IT: Maps of Meaningness and Investing Amid Low Expected Returns

“The solution, by contrast, is to make the everyday appear to us anew, to be seen again as it is in itself, therefore to discover rather than to invent, to see what was there all along, rather than put something new in its place, original in the sense that it takes us back to the origin, the ground of being. This is the distinction between fantasy, which presents something novel in the place of the too familiar thing, and imagination, which clears away everything between us and the not familiar enough thing so that we see it itself, new, as it is.”

—Iain McGilchrist, the Master and His Emissary

Articles and Podcasts

Maps of Meaningness [Podcast]

Meaningness

David Chapman was an AI researcher who has written an excellent book on meaning. In this podcast, he contrasts his work with Jordan Peterson.

Both of them sit in Nietzsche's lineage, grappling with his notion that Christianity's collapse would produce a nihilistic crisis. Both Chapman and Peterson treat this as the central problem of our era: how do you construct meaning when the old certainties have dissolved?

Chapman offers a useful genealogy of how we got to the present. In his telling, the Romantics of the late 18th century were the first organized reaction against Enlightenment rationalism, championing emotion, poetry, and myth against systematic reason. The 1950s Beats revived this impulse, which then flowered into the 1960s counterculture. The hippies merged with the New Left and this sensibility eventually conquered: it became the operating system for the entire political left of Western culture for decades.

A parallel Christian counterculture emerged arguing that modernity had lost touch with God. Chapman argues both movements are now exhausted—no longer the animating force behind cultural or political conflict, even if their rhetoric persists. I interpret Peterson as mostly agreeing with this, though more focused on something like a Christian revival than Chapman (who has Buddhist roots).

Their shared intellectual heritage includes cognitive science, particularly the 4E tradition, emphasizing embodiment and interaction. Both draw heavily on James Gibson's concept of affordances—the idea that we perceive the world not as a collection of objects but as a field of action possibilities. A coffee mug isn't primarily a cylinder of ceramic; it's something graspable, liftable, and excellent for hot-cocoa-drinkableness. This reframes meaning as fundamentally about what you can do, not what you can know. Peterson made Gibson's ecological approach to visual perception a cornerstone of his work; Chapman arrived at similar conclusions through Heidegger and his own AI research in the 1980s.

To the differences, Chapman and Peterson diverge in their response to the nihilism problem.

Peterson's framework grows out of the Western mythical structure of opposition between chaos and order. To use the Babylonian example, the hero (Marduk) ventures into the unknown, confronts the dragon (Tiamat, the symbol of chaos), extracts treasure, and returns to fortify order. In his paradigm, society can have too much order (the tyrannical father) or too much chaos (the unpredictable mother). The hero's role is to reconcile the two.

Chaos has both creative and destructive aspects, but the fundamental posture is one of managing threat—riding out on your armored steed for a dangerous but temporary expedition before returning to safety.

Chapman's Buddhist lineage comes from a not-unrelated, yet different angle. Chapman starts with a framing that everything exhibits both nebulosity and pattern simultaneously.

Nebulosity refers to the aspects of reality that are fluid, constantly changing, impossible to pin down. Pattern refers to what's solid, enduring, well-defined. This maps reasonably well onto Jordan Peterson's order/chaos framework, but the difference is illuminating.

Nebulosity and pattern aren't opposites to be reconciled, but an inseparable pair present in all phenomena. Nebulosity isn't something to be conquered. It merely is.

This produces different orientations toward uncertainty. Peterson's model preserves the notion of a safe city to return to, a domain of order worth defending. His model suggests something more like “order/chaos balance” whereas Chapman's suggests there's no such refuge: nebulosity pervades everything already.

The practice becomes making friends with unformedness rather than conquering it. Peterson’s framework says: venture out, bring back what's valuable, reinforce the walls. Chapman's says: recognize the walls were always illusory, and dance with what you find.

Investing Amid Low Expected Returns [Book]

Antti Ilmanen

Antti Ilmanen's Expected Returns (2011) is my go-to recommendation for an introduction to quantitative investing. It is a dense but rewarding tour through the building blocks of asset class returns with some look at portfolio construction and risk management. Investing Amid Low Expected Returns (2022) is a follow-up that updates some of his past research in light of the enormous run up in valuations of most risk assets over the 2010s.

