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Raise pre-seed money when you can show a real customer problem, tangible progress toward a solution, a team credible enough to execute, and a specific plan for turning capital into the next fundable milestones. For an AI startup, that usually means demonstrating the product in use, explaining the data and model strategy, and showing that inference costs, rights to data, and dependence on outside providers are understood.
Set the amount from the time and cost of reaching those milestones—not from a fashionable headline number. Prepare a clean cap table, a concise pitch, an organized diligence folder, and a targeted investor list. In the United States, SAFEs dominate current pre-seed financings, but their valuation caps and other terms can materially change ownership, so model the outcome and obtain qualified legal advice before signing.
What “pre-seed” means for an AI company
Pre-seed has no universal legal or market definition. It generally describes the earliest outside investment used to move from a raw idea toward a validated concept, prototype, minimum viable product (MVP), or initial team. Some founders include friends-and-family money and angel checks; others reserve the label for institutional investors. The useful description is what you have built and what the financing will prove, not which label you choose.
At this stage, the company is testing whether a particular problem is important, whether the proposed solution works, and whether the initial team can build and distribute it. Seed financing usually follows with more evidence: product-market-fit signals, user growth, revenue, or other proof appropriate to the business. Those are broad stage descriptions, not rigid market rules.
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Describe your stage in one sentence, such as: “We have a working workflow assistant used by 12 design teams, and this round funds reliability improvements, three paid pilots, and the first production deployment.” That is more informative than simply calling the company “pre-seed.”
Decide whether the company is ready to seek capital
Investors do not require a finished product at pre-seed, but an idea alone is a weak proof of execution. Before beginning a broad process, check four signals.
1. Tangible progress
Show a prototype, product mock-up, technical demo, or MVP. A live demonstration is especially useful for AI products because it lets investors see the user experience, latency, failure handling, and the difference between a scripted demo and a repeatable workflow. If the product is not yet demoable, show the smallest credible artifact and state exactly what remains to be built.
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2. A defined and costly customer problem
Name the user, the job being improved, and the cost of leaving it unsolved. Conduct discovery conversations with the specific audience you intend to serve. Record recurring pain, existing workarounds, purchasing authority, and evidence that the problem is frequent or expensive enough to justify a new product. General enthusiasm for AI is not customer validation.
3. A credible founder story
Explain why this team understands the problem and can build and distribute the solution. Relevant domain experience, technical capability, customer access, or a demonstrated history of shipping can all help. Address gaps directly—such as a missing enterprise-sales skill or an unfilled machine-learning role—and show how the round closes them.
4. A capital plan
Connect every requested dollar to work and measurable milestones. Investors should be able to see what the company will accomplish, how long it will take, and what evidence should exist before the next financing. This turns “we need money to grow” into an underwritable plan.
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Apply the readiness test to an AI product
General pre-seed standards apply to AI startups, but the product can create additional questions. Prepare evidence appropriate to your architecture and market rather than presenting a universal checklist—there is no validated, universal AI-specific diligence list.
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- Evaluation method: show how you measure quality over time and how human review, automated tests, or customer feedback detects regressions. Distinguish an internal experiment from a result observed in production.
- Unit economics: estimate token, inference, storage, retrieval, and human-review costs per task or account. State assumptions about volume and model pricing, and identify which costs fall with scale.
- Data rights and governance: document where training, retrieval, or customer data comes from, what permissions apply, how confidential information is isolated, and how deletion or retention requests are handled.
- Provider dependency: identify outside model, cloud, vector-database, or data providers that are critical to the product. Explain fallback options, portability, rate limits, and how a price or policy change would affect margins.
- Safety and reliability: show guardrails, monitoring, human escalation, and an incident-response plan where an incorrect output could cause material harm.
These preparation areas are practical ways to make an AI business legible to investors; they are not claims that every investor demands identical metrics.
Size the round from milestones and runway
Work backward from the next proof point that should make the company fundable at seed. Possible milestones include a reliable MVP, a number of active or paying customers, validated retention, paid pilots, revenue, a production integration, or key hires. Choose measures that fit the product rather than copying another startup’s dashboard.
