AI/ML Startup Diligence for Non-Technical VCs: A Practical Framework

The pitch deck looks impressive. The founder, a former Google AI researcher, is articulate and confident. The demo shows AI-powered predictions that seem almost magical. Revenue is growing 25% month-over-month. But as a VC without a technical background, you're wondering: Is this actually innovative technology, or is it just a wrapper around GPT-4? Are these metrics sustainable, or will they hit a wall when they scale? How do you separate the signal from the noise in an industry drowning in AI hype?

You're not alone. According to PitchBook data, AI/ML startups raised over $42 billion in venture capital in 2024, accounting for nearly 18% of all VC investment. Yet McKinsey reports that 70% of AI projects fail to move beyond the pilot stage, and Gartner estimates that through 2025, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams managing them.

The stakes are high. Miss the next OpenAI or Anthropic, and you've passed on a generational opportunity. Back the wrong AI startup, and you've burned capital on technology that will never scale past the demo stage. The challenge is particularly acute for non-technical investors: how do you conduct rigorous diligence on technology you don't fully understand?

This framework provides a systematic approach to evaluating AI/ML startups without requiring a PhD in computer science. Over the past three years, we've synthesized insights from analyzing over 300 AI/ML deals at VCOS, interviewing 50+ technical due diligence advisors, and studying both successful and failed AI investments. What emerged is a practical, question-driven methodology that non-technical investors can apply immediately.

The Seven Pillars of AI/ML Due Diligence

1. Data Quality & Availability

Data is the fuel that powers AI systems. Without high-quality, relevant data, even the most sophisticated algorithms fail. Yet data quality is where many AI startups stumble, and it's often the hardest aspect for non-technical investors to evaluate.

The Core Questions:

Where does the data come from? The best AI companies have proprietary data moats. Ask founders to walk you through their data acquisition strategy. Are they collecting first-party data from users (gold standard), licensing third-party datasets (acceptable but commoditized), or scraping publicly available information (weak moat)? Databricks built a multi-billion dollar business partly on the strength of customer data flowing through their platform. Contrast this with startups that rely entirely on public datasets anyone can access.

How much data do they actually have? Get specific numbers. How many training examples? How many users generating data? How much historical data? A computer vision startup claiming breakthrough accuracy but training on 10,000 images is a red flag when competitors use millions. However, size isn't everything. A healthcare AI company with 50,000 expertly labeled medical images from partner hospitals may have higher quality data than one with 5 million unlabeled web images.

What's the data quality and labeling process? Garbage in, garbage out. Ask how data is labeled and validated. Do they use expert annotators, crowd workers, or automated labeling? What's their inter-annotator agreement rate (above 80% is generally good)? How do they handle edge cases and ambiguous labels? Scale AI built a billion-dollar business around solving this exact problem for other companies.

Do they own the data rights? This is critical and often overlooked. If they're training models on customer data, what do the terms of service say? Can they use it for model training? What happens if a major customer leaves and demands their data be deleted? Is the data subject to GDPR, HIPAA, or other regulations that restrict its use?

What's the data refresh and decay rate? Some AI applications require constantly updated data. A fraud detection model trained on 2023 data might be worthless in 2025 as fraud patterns evolve. Ask how frequently they retrain models and how they handle data drift. Do they have systems to detect when model performance degrades?

Red flag example: A fintech AI startup claimed proprietary alternative credit scoring data but couldn't clearly articulate their data sources beyond "web scraping and public records." When pressed, they had less than 100,000 training examples and no exclusive data partnerships. Their moat was essentially non-existent.

Green flag example: A vertical AI SaaS company had contracts where customers granted perpetual rights to use de-identified data for model improvement. Each new customer made their product better, creating a compounding data advantage. They tracked data growth metrics as rigorously as revenue.

2. Model Architecture & Approach

You don't need to understand transformer architectures or backpropagation algorithms, but you do need to understand whether the startup's approach is defensible and appropriate for the problem.

