The reason I’m all-in on AI investment, and there may not be a top to this “bubble”: Unlike tulips, railroads, or internet connectivity, there is no upper bound on the value of intelligence.
Me:
30-year TIPS yield is 3% real currently. So your perspective isn’t shared by the market. Investors wouldn’t lend money to the federal government at 3% (after inflation) if they thought there was no limit to NVIDIA’s value. If investors overall thought that there was a low-risk way of making super high real returns over the next 30 years they the government would have to pay 8% real, for example, if buying an AI index was expected to yield 12% real for the next 30 years.
I think AGI is already here from the perspective of most users of ChatGPT and similar. A generally intelligent human isn’t great at everything and makes mistakes… just like ChatGPT! People ask ChatGPT all kinds of questions and give the answers at least the same weight that they would give to an answer from a typical human. In that sense, ChatGPT has passed a Turing Test for general intelligence. Maybe Advanced Superintelligence is already here. The typical human is not good at mathematics. A math professor could have been described as “super intelligent” before WWII. LLMs are supposedly doing all kinds of advanced work in mathematics right now, even if they might fail at a plumbing task. The math professor in 1935 who was a failure at plumbing would still have been considered superintelligent, right?
In other words, we can’t expect hockey stick growth for the economy due to AGI/ASI arriving because the current growth is already an example of what an economy does with the gift of AGI and ASI (but maybe not robotics!).
Will the $40 trillion in federal debt be a drag on economic growth? Scott Bessent says “no”:
Let’s use a 20-year time horizon for the U.S. to potentially get out of Argentina territory (150% debt-to-GDP max; we’re at over 125% right now). For US debt to GDP to fall to the level of a high IQ society (e.g., Taiwan, which has lower-than-US tax rates and debt of about 20% of GDP), GDP growth would need to be 10%/year real for 20 years with Congress not borrowing any more money (the latter condition seems unlikely to be met, since Congress now borrows even in the most robust economies, contrary to Keynes). Investors plaintly don’t believe that this will happen because they’re willing to lend to the Feds at 2.75% real (20-year TIPS current price) and they wouldn’t do that if investing money in domestic stocks would generate a roughly 12% real annual return (real GDP growth plus 2% as a return on investment from corporate earnings).
Does this mean that we’re in an AI bubble? Not necessarily. Only that AI by itself apparently doesn’t hugely lift the overall U.S. economy (a huge part of which is government spending/welfare state!). We’ve got about 1.5% per capita real GDP growth right now. Maybe that includes the AI lift? This NBER paper by a Nobelist (sort of) predicts minimal per capita growth, but cites estimates as high as 3.4 percent per year as the boost (nowhere close to the 10% we’d need to get out debt down to Taiwan’s relative level):
The only way to make $40 trillion in debt insignificant, therefore, would be to grow the U.S. population to about 1 billion humans at roughly the same level of skill/income as the current U.S. population. Until Donald Trump showed up (again!), our wise politicians were working on this, but they forgot to apply and skills test for immigrants.
(In case this blog post is going to be a source for an NPR or PBS story (example), let’s not forget that both Turing and Bessent were/are members of the 2SLGBTQQIA+ community.)
I hope that everyone has made tons of money taking my investing advice and doing the opposite. Loyal readers may remember that I’ve been skeptical of Nvidia becoming worth more than all of the European Union combined and, therefore, never purchased NVDA (current market cap: $5+ trillion). My theory was that 50 percent profit margins for Nvidia would motivate other companies to develop chips and then the AI nerds would recompile their code (or ask AI to recompile their code). Google did this a long time ago with its Tensor Processing Unit. Intel tried and apparently failed with Gaudi (but they were very successful in DEI!). Elon Musk has his AI5 chip and, eventually, his own fab.
The company plans to start using the new chips to support its artificial intelligence models later this year, which should significantly reduce costs for OpenAI as it rolls out more widely.
OpenAI, losing enough money every year to be worth almost $1 trillion, created Jalapeno in a partnership with Broadcom Inc., which makes custom chips for a variety of clients. The two companies, which announced their pact last year, touted the speed at which the processor was developed in June, saying it came together in record time.
Even if this thing succeeds, there will still be a huge market for Nvidia, apparently:
Jalapeno wasn’t tested against the new generation of Nvidia chips, Vera Rubin, which just began shipping. It’s also not designed for training AI models, an area where Nvidia technology excels. Jalapeno is intended for the inference phase of AI — the stage when models have already been trained and can begin responding to prompts and handling tasks.
