At the link, I speculate on doom, hardware, human jobs, the jagged edge (via a Joshua Gans working paper), and the Manhattan Project. The fun thing about being 6 years late to a seminal paper is that you can consider how its predictions are doing.
Sutton draws from decades of AI history to argue that researchers have learned a “bitter” truth. Researchers repeatedly assume that computers will make the next advance in intelligence by relying on specialized human expertise. Recent history shows that methods that scale with computation outperform those reliant on human expertise. For example, in computer chess, brute-force search on specialized hardware triumphed over knowledge-based approaches. Sutton warns that researchers resist learning this lesson because building in knowledge feels satisfying, but true breakthroughs come from computation’s relentless scaling.
The article has been up for a week and some intelligent comments have already come in. Folks are pointing out that I might be underrating the models’ ability to improve themselves going forward.
Second, with the frontier AI labs driving toward automating AI research the direct human involvement in developing such algorithms/architectures may be much less than it seems that you’re positing.
If that commenter is correct, there will be less need for humans than I said.
Also, Jim Caton over on LinkedIn (James, are we all there now?) pointed out that more efficient models might not need more hardware. If the AIs figure out ways to make themselves more efficient, then is “scaling” even going to be the right word anymore for improvement? The fun thing about writing about AI is that you will probably be wrong within weeks.
Between the time I proposed this to Econlog and publication, Ilya Sutskever suggested on Dwarkesh that “We’re moving from the age of scaling to the age of research“.
The flu and covid-19 vaccines don’t work super well. Both vaccines permit infection and transmission at quite high rates. The benefit from these vaccines come largely from reductions in mortality or severe symptoms conditional on infection. The covid-19 vaccine is itself especially risky or ineffective depending on the age and health of the individual. Plenty of people eschew vaccines.
I live in Collier County, Florida where there have been 61 confirmed cases of measles so far this year. I have since learned that Measles is EXTREMELY contagious. It floats around the air and on items and just sort of hangs out and waits for a place to replicate. I’ve also learned that symptoms include a fever, eye irritation, possible brain swelling, severe dehydration, and a characteristic rash. The severe dehydration easily puts people in the hospital, the eye irritation can lead to permanent vision loss, and the brain swelling can be acute, or a symptom delayed by 5-6 years, which can also be fatal. I’ve also learned that having the vaccine, which is usually administered in two doses, provides about 97% immunity. The vaccine works so well, that the department of health recommends no behavioral change among the vaccinated population when there is a measles outbreak. Barring unique circumstances, measles immunity can persist for a lifetime.
Unfortunately, a large segment of the anti-vaccine mood affiliation retains the salience of the covid-19 vaccine characteristics. Other vaccines and diseases in the typical pediatric schedule are not similar. Most of these prevent infection >90% of the time (TDAP is low at 73%), prevent transmission, reduce mortality when there are breakthrough infections, are effective for years or decades, and are extremely safe for all age groups.
The risks of disease versus the corresponding vaccine are orders of magnitude away from each other. The tables below summarize the data (with sources). I did not double check the source on every single figure. If you glance below, then you’ll see why: Even if the numbers are closer by 10 or 100 times, vaccines still look really good.
First, mortality: The data is divided by disease and age group, and provides mortality rates for both the disease and for the vaccine. The numbers are proportions, conditional on infection or vaccination. There are a lot of zeros in the vaccine mortality rates and certainly more than for the diseases. For example, a measles infection is 10,000 more lethal than the MMR vaccine which prevents it. In fact, all of those zeros in the vaccine rates reflect mortality that is so uncommon, that the estimated one out of every 10 million is just rounded up because researchers don’t think that the risk is zero.
Tech stocks (e.g. QQQ) roared up and up and up for most of 2023-2025, more than doubling in those three years. A big driving narrative was how AI was going to make everything amazing – productivity (and presumably profits) would soar, and robust investments in computing capacity (chips and buildings), and electric power infrastructure buildout, would goose the whole economy.
Will the Enormous AI Capex Spending Really Pay Off?
