Blockchain Engineers
15-1299.07Median wage $116,580/yr435,370 employed (US)Rank #172 of 923 scored · top 19% by substitution
Maintain and support distributed and decentralized blockchain-based networks or block-chain applications such as cryptocurrency exchange, payment processing, document sharing, and digital voting. Design and deploy secure block-chain design patterns and solutions over geographically distributed networks using advanced technologies. May assist with infrastructure setup and testing for application transparency and security.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution40
Exposure35
Augmentation71
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
17 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
12%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%35
panel mean rating 2.4/5 → substitution pressure 35/100
Technical feasibility todayw 20%35
panel mean rating 2.4/5 → substitution pressure 35/100
Cost vs. human wagew 15%37
panel mean rating 2.5/5 → substitution pressure 37/100
Adoption barriersw 20%inverted — strong barriers lower the score53
panel mean rating 2.9/5 (barrier strength) → substitution pressure 53/100
Sector adoption velocityw 10%49
panel mean rating 3.0/5 → substitution pressure 49/100
Task breakdown (17 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Automate the deployment of software updates over geographically distributed network nodes.
75CI 64–86 · exposure 75 · augmentation 75 · click for rater detail
Automate the deployment of software updates over geographically distributed network nodes.
75| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Blockchain, fintech, and Web3 companies operate in highly digitized environments and have aggressively adopted CI/CD and automated deployment pipelines as industry standard practice. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Software/DevOps and blockchain sectors are moderately fast adopters of automation and AI tooling, but decentralized node deployment often involves conservative, security-conscious practices that slow full automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Automation tools greatly augment blockchain engineers by handling routine deployment mechanics, freeing them to focus on architecture, validation, and incident response; the human remains in the loop for critical decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI coding assistants and infrastructure-as-code tools substantially speed up writing, testing, and debugging deployment scripts and configurations for distributed systems, meaningfully boosting engineer productivity. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current CI/CD tools and container orchestration systems (Kubernetes, Docker) can fully automate deployment of software updates across distributed nodes with minimal human intervention. The task is largely technical and repeatable, meeting the ≥50% time-saving threshold, though some validation and monitoring oversight may remain. |
| Task automatability | claude-sonnet-5 | 4/5 | Deployment automation over distributed nodes is a well-established DevOps pattern with CI/CD tools; AI coding assistants can generate and maintain such pipelines, though blockchain-specific node coordination and rollback safety still need engineering oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While security and regulatory compliance (especially in regulated blockchain contexts) may require human review and sign-off, the automation itself faces minimal legal barriers. Some organizations require additional oversight, but the task can be substantially automated without licensing requirements. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform deployments, but blockchain systems carry high liability for consensus-breaking bugs or downtime, creating some organizational caution around full autonomy. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated deployment infrastructure costs a fraction of the loaded wage for a human DevOps/blockchain engineer managing manual deployments; cloud platforms offer economies of scale that make per-deployment costs negligible. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Building and maintaining automated deployment pipelines requires significant upfront engineering and ongoing oversight, so while cheaper than fully manual deployment, it isn't dramatically cheaper than a competent engineer using existing tools with AI assistance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature, production-deployed systems like Kubernetes, Ansible, GitOps platforms (ArgoCD, Flux), and cloud-native deployment services reliably perform this task at scale across thousands of organizations today. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Mature CI/CD and orchestration products (Ansible, Kubernetes, Terraform) already automate distributed deployments, and AI copilots assist in writing/maintaining these scripts, but fully autonomous, blockchain-aware node update orchestration without human review is not yet standard practice. |
Determine specifications for, or implement, logging.
74CI 61–86 · exposure 62 · augmentation 88 · click for rater detail
Determine specifications for, or implement, logging.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Software engineering, including blockchain development, is a digitally mature, fast-adopting sector. AI code-generation and logging tools are already in production use across crypto/blockchain teams, venture-backed startups, and enterprises. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Software engineering, including blockchain development, is a fast-adopting sector for AI coding tools, with widespread use of AI pair-programming in production pipelines. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI dramatically accelerates logging specification and boilerplate generation, freeing engineers to focus on higher-level logging strategy, monitoring integration, and debugging. Engineers remain in control and the assistant raises throughput significantly. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting logging specs, boilerplate code, and documentation, letting engineers focus on blockchain-specific edge cases while remaining in the loop. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Logging implementation is highly structured and rule-driven: specifying log formats, output destinations, levels, and implementing logger configuration can be largely automated by AI. Current systems can generate logging code with consistent quality and style, though human review of business logic implications may be needed. |
| Task automatability | claude-sonnet-5 | 3/5 | AI coding assistants can draft logging specifications and implement standard logging code (log levels, formats, storage) from requirements, but tailoring to blockchain-specific node/transaction monitoring and security needs still requires human judgment.: |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Logging is a support task with no legal/licensing requirement and no regulatory barrier specific to automation. Code review and integration oversight remain, but do not prevent automated logging implementation; few organizational policies restrict AI-assisted logging configuration. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or human-contact requirement blocks AI-assisted logging design or implementation for blockchain systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven logging implementation costs nearly nothing per task (marginal inference cost), while a human blockchain engineer's loaded hourly rate is substantial. The cost ratio strongly favors AI once the tool is deployed. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating logging code and configuration is inexpensive via LLM-based coding tools compared to an engineer's time, though review and blockchain-specific tuning still requires paid human oversight. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed code generation tools (GitHub Copilot, Claude, ChatGPT) reliably assist with and in many cases fully implement logging specifications and code. Production systems in software engineering teams use these tools for logging setup regularly, with minimal error rates for standard logging tasks. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Code-generation tools like Copilot or Cursor reliably scaffold logging frameworks in production settings, but determining what to log for blockchain-specific compliance/security needs is less mature and not fully automated in deployed products. |
Design and implement dashboard and data visualizations to meet customer reporting needs.
