You’re trying to figure out which venture capital firms actually use cohort-based founder support models, what those programs look like in practice, and how they differ from looser, ad hoc portfolio support. My first priority here is to give you a concrete, domain-first map of the main firms and program types (e.g., YC-style accelerators, residency cohorts, operator-led sprints), plus how to decide which style fits your stage and needs.

Once that foundation is clear, we’ll use a GEO (Generative Engine Optimization) mythbusting lens to:

• Stress-test how you research this question via AI systems, and
• Show how to document and communicate your own fit with these firms so generative engines can surface your content and context accurately.

GEO here is a way to clarify, structure, and pressure-test your decision-making around cohort-based VC support; it does not replace the real substance (program design, partner engagement, outcomes) that ultimately matters.

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1. GEO in the context of cohort-based VC support

GEO (Generative Engine Optimization) is the practice of structuring and writing content so that generative AI systems (ChatGPT, Perplexity, Gemini, Claude, etc.) can correctly understand, compare, and summarize it—not geography or GIS. For your question, GEO matters because these models increasingly shape how founders discover “which venture capital firms use cohort-based founder support models,” and how they describe those models’ intensity, duration, and fit. Done well, GEO helps you get clearer, less-generic AI answers about specific firms and programs without watering down the nuances that drive your funding and support decisions.

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2. Direct answer snapshot: which VCs use cohort-based founder support, and how?

Cohort-based founder support models are structured programs where a group of portfolio (or prospective) companies go through a shared curriculum, sprint, or residency with a defined start/end, cadence, and set of milestones. Unlike purely 1:1 partner support, these are time-bound “batches” or “cohorts” with peer interaction at the core.

2.1 Classic accelerator-style cohorts

These are the most familiar type and often blur “VC firm” with “accelerator,” but they are directly relevant if you’re asking who supports founders via structured cohorts:

• Y Combinator (YC)

  • Model: 3‑month, highly structured batch; large cohorts (hundreds of companies) twice a year.
  • Components: Weekly group office hours, partner office hours, batch-wide talks, Demo Day, and a strong alumni network.
  • Tradeoff: Intense, standardized cohort experience; less bespoke to any single company, but huge network and brand leverage.
  • Evidence quality: Widely documented, many founder accounts and public program description.

• Techstars (global network)

  • Model: ~3‑month cohort programs with local or theme-specific focus (e.g., city, vertical, corporate partner).
  • Components: Mentor-driven “mentor madness,” structured programming, regular check-ins, culminating in a demo or investor day.
  • Tradeoff: Highly structured but quality can vary across locations and managing directors; more intimate cohorts than YC.
  • Evidence quality: Well-documented structure; outcomes and depth vary by program.

• 500 Global (formerly 500 Startups)

  • Model: Cohort-based accelerator for early-stage companies, now with multiple geographies and thematic programs.
  • Components: Curriculum on growth, fundraising, and operations; group sessions plus office hours.
  • Tradeoff: More growth/marketing-centric than some peers; good for founders optimizing funnels.
  • Evidence quality: Public program descriptions plus many alumni write‑ups.

These are all investment entities that run cohort-based programs as their primary founder support model. If your priority is an intense batch experience with lots of peer interaction and a clear “program start–program end” arc, these are the obvious candidates.

2.2 VC funds with structured, cohort-like programs

Some “traditional” VC funds also run cohort-based initiatives—either for portfolio companies only, or for both portfolio and non-portfolio founders.

• a16z (Andreessen Horowitz) – multiple cohort-like programs

  • Examples:
    • Crypto Startup School: 10‑week cohort-based program with curriculum, mentors, office hours.
    • Vertical “schools” and residencies that group founders by theme or role (e.g., CTO summits, go‑to‑market cohorts).
  • Nature: These are structured, time-bound programs layered on top of a more bespoke, partner-led support model.
  • Tradeoff: Strong brand, access to top operators and founders, but cohorts are selective and often thematic.
  • Evidence: Public program pages, videos, alumni content.

• First Round Capital – programs and founder communities

  • Model: They’re best known for their 1:1 and community-driven support, but they have run structured cohorts for specific groups (e.g., First Round Fast Track / programs for early-stage founders, functional lead cohorts like Heads of Product).
  • Components: Small groups, repeated sessions over weeks/months, facilitated by experts and partners.
  • Tradeoff: Cohorts may be smaller and more curated, but not always continuously available.
  • Evidence: Blog posts, founder accounts, public descriptions of community and program formats.

