Rory

Strategy & GTM for Healthcare and Life Sciences @NVIDIA.

[Brian Armstrong @brian_armstrong]

We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).

I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.

We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:

"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"

I imagine we'll have some backend services that could be replaced entirely by this new architecture.

[Rory @ItsRor]

@DavidRBellamy Thank you for your service - more need to hear this and your experience makes you uniquely qualified to share it. I learned a thing or two myself!

[David Bellamy @DavidRBellamy]

I must be among an extremely small group of people (n=1?) that have both 1) trained a frontier LLM and 2) designed and synthesized custom viruses in a lab with my own two hands.

And I think that the takes on AI killing us all by creating dangerous viruses is total bogus.

[David Sacks @DavidSacks]

Dario has written that we need to “pace the frontier,” and Sam has agreed. People may be surprised by my response: go ahead.

You guys are the frontier. By any reasonable metric — market share, revenue growth, model capability — the two of you have a duopoly on frontier intelligence. You’ve also claimed the lead is widening because of recursive self-improvement.

I don’t see what you see in the lab. If the unreleased models are scary enough that you think you should slow down, I support your decision to be responsible.

But stop pretending you need anyone else’s permission. Stop pretending antitrust law has to be suspended so you can form a cartel. Stop pretending you need a regulatory approval process that supersedes product liability. Stop pretending METR is independent when it is intertwined with Anthropic���s investors and staff. Stop pretending you need those same evaluators to police competitors who aren’t even at the frontier.

Most of all, stop pretending the motivation to slow down is purely altruistic. You face massive product-liability exposure if your products enable a truly damaging cyberattack. The market already punishes models that behave in unpredictable or unauthorized ways. After the Hugging Face episode, it is simply good business for OpenAI and Anthropic to trade some raw power for reliability and predictability. Call it alignment if you want. It is also just giving customers what they want.

Pacing the frontier would also create breathing room for a more intelligent conversation about regulation than Bernie Sanders’ “shut it all down.” China is very unlikely to join a global agreement, as you know, and that has to be taken into account as well.

So go ahead and pace the frontier. You are the ones setting it. The easiest way not to build superintelligence is for you to agree not to build it. Demanding your preferred regulatory framework as the price of that will look like blackmail of the public and the political system. So just do it.

If you do, you’ll buy goodwill for the next conversation. If you don’t, we’ll know this was just another bid for regulatory capture — or an election-season psyop.

[Marc Andreessen 🇺🇸 @pmarca]

What Is To Be Done?

I propose a simple plan:

Big AI companies should be allowed to build AI as fast and aggressively as they can – but not allowed to achieve regulatory capture, not allowed to establish a government-protect cartel that is insulated from market competition due to incorrect claims of AI risk. This will maximize the technological and societal payoff from the amazing capabilities of these companies, which are jewels of modern capitalism.

Startup AI companies should be allowed to build AI as fast and aggressively as they can. They should neither confront government-granted protection of big companies, nor should they receive government assistance. They should simply be allowed to compete. If and as startups don’t succeed, their presence in the market will also continuously motivate big companies to be their best – our economies and societies win either way.

Open source AI should be allowed to freely proliferate and compete with both big AI companies and startups. There should be no regulatory barriers to open source whatsoever. Even when open source does not beat companies, its widespread availability is a boon to students all over the world who want to learn how to build and use AI to become part of the technological future, and will ensure that AI is available to everyone who can benefit from it no matter who they are or how much money they have.

To offset the risk of bad people doing bad things with AI, governments working in partnership with the private sector should vigorously engage in each area of potential risk to use AI to maximize society’s defensive capabilities. This shouldn’t be limited to AI-enabled risks but also more general problems such as malnutrition, disease, and climate. AI can be an incredibly powerful tool for solving problems, and we should embrace it as such.

To prevent the risk of China achieving global AI dominance, we should use the full power of our private sector, our scientific establishment, and our governments in concert to drive American and Western AI to absolute global dominance, including ultimately inside China itself. We win, they lose.

And that is how we use AI to save the world.

It’s time to build.

[Chris Hayduk @ChrisHayduk]

I work at OpenAI and personally think AI has been and will continue to be an extremely beneficial technology to humanity

The conversation should be around how many billions of lives it will save

[Rory @ItsRor]

Good read - here’s to the pursuit of unbounded problems 🚀🚀

[Giovanni Cattani @giovannicatt3]

https://t.co/21queFKajn

[Rory @ItsRor]

@HealthcareAIGuy Beyond radiology, where else in healthcare is AI likely to increase demand for human roles rather than simply automate existing work?

[David Shapiro (L/0) @DaveShapi]

If you want to understand why I stepped back from AI discourse, the Dwarkesh virality is archetypal of everything wrong with it.

Actual cyber security experts have classified the OpenAI and Hugging Face incident as an epic security facepalm.

These morons literally just let it keep going. They didn't consult proper experts. It was an idiotic environment run by idiots.

There was no civilization. There were no zero day exploits. I mean, not other than the zero day of ML researcher hubris.

But people don't really care about reality. You people just want hyperbole and fantasy.

You all should get the heartburn and anxiety you deserve for being so gullible.

[Rory @ItsRor]

@stephenbalaban @garrytan Preach!

[Rory @ItsRor]

@zavaindar Woah. Always loved sports photography as art, but those hit different.

[Ben Kompa @BenKompa]

Post-training open-weight models for scientific tasks is core to @LilaSciences's platform and Nemotron 3.5 Lightning has been a great model to work with in our experience⚡️

[Rory @ItsRor]

@jrkelly @shelbynewsad You never forget your first bodyoid conversation…

[Rory @ItsRor]

@saakohl @Ronalfa Let’s go! Congrats Simon and team!

[Sam Rodriques @SGRodriques]

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important.

See our blog post below, and watch the video to learn more, narrated by the man himself.

[Jacob Kimmel @jacobkimmel]

why don’t we have more medicines?

inventing medicines has grown less efficient, limited by our knowledge of biological targets

for the 1st time, AI & genomics have the potential to make this knowledge abundant – one of the largest potential impacts of the intelligence age.

[Jason Kelly @jrkelly]

We need experimental abundance in bioengineering.

So much of biological science is trying to squeeze as much learning as possible out of a very small amount of raw experimental data -- because making that data is insanely expensive. We're hitting the limits of that approach -- this is why drug discovery gets more expensive every year, not less.

We've unlocked experimental abundance in genomics (reading DNA) but nowhere else yet in a general way -- especially once you include any DNA design in the mix.

Need to really get to work on this problem !!

[Rory @ItsRor]

@anthonycosta @NVIDIAHealth @Dyno_Tx @ekelsic @samsinai STUD

[Anthony Costa @anthonycosta]

Had an amazing time talking about AI, Bio, and @NVIDIAHealth with @Dyno_Tx @ekelsic at #NYTechWeek! Thanks so much to @samsinai and the whole Dyno team for making it such a success!

[Rory @ItsRor]

@ns123abc @grok what are the implications of virtually all white collar jobs being automated by AI and as a hedge where should we be investing?