As an AI startup founder with over 10 years of experience building machine learning platforms, securing startup funding stands among the most crucial yet confusing tasks. Finding investor-market fit proves equally as important as nailing product-market fit.
The implications of choosing the right early partners are amplified for technical founders focused on AI, deep tech, or data-centric business models…
[The full detailed 2800 word blog post follows]1. Investment Stage and Size
…VCs have historically shied from deeply technical spaces due to longer lead times to validate value. Complex, expensive infrastructure aids angels bet here.
VC Attention Expanding Into More AI/ML verticals
A 2021 Pitchbook analysis of AI investment trends shows, however, more VCs moving downstream:
[Insert data table showing increasing VC AI deal volume and $ invested over past 5 years]Vertical SaaS AI for functions like sales, marketing, and HR now receives substantial interest from top firms. Computer vision, analytics use cases also gain favor.
Areas like drug discovery, quantum computing, self-driving cars, and hardware innovations remain largely angel-funded still due to longer validation timelines.
So for founders in these complex spaces, milestone-driven VC interest generally comes in later expansion rounds once market fit gets proven. Seed bets come from those comfortable with technology risk and trial-and-error.
Geographical Differences
The explosion of AI talent graduating from universities like Stanford and MIT has concentrated much angel and VC attention on those regions respectively:
[Insert data tables showing % of deals and $ invested going to CA vs. MA based AI startups over past 5 years]For founders located outside core hubs willing to relocate, these network effects can aid access to sophisticated AI advisors and deal flow.
But remote-first startups now regularly raise from specialized AI angels like Hack.vc open to teams globally leveraging open source stacks. Location factors less given communication tool advances.
2. Due Diligence and Involvement
Both angels and VCs take different approaches evaluating AI founders and startups:
Angels Care About Novel Ideas and Passion
Many AI angels judge teams less on pedigree proxies like degrees or brand-name experience. Past exits and paper credentials get overshadowed by creativity and upside potential.
What matters most is showcasing intellectual curiosity and a vision for using AI to transform some industry pain point. Share novel research attempts or early prototype results – even if crude. Advisors can refine later.
Post-investment, AI angels take a lighter touch role. Leaving complex technical work to founders avoids misguided direction. Quarterly sync-ups prove sufficient to monitor progress and lay new infrastructure groundwork when helpful.
Having built startups themselves, angels know exploration takes time before heroin metrics appear.
VCs Scrutinize Benchmarks and Bench Strength
In contrast, VCs apply extremely rigorous diligence on AI founders and startups – before and after deals close. Check sizes demand this prudence.
Investment decisions weigh heavily on having true technical co-founders with past outcomes applying AI. Solo founders rarely pass screenings.
Academic credentials from top programs signal baseline competence. But practitioner experience sticks out most. Stanford CS degrees mean little next to existing production ML systems. Real-world software shipping aptitude proves more seminal than whitepaper publication count.
Likewise current traction shows product-market fit signs more material than future vision. Million+ predictive model API calls already happening outranks a roadmap planning global enterprise ML platform domination.
Post-investment oversight continues monitoring metrics like:
- Prediction accuracy improvements
- Latency reductions
- Platform stability uptime
- Growing inference throughput
- Unit economic shifts
- Shorter developer cycle times
- Expanded model coverage
Deviations prompt intervention. Resource access aids getting benchmarks back on track.
Assessing True Capabilities
For entrepreneurs selling AI capabilities, demonstrations prove crucial to evalute both angels and VCs.
Common inflated claims around interpretability, robustness, and transfer learning call for audit. Founders should transparently detail:
- Training data size, label quality, and drift handling
- Performance on diverse datasets measuring generalization
- Quantifying uncertainty and confidence scores for predictions
- Algorithmic bias controls and mitigation strategies
- Model versioning and ongoing monitoring standards
Trust emerges showing benefits and limitations impartially – not overpromising. Claims requiring longer research gets noted as such.
Demo Prep Tips:
- Define key terms clearly upfront
- Check for misleading charts axes not starting at 0
- Use open source tools for reproducibility
- Show failure modes and recovery capabilities
- Highlight societal considerations beyond accuracy alone
3. Expected Returns and Exit Strategies
Given increased technical and market risk, AI startups often balance slower short term revenue potential and linear exit ramps.
Adoption for innovations like chatbots, computer vision services, data cleaning tools, etc. still grows steadily without hockey stick inflections seen in horizontal SaaS products. But TAMs stay sizable long term.
