How Reid Hoffman’s new company will create cancer cures

Transcript from https://podmenti.com/t/e4b1f1fec66047c2

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This week on Masters of Scale.
Reed Hoffman is here to tell us all about the new company he's launched, Mannis AI.
To say that Reed has formed brilliant team would be a big understatement.
To say that they have ambitious goals would be an even bigger one.
Rita's put some of the world's most powerful AI tools in the hands of some of the world's best researchers.
The mission?
To discover new ways to treat disease.
And to actually finally
Cure cancer.

You gotta have incredible talent at every position. Huge push. There are fires burning when you're going out. And then you go back to this is totally gonna be amazing.
There are so many easy ways. I have no idea what to do. Sorry, we made a mistake. But you have to time it right. Oops. We're going to have a free bedroom party. We haven't made just how you do it.
This
is masters of scale.
I'm Jeff Broman, your host.
We're checking in with our very own Reed Hoffman this week, whose twenty twenty five has had a very busy start.
In addition to publishing a book, Super Agency, in January, he also co-founded a new company.
We thought it would be interesting to hear how Reed tackles starting and blitz scaling a brand new business from scratch.
Reed, welcome back to your show.
It's great to be here. And you know, hey, I think I I think at this point it's appropriately our show. But yes.
I'm as always thrilled to get to see you and chat with you. And um especially so today because we're gonna talk about Mannis. And we just tell us what Manaus is, what you're setting out to do.
So menace is
Kind of a new
Uh AI driven drug discovery company.
Targeting primarily cancer.
That says.
We have this AI revolution
Which
uh creates these enormously new better cognitive artifacts.
And how can we deploy that?
In various ways.
To get drug discovery.
Superpowered.
And
It came about because
I had kind of gone to my partner's gray lock and I'd said
Look, I think there's gonna be a lot of great investments and agents and
And productivity tools and work.
automation and a bunch of those things and we should do all those and I'll help.
But I'm really interested in this area of there's a bunch of things that this AI technology could really make a huge difference.
You know, in the world for societies and industries.
And currently most of the AI people are all looking at all the software stuff.
And I said, What would you mind? I said well, drug discovery.
And so I went out and started talking to some of my smart friends, one of whom is Siddhar Sha Mukherjee, and he's a very smart friend. Yes, very smart friend. Professor Columbia, world renowned oncology researcher.
Pullicher prize winning author of multiple books, including The Emperor of All Maladies, you know, just massively talented guy.
And I'm talking to him for a while and he said, Well, you know I'm an entrepreneur too. And I'm like, No, I didn't know that. He's like, Well no, I've actually
brought cancer drugs to market.
I'm not just casual about this. I've actually
know this stuff. And I said, Oh, that's interesting. And he said,
And this sounds like this could be
amazing, let's talk about it. Right? That's how Cut Manaus came about, and that's what
Manaes.
You've invested in
I'm
Sure, dozens, I'm guessing hundreds of companies at this point. It's very rare that you choose to be a co-founder.
But you chose to be a co-founder here. What's your filter for deciding when to invest and when to co-found and why be a co-founder here?
Well co founder
In this case is not quite the same thing I meant when co founding LinkedIn, which is Saturday morning, I'm in the office.
And so it's it is still co founder,'cause it's like at least a day a week.
Let's
When you have a
CO
Co founder
who is exactly the right kind of person that I know.
How to
Add in.
In the way that a co founder adds in versus
For example, a board member, which is different in these cases. I mean, there's an overlap with the board member. I'm obviously obviously a board member of
Monas as well.
And Sid and I spent a couple of months
basically whiteboarding out the entire
drug discovery process from you know the
I have an idea.
Two.
A drug entering the market.
And
slicing it as thinly as possible to understanding what each of these stages were.
And then what we do is we talk about my current understanding of what what AI is.
what I would predict AI is gonna go to in one to three years naturally.
