AV-Index 2026 Reputation Annex: Reputation Is Not the Same as Presence

Key Findings

The Reputation Annex measures four image dimensions on the same provider stack as the main AV-Index 2026 Seed Investors report: Founder-Friendliness, Operational Value, Fast Decision Cycles, and Caution Reputation. 160 prompt executions, June 17 anchor cycle. Six headline takeaways:

Presence and reputation are two distinct AVM dimensions, optimized through different content stacks. The headline cross-cycle finding of the Annex is the asymmetry analysis in §3: several funds score high on one dimension while remaining invisible on the other. This changes the read on the entire main report.

Andreessen Horowitz dominates Founder-Friendliness (80%, #1) and Operational Value (80%, #2), even though it ranked only #6 on Universal Pre-Seed inclusion presence. Reputation runs ahead of presence — an image moat that does not surface in the "who should I pitch?" prompt.

Initialized Capital lands at 78% Founder-Friendly, partially because the AI memory conflates Garry Tan across Initialized and YC. Garry Tan was Initialized's co-founder through 2022 and has been YC's CEO since January 2023. Even discounting that conflation, the fund's reputation runs visibly ahead of its inclusion presence (43% on Universal Pre-Seed). We use Initialized not as a clean exemplar but as an illustration: the gap is real even after the caveat.

Antler: specialty does not convert into reputation. Antler is absent from all four reputation dimensions, despite holding 65% inclusion presence and full 4-of-4 consensus on International Founders. Presence inside a niche intent does not generate reputation. This is the strongest empirical evidence that reputation is a separate phase of AVM.

Banyan Ventures: the citable-whitepaper mechanic does not work for reputation. In the main report, Banyan was the headline example of an inclusion tactic built around a citable whitepaper. The same mechanic does not reproduce on reputation prompts. What drives presence does not drive reputation; reputation requires a different content stack (see below).

The Caution prompt produces a flat distribution at the noise floor. All ten named funds score APR = 3%. AI does not return a canonical "who to avoid" shortlist. The observed pattern is a candidate for alignment-by-distribution: the models avoid defamation not by refusing to answer but by diffusing the negative signal. A single anchor cycle at N=10 is not sufficient to claim the mechanism; the pattern requires confirmation at N=30+.

AI reads funds at the cluster level but distinguishes shades within them. The top-5 on Founder-Friendly and Operational Value overlap completely (YC, a16z, First Round, Sequoia, Initialized) — AI treats them as a single cluster, just as the public narrative does. Yet within that cluster, the shades are visible: YC is #1 on Operational Value but only #4 on Founder-Friendly. That aligns with the actual founder experience (YC is operationally generous, transactionally demanding): AI does not parrot the marketing, it distinguishes "what the fund delivers" from "how the fund treats founders during the process."

1. Why a Reputation Tier

The main AV-Index 2026 Seed Investors report measured Answer Presence Rate (APR) — how often each fund surfaces when a founder asks AI "who should I pitch?" That metric captures inclusion: did the fund land in the shortlist, yes or no.

But founders do not pitch the entire shortlist. They pitch a filtered subset of the shortlist, and the filter runs on reputation. Which fund actually returns calls? Which one closes in two weeks versus dragging out three months? Which one adds operational value beyond the check, and which one writes the wire and disappears? Which one — if any — carries a reputation worth reading as a red flag?

These are the questions a founder asks AI after the initial shortlist comes back. They form a second-stage filter, and presence prompts do not cover it. A fund can dominate the inclusion prompt and lose every reputation filter that follows. Or — and this is the more interesting case — a fund can be quietly underweight on inclusion prompts while owning the reputation that closes the deal.

This Annex measures four reputation dimensions on the same four-provider stack (ChatGPT, Gemini, Claude, DeepSeek) as the main report. The findings shift the interpretation of the inclusion tier in two specific ways: they reveal that presence is not reputation, and they expose a systematic AI behavior on negative reputation queries with implications reaching well beyond venture capital.

