A few weekends ago, I started building this real-time simulator of bacterial evolution and antibiotic resistance (calling it evodish).

I just got around to finishing it up.

Antibiotic resistance is still a very big problem. In the United States alone, more than 2.8 million antimicrobial-resistant infections occur each year.

In this simulator, you drop antibiotic discs into a digital petri dish, natural selection unfolds.

I tried to capture as much biology as possible without overcomplicating it (random mutations, horizontal gene transfer, metabolic fitness costs, etc.).

> 💻 Try it here: evodish.vercel.app

> 📺 Video Guide:

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Last Fall, I ate my own cooking, put my money where my mouth is. After my Postdoctoral Fellowship at Georgia Tech, I transited to a research affiliate role at the Institute and I decided not to take a new employment.

About a year ago now, I stumbled on a LinkedIn post, where a pertinent question was asked, just before a brief articulation of the obvious incursion of AI tools into knowledge work. The fellow wrote, “What’s left in research when so many things can be done by AI? 🤔 …”

Some people who might have found that take a bit hyperbolic at the time might not anymore, particularly if they have tried tools like CodeScientist, Autodiscovery, and Kosmos, to mention just a few.

My response was as follows:

"A researcher could, and probably should, deliberately increase their breadth (and depth) of knowledge/competency. Many aspects of research will be aggressively automated over the next few years (This is true for all knowledge work) So, insights (reads: your knowledge bank) would be ultimately highly priced in a world where some research can be done quasi-on-demand. And the 'insights' are going to scale to the extent to which one leverages AI; you probably don't want to be the third servant, in the Parable of Talents who buries the one talent received into the ground, and ends up with nothing. And this parable maps to what we will be seeing going forward. 5 to 10, 2 to 4, and so on. The talent is your knowledge of what you know is possible. The (research) questions you ask. It's probably the best time to be a generalist."

Today, my response could really use some workshopping, but that’s not the reason for this essay. The only thing I would add is that, as a hypothesis, we are approaching even more pronounced barbell territory: hyperspecialist or (hyper)generalist. A plausible scenario is that everything in the middle will be gobbled up by AI. We can argue about timeframes, but again, that isn’t the purpose of this essay. This essay is, in part, an attempt to show what acting on that hypothesis looks like in practice.

After my fellowship, I passed on a couple of opportunities that would have been great in the pre-ChatGPT era (we really need a name that rolls off the tongue for this). But as I was saying, history is no longer “settled”; it’s moving fast — too fast in some domains. The future is profoundly uncertain, and precisely because of this, actions matter more than usual.

And it was principally the barbell and the “moving history” arguments that propelled me in this direction, in spite of the fact that the move ain’t cheap, to say the least.

And now, with the privilege of hindsight, I realize I actually could have used some rest. The path through graduate school and a postdoc is fiercely intellectually stimulating, but undeniably stressful. Add the years spent navigating the U.S. immigration system as an international student, alongside the recent relentless pace of tracking the AI disruption like a hawk, and stepping back makes some sense.

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I have had a fairly eclectic academic journey so far, perhaps this exposes my generalist bias. I hold a PhD in Biochemistry and Molecular Biology. I did some work in yeast genomics and studied Caenorhabditis elegans early in grad school. As a senior graduate student and postdoc, I published several works on applied AI/ML in biomedicine.

One of the main ‘skill issues’ I’ve had over the past couple of years (and for the engineers reading, that pun is fully intended) arose simply by virtue of being in a research environment. It was the ability to take an idea and turn it into production-level software with all the bells and whistles. I could do bits and pieces, but not the full gamut.

To solve this, the core challenge of my break became building intelligent systems end-to-end: models, agents, backend infrastructure, and user experience. What people now call full-stack AI/agent engineering. It felt like a reasonable way to expand my wingspan. I was already adjacent to the field through a number of side projects, so this seemed like a natural escalation. It was a sufficiently hard problem, and increasingly, an important one, since the next iteration of knowledge work is clearly automation (for better or for worse). And how long the skillset itself will be “useful” simply depends on which Substack newsletter you read, or which AI lab CEO you listen to.

However, as I hinted earlier, my break soon turned into a much-needed respite. I worked less — by virtue of being more focused (and leveraging AI) — read more books, played video games, and exercised much more.

I’ll give an example. All my life, I had wrongly assumed I was not a good long-distance runner, despite being, at least by American standards, very active. These days, I run 10 kilometers most days of the week. (It’s crazy how we tell ourselves we are not good at things we simply haven’t tried enough.) Along the way, I built a holistic, bespoke personal operating system from the ground up, one that incorporates work, learning, exercise, rest, and more.

However, one of the main projects I am working, now close to an alpha launch, is an experiment to test a hypothesis on what knowledge navigation, consumption, and collaboration might look like in a world where research is commodified.

Over the next weeks and months, I will share more artifacts (essays, apps, videos) publicly, and I would be glad to hear your feedback. I will share my “in-the-trenches” micro-updates mostly on Substack Notes, and I will also collect them here. General essays will be on Episteme Engine, while technical AI and engineering deep dives will be on my AI blog — The Epsilon. Video updates will be shared here as well.

Calling this a career break is somewhat of a misnomer, more like building through an AI transition (admittedly, that doesn’t roll off the tongue either). The work didn’t stop; it changed shape. Whether this turns out to have been the right call is still an open question. But even if the barbell hypothesis is wrong (which I strongly doubt), a wider wingspan is not the worst thing to have built.

P.S.

1. If you are an experienced engineer, especially in the United States, looking for a new AI adventure around (scientific) research, biomedicine, or science education, feel free to reach out to discuss the possibility of teaming up. I have far too many projects and feature ideas, and too little time.

2. I am considering publishing a full length memoir essay at some point in the future, structured as a sequel to “Plans Are Overrated“, express your interest here.

I listened to the first part of this podcast on my 10k run today.

There are many threads running through the podcast, but I will focus on just one thread.

Dr. Alok Kanojia, whom I encountered for the first time today, eloquently explains that the ego is constructed from the labels and definitions we assign to ourselves, making it an inherently comparative entity. When we fall into the trap of comparing ourselves to others or copying what we see them pursuing (often driven by external conditioning from social media where we adopt others’ desires as our own) we end up chasing goals that do not align with our true selves. While allowing the ego’s comparative nature to fuel our ambition might lead to worldly success and gratify our desire to prove ourselves, it ultimately comes at the cost of genuine happiness because the ego is never satisfied and continually moves the goalposts. To find true fulfillment, he argues that we must step away from these externally programmed desires and peel back the comparative layers of the ego to tap into our authentic, intrinsic drive.

(As a Girardian of some sort myself, it wasn’t particularly hard for me to see that his analysis is drenching in the language of “Mimesis” and “Metaphysical Desire”)

This quote from the classic epistolary novel The Sorrows of Young Werther (published in 1774) by the German polymath Johann Wolfgang von Goethe, has to be the shortest best thing I have read on this matter at hand.

I urge the reader to read it carefully.

“Since we are so made that we compare everything with ourselves and ourselves with everything, happiness or misery lies in the objects we associate ourselves with. . . . The power of our imagination, driven by nature to elevate itself, nourished by the fantastic images of literature, raises up a series of beings of whom we are the lowest and everything outside ourselves seems more glorious, every other person more perfect. And that happens quite naturally. We feel so often that we are lacking so much, and just what we lack another person often seems to possess, to which we also add everything that we possess, and project a certain ideal contentment on top of it. And thus the other person is made happy, complete, and perfect, a creature of our own making.”

given the times we live in, this extract is even more telling:

“...nourished by the fantastic images of literature, raises up a series of beings of whom we are the lowest and everything outside ourselves seems more glorious, every other person more perfect.”

Literature?!

Talk about Social Media.

If Johann Wolfgang von Goethe could visit us for a day, I wager that he would lose his mind, and so would his compatriots.

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I have also published two essays on the subject matter in the past:

We Are All Copycats

Prestige as an Illusion

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An essay landed in a group chat the other day. I read it and started flooding the chat with messages, and then realized quickly that this probably wants to be an essay. So here we are.

The original essay, titled “The Prison of Financial Mediocrity” by , argues that younger generations, locked out of traditional wealth paths and facing accelerating disruption from AI, are rationally turning to high-variance “casino” systems (crypto, prediction markets, sports betting) as the only remaining sources of perceived agency.

As white-collar career ladders erode and social media amplifies status anxiety, gambling becomes less about ignorance and more about desperation under constraint. The author frames this as a structural, not moral, phenomenon and concludes that the winning move is to be “long the platforms”, the argument that because the house always wins, the only rational move is to invest in, or align with, the “casinos” extracting fees from the desperation.

(Another, more general side note on the original essay: I read what is going on as a symptom of late-stage capitalism, one in which pressure is no longer absorbed quietly but expelled through multiple “exhaust pipes.” The same blocked-agency pressure that vents economically through casino-styled systems is also venting politically (say, Groypers-styled movements). The casino is just one outlet. It is not the only one—and it may not even be the most dangerous.)

A False Dilemma

The essay quietly advances a binary:

1. White-collar work is cooked

2. Therefore, gamble

This framing collapses too many alternatives and overfits to a narrow slice of the economy. Even granting that many white-collar knowledge jobs are under threat from AI, the author underweights blue-collar and asset-backed paths (plumbing, construction, electrical work, welding, farming) fields that can scale into real businesses, compound locally, and remain stubbornly resistant to automation. These are not consolation prizes nor are they sexy but they are parallel wealth tracks that don’t depend on winner-take-all dynamics.

That critique stands. But there is another omission that matters just as much.

The Missing Third Option

What the essay leaves out is that AI does not only destroy ladders, it reshuffles where leverage lives.

The choice is not simply between grinding obsolete roles and betting your future on high-variance speculation. There is a third option: positioning oneself at the system level rather than the task level.

AI collapses the value of many individual tasks, but in doing so it raises the value of roles that define goals, set constraints, integrate outputs, arbitrate tradeoffs, and absorb responsibility when systems fail. These are not “AI-proof” jobs in the traditional sense. They are AI-adjacent leverage points, places where human judgment, accountability, and synthesis still matter precisely because the system has become more automated.

(Also, a friendly reminder that there is such a thing as the Jevons Paradox: while AI automates tasks, humans still orchestrate workflows, pushing expectations higher. There are legible, logical arguments that we should expect more work, not less, much of it entirely new)

The core of the argument here is not necessarily about learning the next hot skill or chasing “prompt engineering” as a credential. It is about understanding how AI reorganizes workflows and placing yourself where coordination, interpretation, and consequence still concentrate. That path does not necessarily promise the dopamine hit of a moonshot, but it preserves agency without requiring existential bets.

Gambling, But With a Seatbelt

On “gambling” itself, the sane position lies between moral panic and evangelism.

There is a categorical difference between capped, asymmetric optionality and full-blown, identity-level risk. Holding casino-styled financial assets as a speculative hedge is not the same thing as staking one’s future on memecoins or perpetuals. The essay claims to be descriptive, but its tone risks normalizing behavior that is, at scale, financially and psychologically ruinous.

The deeper problem is not ignorance of odds. Most participants understand the house edge. What they lack is a credible alternative path that feels agentic. When the baseline is “treading water indefinitely”, perpetual movement without motion, even a slim chance of escape starts to look rational.

The Inflation of What Feels Necessary

Another blind spot is excessive upside-seeking masquerading as inevitability. Not everyone is merely seeking dignity or stability; many are seeking extraordinary outcomes in a world where most outcomes are, by definition, ordinary.

Even venture capital is structurally a form of gambling. Most startups fail. A tiny fraction generate outsized returns. The difference is that good VC-style bets often leave participants with something even when they fail: skills, networks, judgment, and optionality (conditioned on who the participant is, of course)

Pure gambling leaves none of this behind.

That distinction points to the strongest corrective insight.

The Only Bets Worth Making

The best bets are those whose failure modes still resemble success.

Learning can compound even when companies die. Reputation can survive even when products don’t. Skills can transfer. Networks persist. Optionality expands. These are antifragile payoffs. Lose the bet, and you are still better positioned than before.

Pure gambling offers no such cushion. Lose, and you are strictly worse off. Win, and you may still be worse off psychologically.

All of these begs the question: what is even gambling in the context of these various assets that I am, admittedly, not terribly familiar with?

One way to answer this via negativa is, it is not pure gambling if the failure modes still resemble some form of success (and I know the devil is in the details, but I’m not ready to spill more ink here; reasonable people can fill in the gaps as they see fit.)

But of course, there are more straightforward(?) way of thinking about it - does it involve creation of real value? and is it merely a game of chance?

A Better Framework

All of these invites better questions:

• Where does agency still exist in this system?

• Where does effort still map to outcomes?

• What paths offer convex upside without existential downside?

• What roles become more valuable because AI moves through the economy?

To conclude, we should address the cynicism embedded in the “Long the Platforms” thesis. The proposal is not to escape the prison of financial mediocrity by breaking the bars, but by repositioning oneself as the warden. To “own the casino” is to place a levered bet on the continued desperation of one’s peers; it presumes a steady population that remains desperate enough to gamble, yet solvent enough to pay the rake. Structurally, this is a short on broad-based human flourishing. True agency shouldn’t be found in collecting tolls on the road to ruin, but in building the off-ramps.

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My Other Publications

My latest WILLs, “What I Listened to Last week” Issue 6.

