- It took us three months, ultimately,

lots of people working day and night in the lab

to fix the problem.

I posed this problem to Claude.

I said, "Hey, what should we do to get unstuck?"

And just in one minute, you know, one response,

Claude actually just one shotted the answer.

- Hi, I'm Jonah Cool.

I am the Head of Life Sciences focused on partnerships

and deployment here at Anthropic.

- Hi, I'm Eric Kauderer-Abrams.

I'm the Head of Biology and Life Sciences here at Anthropic.

I'm focused on research and product development,

and together we're trying to teach Claude to be a biologist.

- All right, Eric, let's talk science.

I'm really excited about this

and also excited about the fact that Anthropic

and, you know, we're leaning into this space,

and, you know, maybe the place to start

is just thinking about why the life sciences,

why Claude, and what Anthropic brings to, you know,

what is already a really big ecosystem,

but one that's moving really fast.

- Yeah, I think that's a really important question.

So I'll start with why are we focused on the life sciences?

And this goes right to the heart of our mission.

I think it's something that a lot of people may not realize,

but when we talk about the beneficial use cases of AI

and all the amazing things that we can do in the world

with the frontier AI that we're developing,

actually the number one place

that we at Anthropic are excited about applying it is

within biology and the life sciences, right?

If you read our foundational material

and, you know, you talk to people in the hallway here,

that's the primary area where we're really focused

on delivering the beneficial impact.

For me that's been a super exciting thing to come in

and plug into is all of that pent up energy

and excitement to apply everything

that we have to this space.

And then starting to get more specific in talking about

why Claude, and how is our approach as Anthropic,

you know, maybe different from some of the other approaches

that are out there.

I think there are two things that that come to mind.

You know, Jonah, you and I

have talked a lot about this,

but the first is that we're interested in building tools

that empower individual scientists

and enhance the experience of being a scientist,

going about your life, you know,

doing all the work that you're doing, right?

So we want to give people the same experience

that software engineers have had of, you know,

having a brainstorming partner to work with

and delegate tasks to throughout the process.

We wanna bring that to biologists in the lab

and on the computational side.

And so our initial focus is really about building tools

that make scientists more productive.

It also makes science more fun.

Right, take away some of the grunt work that, you know,

everyone would rather get out of

and allow you to focus more on

the creative high leverage side.

So that's the first part, and then the second one is

we're really focused not just on the really exciting

early stage discovery problems, right?

Molecule design and protein folding,

and these, you know, incredibly impactful problems

that many people in the field have focused on.

But we want to address the whole spectrum

from early stage discovery

all the way through development and translation.

And so for us, that means, you know,

breaking it down into the whole world of different tasks

that exist in the space.

Everything from, you know, drafting and reviewing protocols

and debugging them to performing bioinformatics analyses

and writing up your results in slides

and papers and that sort of a thing, right?

There's a whole world of tasks out there that are important,

and we're taking a holistic view and addressing all of them.

- Yeah, I think it's a really interesting time

to think about science and maybe AI more generally.

And, you know, there's this inclination towards, you know,

what problem will AI solve for me?

But, you know, the way

that I think we're thinking about it,

the way that you just really nicely described

and the way that in "Machines of Loving Grace" is,

maybe also thinking through

that like slightly orthogonal point, which is like,

how do we change how we do science?

And that will then impact, you know, solving, you know,

structures and molecules and tissues and imaging

and starting to like think through that world.

So, you know, with that in mind, maybe then to transition,

so, you know, we've seen the power of Claude.

I think both of us have experienced this

and like the delight

and the joy in doing science with Claude

and its current capabilities,

but then also now starting to think in the research group

that you're leading

and how we advance those capabilities.

And so maybe for a minute we can just chat a little bit

about how kind of current capabilities and ecosystems

and maybe even like extended context through MCPs

and life sciences start to create this base case

and then your thoughts on how we extend that

and even like push it further,

and you know, what that starts to look like and take shape.

- Yeah, totally.

So, you know, we've talked about this a lot.

I think that, you know, it's important to crawl,

walk, run in this space.

Right?

There's a lot of things about doing biology

and having AI, you know, be useful in science

that are different from having AI be useful-

- It is the AI space.

So maybe you like to use a old running analogy, you know,

you start fast,

you pick it up in the middle and you sprint home.

