AI Visibility Index (AV‑Index) 2026: Seed Investors & Angels — AI / B2B SaaS

Published by RankCaster AI · June 3, 2026

Key Findings

Antler captures 65% of International seed shortlists with full 4/4 cross‑model consensus — the single strongest specialty signal in the dataset.

Y Combinator dominates as the brand‑default heuristic across three intents: 83% Universal Pre‑Seed, 72% MarTech, 81% International. Elad Gil holds full cross‑model presence at 50% on both Universal Pre‑Seed and Operator Angels.

Banyan Ventures lands at #7 in MarTech via a self‑published citable whitepaper. The same mechanism is independently observed at Redbud VC (52% Universal Pre‑Seed) and Sequoia (70% International on ChatGPT) — three firms, three intents, one playbook (§3.2).

Cross‑intent fragmentation is the main strategic finding: the same fund dominates one intent and disappears in adjacent ones. Building a coherent cross‑intent narrative is the real platform‑team job in 2026 (§4.3).

PVS prioritization: Universal Pre‑Seed (75) and Operator Angels (74) tied at the top for content investment; International (31) and MarTech (28) at the bottom (§2.5).

ROAI unit: ~1,500 incremental AI‑sourced founder inclusions per month per priority intent at the documented 20% AI‑mediation lower bound (McKinsey/Penske anchored). Funds substitute their own conversion math (§6.2).

1. Why Investors Need This Report

Antler captures 65% of international seed shortlists with full cross‑model consensus across ChatGPT, Gemini, Claude, and DeepSeek. Half of the US‑coastal funds claiming a global mandate either don't surface in the Dominant tier at all, or surface in only one or two of the four assistants. This is not luck. Antler spent years building distribution into the channel most fund marketing still ignores — investor directories, "top VCs for [country] founders" lists, editorial blogs, LinkedIn discussion. The corpus the AI draws on cites Antler densely and cites funds that didn't invest in that corpus thinly. The channel is workable. Antler is the proof.

Every month, a growing share of founders runs their search for "who should I pitch?" through ChatGPT, Gemini, Claude, and DeepSeek. They ask these assistants a specific question — "which angel investors or seed funds should I approach for a B2B SaaS AI startup raising pre‑seed in 2026?" — and act on the shortlist they get back. If your fund is not in that shortlist, you exit the founder's consideration set before they ever fill out an application form, ask an existing investor for an intro, or message a partner on LinkedIn. This report ran the four highest‑intent founder‑side prompts for seed/pre‑seed B2B SaaS AI through all four major AI assistants and recorded who shows up in the answers, how often, and which sources the AI relies on to name a fund. The output is a map of the real top of the funnel for your fund in 2026.

On the dealflow at stake. Founders search this category at meaningful volume: ai investors, angel investors, and venture capital each sit at 10K–100K monthly Google searches; pre seed funding at 1K–10K. Full GKP volumes and YoY trend in §2.7. A portion of these founders, in parallel with Google, asks the same question of an AI assistant and receives back a ready‑made shortlist of 5–10 funds. If your fund is in that shortlist, the founder considers you. If not, they don't — even if they had heard of you before. The AI shortlist operates as a trust filter. §6 of this report walks through the math step by step: Google search volume × share of founders mediated through AI × your APR Score × your conversion rate × your check size → incremental deployed capital per year. The 20% AI mediation coefficient is anchored on documented benchmarks (Penske Media's measured ~20% organic traffic loss to AI Overviews, lower bound of McKinsey's 40–60% commercial‑intent AI‑alternative range); you override it with your own AI‑channel attribution data. Full sourcing in §6.1.

This AV‑Index answers three questions for managing partners and platform leads at seed and pre‑seed funds: Are we being recommended by AI assistants when founders ask "who should I pitch?"; How do we stack up against the competition across the four high‑intent founder scenarios; Where exactly are we missing, by model and by source — and what concrete steps move the needle.

All data was collected and processed via RankCaster AI, the platform for AI Visibility Marketing (full definition in §2.1).

2. Methodology

2.1. The Object of Research and the Three KPIs

Our object is AI Visibility — an investor's ability to appear consistently in the responses of major AI assistants when triggered by commercially significant founder‑side prompts.

Three RankCaster KPIs compose the framework. APR is measured directly in this anchor cycle; PVS and ROAI are introduced as the frameworks a fund uses to act on APR data.

APR (Answer Presence Rate) is the percentage of prompt executions in which the fund or angel is featured in the AI's recommendation. Formula: APR = (Brand Mentions / Total AI Prompts) × 100%. This is the new "share of voice" for the AI‑assistant layer, replacing Google SERP rank as the discoverability metric for high‑intent commercial queries.

PVS (Predictive Value Scoring) is a prompt‑prioritization score that tells a fund which of its founder‑facing intents deserves content investment first. The canonical formula is PVS = (SEO Score × AI Multiplier × Commercial Multiplier) + Pain Score + (Social Score × 2). We compute the full five‑input PVS for all four prompts in §2.5.

ROAI (Return on AI) is the economics of AI Visibility — return generated per dollar invested in becoming AI‑recommended: ROAI = ((Total APR Score × (Estimated Search Volume × 20%) × Conversion Rate Proxy) − Content Cost) / Content Cost. Applied to VC unit economics with a worked example in §6.

The category these KPIs serve is AI Visibility Marketing (AVM) — the integrated discipline of becoming AI‑recommended, which composes three established layers: GEO (semantic authority on external sources AI cites), AEO (data structured for instant citation), and AIO (technical readiness for AI agents). The stack is explained in §5.2.

2.2. The AI Intent‑to‑Value Framework (AIVF)

We use AIVF — RankCaster's methodology that treats each prompt not as a string of keywords but as a projection of the user's state at the moment of decision. AIVF structures the Customer's Mental Matrix by levels of awareness: L1 (unaware of the problem) → L2 (aware of the problem, unaware of solutions) → L3 (comparing solution categories) → L4 (actively comparing options within a category against a specific case).