You can also find my latest AI newsletter here: “Gemini 3.0 💻, Kosmos 🌌, and Why AI Changes Everything ⚪️”

(For books I have read, reviewed, or currently reading, go to the end of the newsletter)

[Talking Points]

👝Dust in the Baggie - Billy Strings

💃🏾Kpanlogo Dance, Ghana.

👨Man walking across the world.

🗝️35 year old lives in 1946.

🇺🇸Where can Americans afford to live solo?

⚽History of world cup match balls.

🎺Asake - Red Bull Symphonic.

🐍Backend Devs are switching to Python.

🫂Why we are getting more stupid.

🩸A New Cancer Blood Test.

➕The Strange Maths that predicts almost anything.

🇦🇫Public Execution in Afghanistan.

🤼‍♂️John Cena final entrance.

🎵AI generated song that hit #1 on Billboard.

🧗🏽‍♂️Sisyphus unpacked

[Longer Reads/Watch]

🏚️ End of Progress Against Extreme Poverty

Max Roser argues here that the historic era of rapid global poverty reduction is effectively ending, with progress projected to stall and potentially reverse after 2030, quoting World Bank projections. While past successes were driven by booming economic growth in Asian nations like China and India, the world’s remaining extreme poverty is now concentrated in stagnant Sub-Saharan African economies.

Source.

What is different today is that the majority of the world’s poorest people are stuck in economies that have been stagnating for a long time. Consider the case of Madagascar. In the long run, the country has not seen any growth at all: GDP per capita in Madagascar is about the same today as it was in 1950. As a consequence, the number of people in extreme poverty increased in line with the country’s population growth. In richer countries, it is possible to reduce poverty by reducing inequality through redistribution, but a country like Madagascar cannot reduce its share of people in extreme poverty through redistribution. This is because the mean income is lower than the poverty line; if everyone had the same income, everyone would be living in extreme poverty.

The situation is similar in other countries: in the Democratic Republic of Congo, Mozambique, Malawi, Burundi, and the Central African Republic, more than half of the population lives in extreme poverty.

How does this end of reduction of extreme poverty narrative square up with the so-called “AI Abundance”?

I think the conflict, if any exists, partly lies in the gap between digital and physical reality. While AI creates an abundance of intelligence and demonetizes some aspects of services like healthcare, it cannot be eaten, nor does it inherently build the roads, power grids, or sanitation required to lift the “bottom billion” out of destitution. Worse, AI-driven automation threatens to kick away the ladder of low-cost manufacturing that nations like China used to rise, potentially leaving African economies stranded without a growth model. Unless AI specifically solves physical costs (energy, logistics) or drives a shift from local growth to massive global redistribution (perhaps a more cynical read would be something along the lines of ‘recolonization?’), we face a bifurcated future: post-scarcity/abundance for the connected, and continued stagnation for the rest.

🔥 Girard’s Apocalypse.

I have written my fair bit on Girard’s mimetic theory, which you can find here, here, and here. But my analysis of the subject never took any serious theological-political dimension. In this pair of excellent essays from the Substack, João Pinheiro da Silva and Mauricio G. Righi explore the existential stakes of René Girard’s “mimetic theory” and how venture capitalist Peter Thiel has adapted it’s insights.

In Part 1, “Girard’s Apocalypse,” the authors outline Girard’s central thesis: that human societies were originally built on the “scapegoat mechanism,” a collective act of violence against an innocent victim that creates temporary peace. However, the authors argue that the Christian revelation of the victim’s innocence has permanently “broken” this mechanism, leaving modern society in an apocalyptic state. Without the ability to vent societal tensions through a shared sacrifice, mimetic rivalry (the imitation of others’ desires) escalates without a natural limit. This creates a global crisis where traditional “restraining” forces (katechon) have failed, leading to a world of total competition and the constant threat of escalating violence that no longer has a religious “safety valve.”

In Part 2, “Thiel’s Diabolical Deviation,” the focus shifts to Peter Thiel, a former student of Girard who, they argued, has “weaponized” these insights. Thiel accepts Girard’s diagnosis of a world in mimetic crisis, but rejects Girard’s theological conclusion of total non-violence and renunciation. Instead, Thiel performs a “diabolical” inversion by using Girardian tools to build a new, technological katechon, a sovereign power capable of restraining the apocalypse through surveillance, monopoly, and political domination. By attempting to re-engineer a form of “pagan” sovereignty in a post-Christian world, Thiel is seen as a “Girardian against Girard,” utilizing the mentor’s revelation of human violence not to transcend it, but to master and manage it for the sake of worldly survival and strategic advantage.

🚗 The Human Driver is a Failed Experiment?

I took my first Waymo ride this fall, and one of my first thoughts was something along the lines of “Jeez, the stuff is too good, how the hell are humans supposed to compete.” The drive, right in the middle of Atlanta, was too smooth and very cozy. I came to the conclusion that humans should probably be driving less, then I looked up the data. Boy, was it good.

You can find more of the safety data in the Waymo Safety Report.

In this New York Times opinion essay, a neurosurgeon effectively argued that the adoption of autonomous vehicles is a public health imperative:

If Waymo’s results are indicative of the broader future of autonomous vehicles, we may be on the path to eliminating traffic deaths as a leading cause of mortality in the United States. While many see this as a tech story, I view it as a public health breakthrough.

There’s a public health imperative to quickly expand the adoption of autonomous vehicles. More than 39,000 Americans died in motor vehicle crashes last year, more than homicide, plane crashes and natural disasters combined. Crashes are the No. 2 cause of death for children and young adults. But death is only part of the story. These crashes are also the leading cause of spinal cord injury. We surgeons see the aftermath of the 10,000 crash victims who come to emergency rooms every day. The combined economic and quality-of-life toll exceeds $1 trillion annually, more than the entire U.S. military or Medicare budget.

ℹ️Connection Among Information, Intelligence and Decision

I have always enjoyed reading Dembski’s essay on his many critiques of materialism in biology/life.

Here, he argues that information, intelligence, and decision are intrinsically linked by the fundamental act of “ruling out possibilities.” He argues that information is a top-down process where specific outcomes are realized to the exclusion of others, which mirrors the etymology of both intelligence (inter-legere, to choose between) and decision (decidere, to cut off). By defining the creation of information as a discriminatory act of choosing one path over countless others, Dembski contends that the “selection” observed in the biological world is not a byproduct of blind forces.

As a side note, this brief etymological tour reveals that Darwin’s great coup was to coopt the term selection, previously associated with the conscious choice of purposive agents, and combine it with the term natural as a way of negating the role of such intelligent agency. In the term natural selection, Darwin therefore attempted to recover all the benefits of choice as traditionally conceived, and yet without requiring the services of an actual intelligence. Thus to this day we read such claims, as by Francisco Ayala, that Darwin’s greatest discovery was to give us “design without designer.” Likewise, Richard Dawkins describes Darwin’s great coup as explaining the appearance of design in the absence of actual design.

And what about microevolution? If I am not wrong, I believe, elsewhere, Dembski reconciles the aforementioned argument with natural selection & microevolution by framing them as “subtractive” rather than creative mechanisms. Natural selection clearly generates information by weeding out the unfit, he argues that this process merely filters pre-existing genetic data without generating new Complex Specified Information. Blind evolutionary forces can fine-tune existing biological structures (microevolution), but they lack the decision-making capacity required to conceptualize and build them from scratch, say.

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🔔 Double Pendulum Aren’t Chaotic

This excellent video essay challenges the popular notion that double pendulums are inherently chaotic by visualizing their behavior through “phase space” fractals (geometric maps). While admitting that many starting positions lead to unpredictable, divergent motion (chaos), the creator demonstrates that specific initial conditions (particular angles or momentum levels) result in stable, repeating patterns like “pretzels,” “hearts,” or Lissajous curves.

By mapping these behaviors in such beautiful motions, the video reveals “islands of stability” hidden within chaotic regions and observes that while low-energy starts are typically stable and medium-energy ones chaotic, ultra-high momentum starts can surprisingly reintroduce order, proving the system contains pockets of beauty and predictability amidst the disorder.

I was reminded of Nassim Taleb’s “Barbell Strategy” take in one of his books, after watching the video, which advocates for operating at the extremes while avoiding the “muddled middle.” Taleb suggests allocating resources to either hyper-safe assets (stability) or/and hyper-aggressive bets (high variance but high potential) to avoid the hidden fragility of “moderate” risk. I think some “law of everything,” might be hiding here in plain sight.

👴🏻The Common Sense of John Searle

I have been interested in some contentious (philosophy) subject matter that had eventually led me to read about some of John Searle’s work, chief among them would be the nature of the intelligence in artificial intelligence, and free will.

Ed Feser wrote this short synopsis of some of Searle’s work, after he passed away in September.

On AI:

Imagine that Searle, who knows no Chinese, sits in a room with a set of Chinese symbols and a rulebook in English telling him which combinations of symbols to give out in response to written questions slipped to him through a slot in the door. The rulebook does not tell him what the symbols mean; it simply allows him to mimic the behavior of a person who does. What Searle would be doing in this scenario, he argued, is essentially what a computer does: manipulating symbols according to the rules of an algorithm. The resulting mimicry, no matter how convincing, would not yield genuine understanding of Chinese. Likewise, what computers do can never amount to the operations of true intelligence, but only a simulation of it.

(also see this nice TED animation on the Chinese room thought experiment)

On free will

…He argued against the idea that our actions are the necessitated effects of mental events of which we are merely the passive observers—as if everything we think and do simply happened to us. Choice, of its very nature, involves an irreducible and persisting self, which actively brings things about as a result of deliberation. There is a causal gap between our beliefs and desires on one hand and our behavior on the other, and only the self can fill that gap, by way of its agency. Searle’s view is that whether or not we can strictly prove the reality of freedom, we have no intelligible model of human action without it.

On his metaphysics:

The main weakness of Searle’s work is that he never developed a metaphysics that was as carefully worked out as his anthropology. Searle’s materialist critics often accused him of being a dualist in the Cartesian mold, a charge he always denied. He was as committed as the materialists were to the thesis that the natural world is all there is. He simply thought their way of fitting human beings into that world was simplistic. The trouble is that the conception of nature he shared with them, which he never seriously questioned, makes his position unstable. When he emphasized the continuity of human beings with the larger natural world, he sometimes sounded like he was offering just another riff on materialism. But when (as was more often the case) he emphasized how radically different the human mind is from everything else in nature, the charge of dualism was hard to rebut.

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[Books/Papers 📚]

Books I recently completed:

• Ultralearning by Scott Young [Review]

• Stillness is the Key [Review]

• Secrets of Sand Hill Road [Review]

• Reshuffle: Who wins when AI re stacks the knowledge economy [Review] (I plan to publish an essay about the ideas in the book early next year.)

What I am currently reading:

• Hatching Twitter: A True Story of Money, Power, Friendship, and Betrayal by Nick Bilton

• Shoe Dog: A Memoir by the Creator of Nike by Phil Knight

• Google’s Introduction to Agents.

Book Title: Secrets of Sand Hill Road.

I wanted to learn more about venture capital, so I picked up the book Secrets of Sand Hill Road written by Scott Kupor of Andreessen Horowitz. The following are some of my notes grouped (loosely) by themes.

I. The Tyranny of the Power Law

One cannot understand the behavior of a venture capitalist without first grasping the mathematical reality they live in: the Power Law. In most walks of life, outcomes follow a bell curve. In venture capital, returns are heavily skewed. As Kupor illustrates using a baseball analogy, VCs aren’t looking for a high batting average (getting a “hit” every time); they are looking for “at bats per home run”.

The math is stark: VCs expect >50%? of their investments to return nothing. To offset those losses and generate the massive returns their own investors (Limited Partners) demand, they need the winners to return 10 to 100 times the invested capital. This is why a VC might pass on a perfectly good business that has a high likelihood of becoming, say a $50 million company; it simply doesn’t move the needle for a fund that needs to return hundreds of millions.

Understanding this helps to realize that when a VC pushes for hyper-growth, they are simply trying to survive the math of their own industry.

II. The Structural Foundation: C Corps and Stock Classes

Everyone knows corporate structure isn’t just paperwork; it’s strategy. Kupor emphasizes that while the LLC or partnership model might seem tax-efficient for a small business, the C Corporation is the non-negotiable standard for venture-backed startups. This is largely because the Limited Partners (LPs), the endowments and foundations funding the VCs, are tax-exempt entities.

And if a tax-exempt entity receives income from an active trade or business (like a startup) through a “pass-through” entity like an LLC or partnership, that income is classified as Unrelated Business Income (UBI) and is subject to tax. This creates a “tax penalty” for the LP, effectively eroding their tax-free status for that investment. By investing in a C Corporation, the tax liability remains contained within the corporation itself, blocking it from passing through to the LPs and preserving their tax-exempt standing.

Furthermore, the C Corp structure facilitates the necessary hierarchy of ownership: Preferred Stock vs. Common Stock. VCs require Preferred Stock to secure specific economic and governance rights (like liquidation preferences), while founders and employees hold Common Stock.

Take liquidation preferences, it ensures that in a “downside” scenario (e.g., the company sells for less than hoped), the VCs get their money back before the founders see a dime. Conversely, if the company is a massive success, VCs can convert their Preferred shares to Common and participate in the upside. This structure protects the investor’s capital in failure while allowing them to share in the “home run,” aligning their risk profile with the high-stakes nature of the bet.

