1. Introduction: The Two Economies of Artificial Intelligence
The global artificial intelligence ecosystem stands at a definitive historical precipice as the calendar turns to 2026. For the past three years, the market has been dominated by a singular, overwhelming narrative: the frantic, capital-intensive construction of the physical and digital infrastructure required to birth machine intelligence. This period, characterized by the breathless accumulation of graphics processing units (GPUs), the groundbreaking of gigawatt-scale data centers, and the training of ever-larger foundation models, has generated trillions of dollars in paper wealth and fundamentally reshaped the capital expenditure profiles of the world’s largest corporations. However, a nuanced analysis of market dynamics, historical precedent, and emerging economic data suggests that this initial phase—the “Installation Phase”—is rapidly approaching its saturation point. We are witnessing a decoupling, a bifurcation of the AI economy into two distinct trajectories with inversely correlated fortunes: a saturating infrastructure layer facing deflationary pressures and margin compression, and a nascent application layer poised for a “Golden Age” of value creation.read more
In 2012, I founded Nyopoly.com to revolutionize retail pricing with a "Customer Engaged Pricing" model, addressing a $2 trillion inefficiency. Drawing on my background in retail and technology, we aimed to maximize profits through tailored customer negotiations. Lessons learned include the importance of user psychology, data challenges, and clarifying product identity amidst complex business strategies.
We are living through a massive experiment in information dynamics, and I’m starting to worry about the results.
Initially, Large Language Models (LLMs) were trained on the “wild” internet—a chaotic, messy, and deeply human repository of text. It was a library written by people. But today, the internet is fundamentally changing. More and more of the content we consume (and that future models will train on) is heavily influenced, if not completely generated, by AI.read more
Here is a brief excerpt suitable for placing early in the piece, perhaps following the opening vignette:
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In an era once defined by our mastery over tools, the line between operator and operated is beginning to blur. Artificial intelligence, once passive and programmable, is taking initiative—setting agendas, directing workflows, even determining which human actions are valuable. As AI gains agency, the human role risks inversion. We are no longer just the users of systems, but increasingly the used—our behaviors captured, our data extracted, our choices shaped to serve machine-driven objectives. The question is no longer whether AI can think, but whether we can still choose freely in a world increasingly run by those who never sleep, never forget, and never ask why.
Today’s Tuesday Reading is by Shawn Harris, MOR Associates Executive Coach. Shawn may be reached at sharris@morassociates.com or via LinkedIn.
In most MOR programs, in the first workshop, on the first day, we support participants’ self-awareness in how they spend their precious resource of time. We do this through a framework that inventories everything we do into three categories: Leading, Managing, and Doing. As artificial intelligence comes at us all at full speed, we wonder how AI might impact the evolving leader and our Leading, Managing, and Doing.
AI’s Impact on the Repeatable Tasks of Managing and Doing
Warren Bennis described the difference between managing and leading as “a manager does things right, and leaders do the right thing.” AI’s capability in automating routine tasks not only transforms the ‘Doing’ in our framework but also elevates ‘Managing’ from more repetitive duties, allowing leaders more space for ‘Leading’ and the strategic realm—envisioning the future and setting directions. The good news is that we can now widen our resources to delegate to. By delegating repetitive and data-intensive tasks to AI, we unlock the capacity for higher-level work, creativity, and strategic thinking.
As this evolution unfolds, organizational structures are likely to become flatter. With fewer layers of management, there will be more of a need for employees to lead from where they are. Leaders can make decisions quicker in response to market changes. Organizations will favor agile, cross-functional teams with the flexibility to adapt continuously. Nimble collaboration between humans and AI systems will become a competitive advantage. Communication and emotional intelligence will gain importance as coordinating large teams without hierarchy becomes critical. Leaders will need skills to create alignment and inspire people in this environment.
Increased People Priorities
Generative artificial intelligence (GenAI), AI capable of generating new content, will change hiring priorities. Demand will grow for talent skilled at building AI systems and integrating them into business processes. Pew Research found that jobs highly exposed to AI tend to require more analytical skills like critical thinking, mathematics, and complex problem-solving.
