My name is Max Hodak. I'm the CEO of a

company called Science. Um, we're going

to talk a little bit about

infrastructure at startups. So, I've

spent most of my life working on brain

computer interfaces. This is almost 20

years ago now. Um, I started my career

as an undergrad working in a lab at

Duke. This is from our very first

Society for Neuroscience conference. The

experiment I was working on back then

was if you put electrodes in the brain

of a monkey and then give a monkey a

joystick and you record the neural

activity as it's playing a game. If you

make the joystick say go the cursor go

sideways when the when they push forward

on the joystick which does the brain do?

Does the brain represent the joystick or

the screen or something else? Um there

turns out that there are neurons that do

both. At our company science our main

product is a retinal prostthesis. It's a

chip that's implanted under the retina

in the back of the eye to restore vision

to patients that have gone blind due to

loss of the rods and cones in their eye.

Um on the left, this is one of our

patients on the cover of Time last uh

last November. Um on the right, you can

see there's a picture of the implant

with the glasses. So every little one of

those hex grids that you see on the

implant is essentially a solar cell. So

when this is implanted under the retina,

the patient wears glasses that have a

camera that sees the world and a laser

projector that projects onto the

implant. And wherever the when it

projects the image in infrared, wherever

the light is absorbed on the implant, it

creates a little electric field to

excite the retina, thereby directly

bypassing the dead rods and cones to

stimulate um this visual signal back

into the into the retina at the first

possible opportunity. And this is this

is pretty this is a pretty cool product.

It finished major clinical trials uh

last year. It's been in three clinical

trials now. Um was covered in the the

BBC last fall. One of our patients

finished a 300page novel with the device

and mailed us the book. But I'm I'm not

going to talk about this work for the

most part for the next 30 minutes. Um,

we're going to talk about infrastructure

and lessons. I mean, this is startup

school. Maybe there's some things that

you'll find useful in your company. So,

Picasso uh was noted for saying that

when art critics get together, they talk

about form and structure and meaning.

And when artists get together, they talk

about where to buy cheap tarpentine.

This is also often phrased as amateurs

talk strategy, professionals talk

logistics. a quote from a guy that the

United States named a tank after.

And there are so there are surprisingly

few lessons that are really broad across

companies. Typically the experience of

running a startup is you're just looking

at kind of a continual stream of facts

that hit your desk every day and you're

trying to make the best local decision

that you can in the for those facts and

if it looks inconsistent over weeks

that's usually the way to go. But there

are a couple topics that keep kind of

very repeatedly coming up that are kind

of universal experiences at least for uh

deep tech companies which is the thing

that I I kind of know most of my

experiences in not just pure software.

Um and so there are things that keep

coming up like buying things. Um so your

first reaction might be that if you do

software you don't need to buy things.

It will be me alone in an empty room

with some computers writing software and

this is going to be how we build a

company. And if this is you, yes, you

have figured out a reason why VCs love

funding software and why they've done so

much of it for the last 25 years. But if

you do anything other than pure

software, you will be buying many many

thousands of things. This is us uh about

I think six months into the company. Um

and it's a little tough to make out.

There are a lot of computers. There's

also a bunch of microscopes and other

electronics and 3D printers and resin

and PCBs. You're buying things really

continuously. And so it might sound

really obvious, like a really basic

question like you like you surely just

buy things. Um, so for you as the

founder, you can use a credit card.

Credit cards work great. You can buy

lots of things with credit cards. You

can also send a wire transfer. The

question is, how does your 17th employee

buy things? Do they have a credit card?

Let's say you hand out credit cards to

all of your employees and tell them to

buy what they need. So, you're going to

you start getting messages like this.

Um, and

the and you think like, you know, I care

about burn. We have to spend

efficiently. I'm going to approve all

the purchases as they happen. And you

get a message like this. And then you

think, $3,000 sounds like a lot for a

power supply. Do we need a $3,000 power

supply? What if we get one from an

auction? Like, in 3 days there's an

auction. maybe we'll get it for half

off. We can get it in two weeks. But

then you also remember that you've hired

some very highly paid and talented

employees. And are you saying they can't

get the tools that they need? That

you're spending $100,000 a week. If you

wait a week to get a power supply half

off, you have certainly dwarfed any

possible benefit from getting it. And

then, you know, and if they were

anthropic, they're not going to be

getting hassled over a $3,000 purchase.

They're just going to have a power

supply. And so what you realize is not

only is this very hard to keep burn

under control, but also it's just this

is the inappropriate place to exercise

spending review. Spending review has to

come earlier. You have to have some

concept of budgeting. It's not really

about even just the payment rail of

buying a thing. It's how do you

understand the bucket of money that you

have. And I don't want to be making the

$1,500 power supply versus $3,000 power

supply trade-off. They need to

understand the resources that they have

so they can make trade-offs within those

available resources. So, you set up a

procurement system and now your highly

paid employees are spending their days

clicking around B2B enterprise ass. And

it turns out that from the time that

they place an order for a power supply,

it takes two weeks to arrive because you

can't actually buy that with a credit

card. You have to set up an account with

the vendor and deal with insurance and

certification paperwork and get an

account set up. And they have to

generate a quote so that you can

generate a purchase order so that you

can generate an invoice. And now

everyone's upset that things are taking

super long to get ordered. And and so

this actually really requires like a

like this is a living organism. I like

when people move from academia to

startups, I think one of the reactions

that people often have is like why are

there people whose job there is to

purchase things? Surely I can just buy

things. But absolutely there are people

whose job is to buy things. From the

time that you submit the order, going

back and forth with the vendor to set

all of this up is very timeconuming and

it can easily stretch out. It takes like

active management to metrics to cause

this thing these things to go fast. And

I think part of why like when we think

about this, we have a a reputation I

think of often being very quick and

people are unsure like how does that

happen? It is mostly not that we are

smarter. It is infrastructure like this.

That is the the like how speed is built.

So now people can buy things at least

you can keep overall burn under control.

