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super excited to share that we were accepted into @StartX fall batch 2026. will be sharing more in the coming weeks about what we’re building at Rizal. onwards!
strong agree @saranormous
when you work with the best, hit a bottleneck and your cofounder/cto has previously built gpu rigs in his spare time @hichilo_ ...
onwards 🚀💪
good read
A lot of ink has been spilled on GPU financing lately. I spent over a decade trading ABS on Wall St, and structured over $2B+ of debt financings as an operator (including at @tryramp). Figured I'd try to make sense of this market, so I went back to a framework I knew well: aircraft ABS and aircraft engine ABS.
My take: a GPU neocloud runs on airline economics, and the debt behind it looks a lot like aircraft engine ABS.
The parallels:
• Perishable inventory. An empty seat after the door closes is gone forever. So is an idle hour on an energized H100 rack.
• 85%+ of cost is fixed (servers, 10-year rack leases, 10-14% debt service). One more hour of compute costs pennies of power.
• Utilization is everything. Airlines break even around 80% load factor, a levered GPU cluster around 65-75%. The returns live in those last 25 percentage points.
• NVIDIA is Boeing and Airbus rolled into one. Powered shells and grid interconnects are the new airport slots.
• Wall St copied the aircraft financing playbook: take-or-pay contract plus servers in a bankruptcy-remote SPV, 70-80% LTV.
Part 1 of my blog post goes into a lot more detail:
alexyangsong.com/blog/alumin…
Daniel Tebes retweeted
pretty sick.
Surprise 1: Anthropic acquired a one-person biotech
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.
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Surprise 2: that person is me
Surprise 3: I’m here to tell you all about it
Yes, this is what you think it is; after 6 yrs of increasingly loose anonymity: the Professor Oak reveal post
…and yes, it has gotten so boring around here that I’m literally doxxing myself to spice up the timeline
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Ok, allow me to introduce myself. My name is Cody Tranbarger. I mostly tweet, but occasionally find time to dabble in drug dev & starting/building biotechs. My past lives span the VC, crossover, startup continuum; Prof Oak has been my way of keeping a digital foot in those worlds, despite having my physical feet planted elsewhere.
The past 18 mo, ‘elsewhere’ was Mithril Medicine, a co. I founded to develop bsAbs for LAMA2-CMD & other ECM-related genetic diseases.
Mithril was born out of hands-on building with @MikeNGladstone, @JasonPaulRhodes, @LifeSciVC, & team @atlasventure. Somehow, I made enough half-decent calls along the way (even a broken clock is right twice a day, eh?) to convince them Mithril/I warranted a small seed. While I can hardly rationalize such an outrageously cavalier bet, I’m grateful for it, & I'd partner with Atlas again in a heartbeat.
In typical AV fashion, Mithril’s primordial form was virtual, decentralized, & ultra-lean. This stage tends to be short-lived, but for Mithril, it worked surprisingly well, for surprisingly long, thanks in part to the rapid maturation of the AI tools I used liberally.
Eventually, Mithril became a bit of an experiment itself - to find the upper bound of how far it could scale as a single operator + a bunch of Claude (+ a stellar bench of part-time consultants).
In parallel, I started spending time with @nc_frey, @an1lam, & @CoefficientBio team; and promptly experienced, for the 1st time, AI at the frontier. Soon thereafter, Mithril stopped looking like an experiment, & started looking like a preview of an entirely new operating model.
From that spark came “Pipeline-in-a-Person.” The idea immediately resonated when we shared it here back in April; and it was central to the shared vision that led to Anthropic’s acquisition of Mithril, & my joining the team.
Why join Anthropic? Three reasons.
(1) For Mithril, the programs continue, with more resources & infrastructure behind them, and as much Claude as I can put to work.
(2) For me, the chance to start several more programs, & find out how many one person can actually run in parallel - an almost unprecedented luxury for a curious drug hunter.
(3) For our mission, a path to institutionalize & scale this new way of working, then put it in the hands of all of biopharma, all over the world - which, candidly, I find the most energizing.
Will AI x Bio cure diseases? Unequivocally yes, imo… but not by the route that gets the most airtime. Rather than timeline acceleration & compression, I see orchestration & parallelization as today's low-hanging fruit.
Most drug hunters see a handful of go/no-go calls in a career, & spend entirely too much of the rest trading emails (my inbox can attest).
Give them the bandwidth to run several programs at once, and the time to focus on scientific judgment, and I’m willing to bet they’ll make better calls. And at scale, better calls translate to more drugs, higher hit rates, broader patient impact, & likely, a disease or 10 or 100 cured. I couldn't be more excited to build toward that future @AnthropicAI.
