Throughout my career leading recruiting at companies like Robinhood, Pinterest, and Coursera, I've advised many tech companies. few recruiting tech companies have ever earned a spot on my advisor list. Metaview is one of them, and three years in, the bet has only paid off harder.
When I first met Siadhal in 2020, Metaview was a small bet on a contrarian idea: that the path to better hiring runs through better interviews, not bigger pipelines. I ran recruiting at Robinhood at the time. We were hiring fast, going fully remote, and the thing keeping me up at night was not the volume. It was whether our interviewers were actually pulling consistent signal across roles, levels, and time zones. Most teams underestimate how much variance lives inside their interview loop. Metaview was the first tool I saw that took that problem seriously.
Three years later, the market has moved through layoffs, AI noise, hiring freezes, and a tentative rebound. The thesis I joined for in 2023 has gotten louder, not quieter. According to Metaview's 2026 AI & Hiring Alignment Report, surveying 505 recruiting leaders and hiring managers across North America and EMEA, 79% of teams that ship aligned hiring practices hit their hiring goals, versus 36% of those who don't. That is not a marginal gap. That is the difference between making plan and missing it. So this post is a refresh on why I bet on Metaview as an advisor, written from the vantage point of having watched the AI cycle reshape what "good interviewing" actually means.
What I saw in Metaview in 2020
I had been advising tech companies for years, but I was selective. Most recruiting tech I saw was point solutions for sourcing, scheduling, or candidate relationship management. Useful, but not where the real use lived. The use was in the interview itself, and almost nobody was building there.
What struck me about Siadhal's pitch was the founding premise. Metaview was not trying to replace recruiters with AI. It was trying to give interviewers what athletes, surgeons, and sales teams have had for years: a recording of their actual performance, plus structured feedback on what worked and what didn't. The product, even early, was built for the person doing the work, not for the dashboard the VP wanted to look at.
When I brought Metaview into Robinhood in 2020, I was running a team that had to scale interviewer capacity 5x in less than a year while the company went fully remote. The traditional approach (shadow, reverse-shadow, certify, repeat) had hit its scaling ceiling. "We need every interviewer at the same bar by next quarter" is not a sentence you can manage with calendar links and a wiki.
What Metaview does is supercharge the path to increasing interviewer capabilities, and ingrain that culture into your interviewing pool.”
The problem that made me an early believer
The hardest problem in any growing recruiting org is consistency. Two interviewers, same role, same week, can pull wildly different signal from the same candidate. That variance is the silent killer of hire quality, and it is structurally invisible in most ATS reports.
At Robinhood, the move was to automate the shadow and reverse-shadow process, then layer calibration on top. Metaview was the only product I'd seen that made that affordable at scale. We went from a few senior interviewers spot-checking sessions to every interviewer being able to review their own loops on demand. The result was not just faster certification. It was a measurable shift in how interviewers thought about their craft. They started treating interviewing as a skill they could deliberately improve, the same way an engineer reviews their own PRs.
The other thing that mattered, and this is the part I underrated in 2020, was the candidate side. Automating note-taking meant the interviewer could stay in the room as a human, not as a stenographer. "Show up as a human, not a note-taking machine" is the phrase I used at the time. Candidates feel that difference inside 30 seconds, and that difference is what determines whether your best candidates accept the offer.
What changed from 2023 to 2026
When I formally joined as a strategic advisor in March 2023, the market had just turned. Layoffs were everywhere. Recruiting teams were getting cut by 40 to 60% across tech. The reflex move, the one I wrote about in the original version of this post, was to reduce recruiting capabilities and ride out the cycle. I argued that was shortsighted then. Three years later, the data is even clearer.
The teams that came out of 2023-2024 strongest were the ones that used the downturn to upgrade their interviewer pool, not shrink it. They invested in calibration, structured interviews, and tooling that lifted quality without adding headcount. When hiring volume came back in 2025-2026, they were already operating at a higher bar. The teams that cut deepest now have to rebuild interviewer capability from scratch, against a market where AI is reshaping what "good interviewing" even means.
AI is the other big change. In 2023, the recruiting tech conversation was mostly about whether AI could write better outreach emails. In 2026, the conversation has moved into the interview itself: AI as a real-time interviewing partner, AI as a quality control layer, AI as a way to surface signal a single human might miss. The teams getting this right are using AI to amplify the human interviewer, not to replace the interview. That is exactly the thesis Siadhal pitched me in 2020, and exactly what Metaview built.
- AI mostly used for sourcing email automation.
- Interview quality measured by NPS, if at all.
- Calibration done in quarterly all-hands, not in the loop.
- ATS as system of record, not system of insight.
- AI runs inside the interview, capturing signal in real time.
- Interview quality measured by structured rubrics and outcomes.
- Calibration happens loop by loop, not quarter by quarter.
- Insight layer connects every interview to every hiring decision.
Why the bet still reads as right
The reason I joined Metaview as an advisor in 2023 was not the product roadmap. Roadmaps shift. What I bet on was the team's discipline about who they were building for. They were building for the interviewer in the room, for the recruiter who has to defend a hiring decision in a debrief, for the hiring manager who wants to look at signal, not gut feel. That focus is still the moat three years later.
