Tag: philosophy

[Power and Prediction] Chapter 3: AI Is Prediction Technology

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Strip away the hype and the current wave of AI does one thing: it predicts. Chapter 3 is a reset on what that actually means and why it matters economically.

Prediction is filling in missing information. The authors define prediction as the process of taking information you have and using it to generate information you need but don’t have. A bank has a record of millions of past transactions and whether each turned out to be fraudulent. It does not have the answer for the transaction happening right now. Prediction bridges that gap. The same logic covers medical diagnosis (symptoms in, likely condition out), translation (a sentence in one language in, its counterpart out), and driving (what the sensors see in, what the car should do next out). What looks like a dozen different capabilities is one capability pointed in different directions.

Cheap prediction means more prediction. This is where the economics comes in. Prediction is an input to decision-making, and the recent advances in AI have made that input dramatically cheaper. When the cost of an input falls, we use more of it, and we start using it in places where it was previously too expensive to bother. Nobody used to “predict” what a sentence in Mandarin meant; they hired a translator or did without. Now prediction is cheap enough to deploy on every email and menu. The authors’ earlier book, Prediction Machines, was built on this one idea, and it still holds: as prediction gets cheaper, AI will spread.

Substitutes fall, complements rise. The second economic consequence is about what happens around prediction. When something gets cheaper, the things that do the same job become less valuable, and the things that pair with it become more valuable. The main substitute for machine prediction is human prediction, so the value of a person whose job is mostly forecasting, classifying, or estimating will decline. The main complements are two: data and judgment. Data is what the machines learn from, so organizations that have it, or can collect it, are sitting on an appreciating asset. Judgment is what you combine with a prediction to actually decide something.

Prediction is likelihood; judgment is desire. This distinction is the heart of the chapter. A prediction tells you how likely each outcome is. Judgment tells you how much you care about each outcome. Every decision is the two together: you weigh what might happen against what you want to happen. The machine can tell you there is a 3 percent chance this transaction is fraudulent. It cannot tell you whether blocking a legitimate customer is worse than letting a fraudulent one through, or by how much. That’s a question of values, trade-offs, and context, and it stays with humans. As prediction becomes abundant, the scarce and valuable skill is knowing what you want.

Correlation is not causation, and AI doesn’t know the difference. The authors flag what may be the most common misuse of AI predictions: treating the patterns a model finds as if they explain anything. Models find correlations. For many purposes that’s enough; if the goal is to guess which customers will churn, you don’t need to know why. But if you want to act on the world, to change the thing rather than predict it, correlation can lead you badly astray. A model might learn that customers who call support are more likely to leave, but that doesn’t mean cutting the support line will keep them. When the question is causal, the right tool is not a bigger model but a randomized experiment, which remains the most reliable method statisticians have for discovering what causes what. Good AI practice involves knowing which kind of question you’re asking.

The Amazon thought experiment, revisited. In Prediction Machines, the authors ran a thought experiment about Amazon’s recommendation engine. Imagine it getting steadily more accurate. At first, the effect is modest: better recommendations, slightly more sales. But keep turning the dial and at some point a different question becomes possible: if we’re this good at predicting what people want, why wait for them to order it? Just ship it. Shopping would flip from “order, then ship” to “ship, then keep or return.”

Amazon did file a patent on something like this, which the authors call anticipatory shipping. It has not adopted the model. Chapter 3 asks why, and the answer is the book’s thesis in miniature. Better recommendations on the existing platform are a point solution; they ride on the Amazon system exactly as it is. Shipping before ordering would be a system solution, and the system it would strain most is returns. Amazon’s returns process is so costly that in many cases it’s cheaper to discard a returned item than to restock it. A business model built on sending people things they didn’t ask for, and taking back a meaningful fraction of them, would collapse under that cost structure. To make it work, Amazon would have to redesign logistics, returns handling, inventory, and pricing all at once.

The authors are candid that this is what they got wrong the first time. The threshold in their thought experiment wasn’t a matter of accuracy crossing some number. It was the point where capturing the value of the prediction required leaving the point solution behind and rebuilding the system around it. Prediction Machines saw the dial; Power and Prediction is about what’s on the other side of it.

[Power and Prediction] Chapter 1: Three Entrepreneurs and a Technology

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Power and Prediction opens not with artificial intelligence but with electricity, and with a parable. Picture three entrepreneurs in the decades around 1900 — the stretch from the 1880s to the 1920s when factories were moving from steam power to electric power. All three have access to the same technology. What separates them is what they decide to do with it.