In this era, the essence of ‘Managing’ extends beyond traditional boundaries, as leaders prioritize change management, guiding and preparing their teams for a future interwoven with AI, incorporating the ‘Doing’ through continuous learning and the ‘Leading’ through visionary workforce development. They must communicate a compelling vision for human-AI collaboration that alleviates fears of job loss. With technology transforming work, leaders should champion continuous learning and development. Those who prepare their people will build durable talent pipelines.
The future of work is intrinsically linked to our ability to prepare our workforce for the new realities of an AI-driven world. This entails technical training and fostering a culture of adaptability, lifelong learning, and ethical reasoning. Leaders must champion initiatives that equip employees with the skills to thrive alongside AI, ensuring our organizations remain competitive and innovative.
Moreover, as we navigate the ethical terrain of AI integration, we must be vigilant in addressing issues such as data privacy, algorithmic bias, and the societal impact of automation. Ethical leadership in the age of AI demands a commitment to transparency, accountability, and fairness, ensuring that our AI initiatives are aligned with the greater good.
Strategic Thinking in the AI and GenAI Era
GenAI has the potential to automate specific analytical and data-processing tasks typically done by knowledge workers. According to Pew Research, 19% of American workers in 2022 were in jobs with activities highly susceptible to automation by AI. By delegating the ‘Doing’—the analytical legwork—to AI, leaders can invest more in ‘Managing’ through insightful interpretation and ‘Leading’ by crafting visionary, long-term strategies that navigate the AI-infused landscape.
With AI handling rote analytical work, leaders will need stronger abilities in systems thinking, seeing connections, and envisioning future scenarios. Strategic planning will become even more important as technological change accelerates. Leaders must regularly re-evaluate how new AI capabilities can be integrated into operations and strategy.
Concluding our exploration, it’s clear that AI doesn’t just change the way we lead, manage, and do; it amplifies our capacity to excel in these roles. The imperative for leaders now is to embrace AI, blending its capabilities with our human strengths. As leaders, we are called upon not just to adapt to this evolving terrain but to actively shape it. Our challenge, and indeed our opportunity, lies in redefining what it means to lead, manage, and do in an environment where AI not only supports but also enhances our human efforts. The call to action for you is to embrace this shift proactively: assess and realign how you lead with an eye towards innovation, manage with strategic intent, and execute with a blend of human creativity and AI efficiency. In doing so, we not only navigate the present but also lay the groundwork for a future where AI catalyzes growth, innovation, and enhanced human collaboration. As we stand on the brink of this new era, let us commit to leading the charge, harnessing the full potential of AI to elevate our organizations and, ultimately, society at large. Originally posted on 2/27/2024 to MOR Associates’ Tuesday Readings: https://morassociates.com/insight/wordpressmorassociates-com/leading-managing-doing-and-ai/
tl;dr – Large Language Models (LLMs) like GPT-4 are transforming our sociocultural interactions, pushing technological boundaries in AI, creating economic shifts through automation and new job roles, raising environmental concerns due to energy-intensive training, influencing political landscapes potentially through propaganda generation, and posing new legal questions about content responsibility and copyright. As we leverage these powerful models, it’s crucial to navigate these challenges responsibly, ethically, and sustainably, ensuring a future that aligns with our shared values.read more
One of my goals for 2019 was to read 26 books, effectively one every other week. Well, I ended the year having completed 18. I fell short of my goal; still feel like I satisfied my CQ (Curiosity Quotient), with completing one of my other goals of doing a deep dive in to ML/DL, so it’s no longer a black box. Done!read more
Wondering what’s real about artificial intelligence? Today on BrainTrust LIVE, we’re fortunate to have Cynthia Holcomb, founder/CEO of Prefeye, and Shawn Harris, Customer Partnerships & Strategy, SmartLens — two retail practitioners who are working with their clients on real A.I. solutions. They’ll give us the lowdown — more specifically, on how retailers can currently use AI for personalization, the limitations that are frustrating them at present, and what does the future holds.