Now you know that you're not going to

exceed some large amount of spending

every month. And then you realize that

that wasn't really the problem. You

could figure out your runway. The

problem is attribution is when you're

doing whether you're working on rockets

or cars or drugs or brain computer

interfaces or um all like anything that

involves dealing with the real world,

you realize that one of your other

problems is that you're buying stuff in

bulk. Like we buy gases from argon to

silene to nitrogen

um to resins to media and then we we buy

these things in bulk and we part them

out to lots of different experiments.

Um, now when you do this, it breaks this

attribution is pretty difficult. And so

if nobody knows how much an experiment

costs, like every time you like grow up

a new cell line or every time we make a

new a new probe in in the fab, how much

does that loop cost? Nobody knows.

Therefore, experiments are free. Um, it

doesn't cost dollars. It costs media.

And media comes from the fridge. And we

want to know what how do we price a

thing that we make? Like we make a bunch

of things in volume in the foundry. We

want to know what can we sell that for?

That requires all of these spreadsheets

to get an estimate of the pricing. And

there's opinions in here. Like these are

not all facts like how much do you

include rent? How much do you include

depreciation of the tools? This comes

with opinions about your future volume.

All of this is required to understand

not just what you should charge but also

what you're spending and what your

runway is. And so to deal with this,

we've built a huge amount of internal

software at the company um for for

managing this. One of the first things

that we did is almost everything that

you can do in the company is a button

somewhere in the software. We call it

Helix, including stuff like purchasing.

But because this extends all the way

through to manufacturing where we have

every step that happens in the lab in

the database, we can correlate all of

this through and get this information.

And these like again, so it turns out

that for every iteration of a wafer that

we make, in this case, for this

protocol, it cost $40,000. This is like

a lot of money. And you like you might

have raised let's say you raised $20

million in a series A you think you need

four years you need 20 people in my

experience about half the burn is

headcount so that's so let's say 20

people that's probably three three and a

half that's like three million a year in

revenue that's half of your burn um you

need 20,000 square feet about $4 a

square foot that's another 750,000 to a

million a year so now suddenly you've

got really it's a $3 million a year

research budget for three or four That

goes way faster than you think. But

again, your team see saws um just saw

that you raised a larger amount of money

than they've ever seen in their lives

and they think that the $3,000 power

supplies are free. Um this is pretty

important. This is a thing that actually

this infrastructure actually determines

success or failure in many companies.

Another universal experience is hiring.

Um so hiring also I think really

separates the successes from the

failures. Startups usually don't come

out of nowhere. I think the best

companies in my experience come from

what might be characterized as scenes.

There's like a moment that enables a new

company to be born and there's a bunch

that comes together that really creates

this like unique nucleation for the new

company. And once that moment is passed

because some some company has has

executed on it or just the time has

gone, it's kind of tough to get back.

And so the the best hiring comes from

within your network, people that you've

worked with before, you know are good.

Um, and the extended version of that is

to hire from the network that produced

the startup. There's usually some

extended scene that the thing came out

of. There's a bunch of co-founders that

come together out of that crystallize

out of that, but then there's an

extended community and that should

really be the target of your initial

marketing. Um, these are the people that

already speak your language, already

familiar with it, but there's never

enough of them to really fill an entire

company. You have to hire from the

general public. Um, so there's different

companies hire in different ways.

There's different processes that make

sense to different founders. this is the

thing that really is going to be matched

to who the founders are and how they

view the world. Um, and there's no one

right answer, but this is a thing where

you need a really defined process. There

is no right answer, but a wrong answer

for sure is not having something that

you do very religiously as a company.

Um, and this is an area where reality

has a surprising amount of detail. It

seems really straightforward like, oh,

you'll you like have a job board, you'll

get applications, you'll review them.

This very quickly becomes a huge huge uh

drag on the rest of your team. You could

easily spend almost all of your time

recruiting if you're not doing it

efficiently to get a to suboptimal

outcome. And so for us again, we've

built a lot of software to do this.

There are four steps to our process. The

first is that we've built a a software

interface for users to apply for

applicants to apply online where we can

capture some structured information from

them upfront um including the ability to

apply to multiple jobs um in parallel.

And so we originally used a a commercial

applicant tracking system. We've moved

this to our internal tools. Um, and one

of the reasons we did that is because

this allowed us to do something that we

couldn't find in any of the commercial

ATS's. So, when we the first step of our

process when users apply is it goes to

companywide voting. Um, this is a

heavily redacted uh version of of the

internal interface, but hopefully you

can make out the idea of what's going on

here. So in the the the applicant's

resumes in the middle, we collect a

little bit of other structured

information, but the most important

thing is on the the far right you see

this there's a question like how would

you vote for this candidate? Are they

known good, strong, yes, yes, no,

strong, no. And so when a person

applies, the system picks out seven or

eight current employees that it thinks

look something like their backgrounds

and it pings them all for votes. And so

we can distribute the voting across a

lot of the company for this initial

review, which is essential because if

you're doing anything cool, by the time

you get a couple years into it, that top

of funnel is overwhelming. And if you

place any in any small group of

employees or any one person in the way

as a bottle bottleneck on this, they

will absolutely bottleneck the whole

rest of the organization. And um it's

also you want to I think average over

judgment. I think there's different

people that are better or worse at

hiring and have different perspectives

on what you're looking for at that

stage.

And the so in the beginning as the

founder you can meet with everybody and

you should that will take you quite far.

You should definitely interview

everybody for quite a while but even

beyond that you still want to you want

ways to average over the judgment of the

rest of your team and voting mechanisms

are usually a really good way to do

that. So these are these are our actual

statistics over the last couple years.

So 17% of the top offunnel applications

that we get go to a phone screen. Again,

that first initial app voting stage is

drawn from a companywide pool so that we

can get fast we can get the voting done

quickly within usually 24 48 hours and

not bottleneck that on any small group

of people. The phone screen is again

drawn from a companywide pool of people.

This is not team specific. This is a

companywide bar really looking for three

things. Judgment, horsepower, and

agency. Like if we throw you into a

complex vaguely defined situation, will

you tell tend to make good decisions or

will you create diplomatic incidents?