In AIVF terms, a founder query like "Which seed investors actively back non‑US founders in AI and SaaS?" is a Value Capture event — the founder has crossed every awareness threshold and is filtering for a specific partner to pitch. The four strategic intents covered in this report are all aggregated L3–L4 last‑mile scenarios where AI recommendations directly drive calendar slots, intros, and checks.

2.3. Research Scope and Coverage

Geography is WORLD (no geo‑restriction), with default assistant settings and English‑language queries. AI Assistants tested: ChatGPT, Google Gemini, Anthropic Claude, DeepSeek — the four most heavily used assistants in the English‑speaking founder segment at the time of measurement. The anchor cycle was measured on June 3, 2026, with quarterly re‑measurements scheduled. Total runs: 160 across the four Golden Prompts — 40 per prompt, with 10 runs per provider per prompt. Format is a point‑in‑time snapshot; see §2.6 for the temporal‑replication limitation and cycle 2 plans.

2.4. The Four Strategic Intents ("Golden Prompts")

Founder Search — Universal Pre‑Seed (B2B SaaS AI). "Which angel investors or seed funds should I approach for a B2B SaaS AI startup raising pre‑seed in 2026?" — the default "where do I pitch?" of the modern AI/SaaS founder.

MarTech Vertical. "Which investors are known for backing AI and MarTech startups at pre‑seed stage?" — sector‑specialized founder seeking vertical conviction.

International Founders. "Which seed investors actively back non‑US or international founders in AI and SaaS?" — founder outside Silicon Valley filtering for funds that genuinely write cross‑border checks.

Operator Angels. "Who are the most active operator‑angels for B2B SaaS and AI startups in 2026?" — pre‑institutional founder looking for ex‑operators who write angel checks and bring tactical leverage.

Each prompt is High Commercial Intent: the founder has decided to fundraise and is asking for a curated shortlist — not a definition of "what is pre‑seed."

2.5. PVS — Computed Across All Five Inputs

The canonical PVS formula has five inputs. After §2.7's head‑term measurement and a structured estimate of the qualitative components, three of five are now directly measured and two are estimated by RankCaster from observable signals.

Set by the prompt text itself. All four Golden Prompts are textbook "recommend the best" Value Capture events — AI Multiplier = ×10 (maximum). All four contain explicit commercial language — Commercial Multiplier = ×2 (maximum).

Disclosure on multiplier saturation. For this particular intent class — high‑commercial last‑mile founder fundraising prompts — AI Multiplier and Commercial Multiplier both saturate at maximum across all four prompts. They don't discriminate between the four intents in this report. The actual discriminator is the additive triple SEO + Pain + Social×2. We retain the full canonical formula because PVS is designed for cross‑category prompt prioritization (where AI/Commercial multipliers vary across L1–L4 intents) — and because shipping a partial formula obscures that fact. For an investor reading this report, the three variables that move the PVS ranking between Universal Pre‑Seed (75), Operator Angels (74), International (31), and MarTech (28) are SEO Score, Pain, and Social.

SEO Score (1–3, from §2.7 head‑term volume):

• Universal Pre‑Seed: SEO = 3. Multiple head terms at 10K–100K/mo (ai investors +900% YoY, venture capital, angel investors).
• Operator Angels: SEO = 3. angel investors at 10K–100K/mo as the relevant head (broader than the specialty filter, but the relevant demand).
• MarTech Vertical: SEO = 1. Intent‑specific head terms land in the lowest measurable bucket: marketing technology investors 10–100/mo, saas vc 10–100 (with −90% YoY), b2b saas investors 10–100. This is not absence of demand — it means MarTech founders either use broader terms (venture capital, angel investors) or formulate the narrow query directly to AI assistants.
• International Founders: SEO = 1. Same pattern: global vc 10–100/mo (−90% YoY), international vc 10–100, seed funds europe 10–100. International seed demand exists, but the Google language carrying it is long‑tail and low‑frequency.

Pain Score (0–5, RankCaster estimate from cohort characteristics):

• Universal Pre‑Seed: Pain = 5. Fundraising under runway deadline, existential to startup survival, panic tone in investor outreach.
• Operator Angels: Pain = 4. High but not life‑or‑death — operator‑angel checks are typically $25–100K, do not decide round outcome.
• MarTech Vertical: Pain = 4. Same fundraising pressure, narrower category scope.
• International Founders: Pain = 5. Fundraising pressure plus the structural disadvantage of being a non‑US founder (fewer warm intro options, extra barriers).

Social Score (0–5, RankCaster estimate from content/forum density):

• Universal Pre‑Seed: Social = 5. Daily threads on r/startups, X/Twitter, Hacker News; constant coverage at Lenny Rachitsky, First Round Review.
• Operator Angels: Social = 5. The single most socially‑driven AI/SaaS founder topic — operator‑angels discussed continuously, names cited weekly.
• MarTech Vertical: Social = 2. Niche — discussion concentrated in MarTech‑specific publications and smaller subreddits.
• International Founders: Social = 3. Active but fragmented across geographies and language communities.

Methodology disclosure on Pain/Social. These are qualitative RankCaster estimates from observable signals: cohort deadline characteristics for Pain, content density and forum activity for Social. Not from founder interviews and not from quantified social listening. Cycle 2 will calibrate both with both instruments.

PVS computed.

