Building at the intersection of science + finance. Co-founder, Parker Institute for Cancer Immunotherapy. Co-founder, Related Sciences. Dog lover.
Joined July 2011
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for most diseases, the best medicines are soon to come. why?
better disease targets x 10 new/better modalities x better discovery x better clinical precision = golden decade for new drug creation, addressing 2 big opportunities:
1. treat the ~75% of 13,000+ disease segments w no approved medicines
2. replace last generation medicines w more effective and safer meds
nytimes.com/2023/06/23/magaz…
Adam Kolom retweeted
Josh was my first mentor when I came to the valley. This clip in particular was inspired by him and seeing him operate. So grateful @joshelman and excited to see him back in the Arena.
Have known Peter Sellis since 2009. He is a legend and helped build the path toward the first $1B revenue for Snap and Discord.
Glad to see him sharing his insights. Great PMs should eat this up
Adam Kolom retweeted
A lot of my friends have watched this video and expressed some melancholy over the fact that we’ve already seen robots do this. “Why, Chris? Why is this impressive? It’s just a robot folding towels, making beds and picking stuff up again.”
Well yes, we have seen robots make beds, fold towels and clean rooms before. What we have not seen is a humanoid take those trained behaviors into 30 completely unseen homes, with unseen objects, with zero data collection or fine-tuning in any of them, while also showing a predictable human-to-humanoid scaling law as its pretraining data increases. Holding everything else fixed, Index pretraining increased full-task zero shot success from 9% to 56% over a 522% INCREASE in success rate.
The only thing I was personally disappointed by is that it’s only three tasks. I wish we got wiping down mirrors, putting food away, cooking, etc. But this is basically the beginning. If Figure can scale this from 3 behaviors to 10–12, while pushing zero-shot success toward 80–90%, then you start getting very close to a robot you can actually deploy into a home without collecting data from that home first. And that 56% doesn’t mean the robot necessarily fails forever on the other 44% either Helix 2.5 already shows self-correction and retries during long-horizon tasks but Figure has not published a separate “retry until success” number, Just like how when you're a kid, you fold clothes or you make your bed, and you might need to retry a couple of times to get it right.
If Figure is already at 56%, but let's say after two tries they're at 80% or 90%, they could begin deploying autonomously in homes by next year and just add tasks as updates.
That’s why this shortens my robot timelines. Index is already collecting human experience at massive scale, Figure says transfer improves predictably as that data increases. Now I want to see what happens after another few large Index training runs and a much broader task set. And that’s before Figure 4, which is already in design lock with parts being shipped and which Figure says is its largest hardware jump yet.
Adam Kolom retweeted
I've attempted to map everything in oncology in a public website and open source repo for all.
The site is trying to get all cancers, products, technologies, bottlenecks, people, startup opportunities with 1000+ ideas for upgrading the field.
If you have a loved one with cancer and you are technical or can engineer, go take a look, file improvement requests or bugs or help with the open repo and make this the best open and free info resource for individuals, researchers and educational use.
This should save people time, aid AI oncology projects and generate positive action. If you are not technical just complain in this thread about broken or annoying or things you want and I'll fix them live.
Some of the interesting pages:
Treatments: onco.cc/drugs
A gallery of the molecules being used onco.cc/molecules/
And targets: onco.cc/targets/
The technologies in oncology: onco.cc/technologies/
1100 ideas for helping oncology: onco.cc/ideas/
Bottlenecks on oncology: onco.cc/bottlenecks/
Open questions (LETS GO RESEARCH PEOPLE) onco.cc/open-questions/
Startup requests (LETS GO STARTUP PEOPLE) onco.cc/startup-requests/
Mechanics of cancer: onco.cc/mechanics
Battlefronts: onco.cc/fronts/
Isotope supply: onco.cc/isotopes
Key papers: onco.cc/key-papers
Pipeline funnels: onco.cc/pipeline
Cancer by type: onco.cc/cancers/
Institutional rankings: onco.cc/institutions/
The startups: onco.cc/startups/
Heros and heroines : onco.cc/heroes/
Key medical people: onco.cc/people/
Here is the project roadmap: onco.cc/roadmap/
Models and data sets: onco.cc/models/
There are other views as well, take a browse.
Try making a PR if you have an upgrade to this on the repo here: github.com/judegomila/OnCo
If you are biologically/medically minded and something is wrong, file a bug as well or say on the thread and we will get it fixed live.
If this is a useful project star the repo and help get it calibrated.
I've tried to add some other languages but I cannot speak them so tell me if that doesnt work well.
I believe we will crack oncology and having total information dominance is key to the problem.
Let the feedback flow!
very cool first person POV video gets to the heart of the amazing ( canary-in-the-coal mine) hugging face openAI testing swarm incident. 👏
also interesting that we required AI models to interrogate all that happened over the course of months, so future rogue agent events may become inscrutable to humans should the detectives also be the criminals
TECHbio: "we will cure all diseases in 5-10 years"
BIOtech: "our candidate 1374 shows extraordinary early promise to lightly improve the standard of care in a single disease pending 8 more years of studies and 3 significant capital raises, public markets-willing"
Proud (years ago) cofounder of this skin science discovery firm. discovery of significantly more efficacious actives for every skin condition is definitely possible!
Meet @outerbio and YUNA. We are building the future of skin health. outerbio.com
Welcome from Michael, Me and the entire team at Outer Bio.
historic threshold.
An internal version of Astra, @OpenAI’s next major model family, solved 10 major open problems in mathematics, quantum complexity, and theoretical computer science.
We believe it will be a major step for scientific reasoning. openai.com/index/ten-advance…
why is AI so bad at writing clearly and simply? it read the entirety of the human corpus and came out in love with convoluted, dialectical, labyrinthine complexity?