Like do you have the like do you meet a

just a basic hurdle for technical

competence and like demonstrated ability

to learn things? And do you are you

effective at causing the world to look

like you wish it were? Like how how does

your life look or not like whatever

ambitions you had? And like do you have

specific ambitions for your life? And so

this we can distribute over the entire

company and then half of those tended to

go to homework. Um ideally we'd be using

entirely AI resistant homeworks now. So,

our favorite types of homeworks are

things that um don't saturate, have a

very high ceiling, and are naturally

scorable to two or three numbers that we

can put on a plot so that when we get

responses to homeworks, we can just plot

them all and it's very obvious when

someone has really beaten the PTO

frontier and we otherwise don't care

whatever AI models they use like that

can make you better. um in cases where

that's not possible uh for homework

right now we've we're doing increasing

number of technical phone calls or pra

on-site practical tests but ideally we

would have an AI resistant take-home uh

for each of these had a really

interesting take on the AI resistant

homework where they've had a couple

tasks where there's like it's like uh

the GPU kernel optimization like what is

the minimum number of cycles you can get

it down to and this is naturally

adjusting like the hurdle for a while

was I think it was sonnet's performance

if you could beat that then you could

get an interview I think that there's a

bunch of ways to construct a resistant

homeworks.

And then by the time you get to the

interview, it is important that you have

a from there reasonably high like at

least 25% conversion to an offer because

otherwise it is just you're going to

waste too much of your time doing

on-sites for employees that don't

convert. You can't get that down. Um,

and so this is there's four steps to

this. Initial voting, the phone screen,

homework, and a full interview. And this

is as far as like from what my

experience,

this is the minimum set of information

that we need to make a like a full

decision. And I don't think that there's

a a more efficient way to elicit this.

Like I don't think there's a smaller

number of steps that we could use. So

this has become our process. So you're

hiring people, they're coming into work,

they're starting, you're incurring

payroll. Um, but how do you know that

you're good at this? Like eventually

you'll get feedback from the market on

how good you are at hiring because the

company will work or it won't. Like your

team will be capable of accomplishing

the stuff that you've set out and

they'll help you course correct through

that. But this is a very very long

feedback and it's very poorly behaved

loss function. Um and so it's kind of

your job as management to design

synthetic gradients that allow you to

find out earlier and along the way how

recruiting is going and if you need a

course correction.

A conventional answer to this is the 360

review process. So once a year you send

out a lot of forms, you gather up a

bunch of feedback uh around each

employee, you set up a bunch of meetings

with HR and with the various managers

and you can do the conventional

performance review cycle. Um which based

on my experience is like this is a a

very disruptive process that doesn't

tend to surface issues that you don't

already know about but haven't acted on

because you knew that thing was there.

But firing people is hard and so pe like

people drag their feet on it and this

this is kind of reinforcing things you

already knew and it only happens once a

year. Um maybe twice a year if you split

up the company into into cohorts. But I

mean I think really what would be nice

to have is a signal that gives you this

kind of natural feedback from across the

company about who's good and who isn't

and what's working and what's not in a

way that is largely unbiased and is more

continuous. Imagine if you could get

feedback kind of every few weeks on

where there are issues and where things

are going well. And so the process that

I had developed um which I've now used

for the last really six or seven years

is every few like every couple weeks

every four to six weeks it's not that

often people around the company get

pinged with a question through the

software through Helix and it's there's

a form but really there's only one

question that really matters which is

knowing how this person turned out would

you vote again today for their hire?

It's the same questions we use on the

initial voting. Um, and so you'll get a

prompt to say like this person you work

with like how would you vote for their

hire today? And then what we can do is

we construct a graph over the company of

all of the feedback. And so the basic

intuition is that like your vote should

be weighted more highly if everybody

else has rated you highly. And the

astute may notice that this looks a lot

like the original Google algorithm page

rank which is an idea called IGEN vector

centrality where you can create a weight

over the over the graph by looking at

how the graph points together. This is a

little bit different than actually

literally I vector centrality but it's

very similar and so we call this

technique IGEN reviews and I've become

convinced that this is more or less the

right way to do performance reviews.

There's some other tricks that you have

to apply to get this to work really

well. For example, um we apply dropout

where we'll run a thousand iterations

where we'll randomly remove some

percentage of the edges each iteration.

And then when you look at the

distribution of scores that you get out

of that, if you see additional peaks,

for example, this is a clue that there

could be voting clicks that need further

investigation. But as a whole, this is

it distributes the judgment across the

company, updates more or less

continuously with about a month lag, and

gives you just way better insight into

what's going on really around the

company.

So this is and it and it also totally

gets rid of that kind of traumatic super

heavy once a year HR driven performance

view process. Um so the the the point of

this talk is not the spec is not that

you should use this in particular

although you should consider it and if

you're if you actually roll this out at

your company um you can email me and

I'll send you a doc with more specific

tricks on how to actually get this to

work well. But the the real theme of the

talk is that rate of iteration separates

success from failure. And if you can get

a fast iteration loop, that really

overcomes many other things you're going

to run into. And this effect is so

severe. I mean, if you can learn one

thing every week and there's a

competitor that's learning a thing every

month, they will they will never matter.

Um o overwhelmingly if there's you're

looking at different way like two

different approaches to solve a problem.

If there's one that allows you to

compound like in half in a much shorter

amount of time than the other, even if

the other approach has significant um

like redeeming characteristics, you

should really consider going with the

shorter iteration cycle because the

compounding effect is just so dramatic.

And so speed determines success and

failure and speed is determined by

infrastructure. This is driven by really

boring sounding things like how well do

your purchasing and recruiting and

spending processes work. This is as

important as how well do you understand

understand the object level technical

content of the thing that you're

building. I see companies founded by

just like stellar pedigree scientists

and engineers all the time that die on

the vine because this execution is tough

to follow through and your job is to

organize. It's like it's it's uncommon

that these deep tech companies that fail

because the technology doesn't work.

They fail because once you end up with

this organization of hundreds of people

and thousand hundreds of thousands of

square feet of physical infrastructure,

you haven't built the systems to manage

that. becomes unwieldy and then you

can't make like you can't connect

strategy to execution.