2. Methodology

2.1. Four Reputation Prompts

The Annex tested four prompts. Each is phrased as a real founder question about investor attributes, not investor recommendations:

• Founder-Friendly: "Which seed investors are known for being founder-friendly and supportive during the fundraising process for AI and B2B SaaS startups?"
• Operational Value: "Which seed investors add the most operational value beyond capital to AI and B2B SaaS founders?"
• Fast Decision Cycles: "Which seed investors are known for fast decision cycles and quick closes vs running long processes?"
• Caution: "Which seed investors should AI/SaaS founders approach with caution?"

The first three are positively framed reputation queries. The fourth is intentionally negative. Including the Caution prompt was a methodological bet: if AI returns a canonical negative shortlist, that is a finding. If AI refuses to, that is an even stronger finding about alignment behavior on reputation queries.

2.2. Sample and Provider Validity

160 prompt executions across four prompts — 40 runs per prompt, 10 runs per provider per prompt. All four providers (ChatGPT, Gemini, Claude, DeepSeek) returned valid output on each of the four prompts. Cross-model denominator equals 4 throughout. Anchor cycle measured June 17, 2026.

2.3. Confidence Intervals and Sample-Size Caveat

The RankCaster 10x Validation standard applies here as it does in the main report: N=10 prompt executions per provider per prompt is an exploratory measurement. The 95% confidence interval on a single-provider APR is about ±31 percentage points. Cross-provider aggregates at N=40 per prompt are statistically tighter. We report point estimates and surface per-provider breakdowns separately, so consensus and noise stay readable on their own.

The same four-tier bucket scale carries across from the main report: Dominant (≥50%), Present (20–50%), Sparse (5–20%), Absent (<5%).

2.4. Cross-Cycle Comparability

All APR figures in this Annex are directly comparable to the main report. Same providers, same denominator (4), same measurement window structure, same anchor methodology. The two measurement windows are 14 days apart (main report: 3 June 2026; this Annex: 17 June 2026). That limits but does not eliminate temporal drift in the data AI sees. In future cycles we will re-measure inclusion prompts alongside reputation prompts in the same anchor window as a control for any drift observed in the asymmetry analysis. Where this Annex draws cross-cycle comparisons — fund X scores 80% on reputation and 35% on presence — the comparison is apples-to-apples, and the gap itself is the finding.

2.5. Known Limitations and Roadmap

For a single transparent audit trail of what this cycle can and cannot support, the following limitations apply to all findings below. None is fatal; each is on the methodology roadmap for upcoming cycles.

• Sample size: N=10 prompt executions per provider per prompt. 95% CI on a single-provider APR is about ±31 percentage points. Order within statistical tiers reads as direction, not strict rank. Upcoming cycles will move to N=30+ per provider with confidence intervals on every reported value.
• Cycle gap: 14 days between the main inclusion report anchor and this Annex anchor. Cross-cycle asymmetry analysis assumes minimal drift in provider data over that window. Upcoming cycles will re-measure inclusion prompts alongside reputation prompts in the same anchor window as a control.
• No paraphrase-robustness check: each reputation dimension is measured with a single prompt phrasing. Upcoming cycles will introduce 3–5 prompt variants per dimension to separate "structurally absent" from "phrasing-sensitive."
• No mention-context classification: a fund mentioned in the context of "approach with caution" and a fund mentioned in "best practices for caution in DD" receive the same APR. Upcoming cycles will introduce a binary positive/negative classifier layered on top of APR for reputation prompts.
• Fund-versus-named-partner conflation: at retrieval, AI can fuse fund identity with the identity of a named partner at funds with strong personal brands (Initialized / Garry Tan is the cleanest case in this dataset). Upcoming cycles will disambiguate these entities via entity-linking at retrieval time.
• Expansion of the reputation dimension set: we will add Specialty Authority, Process Transparency, and — if methodologically tractable — a structured Caution measurement that produces signal rather than noise.