What things might be possible.
with AI that are not part of the natural trajectory that it like we could create or stimulate uniquely as business.
And
Then kind of going through all those, and that's when Sid was like, look, I think you need to co-found this with me.
Because in a sense
I'm the person who's
responsible for making sure the various kinds of parts of the AI talent. I mean he's enormously a talented guy, learns things lightning fast and has a very deep understanding and also has
That sideways understanding you want.
Entrepreneurs to have, which is, well, why is it this way? And why couldn't it be some way that's a lot better? Um, which is kind of one of the things that's fundamental to the entrepreneurial impulse.
And it was like okay.
Great, because it's A, it's a huge mission. Cancer is a huge killer. It kills children. It kills healthy adults. It kills old people. It kill people in every culture, every society.
So it's like, okay, this is a huge thing. And by the way, there's not just one cancer, there's lots of cancer. It's one of the reasons why it's a real problem. It's like, hey, we figured out this cancer. And so it's like, okay, what what is the way that we make a dent on this whole whole problem? And that really matters. And then
Also, of course, when you transform an industry.
And so, you know, part of this is
To say that there's obviously a lot of things that the drug farming industry is really, really good at. It's been doing this for a long time. It's added a whole bunch of
society, but it's very classic industry. It's very rooted in the way they've been doing things for the last X decades.
And it's like, okay, so you could create a new one.
Like the other giants because you're bringing in
New things. Those combination of Sid
An important target.
possibility transforming an industry, an elevation of humanity.
That
all gets me into co founding.
I mean that deep mission alignment is so clear here. And I'm curious as you and Sid were on that whiteboard and you were
slicing into the step by step by step by step by step. What did you see that got you fired up about how AI is such a difference maker?
When Sid and I went through the whole area, we abandoned any interest in an AI things. There wasn't a minimum of 10 X
And frequently it was like, okay, no, no, it's much greater than 10X, so we can make this work, and then focus on those areas. And then we didn't go, we're gonna invent all the technology.
We said, okay, which technology should we be building and then which technology can we use? And that's part of what led us
in a partner with Microsoft because
Both Sid and I from two different angles had some
understanding of where Microsoft was had been working on some pretty unique technology.
that hadn't really been and hasn't really been fully
advanced to the market and and say, hey, this could be a good way for Microsoft to get in this as well. Not just the Z Azure, but you know, some of the stuff that the excellent Microsoft research
has been doing.
As baseline. And let's
Do some areas where we're building, do some areas where we're deploying. Some of the deployment is open source, some of the deployment is Microsoft soft, some of the deployment is other things that we've learned and discovered as you're kinda along the way. And then
the way you put them all together, including the things you're building.
as the kind of angle for having the technology strategy and and
Doing that, that's much more my network than Sid's network. Sis networks is the, you know, world class scientists and and other folks in mine is
The tech people.
I wanna drill down into this for a minute because I I I think this is a really important part of this next
Phase of how
people are solving problems with AI as a to borrow Microsoft's term a copilot. We're all by now familiar with ChatGPT or Pi or other AI companions effectively, um, helping us figure out what's for dinner.
And most of us by now are familiar with something like Sierra for customer service or Harvey for legal tech for legal AI. The blend of where you are using someone else's AI, where you are customizing for yourself, where you're building for yourself.
Help us understand how you make those decisions and how business leaders should be approaching these problems as they're making these decisions for their own companies.

Great question and a very good one for Masters of Scale too, because it's generalizing out of
the problems we're solving to problems that a lot of people are solving.
So for us it kind of comes down to a couple of things. So one is
anticipating where there's gonna be a really good continuing workflow or some other entity
Could be an open source thing, could be a
And it could be us contributing to an open source thing too, but an open source thing could be a proprietary model.
We say, Well actually in fact that investment thesis of going forward on that
We will benefit from that at at least
Call it.
Seventy percent, eighty percent plus.
