After the Halving of Developer Numbers: Crypto Isn't Dead, It's Just Ceded Talent to AI

Original Authors: Xinyang, Ethan, IOSG Ventures

In 2026, the GitHub activity curve of the Crypto open-source community completed a remarkable "bottoming out." Monthly active developers fell from a peak of 45K in 2022 to around 23K. This apparent halving on paper sparked discussions about "narrative exhaustion" on social media. However, when we dissect this curve, what we see is not an industry contraction, but a profound "deleveraging of talent."

▲ Data Source: Electric Capital Developer Report, based on Crypto Ecosystems Github

Who Left? Who Stayed?

Those who left were mainly newcomers. In February 2024, the number of new developers added in a single month reached 5,462, followed by a sharp decline, with a churn rate of 52% among those with less than a year in the industry. Most of these people flooded in during the bull market, working on NFT minting contracts, forking DeFi protocols, or building front-ends for new L2s.

These roles were highly dependent on market sentiment. Once the heat subsided, projects ceased operations, and the jobs disappeared. Data shows that the code contributions of newcomers never exceeded 25% of the total. These individuals were never really in the core circle of the industry from the start.

▲ Newcomers surge in during bull markets and exit in bear markets; Established devs (2+ years of experience) hit an all-time high during the same period.

Data Source: Electric Capital Developer Report

On the other hand, the number of developers with over two years of experience in the industry not only did not decline during the same period but increased, reaching an all-time high and contributing approximately 70% of the code. Maria Shen, GP at Electric Capital, puts it bluntly: "When we look at the established developers cohort, it's growing, and it looks very healthy."

They stay not because they lack other options.

Technologically, the core work in crypto now revolves around infrastructure development that generally requires years of accumulation to master: protocol development, security auditing, cross-chain architecture. These tasks cannot be picked up quickly and are not jobs that vanish just because market hype fades.

Economically, many veterans hold tokens that haven't fully vested, governance power within protocols, and equity ties. Their accumulated stake in this industry has formed real barriers to exit and tangible returns.

Looking at ecosystem distribution, they are voting with their feet: Bitcoin developer numbers grew by 64.3% in two years, Solana by 11.1%, while Cosmos saw a decline of 51.1% and Polkadot a decline of 46.9%. Veterans are consolidating into ecosystems with real users and revenue, leaving projects that still rely on narratives.

▲ Source: Coincub Web3 Jobs Report 2025

Data Source: Web3.Career

The changing structure of job roles confirms the same trend. Among new Web3 positions created in 2025, the highest proportion wasn't for developers, but for Project & Programme Management, accounting for over 27%.

This is counterintuitive for an industry often touted as technology-driven. However, the logic is not complex: the industry is transitioning from a construction phase to an execution phase. Over 100 chains need integration. Institutional clients entering the space demand completely different standards for compliance and security. DAO governance requires balancing the interests of various stakeholders.

This isn't project management in the traditional sense; it's coordination and judgment in an environment where the rules are still being formed.

While the industry appears to be shrinking on the surface, its core density is increasing. The bear market of 2018-2019 also saw significant developer attrition, yet it spawned phenomenal projects like Uniswap, Aave, and OpenSea, which defined the 2020-2021 bull run. The builders who remain this cycle have more mature infrastructure, and the AI era provides them with an even larger stage than before.

What Capabilities Do Those Who Stayed Possess?

What specific capabilities has the crypto industry actually forged in its builders? To answer this, we must return to the foundational principle of blockchain. Through bull and bear market cycles, this industry has always operated on the same underlying rule: code is law, execution is finality.

During The DAO event in 2016, an attacker exploited a recursive call vulnerability to divert $36 million. The code had no bugs; the logic executed exactly as expected; only the boundaries weren't anticipated by the designers. In 2021, the Poly Network cross-chain bridge was attacked, with $610 million transferred within hours.

No platform could stop it, no institution could reverse it, no legal clause could recover it. This is a structural characteristic that differentiates crypto from almost every other industry: zero margin for error, virtually no room for post-hoc intervention.

This environment has forged a set of abilities rarely needed in other industries: the ability to build a functioning system from scratch that strangers are willing to participate in, under conditions where both rules and trust are absent.

This capability has two levels. The first is establishing trust from zero, relying on no external authority, using only code and mechanisms to make strangers willing to place real assets into the system. The second is making judgments amid technical and economic uncertainty, designing a system that can operate without a regulatory framework, historical data, or industry standards to reference.

Both levels have specific validation within crypto. Uniswap has no corporate guarantee, no KYC, no customer service. Anyone placing funds into a liquidity pool relies solely on trust in a few hundred lines of code and an economic mechanism, yet it achieves billions of dollars in daily trading volume.

MakerDAO has no central bank backing, no deposit insurance. It maintains DAI's stability purely through on-chain governance and collateral mechanisms.