(Why is the chip named for Vera Rubin, someone who was neither an electrical engineer nor a software developer? Google AI:
Honoring Women in STEM: The company has a specific practice of naming key platforms after pioneering women scientists. Previous examples include Grace Hopper (the Grace CPU) and Ada Lovelace (the RTX 40-series architecture)…
I guess we can’t expect the William Shockley CPU/architecture any time soon, despite the man’s Nobel for the invention of the transistor, but why not one for Jack Kilby? Kilby died in 2005, prior to the full flowering of Rainbow Science, and therefore Kilby’s gender ID is unknown (we could say the same regarding Vera Rubin, who died in 2016, some years before the trans-enhanced Rainbow Flag flew at the White House))
How much do journalists and editors need to know about electrical engineering in order to cover cutting-edge AI hardware?
Because the chip is achieving strong results at low voltage — 700 watts — Jalapeno will allow OpenAI to save money when running its data centers, where power is a key cost, he said.
Speaking of high voltage, let’s have a look at the cooling system for Google’s latest TPUs (blog entry, which Google’s massive AI brain couldn’t figure out should have a date on it; I think it is from April 2026):
It looks like they got retired Pratt PT6 engineers to work on this!
(Actually, this suggests a career path: instead of fighting it out on the crowded software front, a young person could study to become a packaging/cooling engineer.)
Vaguely related… let’s see if we can figure out where Nvidia’s passion for “Honoring Women in STEM” comes from. Perhaps nerds identifying as “women” were the principal designers of Nvidia’s hardware and software?
Prompt to Gemini (to run on Google’s TPUs so that the results aren’t biased by running on Nvidia chips):
Who were the principal engineers of Nvidia’s historically important GPUs and software architectures, including CUDA? In other words, who are the people who are primarily responsible for making Nvidia as valuable as it is today?
The Role: Co-founded Nvidia in 1993 alongside Jensen Huang.
Their Contribution: Priem was the chief architect of Nvidia’s early graphics chips (including the NV1 and the breakthrough RIVA 128), while Malachowsky led system engineering and hardware design operations.
Impact: They established Nvidia’s core hardware engineering practices and designed the early pipelines that allowed Nvidia to survive the “3D accelerator wars” of the late 1990s and create the original GeForce 256—the chip Nvidia marketed as the world’s first true “GPU.”
2. Dr. David Kirk (The “Father of CUDA” & Chief Scientist 1997–2009)
The Role: Chief Scientist and VP of Architecture.
Their Contribution: David Kirk was the primary visionary behind transforming Nvidia’s fixed-function 3D graphics hardware into programmable, general-purpose parallel processors.
Impact: In the mid-2000s, Kirk persuaded Jensen Huang to mandate that every single Nvidia GPU include dedicated general-purpose execution units—a massive financial gamble at the time. This decision laid the groundwork for CUDA (Compute Unified Device Architecture), launching general-purpose GPU computing (GPGPU) and making GPUs usable for scientific computing and later AI.
3. Ian Buck (VP of Accelerated Computing & Original CUDA Architect)
The Role: Creator of the BrookGPU programming language (at Stanford) who joined Nvidia to lead the CUDA project.
Their Contribution: While pursuing his PhD at Stanford, Buck created Brook, an extension of C for streaming coprocessors. Jensen Huang hired Buck to lead the software and systems team that built the original CUDA software stack and runtime environment, released in 2006.
Impact: Buck turned the raw, complex parallel hardware engineered by Kirk’s team into an accessible software platform for C/C++ developers. Without Buck’s software stack, AI researchers in 2012 (like Alex Krizhevsky and Geoffrey Hinton) would not have been able to train AlexNet on Nvidia GPUs—the spark that triggered the modern deep learning boom.
4. John Montrym (Chief Architect, GeForce/Tesla Architecture)
The Role: Senior Architect and Fellow.
Their Contribution: Montrym was a key architect behind the NV40 (GeForce 6 series) and the landmark G80 (Tesla architecture) released in 2006.
Impact: The G80 was the first unified shader architecture in a PC GPU and the hardware home where CUDA was born. It abandoned separate vertex and pixel pipelines in favor of unified streaming multiprocessors (SMs), setting the blueprint for every modern GPU architecture designed since.
5. Jonah Alben (Senior VP of GPU Engineering)
The Role: Longtime leader of Nvidia’s hardware silicon development.
Their Contribution: Alben joined Nvidia in 1997 and has spent over two decades directing the physical architecture, design execution, and engineering teams behind generations of GPU microarchitectures: Fermi, Kepler, Maxwell, Pascal, Volta, Ampere, Hopper, and Blackwell.
