That is what is happening now across every function, all at once, and most companies have not figured out what it actually means for how they hire and structure their teams.

AI was cited as a factor in over 55,000 U.S. layoffs in 2025 CNBC, and the pace is accelerating into 2026. But the default narrative that AI is simply replacing people misses the more important shift. Research from Harvard Business Review found that companies have been laying off employees because of AI's potential rather than its current performance. Salesforce Ben These are not efficiency cuts. They are structural bets. Companies are going lean now so that when their AI investments start compounding, the org is built to absorb them. Only 14% of CFOs say they have seen a clear, measurable return on their AI investments so far. CFO.com They are spending more, seeing less than expected, and restructuring anyway. Because the real move is not about today. It is about building a team that can actually operate in what comes next. And that means every role that survived is being asked to own something fundamentally different than what it owned before.

Product, Engineering, and Design

The modern product team was built around the handoff. A PM owned the roadmap and wrote the specs. An engineer owned the build. A designer owned the experience. Each person was excellent at their lane and the work moved through them sequentially. This model made sense when the scope of each discipline was genuinely too wide for one person to hold.

AI is changing that math.

What the roles are today:

• A Software Engineer who owns implementation within a defined technical scope

• A Product Manager who translates business needs into requirements and coordinates across functions

• A Product Designer who owns the visual and interaction layer and relies on engineering to execute it

What the roles are becoming:

• People who own a surface area, not a function. Someone who can move from identifying a problem to designing a solution to shipping it without losing momentum at every handoff

• Contributors who use AI to cover the gaps across disciplines rather than waiting for the right specialist to pick it up

• Fewer people with broader ownership, where the expectation is not that you do everything perfectly but that you can hold the full lifecycle of a problem and know when to go deeper

This is not about shrinking teams for the sake of cost. It is about recognizing that deep specialization in narrow lanes was always a workaround for tooling limitations. When one person can now move fluidly across design, scoping, and implementation with AI filling the gaps, the fragmented team structure becomes a liability, not an asset. Handoffs slow things down. Silos create misalignment. Waiting for the right person to pick something up kills momentum.

The teams winning right now are the ones where each person's scope of ownership is growing, and their compensation and title are reflecting that reality. That is not a reduction in value. It is an elevation of it. The same way a graphic designer in 1992 who mastered desktop publishing did not become less valuable than the five specialists they replaced. They became significantly more valuable because they could hold more of the outcome.

Marketing and GTM

The traditional go-to-market team was built around clear handoffs and defined lanes. A Product Marketing Manager owned positioning, messaging, and launch. A Business Development lead owned partnerships, pipeline, and relationship cultivation. An Account Executive owned the close. Each motion had a dedicated owner and the machine scaled by adding people to each layer.

AI is collapsing the distance between those lanes.

What the roles are today:

• A Product Marketing Manager who owns messaging and positioning, produces launch materials, and hands off to sales with enablement assets

• A Business Development Manager who sources and qualifies partnership opportunities, manages relationship pipelines, and passes warm deals to an AE to close

• An Account Executive who receives qualified pipeline, runs a defined sales process, and closes within their assigned territory or segment

What the roles are becoming:

• A product marketer who owns the full narrative of a product, from positioning and competitive intelligence to customer-facing content to influencing how the product gets built, not just how it gets sold

• A BD operator who can identify a partnership opportunity, build the relationship, structure the deal, and see it through to revenue without a clean handoff to someone else

• An account owner who prospects within a defined market, closes business, and retains and expands the relationship over time, using AI to handle the research, outreach cadences, and reporting that used to require a supporting cast

The volume game is over. One person with strong instincts and the right AI tooling can own more of the customer journey than an entire segmented team could eighteen months ago. The hire that matters now is not the person who can execute one motion exceptionally well. It is the person who understands the full arc from awareness to close to expansion and can hold more of it than their job title used to suggest was possible.

Operations and People

Operations and HR were built around a simple premise: as the company grows, the volume of coordination, administration, and process management grows with it. More employees means more onboarding. More vendors means more contracts. More complexity means more people to manage it. Headcount in these functions was essentially a derivative of headcount everywhere else.

That relationship is breaking down.

What the roles are today:

• An HR Generalist who manages onboarding, compliance, benefits administration, and employee relations

• An Operations Manager who owns process documentation, vendor management, and cross-functional coordination

• A Recruiter who sources candidates, manages pipelines, and coordinates interviews across open roles

What the roles are becoming:

• A people strategist who uses AI to handle the administrative layer and spends their time on organizational design, culture, and talent judgment at a level that directly influences how the company is built

• An operations lead who architects systems and workflows rather than managing them, using AI to automate the coordination layer so human attention is reserved for decisions that require real context and judgment

• A talent partner who understands how AI is changing every function they hire for, redesigns roles in real time, and challenges hiring managers on what they actually need versus what they are used to asking for

IBM laid off around 8,000 employees from its HR department, replacing those functions with an internal AI tool, then began hiring software engineers and data analysts instead. FinalRoundAI The signal is clear. Transactional work in operations and people is being automated. What remains is more strategic, higher stakes, and requires a fundamentally different skill profile than the roles that came before. The people who will thrive here are not the ones who were best at managing the process. They are the ones who were always asking why the process existed and whether it could be better.

The Window to Adapt Is Narrower Than You Think

Here is the part of this conversation nobody wants to have openly. Not everyone currently in these roles is going to make the transition. That is not a judgment. It is just true. The paste-up artist who had spent fifteen years perfecting a physical craft was not a lesser person than the designer who picked up Photoshop in six months. They were just optimized for a world that was moving on. Some of them adapted. Some did not. And the ones who waited to see if the industry would stabilize before committing to the new way of working almost uniformly lost the window.

The same dynamic is playing out right now, and the window is moving faster than most people realize.

If you are an individual contributor in any of the functions above, the single most important thing you can do today is start tinkering outside your lane. Not to become a generalist in the abstract sense, but to build enough fluency in adjacent disciplines that you can hold a conversation, make a decision, and move without a handoff. A PM who has never touched a design tool should be prototyping with AI assistance. An engineer who has never thought about distribution should be running their own content experiment. A marketer who has never looked at a pipeline dashboard should be learning what good GTM data actually looks like. None of this requires becoming an expert. It requires becoming dangerous enough to own more.

If you are a founder or a talent leader, the hiring signal for the new version of these roles is simpler than most people make it. Stop asking candidates to demonstrate depth in their primary discipline. Everyone who makes it past a first screen can do that. Start asking them to show you something they built or owned outside their core function. A designer who shipped a feature. A marketer who closed a customer. An ops person who built a product workflow. The people who are already living the new model of work are not waiting to be asked to expand. They are already doing it and looking for an environment that rewards them for it. Those are the people you want.

In a Nutshell

The layoffs are real. The AI spending is real. The ROI gap is real. But the story underneath all of it is not about replacement. It is about recalibration. Every major function is contracting around a smaller number of people who own more, move faster, and use AI to cover ground that used to require an entire team. The roles are not disappearing. They are expanding in scope and compressing in headcount, which means the people who fill them need to be built differently than the people who filled them before. For founders and talent leaders, this is a design problem: figure out what ownership actually looks like inside your org today and hire people capable of holding it. For individuals, this is an urgency problem: the window to adapt is open right now and it will not stay open indefinitely. The paste-up artist who learned Photoshop did not just survive the shift. They redefined what the role was worth. That same opportunity exists today, in every function, for anyone willing to pick up the full lifecycle and run with it. The ones who wait to see how this plays out are already behind.

AI compressed headcount plans across the board. Engineering teams of 8 are shipping what used to take 30. One content strategist with the right AI workflow is replacing a team of five writers. GTM teams built around AI-powered outbound are outperforming traditional SDR armies at a fraction of the cost.

Fewer people. Higher output. Sounds like hiring gets easier.

It doesn't. It gets exponentially harder. Because when you go from 50 hires a year to 15, every single person you bring in carries the weight that used to be spread across three or four roles.

Welcome to the era of Precision Hiring.

The old playbook is dead

For the last decade, the talent playbook at startups was simple. Raise a round, hire fast, scale the team, figure out efficiency later.

Talent leaders were measured on speed and volume. How fast can you fill 50 roles? How quickly can you ramp a recruiting team to keep up?

That game is over.

When you're only making 15 hires, you're not filling seats. You're placing bets. Each one determines whether your company executes or stalls.

That's not recruiting. That's portfolio construction.

The new category of mistake nobody is talking about

Here's what happens when founders try to navigate this shift without a strong talent leader. They either run hiring themselves or hand it to a generalist.

Then something like this plays out:

Your company needs an engineering leader. You find someone with a great resume, strong technical depth, a track record of managing teams. They start. Within 90 days it's clear they want to build the way they've always built: a team of 25, clear specializations, traditional sprint structures. But your company doesn't need that anymore. You need someone who can run a team of 8 at high leverage, architect systems where AI handles implementation, and rethink what developer productivity even means in 2026.

That's not a "bad hire." That person might be exceptional. They're just exceptional at a job that doesn't exist at your company anymore.

This is happening everywhere right now. Marketing leaders building traditional content teams when the role has shifted to AI-native strategy. Sales leaders hiring SDR armies when one person with the right AI tooling can outperform ten. Ops leaders staffing for manual processes that are about to be automated.

Nobody made a "mistake." They just hired for 2022 in 2026. And the reason it keeps happening is that nobody on the team was thinking about how AI is reshaping what each function actually needs.

That's your talent leader's job. Or at least, it should be.

What Precision Hiring actually requires from a Talent Leader

The talent leader you need today looks nothing like the one you needed three years ago. Here's what actually matters now, and here are some questions to help find them.

They think in capabilities, not headcount

Old question: "How many people does this team need?"

Precision Hiring question: "What capabilities does this team need, and what's the right combination of people and AI to get there?"

The talent leader you want pushes back when a hiring manager asks for three additional engineers and instead asks: what are those three people actually going to do, and could one senior engineer with the right AI tooling cover it?

They should also understand how AI is changing what "great" looks like for every role they're hiring. The skills that made someone a top VP of Engineering in 2023 are not the same skills that matter in 2026. If your talent leader can't articulate that difference for every function they recruit for, they're sourcing yesterday's talent for tomorrow's problems.

Ask them: "A hiring manager comes to you requesting 4 new roles. Walk me through your process before you open a single search."

Then ask: "How has AI changed what 'great' looks like for a [role relevant to your company] in the last 18 months?"

You want someone who starts with understanding the work and the shifts in the work. Not posting the job.

They're embedded in the industry, not just the function

You can't recruit what you don't understand.

A talent leader in crypto who doesn't know the difference between an L1 and an L2. Who can't speak intelligently about how DeFi teams are structured versus infrastructure teams. Who doesn't know where the best protocol engineers are coming from or where they're going.

That person is filling job descriptions. They're not building a team that fits how your company actually competes.

Same in AI. If your talent leader doesn't understand how a breakthrough in multimodal models changes the kind of researcher you need, or how the shift from training to inference changes your infrastructure hiring, they're always a step behind.

Talent can't be abstracted from the industry anymore.

The best talent leaders are reading the same things your CTO is reading. Attending the same conferences your engineers attend. They understand the competitive landscape not just for customers, but for people.

Ask them: "What's a trend in our industry right now that should change how we think about our next three hires?"

Generic answer about "the market is competitive"? That tells you everything. Specific insight about how a particular shift creates a talent opportunity or risk? That's someone who's embedded.

They operate across the entire business and treat it like their own

When every hire is a high-stakes bet, your talent leader needs to understand enough about engineering, product, design, GTM, and ops to have real opinions about what each team needs.

They can't just take orders from hiring managers. They need to be the person who sometimes knows what a team needs before the team does. One wrong hire in a team of 8 is a 12% error rate. One wrong hire in a team of 40 is noise.

They also manage one of your biggest line items. Recruiting spend, agency fees, tooling, team salaries, employer brand. In a world where efficiency matters more than ever, they need to think about their budget with the same rigor your CFO brings to capital allocation.

Ask them: "Tell me about a time you told a hiring manager they were hiring for the wrong role."

Then: "You have a $400K recruiting budget and 15 critical hires this year. Five are roles that didn't exist 18 months ago. How do you allocate?"

You're looking for conviction, cross-functional fluency, and financial discipline. Not just execution speed.

They're building for where AI is going, not where it is today

The best talent leaders are thinking 12 to 18 months ahead.

Which functions will be further compressed by AI next year? Which roles will become more valuable as AI handles more routine work? What new roles will emerge that don't have names yet?

This is the difference between hiring for today's org chart and building the team that will actually exist in 18 months.

Ask them: "How should our headcount plan look different a year from now based on where AI is heading?"

You're not looking for a perfect prediction. You're looking for someone who's thinking about it at all.

In a nutshell

We're not going back to the 50-hire playbook. The companies that win from here are the ones that master Precision Hiring: fewer people, higher stakes, every role a strategic bet.

That's not a job for a generalist. It's not something you run yourself between board meetings. It's a function that requires someone who understands your business model, your industry, how AI is transforming every function, and what "great" looks like in roles that are being redefined in real time.

The question isn't whether you can afford a great talent leader.

It's whether you can afford to make 15 of the most important hires your company will ever make without one.

You look at their resume. Impressive pedigree—they worked at top companies, placed senior roles, have 8,000+ LinkedIn connections. On paper, they look perfect.

But here's the question that actually matters: Can their network move when you need it to?

Most founders evaluate talent leaders and recruiters by the wrong metrics. They look at tenure, company logos, network size. And while pedigree and work history matter, they tell you almost nothing about whether someone's network is actually invested and curated in a way that helps you hire in fast-moving markets.

You can work at incredible companies for years and still have shallow relationships that don't activate when it counts. You can have an impressive resume and a network full of names that won't pick up the phone. In crypto and AI—where entire categories emerge in months and talent gets snapped up in days—what matters isn't who someone worked with, but how quickly they can leverage those relationships for real impact.

That's why I'm introducing a framework I call Network Velocity.

What Is Network Velocity?

Network Velocity = Activation Speed + Cross Functional Reach+ Ecosystem Integration

Network velocity measures how quickly someone can activate the right parts of their network to deliver real outcomes. It's physics applied to relationships: velocity = speed + direction.

Think about two recruiters you're considering for a founding recruiting partner role:

Recruiter A has 10,000 LinkedIn connections. Spent five years at a top firm placing senior engineers at FAANG companies—and they're exceptional at it. Impressive resume, proven track record. But when you ask them to find you a Head of Design outside their core network, they need to start from scratch. They'll get there, but it takes time to build those relationships.

Recruiter B has 900 connections. Spent three years moving between crypto infrastructure companies, a fintech startup, and a gaming studio—recruiting across engineering, product, design, and growth. When you ask for a Head of Design, they text four people that afternoon. By the next day, you have two warm intros to designers who've shipped products at scale and are genuinely curious about what you're building.

Both are good at what they do. But they have fundamentally different network velocity. That's the distinction that matters when you're building fast.

The Network Velocity Framework

Activation Speed
How fast does your network respond when you need it?

If they reached out to 10 people right now, how many respond within 24 hours? High-velocity networks are built on real relationships, not transactional LinkedIn connections. The test is simple: response rate plus response quality.

• Lower Velocity: "I'll post this role and start reaching out"

• High Velocity: "Let me text three people right now—I should have intros by tomorrow"

Cross-Functional Reach
Can your network move beyond its core domain?

The best networks aren't siloed—they span engineering, product, design, GTM, and ops. In crypto and AI, this matters even more. Your best Rust engineer might come from gaming, your growth lead from fintech, your first designer from a B2B SaaS company. Can they reach outside their specialty effectively?

• Specialized (high depth, lower breadth): Deep expertise in one vertical—invaluable for scale hiring in that area

• High Velocity (depth + breadth): Deep expertise in one area PLUS relationships across functions and industries

Ecosystem Integration
Are they transactional, or truly embedded in their community?

Do they show up, give value consistently, and operate on reciprocity? High-velocity people don't just have networks—they are part of the network. Who knows them beyond transactional recruiting relationships?