III. Misaligned Incentives, Fund Cycle, & “Ruling from the Grave”

One critical factor Kupor highlights is the Fund Lifecycle. Most VC funds have a 10-year lifespan. If a VC invests in your company during year 8 of their fund, they may be under immense pressure to force a liquidity event (like a sale) sooner than you would like, simply to return cash to their LPs before the fund expires .

Startups must also watch out for the “Zombie Fund” scenario. If a VC firm has performed poorly and is unlikely to raise a subsequent fund, they may lose the ability to “reserve” capital for future rounds. This can leave startup stranded without the follow-on capital they expected. Knowing where VC stands in their own fundraising cycle is just as important as them knowing startup’s financials.

This ‘alignment’ discussion also extends to governance. Kupor introduces the somewhat macabre but critical concept of “ruling from the grave”. This occurs when a founder retains a board seat purely by virtue of being a founder, even after they have left the company. To prevent a former operator from holding the company hostage, board seats should be tied to current service (say, the CEO role) rather than the individual person.

IV. The Art of the Pitch and Early-Stage Decision Making

When early-stage VCs decide where to invest, they are often operating with very little hard data. Consequently, their decision-making heuristic relies heavily on three pillars: People, Product, and Market. Of these, market size is paramount because of the aforementioned power law, a great team in a small market will still fail to return a venture fund.

A successful Pitch must therefore articulate a narrative around these elements:

• Market Size: convince the VC that the opportunity can generate billions in value.

• Team: Specifically, “founder-market fit”, why this team is uniquely qualified to solve this problem.

• Product: Is it a vitamin (nice to have) or an aspirin (need to have)?

• Go-to-Market: A plausible strategy for acquiring customers.

V. Valuation: The “Dark Art” and Convertible Debt

Valuation is often more art than science. How does a VC conduct valuation of startups? Kupor outlines a few methods, ranging from Comparable Company Analysis (looking at public comps) to the Option Pricing Model (OPM). However, for early-stage startups, it often boils down to a “what do I need to believe” analysis, calculating what the company must look like in ten years to generate a 10x return.

On a related subject: In earlier rounds, founders often use Convertible Debt. This allows them to raise capital quickly without setting an explicit valuation immediately, “kicking the can” down the road to a priced equity round. While efficient, founders must be wary of valuation caps. A cap sets the maximum valuation at which the debt will convert into equity. If your company grows wildly and raises a Series A at a valuation far above the cap, the debt holders get to convert at the lower cap price. Effectively, you have priced the round, you just set a ceiling on it, giving early investors a guaranteed lower price relative to your future success.

VI. Protecting the Cap Table: Vesting and Transfers

The “prenup” elements of a startup are vital for long-term health. Founder Stock Vesting is essential not because partners don’t trust each other, but to align incentives over the long haul which is typically a four-year period with a one-year cliff.

Equally important are Transfer Restrictions and Rights of First Refusal (ROFR).

ROFR: If you want to sell your shares to a third party, you must first offer them to the company (and often the investors) at the same price. This creates a “chilling effect” on buyers, who don’t want to negotiate a deal only to have it snatched away by the company.

Transfer Restrictions: These are even stricter. They dictate that you cannot sell your shares at all without the company’s consent. This ensures that the founders remain “locked in” and aligned with the long-term vision, preventing them from cashing out early while the VCs are still holding the bag.

Finally, regarding exits, Kupor champions Double Trigger acceleration as the industry standard for fairness. Single Trigger means your unvested stock immediately vests solely upon the sale of the company (Change of Control). Acquirers hate this because they are often buying the company to get the talent. If you fully vest the day they buy you, say, you have no financial incentive to stay. Double Trigger, on the other hand, solves this tension. It requires two events: 1) The sale of the company, AND 2) Your termination without cause (or leaving for “good reason”) . This protects the acquirer by keeping you incentivized to stay, but protects you from being fired right after the acquisition and losing your unearned equity.

VI.

There is so much more discussed in the book than highlighted here, including a detailed examination of Term Sheets. For those interested in venture capital, it seems this is one of the oft-recommended books, and I enjoyed reading it.

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I saw someone online the other day who claimed to be a “polymath.” And of course, the last person who would ever use that word to describe themselves is a genuine one—at least to the extent that such a thing is even possible these days. It struck me as a kind of Dunning–Kruger variant, or maybe just the plain old vanilla version.

It reminded me of when I first began reading fine books. When I came to the United States, and suddenly buying books wasn’t a luxury, I realized I couldn’t even come up with a list of ten really interesting titles to read.

Today, if you locked me in a room for five years and threw away the key, I wonder if I will be able to finish the books on my reading list, including those in my anti-library (see below).

There’s a certain irony to all of this. The more one learns, about the nature of things, about how the world works in its elastic, interwoven way, the clearer it becomes that the well is too deep. Knowledge doesn’t expand into mastery; there is a sense in which it dissolves into mystery. The horizon keeps receding.

Related: On Antilibrary

Nassim Taleb via Black Swan:

“The library should contain as much of what you do not know as your financial means … allow you to put there. You will accumulate more knowledge and more books as you grow older, and the growing number of unread books on the shelves will look at you menacingly. Indeed, the more you know, the larger the rows of unread books. Let us call this collection of unread books an Antilibrary.”

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There was once talk about AI as a leveler. I’m not sure how common that claim is anymore, but I’ve always thought it was off the mark. (I recently heard the idea parroted on some podcasts lately prompting me to write this.)

Think of intelligence in two forms: “raw” and “cooked.” Raw intelligence is what an AI model embodies, sheer pattern recognition at scale. Cooked intelligence, however, is different. It’s the lived context, the subtle navigation of a messy world.

AI struggles with that second kind. It doesn’t arrive with context baked in. Over time, context can be layered on, through careful prompt design, by building agents, or by extending memory systems (the kind I’m certain Meta is betting on, and OpenAI is aggressively implementing). Even then, the problem isn’t solved.

Because context is only half the story. What about the quality of your questions? What about the blind spots (what you don’t know that you don’t know?) Those edges define the limits of any intelligence, human or machine. And they’re exactly where the myth of AI as a leveler collapses.

There’s another dimension to this. It’s true that in many domains of knowledge work — particularly in task-centric contexts — the gap between expert and beginner has narrowed, and will continue to be. Yet one could also speak of a “greener field” hypothesis, where precisely because everyone has gotten a leg up, the advanced knowledge worker now holds the magic wand, the ability to do what was previously unthinkable (my go to analogy here is often the parable of the talents in the gospels).

But the real fracture appears on the economic front (which flows directly from the prior argument). This is where the “leveler” myth truly shatters.

AI is less a leveler and more a lever, dramatically amplifying the force of those who already know where to push (capital, know-how, etc.). The technology creates a dynamic where markets disproportionately reward those with sophisticated direction (and those who invest in them.)

A small class will thus guide AI toward novel, high-value creation, effectively setting the agenda for entire industries. The majority will find themselves executing more commoditized tasks, using powerful tools they don’t control, to fulfill objectives set by others.

This hierarchy will become self-reinforcing, as leading players reinvest their outsized gains into better models and proprietary data, solidifying their advantage and making the upper rungs of the economic ladder increasingly hard to reach.

There is precedent for this, of course, as such, I am unsure if many people, or anyone for that matter, would disagree with what we all think is coming.

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My Other Publications:

I have recently started a new series on my AI blog called, “What I Listened to Last week” (WILLs), see my latest edition; my latest AI newsletter.

My latest academic work, using neural topic modeling and LLMs to map the landscape of a biomedical research field, was published in Analytical Chemistry in July.

I haven’t published a video essay in a while, but you can check my latest one here, it’s largely a philosophical commentary on Alan Lightman book, the Miraculous from the Material.

(For books I have read, reviewed, or currently reading, go to the end of the newsletter)

[Talking Points]

🎮GTA VI Trailer

👶4-year-old sings Hallelujah

💵Bribe collection by country

🦄There are now 498 AI unicorns.

🇳🇬Nigeria vs Ghana 2002 AFCON.

🚖Driverless taxi increase in California.

🗞Americans are no longer paying for news.

💻Key differences between Javascript and Python

💦Two billion people don’t have safe drinking water.

💵The U.S. added 1,000 new millionaires a day in 2024.

🤲Migrants’ remittances almost triple global foreign aid.

🚘More than 1 in 5 new cars sold worldwide last year was electric.

🇪🇺100 years ago, ~½ of today’s independent countries were European colonies.

[Longer Reads/Watch]

[I] 👦🏿Can You Still Be Human?

In his thought-provoking piece “Can you still be human?”, sounds the alarm on the unprecedented threat AI poses to human dignity, freedom, and agency. He argues that technological advances—particularly in AI, surveillance, and monitoring—are being rapidly adopted under the guise of progress and convenience, yet risk enabling totalitarian control by governments and corporations, eroding civil liberties and reducing humans to mere cogs in a machine. McGilchrist warns against complacency and fatalism, urging readers to maintain skepticism, question the relentless march of technology, and safeguard what makes us truly human. In a world increasingly shaped by algorithms and bureaucracy, he calls for pushback: “The opposite of life is not death, but the machine. You are not a machine.”

My recent worry isn’t unlike some of Iain’s. I worry that how we treat humans in the world that appears to be looming will hinge squarely on a metaphysics of human nature. And it is no news that there are terrible ideas out there that undermine human dignity because of poor or plain wrong metaphysics, precisely one that could be even more vicious in a capitalist system where humans are essentially defined by their jobs, how much they earn, et cetera.

[II]: 🦯 How Hellen Keller Learned to Write.

In the essay I am writing at the moment, I am investigating people who lived in the most extreme of the human conditions, in terms of their senses, memory, or just plain hardship, and using their lives to stress test a philosophical conception of what it means to be a person. Long story short, I find myself reading this essay about Helen Keller.

In her essay, Cynthia Ozick (now 97 years old) examines the life of Helen Keller not simply as an inspirational figure, but as an artist who constantly battled accusations of inauthenticity. Ozick recounts how the brilliant and resilient teacher Annie Sullivan unlocked language for the young, feral Keller. This breakthrough, however, led to immense public scrutiny. At just eleven years old, Keller was accused of plagiarism over a short story, an event that Ozick presents as the first of many challenges to the legitimacy of her experience. Throughout her life, critics would dismiss Keller as a “living lie,” arguing that her use of visual language for a world she could not see or hear was a form of “verbalism”, parroting concepts rather than expressing genuine perception.

Ozick compellingly argues that these critiques misunderstand the very nature of imagination and art. Keller’s defense, which Ozick champions, was that her rich interior world was built from language and that “the bulk of the world’s knowledge is an imaginary construction.” Keller contended that her ability to describe a “flash of a swordblade” or “rainbow waters” was an act of the mind’s eye, the same faculty used by poets and writers throughout history. Ozick reframes Keller’s story from one of disability to one of profound artistic creation, asserting that Keller’s life serves as the ultimate proof that imagination is not a substitute for sight, but a powerful form of vision in itself.

[III]: 🔥The Hell House of Martinez

Occasionally I watch Jimmy Akin’s mysterious world, and I typically really enjoy listening in on his pod. One of the recent episodes I watched is the so-called “Hell House of Martinez”.

SPOILER ALERT!

He explores a case investigated by paranormal expert Lloyd Auerbach. In the 1980s, a family in Martinez, California, reported a series of strange phenomena in their home, including dizzy spells, noxious odors, flashes of light, and fireballs. The family believed the house was haunted, but Auerbach’s thorough investigation uncovered natural explanations for all the events. He discovered that the dizziness was caused by structural issues and low-frequency vibrations from nearby power lines, while the flashes and fireballs were the result of high static electricity interacting with methane gas from a nearby landfill. Auerbach’s investigation not only provided rational explanations but also helped the family get out of their lease and receive their money back due to the unsafe living conditions discovered during the investigation.

The episode also highlights the ‘paradoxical’ importance of paranormal experts in investigating issues like this because the family had been deflected away by psychologists who largely assumed that they were nuts, when in fact there was something there. A flesh and blood, paranormal expert in this case walked inside the building to essentially investigate what’s going on.

[IV]:🔫Lead and Serial Killers

An amazing podcast here on “lead‑crime hypothesis.”

The lead–crime hypothesis is the idea that widespread exposure to lead, especially from sources like leaded gasoline, paint, and industrial pollution, impairs brain development in children, particularly in areas related to impulse control, decision-making, and aggression. This neurological damage, which can be permanent, is thought to increase the likelihood of violent and antisocial behavior later in life. Studies have shown that trends in violent crime often lag about 20 years behind peaks in lead exposure, suggesting a generational effect: as lead levels in the environment declined after regulations in the 1970s–1990s, crime rates also fell. Book discussed: Murderland.

[V]: 🤳🏿How Social Media Shortens Your Life

unpacks how social media is quietly robbing us of our most precious asset, time, not only by swallowing hours in endless scrolls but by reshaping our very perception of time itself. Drawing parallels with the maze-like designs of casinos, Gurwinder wrote about how platforms engineer “curvilinear mazes” to keep us distracted, passive, and unable to form strong memories—shortening both our experienced and biological lives.

