The jury’s speedy decision to reject Elon Musk’s lawsuit against the other founders of OpenAI and Microsoft confirmed what we saw in the courtroom: Musk’s case was a weak one, in part because he waited so long to file it.
Watching the closing arguments last week, OpenAI’s attorneys detailed point-by-point how the law was on their client’s side, while the plaintiffs team focused on Sam Altman’s apparent lack of credibility and expressed disbelief that anyone would disagree with Musk’s accusations.
The final effect was that, after the verdict, some found it hard to believe Musk had lost — including the man himself. In a post he later deleted, Musk called Judge Yvonne Gonzalez Rogers a “terrible activist Oakland judge,” then announced his plans to appeal, declaring “there is no question to anyone following the case in detail that Altman & Brockman did in fact enrich themselves by stealing a charity.”
But Altman and Brockman weren’t the only figures who benefitted from OpenAI’s non-profit investments. As much as Musk and his legal team tried to make the trial about Altman, the proceedings revealed just as much about Musk himself.
One incident that came out in court showed Musk benefiting from OpenAI in an uncomfortably familiar way. Greg Brockman testified that in 2017, Musk asked him to bring a team of OpenAI researchers down to Tesla’s headquarters to help with the autopilot team for a few weeks. “It was pretty clear that was not something we could say no to,” Brockman said.
Brockman described taking a team of leading scientists, including Andrej Karpathy, Ilya Sutskever, and Scott Grey, to consult with the “demoralized” Tesla workers. They helped come up with ideas to improve the vehicle’s self-driving technology, with Sutskever telling the team that if they could find 10,000 images of a tricky corner case, they would be able to fix their software. Musk even asked Brockman to recommend employees to fire, which he declined to do.
Another person familiar with the episode confirmed Brockman’s account, and said Tesla did not reimburse OpenAI for the time and effort of its employees. Musk’s family office, Excession, didn’t reply to a request for comment.
The heart of Musk’s case is that Altman, Brockman and OpenAI committed a “breach of charitable trust” — that Musk donated funds for a specific charitable purpose, and his cofounders instead used them for something else. He also accuses them of “unjust enrichment” due stock and other benefits from OpenAI’s for-profit.
In the case of the OpenAI scientists parachuting into Tesla, Musk’s charitable donations were intended to hire scientists focused on securing the benefits of AGI. Instead, he had them work for free at his for-profit company.
Dorothy Lund, a Columbia Law School professor and the co-host of the Beyond Unprecedented podcast, told TechCrunch that this arrangement wouldn’t be legal, calling it “a bit rich for Musk to be suing for breach of a charitable trust, when he appears to have been redirecting assets in a way that was inconsistent with that mission.”
It’s true that the self-driving work involved artificial intelligence, but witnesses for Musk emphasized that Tesla’s self-driving project was very different from OpenAI’s research agenda. That’s in part because Karpathy left OpenAI for Tesla shortly after this incident. OpenAI’s attorneys portrayed the departure as Musk violating his duty to the lab, where he was co-chair of the board, by recruiting one of its key researchers to his own company.
The other fact that no doubt influenced the jury was the amount of time Musk spent trying to gain sole control of a potential OpenAI for-profit affiliate in 2017. Musk deployed good cop, bad cop tactics in an attempt to convince his cofounders to let him have total control of OpenAI’s for-profit affiliate — giving them free Teslas, and threatening to withhold his donations.
His efforts put his attorneys in a tricky spot, facing a need to convince the jury there was a significant difference between what Musk envisioned, and the for-profit that was ultimately created. They suggested a “small adjunct” for-profit would be permissible, though OpenAI’s witnesses showed non-profits with large commercial arms are common.
Indeed, there’s a very plausible counter-factual where Musk took one of the offers his cofounders made to split their equity more evenly, and finds himself today as one of OpenAI’s largest shareholders — just not the controlling one. But several times during the trial, Musk’s associates testified that he refuses to invest in any business he could have sole control over.
The failure of Musk’s claims because he filed them too late has been cited as a technicality, but the statute of limitations has substance behind it: People and businesses make important decisions and spend resources based on their understanding that what they are doing is permissible. If someone like Musk waits too long to sue, then the cost of unravelling all those decisions can outweigh a just reimbursement.
No members of the jury have spoken about how they arrived at their verdict. However, they were asked to consider if, before Aug. 5, 2021, Musk should have known that OpenAI was spending resources outside its mission or launching for-profit affiliate. The answer to that is clear: Musk himself was doing those things.
![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](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")
