2026-2028 Forecast
Where is the world going in the next two years — and what should you do about it?
This forecast applies the superforecaster methodology to six domains. Each prediction follows the three-view protocol: outside view (base rate), inside view (current signals), synthesis (calibrated probability). Every prediction has falsifying conditions. If you disagree with a probability, that disagreement is the most valuable signal — examine why.
Forecast date: March 2026. Resolution horizon: March 2028.
The Five Anchor Predictions
These predictions were positioned in March 2026. The six domain analyses below decompose and stress-test each one.
| # | Prediction | Probability | Conviction | Domain |
|---|---|---|---|---|
| 1 | AI agents handle 40% of knowledge worker tasks by end 2027 | 70% | 5/5 | AI + Work |
| 2 | Healthspan becomes top-3 consumer spending category by 2028 | 55% | 4/5 | Healthspan |
| 3 | AI-generated content becomes commodity; human-curated becomes premium | 80% | 4/5 | AI + Work |
| 4 | Crypto rails become default for agent-to-agent settlement by 2028 | 40% | 3/5 | Crypto |
| 5 | Prediction practice separates top-decile performers by 2027 | 60% | 3/5 | Cross-cutting |
Domain 1: AI Trajectory
Base Rate
Technology adoption S-curves show consistent patterns. Electricity took 40 years from invention to 50% household adoption. The internet took 15 years. Smartphones took 7 years. Each wave compresses the adoption timeline of the next. AI adoption in enterprises went from ~10% (2020) to ~79% (2025) in five years — faster than any prior technology wave.
Current Signals
| Signal | Data | Source |
|---|---|---|
| Enterprise adoption | 79% of organizations have adopted AI agents in some form; only 1 in 9 runs them in production | Gartner/industry surveys, 2025 |
| Application embedding | 40% of enterprise apps will embed task-specific AI agents by 2026, up from <5% in 2025 | Gartner forecast, 2025 |
| Market growth | AI agents market reached $7.8B in 2025, projected $10.9B in 2026 (45% CAGR) | Market analysis, 2025 |
| Cancellation risk | 40%+ of agentic AI projects at risk of cancellation by 2027 without governance | Gartner, 2025 |
| Protocol maturity | MCP crossed 97M monthly SDK downloads; A2A launched by Google with 50+ partners; both donated to Linux Foundation AAIF | Anthropic/Google, 2025 |
Trend Direction
Accelerating — but with a governance gap. Adoption is ahead of infrastructure. The 79% adoption vs 11% production gap is the critical number. It means the capability exists but the plumbing (governance, observability, ROI measurement) lags. This is the classic "installed base waiting for the unlock" pattern.
Predictions
P1: By end 2027, 40% of knowledge worker tasks will be handled by AI agents.
| View | Reasoning | Weight |
|---|---|---|
| Outside | Prior automation waves displaced 20-30% of targeted tasks within 5 years. Base rate: 25% | 30% |
| Inside | Adoption rate (79%) far exceeds prior waves at same stage. But production deployment (11%) suggests a gap between experimentation and impact. Coding assistants already handle 30-50% of junior tasks. | 50% |
| Synthesis | The base rate says 25%. The inside view says this wave is faster but governance-gated. 40% is ambitious but within range given the adoption velocity. | Probability: 70% |
P2: Agentic commerce redirects 10%+ of e-commerce transactions through agent channels by 2028.
| View | Reasoning | Weight |
|---|---|---|
| Outside | New commerce channels typically capture 5-15% of adjacent market within 3 years of infrastructure maturity. Base rate: 8% | 40% |
| Inside | MCP + A2A protocols now standardized. McKinsey projects $3-5T redirected by 2030. Bain estimates 15-25% via agentic channels. But protocol maturity ≠ market adoption. | 40% |
| Synthesis | Infrastructure is ahead of adoption (protocols exist, transactions don't yet). 10% by 2028 requires the "1 in 9 production" ratio to flip by mid-2027. | Probability: 45% |
Falsifying Conditions
- Enterprise AI production deployment stays below 20% by end 2026 → lower P1 conviction by 2 points
- Major AI governance failure (data breach, runaway agent incident) triggers regulatory pause → lower both predictions
- MCP/A2A adoption stalls below 200M monthly downloads by end 2026 → lower P2 conviction
Watch Signals
- Gartner quarterly adoption surveys (production deployment %, not experimentation %)
- AI agent market revenue actuals vs projections
- MCP/A2A SDK download trends (monthly)
- Enterprise AI cancellation rate (Gartner tracks this)
Domain 2: Crypto and DePIN
Base Rate
Financial infrastructure adoption follows a pattern: SWIFT took 15 years from founding (1973) to global dominance. Internet payments took 10 years from PayPal (1998) to mainstream. Crypto settlement is at year 5 of institutional adoption (post-2020 DeFi summer). Stablecoin volumes doubled in one year ($19T to $33T). This is faster than prior rails.
