Landing an AI Startup Job
Key Points
- The video shifts focus from common job‑search tactics (resume tweaking, interview prep, AI tools) to the often‑overlooked strategy of carefully selecting which companies to target.
- It advises job seekers not to chase the most high‑profile AI firms (e.g., OpenAI, Anthropic, Microsoft) because their valuations are already inflated and employee equity upside is limited.
- Startup equity today often only offers modest multiples (doubling or tripling), while the risk of failure remains high despite large funding rounds.
- An individual’s best “investment” is their time, which VC money can’t replace; therefore, choosing a company where that time can have meaningful impact is crucial.
- Only in rare cases of truly extraordinary offers (e.g., a direct pitch from a tech giant’s founder) should you prioritize prestige over the strategic fit of the role.
Sections
- Targeting Startups Over Big AI Firms - The speaker urges job seekers to focus on AI startups rather than established giants, highlighting the greater risk‑reward potential and equity upside while promising to cover overlooked job‑search strategies.
- Target A-Stage AI Investments - The speaker warns that seed and pre‑seed AI startups are oversaturated and high‑risk, recommending investors focus on companies at or just after Series A where business models have been validated.
- Cold Applications and Startup Passion - The speaker advises targeting only roles where you’re a 95%+ fit, acknowledges that cold‑application tactics have become increasingly inefficient yet still viable, and suggests refocusing on genuine enthusiasm for a startup’s problem space as the key to breakthrough opportunities.
- Hyper-Targeted Job Hunt Success - A previously deemed unemployable candidate leverages deep personal values and intensive, customized outreach to win a role at their ideal company.
- Passion Over AI in Problem Solving - The speaker argues that genuine passion for a problem space, not AI efficiency, is what builds lasting companies and advises focusing on targeted, risk‑reward‑balanced efforts rather than feigning enthusiasm.
Full Transcript
# Landing an AI Startup Job **Source:** [https://www.youtube.com/watch?v=lZw9G1er8eE](https://www.youtube.com/watch?v=lZw9G1er8eE) **Duration:** 00:14:23 ## Summary - The video shifts focus from common job‑search tactics (resume tweaking, interview prep, AI tools) to the often‑overlooked strategy of carefully selecting which companies to target. - It advises job seekers not to chase the most high‑profile AI firms (e.g., OpenAI, Anthropic, Microsoft) because their valuations are already inflated and employee equity upside is limited. - Startup equity today often only offers modest multiples (doubling or tripling), while the risk of failure remains high despite large funding rounds. - An individual’s best “investment” is their time, which VC money can’t replace; therefore, choosing a company where that time can have meaningful impact is crucial. - Only in rare cases of truly extraordinary offers (e.g., a direct pitch from a tech giant’s founder) should you prioritize prestige over the strategic fit of the role. ## Sections - [00:00:00](https://www.youtube.com/watch?v=lZw9G1er8eE&t=0s) **Targeting Startups Over Big AI Firms** - The speaker urges job seekers to focus on AI startups rather than established giants, highlighting the greater risk‑reward potential and equity upside while promising to cover overlooked job‑search strategies. - [00:03:09](https://www.youtube.com/watch?v=lZw9G1er8eE&t=189s) **Target A-Stage AI Investments** - The speaker warns that seed and pre‑seed AI startups are oversaturated and high‑risk, recommending investors focus on companies at or just after Series A where business models have been validated. - [00:06:33](https://www.youtube.com/watch?v=lZw9G1er8eE&t=393s) **Cold Applications and Startup Passion** - The speaker advises targeting only roles where you’re a 95%+ fit, acknowledges that cold‑application tactics have become increasingly inefficient yet still viable, and suggests refocusing on genuine enthusiasm for a startup’s problem space as the key to breakthrough opportunities. - [00:09:47](https://www.youtube.com/watch?v=lZw9G1er8eE&t=587s) **Hyper-Targeted Job Hunt Success** - A previously deemed unemployable candidate leverages deep personal values and intensive, customized outreach to win a role at their ideal company. - [00:13:00](https://www.youtube.com/watch?v=lZw9G1er8eE&t=780s) **Passion Over AI in Problem Solving** - The speaker argues that genuine passion for a problem space, not AI efficiency, is what builds lasting companies and advises focusing on targeted, risk‑reward‑balanced efforts rather than feigning enthusiasm. ## Full Transcript
All right, settle in. We're going to
have a talk about how to get a job in
the age of AI. It's one of the biggest
questions I get asked. I want to pack
this video with absolutely everything I
know. And I'm going to start with the
startup targeting site. That's actually
something most people don't talk about.