The Core Questions:

Are they building proprietary models or fine-tuning existing ones? There's nothing wrong with building on foundation models like GPT-4, Claude, or Llama, but the business implications differ dramatically. Companies fine-tuning existing models typically have lower R&D costs but thinner moats and dependency risk. Those building models from scratch face higher costs but potentially sustainable differentiation. Both can be great businesses, but you need to understand which you're investing in.

What's their technical approach and why is it suited to this problem? Ask founders to explain their methodology in simple terms. Why did they choose deep learning over traditional machine learning? Why neural networks instead of gradient boosting? The best founders can explain complex technical choices in accessible language. If they can't explain why their approach fits the problem, that's a red flag.

How computationally expensive is their approach? This directly impacts unit economics. A computer vision model that requires 30 seconds of GPU processing per image will have very different margins than one processing images in 100 milliseconds. Ask about inference costs per prediction and how they scale. OpenAI's GPT-4 is powerful but expensive to run. Anthropic's Claude achieved competitive performance with better efficiency, creating a business advantage.

What's their model development velocity? How quickly can they iterate and improve models? Do they have robust testing infrastructure? Can they A/B test model versions in production? Companies with strong ML operations (MLOps) can experiment faster and improve continuously. Those treating model development as a one-time science project will struggle to maintain competitiveness.

How explainable are their model outputs? This matters more in regulated industries. A lending AI that can't explain why it rejected a loan application creates legal liability. A medical diagnosis AI that's a "black box" won't get FDA approval. Ask whether they can provide interpretable explanations for model decisions.

3. Technical Team & Expertise

AI/ML is a hits-driven business where team quality matters enormously. The gap between top-tier and mediocre AI talent is vast, and the compensation required to attract the best is eye-watering.

The Core Questions:

What's the team's relevant experience? Look beyond brand names. A former Google engineer sounds impressive, but were they actually working on AI/ML, or were they a backend engineer? Did the founding team publish papers in top conferences (NeurIPS, ICML, ICLR)? Have they shipped AI products at scale before, or is this their first rodeo?

Do they have the right specialist skills? Different AI applications require different expertise. Computer vision requires different skills than natural language processing or reinforcement learning. Does the team's background match the problem they're solving? A team of NLP experts building a robotics company might struggle.

Can they attract and retain top AI talent? AI engineers are among the most expensive and competitive hires in tech. Senior ML engineers at top companies command $400,000-$800,000 total compensation. Can this startup compete? Do they have a compelling research mission that attracts talent beyond just money? Look at who they've hired recently and their retention rates.

4. Scalability & Infrastructure

A model that works beautifully on a thousand users might collapse at a million. Infrastructure costs can make or break AI business models.

The Core Questions:

What are the unit economics at scale? Get specific projections. What does it cost (compute, storage, bandwidth) to serve one user or process one transaction? How do those costs scale? Are there economies of scale, or do costs scale linearly or worse? A company spending $5 in cloud costs to generate $10 in revenue has a problem.

What's the compute infrastructure strategy? Are they using cloud providers (AWS, GCP, Azure), building on-premise infrastructure, or using specialized AI platforms? Each has trade-offs. Cloud is flexible but expensive at scale. On-premise requires capital and expertise but can be cheaper long-term. Understand the cost trajectory as they grow.

5. Competitive Moats in AI

AI moats are different from traditional software moats. Network effects and switching costs still matter, but data, specialized talent, and algorithmic advantages create new dynamics.

The Core Questions:

What prevents Google, Microsoft, or OpenAI from replicating this? Be blunt about this question. Large tech companies have nearly unlimited compute resources, access to top talent, and massive datasets. If the startup's advantage is just "we use AI for X," that's not a moat. Vertical specialization, proprietary data, or unique distribution can be defensible.

Is there a data flywheel? The best AI businesses get better as they get more users because more users generate more data that improves the product. Ask founders to diagram their data flywheel. Does more usage actually improve the product, or is each customer independent?

6. Validation & Performance Metrics

Impressive demos and cherry-picked examples are easy. Rigorous validation is hard. Non-technical investors must learn to distinguish between the two.

