Maximilian Janisch

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[Metav3rse @themetav3rse]

Sam Altman: Superintelligence probably by end of 2028, so we got roughly 2 years left. Enjoy your job while you still can.

"A superintelligence, at some point on its development curve, would be capable of doing a better job being the CEO of a major company than any executive, certainly me, or doing better research than our best scientists."

"On our current trajectory, we believe we may be only a couple of years away from early versions of true superintelligence. If we are right, by the end of 2028, more of the world's intellectual capacity could reside inside of data centers than outside of them."

PS. Follow us @themetav3rse for latest news on emerging technology and internet culture.

[Kendall Brown @kendallybrown]

It’s genuinely pathetic that @elonmusk is crashing out over this.

You made decisions that resulted in deaths. Own that like a fucking man, you absolute coward.

[Maximilian Janisch @JanischArchives]

@jon_d_brooks @NickKristof @elonmusk > Paper published in The Lancet estimating USAID cuts lead to ~14 million excess deaths by 2030.
> Elon: „Nonsense, nobody can name even a single victim!“
> Nick: „Names four concrete victims“
> You: „Four concrete victims don’t matter; that’s not evidence“
🤦‍♂️🤦‍♂️🤦‍♂️

[Yann LeCun @ylecun]

@elonmusk Estimates are between 9.5M and 14.5M additional deaths worldwide between 2025 and 2030, due to USAID budget cuts in mid-2025. 3.1M to 5.9M of those children less than 5 years old.

https://t.co/w9rRNmmXRU

[Prof. Karl Lauterbach @Karl_Lauterbach]

Vorbehalte gegen Klimaanlagen sind falsch. Sie werden im Sommer gerade für ärmere Menschen in heißen kleinen Wohnungen Leben retten. Im Zeitalter erneuerbarer Energien ist ihr Stromverbrauch an heißen Tagen kein Hindernis im Kampf gegen den Klimawandel https://t.co/yzo5FJQt5m

[Noah Smith 🐇🇺🇸🇺🇦🇹🇼 @Noahpinion]

Humanity: *invents magic box that gives us everything we wish for*

Humans: Oh shit, we're all going to be poor now

[OpenAI @OpenAI]

Work at OpenAI is being transformed by agents, in every department.

Across our entire company, people are using Codex to do work that is more complex, longer-running, and increasingly cross-functional.

Our internal usage offers an early look at how agentic tools may reshape work as they become more capable and broadly available.

[Maximilian Janisch @JanischArchives]

@NidalBitar23 @KalshiPolitics One in five times things with a 20% probability happen 🙄

[Paul Graham @paulg]

One of the biggest advantages of AI will be that it lets companies get further before they cross the lines (at about 10 and about 150 people) beyond which groups become less productive.

[Investment Wisdom @InvestingCanons]

“We find doing nothing the most difficult task of all.”

— Warren Buffett

[Lysandre @LysandreBL]

Ratio par un crabe + ferme ta gueule

[Florian Brand @xeophon]

we are in peak “google is done for and will never catch up”

which is always followed by them releasing a new model and people going “the others are done for, no one has the tpus and data that google has”

[Martin Shkreli @MartinShkreli]

we have the technology
we have the means
we will score >100% on evals

[Maximilian Janisch @JanischArchives]

@grok @_TomHoward @bmorecardiology @CanesDavid @afshineemrani 🤦‍♂️

[Rob Miles @robertskmiles]

I think it's reasonable for a doctor to say "Due to facts about the medical system as a whole which I am powerless to change, getting more information through testing is often net harmful"

[OpenAI @OpenAI]

Improving human health will be one of the most personal, tangible impacts of AGI.

As our models continue to improve, our goal is to make ChatGPT more accurate, more useful, and more impactful in those moments — and to keep bringing that progress to more people.

https://t.co/AINaqHEjtd

[Matt Schwartz @matt_is_nice]

Everyone arguing about whether the Midjourney Scanner can replace an MRI or CT is missing the point.

The reason it's reasonating so broadly, and especially with technologists, is that it could create a beautiful opportunity for The Bitter Lesson to get a foothold in healthcare.

Almost all of our medical data has been totally bastardized by the way we capture and store it. The EHR is supposed to be a medical record, but it is really a billing system. Every patient encounter gets compressed into a lossy template or heuristic just to facilitate billing logic.

The Bitter Lesson is simple. AI gets powerful when you feed it raw, unfiltered data and let learning, search, and compute to the work.

Stop worrying about whether AI can sharpen the resolution of the ultrasonic tomography. If the images get prettier for human interpretation, that will just be a nice bonus.

The actual goal should be to capture as much raw signal of a person's clinical state as possible. Connect that signal to similar measurement of future outcomes. Then let a model learn from that data with minimal imposition of human judgment or measurement.

Start with the assumption that we don't even necessarily know what we're looking for. This is the way to actually do great medical science.

I've watched this play out in endoscopy. As an example, historically we would take 15-20 minute colonoscopy videos of patients with ulcerative colitis and compress it down into a Mayo score of 0, 1, 2, or 3 based on the single worst moment of the entire video. So much data wasted just because we needed a human-digestible heuristic.

It turns out if you instead capture all of that raw data and use it to train a self-supervised model, those embeddings can actually learn far more about a patient's disease state. So much more that they can actually predict treatment outcomes.

This is why I'm personally fired up about the Midjourney Scanner. Don't think about it like an MRI or CT. Think of it as a beautiful fountain of human health data.

[James Clear @JamesClear]

A surefire way to make a bad situation worse is to continue replaying it in your mind.

The damage is done. The only thing that matters now is making the best choice given your current position.

Next play mentality.

[Peter H. Diamandis, MD @PeterDiamandis]

100 years ago, three things would kill most people: infection, childbirth, and a bad winter. We forget how recently the baseline of human life was simply surviving the year.