integrating AI into existing systems is a powerful leverage to increase efficiency. Consequently, generative AI (gAI) lower ranks lower, even though it is currently a popular topic. The ongoing media hype seems more of an awareness wave, with models like CPT offering more manageable ways to integrate AI into existing structures or train custom models.
The Waves
Let's consider some criteria for prioritization and examine the Waves:
Awareness and New Ideas
Preparation and Proof of Market
Implications and Scaling
1st Wave: Awareness and New Ideas
In the first wave, lasting until 2025, companies will integrate AI into existing products and processes to generate value for customers and companies. This movement represents a hype driven by the fear of missing out on AI adoption. Many new services and ideas will emerge during this hype phase, but only a fraction will survive. This first wave also promotes increased investment in AI-driven solutions for the future. However, since implementation takes time, the real implications of novel AI solutions will likely be seen between 2027 and 2029. Even though Goldman Sachs estimates that 300 million full-time employees may lose their jobs to AI, the impact will not be immediate, giving society and governments time to prepare for potential scenarios. Whether people know this and how governments can identify and support affected groups before the changes occur remains.
Implications:
The rise of intelligent companions: From healthcare professional companions to co-authoring, programming, and sales management, AI will act as a companion for suggestions and alerts, requiring human validation. The initial adoption of synthetic relationships will depend heavily on transparency, ethics, and respect. Society's acceptance of AI will significantly influence the implementation of these companions in the coming years.
Integration into high-revenue processes such as risk and fraud detection, payment and billing, pricing, warehousing, production, and customer service will expand. In these cases, AI will increase efficiency and reduce costs. Although this aligns with organizational strategies, the argumentation for extra investments is more likely to succeed. Still, it is already happening and will likely continue.
More accessibility due to an improved understanding of voice, context, and written communication may have positive effects in various directions. For example, we can expect better voice assistants and create a more inclusive society for persons with disabilities, increasing GDP and reducing healthcare costs. To provide some figures, currently, more than 1.5 billion people (nearly 20% of the global population) live with hearing loss (WHO), and 33 million people live with blindness, with an additional 260 million experiencing visual impairment (Orbis). Although the real impact will be more evident in 2029 with new mass products, by 2025, we might see more precise transcription and voice bots launched.
Some questions already being raised, which could transform into counter-movements, include privacy and IP concerns related to provided input, the demand for ethical, less biased, and transparent AI, and the initial attempts at global AI regulations.
2nd Wave: Preparation and Proof of Market
The awakening and further improvements of efficiencies and the implementation of AI into existing processes, the expansion to robotics, and a leap in sentiment analysis represent the second wave. Early adopters have reached a limit on efficiency gains in critical functions. Therefore, they will implement more AI in supporting processes within organizations. Furthermore, we see a new maturity level in product concepts and the legal framework regarding when and how to use AI.
Implications:
AI integration into high revenue, volume processes like risk and fraud detection, payment and billing, pricing and warehousing, production, and customer service is underway. These exemplary cases showcase AI's potential to increase efficiency and reduce costs. Most organizational strategies align with this extra efficiency, making it easy for organizations to justify investments in AI for these processes.
We might see a transition from companions to more physically embodied companions, robotics. The current AI focus accelerates the field of robotics, with the highest acceptance in the field of transport service robots. Further applications include advancing in agricultural robotics combined with crop yield and agriculture management, hospitality robots (in certain cultures), more intelligent children's toys for toddlers and up, and sexbots. Virtual companions might increase complexity and topics, such as wellness coaches, nutrition, or broader acceptance of legal companions.
We see process integration maturing toward more complex and IP-protected processes and increasing the quality of existing AI models. Finance AI will likely span the entire spectrum from wealth to tax management, production processes will see strong integration in resource management, and simulations will gain broader acceptance, currently known as "digital twins." The focus will be on pre-production, supporting more complex projects from manufacturing to infrastructure.
Leap in sentiment analysis: This interesting parallel development will lead to further discussion points in ethics, surveillance, and product integration. Although there might be some benefits, current commercial end-customer solutions, or those in education, have faced high resistance. Sentiment analysis is also crucial to the Metaverse (emotional tracking and mirroring to the avatar). Although the Metaverse is approximately a decade out, the leap in sentiment analysis with new AI models in 2027 might fit into the development cycle. One reason for the limited broader acceptance is a lack of a "viral value-added case" for the end customer.
Until 2027, we will see an increasing digital divide, as not everyone will have access to the first-level features of AI in products or the products themselves. There is a moderate to high risk of further segregation of social classes, leading to educational gaps. Although governments might offer upskilling programs, their acceptance, spread, and inclusion will be challenging.
3rd Wave: Implications and Scaling
The third wave, which may emerge around 2029, brings more uncertainty and speculation. The primary issues surrounding IP, privacy, biases, transparency, and ethics should have solutions by then. After a decade of AI, people will likely firmly accept it. Yet, this acceptance comes hand-in-hand with the earlier wave of job replacements, expected to be felt between 2027 and 2029. On the positive side, technology transitions always create new jobs in fields like Big Data, Drones, 5G, IoT, IIoT, Robotics, Green Frontier Technologies, Nanotechnology, and Gene editing. Moreover, the companions from the earlier waves may evolve into "real synthetic collaboration" and co-creation by 2029, with people trusting AI and its judgments more than before. These judgments won't be perfect, but neither are human ones.