57CI 51–62 · exposure 50 · augmentation 75 · click for rater detail
Design and implement dashboard and data visualizations to meet customer reporting needs.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Blockchain and fintech sectors show moderately fast AI tool adoption for development workflows, but dashboard-specific automation remains in pilot and early production phases; most teams use AI as a code-generation aid rather than end-to-end replacement. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Software engineering and data visualization tooling is in a fast-adopting sector (tech/software), with AI-assisted coding tools seeing rapid, deep uptake among developers. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at accelerating dashboard development by suggesting visualization code, auto-generating boilerplate, and proposing design patterns, significantly raising productivity of human engineers who retain control over requirements and design validation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI coding assistants and BI copilots meaningfully speed up writing visualization code, suggesting chart types, and debugging, while the engineer still steers requirements and final design choices. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can assist with code generation for dashboard templates, data visualization libraries, and standard layouts, but requires substantial human direction on requirements gathering, design decisions, and custom logic specific to customer needs. End-to-end automation would require detailed specifications and significant oversight, achieving only moderate time savings on routine components. |
| Task automatability | claude-sonnet-5 | 3/5 | AI coding assistants can generate dashboard scaffolding and visualization code from requirements, but translating ambiguous customer needs into a finished, polished dashboard still requires significant human iteration and design judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Weak barriers exist since dashboard design is not legally restricted and doesn't require licensure, but customer expectations for reliability, security of financial/blockchain data, and organizational preference for human accountability on critical reporting create moderate friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or liability barriers restrict who builds dashboards; this is a purely technical/business task with no legal requirement for human sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI can reduce initial development time on boilerplate code, the full cost of AI-generated dashboards including quality assurance, integration troubleshooting, and human refinement remains comparable to or potentially higher than experienced engineers for production-grade systems meeting customer requirements. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cut development time for boilerplate visualization code substantially, but integration, requirements-gathering, and customization still need engineer oversight, keeping costs roughly comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed AI tools (GitHub Copilot, ChatGPT) can generate dashboard code and visualization components reliably for standard patterns, but real-world production systems require integration with specific backend APIs, custom styling, and validation—areas where AI performance remains inconsistent and error-prone without expert review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like GitHub Copilot, Cursor, and low-code BI tools with AI features (e.g., Power BI Copilot, Tableau's AI features) exist and are used in production, but they handle narrow slices of the workflow rather than the full design-to-implementation pipeline reliably. |
Run infrastructure tests to examine the behavior of large peer-to-peer networks.
56CI 35–77 · exposure 50 · augmentation 63 · click for rater detail
Run infrastructure tests to examine the behavior of large peer-to-peer networks.
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Blockchain and distributed systems engineering is a high-tech, digitally native sector with rapid adoption of CI/CD, automated testing, and infrastructure-as-code patterns. Major projects and exchanges have already deployed extensive automated test infrastructure. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Blockchain engineering is a niche, still-maturing field with uneven AI tooling adoption; testing automation exists but deep AI-driven adoption for this specific task is limited compared to mainstream software sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted analysis of test results, anomaly detection, and test-case generation can significantly enhance engineer productivity, flagging issues and suggesting root causes while the engineer remains in the loop for validation and decision-making. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help generate test scripts, analyze logs, and flag anomalies in network behavior, meaningfully assisting engineers though human interpretation of complex distributed system dynamics remains essential. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Running infrastructure tests on P2P networks can be substantially automated using current tools: test frameworks, simulation environments, and monitoring systems can execute predefined test suites, collect metrics, and generate reports with minimal human intervention. However, interpreting complex network behavior anomalies and designing novel test scenarios still typically requires human expertise, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 2/5 | Test execution can be scripted, but designing meaningful large-scale peer-to-peer network tests, interpreting emergent distributed-systems behavior, and diagnosing failures requires significant human engineering judgment that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No regulatory or licensure barriers exist; infrastructure testing is not legally gated. Minor organizational friction may arise from team buy-in and integration with existing DevOps practices, but nothing prevents substitution with automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human execution, but organizational trust in correctly validating critical blockchain infrastructure creates moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated test infrastructure costs (cloud compute, CI/CD pipelines, monitoring tools) are typically an order of magnitude cheaper than hiring full-time test engineers to manually run and analyze network tests. Ongoing oversight costs are minimal once systems are configured. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Running large-scale network simulations still requires substantial compute and human oversight to interpret results, so AI-assisted approaches are not yet dramatically cheaper than skilled engineer time for this specialized task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products and frameworks (e.g., Kubernetes, Prometheus, test harnesses, network simulators) demonstrably perform infrastructure testing at scale in production blockchain and distributed systems environments. These tools are widely deployed, though integration complexity and domain-specific configuration still require skilled operators. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CI/testing automation and monitoring tools exist for distributed systems, but no deployed AI product autonomously designs and runs comprehensive P2P network infrastructure tests reliably in production. |
Discuss data needs with engineers, product managers, or data scientists to identify blockchain requirements.
