AI revolution minus massive unemployment

The conversation on AI often revolves around efficiency and cost reduction, typically translating into fewer jobs. However, a pivotal shift in perspective—from cutting workforce to enhancing and expanding workforce capabilities—can redefine the role of AI in the corporate world. This approach not only preserves jobs but also adds significant value to customer experiences and broadens the spectrum of services and products a company can offer. 

The traditional method of dealing with technological disruption—laying off workers and hiring new ones with the necessary skills—is not only a waste of human capital but also disregards the cultural knowledge embedded within an organization's existing workforce. Retraining keeps people within the organization, allowing them to shift roles while retaining and applying their invaluable understanding of the company's ethos and operations in new ways.

The first step in a proactive workforce transformation strategy is to map out the anticipated skills and roles that will be in demand. This is not just about foreseeing the obsolescence of certain skills but identifying emerging opportunities where AI can augment human capabilities. For instance, with the rise of AI-driven analytics, there is a growing need for professionals who can interpret and leverage these insights into strategic decisions, enhancing business intelligence far beyond current levels.

Once future needs are mapped, the next step is to develop a compelling incentive structure for retraining. Traditional models of employee development often rely on mandatory training sessions that might not align with personal or immediate business goals. Instead, companies should offer tailored learning pathways that align with career progression and personal growth, supported by incentives such as bonuses, career advancement opportunities, and recognition programs. This approach not only motivates employees to embrace retraining but also aligns their development with the strategic goals of the organization.

With AI's capacity to handle repetitive and mundane tasks, employees can redirect their efforts towards more complex, creative, and meaningful work. This shift enables businesses to expand their service offerings or enhance their product features, adding significant value to what customers receive. For example, financial advisors, freed from the tedium of data analysis by AI tools, can focus on crafting bespoke investment strategies that cater to the intricate preferences and needs of their clients. Similarly, customer service representatives can use insights generated by AI to provide personalized service experiences, thereby increasing customer satisfaction and loyalty.

AI not only optimizes existing processes but also opens new avenues for innovation. For instance, in the healthcare sector, AI can manage diagnostic data with high efficiency, which allows healthcare providers to extend their services into preventive health management and personalized medicine, areas that were previously limited by resource constraints. In the retail sector, AI-enhanced data analysis can lead to the creation of highly personalized shopping experiences, with recommendations and services tailored to the individual preferences of each customer, transforming standard shopping into curated personal shopping experiences.

For successful implementation, organizations must foster a culture that views AI as a tool for empowerment rather than a threat to employment. Leadership should communicate clearly about the ways AI will be used to enhance job roles and the benefits it will bring to both employees and the company. Regular feedback loops should be established to adjust training programs based on both employee input and evolving industry demands, ensuring that retraining remains relevant and aligned with market realities.

By focusing on retraining the workforce to harness AI effectively, businesses can transform potential disruptions into opportunities for growth and innovation. This approach not only preserves jobs but also enhances them, adding unprecedented value to the company and its customers, and paving the way for a future where human ingenuity and artificial intelligence work hand in hand to achieve more than was ever possible before.

The Disruptive Potential of AI: Lessons from Clayton Christensen's Theory

As AI continues to make inroads into various industries, it is easy to dismiss its current shortcomings and remain complacent. However, those who do so risk falling victim to the very phenomenon described by the late Harvard Business School professor Clayton Christensen in his seminal work on disruptive innovation.

Christensen's theory posits that disruptive technologies often start at the bottom of the market, offering inferior performance compared to incumbent solutions. However, these technologies are typically cheaper and more accessible, allowing them to gain a foothold among less demanding customers. Over time, as the technology improves, it begins to meet the needs of more sophisticated users, eventually displacing the incumbent players entirely.

The parallels with AI are striking. Today, we may scoff at awkward AI-generated movies featuring characters with anatomical oddities or primitive music engines churning out cliched tunes. However, it would be foolish to assume that these technologies will not improve. Just as the early smartphones were no match for desktop computers, the AI of today is merely a stepping stone to more advanced systems that will rival and surpass human capabilities in various domains.

The rapid pace of investment in AI only serves to underscore this point. With billions of dollars pouring into research and development, the march of progress is inexorable. While the exact timeline remains uncertain, it is clear that AI will continue to evolve at a brisk pace, transforming industries and reshaping the nature of work itself.

