Brian Gordon

Strategy | innovation | knowledge creation in science, engineering, and technology | organization | cognitive science | AI

[Brian Gordon @GordonBrianR]

@f_j_j_ I don’t know if this will work or how state capacity might impact execution, but I feel like this is the gambit, no? That Argentina is being used as a credible alternative (read credible as you will) so as to exert pressure on the legislative and regulatory outcomes here?

[Brian Gordon @GordonBrianR]

There is a capability race with China in AI and now a regulatory race with Argentina. Both realistically pressure U.S. political response possibilities and realities.

https://t.co/cW986lvVpf

[Brian Gordon @GordonBrianR]

This is the most interesting part of the announcement, I think.

[Brian Gordon @GordonBrianR]

🤯

[Brian Gordon @GordonBrianR]

🤯

[Ruxandra Teslo 🧬 @RuxandraTeslo]

China is winning the drug discovery race. There's no better example of this than multiple myeloma.

https://t.co/YaJSUquRoa

It's one of the most painful cancers, destroying bone from within. For decades, patients endured cycles of brutal treatment and relapse. Then came Carvytki: a one-time CAR-T infusion that appears to cure some patients who have failed multiple treatments.

Its development story, beginning in 2016, was an early signal of a shift now making headlines: the US is losing biotech dominance to China. Though the foundational science was largely American, a nimble Chinese company moved faster with a better molecular engineering idea.

Unless the US addresses clinical-trial bottlenecks slowing early in-human data, more breakthroughs will be developed elsewhere, weakening the ecosystem American biopharma depends on.

Some key points from my article for @WorksInProgMag, with my friend Amol Punjabi, of @EvidenceOpen:

1) Multiple myeloma is not only extremely painful in and of itself, but also one of the most brutal cancers to treat. As first-line therapy, patients endure four drugs simultaneously, then a stem cell transplant, followed by continuous maintenance therapy. And most still relapse, with each treatment round carrying worse chances.

2) A drug called Carvykti, approved in 2022, is changing the treatment landscape. Carvytki acts as a single, one-time infusion. It's a CAR-T therapy, part of a new wave of transformative immunotherapies: made from the patient's own immune cells and reprogrammed to hunt cancer. In patients who had already failed 4+ other treatments, 33% were still disease-free after 5 years. The results as earlier line therapy look even more promising.

3) Most of the foundational science was American. Decades of CAR-T research, and in 2013 the NCI showed BCMA-targeted CAR-T cells could kill myeloma in the lab.

4) But the drug that ultimately changed myeloma, Carvytki, originates from China. Carvytki beats Abecma (the American CAR-T for myeloma) by a wide margin: 36 months of progression free survival in heavily pre-treated patients versus Abecma's 9 months.

5) In 2016, Legend Biotech was just beginning clinical trials. This was the same year the American team was publishing their first-in-human results. Legend started later, but moved faster. Clever engineering and China's ability to get drugs into humans quickly gave them the edge. Large American biopharma J&J ended up striking a deal with Legend and developing the therapy.

6) Never underestimate the llama: US-developed Abecma used mouse antibody fragments to target BCMA. Chinese startup Legend used llama nanobodies instead. These are smaller, more stable and bind more cleanly to BCMA. The usage of llama as opposed to mice antibodies is what is believed to lead to Carvytki's superior efficacy.

7) In retrospect, Carvytki should have been an early warning. China is winning the drug discovery race through deliberate policy. Their first-in-human clinical trials can launch in 6 months vs 18+ months in the US, letting them iterate faster between lab and clinic. The @nytimes recently reported that ~50 percent of major drug deals this year involve Chinese-origin drugs, up from nearly zero a decade ago.

8) The US still leads in late-stage development, as shown, but the pipeline feeding it is increasingly Chinese. The worry is that this will mirror what happened in solar, batteries, and EVs, where early-stage dominance eventually became control of the entire chain.

9) A proposal to streamline early stage trial regulatory requirements to keep the US competitive has made it into the President's 2027 budget for the FDA. But Congress has to act to make it a reality.

[Brian Gordon @GordonBrianR]

The space of possible minds and the space of possible agencies is probably bigger and weirder than we intuitively suspect. But that suggests we need to be epistemically humble about our predicate projections and generalizations too.

[Brian Gordon @GordonBrianR]

Human consciousness involves phenomenal elements, self-awareness/modeling, narration, and allostatic agency in a bundle (at a minimum, maybe more?). But these elements can all come apart. Is consciousness additive? Synergistic? Does it require all elements simultaneously?

[Brian Gordon @GordonBrianR]

Human consciousness involves phenomenal elements, self-awareness/modeling, narration, and allostatic agency in a bundle (at a minimum, maybe more?). But these elements can all come apart. Is consciousness additive? Synergistic? Does it require all elements simultaneously?