Current Signals
| Signal | Data | Source |
|---|---|---|
| Stablecoin volume | $33T in 2025 (72% YoY growth), USDC leading with $18.3T | Bloomberg/Artemis Analytics, Jan 2026 |
| B2B payments | B2B stablecoin payments surged from <$100M/month (early 2023) to $6B/month (mid-2025) | Artemis Analytics, 2025 |
| Regulatory clarity | US GENIUS Act passed July 2025 — first comprehensive stablecoin framework | US Congress, July 2025 |
| DePIN market | ~250 DePIN projects, combined market cap $19B (up from $5.2B a year ago) | CoinGecko, Sept 2025 |
| Helium traction | 2,721 TB data offloaded in Q2 2025 (138% QoQ growth), 600K mobile sign-ups, $13.3M annualized revenue | Helium Network, 2025-2026 |
Trend Direction
Accelerating — stablecoin rails are crossing from crypto-native to enterprise. The B2B payment surge ($100M to $6B monthly in 2 years) is the strongest signal. DePIN is growing but from a small base. The WEF $3.5T projection for DePIN by 2028 is aspirational.
Predictions
P3: Crypto rails become default settlement for agent-to-agent commerce by 2028.
| View | Reasoning | Weight |
|---|---|---|
| Outside | New payment rails historically take 7-10 years from regulatory clarity to default status. GENIUS Act = year 0. Base rate: not default by 2028. | 50% |
| Inside | Stablecoin growth (72% YoY) is unprecedented. Agent commerce protocols (A2A, MCP) exist. But "agent-to-agent commerce" barely exists as a category yet. The agent commerce market needs to exist before its settlement rails become default. | 40% |
| Synthesis | The rails are being laid faster than any prior system. But "default" requires both the rails AND the traffic. Agent commerce is pre-product-market-fit. | Probability: 40% |
P4: DePIN networks collectively exceed $50B market cap by 2028.
| View | Reasoning | Weight |
|---|---|---|
| Outside | Infrastructure networks with real revenue (Helium: $13.3M ARR) grow 3-5x in 2-year bull cycles. From $19B base, 3x = $57B. | 40% |
| Inside | 250 projects but most pre-revenue. Helium is the standout. Regulatory tailwinds (GENIUS Act). But crypto market cap is cyclical — a bear market wipes 60-70% regardless of fundamentals. | 40% |
| Synthesis | Fundamentals support growth but market cycle dominates. $50B requires sustained bull market OR breakout real-world adoption. | Probability: 50% |
Falsifying Conditions
- Stablecoin volume growth decelerates below 20% YoY → lower P3 conviction
- No major enterprise adopts crypto settlement for agent transactions by end 2026 → P3 conviction drops to 1
- Crypto bear market drops DePIN market cap below $10B → P4 timeline extends 2 years
Watch Signals
- Monthly stablecoin settlement volumes (Artemis Analytics)
- B2B stablecoin payment growth rate
- First documented agent-to-agent crypto settlement in production
- Helium quarterly revenue and subscriber growth
- DePIN aggregate market cap (CoinGecko DePIN category)
Domain 3: Work Transformation
Base Rate
Historical automation waves show a consistent pattern: ATMs were supposed to eliminate bank tellers — instead, teller employment grew 10% as cheaper branches proliferated. Spreadsheets were supposed to eliminate accountants — instead, accounting employment grew as analysis became cheaper. The pattern: automation of tasks ≠ elimination of roles. Roles reshape around the remaining human-judgment tasks. Displacement concentrates in the most routine subset.