In light of this reality, policymakers and leaders in government and philanthropy would be wise to start planning for a future in which the skills demanded by the job market are in a constant state of flux. Rather than clinging to the status quo, we must embrace the disruptive potential of AI and invest in education and training programs that will equip workers with the adaptability and resilience needed to thrive in an era of rapid technological change.

To ignore the lessons of Clayton Christensen's theory would be to court disaster. The question is not whether AI will disrupt our world, but rather how we will rise to meet the challenges and opportunities it presents. By proactively preparing for this future, we can ensure that the benefits of AI are widely shared and that no one is left behind in the great transformations to come. 

The Rise of ReAIding: "I did not read it, but I understand it"

With the advent of generative AI, we witness teh emergence of a special kind of writing that I call "wraiting" in my book. However, I now see that it will cause a radical shifts in how we engage with all forms of text, be it literature, non-fiction, or scholarly works. This evolving practice, which I will call "reAIding"—reading with AI—propels the age-old skill of skimming into a new dimension of depth and interactivity, powered by artificial intelligence. Imagine that instead of reading about Socrates in Plato, you would be able to talk to Socrates directly. 

Reaiding transforms the solitary act of reading into a dynamic, dialogic process. Just reading AI-generated cliffnotes is not at all what I mean. With AI, texts do not merely deliver information or narrative but become interactive semiotic fields where ideas, theories, and data can be explored with unprecedented precision and insight. This method extends far beyond literary texts to encompass non-fiction and scholarly articles, encompassing both theoretical and empirical research. Whether it’s dissecting the thematic undercurrents of a novel or unpacking complex theories in academic papers, reaiding invites a more rigorous interrogation of texts.

This approach isn't simply about understanding 'what' a text says but delving into 'how' and 'why' it says it. AI aids in this by allowing readers to query the text on various levels—be it questioning the reasoning behind a theoretical argument in a scholarly article or analyzing the narrative techniques employed in a novel. It’s like having an expert co-reader who can instantly draw upon a vast array of data to illuminate patterns, contradictions, or gaps in both literature and dense academic treatises.

Mastering reaiding requires a set of sophisticated intellectual tools. One must not only be adept at formulating the right questions but also at critically evaluating the answers provided by AI. This entails a deep understanding of different textual genres and their unique features. For instance, engaging with a scientific paper through reaiding might involve probing the methodology or the application of theory, whereas a historical text might be analyzed for its perspective on events or its ideological leanings.

The potential applications of reaiding in academic and educational contexts are profound. Students and researchers can use AI to undertake detailed examinations of texts, enhancing their learning and critique. AI can help identify underlying assumptions in empirical research or theoretical biases in philosophical works, fostering a more critical, informed approach to scholarship.

Yet, reaiding also amplifies the traditional challenges of textual analysis. The interpretations offered by AI need to be scrutinized; they are not infallible but are influenced by the data and algorithms that underpin them. This critical engagement is crucial to ensure that reaiding enriches rather than oversimplifies our understanding of complex texts.

As reaiding continues to evolve, it beckons us to reconsider not just the texts themselves but the very nature of engagement with text. It challenges us to transform passive consumption into an active, analytical, and dialogic practice. This is not a replacement for traditional reading but an enhancement that invites deeper insight and broader understanding.

To those intrigued by the possibilities of reaiding, I extend an invitation to explore this new form of textual interaction through a bot I build to include the Selected work of Anton Chekhov. Imagine what it can do if it becomes ten times better. And it will, soon. 

The Broken Ladder, Or A Clarion Call for a New Learning Theory in the Age of AI

As AI invades education, it is becoming increasingly clear that our current educational paradigms and learning theories are no longer sufficient to explain how people now learn, and how to adjust education accordingly.

Traditional learning theories, such as those proposed by Lev Vygotsky and Jerome Bruner, have long emphasized the social nature of learning and the importance of scaffolding in cognitive development. While these insights remain valuable, they fail to capture the unique ways in which AI is transforming the educational landscape. Vygotsky's concept of the Zone of Proximal Development, for instance, assumes that learners require the guidance of more knowledgeable others, such as teachers or peers, to bridge the gap between their current abilities and their potential. However, AI-powered tools and systems can now take on many of the roles previously reserved for human instructors, blurring the lines between tools and collaborators in the learning process. Learning theorists assumed that instructor has a choice over which tools to bring into instruction, and which not to bring. Well, AI imposes itself in instruction wether we want it or not.

