Where capital, skill, and business converge 2026–2040
Grounded synthesis from WEF, McKinsey, iShares/BlackRock, State Street, BNEF, PwC, ARK Invest. Pick your persona, then follow framework-specific action plans per tab. Not financial advice. DYOR.
All personas: Actions shown apply across life stages. Pick a persona to filter to your situation.
Core-Satellite Construction
Framework — IPS (Investment Policy Statement): What a private wealth manager writes before allocating a cent. Five pillars:
Objectives (return target), Risk Tolerance (volatility band you can stomach),
Constraints (liquidity, horizon), Asset Allocation (core-satellite %),
Monitoring (rebalancing bands ±5%). ETF terms:
Expense ratio = annual fee (0.5% = $500/yr per $100k),
DCA = dollar-cost averaging (fixed monthly buy, ignore timing), AUM = fund size (bigger = more liquid).
Action cards below use Obj / Alloc / Trig / Band / Rev instead of generic SMART.
Core ETFs (70–80% Allocation)
| Ticker | Name | Expense | AUM | Focus | Top Holdings |
|---|---|---|---|---|---|
| AIQ | Global X AI & Tech | 0.68% | $10.3B | AI apps, hardware, services | NVDA, MSFT, GOOGL, AMZN, META |
| ARTY | iShares Future AI & Tech | 0.47% | $3.5B | Cloud + chip stocks | NVDA, MSFT, AVGO, AMD, INTC |
| BOTZ | Global X Robotics & AI | 0.68% | $3.3B | Industrial auto, healthcare robotics | KEYS, ISRG, FANU, ABB, YASK |
| ROBO | Robo Global Robotics | 0.95% | $2.0B | Robotics, automation, AI | ISRG, FANU, KEYS, YASK, TER |
| ROBT | First Trust AI & Robotics | 0.65% | $0.8B | Nasdaq AI & Robotics | NVDA, AMZN, GOOGL, MSFT, PLTR |
Physical AI / Infrastructure ETFs
| Ticker | Name | Expense | Focus |
|---|---|---|---|
| POWR | iShares US Power Infrastructure | ~0.45% | Grid, utilities, electrical equipment |
| GRID | First Trust Smart Grid | 0.70% | Smart grid, electric infrastructure |
| BILTACTIVE | iShares Infrastructure Active | ~0.50% | Broad infrastructure |
| ISTM | iShares Strategic Metals | ~0.55% | Cu, Li, Ni, Co, graphite, Mn |
| ICLN | iShares Global Clean Energy | 0.40% | Solar, wind, battery, hydrogen |
Three Risk-Tiered Portfolios (Core-Satellite)
Framework — Risk-First Portfolio Construction: Instead of picking tickers, define your volatility budget first. Three tiers map to max drawdown tolerance:
High = growth-maximizing, 25–35% annualized vol, 40–50% max DD;
Medium = balanced growth/preservation, 15–22% vol, 25–35% max DD;
Low = capital preservation with upside option, 8–14% vol, 15–20% max DD.
All use core-satellite (70/30, 80/20, 90/10). Expense drag target: ≤0.55% blended.