Implications:
In the last wave, we might see broad regulatory acceptance of AI in healthcare, from personalized diagnostics and medicine, rehabilitation, telemedicine, and "patients like me," as well as integration into prosthetics and a new generation of hearing aids. Additionally, driven by an aging society and a shortage of healthcare workers, we might see the adoption of AI for elderly care and nursing, including more robotic solutions. Such solutions were not very successful in the past when fully autonomous, and we might see more of a "companion" perspective or full autonomy for simple tasks. Again, however, the substitution of healthcare workers is difficult to foresee.
Organizations that have not yet implemented AI will struggle as competition in the offering, pricing, and quality gains market share. Furthermore, we will see simulations (digital twins) maturing into many new segments, from simulations of sustainable impacts to even the effect of governmental policy-making.
With broader AI adoption, we might also see its use for critical infrastructure such as energy management, traffic control, water, and waste management, where physical changes to grids and networks are necessary.
As AI technology grows, the exploration of edge cases is likely, which might lead to public outcry regarding "crime prevention" due to high volume video stream analysis, not acting after the crime but anticipating possible actions. AI triage and disaster management applications might be another beneficial but also a delicate ethical case, as biases might exist. Although AI features are now fundamental and upskilling programs are ongoing, a divide is probable to prevail among younger generations without access to the features, older people, and lower social class citizens.
Potential larger counter-movements to AI adoption
We can define counter-movement as a reaction against technological advancement or social change. When one social movement supports change, another group often opposes it. In essence, there is a counter-movement for almost everything. For instance, the women's pro-choice movement is a counter-movement to the anti-abortion movement. In contrast, the fast-paced society's Slow Education movement counters the increasing speed of modern life. The European culture of welcoming migrants opposes groups like PEGIDA. Change inevitably creates a counter-reaction, and it is crucial to examine how rapidly it grows and its relevance to the research. (Daniel Egger, 2015)
Privacy & IP Concerns
Amid the AI hype, organizations show eagerness to adopt new technologies to maintain competitiveness. Nevertheless, unclear privacy and intellectual property (IP) rights can cause organizations to hesitate when implementing AI in sensitive processes like quality control, manufacturing, digital twins, hiring, and legal matters. Even with potential advancements in regulation, organizations may resist, mainly when privacy and IP breaches arise. Governments could respond to these issues, but achieving global alignment on AI regulations may pose a challenge due to competition among nations.
Ethical, Transparency, and Biases
There is growing concerned about using AI in delicate hiring and education grading processes. AI models lack balanced training on cultural inputs, ethnographies, social classes, and education levels, leading to a high likelihood of biased outcomes. Cases like the Gang Violence Matrix used by the London Metropolitan Police Service demonstrate the potential for AI to discriminate against certain ethnic and racial minorities. Ethical behavior in AI will emerge as a leading topic of research and discussion in the coming years. The AI Index Report reveals a tenfold increase in ethical AI research publications since 2018. This trend likely continues as ethical considerations become crucial for widespread AI adoption.
Digital and Social Divide
Over the next ten years, the digital and social divide may become a significant challenge. Not all social classes can access AI due to paywalls and other limitations. As AI technologies follow the typical product cycle, extra features may become commonplace, but this will take time. In the meantime, the gap will grow, leading to emotional and likely polemic counter-movements and a difficult political situation. Governments may introduce AI literacy programs in schools, community centers, and workplaces to help individuals effectively use AI-driven technology and navigate the digital world.
Layoffs and Mental Health Support:
Organizations will face challenges finding the right balance when integrating AI into their processes. In the short term, the focus will be on quick wins. However, at the same time, deeper integration in production and IP-sensitive processes may lead to significant layoffs within a six-year horizon. Moreover, these layoffs may disproportionately impact the generation approaching retirement age, potentially leading to increased discrimination.
AI's Impact on Employment and GDP: Recent data from Goldman Sachs (March 26, 2023) indicates that AI automation might affect two-thirds of current jobs and replace up to one-fourth. This assumption puts 300 million full-time jobs at risk of automation globally. However, history shows that worker displacement often balances with the creation new jobs, and technological innovations contribute to most long-term employment growth. AI could ultimately raise annual global GDP by 7%. While this estimation raises concern, we must compare this number to our more considerable societal challenges: Consider pressing matters such as air pollution affecting 90% of the global population (WHO), gender inequality impacting 3.8 billion, water scarcity affecting 2.7 billion people (WWF), mental health crises at 1 billion (WHO), food scarcity affecting 882 million individuals (WFP), access to energy and electricity for 789 million people (Worldbank), aging population and elder care affecting 703 million in 2019 (UN), and inadequate access to healthcare for 400 million people (WHO).