Impact: Alben’s team managed the scaling and execution challenges as GPUs grew from millions to over 200 billion transistors on single silicon packages. His team integrated specialized hardware engines directly into the GPU pipeline—most notably Tensor Cores (introduced in Volta/Ampere) and Transformer Engines (in Hopper/Blackwell)—which gave Nvidia a 10x-100x efficiency advantage in AI workloads over traditional CPUs and competing chips.
6. Dr. Bill Dally (Chief Scientist & Head of Nvidia Research)
The Role: Former Chairman of Stanford’s CS Department, appointed Nvidia Chief Scientist in 2009 (succeeding David Kirk).
Their Contribution: A pioneer in computer architecture and high-speed interconnect networks, Dally leads Nvidia’s long-term research division.
Impact: Dally pivoted Nvidia’s research toward optical interconnects, low-precision AI math (FP8, FP4 formats), ray tracing hardware cores (RT Cores), and multi-chip network scaling (NVLink). His architectural research predicted the massive bandwidth bottlenecks of large AI models years before ChatGPT emerged.
Fun fact from Oshkosh/AirVenture 2026: I ran into a mid-level Nvidia employee. He had shown up in his new airplane, a perfectly restored P-51 Mustang ($5 million?).
Our clay tile roof was installed a year ago and came with a 7-year warranty with the following condition: “All warranties require free annual maintenance inspections, in order to maintain validity throughout the duration.” During this year’s inspection, done by drone because the roof was slightly wet and clay is extremely slippery/hazardous when wet, a cracked tile was discovered.
The roofer has to fix this at no charge? Email from the roofer: “Clay tiles will not crack like concrete tiles since they aren’t absorbing water and would only crack from impact of some kind.”
There haven’t been any real wind storms here in the past year (the last “major hurricane” in Palm Beach County was in 1949). The cracked tile isn’t near a coconut palm that could have dropped a heavy nut onto it. I uploaded the photo to my warranty analyst (ChatGPT):
The crack looks more like a structural/installation crack than a classic impact break. It is a fairly clean, narrow crack running longitudinally through the tile, without an obvious crater, chip, crushed spot, or radiating fracture pattern where something struck it. Impact from something substantial—fallen branch, tool, golf ball, worker stepping hard on an unsupported portion—often leaves some evidence at the point of contact.
On a one-year-old roof, plausible non-impact causes include a hairline crack present when installed that subsequently opened up; a tile stressed by its nail/screw/clip or adhesive; inadequate support beneath part of the tile; somebody walking on it during installation or later work; or occasionally a manufacturing defect.
(I’m pretty sure that we’ll still have to pay the roofer to fix the problem that his crew created, or maybe that the tile manufacturer (Verea in Spain (maybe they’re too busy freeing “Palestine” to go the last kilometer in quality control?)) created, but at least now we know that we’re being cheated!)
Also at the intersection of AI and Florida homeownership, I asked ChatGPT about what stain to use for the front door, beginning to look a bit shabby. Absent AI, I would have tried to buy Sikkens, making stain in the Netherlands since pre-Islamic times (1792). ChatGPT had a different perspective:
I bounced the question to Grok and Claude and learned… that Sikkens in the U.S. is marketed as “PPG”. The AI was still useful, though, potentially, because it calculated how many quarts are needed and that there is a specific stain for doors as opposed to siding.
The smartest people in the Islamic Republic of the UK hired Jason A. K. Arday to hold a professorship at Cambridge University in 2023, which is a huge appointment (what we call “assistant professors” they call “mere lecturers”). He’s been in the news recently for Claudine Gay-style plagiarism combined with an unusual life story. From “Icarus in the Faculty Lounge” (The Atlantic):
According to a cover letter that his agent sent to publishers at the time [2023], Arday’s book, to be titled Great and Unfortunate Things, would be “an awe-inspiring tale of near mythic proportions.”
That now appears to be an understatement. Arday’s story was indeed astonishing: As a toddler, he was diagnosed with autism and developmental delays; he didn’t fully speak until the age of 11; he learned to read and write only as an adult. Arday’s parents are Ghanaian immigrants [enrichers!]
Arday’s grad-school alma mater, Liverpool John Moores University, which first received direct complaints about the dissertation last fall, did its own investigation and cleared Arday of wrongdoing. “A confidential procedure was conducted,” the university told me in a statement. “Professor Arday’s PhD still stands.” But some of Arday’s other academic papers had potential problems too—and they seemed much worse than the mere borrowing of language. Quotes from structured interviews that Arday claimed to have performed closely resembled those used by other researchers; parts of some interview responses also reappeared across different studies under Arday’s name, attributed to different people. This duplication, if deliberate, would be something other than plagiarism—it would be academic fraud.