The DeFi Summer period was even more extreme: no regulatory framework, no audit standards, no historical data to reference. Builders designed AMMs, lending protocols, and liquidity mining, going from concept to billions of dollars in TVL in just months. While this capability manifests differently in builders at the protocol, application, and governance layers, the underlying principle is the same.

The AI era is creating a structurally similar problem. Model decision-making processes are opaque, and output results cannot be independently verified. AI agents are starting to execute transactions and allocate funds autonomously, yet the accompanying rule systems and constraint mechanisms do not yet exist.

Large model companies control both the models and the evaluation standards, leaving users with little effective means of verification. Computing power is highly concentrated among a few top tech giants, leading to monopoly pricing when demand surges. These issues point to the same core: the trust problem of autonomous systems, replayed on the grander scale of AI.

Crypto builders have been dealing with these types of problems for years in environments without external authoritative rules, except previously the arena was on-chain protocols, and now it has shifted to AI. A cohort of people has already directly applied the capabilities honed in crypto to AI, and achieved results.

How Are These Capabilities Being Repriced in the AI Era?

Cases of transitioning from crypto to AI are common in recent years, but upon closer inspection, what they bring with them differs.

The most straightforward path is the direct transfer of hardware and experience. The three co-founders of CoreWeave, Michael Intrator, Brian Venturo, and Brannin McBee, started mining Ethereum with GPUs in 2017, scaling from a single unit to thousands. They shut down their mining operations in 2022. Two months later, ChatGPT launched, transforming their GPUs directly into AI computing supply. In March 2025, they listed on Nasdaq with an IPO valuation of ~$23 billion, with their market cap peak later approaching $70 billion.

OpenSea co-founder Alex Atallah dealt with aggregation and routing issues for highly heterogeneous assets in the NFT marketplace. He applied this same experience to AI model routing, founding OpenRouter, which served over 5 million developers within two years, reaching a valuation of $500 million.

Another type of migration is more noteworthy. NEAR founder Illia Polosukhin is a co-author of the seminal "Attention Is All You Need" paper. He left Google initially intending to build AI applications using natural language. During development, he encountered a real-world problem: needing to make cross-border payments to data-labeling workers globally, many of whom lacked bank accounts. Blockchain technology became the optimal solution for this payment challenge.

Now, NEAR is transitioning into an AI infrastructure platform, with core directions in user-owned AI and decentralized confidential machine learning (DCML), allowing users to utilize AI services without exposing their data. The experience in decentralized architecture accumulated at NEAR has become its most difficult-to-replicate starting point in this direction.

Circle co-founder Sean Neville left to found Catena Labs, positioned as an AI-native bank, directly transferring understanding of stablecoin infrastructure to AI agent financial scenarios. a16z crypto led an $18 million seed round.

Nader Dabit, a veteran developer from Aave and Lens Protocol, moved to Cognition, bringing experience in building developer ecosystems from multiple crypto protocols into the AI agent tooling space.

What this group takes with them is not just GPU hardware or user networks. It's an intuition for mechanism design, experience in building developer ecosystems, and the judgment to construct trustworthy systems from scratch when rules are absent. These capabilities correspond precisely to the three structural gaps encountered during AI scaling.

Aggregation and Optimization of Computing Power

Computing power is the most immediate bottleneck for AI scaling. Training and inference require vast numbers of GPUs. Demand fluctuates wildly, cloud vendors are expensive and have queues, and enterprises don't want to stockpile hardware themselves. This problem has two layers: how to aggregate and allocate computing power, and how to use the aggregated power more efficiently. Crypto builders have directly transferable experience in both layers.

Hyperbolic addresses the issues of allocation and trust. Founder Jasper Zhang brought decentralized mechanism design into the AI computing power track: tokens incentivize scattered GPU holders to contribute idle computing power. But the more critical problem is trust.

Why should you believe a stranger node's computation result is correct? The core innovation, PoSP (Proof of Sampling and Punishment), uses random sampling plus game theory to make honesty the dominant strategy for nodes. It requires no full verification, has low overhead, is scalable, and provides reliable results. This mechanism is a direct migration from the crypto logic of verifying the behavior of unknown nodes.

MoonMath addresses the efficiency problem. Its predecessor, Ingonyama, focused on ZK hardware acceleration, increasing the speed of ZK proof generation several times under extreme computational constraints.

Now its direction has shifted to a Physical AI performance layer, working on sparse attention acceleration for video diffusion models (LiteAttention), low-rank decomposition for FFN layers (LiteLinear), and acceleration of training backpropagation (BackLite). From ZK acceleration to AI inference acceleration, the underlying skill is the same: making mathematics run faster under extreme computational constraints. The track has changed, but the accumulated expertise has not been wasted.

AI Governance and Incentive Mechanism Design

When multiple AI agents begin collaborating on tasks, how do you ensure they don't undermine the overall system while pursuing their individual objectives? Each participant pursues its own goal function, with no guarantee the system will function correctly when they are combined, and agent execution speeds far exceed the window for human intervention.

