AI companies hire from Stanford and MIT. Crypto companies hire builders who ship onchain. On paper, these look like opposite philosophies—one hyper-institutional, one anti-institutional.

But when you look at who actually succeeds in both industries, they're the same people. Extreme owners who treat their role like they're the founder, ship without waiting for permission, and make work their identity.

The hiring signals differ. The trait being selected for is identical.

Both industries are missing massive talent pools because they're over-applying filters to roles where those filters don't actually matter.

As Head of Talent at Lazer Technologies—a leading consulting firm across AI, Crypto, and Commerce—plus 11 years recruiting across Tumblr, Kickstarter, Twitter (X) and three years deep in crypto, I have a unique vantage point on this pattern.

These hiring patterns exist for legitimate reasons.

Protocol engineers genuinely need crypto-native thinking. Smart contract security, MEV, consensus mechanisms—these aren't things you just "pick up." A bad hire in a security-critical role could cost the protocol millions. The "must have crypto experience" filter protects against catastrophic mistakes.

ML researchers genuinely need deep technical foundations. Transformer architecture, training at scale, novel research—that's hard, specialized work where pedigree is a real signal. You can't fake your way through frontier AI research.

But here's where both industries go wrong: they apply these filters to most technical roles, not just the ones where domain expertise actually matters.

Crypto won't hire a great product manager from Airbnb unless they have "crypto credibility." AI won't hire a talented engineer who's been shipping at a startup unless they have the Stanford stamp.

The protective filter becomes a blind spot. Both industries end up fishing in tiny ponds while an ocean of talent sits right next to them.

There's something deeper happening beneath the surface-level differences.

Both technologies are fundamentally about autonomy and control. Crypto gives you control over your money, identity, governance, and economic participation. AI gives you control over your productivity, creativity, problem-solving, and leverage.

So of course they require people who are themselves highly autonomous and ownership-driven. The technology philosophy maps directly to the talent philosophy. You can't build empowerment technology with people who need constant direction.

What ownership looks like in crypto: shipping in public, iterating based on community feedback in real-time, making protocol decisions without perfect information, building with urgency in ambiguous environments.

What ownership looks like in AI: taking research from paper to prototype to production, making architectural decisions with incomplete data, shipping rapidly and iterating based on user input.

Different contexts, different vocabulary, same core trait. The person who thrives in crypto—who treats uncertainty as opportunity and moves with conviction—is the same personality type who thrives building AI products. The industries just use different filters to find them.

At Lazer, our bar is high. We hire talent from some of the best companies in tech. Every candidate goes through a rigorous interview process that deeply assesses CS fundamentals and their ability to build and scale products. We don't compromise on technical excellence.

But we've also learned something important: the most impactful hires often don't fall into the typical buckets. The through line isn't always the brand name on the resume—it's a deep sense of ownership.

Our hiring philosophy requires both: exceptional technical ability AND a proven track record of consulting, entrepreneurship, or founding their own company. That's core to our business model and what allows us to deliver the level of work our clients expect.

This dual focus has given us unique insight into what truly drives success across industries. Ownership matters as much as pedigree—sometimes more.

Two examples:

The engineer most companies would've passed on. Younger, no big brand names, wasn't actively working in crypto at the time. He was writing AI research papers and dabbling casually in crypto while researching and playing with AI. But in our technical interviews, his CS fundamentals were rock solid. And he'd run a web3 studio from his college dorm room—clear entrepreneurial mindset and bias to action. We hired him. He's now making major contributions to one of the biggest crypto apps in the industry.

The founder who barely touched crypto. He'd used stablecoins a little but didn't have much crypto experience. What did he have? Proven experience building and scaling consumer products—he drove around New Zealand testing his app with farmers who were the actual users, IRL. He founded and is creating his own React Native developer tooling. In our interview process, he demonstrated both strong technical problem-solving and the ability to pick up new domains quickly. Now he works with some of the biggest names in crypto building mobile apps.

Both cleared our technical bar. Both had the ownership mindset. Neither fit the traditional mold. Both became some of our highest-impact hires.

This is what happens when you maintain rigorous standards but expand where you look for talent—instead of filtering by the proxy signals your industry happens to prefer.