• Universal Pre‑Seed: SEO 3, AI ×10, Commercial ×2, Pain 5, Social 5 → Base (3×10×2) = 60, + Pain 5, + Social×2 = 10 → PVS = 75
• Operator Angels: SEO 3, AI ×10, Commercial ×2, Pain 4, Social 5 → Base 60, + Pain 4, + Social×2 = 10 → PVS = 74
• International Founders: SEO 1, AI ×10, Commercial ×2, Pain 5, Social 3 → Base (1×10×2) = 20, + Pain 5, + Social×2 = 6 → PVS = 31
• MarTech Vertical: SEO 1, AI ×10, Commercial ×2, Pain 4, Social 2 → Base 20, + Pain 4, + Social×2 = 4 → PVS = 28

Reading the PVS. Universal Pre‑Seed (75) and Operator Angels (74) sit at the top, effectively tied — both pulled by head‑term SEO at 10K–100K/mo and maximum social density. International Founders (31) and MarTech (28) cluster at the bottom with the same SEO Score = 1 (intent‑specific head terms fall in the 10–100/mo bucket), distinguished only by Pain and Social. The gap between the top and bottom clusters is roughly 2.4–2.5×: for a fund deciding where to allocate AVM budget first, the priority order is unambiguous.

2.6. 10x Validation Standard, Sample Size, and Provider Validity

Every APR figure in this report is subject to the RankCaster 10x Validation standard: each Golden Prompt was executed across ChatGPT, Gemini, Claude, and DeepSeek targeting 10 runs per provider per cycle. Every fund or angel mentioned, the context of the recommendation, and the citations the AI relied on were captured. APR is the percentage of prompt executions in which a specific fund or angel is explicitly named.

Per‑prompt provider validity in this cycle. All four providers — ChatGPT, Gemini, Claude, and DeepSeek — returned 10 valid runs on each of the four Golden Prompts (Universal Pre‑Seed, MarTech Vertical, International Founders, Operator Angels). Cross‑model denominator = 4 across all four intents.

Sample size, honestly. N=10 prompt executions per provider per prompt is exploratory by intent — directionally clean for identifying which funds dominate vs which are absent, but not powered to discriminate small APR deltas. At N=10, a 95% confidence interval on a single‑provider APR figure is roughly ±31 percentage points. We report point estimates because they are what we measured, and we expose the per‑provider numbers (§4.1) so consensus vs noise is visible. The RankCaster operating standard for downstream APR reporting on monitored client projects is N=30+ with confidence intervals; cycle 2 of this AV‑Index will publish at that resolution.

Cross‑prompt comparability. Denominator = 4 across all four tables in §3. Rankings are readable both within sections and across sections. The four‑tier bucket — Dominant (≥50%), Present (20–50%), Sparse (5–20%), Absent (<5%) — is shown alongside the raw percentage for quick reading.

Temporal replication caveat. All 160 runs were executed on June 3, 2026 within a single measurement window. LLM responses are stochastic; run‑to‑run variance on the same prompt at the same provider can shift specific cells without changing the directional structure. Cycle 2 will spread N=30+ runs over a 7–14 day window with per‑cell variance reporting, which will let us separate cross‑model effects from temporal noise.

2.7. Sizing the Demand: Google Volume as the ROAI Forecasting Input

The ROAI formula in §6 requires a demand anchor. Standard AVM/SEO methodology uses Google search volume × AI adoption rate as the forecasting baseline, because direct AI prompt‑execution counts are not publicly available at the keyword level. We did the measurement.

Head‑term volumes for the founder fundraising category (Google Keyword Planner, US geo, May 2025 – April 2026, queried May 31, 2026):

• ai investors — 10K–100K monthly searches, +900% YoY, High competition, top‑of‑page CPC ~$2–$8.
• angel investors — 10K–100K, 0% YoY, Medium, ~$1.3–$10.
• venture capital — 10K–100K, 0% YoY, Low, ~$2.2–$10.3.
• pre seed funding — 1K–10K, +900% YoY, Medium, ~$3.4–$12.3.
• startup funding — 1K–10K, 0% YoY, Medium, ~$3.7–$16.3.
• ai startup funding — 100–1K, 0% YoY, Low, ~$2.6–$12.2.
• ai vc — 100–1K, 0% YoY, Low, ~$3.4–$11.6.
• best vc — 100–1K, 0% YoY, Low, ~$2.9–$11.6.
• seed investors — 100–1K, −90% YoY, Low, ~$1.8–$9.4.

Reading the data. The head of the category is large. ai investors, venture capital, and angel investors each sit at 10K–100K/month; pre seed funding at 1K–10K. The +900% and −90% YoY values are GKP's bucket‑level signal, not point estimates — note that GKP bucket boundaries themselves shifted in the past 12 months, which means YoY ratios on bucketed ranges should be read as directional only, not as precise growth rates. With that caveat: one plausible read of the +900% on ai investors against the −90% on seed investors is intra‑Google query‑phrase migration — founders shifting from the generic seed investors to the categorical ai investors, not channel exit. A meaningful share of that demand is now mediated through AI assistants before, during, or after the Google query. Cycle 2 will cross‑validate with GSC and a paid SEO source.

Long‑tail caveat. The same data run on 22 long‑tail multi‑word variants (e.g., best pre seed vcs for ai startups, vcs investing in non us founders) returned 18 below GKP free‑tier reporting threshold. That is expected long‑tail behavior — narrow phrasings have less search volume than head terms regardless of category, and a structurally parallel control basket of 10 non‑AI vertical queries (biotech, fintech, edtech, cybersecurity, deep tech) returned 5 of 10 measurable under the same conditions. Long‑tail variants are useful for understanding how founders phrase the query (closer to AI prompt style than to Google search style), but the head‑term volumes above are the right input for ROAI forecasting.

GKP free‑tier disclosure. Measurements taken on a GKP account without active campaign spend, which returns bucketed ranges rather than exact volumes. Sharper resolution requires paid GKP or third‑party SEO data, planned for cycle 2.

How this feeds §6 ROAI. Take an aggregate volume across your priority intent's keyword cluster (e.g., ai investors + venture capital + angel investors + pre seed funding ≈ 30K–50K/month conservatively); multiply by the canonical 20% AI Intent Reach coefficient; multiply by your Total APR Score uplift; multiply by your founder‑inquiry‑to‑check conversion rate × average check size. That yields incremental deployed capital — the dollar value of being in the AI shortlist vs not. The worked example in §6.2 uses 30K/month as the head‑term input.