Current Signals
| Signal | Data | Source |
|---|---|---|
| Tech layoffs | AI-attributed tech job losses reached 77,999 in H1 2025 | Industry data, 2025 |
| Junior developer impact | 20% decline in employment for software developers aged 22-25 vs late-2022 peak | Bureau of Labor Statistics, 2025 |
| WEF projections | 92M roles displaced by 2030, 170M new roles created (net +78M) | World Economic Forum Future of Jobs Report, 2025 |
| High-risk categories | Data entry (95% automation risk), customer service (80%), administrative roles (7.5M jobs by 2027) | Multiple sources, 2025 |
| ROI evidence | Enterprise AI ROI averaging 171%, US enterprises 192% — 3x traditional automation ROI | Industry surveys, 2025 |
Trend Direction
Accelerating displacement in routine tasks, steady creation of new roles. The junior developer signal is the canary — entry-level knowledge work is being compressed first. But the WEF net-positive projection suggests the "lump of labor" fallacy still holds: technology creates more work than it destroys, just different work.
Predictions
P5: Entry-level knowledge worker roles decline 30% by 2028.
| View | Reasoning | Weight |
|---|---|---|
| Outside | Prior automation waves displaced 15-25% of targeted entry roles within 5 years. Base rate: 20% | 35% |
| Inside | Junior developer employment already down 20%. AI handles the tasks that were traditionally learning-by-doing for juniors. The ROI is clear (171%). But hiring cycles are also affected by macro economy, not just AI. | 45% |
| Synthesis | The 20% decline in junior developers is a leading indicator. Other entry-level knowledge roles (admin, data entry, customer service) face similar pressure. 30% decline is aggressive but the leading signal supports it. | Probability: 60% |
P6: "AI operations" becomes a top-10 job category by 2028.
| View | Reasoning | Weight |
|---|---|---|
| Outside | New technology categories (web developer, social media manager) took 3-5 years from emergence to top-10 job category status. AI operations emerged ~2024. | 40% |
| Inside | Every enterprise deploying AI agents needs governance, observability, ROI measurement (the Gartner governance gap). But the role is not yet standardized — titles vary (AI ops, prompt engineer, AI governance). | 40% |
| Synthesis | The demand exists. The role is crystallizing. But "top-10" is high bar. More likely top-20 by 2028. | Probability: 35% |
Falsifying Conditions
- Junior developer employment rebounds above 2022 levels → P5 is wrong, the decline was cyclical not structural
- Enterprise AI ROI drops below 100% (below traditional automation) → the economics don't support mass adoption
- WEF or McKinsey revise net job creation estimates negative → the "new roles" thesis fails
Watch Signals
- Quarterly employment data by role category (BLS, LinkedIn Economic Graph)
- Enterprise AI headcount data (how many people work on AI operations?)
- Entry-level job posting volumes (Indeed, LinkedIn)
- University CS enrollment trends (leading indicator of labor supply expectations)
Domain 4: Healthspan
Base Rate
The fitness industry grew from a niche ($2B in 1980) to mainstream ($100B in 2020) over 40 years. The supplement market followed a similar curve, reaching $50B by 2025. "Healthspan" as a consumer category is where fitness was in ~1995 — early mainstream, growing fast, but not yet a default budget item. The GLP-1 revolution is the equivalent of the home gym — it makes the category accessible to mass consumers.
Current Signals
| Signal | Data | Source |
|---|---|---|
| Market size | Longevity market projected at $29B in 2026, growing to $63-78B by 2033-2035 | Emergen Research/Business Research Company, 2025 |
| Deal size growth | Average longevity deal jumped from $20M (2023) to $69M (2025) — institutional-grade rounds | Longevity Market Q4 2025 |
| GLP-1 crossover | Nature Biotechnology asking "Are GLP-1s the first longevity drugs?" Multiple aging biology programs entering mid-stage trials | Nature Biotechnology, 2025 |
| Consumer adoption | 50% of new dietary supplement SKUs launched since July 2024 carry a healthspan claim | Euromonitor, 2025 |
| Big pharma entry | Longevity went mainstream within big pharma in 2025 — metabolism, inflammation, and aging biology connected through GLP-1 data | Lifespan.io expert roundup, 2025 |
Trend Direction
Accelerating — the GLP-1 revolution is pulling longevity from biohacker niche to mass consumer. The deal size jump ($20M to $69M) signals institutional conviction. But "top-3 consumer category" requires displacing established categories (food, housing, transportation, healthcare are all larger).
