Meaningful Work
The future of work is a decentralized marketplace where people connect to level up at their Zones of Proximal Development.
Social glue is the most powerful force for influencing human behavior. Where will you add value to your network?
Engineering positive collisions and make meetings matter
Purpose
Explore future of work by leveraging AI and driven Crypto incentives.
Predictions
- AI will lead to flatter organizational structures with fewer managers.
- People will need to become more multidisciplinary as AI takes over some specialized roles.
- Rise of gig economy with quality real world experiences priced at premium.
- People will need to become more multidisciplinary as AI takes over some specialized roles.
- Education will be transformed by AI teachers reaching millions globally.
Limitations
- AI still struggles to fully capture individual writing voices and styles.
- Open-source efforts by large tech companies may pose challenges for AI startups.
- There are concerns about job displacement as AI becomes more capable, particularly for white-collar jobs.
Teamwork
What are the most important human capabilities to focus attention on, that will compliment working with AI teammates?
- Do you select players for positions, or create positions for players' potential?
- What core competencies are demanded across all team members?
- What is the optimal mix of specialist capabilities and responsibilities?
- Is the one person billion dollar company something worth celebrating?
- How much money is enough to live happily?
See Human Capabilities and teamwork for more detailed analysis.
Character first. A rotten apple spoils the barrel.
Core Roles
Predict that organisations will be flat, with fewer managers and more cross-disciplinary roles. Primary roles in the future of work:
Business Roles
Analyse key deliverables to determine the percentage of work that has been and should be assisted by AI. Identify how much time is spent on value-creating activities versus administrative tasks.
- Executive/Founder
- Commercial Officer
- Financial Officer
- Operations Officer
- Human Resources Officer
- Legal Officer
- Marketing Officer
- Risk Officer
- Information Security Officer
- Information Officer
- Technology Officer
- Data Officer
Roles and AI Products that can be "employed" to scale business administration operations.
- Business Administration
- Business Analyst (holistics / metabase / preset)
- Data Scientist (obviously.ai / h2o.ai / datarobot / bigml / rapidminer)
- Financial Analyst (finta /runway / pulse)
- Legal Assistant: (donotpay / lawdepot / wonder.legal / spellbook)
- Project Manager (clickup / notion / coda / linear)
- Recruiter (dover / fetcher / gem)
- Sales Pipeline
- Customer Success: (intercom / front / chatdesk)
- Lead Magnet: (gamma.io / mailerlite / systeme.io)
- Pitch Deck Creator: (beautiful.ai / gamma / pitch.com)
- Public Relations: (prezly / onepitch / prowly)
- Sales Flow: (apollo.io / outreach / gong)
- Marketing
- Copywriter (jenni.ai / copy.ai / anyword)
- Email Marketer (beehiiv, substack, convertkit)
- Faceless video producer (revid / pictory / vidyo.ai)
- Marketing Strategist (semrush / ahrefs / moz)
- Growth hacker (mutinyha / growthbook/ voyantis / phlanx / viral-loops)
- Community Manager (tribe.so / circle.so / mighty networks)
- Podcast producer (synthesia / riverside / descript)
- SEO Specialist (surfer / clearscope / marketmuse)
- Social Media Manager (zaap.ai)
- Video Producer (runway / descript / veed)
- Growth
- Idea Machine: (gummysearch / treendly / glimpse)
- Startup Founder: (stripe / producthunt / angellist)
- Venture Capital: (crunchbase / pitchbook / cb insights)
See SaaS Products for more detailed analysis.
Product and Software Roles
Determine best practice protocols for using AI coding agents to build software at a fraction of the cost of employing humans.
Identify key activities and protocols for best practices across each unique discipline.
Roles and AI Products that can be "employed" to develop custom in-house software.
- Product Manager (productboard / aha / airfocus)
- Market Researcher (exploding topics / trendhunter / sparktoro)
- Product Validator (validately / userfeed / prelaunchhero)
- Programmer (cursor.ai / replit / claude 3.5)
- UX Designer (v0 / playground / galileo ai)
- UX Researcher (maze / hotjar / usertesting)
We are all software engineers and we are all investors now
Roles by Industry
Tomorrow's engineers must have cross discipline expertise; time spent learning to code should instead be invested in expertise in industries such as farming, biology, manufacturing and education - Jensen Huang
AI Use Cases
Actionable use cases for practical day-to-day productivity gains with AI.
- Automating coding tasks
- Building entire web applications with unfamiliar technologies
- Converting programs to more efficient languages (e.g. C or Rust) for 10-100x performance improvements
- Simplifying and trimming down large codebases
- Writing initial experiment code for research papers
- Automating monotonous tasks and one-off scripts
- Accelerating learning and skill acquisition
- Teaching how to use new frameworks and technologies
- Replacing web searches for setting up/configuring new packages and projects
- Assisting with debugging error messages
- Enhancing productivity
- Improving coding speed by 50% or more
- Automating boring/repetitive tasks to allow focus on higher-level problems
- Replacing time-consuming web searches with direct AI assistance
- Rapid prototyping and development
- Quickly building proof-of-concept applications
- Iterating on designs through conversation with AI
- Research assistance
- Generating initial code for experiments
- Analyzing and visualizing results
- Task automation
- Automating data processing and analysis workflows
- Creating scripts to automate repetitive tasks
- Code optimization and refactoring
- Identifying opportunities to improve code efficiency
- Suggesting refactoring to simplify complex codebases
- Documentation and explanation
- Generating code documentation
- Explaining complex code or algorithms
See AI Business Models for more detailed analysis and Engineering for how to build your own agents.
Scorecard
How would you create a scorecard to qualify and quantify the impact of AI agents on demands of traditional job specifications?
Attachments
Related
- AI Automation
- Job Hunting
- Capabilities
- Teamwork
- Predictions
- Jobs to be Done
- Business Operations as a Service
Links
What is the most important question you could ask yourself to make progress?