52CI 38–67 · exposure 45 · augmentation 75 · click for rater detail
Discuss data needs with engineers, product managers, or data scientists to identify blockchain requirements.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Blockchain/crypto firms are tech-forward and increasingly adopt AI tooling for development workflows, but adoption of AI for requirements-gathering specifically is still in pilot/early production phase rather than widespread deployment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Software/tech sectors (where blockchain engineers work) have moderate-to-fast AI tool adoption for meetings and documentation, though not for full requirements elicitation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist human engineers by drafting requirements summaries, flagging missing acceptance criteria, and synthesizing input from multiple stakeholders, allowing engineers to focus on judgment and final validation rather than transcription and synthesis. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can transcribe, summarize, generate clarifying questions, and draft requirement documents from these discussions, meaningfully boosting productivity while humans remain central to the interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems (including agents with document parsing and meeting transcription) can identify and summarize blockchain requirements from technical conversations with high accuracy, meeting or exceeding a 50% time-saving threshold for requirements compilation and initial documentation. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a collaborative discovery/requirements-gathering conversation involving stakeholder alignment, negotiation, and domain judgment, which current AI cannot conduct autonomously end-to-end despite being able to assist with summarization or note-taking.atibility. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some organizations may prefer human stakeholder judgment for final sign-off, there are no hard regulatory or licensing barriers to AI-assisted or semi-automated requirements analysis in blockchain engineering contexts. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier, but organizational trust, need for real-time judgment, and stakeholder relationship management create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | The cost of running AI for automated requirements extraction and summarization is substantially lower than the loaded hourly cost of multiple senior engineers and product managers sitting in requirements-gathering meetings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human labor cost for facilitating stakeholder discussions is not meaningfully replaced by AI; AI tools add cost as an aid rather than a substitute, so cost ratio favors humans still doing the core task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like Claude, GPT-4, and meeting transcription tools can parse discussions and extract requirements, but they struggle with nuanced technical judgment calls and cross-functional priority weighting that human engineers currently perform in these meetings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product independently runs cross-functional requirements discussions with engineers and product managers; AI meeting assistants exist but only support (transcribe/summarize) rather than perform the discussion itself. |
Develop a maintainable code base using object-oriented design principles, practices, or patterns.
44CI 28–61 · exposure 38 · augmentation 88 · click for rater detail
Develop a maintainable code base using object-oriented design principles, practices, or patterns.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Tech firms are rapidly adopting AI copilots for code drafting (pilot and production use common), but end-to-end architectural design and maintainability decisions remain human-led. Adoption is brisk for assistance but not for autonomous full-stack development. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Software engineering, including blockchain/crypto development, is among the fastest-adopting sectors for AI coding tools, with widespread production use of copilots and agentic coding assistants. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI code generation and pattern-suggestion tools significantly augment developer productivity, helping sketch implementations and reduce boilerplate. Engineers using these tools can iterate faster while maintaining human oversight of architectural integrity. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially boosts developer productivity for boilerplate generation, pattern application, refactoring suggestions, and documentation while the engineer retains control over architecture and design decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with writing some boilerplate code and suggesting design patterns, but cannot consistently make the high-level architectural decisions, refactoring trade-offs, and maintainability judgments required for a full codebase. Human expertise in evaluating long-term maintainability constraints is still essential. |
| Task automatability | claude-sonnet-5 | 3/5 | AI coding assistants can generate substantial OOP code and even scaffold entire modules, but ensuring long-term maintainability across a large codebase still requires human architectural judgment and iterative review, so only part of the task meets the 50% time-saving bar. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Code quality and maintainability are critical business risks; organizations require human architects and senior engineers to sign off on designs, and liability for buggy or unmaintainable code falls on the team. Professional standards and team accountability create strong barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted coding, though liability concerns around smart contract bugs and security audits create some organizational caution before fully trusting AI-generated code. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI can reduce drafting time per line of code, the cost of AI generation plus necessary human oversight and refactoring to ensure maintainability approaches the cost of experienced engineers writing it correctly the first time. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI coding assistants cost a small fraction of an engineer's salary per unit of code output, though human review and correction for blockchain-specific security concerns adds oversight cost that tempers the savings somewhat. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI code generation tools (Copilot, ChatGPT) can produce syntactically correct object-oriented code and suggest patterns, but deployed systems lack the contextual judgment to ensure designs are truly maintainable at scale. Code review and architectural validation remain human-dependent in production. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like GitHub Copilot, Cursor, and Claude-based agents are deployed in production for code generation and refactoring, but reliability on complex, maintainable architecture for blockchain-specific systems (smart contracts, consensus logic) remains inconsistent. |
Update client and server applications responsible for integration and business logic.
42CI 28–57 · exposure 38 · augmentation 88 · click for rater detail
Update client and server applications responsible for integration and business logic.
42| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Blockchain development teams actively use AI coding assistants for productivity gains, but adoption remains cautious due to security and correctness concerns. Pilots and assistive use are common, but full automation of application updates without human oversight is rare in production environments. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Software engineering, including blockchain and fintech-adjacent roles, is among the fastest-adopting sectors for AI coding tools, with widespread production use of AI-assisted development. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI coding assistants substantially augment blockchain engineers by accelerating code generation, suggesting refactorings, and helping with documentation—meaningfully raising productivity while engineers retain full control over design, architecture, and validation decisions. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI coding assistants substantially speed up writing, refactoring, and updating client/server integration code while engineers retain control over architecture, security, and final decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Updating client and server applications for blockchain integration requires understanding business requirements, architecture decisions, and testing—tasks involving judgment and domain context that current AI cannot reliably perform end-to-end. AI can assist with code generation and refactoring, but cannot independently gather requirements, design integration approaches, or validate correctness across complex systems. |
| Task automatability | claude-sonnet-5 | 3/5 | AI coding assistants can generate and update integration/business logic code, but blockchain-specific correctness, security, and system context require significant human review, limiting full end-to-end automation to roughly half the effort saved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: blockchain applications carry high financial and security risk, requiring developer accountability; regulatory frameworks in some jurisdictions mandate human review of financial system updates; and organizational liability concerns prevent full automation of business logic changes without expert sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but financial/security stakes in blockchain systems create real liability concerns that push teams toward mandatory human code review before deployment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI coding assistance reduces time on routine coding tasks, but blockchain application updates involve architecture decisions, security validation, and integration testing that require highly paid engineers. The total cost (engineer oversight + AI inference) remains comparable to or higher than having skilled engineers perform the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce coding time substantially but still require skilled engineer oversight for review, testing, and deployment, so total cost is lower but not an order of magnitude cheaper than a human alone. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI coding assistants (GitHub Copilot, Claude) can generate code snippets and suggest updates, no deployed product reliably performs full application updates with business logic integration without substantial human review and oversight. Error rates in complex system-level changes and integration logic remain too high for production deployment without expert validation. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed coding assistants (Copilot, Cursor, Claude Code) are used in production for code updates including smart contract and backend integration, but reliability on complex, security-sensitive blockchain logic is inconsistent. |
Design and implement data repositories to integrate data.
35CI 32–38 · exposure 25 · augmentation 75 · click for rater detail
Design and implement data repositories to integrate data.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Blockchain and cryptocurrency firms are generally fast adopters of developer tooling, but adoption remains concentrated in tech-native organizations. Mainstream enterprises remain slower, and the specialized nature of blockchain data integration limits the velocity compared to general software engineering. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Software engineering, including blockchain development, sits in a fast-adopting tech sector with growing use of AI coding tools, though full design automation is less mature than code completion. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI code generation and repository pattern suggestions significantly assist blockchain engineers in writing boilerplate, documentation, and test scaffolding, accelerating the implementation phase while the engineer retains design and validation responsibility. This is already producing measurable productivity gains in practice. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI coding assistants substantially speed up schema drafting, boilerplate integration code, and documentation, meaningfully raising engineer productivity while they retain design control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Designing data repositories requires architectural judgment about system requirements, constraints, and long-term maintainability that current AI cannot reliably do end-to-end. AI can assist with boilerplate code generation and documentation, but human architects must make critical design decisions about schema, scalability, and integration patterns. |
| Task automatability | claude-sonnet-5 | 2/5 | Designing data repository schemas and integration architecture requires domain-specific judgment about data models, consistency requirements, and blockchain-specific constraints that current AI can assist but not fully own end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organizations deploying blockchain systems face moderate friction: data repository design must meet regulatory and security standards specific to blockchain (e.g., immutability guarantees), and companies typically require senior engineer sign-off on architecturally critical decisions. Liability and correctness requirements are material but not absolute legal gatekeeping. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational review, security auditing, and engineering sign-off create moderate friction before deployment of AI-generated designs. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI code generation may reduce coding time by 20-30%, but blockchain engineers command high salaries and the AI tools still require expensive specialized oversight. The cost savings do not approach order-of-magnitude reduction when accounting for review, debugging, and architectural validation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can reduce some coding time but the human engineer must still design, review, and validate the architecture, so overall cost savings are modest rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While code generation tools can produce working snippets for data repository implementations, no deployed product reliably designs and implements complete repository systems without significant human oversight and revision. Current AI excels at coding but not at the systems-thinking aspects of integration. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI coding assistants can generate boilerplate schema and integration code, but production-grade repository design for blockchain systems still requires significant human architectural decisions and validation; no product autonomously performs this reliably. |
Evaluate new blockchain technologies and vendor products.
31CI 25–38 · exposure 25 · augmentation 63 · click for rater detail
Evaluate new blockchain technologies and vendor products.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Blockchain engineering remains concentrated in specialist teams and smaller, high-risk sectors; adoption of AI-driven evaluation tools in blockchain is still early-stage and pilots are rare compared to general software development. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Blockchain engineering sits within tech/software sectors with generally fast AI tool adoption, though the niche and specialized nature of blockchain vendor evaluation limits deep penetration currently. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by summarizing technical specifications, comparing feature matrices, identifying documentation inconsistencies, and flagging security patterns, helping engineers spend more time on critical judgment; however, the core evaluative work remains human-led. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up research, summarize technical docs, compare vendor claims, and draft evaluation criteria, significantly aiding an engineer's productivity while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with comparative analysis and documentation review of blockchain technologies, evaluating new vendor products requires nuanced judgment about technical trade-offs, security implications, and organizational fit that heavily depends on context-specific requirements and human expertise. Current AI systems cannot reliably perform end-to-end evaluation at 50% time savings without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Evaluating emerging blockchain technologies and vendor products requires synthesizing technical documentation, real-world testing, security assessment, and strategic judgment about business fit, which current AI can support but not perform end-to-end reliably. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizational and liability barriers are substantial: technology selection decisions carry significant financial and security risk, require executive or architect sign-off, and depend on domain expertise and trust that organizations currently vest in senior human engineers. Regulators and stakeholders expect human accountability for blockchain infrastructure choices. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational trust and risk of costly technical missteps (e.g., choosing insecure blockchain infrastructure) create moderate friction against fully automated vendor evaluation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools (document analysis, code review assistants) cost less than senior engineer time in isolation, but the overhead of integration, result validation, and human review for critical technology decisions keeps the effective cost-per-task comparable to or higher than direct human evaluation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply produce comparison summaries, but genuine evaluation requires hands-on testing, security audits, and engineering judgment that still require costly human expert time, keeping the ratio close to comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs independent technology evaluation for blockchain systems; existing tools can summarize documentation or flag obvious issues, but do not match the judgment-heavy, context-dependent evaluation that blockchain engineers actually perform in production settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously evaluates blockchain vendors or technologies; AI assistants can summarize whitepapers and compare features but lack production-grade tooling for full technical/security vetting. |
Design and deploy blockchain design patterns to make transactions secure, transparent, and immutable.
