title: @courtne: In 2016, an email landed in the inbox of Ramp CEO Eric Glyman. A 17-year-old was...
author: courtne
content_type: twitter_article
published: 2026-03-04T19:29:48+00:00
source_url: https://x.com/courtne/status/2031432006119338276
word_count: 2151
In 2016, an email landed in the inbox of Ramp CEO Eric Glyman. A 17-year-old was asking for an inter
In 2016, an email landed in the inbox of Ramp CEO Eric Glyman. A 17-year-old was asking for an internship. Eric skimmed past it, but his co-founder Karim didn't.
The kid was a 17-year-old high school dropout. He'd just represented the United States at the International Olympiad in Informatics which is one of the most selective computing competitions in the world. He was about to start MIT at 16.
3 weeks into his internship at their tiny startup Paribus, he had built an AI model to automate refund decisions. Nobody expected it so quickly.
Years later, when Karim was starting Ramp, his first call was to that same kid who by then had internships at Jane Street and Facebook AI Research on his resume.
Today, Ramp is worth $32 billion. And that kid is Calvin Lee, Ramp's Technical Chief of Staff.
What Karim had spotted in Calvin wasn't his resume, but the spike.
Every hiring process ever designed solves the same problem: how do you avoid hiring someone bad?
And it works. The ten-box competency checklist, the behavioral panel, the case study, and the culture fit round were engineered by large companies to protect themselves from downside.
These orgs have thousands of employees, and for them, a single bad hire is a rounding error on the balance sheet, but a catastrophe for the team that has to carry them for 18 months before HR moves.
The issue is that this machinery has a second-order effect.
The same filter that screens out weak candidates also screens out exceptional ones.
Exceptional people are almost never well-rounded. They are spiky, meaning, freakishly good at one thing, conspicuously deficient in others.
The standard hiring process is a defense mechanism. It selects for the absence of weakness. But the absence of weakness is not the presence of greatness.
The core of Karim's hiring philosophy is a concept you could call the spikiness principle. Exceptional people are often “very spiky.” They do not have evenly distributed skill sets. That means a hiring process built to reward balance and box-checking can end up selecting competent, legible candidates while systematically underrating people with rare, outsized strengths.
The pattern repeats over and over again.
The best hires are totally unqualified on paper.
If you are screening for balance, you are screening against greatness.
The checklist doesn't accidentally miss 10x people. It's optimized to miss them because 10x people almost always fail at least 3 of your 10 boxes.
When Ramp looked at the people who became foundational, a consistent pattern stood out: they tended to be unusually strong in one area. Not well-rounded in the conventional sense, but clearly exceptional somewhere.
Karim's view is that the best candidates are often spiky, with obvious strengths and uneven backgrounds, which means traditional hiring screens can underrate them.
What they had instead was slope , i.e., a rate of learning so steep that the gaps closed in months while the spike only sharpened.
One of the first people Ramp hired had sold a company to Apple while he was still a freshman at Stanford. He was an incredible iOS developer, but he didn't know anything about fintech or how a corporate office worked. Most hiring processes would have labeled him a risky candidate immediately.
Karim saw it differently. He saw a person with a rare, clear talent and realized that his lack of specific industry experience would fade over time.
This difference is important.
A gap is just something you haven't learned. A spike is a specific talent or trait that probably can't be taught at all.
Ramp chose to hire people for those spikes and handled the training for everything else themselves. This kind of lopsided bet only makes sense and becomes obvious if you've already decided that's what you're looking for.
But how do you see spikes before anyone else can?
This is where Karim and Eric Glyman had a specific, non-replicable advantage and then built a replicable system on top of it.
Both co-founders went to Harvard. Karim studied electrical and computer engineering before getting his master's in computer science. Because of that, they knew which classes at Harvard and MIT were actually difficult.
They weren't looking for "college-level" hard, but the kind of work that separates a capable student from someone operating on an entirely different plane. They knew which problem sets were designed to break people and which programs were strong signals of technical ability. They understood that if a freshman aced certain specific courses, it was a sign of talent that wouldn't normally be obvious on a resume for another 5 years.
They used what they knew to identify exceptional candidates unusually early.
Karim has said they looked at freshmen while many companies focused on later-stage candidates, and used specific classes and programs at Harvard and MIT as early signals of unusual ability. That gave them an informational edge in spotting talent before it showed up in a polished resume.
These candidates didn't cost as much because nobody else was paying attention to people with massive potential but no credentials or work history.
This is the same dynamic that exists in every market where price is determined by consensus. The best deal is always in the place where the consensus hasn't formed yet. In public equities, it's the company no analyst covers. In real estate, it's the neighborhood nobody's heard of. In talent, it's the freshman with an Olympiad medal and no LinkedIn profile.
Calvin Lee is a good example of how Karim thinks about talent.
