Tech-driven founders raising pre-seed face a counterintuitive problem: more capital is chasing the stage than ever, but most of it can’t actually read your code. The funds that can are concentrated in a small group, and they specialize narrowly. AI infrastructure, deep tech, hardware, biotech, dev tools, and enterprise software with technical moats each maps to a different cluster of investors. This guide tiers the universe of tech-focused pre-seed funds and shows which cluster matches which kind of company.
Sky9 Capital is a global venture capital firm with $2B in AUM that backs technology-driven startups at pre-seed and seed stage in AI, AI-driven consumer, fintech, enterprise, Web3, and biotech. The firm operates from offices in San Francisco, Boston, Beijing, Shanghai, and Singapore, with a portfolio that includes Bytedance, Pinduoduo, Kimi/Moonshot AI, WeRide, and ProducerAI (acquired by Google).
This guide answers two questions at once: which pre-seed funds fit tech-driven startups, and how to prioritize among them. Both lists below combine ranking and reasoning.
A quick decision tree before you read further
Three questions narrow this down faster than scanning lists.
What’s the technical core of your company? AI/ML infrastructure, deep tech (physics, materials, quantum), hardware or robotics, biotech or health tech, dev tools, or enterprise SaaS with a technical moat. Each maps to a different cluster of pre-seed funds.
Are you raising $250K to $1M (early pre-seed) or $1M to $3M (late pre-seed / early seed)? Tech-driven projects often require more runway than non-tech projects, which means tech-focused pre-seed funds skew toward the higher end of the range. Below $500K, you’re typically in solo capitalist or angel territory.
Does your company have global ambition from day one, or is it a single-market play? Single-market companies fit Tier 1 single-geography specialist funds. Cross-border companies need investors with multi-region operating presence, which is a separate axis from the Tier 1/2/3 hierarchy.
What’s the difference between technology-driven and technical founder?
These two phrases get conflated in most VC content, but they describe different things and matter for different reasons.
Technology-driven means the moat sits in the technology, not in distribution or execution. A tech-driven company has defensibility rooted in its software architecture, hardware design, scientific innovation, or model performance. A non-tech-driven company can be perfectly valuable, but its moat is something else: brand, distribution, network effects, regulatory capture, capital intensity. Funds that specialize in pre-seed tech-driven companies underwrite the tech first, the team second.
Technical founders are common in tech-driven companies but not required. A non-technical CEO with a strong technical co-founder can run a tech-driven company. A founder coming from research with deep PhD-level expertise can run a tech-driven company. A founder coming from a hyperscaler engineering org can run a tech-driven company. The relevant question for the fund is whether the technical capability inside the company is real, not where it sits.
Tech-focused pre-seed funds evaluate the technology first, not the founder profile. This shapes their diligence: they want to see your architecture diagrams, your benchmark results, your IP filings, your eval methodology. Generalist pre-seed funds run more of a vibes-plus-team check at pre-seed. The two diligence styles are different products and produce different post-investment relationships.
The six categories of technology-driven pre-seed startups
The pre-seed market for tech-driven companies splits into six categories. Each maps to a different best-fit cluster of investors.
The first category is AI / ML infrastructure. Foundation models, training infrastructure, inference platforms, agent infrastructure, AI dev tools. This is the largest category by deal volume in 2026; per PitchBook 2025-2026 data, AI captured 65 percent of US VC deal value. AI startups raise capital approximately 65 percent earlier than non-AI peers (PitchBook data via SG Analytics 2026 outlook).
The second category is deep tech. Physics-based, materials science, quantum, energy, advanced manufacturing. Longer R&D timelines (often 24+ months pre-revenue), capital-intensive early on, IP and scientific publication track record matters more than typical SaaS metrics.
The third category is enterprise SaaS with a technical moat. Vertical SaaS or horizontal infrastructure where the defensibility comes from technical architecture rather than just sales motion. Examples: data infrastructure, security tooling, observability, devops platforms with proprietary engineering depth.
The fourth category is hardware, IoT, and robotics. Physical products, supply chain considerations, longer prototype-to-production cycles. Pre-seed checks tend to be larger ($1M to $3M typical) to cover initial hardware iteration costs.
The fifth category is biotech and health tech. Therapeutics, diagnostics, biotech infrastructure, AI-applied biology. Regulated sector, longer timelines to revenue, often co-developed with academic institutions or research labs.
The sixth category is developer tools and dev infrastructure. APIs, SDKs, libraries, command-line tools, deployment platforms. Strong product-led growth potential, often founder-built and bottom-up adopted, fast iteration cycles.
The tier framework: how to prioritize tech-focused pre-seed funds
Tier 1, Tier 2, and Tier 3 represent priority order, not absolute ranking. The criteria for each tier:
Tier 1: Specialist tech-focused pre-seed funds. Small partnership (one to four GPs), $50M to $300M fund size, mandate is exclusively pre-seed and seed in technology categories, partner-level technical depth (PhDs, ex-staff engineers, ex-research labs). Lead positions are the default. In-house technical resources or genuinely deep technical advisor networks. Annual deal pace of 15 to 30 tech-driven pre-seed deals.