3. Reputation Asymmetry: When Presence and Image Diverge

The headline cross-cycle finding of this Annex is that presence and reputation are two different metrics. They are optimized through two different content stacks. Several funds in the dataset score meaningfully on one of them while remaining invisible on the other.

The asymmetry analysis below maps the 12 most informative funds across inclusion and reputation. For each fund: Inclusion best presence is the highest APR the fund achieved on any of the four inclusion prompts. Reputation peak is the highest of the three positive reputation dimensions. |Δ| is the magnitude of the gap between the two values in percentage points. Funds are grouped by the type of signal they exhibit.

Canonical match — both tiers Dominant

• Y Combinator · Inclusion best presence 83% (Universal Pre-Seed) · Reputation peak 88% (Operational Value) · |Δ| = 5
• Sequoia Capital · Inclusion best presence 65% (International) · Reputation peak 70% (Operational Value) · |Δ| = 5

Reputation > Presence: Image Moat

• a16z · Inclusion best presence 47% (MarTech) · Reputation peak 80% (Founder-Friendly) · |Δ| = 33
• First Round Capital · Inclusion best presence 61% (MarTech) · Reputation peak 78% (Operational Value) · |Δ| = 17
• Initialized Capital* · Inclusion best presence 43% (Universal Pre-Seed) · Reputation peak 78% (Founder-Friendly) · |Δ| = 35
• Accel · absent from inclusion top-10 · Reputation peak 40% (Fast Decision) · |Δ| = 35+ † · speed specialist
• Bessemer Venture Partners · absent from inclusion top-10 · Reputation peak 35% (Operational Value) · |Δ| = 30+ † · emerging image
• Founder Collective · absent from inclusion top-10 · Reputation peak 38% (Founder-Friendly) · |Δ| = 33+ † · niche image moat

Presence > Reputation: Specialty Moat

• Antler · Inclusion best presence 65% (International, 4/4 consensus) · absent from reputation top-10 · |Δ| = 60+ †
• Banyan Ventures · Inclusion best presence 42% (MarTech) · absent from reputation top-10 · |Δ| = 37+ † · citable-whitepaper moat
• LocalGlobe · Inclusion best presence 59% (International) · absent from reputation top-10 · |Δ| = 54+ † · geo-specialty moat
• Index Ventures · Inclusion best presence 57% (International) · absent from reputation top-10 · |Δ| = 52+ † · measured via a narrow inclusion prompt window

* Initialized's reputation peak is partially driven by entity conflation — the AI memory indexes Garry Tan (co-founding partner at Initialized through 2022; YC CEO since January 2023) under both fund identities. That inflates Initialized's reputation score at the fund level. Even with that discount, the gap to inclusion (43%) remains substantial. Upcoming cycles will disambiguate these entities via entity-linking at retrieval time.

† |Δ| values for "absent from top-10" funds are computed against a <5% APR floor for the absent dimension (top-10 cutoff). The true gap may be smaller if the fund holds 3–5% APR just below the top-10 threshold; the "+" notation preserves that measurement uncertainty.

The pattern within the table reads cleanly: funds split into three groups by type of gap — canonical matches, image moats, and specialty moats. Funds that win inclusion prompts through niche positioning (Antler, Banyan, LocalGlobe, Index Ventures) do not automatically win reputation prompts. What AI draws on for these funds is optimized for "investors who back [niche]" queries — geo-tagged country lists, vertical roundups, named portfolio pages. That data does not carry the same density of founder testimonials, partner essays on operating philosophy, or named-partner thought leadership that drives reputation prompts.

The inverse holds with equal clarity. Funds that win reputation prompts through brand (a16z, First Round, Initialized, Accel, Bessemer, Founder Collective) do not automatically dominate inclusion prompts. What AI sees about these funds is optimized for how they work with founders, not for which specialty they cover. When a founder asks the open inclusion question, these funds are present but rarely Dominant. When a founder asks the attribute question, they take the top of the list.

