Annual Reflection Questions

My 2025 self-inquiry list—covering habits, energy, decision-making, AI, entrepreneurship, assets, compounding, family, freedom, and education:

  1. Habits
    • Which three habits contributed most to tangible outcomes this year—and which three merely made me feel productive?
    • Is there a habit I could stop now, endure short-term discomfort, and emerge stronger long-term?
    • Do my habits amplify my judgment—or mask my indecision?
  2. Energy
    • What did I do with my top 20% highest-energy hours this year?
    • Which activities drained energy without meaningful return?
    • If I could guarantee only three high-quality hours per day next year—who or what would get them?
  3. Decision-Making
    • Of my three most consequential decisions this year, how many held up? Why?
    • Am I using tactical busyness to avoid confronting strategic ignorance on key issues?
  4. AI
    • Is AI for me an efficiency tool, a capability amplifier—or a catalyst for identity reshaping?
    • Which skills I built this year would still hold value without AI?
  5. Entrepreneurship
    • Is my current venture building long-term momentum—or quietly depleting personal reserves?
    • Am I running “what I’m good at”—or “what I can’t let go of”?
  6. Assets
    • Where did I trade time for money this year—and lose optionality in the process?
    • Have I developed a more systematic understanding of assets—and begun building genuinely healthy ones?
  7. Compounding
    • What actions show no visible results in one year but will define my trajectory in three?
    • Am I consistently investing in at least one compounding system that doesn’t rely on luck?
  8. Family
    • Is my current pace eroding future family relationships?
    • To me, is family a sanctuary, a duty—or an overlooked co-founder?
    • If my child modeled my behavior (not my advice), would I be comfortable with that?
  9. Freedom
    • Which choices are irrevocably narrowing my future options?
    • If I wanted to live differently in five years, which doors am I closing right now?
    • Is my current busyness moving me toward freedom—or postponing the reckoning with unfreedom?
  10. Education
    • In the AI era, which abilities deserve lifelong cultivation—and which should we stop teaching altogether?
    • If my child copied how I spend my time, not what I say, would I be satisfied?
    • If exams vanish as proof of worth in 20 years, what should I nurture today?
    • Which of my “obvious” educational beliefs are just survivorship bias from my own path?
    • If future education proves me wrong, where am I most likely mistaken?

The AI World of 2026

The venture firm a16z recently released its Big Ideas 2026 series—a set of forward-looking reports synthesizing insights across investment teams. Its central thesis: AI is no longer just a tool. It’s becoming an environment, a system, and—increasingly—a parallel agent acting alongside humans.

This shift is the essential lens for understanding the next decade—not just in tech, but in education, entrepreneurship, careers, and trust itself.

1. Education: AI-Native Learning & Personalized Ecosystems

Classrooms and lecturers won’t disappear—but their role will transform radically. Education is shifting from passive knowledge transfer to AI-driven learning ecosystems and talent engineering.

The first true “AI-native university” will launch in 2026—not just teaching about AI, but orchestrating curriculum, research collaboration, and talent development in real time via adaptive AI systems.

Three foundational changes:

  • From passive learning → self-adapting learning systems: AI delivers truly individualized pacing, feedback, and scaffolding—no two learners follow identical paths.
  • From teacher-as-transmitter → teacher-as-system-guide: Educators focus on cultivating higher-order judgment, ethics, and synthesis; AI handles knowledge delivery, assessment, and iterative feedback loops.
  • New talent paradigm: “AI collaboration fluency” replaces rote knowledge mastery. Core skills include task design for AI agents, output verification, and reasoning-chain auditing—not memorization.

2. Entrepreneurship: From Tools to Intelligent Execution Systems

Traditional startups treat software as a UI layer or information dashboard. Big Ideas 2026 argues the next wave moves beyond isolated AI features toward multi-agent systems and coordinating intelligent teams.

AI isn’t just speeding things up—it’s rebuilding the execution layer itself.

That means product strategy pivots from “generating responses” to enabling intelligent execution, cross-process coordination, and autonomous decision engines. Founders won’t just add AI features—they’ll architect AI agents that actively intervene in workflows, complete tasks end-to-end, and iteratively optimize performance.

3. Career Development: AI Collaboration & Systems Thinking

As AI evolves from assistant to collaborator, professional value is redefined—not by what you know, but by how you orchestrate human-AI systems.

Four dimensions will define competitive advantage:

  • AI collaboration design: Structuring complex, cross-module tasks for AI agents—not just calling an API, but composing agents, defining handoffs, and managing dependencies.
  • Output review & explainability: Interpreting AI reasoning, validating logic, tracing evidence chains, and stress-testing conclusions—not accepting outputs at face value.
  • Cross-modal product & process design: Designing for AI that understands audio, video, images, sensor streams, and text—not just prompts, but coherent multi-sensory instruction sets and evaluation frameworks.
  • Systems thinking & workflow redesign: Moving beyond skill stacking to fundamentally re-engineering how work flows through organizations—so human and AI capabilities multiply, not merely add.

4. Healthcare: From Treatment to “Prevention + Health Action Engine”

a16z introduces a telling metric: Health MAUs (Monthly Active Users)—shifting focus from patients with disease to people managing health.

This signals three structural shifts:

  • Healthcare’s center of gravity moves from treatment-centric models to health management and outcome accountability.
  • Health services become continuous experiences—not episodic transactions—with embedded engagement, feedback, and adaptation.
  • Health data becomes strategic infrastructure: AI uses real-time monitoring, predictive modeling, and personalized nudges to drive sustained behavioral change.

5. The Future: Multimodal Systems & Silent Interaction

A unifying thread across the report: AI is moving from interface-driven to environment-driven.