The first sees a point solution. Electricity is cheaper than steam and loses less energy to friction, so the obvious move is to swap the power source and leave everything else alone. The factory keeps its central shaft, its belts and pulleys, its layout. Nothing about how the plant works changes; it simply runs a little cheaper.

The second sees an application solution. Instead of one big motor driving a central shaft, why not put an individual electric drive on each machine? Now the machines are modular. If one breaks down, the others keep running. It’s a real improvement, but the factory itself is still designed as if the power came from a single source.

The third sees a system solution. Steam had dictated factory design: heavy multi-story buildings clustered around the power plant, machines arranged by their distance from the shaft rather than by the logic of the work. Electricity broke that constraint. The power source could now be anywhere, so the factory could be anything. This entrepreneur builds lightweight, single-story plants and lays out the floor around the flow of workers and materials. The productivity gains come not from cheaper power but from an entirely new way of organizing production.

Not all value propositions are created equal. The authors’ point is that the first two solutions were framed as substitution: electricity standing in for steam. That framing explains why industry was slow to adopt electricity at first; a modest saving on fuel wasn’t enough to justify ripping out working infrastructure. The lag was long. Electric motors were available in the 1880s, and by about 1919 only around half of U.S. factory power came from electricity. The productivity surge from redesigned factories didn’t arrive until the 1920s, roughly forty years after the technology first appeared. The payoff came only when some entrepreneurs recognized that electricity’s real gift was decoupling the machine from the power source, something steam made impossible or prohibitively expensive. Once that was understood, the value on offer shifted from “lower energy costs” to “a vastly more productive factory,” and system-level solutions delivered returns that dwarfed the point solutions.

The bridge to AI. The parable is the book’s organizing lens. AI, the authors argue, is at the same stage electricity was a century ago. Most current deployments are point solutions (cheaper, faster predictions dropped into existing workflows) or application solutions (new tools built around a prediction, but still plugged into systems designed for a world without them). The larger opportunity lies in asking what AI decouples. Their answer: prediction from judgment. Just as electricity let the machine move away from the power source, AI lets the prediction move away from the human decision-maker. And just as that earlier decoupling shifted the value proposition from “lower fuel costs” to “a redesigned factory,” AI’s decoupling shifts it from “lower cost of prediction” to “a vastly more productive system.”

The rest of the book is about why so few organizations have made that leap yet, and what it will take to get there.

The Power of Subtraction: Understanding Via Negativa in Antifragile

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In a world obsessed with addition—more features, more solutions, more interventions—Nassim Nicholas Taleb presents a counterintuitive approach in his groundbreaking book “Antifragile”: the principle of Via Negativa, or “the negative way.”

What is Via Negativa?

Via Negativa is the profound idea that we often achieve better outcomes by removing harmful elements rather than adding new ones. It’s built on the premise that knowing what to avoid is clearer and more valuable than knowing what to pursue.

In Taleb’s own words: “In practice, it is easier to figure out if something is fragile than to predict the occurrence of an event that may harm it.” This simple insight has far-reaching implications.

Why Subtraction Trumps Addition

Our natural tendency when faced with problems is to add something—a new feature, medication, policy, or regulation. Yet Taleb argues that this additive approach frequently creates more problems than it solves:

  1. Unintended Consequences: Every intervention introduces potential side effects that are difficult to predict.
  2. Complexity Costs: Added elements increase complexity, which creates fragility and vulnerability.
  3. Iatrogenics: Often, the harm caused by intervention exceeds its benefits (like doctors who make patients worse).

Via Negativa in Action

This principle applies remarkably well across numerous domains:

Health

  • Avoiding harmful substances (smoking, processed foods, excessive alcohol) delivers more reliable benefits than adding any specific supplement or superfood.
  • Intermittent fasting (the removal of food intake) often produces better health outcomes than complex diet regimens.

Productivity

  • Removing distractions (social media blocks, notification silencing) can be more effective than adding productivity tools.
  • Subtracting unnecessary meetings may do more for team effectiveness than adding new collaboration frameworks.

Decision Making

  • Eliminating obvious mistakes will take you further than pursuing brilliant insights.
  • Warren Buffett’s approach reflects this: “The first rule is not to lose. The second rule is not to forget the first rule.”

Innovation

  • Many breakthroughs come from removing constraints or unnecessary steps in a process.
  • The iPod succeeded largely by eliminating complexity (buttons, features) present in competing devices.

The Wisdom of Ancients

Interestingly, Via Negativa isn’t new. Ancient philosophical and religious traditions have long embraced this approach:

  • The Ten Commandments are mostly prohibitions: “Thou shalt not…”
  • In apophatic theology, the divine is approached by understanding what it is not rather than what it is.
  • Buddhist practice emphasizes the removal of attachments and illusions rather than the acquisition of new beliefs.

