Author: Xinyang & Ethan, IOSG
In 2026, the GitHub activity curve of the Crypto open-source community completed a remarkable "bottoming out." The monthly active developers dropped from the peak of 45K in 2022 to around 23K. This halving on paper sparked discussions about "narrative exhaustion" on social media. However, when we dissect a cross-section of this curve, we see not an industry in decline, but a profound "talent deleveraging."
▲ Data Source: Electric Capital Developer Report, based on Crypto Ecosystems Github
I. Who Left? Who Remains?
The majority who left were newcomers. Monthly new developers peaked at 5,462 in February 2024, then dropped sharply, with a 52% churn rate for those with less than a year of experience. This group mostly flooded in during the bull market, working on NFT minting contracts, forking DeFi protocols, and building frontends for new L2s. These roles were highly dependent on market hype. Once the hype faded and projects ceased operations, the jobs disappeared. Data shows newcomers' code contributions never exceeded 25% of the total; they were never in the industry's core circle from the start.
▲ Newcomers flooded in with the bull market, left with the bear market; Established devs (2+ years experience) reached a historical high during the same period.
Data Source: Electric Capital Developer Report
On the other hand, developers with over two years of experience not only didn't decline but actually increased during the same period, hitting a historical high and contributing about 70% of the code volume. The judgment from Electric Capital GP Maria Shen is straightforward: "When we look at the established developers segment, it's growing, and it looks very healthy."
They didn't stay because they lacked other options.
Technically, core crypto work now is infrastructure development—protocol layer development, security audits, cross-chain architecture—which typically requires years of accumulation to truly master. This work isn't easily eliminated when market heat fades.
Economically, many veterans have unvested tokens, governance power within protocols, and equity relationships. Their accumulated experience in this industry has formed real barriers and returns. Looking at ecosystem distribution, they are voting with their feet: Bitcoin developers grew 64.3% in two years, Solana +11.1%, while Cosmos fell 51.1% and Polkadot fell 46.9%. Veterans are converging on ecosystems with real users and revenue, leaving projects still reliant on narratives.
▲ Source: Coincub Web3 Jobs Report 2025
Data Source: Web3.Career
Changes in job structure also confirm this trend. In 2025, among new Web3 positions, the highest proportion wasn't developers, but Project & Programme Management, exceeding 27%. This is counterintuitive for a technology-driven industry, but the underlying logic is clear: the industry is transitioning from a building phase to an execution phase. Over 100 chains need integration; institutional clients bring completely different compliance and security requirements; DAO governance requires balancing stakeholders with diverse interests. This isn't traditional project management but coordination and judgment in an environment where rules are still being formed.
The industry appears to be shrinking on the surface, but its core density is actually increasing. The 2018-2019 bear market also saw significant developer outflow, but was followed by the emergence of phenomenal projects like Uniswap, Aave, OpenSea, which defined the 2020-2021 bull market. The builders who remain this round have more mature infrastructure, and the AI era provides them with a larger stage than the previous cycle.
II. What Are the Capabilities of Those Who Stayed?
What special skills has the crypto industry actually developed in builders? To answer this, we need to return to the underlying principles of blockchain. Throughout bull and bear cycles, this industry has always operated under the same fundamental rule: code is law, execution is finality.
The 2016 DAO hack, where an attacker exploited a recursive call vulnerability to drain $36 million. The code had no bug; logic executed exactly as intended, but boundaries weren't anticipated by the designer. The 2021 Poly Network cross-chain bridge hack, where $610 million was transferred within hours. No platform could halt it, no institution could reverse it, no legal recourse existed. This is the structural feature distinguishing crypto from almost all other industries: zero tolerance for error, and almost no possibility for post-facto intervention.
This environment forces the development of a set of skills rarely needed in other industries: building functioning systems from scratch, under conditions lacking rules and trust, that strangers are willing to participate in.
This capability has two levels. First, establishing trust from scratch, relying on no external authority, only code and mechanisms to make strangers willing to deposit real assets. Second, making judgments under dual technical and economic uncertainty, designing functioning systems without regulatory frameworks, historical data, or industry standards to reference.
Both levels have been concretely validated in crypto. Uniswap has no corporate guarantee, no KYC, no customer service. Anyone providing liquidity to its pools relies solely on trust in a few hundred lines of code and an economic mechanism, achieving tens of billions in daily volume. MakerDAO has no central bank backing, no deposit insurance, maintaining DAI's stability purely through on-chain governance and collateral mechanisms. During DeFi Summer it was even more extreme: no regulatory framework, no audit standards, no historical data to reference. Builders designed AMMs, lending protocols, liquidity mining, moving from concept to tens of billions in TVL within months. This capability manifests differently in protocol layer, application layer, and governance layer builders, but the underlying principle is the same.
The AI era is creating a structurally similar problem. Model decision-making is opaque, outputs cannot be independently verified. AI agents are beginning to autonomously execute transactions and allocate funds, with no accompanying rule systems or constraint mechanisms. Large model companies control both models and evaluation standards, leaving users without effective verification methods. Computing power is highly concentrated among a few major players, creating monopolistic pricing when demand surges. These issues point to the same core: the trust problem of autonomous systems is replaying at AI's larger scale.