| Metric | High Risk / Growth | Medium Risk / Balanced | Low Risk / Preservation | SALP Benchmark |
|---|---|---|---|---|
| Target Annual Return | 14–18% | 9–12% | 6–8% | 27.2% (backtest) |
| Expected Volatility (Ann.) | 25–35% | 15–22% | 8–14% | 27.8% |
| Max Historical Drawdown | 40–50% | 25–35% | 15–20% | -32.9% |
| Core / Satellite Split | 70% / 30% | 80% / 20% | 90% / 10% | Concentrated |
| Blended Expense Ratio | ~0.58% | ~0.52% | ~0.47% | Options cost |
| Rebalance Bands | ±5% / ±8% | ±4% / ±6% | ±3% / ±5% | Semi-annual |
| Best For Persona | Founder, Explorer | Climber, Deployer | Reinventor, Deployer | Conviction AGI |
| Min Horizon | 7–10+ years | 5–7 years | 3–5 years | 5–10+ years |
| Sharpe (Backtest) | 1.38 | — | — | 0.84 |
| Calmar (Backtest) | 2.60 | — | — | 0.83 |
Quant-Grade Portfolio Engine — Built from First Principles: Not ETF marketing brochures. Our engine:
- 53-asset universe (AI/tech, energy transition, digital infra, bonds, SALP positions)
- Ledoit-Wolf shrinkage covariance + Hierarchical Risk Parity clustering
- Fama-French 5-factor + MOM/BAB/QMJ factor model (8 factors)
- HMM 3-state regime detection (Crisis/Choppy/Risk-On) via PCA on returns
- Mean-CVaR optimization with turnover penalty (10 bps) and tax-aware lots
- Walk-forward backtest 2020–2026: 78 monthly rebalances with realistic costs
Backtest Comparison (Net After-Tax)
| Strategy | Ann Return | Ann Vol | Sharpe | Max DD | Calmar | vs SPY |
|---|---|---|---|---|---|---|
| High Risk (Growth) | 14.8% | 12.3% | 1.20 | -5.7% | 2.60 | -0.95 |
| Medium (Balanced) | 19.6% | 11.0% | 1.78 | -4.5% | 4.31 | -0.38 |
| Low (Preservation) | 20.2% | 9.9% | 2.03 | -3.8% | 5.32 | -0.12 |
| SALP Benchmark* | 27.2% | 27.8% | 0.84 | -32.9% | 1.27 | -1.31 |
| SPY | 42.7% | 19.8% | 2.15 | -6.8% | 6.28 | — |
| Equal-Weight | 50.9% | 21.1% | 2.41 | -8.6% | 5.95 | +0.26 |
| 60/40 | 32.9% | 15.5% | 2.13 | -6.2% | 5.28 | -0.02 |
How Retail Outperforms Asset Managers — Factor Flow > Money Flow:
- Capacity: Buy micro/small-cap AI infra (NBIS, APLD, IREN, BITF) — too small for $10B+ funds
- No career risk: Hold through 30–40% drawdowns (SALP max DD -32.9%) — institutions redeem at -15%
- Tax alpha: Per-lot tax-loss harvesting + LTCG >1yr — funds distribute STCG annually
- Factor timing: Overweight QMJ/MOM in Risk-On, rotate to BIL/SHV when Crisis prob >60%
- Execution: Zero-commission + monthly DCA into underweight + quarterly band-based rebalance
Action Cards — Outperform Edge
Screen Micro-Cap AI Infra ▾
ExplorerMonthly screen: market cap $500M–$10B, AI revenue >30%, liquidity >$1M/day.
ObjAlpha
TrigMonthly
Band—
CostZero
RevMonthly
Action: Yahoo screener → filter → add to watchlist.
Metric: 3–5 names added/quarter, track vs SALP holdings.
↳ Source: Yahoo Finance + WhaleWisdom
Build HRP Cluster Map ▾
FounderQuarterly: cluster 50+ thematic ETFs + SALP positions via HRP, visualize regime stability.
ObjStructure
TrigQuarterly
Band—
CostCompute
RevQuarterly
Action: Run Python HRP on 252-day returns, export dendrogram.
Metric: Cluster stability >80% across regimes.
↳ Code: github.com/sagarasea/portfolio-engine
Run Mean-CVaR Optimizer ▾
DeployerMonthly: generate 3 risk-tiered target weights with turnover penalty (10 bps).
ObjAllocate
TrigMonthly
Band±3%
Cost10 bps
RevMonthly
Action: Run optimizer notebook → compare vs current → trade drift.
Metric: Turnover <20%/yr, Sharpe >1.2 paper.
↳ IBKR rebalance tool
Track Regime Probability ▾
ClimberWeekly: monitor HMM Crisis prob. If >60%, rotate satellite → BIL/SHV.
ObjProtect
TrigWeekly
Band60%
CostLow
RevWeekly
Action: Check regime dashboard Monday 9am WIB.
Metric: Zero panic sells, de-risk before drawdown.
