+0.0
net board stance
what this means
3.52
+0.6% · close 2026-11-06
-18% / +9% / -24%
1m / 3m / 12m
-58%
vs SPY since 2025-08-01
32%
of 52w range · -29.0% off high
0/1
hit rate as primary · vs SPY -35
Where All-In Alpha stands — 0 live ideas
No active idea holds this ticker. Anything below is history.
Where the winds are blowing
NET BOARD STANCE, LAST 60 EPISODES —
rising = the besties are building this position, falling = abandoning it. From the
conviction ledger; an idea counts from birth until its window closes. Dots mark episodes
where this ticker's ideas were actually mentioned; a dashed stretch is silence.
Last mention E216 · silent for 79 episodes.
PRICE VS SPY OVER THE SAME WINDOW, % — did the
talk lead the tape or follow it?
Who's pushing which way
each voice's net push ON THIS TICKER — their most recent stance per idea × that idea's direction ON THIS TICKER × strength, so supporting a bearish idea pushes down, unless this ticker is listed as the other side of it. Not conviction (that lives on the idea); this is direction of travel per person. what w= means
Track record on RXRX
As a PRIMARY play the besties are 0 hit / 0 partial / 1 miss over 1 closed window — credit 0.0, average vs SPY -35.0. Adjacent plays are listed but never scored.
| idea | call | play | result | return | vs SPY | scored |
|---|---|---|---|---|---|---|
| 🤖 AI-designed open-source gene editors break the CRISPR patent toll | ▲ LONG | adjacent | WRONG | -9.5% | -21.7 | 2025-05-03 |
| 🤖 AlphaFold 3 turns drug discovery into software — Google/Isomorphic captures it | ▲ LONG | adjacent | WRONG | -9.0% | -18.7 | 2025-05-10 |
| 🤖 AI drug discovery hasn't lifted pharma hit rates — the M&A bid is falling | ▼ SHORT | primary | WRONG | -11.3% | -35.0 | 2024-01-20 |
The tape — what was actually said
every capture on any idea holding RXRX, newest first · quotes verbatim, timestamps deep-link into the episode
The biological models are different in that I think it's a much smaller population of people that will use it. I think they do have to figure out how to take these models and complement the existing pipeline they have. The pipeline they have right now I think is pretty brittle.
we talk a lot about the idea that you can use a computer, state the phenotype or the physical characteristics you want in a biological organism and have the software resolve the whole genome, all the DNA needed to make that physical organism real.
I don't know if you analogize Evo 2 to, I don't know whether it's GPD 2 or 3 or something, but I think we're going to see a similar Cambrian explosion of applications over the next couple of years.
there have been literally dozens of companies that have been started since DeepMind was published, and probably several billion dollars of capital that's gone into companies that are creating new drugs, creating new industrial biotech applications, using this protein modeling capability that was unleashed with DeepMind a number of years ago. So it really has transformed the industry. It'll be a couple of years before we see a transform in the world
And they've basically kept all the IP for AlphaFold 3 in Isomorphic. So Google is going to monetize the heck out of this capability.
health care, I believe, should be pretty transformed by this. But the thing I'm personally most excited about is the sort of doing faster and better scientific discovery.
There's a simple truth to all this, which is every single life sciences lab on Earth is using this technology today. It has absolutely revolutionized life sciences. It has changed everything. It has reset the trajectory of human health, of agriculture, and of industrial biotechnology.
And so it's fascinating to me taking nature and trying to patent nature.
And so what's really happened is the value of acquisition in M&A has gone down, even as the technology capability has gone way, way up. And why is that? It's because this stuff has yet to be proven to actually meaningfully improve the hit rate for these drug companies.
So if you take 99% of the junk out of the top of your pipeline and you only focus on the 1% that the software predicts will be more successful, you much more quickly get drug discovery through the pipeline and you have a much higher hit rate.