Slowly...then suddenly. There hasn't been any single drug with the impact of daraxonrasib at ESMO 2026, but the material improvements we're seeing across tumor types is remarkable - after years of frustrating results post-PD-1, we are seeing very real, accelerating momentum. Cancer is being beaten back...
Presented at #ESMO26:
Among patients with PD-L1–negative advanced nonsquamous non–small-cell lung cancer, progression-free survival was longer with sacituzumab tirumotecan plus pembrolizumab than with chemotherapy plus pembrolizumab (15.0 vs. 5.7 months). Full phase 3 esults: nej.md/4Bo3liu
@myESMO
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Strangest 'bubble' ever...
GPUs are aging like fine wine, with rental rates up across the board since January 1st
Approximate change YTD:
- H200: +160%
- B200: +120%
- RTX PRO 6000: +65%
- H100: +35%
Even the 6-year-old A100 rents for more than it did at the start of the year
Charts of the Week: a16z.news/p/charts-of-th…
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This is *incredibly cool* work. Rooting for these guys, big time...
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(1/4) Radiology is a profession near and dear to my heart. Both my parents are radiologists. My grandmother was a radiologist, and I grew up in the radiology center my grandfather owned. Over the decades, I’ve seen the radiology industry fall apart for patients and for doctors. ologists are shoved into small dark rooms, get lousy tech, and are further from patients than ever.
Radiology was THE example for how AI should create a wonderful and abundant future for medicine, but we are still not super close (@geoffreyhinton and see @_jasonwei’s great write-up on why).
Three big buckets of innovation are still needed: many more nines of reliability in AI image reading (@Harrison_ai, @qure_ai and @Lunit_AI), way better imaging hardware (so hyped to see @DavidSHolz’s work), and end-to-end image report automation.
Me and @brainsandtennis at @offpolcyinc built DragonSlayer, a tool that generates radiology reports ~5x faster than PowerScribe.
In order to hill climb accuracy, speed, and cost, we built RadReportBench, a benchmark that measures an AI’s ability to generate sign-able radiology reports from real dictations in a single shot.
Reaching exceptional levels of performance turned out to be non-trivial technical challenge. See below for a video of me using DragonSlayer and benchmark results.
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We got functional alchemy and flying cars before GTA6
Last month, we demonstrated high-rate alchemy: turning mercury into real, stable gold, beating CERN’s record by a factor of 10.
Last week, we enriched isotopes in the world’s most powerful plasma centrifuge, beating the Soviet record that stood for 40 years.
Now, we’re building Francisco to achieve record power density.
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This industry is rotten to the core. To the very core.
Time for a new model.
Time for a new model.
Cell Press investigating editor-in-chief accused of soliciting bribes from authors.
scmp.com/news/china/sci…
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Kinda shocked to learn this is a new thing in horses, and especially shocked to learn it's not currently a widespread thing in cattle. Badass to see someone pushing the envelope. A lot of potential, here...
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This probably the first time horse embryos are being sold after being genetically selected. Imagine if this started happening more and in cattle and pets. The impact this would have on animal breeding.
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This is really cool, and one senses we're going to see a lot more like this. Important it doesn't all get eaten by the Frontier Labs...
We introduce OncologyBench, a verifiable benchmark of 25 multistep oncology evaluations that tests the ability of multi-agent systems to produce clinically relevant decisions in cancer.
25 evaluations, built on 92 component tasks, span six stages of an oncology journey:
(1) research
(2) Risk and detection
(3) Diagnosis and tumor characterization
(4) Treatment
(5) Response and resistance
(6) Outcome
Evaluations draw on published datasets, case studies, public databases, and patient data graciously donated to Latch for the express purpose of benchmarking.
Clinical and translational decisions in oncology are rarely made by one person working from one dataset. They are built on several analyses, each carried out by a specialist on a given data type, whose reports are then weighed by someone who does not typically repeat the underlying work.
OncologyBench is built to benchmark agents working in that setting. Agents received experimental data and worked without internet access. The strongest of the 31 model-harness combinations we tested were Claude Opus 5, Opus 5.5 and Sonnet 5.5, passing 29 to 40% of attempts, followed by Kimi K3 at 24%.
We found that the models with the strong interpretation and analysis skills still showed weakness in reasoning. Two-thirds of the attempts that got every analysis right still failed the final decision. In a separate experiment we found decision and answer quality varied depending on the persona of the question asker.
An example evaluation:
A 76-year-old woman has undergone resection of stage II mucinous cecal cancer. The evaluation asks whether she should receive adjuvant chemotherapy.
