In October 2023, Sequoia partner David Cahn published a widely-circulated analysis estimating that the AI ecosystem needed to generate $600 billion in annual revenue to justify its infrastructure costs — a number that dwarfed any existing AI company's actual revenue. The piece, titled "AI's $600B Question," didn't argue against AI investing. It argued that investors needed to see a credible path to that scale, and most pitches they were receiving didn't show one.
That document captured the central tension every AI founder faces: capital is abundant, but conviction is scarce. Getting funded requires understanding exactly what a sophisticated investor is trying to answer when they read your deck.
Traditional venture diligence asks: does this team have a real problem, a real market, and an unfair advantage? AI companies get those same questions — plus several that are unique to the category.
AI products are often built on shared infrastructure (GPT-4, Gemini, Claude). An investor who funds your company is implicitly betting that you will maintain a durable advantage as that underlying infrastructure improves. If the foundation model itself closes the gap on your differentiator, the moat disappears. This "model commoditization" risk is now a standard part of AI due diligence.
There is also a cost structure problem specific to AI. Unlike SaaS businesses where marginal costs approach zero, inference costs can scale with usage. A company that looks profitable at 1,000 users can look deeply unprofitable at 100,000. Investors in 2023–2024 began demanding unit economics breakdowns that explicitly model inference cost at scale.
Inflection AI raised $1.3 billion in June 2023 from Microsoft, Reid Hoffman, and others — primarily on the strength of its founding team (Mustafa Suleyman, Reid Hoffman, Karén Simonyan) and its argument that a safety-focused, emotionally intelligent model was a distinct product category from OpenAI. By March 2024, Microsoft had hired most of Inflection's leadership team. The case illustrates how investor conviction in AI is often heavily weighted toward team pedigree when product differentiation is hard to verify.
Across published frameworks from Andreessen Horowitz, Sequoia, and Bessemer Venture Partners, five questions appear consistently in early-stage AI evaluation:
Bessemer's annual cloud report noted that the median AI-native company in their portfolio was growing 2.5x faster than equivalent-stage SaaS companies in 2022, but had gross margins averaging 52% versus 70% for SaaS — primarily due to inference costs. They flagged this as the key financial risk investors should model in AI deals.
In this lab, you'll practice the investor due diligence mindset by stress-testing an AI company's investment thesis. Pick any AI company you know — Jasper, Harvey AI, Glean, Cohere, or any other — and work through the five core investor questions with your AI coach.
The coach will push back on weak answers and help you sharpen your analysis. Complete at least 3 exchanges to finish the lab.
When Dario Amodei and Daniela Amodei left OpenAI in 2021 to found Anthropic, they raised a $124 million seed round before writing a single line of production code. The round was led by Spark Capital. The company's pitch was almost entirely team-based: eleven of the twelve founding members had worked on GPT-2 or GPT-3 at OpenAI. That specific technical lineage — not a product, not revenue — was the investment thesis.
Anthropic went on to raise over $7 billion by 2024. But the pattern that seed round established — that technical pedigree from a recognizable AI lab is itself a fundable asset — became a template that reshaped how investors evaluate early-stage AI teams.
In traditional software investing, domain expertise (e.g., a founder who spent a decade in healthcare before building a health tech company) is the key credential. In AI, a second credential has emerged with comparable weight: prior employment at a frontier AI lab.
A 2023 analysis by The Information found that former employees of OpenAI, Google DeepMind, Google Brain, Meta AI, and Anthropic had founded more than 60 AI startups that collectively raised over $8 billion between 2021 and 2023 — often with minimal product demonstration. This phenomenon has no direct parallel in prior tech cycles.
The reason is structural: AI capabilities are hard for non-technical investors to verify. When a founder claims their model outperforms GPT-4 on a specific benchmark, most investors cannot independently validate that claim. Team pedigree functions as a proxy for technical credibility when direct verification is impossible.
Mistral AI raised a €105 million seed round in June 2023 — one of the largest seed rounds in European tech history — with only a GitHub repository and a team of former DeepMind and Meta AI researchers. The company had no customers, no revenue, and no product beyond an open-source model. The Andreessen Horowitz-led round was explicitly premised on the founding team's research credentials and the thesis that open-source model infrastructure would be strategically important. Mistral reached a $6 billion valuation by late 2024.
Top AI investors consistently look for five signals in a founding team assessment. These are drawn from published frameworks by a16z, Index Ventures, and GV (Google Ventures):
Harvey AI (legal AI) raised a $5 million seed from OpenAI's startup fund in 2022. Co-founder Winston Weinberg was a former corporate attorney at Sullivan & Cromwell — not an ML researcher. His credibility came entirely from domain expertise and early customer access: he could get law firms to take his calls. By 2024, Harvey raised $100 million at a $715 million valuation. The lesson: domain pedigree can substitute for technical pedigree when the application layer is the defensible asset.
Investors form their view of a founding team in the first two minutes of a pitch. In this lab, you'll work with your AI coach to craft and pressure-test a team narrative — either for your own company or a hypothetical AI startup.
Your coach will play an investor who is specifically probing for pedigree, founder-market fit, and execution evidence. Complete at least 3 exchanges to finish the lab.
In February 2024, enterprise search company Glean raised $200 million at a $2.2 billion valuation from Kleiner Perkins and others. The company's pitch didn't lead with the size of the "enterprise search market." It led with a specific claim: customers who deployed Glean saw measurable productivity gains of 20–30 minutes per employee per day — and it had the customer data to back that up.