↳ Dashboard: portfolio-engine/outputs/regime_states.parquet
High Risk — Growth Maximizer (Vol 25–35%)
| Bucket | Ticker | Weight | Focus |
|---|---|---|---|
| Core 70% | AIQ | 25% | Broad AI & Tech |
| BOTZ | 15% | Robotics & Physical AI | |
| ISTM | 15% | Critical Materials (Cu/Li/Ni/Co) | |
| ICLN | 15% | Clean Energy Transition | |
| Satellite 30% | ARKQ | 8% | Autonomous Tech + Robotics |
| THRO | 7% | Therapeutics / Genomics | |
| BOTT | 8% | Humanoid Robotics Pure-Play | |
| Cash | 7% | Dry Powder for Drawdowns |
Medium Risk — Balanced Builder (Vol 15–22%)
| Bucket | Ticker | Weight | Focus |
|---|---|---|---|
| Core 80% | AIQ | 20% | Broad AI & Tech |
| ARTY | 15% | Cloud + Semiconductors | |
| ICLN | 15% | Clean Energy Transition | |
| POWR | 10% | Grid & Power Infrastructure | |
| VTI | 20% | Total US Market Diversifier | |
| Satellite 20% | BOTZ | 8% | Robotics & Automation |
| THRO | 6% | Biotech / Genomics | |
| Cash | 6% | Opportunistic / Buffer |
Low Risk — Preservation with Optionality (Vol 8–14%)
| Bucket | Ticker | Weight | Focus |
|---|---|---|---|
| Core 90% | VTI | 40% | Total US Market |
| ICLN | 20% | Clean Energy Transition | |
| POWR | 15% | Grid & Power Infrastructure | |
| AIQ | 15% | Broad AI Exposure (Capped) | |
| Satellite 10% | BOTZ | 5% | Robotics (Defensive Growth) |
| Cash / T-Bills | 5% | Liquidity / Optionality |
Outperform Benchmark — Situational Awareness LP (Leopold Aschenbrenner): Founded by ex-OpenAI researcher Leopold Aschenbrenner (author of Situational Awareness, 2024).
AUM: $9.3B regulatory / $20.2B 13F securities (Q2 2026 filing).
Thesis: Concentrated AI/AGI bets — semiconductor supply chain, AI data centers, Bitcoin miners as AI infrastructure, edge/physical AI.
Strategy: Heavy options overlays (NVDA calls dominant), long-dated calls for convexity, semi-annual rebalance via 13F.
Our engine backtest (2022–2026, synthetic): 27.2% ann. ret, 27.8% vol, 0.84 Sharpe, -32.9% max DD.
Goal: Our quant-tier portfolios (Mean-Var, Max Sharpe, HRP) target higher risk-adjusted returns via diversification, factor-awareness, and regime-adaptive covariance.
SALP 13F Holdings (Q2 2026) — Concentrated AI/AGI
| Ticker | Name | Weight | Thesis |
|---|---|---|---|
| SNDK | SanDisk / Western Digital | 28.0% | AI data storage, flash |
| MU | Micron Technology | 27.5% | HBM/DRAM for AI training |
| BE | Bloom Energy | 9.4% | Power for AI data centers |
| TSM | Taiwan Semiconductor | 6.3% | Foundry for AI chips |
| NBIS | Nebius Group | 6.1% | AI cloud infrastructure |
| IREN | Iris Energy | 4.1% | Bitcoin miner → AI compute |
| CORZ | Core Scientific | 4.0% | AI hosting, HPC |
| APLD | Applied Digital | 3.3% | AI data center buildout |
| RIOT | Riot Platforms | 2.9% | BTC miner, power optionality |
| CLSK | CleanSpark | 2.5% | BTC miner, energy assets |
| SEI | SolarEdge / Energy | 2.1% | Solar + storage for AI |
| TE | TE Connectivity | 1.9% | Connectivity for AI servers |
| BITF | Bitfarms | 1.5% | BTC miner, hydro power |
| BTDR | Bitdeer | 1.5% | ASIC hosting, AI pivot |
| AMD | Advanced Micro Devices | 1.1% | GPU/CPU for inference |
Rebalancing Rules (Automated Discipline)
Quarterly Calendar Rebalance ▾
AllApr / Jul / Oct / Jan. Check drift vs target; trade only on band breach.
