Daisy Wolf

Partner @a16z leading bio + health technology investments. Reluctant tweeter. Alum: @Meta @YaleLawSch @StanfordGSB

[Daisy Wolf @daisydwolf]

1/ In the past few months, the founders of Instacart, Spotify, and Coinbase have all started new healthcare companies. This may seem odd to some – why are these seasoned tech founders jumping into tech-driven healthcare? A 🧵: https://t.co/4hE1y5ftcA

[Daisy Wolf @daisydwolf]

@julesyoo waaaaaahooooooooo!

[Daisy Wolf @daisydwolf]

New @a16z podcast series in which we ask people at the forefront of health & tech to walk us through a day in their life – food, sleep, exercise & meds. First up, @hubermanlab and I talk about his entire supplement stack and how he got @pmarca to stop drinking.

[a16z @a16z]

Stanford neuroscientist Andrew Huberman built one of the most popular podcasts in the world by turning health research into protocols millions of people actually use.

His top three habits are surprisingly straightforward: get bright light shortly after waking, resistance train, and make the sleep you can get as high-quality as possible.

He joins a16z’s Daisy Wolf to walk through the protocols he actually follows, from his 35-year workout to the lockbox he keeps his social media phone in, and explains why packing in every protocol can backfire if it gets in the way of the rest of your life.

For founders, he explains why the hours your brain does its best work are worth guarding like a fight, how to find the most real work you can sustain, and why sleep is where neurotech will win first.

0:47 Why zero drinks is easier than two
4:39 Meditation means watching the chaos
6:25 Sunlight first, caffeine if training
7:20 The workout he's done for 35 years
10:09 Two sets to failure, low volume
13:11 Find foods that make you feel good
15:55 Why eating too clean backfires
17:29 Why non-sleep deep rest beats a nap
19:05 A sunset habit from a sleep lab
21:18 Who needs blue-light glasses
23:32 Every supplement he takes
30:46 Why his phone lives in a lockbox
32:40 His top three habits for health
35:47 Guard your two daily focus windows
42:47 Neurotech that puts you to sleep
46:38 The best advice he ever got

YouTube: https://t.co/S8k5EiDdta

@hubermanlab @daisydwolf

[Daisy Wolf @daisydwolf]

@a16z State of Longevity report is out!

@evajsteinman and I surveyed 500 Americans to predict what's next.

We're also interviewing people at the cutting edge of health & tech about what they do themselves. My interview with @hubermanlab is out today. https://t.co/CApCjv8xv9

[Daisy Wolf @daisydwolf]

Inspiring generosity by @bhorowitz & @FeliciaHorowitz who donated $150M to build a children's hospital – the largest gift ever made to a children's hospital in the USA

[Marc Andreessen 🇺🇸 @pmarca]

https://t.co/HKKyE3njHV

[Daisy Wolf @daisydwolf]

@OpenEvidence @MSKCancerCenter Amazing!!

[Daisy Wolf @daisydwolf]

Clinical AI can be life or death, especially as busy doctors increasingly rely on it to inform their decisions.

We shouldn't let AI companies grade their own homework. @withprotegeai is solving this, and today launched Independent Evaluations: double-blind evals built by healthcare experts on de-identified, real-world data that model builders have never seen.

This is the kind of trust infrastructure healthcare AI needs before it reaches patients. Congrats @BobbySamuels, @engyziedan, and team!

[Bobby Samuels @BobbySamuels]

https://t.co/OlZ27vKkEd

[Daisy Wolf @daisydwolf]

AI is making healthcare advice cheap and ubiquitous. But once you know what you need, there’s still no good way to actually get it. And knowing what it will cost you? Seemingly impossible!

The AI doctor is here. Nobody’s built the store. That’s why we @a16z are proud to lead the $120M Series B of @GeneralMedicine, the first true healthcare store.

Building this takes insanely detailed knowledge of the infrastructure and weeds of healthcare, pharmacy, & insurance. There are no people better equipped than @tjparker, @elliotcohen, and @ashwinkmurali, the team behind @PillPack (one of the largest exits in the space!) and @amazon Health & Pharmacy. cc @vintweeta @JorgeCondeBio @evajsteinman @EliLillyandCo
@matrixvc @mercy_health @BoxGroup

[General Medicine @GeneralMedicine]

General Medicine has raised $120M in Series B financing led by @a16z.

We’re building the healthcare store: a place where customers can search, compare, and buy care, see prices before deciding, and get guidance from a licensed clinician.

Today, the store includes more than 2,900 products and services across medications, labs, telehealth, specialist e-consults, and select in-person visits and procedures. This funding will help us expand the store into more categories of care for customers nationwide.

Come shop for your healthcare: https://t.co/RyuBkGbXS4

[Daisy Wolf @daisydwolf]

@skupor @ODNIgov @USWREMichael @AFergusonFTC @POTUS Amazing news, @skupor!!

[Vineeta Agarwala @vintweeta]

Most of the discussion on tasks for AI in medicine has been focused on purely cognitive decision-making

But most are underestimating the pace at which some (not all!) tasks in the world of surgery and other physical interventions may also be automated — it’s not “20 years away” as I often hear from folks

Love that the Aleph team is focused on finding tasks where generalizable autonomy will be impactful in the OR

[Daisy Wolf @daisydwolf]

@dylanmcguir3 So excited to be working with y'all!

