Decoding Company Strategy Through Job Posts
Key Points
- The speaker demonstrates how large language models (LLMs) can transform the traditionally manual process of reading job postings into a strategic, automated analysis that reveals company direction, product focus, and hiring gaps.
- By crafting strategic prompts, users can instruct an LLM to scan large sets of recent job listings, categorize themes, detect weak points, and infer broader business tactics without needing to manually review each posting.
- A live example using an app built on the Lovable platform shows the LLM analyzing Anthropic’s job ads, deducing a focus on scaling core AI technology, a shortage of platform engineering hires, and a strong emphasis on alignment science and model welfare.
- The speaker promises to share the exact prompts and API‑based tool (with a simple setup for engineers and a no‑code alternative for non‑technical users) so the audience can replicate the approach on any company’s job postings.
- This methodology not only aids job seekers in targeting opportunities but also provides B2B sales teams and analysts with actionable intelligence on competitors’ strategies derived directly from hiring signals.
Sections
- Decoding Company Strategy via Job Postings - The speaker shows how modern LLMs can instantly analyze large sets of job listings to reveal hiring trends, infer corporate tactics, and empower both job seekers and businesses, providing ready‑to‑use prompts for immediate replication.
- Identifying Hiring Gaps and Risks - The speaker explains how analyzing Anthropic’s current job postings reveals a scarcity of internships, entry‑level, and platform‑engineering roles, suggesting looming technical‑debt issues, scaling challenges, and a possible need for extra capital investment.
- From Trivial to Treasure: Data Reimagined - It explains how AI models turn once‑ignored information—like job postings and public selfies—into valuable insights, highlighting privacy risks and new opportunities for job seekers, product managers, and salespeople.
- LLM‑Powered 3D Company Insight - The speaker explains how large language models can deliver rapid, multi‑angle analyses of a firm’s culture, strengths, weaknesses, and strategic signals—like engineering dynamics and scaling issues—and encourages sharing prompts so product teams, job seekers, investors, and salespeople can harness this new class of accessible data.
Full Transcript
# Decoding Company Strategy Through Job Posts **Source:** [https://www.youtube.com/watch?v=DHZgLzwWXfs](https://www.youtube.com/watch?v=DHZgLzwWXfs) **Duration:** 00:12:58 ## Summary - The speaker demonstrates how large language models (LLMs) can transform the traditionally manual process of reading job postings into a strategic, automated analysis that reveals company direction, product focus, and hiring gaps. - By crafting strategic prompts, users can instruct an LLM to scan large sets of recent job listings, categorize themes, detect weak points, and infer broader business tactics without needing to manually review each posting. - A live example using an app built on the Lovable platform shows the LLM analyzing Anthropic’s job ads, deducing a focus on scaling core AI technology, a shortage of platform engineering hires, and a strong emphasis on alignment science and model welfare. - The speaker promises to share the exact prompts and API‑based tool (with a simple setup for engineers and a no‑code alternative for non‑technical users) so the audience can replicate the approach on any company’s job postings. - This methodology not only aids job seekers in targeting opportunities but also provides B2B sales teams and analysts with actionable intelligence on competitors’ strategies derived directly from hiring signals. ## Sections - [00:00:00](https://www.youtube.com/watch?v=DHZgLzwWXfs&t=0s) **Decoding Company Strategy via Job Postings** - The speaker shows how modern LLMs can instantly analyze large sets of job listings to reveal hiring trends, infer corporate tactics, and empower both job seekers and businesses, providing ready‑to‑use prompts for immediate replication. - [00:03:28](https://www.youtube.com/watch?v=DHZgLzwWXfs&t=208s) **Identifying Hiring Gaps and Risks** - The speaker explains how analyzing Anthropic’s current job postings reveals a scarcity of internships, entry‑level, and platform‑engineering roles, suggesting looming technical‑debt issues, scaling challenges, and a possible need for extra capital investment. - [00:06:48](https://www.youtube.com/watch?v=DHZgLzwWXfs&t=408s) **From Trivial to Treasure: Data Reimagined** - It explains how AI models turn once‑ignored information—like job postings and public selfies—into valuable insights, highlighting privacy risks and new opportunities for job seekers, product managers, and salespeople. - [00:11:33](https://www.youtube.com/watch?v=DHZgLzwWXfs&t=693s) **LLM‑Powered 3D Company Insight** - The speaker explains how large language models can deliver rapid, multi‑angle analyses of a firm’s culture, strengths, weaknesses, and strategic signals—like engineering dynamics and scaling issues—and encourages sharing prompts so product teams, job seekers, investors, and salespeople can harness this new class of accessible data. ## Full Transcript
In the next 10 minutes together, we are