Carta reports an average pre-seed instrument of $276,000 in the second quarter of 2026. Its guide says many U.S. pre-seed rounds fall between $150,000 and $1 million, and that 18% of rounds were in the $1 million–$2.5 million range as of the first quarter of 2026. These are broad Carta benchmarks, not AI-specific targets and not recommendations for your company.
| Planning input | How to calculate it | What to show investors |
|---|---|---|
| People | Salary, payroll taxes, benefits, contractors, and planned hires over the period | Role, start date, hiring rationale, and monthly cash cost |
| Product and infrastructure | Cloud, model or API usage, data, software, security, and testing costs at expected volume | Usage assumptions, sensitivity to volume and provider pricing, and mitigation plans |
| Go-to-market | Customer research, pilots, travel, sales tools, and implementation support | Which activities produce the stated validation or revenue milestones |
| Company overhead | Legal, accounting, insurance, compliance, and ordinary operating expenses | One-time versus recurring costs and timing |
| Buffer | Additional months for hiring delays, technical rework, or a longer fundraising process | The assumption behind the buffer, not an unexplained percentage |
Carta suggests planning for 12–24 months of runway. Treat that as a planning range, not a guarantee. Build a monthly cash model, identify the month in which cash reaches a minimum acceptable level, and include the time needed to raise the next round. A smaller round that runs out before the milestone can be more damaging than a carefully justified larger one.
A practical raise formula
- List the milestones required before a seed process and assign an owner and completion date to each.
- Estimate the monthly cash cost of the team and product at the volume those milestones require.
- Calculate the cost from closing to the final milestone, then add a fundraising and execution buffer.
- Test downside cases: a delayed hire, slower customer conversion, higher inference cost, or a provider price change.
- Set a target and a minimum acceptable close. State what gets deferred if only the minimum is raised.
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The National Venture Capital Association’s 2026 Yearbook reports $320 billion deployed across 15,352 U.S. venture deals in 2025, with 65.4% of U.S. venture deal value going to AI. It also reports $22.3 billion across 5,049 U.S. pre-seed and seed deals in 2025.
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Those totals are highly concentrated. NVCA notes that a small number of financings of $100 million or more heavily shaped the year; excluding those mega-deals leaves roughly $105 billion across the remaining 14,865 deals. The implication for a normal early-stage company is important: a large share of dollars going to AI does not mean every AI startup can expect an unusually large or fast check. Investors still select for problem quality, evidence, team, economics, and fit.
Choose a funding route deliberately
Different routes trade speed, capital, expertise, ownership, and obligations. Compare them on the same five axes: amount and timing; ownership, fees, or other economic terms; investor expertise and fit; access to customers, hiring, compute, or follow-on capital; and time, geography, and reporting requirements.
| Route | Potential advantages | Questions to resolve |
|---|---|---|
| Angels and syndicates | Operator knowledge, introductions, and pooled individual checks | Can the lead investor help with your customer or hiring problem? Who coordinates decisions and updates? |
| Pre-seed venture firms | Experience with very early companies and possible follow-on capital | Does the firm invest at your stage, sector, and business-model type? What ownership and governance does it expect? |
| Accelerators and incubators | Capital, mentorship, a cohort, investor access, and sometimes a demo day | Compare current terms, dilution, location, time commitment, network relevance, and support after the program. Verify terms directly. |
| Friends and family | May be accessible before institutional proof exists | Can each person understand the risk? Are the investment, disclosures, and records formal even where relationships are personal? |
| Venture studios | May help originate the company, recruit founders, and provide operating resources | Clarify founder roles, decision rights, services, vesting, and long-term ownership before accepting the relationship. |
| Equity crowdfunding | Can reach many investors, including non-accredited investors where rules and the platform allow | Check platform fit, disclosure workload, investor-management burden, and jurisdiction-specific securities compliance. |
Programs such as Y Combinator and Techstars illustrate the accelerator category, not a promise of current availability or standard terms. Confirm any program’s present offering directly before relying on it in a financing plan.