[roon @tszzl]

I think you can make an equally convincing argument that waymo, claude, and the Ford Motor Company are conscious

[roon @tszzl]

I think you can make an equally convincing argument that waymo, claude, and the Ford Motor Company are conscious

[Brian Gordon @GordonBrianR]

An LLM inducing the latent space of a text corpus (or post training modifying the distribution of outputs) is NOT learning a rich model of the human mind because language is a small, almost provincial part of what human minds do.

We are language using, not made of language.

[Liv @livgorton]

@kasratweets I think the reason people say LLMs might be conscious and don't say the same of AlphaFold is that LLMs learn a very rich model of the human mind. It's hard to fully express the intuition in a tweet but it's about the emergent properties from learning from so much human text.

[Brian Gordon @GordonBrianR]

This ‘dark vision’ of positional relational goods in a post-AGI world is eye opening, suggesting how abundance in the material conditions of life can actually intensify conditions of stratification, social competition, and division on some margins even as it ameliorates others.

[Bo Cowgill @bocowgill]

Many parallels between @alexolegimas's “What Will Be Scarce?” and The Sneetches (1961) by Dr. Seuss -- although Seuss ends differently, at least on my read. 1/N

https://t.co/ZBlTFYH5fJ

[Brian Gordon @GordonBrianR]

Scope and scale objective function diversity

[Jake Wintermute 🧬/acc @SynBio1]

https://t.co/gr34TbfcmN

[Brian Gordon @GordonBrianR]

@inferredbylisa YES! This would be amazing. I’m in.

[Brian Gordon @GordonBrianR]

@thatrobiam Well whatever you do, don’t put any quarters into the fortune telling machine!

[Brian Gordon @GordonBrianR]

If I have learned one thing watching The Twilight Zone, it’s never go to a cafe in a strange town.

[Brian Gordon @GordonBrianR]

Manifold realism

[The Professor @thatrobiam]

A river remains a river even though the water is never the same. Representational drift suggests the brain may work similarly: not preserving every neuron, but preserving the pattern that holds them together. #talesfromtheshed ✨

[Brian Gordon @GordonBrianR]

@oliverbeige I just remembered the C

[Brian Gordon @GordonBrianR]

de gustibus non est disputandum

[Nicholas Decker @captgouda24]

And here is my tier list of Econ Nobel Laureates

[Brian Gordon @GordonBrianR]

@oliverbeige Wait, fourth. There is a superstar category too.

[Brian Gordon @GordonBrianR]

🤯 I know what I’m reading today

[Matt Esche @matthewesche]

At @IFP, we’ve spent the past 3 years thinking about all the different ways the US government & philanthropy fund R&D.

Until now, R&D funders haven’t had a systematic way to match the innovation problem to the right funding tool.

We built THE ATLAS OF INNOVATION to fill that gap.

https://t.co/XZshJ7pr1f

Alongside @UChi_MSA, we’ve boiled down thousands of hours of research into a handful of questions covering how much the R&D funder knows about:
- the problem they want to solve
- the solution it should have
- the team that should build the solution

Why the Atlas matters:

The US government spends close to $200 billion every year on R&D. And after the Anthropic and OpenAI IPOs, there will be hundreds of billions of dollars in new philanthropic giving.

Choosing the correct funding approach to the social problems they’re trying to solve will mean the difference between success and failure.

For example, NSF research grants have helped seed breakthroughs from MRI machines to search engines, but grants aren’t built to deliver the kind of industrial speed and scale that a project like Operation Warp Speed required.

Picking the wrong funding approach can leave programs behind schedule, over budget, or without anything to show for all the money they spent.

How we built the Atlas:

1. We began by creating a matrix of dozens of considerations that a thoughtful policymaker or funder would ideally weigh before deciding how to fund a project.

2. We looked at every major funding approach, from grants to R&D tax credits to advance market commitments, analyzing when they work well and when they fail to meet the mission.

3. We spent months deep in the weeds of contract theory and incentive design, looking at historical examples and the state-of-the-art research in innovation economics.

4. We then worked to turn that research into a tool that time-strapped policymakers and philanthropic funders could rely on at the start of an innovation funding cycle.

5. Three years later, we are launching just that: a new (and visually stunning) website to help funders decide how to best incentivize innovation. And all they have to know… is what they currently know about their innovation goal! The Atlas takes care of the rest.

How to navigate the Atlas:

Answer questions about your goal to find the funding approach aligned with the information you have.

Each funding mechanism has its purpose for particular technologies and specific moments in development.

There shouldn’t be an ARPA for every field, just like we don’t need a prize or AMC for every innovation. The Atlas helps you navigate those tradeoffs.

[Alex Imas @alexolegimas]

This is not a mystery: because of grade inflation, many students don’t do the reading because they’re not incentivized to do the reading.

Students are just as smart and ambitious as they always have been, they’re just put in an environment where working hard isn’t explicitly rewarded. We are not cooked if faculty can reverse course (as many are already doing). Assign the readings, design the course to reward hard work and learning, and you’ll be amazed at the results.