But in the past few months, a different narrative seems to have taken hold. Now the buzz is “the dark side of AI”. First, there is growing angst among investors over how much money the Big Tech hyperscalers (Google, Meta, Amazon, Microsoft, plus Oracle) are pouring into AI-related capital investments. These five firms alone are projected to spend over $0.6 trillion (!) in 2026. When some of this companies announced greater than expected spends in recent earning calls, analysts threw up all over their balance sheets. These are just eye-watering amounts, and investors have gotten a little wobbly in their support. These spends have an immediate effect on cash flow, driving it in some cases to around zero. And the depreciation on all that capex will come back to bite GAAP earnings in the coming years, driving nominal price/earnings even higher.
The critical question here is whether all that capex will pay out with mushrooming earnings three or four years down the road, or is the life blood of these companies just being flushed down the drain? This is viewed as an existential arms race: benefits are not guaranteed for this big spend, but if you don’t do this spending, you will definitely get left behind. Firms like Amazon have a long history of investing for years at little profit, in order to achieve some ultimately profitable, wide-moat quasi-monopoly status. If one AI program can manage to edge out everyone else, it could become the default application, like Amazon for online shopping or Google/YouTube for search and videos. The One AI could in fact rule us all.
Many Companies May Get Disrupted By AI
We wrote last week on the crash in enterprise software stocks like Salesforce and ServiceNow (“SaaSpocalypse”). The fear is that cheaper AI programs can do what these expensive services do for managing corporate data. The fear is now spreading more broadly (“AI Scare Trade”); investors are rotating out of many firms with high-fee, labor-driven service models seen as susceptible to AI disruption. Here are two representative examples:
Wealth management companies Charles Schwab and Raymond James dropped 10% and 8% last week after a tech startup announced an AI-driven tax planning tool that could customize strategies for clients
Freight logistics firms C.H. Robinson and Universal Logistics fell 11% and 9% after some little AI outfit announced freight handling automation software
These AI disruption scenarios have been known for a long time as possibilities, but in the present mood, each new actual, specific case is feeding the melancholy narrative.
All is not doom and gloom here, as investors flee software companies they are embracing old-fashioned makers of consumer goods and other “stuff”:
The narrative last week was very clearly that “physical” was a better bet than “digital.” Physical goods and resources can’t be replaced by AI like digital goods and services can be at an alarming rate
As I write this (Monday), U.S. markets are closed for the holiday. We will see in the coming week whether fear or greed will have the upper hand.
Use the above game to generate interaction in a class setting. Students collectively form an LLM and have fun seeing the final sentence that gets produced. I call this game “LLM Telephone” based on the classic game of telephone. I suggest downloading the file LLM_Telephone_Game_Sheet and handing out printed copies. However, this game could be adapted to a virtual setting.
The nice thing about passing papers in the classroom is that you can have several sheets circulating in a quite room, so when the final sentence is read allowed it comes as a surprise to most people.
If you’d like to have a handout to follow the game with a more technical explanation, you can use this two-page PDF:
The game relies on a player presenting two tokens of which the next player can select their favorite. Participants should be bound by the rules of grammar and logic when making their selection and presenting two tokens to the next player.
This game works as a fun ice breaker for any type of class that touches on the topic of artificial intelligence. It is suitable for many ages and academic disciplines.
There was a seismic shift in the AI world recently. In case you didn’t know, a Claude Code update was released just before the Christmas break. It could code awesomely and had a bigger context window, which is sort of like memory and attention span. Scott Cunningham wrote a series of posts demonstrating the power of Claude Code in ways that made economists take notice. Then, ChatGPT Codex was updated and released in January as if to say ‘we are still on the frontier’. The battle between Claude Code and Codex is active as we speak.
The differentiation is becoming clearer, depending on who you talk to. Claude Code feels architectural. It designs a project or system and thrives when you hand it the blueprint and say “Design this properly.” It’s your amazingly productive partner. Codex feels like it’s for the specialist. You tell it exactly what you want. No fluff. No ornamental abstraction unless you request it.
Codex flourishes with prompts like “Refactor this function to eliminate recursion”, or “Take this response data and apply the Bayesian Dawid-Skene method”. It does exactly that. It assumes competence on your part and does not attempt to decorate the output. It assumes that you know what you’re doing. It’s like your RA that can do amazing things if you tell it what task you want completed. Having said all of this, I’ve heard the inverse evaluations too. It probably matters a lot what the programmer brings to the table.
Both Claude Code and Codex are remarkably adept at catching code and syntax errors. That is not mysterious. Code is valid or invalid. The AI writes something, and the environment immediately reveals whether it conforms to the rules. Truth is embedded in the logical structure. When a single error appears, correction is often trivial.