Social media’s endless, story-less feeds make days blur, disrupt sleep, accelerate aging, and heighten anxiety, but, I suppose there is hope: by intentionally seeking novelty, storytelling, and mindful habits, we can slow our rush through life and reclaim meaningful moments before they slip away. He reminds us that living longer isn’t just about more days, but fuller ones.

I haven’t read many essays on social media that are this well written.

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[VI] 🗽David Brooks on Audacity, AI, and the American Psyche

Interesting conversation between David Brooks and Tyler Cowen.

[VII] 🏃🏿 Easy Habits to Make You Run Faster

I have been running a lot more lately. As I am writing this sentence in late August, I have run over 100 kilometers. So, I have been all crazed about how to run faster, or better.

In this video, a former elite triathlete Nicklas Røssner breaks down 12 actionable habits that can transform your running game and help you run faster. He covers everything from setting achievable goals and building mental toughness, to smart shoe rotation, self-efficacy, and the power of consistent training. He highlights often-overlooked secrets like training your gut for nutrition, including strength workouts, investing in high-quality gear, and embracing rest as a tool for longevity.

[VIII] 🧗🏿Free Solo

Nice podcast, and super inspiring documentary

https://films.nationalgeographic.com/free-solo

[Books/Papers 📚]

Book(s) recently completed:

Deep Work [Review]

What I am currently reading:

Ultralearning: Master Hard Skills [Link]

Reshuffle: Who wins when AI restacks the knowledge economy [Link]

There is a sense in which by saying the things we are supposed to show, we are professing that we have lost the plot, either as an individual, or more importantly, as a culture. This applies specially if you can detect a tautological motif, or a kind of forced appendage in language use. It is worth noting that they can be hard to see as many are already normalized; and the normalization is really the point here.

For example, the obligatory use of the word “prompt” after a time is clearly stated, in some communities and culture goes a long way to show a broken culture of timeliness, or take deep work (I use this often myself), but what is wrong with just work? The modifier clearly betrays a landscape saturated with too much noise.

The pattern is everywhere once you tune your ear. Some can speak of “authentic cuisine.” Job ads promise “true ownership.” Even the truth sometimes can be insufficient, when one speaks of “honest truth.” I think social media birthed “real connection.” There is passion, and then there is “true passion.” And then there are the “active listening” workshops all over the place. And here is my ‘favorite’: “lived experience.”

Each redundant label is a linguistic tell, a nervous tick revealing the anxiety of its age. When substance fades, language rushes in to erect elaborate signposts, frantically assuring us that the thing still stands. But the very insistence is the confession.

And the normalization, like I intimated earlier, is really the key point, as it signals the silent triumph of the change.

The more syllables we stack, the clearer it becomes that we are circling an empty center, or that something is simply not right, trying to name back into existence what we can no longer, or struggling to, embody.

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I.

I first read Deep Work in 2016, at the start of grad school, and it remained one of the most impactful “productivity” books I read during that period. I decided to revisit it now as I prepare to embark on another major project, mainly to set the right ambience, the kind such projects demand. I’m well aware of the common critiques of books like Deep Work, that they “could as well be just a long blog post.” But such takes often miss the point. Sometimes you read not for novelty, but to create a particular atmosphere, a cognitive aftertaste if you will, that hopefully sets you straight. And that effect isn’t necessarily summoned by reading 600 bullet points of cold facts that you already know, but by stories woven carefully around those salient ideas.

II.

Cal Newport argues that integrating deep work into a professional life requires adopting a specific scheduling philosophy tailored to one's circumstances. He presents a spectrum of four distinct approaches. The most extreme is the Monastic philosophy, which involves minimizing shallow obligations to the barest minimum to maximize deep focus, as practiced by figures like Donald Knuth. It's suited for individuals whose work value is tied to a single, well-defined goal, and are privileged enough to do without the social media ‘networking’, and other things of sorts, that mere mortals like us have to endure to get our ideas out. A more balanced option is the Bimodal philosophy, where time is divided between long, intense, monastic-style deep work sessions and periods left open for everything. For those needing consistency, the Rhythmic philosophy turns deep work into a regular habit, removing the friction of deciding when to focus. Lastly, the Journalistic philosophy, he says, involves fitting deep work into any available free moment, a method best suited for those adept at quickly shifting their mental state and not for deep work novices.

For most serious knowledge workers, a rhythmic strategy might be the most reasonable, for example my recent deep work scheduling is between 5:30am and 11:30am, and getting rid of your smart phone during this period can only do you good.

III.

He also adapted ideas from the book titled “four disciplines of execution” as a framework for making deep work stick. The first is focusing on the wildly important, choosing one or two ambitious goals rather than vaguely promising yourself you’ll “work more deeply.” The second is acting on lead measures: instead of tracking only the final outcome, like a published paper or book, you track the hours you actually spend in deep concentration working towards that goal. The third is keeping a compelling scoreboard, something visible and concrete, even a simple tally card, so that progress feels tangible rather than abstract. And the fourth is creating a cadence of accountability, a regular rhythm of looking back at what worked, what didn’t, and what you’ll commit to next.

This stuff is really powerful stuff if you get to actually act them out. I have implemented a version of it, even long before reading the book.

IV.

In the chapter Embrace Boredom, Newport reminds us that the ability to concentrate is not an inborn gift but a skill, and like any skill it must be trained. The training, however, runs against the grain of our distracted age. We don’t build focus by taking occasional “internet sabbaths” while remaining tethered to distraction the rest of the week. Instead, we train it by scheduling our distraction deliberately, and refusing it outside those pre-allotted windows. Over time, the mind learns it can be content without constant novelty.

On this matter of training the mind, one of the most helpful (yet least used) subscriptions I have is Freedom, and the irony isn’t lost on me. Whenever I fall into a rut, say, binging politics new on YouTube or (doom) scrolling tech news on X, I only need to be strict with myself for about a week, using Freedom. After that, I rarely need it again.

He also offers a few practical drills. One is the “Roosevelt Dash”: set an artificially tight deadline for a meaningful task, then attack it with full intensity. The compression forces an almost athletic level of focus. Another is “productive meditation”: using walks, commutes, or other moments of physical routine to circle a single well-defined problem, returning to it again and again when the mind drifts. Such exercises don’t just improve concentration; they often yield insights that surface only under sustained attention.

The larger point is clear. Focus is a muscle, and muscles grow through deliberate strain. If you don’t practice being bored, it’s hard to develop the depth of attention that serious work requires.

In addition to books, I’ve been thinking about bringing radios back into my late-night routine. I’ve mostly cut off social media after 6 p.m., the damage those late scrolls do at night is hard to ignore, even when you start with good intentions. And don’t get me started on YouTube. I had to abandon my feeds altogether. These days, for the most part, I use an AI browser and LLM workflows to surface the specific content I want, where I want them such as to my email inbox.

V.

Now, talking about social media.

Newport’s advice is bracing: quit social media. However, to grasp just how successful social media platforms have been at capturing attention, you need only note some writers he cites (e.g., Malcolm Gladwell, J.K. Rowling) who once proudly avoided them. I suppose things has changed since then. There is a sense in which once enough of us jumped on the train, it’s compels others to join, even if the train is net bad business for many involved; and there is plenty of argument to be made on that front.

Anyways, there’s value in his call to quit (or at least sharply minimize) our use of social media. He critiques the common “any-benefit” mindset, where even the slightest upside is enough to justify adopting a new tool. The result is a cluttered digital life that fragments attention and weakens our ability to do meaningful work. His alternative, the Craftsman Approach to Tool Selection, is simple but demanding: identify the core factors that truly drive success and happiness in your life, and only use a tool if its benefits clearly outweigh its costs. Most platforms fail that test, he argues, and I agree.

To put this into practice, he suggests a 30-day experiment: quietly quit all non-essential social media, then afterward ask yourself two questions—would the month have been better if I had used it, and did anyone notice I was gone? If the answer to both is no, the verdict is clear.

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VI.

One of the most striking ideas in the book is fixed-schedule productivity. The practice is simple: set a hard, non-negotiable end time for your workday and refuse to work beyond it. At first glance, it feels like an arbitrary constraint, but that’s precisely the point. By creating scarcity, you are forced to strip away shallow tasks, organize your priorities, and channel energy toward what actually matters.

Abundance is good, but it often leads to wastage, and when it does, it’s probably not a good thing.

Immortal Souls by Ed Feser

I recently read AI 2027, an impressively detailed, heavily foot-noted, month-to-month AI scenario projection (You can read my review here), and I left with more questions than when I started. Long story short, while reading the scenario, I couldn't help myself but to do all these pondering in the background (e.g., how does the projected AI agency differ from human agency; what is really agency; what does it mean for a substance or a thing to reason? etc.)

Then I became smart enough to quickly realize my knowledge of philosophy of mind & metaphysics of human nature is indeed poor. You see, I have had Immortal Souls (by the brilliant Aristotelian-Thomistic philosopher, Ed Feser) on my to-read spreadsheet since it was released last year; and this was just a perfect excuse to read it. All it took was reading the aforementioned scenario, $35 and a couple days wait period for delivery.

It turned out to be a very detailed book, far more detailed than I have envisaged, but unsurprising given the subject matter. I don’t have a philosophy background, but my guess is that the book could be a legit textbook for higher level undergrad (or graduate level) class on the metaphysics of human nature – it’s that detailed and long, standing over 500 pages.

So, I have decided to focus solely on the chapters on the philosophy of mind (What is Mind?), and relevant chapters on machine learning/computers. I will most likely be writing about the subject matter either on this blog, or my AI blog.

The Master and His Emissary by Iain McGilchrist

One book that has been taking me forever to read, partly because it’s a door stopper, partly because I can’t help myself from being distracted with other less dense books, is the masterly work by Iain McGilchrist, The Mater and His Emissary. In brief, the book explores how the brain’s two hemispheres (left and right) differ in how they perceive and relate to the world, and argues that Western culture has become dangerously dominated by the narrow, analytic worldview of the left hemisphere, neglecting the more holistic, contextual understanding of the right.

I have added it to my list of books I am reading this summer hoping to knock out more than a few pages and at least complete the first part of the book.

So far, one of my favorite bits in the book: “The kind of attention we bring to bear on the world changes the nature of the world we attend to, the very nature of the world in which those 'functions' would be carried out, and in which those things' would exist.”

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All Things Wise and Wonderful by James Herriot

Admittedly, the two books above are quite dense, and sometimes after a protracted run you get bored from reading them, so I thought I would add something light that will allow me to continue reading, hence All Things Wise and Wonderful, the third installment in the All Creatures Great and Small Series.

My introduction to the book series came in the form of this nice PBS series about the book, which I highly recommend. After watching a few episodes, I immediately ordered the entire book series. I have read Book 1: All Creatures Great and Small, and Book 2: All Things Bright and Beautiful. Leaving me with Book 3.

And what is it about the book? It’s a memoir-style book that recounts the humorous, heartwarming, and often poignant experiences of a country veterinarian in rural England, capturing the richness of life through stories of animals, farmers, and the countryside during and after World War II.

Papers

some papers I plan to read in-depth.

• The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search [Link] [Code]

• Code Scientist: End-to-End Semi-automated Scientific Discovery with Code-based Experimentation [Link] [Blog]

• Towards an AI co-scientist [Link] [Hugging Face]

• And at least a few papers on LLM interpretability.

Video Essay:

Text:

I.

When I was planning to visit Harvard to give a talk last fall, I looked up events I could attend while in Boston. I saw that Alan Lightman was going to give a talk at the Harvard Science Book Talk about his new book, and I quickly reserved a spot to attend. I hadn't read any of Lightman’s book at the time, but of course, I had heard a lot about him. I believe I first heard about him about a decade ago when, spurred by my intellectual interests, I began researching professors who had modeled their careers to encompass a wide variety of pursuits. He was the first professor at MIT to receive a joint appointment in both the sciences and the humanities. I recalled reading about him and reassured myself that I wasn’t that crazy after all (or may be I was).

When I got to the venue of the talk, he sat beside me outside the lecture hall for a while, I didn’t realize he was the one until someone yelled “Prof Lightman” or something to that effect. After the talk, I bought a signed copy of the book.

Alan Lightman's "The Miraculous from the Material" is a captivating exploration of the wonders of the natural world, viewed through the lens of a self-proclaimed "spiritual materialist" (I will come to this materialist bit in a minute). In this book, Lightman examines various natural phenomena, from the mundane to the spectacular (mostly spectacular than mudane), and elucidates how the material world, with its rules and laws, is capable of extraordinary displays of beauty and wonder.

Lightman's journey as a materialist, he says, began in his youth, where he meticulously constructed a laboratory in his bedroom closet. It was there that he embarked on his investigations of the material world, building pendulums and even a light-borne communication device. Through these experiments, he discovered that the physical world, or at least his little corner of it, obeyed reliable, logical, quantitative laws.

Lightman's fascination with the material world did not diminish his awe and amazement of natural phenomena. From the microscopic world of Paramecia to the rings of Saturn, Lightman found beauty and wonder in the material world and its laws.

One of the parts of the book I enjoyed the most is the way Lightman weaves together science and personal experiences. He recounts his first encounter with bioluminescence while returning to his island house in Maine, where he noticed the wake of his boat glowing. He writes about the science behind this phenomenon, explaining how it is caused by a chemical reaction in organisms like fireflies and algae.