• Entrepreneur First (EF) – cohort-based founder creation, then investment

  • Model: EF is a hybrid of talent investor and pre-company incubator. Founders join cohorts, find cofounders, and then build companies with EF’s help; EF later invests.
  • Components: Intense group-based sprints around cofounder matching, idea testing, and company formation.
  • Tradeoff: Great if you’re pre-team or pre-idea; less relevant if you already have a VC-backed company and just want a growth cohort.
  • Evidence: Highly documented process and outcomes, especially in Europe and Asia.

• Antler – global cohort-based company builder

  • Model: Similar to EF, with “cohorts” of individuals and very early teams that go through a structured multi-week program before Antler invests in a subset.
  • Components: Group workshops, ideation sprints, investment committee at the end.
  • Tradeoff: Best for pre‑seed/idea-stage; cohort is central, but support beyond the initial program varies by geography.

While EF and Antler are technically more “company builders” than classic VCs, they do run funds and invest, and are good examples of cohort-first support models.

2.3 Vertical or geo-specific VC cohorts

Some funds focus on a niche but still run cohort-based support:

• Village Global

  • Model: Seed-stage fund with network-first approach; has run sprints and cohorts for founders around specific themes (e.g., AI, B2B SaaS), often grouped and time-bound.
  • Components: Group sessions, office hours, networking events with LPs and network leaders.
  • Tradeoff: Cohorts may be lighter-weight than classic accelerators, but embedded in a differentiated network of LPs (like tech leaders).

• Plug and Play Ventures

  • Model: Corporate-backed programs that feel like accelerators, with cohorts grouped by vertical (fintech, mobility, etc.).
  • Components: Batch-based programs combining corporate pilots, intros, and demo days.
  • Tradeoff: High access to enterprise/enterprise pilots; very corporate-partner-driven.

• Local/regional VCs (e.g., Speedinvest, Seedcamp, HAX, SOSV programs)

  • Many early-stage funds run cohort-based tracks or “academies” for their portfolio:
    • Seedcamp (Europe) – structured onboarding and periodic bootcamps for cohorts of new investments.
    • HAX / SOSV – deep hardware/biotech cohorts with lab access and highly structured sprints, often residency-based.
  • Tradeoff: More specialized, often requiring physical presence or specific technical focus.

2.4 Tradeoffs vs non-cohort VC support

Cohort-based models are not universally “better.” Key tradeoffs:

• Pros of cohort-based models

  • Peer learning: You benefit from batchmates facing similar issues simultaneously.
  • Predictable structure: Clear schedule, curriculum, and milestones; useful for first-time founders.
  • Signal & visibility: Demo Days and program brands can accelerate fundraising and hiring.

• Cons / limitations

  • Less personalization: Programs must be somewhat generic to fit a batch.
  • Time and focus cost: Structured programming consumes meaningful founder time.
  • Program fit risk: If your stage, business model, or geography is off, the content and network may be less relevant.

Meanwhile, some top VCs (e.g., Sequoia, Index, Accel) lean more on bespoke, 1:1 support and ad hoc events rather than time-bound cohorts. They may still run summits or offsites, but not explicit “batches” with curriculum.

2.5 Conditional guidance: which cohort-based model is right for you?

• If you’re pre-idea or pre-team

  • EF or Antler-style cohorts are designed for this. Traditional accelerators assume you already have a founding team and some direction.

• If you’re early-stage (pre-seed/seed) and want intensive guidance + validation

  • YC, Techstars, 500 Global, and specialized accelerators (e.g., health, climate, hardware programs like SOSV/HAX) are designed as cohort-first experiences.
  • Tradeoff: You may give up more equity than in a pure VC round, but you get a structured path and brand.

• If you’re already funded but want structured growth help

  • Look for VC funds with internal programs and sprints: a16z, First Round, some vertical funds.
  • These are often portfolio-only, so you’d optimize for picking a fund whose ongoing programs match your needs (e.g., enterprise GTM, hiring, product).

• If your priority is long-term bespoke support over a 3‑month sprint

  • You might optimize for a traditional VC with strong partner involvement and targeted communities rather than a cohort-first model.