Um so we we heavily lean towards things

that have shorter iteration cycles um

kind of etc like all else equal. Um but

that doesn't that's not a blanket rule

like there are no blanket rules in

startups. You're looking at each new

fact pattern that comes in as its own as

its own unique thing and then making

decisions that make sense to you. And

one of the harder lessons as a startup

founder, one of the harder things I

think to to really deal with is the fact

that you cannot delegate your judgment.

As as the CEO, you must always make

decisions that make sense to you, no

matter how much momentum or inertia

alternatives seem to have. Um, so in

school, if you're let's say there's like

somebody sitting next to you and you

cheat on the test by looking over at

them, you'll your grade will like all

else equal, your grade will be dragged

towards the average of the class. That

is not good enough to succeed in

startups. you have to do things that

like are at the long tail that you're

you the successful companies are the

exceptions by becoming an average that

is not good enough. And so um in order

to succeed your judgment has to be

differentiatedly good. Now the reality

might be that you don't know if your

judgment is good yet. And so um one way

or another you will have to find out and

that means making decisions that make

sense to you even when you are totally

alone in that realization.

um that is the only way to get to to the

really big outcomes. Now, it's not that

often that everyone else will think one

thing and you'll be like, "You're all

totally wrong." But it is a really eerie

feeling like you'll get to a point four

or five years into the company when

there's hundreds of millions of dollars

on the line and there's some really high

stakes decision and only you can make it

and then you will look around for advice

because like in the beginning you'll get

lots of like there's a bunch of things

that are easily advised or easily

figured out but you'll get to a key

point years in and you'll look for

advice and there is nobody to ask and at

that point you must have a really good

sense of the limits and boundaries of

your judgment. That is a very eerie

feeling and you have to be able to

commit to it regardless. Now, the good

news is that in my experience, it's very

difficult to actually get stuck. Um, you

can get yourself into trouble and the

action space is always larger than it

appears. Um, you can kind of no matter

what happens, there's usually like when

you get like I think it's very easy to

try and anticipate all kinds of problems

that you'll never actually run into. Um,

and then you go and do it and then you

get to a point where the system like you

run into some real limitation. There's

always a hundred ideas about how to make

it better. This is sometimes phrased as

action produces information. Um, this

idea is is I think much deeper than it

sounds. Like the so in physics there's

this there's this quantity called

action. And so if I throw a ball and it

follows a ballist like a like a

parabolic trajectory that trajectory is

totally set like when it leaves my hand

unless it gets blown by wind some other

like action is exerted on it. It will

follow this ballistic trajectory which

is in this sense like kind of an

information minimizing trajectory. I can

say it just followed it was ballistic

that totally determines it. If it

something else happens you had to spend

some energy time to cause that cause

that to happen. And so when whenever you

exert like action into the universe that

creates information like in a like in a

very fundamental sense and whenever you

get stuck like you have to you have to

start like injecting action producing

entropy. Um and this is this produces

some fairly counterintuitive effects.

Like I've seen situations where the

company is stuck in a deep local minimum

and there's someone who is great in many

ways but it's just the wrong fit for

what that company needs at the time and

removing them even though they

individually are very strong unblocks

the company and allows it to kind of

enter a new phase. Um when you're when

you get stuck you have to start doing

things. And so all the thing underneath

the the object level content of what the

product you are building is you've got

this you have all these support systems

kind of the company like how the company

does purchasing and accounting and

recruiting and performance reviews and

budgeting and safety and quality is the

operating system of the company and that

has a huge impact

on how far you can take it. So

speed is determined by infrastructure.

Speed determines success and failure.

You need to put more thought into these

into getting these foundations right. If

you do them right at the beginning,

everything else is much easier. If you

get them wrong, you'll end up like

spending $5 million a month and feel

like you have very little control over

it. Um and then you're forced into into

coarser levers and harder decisions. Um

thank you for coming to my TED talk.

Okay.

Do you have advice for people trying to

choose between industry and academia,

starting a company now versus getting a

PhD first?

So, it really it depends on specifically

what you're doing. If

if your field only exists in basic

research, then getting a PhD might be

very reasonable. Um

the

so when things really start to work um

like if

20 years ago the best computer

scientists were at CMU and Harvard and

50 years ago the best rock like if you

wanted to work on rocket engines you

were at NASA you were at a university

you're at University of Maryland or

somewhere and now they're at now the

best computer scientists are at Google

and Apple and um and OpenAI And the best

rocket scientists are at SpaceX and Blue

Origin and others. So when a field

really starts to work, industry can just

marshall such larger levels of resources

and can just move so much faster. Um,

and so I think a question has been why

has academia stayed so relevant in the

life sciences. And it's just the reality

is that it doesn't work that well for

most things. Like humans just aren't

that good at drug discovery. And so if

your if your field is really only in

academia, then it can make total sense

to get a PhD. But

um

I think you know a lot of

it is uncommon that startups don't get

the technology to work. It is more

common that they can't organize the

human organizations to accomplish their

goals and learning that is also a skill

set. The only way to learn it. I think

it's an oral tradition. You have to do

it. And so if the choice is working at a

really high performing company adjacent

to where you want to be versus getting a

PhD, I'd probably recommend the company.

But it's not an absolute rule and it

really depends on the field.

what counts as evidence of exceptional

ability to you? Um, anything that

concretely you can put your finger on

that separates that person from their

high school class. Um, like what is you

like if you have your average high

school student? We just like want some

concrete fact that is that

um

I mean ideally the the best evidence of

exceptional ability is are winning at

legible competitive games. So this could

be being a like a chess grandmaster. It

could be winning design, build, fly or

formula SAE competitions. Um there's a

bunch of Silicon Valley deep tech

companies that are basically built out

of Formula SAE winners from college. Um

people that just spent their college

experience building things and racing

them and finding out. I think you have

to have that type of competitive

feedback. It is tough to know if you're

exceptional um without having some

legible competitive game.

How do we hire engineers now? Do we

still use leak code or do we have better

ways? If we allow AI use, how do you

understand the skills of the applicant?