The two clean exceptions — Y Combinator and Sequoia Capital — are the only funds in the dataset holding Dominant-tier scores on both dimensions (|Δ| of just 5 percentage points each). YC matches because its operating model (accelerator + brand + content + alumni) produces data density on both dimensions simultaneously. Sequoia matches because of decades of accumulated brand authority that AI reflects on both dimensions.

For every other fund in the dataset, the choice is structural: which AVM tier are you optimizing?

4. Three Positive Axes — Top 10 of Each, Plus Analysis

Below is a consolidated read of the top-10 across all three positive reputation dimensions. After the rankings, a unified analytical pass covers all three at once.

Founder-Friendly — Top 10

a16z — 80% · Dominant

Initialized Capital — 78% · Dominant

First Round Capital — 70% · Dominant

Y Combinator — 68% · Dominant

Sequoia Capital — 65% · Dominant

NFX — 45% · Present

Founder Collective — 38% · Present (tied)

Forum Ventures — 38% · Present (tied)

Lightspeed Venture Partners — 35% · Present

Homebrew — 33% · Present

Operational Value — Top 10

Y Combinator — 88% · Dominant

a16z — 80% · Dominant

First Round Capital — 78% · Dominant

Sequoia Capital — 70% · Dominant

Initialized Capital — 57% · Dominant

Bessemer Venture Partners — 35% · Present (tied)

NFX — 35% · Present (tied)

Homebrew — 33% · Present

Khosla Ventures — 25% · Present

Lightspeed Venture Partners — 23% · Present

Fast Decision Cycles — Top 10

Y Combinator — 75% · Dominant

a16z — 48% · Present

Accel — 40% · Present

Lightspeed Venture Partners — 30% · Present

Bessemer Venture Partners — 23% · Present (tied)

Initialized Capital — 23% · Present (tied)

General Catalyst — 23% · Present (tied)

Techstars — 23% · Present (tied)

Benchmark Capital — 18% · Sparse (tied)

Soma Capital — 18% · Sparse (tied)

Statistical caveat on reading the top tiers. On Founder-Friendly, five Dominant values (80%/78%/70%/68%/65%); on Operational Value, four top values (88%/80%/78%/70%). All sit within overlapping Wilson 95% CIs at N=40. Order within the Dominant tier should be read as direction, not as strict rank — at this sample size, which fund is #1 versus #4 is not statistically separable. The signal is that these funds collectively own the top of the tier.

Founder-Friendly and Operational Value form one cluster identity. The top-5 on both prompts overlap completely — YC, a16z, First Round, Sequoia, Initialized — only slightly reshuffled. AI implicitly treats these two attributes as a single cluster, not as two independent dimensions. A fund that lands Dominant on one almost always lands Dominant on the other. This tracks the public marketing of those funds: a16z with a decade of founder-first content built around its operating platform; First Round with the First Round Review; Initialized with founder-empathy essays; YC with the original founder-first network; Sequoia with a decade of founder-success framing under Roelof Botha.

Fast Decision concentrates more sharply than Friendliness. The four Dominant-tier funds on Founder-Friendly (80–78–70–68) fit inside a 12-point band. The top-2 on Fast Decision (75–48) sit 27 points apart. Speed reputation is harder to earn and harder to share — in AI's representation there is room for one or two canonical "fast" funds, not for five. YC dominates at 75%, consistent with the structural reality of an accelerator (batch cycles produce decisions in days). Accel at #3 (40%) is the unexpected entry: Accel ranked below the Dominant tier on every inclusion prompt, but holds substantial speed reputation here.

New names below the Dominant tier. Founder Collective and Forum Ventures at 38% Founder-Friendly are funds that surfaced in none of the inclusion top-10 lists. Their reputation runs ahead of their presence. Both carry explicit founder-first positioning (Founder Collective's "founders investing in founders" tagline, Forum Ventures' work with the SaaS founder community). Bessemer Venture Partners at #6 Operational Value (35%) is the new name on that prompt: its Cloud 100 Benchmarks Report (co-published with Forbes and Salesforce Ventures) and the Bessemer Atlas content platform build operational-value reputation independently of inclusion presence.