Crypto builders have been dealing with this type of problem in environments with no external authoritative rules for years, just in the context of on-chain protocols. Now the context is shifting to AI. And a group of people have already taken capabilities accumulated in crypto directly into AI, producing results.
III. How Are These Capabilities Being Repriced in the AI Era?
Cases of transitioning from crypto to AI have become common in recent years, but upon analysis, what they bring with them differs.
The most direct path is the literal transfer of hardware and experience. CoreWeave's three founders, Michael Intrator, Brian Venturo, and Brannin McBee, started mining Ethereum with GPUs in 2017, scaling from one machine to thousands. They shut down mining operations in 2022. Two months later, ChatGPT was released. Their GPU holdings directly became AI compute supply. They listed on Nasdaq in March 2025 with an IPO valuation of approximately $23 billion, later peaking near $70 billion in market cap.
OpenSea co-founder Alex Atallah dealt with aggregating and routing highly heterogeneous assets in the NFT marketplace. He applied the same experience to AI model routing, founding OpenRouter, which served over 5 million developers within two years, reaching a $500 million valuation.
Another type of transition is more noteworthy. NEAR founder Ilia Polosukhin is a co-author of the Transformer paper. After leaving Google, his initial goal was to build AI applications using natural language. During development, he encountered a practical problem: making cross-border payments to data annotation workers worldwide, 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, focusing on user-owned AI and decentralized confidential machine learning (DCML), enabling users to utilize AI services without exposing their data. The decentralized architecture experience accumulated at NEAR has become the most difficult-to-replicate starting point in this direction.
Circle co-founder Sean Neville left to found Catena Labs, positioning it as an AI-native bank, directly transferring his understanding of stablecoin infrastructure to AI agent financial scenarios, with a16z crypto leading an $18 million seed round. Aave and Lens Protocol veteran developer Nader Dabit shifted to Cognition, bringing his experience building developer ecosystems across multiple crypto protocols into the AI agent tooling space.
This group took with them not just GPU hardware or user networks, but intuitions for mechanism design, experience building developer ecosystems, and judgment to build trusted systems from scratch in the absence of rules. These capabilities precisely address three structural gaps in AI's scaling.
Compute Aggregation and Optimization
Compute is the most direct bottleneck for AI scaling. Training and inference require massive GPUs, demand is volatile, cloud providers are expensive with queues, and companies don't want to stockpile hardware. This problem has two layers: how to aggregate and allocate compute, and how to use aggregated compute more efficiently. Crypto builders have directly transferable experience on both layers.
Hyperbolic addresses the distribution and trust problem. Founder Jasper Zhang brought decentralized mechanism design into the AI compute sector: tokens incentivize distributed GPU owners to contribute idle compute, but the core problem is trust.
How can you trust the computational results from an unknown node? The core innovation, PoSP (Proof of Sampling Participation), uses random sampling and game theory to make honesty the dominant strategy for nodes, avoiding full verification, with low overhead, scalability, and reliable results. This mechanism is directly migrated from the logic of verifying unknown node behavior in crypto.
MoonMath addresses the efficiency problem. Its predecessor, Ingonyama, focused on ZK hardware acceleration, boosting ZK proof generation speed several times under extreme computational constraints. Now pivoting to Physical AI performance layer, working on sparse attention acceleration for video diffusion models (LiteAttention), low-rank factorization for FFN layers (LiteLinear), and training backpropagation acceleration (BackLite). The underlying capability is the same: making math run faster under extreme computational constraints. The sector changed, but the accumulated expertise wasn't wasted.
AI Governance and Incentive Mechanism Design
When multiple AI agents begin collaborating on tasks, how do we ensure they don't disrupt the overall system while pursuing their own objectives? Each participant pursues its own objective function, with no guarantee the combined system functions properly. And agent execution speed far exceeds the window for human intervention.
This is the type of problem crypto builders have repeatedly dealt with in DAO governance and tokenomics design: making participants with completely different interests operate according to the system's preset direction without a central authority. Crypto's answer is economic mechanisms: violations incur real economic costs, rules are coded, automatically executed.
EigenLayer directly migrated this mechanism to the AI scenario. Through its restaking mechanism, nodes must stake assets before participating in collaboration. Non-performance or violation triggers automatic slashing. Rules aren't suggestions; they are rigid boundaries with real economic consequences. EigenCloud extends this logic to verifiable computation and collaborative governance for AI agents, forcing agents to stay within preset bounds while pursuing their goals. Constraining agents with economic mechanisms is far more reliable than with ethical guidelines.
AI Agent Autonomous Payments
There's an even more fundamental problem: how do agents pay? Traditional payment systems are designed for humans: credit cards require accounts, bank transfers require authorization, each step assumes a human operator with identity and patience. Agents don't wait; they may initiate numerous requests per second, each potentially involving micropayments. Traditional payment pipelines fail directly in this scenario.