Adam Kolom retweeted
I think it’s time to revisit the accredited investor laws in the US.
Companies are staying private longer, where only accredited investors (aka rich people!) can invest. Retail investors can only come in after IPO, when much of the upside has already been captured.
These rules were created with the best of intentions, to protect regular people from scams - a noble idea. Unfortunately, in practice they've often made it illegal to get richer, unless you're already rich. A regressive tax!
We have to judge policies based on their outcomes, not on their intentions.
These are two possible routes I see:
1) Replace the rule with something merit-based, like a financial literacy test. Pass it and you're accredited. Having a qualification based on competency rather than your bank balance or income seems far more fair.
2) Remove the rule entirely. Let consenting adults assess their own risk. Disclosure requirements stay and fraud enforcement stays to punish bad actors.
wonder how long until openAI releases an equally capable "dual use" model, yet gets exactly zero government friction (because they donate to and ingratiate the trump admin circles)
wonder how much this experience changes anthropic willingness to publicly talk about model safety
wonder how soon USG ends up with a stake in the frontier labs
Another great recent interview with $NU's CEO. Nubank started because the traditional banking experience was terrible. David Vélez faced huge delays, revolving doors, and extreme frustration just to open a simple bank account in Brazil. He realized that major banks were failing the consumer. To solve this, Nu focused on fighting complexity and empowering people.
Today, this mission has turned Nu into a massive financial giant. They now have 135M customers across Brazil, Mexico, and Colombia. In the first quarter of 2026 alone, Nu reached a record $5B in revenue and $871M in net income.
From the beginning, tech gave Nu a massive edge. In 2012, the rise of smartphones and cloud computing allowed Nu to build a bank without spending billions on physical branches and old computer systems. This digital-first model makes Nu 20 to 30 times more efficient than traditional banks. This incredible efficiency resulted in a record-low operating efficiency ratio.
Now, Nu is pushing this advantage even further through AI. They are completely rebuilding the bank around it. AI now handles real-time credit pricing in under one second and has sped up engineering testing cycles by 90%.
Because Nu operates with such low costs and deep data insights, they can safely lend money in ways that others cannot. They are aggressively growing their credit card and unsecured lending businesses. These products made up 98% of their new exposure in the quarter.
Nu does not worry about minimizing late payments. Instead, they use AI to price risk extremely well. This ensures their loans remain highly profitable over the long term. They keep loan durations short so they can react to market changes much faster than incumbent banks. They are also using these advantages to capture the massive small business market at nearly zero acquisition cost.
They are quickly growing credit limits to win over high-income customers. Nu does not view itself purely as a bank. Their true goal is to make customers love them. Because 100% of their customers are digital, Nu can easily sell non-banking products. They are now expanding into marketplaces, travel, and telecom services like NuCel to make daily life simpler.
Nu is also taking its winning formula to new countries. Their operations in Mexico and Colombia are growing very fast. They believe their Latin American culture helps them build strong emotional connections with customers. This gives them a major edge over traditional banks.
They are also testing an expansion into the US. This move is a low-cost bet that risks less than 100 basis points of their efficiency ratio but offers massive upside. Despite their massive success, Nu is still at the very beginning of its journey. The global financial market produces around $7T in profits, and traditional banks still control 97% of it.
By combining a pro-consumer culture with top-tier AI and an unbeatable cost structure, Nu plans to keep taking market share from legacy banks. They aim to eventually serve hundreds of millions of people across many countries. It is pretty wild to see that despite their growth over the last decade that a large part of their future growth will involve winning over even more customers from legacy banks.
Adam Kolom retweeted
turning out that the biotech winter is not temporary, not thawing when rates drop. We are in a structural consolidation where sub $300M funds doing early stage discovery seem to be hollowing out. I doubt the science has gotten worse. The funding architecture around it has become increasingly inhospitable to anyone who isn’t already at scale. That is a long term problem for therapeutics discovery even if mega fund IRRs look fine in the near term
Just got back from a Life Science investor conference.
The mood of sub-$300m VC funds is not good.
They are having a very hard time raising money. Many people got laid off just in the past 6 months. Apparently it is the same in EU and US.
But Mega funds have no problem raising.
At its most extreme concentration, capitalism conceptually morphs into a technocratic form of communism.
Adam Kolom retweeted
We are back. After one year of quiet building.
Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability.
For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans.
Solving it means rethinking the whole stack from the ground up:
- A robotics-native foundation model.
- A 1:1 human-like robotic hand.
- A noninvasive data collection glove for motion, force, and touch.
- A simulator that turns weeks of experiments into minutes.
GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm.
Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on)
We are approaching the endgame for robotics.
And this is just a beginning.
futuristic AF. hope time rules of inception don't apply
AI seems to be all about thresholds.
Right now, appearance of infinite exponential demand for intelligence/tokens as API at anthropic etc
but above a certain capability level, why wouldn't apple just ship a mac with an opus 4.7 level model fully harnessed?
open model intelligence will eventually be *enough* for 99% of even hard tasks.
if china and google distill powerful models into local ones, this should really hurt the AI growth story in a year or two above some key intelligence threshold we are already approaching
sorry but this is seriously fucking impressive
china just shipped a claude code-level ai model small enough to run on your laptop.
it codes better than opus 4.5 and its tiny. 27B beating models 15X LARGER than it.
best part? shit is fully open source.
no cloud, no rate limits, no api keys
this model plus kimi k2.6 tells me open source has caught up to the frontier models
how the fuck did china pull this off?
amazingly cool sentence / finding
What's especially cool is that the flagellar motor exploits the indivisibility of 5 by 2 to run on a fuel of rising entropy as protons diffuse into cells. For me as a physics and math person, this is biology at its best. quantamagazine.org/what-phys…