In 2016, Calvin was a 17-year-old high school dropout who had already competed for the U.S. in high-level programming contests. He cold-emailed Paribus to ask for an internship. Eric Glyman almost ignored the email, but Karim didn't.
Within three weeks, Calvin built an AI model that handled refund decisions automatically. Karim and Eric kept in touch with him, and when they started Ramp in 2019, one of the first things they did was convince Calvin to join as a founding engineer. He is now the Technical Chief of Staff at Ramp.
There's a simple way to see why this hiring problem doesn't go away.
If a hiring process is built mostly to filter out bad candidates, you usually end up with people who are just competent. They meet every requirement, look safe on a resume, and probably won't cause any trouble. But that same system often misses people with spikes.
As a result, you get a team that does okay but rarely produces the kind of massive work that actually changes how a company grows. That's the tradeoff Karim is talking about when he says that the best people are often spiky.
Karim’s description of the engineering team at Ramp shows what the alternative looks like: a mix of founders, math olympiads, and AI researchers.
It isn't a group built on standard fintech experience. Rather, it functions more like a collection of specialists where every person has a specific skill that would be difficult to replace.
His philosophy is definitely similar, if not identical, to Keith Rabois's "one person, one problem" framework, which he learned from Peter Thiel.
That’s why Karim talks about putting together a team like the Avengers instead of just hiring generalists. The goal isn't for everyone to have the same basic level of skill across the board.
Instead, find people with deep, specific talents in different areas and bring them together to see how much the entire team can actually achieve.
The competitive calculus that makes the spikiness strategy not just philosophically interesting but structurally necessary for startups
Google, Meta, and Stripe have a reputation for hiring anyone who has already made a name for themselves. They don't have to take gambles early on because they can just pay for certainty later. A founder who has already sold two companies and has a PhD from Stanford is going to pick up the phone when their recruiters call.
Those hiring decisions are easy. These big companies are set up to hire people with established records and the right credentials, and they usually win those battles because they can offer more money and stability than a startup.
This creates a gap in the hiring market. People with massive potential but no traditional credentials (the Calvins of the world) don't get as much attention.
It isn't because these people are hard to find. Calvin Lee had a medal from an Olympiad. The Stanford freshman I mentioned earlier had already sold a business to Apple. The signs were obvious, but large companies avoid this group because their hiring systems are built to avoid risk, and people without a long track record are seen as risky.
A startup that ignores this gap and tries to compete for the same candidates that Google is recruiting is playing a losing game. They have less money, no brand, no stability, no career ladder, no free meals, etc.
The winning move is to play the game they can't:
Hire earlier, when you see that the signals of talent are already there, but before the market categorizes them into conventional credentials.
Olympiad medals, unusually hard technical work, or rare problem-solving ability are usually great signs that often appear years before the resume that large companies usually wait for.
This is the only asymmetric advantage in talent that a startup actually has.
What does this look like in practice? How do you actually interview for spikes?
Look at the resume for whatever the candidate claims they're best at. Then drill into exactly that. Exclusively into that one thing. "You say you're great at poker? Prove it. You'd better know way more than me after I spend two hours researching it."
The goal is not to verify breadth, but to confirm the spike is real.
If the candidate claims to be exceptional at distributed systems, the entire interview becomes a stress test of that claim.
How deep does their knowledge go?
Can they reason about edge cases that aren't in the textbook?
Do they have opinions that suggest they've actually lived inside the problem, not just read about it?
If the spike doesn't hold under pressure, if they're overrepresenting a strength that collapses when you push on it, the candidate is done because the one thing they claimed was their superpower ended up being ordinary. There's nothing left.
But if it holds, if the depth is real, if they know things you didn't expect, if they've thought about the problem in ways that surprise you... The rest of the resume becomes irrelevant. You've found what you came for. Everything else can be trained, compensated for, or covered by someone else on the team.
One question, pursued to its limit, replaces ten questions pursued to their surface.
This is not to say that every unconventional hire works out, or that spikiness alone guarantees a great team. The nuance is that this strategy requires tolerance for visible weakness, which is something most companies are unwilling to provide.
When you hire someone who is world-class at one thing and noticeably deficient at others, the deficiency is not theoretical. You can see it in meetings and in communication. It creates friction that a team of well-rounded generalists would never produce.
The spike-based team is harder to manage, harder to evaluate by standard metrics, and harder to defend to a board that wants clean org charts and predictable outputs.
Karim’s solution is structural.
He doesn't try to make every exceptional person look well-rounded on paper.
He builds teams where people with distinct strengths can combine effectively, so the team as a whole covers more ground than any individual could. In that model, the goal is high capability in different domains, assembled into a team with a higher ceiling.
This requires a different kind of leadership than most companies practice.
It requires knowing what each person's spike actually is, deploying them where it matters most, and protecting them from the parts of the organization that would grind their edges down.