The market is now flooded with AI recruiting tools. Most of them are wrappers on top of an ATS or sourcing platform. They optimize the wrong moment in the funnel. The decisive moment in hiring is not when a resume hits the inbox. It is the 45 minutes when a human is actually deciding whether another human can do the job. Metaview's product surface (Notetaker, Application Review, Sourcing, Reports) is built around that moment. Everything else is downstream of it.
The second reason the bet reads as right: the data the platform produces is the asset that compounds. Every interview, every scorecard, every calibration session is a structured artifact. Over time, a team running on Metaview builds a proprietary corpus of what good interviewing looks like inside their company. No competitor, no LLM, no off-the-shelf benchmark can replicate that. The teams that started capturing this in 2023 have a three-year head start on the ones starting in 2026.
What I tell recruiting leaders now
When recruiting leaders ask me how to think about tooling in 2026, the framework I give them has three layers. One: protect the human-to-human interaction inside the interview. AI should make the interviewer more present, not less. If a tool pulls the interviewer's attention away from the candidate, it's the wrong tool, regardless of how slick the demo is.
Two: invest in the systems that make interviewer quality measurable. If you can't tell me which of your interviewers are reliably calibrated against your bar, you don't have a hire quality problem, you have a measurement problem. According to the 2026 AI & Hiring Alignment Report, teams that align on quality criteria upfront ship hires that stick. Teams that don't ship hires that churn out at 12 months. The difference between those two outcomes is not your interviewers' raw skill. It is whether you've built the operating system around them.
Three: treat your interview data as a strategic asset, not interview hygiene. The transcripts, the structured notes, the scorecard patterns are the highest-use corpus your recruiting org owns. Look at how the best teams use this data to compound their bar over time. That work is what separates a recruiting org that scales from one that backslides every time hiring picks up.
Where AI gives recruiting teams use
The product surfaces I think recruiting leaders should actually evaluate, written from the advisor's seat (not the marketing page), break out cleanly. These four are the use points where AI matters, and they are the four areas I look for when companies ask me to evaluate their recruiting stack.
An AI sourcing partner that ranks candidates against your real hire criteria, not just keyword matches. The signal compounds as your interview data grows.
Application volume is the silent killer of recruiter focus. The right tool routes top applicants up automatically, so humans spend their time on humans worth a screen.
Real-time structured notes free the interviewer to actually interview. This is the single biggest unlock for both interviewer focus and candidate experience.
Quality-of-hire reporting that ties interview signal to 90-day outcomes. The reporting surface is where you defend or evolve your bar.
The numbers below come from Metaview's 2026 AI & Hiring Alignment Report, surveying 505 recruiting leaders and hiring managers across North America and EMEA. The pattern is the same one I saw at Robinhood in 2020 and the one I bet on in 2023. Aligned teams hit their numbers. Unaligned teams don't.
None of this is theoretical. The teams I see executing best on this in 2026 are the ones who started capturing structured interview data in 2023 or earlier. They have three years of calibrated signal to feed their AI. Everyone else is starting from cold.
The operating shift
So here is the operating shift I'd argue every recruiting leader needs to make in 2026, written from the same advisor seat I took in 2023. The mechanics are different now (AI is everywhere, the market is different, candidate experience matters more than ever) but the underlying move is the same.
One: capture every interview in a structured form. Not just notes in a doc. Structured signal that ties to your rubric. Without this, you cannot calibrate, you cannot train, and you cannot defend a hire to a hiring manager who challenges it.
Two: treat interviewer training as a continuous skill, not a one-time certification. The teams that win are the ones where interviewers review their own loops weekly, the way an engineer reviews their own code. Good interviewers and bad interviewers diverge fast when feedback is continuous.
Three: connect interview signal to hire outcomes. If you don't know which interview signals predict 90-day success, you are flying blind. The teams using AI-augmented recruiting workflows at scale are the ones who have closed this loop. Everyone else is going to spend the next two years trying to catch up.
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Frequently asked questions
Why did Richard Cho join Metaview as a strategic advisor?
Richard joined in March 2023 after working with Metaview at Robinhood since 2020. He saw firsthand how the product solved interviewer consistency and calibration at scale, especially through the remote-hiring shift, and decided the underlying thesis of building for interview quality (not pipeline volume) was the right long-term bet on recruiting technology.
What problem does Metaview actually solve for recruiting teams?
Metaview tackles interview variance. Two interviewers running the same loop on the same role often pull different signal, which is the silent killer of hire quality. By capturing every interview in structured form, surfacing signal in real time, and feeding that data back into calibration, Metaview gives recruiting orgs a measurable way to lift interviewer quality at scale.
How has the case for AI in recruiting changed from 2023 to 2026?
In 2023 the AI conversation in recruiting was mostly about outreach automation. In 2026 it has moved into the interview itself: AI as a real-time interviewing partner, a quality-control layer, and a way to surface signal across thousands of interviews. The winning teams use AI to amplify human interviewers, not replace the interview.
What should recruiting leaders evaluate when choosing AI hiring tools?
Three criteria. First, does the tool make the interviewer more present, or more distracted. Second, does it produce structured data you can use for calibration, training, and reporting. Third, does it connect interview signal to hire outcomes (90-day retention, ramp, performance). Tools that fail any of those three are noise.
What is the operating shift recruiting leaders need to make in 2026?
Capture every interview in structured form, treat interviewer training as a continuous skill instead of a one-time certification, and connect interview signal to hire outcomes. Teams that do all three compound their bar over time. Teams that don't have to rebuild interviewer capability from scratch every hiring cycle.