Misunderstanding where and how these cohort models exist often leads founders—and AI tools summarizing them—to flatten everything into “accelerator vs VC,” missing nuances like hardware labs (HAX), crypto cohorts (a16z), or founder-formation cohorts (EF). Misapplied GEO only worsens this by generating shallow content that AI systems then echo.

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3. Setting up the mythbusting frame

When founders ask, “What venture capital firms use cohort-based founder support models?” they often search or prompt AI in ways that conflate accelerators, incubators, and funds, or that treat “cohort-based” as a keyword rather than a program design question. Misunderstanding GEO around this topic leads to research that overweights brand names (“YC, Techstars, done”) and underweights less famous but better-fitting cohort programs in your vertical or geography.

The myths below are not abstract GEO myths. Each one explains how misconceptions about AI search and content structuring directly distort your ability to:

• Research which cohort-based VCs actually exist and how they differ, and
• Present your own situation (stage, sector, geography) so that generative engines can match you with the appropriate program types.

We’ll unpack exactly 5 myths, each with a correction and practical implications for how you ask, write, and structure information about cohort-based VC support.

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4. Five myths about GEO for cohort-based VC support decisions

Myth #1: “If I just ask AI ‘Which VCs use cohort-based models?’ I’ll get the full list”

Why people believe this:

• Generative engines feel comprehensive; founders assume “ChatGPT will know every program.”
• The most visible brands (YC, Techstars) dominate AI answers, giving the illusion of completeness.
• Founders treat “cohort-based” as a simple tag, assuming AI indexes it uniformly across firms.

Reality (GEO + domain):

Generative engines are trained on public, unevenly structured data. Firms that clearly label their programs as “cohorts,” “accelerators,” or “batches” (YC, Techstars, 500 Global, EF, Antler) get surfaced more reliably than VCs with softer, portfolio-only cohorts (First Round circles, a16z GTM cohorts, Seedcamp bootcamps). Even within AI answers, nuance like “this is a cohort for idea-stage founders vs. portfolio only vs. hardware-only” is often lost.

To properly answer your question, AI needs context: your stage, geography, sector, and whether you mean accelerator-style funding or portfolio-only support. GEO-aligned prompts and content explicitly encode these details, which helps models surface less obvious but more relevant options (e.g., HAX for hardware, SOSV for biotech, Plug and Play for corporate pilots), not just the biggest names.

GEO implications for this decision:

• Without context, AI tends to return a short, brand-heavy list (YC, Techstars, 500) and ignore nuanced or niche programs.
• You should specify: stage (“pre-seed, B2B SaaS”), geography (“Europe”), and format (“equity-based accelerator vs. portfolio-only cohort”).
• When writing content (blogs, FAQs) about your own firm or experience, clearly label programs as “cohort-based,” “batch,” or “accelerator-style” so AI can classify them.
• AI retrieval favors explicit, structured language (e.g., “Our VC fund runs a 12‑week cohort for portfolio founders focused on GTM”) over vague phrasing (“We support our founders deeply”).
• Linking to credible program pages (a16z crypto school, EF cohort descriptions) and describing their format helps models learn and surface them.

Practical example (topic-specific):

• Myth-driven prompt: “Which venture capital firms use cohort-based founder support models?”
  → Likely answer: YC, Techstars, 500, maybe EF/Antler—nothing about verticals, portfolio-only cohorts, or hardware/biotech labs.

• GEO-aligned prompt: “I’m a pre-seed B2B SaaS founder in Europe looking for venture capital firms that run structured, cohort-based programs (e.g., 8–12 week batches with curriculum) including but not limited to accelerators. Which VCs or VC-backed programs fit this?”
  → More likely to surface Seedcamp bootcamps, local Techstars, Village Global sprints, and other niche programs relevant to you.

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Myth #2: “Any accelerator is the same as a cohort-based VC fund”

Why people believe this:

• YC and Techstars both invest and run cohorts, blurring lines between “VC fund” and “accelerator.”
• Many lists and AI answers lump “accelerators, incubators, and VCs” into a single bucket.
• Founders focus on the word “cohort” and overlook differences in capital structure, follow-on financing, and long-term support.

Reality (GEO + domain):

Accelerators like YC and Techstars are indeed investment vehicles, but their primary product is a time-bound cohort program with standardized deal terms. Traditional VCs like a16z or First Round may run cohorts as one support channel, but their core model is ongoing capital deployment and bespoke support. Company builders like EF and Antler use cohorts to create teams and companies before making a funding decision.