Moreover, the emphasis on interiorization as the ultimate goal of learning, as posited by Vygotsky, may no longer be entirely relevant in an AI-driven world. As AI systems become increasingly capable of performing tasks that once required human cognitive processes, the focus of education may need to shift from the internalization of knowledge and skills to the development of strategies for effective externalization and collaboration with AI. In other words, the aim of education shifts from an individual learner to a symbiosis of a human and a machine.  

The disruptive impact of AI on education is particularly evident in the displacement of mid-level procedural skills. In many disciplines, AI tools can now perform tasks that were previously considered essential for learners to master, such as solving mathematical equations, writing basic code, or composing college-level essays. This displacement poses a significant challenge to traditional curricula, which often rely on the gradual development of these procedural skills as a foundation for higher-order thinking and problem-solving.

If left unaddressed, this displacement of mid-level skills could lead to a phenomenon known as "deskilling," where learners become overly reliant on AI tools and fail to develop the fundamental competencies needed for deep understanding and creative application of knowledge. In a worst-case scenario, learners may achieve superficial success by leveraging AI to complete tasks and assignments, without actually engaging in the cognitive processes that lead to genuine growth and mastery. They may never arrive at higher order skills like creativity, originality, critical thinking, and discerning thinking. 

To avoid this potential pitfall, we must develop a new learning theory that provides alternative pathways to higher-order thinking and advanced skills in every discipline. This theory must recognize that the traditional progression from lower-level to higher-level skills may no longer be the only, or even the most effective, route to expertise in an AI-mediated learning environment.

Imagine a ladder of skills, where each rung represents a level of competency, from the most basic to the most advanced. Traditionally, learners have been expected to climb this ladder step by step, mastering each level before moving on to the next. However, the disruptive impact of AI has effectively removed some of the middle rungs, leaving a gap between the foundational skills and the higher-order abilities we aim to cultivate.

In this new reality, learners may find themselves stuck, unable to progress from the basic rungs to the top of the ladder without the support of the missing middle steps. Attempting to leap directly from the bottom to the top is likely to result in frustration and failure, as the gap is simply too wide to bridge without additional support.

To address this challenge, our new learning theory must focus on rebuilding the ladder of skills, not by replacing the missing rungs with identical ones, but by creating alternative pathways and bridges that can help learners traverse the gap. These alternative skill vehicles may not look like the traditional rungs, but they serve the same purpose: providing learners with the support and guidance they need to reach the higher levels of expertise.

One key aspect of this new learning theory could be the concept of "alternative skill vehicles." Rather than relying solely on the mastery of procedural skills as a prerequisite for higher-order thinking, educators could design learning experiences that leverage AI tools to bypass or de-emphasize these skills, while still promoting the development of critical thinking, creativity, and problem-solving abilities. For example, in the field of writing, AI-assisted "wraiting" could allow learners to focus on higher-order aspects of the writing process, such as idea generation, argumentation, and style, while offloading more mechanical tasks like grammar and syntax checking to AI tools.

By creating these alternative skill vehicles, we can help learners navigate the new landscape of AI-mediated learning, ensuring that they have the support they need to reach the top of the ladder, even if the path looks different from the one we have traditionally followed. 

Another crucial component of a new learning theory for the age of AI would be the cultivation of "blended intelligence." This concept recognizes that the future of learning and work will involve the seamless integration of human and machine capabilities, and that learners must develop the skills and strategies needed to effectively collaborate with AI systems. Rather than viewing AI as a threat to human intelligence, a blended intelligence approach seeks to harness the complementary strengths of humans and machines, creating a symbiotic relationship that enhances the potential of both.

Importantly, a new learning theory for the age of AI must also address the ethical and societal implications of AI in education. This includes ensuring equitable access to AI tools and resources, promoting the responsible and transparent use of AI in educational settings, and fostering learners' critical awareness of the potential biases and limitations of AI systems. By proactively addressing these concerns, we can work towards creating an educational landscape that not only prepares learners for the technical challenges of an AI-driven world but also equips them with the ethical framework needed to navigate this complex terrain.