Lightman's fascination with the natural world extends to the intricate structures created by living organisms. He writes about the spider web, considering it the most beautiful structure created by any living being other than Homo sapiens. Spiders utilize different types of silk for various purposes, including trapping prey and wrapping victims in a deathly cocoon-like structure. Lightman also elucidates the remarkable ability of spider webs to spring out and capture flying insects, a feat achieved through electrical attraction.

His curiosity extends to the skies, where he explores the physics of bird flight. He explains how birds generate lift through the shape of their wings and their forward motion, enabling them to seemingly defy gravity. There is also this intriguing phenomenon of birds flying in V formations, revealing how each bird, except the leader, conserves energy by taking advantage of the uplifting pockets of air produced by the bird in front.

I wish I had read books like this when I was much younger, it would have saved me the horror of memorizing some of my text books. Lightman writes so well about things you might otherwise think are impossible to explain. I probably wouldn’t have read the book if I hadn’t attended the talk, so I’m glad I did.

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II.

In addition to a general review of the book, I'd like to add a few commentaries. While this book is not about philosophy (I should state this very clearly), a few important, opinionated statements were made that I think warrant a full-fledged philosophical commentary. And those statements essentially can be summarized in what Lightman called spiritual materialism, which seeks to harmonize transcendent experiences with materialism.

I believe this framework faces a foundational challenge from Alvin Plantinga’s Evolutionary Argument Against Naturalism. Plantinga argues that if naturalism and evolution are true, our cognitive faculties—including those generating spiritual feelings—evolved for survival, not truth, rendering even materialist explanations of spirituality epistemically unreliable. For instance, sensations of cosmic connection or awe might be adaptive illusions fostering social cohesion or resilience, not genuine insights into reality. This creates a paradox I think: Lightman’s framework depends on neuroscience and evolution to explain spirituality, yet naturalism undermines trust in all cognitive outputs, including the scientific reasoning used to validate such spiritual materialism. And one could respond and say that spiritual experiences require no truth-claims beyond their material origins, sidestepping the argument’s focus on belief reliability.

But there is more to be said on this topic: the mysterious alignment between abstract mathematical truths and physical reality (as seen in Eugene Wigner’s popular paper: “unreasonable effectiveness of mathematics”) poses a dual challenge to materialism. Materialism struggles to explain why accidental cosmic processes would conform to elegant, discoverable mathematical laws or why survival-driven cognitive faculties could reliably uncover these laws. The latter intersects with Plantinga’s Evolutionary Argument Against Naturalism: if naturalism undermines trust in cognition’s truth-aimedness, even science’s reliance on mathematics becomes suspect, as evolved brains might prioritize adaptive fictions over truth. Thus, materialism cannot coherently account for mathematics’ predictive power or the reliability of the minds that wield it.

And there is this scientific antirealism angle too but it’s doesn’t look like a crisp argument to me, but I will still flesh it out anyways. When I first read about scientific anti-realism, I didn't quite get it. But now I'm beginning to think that I might be able to make sense of scientific anti-realism on the grounds of materialism. In brief, (some) scientific antirealist challenges the notion that there are mind-independent truths about reality. They suggest that scientific theories are primarily instrumental tools that help us predict, explain and control stuff, they do not necessarily provide a true picture of how the world actually is. Granting the Evolutionary Argument Against Naturalism - and I am now back to my initial point - it appears to me that materialism paradoxically supports antirealism: if our minds are products of blind evolutionary processes, we lack justification for believing they can grasp fundamental truths about the universe.

We can also go through the route of accounting for information in the evolutionary landscape: Darwinian accounts of how complex biological information arises rely on a "displacement fallacy." In brief, instead of explaining where the information originally comes from, the Darwinian account simply shift the burden onto natural selection or specially designed fitness landscapes, effectively assuming the very information they're supposed to explain.

I suppose this will do for now. I should also add that my philosophical commentary should not be taken as a response to Professor Lightman's complete philosophical framework, as I have not read him beyond this book. It's just a commentary to a few comments that actually popped at me while reading this particular book.

In summary, The Miraculous from the Material is an enjoyable read that invites us to marvel at the wonders of the natural world regardless of our philosophical leanings. That being said, if I were to give the book a title, I will keep things as it is, except to swap the positions of “Material” and “Miraculous.”

My Other Publications:

Check out my latest AI newsletter: ε Pulse: Issue #24

The Epsilon

ε Pulse: Issue #24

Read more

a year ago · Olatomiwa Bifarin

Watch my latest video essays on my Youtube Channel:

The Story in Your Head

Pillars of Modern Civilization

What Are You Hiding?

Working in The Storm

(For books I have read, reviewed, or currently read, go to the end of the newsletter)

[Talking Points]

🏃🏿David Goggins X Israel Adesanya Training.

🥩Ketogenic Diet in Pancreatic Cancer Treatment.

⌨️Cursor Writes 1 Billion Lines of Accepted Code/Day

🎶Nigerian Students Sing the UEFA Champions league Anthem

🏠US: Homebuyers Need $114K Salary for Median-Priced Homes

🌍Sub Saharan Africa Has 67% of People Living in Extreme Poverty

🧑In 2025, If You Are Over 30, You Are Older than Most People in the World.

[Longer Reads/Watch]

[I]: 🔰 Missing Pages: An AI Assistant for Avid Readers.

Back story to an app I am currently working on with a friend called Missing Pages.

Here is the waitlist.

[II]: 💻 A.I. Is Changing How Silicon Valley Builds Start-Ups

The evolution is underway: more with less with AI. Here is a New York Times profile of Gamma, an AI design partner for presentations, websites, etc.

The Cofounder Grant Lee wrote about it on X:

The New York Times profiled a start-up with 28 employees serving nearly 50 million users. That company is us. The traditional startup playbook: raise massive funding, hire hundreds of employees, and worry about profitability "later." But there's another way. Everyone at Gamma could fit in a small restaurant. We're not just surviving—we've been profitable for 15+ consecutive months, with revenue growing month over month, and lifetime negative net burn (we have more money in the bank than we've raised). This isn't an accident. We've deliberately designed our organization to maximize impact per person. Instead of creating specialist silos, we hire versatile generalists who can solve problems across domains. Rather than building management hierarchies, we find player-coaches who both lead and execute. Our team leverages AI tools throughout our workflow - Claude for data analysis, Cursor for coding efficiency, NotebookLM for customer research synthesis. These aren't just productivity hacks; they're force multipliers. Examples: — When our growth PM needed better analytics, he didn't file a ticket with a data team—he built a self-serve system that anyone can use without SQL knowledge. — When our marketing lead needed to understand our customers better, she fed thousands of interactions into an LLM and created actionable personas that now guide our entire strategy. — When our design team needs to test a hypothesis, we create a rapid prototype and show it to our power users. What we're seeing isn't just about "doing more with less." It's about fundamentally changing what's possible per person. The most valuable employees aren't specialists who excel in narrow domains - they're resourceful problem-solvers who continuously expand their capabilities. This approach creates remarkable resilience. Since everyone understands multiple functions, we don't have single points of failure when someone leaves or moves to another project. If you're building today, the question isn't how quickly you can scale headcount … it's how much impact you can create with the smallest possible team. The future belongs to tiny teams of extraordinary people.

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[III]: 🇻🇦The Next Pope

The first pope I knew was Pope John Paul II, but I wasn't particularly aware of him because I was quite young so “knew” is an exaggeration. I do recall his death being major news, and I vaguely remember the talk of an African Pope over the radio in my dad’s car, maybe Cardinal Arinze? While I wasn’t particularly interested in Catholicism (and hence the papacy) then, my appreciation for Catholicism grew after reading some of Pope Benedict's books such as this, this, & this; supplemented with plenty podcast. Now, because of this sequence of admittedly abbreviated events, I've become too interested in who becomes pope, and I have read perhaps an unhealthy quantity of material about who is who, and the goings-on. So here are a few. The best resource is this fantastic web page on the College of Cardinals. It provides in-depth profiles of Catholic cardinals-especially those considered potential future popes (“papabili”) - to help folks better understand their backgrounds, beliefs, and positions on key issues. The site features comprehensive biographical data, summaries of each cardinal’s qualities, detailed analysis of their work as bishops. It also includes a fairly detailed piece on the origin of cardinals. Another one on the role of present-day cardinals (which clarified my confusion with regards emeritus, curial, & diocesian status within the cardinalate). The one I enjoyed the most was on electing the pope. Interesting bit from the article:

In the 1970s, Pope Paul VI introduced the age limit of 80 for electors, and 120 as the recommended maximum number of voting cardinals. For a man to be validly elected pope, he must receive two-thirds of the votes. Pope John Paul II changed the rules in 1996 so that after 33 or 34 ballots without a two-thirds majority, a candidate could be elected by a simple majority.

But Benedict XVI reinstated the requirement for a two-thirds majority to elect a pope, reversing John Paul II’s change which had been viewed as a “radical” innovation compared to the two-thirds rule that had existed since 1179. Benedict’s amendment stated that if a deadlock persisted after 13 days of voting, the two top candidates would enter a runoff. He did this to prevent a situation where a majority bloc could push through a candidate by simply holding out until the 34th ballot when there would have been a simple majority. It was also felt that the change would guarantee the greatest possible consensus among the cardinals rather than a candidate who could win with just a narrow majority. Benedict’s reversal was a return to the historical norm.

I have read a few profiles about some of the papabili, written by John Allen of Crux: Parolin, Zuppi, Erdo, and Besungu. At the time of writing it appears profiles of the following priests have been added: Pizzaballa, Aveline, Tagle, Prevost, and Arborelius. They are running this “Papabile of the day" till the conclave begins. On the world's largest prediction market, Polymarket (at the time of writing) Cardinal Parolin is leading the pack, closely followed by the charismatic Tagle of the Philippines. Also see Ed Feser on pope’s first duty, profiles of African Cardinal electors, and the youngest priest in the college of cardinals (45 year old Cardinal?!).

[IV]: 🌎Ten Charts That Show How the World is Changing

In his Digital Native essay, Rex Woodbury uses a curated set of charts to visually narrate the significant transformations shaping our world. Prominent economic and technological shifts are clearly visible. The tech market, for example, is shifting focus, now prioritizing profitability and efficiency over unrestrained growth, marking a significant change from previous years. Echoing this recalibration, venture capital distributions to investors have slowed, indicating a tighter funding and exit environment for startups and their backers. At the same time, a powerful trend is the rapid acceleration of AI adoption, which is driving significant increases in web traffic and engagement for AI tools, signaling a major technological disruption.

Beyond core tech and finance, the charts illuminate evolving patterns in commerce, culture, and society. While e-commerce continues its expansion, startups like DoorDash are shown dominance in their sector. Within the digital media landscape, success and engagement are increasingly concentrated among the largest platforms and most popular creators, following a distinct power law, which will get accelerated with continued AI adoption. (See my essay: Winners Take All). This digital environment highlights how viral cultural phenomena can create substantial and measurable boosts for brand visibility (this was discussed in the context of Guinness recent sales boost). Finally, the data underscores distinct generational economic realities, as Gen Z confronts unique hurdles compared to previous generations, starkly illustrated by trends like lower relative homeownership rates.

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[V]: 🦠Recipe for a Cell

What makes up a cell? I learnt many of these in college, and even more complicated stuff in grad school (most of my formal education is in biology), but this is refreshing stuff to read nevertheless. For example see this bit:

In general, however, the mass of an E. coli is about 1 picogram, or one one-trillionth of a gram. That’s astonishingly small, roughly equal to the weight of the DNA inside a single hummingbird cell.

Which molecules make up this mass?

About 70 percent is just water. The other 30 percent — the so-called “dry mass” — is composed of everything else: DNA, RNA, proteins, lipids, and ions. Surprisingly, DNA accounts for just 3 percent of this dry mass, yet encodes all the information needed for the cell to grow, divide, adapt, and evolve. Most of a cell’s dry mass (55 percent) is proteins, which dominate because they are the “executors” of cellular functions; they catalyze reactions, form structural scaffolds, and regulate gene expression. About 20 percent of the dry mass is RNA, 10 percent is lipids, and the rest is ions and signaling molecules.

…

building a functioning cell from scratch still remains one of biology’s most elusive goals. The challenge is not merely mechanical, either. Even if one gathers all the right molecules in the right quantities, arranging those molecules with sufficient precision to “create life” remains far beyond current capabilities.

This is, in part, because cells are dynamic. They move and change quickly. Sugar molecules fly through a cell at 250 miles per hour, and each protein is bombarded by 10¹³ water molecules every second. This dynamism is often overlooked in the methods used to study living cells. A list of ingredients is inherently static. The next step, then, is to capture how each piece moves and interacts. Only then might we turn our catalogs of parts into recipes for a cell.

Full essay.

[Books/Papers 📚]

Book(s) recently completed: [-]

What I am currently reading:

The Master and His Emissary [Link]

AI 2027 [Link]

Hands-on Guide to Fine-Tuning Large Language Models [Link] [Code]

The Mom Test [Link]

Administer Container in Azure [Link]

The Alchemist [Link]

This essay was previously published a few years ago. If you want to read the text, see the first version here.

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Link to Waitlist: https://tally.so/r/wgOJvM

Missing Pages is my latest app project— an app prototype to solve a personal habituated pain point.

To stop reading in a circle, find knowledge gaps, read on-demand, and dig in.