Almost all of the job advice I see is
what do you do with your resume? How do
you work with Chad GPT? How do you
prepare for an interview? Etc., etc. I
want to focus this video on stuff that
doesn't get talked about as much and how
important those pieces are and then
we'll get to the basic advice as well.
So, first targeting a company.
I'm going to suggest to you that your
goal is not to get hired by OpenAI. Your
goal is not to get hired by Anthropic.
Your goal is not to get hired by
Microsoft or any of the other major AI
players. I'm not saying it's bad if
you're employed there, and I'm not
saying it's bad to get a job there. But
if you're looking at it from a
riskreward perspective, capital has been
invested very very heavily in those
businesses for AI and those businesses
are already soaring in valuation.
They already have most of the multiple
for AI baked in and a lot of the money
coming in is follow me money. Now why do
you care as a job seeker? Because
frankly the deal for a long time with
startups and even with the
entrepreneurial parts of big companies
has been if you participate and you take
the risk you get some of the upside you
get some of the equity. But the problem
is if the rounds are too juicy now you
don't really get the upside. It is not
really worth it to you to participate if
your equity only doubles or triples in
size. And I say that not because I I'm
trying to say that the money isn't worth
it, but because I'm trying to say the
risk is too high. There is real risk at
these startups. And I know it may seem
like there's no risk because of all the
money that's gone into it. But I have
been at companies that have done very,
very well and look like they were
unsinkable and headed for the public
markets and it didn't turn out that way.
Anyone who has worked in startups for a
long time has those kinds of war
stories. We have stories where things
didn't go as well as the nice little
offer letter said. The multiples didn't
turn out.
So when you're targeting companies, one
of the biggest things you can do is
invest your time in a company. You are
investing something that a VC cannot
invest. They don't have time to invest.
So they invest money and they hedge
their risk because they're investing
money in multiple startups. You're not.
You're just you. You're investing your
time, which you will never ever get back
in a company. So, choose wisely. You got
to pick well. And that's why I say I
don't know that the hot model makers are
really the best spot. Now, look, if you
are the one in 100 million person who is
being courted by Mark Zuckerberg for a
generationally changing wealth
compensation, sure, take the money.
Absolutely, we all would. But if you're
not, most of us aren't, then think real
carefully before trying to aspire to the
big model makers. I'm going to make
another somewhat controversial
assessment here. I have been through
multiple bubbles. Now, I also think that
you should be thoughtful about targeting
seed at this point in the cycle. Seed
and preede is very, very crowded. There
are a lot of seed and preede companies
that are going to go to the wall in the
next 12 to 18 months. They are companies
that looked great on paper. They could
raise a million on five and they are not
going to make it to their aim and they
are burning too much to be seedstar or
like seed to profitability. It's just
not going to happen for them. There is
roughly there's 70 to 100,000 startups
out there right now in the AI space.
It's like a feeding frenzy and you have
only one shot. And if you have only one
shot, I would not take that shot on a
seed stage company. I don't think that's
your best bet. I think the risk is
really high. And so you might say,
"Well, Nate, you've just spent time
saying the model makers aren't the best.
You've said the big companies don't
reward you. You've said the seed stage
and preede stages is too risky. What's
left?"