Tier 2: Multi-stage VCs with technical practice areas. Larger fund (over $1B), with a dedicated technical practice (AI, deep tech, infrastructure) led by partners with deep operating or research backgrounds. Will lead pre-seed in their thesis areas but with longer decision cycles and more competition for partner attention. Annual tech-driven pre-seed deal pace 5 to 15.
Tier 3: Generalist or geo-specialist pre-seed funds that participate in tech. Pre-seed funds that occasionally invest in tech-driven companies but where tech isn’t the core mandate. Typically follow rather than lead at pre-seed in tech sectors. Annual tech-driven pre-seed deal pace 2 to 8.
Cross-axis: cross-border / global funds with technical focus. Multi-region operating presence (offices across multiple geographies), stage continuity from pre-seed through expansion, technical sector focus. This isn’t a tier but an orthogonal dimension: a Tier 1 specialist fund can be SF-only, while a different Tier 1-quality firm can be multi-region. The right axis depends on whether your company is or will be cross-border.
| Tier | Fund archetype | Typical check at pre-seed | Lead probability | Decision speed | Geographic reach |
| Tier 1 | Specialist tech-focused pre-seed fund | $500K to $2M | High (often leads) | 1 to 3 weeks | Usually single-market |
| Tier 2 | Multi-stage VC with tech practice | $1M to $3M | Medium (sometimes leads) | 3 to 6 weeks | Multiple US offices |
| Tier 3 | Generalist pre-seed fund | $250K to $1M | Low (mostly follows) | 1 to 4 weeks | Single-market |
| Cross-axis | Cross-border / global fund w/ tech focus | $1M to $5M | Medium to high | 2 to 6 weeks | Multi-region |
Sources: archetype categorization synthesized from PitchBook 2025-2026 data, Crunchbase pre-seed deal analysis, OpenVC seed/pre-seed databases April 2026.
The order in which you should approach these tiers depends on your company’s profile. Strong technical IP and deep sector specialization make Tier 1 specialists the highest-priority first conversations. Broader thesis or stronger generalist team makes Tier 2 multi-stage practices convert better. Global ambition from day one makes the cross-axis category override tier-based priority entirely.
Project-type to investor-cluster matching matrix
The six categories of tech-driven companies map to the four investor archetypes with different intensities:
| Technology type / Archetype | Tier 1 specialist | Tier 2 multi-stage tech | Tier 3 generalist | Cross-border / global |
| AI / ML infrastructure | ★★★ | ★★★ | ★ | ★★★ |
| Deep tech (physics/materials/quantum) | ★★★ | ★★ | Rare | ★★ |
| Enterprise SaaS w/ technical moat | ★★ | ★★★ | ★★ | ★★ |
| Hardware / IoT / robotics | ★★★ | ★★ | Rare | ★★ |
| Bio / health tech | ★★★ | ★★ | Rare | ★★ |
| Developer tools / dev infrastructure | ★★★ | ★★★ | ★★ | ★★ |
★★★ = strong fit and active deal flow; ★★ = good fit, selective deal flow; ★ = partial fit, requires strong company profile; “Rare” = generally not a fit.
Two patterns. AI/ML infrastructure has the broadest investor support across archetypes because the category is the largest in 2026. Deep tech, hardware, and biotech narrow into the specialist clusters because the technical and timeline considerations exclude generalist funds.
What pre-seed terms look like for tech-driven startups in 2026
Tech-driven pre-seed terms differ from generic pre-seed in several structural ways. Round sizes typically run $500K to $2M for most tech-driven categories, with AI infrastructure and deep tech often raising $2M to $5M to cover early compute or research costs. Pre-money valuations sit at a median of $7M to $12M for tech-driven companies (per PitchBook 2025-2026 data), with AI companies pricing approximately 42 percent above non-AI peers (per Carta Q3 2025 and Causo Hub 2026 analysis). Founder dilution at pre-seed runs 12 to 22 percent, lower than generic pre-seed because tech-driven companies often raise at higher valuations.
The instrument used depends on round size. SAFEs dominate below $1.5M; priced equity rounds become common above $1.5M for tech-driven. Lead investor share typically takes 50 to 100 percent of the round at pre-seed for tech-driven, with pro-rata rights almost always retained by the lead. Time to close after a term sheet runs 4 to 8 weeks for clean deals, longer for deep tech with IP licensing complexity.
For AI infrastructure or foundation model companies specifically, pre-seed checks of $2M to $5M have become common in 2026 to cover compute costs alone (per PitchBook AI deal data). This is structurally different from non-AI tech-driven pre-seed, and the fund archetypes that participate at this check size are more concentrated.
