The most powerful digital platforms in

our lives lost their edge in late 2025

and early 2026, and almost nobody has

noticed it yet. For as long as we've

used digital platforms, we've existed in

a state offormational asymmetry.

LinkedIn knows everything about your

professional network, right? Every

connection, every message, every

endorsement, every job change, but they

only show you what you will be inclined

to scroll and click on. It's optimized

for engagement. Spotify is the same way.

It knows your listening patterns better

than you do, but it only surfaces the

playlists the algorithms decide to

serve. Your bank has a complete picture

of your financial behavior, but it only

presents a chronological list of

transactions, the least useful possible

format. These platforms hold your data

and show you their interpretation. And

they optimize for their metrics, right?

They optimize for engagement, for time

on site, for premium conversions,

whatever drives their business model.

The questions their interfaces let you

ask are only the questions that serve

the platform's interests. The questions

that would serve your interests, my

interests, are the ones that might

reveal you don't need the premium tier

of LinkedIn, or that their

recommendations aren't actually helping

you. And those questions have no button,

and they never get surfaced. The

asymmetry has always felt permanent.

baked into the architecture of how we

relate to technology. You generate the

data, they get to analyze it, and you

accept the filtered view that they give

you back. That arrangement is now

optional, guys. It's optional. The

unlock is deceptively simple. Just

export your data from the platform of

your choice, feed it to an AI, and ask

your own questions. Not the questions

the platform anticipated, not the

questions they built the interface for,

but whatever questions matter to you.

The combination of legallymandated data

exports and AI systems capable of

analyzing unstructured data in response

to natural language queries means that

we have fundamentally changed the power

dynamic. Because now with a simple plain

English question, you can spin up a

complicated Python function that will

query deep deep banks of CSV data from

LinkedIn and get profoundly useful job

insights. And if you're wondering if I'm

just making that up, I'm going to show

you what this looks like in practice

using LinkedIn and the job market as an

example because that's where the stakes

are highest right now and the asymmetry

is most painful. But you need to

understand that the principle applies

everywhere. Once you see the pattern,

you're going to recognize it in every

platform relationship you have. Now, the

job market in 2026 runs on relationships

and the platforms that mediate those

relationships have structured their

interfaces to obscure the information

that would make you most effective. So,

let me give you a very specific example.

Consider what LinkedIn actually knows

about your network. They have your

complete connection graph, right? Every

message you've sent, the timestamps of

every interaction, the reciprocity

patterns and your endorsements and

recommendations. They know the career

trajectories of every single person, you

know, and the overlapping company

histories that create institutional

bonds. They could tell you which

relationships are decaying toward

irrelevance, who would actually vouch

for you if asked, which dormant

conversations have natural re-engagement

hooks, and what your warmest path is to

any company you want to reach. But they

don't tell you any of this. Instead,

they show you a feed optimized to keep

you scrolling and a premium tier that

promises better access to the same data

that you generated. The interface

answers their questions. How do we

increase engagement? How do we convert

free users to paid? How do we keep

people coming back? Your questions like,

who should I reach out to this week? Or

what relationships need maintenance

before they go cold? Or what's my

realistic path to whatever company you

want to work for? Those have no button.

