// Portfolio Intel · embedded interactive // Adapted from uploads/portfolio-intel.jsx for AGI Intelligence Report v8. // Changes from source: // - ES module import → React globals // - Scoped under .report-portfolio-intel so the warm palette doesn't leak // - 'Cormorant Garamond' → 'Fraunces' (matches the rest of the report) // - body{margin:0} removed; noise overlay scoped to mount root (not viewport-fixed) // - Added ReactDOM mount at the bottom (function () { const { useState, useEffect, useCallback } = React; // ============================================================================ // COMPANIES — broadcast slides // ============================================================================ const COMPANIES = { elorian: { name: "Elorian", tag: "Different Base Model", layer: "L3 · Foundation", lenses: ["Multimodal", "World Models", "Robotics"], bigStat: "Vision-first", bigStatSub: "No text bottleneck", sideStat: { value: "$55M", label: "Seed · Apr 2026" }, altStat: { value: "$300M", label: "Valuation" }, script: "When Gemini-class models compress image and video into text-first attention, they inherit a text-first prior. Elorian's bet is that vision-native architectures see the world differently — and that gap widens, not narrows, as the giants merge more modalities into one stack.", backers: "Striker · Menlo · Altimeter · NVIDIA · Jeff Dean", pedigree: "ex-DeepMind · ex-Apple", transcript: null, visual: "eye", }, cartesia: { name: "Cartesia", tag: "Different Base Model", layer: "L3 · Voice", lenses: ["Multimodal", "Robotics"], bigStat: "<100", bigStatSub: "ms · text-to-speech", sideStat: { value: "$191M", label: "Total raised" }, altStat: { value: "Sonic-3", label: "Oct 2025 launch" }, script: "Gemini Live and ChatGPT voice mode set the latency bar at sub-second. SSM/Mamba architectures hit sub-100ms structurally — not through optimization. As multimodal agents demand voice as a first-class output, the architecture choice compounds.", backers: "Kleiner · Index · Lightspeed · NVIDIA", pedigree: "Stanford AI Lab · Mamba lineage", transcript: { quote: "For voice to be viable, cost has to be closer to $5 per million characters — a big jump from $100.", source: "Inworld AI · TTS interview · Aug 2025", }, visual: "waveform", }, livekit: { name: "LiveKit", tag: "Delivery Layer", layer: "L4 · Real-time transport", lenses: ["Multi-agent", "Multimodal", "Robotics"], bigStat: "3", bigStatSub: "ChatGPT · Grok · ElevenLabs", sideStat: { value: "$45M", label: "Series B" }, altStat: { value: "OSS", label: "+ cloud" }, script: "Every merged multimodal agent eventually has to stream — video in, audio out, telemetry, actions. LiveKit already won this layer for voice. Robotics is the same shape with higher stakes, and the merge thesis turns robots into the next obvious customer.", backers: "Altimeter", pedigree: "Open-source · neutral transport", transcript: { quote: "Robotics foundation models follow the LLM trajectory — they become so big they can only live in the cloud.", source: "David Chen, GM Robotics · LiveKit · Feb 2026", }, visual: "network", }, nomadic: { name: "Nomadic ML", tag: "Vertical Specialization", layer: "L5/L6 · Vertical VLM", lenses: ["Multi-agent", "Multimodal", "Robotics"], bigStat: "99%", bigStatSub: "vs 80% on general VLM", sideStat: { value: "+19pts", label: "Above generalist" }, altStat: { value: "Seed", label: "Pear VC PearX" }, script: "The merge thesis assumes general models will be good enough. They aren't, at 95-99% accuracy on robotic footage. Nomadic ensembles fine-tuned vision-language models per vertical, per sub-vertical, per customer mental model. The gap between Gemini-grade describing and DMV-grade reviewing is the moat.", backers: "Pear VC", pedigree: "ex-Microsoft GPT-3 · ex-Snowflake", transcript: { quote: "Enterprises don't want labeling at 50% or even 80%. Minimum 95%, if not 99%.", source: "Mustafa, CEO · Nomadic ML · Feb 2026", }, visual: "accuracy", }, higgsfield: { name: "Higgsfield", tag: "Creator Surface", layer: "L6 · Consumer creative", lenses: ["Multimodal", "World Models"], bigStat: "4", bigStatSub: "Sora · Veo · Kling · Seedance", sideStat: { value: "~$50M", label: "Total raised" }, altStat: { value: "1 UX", label: "Across model wars" }, script: "The model wars are Higgsfield's tailwind, not their threat. Every frontier video model wrapped under one Cinema Studio UX. When Veo 4 leapfrogs Sora 3 next quarter, Higgsfield users don't switch products. The wrapper compounds with every model release.", backers: "Menlo Ventures", pedigree: "Alex Mashrabov · ex-Snap GenAI head", transcript: { quote: "Veo's goal is to be the world's best model — 'best' depends on user and use case. Flow is a tool.", source: "Ricky Wong, Veo3 lead PM · July 2025", }, visual: "stack", }, }; const COMPANY_ORDER = ['elorian', 'cartesia', 'livekit', 'nomadic', 'higgsfield']; const SLIDE_DURATION_MS = 9000; // ============================================================================ // SLIDE VISUAL — varies per company (using report palette) // ============================================================================ function SlideVisual({ kind }) { if (kind === "eye") { return ( {Array.from({length: 8}, (_,i) => ( ))} ); } if (kind === "waveform") { const bars = Array.from({length: 36}, (_, i) => { const t = i / 36; return 12 + Math.abs(Math.sin(t * 14) * Math.cos(t*4)) * 80 + Math.sin(i*0.9)*14; }); return (
{bars.map((h, i) => (
))}
); } if (kind === "network") { return ( MODEL LIVE KIT VOICE ROBOT VIDEO ); } if (kind === "accuracy") { const rows = [ { name: "Bounding boxes", pct: 50, hot: false }, { name: "General VLM", pct: 80, hot: false }, { name: "Enterprise floor", pct: 95, hot: false, target: true }, { name: "Nomadic ensemble", pct: 99, hot: true }, ]; return (
{rows.map((r, i) => (
{r.name}
{r.pct}%
))}
); } if (kind === "stack") { const models = ['SORA 2', 'VEO 3.1', 'KLING 3.0', 'SEEDANCE']; return (
HIGGSFIELD · CINEMA STUDIO
↓
{models.map((m, i) => (
{m}
))}
); } return null; } // ============================================================================ // SLIDE // ============================================================================ function BroadcastSlide({ company, slideKey, total, idx }) { return (
{/* corner brackets */}
{/* top strip — segment counter + timecode only */}
SEGMENT {String(idx+1).padStart(2,'0')} / {String(total).padStart(2,'0')}
{/* main content */}
{company.tag} · {company.layer}
{company.lenses.map((l) => ( {l} ))}

