The Investor

The Macro Setup

Yesterday's Fed decision was technically a non-event — rates held at 3.5-3.75%. The context around it is a regime shift. Three forces converged simultaneously that haven't been present since early 2022:

• Energy shock: Iran declared Gulf energy infrastructure "legitimate and prime targets" and struck a Qatar fuel hub. Oil spiked above $111/barrel. The Strait of Hormuz carries 20% of global oil supply.
• Inflation reacceleration: Wholesale prices rose more than 2x faster than expected. The Fed projects 2.7% year-end inflation — and energy pass-through hasn't hit CPI yet.
• Rate persistence: The dot plot held at one cut for 2026. Markets had priced two. The gap between hope and reality just widened.

The administration's response signals severity: a Jones Act waiver (a rarely-used emergency lever) and VP Vance personally convening oil executives. These aren't confidence-building measures — they're crisis management.

What Broke Yesterday

Every asset class sold off in correlation — the signature of genuine risk repricing:

Bitcoin's 4.6% drop is notable — it's supposed to be a geopolitical hedge. It didn't hedge anything. Meanwhile, Micron nearly tripled revenue on a memory-supply crunch, confirming AI infrastructure bottlenecks are creating winner-take-most dynamics even in a risk-off environment.

The Fed Leadership Vacuum

Powell's term expires in May. His nominated replacement, Kevin Warsh, is blocked by a GOP senator until the DOJ drops an investigation into Powell over — remarkably — the Fed's headquarters renovation. Powell stays indefinitely as a lame duck during a war-driven economic crisis. Whether or not the DOJ probe is political, the perception that monetary policy independence is compromised is itself a risk factor.

Portfolio Implications

Any deal model assuming 2+ rate cuts in 2026 is stale as of yesterday. The 10-year above 4.25% and rising means discount rates need to go up 50-100bps across your pipeline.

Three immediate actions:

1. Late-stage growth equity valued on 2024-25 public comps needs 15-25% haircuts in realistic exit scenarios under a no-cut, $100+ oil environment.
2. Energy cost exposure across portfolio companies needs auditing. Any company where energy/logistics exceeds 15% of COGS faces margin compression at sustained $111+ oil.
3. Defense tech and energy security companies that seemed expensive 6 months ago may now be fairly valued — the geopolitical premium is structural, not cyclical.

A New Platform Category Is Being Capitalized

World Models — AI systems that learn the causal structure of environments by predicting next states conditioned on actions, not next tokens conditioned on text — just attracted $4B+ in funding across 8+ startups in roughly 12 months. This is not a feature of LLMs. This is a parallel compute paradigm for embodied intelligence.

The funding velocity is staggering:

The Architecture War

The field is split three ways, each with fundamentally different data requirements, compute profiles, and investment characteristics:

• Generative World Models (General Intuition, Wayve, Runway): Predict future frames directly. Interpretable but compute-intensive. DIAMOND built a playable Counter-Strike engine from 87 hours of footage on a single GPU.
• Latent World Models (AMI Labs/JEPA, Embo/Dreamer): Predict in abstract representation space. Efficient but opaque — you can't see what the model is thinking. No agent demonstrations from AMI Labs despite their $1.03B raise.
• Vision-Language-Action models (Physical Intelligence, Skild): Piggyback on LLM infrastructure. Shipping real demos today. But architecturally suboptimal for spatial-temporal reasoning.

The Hardware Lottery thesis is the critical lens: markets often converge on the technology that fits existing infrastructure, not the one that's technically superior. VLAs inherit the entire LLM stack. World Models need new infrastructure — and that's both their risk and their opportunity.

Where the Alpha Is

The talent migration is the strongest signal. When Danijar Hafner (DreamerV2, DeepMind), Ian Goodfellow (GANs inventor), Bob McGrew (OpenAI CRO), and Anthony Hu (GAIA-1 lead) leave prestigious positions to found World Model companies, they're voting with their careers. This pattern preceded the founding of Anthropic, Cohere, and Mistral in the LLM wave.