When multiple errors appear, the problem becomes combinatorial. Fix A? Fix B? Change the type? Modify the loop? There are potentially infinite branching possibilities. Even then, the space is constrained. The code must run, or time out. That constraint disciplines the search. The reason these models code so well is that the code itself is the truth. So long as the logic isn’t violated, the axioms lead to the result. The AI anchors on the code to be internally consistent. The model can triangulate because the target is stable and verifiable.
A big narrative for the past fifteen years has been that “software is eating the world.” This described a transformative shift where digital software companies disrupted traditional industries, such as retail, transportation, entertainment and finance, by leveraging cloud computing, mobile technology, and scalable platforms. This prophecy has largely come true, with companies like Amazon, Netflix, Uber, and Airbnb redefining entire sectors. Who takes a taxi anymore?
However, the narrative is now evolving. As generative AI advances, a new phase is emerging: “AI is eating software.” Analysts predict that AI will replace traditional software applications by enabling natural language interfaces and autonomous agents that perform complex tasks without needing specialized tools. This shift threatens the $200 billion SaaS (Software-as-a-Service) industry, as AI reduces the need for dedicated software platforms and automates workflows previously reliant on human input.
A recent jolt here has been the January 30 release by Anthropic of plug-in modules for Claude, which allow a relatively untrained user to enter plain English commands (“vibe coding”) that direct Claude to perform role-specific tasks like contract review, financial modeling, CRM integration, and campaign drafting. (CRM integration is the process of connecting a Customer Relationship Management system with other business applications, such as marketing automation, ERP, e-commerce, accounting, and customer service platforms.)
That means Claude is doing some serious heavy lifting here. Currently, companies pay big bucks yearly to “enterprise software” firms like SAP and ServiceNow (NOW) and Salesforce to come in and integrate all their corporate data storage and flows. This must-have service is viewed as really hard to do, requiring highly trained specialists and proprietary software tools. Hence, high profit margins for these enterprise software firms.
Until recently, these firms been darlings of the stock market. For instance, as of June, 2025, NOW was up nearly 2000% over the past ten years. Imagine putting $20,000 into NOW in 2015, and seeing it mushroom to nearly $400,000. (AI tells me that $400,000 would currently buy you a “used yacht in the 40 to 50-foot range.”)
With the threat of AI, and probably with some general profit-taking in the overheated tech sector, the share price of these firms has plummeted. Here is a six-month chart for NOW:
Source: Seeking Alpha
NOW is down around 40% in the past six months. Most analysts seem positive, however, that this is a market overreaction. A key value-add of an enterprise software firm is the custody of the data itself, in various secure and tailored databases, and that seems to be something that an external AI program cannot replace, at least for now. The capability to pull data out and crunch it (which AI is offering) it is kind of icing on the cake.
Firms like NOW are adjusting to the new narrative, by offering pay-per-usage, as an alternative to pay-per-user (“seats”). But this does not seem to be hurting their revenues. These firms claim that they can harness the power of AI (either generic AI or their own software) to do pretty much everything that AI claims for itself. Earnings of these firms do not seem to be slowing down.
With the recent stock price crash, the P/E for NOW is around 24, with a projected earnings growth rate of around 25% per year. Compared to, say, Walmart with a P/E of 45 and a projected growth rate of around 10%, NOW looks pretty cheap to me at the moment.
(Disclosure: I just bought some NOW. Time will tell if that was wise.)
Usual disclaimer: Nothing here should be considered advice to buy or sell any security.
Snowmageddon!! Over 20 inches of snow!!! That is what we in the mid-Atlantic should expect on Sat-Sun Jan 24-25 according to most weather apps, as of 9-10 days ahead of time. Of course, that kept us all busy checking those apps for the next week. As of Wednesday, I was still seeing numbers in the high teens in most cases, using Washington, D.C. as a representative location. But my Brave browser AI search proved its intelligence on Wednesday by telling me, with a big yellow triangle warning sign:
Note: Apps and social media often display extreme snow totals (e.g., 23 inches) that are not yet supported by consensus models. Experts recommend preparing for 6–12 inches as a realistic baseline, with the potential for more.