So very little crawling,

but just, you know,

sprinting and then sprinting faster.

- Sprinting, sprinting faster, then flying in a rocket ship

is what we're going for here.

But you know,

the very base level is we need Claude to be conversant

with all of the tools

that scientists are using every day, right?

And so there's a whole ecosystem of important tools

and partners out there that we are integrating with, right?

So we talk about Benchling on the, you know, experiment,

administration, lab notebook side of things,

10x Genomics with Cell Ranger, right?

Incredibly important platform for

analyzing single cell experiments.

And then PubMed, for example, for being able

to query the literature, right?

And so these are just three of a,

three incredibly important partners

in a much larger ecosystem.

And so that base level is we need to

make sure that Claude can talk to all the major sources

that scientists are using throughout, you know,

their daily work.

And then I think the next level is we want to bring Claude

to performing at the level of

a superhuman research assistant that can assist you

as a scientist throughout all stages of your project, right?

From the early stage hypothesis generation

when things are more creative,

and you're reviewing the literature

and you're brainstorming,

to the experiment execution phase

where you're drafting protocols

and you're debugging things in the lab,

and even actually running those experiments in the lab,

to the computational

and data analysis side of things, right?

When you're running your bioinformatic scripts,

you're doing machine learning on top of that

or some statistics

and you're presenting the results to colleagues

or for yourself, right?

And so here, this is where

we've broken down tasks into all of those areas, right?

And we're figuring out,

"All right, how do we evaluate

how well our models and are doing those tasks

and how do we, you know,

rapidly improve performance in all of those areas?"

So we are making a big investment

in doing that right now.

And I think it's also important to say that we're, you know,

we're not just doing this generically, right?

Like in some ways,

you can't speak of life sciences

as one monolithic thing, right?

There's all these different subfields within it.

And we have a particular sequencing in mind

where we like to think of it as being,

consisting of this core of,

you know, important tasks that are shared

throughout many different fields.

And then within that there are different subdomains

that are really important, right?

And we want to address all of it,

but we're really focused on starting with that core,

that's gonna be useful throughout the whole journey.

- Yeah, I mean, you know, one thing that I'm really

ecstatic about with our current partners

and you know, folks that are involved early on here

to build this foundation, especially with MCPs,

is, you know, you mentioned 10x Genomics,

you mentioned PubMed,

you mentioned groups like Benchling

and then, you know, Sage Bionetworks, BioRender,

you know, kind of going back to that last point,

it really demonstrates

and hopefully puts to action

the fact that it's not just solving a problem,

but you know, in that group you've got the literature,

you've got instrumentation, you've got analytical workflows,

you've got the cherry on top

with that like perfect image

or, you know, network diagram in BioRender.

And I expect that over the weeks, months to come,

like that whole ecosystem is just gonna grow exponentially,

and with that, like the power for more and more scientists.

And I think that's just like

incredibly cool and exciting.

- Yeah, I think that's a great point

because that's the experience that, you know,

a lot of us have had on the software side, right?

For me, I've always been on the one hand,

a part of the software world,

the other hand, a part of this bio world, and you know,

things started on the software side where you'd give Claude,

you know, these little snippets of tasks, right?

And over time, those tasks become longer horizon,

Claude becomes more autonomous, you know,

it can more seamlessly integrate

through the different tools there.

And I think we're right at that takeoff point

in the life sciences where we're just now

with all of these connections that we're introducing,

able to unlock that next stage where, you know,

you don't have to just ask Claude to go perform an analysis

and then you do some work, and then you come back

and you make a BioRender figure

and you ask Claude to revise it, right?

We could actually give Claude a whole, you know,

meaningful chunk of the work

that would take a human scientist a couple hours to do.

I think that transition is the really exciting point

in a field where it goes from being, you know,

a useful kind of utility to actually

a brainstorming partner, which is what I'm after.

- Yeah, it's just kind of like embedded

in the process. - A collaborator.

- Yeah.

So we recently released Sonnet 4.5,

a really exciting, super powerful model.

And I think one of the things that we've seen,

and you know, eager to hear your perspective

on the research side is just like

seeing how that model performs in the context

of different areas of science.

And so, you know, what have you seen maybe in the,

like the evolution and the power of those models

and maybe some of the early evals

or benchmarks that we've been seeing in different tasks

that are relevant to scientists.