• Lower Velocity: Only reaches out when they need something

• High Velocity: Active in community for years—hackathons, events, intros, contributions

Why Network Velocity Matters (Especially in Crypto and AI)

Here's the trap: founders evaluate talent leaders through proxies that don't predict performance. Someone has 8,000 LinkedIn connections? Great—but how many would actually take their call with urgency? They spent five years at a prestigious company becoming the best in their vertical? Valuable—but if you need to hire outside that vertical, you're starting from scratch together.

Why this matters acutely in crypto and AI:

These markets move at a fundamentally different pace. In crypto, entire categories—DeFi, NFTs, L2s, restaking—emerge, mature, and evolve in 12-18 months. In AI, breakthroughs happen quarterly, reshaping what's possible and who you need to build it.

Traditional hiring timelines don't work. If it takes you three months to fill a critical role, the market has already shifted. Your competitors have shipped. The opportunity window has narrowed. You need people who can move now. And that requires network velocity.

The hidden cost of low velocity:

I've seen this repeatedly. A founder hires a "connected" recruiter—great pedigree, strong resume, 6,000+ connections. Three months in, they've filled one role. Maybe two.

Why? Because when they needed to activate their network, nothing moved. Their relationships were shallow. Their reach was narrow. They knew a lot of names, but couldn't actually get anyone on the phone.

Meanwhile, a founder working with someone who has high network velocity fills four roles in the same timeframe—across engineering, product, design, and ops—because that person can reach into different parts of the ecosystem and get real humans engaged quickly.

In fast markets, that's the difference between building momentum and stalling out.

To be clear: Specialized recruiters are incredibly valuable. If you're scaling a function and need to hire 20 engineers or 15 sales reps, someone with deep vertical expertise will outperform a generalist every time. The issue isn't specialization—it's context.

For early-stage companies with evolving needs, or companies moving into new categories, network velocity across multiple domains becomes critical. You're not hiring for one function—you're building an entire company. That requires a different kind of network.

How Founders Should Build Network Velocity

You can't outsource this entirely. Your network velocity as a founder is as critical as anyone you hire.

Be T-Shaped

The most effective founders aren't just deep in their domain—they're broad across the ecosystem. You need depth (technical expertise, domain knowledge, hard-won insights from building) and breadth (relationships with operators who've scaled companies, investors who open doors, talent across engineering, product, design, marketing, and ops).

When you hit a wall—whether it's a key hire, a strategic question, or navigating a market shift—you need to activate different parts of your network quickly. That's what velocity enables.

Invest in Reciprocity

High-velocity networks aren't built by taking. They're built by giving, consistently, over time.

What this looks like in practice: Make intros without being asked. Share opportunities that aren't yours. Offer help before it's needed—tactical advice, feedback, connections. Show up to community events, hackathons, dinners. Contribute publicly through writing, speaking, open-sourcing knowledge.

The founders who do this find that when they need something critical—a tough hire, a crucial piece of advice, an important intro—their network responds immediately.

Curate Ruthlessly

Not every connection deserves ongoing attention. Your network should be filled with people who actually build things (operators, investors who ship, founders executing), have strong track records and pedigrees, and will pick up when you call.

Quality over quantity. Always.

How to Evaluate Network Velocity in Talent Partners

When bringing on a founding recruiting partner or talent leader, resumes only tell you so much. You need to evaluate network velocity directly.

Questions That Reveal Velocity:

"Can you reach someone in a function you've never recruited for?"
This tests cross-functional reach. If they've only done engineering recruiting, can they still get you a world-class marketer or operator through their network? If the answer is "I'd need to source for that," their network lacks breadth.

"Who would vouch for you outside of candidates you've placed?"
High-velocity recruiters have relationships with operators, founders, and investors—not just people they've helped get jobs. If only candidates would vouch for them, that's a signal their network is transactional, not embedded.

"If you reached out to five key people in your network right now, how many do you think would respond by end of day?"
This is a hypothetical but revealing question. You're testing their honest self-assessment of relationship strength. High-velocity people will confidently say "all five" or "four of five" and can tell you exactly who those people are and why they'd respond. Low-velocity people will hedge or give vague answers. It reveals whether they truly understand the quality of their relationships.

"Walk me through how you'd find me a [role they've never recruited for]."
Listen carefully. Are they describing reaching into their existing cross-functional network, leveraging relationships across industries? Or defaulting to "I'd post on LinkedIn and start sourcing cold"? The former is velocity. The latter is just hustle.

"Tell me about a time you filled a role in under four weeks. How did you do it?"
This reveals process. High-velocity recruiters describe activating specific relationships and moving candidates through trust. Low-velocity recruiters talk about volume and outreach.

The Non-Negotiable: T-Shaped Expertise

A founding recruiting partner must be T-shaped. You need someone with vertical depth (exceptional at one thing—maybe engineering recruiting, or building entire GTM teams from scratch) and horizontal breadth (relationships across functions, industries, and contexts).

Your needs will evolve. In six months, you'll need someone they've never recruited for before. In a year, you might pivot and need an entirely different type of talent. If their network can't flex with you, they're a vendor filling roles—not a partner building your company.

This doesn't mean specialized recruiters aren't valuable—they absolutely are when scaling a specific function. But at the founding stage, or when building in fast-moving markets like crypto and AI where your needs shift rapidly, you need someone whose network can flex with you.

In a Nutshell

The pace of company-building has fundamentally changed.

In crypto, entire categories emerge and mature in 12-18 months. In AI, the talent landscape shifts every quarter. When markets move this fast, you can't afford three-month hiring cycles. You need networks that activate in days, not weeks.

The shift:

• Network effects measure scale

• Network velocity measures execution

Stop asking "who has the biggest network?" Start asking "who can move fastest when it counts?"

The companies that win hire for velocity, not pedigree. Who build relationships that activate when it matters. Who understand that your network isn't just an asset—it's a tool that either works or doesn't.

Network velocity is the new competitive advantage. Measure it. Hire for it. Build it.

For generalist roles, yes—companies have leverage. According to Deloitte, 72% of companies cite talent shortages as a major challenge, but that hasn't stopped them from being selective. McKinsey's global survey found 87% of executives say their organizations either face skill gaps already or expect them within five years—yet only 28% are planning to invest in upskilling programs to close them.

But in AI and crypto? Different story. The a16z State of Crypto 2025 report found that while roughly 1,000 workers left crypto for AI startups since ChatGPT launched, the industry gained the same number from traditional finance, tech, and consulting. Talent is flowing in, not out. And the Microsoft/LinkedIn Work Trend Index reports that 66% of leaders say they wouldn't hire someone without AI skills—but the supply isn't there. McKinsey found that fewer than half of potential candidates have the high-demand tech skills listed in job postings.

Two markets. Two sets of rules. Most founders are operating with the wrong mental model for the talent market they're actually in.

As Head of Talent at Lazer Technologies—a consulting firm working across Crypto, AI, and Commerce—I see this confusion play out across hundreds of hiring conversations. Founders apply employer's-market tactics to candidate's-market roles. They move slowly when they should move fast. They filter aggressively when they should be expanding their aperture.

Here are the three shifts that matter most heading into 2026.

1. Know Which Talent Market You're Actually Competing In

The rise of "996 culture" in tech tells you something important about how founders are thinking about the talent market—and where the nuance gets lost.

996—working 9am to 9pm, six days a week—originated in China's tech industry and was declared illegal there in 2021. But it's now showing up as a hiring filter at U.S. startups. AI startup Rilla, based in New York, tells candidates in job listings not to bother applying unless they're excited about working 70+ hours per week in person. Multiple recruiters report that clients require candidates to confirm willingness to work 996 before interviews even begin.

996 might work for where some teams are at. Early-stage, all-hands-on-deck, shipping v1—there's a time when intensity is the strategy. But most founders aren't thinking carefully enough about what talent they need for specific roles, and how that maps to the culture they're building.

Consider: you need a senior ML engineer to optimize your inference pipeline and reduce latency on your LLM serving infrastructure. This is highly specialized work—distributed training, GPU optimization, model quantization. With that experience comes efficiency. A senior engineer who's done this before can architect a solution in days that a junior team might take months to figure out. They produce faster and at higher quality working 45 hours than an inexperienced team grinding 70. But that senior talent has options—and they're often at a life stage where 996 isn't appealing. If you filter them out with an intensity requirement, you might end up with a less experienced team working harder but delivering less.

The market bifurcation makes this more acute. According to Deloitte, nearly 90% of tech industry leaders say recruiting and retaining tech talent remains a moderate or major issue—even during layoff periods. McKinsey found that 92% of companies plan to increase their AI investments over the next three years, but only 1% describe their current AI deployment as "mature." The demand is accelerating. The supply isn't keeping up.

AI and protocol engineers will work hard—that's not the issue. Specialized talent isn't averse to intensity; they just don't need to accept it. They have options. So it becomes about two things: finding the talent that genuinely wants to build with you, and creating the incentive structures that make the grueling work worth it. Equity, ownership, mission, technical challenge—these matter more than mandating hours.

The best teams work hard strategically, then recover intentionally. Sprint during launches, recharge to maintain peak performance. The founders who treat every week like a sprint will burn out their best people. The founders who never ask for a push will get outpaced.

The 996 framing presents a false binary. The reality: know when to flex, know when to pull back, and be transparent with your team about which mode you're in.

What to do about it:

• Audit your open roles: which are in an employer's market, which are candidate-driven? Calibrate your speed, selectivity, and pitch accordingly.

• For candidate-driven roles, compress your interview process to under three weeks.

• Before requiring extreme intensity, ask: does this role actually need 996, or would a more experienced hire working normal hours deliver more?

• Create incentive structures that make intensity worth it—meaningful equity, real ownership, technical challenge. Mandating hours without aligned incentives breeds resentment.

2. Crypto Is Going Enterprise—And Your Talent Profile Needs to Shift

In June, Stripe acquired Privy—a crypto wallet infrastructure company supporting 75 million accounts across 1,000+ developer teams. Stripe CEO Patrick Collison said the goal is to connect "Privy's wallets to the money movement capabilities in Stripe and Bridge" to enable "a new generation of global, internet-native financial services."

That's the CEO of a $91.5 billion payments company describing crypto infrastructure as core to their future. Not adjacent. Core.

The numbers are staggering:

• Coinbase acquired Deribit for $2.9 billion in May 2025—$700 million cash plus 11 million shares—gaining the world's largest crypto options exchange with $30 billion in open interest and over $1 trillion in annual trading volume.

• Kraken bought NinjaTrader for $1.5 billion in March 2025, described as "the largest-ever deal combining traditional finance and crypto."

• Ripple acquired Hidden Road for $1.25 billion in April 2025, becoming the first crypto company to own and operate a global multi-asset prime broker clearing $3 trillion annually.

• Stripe acquired Bridge for $1.1 billion in late 2024, Stripe's largest acquisition to date.

This is the maturation moment. The talent profile that built crypto isn't the same profile you need to scale it.

The a16z State of Crypto 2025 report captures this shift: the industry's talent pipeline is diversifying beyond developers, with growing demand for compliance, infrastructure, and product specialists. Workers entering crypto now tend to come from traditional finance and consulting backgrounds—a sign that the line between TradFi and crypto is blurring.

Electric Capital's 2024 Developer Report shows the technical evolution too. One-third of crypto developers now work across multiple blockchains, up from less than 10% in 2015. The work has become about orchestrating complexity across ecosystems.

What enterprise crypto needs:

Engineers with deep distributed systems experience—people who understand consensus at scale, not just in theory. Strong CS fundamentals matter more than ever, particularly around security: smart contract auditing, cryptographic primitives, secure key management. These aren't skills you pick up casually. A bad hire in a security-critical role can cost a protocol millions.

What crypto-native talent brings:

You can't build enterprise crypto with TradFi talent alone. You need people who understand crypto primitives—how wallets work, how on-chain governance functions, how tokenomics shape incentives. You need builders comfortable with modern frameworks and languages (Rust, Move, Solidity), who've shipped in public, who understand the culture of building in the open. The innovation mindset that characterized early crypto is still essential—it just needs to be paired with operational rigor.

You need both. The scrappy crypto-native builder who understands the ecosystem AND the distributed systems engineer who's scaled infrastructure at a fintech or cloud company.

At Lazer, we've proven this out. One of our highest-impact hires had barely touched crypto—he'd used stablecoins a bit but didn't have real web3 experience. What he had: proven experience building and scaling consumer products. He'd driven around New Zealand testing his app with farmers who were the actual users. He founded his own React Native developer tooling. In our interviews, he demonstrated strong technical problem-solving and the ability to pick up new domains quickly. Now he builds mobile apps for some of the biggest names in the industry.

He didn't have the crypto pedigree. He had the engineering fundamentals and the ownership mindset. The domain knowledge came fast.

What to do about it:

• Stop requiring "crypto experience" for roles where it doesn't matter. Your product manager doesn't need to understand MEV to understand users.

• For security-critical roles, don't compromise on CS fundamentals. Distributed systems experience, cryptography knowledge, and security mindset are non-negotiable.

• When evaluating candidates from TradFi or tech, ask: "Have they scaled something to real users? Can they learn new domains quickly? Do they have the ownership mindset?"

• For crypto-native hires, evaluate their understanding of primitives—not just years in the space. Someone who deeply understands how different L2s work is more valuable than someone who's been around since 2017 but can't explain rollups.

3. Proof of Work Is Beating Credentials

The companies that gutted teams expecting AI to fill the gaps are about to learn an expensive lesson.

McKinsey's January 2025 research found that while nearly all employees and C-suite leaders have some familiarity with gen AI tools, nearly half of employees want more formal training. The gap isn't in awareness—it's in capability. And according to the Microsoft/LinkedIn Work Trend Index, 66% of leaders wouldn't hire someone without AI skills, but only 39% of users have received AI training from their company.

The demand for AI-capable talent is outstripping supply. McKinsey's analysis of 4.3 million job postings found fewer than half of potential candidates have the high-demand tech skills listed. The World Economic Forum estimates that nearly six in ten workers will require training before 2030.

This is where credentials start to break down. The Stanford grad with two years at a big lab might be perfect for frontier research. For building AI products—taking models and shipping things people actually use—the person who built and scaled three apps at a startup no one's heard of might be better. But you'll never know if they don't make it past your ATS.

The market is catching up to this reality. According to McKinsey's Workforce Transformation Report, the percentage of companies adopting skills-based hiring practices increased from 40% in 2020 to 60% in 2024. Deloitte found that 72% of companies cite talent shortages as a major challenge, driving them to explore alternative hiring strategies beyond traditional degree requirements.

This isn't about lowering the bar. It's about measuring the right things.

That includes AI fluency—and most companies still aren't evaluating it.

According to HR Dive, "Hiring someone today without assessing their AI skills is like hiring someone in the 1990s without checking if they could use the internet." Yet most interview processes were built before AI tools became ubiquitous.

The skills to evaluate aren't monolithic. Prompt engineering—do they know how to ask AI the right questions? Critical thinking—can they evaluate AI-generated outputs, spot flaws, and refine them? AI-human balance—do they know when to use AI for efficiency and when to apply human judgment?

Platforms like HackerRank now offer AI-assisted IDE environments where interviewers can observe how candidates collaborate with AI tools in real time, with all conversations captured in detailed reports. This reveals something traditional coding interviews miss: not just whether someone can solve a problem, but how they think about when to leverage AI and when to rely on their own judgment.

At Lazer, our bar is high. Every candidate goes through rigorous technical interviews that assess CS fundamentals and ability to build at scale. But we've learned that the most impactful hires often don't fit the typical mold.

One example: an engineer most companies would've passed on. Younger, no brand names, wasn't actively working in crypto at the time. He was writing AI research papers and dabbling in crypto while playing with AI tools. 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 apps in the industry.

He didn't have the pedigree. He had proof of work.

What to do about it:

• Before adding any credential filter to a job description, ask: does this role actually require this? Challenge "3 years crypto experience" or "top-tier CS degree" as defaults.

• Change your interview questions. Ask "What have you built that nobody asked you to build?" and "Walk me through a time you learned a new technology quickly and shipped with it."

• Build AI fluency evaluation into every role. Ask candidates: "Walk me through how you use AI tools in your current workflow. When do you use them? When do you not?"

