According to the Sunday Times, a former analyst at the Bank of England (BoE), which is the central bank of the United Kingdom, has written to the bank’s governor, Andrew Bailey, regarding the need to prepare for the potential disclosure of alien life by the Trump administration in the United States. The former BoE analyst, Helen McCaw, previously worked in financial security at the bank and helped develop plans for theoretical economic crises, such as the one she now sees as a possibility in a full disclosure scenario.
According to prediction market Polymarket, the potential for alien disclosure is not as farfetched as previously thought, with the odds currently sitting at 12% for such a revelation before 2027, albeit on low trading volume.
There has been an increasing number of credible, high-level government officials speaking up about the UFO (now referred to as UAPs) phenomenon over the past few years, culminating in appearances by Secretary of State Marco Rubio, former Director of National Intelligence James Clapper, Senator Kirsten Gillibrand, and many others in an associated documentary, the Age of Disclosure. The director of the documentary, Dan Farah, has shared his belief that it’s only a matter of time until a sitting U.S. president makes such a disclosure.
Disclosure could cause widespread cases of ontological shock among the general public, which is why McCaw sees the need to make economic preparations in advance. Notably, it is claimed in the Age of Disclosure that former U.S. Treasury Secretary Steve Mnuchin once received a briefing on the topic of extraterrestrials due to similar concerns.
“There might be extreme price volatility in financial markets due to catastrophising or euphoria, and a collapse in confidence if market participants feel uncertain on how to price assets using any of the familiar methods,” McCaw reportedly wrote in her letter. “If there is an official announcement and we get presented with very clear evidence that nobody is going to dispute, I would say that in a matter of hours, you are going to have total financial instability.”
McCaw also outlined a few different potential scenarios that could occur if disclosure of alien life does indeed take place. One of those scenarios included a flight to bitcoin as a safe haven asset due to the potential destruction of trust in traditional, governmental power structures.
“There might be a rush to digital currencies such as bitcoin, which may prove appealing if people question the legitimacy of government and lose trust in government-backed assets,” wrote McCaw.
The former BoE analyst also shared two differing theories on what could happen with the traditional, analog alternative to bitcoin, which is gold. On the one hand, McCaw saw the potential for the precious metal to enjoy a similar experience to bitcoin as a source of financial safety and security, but on the other hand, there is the potential for fear to enter the gold market due to the potential for “new space-faring technologies” to massively increase the supply of precious metals via new mining projects.
This point about gold eventually facing a large devaluation due to space-based mining projects is one that is brought up in debates by bitcoin proponents somewhat often. For example, it was a key point made by the Winklevoss twins when they tried to make the case for bitcoin to Barstool Sports founder Dave Portnoy in a somewhat infamous discussion a few years ago. While mining in space is currently thought to be uneconomical, NASA did launch a research mission to a metal-rich asteroid in 2023.
Bitcoin’s difficulty adjustment algorithm (DAA), where the difficulty associated with mining bitcoin is increased when more computing power is pointed at the network, prevents a similar massive increase in supply occurring with the crypto asset. In terms of changes to Bitcoin’s monetary policy, social attack vectors are likely the bigger issue, where users are convinced to simply opt into a new, alternative crypto network where the 21 million bitcoin cap has been removed. Quantum computers may also eventually pose a threat to Bitcoin; however, developers are already working on improvement proposals that could be implemented, if necessary.
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![In Harvard study, AI offered more accurate emergency room diagnoses than two human doctors | TechCrunch
A new study examines how large language models perform in a variety of medical contexts, including real emergency room cases — where at least one model seemed to be more accurate than human doctors.
The study was published this week in Science and comes from a research team led by physicians and computer scientists at Harvard Medical School and Beth Israel Deaconess Medical Center. The researchers said they conducted a variety of experiments to measure how OpenAI’s models compared to human physicians.
In one experiment, researchers focused on 76 patients who came into the Beth Israel emergency room, comparing the diagnoses offered by two internal medicine attending physicians to those generated by OpenAI’s o1 and 4o models. These diagnoses were assessed by two other attending physicians, who did not know which ones came from humans and which came from AI.
“At each diagnostic touchpoint, o1 either performed nominally better than or on par with the two attending physicians and 4o,” the study said, adding that the differences “were especially pronounced at the first diagnostic touchpoint (initial ER triage), where there is the least information available about the patient and the most urgency to make the correct decision.”
In Harvard Medical School’s press release about the study, the researchers emphasized that they did not “pre-process the data at all” — the AI models were presented with the same information that was available in the electronic medical records at the time of each diagnosis.
With that information, the o1 model managed to offer “the exact or very close diagnosis” in 67% of triage cases, compared to one physician who had the exact or close diagnosis 55% of the time, and to the other who hit the mark 50% of the time.
“We tested the AI model against virtually every benchmark, and it eclipsed both prior models and our physician baselines,” said Arjun Manrai, who heads an AI lab at Harvard Medical School and is one of the study’s lead authors, in the press release.
