Today, the Financial Times published an article titled “Russia Plotting Sabotage Across Europe, Intelligence Agencies Warn,” which struck me as fascinating and somewhat hypocritical. The tone of surprise in the article, as if the notion of Russia attempting sabotage is unfathomable, ignores the clear context: Europe is actively supporting Ukraine, Russia’s adversary. This isn’t just a regional conflict; it’s a proxy war where the stakes are high, and the repercussions are global.
The bewilderment displayed by Western media and governments appears disingenuous when considering the direct military and financial support flowing from Western capitals to Kyiv. With over 18 Leopard tanks, 100 MARDER infantry fighting vehicles, and nearly 30 billion euros committed by Germany alone, the scale of involvement is not trivial. It’s substantial and consequential.
Yet, as infrastructure sabotage incidents unfold across Europe — with individuals being charged and others caught in acts of sabotage — there remains a glaring omission in the dialogue: the sabotage of the Nord Stream Pipeline, widely accepted as a deliberate act by entities possibly including the United States and Ukraine. The reaction to this has been muted, especially compared to the loud condemnations and promises of repercussions aimed at Moscow.
“As ever with Russia, it’s wise not to look for a single explanation of why they are doing anything. There’s always a combination of things going on,” said Giles. “These pinprick attacks we’ve seen so far are of course to create disruption, but they can also be used for disinformation. And then there is what Russia learns from these attacks if they want to immobilise Europe for real . . . They’re practice runs.”
This situation begs a critical reflection on the lack of peace dialogues. Why is there no substantial push for peace talks or negotiations? In the West, why are we not initiating a process to broker at least a ceasefire, if not a long-term peace agreement? The absence of these efforts is as much a failure of NATO and the United States as it is of Russia.
The human cost is staggering. A declassified U.S. intelligence report from December 2023 estimated that between 15,500 and 17,000 Ukrainian soldiers had been killed by that point, with countless civilians caught in the crossfire. Yet, the prevailing narrative often skirts these harsh realities, instead focusing on a one-dimensional portrayal of Russian aggression.
The quote from the article, “As ever with Russia, it is wise not to look for a single explanation,” misses the mark. In reality, the explanation is straightforward: the West is engaged in a proxy war, and Russia’s actions, though aggressive, are a mirror of what any state would do in retaliation. It is time for the West to acknowledge its role, take responsibility, and earnestly seek peace. Only through such efforts can we hope to spare further loss and resolve this devastating conflict.
In his 2016 letter to shareholders, Amazon founder and then-CEO Jeff Bezos introduced the concepts of Day One and Day Two. Day One is a metaphor that includes perpetual innovation, urgency, and a zealot-like customer focus, while Day Two is a metaphor for stasis, irrelevance, decline, and the eventual failure of an organization. Bezos argued that for an organization to stay vibrant, energetic, and relevant, it must embody a “Day One” mindset.
The Day Two mindset is dangerous for organizations because it symbolizes stasis, irrelevance, and ultimately, the death of an organization. It manifests in slow decision-making, bureaucratic processes, prioritizing internal politics over customer satisfaction, and reluctance to take risks and explore new opportunities. When an organization is stuck in Day Two, customers suffer the most.
Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. Jeff Bezos
The Blockbuster/Netflix saga has been used so many times to highlight the incumbent losing to the startup that it verges on becoming a cliche. It has mainly been used to support Clayton Christensen’s “The Innovator’s Dilemma” thesis, and I agree it does, but I also think it serves as a simple heuristic for the Day Two mindset. One of the reasons Blockbuster was unwilling to expand their streaming service was the dependency of their profits being linked to customer late fees. According to a 2010 NBC News article, “In 2000, Blockbuster collected nearly $800 million in late fees, accounting for 16 percent of its revenue.” By the time of the article’s posting, those numbers had dropped to $134 million. Slow decision-making, bureaucratic processes, prioritization of internal politics over customer satisfaction, and reluctance to take risks and explore new opportunities led Blockbuster first into stasis, then irrelevance, and ultimately, its death via bankruptcy.
