Osvald Nitski
Head of Product, Mercor. On 1 show, 1 appearance. The Shows tab opens the full record on each.
executiveoperatorscientistathlete@OsvaldNitski ↗mercor.com ↗Wikipedia ↗
Osvald Nitski leads product as Head of Product at Mercor, an AI-training and expert-data marketplace serving frontier AI labs. Prior to his work in artificial intelligence and machine learning research, he was an Estonian national record-holding swimmer who competed internationally and for the University of Toronto.
1shows
1appearances
33statements
2resolved
1supported
1contradicted
50%fully supported
Everything Osvald Nitski said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Nitski: Forward-deployed AI services are only a short-term enterprise trend
“I think it's the future for the short term as the knowledge of how to use AI gets disseminated throughout industry. We have basically a concentration of a bunch of people in San Francisco who really know how to deploy agents, eval agents, Be AI first in engine…”
Nitski: Mercor adds millions of dollars in cash to bank weekly
“It doesn't annoy me, no, because the, you know, we end every week with millions more in the bank, right? So it's funny how you can have, I've been at other Companies where I've seen, you know, interesting financial engineering and accounting, and people can ha…”
Nitski: Open-source AI does not cannibalize frontier data demand
“I wouldn't say that open source model improvements cannibalize our core business because data is most valuable on the frontier of model performance, so each of our customers has their own unique goals and is purchasing eval and training data sets To fill gaps …”
Nitski: Specialized AI models will require custom enterprise training data
“I buy it. I think it's also self-serving towards Mercore, in that we think that every specialized model will need enterprise specific eval and training data to show the model how it's performing in its setting, and I think it depends on I think the diversity a…”
Nitski: AI is driving higher PM-to-engineer headcount ratios
“So the trend that we see is we're, as a product team, constantly fighting to reduce surface area and simplify things, and we also see a higher ratio of PMs to ENG because engineering is less bottlenecked.”
Nitski: AI removes skill barriers, making judgment the key PM differentiator
“I can do things very quickly now, you know, it's like there's, ah, skill issues have almost gone away. So now it's all about, ah, judgment, and am I doing what is going to drive the most business value?”
Nitski: Mercor shifts to senior hires as AI obsoletes junior skills
“We've biased towards more senior hires. Who are better at understanding what drives the business forward, finding, kind of, like, really crocking how we operate, how we make more revenue, how we deliver better services to our customers, how we keep our custome…”
Nitski: Teams must never delegate core decision-making to AI
“I want very careful never to delegate judgment or decision making to models because it's, they make you think that it's doing the right thing, but You have to be paranoid with them still, right? You still have to, like, double check everything, and that's what…”
Nitski: Founder-led AI data startups cannot scale to enterprise lab demands
“This is just, like, VC-subsidized work that labs love. The problem is scaling it beyond a few data points or what one founder or full-time employees can do, and this is the position that we're in, is we're having to compete against Basically founder-led annota…”
Nitski: AI will never automate 90% of cybersecurity workflows
“There's never going to be that 90% for security because the goal posts are always going to move.”
Nitski: Robotics adoption will scale like driverless cars, not ChatGPT
“Yeah, I think so, but I think it might play out similar to driverless cars, where it's really hard to scale physical things as opposed to software, so it might be more of a, more of like a Waymo robo-taxi-cruise type moment than a ChatGPT moment, but I think t…”
Nitski: Mercor's fix for lab concentration is moving down market
“I can answer this from kind of like a, how it affects the product team. We would love to move like our biggest challenge is moving down market so that every single enterprise can efficiently run human data projects for eval and training. And that'll diversify …”
Nitski: judge token spend by outcomes, not one salary ratio
“I hope that we can move towards a future of better accounting of the outcomes being driven by token spend. Because even here, I think in a company like Salesforce we have so many, a company of that size, certainly you're getting, you should have different spen…”
Nitski: Mercor cannot spend money fast enough to meet customer demand
“We can't spend money fast enough to service all of the demand that we have.”
Nitski: Material learned in school will quickly become outdated
“Get a real internship as soon as possible because whatever you learn in school is probably going to be outdated quickly.”
Nitski: Top AI models cannot handle 90% of enterprise workflows
“I'm not convinced that 90% of enterprise workflows can be handled by open models or Frontier models right now.”
Nitski: Enterprise AI does not currently have an ROI problem
“I don't think there's an ROI problem right now. I think we're in a period of exploration and experimentation where there's more tolerance, more patience To get that ROI calculation right now.”
Nitski: Mercor team is shifting away from Figma to Claude
“To be honest, I let the team do whatever is best for them, and this is a trend I've just observed amongst almost everybody, is that cloud design has done a great job. People really like using it, it's easy to use, and we've just had a natural movement towards …”
Nitski: Evals and training data are AI's primary bottleneck
“Evals and training data are the primary bottleneck to model performance right now.”
Nitski: Robotics is the most underhyped tech opportunity three years out
“Probably the same answer as before in that, like, the three years out opportunity of robotics.”
Nitski: niche data providers exist already, and the small ones struggle
“To an extent, we're already in this world. It's not that successful though for the small players always. So how I would describe it is we're facing what looks like a cottage industry of founders doing annotation themselves, right?”
Nitski: Top AI models score 50% on Mercor's long-horizon benchmarks
“Well, in our Apex benchmarks, we're getting closer to around 50% of Long Horizon workflows. Top models are scoring around around that much.”
Nitski: Enterprise AI token spend will exceed 3% of developer salaries
“I think macro, the percentage will increase over time to more than three percent.”
Nitski: Mercor should have restricted supported annotation workflows much sooner
“We made a tool that's maximally flexible, has all sorts of, we had, like, hundreds of different projects running on it. That's just chaos to manage, and what we needed to do sooner was to put guardrails on the type of services that we support, and work closer …”
One line per show, most statements first. The link opens Osvald's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 20VCLEDGER | Head of Product, Mercor | 1 | 33 | 50% 1/2 | full record on 20VC → |