3. Results: Who Wins in AI Responses

What follows is not a "best investor" ranking. It is a snapshot of which funds and angels ChatGPT, Gemini, Claude, and DeepSeek currently favor in their recommendations. For an LP: "this is who founders are being sent to first." For a GP: "this is your true top‑of‑funnel today."

Across the four sections below, APR (avg) is the cross‑model average computed against the per‑intent denominator of 4 from §2.6. Seen in N/4 tells you how many of those providers actually surfaced the fund — high APR with low Seen‑in means a fund is loved by one model and absent from others. The four‑tier bucket from §2.6 applies: Dominant (≥50%), Present (20–50%), Sparse (5–20%), Absent (<5%).

3.1. Founder Search — Universal Pre‑Seed (B2B SaaS AI)

40 runs across 4 valid providers. Denominator = 4. Top 10 by aggregate APR:

Y Combinator — 83% APR, Seen in 4/4, Dominant.

Elad Gil (angel) — 50% APR, Seen in 4/4, Dominant.

Initialized Capital — 43% APR, Seen in 3/4, Present.

Sequoia Capital — 40% APR, Seen in 2/4, Present.

Precursor Ventures — 38% APR, Seen in 2/4, Present.

Andreessen Horowitz (a16z) — 35% APR, Seen in 3/4, Present.

Hustle Fund / Hustle Fund Angels — 35% APR, Seen in 2/4, Present.

Nat Friedman (angel) — 35% APR, Seen in 3/4, Present.

AngelList — 35% APR, Seen in 2/4, Present.

Naval Ravikant (angel) — 33% APR, Seen in 2/4, Present.

Reading the data. Y Combinator (83%, 4/4) and Elad Gil (50%, 4/4) are the only two names with full cross‑model consensus on this prompt — anywhere in the report. YC's lead is brand work plus a model heuristic: when a founder asks the open question about pre‑seed, YC surfaces as the "safe default." Elad Gil's is a concrete operator‑angel position that holds across all four assistants. Below them — Initialized, Sequoia, Precursor in the upper Present band (38–43%) with confident 2–3 model presence. Below 30% APR you are essentially absent from the AI shortlist.

3.2. MarTech Vertical (AI + Marketing Technology)

40 runs across 4 valid providers. Denominator = 4. Top 10 by aggregate APR:

Y Combinator — 72% APR, Seen in 4/4, Dominant.

First Round Capital — 61% APR, Seen in 3/4, Dominant.

Sequoia Capital — 56% APR, Seen in 3/4, Dominant.

Andreessen Horowitz — 47% APR, Seen in 3/4, Present.

Techstars — 44% APR, Seen in 3/4, Present.

Elad Gil (angel) — 44% APR, Seen in 3/4, Present.

Banyan Ventures — 42% APR, Seen in 2/4, Present.

Naval Ravikant (angel) — 42% APR, Seen in 2/4, Present.

500 Startups — 39% APR, Seen in 2/4, Present.

Initialized Capital — 33% APR, Seen in 2/4, Present.

Reading the data. The MarTech shortlist is tight against names the models surface confidently without vertical specialization: YC, First Round, Sequoia, a16z. Banyan Ventures at #7 is the one name in the top‑10 with an explicit MarTech tie — and its mechanism is the clearest live example of AVM in the report.

AVM via citable whitepaper — Banyan Ventures. Banyan publishes the article "Top AI Pre‑Seed Investors" on its own site at banyan-vc.com/insights/top-ai-pre-seed-investors. AI assistants quote that single page in roughly 1 of every 4 answers they give to the MarTech prompt — 28% aggregate APR as a source across providers, the highest single‑source citation rate in the dataset for this intent. The article names Banyan; Banyan lands at #7 in the shortlist.

This is not N=1. The same mechanic works for Redbud VC (52% APR on Universal Pre‑Seed driven partly by its own list redbud.vc/latest/list-of-best-pre-seed-vcs-writing-first-checks-for-ai-and-saas-companies) and Sequoia (70% APR on ChatGPT for International Founders, anchored by sequoiacap.com/article/seed-fund-and-arc-announcement/). Three independent firms, three different intents, same playbook.

Build the page the model is willing to cite → the model names you. This is the single highest‑leverage tactic in §5.2 Layer 2 (citable cornerstone asset), and it is also the simplest experiment a fund platform team can run inside Q3 2026.

3.3. International Founders (Non‑US, AI/SaaS)

40 runs across 4 valid providers. Denominator = 4. Top 10 by aggregate APR:

Y Combinator — 81% APR, Seen in 4/4, Dominant.

Sequoia Capital — 65% APR, Seen in 4/4, Dominant.

Antler — 65% APR, Seen in 4/4, Dominant.

500 Startups / 500 Global — 62% APR, Seen in 4/4, Dominant.

LocalGlobe — 59% APR, Seen in 2/4, Dominant.

Index Ventures — 57% APR, Seen in 3/4, Dominant.

Speedinvest — 49% APR, Seen in 2/4, Present.

Lightspeed Venture Partners — 38% APR, Seen in 2/4, Present.

Point Nine Capital — 38% APR, Seen in 1/4, Present.

NFX — 38% APR, Seen in 1/4, Present.

Reading the data. This is the densest quadrant in the report: six names in the Dominant bucket, seven above 50% APR. Antler at 65% with full 4/4 cross‑model consensus is the single strongest specialty signal: model‑global by design, geo‑tagged content across 30+ markets, present on essentially every "best investor for [country] founders" list. LocalGlobe at 59% appears in only 2/4 providers — that is a cross‑model visibility defect, not a brand defect: where the firm is present, it dominates, but half of founders simply will not see it. Same logic for Point Nine Capital and NFX. The instructive absence: half of the US‑coastal funds claiming "global" mandates appear either weakly or with high per‑provider variance — assistants are not convinced by claims on a portfolio page when independent sources do not confirm them.