Bureaucracy – Prompt by George Estrada, Image by MidJourney
So, how does an organization get out of Day Two if they are already there? An organization will need to create a mechanism that will allow it to generate enough escape velocity to propel itself back into a Day One mindset. One such mechanism is developing a leadership team (C-suite) with a robust data strategy.
A symptom of the Day Two mindset is siloed data. Where there is siloed data, there is a bureaucracy that justifies the need to keep it siloed, followed closely by internal politics that prioritize ownership over customers. See how it is all woven tightly together? If you are stuck in this Day Two mindset, you most certainly do not have a robust data strategy. While organizations can operate and function with siloed data, bureaucracy, and politics, with every day that passes, they risk not being able to anticipate, adjust, and remain relevant to their customers.
To escape the Day Two mindset, organizations must develop a robust data strategy. It has to be organized and led by the C-suite and complement and support the organization’s overall strategy and mission. A robust data strategy will be customer-centric, focused on innovating for the customer. It will improve the organization’s agility by providing its leaders and staff with the information they need to make quick decisions. It will take outside trends and help identify new opportunities while allowing for continuous improvements measured in efficiencies, increased productivity, and reduced costs, all while improving the organization’s posture on compliance requirements and risk to the business.
Data can be a real game-changer in helping drive change in an organization. It has the power to give you insights that make you rethink what you know, identify new opportunities, and guide you in developing strategies. By making data your starting point, you can:
Understand your customers’ needs
Improve operational efficiencies
Innovate new products and services
Increase speed to market
But here’s the catch: for your data strategy to be effective, you need your leadership team to be fully committed. Your C-suite needs to be the biggest cheerleaders for a data-driven culture. Just wishing or saying you want to be a data-driven organization doesn’t make you one. Leadership has to put the resources behind your data initiatives and ensure that everyone in the organization treats data as a strategic asset.
When it comes to building up your data capabilities, you must cover all your bases:
Governance and management of your data is imperative
Identifying the right infrastructure and technology platforms
Developing your staff’s data skills and fostering a data-driven mindset
Cultivating a culture that embraces data at every level
Remember, your data strategy isn’t a one-and-done thing. It’s a continuous process of improvement and iteration. You must regularly review and refine your strategy to ensure it’s always aligned with your changing business needs and new technological developments.
Finally, an effective data strategy requires collaboration and alignment across all the different functions and departments in your organization. You need cross-functional teams working together to ensure your data initiatives are in lockstep with your overall business objectives and that data is being shared and used effectively across the board.
For organizations that have ambled into a Day Two mindset, I am proposing that they use data as a catalyst for change by developing a robust data strategy that will provide insights and challenge existing assumptions.
Remember, a Day One mindset welcomes challenges to conventional thinking and established norms and is contemptuous of bureaucracy and anti-customer politics.
On January 2nd, Bloomberg published a piece titled “Nobel Prize Winner Cautions on Rush into STEM After Rise of AI.” Now, I have been struggling with this article and its overall message of “don’t study STEM,” it has taken me a while to process it because I wanted to be nice about it, but I was having a hard time doing so. The Nobel Prize-winning economist mentioned is Christopher Pissarides, and essentially, he says that people should not be studying STEM anymore because “This demand for these new IT skills, they contain their own seeds of self-destruction.” Instead, he suggests that people should develop skills for “jobs requiring more traditional face-to-face skills, such as in hospitality and healthcare.”
So, why does this bother me?
Experts get things wrong all the time. And for someone of Pissarides’ stature to make such a recommendation exemplifies his lack of understanding of this new and dynamic field. Furthermore, it spreads fear around AI and its inevitable destruction of the human labor force.
There is so much that we don’t know about AI. Even the so-called experts in the field can’t fully explain what’s happening or why it’s (AI systems) doing what it does. We have an idea now about how and why hallucinations happen, but it was not something anyone predicted. And while we are working hard to eliminate or minimize them, we might not be able to eliminate it completely. Every week, a new paper shows some kind of anomaly or strange effect that is inspiring, bizarre, or frightening.