  • AI will parse the internal structure of video—not just generate captions or tags, but understand scene transitions, causal sequences, and emotional arcs.
  • Applications evolve from “help me do X” to “understand me / see me”—inferring intent, context, and needs from behavioral traces, interaction history, and multimodal signals.

6. Trust & Infrastructure: Privacy as the New Moat

In the crypto trends section, the report makes a sharp observation: Privacy is becoming the strongest moat for encryption and blockchain.

Why? Because migrating secrets is far harder than migrating value—creating a new kind of network effect rooted in confidentiality.

Future value and identity flows won’t depend on centralized gatekeepers—but on verifiable protocols, privacy-preserving mechanisms, and open economic architectures.

Key 12-Month Takeaways

  • Education will restructure around AI-native learning ecosystems.
  • Entrepreneurship will pivot from efficiency gains to intelligent coordination and workflow value.
  • Career relevance will hinge on AI collaboration fluency and systems redesign ability.
  • Healthcare products will shift from diagnosis/treatment to lifelong health lifecycle management.
  • Trust infrastructure will migrate from institutional rules to technically enforceable, privacy-aware protocols.

Original reports:
a16z.com
a16z.com
a16z.com

How to Spot a Real Expert

In the AI era, trustworthy expertise matters more—not less. But distinguishing genuine experts from convincing impostors requires attention to four markers:

  1. Rapid elimination of noise: They instantly dismiss irrelevant options—not because they’re dogmatic, but because they recognize what doesn’t matter, letting them zero in on the core issue.
  2. Judgment under uncertainty: They make useful calls even with incomplete data—by identifying primary variables and dominant tensions, not waiting for perfect inputs.
  3. Actionable clarity: Their advice lands concretely—not as abstract principles, but as steps, heuristics, or constraints that can be applied tomorrow.
  4. Explainability + verifiability: They don’t just state conclusions—they walk through why, and invite scrutiny, testing, and post-hoc review.

Beware of “pseudo-experts”: They sound authoritative, cite widely, and rarely err—but never commit to specific judgments, actionable methods, or measurable outcomes. Their currency isn’t insight—it’s information asymmetry.

2025 in Review

In 2025, I interacted with ChatGPT over 10,000 times. Its memory function and steady model improvements were the main reasons I kept returning—consistently.

Near year-end, I asked it to summarize my 2025. The result was uncannily accurate—not just in content, but in tone, rhythm, and depth. That level of coherence and contextual awareness remains striking.

AI Productivity Survey Report

This summary draws from a survey of 1,750 tech professionals—a grounded, practitioner-level view of how AI is actually being used today.

  1. Overall satisfaction?
    Most respondents report AI exceeding expectations, significantly lifting both work quality and efficiency. Time savings average half a day per week.

  2. How different roles use AI:
    • Founders: Treat AI as a “thinking partner”—for strategy, vision, product framing, and risk mapping.
    • Product Managers: Benefit most on the output side: PRDs, wireframes, stakeholder comms.
    • Designers: Gain traction in adjacent areas—user research synthesis, copywriting, concepting—but visual design automation remains limited. Pixel-perfect fidelity and stylistic consistency still resist one-click replacement.
    • Engineers: Demand has shifted from “write code” to “write after code”: AI is increasingly expected to handle docs, tests, code reviews, and governance—freeing engineers for architecture and complexity management.
  3. Tool preferences by role:
    • ChatGPT dominates among PMs, designers, and founders.
    • Engineers lean toward specialized tools like Cursor and Claude Code.
  4. Where time is saved:
    • High-frequency: Writing & rewriting, research synthesis, rapid prototyping, structured doc generation.
    • Engineering “post-code” work: Test case generation, API documentation, linting, static analysis—building audit trails.
    • Founders: Competitive scanning, counterfactual stress-testing, strategic framing, org design support.
  5. Bringing AI into upstream thinking & research:
    • Process formalization: Cluster topics, map evidence chains, flag counterexamples, link sources—and demand transparent reasoning.
    • Question-framing discipline: Prompt AI to structure outputs as hypothesis → evidence → counter-evidence → conclusion.
    • Human veto power: Reserve final judgment for people—use AI for alignment, synthesis, and option-scanning.
  6. Measuring AI ROI:
    • Go beyond “hours saved”: Track quality scores, rework rates, defect density, alignment speed, and delivery cadence.
    • Audit evidence transparency: Are sources cited? Counterpoints considered? Reasoning traceable?
    • Review by iteration: Each week, log where AI intervened, what worked/didn’t, and adjust next week’s prompts or workflows.
  7. Key shifts ahead (2026–2027):
    • From “output acceleration” → “collaborative ambiguity resolution”: AI as a long-term thinking partner for ill-defined problems.
    • Vertical workflow maturity: Engineering “post-code” pipelines, design system governance, standardized research evidence chains.
    • Organizational AI governance: Formal policies, risk controls, audit frameworks, and redesigned talent roles.

Full report: lennysnewsletter.com

A few I’ve found especially effective lately:

  1. Banana Slides — Open-source AI-powered presentation tool built on nano banana pro. Truly liberates PPT creation.
    github.com

  2. Stitch — Google’s new AI prototyping tool. Turn an app idea into a clickable, interactive UI prototype in under a minute.
    stitch.withgoogle.com

  3. NotebookLM — Google’s AI-native knowledge companion. Universally praised for learning and knowledge organization. Upload documents, then generate reports, slides, tables, mind maps, audio summaries—even video recaps—all from your own sources.
    notebooklm.google.com

  4. Antigravity Tools — A lightweight manager for Google’s Antigravity platform. Solves the frequent auth hang-ups by enabling seamless, one-click account switching.
    github.com