Author: Zackary Skelly (Head of Talent at Dragonfly)

Translated by: Deep Tide TechFlow

Deep Tide Introduction: Dragonfly released the 2026 Crypto Industry Talent Insights Report, revealing a fundamental shift in hiring logic. The industry saw a net reduction of 472 employees in 2025, but compliance positions skyrocketed by 340%, and data science roles increased by 74%. The most critical change is: candidates are no longer impulsive in bull markets; they want clear value explanations and certainty. If you cannot clearly state "why this position is important," the conversion rate will fall off a cliff.

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We have entered the first quarter of 2026, and the hiring situation in the cryptocurrency sector is completely different from any previous cycle.

We have just released the latest Talent Insights Report, which details how we arrived at this point and what it means for founders and talent teams.

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TL;DR

2025 did not kill crypto hiring; it matured it.

Companies are no longer hiring based on price but based on actual demand.

This shift has become the new benchmark as we enter 2026.

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This year can clearly be divided into two halves.

The first half of 2025 (25H1) was tumultuous, with macro shocks causing a rapid reversal of pro-crypto sentiment.

In March, the removal of positions surged (750), with most losses concentrated in the first half.

Approximately 3,700 new positions were added throughout the year, about 4,100 were removed, resulting in a net decrease of -472.

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The second half (H2) brought discipline and recovery.

The total job trend line for H2 was basically consistent with 2024, albeit at a lower overall level.

July saw a reset, August hit bottom, September reopened, and the fourth quarter stabilized.

A more severe reset in spring was the main reason for the overall lower performance in 2025 compared to 2024.

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In our 25H1 report, we made some predictions. Now let's score them:

✓ Late Q3 rebound (new positions in September +26%), Q4 slowdown, compliance hiring initiated early

✗ Underestimated the divergence in traffic and applications; overestimated the resilience of legal positions relative to compliance positions

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From 25H1 to H2, the real transformation was not how many people companies hired, but the types of roles they hired. Core priorities came first to win the right to expand.

→ Engineering: -12%, still a benchmark → Marketing: -27% → Design: -33% → Customer Service: -35% → Sales & BD: -16% → Legal: -41% → Compliance: +340%

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Data science was the most obvious winner of the year, increasing by +74% year-on-year. (Thanks to AI?)

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Interesting changes also occurred on the candidate side.

Traffic remained stable in the second half, while the application volume decreased by about 26%.

People are still browsing but are not submitting applications as easily.

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In early cycles, market excitement carried most of the hiring: salaries rose, and applications flooded in.

This mechanism is failing.

Stronger months can still drive browsing, but the attention conversion rate is not what it used to be.

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What is the reason? Partly because candidates have become more cautious.

They are more stringent in screening for the durability of the company, clarity of ownership, team quality, and technological credibility: open-source evidence, product depth, hardcore questions, GTM roadmap.

Generic category narratives no longer work.

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Areas of concentrated belief: Infrastructure, DeFi, L1 and L2 remain core, but interest in DeFi has narrowed to stablecoins, payments, and RWA.

Fintech-related and institutional use cases have gained significant attention. AI remains a key interest point.

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Stage preference also tells an interesting story.

Seed and Series A stages remain the most attractive to candidates, with high demand for founder and first employee roles. However, larger, more established companies can still attract interest.

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The most common factors causing candidates to drop out are not compensation, stage, or size, but ambiguity.

If you cannot clearly explain why the company is important, what specific scope they will have, and why the opportunity has lasting power, the conversion rate will significantly decline.

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Geographically, remote work is still the norm, but the most active hiring teams are more concentrated in New York and are more inclined toward in-person work.

Talent remains global, but New York + the Bay Area still dominate. Europe is the largest non-U.S. hub.

(Note: Specific geographic hiring = smaller TAM, longer hiring cycles.)

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Another factor shaping the current landscape is that recruiting is concentrating more toward late-stage teams and significantly in verticals where candidates are most interested.

We expect that hiring for the remainder of 2026 will be driven more by acquisitions, transformations, and integrations rather than pure new growth.

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So what should founders do?

Base hiring on milestones—product releases, revenue, partnerships, regulatory progress—rather than market cycles or calendar timelines.

Companies that did well in hiring in the second half of 2025 were able to clearly articulate and uphold the reasons for each role's existence.

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Recognize that teams are different; therefore, thoughtfully arrange personnel sequences:

→ Core builders first (Engineering, Security, Data/Protocol) → BD exploring fit → Product flexibility based on type (consumers earlier, infrastructure more streamlined) → Compliance, Finance, Risk → Marketing/Support to scale after leverage appears

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Keep a constant pipeline for scarce talent.

Supply for Engineering, AI/ML, and Security roles is severely limited, so it cannot restart from zero each cycle. Even when specific demands are closed, relationships should remain warm.

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Recognize that the role selling approach has changed.

Candidates want runway clarity, clear ownership within the first 30-60 days, and transparent upside mechanisms.

You must sell differentiation. You are not selling your category; you are selling why you will win and the specific roles they can play.

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You also need a real AI story. Not "we are an AI company."

Candidates want to know:

→ How AI is used internally → How it changes the product → Whether it creates real advantages

Ambiguous answers will lose talent.

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Specific advice for talent teams:

Put your strongest people at the front of the process (first impressions are important), keep the interview loop tight, and provide clear feedback.

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An open question: AI makes 2026 harder to predict.

People can do more with fewer resources. Better tools allow some to start their own ventures. Some may go directly into AI work.

Meanwhile, higher output per employee means faster scaling, and the positioning of crypto is broader than ever.

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Our current view on the impact of AI: The signals of deceleration outweigh the signals of acceleration before clear AI × Crypto use cases solidify.

📎 Further reading: The Agentic Economy Will Be Massive, Agentic Commerce Won't

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Our benchmark expectation for 2026: flat to moderate growth, led by engineering, AI/data, and security. Integration will continue.

Regardless of bull markets, benchmarks, or bear markets, this is a year that emphasizes quality building.

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The teams that can win talent will be those with the most credible stories, not the loudest voices.

Disciplinary execution, durable business models, and good explanations of both are essential.