• For technical roles, consider assessment environments that let you observe AI collaboration in real time—how candidates prompt, evaluate outputs, and integrate AI with their own judgment.

• Track the correlation between your credential filters and actual performance. Most companies have never done this analysis. When they do, they find the correlation is weaker than they assumed.

In a Nutshell

The market is bifurcated. It's an employer's market for generalist roles and a candidate's market for specialized AI and crypto talent. Most founders are applying the wrong market's rules to their hiring. Know which market you're competing in—and remember that today's leverage can become tomorrow's attrition if you don't calibrate intensity intelligently.

Crypto is going enterprise. The M&A numbers tell the story: $2.9B, $1.5B, $1.25B, $1.1B. You need both crypto-native builders who understand primitives and innovation AND engineers with distributed systems depth, strong CS fundamentals, and security expertise. The domain knowledge can be taught. The engineering foundation can't be faked.

Proof of work is beating credentials. McKinsey found 87% of companies face skills gaps—and skills-based hiring adoption has jumped from 40% to 60% since 2020. The ability to ship, learn fast, and leverage AI tools matters more than the logos on a resume. Evaluate accordingly.

Your competitors are still hiring for the market they think they're in. The best founders are hiring for the market that's coming.

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.

Why The Orthodoxies Exist (And When They're Right)

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.

What They're Actually Selecting For

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.

What This Looks Like In Practice

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.

The Two Questions That Actually Matter

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.

What Crypto Can Learn From AI

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.

What AI Can Learn From Crypto

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.

What This Means For You

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.

In a Nutshell

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?

Stripe just launched Tempo—a payments-focused L1 blockchain built with Paradigm and design partners including Visa, Deutsche Bank, Revolut, Nubank, and Shopify. It's purpose-built for stablecoins and real-world payments, with features like ISO 20022-compatible payment memos and compliance hooks built into the protocol layer. This isn't a crypto company trying to do payments. This is a payments company rebuilding financial rails on blockchain infrastructure.

If you're building crypto infrastructure and your roadmap includes "institutional adoption," "enterprise integration," or "mainstream financial services," it's time to add a different type of engineer to your team. The talent you need isn't in crypto Discord servers—they're shipping production fintech systems at neobanks and payment processors. Right now, before this becomes obvious to everyone, you have a window to hire them.

Why This Matters Right Now

Tempo promises to process over 100,000 transactions per second with sub-second finality—designed for business payments with predictable, dollar-denominated fees. This is modern fintech scale and UX expectations, not "gas fee roulette."

The institutional wave isn't theoretical anymore. Stablecoins process real economic activity. Tokenized deposits moved from pilots to production. Cross-border payments shift to blockchain rails because they're faster and cheaper.

The companies that dominate this phase won't just have sophisticated consensus mechanisms. They'll build blockchain infrastructure that delivers fintech reliability and UX while maintaining what makes crypto valuable. And right now, while most crypto teams hire exclusively for Solidity expertise, there's a narrow window to bring in the talent that actually gets you to institutional scale.

The Engineers You Actually Need

Stop filtering exclusively for protocol knowledge. Start looking for engineers who've shipped scaled, secure financial products where downtime costs millions and compliance isn't optional.

Modern Fintech Product Engineers:

• From neobanks at real scale: Revolut, Chime, Nubank, N26 (millions of users, not thousands)

• Built payment processing, multi-currency platforms, real-time settlement infrastructure

• Architected backends where 99.9% uptime is the floor and "fix it next deploy" isn't acceptable

• Shipped products where bugs cause regulatory issues and financial losses, not just user frustration

Payments Infrastructure Specialists:

• Engineers from Stripe, Adyen, Square who understand high-volume transaction processing

• Built real-time payment systems and settlement mechanics at scale

• Know the complexity of moving money across currencies and jurisdictions

• Created developer APIs that other companies build products on top of

What They Bring That Crypto Engineers Often Don't:

Production-grade reliability standards. They think about failure modes, rollback procedures, and incident response before writing code. They ship with proper observability, comprehensive testing, and deployment processes that catch issues before production. They don't ship and iterate toward stability—they ship stable.

Integration expertise that scales. They know the difference between "we shipped an integration" and "we built integration infrastructure that handles edge cases, maintains backward compatibility, and scales to thousands of partners." They've designed APIs that financial institutions actually want to integrate with.

Compliance-first architecture. Security and compliance aren't bolted on later. They've designed systems where KYC/AML, transaction monitoring, and audit trails are fundamental from day one. Like Tempo's compliance hooks at the protocol level—built for regulatory requirements from the start.

User-centric product thinking. They've built financial products for people who don't know what a blockchain is. When competing with Cash App and Venmo for mainstream adoption, "crypto UX" isn't good enough.

What Fintech Engineers Need to Learn

This isn't one-way. They bring critical infrastructure expertise but need to understand crypto-native patterns.

Decentralized architecture patterns. They're used to systems where you control the database and can fix mistakes. Blockchain's immutability requires fundamentally different thinking—"just update the database" isn't an option.

Smart contract integration. Traditional APIs assume you control the service. Smart contracts require understanding gas optimization, handling failed transactions, and building on infrastructure you don't control.

Different security models. Their model is authentication tokens and access control. Crypto's model is private keys and irreversible operations. Both matter, but failure modes are completely different.

The best hires get genuinely excited about these problems. They see blockchain enabling new products, not as inferior infrastructure.

The Team Composition That Works

Understanding what fintech engineers bring and need to learn is only half the equation. The real question is how you structure your team so both perspectives actually work together.

Don't hire only fintech engineers or create separate "crypto team" and "fintech team" silos with handoffs.

The magic is in the mix. Crypto-native engineers understand blockchain architecture and what makes decentralized systems valuable. Fintech engineers bring experience shipping at scale with compliance-first thinking.

The winning pattern: Small teams where both perspectives directly influence every technical decision. Your smart contract engineer and payments infrastructure engineer argue about system design together. The person who scaled Revolut's transaction processing learns from the person who designed your protocol's security model.

You need translators who've worked in both contexts and prevent the two cultures from talking past each other. These people are your most valuable hires.

The Reality of Hiring From Fintech

The engineering culture is different. Understanding these differences prevents friction that kills velocity.

Expect more structured engineering practices. Fintech engineers expect rigorous code reviews, comprehensive testing, and staged rollouts. This isn't bureaucracy—it's learned behavior from environments where bugs cause financial losses. They'll push back on shipping without test coverage, ask about rollback procedures before deploying, want monitoring before code hits production. This keeps systems reliable at scale.

Don't mistake production discipline for "not scrappy enough." The best fintech engineers move fast—they've just learned which corners you can cut and which you cannot.

Security and compliance come as default. They ask about compliance early in design, not as an afterthought. KYC processes, sanctions screening, data retention, regulatory reporting. Building these after you have users is exponentially more expensive than building them correctly from the start.

In crypto, security often means "can the contract be exploited." In fintech, it also means audit trails, regulatory compliance, and anti-money laundering controls.

Different production standards matter. They won't ship code that works on their laptop without understanding behavior under load. They won't deploy database migrations without rollback plans. They won't change payment logic without extensive testing. This discipline allows fintech products to scale from thousands to millions of users without constant production incidents.

What to Avoid: The Legacy Tech Trap

Not all financial institution experience is valuable. Be wary of engineers from large, traditional banks.

Why large financial institutions are different:

Engineers from major banks often bring legacy tech mindsets that slow you down:

• Speed and decision-making: They're used to committees, approval processes, and quarters-long deployment cycles. Moving from "6-month release cycles" to "ship multiple times per week" requires complete mental model shifts many struggle with.

• Technology choices: Large banks run on mainframes, COBOL, and decades-old core banking systems. Understanding how these work has value. Being unable to build without that full enterprise stack kills your velocity.

• Over-engineering everything: Enterprise architects build for every possible future scenario. This creates systems that take 18 months to ship and solve problems you don't have yet. You need people who build for the problem in front of you, then refactor as you scale.

What you actually want:

Engineers who understand how traditional financial systems work but aren't constrained by how those institutions build. People from scale-stage fintechs or modern financial infrastructure companies who've seen both worlds—they know the regulations and integration points that matter without the legacy baggage.

Look for people who've built modern fintech products that integrated with traditional banking rails. They understand the constraints without being limited by them.

How to Compete for This Talent

Blockchain developer starting salaries are 87% higher than web developers, but fintech engineers are used to fintech compensation bands—competitive salaries, meaningful equity, and usually more stability than early-stage crypto.

You're competing with traditional fintech companies offering stability AND crypto companies offering upside. Your value proposition needs to be clear: why should someone with options choose your specific problem?

The best positioning isn't "we're doing cool crypto stuff." It's "we're building infrastructure that will process trillions in value, and you'll architect systems that define how mainstream finance works for the next decade."

Focus your search:

• Neobanks that scaled to millions of users (Revolut, Chime, Nubank)

• Payment processors and infrastructure companies (Stripe, Adyen, Square)

• Scale-stage fintechs that shipped compliant products fast

• Modern financial infrastructure companies, not legacy banks

Emphasize in your positioning:

• Specific technical problems they'll solve at unprecedented scale

• Direct impact on how billions of people interact with money

• Working with a small, talent-dense team during the most critical technical decisions

• The chance to define standards for an entire industry

Red flags to watch for:

• Engineers who view crypto as "fintech with extra steps" rather than genuinely excited about the paradigm shift

• People who can't operate without the full enterprise tooling stack they're used to

• Those who need six months of process review before shipping anything

• Anyone who thinks blockchain is just a less efficient database

In a Nutshell

Tempo isn't the end of this trend—it's the starting gun. Stripe, Paradigm, Visa, and Deutsche Bank just signaled that institutional adoption requires engineers who've actually built institutional-grade financial products. If you're not already thinking about this hire, you're behind.

Crypto infrastructure teams pursuing institutional scale need engineers who've shipped financial products serving millions of users, not just protocol developers. The magic happens when crypto-native engineers who understand decentralization work alongside fintech engineers who understand compliance, reliability, and traditional finance integration.

But avoid the legacy tech trap. Engineers from scale-stage fintechs and modern payment processors bring the right balance—they understand financial systems without the enterprise baggage that kills velocity.

In six months, every crypto infrastructure company will be competing for this same talent. The compensation will inflate. The pitches will get more sophisticated. The hiring bar will get higher.

The teams that move now—while this playbook isn't obvious to everyone—will build the talent-dense organizations that define how the next generation of financial infrastructure actually works.

I see this pattern constantly—founders who've confused performative intensity with competitive advantage, teams that mistake suffering for progress, and job descriptions that repel exactly the people they're trying to attract.

The math is brutal but clear. In today's talent market, leading with "grind culture" is a systematic filtering mechanism that selects against the people who can actually build generational companies.

The Performance Art Problem

Something broke in how we talk about work intensity. What used to be an understood reality—that building meaningful companies requires deep focus and strategic sacrifice—became explicit theater around hours and suffering.

Walk through any accelerator demo day or scroll through founder Twitter. You'll see the same performative playbook everywhere:

Job descriptions leading with "we work 6 days a week" instead of "you'll architect systems that process billions in transaction volume." Founders competing on LinkedIn about weekend coding sessions rather than customer wins. Teams broadcasting their grinding schedules like cultural credentials instead of shipping velocity metrics.

Here's what's actually happening: You're optimizing for people who think hours equal impact while systematically filtering out people who understand the difference between activity and results.

The companies quietly winning—the ones capturing market share, retaining top talent, and building products that scale—operate completely differently. They hire small, talent-dense teams that know when to sprint hard, when to recover strategically, and how to deliver exponential outcomes through focused execution rather than chronic overwork.

Why Smart Founders Fall Into This Trap

Three market forces created this systematic dysfunction:

Execution Speed Pressure: In fast-moving markets like AI and crypto, shipping weeks ahead of competitors often determines who captures market share. When everything moves this fast, "we work harder than them" becomes founders' shorthand for "we'll out-execute them." The logic feels sound until you realize that working harder and working faster are often inversely correlated.

Junior Talent Market Dynamics: Years of companies exclusively chasing Senior+ engineers created massive pent-up demand among hungry mid-level talent. These engineers are often exceptionally capable and eager to prove themselves. They're also more culturally adaptable and typically face fewer constraints around availability. This created perfect conditions where grinding became both appealing to this segment and operationally feasible—at least short-term.

Social Media Amplification Loop: Platforms reward hustle content because it's aspirational and shareable. Founders discovered that posting about 4am coding sessions generates more engagement than sharing technical architecture decisions. This creates a feedback loop where performative intensity becomes startup marketing, which becomes actual culture, which attracts people who mistake the performance for the substance.

The result: selection pressure for exactly the wrong behaviors.

The Tale of Two Cultures

Mission-Driven Intensity (What Strong Teams Actually Do)

How they communicate opportunity:

• "You'll define how the next generation of financial infrastructure scales to billions of users"

• "This role involves solving distributed systems problems that don't have existing solutions at this scale"

• "You'll work directly with our founding team during the most critical technical decisions in our company's trajectory"

How they operate internally:

• People work long hours because they're genuinely excited about problems they're uniquely positioned to solve

• Leadership creates guilt-free recovery periods and systematically rewards sustainable high performance

• Teams sprint intensely during critical launches, then deliberately recharge to maintain peak cognitive capacity

• Work intensity fluctuates based on business necessity, not cultural performance

• Recognition flows to impact and strategic thinking, not face time and availability

Take Coinbase's Base team. They use explicit "intensity zones" that signal when the entire team needs to operate in high-execution mode. During these periods, everyone understands that longer hours and focused execution are necessary. But they balance this with structured recovery weeks where people completely disconnect to recharge.

This isn't just nice-to-have culture design—it's competitive strategy. The result is they've shipped one of the fastest-growing Layer 2 networks while maintaining team cohesion and continuing to attract senior talent who have options.

Performance Theater Culture (What Doesn't Work)

How they communicate opportunity:

• "We work 6 days a week because that's what it takes"

• "Looking for engineers who aren't afraid to grind"

• "We move fast and break things—no 9-5 mentality here"

How they operate internally:

• Constant emphasis on hours worked rather than problems solved

• Working late becomes performance for leadership rather than response to necessity

• Intensity becomes team identity regardless of business context

• Face time and responsiveness drive recognition over actual output

The filtering effect: This approach systematically selects for people who haven't yet learned to optimize for leverage while filtering out those who have.

The Compound Mathematics of Strong Teams

Here's what most founders miss about talent density: sustainable high performance creates exponential advantages that grinding cultures can't match.

The Small Team Advantage: A focused team of 8 exceptional people will consistently outship a grinding team of 15 exhausted people. This isn't motivational—it's mathematics:

• Smaller teams make better architectural decisions because they preserve cognitive capacity for strategic thinking

• They ship higher-quality code because they're not constantly operating through fatigue

• They build features that require fewer post-launch fixes because quality gets built in from the start

• They attract other exceptional people because talent gravitates toward environments where they can do their best work

The Hidden Costs of Grinding:

• Constant turnover means you're always rebuilding institutional knowledge

• Fatigued people make expensive mistakes that compound over time

• Teams optimizing for activity instead of impact accumulate technical debt faster than customer value

• Senior engineers opt out entirely, leaving you with people who haven't learned efficiency patterns yet

Speed vs. Hours: The Critical Distinction True execution speed comes from working on the right problems with clear thinking and systematic elimination of waste. Hours spent often indicates the opposite—unclear priorities, poor tooling, or inefficient processes disguised as dedication.

A senior engineer who architects and ships a critical system component in 35 focused hours creates more lasting value than someone who spends 60 hours building something that needs to be rewritten. The difference isn't just individual capability—it's having organizational systems that enable deep, efficient work consistently.

Building Systems That Unlock Peak Performance

The secret isn't managing work-life balance—it's creating conditions where exceptional people can do the most impactful work of their careers.

Elite talent already knows how to work intensely on problems that matter. They don't need cultural pressure to perform at their peak. They need clarity about what moves the business forward, autonomy over how they solve problems, and organizational systems that amplify rather than drain their effectiveness.

Start With Precision About What Matters

Before you can expect strategic intensity from your team, you need surgical precision about which activities drive results versus which create the appearance of progress.