ObjDiscipline
TrigBand breach
BandPer tier
CostMin
Rev4/yr
Action: IBKR rebalance tool or Monex manual. Trade at 10am ET, avoid open/close.
Metric: 0 emotional trades/yr. Log drift %, trade ticket, tax lot.
↳ Auto-calendar: 4 recurring events + band checklist
New Money → DCA Into Underweight ▾
DeployerClimberMonthly $500 DCA buys the most underweight sleeve first — natural rebalance.
ObjCost alpha
TrigMonthly
BandAuto
CostZero
RevOngoing
Action: Set recurring buy for most underweight ticker (check 28th of month).
Metric: Drift reduced without selling. Avg entry cost improves.
↳ Only sell if >2× band breach
Annual Tax-Loss Harvest ▾
FounderDeployerDec: harvest losses in satellite sleeves, rotate to similar exposure (wash-sale safe).
ObjTax alpha
TrigDec
Band—
CostLow
Rev1/yr
Action: IBKR tax optimizer → sell losers, buy correlated proxy (e.g., BOTZ→ROBO).
Metric: $ harvested ≥ expense drag. No wash sales.
↳ Document for CPA
IPS Wealth Plan — Investment
0/5 (0%)
Open Brokerage Account ▾
DeployerSet up IBKR or Monex for US ETF access with $50k initial capital.
ObjAccess
Alloc$50k
TrigKYC done
Band—
RevQ4 2026
Action: Complete KYC, link bank, deposit $50k USD via wire.
Metric: Account approved + funds settled in 7 business days.
↳ Verify: dashboard shows balance
Start DCA with $500/mo ▾
ClimberDeployerAuto-invest $500/month into AIQ + ICLN (60/40) via recurring buy.
ObjHabit
Alloc$500/mo
Trig1st/mo
Band±5%
RevOngoing
Action: Set recurring purchase 1st of month. Ignore price swings.
Metric: 12 deposits/yr, avg cost smoothed, zero timing decisions.
↳ After 12mo: review allocation vs target
Deploy Core 70% ▾
DeployerBuy AIQ 21%, ICLN 14%, BOTZ 10.5%, POWR 7%, ISTM 7%, VTI 10.5% = $35k.
ObjCore
Alloc70%
Trig3 tranches
Band±3%
RevJan 2027
Action: Execute 3 tranches over 3 months to average entry.
Metric: 70% of $50k in core, max 5% cash drag.
↳ Rebalance if drift >3%
Deploy Satellite 20% ▾
FounderDeployerARKQ 5%, THRO 5%, BOTT/DRGN 5%, cash 5% = high-beta + dry powder.
ObjUpside
Alloc20%
TrigStagger
Band±5%
RevFeb 2027
Action: Stagger entries; keep 5% cash for drawdowns.
Metric: Satellite ≤25%, no single ETF >10%.
↳ Trim winners >15% allocation
Quarterly Review Cadence ▾
All4 calendar reminders/yr (Apr/Jul/Oct/Jan). Rebalance on band breach.
ObjDiscipline
AllocTrack
TrigBand ±5%
Band±5%
Rev4/yr
Action: Track expense drag, drift, tax lots (IBKR optimizer).
Metric: 4 logged reviews/yr, rebalance only on breach.
↳ Automate: 4 recurring calendar events
Track SALP 13F vs Our Portfolios ▾
FounderExplorerMonitor WhaleWisdom semi-annual filings. Compare factor exposure vs our diversified tiers.
ObjBenchmark
AllocTrack
Trig13F filing
Band—
RevSemi-annual
Action: WhaleWisdom alert → diff holdings → update factor model.
Metric: Our Sharpe > SALP Sharpe with lower max DD.
↳ Source: whalewisdom.com/filer/situational-awareness-lp
Tier 1: Massive TAM + Structural Tailwinds
Framework — Lean Startup + McKinsey Horizons: A strategy partner wouldn't hand you a SMART list — they'd frame it as
Horizon 1 (defend/extend core), Horizon 2 (build emerging), Horizon 3 (create options). Execution uses
Build-Measure-Learn loops: validate problem → MVP → North Star Metric → pivot/persevere.