AMI Labs CEO Alexandre LeBrun explicitly warned: 'World Models will be the next buzzword and in six months every company will call itself a World Model to raise funding.' When the CEO of a $3.5B company tells TechCrunch the category is about to be flooded with pretenders, believe him.

Google DeepMind is the existential threat. They're pursuing every approach simultaneously — Genie 3, SIMA 2, Waymo fleet data — with near-infinite compute and talent. The startups that survive are those with data assets Google can't easily replicate: General Intuition's 15M-user Medal gaming platform (ground-truth action labels), Wayve's driving fleet, Physical Intelligence's multi-robot task data.

The hardware layer is the de-risked proxy play. Decart's migration from Nvidia GPUs to Etched's custom Sohu ASICs signals real-time World Model inference needs specialized silicon. You profit from the sector's growth regardless of which architecture wins.

The Proof Point

Brian Spanswick, CIO of Cohesity ($2B+ revenue, 400-person IT department), just gave the market a preview of SaaS cannibalization — and it's not the 'AI replaces Salesforce' narrative everyone's been debating. It's surgically worse: AI agents eat the highest-margin automation add-ons while enterprises keep paying for core platforms.

The specifics are damning:

• A Cohesity cybersecurity executive built a ServiceNow ITAM equivalent — priced at hundreds of dollars per user per month — using Claude Code in under two days
• Cohesity replaced Splunk's security monitoring with a consultant-built AI agent at lower operating cost
• Spanswick plans to keep Salesforce, Workday, and ServiceNow for 1-2 years but will not spend on their automation add-ons
• He estimates 50% cuts to automation add-on budgets based on early tests

The Macro Confirmation

This isn't isolated. Enterprise AI spend is surging 81% YoY while total IT budgets grow just 3.4%. Anthropic and OpenAI are extracting $40-50B annually from enterprise budgets — the equivalent of 2-3 Salesforces' annual revenue. The SaaS incumbents' responses confirm they see it too:

The divergence between Salesforce (buyback-defensive) and SAP (partnership-forward) will compound. SAP's ERP data is the business context that enterprise AI needs — its moat actually strengthens in the AI era.

The Unpriced Risk: NRR Compression

Wall Street models ServiceNow and Salesforce on 120%+ net revenue retention. If CIOs retain core platforms but zero out add-on budgets, NRR drops to 105-115% — a 10-15 point compression that triggers 30-40% multiple contraction even without headline revenue declines.

This is a 2026-2027 earnings risk that current multiples ignore. ServiceNow's defensive response — citing compliance, integrations, and auditability as moats — reveals the exact fault line. Those moats are real today but codifiable. The moment a startup ships SOC 2-compliant AI agent orchestration, ServiceNow's most defensible argument evaporates.

The credit market is already reacting. JPMorgan's suspension of Qualtrics' $5.3B debt deal because investors balk at AI displacement risk to survey software confirms this has jumped from equity to debt markets. Three of the five largest enterprise SaaS companies simultaneously executing debt-funded buybacks is a sector-wide signal we haven't seen since 2008.

The Three Investable Categories Emerging

1. AI Agent Governance & Identity: Okta's April 30 launch, 88% of organizations reporting agent security incidents, Workday's Sana performing write actions. The compliance/governance gap is the next $1B+ platform opportunity.
2. AI-Native Enterprise Automation: Workday's $1.1B Sana acquisition sets the valuation benchmark. The key differentiator is safe write actions within HR, finance, and supply chain workflows.
3. Agent Orchestration & Compliance Tooling: ServiceNow's defensive moat (compliance, auditability, integration) is simultaneously the best argument for incumbents AND the clearest market map for what startups should build. The company that ships enterprise-grade compliance for AI-built internal tools becomes the Terraform of the agent era.