The framework that works:

• What specific technical or market barrier are you uniquely positioned to break through?

• Which customer problems become dramatically easier to solve when you succeed?

• How does individual excellence directly translate to exponential company outcomes?

Vague missions attract vague commitment. Specific, compelling problems attract people who can't stop thinking about elegant solutions.

Design Infrastructure for Deep Work

Create systematic conditions where people can work intensely on high-leverage problems without constant interruption or administrative drag.

The infrastructure you need:

• Protected blocks for deep work on complex technical challenges

• Strategic recovery periods that preserve cognitive capacity for difficult decisions

• Decision-making processes that don't require five stakeholders to coordinate calendars

• Tools and automation that eliminate low-value administrative overhead

• Ruthless prioritization that helps people focus energy on highest-impact activities

Office tent sleeping might happen during critical product launches, but it shouldn't be your standard operating procedure. Elite talent builds exceptional systems because they understand how to sustain cognitive performance over time, not because they can endure the most suffering.

Measure What Creates Lasting Value

The best people don't just produce excellent individual work—they make everyone around them more effective.

Track these leading indicators:

• Feature velocity and customer impact metrics, not hours logged or meetings attended

• Quality of technical and strategic decisions, not volume of output or responsiveness speed

• How quickly people ship meaningful improvements that don't require major refactoring later

• How individual contributions accelerate overall team capability and institutional knowledge

How to Position Intensity Authentically

Elite talent wants to work on historically significant problems. The intensity should be a natural consequence of the ambition and opportunity, never the primary selling point.

Your positioning should answer this question: "Why would someone at the top of their field choose to spend their finite career capital solving this specific problem with this specific team?"

Not this question: "Why should someone be willing to work harder for us than anywhere else?"

The highest performers already understand that sustained excellence requires both strategic intensity and systematic recovery. They sprint during launches that matter, then recover to maintain peak decision-making capacity. They work long hours because they're genuinely excited about breakthrough opportunities, not because someone expects them to prove dedication.

When you attract people whose personal growth trajectory aligns with company success metrics, you don't need to manage their motivation—you need to create systems that help them perform at levels they didn't know they were capable of reaching.

The positioning test that matters: When someone exceptional reads your opportunity description, what gets them genuinely excited? The chance to solve technical problems that haven't been solved at this scale, or the opportunity to demonstrate how hard they're willing to work?

The Strategic Advantage of Getting This Right

Companies that understand sustainable intensity have massive competitive advantages that compound over time.

While competitors exhaust talented people with performance expectations, you're building systems that help exceptional performers do career-defining work. While they optimize for signaling dedication, you optimize for multiplicative impact. While they measure inputs, you measure outcomes that matter for customers and company trajectory.

The network effects are powerful: Elite talent attracts more elite talent. People who've experienced environments where they can do their best work consistently become evangelists for that approach. They refer other exceptional people. They stay longer. They take ownership of outcomes instead of just completing tasks.

In today's talent market, this isn't just better culture—it's better competitive strategy.

In a Nutshell

Exceptional people work intensely when they believe the outcome justifies the effort and feel genuine ownership of the process. They don't need cultural pressure to perform—they need clarity about problems worth solving and systems that amplify their effectiveness.

The shift: Stop competing on who works harder. Start competing on who can create conditions where exceptional people do the most impactful work of their careers.

Define intensity based on impact, not hours. Build infrastructure that attracts intrinsically motivated talent. Use positioning that emphasizes breakthrough opportunities over grinding expectations. Measure sustainable excellence, not performative dedication.

In a talent market this competitive, that's not just better culture—it's the only sustainable path to building something generational.

The stakes have never been higher. In AI and crypto, talent density isn't just about building better products—it's about survival. When foundation models can be trained in weeks and DeFi protocols launch with billion-dollar TVLs, the difference between an exceptional engineer and a mediocre one isn't linear. It's exponential.

Here's the brutal math: a weak hire doesn't just underperform. They turn your senior engineers into mentors instead of builders. They make your code reviews teaching sessions instead of quality gates. Most importantly, they occupy a seat that could have gone to someone who accelerates your entire team instead of slowing it down.

Yet the pressure to hire quickly has never been more intense. Same-day offers are becoming standard. Interview processes are shrinking to single conversations. Equity packages that seemed impossible five years ago are now table stakes.

The companies that win long-term have solved a different problem entirely. They've figured out how to make speed and quality reinforcing, not competing.

What Talent Density Actually Means

Talent density isn't about hiring only 10x engineers or building teams of former FAANG principals. It's about intentional composition—ensuring every hire raises the average capability of your team rather than diluting it.

The best way to understand this is through outcomes, not credentials. High-density teams ship faster, make fewer mistakes, and solve harder problems with smaller groups. They spend less time in meetings because communication is efficient. They write code that rarely needs major refactoring. Most importantly, they attract other high-performers because talented people want to work with talented peers.

What this looks like in practice:

Your senior engineers can focus on architecture and complex problem-solving instead of constantly reviewing basic code. Your team consistently hits sprint commitments without heroics. New features ship with fewer bugs and require minimal post-launch fixes. Technical debt stays manageable because everyone thinks about long-term implications, not just immediate solutions.

The calibration exercise that matters most: Take your last five hires. Rank them by actual impact six months later—not interview performance, but real contribution to team velocity and product quality. What separated the top performers from the rest? This isn't about technical skills alone. It's about judgment, communication, ownership, and how they elevate others around them.

For early-stage teams, this is critical infrastructure work. When you're 10-15 engineers, defining what talent density means for your specific company creates the foundation for everything that follows. The teams that figure this out early have a massive advantage when they're ready to scale to 50-100 employees—they have a proven playbook of exactly what impactful performance looks like in their context.

This early investment pays exponential dividends. When you bring in a talent leader to build hiring systems around your 50-person growth phase, they're not guessing what good looks like. They're designing processes around proven patterns of success at your company. This enables both speed and consistency as you scale.

The Reality of Scaling Speed

Scaling speed isn't just about moving through interviews faster. It's about compressing the time from "we need this role" to "this person is productive on our team."

In today's market, speed determines who you can hire. The best candidates have multiple offers, often with tight decision timelines. The longer your process, the more likely you are to lose them to competitors who can move decisively.

But speed has hidden costs. Rush through technical evaluation and you miss critical gaps. Skip culture fit conversations and you hire people who can't collaborate effectively. Compress reference checks and you miss red flags that would have saved months of performance management.

Smart speed looks different than fast speed:

Front-load elimination criteria so you spend senior engineer time only on candidates who meet your bar. Use technical screens to filter out 80% of applicants before they meet your team. Structure interviews to gather maximum signal in minimum time—every conversation should answer specific questions about capability and fit.

Create feedback loops that help you learn from every hire. Track which interview signals predict success and which are false positives. Measure time-to-productivity, not just time-to-hire. A candidate who takes three weeks to close but ships meaningful code in their first month beats someone you hired in three days who needs six months to contribute.

The infrastructure that enables speed: Streamlined scheduling systems, consistent interview guides, rapid feedback collection, and decision-making processes that don't require five stakeholders to align calendars. The best hiring teams can move from screen to offer in a week not because they skip steps, but because they've eliminated everything that doesn't add evaluation value.

Marrying Speed and Density

The secret isn't balancing these forces—it's building systems where they reinforce each other. High talent density actually enables faster hiring because exceptional people help you identify other exceptional people. Clear standards enable speed because everyone knows what they're evaluating for.

Start with market intelligence. While competitors react to talent becoming available, anticipate it. Track funding announcements, team changes, and industry signals. Build relationships before people are actively looking. The best hiring happens when you're the first call someone makes when they're ready to move.

Design for rapid, accurate evaluation. Create interview guides that help any team member consistently assess candidates against your standards. Build rubrics that translate "strong technical judgment" into observable behaviors. Front-load the hardest elimination criteria so you spend senior engineer time only on candidates who clear your bar.

Optimize your offer process. Speed often dies in decision-making, not evaluation. Establish compensation bands and approval processes before you start interviewing. Know your competitive position on equity, benefits, and growth opportunities. Be ready to move when you find the right person.

Create feedback loops that improve both speed and quality. After every hire, track back to interview performance. Which signals predicted success? Which took too long to gather? Which screening criteria eliminated good candidates or let weak ones through?

Tools like Ashby's Quality of Hire survey can systematize this feedback collection, making it easy to gather consistent data from hiring managers about new hire performance. Use this data to constantly refine your process.

Leverage your existing talent. Your best engineers are your best sources for finding other great engineers. They understand your standards, they have networks of peers who meet those standards, and they can speak authentically about why someone should join your team. Make referrals easy and rewarding.

Build decision frameworks for different scenarios. When funding runway is short, prioritize roles that directly impact revenue. When competitors are hiring aggressively, focus on offer competitiveness and decision speed. When scaling rapidly, invest in hiring infrastructure before you need it.

Measuring What Matters

Track both process metrics and outcome metrics. Process metrics help you optimize efficiency: interview-to-offer conversion rates, offer acceptance rates, time from screen to offer. Outcome metrics help you optimize quality: 90-day performance scores, time to first meaningful contribution, 12-month retention rates.

But go deeper. Which interview signals actually correlate with long-term performance? Which hiring managers consistently identify top talent? Which sourcing channels produce candidates who both accept offers and succeed in role?

The goal isn't perfect prediction—it's continuous improvement. Every hire teaches you something about what works in your context, with your team, at your stage.

In a Nutshell

The hiring paradox resolves when you stop seeing speed and quality as competing forces. They're both outcomes of the same thing: systems that help you identify and close exceptional talent faster than everyone else.

Define talent density based on actual performance outcomes, not theoretical ideals. Build hiring infrastructure that eliminates waste while maximizing evaluation signal. Use market intelligence to source proactively rather than reactively. Create feedback loops that help you learn from every hire.

The companies building the future aren't choosing between speed and quality. They're building hiring engines that deliver both. In a talent market this competitive, that's not just an advantage—it's survival.

AI has fundamentally changed how engineers work. How we recruit needs to catch up.

I've seen candidates use AI in technical interviews and get to working solutions, but then struggle to explain the code or understand why specific frameworks were chosen. On the flip side, I've watched engineers who avoid AI tools completely struggle to work at the velocity and efficiency that today's engineering environment demands.

The rise of AI-powered development tools has transformed software building—code generation, automated testing, and intelligent debugging assistance are now standard parts of the development workflow. Yet most technical interviews still operate as if these tools don't exist, testing computer science fundamentals in abstract environments disconnected from how engineers actually ship software today.

These fundamentals are still inherently what we need to evaluate, but how we assess them needs to adapt. AI is an incredible tool that unlocks engineering teams to move more efficiently and quickly. However, you need engineers who know how to use it correctly as a supplemental tool to their CS knowledge and real-world building experience, not as a replacement for understanding core principles.

While your current technical interviews probably work fine for evaluating traditional coding skills, they need to reflect how engineers actually work every day in an AI-powered world. You're missing out on understanding how your candidates will really perform in this environment. When AI can generate working code in seconds, what matters isn't whether someone can code without assistance—it's how well they can combine AI tools with solid engineering fundamentals to build great software.

3 Fundamental Shifts in Technical Interviewing

The philosophy behind technical assessment needs to change completely. Here are the three core shifts in how we think about evaluating engineering talent in the AI era.

AI Collaboration, Not AI Dependency

The best engineers treat AI like a really solid junior developer—they know how to guide it, when to trust it, and when to step in themselves. They write clear prompts, quickly spot when AI goes off track, and can walk through every line of generated code.

AI-dependent engineers, on the other hand, tend to accept whatever the model outputs without fully understanding it. They struggle when AI produces incorrect solutions and have difficulty modifying code when requirements change.

You want engineers who use AI as a force multiplier for their expertise, not as a replacement for understanding fundamentals.

Process Over Perfect Solutions

In the pre-AI world, interviews tested whether candidates could produce correct answers under artificial constraints. Now you need to evaluate their problem-solving methodology when they have access to real tools.

Watch how they break down problems, structure their approach, and iterate toward solutions. Do they validate AI outputs critically? How do they handle edge cases the AI missed? Can they debug when something goes wrong?

Strategic Thinking Beyond Immediate Solutions

AI can solve immediate coding needs quickly, but engineers need to think beyond the quick fix. We need people who can evaluate not just whether code works, but how solutions are organized and structured for long-term success—performance implications, security considerations, and eventual scalability challenges.

The best engineers use AI to handle immediate implementation while keeping their focus on the bigger picture: building systems that won't become technical debt down the road.

This isn't just about catching bugs—it's about maintaining high standards when the pressure to ship fast is intense. The best engineers are rigorous reviewers of their own and others' work, even when AI makes it tempting to skip thorough evaluation.

Engineering Interviewing 2.0

AI Coding Interview

Give candidates a realistic feature-building task using whatever tools they'd normally use. Start them off with an existing codebase sample (potentially AI-generated itself) that has a subtle bug or issue they need to find and fix, then have them build a new feature on top of it.

This mirrors actual development work—you're often inheriting code (AI-generated or otherwise) that needs debugging before you can extend it. The exercise combines code review skills with feature development, just like the real job.

What to watch for:

• Do they spot the initial issue in the provided code?

• How do they break down the problem before jumping into implementation?

• Are their AI prompts clear and specific?

• Do they test their solutions properly?

• How do they handle when AI gives them something that doesn't work?

• Can they explain their decisions without getting too in the weeds?

Red flags:

• Missing obvious bugs in the starter code

• Just copy-pasting AI suggestions without understanding them

• Can't debug when things break

• No clear approach to testing or validation

• Gets lost explaining their choices

Make it collaborative—engage with their process, ask follow-up questions, see how they handle discussion about their technical decisions.

System Design

This is where we remove AI support entirely. While AI can suggest architectural patterns and solutions, system design interviews test whether candidates can think strategically and independently about complex problems. These are the foundational skills they'll need to guide AI toward the right architectural decisions in real-world scenarios.

We need talent who can establish the guardrails and constraints that direct AI tooling toward desired outcomes. Without this independent thinking ability, engineers become dependent on AI suggestions rather than using AI as a tool to implement their vision.

What to watch for:

• Do they break down complex systems into manageable components?

• Can they identify potential bottlenecks and failure points?

• How do they handle trade-offs between different architectural approaches?

• Do they consider operational concerns like monitoring and deployment?

Red flags:

• Over-engineering simple problems or under-engineering complex ones

• Can't explain why they chose specific technologies or patterns

• Ignores real-world constraints like team size or timeline

• Focuses only on happy path scenarios

Strong candidates understand that when AI handles more of the implementation work, they can focus more time on getting the architecture right and spending time with users to make sure they're building the right thing.

In Practice

At Lazer Technologies, we've started incorporating AI tools into our technical interviews. While we're still figuring out what works best for our team, this approach has given us better insights into how candidates actually work with AI versus being overly dependent on it.

What we've learned is that the fundamentals still really matter. The engineers who perform best aren't necessarily the ones who use AI the most—they're the ones who combine AI effectively with solid engineering principles. They validate solutions, write good tests, and can clearly explain their technical decisions.

The space is evolving rapidly, but early signs are promising. We're getting a much clearer picture of how candidates will actually perform on the job.

In a Nutshell

Stop testing for skills that AI has commoditized. Start testing for the abilities that become more valuable when combined with AI: problem decomposition, solution validation, architectural thinking, and quality gatekeeping.

The best engineers in the AI era aren't the ones who can code without AI—they're the ones who can build better software faster by leveraging AI effectively while maintaining high standards. Your technical interviews should reflect this reality.

AI is a tool, not a solution. You need engineers who know how to use that tool well while still thinking critically, reviewing thoroughly, and designing systems that scale. Test for those skills, and you'll hire the engineers who can thrive in the AI-powered future.

The technology landscape moves fast, and talent practices need to move with it. This framework works today, but the key isn't having the perfect interview process—it's staying close enough to how engineers actually work that you can evolve your hiring alongside the technology. The strongest builders are always adapting to new tools and environments. Your talent practices should too.

X is an incredible tool for connecting with the crypto community and sharing knowledge. But we need to own what we post. While it's great for building relationships and learning from others, it can sometimes bite us in the ass when we're not thinking about the impact of our words.