29CI 25–32 · exposure 25 · augmentation 63 · click for rater detail
Design and deploy blockchain design patterns to make transactions secure, transparent, and immutable.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Blockchain adoption itself remains niche (finance, supply-chain tracking), and organizations deploying blockchain systems are highly cautious about automating security-critical design. Adoption of AI in this domain is in early pilot phase, not production at scale. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Blockchain/crypto and fintech sectors are moderately fast adopters of AI coding tools, though this specialized security-critical task sees more cautious, pilot-level AI use rather than full production reliance. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist blockchain engineers by generating code templates, suggesting pattern libraries, and scanning for common vulnerabilities—raising productivity on lower-risk implementation tasks. However, the human must validate and architect the core design logic, limiting augmentation to roughly half the workflow. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI coding assistants and vulnerability-scanning tools meaningfully speed up drafting, code review, and pattern selection, letting engineers focus on higher-level architecture and security validation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with code generation and pattern suggestions, designing secure blockchain architectures requires deep domain expertise, threat modeling, and nuanced tradeoffs between security, transparency, and performance that demand human judgment. End-to-end automation with 50% time savings and equal security quality is not demonstrated in production systems today. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist in drafting smart contract templates or explaining design patterns, but designing secure blockchain architectures requires original judgment, threat modeling, and system-level tradeoffs that current AI cannot reliably execute end-to-end without substantial human oversight.atile. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong professional and organizational barriers exist: security liability for flawed blockchain designs falls on the organization, regulatory scrutiny of blockchain systems is increasing, and client/stakeholder trust demands human architect sign-off. Enterprise adoption requires human accountability for immutability guarantees. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement exists for blockchain engineers, but liability for security failures (hacks, exploits) creates strong organizational incentive to keep experienced humans accountable for design decisions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Blockchain engineers command high wages ($120k–$200k+), and the cost of AI inference plus integration plus mandatory expert oversight for security-critical design still exceeds or roughly matches the cost of direct human engineering effort. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI coding assistance reduces some drafting time, but the high cost of security errors (exploits, fund loss) means extensive human expert review is still required, keeping all-in costs closer to human-level. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI code assistants (e.g., GitHub Copilot) can generate boilerplate and suggest patterns, but no deployed product reliably designs and validates complete blockchain security architectures autonomously. The task's security-critical nature means current AI output requires substantial expert review and modification. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed products autonomously design and deploy production blockchain security architectures; coding assistants can generate boilerplate smart contract code but require expert review for actual deployment. |
Evaluate blockchain processes or risks based on security assessments or control matrix reviews.
29CI 25–32 · exposure 25 · augmentation 63 · click for rater detail
Evaluate blockchain processes or risks based on security assessments or control matrix reviews.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Blockchain engineering remains concentrated in specialized crypto/finance firms with high risk aversion; even digitally native sectors show slow automation of security review functions due to liability concerns and the criticality of human judgment in threat assessment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Blockchain/crypto security is a tech-forward niche adopting AI tooling for code analysis, but full risk assessment workflows still rely heavily on specialized human auditors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully draft preliminary risk summaries, flag known vulnerability patterns, and organize control matrices for review, enabling engineers to focus on novel threats and business-context judgment; this is materially helpful but incremental rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by scanning code for known vulnerability patterns, summarizing audit findings, and mapping risks to control matrices, significantly speeding up the human reviewer's work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with identifying common patterns and flagging obvious risks in control matrices, but evaluating blockchain processes requires domain expertise, threat modeling judgment, and contextual business reasoning that current systems cannot reliably perform end-to-end at equal quality with 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires nuanced judgment about novel attack vectors, protocol-specific risks, and threat modeling that current AI can assist with but not fully replace end-to-end at production quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Security and risk evaluation carry significant liability exposure; regulatory frameworks (SOC 2, financial oversight) often require licensed or credentialed personnel to sign off on control assessments, and clients typically demand human accountability for blockchain risk evaluation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but high liability exposure (smart contract exploits, financial loss) creates strong organizational incentive for human sign-off and audit trails. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Blockchain security assessment by specialized engineers commands premium labor costs, and the AI infrastructure, specialized training, and required expert oversight for validation are still comparable to or exceed the cost of human assessment for high-stakes blockchain work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply flag common vulnerabilities, but the residual need for expert human review of nuanced blockchain-specific risks and control frameworks keeps overall cost comparable to skilled engineer labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can parse and summarize security documents, no deployed product reliably performs comprehensive blockchain security evaluation or validates control matrices independently in production; current tools are research-stage or narrow demo systems without proven real-world reliability at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI-assisted security scanning and static analysis tools exist for smart contracts, but comprehensive risk evaluation against control matrices remains largely manual and expert-driven in practice. |
Implement catastrophic failure handlers to identify security breaches and prevent serious damage.