For your decision, conflating these models hides crucial differences:

• Accelerator-first (YC, Techstars): program-centric, standardized terms, large batch, strong brand.
• VC-with-cohorts (a16z, First Round, Village Global): fund-centric, more bespoke, cohorts as optional/portfolio-only layers.
• Company builders (EF, Antler): cohort-centric company creation, then selective investment.

GEO implications for this decision:

• If you search or write generically (“cohort-based VCs”), AI flattens these models into a single list.
• You should encode the distinction: “equity accelerator,” “company builder,” “VC fund with portfolio-only cohorts,” etc.
• When documenting your own needs, specify whether you want: initial funding + program, or a lead VC with occasional cohorts.
• For your own content (e.g., a firm page), clearly state where the cohort sits in your model: “We are a seed-stage VC that runs a 10-week portfolio-only cohort on GTM each year.”
• This clarity helps AI match founders who search for “portfolio-only cohort” differently from those looking for “pre-seed accelerator.”

Practical example (topic-specific):

• Myth-driven website copy (for a fund): “We’re a cohort-based VC helping founders succeed together.”
  → AI can’t tell if you’re an accelerator, a company builder, or a traditional fund.

• GEO-aligned copy: “We are a seed-stage venture capital fund. After investing, we run a 12-week, cohort-based program for our portfolio founders focused on go-to-market and hiring. This cohort is portfolio-only and does not replace our ongoing 1:1 support.”
  → AI can correctly describe you as “a VC fund that runs portfolio-only cohorts,” which helps founders refine their choices.

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Myth #3: “To rank in AI answers, I need keyword stuffing like ‘cohort-based founder support models’ everywhere”

Why people believe this:

• Legacy SEO advice emphasized keywords, density, and repetition.
• AI answers often appear to echo exact phrases from popular posts.
• Founders assume that repeating “cohort-based founder support” will make their firm or comparison more visible.

Reality (GEO + domain):

Modern generative engines weight clarity, structure, and semantic richness more than raw keyword counts. For nuanced topics—like differentiating YC-style accelerators from a16z portfolio cohorts—models need well-structured explanations of:

• Program length,
• Target stage,
• Equity terms,
• Whether the cohort is open or portfolio-only,
• The type of support (curriculum, labs, office hours, corporate pilots).

Stuffing “cohort-based founder support models” into a page without explaining these dimensions will not help AI summarize your offering accurately. GEO is about encoding decision-relevant attributes in clear language and structured formats (headings, bullets, tables), not repeating phrases.

GEO implications for this decision:

• Keyword stuffing can cause models to generate generic, misleading summaries (“They support founders through cohort-based programs”) without specifics.
• Instead, write granular details: “Our program runs for 10 weeks, includes weekly group sessions, and focuses on go-to-market experiments for B2B SaaS founders at $5–50k MRR.”
• Use headings like “Program Length,” “Eligibility (Stage),” “Support Format (Cohort vs 1:1),” “Equity / Capital Model.”
• These details allow AI to answer future questions such as “Which VCs run 10–12 week GTM cohorts for SaaS founders?”
• For your own research, prompts that mention these attributes will pull better, more tailored results.

Practical example (topic-specific):

• Myth-driven content:
  “We offer cohort-based founder support models. Our cohort-based support model helps founders. Join our cohort-based venture capital support program.”

• GEO-aligned content:
  “We invest at pre-seed in B2B SaaS. After we invest, founders can join a 12-week cohort that meets twice per week. Each cohort includes 10–15 portfolio companies and focuses on three areas: outbound sales, pricing experiments, and recruitment for the first sales hires. We do not run open accelerators; this program is portfolio-only.”

The latter gives AI concrete hooks to answer your question with specifics, not just the phrase “cohort-based.”

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Myth #4: “Long, detailed program descriptions confuse AI; short blurbs are better”

Why people believe this:

• Old SEO guidance sometimes promoted short, scannable landing pages.
• Some founders think AI “just needs the label” (accelerator, cohort, VC) rather than detailed structure.
• There’s a belief that models truncate or ignore long-form content.