Might this be Donald Trump’s fault?
Some of the people who were going after Arday had a political agenda. Cofnas, the philosopher who put Arday’s dissertation through a plagiarism checker last month, argues for a “hereditarian revolution” based on the idea that innate differences in intelligence and other traits exist across racial groups. (He also claims that “the humanities and much of the social sciences have been slain on the altar of DEI.”)
Everyone loves an abuse survivor:
The doctors who assessed him as a toddler told his mother that “there’s no one in there” and that he was “no better than a vegetable.” Even his speech therapist wasn’t optimistic that he’d ever learn to talk. (At one point, she also physically abused him, the memoir says.) As a boy, Arday rocked and hummed and banged his head until he bled; he slammed cupboard doors and smeared his feces on the walls. Yet still, eventually, he started to verbalize.
Arday is nearly deaf in one ear; he’s dyslexic; he finds simple math befuddling; he has “marked difficulties” with memory, reading, and processing skills. As a teen, he was beaten so severely by a group of kids his age that he became an epileptic. To make his way through secondary school while—by his own account—illiterate, he needed ample help: support assistants who sat beside him in class and took notes on his behalf.
(What good are “notes” if a person is “illiterate” (unable to read)?)
By the time Arday started on his master’s degree, he writes, he was reading at about the same level as his 3-year-old daughter.
Money was tight, so after each day of teaching, Arday writes, he had to work an overnight shift in a supermarket, from 11 p.m. to 5 a.m., and then a morning shift as a cleaner from 5:30 to 7:30. The only time he had left to write his dissertation was when he was commuting between London, where he lived, and Liverpool, where he taught. On those semiweekly five-hour rides, the book explains, he would open up his laptop on the plastic seat-back table and start typing out his thesis, hunt-and-peck style.
As his dissertation deadline approached, Arday started having blurry vision; medical scans revealed a brain tumor. After it was removed, he had a ministroke and lost his short-term memory. “I’d worked on my thesis for over thirty months, and now I was reading it as if for the first time,” he writes. As one might expect, his thesis defense did not go smoothly. One examiner was suspicious of his work: “I am struggling to see that there is any original contribution here,” she said, according to the book. But Arday, who often likens himself to Rocky Balboa, managed to fight back, and in the end, he passed. (The memoir brings up the fictional boxer in about a dozen different contexts.)
(Note that Rocky is now a Florida resident (Journal of Popular Studies) and stopped paying taxes to Gavin Newsom in 2024)
More troubles follow in the years ahead: Arday’s brain tumor returns; he splits up with his wife; he suffers autistic burnout during the coronavirus pandemic and can’t get out of bed. He decides to kill himself by jumping off a bridge, but at the very last minute, he listens to his favorite song, “Take It to the Limit”—delaying his attempt just long enough for a member of his family to rescue him. “In the end,” he writes, “my decision to listen to the Eagles likely saved my life.”
I reached out to the book’s fact-checker [at Simon & Schuster] to ask whether this and several other stories from the book had been verified, but she did not respond. … the book’s release is still on track and that it has even been designated as an “Editors’ Pick” on Amazon
This wasn’t part of the book proposal, but it seems that Prof. Dr. Arday, Ph.D. was also an RAF Red Arrows pilot:
The Professor’s PhD thesis is available online. I downloaded it and asked ChatGPT to “evaluate the quality of research and writing”. Some excerpts from the full interaction:
The biggest problem is the sample and selection mechanism. There are only four intervention participants, selected from 46, and selection expressly included whether the candidates demonstrated characteristics that the researcher thought “resonated with the study’s aims and objectives” and a desire/capacity to improve their teaching. … More seriously, the researcher was a lecturer at the university attended by the subjects and already had relationships with them.
There is also no convincing counterfactual. There is:
no control/comparison group, no random assignment, only four highly selected subjects, an intervention that the researcher helps facilitate, and outcomes that are overwhelmingly based upon the subjects’ own narratives and perceptions.
The thesis itself eventually admits a crucial point: it cannot determine with certainty whether improved reflective practice would have occurred naturally rather than because of the peer-mentoring intervention.
Contribution to knowledge: This is probably the weakest part of the intellectual case for a PhD.
Writing quality: This is much easier to judge: the prose is substantially below the standard I would expect in the final, examined copy of a PhD thesis from a British university. The problems aren’t occasional typos. They’re pervasive. Even the title contains an erroneous apostrophe… Throughout the thesis there is a characteristic tendency to take a simple proposition and turn it into an elaborate nominalized construction. … Most problematic is the repeated use of inflated pseudo-technical expressions that obscure ordinary ideas.