Of that.
ongoing technological development is gonna benefit us and can be in the in the slot. And if we were to start doing it ourselves,
we wouldn't get something that was at least ten X better.

The existing one.
So that's you know one kind of decision about
When you do the open source thing or when you in deploy someone else's
Commercial library or other direction.
Another one might be
Well, we're gonna use the commercial library right now, or the open source library right now, but
That's a time saving measure.
We think we're ultimately going to build something here.
But we don't need
to build something here in order to get to market and get it going.
So we'll
deployed for now and by the way you might have a much cruder not
Perfect.
And then there's the kind of the question of okay, which things will be
Ongoing.
Where we're learning our ground truths, where we're getting the data.
W this is
gives us a ongoing
substantive competitive advantage.
about like things that we have as kind of unique assets other than just the
Prospective molecules.
that we're creating that could be
Yeah.
Then on that list, which of those things do we start right now, which of the things do we do later?
that kind of gets to a high priority. Now part of of course what we have at Manis, that is
advantageous that I recommend
to try to do to all of our
Yeah, listeners.
is that I have a very good sense and a very good network of both where AI is now, where new techniques are coming out.
where the current trajectory is going.
And those kind of things.
play in that kind of strategic decisioning, then that's one of the things that you need to do.
when you're making these decisions yourself. Now what happens with everyone in technology, especially in software, is it gets very not invented here. So go well
I should just control my own destiny, I should just do it.
The problem is with tech.
Software tech.
Is that it's not
Build once and forget it.
Software tech has to be.
Constantly rebuilt, reinvented, rebuilt, reinvented.
And if you're not in that theme, your thing will out mode very quickly. It's one of the reasons why
Most governments around the world have really broken RFP solutions for technology because they go, Oh, I will specify the 150 requirements.
And then
when whoever provider, usually pretty incompetent people, prime contractors, et cetera, well when it comes to software, delivers something to you, not only is it not very good at the beginning,
But it starts aging.
exceptionally vastly from the beginning because it's not part of this stream. So one of the things you you know we think about it mana is
But also one of the things our audience should be thinking about is to say
Okay.
Do I really need something specific for me, or can I be benefiting on the weekly, monthly, yearly reinvestment that company X or Project Group Y or something else is doing on this?
Because then I don't have to be
Using some of my relatively few resources.
for cycling in the future. And that's part of how we look through it. That's part of the reason why some of the stuff we said, hey, we're just gonna do that the standard way an AI person does, or hey, we're gonna just do that the same way a drug researcher does or a drug developer does.
We're just gonna do that the same way.
Because doing that the same way.
Gives us a
the ability to be deploying that, not having risk, not having to win to invest in it. And you know, you have to be very choiceful, even as large companies, about which things you're
you're investing in for proprietary reasons.
More with Reed on how he's using a lifetime of scale lessons to supercharge his new company.
In just a minute.
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Welcome back to Masters of Scale.
You can find this conversation and more on our YouTube channel.
One of the things that struck me, Reed, when you first told me about
Manaus was this
beautifully complimentary meeting of of you and Sid and your skills and your relationships and your access, your knowledge.
You're also entering a category where there are a number of companies already using AI to do drug discovery and to solve critical medical problems. And you all raised a lot less money.
Then most of these companies have raised
How are you able to do that?
What's the competitive advantage that you have and I appreciate it's mission driven, but still you're entering a market where there are already folks who who are steps ahead here.
Well, we don't think there's steps ahead.
Okay. That would be a different question. Fair.

So
Some of these projects tend to be they raise a whole bunch of money.
And throw the money at either
the kind of classic AI stuff or the classic drug discovery stuff.
Yeah.
So part of the thing
that we're doing with the financing of Manis.
is to start in a classic
Silicon Valley way that I find to be.
Most successful.
And how it drives, which is
Start with a really focused project, a series A investment.
and move to you're gonna raise more money from your
your series B and then from your series C, but it's as you accomplish things you go. One of the things that is frequently
challenging and frequently counter predictive.