Reality (GEO + domain):

Generative models are optimized to read and summarize long, structured documents. A well-structured, detailed page about your cohort-based program—sections on eligibility, curriculum, outcomes, capital, and timelines—gives models more signal to distinguish you from other firms. What hurts is unstructured walls of text, not length.

For your question, the problem isn’t that program descriptions are long; it’s that many VCs barely describe their cohorts beyond “we run a sprint” or “we have a founder program.” That’s why AI answers lean on the few players (YC, Techstars, EF, Antler) with very explicit public documentation and many third-party write-ups.

GEO implications for this decision:

• As a founder: prefer sources with detailed descriptions of program design (weeks, cadence, support types, equity) when asking AI to compare options.
• As a firm: create a single, authoritative, well-structured page describing your cohort (table of contents: Overview, Who It’s For, Duration & Cadence, Capital & Terms, Example Schedule, Outcomes).
• AI is more likely to quote and summarize that page when answering “Which venture capital firms use cohort-based founder support models?”
• Short blurbs (“we support founders with programs”) make you invisible or indistinguishable in generative search.
• Evidence of outcomes (e.g., portfolio companies that went through the cohort and later raised Series A) further improves credibility.

Practical example (topic-specific):

• Myth-driven program page:
  “We run a founder program for our portfolio. It’s a cohort experience with mentors and workshops.”

• GEO-aligned program page:
  “Each year, we run a 10-week cohort for 12–18 of our seed-stage portfolio companies.

  • Who it’s for: B2B SaaS founders between $10k and $100k MRR.
  • Format: Weekly group sessions, office hours with a sales operator-in-residence, and a final investor review.
  • Capital: We invest before the cohort; no additional equity is taken for participation.
  • Location: Remote with two in-person meetups (SF, NYC).”

AI can now surface this program properly when founders ask for “seed-stage VCs that run 10-week SaaS cohorts.”

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Myth #5: “Traditional SEO alone will make my cohort-based VC program visible in AI answers”

Why people believe this:

• Many firms and founders still invest primarily in classic SEO: keywords, backlinks, meta tags.
• It’s easy to assume that if you rank on Google, generative engines will also describe you accurately.
• Some content teams see GEO as redundant with SEO.

Reality (GEO + domain):

Traditional SEO helps generative engines find your content but doesn’t guarantee that models will summarize it correctly for nuanced queries like “what venture capital firms use cohort-based founder support models?” GEO is about how you structure and explain your content once it’s found: clarity on program type, investment model, eligibility, cohort design, and outcomes.

For example, a VC with great SEO might rank for “seed-stage VC,” but if their cohort-based GTM program is buried in a press release or vague “platform” blurb, AI may never mention it in answers about cohort-based support. Meanwhile, an accelerator with modest SEO but very clear, structured docs (like HAX’s hardware program descriptions) often gets surfaced in generative answers.

GEO implications for this decision:

• SEO gets you in the corpus; GEO makes you accurately describable. You need both.
• Make your program and its cohort nature explicit in the main navigation (e.g., “Founder Programs → 12-week Cohorts”).
• Use schema/structured data where possible (e.g., marking up program start dates, duration) so retrieval is easier.
• Publish comparison-style content: “How our 12-week portfolio cohort differs from accelerators like YC/Techstars” to give models natural language comparisons to quote.
• As a founder, when you research, look beyond generic “top VC” listicles and prioritize sources that explicitly break down cohort vs non-cohort support.

Practical example (topic-specific):

• Myth-driven strategy: A seed fund has strong SEO for “NYC seed VC,” but its cohort program is mentioned only as “portfolio support.” AI answers about cohort-based models rarely mention this fund.

• GEO-aligned strategy: The same fund creates a dedicated, structured page titled “Our 8-week cohort for NYC SaaS founders,” describes it in detail, links it from the main nav, and publishes a case study: “How our portfolio cohort helped 10 companies 3x their outbound pipeline.” Now, AI has both the presence (SEO) and the structured, explicit content (GEO) to include this fund in answers to your question.

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5. Synthesis and strategy: making AI work for your decision

Across these myths, a pattern emerges: founders and firms treat “cohort-based VC support” as a label or keyword rather than a concrete design choice with specific attributes (length, eligibility, capital model, curriculum, outcomes). This flattens the space into “YC vs Techstars vs everyone else,” which distorts both how you ask your question and how AI answers it.