- So I think there's two things about Sonnet 4.5

that I'm really excited about that have enhanced my own work

by a great deal.

The first is that it's our first model

that's undergone extensive scientific training.

So Sonnet 4.5, you know,

is skilled in many different domains of science.

And I think one of the exciting things is

that there's a lot there that generalizes, right?

And so Sonnet 4.5 being better at math, you know,

has some effect of uplifting different capabilities in bio,

especially in the computational side.

And so I think it's just really exciting

that it's our first, you know, scientifically, you know,

really capable model.

And you know, there were some new things

on the training side that went into making that possible

that we're just leaning into

and accelerating with all future models here.

And the second thing is its ability

to do long horizon tasks, right?

Consisting of long strings of different tool calls.

So this is something that, you know, for anyone

that's done these sorts of long bioinformatics pipelines

and things like that is absolutely critical.

And we saw a major jump up in those capabilities

with Sonnet 4.5, which, you know, makes it, you know,

uniquely able to start

to do these like really long bioinformatics workflows.

- Yeah, I think in the analysis workflows

and also thinking about how it applies

to the different surfaces of Claude.

So, you know, a lot of people think about Claude

and they think, you know, the chat interface.

But of course I think for many scientists,

maybe some that do, maybe some that don't realize this,

the power of agentic coding tools like Claude Code

or other places where that longer context

and all of that power becomes really interesting

for data analysis, for integration,

for kind of like reasoning over different types

of knowledge and yeah.

It's an incredible starting point, right?

Where then we can start to build.

- Yeah, it really is, and I know this is one of the things

that you and I have been the most excited about,

that Claude Code is amazingly useful

as it is today in biology.

And most people don't realize that, right?

It's called Claude Code,

it's not called Claude Biology, right?

But, you know, underneath the hood there,

there's a really powerful general purpose agent that

I, in particular, you know,

many people that we've talked to throughout the community

have started to use in bioinformatics,

even in things like drafting papers, right?

And in performing literature reviews

and organizing your projects, right?

And so I think that that's definitely something that

we're gonna be putting a lot more energy into.

- Yeah, I mean, you know,

there's those moments, and as a technologist

and someone that loves to develop technologies

and apply them to biology, which is an affinity

that I know we both share,

you know, there are those moments where you see technologies

or kind of like experience technologies

and you just like really feel them.

And I still have that moment of like uplift, you know,

remembering the first time like playing with Claude Code

and making tasks that are either kind of

beyond my technical capabilities, tractable and manageable,

or the tasks of just, you know, like workflow running

and execution that are just time intensive

and cumbersome, trivial, right?

I mean, it just like puts those tools in scientists' hands

in a way that is incredibly powerful.

- It really is.

And you know, you mentioning those moments

where you just viscerally feel, you know,

the new capabilities that are out there,

that reminds me of that moment for me,

that really woke me up for the first time,

and this was actually back in the Sonnet 3.5 days

that "Wow, these LLMs

and these frontier models are really relevant

for what we're doing in the life sciences."

And so for me, that moment was,

at the time I was running a biotech company

that I had founded, and I had this idea that I wanted to try

to see, "Hey, if I had had access to Claude

when I was starting this company five years ago,

how much time would it have saved us?

And how much heartache in trying to navigate

some of these really difficult

R&D problems we were trying to solve,

would it have saved us?"

And I'll never forget this

because the very first huge technical roadblock

that we ran into when we founded this company,

there was a problem, we were developing an assay,

you know, trying to detect in this case COVID.

And it wasn't working.

We were getting inhibited by the sample matrix

and we couldn't figure it out, right?

And it took us three months, ultimately,

and, you know,

lots of people working day and night in the lab

to fix the problem.

And I posed this problem to Claude, I said,

"Hey, we're trying to develop this assay,

and we're seeing that the sample is inhibiting things,

and what should we do to get unstuck?"

And just in one minute, you know, one response,

Claude actually just one shotted the answer,

and said, "Hey, I think you should add

this much of this chemical, you know, into the mix."

And, you know, that was a really eye-opening moment, right?

That here, in conversing with Claude, you know,

you're kind of talking to a distilled version

of the totality of, you know, scientific knowledge, right?

And at the time it was imperfect,

but it's rapidly getting better.