AI is what gives you the power back. You

can feed your data to either Claude

Co-work or Chat GPT. Both work. And

suddenly you are empowered to ask

anything. And if you're wondering like,

I don't know what to ask. Don't worry,

I'm getting to that. The platform's

carefully constructed limitations vanish

because you're no longer operating

inside their interface. You're operating

on the raw material, the data, and you

can query it using your own natural

language. You don't have to go through a

bunch of clicks. You can get exactly

what you want. This is the leverage that

changes outcomes. Not better access to

platforms, but independence from the

constraints they impose because of their

interests in their business models. Get

your data back and you can do what you

want with it and drive your own career

trajectory. So, let me walk you through

what I produced when I built a LinkedIn

analysis. And I'm going to show it in a

moment, but I'm going to go through each

of the principles I use so you

understand it first. So, you'll get that

cool visual at the end. Each piece that

I'm going to show depends on

capabilities that only became accessible

when AI reached its current level. It is

actually pushing current AI systems to

get this stuff done because you're using

quite complex query logic on the back

end. First, I'm calculating relationship

half-life models. And that sounds really

mathematical, but all we're doing is

basically saying relationships we don't

touch get colder. And so we've I created

a very simple algorithm that basically

says a relationship loses half its

strength every 180 days if you don't

touch the person and have a moment of

connection and the model can adjust

based on those signals. And obviously if

you have a different model for halfife

you can change it. But the key is this

kind of analysis allows us to analyze

connections in ways that LinkedIn never

shows you because you can look and

modify those half-life decay curves by

institutional bonds which decay more

slowly by how often you chat by whether

something is a shallow interaction like

congratulations or whether it's a deeper

longer message. This requires AI because

although the calculation is relatively

straightforward mathematics, identifying

which relationships are deep or shallow,

requires parsing potentially thousands

of messages and making very qualitative

judgments about conversation substance.

An AI can read through your entire

message history, assess the depth and

nature of every single thread, and apply

that assessment to modify decay curves,

something that no traditional software

interface would attempt because the

natural language understanding isn't

there. Here's another idea that I'm

going to show you. The reciprocity

ledger tracks the social capital balance

in each relationship you have. So, every

recommendation you've written represents

a particular investment. You can give it

a point total. Say every endorsement

represents another amount. And the same

scoring can apply to endorsements you've

received or any recommendation that

you've received. Now you can go through

and calculate your net balance. Where

are you in an equal state with these

people where you've both recommended

each other, where you've both invested

in each other through endorsements, and

where are you in debt or have endorsed

someone who hasn't responded? Again, the

data exists, but it's scattered across

multiple files. And so, we use AI to

synthesize endorsement data,

recommendation data, connection metadata

into a unified relationship ledger,

which means that we can ask the AI to

figure out the relationships between

files and compute the results. Now, you

can technically hardcode this. It would

just take you hours. The AI means this

takes minutes. Vouch scores are really

interesting because they predict who

would actually advocate for you if

asked. Combining message depth, reaction

recency, recommendations received,

endorsement patterns, shared

institutional history. Someone scoring

above 80 would probably write you a

reference letter tomorrow. Someone below

30 might not remember you clearly enough

to be effective. Right? Again, this

requires AI because it's fundamentally a

prediction problem requiring synthesis

across multiple data types. The AI is

going to read your message history. It's

going to assess your relationship depth.

It's going to incorporate recommendation

and endorsement data and weight all of

those factors into a combined score.

Building this as a traditional software

would require a lot of explicit feature

engineering and I could just ask for it

and get it in a couple of minutes with

Chad GPT or co-work conversation

resurrection. That one scans your

message history for dormant threads with

natural re-engagement hooks.

Conversations where you promised to

catch up and never did or someone asked

for help and didn't follow through. It's

a great way to triage your inbox. And

again, LinkedIn never gives this to you.

Pattern matching on conversational

intent is something large language

models excel at. They can easily find

threads where someone requested help in

a way that traditional query methods

just can't get at. Here's another one.

Network archetype classification. It

sounds super fancy, but all it's doing

is analyzing your individual connection

fingerprint to look at your networking

style. Are you a thought leader? Do you

have high inbound connections? Are you a

connector? Are you widespread across

many organizations? You can use AI to

develop a fingerprint of all of the ways

that you connect on LinkedIn and get an

overall archetype that gives you a

unique strategy to move forward. Now,

here's my favorite warm path discovery.

