it's not laziness. you started with good intentions. prompt engineering seemed important. then someone said it's dead, learn agents. then context engineering became the thing. every month the "essential ai skill" changes. every month you're behind again. honestly, you're collecting ai anxiety.
i spent 30 days on this. talked to people building ai. spoke with the top 1% professionals in our community. median experience 10 years. companies like lovable, sarvam, google, meta, amazon.
i scraped through thousands of job descriptions. extracted the skills they highlight. mapped them against what hiring managers actually ask for.
the market is screaming about 12 skills right now.
it's 4 skills.
1/ AEO. ask engine optimization. getting your brand mentioned when people ask ai for recommendations.
2/ ai ad generation. test an ad the same day you get the insight.
3/ ai led performance marketing. let ai do the grunt work of bid adjustments and budget reallocation.
4/ ai content production. produce 50 pieces at the cost of 5.
if you're in strategy & ops there are three skills
1/ automating business operations. any repeated process where humans touch documents repeatedly.
2/ agentic workflows. yc companies are obsessed with this. agents have goals and figure out their own rules.
3/ ai adoption strategy. you build the automation but your 10,000 employees still need to use it.
if you're in product it's three skills.
1/ ai product strategy. knowing which problem ai solves better than traditional code.
2/ ai prototyping. build working software in an hour, not mockups.
3/ ai evaluations. measuring if your ai feature actually works.
if you're an engineer, it's three skills.
the ai doesn't need better prompts. it needs better context.
2/ RAG. giving ai live information, not stale training data.
3/ ai agents. building systems that decide what to do, not just generate text.
these are real skills. the job descriptions mention them. linkedin shows 10x growth in profiles listing them. the courses are multiplying.
but none of these 12 skill matter. i also asked a different question to members who are actually building in ai. not learning about it. building products. shipping features. deploying systems.
"what got you promoted?” ”what got you hired?"
the answers broke my brain. because nobody mentioned agentic workflows. nobody said "i learned RAG." nobody brought up the ai prototyping course they finished.
systems they built. hours they saved their companies.
an ops lead took vendor onboarding from four days to four hours.
a product manager built an ai assistant that cut churn by 3 points. worth 2 crore annually.
a marketer built a content system that let their team produce 50 variations while competitors produced 5.
same skills the market is screaming about. completely different starting point.
now this seems obvious in hindsight of course you solve problems with skills. what else would you do?
let’s be honest for a second. in the last six months, how much time did you spend thinking about which ai skills to acquire? and how much time did you spend thinking about which problems at your company you could solve with ai?
i'll wait. that's what i thought.
we're all collecting skills. reading about RAG. watching tutorials on agents. saving linkedin posts about prompt engineering. telling ourselves we're "staying current."
nobody is auditing their company for broken processes and asking "can ai fix this?" the skill collector mindset is default. the problem solver mindset is rare.
workflows follow rules. agents have goals and make their own rules to achieve them. he knew this cold.
then his CEO asked him to fix vendor onboarding. four days per vendor. five people touching every document. compliance bottleneck killing their growth.
arjun froze.
he knew how to build agents. he didn't know if this problem needed an agent. maybe a simple workflow was enough. maybe it needed RAG to pull compliance documents. maybe it needed nothing and the real problem was process design.
the skill was in his head. the judgment wasn't.
first one built a workflow. document comes in, extraction runs, matches to purchase order, flags exceptions, routes approvals. simple automation. no agents. four days became six hours.
second one built an agent. same problem. but their vendors sent documents in wildly inconsistent formats. handwritten notes. email threads. verbal approvals referenced in attachments. the agent learned to handle ambiguity. four days became four hours.
third one didn't build anything new. realized their existing systems had APIs nobody was using. connected them with a basic RAG pipeline that could answer "what's the status of this vendor" by pulling from six different tools. the time savings came from people not chasing information. four days became one day.
same problem. workflow vs agent vs RAG.
the skill was variable. the problem was the constant.
if you've read the proof of work series, you remember the cupcake company.
three chefs apply.
one brings a philosophy PDF. second one brings certificates. the third brings three variations of their bestselling cupcake with cost savings calculated.
ai skills work the same way.
certificates about agentic workflows? that's the philosophy PDF.
that's the certificate.
"i reduced vendor onboarding from four days to four hours. here's how. here's the system. here's the annual savings." that's the cupcake.
the market doesn't pay for skills. it pays for problems solved.
which one are you building right now? philosophy PDFs or cupcakes?
product manager at a B2B saas company. 400 crore ARR. their onboarding completion rate was stuck at 41%.
the obvious answer was "improve onboarding UX." she'd done that twice already. marginal gains.
she could have learned ai prototyping. built mockups faster. tested ideas quicker. the skill everyone says PMs need.
instead she spent two weeks watching session recordings. found the pattern. users got stuck at the same three points. not confused about what to do. confused about why they should do it.
she built an ai assistant that watched user behavior in real time. stuck on a feature? surfaced a 30 second contextual video. confused by terminology? inline definitions appeared. dropped off mid setup? triggered a personalized email with exactly where they left off.
the skill underneath? context engineering.
the ai needed to know where the user was, what they'd done, what they hadn't, and what typically worked for users like them.
but meera didn't start with "i should learn context engineering." she started with "41% completion is broken and i need to understand why."
onboarding completion went to 67%. churn dropped 3 points. finance impact: Rs 2.1 crore annually.