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Clean the cap table and records
Maintain a single, current ownership record showing founders, employees, option grants, SAFEs, notes, warrants, and any promised equity. Reconcile signed documents to the ledger and resolve missing signatures or inconsistent names. Carta warns that disorganized equity records can create diligence delays and additional cost.
Create a focused diligence folder
Organize, with appropriate access controls, the documents an investor will need to verify the story:
- Formation documents, governing agreements, board or member approvals, and ownership records.
- Founder and employee intellectual-property assignments, employment or contractor agreements, and option documentation.
- Product demonstrations, architecture notes, roadmap, customer discovery notes, pilots, contracts, usage data, and references where permission exists.
- Financial model, current cash balance, monthly burn, budget, cap-table scenarios, and liabilities.
- AI-specific materials that matter to your product: evaluation methodology, model or provider agreements, data provenance and rights, security controls, incident history, and an explanation of inference economics.
The folder should support verification, not overwhelm a first meeting. Share sensitive material only after a legitimate diligence process and appropriate confidentiality protections.
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Build a pitch that earns the next meeting
Your deck supports a conversation; it does not replace one. Keep the narrative concise and evidence-led:
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- Solution: demonstrate the product and show why AI produces a meaningful improvement rather than a decorative feature.
- Customer and market: define the initial buyer, use case, distribution path, and evidence of demand.
- Why now: explain the technical, regulatory, workflow, or market change that makes the opportunity timely, without treating general AI excitement as proof.
- Traction: report the strongest available evidence—pilots, usage, retention, paid commitments, revenue, or validated technical performance—with dates and definitions.
- Business model and economics: show who pays, pricing logic, gross-margin assumptions, and how model costs affect the path to profitability.
- Team: connect each founder’s experience to the problem and state the next hires.
- Round and milestones: state the target, expected runway, use of funds, and the measurable outcomes that should be complete before the next round.
Use a short written summary for introductions and tailor the first paragraph to the investor’s stage and relevant experience. Avoid indiscriminate mass outreach: a smaller list of well-matched investors produces more useful feedback and a more coherent process.
Run the outreach and diligence process
- Define the investor profile. Filter for pre-seed activity, relevant industry or business model, geography, check size, and willingness to lead or follow.
- Build a ranked list. Separate likely leads, strategic angels, and backup options. Record the source of each introduction and the next action.
- Seek warm introductions where possible. Ask founders, customers, operators, and other investors for a specific introduction. A direct email can still work when it is concise and clearly relevant.
- Use early meetings to learn. Track objections and questions about customer urgency, differentiation, model reliability, data rights, economics, and distribution. Improve the evidence, not just the wording.
- Control the process. Set a target start date, maintain momentum, provide consistent updates, and avoid allowing one investor’s indefinite interest to block other conversations.
- Open the diligence folder after genuine interest. Keep a log of requested documents, open issues, and promised follow-ups so all investors receive accurate information.
- Confirm the close. Verify funds, executed documents, board or member approvals, securities filings, and cap-table updates before treating the money as available.
Understand SAFE and convertible-note mechanics
Carta reports that 93% of pre-seed rounds were SAFEs in the second quarter of 2026. A SAFE gives an investor a contractual right to receive shares later. It is not debt, generally has no interest or maturity date, and typically converts when a priced equity financing occurs. A convertible note is debt, generally carries interest, and has a maturity date; it may also convert in a later financing.
| Feature | SAFE | Convertible note |
|---|---|---|
| Legal character | Contractual right to future shares; not debt | Debt that may convert to shares |
| Interest | No interest | Generally accrues interest |
| Maturity date | None on the standard structure described by YC | Generally has a maturity date |
| Conversion terms | Often valuation cap, discount, or both; some include MFN or pro rata rights | Often cap, discount, interest, and maturity provisions |
| Main planning risk | Multiple instruments can create unexpected dilution at conversion | Debt obligations and maturity negotiations add another layer of risk |
Caps and discounts determine the conversion price. A post-money SAFE’s ownership sold is generally calculated as the investment divided by the post-money valuation cap. Y Combinator’s example shows a $1 million investment at a $6.7 million post-money cap corresponding to about 15% ownership. That example explains the arithmetic; it is not a recommended valuation for your company.