29CI 25–32 · exposure 25 · augmentation 63 · click for rater detail
Implement catastrophic failure handlers to identify security breaches and prevent serious damage.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Blockchain engineering remains concentrated in specialized firms and crypto-native companies with relatively low overall IT workforce scale. Adoption of AI for security-critical failure handling is cautious and pilot-focused, not yet showing the deep production displacement seen in mainstream software development. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Blockchain/crypto engineering is a fast-moving tech sector with notable AI tool adoption for coding, but security-critical failure handling remains conservative and slow to fully delegate to AI due to risk sensitivity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating code templates, suggesting common failure patterns, and documenting handler logic, improving the productivity of blockchain engineers during implementation. However, the human must retain full responsibility for threat assessment and architectural decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI coding assistants can meaningfully speed up drafting of error-handling code, flagging common vulnerabilities, and suggesting failure-detection patterns, significantly aiding engineers while they retain final design and validation responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Implementing catastrophic failure handlers requires domain expertise in security architecture, threat modeling, and system design to anticipate failure modes. While AI can generate code snippets and suggest patterns, end-to-end implementation with equal quality to human specialists—including design decisions, threat assessment, and integration into production systems—falls well short of the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | Designing catastrophic failure handlers requires deep threat modeling, understanding of specific protocol architecture, and judgment about edge cases that current AI cannot reliably automate end-to-end; AI can assist with code snippets but not the full design and validation process. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Catastrophic failure handling in blockchain systems carries high liability risk; security breaches directly cause financial loss. Organizations face regulatory scrutiny, insurance requirements, and customer trust concerns that practically require human expert sign-off and accountability for failure-handler design and implementation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but strong organizational and liability-driven friction exists since security failures in blockchain systems can cause catastrophic financial loss, requiring expert sign-off and audits before deployment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted code generation and documentation tools reduce some overhead, but the core task demands specialized blockchain security engineers whose loaded cost far exceeds the inference and integration cost of current AI systems. Oversight and validation costs remain substantial. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Given the high cost of errors (exploits, fund loss) and the need for extensive human security review and auditing, AI assistance reduces some drafting time but doesn't eliminate costly expert oversight, keeping costs closer to comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production systems reliably implement complete catastrophic failure handlers autonomously. AI can assist with boilerplate code and documentation, but the security-critical decisions, threat prioritization, and architectural validation require human blockchain engineers in current deployments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed products autonomously design and implement blockchain-specific catastrophic failure/security handlers reliably; existing AI coding assistants can suggest patterns but require heavy expert review, especially given the high stakes of smart contract security. |
Test the security and performance of blockchain infrastructures.
29CI 25–32 · exposure 25 · augmentation 63 · click for rater detail
Test the security and performance of blockchain infrastructures.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Blockchain engineering remains a relatively niche, early-stage sector with high concentrations in crypto and finance startups. Adoption of AI for critical security functions in these environments has been cautious, with most organizations still relying on traditional security audits, manual testing, and expert teams rather than adopting autonomous AI security platforms. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Blockchain/crypto sector is tech-forward and adopts AI tooling fairly quickly for code analysis, but full security auditing remains a specialized niche with cautious, incremental adoption due to high stakes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist blockchain engineers by automating routine test case generation, running standard vulnerability scans, and summarizing performance metrics, allowing engineers to focus on novel attack design and risk assessment. However, the assistance is on specific subtasks (test automation, code analysis) rather than transforming the entire security evaluation workflow. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly help with static analysis, vulnerability pattern detection, fuzzing script generation, and performance benchmarking scripts, meaningfully speeding up the work of human security engineers. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Security testing of blockchain systems requires deep domain knowledge, creative adversarial thinking, and judgment about risk acceptance—capabilities current AI systems lack. While AI can run automated test suites and analyze code for common vulnerabilities, the novel and high-stakes nature of blockchain security (designing new attack vectors, assessing novel consensus mechanisms) remains beyond today's AI automation threshold of ≥50% time saving at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Security and performance testing of blockchain infrastructure requires deep understanding of consensus mechanisms, smart contract vulnerabilities, and adversarial thinking that current AI cannot fully replicate end-to-end; AI can assist with parts (static analysis, fuzzing) but cannot autonomously conduct full security audits at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Security testing and sign-off on blockchain infrastructure carries real liability and regulatory exposure (especially in financial blockchains), and organizations typically require human expert validation of security assessments. Reputational risk and the need for qualified human judgment on novel attack scenarios create strong organizational friction against full automation without expert human sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but high liability for undetected vulnerabilities (exploits can cost millions) creates strong organizational incentive to keep expert humans in the loop and use AI only as an aid. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Blockchain security engineers command high salaries ($150k–$250k+), and AI-driven security tools (when suitable for this task) still require significant integration, custom test environment setup, and expert human oversight. The all-in cost of deploying AI security automation remains comparable to or higher than employing domain experts, especially given liability and accuracy expectations. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized security auditing tools plus required human expert review mean AI reduces some labor but doesn't yet dramatically undercut the cost of skilled blockchain security engineers, especially given liability of undetected vulnerabilities. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed AI tools can assist with code scanning and standard vulnerability detection, but no production AI system reliably performs end-to-end blockchain security and performance testing independently. Tools like automated fuzzing and SAST exist, but blockchain's complexity and the need for custom test harnesses mean most organizations still rely on expert human security engineers and specialized services, not general AI products. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI-assisted static analysis and fuzzing tools exist (e.g., for smart contract auditing) but they are narrow-scope, produce false positives/negatives, and are not relied upon as standalone production solutions for comprehensive security/performance testing. |
Design and develop blockchain technologies for industries such as finance and music.
