Author’s Note: On November 1, I’ll be running the 2026 New York City Marathon in memory of my uncle Chris and in support of the Parkinson’s Foundation. As this newsletter lands in your inbox, I’m likely out on a hot, humid training run. If you’d like to learn more about Parkinson’s disease or support my fundraising efforts, you can visit my fundraising page here.
What’s the latest in AI, you ask? Well, apparently, it’s coming to kill us all.
According to ex-Anthropic researcher Jacob Coxon, AI’s meteoric progress (have you checked out OpenAI’s Astra yet?) poses a real possibility of AI-caused human extinction. Coxon argued that AI could cause human extinction by the end of the decade. Current Anthropic researcher Evan Hubinger went further, saying he personally puts the odds above 10% within the next decade.
“What does human extinction caused by AI even mean? This makes no sense.”
Fair point. I was dubious of this statement when I heard it too.
On a recent Prof G Markets podcast episode, guest Nick Bostrom (author of Deep Utopia) gave this example (loosely paraphrasing): Imagine you tell AI to produce as many paper clips as possible, assuming no resource constraints. If AI were to execute this task verbatim, it would find a way to produce a gargantuan amount of paper clips, taking away resources intended for humans. If, say, drinking water was needed to produce paper clips, all drinking water on Earth would be reallocated away from humans. It’s these kinds of resource-constraint issues that could lead AI to make consequential changes to the things humans need to survive.
But take this all with a grain of salt. Coxon and Hubinger’s dialogue has taken the world (or tech world, that is) by storm over the past two weeks. It’s gotten to the point where Sam Altman (OpenAI) and Dario Amodei (Anthropic) are voicing concerns about AI’s rapid acceleration, and suggesting government intervention and a slower pace of frontier AI development.
Although I work in tech and have seen firsthand how incredible recent model developments have been at executing business tasks, I am a bit skeptical of the current narrative that AI will take over the world, rendering humans to be useless along the way. There are so many assumptions and narratives that are being spun to arrive at this possible outcome. It’s like revenge of the robots, but keep in mind, that’s science fiction.
Altman and Amodei’s version of brand building has been fear-based. They’re in a race to prove whose AI model is more capable than the other and have chosen to lead with a self-righteous perspective that “AI is dangerous, but you can trust me”. I view this as nauseating, if not misrepresentative of what this technology can do for good.
It’s either a publicity stunt around which company their models ‘accidentally’ hacked, or it’s about all the awful things that someone can do with AI but that they stopped thanks to their safety guardrails. I don’t disagree that AI models have become incredibly powerful, and we need to be more mindful regarding security risks. However, a doomer’s mindset around this seminal technology isn’t necessarily going to make people want to see how far you can take it with AI.
AI’s biggest branding problem may be that the people building it are disproportionately defining its public narrative around what could go wrong rather than what could become possible when it works. In my opinion, the quickest way to change the current gloomy (if not fatal) narrative about AI is to focus on what can go right with AI. And where can AI produce a massive amount of good? In the medical field. Let’s dive into a few recent wins.
Anti-Aging
Insilico Medicine created a drug with AI originally meant for lung disease patients, that happened to also make patients appear biologically younger according to several aging measures. AI was used to identify the target protein and design the molecule needed to inhibit it (I’m oversimplifying it, but this is as much as I could understand). Insilico used AI to compress the front-end of drug discovery and stumbled upon another win: anti-aging insights.
Cancer Detection
The MASAI trial, involving more than 105,000 women, found AI-assisted screening increased breast cancer detection by 29% while cutting radiologists’ screen-reading workload by 44%. There are only so many radiologists in the world, and they already tend to be quite busy. AI-assisted radiology is producing better results, while making these doctors more efficient. The sooner we have solutions, the sooner more lives get saved from cancer.
Sepsis Detection System
If you go to the hospital, you shouldn’t have to worry about catching an infection and passing away. Unfortunately, sepsis claims more than 250,000 lives per year in the U.S. But thanks to Johns Hopkins researchers there is now an FDA-cleared early-warning system available. They created an AI-based medical tool that compares hundreds of live parameters with electronic medical records to identify potential cases of sepsis earlier. Doctors are moving from patient to patient in hospitals and don’t have the time to stay on top of every little thing regarding their patients. The sepsis detection tool has been shown to reduce the mortality rate by 18% across dozens of hospitals using this new technology.
Is AI getting noticeably powerful? Yes. But if it’s directed toward good, it can materially improve our quality of life (and potentially extend it). Now that’s better branding.