How the Law of Displacement Can Solve the Data Center Crisis

Much of what is written here is guided by the fact I spend a lot of time thinking about the problems the world faces and what solutions could potentially address them, either now or in the future when cultural circumstances change. This year I realized a few longstanding issues were converging into a potential long-term solution that I wanted to have this newsletter help bring to light.

The Law of Displacement

One of the depressing things I have come more and more to terms with as I’ve gotten older is how so, so, many things ultimately come down to money (e.g., a lot of medicine is structured into sales funnels that ensure robust sales of medical services and many seemingly irrational ways the practice of medicine is structured finally make sense once you see it through that lens).

As such, all throughout history, you can cite examples of people being exploited for profit (e.g., for most of human history in most societies slavery was widely practiced). Once science entered the picture, this frequently resulted in profitable technologies being deployed which advanced the national wealth but harmed a significant portion of the population in the process (e.g., Monsanto’s profitable chemical plants poisoning their community’s water supply).

In all the case studies I’ve looked at, a recurring process occurred:

• Industry was aware of health issues from their product, but buried all of it (with this frequently being revealed in subsequent lawsuits).

• The profit margins for the products were typically larger than the cost of keeping it on the market (e.g., they could afford to fund doctored science that cast sufficient doubt on health issues with the product). Likewise, typically a small enough number of people were directly harmed (e.g., an isolated community or less than 1% of the people who took a pharmaceutical) that it was easy enough for the industry to sweep everything under the rug—especially since until recently, corporate news controlled the media landscape and was structured to support narratives from large corporate clients.

• Government typically sided with industry, due to some combination of lobbying (e.g., bribes), the industry effectively controlling the narrative (so government regulators thought the product was “safe”) or the government viewing the product as a strategic national asset which justified a certain degree of sacrifice to promote the national interest.

Because of this, you would again and again see things that were clearly toxic be able to stay on the market for decades despite those harmed by them doing all they could to bring attention to the issue (and supportive researchers providing the data to substantiate their claims).
Note: the most extreme example of this is likely the COVID vaccines, as the scale of harm they have done eclipses arguably any other product in history, there is widespread public opposition to them (to the point a clear majority now refuses to take them) and more researchers have come forward to expose their dangers than virtually any other product in history—but nonetheless—the FDA continues to advocate for them. Why? Because there is so much money to be made off mRNA technology too many parties are simply not willing to let it go.

Rather, what typically gets these products off the market is a commercially viable alternative to them being developed, as at that point, the same profit can be made, but it is no longer necessary to bear the additional cost of defending the old product to keep it on the market (which, for lack of a better term, I call the “law of displacement”).

For example, almost immediately once leaded gasoline was created in 1922, serious health concerns were raised about it—the Surgeon General questioned it before a gallon had been sold, workers making it began dying within months of its 1923 launch, and the engineer who invented it wrote the industry’s reassurances to the government while recovering from lead poisoning himself. However, since GM held the patent and collected a royalty on nearly every gallon of gasoline sold in America, rather than be ethical, GM wrote into its contract with the Bureau of Mines that the government’s safety findings required company approval before publication, then funded a university laboratory that produced essentially all of the “safety” data on lead for the next forty years. When Caltech’s Clair Patterson proved in 1965 that Americans carried roughly a hundred times the lead burden of their ancestors, the industry tried to buy him and then had his government contracts pulled. As such, all of the efforts to get lead out of gasoline were unsuccessful (which is truly remarkable given the massive health consequences of it such as 256,000 premature deaths from cardiovascular disease each year in America).

Rather, what did was the catalytic converter as automakers needed them to meet the 1970 Clean Air Act, lead destroys them on contact, and in 1970 GM (which had conveniently sold its stake in the lead business a few years earlier) announced it was switching away from leaded gas. As such, once the world’s largest carmaker needed unleaded fuel, refiners had a product that was just as profitable to sell, and the same machine that had defended lead for half a century quietly phased it out. Notably, a non-toxic alternative had existed the entire time (GM’s own researchers had tested ethanol blends before lead); it lost because it couldn’t be patented (whereas by 1970 other patentable additives existed).