Predictions
P7: Healthspan becomes a recognized consumer spending category tracked by major market research firms by 2027.
| View | Reasoning | Weight |
|---|---|---|
| Outside | New consumer categories (organic food, wellness, fitness) took 5-10 years from niche to tracked category. Healthspan is at year 2-3. Base rate: tracked by 2028, not 2027. | 40% |
| Inside | 50% of new supplement SKUs carry healthspan claims. GLP-1 drugs are $50B+ market. Big pharma entered. The demand signal is undeniable. | 40% |
| Synthesis | The market exists. The tracking lags. Euromonitor already uses "healthspan" in reports. Formal category tracking by 2027 is likely. | Probability: 75% |
P8: Healthspan reaches top-3 consumer spending category by 2028.
| View | Reasoning | Weight |
|---|---|---|
| Outside | No new consumer category has entered the top 3 (housing, food, transportation) in 50 years. These are structural. Base rate: near zero. | 60% |
| Inside | The category definition matters. If "healthspan" includes GLP-1 drugs + supplements + longevity biotech + wellness services, you're at $200B+ already. But the top 3 are each $1T+. | 30% |
| Synthesis | This prediction needs reframing. Healthspan won't displace housing. But it could become the fastest-growing major consumer category (top 5 by growth rate, not absolute size). | Probability: 55% (as top-5 by growth rate) |
Falsifying Conditions
- GLP-1 safety signal emerges (major adverse event data) → healthspan category takes a hit
- Consumer supplement spending declines (recession-driven) → growth stalls
- No major market research firm creates a "healthspan" category by end 2027 → category isn't crystallizing
Watch Signals
- GLP-1 prescription volumes and market size (quarterly)
- Longevity startup funding rounds (quarterly, New Market Pitch tracker)
- Consumer supplement spending data (Euromonitor)
- FDA aging-related treatment pipeline milestones
Domain 5: Geopolitics
Base Rate
Regulatory response to transformative technology follows a pattern: the internet operated in a regulatory vacuum for ~5 years (1995-2000). Social media had ~6 years (2012-2018) before GDPR. AI regulation began in earnest with EU AI Act (2024). Countries that regulate early attract cautious capital. Countries that regulate late attract aggressive capital. Countries that find the middle (light-touch, principles-based) attract the most diverse capital.
Current Signals
| Signal | Data | Source |
|---|---|---|
| NZ AI Strategy | Released July 2025 — last OECD member to establish one. Projected NZ$76B economic contribution by 2038 | MBIE, July 2025 |
| NZ approach | Light-touch, OECD-aligned, no standalone AI Act. Uses existing laws (Privacy Act, Fair Trading Act) + new principles-based guidance | NZ Digital Government, 2025 |
| Treaty integration | NZ AI Strategy incorporates Treaty of Waitangi obligations — unique globally | NZ AI Strategy, 2025 |
| EU enforcement | EU AI Act enforcement began. High-risk AI systems face compliance requirements. First enforcement actions pending | EU, 2025-2026 |
| US regulatory shift | GENIUS Act (stablecoins) passed but AI-specific regulation remains fragmented across agencies | US Congress, 2025 |
Trend Direction
Diverging. Three regulatory models are crystallizing: EU (prescriptive, compliance-heavy), US (fragmented, sector-specific), NZ/Singapore (principles-based, light-touch). NZ's late entry is actually advantageous — it learned from EU's over-regulation and US's under-regulation.
Predictions
P9: NZ becomes a top-5 OECD destination for AI-native startups by 2028.
| View | Reasoning | Weight |
|---|---|---|
| Outside | Small countries with favorable regulation have attracted disproportionate tech capital before (Singapore, Estonia, Ireland). But NZ has geography and timezone disadvantages. Base rate: top-10, not top-5. | 50% |
| Inside | Light-touch regulation + Treaty-integrated ethics + quality of life + English-speaking. But NZ$76B projection is aspirational. Current AI startup ecosystem is small. | 35% |
| Synthesis | NZ has the policy ingredients but lacks the ecosystem density. Top-10 is realistic. Top-5 requires a catalytic event (major AI company establishing NZ presence). | Probability: 25% |
P10: Principles-based AI regulation (NZ/Singapore model) outperforms prescriptive regulation (EU model) in innovation metrics by 2028.