Here's how to navigate X strategically—understanding the do's and don'ts while making the most of this powerful platform.

1. Anonymous is fine, but know your limits

Being pseudonymous isn't career suicide. Some of the best builders I know are still anons, and top recruiters have gotten really good at spotting anonymous developer talent. You can absolutely build a reputation as @DegenDev69 and get recruited based on your contributions.

But understand the reality: certain companies just can't work with anonymous talent. Enterprise clients, regulated exchanges, and traditional finance companies exploring crypto have different risk tolerance. A native crypto startup might be totally fine with an anonymous lead developer. A bank building blockchain infrastructure probably isn't.

Companies need to verify credentials for regulatory compliance, insurance requirements, and stakeholder trust. No Fortune 500 is hiring an anonymous Chief Product Officer, but plenty of crypto startups will hire anon developers based purely on their GitHub contributions.

The play: Build your rep as an anon, then doxx strategically when the right opportunity comes along.

2. Genuine insights beat all

The engineers getting recruited aren't posting rocket ship emojis when Bitcoin pumps. They're breaking down why Uniswap v4's hooks architecture is brilliant, or explaining the trade-offs in different consensus mechanisms, or analyzing why a particular protocol's tokenomics might create long-term sustainability issues.

This isn't just about technical content—some of the best non-technical hires come from people who provide genuine market analysis, regulatory insights, or business strategy perspectives. The key is adding something new to the conversation rather than just reacting to price movements.

The best crypto X accounts become resources that people bookmark and return to. When someone has a question about cross-chain bridges or MEV or tokenomics, they think of your thread first. That's the kind of credibility that turns into career opportunities.

The play: Focus on insights that add value—code breakdowns with explanations, protocol architecture analysis, regulatory takes with actual reasoning, market dynamics with specific examples. Avoid pure price speculation, regurgitated news without your angle, and generic "this is bullish" takes.

3. Quality engagement over follower count

I've seen hiring managers get excited about candidates with 500 thoughtful followers and pass on people with 20K followers who are mostly retail speculators. Some companies literally say "You don't have 20,000 X followers, so you can't be responsible for our marketing," but that's the exception, not the rule.

Smart hiring managers know the difference between influence and engagement. They can tell when your followers are other builders, protocol developers, investors, and industry participants versus people who just want to know when to buy and sell tokens.

The goal isn't to maximize follower count—it's to build relationships with the right people. One thoughtful reply from a protocol founder or a repost from a respected researcher can be worth more than a thousand likes from anonymous accounts.

Quality engagement also means knowing when not to engage. You don't need to have an opinion on every controversy or participate in every viral thread. Being selective about what you comment on actually makes your voice carry more weight when you do speak up.

The play: Engage with actual builders, add value to technical conversations, build genuine relationships with people shipping real products.

4. Shitpost with levity and kindness

Shitposting is part of crypto culture and honestly, you should go for it. The memes, the jokes, the general chaos—it's what makes the space fun and shows you actually participate in the community. Some of the most successful people in crypto have personalities that come through in their X presence.

But here's the key: do it with a sense of levity and kindness. There's a big difference between being funny and being mean-spirited. Being nasty or too spicy isn't always the best look, especially when people are evaluating whether they want you on their team.

People want to work with team players, not toxic personalities. You can have strong opinions and express them with humor without making other people the target of your jokes. The best crypto X accounts manage to be entertaining while still being people you'd actually want to grab coffee with.

The goal is to come across as someone who gets the culture and participates in it authentically, not someone who's either trying too hard to fit in or completely oblivious to how they're perceived.

The play: Context switching is key. Meme away, but balance it with actual insights. If your last 50 tweets are pure chaos, that's your brand.

5. Be an opinionated contributor, not just a commenter

Some of the best-connected people in crypto are thoughtful reply guys who add genuine value to conversations. There's absolutely nothing wrong with building relationships through high-quality replies and quote tweets. But if that's all you do, you're missing opportunities to show what you actually think about the space.

The issue isn't engaging with other people's content—it's when your entire presence consists of reactions without original opinions. Companies want people who have strong takes about how things should be built, backed up by actually building them. They want opinionated contributors who do the work, not just commenters who validate other people's ideas.

This doesn't mean you need to write long-form threads every day. It could be sharing a code snippet with your take on why the current approach sucks, posting about a tool you built to solve an annoying problem, or documenting something you learned while exploring a protocol. The key is that people can understand your perspective and see evidence that you actually ship stuff.

The play: If you're gonna engage, make it count. Add something new instead of just being first to every thread.

6. Free speech means companies are free to respond

You absolutely have the right to say whatever you want about politics, use whatever language feels natural to you, and express strong opinions about industry drama. Crypto culture values that freedom and authenticity, and trying to sanitize your entire online presence would make you seem disconnected from the community.

But understand that companies will judge you accordingly. When a compliance officer at a major exchange is reviewing your X before signing off on your hire, they're not thinking about crypto culture—they're thinking about regulatory risk and professional representation.

Same with political takes. Going full libertarian manifesto mode can bite you when you're interviewing at companies that need to work within existing regulatory frameworks. It doesn't mean you can't have strong political opinions—just be strategic about how you express them.

Your public posts stick around and crypto is a small space. Be prepared to own your opinions if they're attached to your name. If you want to get really spicy, well... multiple accounts exist for a reason.

The play: Crypto has its own language and that's totally fine, but compliance teams don't care about Discord culture. Read the room.

In a Nutshell

Crypto X is where the real opportunities happen, but it's also where careers die from one bad take. Since crypto companies actively hunt for talent on X instead of traditional job boards, your presence there matters more than most people realize.

You don't need to become some sanitized corporate account. Keep your personality, keep the energy, keep participating in the culture. Just be smart about it. The people who win in crypto aren't the ones playing it completely safe—they're the ones who understand the game and play it strategically.

Every tweet is a career decision, even the fun ones. Your X can either open doors or slam them shut. Choose wisely, but don't choose boring.

The web3 consulting market has exploded alongside institutional adoption. The first half of 2024 saw nearly 20% increase in global Web3 job postings, with consulting firms leading the charge as companies across industries seek blockchain expertise. There are now 37+ major web3 consulting companies, from specialized blockchain boutiques to major firms like Deloitte and EY integrating web3 practices.

Here's why smart engineers are making the switch.

1. Project Variety That Accelerates Your Technical Growth

The Startup Reality: You become an expert in one protocol, one product, one technical stack. A Solana DeFi engineer stays a Solana DeFi engineer. An NFT marketplace developer builds NFT marketplaces.

The Consulting Advantage: Web3 consultants work across DeFi applications, NFT marketplaces, supply chain solutions, and identity verification platforms. You rotate between projects every 3-6 months, building expertise across multiple blockchain platforms, consensus mechanisms, and use cases.

I've seen consultants go from building a supply chain solution on Ethereum to designing a payment system on Solana to architecting a gaming economy on Polygon—all in the same year. Each project teaches new patterns, protocols, and problem-solving approaches that compound into rare, valuable expertise.

The result? Consulting engineers become protocol-agnostic architects while startup engineers become specialists with single points of failure in their skill sets.

2. Elite Network Access That Startups Can't Match

The Startup Reality: Your network is your team, your investors, and maybe some industry contacts at conferences. You're building relationships within one company's ecosystem.

The Consulting Advantage: You work directly with CTOs, technical leaders, and decision-makers across dozens of companies. Every 3-month project becomes a new professional relationship. You're not just delivering code—you're a trusted advisor to technical leadership at Fortune 500s, growing crypto companies, and innovative DeFi protocols.

Consultants get invited to architecture reviews, strategic planning sessions, and technical roadmap discussions. You become the person companies call when they're exploring new blockchain implementations or need expert guidance on protocol decisions.

That network becomes your greatest career asset. Former clients become co-founders, investors, and the source of your next opportunity. The startup engineer knows 50 people well. The consulting engineer knows 500 people professionally.

3. Stability in Crypto's Choppy Waters

The Startup Reality: 2022 saw 26,000+ employees lose web3-related jobs during the crypto winter. Many startups made quiet layoffs without public announcements. Equity becomes worthless overnight. Runway evaporates. Teams get cut by 50%.

The Consulting Advantage: Diversified revenue streams create anti-fragile business models. When DeFi startups are laying off, enterprise clients are increasing blockchain budgets. When NFT companies struggle, supply chain and finance companies are exploring tokenization.

Consulting firms I work with maintained 90%+ retention rates during 2022's bear market because they serve multiple sectors simultaneously. While startups chase single markets, consulting firms ride the broader wave of institutional blockchain adoption that transcends crypto market cycles.

The best web3 consultants I know have been building consistent, growing income for 3+ years while their startup friends cycled through layoffs, pivots, and equity resets.

4. Portfolio Career Building vs. Concentrated Risk

The Startup Reality: You're making a concentrated bet on one company's trajectory. Great salary plus equity upside if things work out, but your professional development is tied to one product, one team, one market outcome. If that bet doesn't pay off, you've spent 2-4 years building expertise that may not transfer easily.

The Consulting Advantage: You're building a diversified portfolio of experiences, relationships, and expertise that compounds across multiple projects and industries. Each client engagement adds to your professional value in ways that aren't dependent on any single company's success.

Think of it like investment strategy. Startups are like putting all your money in one high-potential stock—massive upside if it hits, but concentrated risk if it doesn't. Consulting is like building a diversified portfolio where each project contributes to your overall career growth regardless of any individual outcome.

Both approaches can be financially rewarding, but consulting gives you more control over your professional development timeline. You're not waiting for an exit to validate years of work—you're building valuable expertise and relationships with every project that becomes part of your long-term career equity.

The question isn't which pays more today, but which gives you better optionality and resilience for building the career you want over the next decade.

5. Future-Proof Career Architecture

The Startup Reality: Your success depends on one company's execution, one product's market fit, one technical bet's validation. If your startup's approach becomes obsolete, so does your expertise.

The Consulting Advantage: You're positioned at the intersection of emerging technologies, working with companies integrating AI and blockchain, exploring new consensus mechanisms, and building next-generation applications.

The Web3 landscape continuously evolves, with new protocols, tools, and best practices emerging regularly. Consulting forces continuous learning across multiple domains while startups create specialization constraints.

As web3 evolves from speculation to infrastructure, consultants become the trusted experts helping institutions navigate technical decisions. You're building authority and expertise that compounds across market cycles rather than betting everything on single company outcomes.

In a Nutshell

The choice isn't between stability and upside—it's between lottery tickets and career architecture. The smartest engineers in web3 are realizing that consulting offers better technical growth, stronger professional networks, financial stability, and future-proof positioning.

As institutional adoption accelerates, the demand for trusted technical advisors will only increase. While your startup friends are developing deep expertise in one product vertical, you'll be getting paid to learn from and collaborate with the best technical minds across the industry. You're amplifying your reach, expanding your network, and building the kind of broad expertise that gives you infinite optionality—whether that's starting your own company, joining as a technical co-founder, or taking your experience anywhere in the web3 ecosystem.

The strongest engineers I know aren't gambling on equity anymore—they're building consulting practices that generate immediate value while positioning them as the technical authorities companies trust most. In an industry where expertise is the ultimate moat, consulting lets you build that moat faster and monetize it immediately.

Teams like Lazer Technologies are growing our crypto engineering team and always looking for builders who want to work across the best projects in web3.

Careers | Lazer Technologies

Clients seek out our help because we have the talent to deeply understand their needs and provide industry, technical, or product insights that are uniquely valuable to their efforts. Our clients range from early-stage startups and venture studios to recognizable commerce brands and exciting enterprises.

https://www.lazertechnologies.com

Welcome to hiring in 2025, where AI makes everyone look like a senior engineer.

Let me be clear: we should all be using AI in our workflows. As recruiters and founders, we're leveraging AI to scan resumes, draft job descriptions, and streamline our processes. The candidates we're evaluating should be using AI too—it's becoming table stakes for productivity.

But here's the challenge: when everyone's using AI to optimize their presentation, traditional evaluation signals break down. With 63% of developers now using AI tools in their development process, we need to evolve our evaluation methods. Your startup needs people who can think alongside AI, not just people who can prompt it effectively.

The goal isn't detecting AI use—it's identifying genuine capability and sound judgment underneath the AI-enhanced presentation. When everyone's resume looks professional, we need to get better at parsing signal from noise.

Step 1: Parse Through the AI Application

Look beyond the polished presentation. Focus on signals of intentionality and authentic experience when reviewing resumes and GitHub profiles.

✅ Green Flags: What Signals Real Experience

Evidence of Intentional Effort Applications personalized to your specific company and role. Clear demonstration of research about your technical challenges. Writing that shows personality while remaining professional—they used AI to enhance their unique voice, not replace it.

Lived-In GitHub Presence Regular contribution patterns beyond just personal projects. Engagement with the broader developer community through issues, pull requests, and code reviews. Projects that evolved over time with visible iteration and improvement cycles.

Specific Impact Over Generic Claims Concrete metrics and business outcomes rather than abstract improvements. Technical challenges described with specific context about constraints and trade-offs. Clear connection between their actions and measurable results that actually matter.

🚩 Red Flags: What Reveals AI Dependency

The Copy-Paste Resume Epidemic You'll start seeing the same bullet points across multiple candidates. AI generates similar language, so you get identical phrases like "Spearheaded cross-functional initiatives that optimized performance metrics" appearing verbatim on different resumes. When everyone sounds the same, nobody stands out.

Missing Personal Voice Cover letters that could have been written by anyone for any company. It's not that candidates can't use AI to help write—it's that they're not adding anything unique to make themselves memorable. Generic applications that show zero research about your specific company or role.

Performative GitHub Activity Repositories that feel like they were built to showcase skills rather than solve real problems—projects that appear fully-formed without evidence of iteration, debugging, or learning from mistakes. GitHub profiles that look like they were assembled specifically for job applications rather than reflecting genuine building over time.

Buzzword Overload Without Substance Technical accomplishments described in abstract terms that could apply to any role at any company. "Enhanced system performance" without specific metrics. Industry jargon that doesn't connect to concrete examples of impact.

Step 2: Validate the Application in the Initial Screen

This is where AI-enhanced resumes meet human conversation. Time to verify whether the polished application reflects genuine capability.

✅ Green Flags: What Reveals Authentic Understanding

Authentic Technical Depth Can walk through their debugging process with specific examples and tools. Explains technical decisions with clear reasoning about trade-offs and constraints. Shares specific frustrations they've encountered with technologies. Shows learning evolution: "I used to think X, but now I understand Y."

Real Problem-Solving Experience Provides concrete examples of approaching unfamiliar problems. Can discuss what didn't work and how they adapted their approach. Shows systematic thinking about complex technical challenges. Demonstrates understanding of business context behind technical decisions.

Effective AI Integration Can explain how they use AI tools as part of their workflow. Shows examples of improving upon or correcting AI-generated solutions. Understands limitations of AI tools and when to rely on human judgment. Demonstrates thoughtful validation processes for AI-assisted work.

Genuine Curiosity and Growth Can articulate specific things they've learned recently and how they learned them. Shows curiosity about your technical challenges and asks thoughtful follow-up questions. Admits knowledge gaps honestly and explains how they would research solutions.

🚩 Red Flags: What Exposes Surface-Level Knowledge

Surface-Level Technical Understanding They can describe what they built but not why they made specific architectural choices. Unable to discuss trade-offs between different approaches. Cannot explain what would break if certain assumptions changed. Lacks strong opinions about tools and technologies they claim expertise in.

Disconnection Between Written and Verbal Skills Cannot elaborate on resume accomplishments when asked for specifics. Struggles to explain basic technical decisions from their own projects. Perfect written communication but difficulty articulating ideas in real-time conversation.

AI-Coached Response Patterns Responses that sound overly polished for casual conversation. Struggle when you ask follow-up questions that go off-script. Cannot explain their thought process in real-time. Defensive or evasive when asked about specific implementation details.

Generic Problem-Solving Stories Challenge stories that sound rehearsed or template-like. Cannot provide specific details when you dig deeper into their problem-solving process. No examples of failed approaches or lessons learned from mistakes.