I tell you, I think your sweet spot at
this point in the cycle is like right
around the A stage, like immediately
before the A would be ideal. Right after
the A maybe that's a place where they've
proven some of the business model, at
least historically,
and there's still growth left on the
bone. So, they're going to grow and
you're going to get some multiple
and there's enough value in the business
that it was worth funding again. Now
these days, as I mentioned, there is
seedstrapping and so you may run into a
situation where you're functionally at a
but you're kind of bootstrapped. That's
okay, too. In fact, that might be ideal
because you get less of the sort of the
drama that goes with VC versus founder,
which can be exciting at times.
So, if you're looking through your
targeting lens, the lesson I want you to
take away is not follow Nate's advice.
The lesson I want you to take away is
think carefully about your values and
your targeting matrix and recognize that
whether you agree with me or not, you
also have only your time to invest. You
don't get more time than I get. So you
have to choose carefully.
I don't think that gets said enough.
Let's move from targeting companies
to talking about the application
perspective. This one gets talked about
more because it's more I think
immediately controllable. It lends
itself to courses, etc. So, a lot of the
advice I see, and I'm just going to
rehearse it here, and then we'll get
into sort of how I expanded, how I think
about it. The general advice I see now
in 2025,
uh, you have to be on LinkedIn. You have
to be active on LinkedIn. You have to
fill in your LinkedIn profile really,
really well with good keywords. You have
to make sure that your resume is
absolutely customized and tailored to
that individual role. You want to make
sure that your cover letter is really
sharp. Some people don't read the cover
letter, so you send the LinkedIn DM as
well. You send cold emails. You send
follow-ups 3 days, 7 days, 14 days
later, maybe 21 days later. You might
send a Loom video introducing yourself.
Overall, you want to make sure you show
passion for that particular startup and
then you have to rinse and repeat it and
do it a lot across every company that
has a target job that you think is a
very good fit. And the advice that I
typically see, which I think is correct,
is don't apply for roles where you're
kind of an okay fit. You should be
looking for 95% or better fit for the
role.
Look, here's what I have to say. At the
end of the day,
clearly
and similar AI application strategies
have poisoned the well so much on the
entire resume system
that I don't know how plausible it is to
get a job as a strategy with cold
applications.
Does the engine still work? Technically,
yes. People get jobs through cold
applications every day. I know someone
who got a job through a persistent cold
application strategy this year in 2025
related to AI.
It does happen. It is rarer than it was.
It is very inefficient. It's hundreds
and hundreds and hundreds and hundreds
of patient applications. It is a long
game. It is a months or even a year and
a half game at this point.
And so it's not that it can't happen,
it's that it's harder and harder and
harder because that engine of jobs is
breaking down.
So where do you go from here? I want to
suggest that we go back to what has made
startups an attractive place to work for
people who build for a long time. It's
actually not the risk. It's a little bit
of the equity and the upside. Let's not
kid ourselves. But it's the passion for
the problem space that is what
distinguishes people who stay in
startups. That is what distinguishes
people who stay in tech. They are
passionate about solving a particular
problem with technical leverage.
And if you don't have that, it doesn't
matter if you're an engineer like you
have to be passionate about the problem
space. If you don't have that passion,
it is going to be difficult for you to
stand out. And I've seen over and over
again, if you do have that passion, you
find a way in a door by hook or by
crook. The person I told you about, cold
application strategy, passion,
incredible passion for the job family
they're in, incredible passion for
startups, for the particular space they
were in, and they showed it every step
of the way. You cannot fake passion.
Clearly, can't make passion go out of
style.
And passion leads to problem solving,
which is what you get paid to do in
startups and tech. The function of
compensation is to reward you to some
degree for the scope of your impact in
problem solving. That is true regardless
of your job level.