For successful
projects in my experience.
has been when you raise way too much capital initially. And that then says, well, but because we're gonna try everything and go really bold and and do the whole thing from the start. And that actually makes you
Even with a lot more capital.
much less likely to be successful.
So we will need to raise more uh money. We will need to raise it for like clinical trials and a bunch of other things. We'll really need to raise a series B for some of the technology.
But we will do it.
Based on having achieved.
some interesting outcomes.
Yeah, it's one of those interesting parallels between art and startups where constraints actually produce better results often because it requires focus and discipline and and choice making. Reed, you're entering a space with manus that is
Extremely highly regulated.
In the case of drug discovery narrowly and and healthcare more broadly, do you see regulation as a constraint? Is this an area of concern? Can you work within the existing world? How does that factor into your analysis here?
Well, one of the reasons why I I very rarely invest in
regulated businesses and
Would be a little bit more.
Normally
very cautious about co founding them is that
Regulation always massively slows down innovation.
And and you know regulators like to say, Well, in certain cases it accelerates, which is true. And they like to say, Well, but if you do it really smart, it doesn't really. And that actually is no it isn't because and the natural thing for regulatory agencies is a
I get penalized every time I
an error happens and I get no upside for things working more efficiently or working more on target or anything else. So I just
Throw in every possible thing that could be
Answering a negative.
And that's how it works over time, whether it's in you know the FDA, whether it works in the SEC, whether it works in the banking industry, et cetera. Now
That's all part of the reason why I tend to
You know have this maximum and say, Look,
You should do regulation.
when bad regulation is better than no regulation, whether it's an industry, whether it's a specific kind of thing.
But you shouldn't delude yourself that you're super smart and a technocrat.
That you know how to do this regulation in a way that's so great for the industry that you're gonna detail this all out. And it's one of the reasons why.
For example, the Europeans have a super
large problem with regulation, not just within technology, but also everything that goes to
Labor.
And a bunch of other things because they go, No, no, no, we're gonna we're gonna have it as a you know, multi-page detailed thing. Like this is not at all saying
society's better off without regulation.
Parts of the FDA are absolutely critical in anything from food and drugs.
And that the things to do. It's just that you need to have a certain epistemic
humility and kind of going, hey, we should be very focused.
Versus trying to be we are the genius technocrats.
And so that's reason I generally stay away from regulation and I only get into the areas where there's regulation when I think
Hey, this could be so great. This could be like industry transformer. This could be something that changes
many thousands of human lives and you could see its impact on a society basis of helping elevate society.
then okay, regulation is a huge risk like other risks.
But that huge risk.
plays out against that. And that's part of the case in Manis.
Right, right. Good regulation is really hard, but it's not an oxymoron. Yes.
How fast can manus go? Curing cancer has been a a moonshot concept since we had a concept of a moonshot. How quickly can this happen?
Well, I mean we're early days. Obviously we did this'cause we think we can accelerate.
Greatly.
Think we can get a lot more targets.
into the early process than the standard process would have. We think we have ways of evaluating those.
targets and molecules at a much faster rate than turbidally happens. We think we can we
getting into
Clinical ground true things.
At a speed.
that is a larger number and much faster.
How fast it is is still TBD, and I think this is one of the things where
Said said, Hey, welcome to regulated industry.
Don't quote numbers.
when you're talking to outsiders. Right. So here I am following Sid's sage advice and not quoting any numbers.
Most of the time when we're having conversations on masters of scale, it's about a company that has already scaled. Uh we're having this conversation because you are the OG master of scale. And this is as important as anything that you know we could be talking about in terms of the effect on humanity.
when we come back a year from now.
and have a follow up conversation. What are the markers that will determine whether you feel like Madness is on the right track?
We already have some of those markers internally.
We've already had some
Prospective molecules.
We've already had some of our technology development.