going to crack the code on reading job
postings. It's a lost art and it's one
that we can absolutely transform in the
era of AI. And I don't just mean for job
seekers, although that's obviously a
huge benefit. You also can infer company
strategy, B2B sales approaches, and all
kinds of other things just from reading
job postings. A year ago, I wouldn't
have been able to recommend this. But
now, with where LLMs are at, I can
actually give you three different
examples in the next few minutes that I
was able to spin up that give you a
comprehensive approach on how to read an
entire company's strategy from just a
set of job postings. And yes, I'll be
dropping the prompts and everything else
in the post, so you'll be able to follow
up and do it yourself. So what are we
talking about when we say a job search
strategy informed by job postings? In
the past, we would have said, "Hey Nate,
go and get a thousand job postings or
the last 100 from the last 90 days and
do this yourself. Conduct a manual
review. I want you to categorize
everything. I want you to identify
commonalities, spot weak points, notice
what they didn't post. Now look at how
the products they offer compared to the
job postings." You see how it goes on
and on. Not anymore. You can get all of
that done in just a prompt. In fact, you
can get more done than you could before
because you not only have the volume
game, which you can play with LLMs, you
also have the strategy game. So, you can
give the LLM a strategic prompt and you
can tell it to reason and infer in a
particular way over a set of job
postings that it searches and it will do
that and it will come back and it will
give you a view. Sometimes I think just
showing it is way easier. So, let me
show you an actual response that I built
about a real job posting situation at
Enthropic, the major AI company. Check
it out. All right. So, I built this
handy little app in Lovable just to
showcase what you can do with it. Don't
worry about these initial fields here.
If you're uh an engineer, it's really
easy to put in an API key and use this
yourself. I'll be sharing it. And if
you're not, you can follow along and I'm
going to give you some prompts that you
can use in other search engines. So, if
you don't know what an API is, you don't
have to care. But look at what you get.
So, this is analysis results generated
today, 9:24 when I'm recording this. And
it gives you so many different
components to look at. It infers a
product strategy, doubling down on their
core AI build, uh, and suggests that
they have a lack of fresh platform
engineering hires, which would indicate
that they're focused more on scaling
existing tech right now. That aligns
with what we see from Anthropic's recent
moves. It seems like a solid insight.
Meanwhile, alignment science and model
welfare roles indicate a willingness to
tackle unsolved safety and ethics
problems. Again, aligns tightly with
what we see from anthropic. We go down
to inferred B2B sales approach. They're
calling out that this signals a push for
rapid enterprise adoption based on
startup account management and B2B uh
marketing. And there's little evidence
of dedicated sales engineering. So, one
of the things that's really interesting
is you can start to infer a B2B strategy
from this. You can start to look at this
and say they don't have dedicated sales
engineers yet. They don't have post-
sales technical support. They're very
early in their B2B startup account story
here. There is an opportunity to come in
and offer solutions for a sales team
that is probably under stress right now.
And you can read that from the job
postings. You can infer that. Now, what
if you're a job seeker? What does that
look like? Well, they don't have
internships or entry- level roles posted
right now. and they have very few roles
for platform engineering. And so what's
interesting about that is that they are
essentially
essentially setting themselves up for a
potential technical debt risk as they
scale. And that is indeed what we see in
some of the recent outages and the uh
work that Anthropic has done to their
credit to talk about why the outages
occur. they are struggling to keep pace
with scaling demand and they haven't yet
invested in platform engineering. And so
another another insight here that you
can see as you read through this if
you're putting another lens on this is
that Anthropic may need an additional
capital injection in order to start to
scale some of these platform pieces out.
