2023: The Year AI Became Real

ai_milestones_2023:
  march:
    - GPT-4 released (major capability jump)
    - Claude launched to public
    - AI safety concerns intensified

  may_june:
    - OpenAI ChatGPT plugins
    - Google Bard improvements
    - AI coding assistants became mainstream

  july_august:
    - Llama 2 open sourced
    - Enterprise AI adoption accelerated
    - Vector databases became essential

  november:
    - OpenAI DevDay (GPT-4 Turbo, Assistants API)
    - Custom GPTs announced
    - 128K context windows

  throughout:
    - RAG became standard pattern
    - AI features in every product
    - Prompt engineering as discipline

paradigm_shifts:
  from_experimental_to_production:
    before: AI as interesting demo
    after: AI as production feature
    evidence: Every major product adding AI

  from_one_model_to_many:
    before: Use GPT-4 for everything
    after: Model routing, specialized models
    evidence: Open source models competitive

  from_basic_to_compound:
    before: Single LLM call
    after: RAG, agents, multi-step pipelines
    evidence: Standard architecture patterns

  from_text_to_multimodal:
    before: Text in, text out
    after: Images, documents, audio
    evidence: GPT-4V, multimodal applications

technology_maturation:
  vector_databases:
    state: Essential infrastructure
    options: Pinecone, Weaviate, pgvector, Qdrant
    adoption: Mainstream

  rag_architecture:
    state: Standard pattern
    evolution: Basic → hybrid search → advanced retrieval
    adoption: Default for knowledge applications

  agent_frameworks:
    state: Experimental → early production
    tools: LangChain, LlamaIndex, custom
    adoption: Growing but cautious

  ai_observability:
    state: Emerging
    needs: Cost tracking, quality monitoring, debugging
    adoption: Early stage

  prompt_engineering:
    state: Recognized discipline
    practices: Templates, versioning, testing
    adoption: Variable maturity

2023_surprises:
  open_source_competitiveness:
    expectation: OpenAI dominates
    reality: Llama 2, Mistral competitive for many use cases
    implication: More choices, less lock-in

  cost_reduction:
    expectation: AI stays expensive
    reality: 10x cheaper over the year
    implication: New use cases viable

  enterprise_adoption_speed:
    expectation: Slow, cautious adoption
    reality: Rapid experimentation, production deployments
    implication: AI skills in demand

  capability_ceiling:
    expectation: Continuous capability improvement
    reality: Impressive but plateauing on some benchmarks
    implication: Engineering around limitations matters

industry_state_2023:
  layoffs_continued:
    - More companies right-sizing
    - Efficiency focus sustained
    - Hiring more selective

  ai_investment:
    - Counter-cyclical AI investment
    - AI skills premium
    - AI startups well-funded

  profitability_focus:
    - Growth-at-all-costs ended
    - Unit economics matter
    - Sustainable business models

  remote_work:
    - Return-to-office pressure
    - Hybrid as compromise
    - Async practices improved

personal_learnings:
  ai_engineering:
    - It's a new discipline, not just ML
    - Engineering practices matter as much as AI
    - Build for failure and iteration

  product_strategy:
    - AI features need clear value proposition
    - "Add AI" is not a strategy
    - User problems first, AI second

  team_leadership:
    - Help team navigate AI adoption
    - Balance experimentation and delivery
    - Skills development matters

  technical_choices:
    - Start simple, add complexity as needed
    - Abstractions help but have costs
    - Own what's core, use services for rest

2024_watch_list:
  ai_agents:
    expectation: More autonomous systems
    challenge: Reliability and safety
    opportunity: Complex task automation

  multimodal_expansion:
    expectation: Video, audio, documents
    challenge: Cost and latency
    opportunity: New application categories

  open_source_ai:
    expectation: Continued improvement
    challenge: Catching frontier models
    opportunity: More deployment options

  ai_regulation:
    expectation: More concrete rules
    challenge: Compliance complexity
    opportunity: Trust and differentiation

  enterprise_maturity:
    expectation: Beyond POC to scale
    challenge: Production reliability
    opportunity: AI infrastructure market

priorities_2024:
  skills:
    - Agent architectures and safety
    - Multi-modal applications
    - AI system reliability
    - Cost optimization at scale

  content:
    - Practical production patterns
    - Real-world case studies
    - Emerging architecture guidance

  professional:
    - Help organizations adopt AI effectively
    - Bridge strategy and implementation
    - Build sustainable AI practices