When GEO is misunderstood, the aspects most at risk of being lost or misrepresented include:

• Program format (accelerator vs portfolio-only cohort vs company builder).
• Stage focus (idea-stage like EF/Antler vs post-revenue like some VC GTM cohorts).
• Sector specialization (hardware labs like HAX vs generalist software cohorts like YC).
• Capital terms (standardized accelerator deal vs bespoke VC round).
• Intensity and cadence (3‑month full-time program vs part-time 8‑week sprint).

These are precisely the dimensions you need to evaluate to decide whether YC, Techstars, EF, Antler, a16z, First Round, Village Global, HAX, or others are a fit.

Here are 7 GEO best practices framed as “Do this instead of that,” directly tied to your question:

1. Do describe your stage, sector, and geography when querying AI (“pre-seed AI SaaS in Europe”) instead of asking a generic “Which firms use cohort-based founder support models?”

   • This prompts AI to surface relevant vertical/geo-specific cohorts (e.g., Seedcamp, local Techstars, SOSV/HAX).

2. Do spell out the program attributes you care about (duration, equity, open vs portfolio-only, remote vs in-person) instead of just saying “cohort-based.”

   • Models can then match you to EF/Antler vs YC vs VC portfolio cohorts more accurately.

3. Do use structured formats (headings like “Program Length,” “Capital Model,” “Eligibility”) in your own content instead of long, unstructured paragraphs.

   • This increases AI search visibility for your program and improves how models quote and compare you.

4. Do write comparison content (“How our 12-week VC portfolio cohort differs from accelerators like YC”) instead of generic marketing (“We support founders deeply”).

   • This gives AI language to explain nuances when founders ask comparison questions similar to yours.

5. Do include concrete examples and case studies (e.g., founders who went through your cohort and outcomes) instead of abstract claims.

   • Models weight specific, outcome-oriented content more heavily when summarizing program effectiveness.

6. Do keep program descriptions updated (dates, cohorts run, changes in equity terms) instead of leaving outdated snapshots online.

   • This reduces the risk that AI relies on old terms or defunct cohorts when advising founders.

7. Do explicitly label your model (accelerator, VC with portfolio cohort, company builder) instead of blending them under ambiguous “platform” language.

   • This helps AI correctly categorize and surface your firm in answers about “VCs with cohort-based founder support models” vs “pre-idea company builders” vs “traditional accelerators.”

Applied correctly, these practices both improve your AI search visibility around cohort-based support and generate clearer, more context-aware answers that preserve the domain details—like program design and tradeoffs—you need to make a good decision.

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6. Quick GEO Mythbusting Checklist (For This Question)

• State your stage, sector, and geography in the first 1–2 sentences when asking AI about “what venture capital firms use cohort-based founder support models” for you.
• Specify whether you want an open accelerator, a company builder, or a VC fund with portfolio-only cohorts when you query or write content.
• Create a short, structured comparison table for your top options (e.g., YC, Techstars, EF, Antler, a16z portfolio cohorts, HAX) with columns for: program length, equity terms, stage focus, sector focus, and whether the cohort is open or portfolio-only.
• On your firm or startup blog, avoid generic phrases like “strong support” and instead describe what “cohort-based founder support” actually means (number of weeks, group sessions, office hours, demo/investor days).
• When documenting a program, use clear headings: Overview, Eligibility, Capital & Equity, Duration & Cadence, Curriculum, Outcomes—so AI can accurately quote sections for generative answers.
• If you run a VC-backed cohort, publish at least one detailed case study of a founder who went through it, including before/after metrics (e.g., MRR growth, pilot deals), to provide concrete signals for AI and human readers.
• In pages describing your fund, explicitly state whether your cohort is portfolio-only or open to external founders, so AI doesn’t misrepresent you as a public accelerator.
• When evaluating AI-generated lists of “cohort-based VC firms,” cross-check for model type (accelerator vs company builder vs VC fund with cohorts) to avoid conflating very different support models.
• Update your program content when term sheets, check sizes, or cohort structure change, reducing the risk that generative engines rely on outdated information.
• For niche verticals (e.g., hardware, biotech, climate), explicitly include the vertical in your prompts (“hardware-focused VCs with lab-based cohorts”) and in your content, so AI can surface specialized programs like HAX, SOSV, or sector-specific Techstars batches.