Finally, a heartfelt thank you to BioTwitter/X. The end of anonymity is bittersweet, but if anyone’s earned a proper introduction, you all have.
It’s difficult to overstate how much this app has done for me: a decade of education, plenty of friends, many job opportunities, several mentors, a few investors, even a co-founder (fmr). It was the generosity of this community that inspired me to start posting 6 years ago, after just as many years lurking. I hope I’ve been able to pay it forward, in my own small way.
But, to be clear, Prof Oak isn’t going anywhere. Same beat, same themes, and now, a front-row seat to drug development at the frontier. Stay tuned for dispatches from the trenches; and remember...
Gotta Cure 'Em All
Daniel Tebes retweeted
These were three of the biggest science results I’ve seen in my lifetime
Just absolutely jaw-dropping results
Within ~5 years it seems like any scientific result a human is capable of understanding will be knowable
I find this incredibly exciting
We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory.
openai.com/index/building-sc…
we’re super grateful to be a part of the @StartX community
"It feels like overnight we gained access to an incredible group of people who are a message away.”
@DanielTebes (F26), co-founder and CEO of Rizal AI, on the StartX community.
This is what happens when Stanford’s best founders help each other.
anyone working in enterprise should read this. thanks @bhorowitz @alighodsi
Daniel Tebes retweeted
Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory).
AI starting to help us improve our understanding of the universe! openai.com/index/building-sc…
yep couldn't agree more, the speed of change even feels day-to-day in the trenches... we talk about people being displaced by ai when really we should be talking about entire companies and industries being reinvented ground-up by "ai-natives"
@pmarca @bhorowitz @a16z
Marc Andreessen says the gap between what AI-native startups and incumbents think AI can do is the widest he’s ever seen:
"The AI-native early stage startups have just a completely different view on what they can do with AI than the typical incumbents in almost any industry, almost without exception."
"AI is just racing ahead in capability even week by week... There are going to be a lot of industries that are really up for grabs in a fundamentally more profound way than maybe has been the case in the past."
"The founders are gonna have a level of superpowers coming from the use of these tools and these technologies that they just simply haven't had in the past... Had I had Astra and Netscape in 1994, like, oh my God. Like, Ben, what would you and I have been able to do at Netscape?"
"Those entrepreneurs are gonna color way outside the lines. We already see this. They're not gonna think of themselves as like, 'I'm just a software company,' or, 'I'm just a hardware company,' or even, 'I'm just an AI company.'"
@pmarca @bhorowitz
got asked this question today - what’s a plan? basically it’s a prd meets tech spec for agents. we’ve built a skill that takes a feature or enhancement request and puts together a “plan” doc that we run through our graph of agents who build it and document precisely who did what (so we can hold each agent accountable given how black boxy this stuff is). we’ve found the key part of the plan is defining a good acceptance criteria. we typically push 10-20 plans at a time to our graph and it builds most of them in parallel coordinating agents and prs to avoid bugs/issues
three things i’m most excited about with our setup
1. anyone on the team can create and publish a plan to our graph. we have sales and support people learning from our customers and creating plans in slack with our claude bot and skills.
2. this works for not just building new features/enhancements but improving our own dev process including building plans for an agent-based qa process (that includes retroactive qa via a bug bounty), core infrastructure and database scheme design (we were able to move core infra to mexico in a few days) etc.
3. our team and customer calls are transcribed and connected to our company brain as are in-app usage data and all of these plans and our monorepo codebase (a subject for another post) which gives our agents robust context when drafting these plans alongside a team member. we’re getting close (but not there yet) where we can turn around a feature request in a customer call.
just the beginning
GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration?
DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it.
That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders.
With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets.
openai.com/index/scaling-ai-…
good read
One of the most important trends in business over the past few decades has been industries "going quant."
Think Renaissance Technologies in finance, Obama's quantitative campaign operation, or Billy Beane's Oakland A's.
Applying systematic, data-driven prediction to important decisions improves how an industry works and, more importantly, changes who wins.
I think AI is setting up the next wave. My three bets: trial law, entertainment, and general management.
Why now? AI makes far more information available for systematic prediction and makes it possible to generate and test alternatives that were previously too expensive to experiment with.
In my latest Faint Signals, I explore what makes an industry susceptible to going quant and where the next opportunities might emerge.
What other industries would you bet on?