Contrary to what this Nobel Prize-winning economist says, I argue that now more than ever, there is the time for people to double down on STEM studies, particularly in fields like computer science, data science, cognitive science, and ethics. We are living in a golden age of technology, and we need more people with diverse backgrounds, interests, and ideas and less of the tunnel vision, institutionalized, and narrow-scope mentality from Silicon Valley. We need more folks from China, Japan, Russia, India, Africa, and Latin America to challenge its echo chambers. It’s not that one should replace the other; we all have a stake in this game. We need people from all walks of life and all industries to weigh in on these technologies and their implications. The study of philosophy has been in decline for decades, and now more than ever, we need people to revisit this subject and become part of the dialog.
Author David Epstein, who wrote the book “Range: Why Generalist Triumph in a Specialized World,” noted on the “World of DaaS” podcast that research has shown that experts get things wrong more often than generalists. Epstein specifically references Philip Tetlock’s book “Expert Political Judgment: How Good Is It? How Can We Know?” published in 2005. The research suggested an inverse relationship between fame and accuracy, meaning that the more famous some experts are, the more they get things wrong. That’s problematic because we listen to these experts, and yet they get a lot more airtime, creating a vicious cycle of wrong forecasting and an audience that is misled. But when these experts’ predictions/forecasts are measured, as Tetlock’s book did, you find that they get things wrong more than they do right.
The Fox and the Hedgehog — prompt by George Estrada, image by MidJourney
Tetlock divided his subjects into two groups, foxes and hedgehogs, where a “fox” is defined as a thinker who knows many things, is skeptical of grand theories, adaptable, and ready to adjust ideas based on actual events, while a “hedgehog” knows one big thing, adheres to a grand theory, and expresses views with great confidence. Below is a summary of the top five findings of the research:
1. Foxes, use a diverse range of experiences and are adaptable in their thinking, consistently outperform hedgehogs, who focus on grand theories, in forecasting accuracy.
2. Foxes’ superior predictions are due to their diverse thinking and openness to various perspectives, while hedgehogs’ adherence to singular theories hampers their forecasting ability.
3. Calibration and discrimination are better in foxes, enabling them to align prediction probabilities with actual outcomes and make nuanced assessments of their confidence levels.
4. Foxes’ willingness to learn from their mistakes, reflected in their self-critical reflection and belief updates, contrasts with hedgehogs’ tendency to rationalize or dismiss contradictory evidence.
5. Open-mindedness, a critical factor in foxes’ success, highlights the importance of fostering openness and cognitive flexibility to enhance individuals’ forecasting abilities.
Paul Krugman’s 1998 prediction about what the internet would look like in seven years is an example of hedgehog thinking that I never tire of highlighting. Note that he is also a Nobel Prize-winning economist. Krugman stated:
The growth of the Internet will slow drastically, as the flaw in ‘Metcalfe’s law’-which states that the number of potential connections in a network is proportional to the square of the number of participants—becomes apparent: most people have nothing to say to each other! By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.
Paul Krugman, 1998
Now, sit and dwell on that for a minute. This was an expert. A future Nobel Prize-winning economist (Krugman was awarded the Nobel Prize in Economic Sciences in 2008). Krugman is responsible for a series of predictions that never came true, yet he is never held accountable and is a fan favorite of the news outlets.
So, why do we need more people to study STEM? Simply put, we don’t know as much as we’d like to think we do.
STEM in the 21st Century — prompt by George Estrada, image by MidJourney
I am a fan and supporter of AI; I don’t fear it. But I do fear the hubris with which we are approaching this technology and its macro and micro implications. I do not want to outsource its regulations and development to Silicon Valley, Government, or “experts.” All of which think that consolidating and centralizing control in the name of safety is a good idea, I disagree. Comments like those by Mr. Pissarides, encouraging people to stay away from STEM studies, are irresponsible and unhelpful, feeding into fears that people will become useless in some fields.