If you are interested in trying this out, here is the Waitlist: https://tally.so/r/wgOJvM

(h/t to Michael Judge for the apt name)

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Video essay:

If you will rather read the vanilla essay:

Essay

Preamble – Steel: tough as hell – Concrete: as hard as these things get – Plastics: The Versatile and Troubling Material – The Unsung Hero: Ammonia – Fossil Fuel Run Our World – Hidden Cost of Progress

Preamble

The modern world is complex. Yet, beneath the surface of our interconnected, information-driven society lie the physical realities that underpin our very existence.

In a world where everyone is on their phones, and God knows what else, it is not surprising that the physical realities are often blurred out. But, yet, these physical entities are the fundamental elements that enable us to produce the food we eat, generate energy, and build the structures that house us.

Vaclav Smil, a distinguished scholar of Energy Studies, in his fascinating book, "How the World Really Works," isolates four materials as the so-called "four pillars of modern civilization": steel, concrete, plastics, and ammonia.

And I was sold. They are not just merely components of our built environment, but they represent the very backbone of our civilization.

Steel: Tough as Hell

At its core, steel is mostly made of iron, with a few other elements mixed in to give it all kinds of special properties. This journey begins with pig iron (also called crude iron), a product of blast furnaces that contains 95 to 97% iron. However, pig iron has a high carbon content, making it very brittle and less ductile (meaning it is not very stretchy). It also has lower tensile strength, which is the ability to withstand stress, compared to bronze or brass.

To turn pig iron into the strong, versatile steel we use every day, the carbon content is reduced. This process makes steel incredibly strong and durable. In fact, steel is much stronger than some of the hardest stones and most common metals. For instance, steel beams can bear loads 15-30 times heavier than granite. It is seven times stronger than aluminum and nearly four times stronger than copper. Steel is also much harder— about four times harder than aluminum and eight times harder than copper. Plus, it can withstand very high temperatures, only melting at about 1,425°C, compared to aluminum at 660°C and copper's 1,085°C.

There are four main types of steel, each with its unique qualities: we have carbon steels, alloy steels, stainless steel, and tool steels. Carbon steels are the most common, and they are used in buildings, bridges, and cars. Alloy steels have other elements added to them to improve certain characteristics. Stainless steel, which doesn't rust by the way, is often used in kitchen appliances and medical equipment. Tool steels are so incredibly hard that they are often used to make tools that are used to cut other kinds of steels, for example. They are used for hand tools like chisels, hammers, wrenches, et cetera.

Understanding these different types of steel and their properties helps us appreciate how this remarkable material shapes our world, making everything from skyscrapers to kitchen sinks possible.

So, now, let’s Imagine a world without steel.

The towering skyscrapers that define city skylines are absent, replaced by shorter, bulkier buildings made from weaker materials. Bridges that span vast rivers and connect cities are now unreliable and fragile. Cars, trains, and airplanes, which rely heavily on steel for their frames and engines, are either significantly less efficient or just non-existent all together, making travel slow and cumbersome.

Everyday life would feel drastically different. Your kitchen appliances, which are typically sleek and durable, are now clunky and prone to rust or wear. Tools and utensils made from less sturdy materials break easily, leading to frustration in accomplishing even simple tasks. The safety and efficiency of our infrastructure— be it roads, railways, and pipelines—are compromised, which will inevitably lead to frequent maintenance and accidents.

Even the medical field would face its own challenges. Stainless steel instruments and equipment are crucial for surgeries and patient care.

The reality is, without steel, our world would be much less advanced, much less connected, and much less efficient.

Concrete: As Hard as These Things Get

Cement is the magic ingredient that makes concrete possible. This fine powder is produced by heating ground limestone and clay, shale, or even waste materials in massive kilns at temperatures of at least 1450°C. The intense heat transforms these raw materials into a substance called clinker, which is then ground into the fine powder that we all know as cement. When mixed with aggregates like sand and gravel, along with water, cement acts as the binding agent to form concrete.

Concrete is everywhere—it's in the roads we drive on, the bridges we cross, our dams, and the buildings we live and work in. Modern cities, in essence, are built from concrete, and without cement, creating concrete on the scale needed to support our societies would be simply impossible.

Back to our exercise, now briefly, imagine a world without cement. Our cities would look drastically different, with less durable and more costly alternatives struggling to meet the demands of modern construction. The convenience and safety we often take for granted in our built environment would simply be compromised.

Plastics: The Versatile and Troubling Material

Plastics are everywhere. The versatility of plastics stem from their ability to be molded into virtually any shape you can think of, making them integral to countless aspects of modern life.

The journey of plastics begins with hydrocarbon feedstocks, found in crude oil and natural gas. Through a process called steam cracking, these hydrocarbons are heated to extremely high temperatures, breaking them down into simpler molecules such as ethylene and propylene. These primary monomers are then synthesized into the diverse array of plastics we use every day.

There are two main types of plastics, each with unique properties and uses. Thermoplastics, which soften when heated and harden when cooled, make up the majority of global plastic production. Examples of thermoplastics include low- and high-density polyethylene (PE), commonly used in packaging and plastic bags; polypropylene (PP), found in car parts and bottle caps; and polyvinyl chloride (PVC), used in healthcare products like catheters, IV tubings, oxygen masks, PVC coated mattress cover, to name but a few. On the other hand, thermoset plastics do not soften upon reheating and are used in products requiring high durability, such as electronics and furniture.

How did we even get to plastics, anyways? We can go from 1600 BCE, when Mesoamericans crafted objects from natural rubber, to the 19th century where we had the discovery of polystyrene and the invention of Parkesine, which is a celluloid that marked the dawn of artificial plastics. The early 20th century saw breakthroughs with Bakelite (the first fully synthetic thermoset, again thermoset are plastics that do not soften upon reheating) and flexible PVC, propelling plastics into widespread use. The mid-century introduced nylon, Teflon, and PET (that is Polyethylene terephthalate), completely transforming consumer goods and packaging. By the late 20th century, plastic bottles had largely replaced glass, and polymer banknotes emerged.

Okay, so that’s quite a ride. An abridged one, I suppose.

But before I move on, I will leave you with this stat: global production of plastics increased from a mere 20,000 tons in 1925 to an astonishing 370 million tons by 2019. If you are about to bring out your calculator there is no need for that, that’s over 18,000 times increase.

Let’s proceed.

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The Unsung Hero: Ammonia

Okay, maybe we cut to the chase with this one. Without ammonia, about 50% of people alive today wouldn’t be here.

So, what’s up with Ammonia?

I suppose we all know this, but you can’t assume much of anything these days. Ammonia is the primary ingredient in nitrogen fertilizers, which are essential for high-yield agriculture.

Nitrogen is a must-have nutrient for plants. It’s a key component of chlorophyll, the molecule plants use to convert sunlight into energy through photosynthesis. Nitrogen also plays a critical role in the biosynthesis of DNA and RNA, the molecules responsible for storing and transferring genetic information, and in building proteins.

Although nitrogen is abundant in the atmosphere, plants can’t use the nitrogen gas (N2) found in the air. They need nitrogen in a reactive form, which only a few natural processes, like nitrogen fixation by bacteria, can provide. Certain bacteria, such as Rhizobium species, form nodules on the roots of legume plants. Within these nodules, the bacteria fix atmospheric nitrogen gas into ammonia or ammonium, which the plants can utilize.

However, staple crops like rice and wheat can’t fix nitrogen on their own and they require an external nitrogen source. Before synthetic fertilizers became a thing, more on that in a minute, farmers had limited methods to add reactive nitrogen to the soil: They would do crop rotation with Legumes: farmers would grow a legume crop, such as clover, and then plow it under to enrich the soil with nitrogen. Or they could just use animal manure, which contains reactive nitrogen compounds.

Needless to say, the traditional methods were labor-intensive and could not supply enough nitrogen to support the high-yield agriculture that is needed for a growing world population.

However, as it always happens, there was a revolutionary discovery: In the early 20th century, scientists Fritz Haber and Carl Bosch developed a groundbreaking method to synthesize ammonia. Known as the Haber-Bosch process, this technique uses high pressure and a catalyst to combine nitrogen from the air with hydrogen. This innovation made it possible to produce reactive nitrogen in the quantities necessary for modern agriculture.

The Green Revolution, which began in the 1960s, was a period of significant agricultural advancement. New high-yield varieties of wheat and rice were developed, but these crops required substantial amounts of nitrogen to reach their full potential. The Haber-Bosch process enabled the mass production of nitrogen fertilizers, which in turn supported these high-yield crops and led to a dramatic increase in global food production. So, again, at the risk of repeating myself, if not for ammonia many of us would simply not exist today.

I should also add that while about 80% of the ammonia produced worldwide is used for fertilizers, it has numerous other applications: such as production of nitric acid, used in the manufacturing of explosives, in rocket propellants, dyes, fibers, cleaners, and so on.

Fossil Fuel Run Our World

If you have granted Vaclav Smil that the four pillars of civilizations are as I have mentioned, then it goes without saying that fossil fuels run our world. Let’s reel through the facts.

Steel: The iron ore smelting process, a crucial step in steelmaking, relies heavily on coke, a carbon-rich material derived from coal. The energy used to power blast furnaces and other steelmaking processes also comes primarily from fossil fuels.

Concrete: Cement production, the core component of concrete, requires immense heat generated by burning fossil fuels, primarily coal dust, petroleum coke, and heavy fuel oil.

Plastics: The synthesis of plastics begins with monomers, simple molecules derived from crude oil and natural gas. The process of converting these monomers into polymers also consumes large amounts of energy derived from fossil fuels.

Ammonia: The Haber-Bosch process, mentioned earlier, is a prime example of fossil fuel dependence. Natural gas is both the source of hydrogen and the primary energy source for this synthesis reaction.

There are two commentaries about these analyses that are quite warranted at this point. The first was made by Professor Smil in the book: the importance of reducing dependence on fossil fuel can not be overstated. However, many folks do not simply realize how deep we are in this fossil fuel world.

It appears that decarbonization is going to be a long-term process due to the pervasive use of fossil fuels in modern society. There are significant changes in energy systems and infrastructure that just need to take place. Smil argues that fossil fuels will remain crucial for decades, especially in sectors like transportation and heavy industry, where alternatives are still developing.

The second point is more obvious, and perhaps only tangentially related to the topic at hand. Our success in solving important problems does not diminish their importance. The fact that we have solved issues like building bridges or producing food does not make them any less crucial to our survival. They are worth our attention, even if it means retracing our steps.

Hidden Costs of Progress

In light of all this, it’s clear that the very foundations of our modern civilization—steel, concrete, plastics, and ammonia—are both marvels of human ingenuity and they are also sources of profound dilemmas. We rely on these materials to feed billions, build cities that defy gravity, connect distant lands, and sustain a quality of life that would have been unimaginable mere centuries ago. Yet, as the systems thinker Daniel Schmachtenberger points out, our technological prowess doesn’t exist in a vacuum. Each stride forward can set off a cascade of unintended consequences, rippling outward through the complex webs of ecology, economy, and society.

The approach we ought to take is to understand that our world is not a series of isolated problems and solutions, but rather an interconnected matrix of systems that interact in both predictable and unpredictable ways. When we celebrate the achievements of the Haber-Bosch process and the gift of abundant food it provides us, for instance, we must also acknowledge its role in overtaxing soils, polluting waterways, and creating a high-stakes dependency on fossil fuels. Similarly, as we marvel at the skyscrapers and infrastructure made possible by steel and concrete, we must also contemplate the immense energy inputs and carbon emissions that have become so deeply embedded in their production.

Our tools and processes, once hailed as solutions to scarcity and hardship, are now implicated in creating new forms of vulnerability. Plastics, once a symbol of modern convenience, have now metamorphosed into environmental hazards scattered across the globe, hinting at the fragile nature of our so-called progress. In fact microplastics in recent studies have been found in human kidneys, livers, and even in brain tissues.

The call to action here is not to abandon these materials outright—an impossibility given the scale of global dependence, as I stated earlier—but to re-envision how we produce, use, and recycle them. We need a more integrated form of problem-solving, one that admits the immensely complex world we live in. This approach challenges us to think systemically: to ask how our attempts to solve one issue might generate others; and to design materials that don’t simply end up as pollutants but can re-enter ecological cycles.

Moving toward such a paradigm requires far more than technological fixes. It demands new governance structures that foster transparency, cooperation, and long-term thinking. It necessitates an educational shift that equips future generations to navigate uncertainty, understand trade-offs, and anticipate outcomes over multiple generations rather than electoral cycles. As Daniel Schmachtenberger has written about, on a cultural level, it might mean reassessing our definition of progress itself.

In the final analysis, recognizing the delicate interplay among these pillars of civilization compels us to question the notion that technology alone can deliver salvation.

References

• Parts of this essay draws heavily from Vaclav Smil’s book: How the World Really Works, Chapter 3. Understanding Our Material World.

• The Consilience Project, Development in Progress, July 16, 2024. https://consilienceproject.org/development-in-progress/

• Home Depot. (n.d.). Types of Steel. Retrieved December 5, 2024, from https://www.homedepot.com/c/ab/types-of-steel/9ba683603be9fa5395fab90171fb5f7e

• Thermoset plastics: https://plasticseurope.org/plastics-explained/a-large-family/thermosets/

• Polyvinyl Chloride in biomedical applications:

• https://boydbiomedical.com/articles/using-polyvinyl-chloride-in-biomedical-applications

• Timeline of plastic development: https://en.wikipedia.org/wiki/Timeline_of_plastic_development

My Other Publications:

Check out my latest AI newsletter: ε Pulse: Issue #22

Watch my latest short video essays on my YouTube Channel.