Applications for Modern Life

How can we apply Via Negativa in our daily lives?

  1. Question additions: Before adding anything new (possessions, commitments, features), ask, “What problem does this solve, and could I solve it by removing something instead?”
  2. Practice subtraction: Regularly identify and eliminate things that cause stress, inefficiency, or harm in your life.
  3. Value simplicity: Remember that elegance often comes through simplification, not complication.
  4. Embrace the pause: Sometimes the best intervention is no intervention.

Building Antifragility Through Removal

Perhaps most importantly, Via Negativa connects directly to antifragility. By removing sources of fragility—debt, dependencies, unnecessary complexity—we often create more robust and antifragile systems without needing to explicitly construct them.

In an unpredictable world, knowing what to subtract may be our most reliable path forward. As Taleb reminds us, “To become antifragile, you need to first reduce your fragility.”

The next time you face a challenge, before asking “What can I add to fix this?”, consider the power of Via Negativa and ask instead: “What can I remove?”

Mastering Human Nature: Insights from Robert Greene

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In his masterwork “The Laws of Human Nature,” Robert Greene undertakes an ambitious project: decoding the complex patterns of human behavior that have remained consistent throughout history. After spending six years researching and writing this book, Greene presents 18 fundamental laws that govern human nature – from our innate irrationality to our confrontation with mortality.

What makes this book particularly compelling is its unflinching look at both the light and shadow sides of human nature. Greene doesn’t shy away from uncomfortable truths: we are all narcissists to some degree; we all harbor aggressive impulses beneath our civilized facades; we all wear masks to fit into society. Yet rather than condemning these aspects of human nature, he shows how understanding and accepting them can lead to greater self-awareness and personal growth.

The Core Message

At its heart, the book argues that we cannot hope to navigate human relationships effectively until we first understand our own nature. Greene systematically dismantles the illusion that we are primarily rational beings, showing how our emotions and unconscious drives shape our behavior far more than we realize. This can be unsettling – no one likes to think of themselves as irrational or driven by hidden motivations. However, it’s precisely this uncomfortable self-awareness that enables genuine growth.

Key Insights

Several themes stand out as particularly relevant to modern life:

  1. The Rationality Paradox
    In an age of information and technology, we like to think we’re more rational than ever. Yet Greene shows how social media and modern life have actually heightened our emotional reactivity. The key to becoming more rational isn’t suppressing emotions, but rather understanding and managing them effectively.
  2. The Power of Character
    In an era obsessed with charisma and surface-level charm, Greene makes a compelling case for prioritizing character. A person’s patterns of behavior over time reveal far more than their words or momentary actions. This applies both to judging others and developing ourselves.
  3. The Importance of Empathy
    Greene presents empathy not just as a moral virtue but as a practical skill essential for success. By transforming our natural self-absorption into genuine interest in others, we can build stronger relationships and become more effective leaders.

Practical Applications

What makes this book particularly valuable is its practicality. Each law comes with specific strategies for application. For instance:

  • Creating space between events and our reactions to become more rational
  • Developing techniques to read people’s true character beneath their social masks
  • Learning to channel aggressive energy constructively rather than suppressing it
  • Using awareness of mortality to create a sense of urgency and purpose in life

Critical Perspective

While the book’s insights are profound, it’s worth noting some limitations. Greene sometimes relies heavily on Freudian psychology, which isn’t universally accepted in modern psychology. Additionally, some of his historical examples might be oversimplified to fit his theoretical framework.

However, these limitations don’t significantly diminish the book’s value. Even if you don’t agree with every interpretation, the underlying patterns Greene identifies ring true to human experience and are supported by modern psychological research.

Final Thoughts

“The Laws of Human Nature” isn’t just another self-help book – it’s a comprehensive manual for understanding human behavior, including our own. Its greatest strength lies in combining deep psychological insight with practical strategies for personal development.

The book’s message feels particularly relevant today. In an increasingly complex and polarized world, understanding human nature – both its creative potential and its destructive tendencies – becomes ever more crucial. Whether you’re a leader, professional, or simply someone interested in personal growth, Greene’s insights offer valuable tools for navigating human relationships more effectively.

Perhaps most importantly, the book reminds us that true power comes not from manipulating others, but from mastering ourselves. By understanding and accepting our own nature, we become better equipped to understand and connect with others, creating more meaningful relationships and achieving more significant impact in our work and lives.

What are your thoughts on human nature? Have you found patterns in your own behavior that reflect these universal laws? I’d love to hear your reflections in the comments below.