So, this takes any target company you

want to work at and ranks your

connections by combined relevance and

warmth to look for a bridge. Basically,

if you asked the question, which person

on LinkedIn do I need to message now

today, in order to reach this other

company that isn't in my network, who

would I reach out to? It's one of the

most popular questions people ask. It's

a hard one. LinkedIn never really tells

you and AI can tell you. AI can go

through the network analysis, identify

the qualities of the company. Is it a

robotics company? They'll identify other

robotics companies in your network,

stuff like that. And then start to build

a bridge based on a combination of your

connection warmth and the relevance of

that person to your search until you're

able to have a high probability set of

people to talk to to get into that

particular company and have a

conversation. The cumulative effect of

these analyses is a view of your network

that the platform never intended you to

have. Each piece leverages something

that AI does well. natural language

understanding, pattern recognition

across data sets, synthesis of

information from multiple sources,

flexible response to novel queries, and

the combination produces insights that

would have required either a dedicated

engineering team or simply weren't

possible before LLMs reached current

capability levels. All right, let me

show you what I built. This is the

network intelligence dashboard. And if

you're wondering, can I build this for

myself with my data? The answer is

absolutely yes. I'm putting all of the

details into the Substack. I have a

collection of prompts. There are

different prompts depending on whether

you're in chat, GPT or Claude. And I go

through the different files you need to

get and get you a complete guide of how

to get it out of LinkedIn. So a whole

guide is there. But let's look at what

we got. Network intelligence. It gives

you a dashboard view. This is by the way

real data for me. Uh and I am going to

use anonymized names. So none of the

names you're going to read are real

names. Concept one relationship

half-life. I talked about this. Uh you

can see my mass names where you actually

can go through and look at messages. You

can look at the halflife. The half-life

can vary. It's not always 240 of course.

And you get a sense of how this is

calculated mathematically and also a

sense of who you have the strongest

bonds with. It's basically a leaderboard

of the people you connect with the most.

You could easily reverse engineer this

and get the people who are perhaps

strategic in a particular company and

who you are least connected to but still

connected to so you could wake them up.

So there's a lot of ways to modify this

and get really interesting and

actionable stuff out of LinkedIn. Here's

your reciprocity debt ledger, right? You

can track social capital flows. Who owes

you? Who who do you owe? And how can you

start to reciprocate? How can you start

to think about who you can ask and

probably get a response from? It's not

perfect. You could probably improve this

further, but it's a really interesting

start on looking at social capital at

LinkedIn. Again, they'll never show you

who would vouch for you. I love this one

because we sometimes need that

recommendation. And it is a combination

of recency and deep conversation to

figure out who would be most likely to

be an advocate for you when it really

matters. And yes, it's it's printing out

the algorithm so that you can see it or

the the formula so you can see it there.

resurrection. You have unfinished

business with people. I love this one.

If your LinkedIn box is just

overflowing, you can identify particular

conversations that are worth picking up

and you can figure out like, do I want

to wake up a 743 day dormant thing and

it gives you a suggestion of how to get

started. And so this feels really

actionable. You can easily filter this

to just dormant conversations in the

last two months if you wanted. Lot of

really fun ideas here. network archetype

classification. How do you think about

your network fingerprint and what is

your strategy? And I love that I get a

different strategy depending on my

particular network and yours is going to

vary. And then warm path discovery. This

is the one that I'm super excited about.

You actually have I built a whole

separate prompt for this. You can

actually give a query to Claude or Chad

GPT with a particular company you want

to reach and have it look at your

LinkedIn data and map a bridge to get

there. And I think that's super super

interesting. Have a little note here and

I'm going to share this when I do the

Substack so you don't have to memorize

all of this. You can sort of go ahead

and grab it. Analysis summary. It's

everything you need to get started. And

really the larger goal here is to free

yourself from the default view that the

platform is giving you. AI really

enables that asymmetry. What I want you

to take away from this ultimately is

less about LinkedIn specifically and

more about what's now possible in your

relationship with any platform that

holds your data. The exports exist often

they're legally mandated and they're

buried in settings menu, but they do

exist and the analytical capability just

now exists. AI systems can take messy

real world data at scale and analyze

with natural language questions

meaningful insights that you would not

otherwise be able to get to. You can ask

the questions the platforms never wanted

you to ask and get real answers that are

actionable for you. This represents the

first genuine shift in power for these

platforms ever. It is not a marginal

improvement. For 20 years, the data you

generated has been analyzed by systems

designed to serve someone else's

interest, showing you only what kept you

engaged in paying. The asymmetry has

felt really structural. That's no longer

true. The analytical capability here is

not the property of the platforms

anymore. It's in all of our pockets. You

can continue accepting whatever filtered

view the platform provides, but you have

a choice now to take your data back and

analyze it the way you want to. And for

professional networking specifically,

your network is not your list of

connections. It's the actual strength of

actual relationships with people who

would actually help you. LinkedIn's

interface treats every connection as

equivalent, just a blue dot in an

alphabetical list. The analysis I'm

describing shows you more ground truth.

Which relationships are warm? Which are

cooling down? Which have decayed past

usefulness? Who would vouch for you? And

what your real path to any company is

that you want to reach. Look, the tools

are available. The data is exportable.

The question is not whether it's

possible. It definitely is. The question

is whether you're going to spend I want

to say it's going to be like half an

hour getting this set up or whether

you're going to continue to accept the

filtered view the platform is giving

you. It's up to you.