The Core Questions:

How do they measure model performance? Different metrics matter for different problems. Classification accuracy, precision, recall, F1 score, AUC-ROC for classification problems. BLEU score or perplexity for language models. Mean squared error for regression. You don't need to calculate these, but ask what metrics they track and why those matter for their use case.

What are the performance benchmarks? How does their model perform against industry standards or academic benchmarks? Be wary of custom benchmarks designed to make them look good. Strong teams compare against published research and competitors on standard tests.

7. Ethical & Regulatory Considerations

AI ethics and regulation are evolving rapidly. What's legal and acceptable today might not be tomorrow. Forward-thinking companies build responsibly from the start.

The Core Questions:

What are the bias and fairness considerations? AI systems often perpetuate or amplify biases in training data. An AI resume screener trained on historical hiring data might discriminate against women if historical hiring was biased. Ask what fairness testing they've done and how they measure disparate impact across demographic groups.

How do they handle privacy and data protection? Are they compliant with GDPR, CCPA, and other privacy regulations? How do they handle personal data? Can users request data deletion? Do they have data processing agreements with customers? Privacy violations can be company-ending.

Red Flags to Watch For

Beyond the framework above, certain warning signs should immediately heighten scrutiny:

• The "AI washing" startup: They claim to use AI, but it's actually simple rules-based logic or heavy human intervention behind the scenes.
• Vague about data sources: Cannot or will not clearly explain where training data comes from.
• Over-reliance on a single foundation model: The entire business is a wrapper around GPT-4 or similar, with no proprietary differentiation.
• Defensive about technical details: Founders who claim technology is "too complex to explain" often have something to hide.
• Unrealistic performance claims: Claiming accuracy far beyond published research without extraordinary evidence.

When to Bring in Technical Advisors

Even with a solid framework, there are times when non-technical investors need expert help. Professional technical due diligence typically costs $15,000-$50,000 and takes 2-4 weeks depending on scope.

Scenarios requiring technical due diligence:

• Late-stage diligence for significant checks (Series B+ or $5-10M+)
• Deep tech or novel approaches claiming genuinely new algorithms
• When technical claims seem suspicious but you can't definitively assess
• Regulated or safety-critical applications (healthcare, autonomous vehicles, fintech)

How VCOS Flow Helps Systematize AI/ML Diligence

The framework above is only valuable if you actually use it consistently. The challenge many investors face is maintaining rigorous diligence processes across dozens of deals while moving quickly.

VCOS Flow is purpose-built to systematize AI/ML due diligence tracking. Instead of diligence living in scattered emails, Slack messages, and documents, Flow centralizes the process with customizable diligence checklists, data room organization, collaboration workflows, deal comparison tools, and historical learning.

Investors using Flow report conducting 40% more thorough diligence in the same timeframe because they're not reinventing the process for each deal.

Practical Checklist for First Meetings

Here's a practical checklist to structure initial conversations:

Before the meeting:

• Review published research, blog posts, or technical content
• Search Google Scholar for publication history
• Check LinkedIn for team backgrounds
• Note recent AI developments in their domain

Questions for the first meeting:

• Problem and Approach: What problem are you solving with AI/ML and why is it the right approach?
• Data: Walk me through your data sources, volume, and what makes it unique
• Technology: What's your technical approach and why is it suited to this problem?
• Team: Walk me through the technical team's background and relevant experience
• Traction: Show me real-world performance data, not just benchmark results
• Moats: What prevents larger companies from replicating this?
• Economics: What are your unit economics and how do they change at scale?

Conclusion

Evaluating AI/ML startups as a non-technical investor is challenging but far from impossible. The key is systematic frameworks that translate technical concepts into business questions you can evaluate.

Remember that technical brilliance alone doesn't create successful companies. Many of the best AI investments aren't the most technically sophisticated but rather combine good-enough technology with exceptional distribution, business model, or domain expertise.

Your advantage as an investor is business judgment, market understanding, and pattern recognition across multiple deals. Combined with the technical framework above, you can make informed investment decisions without becoming an AI researcher yourself.