- There's always this tension,

and I think scientists want perfection, right?

It's something that we all kind of like strive for

and want that specificity.

But for a lot of the work that holds science back,

protocol optimization, you know,

and an imperfect but helpful answer

is the sort of thing that we go to,

you know, the most trusted colleagues

where they might say like, "Eh, I don't know if,

but like, this looks familiar."

Right? Like, I've seen this problem at some point.

They're those kind of like sage professors.

They're the like super sharp student,

you know, down the hall.

And again, it's not looking for perfection,

but it's looking to get unstuck.

It's looking to be helpful.

It's looking to just like keep you moving

and like towards discovery,

which is what we're all looking for, right?

- Yeah, totally. Totally.

And I think the other area that really jumped out for me

early on was, you know,

this is relevant later in the translation phase,

is in the regulatory process.

So I've spent a lot of time writing regulatory submissions,

going through those processes with FDA,

and you know, Claude is really capable there.

And I think there's a huge opportunity

both on the industry side and on the FDA side

to recognize that we have these tools that can, you know,

speed up the process on both sides

and facilitate consistent standards across the board.

And I'm really excited about pursuing that,

and I know people, you know,

across this whole industry are as well.

- Okay. So let's stick on this for a minute.

You know, within biology, within AI,

maybe even if you take a step back from AI

and think about just like engineering and technology,

you know, the life sciences,

biology is this, you know, frequent substrate

where people get really excited about the idea of biology

or how it's just like one step away

from being like immediately programmable.

So some of those ideas and intuitions

I think we probably agree with,

but I think in many cases, you know, it's folks

that are maybe more in love with the idea of biology

as opposed to like really know what the life sciences,

what regulatory frameworks look like.

You know, let's talk a little bit more about, you know,

your experience, our collective experience as scientists,

and kind of like bringing some of that detailed knowledge

to our partnerships, to our research efforts,

and, you know, what those moments have looked like

for you in the past

and how they're maybe like reading onto

priorities or approaches.

- Yeah, I think this is a really important

and also pretty fun topic to talk about, right?

In some ways it's one of the oldest tropes in the space

of the computer scientists, the physicists,

the mathematician, that kind of waltz into biology,

and have all these romantic notions

and then, you know, spend their first year in the lab

and come out kind of shellshocked

and, you know, in some ways disillusioned

of all the things that are possible, right?

I think that, you know,

where you and I both are coming from,

and the way that we're doing things here is,

we know what life of the lab is like,

and we want to solve the real problems that are

the bottlenecks for this field, right?

I will say that that's my own background.

I'm coming more of the computer science side

and the math side of things

and have picked up bio, you know, over the years

in being in the lab, and I'm not disillusioned.

I really believe that we have the opportunity

to massively uplift the capabilities of biologists

in doing incredible, impactful research.

And that with the tools that we have now, you know,

we're finally at that moment

where all these things are possible.

So I remain the same optimism

that I had when I first got into this.

And I think, you know,

all the experience in the lab has been really clarifying

to help point out, okay, there are real problems here

that are not pretty and that require, you know,

lots of grindy work to get in there and disentangle.

But I think we're now set up

to make a dent in that, so.

But I'd love to hear what you think.

- Yeah, I mean, I totally agree, right?

I mean, I share that optimism.

I do think that there are many people

that don't always understand like how difficult science is,

but also how important just persistence

and the fact that, you know, research,

and I think this probably applies, you know,

down the clinical pipeline too.

You know, it's because it's so difficult,

because there's so much knowledge that needs

to be incorporated in every, like, step in debugging

and the complexity of biology,

whether it's that protocol optimization

or data analysis,

it's really hard to hold all that expertise

definitely in one person,

probably not even in one group,

and infrequently in one institution.

And the result of that,

and I think where again,

we provide a really powerful technology in Claude

and a, you know, research assistant collaborator,

is it starts to like, bring more of that fluidity, right?

It lowers the bar for computational analysis for folks

that may not have that computer science background.

It brings some molecular biology

and optimization skills for folks

that haven't spent their whole life, you know, cloning

and doing molecular biology.

And then it also just like helps make discoveries, you know,

transferable across fields, right?

I mean, I was not trained as a neuroscientist.

I used to love to go to neuroscience lectures,

but would then have to like, come back

and either like ask a whole bunch of naive questions.