It has inferred cultural insights which
seem fair for what they're worth, but
it's very easy to get them. It's trivial
to tweak the prompt and get what you
want. Inferred company weaknesses. It
calls out platform engineering. It calls
out a miss on PM, QA, and customer
support. What's interesting here is that
this underlines the sort of research
bones of the company, where the company
came from. This will not always show up.
These are these are individual insights
that you get per company. Now, this is
the part that I love the most. You can
actually see why the model did this,
right? It will give you a table and it
will say, "This is what I read. This is
the link to it. This is the reasoning,
and this is the claims that I'm
confirming here." here. And you can see
that it's all recent stuff. This is not
old postings it's working from. Is it
perfect? No. Does it underline how much
you can get out of just looking at job
postings? Yes, it does. Yes, it does.
Now, I want to give you a couple of
other ways to look at this. This is not
just something where you have to use a
custom lovable app. You can do it
directly in chat GPT. You can do it in a
search engine like Perplexity. I have
prompts for that. The key to take away
is that the quality of this assessment
depends on your ability to ask very
clearly for exactly what is important to
you. And that's why I built different
versions. I built a version for job
seekers that kind of lines in on
available roles and what you can get. I
also built a version for folks who are
looking for competitive intelligence.
And that's not something we've talked
about yet in this video, but as someone
who has had to run competitive
intelligence in the past, this this
would have been a lifesaver a year ago.
Like it would have been huge because all
you have to do is plug this thing in and
you get a full competitive readout on
your competitor just based on their job
listings that they have publicly shared.
You you're not doing anything
inappropriate. You're just looking at
their job listings. And here it is. And
that is one of the larger lessons that I
want to call out here. We are in a world
where there is an entire new class of
data that was previously considered
trivial data. Data that wasn't worth
hiding, wasn't worth securing because
nobody had the time to analyze it. It's
now open season. This data is now
available for analysis. It's available
for strategic understanding. If you're
an investor and you're trying to invest
in a company, why wouldn't you run a
query like this on open job postings and
cross-check that against what the
company's principles are telling to you?
Of course, you would. That makes just
perfect sense. So, this is not just
something that job seekers are
interested in. This is something where
if you need the tea on a company, it is
now easy to get. And I want you to ask
yourself, what other classes of data are
like that? What other classes of data
out there have been trivial for a long
time and we're now thinking maybe that's
not trivial anymore? I'm going to give
you another example and this is actually
a safety tip in the age of AI. Think
about when you last posted a publicly
available selfie outside. And the reason
I say that is because with the advent of
reasoning models, especially the Chad
GPT image recognition models, they are
extremely good at knowing where a
photograph was taken in the world. And
so if you have like your Instagram feed
set to public and you're taking a bunch
of selfies outside, even if you don't
reveal your location, your location can
be inferred from that information. There
are other examples as well, but I think
that gives you a picture. We we are
entering a world where LLMs are making a
whole new class of data that would
previously have been like waste data or
data that nobody cared about. It's now
useful. I picked job postings because I
think it's one of the most useful
examples of this. There are, as I've
been saying, a dozen and a half ways to
use this, right? You can be a job
seeker. You can be a PM who's looking at
competitive intelligence. You can be a
sales guide looking at how to approach
this company. You can be a buyer looking
at whether you want to buy based on the
job postings. You can be an investor.
Right? There's so many different roles
you can take and still find this useful.