Furthermore, this is not an isolated pattern. For example, DuPont fought the science linking CFCs to ozone depletion for fifteen years, then abruptly reversed its position in 1988—once it held the patents on the replacement refrigerants. The Montreal Protocol had been signed the year before, and the “impossible” phase-out proceeded on schedule. READYWISE Emergency Fo... Buy New $68.39 (as of 09:31 UTC - Details)

Likewise, many things in medicine have fit this pattern:

• Seldane (terfenadine) was known from 1990 to cause fatal arrhythmias when taken with common antibiotics, but stayed on the market until 1998—eighteen months after the same company got Allegra approved (which is simply Seldane’s active metabolite without the cardiac risk). The FDA’s own withdrawal proposal stated the reasoning plainly: now that a safer alternative existed, Seldane’s risks were no longer acceptable.
• Rezulin (troglitazone) caused fatal liver failure and was pulled in Britain in 1997. The FDA kept it on the market until 2000, and when it finally withdrew the drug, explicitly cited the fact that Avandia (rosiglitazone) and Actos (pioglitazone) were now available to replace it.
• Bextra (valdecoxib) was withdrawn in 2005 for cardiovascular risk and rare, life-threatening skin reactions, with the FDA noting it offered no advantage over the NSAIDs already on the market—while Celebrex, Pfizer’s bigger-selling COX-2 inhibitor (with the same COX-2 class risk), was allowed to stay.
• Phenacetin was one of the most widely used painkillers in the world and was linked to kidney failure in the 1950s. It wasn’t banned in the U.S. until 1983, by which point Tylenol (phenacetin’s own metabolite) had already taken over its market.
• Barbiturates were known to be lethal in overdose and highly addictive by the 1930s, yet remained the default sedative for another thirty years until Librium and Valium (benzodiazepines) arrived in the 1960s with fresh patents and the same customers.
• Halothane hepatitis, a frequently fatal liver necrosis, was recognized within a few years of halothane becoming the standard anesthetic in the 1950s. It was tolerated for three decades until patented replacements (isoflurane, desflurane, sevoflurane) captured the operating room in the 1980s and 90s. Tellingly, halothane is still widely used in poorer countries where those patented pharmaceuticals are unaffordable.
• Tardive dyskinesia, an often irreversible movement disorder, was identified in patients on Thorazine and Haldol by the late 1950s and accepted as a cost of doing business for forty years. The older drugs were rapidly displaced once patented “atypical” antipsychotics arrived in the 1990s—which then turned out to cause diabetes and metabolic syndrome at rates that arguably made them no safer, only newer.
• The whole-cell pertussis (DTwP) vaccine had a high rate of causing encephalitis and nearly bankrupted the vaccine industry in lawsuits, leading to the 1986 National Vaccine Injury Act being passed (giving the industry immunity from lawsuits). As activists had begged for years for the safer but more expensive Japanese acellular (DTaP) vaccine to replace the DTwP vaccine (which the industry refused to spend the money to do), the 1986 Act was structured to require HHS to promote safer vaccines (leading to the necessary research being Federally funded) and a few years later DTaP quietly replaced DTwP (except in poorer nations where the WHO continues to promote DTwP).
• After the idea of X-raying a fetus throughout pregnancy was proposed in 1923, it was quickly taken up by the medical profession. Before long, evidence accumulated that this was very dangerous, but it was not until 1975 that the obstetric field shifted away from it—a shift that largely occurred because an alternative way (ultrasound) was found to conduct those routine exams.
• Cylert (pemoline), an ADHD drug, carried liver-failure warnings from 1996 onward but stayed on the market until 2005, once the stimulant market was fully covered by other products and it no longer filled a gap.
• OxyContin’s abuse potential was known to Purdue for a decade before it “solved” the problem in 2010 with a crush-resistant reformulation—which conveniently extended its patent protection and let the company argue the original (now unsafe) version should be blocked from generic competition.

So, while there are many harmful products on the market I believe should be banned, rather than continually emphasize how bad they are, I try to have my focus here be directed towards safer alternatives which can displace them, as I feel that is the most realistic way to get them off the market and have them stop harming people.

The AI Boom

One of the topics I’ve seen many people debate is how far AI is likely to go as on one hand, there are projections it will displace many people’s jobs and completely redo the economy, while on the other many argue that dystopian vision is simply hype the AI industry is creating to justify inflated stock values as the companies go public (and to obtain private investor funding prior to that).