This is precisely the type of problem crypto builders have repeatedly tackled in DAO governance and tokenomics design: getting participants with entirely divergent interests to operate according to the system's intended direction without a central authority. Crypto's answer is economic mechanisms. Non-compliance carries real economic consequences; rules are written into code and executed automatically.

EigenLayer has directly migrated this mechanism to the AI context. Through its restaking mechanism, nodes must stake assets before participating in collaboration. Failure to perform or malicious behavior triggers automatic penalties. The rules are not suggestions; they are hard boundaries backed by real economic cost.

EigenCloud extends this logic to verifiable computation and collaborative governance for AI agents, ensuring agents must stay within predefined bounds while pursuing their goals. Constraining agents with economic mechanisms is far more reliable than constraining them with ethical guidelines.

Autonomous Payment for AI Agents

There's an even more fundamental issue: how do agents pay? Traditional payment systems are designed for humans. Credit cards require account opening, bank transfers require authorization. Every step assumes the operator is human, has an identity, and will wait. Agents don't wait. They can initiate a massive number of requests per second, each potentially involving a micropayment. Traditional payment rails fail directly in this scenario.

Stablecoins and on-chain rules are the infrastructure crypto builders have already built. They are natively programmable, permissionless, and operate 24/7. These three characteristics are precisely the hard requirements for the agent payment scenario. What was missing was a protocol layer to connect stablecoins into the agent workflow.

x402 was launched by Coinbase in May 2025. It activates the HTTP 402 status code, embedding stablecoin payments directly into HTTP requests. An agent completes payment when initiating a request. No account is needed, and settlement takes about two seconds.

As of April 2026, the x402 protocol has processed over 165 million transactions, with a cumulative transaction volume of approximately $50 million and 69,000 active agents (data source: x402 Foundation). Cloudflare, AWS, Stripe, and Anthropic MCP have all integrated with it. Agent payment is already a track with real traffic.

These three directions correspond to three structural gaps encountered by AI scaling: aggregation and efficiency of computing power, incentive alignment for multi-agent collaboration, and infrastructure for autonomous payments. Traditional software architectures have no ready-made answers for these problems, but the crypto industry possesses corresponding experience in handling each one. Capabilities haven't disappeared; they've just found new application scenarios.

The Builder's New Role: From Writing Contracts to Setting Rules for AI

The scaling of AI is creating a functional gap that didn't exist before. It's not a gap in technical talent, but a gap in people who can design trust mechanisms for autonomous systems. As the served entity shifts from humans to AI, the role of the crypto builder is being redefined.

The table below compares the dimensional changes in specific functional paradigms:

The core difference between the two paradigms isn't the technology stack, but the method of establishing trust and the logic of rule execution. In the Pre-AI era, crypto builders faced human participants. Rules were written into contracts, the margin for error was zero, but the system's boundaries were relatively clear.

In the AI-Native era, when the interaction target becomes autonomously operating AI agents, the problem to solve is: agent behavior is unpredictable, execution speed far exceeds the human intervention window, and the system's boundaries themselves need redefinition under greater uncertainty.

The functional positioning of the crypto builder is shifting from "writing secure contracts" to "designing trustworthy mechanisms for autonomous AI systems."

Recruitment by top institutions already reflects this change:

▲ Core AI/Data positions actively opened by top exchanges in Q1 2026

Source: Gate Research Institute

Recruitment at top exchanges and institutions in 2026 clearly reflects this trend: They are no longer simply hiring AI engineers or crypto developers, but seeking people who can bridge the two – those who understand on-chain incentive distortions and governance games, can deeply embed AI tools into crypto workflows, and design mechanisms that align agents with regulation and users over the long term.

Capital allocation directions also reflect this judgment. Paradigm is raising a new fund of up to $1.5 billion, expanding its investment scope from crypto to AI and robotics.

Haun Ventures closed a $1 billion Fund II, focusing on the convergence of crypto and AI in financial infrastructure, specifically payments, stablecoins, and agent-to-agent economies supporting autonomous trading and coordination by AI agents.

a16z crypto raised $2.2 billion for its fifth fund (Crypto Fund V), explicitly stating it will be 100% deployed into the crypto space. Facing the complexity and opacity of the AI era, it will focus on application directions leveraging crypto's transparency, verifiability, and decentralization.

Furthermore, according to PitchBook data, approximately 40% of US VC investment in the crypto space in 2025 flowed to companies simultaneously involved in AI business, a significant increase from 2024.

While both involve crypto builders moving into AI, the chosen paths differ significantly based on the market environment.

In the US, as the regulatory environment has become relatively clearer, protocol-level innovation has gained real room to survive. Capital network density is high, the path from idea to funding is short, and there is a larger margin for error.

Projects like Hyperbolic, EigenCloud, Gens