Complex systems, especially AI systems of a generative nature, exhibit emergent behavior. This feature alone should be a reason for wanting more people to study STEM, particularly in fields that can help us better understand, align, and explain these systems, such as:
Computer Science and AI/ML to advance the technical underpinnings
Cognitive Science and Psychology to model and align with human cognition
Philosophy and Ethics to grapple with the moral implications
Social Sciences to study the societal impacts
Domain experts (healthcare, law, education, etc.) to guide application
Here are a couple of things that give me hope and why we need more people to study these areas:
1. AI can make accurate predictions but focus on irrelevant data features, which raises questions about its trustworthiness — because the rationale behind these decisions remains unknown and challenging.
2. Deep learning AI systems are often considered “black boxes,” making it difficult to trust their decisions in critical applications — lack of transparency of these processes is problematic.
3. The AI alignment problem, ensuring that AI systems’ goals align with human intentions, especially in the context of superintelligent AI — misalignment can pose significant risk.
4. Explainability in AI systems remains a challenge, with efforts being made to improve transparency through more transparent machine learning models and interdisciplinary research.
The examples above are enough to start new, lucrative and rewarding fields of study. I don’t want the present and future generations to be myopic about the world of possibilities. And while the hype is that AI will become god-like, my take is that these systems will be powerful tools that will amplify the very best and worst in all of us.
No, I think this gentleman, Nobel Prize or not, is categorically wrong, and his comments are irresponsible. We need to push back on the rush to regulate and centralize AI and on experts who oversimplify how these systems work.
I’ll end with a quote by Karim Lakhani, a professor at Harvard Business School whose focus is on workplace technology:
“It is not that AI machines will replace humans, but that humans who use AI will replace those who don’t.”
Karim Lakhani
NOTE: Diverse thinking, as defined by Philip Tetlock’s research on superforecasters, refers to the practice of integrating multiple perspectives, knowledge sources, and cognitive approaches when analyzing problems or making predictions. This includes actively seeking out conflicting viewpoints and evidence to counteract confirmation bias, leveraging cognitive diversity within teams, and bringing together individuals from different fields and backgrounds in interdisciplinary teams. Additionally, cultivating creative behaviors such as an open mindset and the power of iteration can enhance forecasting abilities and innovation. By embracing diverse thinking, individuals and organizations can improve the accuracy of forecasts, foster innovation, and enhance the overall quality of decisions.
I’ve been thinking about the war in Ukraine, obviously a proxy war between the US, NATO, and Russia. The constant, nauseating references to World War II and Putin as the new Hitler are disturbing and misguided.
Putin is undoubtedly a ruthless, murdering autocrat, if not an outright dictator, balanced somewhat by Russian aristocrats and oligarchs. His regime is dangerous and to be feared, if not respected. But they’re not the Nazi regime.
It’s foolish to imply that if we don’t stop Putin now, he’ll do what Hitler did. It is important to note that the Allies didn’t go after Hitler because of the Holocaust — we might have known through intelligence, that there was a systemic annihilation of Jews and other peoples but that wasn’t the reason why England and the US went to war with Germany. A fact that is overlooked. We were at war because the Nazis challenged the Anglophile world order.
The Nuremberg laws were inspired by American race laws. Blacks in the US were treated with such indifference and hatred that I would argue we were one bad leader away from developing our own flavor of a Holocaust. The revelations of the Holocaust’s horrors held a mirror to post-war America and its treatment of blacks, which I believe enabled the civil rights movement to gain the traction it had lacked for decades. Without it, the holocaust, I believe progress in race relations would have been little to none.
We must be wary of the myths our leaders peddle with little to no context. Especially when they are used to justify foreign entanglements.
World War II was an extension of World War I. A war started in large part as an arrogant power grab by impotent leaders sacrificing the lives of millions of young men and civilians, with the net outcome of some changes in borders. The lack of consequences for any of the men responsible led to an even more horrific World War II.
Today’s leaders hide behind that false moral superiority. Leading us to a potential nuclear confrontation – an existential threat to humanity that we aren’t even upset about, preoccupied instead by petty issues like the “misuse” of pronouns.