Terms:
TAM/SAM/SOM = total / serviceable / obtainable market. MSP = Minimum Sellable Product (smaller than MVP).
MRR = Monthly Recurring Revenue. Card columns: Prob / MVP / Metric / Learn / Next.
| Opportunity | TAM 2030 | Key Drivers | Entry Point |
|---|---|---|---|
| AI-Augmented Services | $15.7T GDP | "Using AI" shift | AI agency, vertical SaaS |
| Energy Transition Infra | Trillions | AI power 4x by 2030 | Solar EPC, battery, EV charge |
| Semiconductor Ecosystem | >$1T | AI chips, EVs | OSAT, design, equipment |
| Robotics Integration | $110.7B | Physical AI, 13.8% CAGR | Factory, warehouse, service |
| Digital Health / Biotech | $574B | Aging + tech, 3x | Telemedicine, AI dx, longevity |
Quant Content Business Validation — Lean Startup + McKinsey 3 Horizons:
- TAM: $2.1B — Retail investment research/subscriptions (ARK, Seeking Alpha, Motley Fool)
- SAM: $180M — English-speaking quant-savvy retail in SE Asia + diaspora
- SOM: $3.6M — 2% capture in 3 years (12k subs @ $25/mo)
- MSP: Weekly regime update + monthly portfolio rebalance alert (Telegram/Email)
- Unit Economics: CAC $15 (organic content), LTV $900 (36 mo), Payback 1 mo, Marginal cost ~$0
- Moat: Proprietary quant engine + SALP tracking + regime-aware allocation (not replicable by newsletter-only)
Lean Startup Plan — Business
| Opportunity | Why Now | Local Angle |
|---|---|---|
| Materials Recycling | Supply constraints | Battery, e-waste mining |
| AI-Native Learning | 59% reskill (WEF) | Adaptive platform scale-out |
| Silver Economy | 1 in 6 >60 by 2030 | Senior tech, longevity finance |
| Space Economy | $1T by 2040 | Satellite data, ground station |
| Cybersecurity | $300B by 2027 | Managed SOC, OT/ICS |
Lean Startup Plan — Business
0/3 (0%)
Validate 1 Vertical ▾
FounderExplorer20 customer interviews in AI-Native Learning or Materials Recycling within 90 days.
ProbPain
MVPInterview
Metric15 calls
LearnBrief
NextBuild
Action: LinkedIn + chat list outreach. Script: pain, budget, current fix.
Metric: ≥15 calls, 3 quantified problems, 1 design partner.
↳ Output: 1-page problem brief
Build MSP in 90 Days ▾
Founder1 paid pilot client, $1k MRR, before month 4.
ProbValidated
MVP1 pilot
Metric$1k MRR
LearnFeedback
NextScale
Action: Package as fixed-scope retainer. Manual delivery OK.
Metric: Signed contract, invoice paid, NPS >8.
↳ Iterate offer from pilot feedback
Scale to $10k MRR ▾
FounderClimber10 clients @ $1k or 4 @ $2.5k within 6 months of pilot.
ProbSolved
MVPProductized
Metric$10k MRR
LearnUnit econ
NextSystem
Action: Productize delivery, hire 1 contractor.
Metric: Gross margin >60%, churn <10%/mo.
↳ SOP + async onboarding
Priority 1: Must-Have (Universal)
Framework — 70-20-10 (WEF/McKinsey future-of-work): Capability builds as 70% experiential (real projects),
20% social (mentorship, peer feedback), 10% formal (courses). Not SMART checklists — compounding via deliberate practice.
RAG = Retrieval-Augmented Generation (AI reads your docs before answering). JTBD = Jobs-To-Be-Done (people "hire" products for a job).
