positioning sprint starts now.
four moves. four days.
read each drop, act on it.
on day four, we’ll have a clear value
prop that the market understands.
you have 7 seconds. that's it.
you're polishing your resume for the 17th time. adjusting bullet points. tweaking action verbs. trying different resume tempaltes. meanwhile the hiring manager just spent 7 seconds on your last application.
that's not laziness.
that's pattern matching. and once you understand how their brain actually works, you'll stop wasting time on shit that doesn't matter.
yes, resume needs a narrative
but even the best story fails if it doesn't match the pattern they're hunting for. today we decode what actually happens in those 7 seconds.
the neuroscience.
hiring managers aren't reading resumes. they're running facial recognition software. except instead of faces, they're scanning for patterns that match their mental model of "person who will succeed here."
google's people operations team studied their own recruiters in 2018. found they spend 6.25 seconds on initial resume scan. not skimming. pattern matching against five specific things:
1/ company logos (where you worked)
2/ role progression (your trajectory)
3/ specific keywords (their problem language)
4/ tenure patterns (how long you stay)
5/ quantified impact (numbers that matter)
everything else? kinda invisible.
your carefully crafted personal summary? skipped. those soft skills? ignored. the creative format that "shows personality"? actively hurts you because it breaks the pattern they're scanning for.
why?
because human brains under time pressure default to system 1 thinking. fast, automatic, pattern based. the same system that helps you recognize faces in a crowd. except now it's recognizing "startup product manager who can scale" or "enterprise sales leader who closes 7 figures."
and it’s not evil. it's human.
you do it too when you're overwhelmed with options.
how patterns become market truth
here's where it gets weird. these patterns aren't objective truth. they're collective beliefs that become real through repetition.
five years ago,
nobody filtered for "ai experience."
today? search any job board. ai/ml appears in 73% of product roles at series b companies. not because ai experience predicts success. because the market has proved ai works, so now everyone pattern matches for it.
back in 2016, i worked in an ai company.
we used diffusion models (the paper for diffusion came out in 2015), transformers, fine tuning, prediction, everything. but we stopped saying ai. why? because ai wasn’t proven and actually people didn’t even trust an ai SaaS to work well. we shifted to “data-driven”, “all in one”, “automation”, and it worked.
same company, same tech, but ai wasn’t proven. data driven was proven. and when something is proven, the entire market wants it. blindly. it’s twitter fomo for a new restaurant but at a market scale ;)
i watched this happen with "growth" titles. 2014: growth meant paid acquisition specialist. then, i think, spotify hired someone who knew product too. they crushed it. suddenly every startup wanted "product led growth" people. the entire market definition shifted because one pattern worked at one visible company.
payu hires ankit from stripe. ankit transforms their infra. cto tells other ctos at founder poker night. suddenly every fintech hunts "stripe alumni." phonepe poaches from google pay. works out. now "gpay experience" becomes the hot pattern.
the pattern isn't predictive.
it's just memorable. but once established, it creates its own reality through repetition.
the cognitive biases in play
1/ confirmation bias:
once they form a hypothesis ("we need enterprise dna"), every data point gets interpreted to confirm it. your consumer experience becomes a negative. not because it's bad. because it doesn't confirm their existing belief.
2/ availability bias:
memorable patterns beat better patterns. "ex founder" sticks in memory. "increased revenue 34%" doesn't. this is why unusual backgrounds either win big or lose hard. no middle ground.
3/ anchoring bias:
the first pattern they notice anchors everything else. open with microsoft? you're enterprise forever. open with a startup? you're scrappy forever. same person, different story, different outcome. man, i’ve seen hiring manager judge folks who use microsoft docs over notion or google docs. not kidding.
4/ halo effect:
work at a hot company? everything you did seems better. work somewhere unknown? every achievement needs more proof. the halo isn't about you. it's about borrowing trust. anyone who tells you this doesn’t matter is fucking lying.
want to see actual demand?
1/ time to fill tells truth
i have seen this for growthx members. hot roles at good companies close in under 14 days. lukewarm roles drift for 90+. that product manager 2 role that's been open since august? they're not really hiring.
2/ salary % growth
delta shows actual supply & demand.
not what companies say they need.
what they pay for.
frontend engineers in bangalore:
15% average increase yoy
data engineers in bangalore
41% average increase
content marketers:
7% increase
3/ poaching patterns
map who's stealing from whom on linkedin. IF zomato's poaching from swiggy but not vice versa, that tells you momentum direction. the talent flow reveals more than any industry report.
3 more nuances i am noticing
1/ the geography clusters
engineering realities:
bangalore + generic saas = balanced market
gurgaon + gaming = talent war
chennai + enterprise = steady, unexciting
data science truth:
bangalore + ecommerce = oversupplied since 2023
mumbai + credit risk ai devs = big shortage
pune + agritech = emerging
your bengaluru GO skills might be commodity. those same skills in kochi for a tech startup? scarce. sometimes you gotta choose your battles even if it means lower pay. advantage is you have more demand due to skill scarcity AND it allows you to stay close to fam.
2/ choice of words
"ai driven analytics service" is beating "used ML in analytics service" even when they describe identical work. not because keywords matter. because keywords signal patterns that match current demand.
real example from last month.
version 1: "led customer analysis to improve retention"
version 2: "built a model predicting churn, reduced churn 23%"
version 1: 0 callbacks from 12 applications
version 2: 4 callbacks from 8 applications
same project. different pattern match. different outcome.
3/ internal vs external hiring.
some roles almost always hire externally:
chief of staff
(no internal pipeline)
revenue leaders
(need fresh networks)
technical architects
(specific expertise)
others promote internally 80%+ of the time:
ops managers
(need company context)
customer success leads
(relationship continuity)
implementation heads
(know the product mess)
if you're competing for an internally promoted role from outside, you need exceptional spike. if you're competing for an externally hired role, you need pattern match.
pattern reality check
find 20 people on linkedin
who got hired into your target role in last 6 months.
list their previous companies.
count their years of experience.
note repeated keywords in their headlines, bio, description. see which companies show up multiple times (talent sources)
that's the market and company’s ask.
not the job description .
the pattern reality.
so, what now?
the uncomfortable truth is that pattern matching is efficient when you have 2000 resumes and 30 minutes. even the AI systems that are told to “screen” are given the same brief that the TA is doing.
you can rage against it. or you can map which patterns you match naturally, find where those patterns have demand, and position yourself accordingly.
tomorrow,
we’ll talk about how to figure out what the market really wants.
i’ll see you soon.