The prose also affects the scholarship: This is more important than cosmetic proofreading. The convoluted language sometimes makes it difficult to determine exactly what proposition is being asserted and how strongly.
Should Arday have been given the big job at Cambridge?
Cambridge’s published criteria for its highest professorial level say that a Professor at Grade 12 should demonstrate: “outstanding achievement in research and research leadership assessed by reference to international levels of excellence”… Against that benchmark, Arday’s 2023 record looks unusually thin.
Cambridge’s February 2023 announcement … emphasizes his work on race … and, very explicitly, the importance of his appointment to Cambridge’s efforts to increase representation of people from disadvantaged and ethnic-minority backgrounds.
Asked to place Arday in a percentile of people in the same field, ChatGPT grudgingly estimates “somewhere around the 30th–50th percentile overall” (i.e., quite a few people with PhDs in education/sociology are far dumber than Dr. Arday!).
Here’s the Amazon page showing that this is, according to their experts in Seattle, among the world’s “Best Biographies & Memoirs”:
Throw out your Life of Johnson. It’s not among the “Best Biographies”.
Related:
“Jason Arday taught me. Here’s what I learned” (Fiona Brown in Unherd): “Before Arday arrived at Roehampton, university leaders enthusiastically told us of his amazing backstory: how he only learned to speak at 11 and write at 18, and how excited they were to have him teaching us. … I can honestly describe Arday’s teaching as terrible. He rarely used the PowerPoint slides. Instead, he usually sat casually on a desk, giving his generalized views on racism … Roehampton is a very diverse university, both in terms of racial background and neurodiversity, and takes inclusion seriously. More than two-thirds of the class were either black or from other non-white backgrounds. Most were women… In the end, Arday gave me 70% for my essay. That sounds good — except that my average for the year was 75%, meaning my overall result was dragged down. … In our meeting, I explained that 70% was a low mark for me and asked him to provide feedback. Suddenly, his friendly, confident demeanor changed. I now saw a side of him I’d never seen before: angry, but also scared. Arday implied that I was racist, and said that the only reason I wasn’t happy with 70% was due to my “white privilege”.”
Today in Evanston, Illinois: “Say No to AI Data Centers”.
A lot of retailers display this sign, but my favorite is GadgetWorks, whose smartphone and game console customers are in no way dependent on data centers:
The Guardian says that the dream would be to find a data center that is used by ICE and involved in demanding IDs from voters:
Speaking of ID, although everyone in Evanston seems to agree that requiring ID to vote is unreasonable, the city requires ID of those who wish to visit the local (Lake Michigan) beach:
OpenAI is at the frontier. There is no reason to use Terra, as there is a Luna or Sol model at an effort level that matches it for cheaper. You can get quite a lot of inference out of the $20/mo plan. $100/mo gives you 5x, but $200/mo gives you 20x, not 10. Claude Fable is marginally the best, but quite expensive and falls back to Opus on anything it considers remotely questionable, while still charging Fable prices.
Gemini’s latest is 3.5-flash and is well off the curve. The latest non-flash is still 3.1-pro.
GLM 5.2 is the best open weight Chinese model, but its price per token is misleading as it eats up reasoning tokens like crazy.
Best local coding model is probably Qwen 3.6 27B. Gemma 4 is a good local all around model to talk to, but not as good at coding.
Where will these various models run? New York State has banned data centers, which is forward-thinking but I prefer to reflect on the exquisite timing of the New York political elite in shutting down their massive nuclear electricity plant just one year before the AI/data center boom began (launch of ChatGPT in 2022):
Here’s a chart from an X post charting answers to the question “The only acceptable religion is my religion” (perfect for Pride Month!):
Iranians living in the Islamic Republic of Iran are, according to this chart, much more likely to tolerate non-Islamic belief than, for example, the noble Palestinians who reside in Jordan (whose territory is about 80% of British “Palestine” and in which over 97% of residents are Muslim (0% are Jewish, which makes puts it on track to be celebrated as an ideal society from a progressive point of view)).
Should we believe this chart? Is it a reasonable size survey, for one thing? I found the cited source and was able to get the raw numbers. 1200 people were surveyed in Bangladesh and only 25 disagreed, consistent with the published bar chart’s percentage.