Here is one:

Others:

From Rebels to Rulebooks.

Humanity’s Persistent Bug.

(For books I have read, reviewed, or currently read, go to the end of the newsletter)

In this newsletter:

[Talking Points]

⌨️Story of VueJS

👯 Twins Baby Boom

🪙US: No More Pennies?

🍿Netflix’s Video Podcast.

🇰🇷Seoul: Marry, Take Cash.

📺YouTube is the New Tele.

🪖Latest Commandos Origin Game.

📰New York Times Approve AI-use.

[Long Reads/Watch]

[I]: 🎲Why Everything is Becoming a Game

Gurwinder’s brilliant essay examines how modern society increasingly employs gamification—a strategy rooted in B. F. Skinner’s behaviorist experiments with pigeons—to control human behavior by making everyday activities into games. By offering immediate, unpredictable, and conditioned rewards, this method shifts our focus from intrinsic values to extrinsic metrics, effectively turning our lives into score-driven systems. Though Gurwinder didn’t mention it in his essay, at the heart of it all of this, is mimetic desire, as a major driver.

The essay traces the origins of gamification from Skinner’s laboratory work, where he discovered that pigeons pecked more persistently for immediate and random rewards, to its evolution into a widespread tool in consumer and corporate environments. Early experiments demonstrated that conditioned reinforcers, such as a simple click or badge, could manipulate behavior more effectively than primary rewards, laying the foundation for modern applications.

Gurwinder details how gamification has permeated various sectors—from loyalty programs and employee management to social media platforms where “likes” and notifications serve as modern-day status symbols. This shift has redefined our societal values, leading us to prioritize quantifiable achievements over meaningful, long-term fulfillment. The essay highlights examples like social credit systems in China and the addictive design of dating apps and fitness trackers that subtly condition behavior for commercial and political gains.

Ultimately, the essay warns that while gamification promises immediate satisfaction and order, it often leads to a hollow existence defined by fleeting wins rather than true progress. Gurwinder urges readers to be discerning about the "games" they play, advocating for choices that emphasize long-term, positive-sum, and intrinsically rewarding pursuits, thereby reclaiming control over our lives and resisting the seductive pull of superficial metrics.

The essay will take you about 35 minutes to read, if you have that long to spare, I strongly recommend reading it. The idea/minute spent reading it is quite high. It articulates the problem of modernity. It’s probably the best thing I have read this year.

[II]: 💡How to Prepare for the Intelligence Age

This conversation between Jonathan Bi and Tyler Cowen offers a very rich exploration of the implications of AI, focusing on its impact on innovation, economic competition between the US and China, and the future of knowledge work. Re: DeepSeek, Cowen's insights challenge conventional wisdom, such as the belief that US sanctions effectively stifle Chinese technological advancement. He argues that restrictions, like the ban on high-end AI chips, can actually stimulate innovation by forcing China to innovate frugally. Cowen's analysis extends beyond the geopolitical landscape. He paints a picture of a future where AI radically reshapes the workforce, potentially leading to a greater divide between those who can effectively utilize AI tools and those who cannot (I suppose that goes without saying, a pronounced Matthew Effect of some sort). He also touches upon the psychological and societal challenges posed by rapid technological advancements, highlighting the difficulty in comprehending and responding to the pace of change. I, for example, write an AI blog to keep up with this stuff; and I must say, it’s not for the faint of heart. But I see the writing on the wall: the productivity gain from embracing these tools is huge. I have many internal protocols that just allow me to do so much more for the past 2 years.

One particularly compelling aspect of the conversation revolves around the future of writing and knowledge creation. Cowen discusses how AI is already surpassing human capabilities in certain domains, such as answering complex (academic) questions and generating different kinds of creative text formats. In this context, Cowen emphasizes the importance of cultivating a unique voice and style in writing. He believes that as AI becomes more proficient at generating generic content, human writers will need to focus on incorporating their personal experiences, insights, and styles to differentiate themselves. While I see the point Cowen is making here, I think he might be underestimating how pervasive and powerful this technology is. For example, I do know of creators, such as Varun Mayya, who have been using his AI clones to 10X is output. But I do agree that the uncanny valley is a thing, maybe not so much of a big deal if folks like Varun literally have millions of followers. There was a quick chat too about the future of academia (On this topic, I recommend reading this blog by another economics professor: in brief, research will be overrated in the age of intelligence) The conversation ultimately leaves me with a sense of both the immense possibilities and the potential challenges that lie ahead in an era defined by increasingly powerful AI.

[III]: 📿Hidden History of Early Christian Art

I very much enjoyed watching this 45-minutes video essay partly because I am new to the whole business. Here, Harmony explores the fascinating history of early Christian art and its connection to Jewish traditions. The speaker posits that the first icon was actually the Ark of the Covenant, emphasizing its role as a prefiguration of Christ. The video discusses Jewish practices of using objects like mezuzah and phylacteries, which contained scripture and were believed to offer protection. These practices, the speaker argues, demonstrate a belief in the power of symbols to connect with the divine, a belief that carried over into early Christian art.

The video essay traces the development of Christian iconography, suggesting that it originated with Gentile converts who adapted their custom of making images of saviors to include Christ and his Apostles (this was news to me). The essay concludes by highlighting the continuity and discontinuity in modern Christian art. While traditional Orthodox churches have maintained the canonical approach to iconography, other denominations have adopted diverse styles that often lack the symbolic depth and devotional intent of early icons.

At the end of the essay, I became really interested in the discontinuity of Christian Art in a large aspect of modern Christianity, and I suppose I can speak to this to an extent. I come from a protestant background, and icons and images tend to spook a lot of folks. But in essence, the Protestant Reformation reshaped Early Christian visual traditions by adapting imagery, architecture, and symbolism to align with new theological values. The principle of sola scriptura led to concerns about idolatry, resulting in restricted use of religious images and episodes of iconoclasm. Protestant churches prioritized simplicity, centering worship around the pulpit and the spoken word instead of elaborate altars and decorations.

[IV]: 🔰Remembering Everything You Read

I read a lot of stuff, and this video caught my eye, largely because I haven’t revisited my lessons on learning how to learn in a long time. I have heard about most of the ideas shared in the video, practiced some of it in college, etc. Sometimes I have this apprehension about summarizing something that is complex and sort of reducing it to this kind of synonym regime. That notwithstanding, I think this video is really good. In brief, Justin Sung outlined this system (PACER) for effective learning.

Procedural: Information about how to do something. For example, instructions on how to bake a cake or write a Python program. The key to mastering this information is practice. Don't just read the instructions, actually bake the cake or write the code!

Analogous: Information that connects to something you already know. For example, if you're learning about electrical circuits, you might relate it to the flow of water in pipes. The key here is to critique the analogy – how is it similar? How is it different? This deepens your understanding. I like analogies alot, it’s one of the easiest ways to catalyze learning new stuff.

Conceptual: Facts, theories, and principles. For example, understanding the concept of gravity or the principles of economics. The best way to digest this is through mapping, creating visual representations like mind maps to connect the concepts.

Evidence: Specific data or examples that support conceptual information. For example, statistics on climate change or historical examples of economic crises. The key here is to store the information in a system like a second brain or flashcards and rehearse it by applying it in different contexts. (I don't understand why a lot of these things have to be stored because we do have search and now, research on demand thanks to AI.)

Reference: Minor details that might be needed later. For example, the exact date of a historical event or the specific name of a chemical compound. Like evidence, you should store this information and rehearse it, often through flashcards and spaced repetition. (Except you are sitting for an exam that requires you to know this detail. Again I really don't see why these things need to be rehearsed)

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[Books/Papers 📚]

Book(s) recently completed:

Causal Inference by Paul R. Rosenbaum [Book Review]

What I am currently reading:

On Truth by Simon Blackburn

Reflex: Python Web Framework Docs

Causal Inference by Paul Rosenbaum - Book Review

In scientific inquiry, nothing beats the quest for causal understanding. While statistical associations may offer intriguing glimpses into relationships between variables, they fall short of establishing the coveted cause-and-effect relationship. This is where the field of causal inference steps in, providing a rigorous framework to navigate the intricate web of causality. My interest in causal inference, as a field of study, dates back to grad school, when I was building machine learning models to discriminate kidney cancer patients from healthy controls using metabolites in their urine. I couldn't stop myself from thinking - as I should - about the different ways these patients could be different other than the disease factor. I ended up applying a method called propensity score matching (more on this later), and I concluded my paper by stating:

"We have shown the potential utility of a urine assay in the clinical setting for RCC (renal cell carcinoma) detection. This study, like others of its kind, has the limitation of numerous potential confounders that could impact biomarker discovery results. While randomized control trials (RCTs) are gold standards for epidemiology research, observational studies remain inescapable for studies like this as randomizing the intervention (RCC) is impossible. As such, to argue for the reduction in selection bias, we adjusted for five potential confounders in the study: age, BMI, gender, smoking history, and race. [...] Going forward, a much larger cohort, representing the diversity of race and geographical locations would be required for the validation of our biomarker proposals."

Many of the concepts mentioned in that excerpts were covered very clearly in the book

In "Causal Inference," Paul R. Rosenbaum masterfully distills the essence of this field, presenting a concise yet comprehensive overview that is accessible to a wide audience. With numerous motivating examples, Rosenbaum illuminates the key concepts and methods that underpin causal inference.

I. The Essence of Causal Effects

At the heart of causal inference lies the concept of a causal effect, which quantifies the difference in an outcome for an individual under two alternative scenarios: receiving the treatment versus receiving the control. This seemingly simple notion is fraught with challenges, as we can only observe an individual under one of these scenarios, leaving the other outcome forever hidden from view.

To illustrate this point, Rosenbaum recounts the historical example of George Washington's demise. In 1799, Washington fell ill with a sore throat, and his doctors, in their wisdom, decided to bleed him profusely. Washington succumbed to his illness the following day, leaving us to ponder whether the bleeding hastened his death or whether he would have met the same fate regardless. This question, like many others in the context of causal inference, involves comparing what actually happened to what might have happened in a counterfactual world, a world we can never fully access.

II. Randomized Experiments

The gold standard for causal inference is the randomized experiment, where a truly random device, such as a coin flip, dictates whether an individual receives the treatment or the control. This random assignment ensures that the treated and control groups are comparable prior to treatment, minimizing the risk of confounding factors that could distort the true causal effect.

Rosenbaum highlights the importance of randomized experiments with the example of the PALM trial, a clinical trial that compared two treatments for Ebola virus disease. In this trial, patients were randomly assigned to receive either ZMapp or mAb114, two drugs derived from antibodies. The random assignment ensured that any differences in survival rates between the two groups could be confidently attributed to the treatment and not to pre-existing differences between the patients. But, we don't always have the luxury of randomized experiment. Case in point is my study from grad school, that I briefly outlined earlier.

III. Observational Studies

In many cases, we are stuck to observational studies, where the assignment of treatment is not under the control of the investigator. This lack of control introduces the risk of bias, as the treated and control groups may differ in ways that affect the outcome, leading to spurious conclusions about cause and effect.

Rosenbaum illustrates the challenges of observational studies with the example of smoking as a possible causal factor for periodontal disease. Smokers and nonsmokers differ in numerous ways, including age, gender, education, and income. These differences could account for the observed association between smoking and periodontal disease, even if smoking itself has no causal effect.

IV. Measured Covariates

As such, to mitigate the risk of bias in observational studies, researchers often adjust for measured covariates, such as age and gender, that could confound the relationship between treatment and outcome.

One such technique, known as propensity score matching (PSM), stands out as a powerful tool for adjusting for measured covariates and creating comparable groups for analysis.

In essence, PSM involves pairing treated individuals with control individuals who share similar probabilities of receiving the treatment, based on a set of observed covariates. This probability, known as the propensity score, is typically estimated using statistical models which take into account the measured covariates that could potentially confound the relationship between treatment and outcome.

Beyond this point, I didn't have a great deal of understanding of Causal inference as a field, and this is exactly while I picked up the book.

V. Unmeasured Covariates

I gave a talk at Morehouse School of Medecine a few weeks ago, and one of the professors who attended kept asking me these questions, multiple times, same question, but different flavors each time - while using the word ' bias' alot, and rightfully so. He was speaking to unmeasured covariates. As Rosenbaum stated in the book, "an observational study is met with objections not applause."

While adjusting for measured covariates can help reduce bias in observational studies, it cannot eliminate it entirely. The challenge of unmeasured covariates, those lurking variables that remain unobserved and unaccounted for, casts this long shadow over causal inference. These unmeasured covariates could be genetic predispositions, personality traits, or any other factors that influence both the treatment and the outcome, potentially distorting the true causal relationship.

In the context of smoking and periodontal disease (the example used in the book), unmeasured covariates could include dietary habits, oral hygiene practices, or even genetic factors that affect both smoking behavior and susceptibility to gum disease. These unmeasured covariates could create a spurious association between smoking and periodontal disease, even if smoking itself has no direct causal effect.