Given the potential cultural implications of AI, I’ve hence put a lot of thought into how they actually generate their answers, the relative accuracy of the different types of responses they get, how AI can be a productivity increasing or decreasing tool, and the overall effects I expect them to create upon the society (e.g., a lot of the responses AI gives are terrible because its conversational style is tuned to please raters, which produces the same pretentious and obnoxious responses that dominates Reddit). From that, I’ve essentially concluded: Emergency Candles 150 ... Buy New $38.99 (as of 11:41 UTC - Details)

• A significant portion of the population prefers to follow the crowd, tends to defer to authority, and is somewhat averse to the hard effort it requires to think for oneself or deal with adversity. For these people, AI (as most currently understand it) will be irresistible, resulting in them being trapped in a bubble where they can’t grow or change (exemplified by the rising phenomenon of AI boyfriends and girlfriends) and it becoming increasingly easier for the system to manipulate them into compliance (in part because AI answers inevitably regress toward the orthodoxy of their training data and the preferences of the people who rate their answers).

• A smaller portion will recognize that AI provides a tool that allows them to greatly increase their productivity. This, I believe, will ultimately lead to a wealth explosion that will be concentrated in the upper classes (hence increasing the income division in society). However, I do not believe this benefit will be seen for many of the people pursuing it due to them failing to recognize what AI is and is not useful for. Put differently, AI will benefit users who make a point to keep their own agency while using it and harm those who hand it over.

• Since many of the things we are trained by the educational system to do are ultimately algorithmic (and hence possible for AI to do), AI will place an increasing pressure upon the population to shift to providing things which offer an inherent value that goes beyond what can be automated and trained (e.g., a genuine authentic human presence in medicine that is naturally therapeutic). However, I do not think many of the people who economically need to do this will do so, and as a result, AI will also amplify the income inequality in society from the opposite direction (by making the less wealthy poorer).

• A significant portion of the value of AI comes from things most users cannot see (AI interfacing with programs rather than individual users talking to chatbots), as this setup makes it possible for programs (or programs connected to physical systems) to do a lot of things that previously were not possible (e.g., to create a variety of highly profitable technologies or meticulously micromanage the population). Because of this, I suspect one of the main purposes of the chat bots (beyond creating positive PR for the AI industry) has been to provide free human training that can be used to develop these far more lucrative invisible systems.

To put all of that in more concrete terms; initially, I was strongly opposed to using AI as I felt it was highly inaccurate (due to it sharing the exact same subtle biases I was used to seeing on websites like Wikipedia) and found its pretentious and condescending style of conversation to be extremely obnoxious (until we realized you could stop a lot of that by telling the chatbots to stop “sounding like Reddit”). Later, I realized it was very useful for rapidly executing second and third order searches (e.g., I am going from point A to point B, based on my criteria, the possible routes I can take, and the time I am leaving, what is the optimal place for me to stop at midway) as it could quickly do all of that rather than requiring me to manually sort through each part of the decision tree (saving a lot of time).

Likewise, with writing, I was initially strongly opposed to using AI at all both because I could not tolerate the errors it continually introduced (e.g., AI hallucinations or regressions to the orthodoxy) and because I did not like the feel of AI writing (which beyond having a “Reddity tone” also just felt empty, which was a deal breaker as I am not willing to ask readers here to read things I myself would not want to read). I then switched to viewing AI as a good way to obtain “orthodox” perspectives (many of which are correct) and edit what you are working on (which for me works best by generating word document copies of articles with suggested edits highlighted so I can go through each one and manually add in the ones that make sense).

Later, I realized that while AI has a high rate of hallucination for anything it generates on its own (and pulls from fairly limited datasets), it’s fairly good with material you directly give it to process. So if you understand the entire pipeline that is required to create a finished output, it often makes sense to save a lot of time by having AI automate certain parts of the pipeline (rather than asking for the whole thing start to finish—which inevitably creates a highly erroneous and useless output). Because of this, one of the main things we’ve done over the last year has been to give them well over ten thousand studies (from all the databases AI systems do not look at) and have them process each one into a paragraph highlighting all the pertinent data in the study (and then sort those paragraphs by category and then sort those categories by overlapping information).