Codex crunched away for about 15 minutes, asking for permission quite a few times (I haven’t ever used it for something like this so maybe the next one will go smoothly). Codex (ChatGPT/OpenAI) concludes that the X chart is a fair representation of the data (i.e., it accomplished a fact check). The Codex-produced chart in Excel is hard to read, though, with the country names buried inside bars of color (not to say “colored bars”):
Note that some countries the X author left out are near the top here, e.g., Maldives (“100% Muslim” according to Google) and Libya (nearly 100% Muslim, according to Google, with the exception of some expats (oil industry workers?)).
I asked “Can you redo the chart so that the country names are in a separate column? Or at least left-justified?” This took another 10 minutes with many failed attempts and several requests for approval. The result was at least off by one, with the Bangladesh label applied to the percentage legend:
Another 5 minutes and much straining by NVIDIA chips in a data center somewhere…
So the AI assistant does work, but I think that asking a Chatbot to produce a chart, without reference to a desktop computer and Excel, might have been faster/simpler.
Separately, why did we attack a country that is far more tolerant than our NATO ally Turkey (recipient of about $31 billion in aid, cumulatively) and far more tolerant than Jordan, a country to which we have provided $34 billion in aid?
(Loosely related, maybe our surrender to Iran isn’t quite as great a deal for them as portrayed in the media. If it were, wouldn’t the Iranians have already agreed to our surrender? Instead, there are merely negotiations.)
As part of preparing my old Harvard Square condo to turn over to its next owner, I decided to look at not only the smoke detectors in the unit (2019, 2023, 2023), but also the hardwired smokes in the common stairwell and basement. A disturbing pattern emerged in which the hardwired smoke detectors may have been made in 1990(!). I asked four LLMs to figure out who should pay for the new detectors and the insane installation cost charged by an immigrant Brazilian.
Prompt:
Here are our building’s condo docs. There is a common stairwell serving just three of the four units. The smoke detectors in the common stairwell must be replaced. Is that a condo association expense or something that the three unit owners served by the stairwell msut pay for? (when the building was first divided, there were originally only two units on the stairwell side, but one was further subdivided, so now we have three)
Grok handled this well.
The replacement of the smoke detectors in the interior stairwell (the one serving Units 2A, 2B, and 3) is the expense of the owners of those three units only—not a full condominium association/common expense shared by all four units (including Unit 1). This is explicitly addressed in the recorded amendment to the Master Deed that subdivided the original Unit 2 into Units 2A and 2B (document I0myQ / the one titled “AMENDMENT TO MASTER DEED OF THE 5-7 IRVING TERRACE CONDOMINIUM” that re-recorded a full updated Master Deed, Book 320806, Page 337 et seq.). On page 17 of that amendment, under the additional provisions / Exhibit-type language dealing with exclusive easements and rights of use, it states (in relevant part): “12. The interior staircase serving Units 2A, 2B and 3 shall be for the shared exclusive use of said Units and shall be maintained at their expense so that Unit 1 shall not be responsible to pay for such maintenance.”
Gemini gave a similar focused answer.
Based on the Master Deed and Condominium Trust documents for 5-7 Irving Terrace, the cost of replacing the smoke detectors in that specific stairwell is the responsibility of the three unit owners served by the stairwell (Units 2A, 2B, and 3), rather than a general condo association expense.
Gemini’s answer was inferior to Grok’s because it failed to cite the page number in what is a long doument.
Claude’s answer was longer-winded and more confusing than Grok’s or Gemini’s. Claude made the idiotic suggestion of hiring a human lawyer to deal with the question of how to divide up spending an amount, even after absurdly overpaying a Brazilian immigrant handyman, that was less than what a lawyer would charge to provide an answer.
Based on the condominium documents you’ve shared, this is a common expense paid by the association — but with an important nuance. The 1990 amended Master Deed (the controlling document, which superseded the original) addresses this directly. The interior staircase serving Units 2A, 2B, and 3 is designated for their “shared exclusive use” and is to be maintained at their expense, so that Unit 1 is not responsible. However, the staircase itself and its fixtures are part of the common elements of the building. Here’s how the relevant provisions interact: The stairwell is a common element. Building entrances, entrance halls, stairways, and all related systems are explicitly listed as common elements under Section 5 of the amended Master Deed. Smoke detectors wired into the building’s systems would fall under conduits, wiring, and utility facilities — also common elements. The maintenance cost allocation is split. Under Exhibit C of the 1990 amendment, the interior staircase serving 2A, 2B, and 3 is maintained at the expense of those three units, not Unit 1. Unit 1 is explicitly carved out. Practically speaking, the smoke detectors in that stairwell would most naturally be treated as a shared cost among Units 2A, 2B, and 3 — not a general common expense chargeable to all four units including Unit 1. This is consistent with the “shared exclusive use” framework: the costs follow the benefit. The proportional split among 2A, 2B, and 3 would likely be based on their respective percentages of common interest (14.585%, 14.585%, and 33.33% respectively), though you could also argue for an equal three-way split given the stairwell serves all three equally regardless of unit size. I’d recommend confirming this interpretation with a Massachusetts condo attorney, as I’m not a lawyer and the documents have layers that accumulated over 40 years of amendments.