Is preventing Putin’s potential tyranny over a small population worth risking all of humanity? If so, then why are we not doing the same for Haiti, the Congo, or South Africa? Do some people count more than others? We do nothing for these and many other countries but are willing to risk Armageddon for a conflict and people we barely understand – essentially for nothing.
No love lost for Lizzy from me. Never been a fan of the archaic practice of royals. But this is further sign that Western Civilization has lost all common sense.
I am starting a series of book recommendations, hoping that some will find them helpful. I believe that a good mix of history, nonfiction, and fiction can lead to insights, inspiration, and “aha!” moments that would otherwise be lost in the noise of narrow-scoped hedgehog expertise.
The Square and the Tower: Networks, Power, and the Rise of Modern America by Niall Ferguson: In this book, Ferguson argues that networks have played a crucial role in shaping American history. He examines how networks of power have been used to build businesses, win elections, and start wars. Ferguson’s writing is clear and engaging, and he does a great job of explaining complex ideas in a way that is easy to understand.
Fracture: Life and Culture in the West, 1918-1938 by Benjamin Carter Hett: This is a brilliant book that provides a comprehensive overview of the events that shaped the 20th century. Hett examines the cultural, political, and scientific developments of the period, and he shows how they all interconnected to create the world we live in today. This book is essential reading for anyone who wants to understand the modern world.
Nonfiction
The Three Marriages: Redefining Work, Life, and Loveby David Whyte: This is a thought-provoking book that challenges our conventional notions of work-life balance. Whyte argues that we need to rethink the way we approach work, life, and love in order to find true fulfillment. His writing is wise and compassionate, and he offers practical advice on how to create a more meaningful and balanced life.
How Innovation Works: And Why It Matters by Matt Ridley: This is a fascinating book that explores the nature of innovation and why it is so important for our society. Ridley shows how innovation is a complex and unpredictable process, but it is also essential for progress. He also discusses the challenges that we face in promoting innovation, and he offers some suggestions for how we can overcome them.
Fiction
Trigger Warning: Short Fictions and Disturbances by Neil Gaiman: This collection of short stories showcases Gaiman’s mastery of a variety of genres, including horror, comedy, and fantasy. The stories are all well-written and engaging, and they will stay with you long after you finish reading them.
Speed is not preordained. Speed is a choice. You’ve got to set up a culture that has urgency and wants to experiment. You can’t flip a switch and suddenly get speed — Andy Jassy
For organizations to stay resilient and competitive, they need to master two interconnected capabilities: speed and innovation.
That’s the key insight from new research by MIT’s Center for Information Systems Research (CISR). They surveyed over 700 companies worldwide to understand what drives top performance. Their findings reveal that the highest growth and profitability come from companies excelling at both accelerating time-to-market and continually introducing new products and services.
Here are three major takeaways from the MIT CISR study:
1. Speed alone provides minimal boost — racing to market faster doesn’t improve results. The companies with real differentiation couple speed with ongoing innovation. The top performers launched more revenue-generating innovations while also getting to market faster than competitors.
2. Digital capabilities are foundational — leading companies leverage cloud platforms, APIs, automation, real-time data, and other digital technologies. These tools allow them to tap ecosystems, enable collaboration, quickly respond to changes, and rapidly deliver innovative offerings.
3. Leadership and organization evolve — the top companies shifted from hierarchical control to empowered teams. Leadership focused on communicating strategic vision and coaching, not commanding. Shared dashboards provided transparency on value creation goals and guided data-driven decisions at all levels. Work was organized around outcomes rather than tasks.
The researchers identified four drivers (a framework) for these high-performing organizations:
1. Empowering leadership, while simplifying work
2. Adopting leading-edge technology
3. Using data as a guide
4. Nurturing an innovative ecosystem
The MIT CISR framework provides a blueprint for management practices and digital technologies that enable top performance. I recommend reading the complete research article. It offers an in-depth look at the approaches, leadership styles, and organizational models that allow some companies to continually adapt and lead their industries. And the highlighted case study on Mercedes-Benz is a model to be mimicked.