In a Nutshell

The goal isn't to eliminate candidates who use AI—it's to identify candidates who use AI thoughtfully alongside strong human judgment. The strongest engineers I've hired recently didn't avoid AI—they used it more effectively than anyone else. They could explain when to rely on AI versus when to think from first principles, improved AI output with domain knowledge, and caught errors others missed.

Teams that master human-AI collaboration will significantly outperform those that don't. The most valuable hires in 2025 combine AI efficiency with critical thinking, domain expertise, and genuine technical curiosity.

As talent professionals, we need to model this behavior: use AI to enhance our processes while getting better at evaluating human judgment, problem-solving ability, and collaborative skills. When you focus on authentic experience signals in applications and validate real understanding through conversation, you'll identify the builders who can actually drive your company forward.

The companies that figure this out first will have significant competitive advantage over those still hiring based on pre-AI signals. While your competitors are looking for perfect portfolios, you're finding the people who can think, build, and adapt in an AI-augmented world.

Every founder knows this intuitively, but most hiring processes systematically miss it. Technical teams default to evaluating what someone knows rather than how they learn. They optimize for current skills over learning velocity. They mistake years of experience for adaptability.

Meanwhile, your startup needs people who can evolve as fast as your product requirements. The engineer who mastered React in 2020 but hasn't explored modern frameworks isn't just behind—they're actively slowing down your development cycles. The difference between engineers who accelerate your team and those who anchor it isn't technical competence. It's intellectual curiosity.

Harvard's Francesca Gino surveyed 3,000 employees about workplace curiosity. She found that 92% of workers believe their most curious colleagues are also the most satisfied, motivated, innovative, and high-performing.¹ When researchers measured different types of curiosity in employees, they discovered that more curious people consistently reported higher job satisfaction, better work engagement, and greater innovation—exactly the outcomes that determine whether someone accelerates or anchors your team.

Why Curiosity Is Critical for Technical Hires

Technical Competence Isn't Enough

In fast-evolving technology landscapes, yesterday's expertise becomes tomorrow's technical debt. Curious engineers approach new technologies with systematic learning methods, question architectural assumptions, and synthesize insights from adjacent domains.

Since joining Lazer Technologies as Head of Crypto Recruiting, I've found the strongest hires to be those who showed me a history of tinkering, building on the side, and learning about what's going on in web3. At Rodeo.club, when hiring for a senior backend engineer, I sourced a candidate who on paper looked well-rounded and solid, but through our initial conversations showed they were playing and exploring Farcaster Protocol in their free time, curious about how we would have explored building on that, frames, etc. This showed me not only were they curious but they were curious if we as a company were as well. They resulted in being a 10x hire for the engineering team.

Curiosity Signals Adaptability and Growth Potential

Technical teams over-index on current skills, but growing startups need people who can rapidly acquire new ones. The uncomfortable truth is that candidates need to understand their value isn't just their hard skills anymore. It's their soft skills and their willingness to truly clock in and understand the space they're operating in so they can be genuinely impactful. The engineer who knows Solidity but doesn't understand tokenomics, DeFi mechanics, or why decentralization matters will write technically correct code that misses the point entirely.

You Need Someone Who Pushes Your Product Forward

Here's what most founders miss: people who don't show genuine curiosity about your company, your space, your industry will not lean in the way you need them to. They'll clock in, write code, and clock out. But they won't stay up reading your competitors' technical blogs. They won't experiment with adjacent protocols in their spare time. They won't push back when a product requirement doesn't make technical sense.

As AI becomes a bigger and more prevalent tool for productivity by the day, technical skills—while still critical—are being overshadowed by soft skills like critical thinking and curiosity. You need people who want to learn how to solve problems, not just someone with an existing manual. ChatGPT can write boilerplate code, but it can't question whether you're building the right thing in the first place.

Critical Indicators: How to Validate Curiosity in Your Interview Process

Recruiter Screen: Excavating Learning Patterns

Skip generic questions about interest in your company and instead probe for curiosity architecture:

Questions that reveal learning patterns:

• "Walk me through how your understanding of [relevant technology] has evolved over the past year"

• "Tell me about something technical you built that nobody asked you to build"

• "What's something outside your current role that you're genuinely excited to understand better?"

Due diligence on their exploration:

• GitHub contributions to interesting projects (not just work repositories)

• Technical blog posts or documentation they've written

• Evidence of tinkering with new tools/frameworks in personal time

• Questions they ask you about your technical challenges or architecture

Green flags: Specific examples of independent learning, systematic approach to skill acquisition, thoughtful questions about your technical trade-offs

Red flags: Generic enthusiasm, no evidence of personal technical exploration, inability to articulate how their thinking has evolved

Check-ins Throughout the Interview Process: Measuring Sustained Engagement

Between formal interview rounds, curious candidates don't wait passively. Schedule brief check-in calls to gauge continued engagement:

What genuine curiosity looks like:

• They've dug deeper into your technical documentation or codebase

• They bring new questions based on continued investigation

• They reference recent blog posts, talks, or technical decisions from your team

• They share relevant insights from their own continued learning

Energy pattern recognition:

• Declining curiosity: Each conversation feels repetitive, questions become generic

• Sustained curiosity: Each touchpoint reveals deeper understanding and more sophisticated questions

• Accelerating curiosity: They're making connections you hadn't considered, bringing external insights

Technical Interviews: Collaborative Assessment Over Isolated Testing

Replace traditional whiteboard coding with collaborative technical assessments where candidates work directly with your engineers:

Structure collaborative sessions around:

• Code review of an existing feature with discussion of improvements

• Architectural planning for a new component with your team

• Debugging session on a real (but contained) technical challenge

What to observe:

• Inquisitive engagement: Do they ask clarifying questions about requirements, constraints, and context?

• Learning orientation: When they encounter unfamiliar patterns, do they ask thoughtful questions or make assumptions?

• Collaborative curiosity: Do they build on your team's ideas while contributing their own insights?

The best technical hires use these sessions to learn about your system while demonstrating their thinking process. They're curious about your architectural decisions, interested in understanding constraints, and engaged in collaborative problem-solving rather than just showing off individual technical prowess.

In a Nutshell

Technical competence is necessary but not sufficient for high-impact hires in fast-moving startups. Curiosity—demonstrated through systematic learning patterns, sustained engagement throughout your interview process, and collaborative technical assessment—is the best predictor of whether someone will accelerate your team's velocity and push your product forward. While technical skills get candidates through the door, intellectual curiosity determines whether they'll thrive in your environment, adapt to rapid changes, and contribute to breakthrough solutions. When you optimize your hiring process to identify genuine curiosity alongside technical ability, you don't just hire better individuals—you build teams that learn faster than market conditions change.

References:

1. Gino, Francesca. "The Business Case for Curiosity." Harvard Business Review, September 1, 2018. https://hbr.org/2018/09/the-business-case-for-curiosity

This approach to talent evaluation may seem intense to those accustomed to conventional tech companies. But anyone who's built in Web3 understands that resource management isn't just important—it's existential. When you're building at the bleeding edge of crypto or AI, having the right team isn't just a competitive advantage; it's the difference between breakthrough success and joining the graveyard of promising-but-failed projects.

Why the First 90 Days are Paramount

The most successful Web3 projects share a common practice: they treat the first 90 days as a deliberate, transparent evaluation period that benefits both the organization and the new hire. This period reveals whether someone can truly translate potential into impact in your specific environment.

1. Day One: Set Crystal-Clear Expectations

In Web3, the luxury of extended ramp-up periods doesn't exist. Market cycles shift overnight, technical paradigms evolve weekly, and runway calculations leave no room for passengers. This isn't about holding new hires to unreasonably high standards—it's about acknowledging the economic reality of building in this space.

Successful founders don't sugarcoat the situation. They have an explicit conversation on day one that sounds something like:

"We're building in an environment where six months feel like six years in traditional tech. Our runway extends X months at current burn. We need contributors who can navigate uncertainty, locate resources independently, and ship meaningful work within weeks, not months. Here's what success looks like at 30, 60, and 90 days..."

This conversation isn't meant to intimidate—it's meant to align. It acknowledges the constraints of building in Web3 and sets expectations accordingly.

2. Immediate Immersion, Not Orientation

Rather than cordoning off new hires in orientation sessions or training programs, elite Web3 teams practice immediate immersion:

• Access to production codebases on day one

• Inclusion in key meetings immediately

• Assignment to real (though carefully scoped) project work within the first week

• Direct exposure to users and community members

During my time at Rodeo.club (previously Foundation), we would identify a small but meaningful feature for new hires to build in their first week. This wasn't a test or throwaway exercise—it was genuine product work. Watching how they dove into the codebase, collaborated with teammates, thought critically about implementation details, and showed proactiveness around what they were building provided a clear signal about their ability to integrate quickly.

This first-week feature served as a powerful litmus test that revealed far more than technical skills. It showcased problem-solving approaches, communication styles, and whether someone could navigate ambiguity while delivering tangible results—all critical capabilities in the fast-moving Web3 space.

The accelerated timeline isn't about unreasonable pressure—it's about acknowledging that in a space where resources are precious and opportunities fleeting, finding alignment quickly serves everyone's interests.

3. The North Stars: Curiosity and Execution

When evaluating new talent during this compressed timeline, three interwoven qualities distinguish exceptional contributors: intellectual curiosity, proactive initiative, and consistent delivery. The best Web3 talent doesn't just ask thoughtful questions—they transform insights into tangible outputs with remarkable velocity.

What separates transformative hires from merely adequate ones isn't hours logged or meetings attended. It's their ability to navigate uncertainty, identify high-leverage opportunities, and ship meaningful contributions within days, not weeks.

Key Behaviors That Predict Success:

• They ship early and often, even if the initial scope is modest

• They intuitively know when to ask questions versus when to make informed decisions

• They identify the right stakeholders for specific questions, respecting senior team members' time

• They document their work proactively so others can build upon it

• They connect theoretical knowledge with practical implementation

• They surface potential issues alongside proposed solutions

• They seek feedback on work-in-progress rather than waiting for perfection

This combination of curiosity-driven learning and execution-focused delivery predicts long-term value with remarkable accuracy. Anyone can appear busy; exceptional talent makes measurable impact within days of joining.

What's particularly telling is how they balance autonomous execution with appropriate collaboration. They don't disappear for weeks building something brilliant but misaligned. Instead, they establish rapid feedback loops—sharing work-in-progress, incorporating guidance, and iterating toward increasingly valuable contributions.

This rhythm of learn-build-share becomes evident within the first two weeks and solidifies by day 30. When someone embodies this pattern, you'll notice the entire team's velocity increasing as their contributions compound.

The 30-60-90 Day Framework

The foundation of an effective evaluation lies in deliberate milestone design. These aren't generic onboarding checklists, but carefully calibrated challenges that test relevant capabilities:

30-Day Milestone: Technical Integration

• Assign substantive contributions to an established workflow

• Provide clear context on existing systems and architectural decisions

• Set specific deliverables that demonstrate technical competence

• Evaluate: How do they absorb product-specific knowledge? How thoughtful are their questions? How quickly do they begin contributing meaningful work?

60-Day Milestone: Collaborative Expansion

• Transition from guided work to self-directed contribution

• Create opportunities for cross-functional collaboration

• Introduce ambiguity that requires independent judgment

• Evaluate: Do they proactively identify high-value work? How effectively do they navigate uncertainty? Are they building productive relationships across the team?

90-Day Milestone: Strategic Impact

• Assign responsibility for a complete feature or significant enhancement

• Require decision-making that balances technical tradeoffs

• Create space for them to demonstrate unique value beyond execution

• Evaluate: Do they understand how their work connects to broader business objectives? Can they make sound technical decisions independently? Are they having an outsized impact relative to their tenure?

What differentiates elite Web3 teams is their ability to make these milestones simultaneously challenging and achievable. The goal isn't to set impossible standards—it's to create clear conditions where true talent can demonstrate their capabilities unambiguously.

Continuous Feedback Is Non-Negotiable

Perhaps the most consistent difference between Web3 teams that retain exceptional talent and those perpetually recruiting lies in feedback velocity. In conventional organizations, substantive performance discussions happen quarterly or annually. In high-functioning crypto projects, meaningful feedback flows weekly or even daily.

This isn't about micromanagement—it's about creating alignment through continuous calibration:

Week 1-2: Daily Touchpoints

• Brief, focused conversations about immediate work

• Clarification of expectations and available resources

• Quick course corrections before misalignments compound

Week 3-4: Weekly Depth Discussions

• Structured reviews of completed work against expectations

• Identification of emerging patterns (both strengths and gaps)

• Explicit acknowledgment of progress and remaining challenges

Throughout: Real-time Specific Feedback

• Immediate recognition of exceptional contributions

• Direct addressing of concerning patterns when they appear

• Continuous connection between individual work and protocol objectives

This approach serves both parties. For the contributor who's thriving, it reinforces positive trajectory and accelerates integration. For someone struggling, it eliminates the possibility of "surprise" termination—they've received clear, consistent feedback about performance gaps.

The most successful teams view feedback as an obligation, not an option. They recognize that withholding concerns about performance isn't kindness—it's a disservice that prolongs inevitable separation while consuming finite resources.

When It's Not Working: The Courage to Conclude

The final component of an effective 90-day framework requires what's often in shortest supply among technical founders: the resolve to make clear decisions when evidence indicates misalignment.

Some warning signs rarely improve with time:

• Persistent resistance to feedback or defensive responses

• Inability to work effectively without constant guidance

• Minimal curiosity about the broader ecosystem and tech landscape

• Consistent delivery delays without proactive communication

• Technical outputs that require substantial rework

When these patterns emerge and persist despite clear feedback, the kindest action for all parties is a swift, respectful separation. This isn't about blame—it's about acknowledging that not every talented person is the right fit for every company at every stage.

The conversation should reflect this reality:

"We've observed these specific patterns over our evaluation period. We've provided feedback and support in these specific ways. While we value your contributions, the evidence suggests this isn't the optimal role for your talents. Let's discuss how to transition respectfully."

This approach preserves dignity while protecting your company's momentum. It acknowledges that hiring decisions, even when made thoughtfully, sometimes prove suboptimal—and that recognizing this quickly benefits everyone involved.

In a Nutshell

In a space where execution velocity often determines survival, the advantages of a structured 90-day evaluation framework compound dramatically. The difference between a team that takes six months to reach conviction about talent and one that achieves clarity in 90 days isn't incremental—it's often existential.

The Web3 projects that emerged strongest from previous market cycles weren't just technically superior or better funded—they were ruthlessly effective at building cohesive teams aligned around clear objectives. Their secret wasn't finding perfect people; it was creating systems that rapidly identified who would thrive in their specific environment.

The 90-day crucible isn't about being harsh or demanding. It's about creating the conditions where exceptional talent can flourish and where misalignments surface quickly enough to address without derailing your company's potential.

Have thoughts on effective onboarding in Web3 or just Talent in general? Connect with me X and Farcaster.

Here's the reality in 2025's $3T crypto ecosystem: products can skyrocket from zero to a million users in weeks. The teams that thrive don't pick sides in the builder-versus-theorist debate—they blend both with finesse. You need builders who ship fast when opportunity knocks, but who design with scale in mind from the start. Too many projects either crawl too cautiously and miss their shot, or race to launch only to crumble when success hits.

After years at the forefront of technical recruiting, I've learned crypto demands a rare mix: people who prototype with urgency while architecting for that explosive growth spike. This isn't about pedigrees or trendy skills—it's about teams with the flexibility to move quickly and the foresight to build foundations that hold under pressure.

At Lazer, we have been able to begin to build a process that we've distilled from a decade of triumphs and tough lessons on threading this needle.

Why This Matters

The crypto landscape is brutal—global users, ever-shifting tech stacks, and financial stakes that can balloon to millions overnight. Your team needs two strengths that seem at odds: deep problem-solving for complex challenges, and the grit to build and iterate fast.

I've watched Web2 teams chase theoretical perfection while their market window slammed shut. I've seen Web3 crews of "builders" flounder beyond their last hackathon when real adoption kicked in. The teams that last blend these thoughtfully—practical building gets you to market; fundamental thinking keeps you there when success lands.