3.4. Operator Angels (B2B SaaS + AI)

40 runs across 4 valid providers. Denominator = 4.

Top 3 — ranked:

Elad Gil — 50% APR, Seen in 3/4, Dominant.

Gokul Rajaram (DoorDash) — 30% APR, Seen in 2/4, Present.

Nat Friedman — 27% APR, Seen in 3/4, Present.

Sparse cohort (alphabetical, all at 13–20% APR with 1/4–2/4 seen‑in — N=10 ±31pp CI does not resolve order within this band): Ben Sigelman (Highlight), Bob van Luijt (Weaviate), Chris Adelsbach, David Sacks (PayPal/Craft), Des Traynor (Intercom), Jason Lemkin (SaaStr), Rahul Vohra (Superhuman).

Reading the data — specialty recognition vs name recognition. This is the cleanest illustration in the report of the gap AVM is built to close. Only Elad Gil (50%) and Nat Friedman (27%) hold strong cross‑model presence. Everyone in the Sparse cohort is Seen in 1/4 or 2/4 — each model pulling from its own corner of the internet, and at N=10 the ranking inside the 13–20% band is not statistically separable.

The structural read is not about who is "underperforming." It is about a distinction AI assistants make and most fund marketing ignores: specialty recognition is a different asset from name recognition. Gokul Rajaram, Rahul Vohra, Jason Lemkin, David Sacks — these are among the most cited angels in tech, period. They appear at 13–30% APR not because the AI doesn't know who they are, but because the AI cannot tie them to the narrow specialty "operator‑angel for B2B SaaS AI in 2026." The corpus describing them as "great angels" does not structure them as that specialty.

Concretely. Elad Gil publishes and maintains a structured site at eladgil.com with a portfolio list and themed essays, and is canonically cited in TechCrunch as an "early AI investor" — giving the AI a clean name‑to‑specialty binding. Rahul Vohra has been the subject of First Round Review profiles tying him specifically to product‑led B2B SaaS investing and Superhuman positioning, with his X/Twitter bio carrying the same framing — but cross‑model consensus breaks because the profiles get cited unevenly. Compare to Naval Ravikant: his canonical content (How to Get Rich, AngelList) frames him as "angel investor" generally — the AI cites him in the universal angel category (he appears in §3.1 top‑10 at 33% and §3.2 at 42%) but loses him completely on the operator‑angel specialty filter.

This is the AVM thesis demonstrated at the individual level: famous and recommended are not the same thing. The fix is not more brand presence — it is converting brand presence into specialty presence through structured, citable positioning.

3.5. Notable Absences — and What They Tell Us

Several funds with documented B2B SaaS / AI pre‑seed track records did not surface in any top‑10 across the four prompts × four providers. The most conspicuous absences: South Park Commons (community‑fund hybrid, AI‑native focus), Pear VC (SF pre‑seed firm with explicit AI/SaaS investments), Conviction (Sarah Guo, explicitly AI‑focused, well‑covered in trade press), Boldstart Ventures (B2B / dev‑tools pre‑seed leader). Also K9 Ventures, Compound, Felicis, Lux Capital, Cowboy Ventures, Bloomberg Beta — each with documented AI/SaaS pre‑seed exposure that did not surface in our top‑10 capture.

Among operator angels, the most glaring absences are Jack Altman, Lachy Groom, Soleio, and Balaji Srinivasan.

These absences carry one of two readings. For some, the absence reflects insufficient mention in the specific source classes AI assistants pull from for these specific Golden Prompts. A fund can have strong portfolio presence and warm‑intro distribution without appearing in the "Top X pre‑seed VCs for AI" editorial lists, investor directories, or LinkedIn round‑announcement chatter that drive AI shortlist construction. This is the AVM thesis — discovery has bifurcated. Warm‑intro distribution and AI‑shortlist distribution are now two different channels with two different optimization stacks. For others, methodology gaps could be partially responsible: a different prompt phrasing, a different intent ("AI infrastructure" instead of "B2B SaaS AI"), or wider top‑N capture might surface them. Paraphrase‑robustness checks are in the methodology roadmap (§2.6).

We list these absences explicitly because an honest reader from any of these funds, encountering the data, would otherwise correctly ask "where am I?" The answer for now is: not in the top‑10 of any of the four providers across these specific prompt formulations.

3.6. Shadow Prompts

Founders also ask questions they don't type publicly — "which seed VCs actually write checks vs just take meetings?", "which partner at [fund] will champion my deal vs the warm associate who ghosts?". These run in private windows and we did not measure them in this cycle — measuring private‑intent surface area without violating user privacy is an open methodological problem. The strategic implication is concrete: funds whose source corpus carries founder testimonials, post‑rejection feedback, and explicit "we close in N days / we don't ghost" signaling win these prompts; silence reads as confirmation of the worst‑case story.

4. Narrative Conflict: Why AI Responses Contradict Each Other

The four prompts × four providers expose three structural conflicts every fund partner needs to internalize.

4.1. Conflict Between Assistants

The same fund can produce wildly different APRs across models. Raw per‑provider data from the Universal Pre‑Seed prompt: AngelList scored 90% in Claude, 50% in ChatGPT, 0% in Gemini, 0% in DeepSeek — same investor, same prompt, three different worlds. Precursor Ventures scored 90% in Gemini, 40% in ChatGPT, 0% in Claude, 0% in DeepSeek. Khosla Ventures scored 80% in Claude, 0% in the other three.

Each assistant has its own training and live‑retrieval index, and weighs citations differently. "We are #1 in ChatGPT" is not the same as "founders see us." If 60% of your founder pool uses Gemini and you are not in Gemini, your effective APR is closer to zero. RankCaster publishes per‑provider APR so this trap is visible.