Stop defaulting to your industry's orthodoxy. Before you add another filter to your job description, ask yourself two questions:

First: Does this specific role actually require these credentials?

Protocol engineer? Yes, probably needs crypto-native thinking. Product manager? No—they need to understand users and ship, and domain knowledge can be learned. ML researcher? Yes, probably needs the technical depth. AI product engineer? No—they need to build and iterate, and they can learn the models.

Second: Are we filtering out ownership because we're over-indexing on pedigree?

This doesn't mean lowering the bar. It means challenging whether "3 years crypto experience" or "top-tier CS degree" is a real requirement for this role or just overt filtering.

It means asking different interview questions:

• "Share a time you had to learn a new technology quickly and implement it.”

• "What have you built that nobody asked you to build?"

• "How do you make decisions with incomplete information?"

Most of the time, when you honestly answer these questions, you realize the domain-specific filter is protecting you from the wrong risk.

Crypto's talent trap is simple: you're only hiring people who are already in crypto.

You're recycling the same 500 people across every project. Bidding wars, inflated comp, everyone knows everyone's salary. You're competing for 100 people when you could be competing for 10,000.

Web2 and AI are full of incredible builders who would thrive in web3—product managers, engineers, operators who have extreme ownership and hunger. They're curious about crypto but haven't made the jump yet. You just need to be willing to bet on their ability to learn the domain instead of demanding they already know it.

Stop requiring "crypto experience" for roles where it genuinely doesn't matter. Your product manager doesn't need to understand MEV to understand users. Your operations lead doesn't need to have shipped a protocol to build scalable processes.

Hire for ownership and hunger. Teach them the domain. The best crypto companies in 2025 will have teams that look more like tech companies than crypto Discord servers.

AI's talent trap is the mirror image: you're filtering out incredible operators because they don't have the right institutional stamps.

The person who built and scaled a product at a no-name startup might be 10x better for your applied AI role than the Stanford grad with two years at a big lab. But you'll never know because they didn't make it past your ATS.

Crypto is full of people who take extreme ownership, ship in public, and build with conviction in uncertain environments. These are exactly the traits you need for applied AI roles—not frontier research, but the actual work of taking models and building products people use.

These people have been operating in ambiguous, fast-moving environments for years. They know how to ship without perfect information.

Expand beyond the Stanford/MIT/Berkeley pipeline for execution roles. The pedigree signal matters for research. For building AI products, hunger and ownership matter more. And there's an entire industry of people next door who's been selected for exactly those traits.

If you're a crypto founder, you're talent-constrained by choice. Stop competing for the same tiny pool. The product manager who scaled growth at a tech startup, the engineer who built infrastructure at a fintech company, the ops lead who scaled teams at a SaaS company—they can all succeed in crypto if you're willing to teach them the domain.

If you're an AI founder, you're missing incredible builders because you're over-indexing on pedigree. The crypto founder who scaled a DeFi protocol, the engineer who shipped products in public, the designer who built for communities—they have exactly the ownership and velocity you need. You just have to look past the unconventional resume.

If you're a candidate, you don't need to be "from" the industry to succeed in it. If you have extreme ownership, hunger, and a history of shipping, you can make the jump. The path looks different but the destination is the same. Focus on demonstrating ownership in ways the new industry will recognize.

If you're a recruiter, the opportunity is in the overlap. The best AI companies will have crypto people on their teams. The best crypto companies will have AI people. Cross-pollination is massively underrated because most people can't see past industry orthodoxies.

AI and crypto appear to hire differently, but they're selecting for the same trait: extreme ownership. The problem is both industries over-apply their domain-specific filters beyond where they actually matter, creating artificial talent constraints.

At Lazer, we've proven that maintaining rigorous technical standards while expanding your hiring aperture works. The ownership trait translates. Domain knowledge can be learned.

In 2025, the best crypto companies will have AI people on their teams. The best AI companies will have crypto people. Your competitors are still fishing in the same tiny pond. There's an ocean of talent right next to you that nobody's looking at.

The question is: are you willing to look beyond the orthodoxy?