| View | Reasoning | Weight |
|---|---|---|
| Outside | Principles-based regulation historically outperforms prescriptive in fast-moving sectors (UK financial services vs US after 2008, Singapore fintech vs EU). Base rate: 70% | 40% |
| Inside | EU AI Act is already being criticized as too burdensome. Companies are routing around it. NZ and Singapore are attracting "regulation tourism." But innovation metrics are hard to define and measure. | 40% |
| Synthesis | Strong base rate and early evidence support this. The definition challenge is real but OECD tracks innovation indices. | Probability: 70% |
Falsifying Conditions
- NZ reverses course and introduces prescriptive AI legislation → P9 and P10 both weakened
- EU AI Act enforcement proves lighter than expected → P10 comparison framework changes
- Major AI safety incident in a lightly-regulated jurisdiction → political pressure for prescriptive approach everywhere
Watch Signals
- AI startup formation rates by country (OECD data, annual)
- NZ AI Strategy implementation milestones (MBIE quarterly updates)
- EU AI Act enforcement actions (frequency, severity, economic impact)
- Corporate AI investment location decisions (where are companies incorporating?)
Domain 6: Education
Base Rate
The university system has survived every disruption attempt for 800 years. MOOCs (2012) were going to replace universities — they didn't. Bootcamps (2015) were going to replace CS degrees — they captured ~5% of the market. No alternative credential has achieved parity with a degree for hiring purposes. However, the base rate for enrollment decline is real: US enrollment has been falling since 2010.
Current Signals
| Signal | Data | Source |
|---|---|---|
| Enrollment decline | 13% projected decline 2025-2041. International enrollment fell 17% in fall 2025 | US higher education data, 2025-2026 |
| Degree skepticism | 63% of US voters say 4-year degree "not worth the cost" | Public opinion survey, 2025 |
| AI skills demand | 57M Americans interested in AI skills, 8.7M currently learning. Ed-tech serves 99%+ of demand | Industry data, 2025 |
| Alternative funding | Workforce Pell (H.R.1, July 2025) allows Pell grants for 8-week credential programs | US Congress, July 2025 |
| CS enrollment paradox | AI program enrollment grew 45% annually for 5 years, but 62% of CS programs saw decline in fall 2025 | UPCEA/university data, 2025 |
Trend Direction
Bifurcating. AI-specific education is booming. Traditional education is declining. The CS enrollment paradox (AI up, general CS down) is the clearest signal: students are routing to AI-specific skills, not general computer science. The Workforce Pell expansion is a structural shift — government funding flowing to non-degree credentials for the first time.
Predictions
P11: Non-degree credentials capture 25%+ of post-secondary career training market by 2028.
| View | Reasoning | Weight |
|---|---|---|
| Outside | Non-degree credentials are currently ~10% of post-secondary. Growing at ~15% annually. 10% growing at 15% for 2 years = ~13%. Base rate: 15% by 2028. | 45% |
| Inside | Workforce Pell is a structural accelerant — $30B+ in federal funding newly accessible to short credentials. But institutional inertia is enormous and employer hiring criteria change slowly. | 40% |
| Synthesis | Workforce Pell moves the ceiling. 25% is ambitious but within range if employer signaling shifts. | Probability: 40% |
P12: "Agency accelerator" programs (teaching initiative, AI fluency, building) become a named category by 2027.
| View | Reasoning | Weight |
|---|---|---|
| Outside | New education categories (bootcamps, MOOCs) took 2-3 years from emergence to named category. "Agency accelerator" as a concept is at year 1. | 40% |
| Inside | The All-In prediction about education splitting into "credentials vs agency accelerators" is gaining traction. The demand is clear (57M interested in AI skills). But the term "agency accelerator" is not yet in common use. | 40% |
| Synthesis | The category will emerge but the naming is uncertain. Some variant of "AI-native career accelerator" will be named by 2027. | Probability: 55% |
Falsifying Conditions
- University enrollment stabilizes or grows → the decline thesis is wrong
- Employers re-emphasize degree requirements (recession-driven risk aversion) → alternative credentials stall
- Workforce Pell implementation is delayed or underfunded → structural accelerant removed
Watch Signals
- US post-secondary enrollment data (NCES, annual)
- Workforce Pell uptake numbers (first available late 2026)
- Employer hiring criteria surveys (LinkedIn, Indeed — quarterly)
- New education program launches with "agency" or "AI-native" framing
Interaction Effects
The highest-value predictions live at domain intersections. Where two or more domains compound, the outcome accelerates beyond what either domain predicts alone.
AI + Crypto = Agent Commerce
MCP and A2A protocols give agents the ability to communicate. Stablecoin rails give them the ability to transact. Together, they enable autonomous economic activity. The interaction effect: if AI agent deployment hits production scale (Domain 1) AND stablecoin rails mature (Domain 2), agent commerce emerges faster than either domain predicts independently.