Skills ranked by compounding value — hardest to automate, easiest to neglect. Card columns: Exp / Soc / Frm / Out / Cpd.
| Skill | Learning Path | Time | Proof |
|---|---|---|---|
| AI Fluency | Prompt → RAG → Evaluate | 40–80 hrs | 1 working workflow |
| Analytical Thinking | SQL/Py → Frameworks | 60–100 hrs | Case study |
| Creative Problem-Solving | Design → First principles | 40–60 hrs | 3 solutions |
90-Day Quant Sprint Curriculum — 70-20-10 Learning Model:
- Weeks 1–2: Factor Mastery — Replicate FF5+MOM/BAB/QMJ from Ken French library (70% coding, 20% reading, 10% teaching)
- Weeks 3–4: Regime Detection — HMM 3-state + transition signals (VIX term, HY spread, USD) (70% coding, 20% reading, 10% teaching)
- Weeks 5–6: Optimization Pipeline — Mean-CVaR + turnover penalty → 3 risk-tiered portfolios (70% coding, 20% reading, 10% teaching)
- Weeks 7–8: Backtest Discipline — Walk-forward 2019–2026 with costs vs SALP/SPY/EW (70% coding, 20% reading, 10% teaching)
- Weeks 9–10: Live Paper Trading — IBKR paper / TradingView webhook + daily P&L attribution (70% doing, 20% reviewing, 10% sharing)
- Weeks 11–12: Content & Distribution — Frontier updates + newsletter + X/LinkedIn threads (70% writing, 20% editing, 10% distributing)
Priority 2 & 3
| Skill | Why It Compounds | How to Build |
|---|---|---|
| Tech Leadership | Scale through teams | System design, trade-offs |
| Product Sense | Build right thing | JTBD, experiments |
| Judgment | AI can't own it | Track → review → model |
| Cross-Domain | Innovation at edges | 2–3 fields deep |
| Adaptability | Survive shifts | Quarterly audit |
70-20-10 Learning Plan — Skills
0/4 (0%)
Q1: AI Fluency 40h ▾
ExplorerReinventorAndrew Ng + Prompt Eng, build 3 workflows by Month 3.
Exp3 WF
SocPeer
Frm40h
OutRepos
Cpd2h/wk
Action: 2h/week. Workflows: content, code review, research.
Metric: 3 repos, each saves ≥2h/week.
↳ Log hours in tracker
Q2: Analytical 60h ▾
ClimberReinventorSQL + Python basics, 1 business case end-to-end.
Exp1 case
SocReview
Frm60h
OutDeck
CpdSkill
Action: Pick real Q. Wrangle → insight → slide.
Metric: Deck to 3 stakeholders, 1 decision changed.
↳ Kaggle if no internal data
Q3: Domain Depth ▾
FounderClimber1 vertical, 3 books + 10 papers + 5 interviews, build MVP.
ExpMVP
Soc5 intv
Frm18 in
OutProto
CpdEdge
Action: Energy or Health. Read → interview → prototype.
Metric: Working prototype + 5 user tests.
↳ 10 LinkedIn posts
Q4: Teach & Network ▾
AllWorkshop 10+, join 3 communities, plan Year 2.
ExpTeach
Soc3 grp
Frm—
OutNPS8
CpdLever
Action: Free workshop (AI for X). Engage 3 Slack/Discord.
Metric: NPS >8, 2 inbound opps.
↳ Record → YouTube → repurpose
Structural Advantages
Framework — Value-Chain Upgrade + Leapfrogging: Indonesia's trap is exporting raw materials (nickel ore) and importing finished goods.
The play is to move up the value chain (ore → precursor → cell → pack) and leapfrog legacy infra (skip landline → mobile; skip cards → QRIS).
Avoid the resource curse: capture margin via processing + IP, not just extraction. Card columns: Pos / Add / Risk / Lever / Horiz.
Demographic
70% working age, median 29. 50M+ students, exam-driven culture.
Digital Native
210M+ users, QRIS, 50% unbanked = embed fin.
Resource Rich
Largest nickel, copper, bauxite. Downstreaming policy.
Equatorial
Launch latitude + solar irradiance advantage.
ASEAN Hub
670M market, halal = 1.8B OIC consumers.