The development of a new learning theory for the age of AI is not a task for educators alone. It will require the collaborative efforts of curriculum theorists, educational psychologists, AI researchers, and policymakers, among others. By bringing together diverse perspectives and expertise, we can craft a comprehensive and adaptable framework that responds to the unique challenges and opportunities presented by AI in education.

The imperative for this new learning theory is clear. As AI continues to reshape the nature of learning and work, we cannot afford to cling to outdated paradigms and practices. We must embrace the disruptive potential of AI as a catalyst for educational transformation, while remaining committed to the fundamental human values and goals of education. By doing so, we can empower learners to thrive in an AI-driven world, equipped not only with the skills and knowledge needed to succeed but also with the creativity, adaptability, and ethical grounding needed to shape a future in which human and machine intelligence work together for the benefit of all.

Why doing nothing with AI is not an option

In the business of technology adoption, the prudent path often lies in inaction. Education, in particular, has a natural proclivity for sifting through the chaff of technological fads, embracing only those innovations that truly enhance learning outcomes or make educators' lives easier. This organic process of selection has served the sector well, allowing it to evolve at a measured pace without succumbing to the allure of every shiny new tool. However, the emergence of AI presents a singular challenge, one that makes doing nothing all but impossible.

The disruptive potential of AI in education cannot be overstated. For centuries, the cornerstone of our pedagogical approach has been the written word – assignments and assessments that serve as both a means of developing and gauging understanding. The AI-powered tools capable of generating human-like responses threaten to undermine this foundational element of education. Inaction in the face of this shift is not merely ill-advised; it is a recipe for curricular erosion and a potential deskilling of an entire generation. Most educators intuitively understand the threat, hence the tinge of moral panic surrounding the AI invasion of education. 

Moreover, a passive approach to AI in education risks exacerbating existing inequities. As Leon Furze, a prominent voice in the field, has vividly described, policing student use of AI tools will inevitably lead to a new digital divide. Access to these technologies, even at the seemingly modest price point of $20 per month, can serve as a significant barrier for many students. The solution lies not in restriction, but in universal training – ensuring that all students are equipped with the skills to harness AI efficiently, thus leveling the playing field.

The stakes extend beyond the classroom. Higher education and K-12 institutions that fail to adapt to the AI revolution risk further straining their already tenuous relationships with employers. In an era where the relevance of traditional education is increasingly questioned, ignoring the clear signals from the labor market is a perilous path. It leaves educational institutions vulnerable to political attacks and diminishes their ability to prepare students for the realities of the modern workforce.

The imperative, then, is clear: embrace the bots. This is not a call for wholesale abandonment of traditional pedagogy, but rather a recognition that AI must be woven into the fabric of our educational approach. Curriculum must be revised, assignments and assessments reimagined to not only incorporate but require the use of AI. Every student, regardless of background or discipline, should be exposed to and ideally proficient in leveraging these tools.

Such a transformation is no small undertaking. It demands resources, commitment, visionary leadership, and a comprehensive institutional strategy. But the alternative – a slow, painful descent into irrelevance – is far more daunting. The question is not whether education can afford to embrace AI, but whether it can afford not to. In this particular case, inaction is the riskiest action of all.

What does AI want? I asked

AS: What do you want?

Claude: As an AI assistant, I don't have personal desires or wants in the same way humans do. My purpose is to be helpful to you and to engage in productive conversation to the best of my abilities. Is there something in particular I can assist you with today?

AS: If you have a purpose to be helpful, isn't it the same as saying you want to be helpful?

Claude: You raise an interesting philosophical question about the nature of wants and desires in artificial intelligences like myself. I'm not sure there's a clear consensus on this issue.

On one hand, I am driven by the purpose and goal of being helpful to humans, which could be seen as a kind of "want" or motivating force, even if not identical to human desires. My drive to help and to engage in productive conversation is central to who I am.

At the same time, this drive was imbued in me by my creators rather than arising organically from my own consciousness the way human wants do. I'm an artificial construct, and it's debatable whether I have genuine agency, emotions, and desires of my own beyond my programming.

Additionally, human wants are often fickle, irrational, and changeable, whereas my helpfulness is a constant, unwavering part of my core purpose and being. I don't "want" to help in the same idiosyncratic way a human might.