But, you know, seeing optogenetics for the first time,

you know, discovered in neuroscience took way too long

to get out to cell biology, to other domains.

And I think the power of Claude,

and Claude as a life scientist is it starts to like,

address some of those core problems in biology,

but also just starts to create that fluidity

and start to break down walls

and some of the parts that makes science hard, right?

- I totally agree.

And the other thing I wanna mention

when we're talking about our outlook

and our research roadmap

and things like that is, you know, we focused a lot

on the meat and potatoes and eat our vegetables of,

you know, all of these practical tasks, right?

That are really exciting,

but more sort of surface level.

I also wanna call out that

we're seeing an increasing trend in, you know,

the field focusing on these bio-foundation models, right?

These models that have savant-like capabilities

on biological modalities, right?

DNA sequences and protein sequences and being multimodal

and expression data and all sorts of things.

And a trend that's really interesting to watch is seeing,

you know, increasing number of papers come out over time

that are demonstrating that these things that

previously looked like you needed these specialized

bio models for, maybe you don't,

and maybe actually with, you know,

really large frontier scale models like Claude,

with the right type of training,

we can start to develop those capabilities.

And so I think we're all as a field at the beginning

of just sort of working through that,

but I think it's a really, really exciting trend

to follow and that we'll be pursuing pretty aggressively.

- Yeah. - Right?

Because I think having these savant-like capabilities

in these bio-modalities is really powerful

for these specific bio-foundation models,

but to really make that accessible to people,

you need to be able to interface it with language, right?

And so I wanted to call that out as one, interesting.

- Yeah, I mean, it's a great point that, you know,

as the field progresses here,

and by field here, I mean both, you know, the field of AI,

but also like many domains of the life sciences.

And I think we're already seeing a whole bunch

of really exciting, you know, partners

that are the AI native startups in the biotech space

that are kind of taking some of these tools,

as well as large pharma partners.

And the way that the different pieces come together, right?

So bio-foundation models, general intelligence models,

specific data sets, you know,

it's gonna be fascinating, right?

And I think a really exciting time,

and maybe this also gets to the point of partnership, right?

So starting to take those different pieces

and like bringing them together

and how we think about partnerships,

maybe some of the early learnings or opportunities

or partnerships that have been front of mind for you,

or kind of a philosophy of like building this ecosystem.

- Yeah, so the way that I think about it is

we know what our North Star is.

We want to enable the amazing world

that Dario writes about in "Machines of Loving Grace"

in which, you know, R&D throughout the life sciences

is accelerated by at least an order of magnitude.

We want to make that happen as soon as possible.

And within that framing,

I think about partnerships is we need to make sure

that all the right pieces exist, right?

Some of those pieces we're gonna do ourselves.

Right, a lot on the model training side,

some on the product side as well,

but other pieces, you know, it makes sense for us

to just find the right partners

and make sure that we're supporting as much as we can.

And so when I think about the different types of partners,

there's really important ecosystem partners, right?

Like I would call out Benchling is one of those for us

where, you know, I think they have, you know,

the majority of working, you know, bio scientists

are using Benchling as how they engage every day

with kind of managing and running their experiments

and their data.

And so that's really, you know, important one

for us to lean into.

And I think there's a lot of exciting things

that we'll be able to share soon

that we're working on together.

So that's one type of a partner.

Another type is a partner that we want to work with

in which they're using what we're building

to actually do science,

and, you know, in a way that wasn't possible before, right?

Whether it's doing more science per unit time, right?

Getting more impactful results per unit time

than they could otherwise,

or making a type of discovery

that wasn't possible before it, right?

And so there, you know, there's a few partnerships

that we're pretty excited about.

I think one that that's worth mentioning

is with the Arc Institute, and I know that

you're thinking a lot about this as well, so,

would love to hear your thoughts.

- Yeah, I mean, I think the, you know,

the affinity that we both had towards Anthropic

because of the unique features of the models,

you know, this like deep thinking,

I don't think it's an accident actually that

so many scientists

have gravitated towards using Claude just,

you know, naturally, but then also Dario's vision,

and I think the vision that we very much believe in,

which is, you know, our goal is to accelerate, right?

It's 100 years of science that is possible in 10,

and that's bold, it's ambitious,

but also I think the more you think about it

and the more you think about what holds science back,

it's achievable.