27CI 16–38 · exposure 17 · augmentation 63 · click for rater detail
Design and develop blockchain technologies for industries such as finance and music.
27| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While blockchain is a growing sector, most organizations still rely on in-house or contracted senior engineers for core protocol and system design; adoption of fully autonomous AI-driven blockchain development is minimal, with AI primarily supporting incremental tasks rather than full system design. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Software engineering broadly shows fast AI tool adoption, and fintech/crypto sectors are digitally native, but blockchain design specifically remains a niche with cautious, security-conscious adoption of AI-generated code. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI code generation, testing suggestions, and documentation drafting can meaningfully assist engineers in scaffolding and boilerplate reduction; however, the strategic and security-critical aspects of blockchain design remain dependent on human expertise, limiting transformative augmentation to partial workflow gains. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI coding assistants substantially speed up smart contract drafting, boilerplate generation, documentation, and debugging, meaningfully augmenting blockchain engineers' productivity while they retain design authority. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Blockchain technology design and development requires novel architecture decisions, cryptographic innovations, and domain-specific problem-solving that go well beyond code generation; current AI cannot autonomously conceive of new consensus mechanisms, security proofs, or end-to-end blockchain systems at production quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This is complex system architecture and design work requiring novel decisions about consensus mechanisms, tokenomics, and industry-specific requirements; AI can assist with code generation and boilerplate but cannot independently design a full blockchain solution end-to-end at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Blockchain systems in finance face strict regulatory approval, security audits, and liability requirements that legally mandate human expert sign-off; additionally, cryptographic correctness and network security carry asymmetric error costs that organizations will not delegate fully to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for blockchain engineering, but financial-sector blockchain applications face regulatory scrutiny and security/audit requirements that create moderate friction against pure AI automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted coding reduces development overhead, but the specialized expertise, auditing, and security validation required for blockchain systems mean total cost remains high; integration and verification costs are substantial relative to junior developer wages. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools reduce coding time but the design, security auditing, and architectural decision-making still require expensive specialized engineers, so overall cost savings are modest rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with code generation and debugging, no deployed product reliably designs complete blockchain systems end-to-end; existing tools lack the architectural judgment and security verification needed for production blockchains in regulated industries like finance. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI coding assistants (Copilot, Claude, etc.) are used in smart contract and blockchain development, but no deployed product autonomously designs blockchain systems for specific industry use cases without heavy engineer oversight. |
Assess blockchain threats, such as untested code and unprotected keys.
26CI 25–28 · exposure 25 · augmentation 75 · click for rater detail
Assess blockchain threats, such as untested code and unprotected keys.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Blockchain development remains concentrated in early-stage and specialized firms with technical depth; adoption of AI-driven security tooling is still in pilot phases rather than production deployment at scale across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Blockchain/crypto security is a fast-moving niche within software engineering with growing AI-assisted tooling adoption, but overall deployment remains limited to specialized firms rather than broad-based transformation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted code analysis and vulnerability scanning tools can substantially augment human blockchain engineers by surfacing suspicious patterns and known threats for review, accelerating the initial assessment phase while the engineer retains final judgment over threat severity and remediation strategy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly aid engineers by flagging suspicious code patterns, known vulnerability signatures, and unprotected key configurations, substantially speeding up the review process while humans retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can identify common patterns of insecure code and known vulnerabilities through static analysis, assessing complex blockchain threats requires nuanced judgment about novel attack vectors, edge cases, and context-specific risks that current AI systems struggle with reliably. The task demands reasoning about cryptographic assumptions and adversarial scenarios that exceed half-time-saving automation at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Threat assessment requires deep contextual judgment about novel attack vectors, economic incentives, and system-specific architecture that current AI cannot fully replicate end-to-end, though it can assist with parts like static analysis. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Blockchain systems carry high financial and security stakes; liability for missed threats or false confidence in automated assessment creates strong incentives for human expert sign-off. Regulatory and organizational pressure to document human accountability in security decisions presents substantial barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Given the high liability of undetected vulnerabilities (loss of funds, exploits), organizations and auditors impose strong requirements for human expert sign-off, and reputational/legal risk creates a de facto barrier against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-powered security scanning tools are relatively inexpensive, but they require skilled blockchain engineers to interpret findings, validate results, and make remediation decisions, keeping overall cost closer to or exceeding human-only review for thorough assessment. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-assisted scanning tools are cheap to run but the residual need for expert manual audit and validation to avoid costly security failures keeps overall cost comparable to or only modestly below human-led review. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some security scanning tools use AI-assisted code analysis, but production systems still generate substantial false positives and miss novel threats; human security experts remain essential for validation. No deployed product reliably performs comprehensive blockchain threat assessment end-to-end without expert oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Static analysis and vulnerability scanning tools with AI components exist (e.g., smart contract auditors), but comprehensive threat assessment covering key management and novel exploits still requires expert human review in production security practices. |
Design and verify cryptographic protocols to protect private information.