Now, one thing I want to do is make sure
that I share with you how this looks. If
you are not a fancy pants engineer and
you do not have a perplexity API key,
what does that look like? Well, actually
got a couple for you. So, let me just
share that quickly and I will show you
what it looks like. Here we are. Same
exact company, by the way. So, this is
Anthropic, right? It runs the query. You
can actually see the query here. I'll be
sharing it in the post. You can see how
it works. Um, and it goes and runs it,
right? It thinks for a bit and it goes
and runs it. It gives you a sense of
what it looked at. It gives you a sense
of the signals it pulled out. So, this
is a little bit different order from
what you saw in the lovable app that I
showed. This is a an order that
emphasizes proving how it got there. And
so, these are the grounds or the inputs
that it's using and it wants to show you
that first. So, if you want to just
scroll, you can scroll down to insights
and you can see where they're investing.
Uh you can see career opportunities. Uh
and this one is absolutely aiming at the
career side. So it it brings out more of
the career piece than I have in the
Lovable app. Although Lovable makes it
really trivial to remix these. So when I
publish this, anybody is going to be
able to just remix it and make it what
you want. So you can make it a career
one really easily that's just about
careers, not just about company
intelligence. And I'll include this
prompt so the career folks are going to
have plenty to work with. Uh, so it has
a Seattle office that's growing rapidly
and an NYC hub. It talks about the comp,
which is of course insane AI comp. And
then it gives you the receipts to show
you kind of how it's thinking about
about it. And it's also talking about
sort of competition, which of course
like that's not surprising, but it's
nice that it pulled out, right? It's
nice that it showed it. Um, and it calls
out automation risk. It calls out less
emphasis on consumer features, so you
sort of know where they're at, which
aligns with what the lovable prompt
found. And so this is sort of like a
lens on the same company from a
different camera angle where you're
looking just at careers and obsessed
about it. And as you can see, it's not a
fancy web page, but it's lots of
information you can use. And you don't
have to have an API key or anything. You
just run the prompt. And by the way, if
you don't have Perplexity or use it,
Chad GPT has its own search engine. It
will also run this prompt. Let me give
you one more peak. I love this one. Um,
this is a company radar that's more sort
of like for the product manager or
someone who wants to do like an overall
analysis and I think it's really cool. I
think it it sort of gives you a sense of
what's in the box. Let me just share it
with you here. All right. So, it's going
to go through and it's going to look at
all the signals. It's going to prove its
way forward and then it's going to get
into product strategy, right? is going
to talk about how it's investing in
claude code, what MCP looks like as far
as a moat goes, which is a really
something I've been calling out is like
it's sort of an engineering mode for
them to build that ecosystem. Um, this
one talks a little bit about how they're
doing sort of B2B sales. And this one
does catch sort of a sales position
piece around healthcare and financial
services, which the lovable prompt
didn't get. If you're looking at sort of
reconciling that out, what I would
suggest you do is you pull all three and
then you start to hybridize them and
harmonize them a little bit and pull out
specific insights you're looking for.
It's almost like getting 3D vision,
right? You get different perspectives on
the same job and they're they're roughly
aligned, but you get different nuances
that come out. Uh you have a call out on
how engineers work, which I really love.
Uh you have a call out on anti-
hierarchy signals which is another great
one. Um and you have some interesting
inferred weaknesses, right? Are there
too many engineering manager positions
with no teams built? Is there euro chaos
because they're aqua hiring teams? Uh
scaling fractures. This feels like it's
really big and really fast. TPU
dependency, which is frankly a really
interesting piece of intelligence. Um
and so I think I think this is a
phenomenal overall perspective on the
company. if I were in any kind of
competitive intelligence, this would be
really exciting for me. So, one of the
things I want you to take away as you
look through this is that this is not
hard to do. Like, I'm going to share the
prompts. I'm going to share how I worked
through it, but what you should be
thinking is, where is there data that I
want to get a hold of that would
previously have just been really hard to
do? How can I get a hold of that data
and make use of it? LLMs make whole new
classes of data accessible and they give
all of us an easier time as a result.
And so if you're in product, if you're a
job seeker, if you're an investor, if
you're a buyer, if you're in sales, I
hope this helps you imagine differently
what you can do. And obviously make use
of the prompts. Obviously go use the
Lovable app and have fun with it. Love
to see what you built. Cheers.