ChatGPT, Esq. failed spectacularly.
Short answer: almost certainly a condo association (trust) expense, not just the #5 side — unless your documents or wiring show something very unusual.
It provided about two pages of additional text, none of which referenced the relevant section of the Master Deed.
It’s a suprisingly interesting book on an uninteresting subject. The author is an economist so he is expecially weak on the engineering challenges of building ever-larger container ships and cranes. Nonetheless, we do learn about some of the engineering that went into designing the containers themselves, e.g., the corner connectors and figuring out how to support the weight of additional containers piled on top.
Like AI today, container shipping was perceived in its early days (1960s) as potentially saving a huge amount of human labor, especially dockside. Breakbulk shipping required large crews of longshoremen working for days to pack items into cargo holds, thus giving the mariners a relaxed week in port. Because longshoremen were usually unionized and had the power to shut down ports completely, they were able to negotiate the transfer of a massive share of the expected profits from container shipping to their members, either for not working at all or for working part-time. Non-unionized workers in the breakbulk shipping industry were completely out of luck. Future workers were also out of luck. As members of longshoremen unions died, the benefits of the union contract flowed only to those who were still alive and/or working. Crane operators in Los Angeles can make over $300,000 per year, but there aren’t many of them.
Practical advice for young people: Get a union job now and in a union that can shut down something important to the rest of the economy and/or the public. If containerization is any guide, unionized schoolteachers will be able to keep their wages even if Optimus can teach better. It would be ideal if one could think of a union that can shut down all AI data centers, but I am not sure there is one. Maybe the people who handle cooling? Even then, however, the data centers theoretically have the right to hire replacement workers during a strike. (School districts have this right too and it would be trivial to hire some adults to take over teaching/daycare responsibilities, but they don’t do it because, I guess, the union and the people who run the city are part of the same political party.)
Container shipping caused a massive shift in employment. Docks and their associated jobs in Manhattan and Brooklyn disappeared. So did factories that had been close to the docks in order to faciliate shipping to Europe. The replacement was Port Elizabeth in New Jersey, set up in 1963 to handle containers for Malcom McLean‘s Sea-Land. The factories moved to Upstate New York, Pennsylvania, Connecticut because with container shipping they just needed to be able to put a box on a railroad car headed for Port Elizabeth.
The advent of container shipping did not highlight the merits of technocratic government or credentialed experts. Governments, armed with expert advice and forecasts, were investing huge quantities of tax dollars in wharves for breakbulk ships just as the container boom was becoming established. Experts predicted minimal savings and disruption from containerization, perhaps partly due to government regulations that stifled the growth of the industry. Until President Gerald Ford kicked off the deregulation trend in the U.S., rates for shipping via rail and truck were set by a central planning agency (the ICC). International shipping rates over water were similarly regulated by a combination of bilateral agreements, cartels among shipping lines, etc. The rate to ship a load of refrigerators, for example, might not be different whether they were in a container or not. It wasn’t until the 1980s that the full benefits of containerization began to be experienced by shippers and consumers. In other words, government intervention in the market delayed the benefits of the technology by 15-20 years.
As with AI, which has made receptionists at NVIDIA richer than 99% of the people who live in Michigan (Detroit was once the richest city in the US and maybe the world!), the benefits of container shipping haven’t been equally distributed. A privately-owned non-union (at the time) port in Felixstowe took away thousands of jobs from unionized government-owned ports in other parts of the UK. Intelligent and efficient countries got dramatically richer, e.g., Singapore and the Netherlands, while countries that couldn’t get organized were left much farther behind than in the breakbulk days, where everyone was inefficient. It’s almost free to ship cargo among the world’s leading container ports and expensive/slow to ship cargo to places that aren’t regularly visited by big ships. The cycle tends to be a virtuous one. Because Panama has a busy container port that’s also the logical place to put factories that divide up and repackage pharmaceuticals for re-export to other Latin American countries. Being a landlocked country was already bad, but the penalty increased with containerization. (Our family experienced this with roofing tile. We got $30,000 of clay tiles from Spain, including container shipping and a truck ride up from Miami. It was going to cost $16,000 for tiles from Ohio…. just for the shipping.)