The core argument: virtually all long-only assets appear expensive compared to their own histories, and investors need to recalibrate their expectations accordingly. Ilmanen estimates that achieving the same retirement income target in a low-return environment requires nearly doubling your savings rate—from roughly 8% to 15% of salary annually for a typical saver.

It’s worth noting that using historical valuation doesn't have a great track record because future highs can be higher and lows can be lower than the past. Using historical valuations, you would have been underweight US equities basically starting in the 1990s to present, the period where US equities have performed phenomenally well (dotcom bubble and GFC not withstanding). Spoiler: The future is out of sample! [insert ergodicity comment here].

It’s a weird thing to say, but my critique of this book (and AQR-style thinking more broadly) is that maybe it’s a little TOO empirical. As noted, the future is not the past and I think you have to think qualitatively about that at some level.

Having said that, most investors I know would be much better off understanding historical returns more closely. Probably, the most helpful contribution in the book is his framing of where investors actually add value.

Most spend their time selecting active managers—nearly a zero-sum game—while underutilizing diversification, risk management, and cost control. He uses a memorable image of apple harvesting: everyone reaches for the top of the tree (alpha) while ignoring the low-hanging fruit. The apples are all in one basket (poor diversification), someone's standing under the ladder (bad risk management), and there's one overseer for each worker (terrible cost control).

I cannot overstate how true this in my experience. While the Vanguard/Boglehead movement has been a net positive in terms of people being more fee conscious (though sometimes blindly so - there are some situations where fees are worth paying), most investors I know have bad calibrations around diversification and risk management.

One litmus test question I saw on Twitter: If someone offered you a fully liquid, guaranteed 6% annual real return investment, how much of your portfolio would you invest in it?

The answer depends on individual circumstance, but should be "a lot" for pretty much everyone. That is on the high end of any major asset class over the last century and it has zero volatility. Most people answer something less than "a lot" in my experience.

Of all the historical data he presents, the one that I think would surprise people the most is the data on commodities. Individual commodities have roughly 30% annual volatility, which creates enough variance drag to bring their compound returns to zero despite positive arithmetic returns. Yet, a diversified basket of commodities has earned over 3% annually for almost 150 years. Erb and Harvey called this "turning water into wine"—the rebalancing bonus from holding volatile, uncorrelated assets. Add to this commodities positive return in periods of inflation and low historical correlations to stocks and bonds and you can see why many portfolios could benefit from more commodity exposure.

Why AC is Cheap but AC Repair is a Luxury [Article]

Andreessen Horowitz

Here’s a fun time travel thought: go back to the 90s and explain to people that in 2025 it would be cheaper to buy a flatscreen TV to cover up a hole in your drywall than to hire someone to fix it.

This paradox captures something systemic about modern economies: the intersection between two economic phenomena that rarely get discussed together: Jevon's Paradox and the Baumol Effect.

Jevon's Paradox explains why productivity gains don't reduce total spending—they explode it. When transistors dropped from $1 to a fraction of a millionth of a cent, we didn't save money on computing, we consumed a lot more computing. We embedded processors in greeting cards and disposable shipping tags. The same logic applies to AI: every efficiency gain unlocks new use cases, creating infinite demand for any chip you can get.

The Baumol Effect is the mirror image. When one sector becomes wildly productive and creates high-paying jobs, every other sector's wages must eventually rise to compete in the same labor market. If you can make $150/hour installing HVAC for data centers, you won't accept less for residential service.

If I have a home appliance break, I usually spend an hour or two trying to fix it with Youtube and then throw it out. Depending on where you live, most entry level appliances (washer, dryer, stove, etc.) probably cost about 4-6 hours of maintenance time.

My dishwasher cost about $600. It costs me $150/hour to get a technician. If it’s already halfway past its useful life and it’s going to take more than 2 hours to fix, the economically rational thing is to just chunk it and buy a new one (with free delivery and install in most cases!).

How does this impact AI’s effects? If you automate 99% of a job but regulations require a human for the final 1%, that human becomes the bottleneck. As a result, their wages should rise tremendously—until that last 1% is automated, at which point they collapse.

The intersection of regulation (and public pressure around changing regulation) and AI seems like the most significant thing for how it will drive cost and economic impact. By all accounts, AI is already much better at reading scans than the median radiologist today. But, how long will it be until the median radiologist is out of a job? I suspect the timing is measured in decades.

This suggests a strange future where the "human required" residue of various professions becomes the essential employable skillset—vestigial limbs of career paths that no longer substitute for one another and can be milked right up until they completely collapse.

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