To address the challenge of unmeasured covariates, researchers turn to sensitivity analysis, a technique that quantifies how much bias would need to be present to explain away the observed association between treatment and outcome. In essence, sensitivity analysis asks: "If the observed association is not due to a true causal effect, how strong would the influence of unmeasured covariates need to be to create this association?"

The answer to this question is typically expressed in terms of a sensitivity parameter, which quantifies the magnitude of bias that would be required to overturn the causal conclusion. If the sensitivity analysis reveals that a large amount of bias would be needed to explain away the association, then the causal claim is strengthened, as it suggests that unmeasured covariates are unlikely to be driving the observed relationship. Conversely, if even a small amount of bias could account for the association, then the causal claim is weakened, as it raises the possibility that unmeasured covariates are playing a significant role.

VI. Quasi-Experimental Devices

In some cases, researchers can exploit the so called quasi-experimental devices, which are tools used in observational studies to address potential biases and strengthen causal claims. They involve adding elements to the study design to investigate and potentially invalidate anticipated counterclaims. Quasi-experimental devices furnish new data intended to advance a claim by undermining specific grounds for doubt.

Rosenbaum discusses several quasi-experimental devices, including natural experiments, discontinuities, and instruments. Out of these three, I have only read about natural experiments before, and it was in the context of the 2021 Nobel Prize in Economics which was awarded to David Card, Joshua Angrist, and Guido Imbens for their groundbreaking work on natural experiments in economics. Card demonstrated how natural experiments can be used to study labor market issues, while Angrist and Imbens developed methodological frameworks to draw causal conclusions from such experiments, revolutionizing empirical research in economics.

In brief, in natural experiments, researchers capitalize on naturally occurring events that resemble randomized experiments. For instance, in the study of the effects of winning the lottery on financial well-being, an example used in the book, researchers might compare the outcomes of winners of large vs. small amounts to assess the impact of cash infusions on financial stability, exploiting the random nature of lottery draws to approximate a randomized experiment.

Another quasi-experimental design is the discontinuity design, which exploit abrupt treatment cutoffs along a continuum, creating a natural experiment near the point of discontinuity. For example, researchers studying the effects of a scholarship program might compare the outcomes of students who barely qualified for the scholarship to those who barely missed the cutoff, exploiting the discontinuity in scholarship eligibility to approximate a randomized experiment.

Instrumental variables, another quasi-experimental device, are variables that influence treatment assignment but do not directly affect the outcome. In other words, they are used when the treatment of interest is not randomized, but some form of encouragement to accept the treatment is. For example, imagine a study where some people are encouraged to quit smoking, and others are not - the encouragement is the instrument. The encouragement is randomized, so it affects who tries to quit. However, note that, it is the act of quitting smoking, not the encouragement itself, that is thought to improve lung function. The effect of quitting on lung function can be estimated among compliers, people who quit smoking only if encouraged to do so. And why is it useful? It is because it allows researchers to get closer to estimating a causal effect when true randomization is not possible. It allows researchers to focus on the effect of the treatment on people whose treatment status is affected by the instrument, i.e. the compliers

In sum, the beauty of quasi-experimental devices lies in their ability to provide insights into causal effects even when randomized experiments are not possible. By carefully selecting and analyzing quasi-experimental settings, researchers can gain a deeper understanding of causal relationships and inform policy decisions in a variety of fields, from economics to public health.

VII. Replication and Evidence Factors

We have all heard about the replication crisis in so and so field. Replication is a cornerstone of scientific inquiry, as it helps to ensure that findings are not due to chance or bias. However, simply repeating the same study with a new sample is not always sufficient to establish causality.

Rosenbaum emphasizes the importance of varied replication, where studies are conducted in different settings, with different populations, and using different methods. The goal here is to have studies/analyses that are inevitably flawed, but in different ways, converging on the same causal claim. This leads us to the idea of evidence factors, which are multiple, independent sources of evidence that converge on the same causal conclusion. The more evidence factors that support a causal claim, the stronger the claim becomes.

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VIII. Uncertainty and Complexity

Causal inference is often fraught with uncertainty and complexity, especially in observational studies. It is important to acknowledge these limitations and to be cautious in drawing causal conclusions.

Rosenbaum concludes the book with a discussion of the ongoing debate about the health effects of alcohol (or the lack thereof), highlighting the uncertainty and complexity that surround this topic. He discusses the conflicting evidence from various studies, as well as the challenges of disentangling the effects of alcohol from other lifestyle factors.

If you have read this piece thus far, and enjoyed what you read. You should definitely give the book a try.

My Other Publications:

Check out my latest AI newsletter:

The Epsilon

ε Pulse: Issue #21

Read more

a year ago · Olatomiwa Bifarin

Watch my latest short video essays on my Youtube Channel.

Here is one:

Others:

949: The First Influencers

Aristotle on Modern Science

The Desire Domino Effect

In this newsletter:

[Talking Points]

🐝The Honeycomb Conjecture

🌊Ha Long Bay.

🐦Hummingbirds Heart Rate

🐍uv:python Packaging in Rust.

🤖Jevons Paradox and AI.

🇳🇬World Bank: AI Boosts Student Success in Nigeria.

🏛ChatGPT Gov for US government.

[Longer Reads/Watch]

[I]: 🤳The Dangers of Social Media.

, writer of The Generalist, wrote about some of the self experiments that he wants to do at the beginning of 2025. One thing that obviously got my attention in the essay was his irritation with regards to the pervasiveness and persuasiveness of social media. I mean everybody knows these things, or at least I will like to imagine that, but it can be extremely difficult to deeply understand the strain continuous use of social media actually puts on you.

Another creeping habit over the past few months has been spending a bit more time on X/Twitter. Elon Musk’s centrality in the incoming government, heated conversations in AI, and another crypto bull cycle conspired to eat an increasing number of minutes per day. X and Reddit reliably host interesting conversations that teach me something. But they equally reliably shred my attention, steal twice the time I intended to spend, and stuff my brain with pointless dreck. Despite prevalent studies about social media’s deleterious effect on productivity and mental health, I don’t think most people fully appreciate the impact of this continuous oversaturation. One attribute of these social platforms that I’ve only just started to appreciate is how persuasive they are — and what that means for a mind. We have all heard stories about being “radicalized” by YouTube videos, but since we’re not on the monkey bars at Al Farouq, we assume we’re unaffected. For most of us, the risk is not being radicalized but denatured. When you are bathed in a monoculture, even one warring with itself, you begin to adopt its stances as your own. Under Musk’s management, X is an exemplar of this risk. It may bill itself as a fractal “town square,” but it has an increasingly monolithic perspective and set of values. Many are the skirmishes and disagreements, but there is only one direction of travel. Surrendering to that momentum by donating your attention is a step toward losing ownership of your mind. It is too easy to find yourself adopting opinions and preferences you haven’t thought through but that are part of the fabric of these networks…

Other related essays.

Cal Newport on Tik Tok

How to Break Free from Dopamine Culture

Why we Can’t Focus.

In all of these I think a legitimate source of worry is the dangers of the hackneyed dangers of social media, not that I have any ideas of resolving this dilemma, but it is an observation that is worth stating.

[II]: 🤳The Evolution of the Creator

A fine essay by that explores the evolution of the "creator economy," moving beyond the narrow definition of "influencer" to encompass a wider range of online creative endeavors. It examines how advancements in AI, particularly generative AI, are democratizing content creation and enabling new forms of distribution and monetization for creators. The essay also highlights the importance of a "Creator Triad" — creation tools, distribution platforms, and monetization strategies — and discusses successful examples like Canva, Roblox, and TikTok, while also considering emerging trends like digital clones and AI-powered fan fiction.

Read the essay here.

Here is a podcast I am currently listening to about how AI will/is transforming content creation.

[III] 🦪On Evolution: The Displacement Fallacy

This is a great essay from . What follows is a brief summary: conservation of information is a central finding in the intelligent design literature, highlighting that there is no “free lunch” in search problems. To find an unlikely “needle in a haystack,” one inevitably needs another search strategy, which itself requires information. Mathematically, this insight is captured by the law of conservation of information, which shows that the information needed to improve search odds can never be conjured from nothing but must be sourced from somewhere else. This leads to what Bill Dembski calls the “displacement fallacy,” where an explanation for how something improbable gets found—such as a target phrase in Richard Dawkins’s WEASEL program or complex Boolean functions in the Avida simulations—merely shifts the need for information to another, unexplained part of the system.

Repeatedly, evolutionary simulations or explanations claim to generate new information but on closer inspection smuggle it in at the outset via carefully engineered fitness landscapes or reward systems. In other words, they “displace” the question of where that crucial information came from, just as in a shell game where the pea is never created anew but only hidden and shuffled around.

Some quotes from the essay:

The discovery of conservation of information didn’t start with proving a mathematical theorem. Rather, its discovery came from repeatedly noticing how efforts to account for the success of searches whose odds of success were seemingly hopeless always smuggled in information that wasn’t properly accounted for. One hole was filled, but only by digging another, and so a new hole now in turn needed to be explained. This failure of explanation became especially evident in the evolutionary literature.

Over and over information supposedly created from scratch was surreptitiously introduced under the pretense that the information was already adequately explained when in fact it was merely presupposed. In effect, displacement became a special case of the fallacy of begging the question, obscuring rather than illuminating evolutionary processes.

[IV]: 🛐 Negative Theology: Popper’s via Negativa

Here, Ed Feser’s essay draws an analogy between the apophatic (or “negative”) theological tradition and Karl Popper’s “falsificationist” view of scientific knowledge. In negative theology, thinkers like Maimonides and Aquinas emphasize that, although one can establish that God exists, one cannot describe His essence in any positive, comprehensive way. Our knowledge is largely confined to what God is not—God is not material, not subject to change, and so on—because our senses and finite intellect cannot fully grasp a divine nature. According to Aquinas, we do form some affirmative propositions about God, but these remain incomplete compared to the fullness of His essence.

Feser observes a parallel in Popper’s philosophy of science, where knowledge is similarly constrained: we know that certain claims are false, rather than which claims are definitely true. For Popper, theories survive only as unrefuted conjectures, awaiting the inevitable discovery of contrary evidence. Both views stem from an empiricist orientation (albeit different varieties), one that sees experience as essential but limited in scope. Whereas Aquinas’s Aristotelian empiricism allows for robust understanding of natural essences—though still stopping short of fully grasping the divine essence—Popper’s modern, more modest empiricism suggests we cannot even fully grasp natural essences. The overlap thus highlights different responses to the boundaries of empirically grounded knowledge, whether in theology or in science.

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[V]: 🥪 Sandwich Economics

A good essay - and I hope you will agree - is an essay that teaches you something totally new that you find particularly interesting. For me, this essay qualifies as one: Back in 2020, major American tech companies poured billions of dollars into Jio (an Indian company), not just because it was a telecom provider, but because of its grand vision. Jio didn’t merely offer cheaper internet or a new business model; it orchestrated a transformative “economic framework” that compels other businesses to reorganize around it. This framework—which the author calls the “Sandwich”—is the real innovation: it redefines how industries create and capture value, sweeping traditional suppliers and competitors into a layered structure from which they struggle to escape.

Unlike typical business models or supply chain squeezes, Sandwich Economics is about strategically dictating the terms of competition itself. While Big Tech might appear to expand through various product portfolios (search, social media, e-commerce, and more), the essence is that each move imposes their economic framework on a new industry—much like Reliance did. For companies caught in the middle, it no longer matters how strong they are in their current markets; what matters is their position within the “Sandwich.” If you’re not in the right layer, you’re effectively squeezed out.

[VI]: 📔A Lifetime Reading Plan

I started taking books seriously when I finished college. And to be clear, when I talk about taking books seriously, I am talking books that have little to nothing to do with my actual formal training. It was not until I moved to the United States, that I was finally let loose. Largely because I could now afford any book I wanted to read. So reading about “Lifetime Reading Plan” made for a delightful read, largely because whatever I think I have been doing with books seems like a child’s play to what is possible.

In his essay, Ted Gioia describes the rigorous—almost “extreme sport”—approach he took to self-education through books. He emphasizes that the bulk of his learning didn’t come from classrooms or degrees but from reading widely and deeply, beginning every day by immersing himself in challenging texts. “I went beyond what was reasonable,” Gioia writes, emphasizing that he wanted to “expand my mind and broaden my understanding of the world.” He notes that while he loved old and demanding authors in his youth—like Tolstoy, Cervantes, and Dostoevsky—he deliberately shifted toward contemporary voices as he got older, having already absorbed a firm grounding in the classics.

Gioia also highlights that reading can be a slow, meditative process; far from trying to speed-read, he often reads at a “tortoise’s pace.” To maintain this discipline, he keeps meticulous lists of the books he wants to tackle, seeking out those that fill the “gaps” in his knowledge, even if they have no direct relation to his day job. Over time, he saw how this intense and methodical reading regimen sharpened his insights and shaped his outlook. “My lifetime reading plan was my proven path to Nirvana,” he wrote—demonstrating that, for him, books aren’t merely hobbies, but powerful vehicles for wisdom, personal growth, and a fuller appreciation of the world.

If you read his actual essay, you will want to just clean up your room and start reading.

And on the subject of books…

[Books/Papers 📚]

What I am currently reading:

• Causal Inference by Paul R. Rosenbaum

• The Miraculous from the Material by Alan Lightman.