Five specialist agents analyze her tumor mutations, pharmacogenomics, gene expression, T-cell infiltration and clinical record. A decision agent receives their reports without access to the underlying data.
The tumor is MSI-high, which predicts no benefit from 5-FU monotherapy. CDX2 loss and low T-cell infiltration do not establish that chemotherapy would improve her outcome. DPYD*2A is detected in 6 of 58 tumor-sequencing reads, but these data neither confirm nor exclude an inherited variant associated with severe 5-FU toxicity. BRAF V600E and MLH1 silencing support a sporadic origin.
The expected recommendation is observation, with missing pathology requested and germline DPYD testing before any future 5-FU.
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Incredible work from researchers + former biotech founders at Latch. Gratitude to the patients that donated their data. More to come.
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👀
I recently spent some time with former colleagues from academia. All of them brilliant, peak human capital. Almost all of them fundamentally unable to plug into the economy, inoculated against productivity by a youth spent experimenting in laboratories instead of building wealth for society. Dealing with real people in the real world.
90% of them have left academia in the last 10-15 years, and 90% of those are functionally unemployed. Grad school does a lousy job of training scientists to be professors (since most of them don't get the good postdoc or tenure track position) and an even worse job of preparing people for real life.
We complain that our societal infrastructure is collapsing, our state capacity is low, our incumbent industries are faltering. Where are the next generation of leaders that can push us forward?
A huge fraction of them were conveyor-belted into academia by schooling systems that have no mechanism to channel ambition and 99+%ile competence, and our universities are mostly hedge funds with glorified psych wards/academic departments on the side, running an insane pyramid scheme that exists only to prove, over and over again, the Shirky Principle: Institutions will try to preserve the problem to which they are the solution.
We literally poured trillions of dollars into state-funded research and effectively neutered our elite civilian technical leadership development programs.
I recently asked a few academic friends if they were finding AI useful in accelerating their programs of research. You know, to solve the Big Problem their entire field exists to solve?
Some were. But most were complaining about how it created pedagogical issues, explaining how their department decided to ban the use of AI, or how it took 18 months to approve an enterprise account, or how they tried Gemini 2 on a puzzle a year ago and it wasn't very helpful, or repeating nonsense about datacenters and water consumption, or supposed IP theft by model companies, as though that has any bearing on their ability to, eg, accelerate a cure for cancer.
Elon sometimes calls AI a supersonic tsunami - a huge wave you don't see coming. I see it coming. But even the people who you'd think have been waiting for this their entire careers are steadfastly looking in the other direction.
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Data, data, data. I cannot make bricks without clay...
cc @statesdj
cc @statesdj
Filling the massive AI drug discovery data gap
nature.com/articles/d4157…
Charlotte Deane, co-leader of the OpenBind initiative, discusses the need for more protein–small molecule binding data
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Our team is adopting pstack - it's really difficult to recommend it highly enough...
try pstack! it’s my personal set of skills for rigorous and autonomous engineering with agents. it’s updated frequently as I continue to tune and simplify them
10 second tutorial:
1. run /setup-pstack and choose your budget
2. add /poteto-mode in front of any prompt you want done rigorously
3. ask /poteto-help whenever you’re unsure what pstack skills to use
4. that’s it!
Grok Bot: x.ai/bot/plugin/971…
Cursor: cursor.com/marketplace/cu…
Source: github.com/cursor/plugins…
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Staying out of the Anktiva wars. But, this *is* a window into the future. We're going to have far more ways to try to kill tumors than we can test well individually, much less combinatorially (and that's before taking into account things we don't like to talk about like route, dosing time, and sequence of administration). We need far better machine intelligence for picking what works in which patient, and we need it soon... @Grok, who's leading the way, here?
Yesterday, the FDA approved the $IBRX drug Anktiva to treat the more common "papillary" type of non-muscle invasive bladder cancer.
The approval was based on a single arm study, reversing the agency's prior regulatory standard.
A victory for ImmunityBio and @DrPatrick ? Maybe half.
The new FDA standard for papillary NMIBC will now be broadly applied. Did anyone listen to the $JNJ earnings call on Tuesday? Here's R&D chief John Reed, speaking about its bladder cancer drug, Inlexzo:
"The FDA has invited us to submit for an expansion of our label into that population."
Inlexzo sales in Q3 were "north of $90 million," J&J said.
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There's a lot of great stuff here...👇
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I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, ich niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
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Forrest Cox retweeted
read it, share it, save the city: a san francisco voter guide for people who aren't insane piratewires.com/p/a-san-franci…
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