From doing this, it’s made it possible to compile a lot of information that simply was never accessible before and to do it in a very short timeframe (thereby making it actually possible to get all of this into the public domain while RFK is still HHS Secretary). For example, papers get exponentially harder to write the more sources they contain, and the capstone paper on DMSO for brain spine and heart injuries the top experts in the field wrote (after spending a decade researching the subject) contained 78 references—yet the four articles I released this year on this topic collectively contained nearly 5,000 references.^1,2,3,4 So while doing these four articles was immensely time consuming, with the help of AI (and very careful vetting of the outputs), it was possible to do orders of magnitude more than anyone ever has been able to do before, thereby, at last making that forgotten literature base accessible.

DEBAOBULB 6 Pack Recha... Buy New $34.18 (as of 11:41 UTC - Details) One of the main reasons I did all of this, in turn, was because I knew it was simply not feasible for independent researchers to ever uncover most of the actual research that had been done with DMSO. As such, my hope was that by making it all easily available, other researchers could copy my work and use it as a foundation for their own works on DMSO (as prior to me beginning this project, I’d realized almost every DMSO book in print rather than independently research the subject had just copied what was in the previously published books and hence all were collectively drawing from a very limited pool of scientific studies). So, if a much larger body of compelling literature was made available to everyone, it would effectively promote the therapy and make something that could help a lot of people widely available, thereby creating the pressure to shift us to a better model of medical care (which is why I chose to start by the writing the longer and far more difficult articles which make that critical literature available rather than jumping to the shorter ones that have more commercial appeal).

This has basically happened, and over the last two years, a lot of books and articles have started to be published on using DMSO which are incorporating many of those forgotten studies that have never before been seen in print. However (and this is the key point), what I’ve noticed is that most of them were AI generated summaries of what I wrote rather than the authors using them as a scaffolding to assist in developing their own DMSO content. On one hand, this has been problematic as the AI summaries introduce a significant amount of errors or misinterpretations (so in cases where authors who did this credited me, many people have asked me why I said something I never actually did but the AI summary erroneously concocted).

However, what I feel is far more important (and why I shared this story) is that even in cases where I’d already done the vast majority of the work for anyone who would want to broach the DMSO topic (and held off on writing the shorter DMSO pieces so an economic niche was created for other people to do it), when authors were faced with the choice between doing the last bit themselves or letting AI do it with the errors doing so inevitably entails, they chose the latter. I mention this because I do not think this issue is at all unique to DMSO, but rather that (as I alluded to in the first bullet point of this section) if AI provides an easier and faster way to do tasks at the expense of quality, human nature dictates that a lot of people will use AI to do that (which amongst other things is why the amount of content on the internet is rapidly increasing while in tandem its quality is rapidly declining).

So to summarize, I feel AI is a very useful tool which makes a lot of things previously not possible possible. However, my fear is that the majority of users will treat it as a crutch and not utilize it in a manner that productively enhances their own lives (or their personal development). In contrast, there will also be a sizable number of individuals who will grasp how to effectively leverage AI, but I think there is a high likelihood most of them will not have the ethics (or wisdom) to prevent their projects from harming humanity (e.g., one of the current AI enabled gold-rushes is AI facilitated mass surveillance while another is using mRNA to cure illnesses by “rewriting the programs your body runs on”—neither of which is likely to provide a net benefit to humanity). Put differently, the tool amplifies whatever its user brings to it, but most people instead rely upon AI to do everything for them.

Note: since America’s tech industry originates from a very left-wing area, it exists within a cultural ethos where presenting the appearance of aiming to benefit humanity is paramount. Because of this, many people I’ve spoken to have collectively seen more investor pitches than I can count from Silicon Valley tech companies over the years that claimed the company would help humanity but in reality were entirely about profit and often later created a negative impact on humanity (e.g., Facebook’s first president recently admitted he deeply regrets what they did to children’s brains). Because of this, I am fairly skeptical about all the (repackaged) utopian language I am seeing being increasingly wrapped around AI—particularly since more and more military spending is going towards developing AI weapon systems, which I feel is very dangerous for humanity (as once human beings are no longer held back by being confronted with the visceral reality of killing another person it opens the doors to unspeakable horrors occurring).

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