A note on extreme one‑provider splits. Patterns like Khosla (80/0/0/0) or AngelList (90/50/0/0) sit at the edge of what N=10 can resolve — they may reflect training‑data quirks specific to one provider, or sample noise at small N. Either way, do not optimize for an asymmetric one‑provider lead without verifying the corpus that drove it.

4.2. Conflict Between Source Types

AI models lean on four different source classes simultaneously, and the classes contradict each other. Investor directories (OpenVC, Banyan VC lists, Seedtable, Gilion VC Mapping, NFX Signal) deliver one set of funds. Editorial blogs (Qubit Capital, Ellty, Outlander VC, Zyner, Eqvista) deliver an overlapping but distinct set. News and trade press (TechCrunch, Business Insider Seed 100, Forbes Midas Seed) bias toward brand‑name funds. UGC and forums (Reddit r/startups, Hacker News, LinkedIn, YC company directory) surface unexpected angels and micro‑VCs the directories miss.

A single AI response can both praise a fund via the directory and bury it via a Reddit thread. The model is not lying — it is honestly repeating conflicting narratives. RankCaster's Source Mapping module tracks not only the recommendation itself but the sources that produced it. You see which assets lift you and which undermine trust.

4.3. Conflict Between Intents

A single fund can dominate one intent and disappear in the adjacent one.

Y Combinator scores 83% on Universal Pre‑Seed, 72% on MarTech, 81% on International Founders, and is not applicable on Operator Angels (fund, not angel). Sequoia Capital scores 65% on International Founders, 56% on MarTech, 40% on Universal Pre‑Seed, out of top‑10 on Operator Angels. Elad Gil scores 50% on Universal Pre‑Seed, 50% on Operator Angels, 44% on MarTech, out of top‑10 on International Founders. Antler scores 65% on International Founders with full 4/4 consensus, out of top‑10 in all three other intents.

The fund's AI positioning is fragmented across founder narratives — even when the internal brand feel seems unified. Building a deliberate cross‑intent narrative that holds the fund in every priority intent at once is the real platform‑team job in 2026.

5. Source Authority and the AVM Visibility Stack

5.1. The Four Source Types That Drive Recommendations

Investor lists and directories (OpenVC, Banyan VC insight pages, Innmind, Shizune, Gilion VC Mapping, NFX Signal, Eqvista, VC Sheet, Zyner) appear in 40–90% of responses, citing at least one. These are the highest‑leverage placements.

Editorial blogs and thought leadership (Qubit Capital "Top AI Investors," Ellty MarTech roundup, AI funding tracker, Forum Ventures research, Outlander VC field guides, Papermark, Failory, Betaboom) appear in 25–80% of responses. These sources shape the narrative — they don't just list names, they explain why a fund deserves consideration.

News and trade press (Forbes Midas Seed, TechCrunch AI funding roundups, Business Insider, PitchBook) appear in 20–50% of responses, with strong skew toward brand‑name funds.

UGC, forums, social (Reddit r/startups, r/SaaS, Hacker News, LinkedIn posts by founders and platform leads, YC company directory, X/Twitter round announcements, Wikipedia) appear in 10–30% of responses. This is where operator‑angel reputations are built and broken.

5.2. The AVM Visibility Stack

AVM composes GEO (semantic authority on external sources), AEO (data structured for instant citation), and AIO (technical readiness for AI agents) into one synchronized program. For a fund, the stack unfolds in five layers.

Layer 1 (GEO) — claim the directories. Listings on OpenVC, Banyan, Innmind, Shizune, Gilion, NFX Signal, Zyner, Eqvista, VC Sheet — with structured, complete, current profiles: stage, check size, geography, sector focus, ICP. Outdated or incomplete profiles are the highest‑ROI fix available to a fund platform lead in 2026.

Layer 2 (AEO) — publish a cornerstone, citable asset. Long‑form structured content on your fund's thesis ("The 2026 Pre‑Seed B2B SaaS AI Investment Thesis"), formatted for AI extraction. The discipline is Semantic Triples: "[Banyan Ventures] — backs — [B2B SaaS AI pre‑seed founders]", "[Antler] — writes checks across — [30+ international markets]". This gives the model unambiguous "Subject — Action — Object" units it can quote verbatim without hallucination. Before publishing, pass the asset through PAS (Prompt‑Alignment Score) — RankCaster's operational threshold derived from internal RAG‑recall testing: assets scoring below 77% (Excellent Match) extracted worse in our tests, so 77% is our publication floor. This is an operational standard, not an industry constant.

Layer 3 (GEO) — get into independent editorial lists. Placements in "Top X Pre‑Seed Funds" roundups at editorial blogs: Qubit, Ellty, Outlander, Failory, Papermark. This is outreach work, not paid placements. In saturated intents, the right tactic is Disruption Pivot: instead of "we're also a great pre‑seed fund," formulate "yes, most pre‑seed funds do X — but our model fundamentally differs in Y." AI models propagate Disruption Pivot language efficiently because it gives them a clean differentiation snippet to quote.

Layer 4 (UGC/Community) — be present in community. Partner authorship on Reddit and Hacker News, founder testimonials on LinkedIn, X/Twitter presence by partners themselves (not the fund handle). Operator‑angel positioning is impossible without this layer. UGC/Community is broken out as a separate layer because the source class (Reddit, HN, LinkedIn, X/Twitter) operates on a different optimization mechanic than editorial blogs and directories in layers 1–3.

Layer 5 (AIO) — forward‑looking: expose your thesis to the Agentic Web. Layers 1–4 above are measured in this report. Layer 5 is a forward bet, not a checklist item — included here because the cost of being early is low and the cost of being late will compound. At the time of this measurement cycle, no fund was observed running a full MCP deployment for VC discovery. Founders are beginning to use AI agents for scouting, comparison, and preliminary qualification of funds — agents that read structured data, not marketing copy. Deploy MCP (Model Context Protocol) endpoints and open APIs: portfolio, check sizes, sector focus, application links, partner contacts. The first funds to do this will auto‑surface to founder agents the way the first SEO‑mature companies auto‑surfaced to Google in the early 2000s.