Um, so we've never I don't think we've

ever really used leak code. Maybe some

other people on the team do it in

secret, but I've never asked it. Um,

so software

in particular,

it the rewards to horsepower are so

great that it really is just it's a

field that attracts really smart people

because it gives you this very rapid

feedback. Like if you think about like

there's a lot of really smart people in

biology, but when you have a biological

idea, it can take you many months to

find out if it's a good one. In

software, if you have an idea, you can

often build it in a couple hours or you

can get feedback within days. And so it

has this really addictive feedback loop

kind of like high frequency trading that

just draws in really smart people. Um

and uh and so we look for kind of over

your life what signals do we have that

you have done something interesting like

it's it's uncommon for someone to get

into their mid20s without having without

there being some thing in their

background that they went out and sought

out and did. Um

but it can but it this is like such an

open-ended criteria. Um it can really be

anything. The we don't we increasingly

more directly to the question we

increasingly don't directly evaluate

programming. We evaluate try to evaluate

thinking. So this is design questions

like if we give you a domain how do you

break it down? Can you understand the

decomposition of the problem clearly? Um

it's really measures of like can you

think clearly rather than can you write

code?

What did you take away from your

experience at Neurolink?

So the question of like should you go

get a PhD? I don't have a PhD. I spent

five years running a company for my CEO

at Neurolink. Um that was I mean there's

one of the biggest lessons I think is

that there are few really generic

there's no generic algorithm for how to

succeed at a startup. There's no like

set of like five bullet points that can

be conveyed that if you just like turn

the crank your company will be

successful. It is a long series of

judgment calls. And so the most

important thing is that those filters

are tuned really well. And so the I

think the most one of the most valuable

things for me at Neurolink was I was

working with someone who has empirically

excellent judgment. Like we could get

into trouble together and there'd be all

like something would happen and there'd

be two possible solutions that would

make sense and I'd go to him and say

like is it option A or is it option B?

you'd look at and be like, "Oh, it's

definitely option B. The problem would

never recur." And having been in those

situations where I was trying to make

these bets kind of with stakes attached,

looking forward in time, not getting

feedback until later with that advice

was incredibly useful for train for

fitting those filters. And I don't know

that there was really a shortcut. And I

think that just hearing the stories when

you're not there making the like really

thinking about it because there are real

stakes and then getting that feedback um

that that is an essential part of the

education of an entrepreneur that I

think many people underrate. I think it

is really worth working for a a company

that has an excellent culture that you

respect before jumping right into your

own into your own startup. It is

relatively uncommon that startup

cultures get rediscovered entirely from

first principles. Usually they're passed

down as as again oral traditions because

there's a founding team that worked at

another company which worked at another

company and so they inherited it or in

some cases where there's really a

breakout where there's just some market

dislocation that really enables a team

kind of out of nowhere to build it.

They'll often get it from the VCs but

it's working with the people that have

that judgment so that you can get that

like you can get that reinforcement

learning as it's long series of facts is

really important. Um,

could BCIs or neural interfaces help us

figure out what consciousness actually

is? How? Absolutely. Um, so

if the end of the artificial

intelligence quest is super intelligent

machines, um, I think the end of the BCI

quest is conscious machines. Um, there

the brain is composed of ordinary matter

arranged according to the rules of

chemistry, only things found on the

periodic table. It seems tough to

believe that there's like some new

physics going on in there. And so

there's we we're looking for some

mapping between the substrate activity

and the phenomenal content. Now, if we

had a tech if we had a magical BCI that

allowed me to see the instant state of

every neuron in the brain and and drive

them, I think we'd figure out

consciousness pretty fast. I don't think

I think this is a practical problem, not

a philosophical problem. And uh but to

prove it but first of all that practical

problem is real and we'll have to do the

stuff in humans and to prove any of this

we'll have to do it in humans. I think

the it is possible that uh you could use

a BCI to prove it. We have some ideas

about how to do those experiments but

they are um

they're still a few some number of years

off right that things going into humans

now are are not designed to to study

consciousness but I do think that that

is further down this path.

What should I study to contribute to

BCIS?

The

this really depends on your background.

Um there neural interfaces are a very

interdicciplinary problem. It uses

everything from uh stem cell biology to

materials and micr fabrication to um to

software to animal behavior to surgery.

Um so there are many different entry

points in it. Um one of the things that

we found is that it's better to have a

smaller team that can fit more of the

problem in their heads and then compress

it together. Um, contrast this to how

academia usually handles

interdisciplinary problems where they'll

have an interdisiplinary center that

pulls in very deep verticalized experts

who are kind of meet at the center. And

the problem is that they're all speaking

different languages. And so it's often

hard to really like even when they can

communicate, typically you end up

shipping the interfaces of those

departments. Whereas for us, if we can

kind of hold the problem in the head of

a smaller number of people, we can shift

around where the bottlenecks are.

Specific example of this is our protein

engineering group has been able to

develop much more sensitive like much

better proteins for some things that we

need to do which has allowed us to relax

some electronics requirements. So if we

have uh so specifically we have proteins

called opsins that allow us to make

neurons light sensitive so that if we

shine light on them we can fire a

neuron. Um the problem was that you

needed to hit a neuron with a lot of

light to fire it which means that you

can't have that many light light sources

because it gets too hot. So, we've been

able to make the protein more sensitive,

which means that we can have more LEDs

because each one can be dimmer. And so,

we can we've turned this electronics

problem into a biology problem that

allowed us to relax those constraints.

You don't get that as much when you have

these interdisiplinary centers where

there's like one group focused on one

thing, there's another group focused on

another thing. Um, and so I would say

being being able to have a broader

perspective of more of the problem is

really valuable. And then just have like

really the as deep and clear an

understanding of this as of the system

as you can get. I think there's no

substitute for being hands-on. It

doesn't really matter like the you want

some hard skill to get you in the door.

Software, electric, electronics,

mechanical, materials, something and

then from there I would try to learn as

much of it as you can. Um

what doesn't AI replace in scientific

research?

Where are humans still necessary if

anywhere?