Stablecoins and on-chain rules are infrastructure crypto builders have already built, natively supporting programmability, permissionlessness, and 24/7 operation. These three characteristics are precisely the hard requirements for agent payment scenarios. What's missing is just a protocol layer connecting stablecoins to agent workflows.
x402, launched by Coinbase in May 2025, activates the HTTP 402 status code, embedding stablecoin payments directly into HTTP requests. An agent completes payment while initiating a request, no account needed, settlement in about two seconds. As of April 2026, the x402 protocol has processed over 165 million transactions, cumulative volume ~$50 million, with 69,000 live agents (source: x402 Foundation). Cloudflare, AWS, Stripe, Anthropic MCP have integrated. Agent payments are already a sector with real traffic.
The three directions correspond to three structural gaps in AI scaling: compute aggregation and efficiency, incentive alignment for multi-agent collaboration, and infrastructure for autonomous payments. These problems have no ready-made answers in traditional software architecture, but crypto has corresponding experience. The capabilities haven't vanished; they've found new application scenarios.
IV. The Builder's New Role: From Writing Smart Contracts to Defining Rules for AI
AI scaling is creating a functional gap that didn't previously exist. It's not a gap for technical talent, but for people who can design trust mechanisms within autonomous systems. As the service target shifts from humans to AI, the role of crypto builders is being redefined.
The table below contrasts the dimensional changes in specific functional paradigms:
The core difference between the two paradigms isn't the tech stack, but the way trust is established and rules are enforced. In the Pre-AI era, crypto builders faced human participants. Rules were written into contracts, with zero tolerance for error, but system boundaries were relatively clear.
In the AI-Native era, when the interacting entities become autonomously operating AI agents, the problem to solve is: agent behavior is unpredictable, execution speed far exceeds human intervention windows, and the system's boundaries themselves need redefining under greater uncertainty. The functional positioning of crypto builders is shifting from "writing secure smart contracts" to "designing trusted mechanisms for AI autonomous systems."
Hiring at top institutions already reflects this change:
▲ Core AI/Data positions actively opened by top exchanges in Q1 2026
Source: Gate Research Institute
Hiring 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 connect both sides—understanding on-chain incentive distortions and governance games, while deeply embedding AI tools into crypto workflows and designing mechanisms to align agents with regulation and users long-term.
Capital allocation also reflects this judgment. Paradigm is raising a new fund targeting up to $1.5 billion, expanding its investment scope from crypto to AI and robotics. Haun Ventures completed a $1 billion Fund II, focusing on crypto-AI convergence in financial infrastructure, particularly payment, stablecoin, and agent-to-agent economic systems supporting AI agent autonomous transactions and coordination.
a16z crypto completed a $2.2 billion fifth fund (Crypto Fund V), explicitly stating 100% allocation to crypto. Facing the complexity and opacity of the AI era, they will focus on applying crypto's transparency, verifiability, and decentralization. According to PitchBook data, in 2025 US crypto VC investments, about 40% of capital went to companies also involved in AI business, a significant increase from 2024.
Similarly, crypto builders transitioning to AI choose different paths under different market conditions, showing clear divergence.
In the US, as the regulatory environment clarified, protocol-layer innovation gained real survival space. High capital network density, short paths from idea to funding, and greater tolerance for error. Projects like Hyperbolic, EigenCloud, Gensyn, Ritual share a common characteristic: designing new mechanisms from scratch, not simple application integration on existing systems. Top VCs have clear investment theses on directions like "verifiable computation, agent coordination, decentralized ML," willing to provide ample room for early technical exploration.
Asia is different. Singapore and Hong Kong play roles in compliance implementation and institutional capital flow, with relatively conservative regulatory frameworks and lower tolerance for pure protocol-layer innovation. Crypto-background builders pivoting to AI more often choose application-layer and industry integration paths—leveraging crypto-accumulated user bases, payment capabilities, or data assets to quickly integrate AI products and services.
This isn't a capability gap, but a divergence in path choice due to different market signals and regulatory environments: the US encourages underlying mechanism innovation and early-stage tech exploration, while Asia emphasizes compliance-friendliness, rapid monetization, and deep integration with traditional industries.
Returning to that initial GitHub curve. Monthly active developers dropping from 45K to 23K superficially looks like industry contraction. But among those who remain, the proportion of established devs hit a historical high, converging on ecosystems with real users, while being repriced by the AI industry in unprecedented ways.
When AI scaling encounters structural bottlenecks like compute aggregation, autonomous agent payments, data/decision verifiability, and privacy coordination, these builders' long-accumulated sensitivity to rules, incentives, and authenticity is gradually transforming into a system-level capability scarce in the AI era.
As an investment firm deeply rooted in crypto infrastructure since 2017, IOSG's judgment on this line isn't just observational. We invested in EigenLayer's restaking mechanism before it was widely recognized, led the seed round for Ingonyama (now MoonMath) betting on ZK hardware acceleration's migration to the AI performance layer, and invested in Hyperbolic in 2024, supporting its path of using crypto-native verification mechanisms to solve decentralized compute trust problems.
The common logic behind these investments is: trust, coordination, and verification problems encountered during AI scaling will ultimately require mechanism design capabilities accumulated in the crypto industry. We believe the convergence of crypto and AI is not a narrative, but a structural opportunity unfolding now.