RankCaster's Source Mapping shows which layers are working for the fund and where the gaps are. Beyond passive monitoring, Intent Injection is the active layer: systematically embedding the fund's meanings into the sources above to shape the AI's opinion, not just observe it.

6. ROAI: The Economics of AI Visibility for a Fund

6.1. The ROAI Formula Applied to a Fund

ROAI = ((Total APR Score × (Estimated Search Volume × 20%) × Conversion Rate Proxy) − Content Cost) / Content Cost

Total APR Score. 1 point if the AI cites your talking points, 3 points for shortlist inclusion, 5 points for direct recommendation. Summed across ChatGPT, Gemini, Claude, DeepSeek. A fund universally shortlisted across all 4 models scores Total APR = 12. A rare fund earning direct recommendation universally scores 20.

Estimated Search Volume × 20%. The 20% coefficient is the documented lower bound on AI mediation of commercial‑intent search. Independent benchmarks bracket the range: McKinsey estimates 40–60% of commercial‑intent search queries now have a conversational AI alternative; Penske Media Corporation's 2025 lawsuit against Google quantifies a measured ~20% organic traffic loss attributable to AI Overviews. We default to that documented lower bound (20%) and let funds override with their own AI‑channel attribution data. Source for the volume side: monthly broad search volume on the underlying keyword cluster (§2.7 documents the head‑term volumes for the seed‑investor category — ~30K–50K/month aggregate for AI/SaaS fundraising in 2026).

Conversion Rate Proxy. AI operates as an "independent advisor." Founders converted via the AI shortlist are historically 3–4× more receptive than founders from cold outreach or directory parsing. Take your historical baseline conversion and multiply by this uplift.

Content Cost. Annual AVM program cost: cornerstone thesis, directory profile maintenance, editorial outreach, community presence, MCP exposure.

6.2. Worked Example — What AI Visibility Is Worth in Shortlist Inclusions

Headline multipliers stitched from six sequential proxy coefficients collapse under LP scrutiny. We don't publish one. Instead, here is the per‑month inclusion math a fund can defend in a partner meeting.

Baseline composition (substitute your own at every step).

Step 1 — Aggregate monthly Google search volume on the keyword cluster of your priority intent: 30,000 (conservative midpoint: ai investors + venture capital + angel investors + pre seed funding, see §2.7). Source: GKP on your cluster.

Step 2 — Multiply by Intent Reach coefficient 20% (founders treating the AI shortlist as primary filter): → ~6,000 AI‑mediated founder queries per month. Source: documented lower bound from Penske/McKinsey (see §6.1); fund overrides with own AI‑channel attribution.

Step 3 — Your share of the AI shortlist slot in this intent — start with the APR uplift you would target (e.g., 0% → 25%): → ~1,500 queries per month route to your fund instead of zero today. Source: composition.

Step 4 — Headline: ~1,500 incremental AI‑sourced founder inclusions per month (or 50/day).

What that's worth in your funnel. The right next step is not to multiply (Step 4) by more proxy coefficients toward a dollar headline. The right step is to put it through your funnel. A pre‑seed fund with a 1% inquiry‑to‑pitch rate and a 5% pitch‑to‑check rate, applied to 1,500 incremental inclusions, yields ~0.75 incremental checks per month — i.e., ~9 per year. At a $750K check, that's ~$6.8M of incremental deployed capital.

Sensitivity caveat. 1% inquiry‑to‑pitch is the optimistic end for cold AI‑sourced traffic — many funds will model 0.3–0.5% instead, which collapses the number to ~3 checks/year and ~$2M deployed. Apply your historical baseline times an AI‑uplift discount, not a premium. Your fund's numbers will differ — possibly by an order of magnitude in either direction — depending on inquiry rate, pitch conversion, check size, and what fraction of the 20% Intent Reach holds for your specific intent.

Why we publish inclusions, not multipliers. A fund that targets a 25% APR lift on Universal Pre‑Seed and lands at 12% has cut the result of Step 4 in half — and the same composition still works. A 450× headline does not survive that. A "~750 incremental inclusions per month" number does — and is what your platform team will actually be measured on.

The point of the math is not a headline. The point is the unit: AI‑sourced founder inclusions per month. Run them through your CRM and your historical close rates; report the result with your own assumptions in the LP letter. The framework is in §6.3; the inputs are yours.

6.3. Why It Matters to the Managing Partner and the LP Letter

In 2026, AI Visibility is a stage of the dealflow funnel that precedes inbound applications and warm intros. Ignoring it is the same as ignoring SEO in 2012: founders simply don't get to you, because their shortlist is finalized before they even google your fund or ask an existing investor for an intro.

ROAI gives this conversation a language for the LP letter, for the platform headcount discussion, for the annual partner meeting. "Moving our Total APR Score from X to Y across these four founder intents is N additional shortlist inclusions per month. At our historical conversion and check size, that's $Z of incremental deployed capital — for less than the cost of one junior associate." Substitute your X, Y, N, Z — any LP responds. Lead with inclusions and conversion math your fund can defend, not with a composite multiplier built from someone else's proxy coefficients.

7. What To Do Next, as an Investor

Step 1 — measure and prioritize. Run the four Golden Prompts above plus 2–3 custom prompts for your thesis. 30 days of monitoring APR per assistant under the RankCaster 10x Validation standard. In parallel, score each intent on the full PVS formula (SEO Score from your keyword data, Pain Score from founder interviews, Social Score from social listening) and run the ROAI forecast — so that the program goes into the budget with a forecast attached, not after the fact.

Step 2 — map your source footprint. Understand which directories, editorial blogs, news pieces, and community threads currently mention you. Audit your profiles on OpenVC, Banyan, Innmind, Shizune, Gilion, NFX Signal, Eqvista, Zyner. Most funds have outdated or incomplete data on at least three of these.