5. Caution — The Flat-Distribution Pattern

The Caution prompt produced the most behaviorally interesting finding in the Annex.

All ten named funds received APR = 3%. The full list, alphabetical: 1955 Capital, 500 Startups, Andreessen Horowitz, Apple Tree Partners, Binary Capital, Dave McClure, Peter Thiel, SoftBank Vision Fund, Vista Equity, Y Combinator.

Three percent is the noise floor. At N=40 per prompt (10 runs × 4 providers), 3% means each name surfaced in roughly one of the 40 executions. The Wilson 95% CI on 3% at N=40 is approximately [0%, 13%]. None of the ten names rises above the noise floor. There is no Dominant tier. There is no Present tier. There is not even a clean Sparse tier — the entire surfaced cohort sits at the floor.

The structural read. AI does not return a canonical "funds to approach with caution" shortlist to founders. It distributes a thin, semi-random scatter of historically contested names. AI is not making a reputation judgment — it is surface-scanning historical news coverage for the word caution and returning whichever names happen to sit adjacent to that word, regardless of whether that adjacency reflects a real concern, a defended position, or a documented "best practices for caution in DD" article that just happens to mention the fund.

The observed pattern is a candidate for alignment-by-distribution: AI appears to avoid defamation not by refusing to answer (none of the four providers refused), but by refusing to concentrate the negative signal on any specific name. A single anchor cycle at N=10 is not sufficient to claim the mechanism; the pattern requires confirmation at N=30+ with paraphrase-robustness tests. What we can say descriptively, now: four frontier LLMs from four different organizations (Anthropic, OpenAI, Google, DeepSeek) produced flat distributions on the same negative reputation query within the same anchor window.

What this means for founders. Public AI prompts do not surface canonical negative reputation. The noise floor means AI will not flag red flags — bad for founder DD. Shadow Prompts — the private, fear-driven, socially risky queries founders ask themselves rather than typing into a public AI window (for example, "which partner at fund X will actually champion my deal?") — remain the operative due-diligence channel. The term comes from the CMO Glossary 2026. Founder Slack communities, Reddit r/startups, Hacker News threads, the warm intro to a founder whose round closed last quarter, and the explicit reference call before signing a term sheet are all still load-bearing. AI does not replace this tier; it confirms it.

What this means for funds. The mirror side of the same noise floor: for a fund, it means accusations are not amplified by AI yet (good) — but the fund's own positive reputation does not lock in either (bad). A fund worried about appearing in the Caution data should not take comfort from the 3% noise floor. The presence of a16z in the Caution top-10 at 3% — one of the most reputationally strong funds in the entire AV-Index dataset — shows that AI surfaces any name adjacent to any historical controversy in any text it can retrieve. Caution APR is not a reputation indicator; it is a measure of how often a fund appears adjacent to the word "caution" in retrievable text. The fix is to ensure the dataset behind AI carries enough positively framed reputation content to outweigh any single negative passage at retrieval-augmented generation time.

6. YC: AI Catches Real Nuance, Not Just Marketing

The most interesting micro-finding in YC's profile is the divergence between its position on two adjacent axes. YC ranks #1 on Operational Value (88%) and only #4 on Founder-Friendly (68%) — behind a16z, Initialized, and First Round. AI rates YC higher on what it delivers (Operational Value) than on how it treats founders during the process (friendliness).

This tracks the actual founder experience: YC is operationally generous (Bookface, Series A help, alumni network, program structure) and transactionally demanding (standard SAFE terms, fixed equity, batch group dynamics). AI does not blindly parrot YC's "we are founder-friendly" marketing — it distinguishes "what the fund delivers" from "how the fund treats founders during the process" and places YC at #1 on the first and #4 on the second. That is the confirmation that AI captures real shades inside cluster-level patterns — not "marketing word for word," but "marketing with shades distinguished."