The recent lawsuit filed by The New York Times against OpenAI and Microsoft over its artificial intelligence system has brought the issue of copyright infringement by AI into focus. As someone who has fielded questions on this topic before, I believe this is fundamentally more a matter of copyright law than technology. This lawsuit will probably draw clearer distinctions between the two domains.
Personally, I have never been a proponent of expansive copyright laws that seem to benefit large media companies at the expense of consumers and up-and-coming artists (writers, filmmakers, visual artists). The notion that any inspiration derived from published works constitutes infringement is particularly problematic to me. As a society, we are constantly exposed to and influenced by information around us, often in ways we don’t even realize or remember. Should fleeting inspiration really be grounds for legal consequences years later?
“But the use of this technology also presents a possible existential crisis for the news industry, which has struggled to find ways to replace the revenue it once generated from its profitable print products. “
Washington Post: New York Times sues OpenAI, Microsoft for using articles to train AI – https://bit.ly/3RXsGuO
I do believe AI developers should pay licensing fees to access certain content, just as any other business would. However, news publishers face revenue challenges tied to numerous industry changes, not just technological advancements. Declining public trust in polarized news coverage deserves as much scrutiny as any technological disruption when analyzing the media’s struggles — for an in-depth assessment of such problems, I recommend James Bennet’s piece in the Economist 1843 Magazine, “When the New York Times Lost Its Way.”
Additionally, while publishers demand recompense when AI systems summarize their content, the same publishers do not pay any form of royalties to the victims featured in their true crime reporting, which certainly drives traffic to their sites. If we consider copyright principles comprehensively, such inconsistencies stand out.
Questions around derivative works also illustrate the complexities of this debate. If fiction authors build on themes and characters from myths and legends in the public domain, should their works also lose copyright protection? Inspiration has murky borders that our legal framework is ill-equipped to navigate.
There is no doubt AI systems raise copyright concerns that technology companies must address responsibly. However, as we balance those interests, we should be wary of arguments that may limit access to information mainly for those unable to pay. The early promise of the internet as an open repository of human knowledge relies on getting this balance right. Legal outcomes here will shape much more than profits.
This is an exciting time to be a technologist, but even more so if you are a business leader, decision maker, or someone with big ideas for improving processes, member or customer experiences, or simply someone with an idea to improve society. Technology is becoming accessible to nontechnical stakeholders in ways only one could dream of a few years ago.
Last month Amazon Web Services (AWS) announced several new artificial intelligence (AI) products and capabilities that will help organizations operate more efficiently and effectively. If you are wondering how these emerging technologies could apply to your organization’s work. I will briefly break down the key announcements and their potential nonprofit(and commercial) use cases in quick and simple terms/ideas.
1. Amazon Bedrock makes advanced AI models easily accessible.
Bedrock offers pre-built foundational AI models from top providers like Anthropic and Meta. This means your developers don’t need deep AI expertise to tap into powerful natural language processing. Possible use cases include building a virtual assistant to answer donor questions or automating tasks like sorting and tagging incoming donations.
2. Amazon CodeWhisperer can suggest code tailored to your needs.
CodeWhisperer is like autocomplete for coding. The new customization feature allows it to make suggestions based on your organization’s own codebase and conventions. This will boost productivity for developers working on your nonprofit’s custom software and websites.
3. Amazon QuickSight Q — turn text into data visualizations.
QuickSight’s new natural language capabilities let analysts generate charts and calculations by describing what they want in plain English. Your data team could quickly visualize important metrics like program enrollment or fundraising progress through conversational commands.
4. Multiple model options for different needs.
With new models like Titan Embeddings and Llama 2, Bedrock offers variety to suit different use cases. This flexibility allows you to test and find the best AI approach for each task, from donor segmentation to grant application screening.
The above services will provide the building blocks and organizations will combine to create customized solutions – organizations of all sizes will benefit. These innovations and productivity boosts will unlock the potential to better achieve social impact missions.
As I noted above, this is a great time to be in technology.
CYBERTONIA 