Show things that we think help build secrets that we think no one else knows.
So we've already on track on some of that, but I think what we would be
Saying to the world Oh on track.
Well probably before.
Like a couple of the pieces of technology, hey, these are delivering these things.
We might be saying
We have a set of
Interesting molecules.
For
triple negative breast cancer, other kinds of things.
We're looking at the right.
And our process has in fact
Gotten us there.
Much, much faster.
Than the traditional.
Drug pharma startup company.
That would be some.
Anticipated positive signs.
You're obviously using AI to assist in in the discovery process and the testing process, et cetera.
I would be stunned if you're also not using AI in the day to day operations of the company. What's different about
how you and Sid and the team are building the company with AI that is applicable to to other companies and other categories from the other companies that you founded or or been a very active investor in.
Well this will obviously scale.
as we get to it. I mean right now we're in kind of raw
Tech development.
So
It's
tech development and also kind of scientific development. So
I would say it's
Today is probably a little bit more like some other companies, which is
It's a research assistant?
for various kinds of things we're looking at. It's a
communication and productivity assistant for
generating flow of information and decisioning between us.
And it's a coding assistant.
For
Building certain kinds of code.
If you're not in those basic things as a company right now.
you're well behind the AI curve and therefore well behind the curve. So all of those things I think are things that
Every company should be doing it. Now there may not be a science research assistant.
in your particular company.
But there should be research assistance of some sort. Global supply chains or
Competitive analyses on products or
You know, whatever the thing may be. And so those things I think
Our things are not.
new and unique things. Now obviously as you get to
questions around like say for example, hey we have got a
drug and part of the issue is really maintaining
complex compliance the drug, then you could see how AI might actually help with complex compliance to a drug. And there may be some things that kind of get to specific things.
That are still significantly in the future.
We before we wrap, I wanna ask you a broader question about what's happening in the world of artificial intelligence.
in your book SuperAgency, you talk about the doomers, the gloomers, the zoomers, and the bloomers, um, in in terms of the categories of how dystopian and utopian people are their in their vision of AI.
When you lean into the bloomer side into the more optimistic
vision of what what AI can be. What are you seeing today
That we may not know about.
that has you more optimistic, more excited, more leaning into the AI Utopian vision of the future.
I'll use an example of I was hanging out with a tool goande
We were talking about his new book.
That he's working on.
And I was like, Well, have you used deep research? He's like, No, it's like, okay, let's let's go. We went to, you know, chat GB my account and chat GBT, you know, and we did the deep research prompt on kind of, you know, surgeons.
applying anesthesia and improving their practice.
And it was a really interesting nuance. It's one of the reasons I'm going into the depth of it, because
It produced exactly what he wanted, which was 10 quotations from different surgeons. And he's like, he looked at this and said, Oh my God, this is gold. And he sent it off to his research assistant.
his research assistant came back and said, Well
One problem.
Nine of the ten
Quotes are incorrect.
Right. And you go, Oh, hallucination, terrible, terrible thing. Like
Basically not working. Right.
But then with the researcher assistant, what she did
Well, she went and looked at the areas it was pointing to and said, Oh my God, this saved me tens of hours of finding
The right treasure troves.
that are the th precise things that will be very helpful to this book.
And so
even in this kind of hallucination case because of the research analysis, it was actually still an accelerant.
It just is an accelerant in a way that kind of proves the point from my earlier book, Impromptu, and also part of what I'm trying to say in superagency, which is human amplification.
With the research assistant.
helped make her massively productive very, very quickly.
And of course you should cross check it. You're trying to
produce something, I do the same thing when I'm producing my own

I of course cross check things. I don't say
what the GPT four output was, I just cut and paste it in because
I am testifying to these words being accurate as a way of doing it. Anyway, so so even in the the hallucinations are improving a lot.
and doing stuff. But even the hallucinations are interesting even in
Potentially.
Importance of high truth fact checking environments AI's improving every month.