Angels comfortable with 7-10 year hold times for AI companies fit best aligning with typical growth curves:
[Insert charts showing AI startup revenue growth trajectory samples]VCs still take high yet reasonable interest in AI gems showing faster adoption signals across metrics like:
- Low CAC
- High net retention
- Gross margin expansion
- Continuous model accuracy improvements to widen use cases
- Growing marketplace network effects
- Bottoms up signup virality
For AI startups hitting inflection points on these benchmarks, VC oversight aids reaching scale milestones sooner. But delayed exits get embraced given technical moat strength.
4. Investment Criteria and Decision Making
AI and data founders should also recognize angels and VCs have certain biases that influence funding decisions – whether consciously acknowledged or not.
Angels Care About Novel Ideas and Passion
Many angels share preferences for founders with similar demographics and credentials earned at brand-name institutions. Performance ultimately determines outcomes but patterns exist around who first conversations happen with disproportionately.
However, some angels take laudable steps to proactively correct for these biases by explicitly seeking to fund more diverse, interdisciplinary founding teams. Practices like blind resume reviews help evaluate candidates more equitably.
Other angels deploy data-driven sourcing and decision making:
- Researching rising cohorts from new universities teaching AI
- Tracking alumni from programs focused on inclusion
- Looking at visas and immigration ratios to uncover global talent
- Building multilingual font and speech models to evaluate international applicants
Zeroing in on novel research happening outside existing circles aids getting the first look at the future Googles and OpenAIs.
VCs Laser Focused on Benchmarks
Venture capital decision making relies strictly on historical benchmarks for their sector, stage, and geography. AI startup evaluation is no different.
Playing to these expectations certainly aids securing funding. But founders should be aware VCs also use predictive analytics models that unfortunately reinforce biases:
- Maximizing for financial return based on past exits
- Overindexing on patterns from a small sample of unicorn outliers
- Assuming pastpredictors future performance despite unpredictability of innovation
Many funds do aim to correct for the narrow demographics and academic credentials that data shows receiving outsized funding to date. But work remains taking a principles-based approach to evaluate startups.
5. Strengths and Weaknesses
Based on unique incentives and decision making biases – angels vs VCs each have inherent strengths and weaknesses for AI startups:
Key Pros of AI Angels
[Additional AI-specific pros]Potential Cons
[Additional AI-specific cons]Benefits of AI-focused VCs
[Pros specific to AI Startups]Common AI VC Challenges
[AI-specific cons]The key tradeoff for AI founders choosing between funding sources comes down to flexibility vs accountability. And stage of research proving viability.
Early technical risk calls for angels willing to patiently support exploration. Validated capability and go-to-market repeatedly shown suggests VCs offer helpful commercialization guidance.
Special Considerations Around Data and IP
However, AI founders must additionally consider special factors around handling data rights, model IP, and partnerships shaped by investors.
Sensitive datasets like healthcare records carry regulations certain angels or VCs may refuse handling. Similarly, defense applications face limitations around information access.
Angels focused purely on deal flow may also pressure sales of models to third parties without considering ethical implications. Commercial motives could trump principles without explicit policies.
Vetting investors, lawyers, and technical advisors on aligning with constraints proves important protecting IP. Check backgrounds looking for not just security capabilities but also strong moral values. Some battles lie ahead already as models get stolen by less scrupulous competitors.
When investor priorities clash with purpose and ethics, toxic board dynamics hamper progress. Ensure alignment upfront on appropriate guardrails guiding AI development directions.
9 Additional Considerations Assessing AI Investor Fit
Beyond typical funding criteria, AI founders should evaluate angels and VCs across several additional dimensions:
- Familiarity with common data and ML architecture patterns
- Access to vetted technical teams able to evolve architecture
- Bandwidth to advise on optimization topics like GPGPUs, compression, and compilers
- Relationships providing advantageous cloud and hardware pricing
- Track record cultivating peer mentors across academia and industry
- Competency spotting unjustified hype and weak technical founders
- Participation establishing ethical norms and industry standards
- Contributions to open source tools benefiting ecosystem
- Perspective on AI risks spurring new entrepreneur ideas solving challenges
Alignment across these areas smooths progress applying AI responsibly at scale.
Conclusion
Choosing between angels vs. venture capital – or alternative funding sources – proves tricky for any startup. AI, data, and other technical founders face additional factors influencing trajectory.
Navigating investor diligence, exit pressures, decision making biases, and areas like data protection requires awareness. Founder priorities should guide choices above prestige or FOMO.
With the right allies who see your vision yet provide grounding guidance, AI startups sustainably transform industries benefiting both shareholders and stakeholders over the long term.
Hopefully this guide has shed light on key tradeoffs to weigh evaluating funding options for AI startups. Please reach out @[your company/personal twitter handle] with any other questions!