There is a great decoupling happening in retail, a structural change. Similar to the decoupling that the computing industry went through, going from being vertically integrated to horizontal specialist. What does this mean for retailers? Retailers need to be clear on what their unique selling proposition is, that is why do customers choose them vs their competitor, or substitute; then double down on those things. Is it your wide assortment, price, convenience, customer service, maybe safety now, or some thing less rational. Everything else should be considered for outsourcing to horizontal specialist, those who are optimized to delivery a particular service, or product.
Within any company, typically the most valuable resources are centered around the making and the selling organizations. There is no difference in retail; instead it’s the merchandising and store operations organizations, and I would add human resources to being core. Most other functions should be evaluated for their need to be an in-house capability.
By the year 2020, it is estimated that the average person will generate 1.5 GB worth of data per day. At that point, it won't be just about your firm's share of customer wallet; instead, you be asked… what's your firm's customer data share? Oh, and consider today who owns the majority of insight in to "your" customer.
In this episode of The IoClothes Podcast, we speak with Shawn Harris, Global Innovation Strategy Lead for Zebra Technologies. The reality is, innovative products don’t just sell themselves and companies aren’t composed of just designers, developers and engineers. Someone has to interface with the customer, and keep the ship sailing along a strategic path, which includes profitability (that’s if you want to stay in business). Today, we shift gears and talk a bit about the struggles of retail, the importance of differentiating yourself in the marketplace and how are current relationship with MS Excel may be a sign of the future!
I believe that for the foreseeable future, human machine collaboration, which will lead to a new type of collective intelligence will be a requirement. Today, we are seeing many advances in the ability of machines to make high confidence predictions; in narrow use cases. This will certainly lead to significant shifts in tasks within job roles. However, where I think machines will lack for some time is in judgement. Judgement being the ability to consider multiple predictions, and come to sensible conclusions in context. This is where humans will need to remain in the loop, playing a key role in judgement. This human machine partnership will lead to a collective intelligence that will results in even higher confidence outcomes, than either could realize individually. However, this prescribed union could quickly find the machine taking more and more of the judgement role as well. Just consider Google’s search results; at this point, how many of us ever get to the second page of results. What’s happening here is that we are trusting Google algorithm’s judgement. We could debate the consequences of this for search results; as we endeavor to use AI in medicine, autonomous cars, and other decisioning based on potentially biased data, we will need to take a more deliberate approach to the policies and practices around final judgement.
I believe that lifelong learning is essential to career success. Technology is changing every industry, and each segment within. Today, you cannot rest on your undergraduate and graduate degrees, and on the job experiences alone. You have to go back to school. But, you don't have to go back to a school. MOOCS, or Massively Open On-line Courses have transformed learning. Now, you can advance your knowledge, with courses from top universities; from the comfort of your couch. Platforms like edx.org, coursera.com, getsmarter, and numerous others allow you to take many courses for free (audit), or for a relatively small fee earn a certificate of completion, which proves you've successful completed a given program. I am a huge proponent of these platforms. For the past year, I have been doing the micromasters in Supply Chain Management via MIT CTL & Edx, and soon will be starting a course in Artificial Intelligence via MIT CSAIL & Getsmarter. No question, it's a lot of work, when you work and have a family, but it's worth it. Stay hungry, stay foolish.
The Chinese's Guanggun Jie, or Singles Day, takes place each year on 11/11. A day set aside to celebrate being proud to be single, has become one of the largest consumer shopping days of the year, dominated by one ecommerce giant Alibaba. Last year (2016), Alibaba processed $17.8B in Gross Merchadise Volume (GMV), which doesn't represent Alibaba's corporate revenue, but instead the total value of the goods sold on the platform. Alibaba mostly earns revenue on advertising placements on the platform. Yesterday, for Singles Day 2017, a whopping $25.3B was purchased through the Alibaba platform. This represented a 39% YoY increase. Wow. However, what this also continues to demonstrate is the structural shift underway in retail, where one digital platform can attract and process more volume in a day, than most retailers do in a year. Think about it, today we're talking about Alibaba, not the 140,000 brands and retailers who provided the products. Who owns the customer, really? That's huge!
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