This isn't about credentials or chasing trends. It's about products that go from prototype to millions of users without imploding. In a space where opportunities flash and fade fast, this balance isn't optional—it's survival.

Two Sides of the Same Coin

Successful crypto teams understand that building products in this space requires two complementary mindsets. Let's examine both sides of this coin and why each is essential to your team's success.

Real-World Building: The Practical Edge

Builders who act—who wield modern tools and thrive in live chaos—get your product to users. Their curiosity keeps their skills fresh and adaptable. Why this matters: in Web3, hesitation means missed chances.

These builders bring key strengths:

• Hands-on skill with tools that speed up development

• Ease with unclear, shifting demands

• Clever fixes using what's at hand

• The pace crypto's tight timelines need

They turn ideas into products users can touch, while others tweak theories on whiteboards.

CS Fundamentals: The Architecture That Scales

First-principles thinking tackles the big problems—how systems grow, stay robust, and last. This isn't about code syntax; it's reasoning that holds when users hit seven figures. Why this matters: skip this, and your product folds just as it takes off.

We've all seen it—protocols raise millions, launch big, then crash under load or get wrecked by exploits because the system wasn't thought through. All that hype, gone, because speed trumped solid design.

These thinkers deliver:

• Efficient code when transaction costs soar

• Security that stops exploits cold

• Designs that scale without constant fixes

• Insight into blockchain's unique limits

Projects that survive cycles—and avoid becoming warnings—have someone who foresaw the cracks "move fast" teams miss.

How to Assess Candidates: Two Key Angles

Finding talent that balances both building and architectural thinking requires a thoughtful interview process. Your goal is to evaluate candidates on their ability to ship quickly while designing for durability. This means going beyond standard tech interviews to assess real-world capabilities and deep technical reasoning.

Your interviews must test both: shipping in real conditions, and thinking for sustainable growth. Here's how:

Applied, Real-World Building

In crypto's fast-moving landscape, theoretical knowledge without practical application is nearly worthless. You need to see how candidates perform when faced with the kind of challenges they'll encounter on your team. This section focuses on testing their ability to turn ideas into working prototypes efficiently.

This checks how candidates create and launch in true settings.

Test MVPs Their Way: Use a take-home or live coding task, but let them work naturally:

• Full access to tools—AI, Google, docs

• Freedom with modern frameworks

• A problem like what they'd face on your team

Instead of obsolete restrictions on tools, explicitly encourage candidates to use AI in your interviews. Yes, it's a spicy take, but let's be honest—you want people who leverage every available tool to build efficiently. Try prompts like: "Build a basic DEX interface for our test API" or "Make a simple wallet connection with transaction signing," and make it clear that AI assistance is welcome. Watch how they integrate AI into their workflow—this is the future of building. Teams that pretend developers won't use AI tools are hiring for yesterday's skills, not tomorrow's competitive edge.

What to Watch For:

• How fast to a working prototype?

• Fluency with today's tools?

• AI as helper or crutch?

• Focus on a usable build?

• Clear on their choices and trade-offs?

Perfect code isn't the goal—shipping something iterable is. Top crypto builders know when to polish and when to launch.

Systems Architecture & Technical Reasoning

While shipping fast is crucial, building systems that collapse under their first success is a recipe for disaster. The second essential component of your assessment should evaluate how candidates think about scale, security, and sustainability in blockchain-specific contexts.

This probes how candidates handle scale—the backbone that keeps success from breaking you. Instead of theoretical computer science puzzles, engage them in meaningful discussions about real challenges your project might face.

Discuss Real Challenges:

Skip whiteboard trivia for talks on actual issues:

• "Transactions fail at peak—how do you fix it?"

• "How do you mix on-chain and off-chain when costs jump?"

• "What smart contract flaws do you catch first?"

What to Watch For:

• Reasoning from basics, not trends

• Grasp of blockchain's quirks

• Sense of scale's hidden traps

• Foresight on failure points

• Balancing now versus later

This reveals how they'd tackle crypto's growth pains. You want reasoning, not textbook answers.

What Not To Do: Hiring Traps

Crypto teams still fall into old habits that waste time and talent:

• Endless interviews: 7+ rounds just test patience, not skill. Keep it to three: build test, architecture talk, culture chat.

• CS trivia obsession: Red-black trees don't predict DEX builders. Focus on real skills, not academic rites.

• Toolset fixation: Hardhat or Foundry can be learned fast—problem-solving can't. Don't skip great minds over frameworks.

Top crypto teams cut hiring from 6+ steps to 3-4 sharp tests. Result: faster hires, better fits, code that launches and lasts.

In A Nutshell

In 2025's $3T crypto world, purity tests don't win. You need teams that ship with urgency and build to endure. Practical speed plus foundational depth—that's the formula.

Founders: test both, pay both well (market rates plus tokens for stars), and foster teams where builders and architects lift each other. Job seekers: show working code (link repos) and explain your design choices.

As crypto matures, the builder-thinker split fades. Winners know it's not either/or—it's blending execution with vision for this wild space.

Let's keep talking—find me at @thebc12.

Lazer is also hiring: https://www.lazertechnologies.com/careers

The Web3 talent hunt is evolving fast, and old-school platforms like LinkedIn or X can't keep up with crypto's pace. I'm a recruiter who thrives in this chaos, and right now, I'm leaning hard into Farcaster—the slickest decentralized social protocol in the game—to snag builders who live and breathe blockchain. At Lazer Technologies, where I steer recruiting, we're growing our team and tapping this ecosystem as a prime spot to pull in top-tier talent. With over 1 million wallet addresses connected as of March 2025, Farcaster's not just a hangout; it's a buzzing hive of innovators, all accessible through Warpcast's casts, channels, and onchain juice. Here's my playbook—five actionable moves to tap Farcaster's unreal potential, straight from my recruiting trenches.

Warpcast channels aren't your average chat spots—they're unique spaces for finding talent where they kick it most authentically. Warpcast has rooms for anything ranging from New York City, React Native, Food, DeFi, Art, etc. These focused rooms can allow you to find talent that can align with skills you're hiring for or any interests that may align with your team.

Warpcast channels make it easy to find the right people fast. I was scouting talent in the /Toronto channel when I came across someone who had just won ETHToronto with a Farcaster-integrated project. I found their product’s channel, saw they were active on Warpcast, and shot them a DM. That turned into a real recruiting conversation—way more natural than cold outreach on LinkedIn.

Why It's Clutch: Channels zero in on active talent by location, skill or interests—no fluff, all signal.

How You Might Use It: Dig into niche or geo-channels (/Toronto, /ReactNative), spot onchain builders, and ping 'em directly.

How I'm Currently Using This: I'm scouting /Toronto and /Vancouver channels to grow Lazer's Canada squad—already got a lead from that ETHToronto champ.

Frames—interactive elements within casts that launched in 2024—allow developers to create mini-applications that users can engage with directly in their feed, including completing onchain transactions without leaving Farcaster. These aren't just technical widgets; they're powerful tools that showcase a developer's ability to combine blockchain functionality with intuitive user experiences. For recruiters, Frame creators represent a goldmine of talent—they demonstrate not just coding ability, but product thinking, user empathy, and the rare skill of making complex blockchain interactions accessible to mainstream users. I've been personally immersed in the Frames ecosystem for months—testing new creations, connecting with builders, and watching how they iterate based on community feedback—which has opened up a rich vein of recruiting opportunities.

At Lazer, I’m hiring dApp engineers, and Farcaster makes it easy to spot builders who are actually shipping. I’ve been deep in the Frames ecosystem, testing out new projects and seeing who’s making cool stuff. That’s led to some great convos with engineers who aren’t just talking about web3—they’re building it.

Why It's Clutch: Frames flex tech chops and a user-first mindset—key for adoption.

How You Might Use It: Test Frames on Warpcast, find the slick ones, DM the brains behind 'em—they're usually down to chat.

How I'm Currently Using This: I'm talking with Frame builders for Lazer—eyeing those whose work could juice our client's consumer projects.

Farcaster's open protocol is incredibly powerful - it allows anyone to build products on top if it and their social graph can follow from product to product. Tools like Icebreaker.xyz and Interested.fyi are game-changers, linking crypto-pilled job seekers with verified IDs to hiring teams. It's onchain cred meets old school resumes, and it's a no brainer for cutting through the noise. These platforms spotlight talent who aren't just dabbling—they're all-in on crypto's future, which is exactly the mindset I'm after for scaling web3 teams.

Why It's Clutch: Merges Farcaster's decentralized mission with real hiring credentials.

How You Might Use It: Go to Icebreaker or Interested.fyi (to name a few), and explore onchain talent and see their profiles across the Farcaster Ecosystem.

How I'm Currently Using This: I'm tapping Icebreaker to find crypto diehards for Lazer—hunting for the right fits.

Posting jobs on LinkedIn or X means your roles get in front of hundreds of eyes across all industries, experience levels, and backgrounds. This broad approach definitely works—I use it regularly myself. But Farcaster isn't about flooding feeds; it's about surgical strikes where specific skills and passion intersect. While volume has its place, Farcaster lets me target the right eyeballs rather than sorting through piles of mismatched applications. I drop thoughtful posts in specialized channels where developers with precise skills actually hang out—think /Rust or /DeFi for those exact skill sets. I've even gotten creative with Frames (tested that at Foundation, turned a cast into an "apply here" interactive element that popped off). The platform lets you place opportunities directly in super niche communities with a much higher likelihood of genuine interest—no guesswork required, just pure signal in an otherwise noisy recruiting world.

Why It's Clutch: Channels like /Rust or /DeFi hit niche crews square-on, not randos.

How You Might Use It: Drop crisp, skill-specific casts (e.g., "Rust devs for crypto consulting") in the right spots—keep it chill, not spammy. Play with Frames; I did, and it worked.

How I'm Currently Using This: I'm posting "Rust devs for crypto consulting" in /Rust for Lazer—hyper-targeting beats the spray-and-pray game every time.

Farcaster offers a unique window into candidates' genuine interests and skills—through the channels they frequent, the casts they engage with, and the projects they showcase. Unlike broader platforms, Farcaster's niche community makes it easier to build multidimensional relationships through natural interactions. I'm active on Warpcast not just as a recruiter, but as a complete person with varied interests. This authentic approach enables me to connect with talent on multiple levels—I'll dogfood an engineer's experimental Frame, share memes, drop food pics in the /cooking channel, or discuss memecoin trends in /defi, all while establishing genuine connections. This results in a response 3× faster than similar outreach on LinkedIn. This multifaceted engagement builds authentic trust that transactional recruiting approaches simply can't match.

Why It's Clutch: Real connections across shared interests create trust before the recruiting conversation even begins.

How You Might Use It: Engage naturally across different channels, support builders' projects, and connect based on genuine shared interests before discussing opportunities.

How I'm Currently Using This: I'm actively participating in the ecosystem—testing Frames, joining discussions, sharing content I genuinely care about—while strategically connecting with talent for Lazer, resulting in triple the response rate compared to traditional platforms.

As the crypto ecosystem matures, finding the right talent requires more than traditional platforms—it demands presence in spaces where authentic builders naturally congregate and create. Farcaster has evolved beyond a social network into a talent ecosystem where merit is visible through contribution, not just claims. The strategies I've shared aren't just recruiting tactics; they're ways to engage meaningfully with a community that values building over broadcasting.

For recruiters and hiring managers, the opportunity is clear: authentic engagement on platforms like Farcaster yields higher quality connections than volume-based approaches elsewhere. For builders, your contributions here speak volumes more than any resume. The ecosystem—now over one million wallets strong—represents a paradigm shift in how Web3 talent and opportunities connect.

Recruiters entering ecosystems like Farcaster need to recognize its primary use case. It's not just for recruiting; it's a community first and foremost. Knowing when and where to post job opportunities is crucial—misreading the room can damage both your personal brand and the company you represent. Respect the vibe, contribute authentically, and the recruiting success will follow naturally.

I invite you to experience this shift firsthand. If you're not already on Warpcast, give it a try—you'll find a vibrant community that goes far beyond recruiting. And if you're looking to discuss talent strategies, crypto careers, or just connect with a fellow Farcaster enthusiast, you can find me at @thebc12. I'm always open to thoughtful conversations about where Web3 talent discovery is heading next.

Note: Lazer Technologies is hiring—if you wanna join a team like this, let me know!

It became clear that the tension between technical excellence and cultural alignment isn't just another recruiting challenge—it's the fundamental people problem at the heart of Web3. After building teams across Tumblr, Kickstarter, Twitter, Foundation, and advising numerous crypto projects, I've seen firsthand how this balance determines which teams thrive and which implode when market conditions shift.

Let's get clear on what cultural alignment actually means in Web3. It's not about finding people who use the same memes or invest in the same tokens. True cultural alignment means building teams that share fundamental values around:

• How decisions should be made (do you default to decentralization or efficiency?)

• Attitudes toward transparency (are you comfortable building in public?)

• Comfort with ambiguity and rapid change (can you thrive when everything shifts weekly?)

• Views on ownership and value creation (do you believe in aligned incentives?)

• Commitment to the mission beyond market conditions (will you build through a bear market?)

These shared values create the foundation for teams that can weather the extreme volatility of this industry. Most failed Web3 startups cite "team misalignment" as a primary factor—not technical challenges. This isn't surprising when you consider that building in Web3 is like trying to change a tire on a moving car... during a hurricane... while the car is still being designed.

Let's dive into the frameworks that actually work for striking this delicate balance.

Different roles require different balances of technical and cultural fit. Early Coinbase growth came from bringing in technical talent from Web2 giants like Google and Amazon, while maintaining a core leadership team with strong alignment on values.

Web2 transplants bring battle-tested skills but may struggle with the public nature of Web3 work. ("What do you mean everyone can see my code commits and critique them on Twitter?")

Practical framework:

• Technical roles (engineers, designers): 70% technical excellence, 30% cultural alignment

• Strategic roles (product, operations): 50/50 balance

• Vision roles (founders, leadership): 30% technical understanding, 70% cultural alignment

Warning sign: If you're rejecting technically brilliant candidates solely because they don't fit a narrow cultural mold, you're likely creating an echo chamber rather than a high-performing team. I've seen this kill projects faster than technical debt ever could.

Not all aspects of Web3 work culture can be adopted quickly. Smart founders identify which elements of their organization's culture are teachable versus which require inherent alignment.

The adaptability hierarchy (from easiest to hardest):

• Technical protocols and tools (can be learned through structured training)

• Communication norms and transparency expectations

• Comfort with uncertainty and rapid change

• Self-direction and autonomous decision-making

• Philosophical alignment on decentralization and ownership

As Chris Dixon notes, "Web3 is about creating a better internet by realigning network participants."[1] This realignment isn't just about technology—it's about people. The most successful Web3 teams don't just build products for users; they build with communities as co-creators.

Pro tip: Create clear documentation of your cultural expectations. The best teams I've worked with have a "culture doc" that's as detailed as their technical docs. Your values shouldn't be like memecoin prices—unpredictable and mysterious.

Web3's remote-first nature creates unique challenges. Culture becomes explicit rather than implicit—you can't rely on office energy or casual interactions to transmit your values.

Strategies that actually work:

• Regular in-person off sites (quarterly minimum) focused equally on strategic alignment and relationship building

• Clear documentation of values, decision frameworks, and communication norms

• Structured opportunities for casual interaction (yes, those virtual happy hours actually matter)

• "Core hours" across time zones for synchronous collaboration

At Foundation, as our team grew, our leadership team quickly wrote documentation around our decision making framework (DRI culture), comms structure and how to hype people up remotely. These async comms best practices and structures ensured our team knew when to collaborate, how to disseminate information, etc. This resulted in faster decision making, better transparency, and a general lack of confusion.

Effective cultural alignment requires concrete evaluation methods that transcend personal bias. The most successful Web3 organizations develop structured frameworks to assess cultural competencies objectively, looking for demonstrated behaviors rather than personality traits. These pioneering teams implement regular pulse surveys (quarterly or bi-annually) to gauge team comfort with key cultural cornerstones, allowing leadership to identify gaps and make targeted adjustments based on real feedback rather than assumptions.