And so think about the problem space you
can be sustainably curious about. It
might be weird. It might not map exactly
to a job family. Let that be okay for a
minute. But I don't hear that get said
enough. If you're not passionate about
the problem space, it's not going to
last.
Another story that's totally at the
opposite end that I'll share. This is
someone who was considered
widely to be unemployable. Not because
they'd had a terrible scandal in their
past, but because they were one of those
square pegs and round holes. The
experience set that they brought to the
table did not neatly fit on any resume
for a given job title. Is that you? You
know folks like that? We've all had that
moment.
in that world. You know what that person
did?
They went out and they hyperargeted.
They picked they they went through the
same process I just laid out for
targeting companies where you think
about your values. You think about what
your upside is going to be, what what
your problem space you're going to be
passionate about. And they found one
company. They said, "I want to learn. My
top value is learning. and I want to
find a particular company and and I want
to make sure that I learn this problem
space really well and I want to tie in
these particular pieces from my
background. They had a whole thing
great. Then they spent hours and hours
and hours preparing an application
strategy for just that company. It was
like that company was the whole product
for them. Like I I'm telling you it must
have been 50 60 hours of work. It was a
video. It was all kinds of stuff. And
they went in with what I call a spear
fishing strategy. And the point is not
the individual tactics they used. The
point is the passion they had for that
company tied to that problem space tied
to their role. And what they did is they
went and said, "I don't care whether a
particular job is open right now. I want
this company so bad, right? I'm going to
like go in and like spearfish and target
it and show my value."
Not everyone has that story. Not
everyone has that background. Not
everyone is suited for spear fishing.
But spear fishing is a way to make
progress in a world that is overwhelmed
by cold AI applications. And I think
there's really only three routes and we
all know the third one. The third one is
you have a cousin that works in the
startup, right? You'reworked. You have a
friend. You met someone at a cocktail
party.
And so I don't need to tell you how to
work that one. You're either in the
network or you're not. And if you're not
in the network, frankly, moved to New
York or San Francisco. Those are
basically the two nodes of the network.
that the only way if you're not living
in those two cities to start to break
into tech, to start to get into the
roles in tech that you're looking for is
either to have the kind of incredible
passion married with incredible grunt
work and frankly tolerance for pain that
goes with a cold application strategy
nowadays. and to go after that with
heart and passion. Make every single one
your best and not give up for months and
months and months and months and months
and possibly years. Or to do the spear
fishing approach where you go in and you
hyperarget a company, you know, it's
absolutely perfect for you and you put
tens of hours into it and you make the
video and you make a personal website
for them and you do this and you do the
other thing. You're like, maybe you're
the one that sends like the special cake
to them. I don't know. Like there's a
dozen ways to do this. All of them are
distinguished by being unique and
creative and tied to the company. So
there's no recipe for this. You have to
use your own creativity and passion for
the problem space to spearfish well. But
that's the other option. You spearfish
and that is the way people get roles.
And what's interesting to me as I think
about this in the context of artificial
general intelligence is that AI does not
have this kind of passion for a problem
space.
AI can do a lot of the individual
activities,
but I think it's fair to say that the
thing that built the companies that are
enduring in Silicon Valley today was
passion for the problem space. I feel
like I feel good saying Steve Jobs would
agree with me on that one. He built
Apple on passion. One of the biggest
critiques right now of Apple is that
they have run out of passion.
Joanie IV has left.
And so the thing that can distinguish
you, the thing that stands out, not just
you versus the AI, but you versus a sea
of cold applications that didn't care
and just sort of yeated their
application in from LinkedIn on a single
click. It's the passion. It's the
passion for the problem space. And so as
we wrap this up, what I want to
challenge you with is one, pay more
attention to your targeting. Think more
about your riskreward and where you
invest your time. And two,
think about how you value problem spaces
and the fact that you can't fake passion
and then work your way into your
application strategy from there. That is
the best advice I can give. I don't see
it very often on the internet and I want
you to have it. Cheers.