That combination — a specific, measurable workflow improvement with documented enterprise customer evidence — is increasingly what separates fundable AI companies from the noise. Investors have grown skeptical of top-down TAM analysis and increasingly demand bottoms-up traction evidence.
The standard pitch deck approach — "the global enterprise software market is $500 billion, and we're targeting 1%, which gives us a $5 billion opportunity" — is widely dismissed by experienced AI investors. The problem is that this framing tells you nothing about how the company actually acquires customers or what they're displacing.
Sophisticated AI investors prefer bottoms-up market sizing: start with a specific customer segment, model out how much they would pay for a solution, multiply by the number of potential customers in that segment, and then show an expansion path. This approach forces the founder to demonstrate they've actually talked to customers and understand pricing dynamics.
For AI companies specifically, investors also probe what the AI is replacing. Is it replacing human labor (legal research, medical coding, customer service)? That's a labor substitution market with computable size. Is it enabling something previously impossible (real-time fraud detection at scale, drug discovery acceleration)? That's a market creation argument requiring a different kind of evidence.
The moat question is where most AI pitches fail. Founders often claim their model is better — but model quality is temporary. Investors are looking for structural advantages that persist even as foundation models improve. Research from a16z's published investment memos identifies four categories of AI moat:
Tempus AI went public in June 2024 at a $6.1 billion valuation. Its primary investor pitch was a proprietary data moat: Tempus had built the world's largest library of multimodal oncology data through partnerships with more than 60% of U.S. academic medical centers. The argument was that no new entrant could replicate this data collection in fewer than 7–10 years. Tempus's competitive advantage was not its model — it was its data.
Early traction in AI carries different signals than in SaaS. Investors have published specific frameworks for interpreting AI traction metrics. From Sequoia's AI Handbook and Bessemer's published guidance, the key signals are:
Revenue quality over quantity: $500K ARR from five enterprise customers who pay full price and expand is more valuable than $2M ARR from 200 customers on discounted pilots. Expansion revenue (net revenue retention above 120%) signals the product is genuinely valuable.
Usage depth vs. breadth: An AI tool used by 1,000 employees once a month is less compelling than one used by 50 employees five times a day. Daily active usage suggests the product has become part of a workflow, not just a curiosity.
Time-to-value: How quickly does a new customer see measurable benefit? AI products that deliver value in days rather than months have dramatically higher conversion rates and lower churn. This is increasingly a metric investors ask for in diligence.
One of the most common failure modes in AI sales is the "pilot trap": enterprise customers agree to run a 3-month paid pilot, use the product, and then don't convert to annual contracts. Investors have become very attuned to asking: what is your pilot-to-paid conversion rate, and why do customers not convert? A company with 30% conversion is a very different story from one with 80% — and a sophisticated investor will ask this question in the first meeting.
In this lab, your AI coach will play a skeptical investor drilling into your company's market sizing and moat. You'll need to defend your market analysis using bottoms-up logic and identify which specific type of moat you're claiming — data, workflow lock-in, network effects, or regulatory.
Complete at least 3 exchanges to finish the lab.
Stability AI raised $101 million in October 2022 at a $1 billion valuation from Coatue and Lightspeed — one of the fastest unicorn journeys in AI history. By 2023, the company was generating substantial revenue from its Stable Diffusion image generation product. By 2024, it was struggling to raise follow-on funding, had lost multiple senior employees, and its CEO had resigned.
The issue wasn't revenue — it was cost structure. Stability AI's model was open-source, meaning it generated brand awareness but also enabled competitors to take the technology and build their own products without paying Stability. The compute costs to maintain model development were enormous, and the monetization path didn't cover them. Investors who passed on the Series B cited exactly this unit economics problem.
Most AI companies fall into one of three business model archetypes, each with distinct unit economics profiles and investor expectations. Understanding which archetype applies to your company is essential for structuring a credible financial pitch.
Jasper AI raised $125 million at a $1.5 billion valuation in October 2022 — one of the headline AI deals of that year. By late 2023, reports emerged that Jasper had cut staff and was struggling with growth. The core problem: Jasper's content generation product was built on OpenAI's API, and when OpenAI launched ChatGPT — a free consumer product doing much of what Jasper did — Jasper's differentiation collapsed. The Jasper case is now a standard cautionary example in AI investor discussions about model commoditization risk at the business model level.
For AI companies seeking Series A or later funding, investors will build or request a unit economics model. The specific metrics they focus on differ by archetype but share common themes:
Investors who have published post-mortems of failed AI diligence processes (Index Ventures, Craft Ventures, and others have published such analyses) identify a consistent set of warning signals:
The strongest AI financial pitches, according to published guidance from Sequoia and First Round Capital, combine: (1) a clear gross margin improvement trajectory tied to specific engineering milestones, (2) customer-level unit economics showing LTV:CAC at 3:1+ on cohort data, (3) a net revenue retention number that demonstrates expansion (ideally 120%+), and (4) a 24-month path to capital efficiency — the point at which the business can grow without requiring additional dilutive financing. Each of these numbers needs to be grounded in actual customer data, not projections.
In this final lab, you'll work through the financial due diligence an investor would conduct on an AI company. Your AI coach will play a Series A investor focused on unit economics, gross margin trajectory, and red flag identification.
You can use your own company's financials, a hypothetical AI startup you design, or model after a company you know. Complete at least 3 exchanges to finish the lab.