The best Web3 teams evaluate core indicators like:

• Comfort with transparency and public-facing work

• Resilience to volatility and uncertainty

• Self-direction and proactive problem-solving

• Collaborative rather than competitive mindset

Pro-tip: Keep your pulse surveys lightweight and focused on the core elements of your team's identity. Always follow up with visible action items based on the results—if team members don't see meaningful changes after providing feedback, they'll quickly stop taking surveys seriously.

The most successful Web3 projects don't create echo chambers of like-minded people. They actively seek diversity of thought while maintaining alignment on core mission, values, and talent. This is never about compromising on talent caliber—quite the opposite. It's about investing more deeply in your recruiting efforts to find exceptional candidates who both meet your high standards and expand your team's perspectives and capabilities.

Unity-building tactics:

• Clear and documented company values that are expected to be held at every level and function of the company

• Mixed team compositions that bring together different backgrounds and expertise

• Structured decision-making frameworks that make space for dissenting opinions

• Town Halls that emphasize and celebrate company Q&A and transparent company updates

Critical insight: The strongest teams align on the "why" of their work while embracing diverse perspectives on the "how." They create cultures of belonging without demanding conformity. You want a team that's a mixing board, not an echo chamber.

The technical vs. cultural alignment balance isn't a zero-sum game. The most successful Web3 founders recognize that both dimensions matter, but in different proportions depending on role, project stage, and market conditions.

Remember: code can be forked, but culture and teams can't. Your technological moat may erode over time, but a well-aligned team creates a sustainable competitive advantage that will carry you through bull and bear markets alike.

Struggling with building the right team for your Web3 project? Connect with me on X or @ejp1205 on Telegram, and let's talk shop! 🚀

References: [1] Chris Dixon, "Why Web3 Matters," https://cdixon.org/2021/10/25/why-web3-matters

Let me be clear: choosing the wrong founder in Web3 isn't just about a bad job experience - it can fundamentally shape your career trajectory. Without proper due diligence, you might find yourself trapped in circles with a founder who can't evolve their product with the market, or worse, in a toxic environment where your expertise isn't valued. I've watched talented engineers pour months into projects that stalled because founders couldn't adapt their vision. I've seen marketing leads burn out under founders who dismissed their strategic input, caring more about short-term hype than sustainable growth. And I've witnessed skilled operators struggle as founders resisted their efforts to implement basic business practices.

But here's the flip side - the right founder can transform your Web3 journey. They can become a mentor who helps you navigate the space, a collaborator who empowers you to own crucial parts of the business, and a leader who amplifies your impact. They'll value your expertise, champion your growth, and help you build a reputation that opens doors.

In Web3, these early career choices matter more than in traditional tech. The space is small, reputation travels fast, and the projects you associate with become part of your onchain resume. Your choice of founder isn't just about the next job - it's about who will shape your trajectory in this industry. Choose wisely!

A founder who knows their strengths and limitations is invaluable. They understand where they need support and how to build a team that complements their skills. Self-awareness is a leading indicator of a founder who will give their team the opportunity to lead in areas where they are weaker.

Look for:

• Technical founders acknowledging business gaps or business founders admitting technical limitations

• A clear vision for long-term sustainability over short-term gains

Founders with a steadfast vision are the ones who weather market storms; they have a true north star. They stick to their mission, proving their commitment through actions, not just words. This resilience is not only required, but inspiring.

Look for:

• Consistency in mission despite bull and bear markets — they build through the noise.

• Decisions driven by user and business needs, not market hype and how to make a quick buck

Effective founders manage resources wisely, fostering a strong and communicative team. They ensure the team's growth aligns with the project's long-term goals.

Look for:

• Clear, market-realistic compensation structures

• An environment that encourages open dialogue and transparency

• Balancing speed with quality in development by optimizing team composition and structure

The interview process reveals more about a founder's priorities than you might think. Strong founders approach interviews as strategic discussions about mutual value creation - they want to understand your expertise, vision alignment, and potential impact on their mission. If a founder can't effectively evaluate your potential contribution or seems fixated on irrelevant factors, it often indicates deeper issues with their leadership approach.

Watch out for:

• Questions about personal habits, politics, etc. instead of your professional skills

• Lack of interest in assessing your core competencies or reasoning for wanting to build with them and their product

A founder who mismanages funds without a strategy for ROI or sustainability is a red flag. Are they over indexing on event sponsorships for user growth, or are they not paying at industry levels for their talent? Their approach should support the project's longevity, not just immediate gains.

Watch out for:

• Rapid cash burn without clear metrics for success

• Focus on short-term market trends over infrastructure

• Lack of data informing compensation practices

Founders need to have a high level of confidence; it's required to inspire and lead teams/VCs/communities. However, an ego or someone who can't handle feedback or engage constructively with their community and team might hinder growth and collaboration.

Watch out for:

• Defensive or dismissive responses to criticism or challenges

• Lack of genuine community engagement or transparency in updates

• Walk me through your revenue strategy and how our protocol ties to it.

• How are you thinking about your strategy as we move between bull and bear markets?

• What is your approach to collaboration and feedback?

• What do you feel are your greatest strengths and opportunities for growth as a Leader?

• What are your short and long term goals for someone like myself coming into this role?

When evaluating Web3 opportunities, remember that the best founders aren't just building for the next bull run - they're building sustainable protocols that generate real value. They welcome tough questions because they know building a strong team requires mutual due diligence.

Watch how founders build in public and engage with their community - the best ones share their journey openly, seek feedback actively, and iterate thoughtfully. This transparency isn't just about marketing; it's a window into their leadership style and team culture.

Don't let excitement about entering Web3 or FOMO from market conditions rush your decision. Take your time, ask the hard questions, and look for founders who demonstrate these green flags consistently throughout your interactions.

Your role in a Web3 project isn't just a job - it's a bet on a team's vision and execution capability. Choose wisely.

Like successful traders who understand market cycles, smart founders know that different talent strategies work in different market conditions. Here's your comprehensive playbook, backed by data from leading Web3 projects.

The most common starting point for early-stage projects, this approach puts you directly in control of building your team. While it requires significant time investment, it can be the most cost-effective way to grow when you have a strong network and clear hiring vision. If you’re scrappy and resourceful, FYOT lets you take full control of your hiring... but be ready to put in the work

✅ Cost-efficient: No agency fees or recruiter salaries. According to a survey by AngelList, startups can save an average of 20-25% on hiring costs by managing recruitment themselves.

✅ Full control: You pick the talent and negotiate directly, crucial for maintaining your project's culture.

✅ Community building: Direct hiring often leads to stronger community engagement, with hires becoming long-term ecosystem contributors. A study by CoinDesk found that community-driven hiring in crypto projects leads to higher retention rates.

❌ Time-intensive: Finding, vetting, and closing candidates is a full-time job. Leading founders report spending 15-20 hours per hire.

❌ Potentially inefficient: Without a talent professional, you may miss out on passive candidates who make up 70% of the talent pool, as per LinkedIn's 2023 Global Talent Trends report.

❌ Security risks: You're a Founder; you have quite a few balls in the air. You may not have the time, or experience, on how to setup a comprehensive interview process. Limited vetting can introduce bad actors. In 2023, 62% of Web3 projects reported attempted infiltration by malicious actors, according to a report by Chainalysis.

For founders who need speed and market expertise, partnering with agencies is often the best way to accelerate hiring. They're particularly valuable during bull markets when you need specialized roles filled quickly. While more expensive than direct hiring, the right agency partnership can dramatically reduce your time-to-hire.

✅ Speed: Average time-to-hire is 23 days vs. 45+ days for in-house recruiting, based on data from the Recruitment Process Outsourcing Association (RPOA).

✅ Market expertise: The right agency knows where top blockchain talent is and how to attract them. The best agencies are dedicated to sourcing.

✅ Industry Immersion: Agencies are often running multiple searches across different companies, which means they have an intimate understanding of the industry's current needs and access to a broad pool of active candidates, ensuring they can match the right talent with the right project at the right time.

❌ Expensive: Expect to pay 15-30% of first-year salary per hire, with top agencies commanding even higher fees for executive roles.

❌ Quality varies: Not all agencies have great reputations with candidates. Pro tip: Check their placement retention rates (should be >80% at 12 months), a standard suggested by the National Association of Personnel Services.

As your project scales and hiring becomes constant rather than sporadic, bringing recruitment in-house often makes strategic sense. This approach requires significant investment but can transform hiring from a reactive process to a strategic advantage.

✅ Cost Savings Over Time: The average cost per hire in the U.S. is $4,700, but agency fees can range from 20-30% of a new hire’s salary (Engagedly, 2023). Investing in an internal recruiter helps reduce reliance on agencies and lowers cost-per-hire over time as processes become more efficient.

✅ Better hiring outcomes: With a recruiter, you're consistently bringing in high-quality talent and can build structured onboarding programs. 

✅ Employer branding: Internal recruiters can build your reputation as a top Web3 employer, boosting retention, elevating your talent bar, and ensuring hires align well with your team, ultimately driving higher-quality hires and reducing turnover costs.

✅ Retention focus: An in-house recruiter can implement strategies tailored to retain top talent, crucial in the volatile crypto market where loyalty can be hard to secure. LinkedIn's 2023 Global Talent Trends report highlights that companies with focused retention strategies see a 50% lower attrition rate.

✅ Specialization: Recruiters can specialize in understanding the nuances of blockchain roles, ensuring hires fit not just skill-wise but also culturally with your team's vision and ethos.

❌ High cost: Expect to pay $140K-$180K for a recruiter who understands blockchain and high-value talent. And this does not include equity (which most talent will expect).

❌ Premature for some startups: If hiring slows down, you may not be getting ROI on this role. Calculate your cost-per-hire carefully.

For projects needing immediate execution power or specialized expertise, product studios, like Lazer Technologies, offer a compelling alternative to traditional hiring. This approach brings you instant access to coordinated, experienced teams who can accelerate your development timeline significantly.

✅ Immediate execution: Teams are already in sync and can start contributing from day one, with established processes and workflows in place. Research suggests this approach can decrease time-to-resource by up to 76% compared to traditional hiring methods, getting your project from concept to execution in days rather than months.

✅ Less hiring overhead: You avoid the complexities of full-time hiring in a competitive market while gaining access to teams that are actively building across the ecosystem and staying current with rapidly evolving technologies and best practices.

✅ Reduced risk: Fixed contracts and deliverables make budgeting more predictable, and these teams typically bring battle-tested security practices from working across multiple projects. Their cross-project experience often helps identify potential issues before they become problems.

✅ Industry expertise: Product studio teams are typically building multiple projects simultaneously across the ecosystem, giving them real-time insights into what works and what doesn't. This means you're not just getting developers – you're getting partners who understand current market trends, user expectations, and technical best practices.

✅ Great for teams of any size: Early founders can work with product studios to get their product live quickly, scaling teams can leverage them to experiment with new user experiences while their core team focuses on infrastructure (especially useful for L1's or L2's), and established teams can bring in a studio to create one-off experiences or supplement their existing team with additional capacity or specialized expertise to accelerate time-to-market.

❌ Costs vary: Rates depend on the studio's expertise and service level, ranging from basic development to full-service product teams. It's crucial to understand their complete service offering to maximize your ROI and ensure you're getting the full value of their expertise.

❌ Alignment is key: You're introducing a new stakeholder into your project, so it's essential to choose a studio that not only has the technical capabilities but also shares your vision and working style. Take time to evaluate their past projects and cultural fit.

In crypto, flexibility isn't just a nice-to-have—it’s a competitive advantage. The best projects blend multiple strategies to scale quickly and adapt to market changes on a dime. They build a flexible talent function that can leverage all these approaches based on:

• Immediate business needs

• Available resources

• Role criticality

• Market opportunities

Smart founders combine multiple approaches:

✅ Keep a network of proven contractors ready for specialized work (like smart contract audits or security reviews)

✅ Leverage specialized recruiting agencies for senior technical roles (like Head of Protocol) while building your everyday engineering team through direct hiring and referrals.

✅ When ready, keep a lean in house talent team to ensure a high talent bar and retention strategies are in place.

✅ Partner with product studios to accelerate development - whether you need to quickly scale engineering capacity, launch an MVP, or build complex features. Their pre-built teams can significantly reduce time-to-market while your internal team focuses on core protocol work.

Crypto founders must be agile; the best hiring strategy hinges on your project's stage, hiring needs, and market conditions. Whether you're scaling with contractors or building a full-time team, understanding the nuances of each approach can significantly enhance your efficiency.

In the unpredictable world of crypto, building an adaptable talent strategy is essential. By combining various hiring methods strategically, you can not only survive the market's cycles but thrive—outpacing competitors and attracting top-tier talent. Looking to optimize your talent strategy or need specific advice on how to scale effectively in crypto? Connect with me on X or Warpcast - and let's talk shop! 🙂  

A POAP (Proof of Attendance Protocol) is an incredibly effective way to engage with open-source contributors. Instead of sending a standard a cold outreach or recognition email, consider minting a POAP as a token of appreciation for their contributions. POAPs can be branded and customized, tying them to your company’s identity and values—creating a lasting impression. Pairing recognition with an opportunity to connect with your company is a winning combination.

For contributors, these POAPs can become a badge of honor or a point of pride, displaying their involvement in the open-source ecosystem. When they look back at their collection, your POAP will stand out as a memorable token of recognition. It’s a unique way to acknowledge their work and ensure they remember your company when the time comes. Also, it shows that as a recruiter you're seeing their work in the community and your company values open source work. It shows that you understand and value the ethos of the Web3 space.

Another fun and impactful way to engage blockchain talent is by tipping them on Warpcast. Built on Farcaster’s protocol, Warpcast allows you to track candidates across different social crypto products built on it—your social graph migrates seamlessly from product to product. Warpcast is structured around topic-specific channels—whether it’s zk projects, data, L2s, or other Web3 areas—so you're not going in blind. You can engage directly in spaces where talent is already discussing what they’re passionate about.

The tip itself is typically nominal, but the gesture carries significant weight. Tipping someone on Warpcast is more about signaling respect and showing you’re tuned into their contributions—whether it’s a sharp take on the space, an insightful post, or valuable community engagement. It’s a playful yet meaningful way to break the ice and start building connections in an organic, Web3-native way.

This approach taps into Degen culture—playful, authentic, and uniquely Web3. Plus, it’s a great way to get their attention without overwhelming them with a sales pitch or generic outreach. When you show up in their feed with a tip, it’s a subtle, yet memorable reminder that you see them and value their voice. It’s a fantastic way to start a conversation, especially in the Web3 world, where community recognition and shared respect are key to building lasting connections. It shows you’re not just another recruiter looking for talent—you’re someone who genuinely understands the space and is willing to participate in its culture.

Not all talent is ready to jump into a new opportunity right away, but by minting your contact information as an NFT, you ensure that you’re in their wallet—not buried in their inbox. Offering a free mint to your digital business card lets you stand out in a way that’s completely onchain and memorable. The NFT can feature a QR code linking to your career page, a personal introduction, or even an invite to connect.

Platforms like Zora make this easy, offering free mints that remain accessible indefinitely. Unlike emails that get lost, an NFT is a digital collectible that stays with them, ensuring your contact info is there when they're ready. It’s a unique, forward-thinking approach that demonstrates you're part of the Web3 ecosystem, offering lasting touchpoints beyond traditional recruitment channels.

In the fast-paced world of blockchain recruitment, standing out and authentically engaging talent requires thinking outside the box. Whether you’re rewarding open-source contributions with a branded POAP, showing respect through a Degen tip on Warpcast, or offering a free NFT business card to stay in talent’s wallets, these Web3-powered tools create real connections in a space that thrives on innovation and community.

These strategies aren’t just about filling positions—they’re about creating a network of blockchain professionals who value your approach, your recognition, and your unique touch points. Using Web3 tools like these demonstrates that you understand the dynamics of the space, you respect its culture, and you’re taking the time to engage with talent in a way that feels personal, innovative, and truly representative of the Web3 ethos. As Web3 continues to evolve, staying ahead of the curve in your recruitment efforts is key.

Try these out today, and elevate your recruitment strategy in the Web3 world! 💪