Um,

we we still definitely need humans. Um,

and in scientific research in

particular, I mean, it's tough to

predict like AI is clearly advancing

very rapidly. I do think that you need

to think about how to have your company

be AI native in the sense that every

like you want you want to gather all of

the context all the stuff happening in

your company and be able to make that

available efficiently to agents because

those are clearly a big part of the

future. So for us in Helix really

everything goes in there and one of the

reasons that we did that was because not

just is it powerful to have everything

in one database to link together

purchasing to quality to batch records

and manufacturing so that we can trace

stuff more efficiently but also so that

we could give it all to to agents. Um

and so we found them to be a a

multiplier for the team not a

replacement. Um

the three biggest areas that AI has had

an impact for us so far are um like well

first of all coding I mean that's like

now basically all this like I don't I've

written a lot of code in my life I don't

think I've looked at the source very

much the last six months um that is

getting really good um regulations. So

this is

so if you're doing anything really

interesting, you're going to end up

regulated and then you'll probably end

up dealing with these things called

quality systems. And so a quality system

I think triggers a lot of scar tissue

for people because it's it's just the

the quintessential heavy bureaucracy.

Slow everything down. But the idea of

quality itself is actually not a

problem. The problem is that humans are

bad at reading and interpreting these

things. And so when we make a product,

one of the things we have to do is

identify all of the standards that might

apply. And there's standards for

everything. There's standards for like

how the lithium-ion batteries plug into

a PCB. There's standards for electrical

insulation of the boards. There's

standards for shipping label like the at

some point you'll have to take your

shipping packaging, print a label on it,

and put it in a vibe box and show that

the the corners of the label don't curl

in a way that might cause it to detach.

And so we you hire regulatory experts to

go find all of the standards that might

apply, make a list of them, and then

have a spreadsheet which is like all of

the evidence that you comply with all of

the standards. So you have this this um

this thing can take many many months.

Historically AI has totally transformed

it. I mean we can very quickly look up

all the standards. We can very quickly

generate the evidence tables. And I

think that um to the degree that there's

kind of over I mean there is we

definitely need to deregulate some

things but I think that the combination

of AI and regulation is is a better fit

than people think and you can use it to

smooth a lot of stuff. Um where the

regulations are written in blood and

largely good ideas it's just hard for

humans to do it.

Um,

why build your own infrastructure

platforms rather than just buying them?

I mean, you can't really buy these

things. There's no there are ERP systems

out there, but there's no company that

like loves their ERP system. Like, I

don't know there's anyone who's really

like, I want to spend more time in

Netswuite. Um, and

on the contrary, there are a bunch of

examples now of companies that grow up

around a piece of software that's really

fit just for them. like YC famously has

a lot of internal software that I think

really makes YC work. Um Facebook also

very famously invested heavily in

internal tools and now has um like I

think gets a lot of efficiency from

that. Um SpaceX and Tesla internally

have a pretty giant piece of software

called Warp Speed that runs a lot of

their manufacturing and R&D processes.

And so when one company grows up around

like a harness fit to it, it can be very

powerful. It is powerful in a way that

the software that you can buy isn't. Um,

but this requires you to really look

into the future because certainly,

especially at the seed stage, this is

not the thing that you would think you

should be focusing on and historically

it has not been. I think this is a thing

that has changed with agents. The fact

that you can vibe code this now makes it

a reasonable thing to think about.

Historically software has been so

expensive, you would have had to buy it

and that's what everybody did for a long

time. That was I think a worse world and

that world has changed and so now there

are better options available. But like I

said, so like we previously had used a

we used greenhouse. Um greenhouse

required us to have a small number of

people as a bottleneck at that at that

first funnel stage. Um replacing that

with software. We we were able to

explore voting mechanisms and fairly

detailed voting mechanisms that can that

can make smart inferences about who

would know about it know about an

applicant. Things that you can't really

do with the commercial software. And so

for a lot of these processes, you should

think about how you want it to work for

you. the

it it matter these are human

organizations these human processes that

have to be staffed and if they aren't

done routinely will atrophy and there's

things that make sense for different

teams and founders in the way they view

the world and think about it it really

is all very different and but if you

build a thing uh for that works for you

and then you like bake that into the

company so it like when you put

something there it stays there it can be

very very useful

what changes should we expect in the

as BCIs start to work and get widely

adopted, do intelligence differences no

longer matter.

So there's this meme that BCI is an

artificial intelligence adjacent story

and there's some of that like eventually

like if AI is building super intelligent

machines and BCI labs are building

conscious machines and we're building

brain-to-brain connections so that the

boundaries between those things become

less meaningful like at some point you

want a super intelligent conscious

machine that we can participate in. But

that actually feels further away to me.

I think in the near term BCI is really a

longevity story. And I view longevity as

really just healthcare. I mean just

biotech. It's just that it hasn't like I

think it is not right to say that the

pharma companies or any of these these

like past healthcare companies are not

interested in cures. I think that that

is what all of them want. It's just that

that's been beyond our capabilities. And

in neural engineering and people hear

BCI think they think of motor decoding

like I put some electrodes in motor

cortex and now they can control it like

a video game. I think neural engineering

is much broader than that. We include

our retinal prosthesis in that. We

include cocar implants in that. Um and

this this I think is a contrarian take

on all of healthcare. It gives you these

effect sizes that you just don't really

see in medicine. Like if you have a

patient on on a dopamineergic drug for

Parkinson's that works for some period

of time, but it's a it's a relatively

small effect after a little while. You

turn on a deep brain stimulator and a

patient goes from not being able to hold

a cup of water to being able to write

cursive in like 10 seconds. You turn on

like if you want to talk about strong

patient testimonials, you should see a

newborn having their coar implant turned

on. Like these are just when you deal

directly with the brain as a computer,

not only do you not have to solve some

of these really hard biology problems

that are just beyond humanity's

capabilities, but you get these results

very like pretty readily that again are

just uh like you can get an engineering

gradient, you can get them more reliably

and they're just large effects. And so I

see this as as a way to to extend and

improve the life of of like of

everybody. I mean there's

the the brain is the thing that makes

you you. It's the only thing that in

principle you can't transplant. You can

get a new heart or a new liver. You

cannot even in principle get a new

brain. And the brain is usually not the

thing that fails. And so if you can deal

with the brain directly, um I think this

is going to

this is more of a of a radical longevity

story than it is an AI one for the

moment. Although all of these things

will come together um over some period

of time

for your IEN review performance system.

