'Intelition' changes everything: AI is no longer a tool you invoke

The article argues that a new paradigm—dubbed “intelition”—is emerging as humans and AI co-produce decisions via shared enterprise ontologies, continuous world-model learning and always-on personal interfaces. It highlights Palantir’s focus on ontologies, Google’s Nested Learning proposal, Meta’s H-JEPA lineage (V-JEPA/I-JEPA), Apple’s on-device UI-JEPA work and device activity around OpenAI and Jony Ive, and privacy standards like Solid and Anthropic’s MCP; together these trends could shift value toward chips, enterprise data-modeling infrastructure and on-device personal data controls. For investors, this signals potential secular opportunities in enterprise software platforms, semiconductors and secure personal-data/agent infrastructure rather than near-term market-moving events.

Analysis

Market structure: Winners are platform/ontology owners (PLTR) and device vendors (AAPL) that can bind models to real-world objects; platform R&D leaders (GOOGL, META) benefit from Nested Learning and world-model IP. Losers include ad-profiling intermediaries and single-app SaaS with siloed data—pricing power shifts to firms that control federated ontologies and secure on-device intent flows. Increased demand for AI chips and secure storage/cloud will tighten supply for advanced nodes and enterprise GPU capacity over 12–36 months, lifting capex and margin dispersion across suppliers.

Risk assessment: Tail risks include regulatory backlash (EU/US AI rules, data sovereignty) and liability from agentic actions—these could cut 10–30% off valuations in worst-case scenarios within 6–18 months. Short-term (days–weeks) volatility will spike around product events and AI policy releases; medium-term (3–12 months) outcomes hinge on enterprise sales cycles and Solid/MCP adoption rates; long-term (2–5 years) depends on successful federation of ontologies and chip supply scaling. Hidden dependency: value realization requires enterprise adoption and standard harmonization, not just model breakthroughs.

Trade implications: Favor concentrated, time-boxed exposure to PLTR and AAPL for ontology and on-device moats, and calibrated option exposure to GOOGL/META for world-model upside; expect IV-led opportunities around Apple product events and Qs. Rotate portfolio overweight to semiconductors and enterprise software (increase exposure by 2–4%) and underweight ad-revenue sensitivity. Use calendar spreads and financed call spreads to capture multi-quarter secular upside while limiting drawdowns.

Contrarian angles: Consensus underestimates integration friction—enterprise ontology rollouts historically take 12–24 months, so immediate multiple expansion may be delayed; regulatory and insurance costs for agentic AIs are underpriced today. The market may overrate near-term on-device substitution of cloud; expect gradual hybrid adoption. Unintended consequence: accelerated centralization around a few ontology owners could invite antitrust scrutiny and create single-point security failure risks.

Key Decisions for Investors

• Establish a 2–3% long position in PLTR over the next 4–8 weeks (scale in 25% increments); complement with a 12-month call spread (buy ATM LEAP, sell +30% strike) to cap cost; target +40% return within 12–18 months or trim to half if no enterprise contract cadence visible in 12 months.
• Build a 2–4% long position in AAPL ahead of the next product event within 3 months to capture on-device AI upside; buy 1% notional of cost-limiting puts (3-month, ~5–7% OTM) as earnings/product-event protection.
• Allocate 1–2% to financed 9–12 month call spreads on META and 1% on GOOGL (each: buy near-ATM call, sell +25–35% call) to play world-model/Nested Learning adoption; take profits at +50% or if regulatory headlines materialize.
• Purchase regulatory tail hedges: 6–9 month 15–20% OTM puts on GOOGL and META totaling 0.5–1% of portfolio value to protect against adverse AI/data regulation tied to EU AI Act milestones expected in the next 90–180 days.
• Sector tilt: over next 3 months rotate 3–5% of portfolio from ad-revenue-sensitive exposure into semiconductors and enterprise software (target companies with >12 month visibility on GPU/node supply); trim on sector outperformance of +30% or when chip supply expansion announcements meet capacity increases.