AI Won't Kill Hiring – It Will Make It Far More Rigorous

01 /The Concept

HelloSky uses AI to help find senior executives – the kind of search that's traditionally been slow, expensive, and painfully manual.

The platform serves three types of clients:

- Executive search firms looking for candidates to present to their clients

- Companies that want to run their own search internally

- Venture and private equity funds sourcing leadership for their portfolio companies

On the surface, HelloSky looks like a "magic black box" – you describe in plain English who you're looking for, and the platform returns a ranked list of potential candidates you can actually reach.

For each candidate, HelloSky provides a relevance assessment, a fit rating for the specific role, and a "hireability index" reflecting how open the person appears to be to new opportunities right now.

HelloSky has already crossed $1 million in ARR and expects to reach operational breakeven in Q4 of this year. On the back of that momentum, it just raised $5.5 million in new funding, adding to $2.5 million raised in early 2022.

02 /Why It Matters

Executive search is a genuinely large market. It was valued at $58 billion in 2025 and is projected to reach $95 billion by 2030. But finding the right senior leader is legitimately hard.

Resumes and past titles are poor proxies for executive effectiveness. The cost of a bad hire at this level isn't just a recruiter's time – it's organizational momentum, team morale, and often a painful severance process.

There's also a supply-side paradox: at any given moment, very few executives who would be relevant for a given role are actively looking. Good leaders tend to already be well-placed, and publicly circulating a resume signals something awkward at this level – the best executives don't search for jobs, they get recruited away from them.

So headhunters spend enormous time and effort finding people through indirect channels: industry databases, conference attendee lists, board composition filings, and relationship networks. It's labor-intensive research that requires both domain expertise and access to specialized data sources.

HelloSky's AI doesn't do anything magical – it systematizes and accelerates the same research process that experienced executive recruiters run manually. But it does it faster, more comprehensively, and from a broader set of data sources, including the financial performance of companies candidates have led. If someone held a VP title, the P&L of the business unit they ran should reflect their actual effectiveness.

Building this took the team nearly five years – first finding experienced recruiters willing to share their mental models, then encoding those models into working algorithms.

The result is an AI that triangulates three data types from three source categories: industry data (e.g., PitchBook), personal data (e.g., LinkedIn), and relationship data that connects the two. Critically, HelloSky doesn't rely on social media connections for relationship mapping – those are too noisy. Instead it analyzes co-employment history, shared board memberships, and documented professional overlap to infer expertise levels and surface warm introduction paths.

Verata ([related review](/review/chtoby-pobedit-konkurentov-nuzhno-znat-chto-u-nih-proishodit)), another Y Combinator company, applies a similar approach for PE fund portfolio companies – explicitly positioning itself as having "married" LinkedIn and PitchBook. Its differentiator is modeling company revenue and overlaying it on executive histories to assess impact.

Spott ([related review](/review/odnimi-rukami-95-000-000-ne-zarabotaesh)), another recent YC graduate, is digging in the same direction.

03 /Opportunities

Y Combinator's attention to a category is always a signal – not that the problem is new, but that the conditions for solving it have recently changed.

The first key shift: resumes are increasingly understood to be unreliable signals. They tell you where someone worked, when, and in what title. They don't tell you what results they produced. Answering that question currently requires too much time and effort, so most companies skip it – leaving recruiters to rely on instinct. Instinct that's further distorted by the fact that a recruiter's core KPI is speed of placement, not quality of fit.

AI can now answer that question by aggregating and synthesizing large volumes of indirect evidence to make well-supported inferences about past performance.

The broad opportunity: hiring platforms whose AI pulls candidate data from multiple sources to make genuinely predictive recommendations – not just who is available, but who is likely to succeed in this role at this company.

Mercor ([related review](/review/a-ty-jetu-ofigennuju-vozmozhnost-mozhesh-razgljadet)) is the headline example – revenue growing 50% month-over-month since last fall, $100 million raised in February at a $2 billion valuation. Its mission: let every person find the best possible job, and every company find the best possible person.

There are smaller angles too. MyCredibility ([related review](/review/kak-poverit-neznakomcu)) built a platform where candidates and freelancers post profiles alongside video recommendations from past employers and clients – with AI that reads the sincerity of the people giving those endorsements.

The second shift: AI won't eliminate hiring; it will make companies far more selective about which humans they do hire. The ones they hire will matter more – which means they'll pay more for tools that help them make those decisions well.

In other words, the market for high-quality hiring platforms is about to expand, not contract. The companies building next-generation hiring infrastructure right now have a clear window.

Resumes have been overdue for retirement for years. They're finally approaching it. The question is just which part of their replacement you want to build.