How do you prevent employees from

colluding on their votes or downvoting

somebody on purpose? So, so as I

mentioned, there are some tricks. So,

for like for example, applying uh marov

chain Monte Carlo dropout allows us to

detect things like voting clicks because

now instead of seeing one peak, you'll

see two peaks. That is a a clue to look

in look into that. Um it's I mean it's

designed to um be tolerant of these

things. I think it is really fairly

transparent. It's also not our only

signal. It's one of several. Um the uh

if anybody's interested in this um send

me an email and I will share a document

with with the specific tricks but I want

to understand a little more about how

you were going to deploy it first. Some

of this is trade craft

when you're building something as long

horizon as Neurotch. How do you figure

out how much runway you actually need to

keep the company alive and how do you

get investors to fund that much?

So sometimes I mean you you often see

founders like especially more

experienced ones pitching VCs for what

they think is reasonable to ask for

rather than what they need to run the

experiment. You're raising some amount

of money to go find out some answer. The

answer to that might be no. the

investors understand this like depending

on what business you're in, but you have

to actually run the experiment. And one

of the the things um like

there are definitely some ideas that are

worth funding with $50 million or zero

dollars, but not $5 million. Um you

won't run the experiment. It'll be

really frustrating experience. You'll

get an ambiguous outcome. And so my

first piece of advice is like you should

figure out what you think it's going to

take to actually run the experiment,

which is not the whole company. That is

what is your next value inflection. You

should have, no matter how ambitious and

open-ended of your plan is, you should

have some sense of like what is your

next key value inflection point. What

are the experiments that need to go into

that? Um, price that out and then raise

twice the money. Um, so I would I mean

there's some amount of waste. I think if

you can get waste down to 20 or 30%,

that's pretty good. Um, and

anyway, the advice is figure out what it

costs to actually run the experiment.

raise twice that and like raise raise

that or not. Um beyond that it's the uh

you'll always discover new things.

There's usually some path through the

mass. Um but you're also like when you

start the company you're not going to

get a guarantee of like that you won't

be on a bridge to nowhere or that it

will work on the funding that you have.

Like you're going to have to get in

there and figure it out halfway through.

Um I think that people should push for

profitability sooner than they often

think that they need to. Um, for us, I

mean, even though we are seen as this, I

think like very open-ended deep tech

company with a very long roadmap, which

is true, we are also relent relentlessly

focused on revenue at this point. Um, we

are trying to get to sustainability. It

feels like I mean, you kind of the

company is kind of constantly dying

slowly of this money cancer that we can

beat into remission every couple years

with the fundraising, but then it like

eventually comes back. And I like want

that feeling to be over. And so you you

no matter like how big of a problem or

big of a vision it feels, you do need to

think about how do you get to revenue so

that not just you can do it forever, but

then you'll be valued on your long-term

road map, not not valued on your

probability of dying. And it really

opens up an another set of investors

that wouldn't um that wouldn't be

relevant otherwise.

What is the best piece of advice you've

received?

Um,

I don't know. I've acquired way too much

brain damage over the last 20 years to

have a memory capable of picking that

out. Um

it

I mean I think if other than

speed being the basis of success and

infrastructure determines your speed um

it is it is important to appreciate that

there like are no general principles. I

think people are looking for shortcuts.

People are looking for um like a a pathy

set of instructions that are like oh I

figured it out and that doesn't exist.

Like every one of these things is

different. When you get to that moment

in history, you're doing something new.

And I mean, we can take we can reflect

for a second on how crazy it is that all

that this is possible. Like for the vast

majority of human history, if you were a

smart 20-year-old that like had an idea

to like make your society better and you

raised this to the people with capital,

the reaction was like you should pay

attention to the harvest. The fact that

like it is not widely available. It's

not universally available, but it's not

like widely available that if you're a

really smart 20-year-old, you can come

to San Francisco and make the case and

if it's an interesting idea, you'll get

millions of dollars to find out. Like

this is not the case for most of the

world today and it's certainly not the

case for most of history anywhere. And

um that but like that shouldn't feel

normal like that that this is given to

push the frontier out. And when you're

on the frontier, they're like you're

figuring it out as you go. You like that

is that is the job. And so I would try

to rely less on things that feel like

startup advice and more on how good is

your judgment, how well is that refined

in your domain and remembering that is

you have to think for yourself.

What are some of the hardest remaining

engineering challenges involved in

getting BCIs to work?

So in BCIS we often feel very limited by

power and thermal constraints on the

implants. Um, and so this creates a

strong pressure to implant as little as

possible and do the rest uh off the

body. You can't pass a wire through the

skin because the skin is a very

important immune barrier. And if you and

the skin won't like fully heal around it

like if you have any connector through

the scalp, you're constantly at risk of

a bacteria crawling down that and into

the brain and then the patient's going

to have a really bad time. And so you

really have to be able to close the

skin. That requires you to have

implanted like a radio or transceiver of

some sort and getting the power on that

down. like there's there's a frontier at

low power electronics which is really

important more as I mentioned earlier a

lot of this is now becoming increasingly

biology as our biological engineering

capabilities increase um but then on

those implants ironically one of the the

harder kind of more open problems is

what we call packaging um our colleagues

in Europe call it tropicalization

um this is the uh your ability to keep

your device in and the body out of an

implant that that you put in the body so

there are no truly passive surfaces

anywhere in the body, even bone is

constantly getting remolded. And so if I

put a device in, it's going to be it's

going to be getting attacked by the body

and it's not regenerating itself. And so

you need a material that is going to

survive that for an extended period of

time. The very like the classic example

of this is the laser welded titanium

can, which like if you've seen like a

pacemaker or deep brain stimulator,

they've got this big titanium box.