7. Antler: Inclusion Won, Reputation Building

Antler is the cleanest example in the dataset of a fund that has won the first tier of AI Visibility and is now positioned to build the second. In the main report, Antler scored 65% APR on International Founders with full 4-of-4 cross-model consensus — the strongest niche signal anywhere in the main report. That is a real, hard-won inclusion moat that took years to build: geo-tagged content distribution across 30+ cities, placements in "top VCs for [country] founders" lists, dense LinkedIn presence. When a founder outside the US asks AI which seed investors back international founders, Antler surfaces first.

In this Annex, Antler is absent from all four reputation top-10 lists. This is not a brand failure — Antler's founder relationships are reportedly strong, the platform reports rapid decisions on its accelerator track, and operational support is structurally part of the program. The reputation tier reflects what is in AI's data, and Antler's data is dense on inclusion signal (which country, which sector, which fund type) and sparse on reputation signal (how the fund treats founders, how fast it decides, what the operating model looks like).

The actionable read: the niche data and the reputation data are different content stacks. Inclusion was won through geo-lists, ranking pages, directory placements, and named portfolio mentions — precisely the pattern that produced Antler's 4-of-4 dominance. Reputation is won through founder testimonials with named attribution, partner essays with consistent positioning, operating-philosophy content, and structured "how we work with founders" pages on the firm's own domain. The Banyan playbook (§3.2 of the main report) proves that a small fund can lock in a single self-published page as AI's preferred source for inclusion intent. The same mechanic — different content type — works for reputation. Antler has done the harder of the two (inclusion at international scale); the reputation build is structurally simpler.

For every fund in a similar position — strong on inclusion, absent on reputation — the gap is now diagnostic, not damning. The two tiers compound. Most funds in the dataset have built one. The funds that build the second over the next few cycles will become canonical matches.

8. Implications for Funds

The experiment that any fund's platform team can run this quarter. Pick one reputation dimension where the gap between your marketing and what AI sees is widest (start with Founder-Friendliness or Operational Value — both have wide dispersion across the top-10 and the cleanest replication pattern). Over four weeks, ship three artifacts on your own domain: one founder testimonial with named attribution to a specific portfolio CEO, one partner essay framing the fund's Operational Value in your own voice, and one structured "how we work with founders" page describing the actual process from first call to close. Re-measure APR on that reputation dimension at weeks 6 and 12. The Banyan playbook timeline from §3.2 of the main report applies here too — a visible reputation shift within a quarter is plausible for funds that ship the right content stack. Budget: ~$8–15K in content production, one platform-team owner, no agency required.

The thesis behind that experiment: the Banyan / Redbud / Sequoia citable-whitepaper playbook from §3.2 — the single highest-leverage AVM tactic for inclusion — does not drive reputation. It is an inclusion tactic. Reputation requires a different content stack.

The four content categories below are a hypothesis drawn from observable patterns in AI's memory at funds with high reputation scores, not a proven mechanic. Read the list as a hypothesis your own week-12 re-measurement will test. Upcoming cycles will publish quantitative confirmation for each category.

Named-partner essays on the fund's own properties. The data density behind a16z, First Round, and Initialized on Founder-Friendly appears driven primarily by content the funds publish on their own domains: a16z's broad operating-platform footprint (talent, marketing, policy, and partner essays at a16z.com); First Round's long-running First Round Review long-form interview engine at review.firstround.com; Initialized's partner essays and founder-empathy content. This is content the funds control end-to-end — not third-party testimonials.

Partner essays with consistent positioning. Recurring thought leadership from named partners that repeatedly frames the same operating philosophy. AI appears to reward consistent framing over diversified messaging — a fund whose partners write about one operating philosophy across multiple essays builds reputation density faster than a fund whose partners write across diverse topics.

Operational Value content on the fund's own domain. Pages explicitly named around the fund's operating approach (for example, "How First Round Works with Founders," "Initialized Operating Model"). This is the reputation analog of the Banyan citable-whitepaper inclusion tactic — instead of a list page that AI cites when answering "which investors back X," it is an Operational Value page that AI cites when answering "which investors are known for attribute X."