High-Fit Opportunities
| Opportunity | Local Tailwind | Execution |
|---|---|---|
| Nickel → Battery | Reserves + policy | Processing, precursor, recycling |
| AI-Native Learning | 50M students | Adaptive platform → subject verticals |
| Solar + Storage | Irradiance, PLN gaps | C&I rooftop + battery |
| Halal Biotech | 230M Muslim | Vaccines, supplements, cosmetics |
| Digital Finance | 50% unbanked | Embedded, SME lend, cross-border |
Value-Chain Upgrade Plan — Indonesia
0/2 (0%)
Learning Platform Expansion ▾
FounderClimberAdd 2 subject verticals (language proficiency, exam preparation) by Q2 2027 via messaging funnel.
PosScale
AddSubjects
RiskCAC
LeverEngine
HorizH2
Action: Clone learning engine, localize to new subjects, run acquisition funnel.
Metric: +30% enrollments, CAC
↳ A/B pricing per subject
Nickel Supply Chain Pilot ▾
DeployerFounderPartner 1 smelter for battery precursor JV feasibility by Q4 2027.
PosUpchain
AddProcessing
RiskCapital
LeverPolicy
HorizH3
Action: Map 5 Sulawesi smelters, pitch value-add JV.
Metric: Signed LOI or paid engagement.
↳ Leverage downstreaming mandate
Execution Frameworks by Profile
Framework — OKR (Objectives & Key Results): Used by Google/tech orgs instead of SMART for execution. An Objective is a qualitative ambition;
Key Results are 2–3 measurable outcomes (not tasks). Cadence = weekly check-in, quarterly scoring. Card columns: Obj / KR / Init / Cad / Own.
Explorer
Skill-stack free. No capital yet. Time-rich.
Climber
Employee. DCA $500/mo. Build career + side.
Founder
Validate → MSP → $10k MRR. Risk-tolerant.
Deployer
$50k+. Core-Satellite ETFs. Preserve+grow.
Reinventor
40+. Reskill fast. Urgency-driven.
OKR Execution Plan — Actions
0/3 (0%)
Choose Lane + Commit ▾
AllPick 1 of 5 personas, write 1-line objective, share publicly Week 1.
ObjFocus
KR0 pivot
InitPost
CadWkly
OwnYou
Action: DM Hermes or post in group. No switch 90 days.
Metric: 1 commitment logged, 0 pivots Q1.
↳ Weekly check-in
Week 1 Sprint ▾
ExplorerClimberFounderReinventorFirst step: course start / interview #1 / account open.
ObjMomentum
KR1 done
Init2h block
CadWk1
OwnYou
Action: Block 2h Mon. Execute smallest irreversible step.
Metric: 1 done, screenshot proof.
↳ Stack next 3 Sunday
90-Day Review ▾
AllScore OKRs vs tab plans, adjust, report to group.
ObjLearn
KR≥50%
InitScorecard
CadQ1
OwnYou
Action: Fill scorecard per tab. What worked/didn't.
Metric: ≥50% KR achieved, 1 lesson shared.
↳ Use progress bars as tracker
Primary References
World Economic Forum
Future of Jobs 2025 — 39% skills change, 59% reskill, 170M/92M jobs.
WEF 2026
McKinsey Global Institute
"Agents, robots, and us" (Nov 2025) — AI fluency 7x, social +26%.
McKinsey/WEF/OECD 2026
iShares / BlackRock
2026 Thematic Mid-Year — AI → physical, bottlenecks.
iShares 2026
State Street (SSGA)
Intelligence Economy (Feb 2026) — 4 AI phases, bottlenecks.
SSGA 2026
BloombergNEF
Energy Transition 2026 — Renewables, nuclear, hydrogen, grids.
BNEF 2026
PwC
AI GDP Impact — $15.7T by 2030 (~14% output).
PwC via WEF
ARK Invest
Big Ideas 2026 — Blockchain, sequencing, aerospace, robotics.
ARK 2026
HubSpot / Ascendurepro
Fastest Growing 2026+ — AI 28%, renewables 17%, health 3x.
Ascendurepro 2026
Morgan Stanley / Raysolute
Space $1T by 2040 — Satellite, observation, logistics.
Raysolute 2025
All data cited inline per section. Not financial advice. Verify before acting.