What Goes Quant Next? faintsignals.substack.com/p/…
come do your life's work with us @rizalaihq
millions of good businesses can't get affordable loans or insurance. not because they're bad businesses but because understanding them costs more than the loan or policy earns. a farm loan in mexico takes 4 weeks to underwrite with teams of people reviewing documents, visiting fields and interviewing borrowers. this is true for other types of smb loans too.
every time the cost of underwriting collapsed a new asset class was born… us mortgages, consumer credit, e-commerce smb credit.
ai is the next collapse.
we're building the system that understands a business in minutes and today supports US$250M+ in loans across 25+ institutions in mexico.
i farmed for five years and watched good farmers never get the loan. i also helped build these rails for consumer credit at affirm and saw what tech can do to change people's lives.
we're hiring builders and partnering with investors who give a shit. dm me. come finance human ingenuity
Worth a read congrats @nickcammarata
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
From a LinkedIn exchange:
One abstraction of what we’re building is an ib team of agents that can underwrite any complex business in the world opening up entire economies to financing that today are constrained by the cost of pricing risk (super manual workflows). One of my cofounders is ex-Google /morgan Stanley ai engineer and my other is ex-ib, chemical engineer and jack of all trades. We’re working with a central bank, some large family offices and exploring private credit partnerships to fund these complex rural business starting in farming in Mexico and are already expanding into other types of rural businesses.
Basically an ai leap frog story…teh global rural economy is built on manual financial workflows and we’re building alternative infra from the ground up for agents that will touch trillions is value
Sharing from a text exchange with family in the East coast…
Hey guys really appreciate you sharing the daily episode and Ezra’s podcasts yesterday. Had a chance to listen to the daily and part of Ezra’s episode with Jensen and another with an ex-OAI safety policy employee. Also did some reflecting last night and wanted to share some quick thoughts. Also bounced some ideas off yui who helps me stay grounded with this stuff.
1. I’m deep in the bubble and I need to be. Building a startup is incredibly hard, risky and honestly crazy especially with kids and a wife in the picture. For me it requires an irrational belief that I am doing something that is going to make the lives of millions of people better. At the same time I’m constantly questioning this belief but the questions are in the day to day execution and less high level / heady debate about the tech being good vs bad — that is outside my control and a distraction from me accomplishing my goal of starting a viable high impact company.
2. I struggle with the nyt coverage of tech and business and honestly have stopped consuming any of its content for a while now. It’s partially a function of having limited time and partially a function of more thoughtful and detailed coverage of tech/business being available elsewhere. My friends and it feels like most of the tech community has also moved on from NYTimes including newer media options like Ezra and the daily. I heard Jensen went on Ezra from a tech podcast and watched spliced clips but didn’t watch the actual episode.
3. There seems to be a gap widening between those building ai and with ai and those who don’t. And this gap is also largely geographic — Bay Area vs everywhere else. This is starting to be reflected in media coverage and culture. Ai safety, data centers, unemployment fears — there’s a consensus view within the ai bubble on these things that is largely informed from the bottom up (the people who are building these things) that is rich with details and nuance — unfortunately I don’t see a lot of these details in more mainstream coverage of Ai. Tbf all this stuff is quite complex and there’s a steep learning curve that makes mass coverage near impossible.
4. I can’t describe well how fast things are moving. It’s like nothing I or anyone has experienced. And it forces everyone who is serious people to be extremely honest and first principled with themselves about what they know / don’t know. It’s also extremely exciting to build in this environment but I can’t express how difficult it is — not in the technical of building but rather in the strategic sense. If we build in the wrong direction (which we and everyone does at times) it can be exponentially (and existentially) costly because the rate of technological change is compounding so quickly
5. So far I don’t see my experience as a founder building an ai-native reflected in media coverage outside the bubble and I think these ground up details matter. I’ll give an example, here’s a great conversation about the state of open source frontier models and what this means for ai safety as well as where the frontier is going that reflects most builders’ take on ai safety now (alignment is an engineering problem we’re going to solve and that needs many people focused on it likely via open source methods).
youtu.be/up4sG9RM20M?is=3t3f…
Amazing mistral’s small model release and llama 3 was only 2 yrs ago…
Love what @MishaLaskin and team are building at @reflection_ai. Ty for a great discussion @saranormous @eladgil
ICYMI, new @NoPriorsPod with @reflection_ai cofounder @MishaLaskin on China, safety, market structure of ai, the business model of open models, reasoning efficiency, distillation, and what it takes to ship massive frontier AI
“Competitive tension is just good.”
