“Huh,” thought I. Well, duh, the more scared they make us, the more eyeballs they get and the more ad revenue they generate. Follow the money…
Unfortunately, I did not log exactly who said what when last week. My recollection is that weather.com was still predicting high teens snowfall as of Thursday, and the Apple weather app was still saying that as of Friday. The final total for D.C. was about 7.5 inches for winter storm Fern. In fairness, some very nearby areas got 9-10 inches, and it ended up being dense sleet rather than light fluffy snow. But there was still a pretty big mismatch.
Among the best forecasters I found was AccuWeather. They showed a short table of probabilities that centered on (as I recall) 6-10”, with some chances for higher and for lower, that let you decide whether to prepare for a low probability/high impact scenario. It seems that the Apple weather app is notoriously bad: instead of integrating several different forecast models like some other apps (and like your local talking head meteorologist), it apparently just spits out the results of one model:
The core issue is that many weather apps, including Apple Weather, display raw data from individual forecast models without the context and analysis that professional meteorologists provide. While meteorologists at the National Weather Service balance multiple computer models, dozens of simulations and their own expertise to create forecasts, apps often pull from a single source and deliver it directly to users.
“Everything that catches attention is mostly nonsense,” said Eric Fisher, chief meteorologist for WBZ-TV in Boston. He points to the viral snowfall maps that spread on social media, noting that extreme forecasts generate the most attention even when they may not be the most accurate.
Anyway, I tried to poke around and find out in dollar terms how much it benefits the weather apps to exaggerate storm dangers. I was unsuccessful there, but by playing with query wording, I was able to coax out of ChatGPT some numbers on how much these exaggerations may cost the rest of us. (A more qualitative search noted, among other things, that the “crying wolf” cost of people becoming jaded to alarmist forecasts may lead them to discount genuine warnings; and I will add it is inconvenient to find the stores out of things because of panic buying).
Everything below the line of asterisks is just a straight dump from the AI, since I could not readily improve on its presentation:
The economic impact of weather apps exaggerating storm dangers isn’t easy to quantify precisely because most formal studies focus on weather warnings overall (from official sources like the National Weather Service), not specifically on private weather apps exaggerating risk. However, economics research on false alarms, over-warning, and exaggerated alerts offers concrete dollar figures that we can use to approximate the kinds of costs involved. Below are the key economic impacts with as many dollar figures as available:
💸 1. Opportunity Costs of False or Overbroad Storm Warnings
Time cost of false warnings:
Traditional county-wide tornado warnings (over-broad alerts) have historically forced many people to shelter unnecessarily, resulting in ≈234 million person-hours per year spent under warnings (1996–2004) — valued at roughly $2.7 billion annually in lost time/productivity in the U.S. alone.
Improved storm-based warnings that reduce false warning area could save up to $750 million–$1.9 billion/year in avoided lost time.
This demonstrates that exaggerated alerts — such as those sometimes amplified or presented without clear context by weather apps — can force unnecessary economic shutdowns of work and business activity.
🏙️ 2. Evacuation and Preparedness Costs from False Alarms
Evacuation costs:
A NOAA analysis estimates that evacuation costs for hurricanes, when done broadly for storm-warnings, can reach about $1.4 billion annually in the U.S., with roughly $1 billion being unnecessary cost due to false alarms (people evacuating where no damaging winds ultimately occur).
Evacuations are one of the most expensive forms of response — from lost wages to lodging and fuel costs — and exaggerated risk messaging can trigger them prematurely or more frequently.
🏭 3. Business Productivity Losses from False Alerts
Example from private sector:
A building management firm reported that by reducing false storm alerts (better targeted data rather than broad app warnings), it saved ~$10 million in lost production time in one year alone.
This is a real dollar example showing how over-warning impacts businesses directly when they preemptively shut down operations.
💼 4. Broader Firm-Level Economic Avoidance
Cost avoidance from better warning info:
A study of nearly 500 firms found that switching from deterministic (binary “storm/no storm”) warnings to probabilistic information could produce $2.3 billion to $7.6 billion per year in avoided costs for businesses, thanks to better decision-making.
While this doesn’t measure exaggeration specifically, it underscores the economic penalty of imprecise or alarmist warnings, which many weather apps unintentionally echo.
📉 5. Insurance & Property Impacts
Although not directly about exaggerated warnings, inaccurate perceptions of storm danger influence behavior and pricing:
Insurance premiums rising: In storm-prone states (e.g., Florida), homeowners’ premiums have risen by ~57% or more because of risk perceptions and loss patterns tied to severe weather.