Obviously can't we can't put a big

titanium box in the eye. Um,

interestingly, one of the the earlier

retinal prostheses before before us, uh,

10 years ago was a device that was a

that did have a titanium box that they

attached to the eyeball. So, they had a

it was a four and a half hour surgery.

They had a little belt that went around

the eyeball with a little titanium box

on the side of the eye with a battery

and a little PCB. Like, this didn't

work. This was not good enough. Um, they

needed to get rid of that somehow. Um in

our case we've solved this with the with

the laser projection trick where we

power it wirelessly but um having type

this next generation packaging some type

of conformal coating that we can use to

protect the implant that is not degraded

by the body is also not harmful to the

body and is is resistant to all the

thing like all of the ways the body will

try and kill it. um th that material

science is a very open-ended field and

if you're interested in material science

that is a thing that uh we need progress

in.

How did you approach interacting with

the medical field to build your retinal

implant?

Um

the

I mean business is just like a fancy

word for talking to people and doing

things like you send you talk to them

like you send them emails. I mean this

is the uh

so for our retinal implant it was

originally invented uh by a professor at

Stanford almost 15 years ago I think um

it was licensed to a European company

that we were tracking um let me back up

a second. So when we started the company

uh I I came from Neurolink four of my

five co-founders came from Neurolink.

uh kind of took a look around the world

in early 2021 and asked like what is the

most valuable thing that we can do that

would be likely to work in the near

future that would may have a big impact

to patients and allow us to be the

foundation for a um the type of scalable

medical device company that we wanted to

build. And we came to the conclusion

that that restoring vision to the blind

by stimulating the retina was was the

thing in that there's you kind of have a

choice of two types of cells in the

retina that you can stimulate these

things called bipolar cells or the optic

nerve. And you could do that

electrically or you could do that

optically. We explored all four

quadrants of that. We developed

internally a state-of-the-art gene

therapy that optically stimulated one of

those cells. And we identified this this

French company as being the

state-of-the-art in electrical

stimulation. And so we I mean it's a

small community. You can meet people,

you can talk to them. Um we uh it

eventually made sense for us to acquire

them. We ended up with the license, the

technology, and we and we work with

surgeons and doctors all the time. Um

you uh like if there's a new surgery

that you want to figure out, I mean

typically this makes this is best going

through networks so that people are more

likely to respond to your email, but we

cold email surgeons all the time saying

like, "Hey, we have a weird surgery to

develop. Do you want to be a

consultant?" and people reply

um there before I get to the next

question there there is a real cultural

thing here um so in my time hanging out

around the periphery of SpaceX I

observed that like at least circa seven

or eight years ago probably like 20% of

that company is what you might

characterize as committed Martian

colonists and 80% % are serious

engineers. I think that those people are

lunatics and they just want to work on

the highest performance methodox engines

in the world. And you need both of those

cultures to be really successful long

term. And that's uh especially tricky in

in in medicine, right? Because that's a

very very conservative, arguably very

authoritarian culture for the most part.

And similarly at at our company we have

I'd say 30%

I mean it's an overtly transhumanist

mission and then 70%

like serious clinicians and scientists

and researchers and people who think

that those guys are crazy but we're

going to build some really valuable uh

medical devices for critical unmet needs

in the process. I think one of the

things that makes science the company

very special is that it has both of

those cultures and is able to integrate

them and we're able to simultaneously do

some really cool research that I think

is really at the edge of the Overton

window while simultaneously running

clinical trials in six countries now

with an approved medical device in

Europe and clinical trial results in the

New England Journal of Medicine. You

have to be able to navigate both of

those things I think to really reshape

the future.

Has biotech gotten easier to break into

for earlier stage founders?

Um,

biotech remains capital intensive. Um,

and so that is like I don't know that

I'd recommend biotech if you have a

choice of other stuff to do. I think for

me this was I got like

I realized almost 30 years ago that if

you could engineer like if you could

alter the brain you could alter reality.

like this was one of the biggest

missions of of the next 30 40 years was

was building these things. And so for

me, I think it's like every now and then

I think that my life would be way easier

if I just gone into AI instead of ECI.

Um but somebody has to do it. And I

think it's important to

like biotech is hard. It's like it's a

it is a much harder path than many other

things that you can do. But when you're

successful, it it has an impact on like

the really what you see elsewhere. Um

the

like I think increasingly there's I mean

it was Paul Graham that wrote a long

time ago that like you get vibes in

different cities and like the vibe in

Cambridge, Massachusetts is you should

be smarter or the vibe in New York is

you should be wealthier. The vibe in San

Francisco is you should be more

powerful. Especially with the rise of

things like artificial intelligence, I

think people realize that this isn't

just about money. And I think for many

the of the most effective startup

founders, it's not about it's not about

the money. It's about changing like

there's some way in which you want the

world to be different. And it just turns

out that for that project, the

for-profit company is an incredibly

powerful way to marshall the resources

required to cause the world to be

different in that way. And this is this

is not about money. This is about power.

And there are many different types of

power. There's economic power. There's

military power. But the power to heal

the sick is is like a very dramatic one.

And when you get that, not only is that

um is that a real force to reshape the

world, it's one that can be shared very

readily. Like you can't share military

power, economic power, but you can share

the power um of restoring sight to the

blind or of giving life to the cancer

patient. And I think that the world is

getting more complicated and there's

there's like there's big impacts of all

the things that are being worked on by

the people in this room. And biotech is

is hard. It's very capital intensive.

It's a long road. when you start a

company in this space, you're committing

to a decade of your life that you will

never get back no matter how it turns

out. Um, but

the results of that when it works, um,

the impact that this has on patients and

their families is really unlike really

any other sector.

So the last question,

what's a popular belief in tech that you

think is wrong? And I don't even know

what the popular beliefs in tech are

now. Well, I mean, okay, even the whole

basis of building Helix is contrarian.

Like, I think that if you raise a series

A and then you tell your investors that

you're going to vibe code a purchasing

system, I think that any reasonable

board is going to like ask you what

you're thinking. Um, and that um

we were able to do that because I never

got those questions because we don't

because I control the company. But

that's one narrow example, I guess.

All right.

Thank you.