And so I agree, you know, within the life sciences

and biology, I think the other thing that's unique

is that it's an ecosystem that is incredibly continuous

and fluid, right?

The student that is in a lab

and finishing their thesis one day

is the founder of an AI native startup, you know, the next,

that is then like acquired or working with

or advancing, you know, major pipelines at, you know,

AI forward pharma companies like Lilly.

And, you know, that fluidity

and thinking about kind of that entire

partnership and that ecosystem,

I think is that that's the beneficial deployment.

It's all of science, and achieving that.

The thing that I'm really excited about,

and maybe one feature that you didn't touch on,

is our AI for Science program.

And this is really looking to put tools

and Claude into the hands of scientists

that have a bold idea or a big project,

and they think that Claude can, you know,

be useful to solving that.

And I think it's a great way to, you know,

power early stage discovery research.

It's a great way for us to kind of lean into those partners

and work with them closely and learn from them

and like start to just, you know,

keep drawing the aperture open

and understanding like in these early days,

you know, what is working really well,

and you know, frankly, equally important, like,

what isn't working well.

And you know, I think we both believe in the power,

but also believe in the current imperfection.

And so that opportunity to, you know, work with scientists,

accelerate their research, judge success based on, you know,

what their success is, their discovery,

their acceleration, their time,

and start to see where we're doing pretty well,

and maybe some areas

where we just need to be doing a lot better.

- Yeah, I'm really excited about that too.

And I think that's such an important point, right?

Like in this conversation we've been emphasizing a lot

breaking the problem down into all these pieces

that we're gonna solve independently,

but the most important part

is when we put it all back together.

- And scientists are actually using these things, you know,

how's it going and what are we doing, right?

And so I think the AI for Science program is critical

for us to get that feedback

and be closing the loop with people

that are using these things every day in the lab.

And so I am super excited about that.

One other point that I think is really important to make

that, you know, speaks to why Anthropic,

and why the experience of doing this within Anthropic

is so exciting, it's such a perfect fit,

is that as we're talking about accelerating

and enhancing capabilities, right?

The other side of that is safety

and the tremendous responsibility that we all have

to making sure

that we are improving the model's capabilities

and releasing, you know, increasingly impactful products

in a way that is responsible

and aligned with our responsible scaling policy

and best practices in the biosecurity community.

It's something I care deeply about.

I've worked in biosecurity for years,

and I think that, you know, at most companies,

right, there would be some tension between the impact

and the commercial aims

of making these models better in biology, right?

And the safety

and responsibility side of, you know,

slowing down when we need to,

and making sure that we're being careful

and have all the right safeguards in place.

But at Anthropic, we don't have that tension, right?

That's our DNA as a company.

I think that's so valuable here.

It's also really familiar, you know,

to everyone in the life sciences, right?

For people that are developing therapeutics

and medical technologies, right?

On the one hand you have your product development arm

and your commercial goals, and on the other hand,

you have a quality management system, right?

Which is a set of procedures

and practices that govern everything you do

in order to make sure that you're doing so safely, right?

And so I think it's just such a natural fit, you know,

our approach here to AI of making sure

that developing really powerful AI goes well,

and is done safely,

and what needs to happen in the life sciences, right?

And so that's something that I'm personally

really excited about,

that I also think is a big part of

who we are as a partner of this field.

- Right, yeah. It's an assumption, right?

Like we have to do that.

We owe it to ourselves, we owe it to scientists,

we owe it to the world

to take those sorts of questions really seriously.

Yeah, and I think the other

thing that I think about a lot is,

like at our core, at our DNA, we're a research organization.

I don't think you can say that about

all other AI companies, you know, frontier labs, et cetera.

But I think being a research organization

allows us to engage with researchers, labs,

other research organizations,

in a way that really creates kind of a shared sense

of like ownership and goals and working together, right?

Like we want to advance the technologies

and see them put to, you know, the full purpose, and power,

and are really invested in seeing that forward.

- Yeah, I think we're really lucky

that that's the case.

And you know, I feel that very viscerally

that so many people on our founding team

and our leadership team

and just throughout all levels

and teams in the organization are scientists, right?

Many by training, many by nature and disposition.

And, you know, I think that that,

you know, you can feel that sort of, you know,

in all the work that we do,

and it makes it, you know, so natural to just go out

and get to work with other scientists and all these things.