Losses from storms: Billion-dollar disasters are common; for example, Hurricane Milton in 2024 had estimated total costs of $21–$85 billion (depending on source).
While not directly caused by exaggeration, inflated perceptions of danger (from apps or media) can affect insurance markets and property values, contributing indirectly to economic impacts.
📈 6. Net Benefits vs. Exaggeration
It’s important to balance:
Early warnings overall clearly save money and lives. Improved forecasts and early alert systems have been estimated to avoid$300 million to $2 billion per year in asset losses and provide up to $32 billion/year in broader economic benefits (through optimized activities and prep).
This highlights that the problem isn’t warnings per se, but inaccurate or exaggerated warnings that cause over-response.
Even before Elon Musk gutted X’s content moderation, James Bailey was tired of the shouting. “It’s like a cursed artifact that gives you great power to keep up with what’s going on, but at the cost of subtly corrupting your soul,” said the 38-year-old Providence College economics professor.
He retreated. This year, he realized he was spending five to 10 minutes a day on a site he used to ignore.
The WSJ reporter contacted me after seeing my previous post about LinkedIn here, explaining how I think LinkedIn has improved as a way to share and read articles, and was always good as a way to keep up with former students. Just in the short time since the WSJ article came out, I finally used LinkedIn for one of its official purposes, hiring, where it worked wonders helping to fill a last-minute vacancy.
If you don’t trust me or the WSJ to identify the hot social network, lets see what the actual cool kids are up to
This will be a longer-than-usual post, since I will try to include all the steps I used to grow salad ingredients in a compact (AeroGarden-type) hydroponics system. I hope this encourages readers to try this for themselves. See my previous post for an introduction to the hardware, including small modifications I made to it. I used a less-expensive ($45), reliable 18-hole MUGFA model here, but all the AeroGardens and its many knockoffs should work similarly. Most plant roots need access to oxygen as well as to water; these hydroponic units allow the upper few inches of the root to sit in a (moist) “grow sponge” up out of the water to help with aerobic metabolism.
Step 1. Unbox the hydroponics unit, set up per instructions near a power outlet. Fill tank close to upper volume marking.
Step 2. Add nutrients to the water in the tank: usually there are two small plastic bottles, one with nutrient mix “A” and the other with nutrient mix “B”, initially as dry granules. Add water to the fill lines of each of these bottles with the granules, shake till dissolved. (You can’t mix the A and B solutions directly together without dilution, because some components would precipitate out as solids. So, you must add first one solution, then the other, to the large amount of water in the tank.)
There is more than one way to do this. I pulled the deck off the tank, used a large measuring cup to get water from my sink into the tank, a little below the full line. For say 5 liters of water, I add about 25 ml of nutrient Solution A, stir well, then add 25 ml of Solution B and stir. You could also keep the deck on, have the circulation pump running, and slowly pour the nutrient solutions in through the fill hole (frontmost center hole in the deck). You don’t have to be precise on amounts.
Step 3. Put the plastic baskets (sponge supports) in their holes in the deck, and put the conical porous planting sponges/plugs in the baskets. Let the sponges soak up water and swell. (This pre-wetting may not be necessary; it just worked for me).
Step 4. Plant the seeds: Each sponge has a narrow hole in its top. You need to get your seed down to the bottom of the hole. I pulled one moist sponge out at a time and propped it upright in a little holder on a table where I could work on it. I used the end of plastic bread tie to pick up seeds from a little plate and poke them down to the bottom of the hole. You have to make a judgment call how many seeds to plant in each hole. Lettuce seeds are large and pretty reliable, so I used two lettuce seeds for each lettuce sponge. Same for arugula (turns out that it was better to NOT pre-soak the arugula seeds, contrary to popular wisdom). If both seeds sprout, it’s OK to have two lettuce plants per hole, though you may not get much more production than from one plant per hole. For parsley*, where I wanted 2-3 plants per hole, I used three seeds each. For the tiny thyme seeds, I used about 5 seeds, figuring I could thin if they all came up. For cilantro, I used two pre-soaked seeds. I really wanted chives, but they are hard to sprout in these hydroponics units. I used five chive seeds each in two holes, but they never really sprouted, so I ended up planting something else in their holes.