Interaction prediction: First $1B in autonomous agent-to-agent transactions settled on crypto rails occurs by Q4 2027. Probability: 30%. This is a low-base-rate, high-impact prediction — the intersection creates a new category.
AI + Healthspan = Computational Biology
AI capability improvements (Domain 1) directly accelerate drug discovery and longevity research (Domain 4). AlphaFold was the proof point. GLP-1 optimization through AI modeling is the current wave.
Interaction prediction: AI-designed longevity therapeutic enters Phase 2 trials by 2028. Probability: 65%. The base rate for AI-assisted drug discovery entering trials is already high — multiple candidates in pipeline.
Work + Education = Credential Disruption
As AI automates entry-level tasks (Domain 3), the value proposition of a 4-year degree (which primarily signals "can complete entry-level tasks") weakens. Alternative credentials (Domain 6) fill the gap.
Interaction prediction: At least one Fortune 500 company publicly drops degree requirements for 50%+ of roles by 2027. Probability: 55%. Several have already dropped requirements for specific roles. The trend is toward generalization.
Geopolitics + Crypto = Regulatory Arbitrage
Countries with principles-based regulation (Domain 5) attract crypto-native businesses (Domain 2). NZ's light-touch approach + GENIUS Act compliance could make it a hub for compliant stablecoin operations.
Interaction prediction: NZ or Singapore becomes a top-3 jurisdiction for licensed stablecoin operators by 2028. Probability: 35%. Requires proactive regulatory positioning beyond current light-touch approach.
Calibration Check
Distribution Assessment
| Probability Range | Count | Healthy Range |
|---|---|---|
| 0-30% | 3 | 15-25% of predictions |
| 31-50% | 4 | 20-30% |
| 51-70% | 5 | 25-35% |
| 71-100% | 2 | 10-20% |
Distribution looks reasonable. Slight lean toward the middle (calibration-conscious, not hedging — the extremes are genuinely uncertain or genuinely likely).
Overconfidence Check
Highest-conviction predictions (P1: 70%, P3: 80%, P10: 70%) all have strong base rates AND supporting inside views. No prediction above 80%. No prediction claims certainty. This passes the basic calibration sniff test.
Consistency Check
- P1 (40% task automation) and P5 (30% entry-level decline) are compatible — task automation and role decline are correlated but not identical
- P3 (content commodity) and P12 (agency accelerators) reinforce — both describe the same bifurcation
- P4 (crypto rails for agents) has lower probability (40%) than P2 (agentic commerce at 45%) — consistent, since P4 requires P2 as a precondition
No contradictions detected.
Review Schedule
| Cadence | Action | Next Date |
|---|---|---|
| Monthly | Scan for signals that affect existing predictions. Flag any that need conviction updates. | April 2026 |
| Quarterly | Full probability review. Update any prediction where evidence shifted by 10%+. | June 2026 |
| Semi-annual | Calibration check. Compare predictions that resolved against assigned probabilities. | September 2026 |
| Annual | Full forecast refresh. Retire resolved predictions. Add new domains if warranted. | March 2027 |
Context
- Superforecaster Methodology — The process that produced this forecast
- 2026 Predictions Review — Archetype analysis of All-In predictions
- Prediction Database — The living record
- Prediction Process — Review cadence and tracking discipline
- Strategy — Where predictions connect to positioning decisions
- Perspective — The perception that enables prediction
Links
- Gartner AI Agent Adoption — Enterprise adoption tracking
- Artemis Analytics Stablecoin Data — Settlement volume tracking
- WEF Future of Jobs Report 2025 — Labor market projections
- NZ AI Strategy — NZ regulatory approach
- ai-2027.com — AI trajectory modeling
- New Market Pitch Longevity Tracker — Longevity investment data
- Bloomberg Stablecoin Analysis — $33T stablecoin volume data
Questions
If you could only monitor one domain for the next two years, which would give you the highest decision-relevant signal — and what does that tell you about where your uncertainty actually lives?
- Which of these 14 predictions has the weakest evidence base — and is that because the evidence doesn't exist or because you haven't looked?
- The interaction effects section predicts outcomes at domain intersections. Which intersection is missing from this analysis — and does its absence reveal a blind spot in your worldview?
- If three of these predictions are wrong by March 2028, which three would teach you the most about how you think?