Hiring technology has expanded rapidly over the past decade, producing a wide range of tools intended to make recruiting faster and more efficient. Yet critics within the industry argue that these tools largely refined an existing system rather than rethinking it. That distinction is central to how Sebastian Scott, Co-Founder and CEO of Clera, describes the current state of hiring and where it may be headed.
Why a candidate-first model has been so slow to emerge
According to Scott, most hiring technology has historically been built around the same core structure: a job post, a funnel, and a set of filters designed to sort applicants efficiently.
“The hiring tech boom of the last decade produced an enormous amount of tooling, but almost all of it was designed to optimize the existing system rather than reimagine it. Resume builders, ATS optimizers, and application automation tools all accept the same premise: that candidates should mold themselves to fit a machine-readable funnel. None of these innovations questioned whether the funnel itself was the problem.”
In this framing, innovation improved the mechanics of hiring without changing its underlying logic. Candidates were still expected to adapt themselves to standardized systems rather than the other way around.
Scott argues that a different approach, often described as candidate-first recruiting, requires a shift in what the system is designed to understand.
“True candidate-first recruiting requires something fundamentally different—hyper-personalized matchmaking at scale, where a candidate’s unique strengths, ambitions, and preferences drive the process. That kind of nuanced understanding simply wasn’t possible with rules-based software. It’s only now, with the emergence of AI capable of deeply understanding both people and opportunities, that we can finally build recruiting around the candidate rather than the job posting.”
What startup founders tend to value beyond resumes
In discussions about hiring, Scott points out that startup founders often describe ideal candidates in ways that do not map neatly to traditional credentials or job histories.
“When you talk to startup founders about their best hires, they almost never start with credentials—they talk about hunger. They want people with a deep, almost restless ambition to build something meaningful, paired with genuine curiosity that drives them to learn constantly and question assumptions.”
He adds that the definition of a strong candidate is also evolving alongside new technologies.
“Increasingly, being AI-native matters too: not just using AI tools, but thinking in terms of leverage, automation, and what’s newly possible.”
Another quality frequently cited, he notes, is sustained interest in a domain that predates the job itself.
“Founders also look for authentic passion for the specific problem space—someone who’s been obsessing over the domain long before they saw the job listing. These traits are nearly invisible on a traditional resume, which is exactly why conventional recruiting keeps missing the candidates founders actually want. A candidate-first model has to surface these signals, not just filter for keywords.”
Outreach versus matching in modern recruiting
Outbound recruiting has long been a standard practice, but Scott suggests its effectiveness depends heavily on specificity and intent rather than scale.
“Cold outreach can still work, but only when there’s real signal and genuine relevance behind it—mass outreach to hundreds of candidates with a templated message is noise, and everyone knows it.”
He contrasts low-signal outreach with more targeted approaches that demonstrate deeper understanding of a candidate or role.
“What actually moves the needle is when someone demonstrates they’ve gone deep on your product or space and put in effort that clearly doesn’t scale across a hundred applications. As a founder, you notice immediately when a candidate has done something thoughtful and specific – it’s rare, and it’s one of the strongest green flags in hiring.”
At the same time, he suggests that traditional outreach methods are limited in their ability to consistently produce that level of relevance.
“The problem is that this kind of high-signal interaction is almost impossible to manufacture at volume through traditional outreach. Proactive matching flips the model: instead of blasting candidates and hoping for relevance, you start with deep understanding of both sides and connect people where there’s a genuine fit.”
As hiring continues to evolve alongside new AI tools, Scott’s perspective reflects a broader shift in the industry: from systems designed primarily for filtering applicants to systems that attempt to better understand them.