I chose all fairly low-growing plants, no basil or tomatoes. Larger plants such as micro-dwarf tomatoes can be grown in these hydroponics units; also basil, though need to aggressively keep cutting it back. It may be best to choose all low or all high plants for a given grow campaign. See this Reddit thread for more discussion of growing things in a MUGFA unit.
Once all the plugs are back in their holders, you stick a light-blocking sticker on top of each basket. Each sticker has a hole in the middle where the plants can grow up through, but they block most of the light from hitting the grow sponge, to prevent algae growth. Then pop a clear plastic seeding cover dome on top of each hole, and you are done. The cover domes keep the seeds extra moist for sprouting; remove the domes after sprouting. Make sure the circulation pump is running and the grow lights are on (typically cycling on 16 hours/off 8 hours). This seems like a lot of work describing it here, but it goes fast once you have the rhythm. Once this setup stage is done, you can just sit back and let everything unfold, no muss, no fuss. Here is the seeded, covered state of affairs:
Picture: Seeds placed in grow sponges on Jan 14. Note green light-blocking stickers, and clear cover domes to keep seeds moist for germination. The overhead sunlamp has a lot of blue and red LEDs (which the plants use for photosynthesis), which gives all these photos a purple cast.
Jan 28 (Two weeks after planting): seedlings. Note some unused holes are covered, to keep light out of the nutrient solution in the tank. The center hole in front is used for refilling the tank.
Feb 6. Showing roots of an arugula plant, 23 days after planting.
Step 5. Maintenance during 2-4 month grow cycle. Monitor water level via viewing port in front. Top up as needed. Add nutrients as you add water (approx. 5 ml of Solution A and 5 ml Solution B, per liter of added water). The water will not go down very fast during the first month, but once plants get established, water will likely be needed every 5-10 days. If you keep trimming outside leaves every several days, you can get away with having densely planted greens, whereas if you only harvest say every two weeks, the plants get so big they would crowd each other if you plant in every hole on the deck.
Optional: Supposedly it helps to keep the acidity (pH) of the nutrient solution in the range of 5.5-6.5. I think most users don’t bother checking this, since the nutrient solutions are buffered to try to keep pH in balance. Being a retired chemical engineer, I got this General Hydroponics kit for measuring and adjusting pH. On several occasions, the pH in the tank was about 6.5. That was probably perfectly fine, but I went ahead and added about 1/8 teaspoon of the pH lowering solution, to bring it down to about 6.0. I also got a meter for measuring Electrical Conductivity/Total Dissolved Solids to monitor that parameter, but it was not necessary.
Feb 16: After a month, some greens are ready to snip the outer leaves. Lettuces (buttercrunch, red oak, romaine) on the right, herbs on the left.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Feb 17: Harvesting a small salad or sandwich filler every 2-3 days now.
March 6: Full sized, regular small harvests. All the lettuces worked great, buttercrunch is especially soft and sweet. Arugula (from the mustard plant family) gave a spicy edge. Italian parsley and thyme added flavor. The cilantro was slower growing, and only gave a few sprigs total.
Closeup March 16 (three months), just before closing out the grow cycle. Arugula foreground, lettuce top and right, thyme on left, Italian parsley upper left corner.
Step 6. Close out grow cycle. At some point, typically 2-4 months, it is time to bring a grow cycle to a close. I suppose with something like dwarf tomatoes, you could keep going longer, though you might need to pull the deck up and trim the roots periodically. In my case, after three months, the arugula and cilantro were starting to bolt, though the lettuce, thyme, and parsley were still going strong. As of mid-March, my focus turned to outside planting, so I harvested all the remaining crops on the MUGFA, turned off the power, and gently pulled the deck off the tank. The whole space under the deck was a tangled mass of roots. I used kitchen shears to cut roots loose, enough to pull all the grow sponges and baskets out. The sponges got discarded, and the baskets saved for next time. I peeled off and saved the round green light-blocking stickers for re-use. I cleared all the rootlets from the filter sponge on the pump inlet. Then I washed out the tank per instructions. It took maybe 45 minutes for all this clean-out, to leave the unit ready for a next round of growing.
Stay tuned for a future blog post on growing watercress, which went really well this past fall. Looking to the future: In Jan 2026 I plan to do a replant of this 18-hole (blocked down to 14-holes) MUGFA device, sowing less lettuce (since we buy that anyway) but more arugula/Italian parsley/thyme for nutritious flavorings. For replacement nutrients and grow sponges, I got a Haligo hydroponics kit like this (about $12).