Consider Haiti, one of the world’s most violent and dysfunctional societies (which is why the U.S. is eager to import as many people from this society as possible?). It also has a violent and dysfunctional container port. UNICEF:
Armed groups breached the city’s main port a week ago, severing one of the capital’s last remaining lifelines for food and supplies as the country edges closer to collapse. Currently, over 260 humanitarian-owned containers are controlled by armed groups at the port.
Even if the rest of Haiti weren’t violent and dysfunctional, no factory could be set up profitably given the violent and dysfunctional nature of the port.
State-owned Transnet Port Terminals is pouring investment into cranes and new equipment after years of corruption and mismanagement that eroded the quality of its operations. The Container Port Performance Index from 2020 to 2024 took note of the upgrades and measures, including better weather forecasting at two local facilities. … Still, Cape Town was 400th in the survey, with Coega and Durban the penultimate and last of the 403 ports ranked.
The countries with high-ranked container ports are likely to be more advantageous spots for factories, at least the parts of those countries connected by good rail links to the efficient ports. Note that even today it can cost more to ship a container a few hundred miles by rail than thousands of miles by ship.
Also interesting from the above-cited report, what happens when you compare the best that Americans can do, considering all union and cultural factors, to our brothers, sisters, and binary-resisters in Asia?
(Note that some of this inferior performance might be robot vs. human. American unions have had a lot of success in obstructing the installation of automation at our ports.)
So… if the AI revolution turns out to have dramatic economic effects, as predicted, the benefits will be radically unequal. Maybe Californians won’t complain so much about inequality if it turns out that nearly all of the wealth of the U.S. ends up in California as a result of the AI economy? Will they be eager for federal tax policy that plucks wealth from California AI Achievers and pays it out to Left-Behind Mainers and Michiganders?
Can we predict the people and places that the AI boom will enrich the most? I hope that SE Florida will be fine, even if money is earned elsewhere in the U.S., thanks to the spectacular mismanagement and consequent high taxes of a lot of other parts of the U.S. (Maybe Jensen Huang will eventually retire and bring his personal $trillions to tax-free Florida?) California is an obvious candidate for a place where a lot of individuals will keep getting richer, but mostly the rich AI nerds will leave the other 40 million Californians in the dust.
Maybe the answer is that AI is most useful to the smartest humans and, therefore, the big winners from AI will be the smartest humans and places where smart humans cluster. This was the core point of the book The Bell Curve, improperly characterized as a book about IQ as a function of race. In fact, the main point is that, unlike in medieval times, the modern industrial economy delivers enormous rewards to the smartest people. A potato-picking peasant in 1500 who happened to have an IQ of 130 wasn’t going to earn a lot more than his counterpart with an IQ of 100. If AI accelerates the trend identified by The Bell Curve then maybe Korea, China, Japan, Singapore, and Taiwan will be the ultimate winners. (Average IQ in the U.S. started to fall shortly after our post-1965 opening of our borders to immigrants from countries with lower-than-100 IQs.)
Containerization was invented by an American. The first purpose-built container ships were built in U.S. shipyards. All of the early leaders in container shipping were American companies. One of the biggest early adopters of container shipping was the U.S. military (to support our ultimately futile efforts in South Vietnam). Today, however, the U.S. is insignificant in building and operating container ships. Merely because the world’s current AI leaders are in the U.S. we shouldn’t be complacent!
Readers: Who wants to make some predictions?
Fresh on X today, from the Financial Times, about how AI makes the cognitive elite more elite (i.e., another reason why the majority of Americans will eventually vote for everything that Bernie Sanders, Elizabeth Warren, and AOC propose):
The highest-earning and most experienced workers are adopting AI in their jobs far faster than others, in a divide that risks widening inequality as the technology spreads through the workplace https://t.co/ekDF96Z6TTpic.twitter.com/VRy0BAL9zP
A lady here in Florida asked me to reassure her that AI wouldn’t kill all humans. “I’ve seen the movies about Skynet, so I know what could happen,” she said. I reflected that an AI fed on a diet of the New York Times and Greta Thunberg could easily come to the conclusion that humans were destroying the planet via CO2 emission and, therefore, the best course of action would be to kill all humans. She responded, “You’re not making me feel better.”
What is the correct answer? If AI is embedded in networked robots and at least one robot is walking near every group of humans, what stops the robots from killing us in a coordinated attack? Maybe the AI will study the Lebanese Civil War in which 150,000 people were killed by their neighbors due to “religious diversity” and say “I can do a more thorough job than the Lebanese did with their neighbors back in 1990.”
