AI Hiring's Double-Edged Sword: Should Startups Bet on Potential?

Amid the AI wave, entry-level technical roles are undergoing a silent squeeze. A developer starting out in the AI field recently asked in a tech community: when Agent calls can replace the repetitive work of junior positions, and when top labs release new features every few weeks that refresh the toolchain, how much room is left for engineers just entering the industry? The answer to this question points to a sharp double-edged sword in the AI hiring market.

Big Tech Rigidity and Startups' Risky Bets

The current AI hiring landscape shows clear divergence. According to the original author's observations, large companies, despite abundant resources, have overly rigid hiring processes and are unwilling to create talent through internal development, preferring instead to poach engineers who are already fully formed. This strategy reduces hiring risk in the short term, but it also leaves big tech without depth in building talent pipelines.

In contrast, startups have become the biggest risk-takers in this field. They believe in an unproven idea, bet on a market that keeps clinging to the past, and try to build products that can truly change the world. The original article points out that startup hiring is closer to "meritocracy" — capable candidates can open that door by impressing founders and technical leads. But the problem is: if a candidate has potential but lacks hard skills that can be deployed immediately, are startups willing to take them in and develop them?

The Dilemma Between Potential and Readiness

"We can't just bet on concepts and theory; in the end, only results count." This statement captures the real dilemma in hiring. The original author also admits that a person cannot be hired just because they wrote a beautiful blog post or have a cool project that hasn't yet scaled. The energy and qualities a candidate brings are key factors that ATS scores cannot measure, yet they are also the hardest part to standardize.

This creates a paradox: startups need people who can solve problems immediately, but engineers with the potential to grow into pillars often need time and room for error to prove themselves. The original article raises an even sharper question — hiring certainly needs merit-based tests and thresholds, but "whether candidates are given the opportunity to prove themselves" may be a more worthwhile question than the thresholds themselves.

ATS (Applicant Tracking System) is a type of software widely used by companies in hiring to automatically collect, screen, and rank resumes. It usually scores candidates based on structured fields such as keyword matching, educational background, and years of work experience, helping HR quickly narrow down a large pool of applicants. The advantage of ATS is efficiency, and that is also precisely its flaw: it is good at identifying "labelable" hard qualifications, but it is almost incapable of evaluating a person's learning speed, collaboration style, problem-solving intuition, or drive to proactively push things forward in uncertain environments. What the original article means by "energy and qualities cannot be measured by ATS" points directly to this structural blind spot. For junior candidates, this means that even if their potential is outstanding, they may be filtered out by the system before ever entering the view of a human interviewer, making the original intention of "meritocracy" hard to realize.

Team Chemistry: The Unquantifiable Variable

The original article also touches on an often overlooked dimension: team atmosphere and rhythm. The author believes that vibe and rhythm are not abstract concepts, but real reflections of a team's coordination and motivation. But while diversity brings energy, it also contains tension — people with strong opinions will inevitably clash. For startups already juggling many things, bringing in a newcomer who needs development may mean additional management costs and team friction.

This may explain why startups hesitate over high-potential newcomers: it is not that they are unwilling to take risks, but that their plates are already too full, and they can only make exceptions for "exceptionally outstanding people."

Team chemistry and rhythm in engineering teams are not mysticism, but organizational characteristics that can be indirectly observed. Chemistry usually shows up in whether members are willing to step in for one another, whether they can keep discussions focused on the issue rather than the person during technical disagreements, and whether newcomers are patiently answered when they ask questions. Rhythm is about the team's breathing pattern in delivery: whether it is steady small steps with frequent reviews and iterations, or long periods of working in isolation followed by concentrated bursts. Both directly affect the cost of onboarding newcomers. A startup team that moves too fast while its chemistry is still unstable often does not have the spare capacity to provide structured feedback for newcomers, and newcomers can easily fall behind while "figuring it out on their own." Conversely, a team with a clear rhythm and an open atmosphere, even with limited resources, is more likely to let high-potential candidates prove themselves through actual contributions.

Who Dares to Wield This Sword?

The article ends with a somewhat playful English proverb: "If 'Ifs' and 'buts' were candy and nuts, we'd all have a Merry Christmas." If assumptions could all come true, everything would be wonderful. But the reality is that the double-edged sword of AI hiring is hanging right there: on one hand, without taking risks, you cannot find early talent that truly fits; on the other hand, the cost and uncertainty of taking a chance on developing newcomers make startups hesitate.

The real question worth asking is: do those groups that claim to be the boldest and most willing to bet on the future dare to wield this double-edged sword in hiring as well? For people just starting out in AI, the answer to this question may determine their career trajectory more than any choice of tech stack.

"If 'Ifs' and 'buts' were candy and nuts, we'd all have a Merry Christmas." is an English saying that literally means, "If 'ifs' and 'buts' were candy and nuts, we'd all have a Merry Christmas." It is used to tease those who indulge in hypotheticals while refusing to face reality: if all conditions were as ideal as imagined, things would of course be wonderful, but reality never runs on assumptions. The original article uses it as a closing reminder to readers: the discussion about "whether startups should give high-potential newcomers a chance" can easily remain at the level of assumptions such as "if they were willing to develop people" or "if candidates had time to grow," while real decisions happen in a reality constrained by resources, time, and risk. For job seekers, understanding the subtext of this proverb may be more practically meaningful than waiting for a perfect answer.

One-line Summary

AI hiring is a double-edged sword: big tech is rigid and poaches talent, while startups dare to take risks but struggle to give high-potential newcomers room to grow.

Paragraph Summary

The article explores the squeeze on entry-level technical roles under the AI wave and the structural contradictions in the hiring market. Large companies have abundant resources but rigid processes, preferring to directly poach experienced engineers rather than develop talent internally; startups are more willing to hire on merit, but due to immediate execution needs and tight resources, they struggle to provide growth space for newcomers who have potential but lack hard skills. The author points out that a candidate's energy and team fit are key factors that ATS scores cannot measure, while team chemistry and rhythm also affect the cost of onboarding newcomers. The core question is: do those startups that claim to be the boldest and most willing to bet on the future dare to give high-potential candidates a chance to prove themselves in hiring?