Growing these salad/sandwich ingredients in the kitchen under a built-in sunlamp provided good cheer and a bit of healthy food during the dark winter months. The clean hydroponic setup removed concerns about insect pests or under/overwatering. It was a hobby; at this toy scale it did not “save money”, though from these learnings I could probably rig a larger homemade hydroponics setup which might reduce grocery costs. This exercise led to fun conversations with visitors and children, and was a reminder that nearly everything we eat comes from water, nutrients, and light, directly or indirectly.
*Pro tips on germinating parsley seeds – – Parsley seeds have a tough coating, and can take weeks to germinate. Some techniques to speed things up:
( 1 ) Lightly abrade the seeds by gently rubbing between sheets of sandpaper.
( 2 ) Soak in warmish water for 24-48 hours.
( 3 ) For older seeds, cold stratification (1–2 weeks in a damp paper towel in the fridge) may help break dormancy.
This quote from the introduction explains the title:
… in the early 2000s… the Motion Picture Association of America (MPAA) released an anti-piracy trailer shown before films that argued: (1) “You wouldn’t steal a car,” (2) pirating movies constitutes “stealing,” and (3) piracy is a crime. The very need for this campaign, and the ridicule it attracted, signals persistent disagreement over whether digital copying constitutes a moral violation.
The main idea:
In contemporary economies, “idea goods” comprise a substantial share of value. Our paper examines how norms evolve when individuals evaluate harm after the taking of nonrivalous resources such as digital files.
We report experimental evidence on moral evaluations of unauthorized appropriation, contingent on whether the good is rivalrous or nonrivalrous. In a virtual environment, participants produce and exchange two types of resources: nonrivalrous “discs,” replicable at zero marginal cost, and rivalrous “seeds,” which entail positive marginal cost and cannot be simultaneously possessed or consumed by multiple individuals. Certain treatments allow unauthorized taking, which permits observation of whether participants classify such actions as moral transgressions.
Participants consistently label the taking of rivalrous goods as “stealing,” whereas they do not apply the same term to the taking of nonrivalrous goods.
To test the moral intuition for taking ideas, we create an environment where people can take from each other and we study their freeform chat. The people in the game each control a round avatar in a virtual environment, as you can see in this screenshot below.
In the experiment, “seeds” represent a rivalrous resource, meaning they operate like most physical goods. If the playerin the picture (Almond) takes a seed from the Blue player, then Blue will be deprived of the seed, functionally the equivalent of one’s car being stolen.
Thus, it is natural for the players to call the taking of seeds “stealing,” Our research question is whether similar claims will emerge after the taking of non-rivalrous goods that we call “discs.”
The following quote from our paper indicates that the subjects do not label or conceptualize the taking of digital goods (discs) as “stealing.”
Participants discuss discs often enough to reveal how they conceptualize the resource. In many instances, they articulate the positive-sum logic of zero-marginal-cost copying. For example, … farmer Almond reasons, “ok so disks cant be stolen so everyone take copies,” explicitly rejecting the application of “stolen” to discs.
Participants never instruct one another to stop taking disc copies, yet they frequently urge others to stop taking seeds. The objection targets the taking away of rivalrous goods, not the act of copying per se. As farmer Almond explains in noSeedPR2, “cuz if u give a disc u still keep it,” emphasizing that artists can replicate discs at zero marginal cost.
We encourage you to read the manuscript if you are interested in the details of how we set up the environment. The conclusion is that it is not intuitive for people to view piracy as a crime.
This has implications for how the modern information economy will be structured. Consider “the subscription economy.” Increasingly, consumers pay recurring fees for ongoing access to products/services (like Netflix, Adobe software) instead of one-time purchases. Gen Z has been complaining on TikTok that they feel trapped with so many recurring payments and lack a sense of ownership.
In a recent interview on a talk show called The Stream, I speculated that part of the reason companies are moving to the subscription model is that they do not trust consumers with “ownership” of digital goods. People will share copies of songs and software, if given the opportunity, to the point where creators cannot monetize their work by selling the full rights to digital goods anymore.
A feature of our experimental design is that creators of discs get credit as the author of their creation even when it is being passed around without their explicit permission. Future work could explore what would happen if that were altered.
Economist Writing Every Day 