[Daisy Wolf @daisydwolf]

@r_mcguire @dylanmcguir3 @a16z @alephsurgery So excited to be working together

[Daisy Wolf @daisydwolf]

We @a16z are proud to lead a $7.5M round into @alephsurgery, which is building an autonomous surgical robot.

These robot surgeons will eventually be able to learn & perform any procedure, making critical care accessible to all.

@dylanmcguir3 and @r_mcguire are visionaries and we're honored to partner with them on this journey, alongside @BoxGroup, Reveille VC, Constellation,
@forward_deploy, @Valkyrie_vc, @DiscipulusVent,
@maniacvc + more. @evajsteinman @oyhsu

[Ryan McGuire @r_mcguire]

@dylanmcguir3 and I just raised a $7.5M pre-seed led by @a16z to build a robotic surgeon @alephsurgery

Much of the early investment in Physical AI has focused on industries where labor is relatively abundant and low-cost. We believe the biggest opportunities to change the world lie at the opposite end of the spectrum.

Surgery is among the most valuable forms of labor on the planet, but it's far too scarce and expensive - due to an exceptionally long training pipeline and highly concentrated expertise.

We’re building the first surgeon that scales: one system that can acquire, refine, and transfer surgical skills across millions of procedures. v1 is already learning to perform surgical tasks autonomously from demonstrations, and improve from its own experience.

Lots of great folks behind us! @BoxGroup, Reveille VC, Constellation, @forward_deploy, @Valkyrie_vc, @DiscipulusVent, @maniacvc + more

[Protege @withprotegeai]

"Who is making sure that AI answers in a doctor's clinical AI tool is safe and correct?"

Frontier models care - but ChatGPT or Claude shouldn't grade itself when it's deployed in a health system

Specialized healthcare AI model builders care - but they have the same grading concern

Healthcare system and providers care - but they're actively looking for the source of truth for whether models are safe for their context and environment

Academics care - and continue to debate health AI effectiveness while looking for representative real-world data

The US government is starting to care - HHS just appointed a deputy commissioner for technology and artificial intelligence

So everyone cares. But there hasn't been an incentives-aligned, expert healthcare organization that exists to help ensure AI alignment that is also able to help improve the models at their training source.

This is something @engyziedan and @datalabresearch think about daily. With Protege's healthcare data, expertise, and scale, we're actively working with model builders to help solve this pressing issue.

Engy, @daisydwolf, @evajsteinman on the @a16zbiohealth Raising Health podcast: https://t.co/clGogZ86Jo

[Julie Yoo @julesyoo]

https://t.co/Joh6JogSEY

[Protege @withprotegeai]

The data curation requests we get from frontier labs have changed shape.

Our co-founder and Chief Scientific Officer @engyziedan joined Andreessen Horowitz's @daisydwolf and @evajsteinman on @a16zBioHealth's Raising Health Podcast to talk about what that shift looks like from where we sit.

Early on, as Engy puts it, the asks were consumer-shaped. Can the model hold an intelligent conversation? Is it aligned? Is it safe? Is it reasonably sound when someone asks it a medical question?

Very large foundation models became our early customers - the consumer was the market they wanted to win first.

Then those same labs started to see how much revenue was sitting in enterprise settings. So now, requests we field today land somewhere between consumer needs and enterprise needs.

Doing both is difficult. Our belief: you only get both from real-world data — the kind generated by people doing their actual work, in their actual context. Can't get this synthetically.

More from Engy in the clip, and full episode in the thread

[Jorge Conde @JorgeCondeBio]

Moderna can now turn around individualized mRNA treatments in only 42 days. A miracle of modern medicine.

[Vineeta Agarwala @vintweeta]

On the Lp(a)HORIZON pelacarsen results:

1. It is still prudent (and evidence-based imo) to obtain Lp(a) diagnostic testing once, ideally early in life. Knowing you are at high risk (~10% of general pop has Lp(a) > 70 mg/dL, which confers ~50% higher risk of ASCVD) should prompt TODAY what this trial called "risk-therapy optimization" in the months PRIOR to enrollment in the study. This means starting the full sweep of already approved CV risk modifying agents: statins, PCSK9i, antihypertensives, etc. This single trial result on Lp(a) perturbation does not negate the predictive value of Lp(a) dx testing

2. The HORIZON trial result definitely does NOT mean that human genetics defined targets 'don't work'. C'mon people, stop the hysteria. It just means that in a group of people with exceptionally well-controlled LDL, whose (very profound!) CV disease biology has already revealed itself as a heart attack / stroke / symptomatic PAD, lowering Lp(a) by 70-80% for a period of only 2.5-6 yrs, starting at an avg age of ~60, is not enough to significantly decrease the risk that the same biology rears its ugly head again. Many possible hypotheses remain

3. To all the investigators and pharma companies who have committed themselves to running CVOTs, applause and gratitude. These studies could potentially produce some of the most widely practice-changing and life-saving results most of us will ever see in a lifetime. Personally I remain optimistic about studies that will 'treat earlier and treat longer', especially for primary prevention. eg Lilly's lepodisiran (siRNA) trial ACCLAIM-Lp(a) includes a primary prevention arm

[Daisy Wolf @daisydwolf]

A legend 💔

[Daisy Wolf @daisydwolf]

It was surreal to chat with Gloria Steinem in her living room!!!!!

Thank you @karinklein for making a girl’s dream come true.

[a16z @a16z]

$1.1B for the Machine Age.