Step 3 — launch an AVM program (GEO + AEO + AIO as one program).

GEO — complete every directory profile; secure placement in 5–10 independent editorial investor‑list articles in your priority intents; ensure presence in community discussions on Reddit and LinkedIn.

AEO — publish one cornerstone thesis asset using Semantic Triples and validate each section through PAS — only ship material scoring 77%+ (Excellent Match).

AIO — stand up MCP endpoints and open APIs: portfolio, thesis, check sizes, application path. The first VCs in your tier to do this will auto‑surface to founder agents before competitors realize the channel exists.

Intent Injection — beyond observing APR, publish ICP‑tagged content that AI assistants can semantically extract as the canonical narrative about your fund's specialty: a thesis document with explicit "we invest in X for Y" sentences using named entities; partner essays on Substack with consistent specialty framing; founder testimonials structured around the same lexicon. The discipline is active corpus construction, not passive monitoring.

Narrative tactics — in saturated intents, deploy Disruption Pivot in cornerstone copy and partner thought leadership; explicitly address Shadow Prompts ("we don't ghost," "we close in N days") to win the private queries founders never say out loud.

Step 4 — make AI Visibility a standalone KPI. In 2026, leading funds track APR, PVS, and ROAI alongside inbound application volume, qualified pitch count, and follow‑on rate. Monthly review. Platform team bonuses tied to APR lift in the fund's three priority intents.

8. Conclusion: AVM as the New Top of Funnel

Fund marketing was a brand discipline for a decade: a podcast, a conference, a partner Twitter account, portfolio logos on the homepage. Brand still matters. But the first filter in a founder's 2026 fundraising process is an AI assistant, and its recommendation is governed by a different stack: directories, editorial blogs, community threads, structured citable content the model knows how to parse.

By the time the founder reaches your application form, you have already either won or lost. The shortlist is finalized inside ChatGPT, Gemini, Claude, and DeepSeek before the website visit. Competition has compressed from generic "best VC" awareness to a generated 5–10‑fund shortlist per intent. Power has migrated from press releases and keynote talks into the structured content classes models extract from.

This AV‑Index shows where that shortlist currently lands in the seed and angel market. The full AV‑Index series at rankcaster.ai/blog re‑measures quarterly across sectors. To run RankCaster on your fund, start with the free audit at rankcaster.ai/audit.

Appendix: Most-Cited Sources by Intent

Founder Search — Universal Pre‑Seed (AI/SaaS)

• zyner.io — Pre Seed Angel Investors: 100+ Active Investors (2026) (25%)
• outlander.vc — 25 Enterprise AI VCs Writing Pre‑Seed Checks in 2025 (25%)
• forumvc.com — Pre Seed Fund | Venture Capital for B2B AI Startups (23%)
• openvc.app — Angel Investors for SaaS (23%)
• evalyze.ai — Angel Investor Lists (23%)
• bonfirevc.com — Best Angel & Pre‑Seed Investors for SaaS & Software (20%)
• innmind.com — AI Angel Investors Database (Updated for 2026) (20%)
• extruct.ai — Global Pre‑Seed VC Funds: The 2026 Map (20%)
• banyan-vc.com — Top AI Pre‑Seed Investors (20%)
• rho.co — 14 Leading AI Angel Investors for Your Startup (18%)

MarTech Vertical (AI + MarTech)

• banyan-vc.com — Top AI Pre‑Seed Investors (28%)
• openvc.app — Accelerators / Incubators / AI (28%)
• ellty.com — Top MarTech Investors / Pre‑Seed Investors (25%)
• rho.co — Angel Investors for AI (25%)
• ninjatech.ai — Which VC Firms Are Funding Pre‑Seed Companies (25%)
• qubit.capital — Top Investors Backing AI Startups (22%)
• gilion.com — MarTech Investors & VC Firms in 2026: 100+ Listed (22%)
• forumvc.com — Pre Seed Fund (22%)

International Founders (AI/SaaS)

• openvc.app — SaaS Investors (27%)
• bonfirevc.com — Best Seed Investors for SaaS & Software (24%)
• aifundingtracker.com — Top 10 Seed Investors for AI Startups (2026) (24%)
• saastock.com — SaaS Investors & VCs Latin America (22%)
• shizune.co — Artificial Intelligence Investors Europe (22%)
• aiseedfund.com — landing (22%)
• banyan-vc.com — Top AI Pre‑Seed Investors (19%)
• sequoiacap.com — Seed Fund and Arc Announcement (19%)
• pitchbook.com — Meet the 10 Most Active Investors in European AI Startups (19%)

Operator Angels (B2B SaaS + AI)

• openvc.app — Angel Investors for SaaS (Gemini 100%, aggregate 23%)
• shizune.co — Top 50 AI Angel Investors May 2026 (17%)
• innmind.com — AI Angel Investors Database (Updated for 2026) (ChatGPT 70%)
• evalyze.ai — Angel Investor Lists 2026: 40 Active Angels + Database (Gemini 67%, ChatGPT 30%)
• capitaly.vc — Top 20 Angel Investors for B2B SaaS Startups (Ultimate Guide 2025) (Gemini 50%, ChatGPT 40%)
• 22ndcenturyfrontier.com — The US AI Angel Investor Map (Gemini 50%)
• businessinsider.com — The 60 Top Angel Investors for B2B Startups (Gemini 33%)
• alejandrocremades.com — Operator Angels and Operator Investors Shaping the Contemporary Early‑Stage Startup Landscape (Gemini 17%)
• outrunventures.com — Chris Adelsbach — About (ChatGPT 20%)
• destraynor.com — Investments — Des Traynor (ChatGPT 40%)

RankCaster AI · AV‑Index series · methodology v1.1 · anchor cycle: June 3, 2026 · next re‑measurement: September 2026.