Model the full dilution, not one instrument in isolation
- List every outstanding and proposed SAFE, note, warrant, option, and promised equity grant.
- Record each amount, cap, discount, MFN provision, pro rata right, interest rate, maturity, and definition of the capitalization base.
- Model conversion under several priced-round valuations and option-pool sizes.
- Include new money in the priced round and show ownership for founders, employees, and each investor after conversion.
- Review the combined result with qualified counsel before signing or promising additional terms.
Stacking instruments with inconsistent definitions can make the cap table difficult to understand. A clean, scenario-based model prevents a seemingly small early check from producing an unwanted ownership outcome later.
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Handle jurisdiction and securities obligations correctly
Fundraising and securities rules vary by country and, in the United States, by offering structure and state. Y Combinator’s SAFE tool currently supports U.S.-incorporated companies; companies incorporated elsewhere should use relevant local forms and legal help. Do not present a U.S. SAFE template as a universal document.
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Before soliciting or accepting money, obtain qualified securities counsel for the company’s jurisdiction. Confirm investor eligibility where applicable, required disclosures and filings, board or shareholder approvals, tax treatment, data-protection obligations, and whether public promotion or crowdfunding changes the rules. Formal documentation matters even when an investor is a friend or relative.
Common mistakes and how to correct them
Copying an average round size
Problem: treating the $276,000 Carta average or a million-dollar headline as the target. Fix: show the milestone budget, runway, sensitivity cases, and minimum close that justify your number.
Confusing AI interest with customer demand
Problem: presenting model novelty without a defined buyer or urgent workflow. Fix: document discovery, usage, willingness to pay, and the specific improvement over existing alternatives.
Hiding inference economics
Problem: showing revenue potential while omitting per-task compute, human review, or provider costs. Fix: include a cost model at current and expected scale, plus fallback plans.
Sending an inconsistent cap table
Problem: spreadsheets, signed instruments, and verbal promises disagree. Fix: reconcile records before outreach and update the authoritative ledger after every grant or financing.
Accepting terms without a dilution scenario
Problem: focusing on the headline cap while overlooking discounts, MFN, pro rata rights, option-pool changes, or other SAFEs. Fix: run combined scenarios and have counsel explain the documents.
Running an unstructured process
Problem: random outreach, no timeline, and long gaps between meetings. Fix: rank investors, batch conversations where practical, track decisions, and communicate a clear close target.
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Days 1–7: establish the evidence
- Write the one-sentence stage description and customer problem.
- List the strongest product, customer, and technical evidence and identify its gaps.
- Reconcile the cap table and collect missing corporate or intellectual-property documents.
- Build a monthly cash model with 12–24-month runway scenarios.
Days 8–14: make the AI business legible
- Document evaluation, failure modes, data rights, provider dependencies, and unit economics appropriate to the product.
- Record customer discovery and pilot evidence with dates and definitions.
- Define milestones, owners, deadlines, and the minimum acceptable close.
Days 15–21: prepare materials
- Finish the concise deck, written summary, product demo, financial model, and diligence index.
- Prepare direct answers to questions about differentiation, reliability, privacy, safety, margins, and distribution.
- Identify investors by stage, fit, check size, geography, and relevant help.
Days 22–30: start a controlled process
- Request targeted introductions and send concise, customized outreach.
- Schedule conversations in a time-boxed sequence and record feedback.
- Update materials only when new evidence warrants it; keep claims and numbers consistent.
- Engage counsel before accepting a term sheet, SAFE, note, or other security.
Bottom line for an AI pre-seed raise
The strongest pre-seed case is not “AI is receiving most venture dollars.” It is a specific customer problem, a working or rapidly testable product, evidence that the team can execute, and a financing plan that reaches the next proof point before cash runs out. Choose investors and programs for fit, keep ownership records accurate, model every proposed instrument together, and treat market statistics as context rather than a promise of access.
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