- Yeah. - And some, you know,

it's a little bit like,

you know, the monkeys are running the zoo, right?

Where we have people that are so passionate about science

driving the ship, and I think that it means that

we get to have a lot of fun.

- Yeah.

But I think it also, it's a lot of fun,

but also that appreciation for core questions like safety

and understanding what the power is

and also core, you know, questions about like

what are the right problems to solve?

And, you know, an appreciation for what makes science hard,

what slows science down, you know,

if we need to make 100 years of progress in 10,

you know, what does that actually look like?

And, you know, you can draw back the veil

of science and, you know, there are some of those things

of just understanding the literature, right?

Like you could spend all day, every day.

As a matter of fact, I think a lot of scientists

would probably love to spend all day, every day

reading the literature,

but like even then you'd get through, you know,

some small, tiny fraction of what was published

or pre-printed at any given moment.

So it's just impossible to keep up. Right?

But Claude can keep up.

- Yeah. - Yeah, yeah.

Okay, so let's talk a little bit maybe here at the end

about, you know, the future of life science work

and, you know, we've talked about

bioinformatics and coding,

we've talked about some, you know, clinical work

and different work that has been like demonstrated by

some early partners.

And then maybe also just ways

that we're thinking about like building this up

and continuing to develop new partnerships,

push the models towards greater capabilities.

Where do you go when you start to think about the future?

- Yeah, so when we started to think about the future,

you know, I think first we need to make sure that

Claude has all of the foundational knowledge

that any scientist in the bio world would have, right?

So things like understanding protein structural biology,

right?

And being able to look at

a molecule from organic chemistry

and understand its structure

and function and things like that, right?

And so once you establish that base,

then I think there's some really exciting places

that we can go after that.

Where one of the ones that I really like

to talk about and that I think is critical

is Claude actually learning

to execute experiments in the lab, right?

I think in order to get to this world where,

you know, we're all going, that needs to happen.

And again, this is a problem that for so long, you know,

we've been making a lot of progress,

maybe not as much as some had hoped for, right,

in terms of this vision of automating the tedious work

of life in the lab.

But I believe that it's possible now.

And I think that that's a really important area

where we have to drill in and focus on.

And I think, you know, just pause for a moment

as to what life will be like when we get there.

It'll be incredible, right?

We'll be able to go from, you know,

talking to Claude about an experiment,

to designing an experimental plan with Claude,

to having Claude draft the protocols,

and, right, you can go back and forth on them,

and then when you're ready, you could say,

"Okay, now go run those experiments

and I'll review the data, right, in the morning."

And so I think that's critical for closing the loop

and enabling that acceleration that we're talking about.

And the other thing

that I think is a really important theme

for our future research is in biology,

as with any domain in science, we have the opportunity

to learn directly from real data from nature, right?

And so, on the one hand, we do a lot of model training

and learning on annotations that are created by humans

and other data sets

that are either curated or created by humans, right?

But there is an opportunity here to really do

sort of lab in the loop,

active learning from high throughput bio measurements.

And the other reason why bio's such a good fit for that

is we really, you know, are every year on a scaling law

of the number of experiments, right?

Per unit that we can do, right?

In terms of the throughput of these systems.

So those are two themes that I'm increasingly excited about,

where, you know,

when you start thinking about

how do we move beyond human capabilities

in these tasks, right?

At some point we're going to saturate

learning from human experts.

The answer is to get the data from the lab.

- Yeah, I think this is a great theme.

And the other thing that maybe I would point to

is I think there's still this huge overhang,

if you will, in terms of like current capabilities and use,

and one of the things that sticks out to me

is like starting to get Claude in the classroom

in basic training, like really, you know,

kind of implemented in a deep way such that, you know,

many scientists are using Claude,

and also that experience and the product, you know,

starts to have this very cohesive feel where Claude is

that virtual assistant and that virtual scientist

that is helping not answer our problem,

but, you know, answer a scientist, answer any problem.

All right, Eric, this has been awesome.

I mean, it's always fun to talk science.

Sounds like we've got a lot of work to do.

So thanks for taking the time

and really looking forward to the future

of Claude, life sciences, and pushing towards the frontier.

- Yeah, thank you Jonah.

This has been a lot of fun

and we're just getting started.

- We are.