{company.name}

{company.bigStat}
{company.bigStatSub}
{company.sideStat.value}
{company.sideStat.label}
{company.altStat.value}
{company.altStat.label}
); } // ============================================================================ // LIVE CLOCK // ============================================================================ function LiveClock() { const [now, setNow] = useState(new Date()); useEffect(() => { const t = setInterval(() => setNow(new Date()), 1000); return () => clearInterval(t); }, []); const hh = String(now.getUTCHours()).padStart(2,'0'); const mm = String(now.getUTCMinutes()).padStart(2,'0'); const ss = String(now.getUTCSeconds()).padStart(2,'0'); return <>{hh}:{mm}:{ss} UTC; } // ============================================================================ // MAIN // ============================================================================ function PortfolioIntel() { const [idx, setIdx] = useState(0); const [playing, setPlaying] = useState(true); const [progressKey, setProgressKey] = useState(0); const next = useCallback(() => { setIdx(i => (i + 1) % COMPANY_ORDER.length); setProgressKey(k => k + 1); }, []); const prev = useCallback(() => { setIdx(i => (i - 1 + COMPANY_ORDER.length) % COMPANY_ORDER.length); setProgressKey(k => k + 1); }, []); const goTo = useCallback((i) => { setIdx(i); setProgressKey(k => k + 1); }, []); useEffect(() => { if (!playing) return; const t = setTimeout(() => { setIdx(i => (i + 1) % COMPANY_ORDER.length); setProgressKey(k => k + 1); }, SLIDE_DURATION_MS); return () => clearTimeout(t); }, [idx, playing]); useEffect(() => { const handler = (e) => { if (e.key === 'ArrowRight') next(); else if (e.key === 'ArrowLeft') prev(); else if (e.key === ' ') { e.preventDefault(); setPlaying(p => !p); } }; window.addEventListener('keydown', handler); return () => window.removeEventListener('keydown', handler); }, [next, prev]); const currentKey = COMPANY_ORDER[idx]; const currentCompany = COMPANIES[currentKey]; return (
{/* PREMISE — no byline */}
The Premise

Image, video, voice, and language are converging into single base models. Google's Gemini, OpenAI's GPT-class, Anthropic's Claude — all racing toward one stack that handles every modality at once. The question is no longer which modality to bet on, it's where a startup still earns defensibility and how to best align with the wave.

{/* BROADCAST */}
{/* player */}
{COMPANY_ORDER.map((k, i) => (
{/* SCRIPT */}
Segment Script

{currentCompany.script}

{currentCompany.transcript && (

"{currentCompany.transcript.quote}"

{currentCompany.transcript.source}
)}
{currentCompany.name}  ·  {currentCompany.pedigree}  ·  BACKED BY {currentCompany.backers}
); } // Mount into the report. The mount node is created in the Field Notes section. (function mountPortfolioIntel() { function mount() { const el = document.getElementById('portfolio-intel-mount'); if (!el) return false; if (el.dataset.mounted === '1') return true; el.dataset.mounted = '1'; ReactDOM.createRoot(el).render(React.createElement(PortfolioIntel)); return true; } if (!mount()) { document.addEventListener('DOMContentLoaded', mount); } })(); })();