25CI 25–25 · exposure 25 · augmentation 63 · click for rater detail
Design and verify cryptographic protocols to protect private information.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Cryptography remains a domain where human experts are closely involved in design decisions due to security criticality. Adoption of AI for full protocol design is slow; most firms use AI only as a coding assistant under heavy expert oversight. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Blockchain and cryptographic security engineering remains a specialized, slower-adopting niche where AI tools are used cautiously for code review and documentation but not for core protocol design, unlike broader software engineering's faster AI adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools meaningfully assist cryptographic engineers by accelerating code generation, suggesting implementations, and flagging common vulnerabilities in reviews, but the human expert remains essential for protocol design and high-stakes security verification. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by suggesting known cryptographic constructions, flagging common vulnerabilities, drafting formal specifications, and aiding literature review, substantially speeding up parts of the design and verification workflow while humans retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with code generation and vulnerability scanning, designing novel cryptographic protocols requires deep mathematical innovation and human judgment. Current AI systems cannot end-to-end design and verify cryptographic protocols that meet the ≥50% time-saving bar at equal security quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Designing and formally verifying cryptographic protocols requires deep mathematical reasoning, novel proof construction, and adversarial threat modeling that current AI cannot reliably perform end-to-end; AI can assist with drafting and checking known patterns but not fully replace expert cryptographic design and security verification. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and liability barriers are substantial: cryptographic failures can compromise financial systems and user data, creating legal and fiduciary accountability. Organizations require human experts to sign off on protocol design, and customers demand human-vetted security properties. |
| Adoption barriers | claude-sonnet-5 | 4/5 | While no formal license is required to design cryptography, liability and error-cost asymmetry are severe (a flawed protocol can lead to catastrophic breaches), creating strong organizational and professional insistence on rigorous human expert review and sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI-generated cryptographic code requiring expert human review, testing, and validation still exceeds the loaded hourly wage of skilled cryptographers who can design trustworthy protocols without heavy downstream review burden. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Given the high stakes of cryptographic errors (security breaches, financial loss), heavy human expert review is mandatory, so AI assistance only marginally reduces cost versus the loaded cost of specialized cryptographers/security engineers required regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed production system reliably designs cryptographic protocols from scratch. AI tools like GitHub Copilot assist with implementation but do not independently verify the security properties required for this specialized task. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No production system autonomously designs and verifies novel cryptographic protocols reliably; existing tools (formal verification assistants, code analyzers) are narrow and require expert oversight, remaining research/assistive stage rather than deployed end-to-end solutions. |
Discuss and plan systems with solution architects, system engineers, or cybersecurity experts to meet customer requirements.
20CI 7–32 · exposure 13 · augmentation 63 · click for rater detail
Discuss and plan systems with solution architects, system engineers, or cybersecurity experts to meet customer requirements.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While blockchain and crypto sectors digitize rapidly, collaborative planning discussions remain heavily human-driven even in tech-forward organizations. Pilots of AI-assisted documentation exist, but production replacement of engineer-architect planning discussions is rare and resisted by customers and risk management. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Tech and blockchain sectors adopt AI tools quickly for coding and documentation, but collaborative planning/discussion work still sees mostly pilot-stage AI assistance rather than deep production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by generating design templates, analyzing requirements documents, or flagging technical inconsistencies in draft plans, raising productivity for the human engineer in the discussion loop. However, it cannot drive the conversation or replace the collaborative judgment central to the task. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by summarizing requirements, drafting architecture diagrams, generating meeting notes, and proposing technical options, enhancing the humans' planning discussions. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires interpersonal discussion, negotiation, and real-time judgment about customer needs and technical trade-offs. Current AI cannot participate meaningfully in dynamic planning sessions with stakeholders or synthesize diverse expert perspectives into cohesive system designs. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a collaborative, judgment-heavy discussion task requiring real-time synthesis of stakeholder needs, technical tradeoffs, and negotiation; AI can support but not replace the interactive planning process end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and organizational barriers are substantial: customers typically require direct engagement with credentialed engineers, architects hold professional liability for system designs, and regulatory frameworks (especially in finance/crypto contexts) expect human expert sign-off on critical infrastructure planning. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but strong organizational friction exists since customer trust, accountability, and cross-team negotiation typically require human presence and judgment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI-assisted planning (including model inference, integration with design tools, and necessary human oversight) remains higher than the benefit gained, especially given the expertise and accountability required. A blockchain engineer's planning work still demands human judgment and accountability. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human architects and engineers remain necessary for these discussions; AI tools add cost as aids rather than substitutes, so all-in cost is comparable or higher when factoring integration and oversight. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While AI can assist with documentation or generate design patterns, no deployed product reliably conducts end-to-end stakeholder discussions and collaborative system planning. This requires sustained contextual reasoning, domain expertise synthesis, and human judgment that current systems cannot perform autonomously. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI meeting assistants and design-support tools exist but no deployed product autonomously conducts cross-functional architecture planning discussions with customers reliably today. |
Related occupations — Computer & Mathematical
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.