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To be clear, the study didn’t claim that AI is ready to make real life-or-death decisions in the emergency room. Instead, it said the findings show an “urgent need for prospective trials to evaluate these technologies in real-world patient care settings.”
The researchers also noted that they only studied how models performed when provided with text-based information, and that “existing studies suggest that current foundation models are more limited in reasoning over nontext inputs.”
Adam Rodman, a Beth Israel doctor who’s also one of the study’s lead authors, warned the Guardian that there’s “no formal framework right now for accountability” around AI diagnoses, and that patients still “want humans to guide them through life or death decisions [and] to guide them through challenging treatment decisions.”
In a post about the study, Kristen Panthagani, an emergency physician, said this is an “an interesting AI study that has led to some very overhyped headlines,” especially since it was comparing AI diagnoses to those from internal medicine physicians, not ER physicians.
“If we’re going to compare AI tools to physicians’ clinical ability, we should start by comparing to physicians who actually practice that specialty,” Panthagani said. “I would not be surprised if a LLM could beat a dermatologist at an neurosurgery board exam, [but] that’s not a particularly helpful thing to know.”
She also argued, “As an ER doctor seeing a patient for a first time, my primary goal is not to guess your ultimate diagnosis. My primary goal is to determine if you have a condition that could kill you.”
This post and headline have been updated to reflect the fact that the diagnoses in the study came from internal medicine attending physicians, and to include commentary from Kristen Panthagani.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Harvard #study #offered #accurate #emergency #room #diagnoses #human #doctors #TechCrunchbeth israel,harvard medical school,OpenAI](https://i2.wp.com/techcrunch.com/wp-content/uploads/2024/01/GettyImages-104509077.jpg?resize=1200%2C800&ssl=1 "In Harvard study, AI offered more accurate emergency room diagnoses than two human doctors | TechCrunch
A new study examines how large language models perform in a variety of medical contexts, including real emergency room cases — where at least one model seemed to be more accurate than human doctors.
The study was published this week in Science and comes from a research team led by physicians and computer scientists at Harvard Medical School and Beth Israel Deaconess Medical Center. The researchers said they conducted a variety of experiments to measure how OpenAI’s models compared to human physicians.
In one experiment, researchers focused on 76 patients who came into the Beth Israel emergency room, comparing the diagnoses offered by two internal medicine attending physicians to those generated by OpenAI’s o1 and 4o models. These diagnoses were assessed by two other attending physicians, who did not know which ones came from humans and which came from AI.
“At each diagnostic touchpoint, o1 either performed nominally better than or on par with the two attending physicians and 4o,” the study said, adding that the differences “were especially pronounced at the first diagnostic touchpoint (initial ER triage), where there is the least information available about the patient and the most urgency to make the correct decision.”
In Harvard Medical School’s press release about the study, the researchers emphasized that they did not “pre-process the data at all” — the AI models were presented with the same information that was available in the electronic medical records at the time of each diagnosis.
With that information, the o1 model managed to offer “the exact or very close diagnosis” in 67% of triage cases, compared to one physician who had the exact or close diagnosis 55% of the time, and to the other who hit the mark 50% of the time.
“We tested the AI model against virtually every benchmark, and it eclipsed both prior models and our physician baselines,” said Arjun Manrai, who heads an AI lab at Harvard Medical School and is one of the study’s lead authors, in the press release.
Techcrunch event
San Francisco, CA
|
October 13-15, 2026
To be clear, the study didn’t claim that AI is ready to make real life-or-death decisions in the emergency room. Instead, it said the findings show an “urgent need for prospective trials to evaluate these technologies in real-world patient care settings.”
The researchers also noted that they only studied how models performed when provided with text-based information, and that “existing studies suggest that current foundation models are more limited in reasoning over nontext inputs.”
Adam Rodman, a Beth Israel doctor who’s also one of the study’s lead authors, warned the Guardian that there’s “no formal framework right now for accountability” around AI diagnoses, and that patients still “want humans to guide them through life or death decisions [and] to guide them through challenging treatment decisions.”
In a post about the study, Kristen Panthagani, an emergency physician, said this is an “an interesting AI study that has led to some very overhyped headlines,” especially since it was comparing AI diagnoses to those from internal medicine physicians, not ER physicians.
“If we’re going to compare AI tools to physicians’ clinical ability, we should start by comparing to physicians who actually practice that specialty,” Panthagani said. “I would not be surprised if a LLM could beat a dermatologist at an neurosurgery board exam, [but] that’s not a particularly helpful thing to know.”
She also argued, “As an ER doctor seeing a patient for a first time, my primary goal is not to guess your ultimate diagnosis. My primary goal is to determine if you have a condition that could kill you.”
This post and headline have been updated to reflect the fact that the diagnoses in the study came from internal medicine attending physicians, and to include commentary from Kristen Panthagani.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Harvard #study #offered #accurate #emergency #room #diagnoses #human #doctors #TechCrunchbeth israel,harvard medical school,OpenAI")
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