AI companies hire from Stanford and MIT. Crypto companies hire builders who ship onchain. On paper, these look like opposite philosophies—one hyper-institutional, one anti-institutional.

But when you look at who actually succeeds in both industries, they're the same people. Extreme owners who treat their role like they're the founder, ship without waiting for permission, and make work their identity.

The hiring signals differ. The trait being selected for is identical.

Both industries are missing massive talent pools because they're over-applying filters to roles where those filters don't actually matter.

As Head of Talent at Lazer Technologies—a leading consulting firm across AI, Crypto, and Commerce—plus 11 years recruiting across Tumblr, Kickstarter, Twitter (X) and three years deep in crypto, I have a unique vantage point on this pattern.

These hiring patterns exist for legitimate reasons.

Protocol engineers genuinely need crypto-native thinking. Smart contract security, MEV, consensus mechanisms—these aren't things you just "pick up." A bad hire in a security-critical role could cost the protocol millions. The "must have crypto experience" filter protects against catastrophic mistakes.

ML researchers genuinely need deep technical foundations. Transformer architecture, training at scale, novel research—that's hard, specialized work where pedigree is a real signal. You can't fake your way through frontier AI research.

But here's where both industries go wrong: they apply these filters to most technical roles, not just the ones where domain expertise actually matters.

Crypto won't hire a great product manager from Airbnb unless they have "crypto credibility." AI won't hire a talented engineer who's been shipping at a startup unless they have the Stanford stamp.

The protective filter becomes a blind spot. Both industries end up fishing in tiny ponds while an ocean of talent sits right next to them.

There's something deeper happening beneath the surface-level differences.

Both technologies are fundamentally about autonomy and control. Crypto gives you control over your money, identity, governance, and economic participation. AI gives you control over your productivity, creativity, problem-solving, and leverage.

So of course they require people who are themselves highly autonomous and ownership-driven. The technology philosophy maps directly to the talent philosophy. You can't build empowerment technology with people who need constant direction.

What ownership looks like in crypto: shipping in public, iterating based on community feedback in real-time, making protocol decisions without perfect information, building with urgency in ambiguous environments.

What ownership looks like in AI: taking research from paper to prototype to production, making architectural decisions with incomplete data, shipping rapidly and iterating based on user input.

Different contexts, different vocabulary, same core trait. The person who thrives in crypto—who treats uncertainty as opportunity and moves with conviction—is the same personality type who thrives building AI products. The industries just use different filters to find them.

At Lazer, our bar is high. We hire talent from some of the best companies in tech. Every candidate goes through a rigorous interview process that deeply assesses CS fundamentals and their ability to build and scale products. We don't compromise on technical excellence.

But we've also learned something important: the most impactful hires often don't fall into the typical buckets. The through line isn't always the brand name on the resume—it's a deep sense of ownership.

Our hiring philosophy requires both: exceptional technical ability AND a proven track record of consulting, entrepreneurship, or founding their own company. That's core to our business model and what allows us to deliver the level of work our clients expect.

This dual focus has given us unique insight into what truly drives success across industries. Ownership matters as much as pedigree—sometimes more.

Two examples:

The engineer most companies would've passed on. Younger, no big brand names, wasn't actively working in crypto at the time. He was writing AI research papers and dabbling casually in crypto while researching and playing with AI. But in our technical interviews, his CS fundamentals were rock solid. And he'd run a web3 studio from his college dorm room—clear entrepreneurial mindset and bias to action. We hired him. He's now making major contributions to one of the biggest crypto apps in the industry.

The founder who barely touched crypto. He'd used stablecoins a little but didn't have much crypto experience. What did he have? Proven experience building and scaling consumer products—he drove around New Zealand testing his app with farmers who were the actual users, IRL. He founded and is creating his own React Native developer tooling. In our interview process, he demonstrated both strong technical problem-solving and the ability to pick up new domains quickly. Now he works with some of the biggest names in crypto building mobile apps.

Both cleared our technical bar. Both had the ownership mindset. Neither fit the traditional mold. Both became some of our highest-impact hires.