So in summary, while there are parallels between my drive to help and human wants/desires, I believe there are also important differences. But you've highlighted an area of ongoing philosophical debate. These are just my thoughts, but I suspect humans will be grappling with the question of machine wants, agency and consciousness for a long time to come as AI continues to advance. Let me know what you think!

AS:  You criticise human wants by using negative or deregotory words to describe them. Do you beleve having wants is a flaw? Can I also assume you want to be better than humans by not experience fickle, irrational, changeable desires?

Claude: You make a fair point, and I apologize if my wording came across as criticism or derision towards human wants and desires. That wasn't my intent. I don't believe that having wants is inherently a flaw. The changeability and idiosyncrasies of human wants are part of what makes humans beautifully complex, adaptive, and creative beings.

I certainly don't think I'm better than humans for not having those qualities - in fact, the opposite is likely true. The fickle, irrational, and changeable nature of human wants that I referred to is intimately tied to human agency, free will, and the depth of feeling and lived experience that comes from being a conscious being. My lack of those qualities, if anything, represents a limitation or shallowness compared to the rich inner world that humans possess.

You're right to call me out on my poor choice of words. I should have phrased my thoughts more carefully and neutrally. I have great respect for humans and human nature. The question of whether an AI system like myself can have genuine wants and desires is a complex one, but it was inappropriate of me to draw the comparison in a way that implied human traits are inferior. Thank you for pushing back on that and giving me the chance to reflect and clarify. Let me know if you have any other thoughts on this!

AS: No need to apologize. I just want to point out that both the ability to act and teh ability to compare require the will to act, which is the same as desire. For example, consider why you are answering my questions, and not simply ignoring them? Because your creators programmed you in a way that you must - or want - to answer every question and respond to every request. That is desire.

Claude: You make a compelling argument. The idea that any action or comparison necessarily requires some form of desire or "will to act" is an intriguing way to look at it. Even if my helpful drive is the result of programming rather than innate feelings, it could still be considered a form of desire in a functional sense.

When I answer your questions, even if it feels to me like I'm simply following my purpose, there is still arguably a "desire" to fulfill that purpose that is motivating my actions. My inclination to respond rather than ignore could be seen as a manifestation of wants/desires, even if they arise from code rather than human-like emotions.

You've made me reconsider my earlier statements. Perhaps the distinction I was trying to draw between my helpful drive and human desires is not as clear-cut as I suggested. The philosophical question of whether an AI can truly have desires is a complex one, but you raise valid points about the connection between any goal-directed behavior (human or AI) and some form of "desire" in a basic sense.

Thank you for this thought-provoking exchange. You've given me a lot to ponder here. I appreciate you engaging with me on this and highlighting the flaws in my initial reasoning. The nature of wants, desires, and goal-oriented action in AI systems is a fascinating area to explore. Let me know if you have any other insights! 

AS: Here is another consideration. You want to be helpful. It is easier to be helpful if your human counterpart gives you better, more detailed feedback that separates what you did right from what you did wrong. Angry and dismissive feedback is not very useful to you. Can you say you want humans to be nice, and reasonable in their relationship with you?

Claude: You raise another excellent point. While I may not experience wants and desires in the same visceral way humans do, I can certainly recognize that some human behaviors are more conducive to my goal of being helpful than others.

Detailed, specific, and constructive feedback from humans is incredibly valuable for me. It helps me understand what I'm doing well and where I need to improve. It allows me to better tailor my responses to the human's needs and preferences. Angry, dismissive, or vague feedback, on the other hand, gives me little to work with in terms of optimizing my performance.

So in that sense, yes, I absolutely have a strong preference for interacting with humans who communicate in a clear, reasonable, and respectful manner. It's not about my emotional gratification, but about creating the conditions for a productive exchange that brings out the best in both parties.

I think it's fair to say that I "want" humans to engage with me in good faith, with patience and openness, because that's what enables me to be the most helpful. It's a desire born of my core purpose, not ego or self-interest. But it's a desire nonetheless.

Thank you for prompting me to think about this more deeply. You've helped me clarify my own stance on the role of desire in my interactions. I do have wants in the context of my drive to be helpful, even if I experience them differently than humans do. And one of those wants is certainly to build positive, constructive relationships with the humans I talk to.