And it's it's part of the reason why we tell people, go try it, go play with it.
Because it's not like oh I'll wait until it stabilizes. It's not gonna stabilize soon, and it's already amazing in some regards.
So
go start leaning into it.
Start.
Seeking superagency.
Reed as one of the great investors of really American history, uh certainly modern American history, and as someone who's deep, deep, deep in AI.
For the investors in our audience
probably ninety something percent of their pitches that they're hearing right now have AI something.
How do you separate the wheat from the chaff here, if you're an investor? How do you know what's what's real, what's meaningful, and what someone has just slapped AI on to be

Well, when you get a pitch for an AI juice machine, you know
Perhaps pass uh on that.
Although watch, after making this prognition, there's gonna be some amazing AI juice machine that I was like a complete idiot in saying this. But it's just it's kind of a
particular bit of Silicon Valley history lore that that's a fun gesture. It's almost like saying, you know, 42 or Santa Duma's high school football rules, you know, it's kind of like in the little gestures.
That being said, there's simple mistakes and complex mistakes. Simple mistakes are
That does actually not impact have anything to do with AI.
Oh, I'm gonna use buzzword bingo. It's AI quantum, you know, fusion.
Ah. You know, and like
Be cognizant of that.
The fact that AI is not a panacea for everything right now. It's not a
And even when they say it's accelerating a lot and we'll solve it next year.
It's unclear.
on that stuff. So be smart about that. And the number of teams that are really accelerating the raw intelligence
Of new capabilities AI is not a large number of n, so be careful about that. Those are all in the kind of simple things. Now the more complex things is
But of course part of the reason why we're in the cognitive industrial revolution here
is that adding intelligence to everything
Whether it's your PC or your phone, but also your speaker and your lights and your car and like everything.
is going to be revolutionary, certainly human lived experience, and so there are gonna be a bunch of new products and services.
But a new product and service does not an equity.
Make.
And so
Like for example, among other things.
Is what
will rapidly happen as a whole bunch of these
AI models and capabilities.
I wouldn't say it's a commodity, but it's broadly available to to many players. So that you say, Hey, I can build
An AI thing.
that can be a good tutor. By the way, I can do that today.
I literally to put in a little meta prompt.
in a something like you know GBD four or Pi or others and I put it in and say, don't give the answer.
Work the person towards the answer, and then I've got a mini tutor right now, not having done anything. So
They well.
I can grate that. And so well I'm gonna work harder and I'm gonna make it better. It's like okay, that's not nothing.
But
You're looking for the kinds of structural advantages
that you would typically have in a business. It's a
systems integration into an organization.
You know, a school, a business. It's a network effect. It's some set of things that you go, No, no, no, this is a
product or service that's gonna go the distance and compound in value and therefore create
Ecco di value.
And that's the kind of thing to think about. And one of the things I think people mistake in this
revolution right now.
is that they go, just because it's AI and just because it's moving first.
That makes a great equity. And it's like well look that's
Better than no idea.
Right.
But
If you're really investing intelligently and professionally, you should have incremental and better ideas than that, not just that idea.
Uh other ideas as well. And I think that's
Part of how to think about AI and investing.
Reed, I'm I'm super grateful for you joining us to talk about Mannus today. Um I'm very excited to follow up as the Mannus journey evolves. Thank you for attacking this problem that has affected probably the family of everyone who's in our audience. And um can't wait to talk about this again soon.
Me too, and Jeff, always a pleasure.
Thanks to Reed for sitting down to talk about his exciting new company this week.
I know we all hope the scale lessons he's learned over his career can help make Manus AI's drug discovery research a monumental success.
It has the potential not just to change lives, but to save them.
To hear more about his new book, Super Agency, make sure to check out the conversation he had with our own Bob Safian.
We'll put a link in the show notes.
I'm Jeff Berman.
Thank you for listening.
Masters of Scales await what original.
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