This is what happens when you maintain rigorous standards but expand where you look for talent—instead of filtering by the proxy signals your industry happens to prefer.

Stop defaulting to your industry's orthodoxy. Before you add another filter to your job description, ask yourself two questions:

First: Does this specific role actually require these credentials?

Protocol engineer? Yes, probably needs crypto-native thinking. Product manager? No—they need to understand users and ship, and domain knowledge can be learned. ML researcher? Yes, probably needs the technical depth. AI product engineer? No—they need to build and iterate, and they can learn the models.

Second: Are we filtering out ownership because we're over-indexing on pedigree?

This doesn't mean lowering the bar. It means challenging whether "3 years crypto experience" or "top-tier CS degree" is a real requirement for this role or just overt filtering.

It means asking different interview questions:

• "Share a time you had to learn a new technology quickly and implement it.”

• "What have you built that nobody asked you to build?"

• "How do you make decisions with incomplete information?"

Most of the time, when you honestly answer these questions, you realize the domain-specific filter is protecting you from the wrong risk.

Crypto's talent trap is simple: you're only hiring people who are already in crypto.

You're recycling the same 500 people across every project. Bidding wars, inflated comp, everyone knows everyone's salary. You're competing for 100 people when you could be competing for 10,000.

Web2 and AI are full of incredible builders who would thrive in web3—product managers, engineers, operators who have extreme ownership and hunger. They're curious about crypto but haven't made the jump yet. You just need to be willing to bet on their ability to learn the domain instead of demanding they already know it.

Stop requiring "crypto experience" for roles where it genuinely doesn't matter. Your product manager doesn't need to understand MEV to understand users. Your operations lead doesn't need to have shipped a protocol to build scalable processes.

Hire for ownership and hunger. Teach them the domain. The best crypto companies in 2025 will have teams that look more like tech companies than crypto Discord servers.

AI's talent trap is the mirror image: you're filtering out incredible operators because they don't have the right institutional stamps.

The person who built and scaled a product at a no-name startup might be 10x better for your applied AI role than the Stanford grad with two years at a big lab. But you'll never know because they didn't make it past your ATS.

Crypto is full of people who take extreme ownership, ship in public, and build with conviction in uncertain environments. These are exactly the traits you need for applied AI roles—not frontier research, but the actual work of taking models and building products people use.

These people have been operating in ambiguous, fast-moving environments for years. They know how to ship without perfect information.

Expand beyond the Stanford/MIT/Berkeley pipeline for execution roles. The pedigree signal matters for research. For building AI products, hunger and ownership matter more. And there's an entire industry of people next door who's been selected for exactly those traits.

If you're a crypto founder, you're talent-constrained by choice. Stop competing for the same tiny pool. The product manager who scaled growth at a tech startup, the engineer who built infrastructure at a fintech company, the ops lead who scaled teams at a SaaS company—they can all succeed in crypto if you're willing to teach them the domain.

If you're an AI founder, you're missing incredible builders because you're over-indexing on pedigree. The crypto founder who scaled a DeFi protocol, the engineer who shipped products in public, the designer who built for communities—they have exactly the ownership and velocity you need. You just have to look past the unconventional resume.

If you're a candidate, you don't need to be "from" the industry to succeed in it. If you have extreme ownership, hunger, and a history of shipping, you can make the jump. The path looks different but the destination is the same. Focus on demonstrating ownership in ways the new industry will recognize.

If you're a recruiter, the opportunity is in the overlap. The best AI companies will have crypto people on their teams. The best crypto companies will have AI people. Cross-pollination is massively underrated because most people can't see past industry orthodoxies.

AI and crypto appear to hire differently, but they're selecting for the same trait: extreme ownership. The problem is both industries over-apply their domain-specific filters beyond where they actually matter, creating artificial talent constraints.

At Lazer, we've proven that maintaining rigorous technical standards while expanding your hiring aperture works. The ownership trait translates. Domain knowledge can be learned.

In 2025, the best crypto companies will have AI people on their teams. The best AI companies will have crypto people. Your competitors are still fishing in the same tiny pond. There's an ocean of talent right next to you that nobody's looking at.

The question is: are you willing to look beyond the orthodoxy?