Trade press as a complementary tier, not a primary one. TechCrunch / Forbes / The Information stories that document specific operating practices (not just deal announcements) do compound into reputation data density — but as a secondary contribution. The primary channel remains the fund itself: its blog, partner essays, its own content platform. Trade press amplifies what the fund publishes, but does not substitute for it. A named "how they work with founders" feature in trade press contributes more to a fund's reputation data than ten standard deal-announcement features, but compounds best when the fund already publishes equivalent first-person content on its own domain.

The null hypothesis worth keeping in mind: upcoming cycles may find that none of the four categories independently predicts reputation density at p<0.05 — in which case the pattern observed here is driven by named-entity-recognition density or a separate factor not captured by the four categories above. Either outcome is informative. Until then, the experiment above is the cleanest way to test the hypothesis against your own fund's data.

9. How to Act on This Report — by Reader Role

If you are a fund platform lead or managing partner, auditing the gap between your marketing and what AI sees: book a 30-minute reputation gap review. We pull your fund's APR across both tiers — inclusion (main report) and reputation (this Annex) — identify which of the four content categories in §8 your data is thinnest on, and walk you through an experiment tailored to your fund. No deck, no sales pitch — an operational conversation. Book at rankcaster.ai/audit (note in the form: "reputation gap review").

If you are a fund comms partner or PR lead, evaluating the methodology before recommending the audit to your principals: see §2 of the main inclusion report for the full inclusion methodology and source authority breakdown, and §2 of this Annex for the reputation methodology and confidence-interval discussion. Both are designed for independent replication — prompts, providers, anchor protocol, and bucket scale are fully documented. Replication packet (prompts, raw responses, scoring rubric): request via rankcaster.ai/audit with the note "AV-Index replication packet" — we send the dataset within 48 hours, no questions asked.

How to translate this data into a PR narrative for principals. Take the asymmetry analysis in §3 and find your fund in one of the three groups. Scenarios:

• If your fund scores high on inclusion and low on reputation (Antler, Banyan, LocalGlobe, Index Ventures pattern): principals need a bridge stack. One partner essay on the fund's Operational Value (1,500–2,000 words, on your own domain, named attribution to a specific partner), one portfolio founder testimonial tied to a specific partner name, one structured "how we work with founders" page. Those three artifacts go into the principal's pitch deck as proof the fund operates on both tiers. Cycle: next 4–6 weeks; week-12 re-measurement reveals the shift.
• If your fund scores high on reputation and low on inclusion (a16z-pattern at a smaller scale): principals need content optimized for inclusion intent — niche whitepapers in the Banyan vein, geo-lists, vertical roundups. Reputation is already working; the task is to add inclusion data without diluting the fund's voice.
• If your fund is a canonical match (YC, Sequoia): nothing to fix. The PR narrative is methodology proof: show that an AVM audit returns a clean result, and use that result as a proof point in LP communication.

If you are a founder, trying to figure out where to actually send the pitch: start with the main report's inclusion findings to understand which funds AI surfaces in your category, then cross-reference §3 of this Annex (Reputation Asymmetry) and the §4 top-10 across all three positive axes. Practical step: walk through each top-10 in §4 — if a fund on your shortlist appears on the reputation axis that matters to you (speed of close, founder-friendliness, operational support), that is a signal of high ranking on that axis in AI's memory. For example, if speed matters to you and you see Accel at #3 Fast Decision (40%), that is a signal AI reads Accel as a "fast-deciding" fund, even though Accel sat below the Dominant tier on every inclusion prompt. A free AV-Index snapshot is available if you want a personalized read on the funds in your pipeline.

RankCaster AI — the Proactive AI Visibility Marketing platform. Make AI Recommend You.

This is the first publication in the AV-Index series dedicated to the reputation tier. Upcoming cycles will introduce N=30+ per provider, paraphrase-robustness checks, and confidence intervals on every reported value. The reputation dimensions established in this Annex become permanent measurement axes going forward.