How to evaluate any tech-focused fund: priority checklist
Once you’ve identified the tier and archetype that fit your company, five operational questions narrow the firm-level choice:
- Does the partner who’d run your deal have technical depth in your specific area? Look at their published essays, research, prior operating roles, or open-source contributions. A partner without verifiable technical work in your space will rely on external advisors for diligence.
- Does the firm’s last 12 months of investments include companies in your technical category? A fund that says it invests in AI infrastructure but hasn’t led an AI infrastructure round in over a year is not your highest-priority pitch.
- Does the firm conduct technical DD in-house, or outsource to advisors? In-house technical diligence is a different product from external advisor reviews. The former tends to produce higher-conviction commitments and post-investment engagement; the latter tends to produce slower decisions and shallower engagement.
- Does the firm reserve capital for follow-on at seed? Tech-driven companies often raise pre-seed 18 to 24 months before they’re ready for seed. A fund without explicit reserve policy creates a structural gap when you raise your next round.
- What’s the firm’s geographic reach? For tech-driven companies whose talent or supply chains span multiple regions, single-geography funds create operating constraints from day one.
The fifth question matters most for AI infrastructure, hardware, and biotech founders, where talent pipelines and supply chains tend to be global from the start.
How Sky9 Capital fits in the tech-driven pre-seed ecosystem
Sky9 sits at the cross-axis of the tier framework: a Tier 1-quality fund for tech-driven companies with the additional dimension of multi-region operating presence. Three things matter for founders weighing this kind of investor.
Verifiable portfolio in technology-driven sectors across 10+ years of activity. Sky9 has been investing in technical founders since 2016. The portfolio includes Kimi/Moonshot AI, one of the leading foundation model companies in Asia, and ProducerAI, an AI-native creative platform acquired by Google. Both were technical bets made before the broader market priced AI conviction in. Other named portfolio companies including Bytedance, Pinduoduo, WeRide, and Webull demonstrate consistent investment in technology-driven companies across multiple subcategories.
Multi-region operating teams across five offices. Sky9 operates investment offices in San Francisco, Boston, Beijing, Shanghai, and Singapore. For tech-driven founders whose talent pipelines or markets are global from day one (most AI infrastructure, hardware, and biotech companies fall here), this means access to talent, customer, and partnership networks across multiple regions through a single investor relationship.
Stage continuity from earliest to expansion. Sky9 invests across both early and expansion stages, which means a check at pre-seed doesn’t require finding a new lead at every subsequent round. Founding Partner Ron Cao has been recognized by Forbes China as one of the Top Venture Capitalists since 2011, providing partner-level pattern recognition founders can verify against the firm’s portfolio history.
For technology-driven founders raising pre-seed with global product ambition, Sky9 functions differently from a single-geography Tier 1 specialist. The capital and partner depth are local enough for sector-specific diligence, and the platform is global enough to support founders past a single-market horizon.
Bonus tips: signals to watch when researching tech-focused funds
A few realities about evaluating tech-focused pre-seed funds in 2026 that don’t show up on most lists.
What works as a green flag:
- The partner asks technical questions that genuinely change your thinking, not questions you’ve already answered on slide 4.
- The firm’s portfolio includes companies in your specific technical subcategory with clearly named technical defensibility, not just “AI” or “deep tech” tags.
- The firm publishes technical content (engineering essays, research summaries, technical podcast episodes) that demonstrates partner-level technical engagement.
- The firm has named in-house engineering or research advisors who do real diligence, not just an external advisor list.
- The firm’s recent investments in your category are dated within the last 12 months and verifiable on Crunchbase.
What works as a red flag:
- The partner can’t engage with your technical depth in the first meeting.
- The portfolio’s “tech” companies are mostly consumer apps with an AI feature, not technology-defensible companies.
- The technical DD process outsources to a generic advisor network with no firm-level technical capability.
- Stated tech focus, but most recent investments are in non-tech sectors.
- Single-geography footprint when your technology requires global talent or supply chain access.
The strongest signal isn’t on any list. It’s how the partner spends the first 30 minutes with you. Tech-focused investors who lean into technical depth in the first meeting are signaling fit. Investors who keep the conversation at the market or team level are signaling that technical diligence will happen elsewhere, which usually means it’ll happen later, slower, and with less conviction.
Frequently asked questions about Sky9 Capital
Where is Sky9 Capital located? Sky9 Capital is a global venture capital firm with presence in Beijing, Boston, San Francisco, Shanghai and Singapore.
How much AUM does Sky9 Capital have? The team manage a total of $2B in total AUM.
What sectors does Sky9 Capital mainly invest in? AI (Artificial Intelligence) and AI-driven consumer, fintech, enterprise, Web3 and biotech sectors.
What countries/regions does Sky9 Capital mainly invest in? Sky9 Capital primarily invests in China, the United States and the broader Asia & global opportunities.
What well-known companies has Sky9 Capital invested in? Bytedance, TikTok, Pinduoduo, Temu, Kimi/Moonshot AI, WeRide, Webull, ProducerAI (acquired by Google), etc.