Predicting Success in AI Startups: A Data-Driven Investment Analysis Part 2 - 2021 Vintage

This version 2 research extends our machine learning approach to identify high-potential AI startups from the 2021 vintage, yielding compelling results that further validate our investment methodology.


Project Framework

We applied our established machine learning methodology to identify promising AI ventures in the 2021 cohort, maintaining consistency with our previous analysis.

Our approach continued to incorporate:

- Six predictive variables: Industries, Company Description, Founder Biography, Founder Gender, Location, and Educational Background

- A dual-model ensemble combining Random Forest and XGBoost algorithms

- Advanced text vectorization for unstructured data


Portfolio Performance

Our model selected 19 companies from the 2021 vintage all the way till 2024 (evenly distributed) with the following current performance:


- 9 companies (47.37%) have already achieved valuations exceeding $500M, including:

  - Perplexity AI: Reached $9 billion valuation in December 2024, with over $100 million in annualized revenue as of March 2025

  - Cyera: Secured $300 million in Series D funding, reaching a $3 billion valuation in November 2024

  - Hippocratic AI: Achieved unicorn status with a $1.64 billion valuation in January 2025

  - Anumana: Showcasing leadership in AI-powered cardiovascular solutions

  - World Labs, Protect AI, Mytra, DatologyAI, and Revefi


- 4 companies (21.05%) demonstrate strong growth trajectories

- 4 companies (21.05%) are too early in their development to evaluate conclusively

- 2 companies (10.53%) have not ceased operations but are unlikely to achieve significant success


This 47.37% high-performer rate significantly outperforms the best venture capital unicorn success rates of 5% (Sequoia), with potential to reach 68.42% as companies currently on track continue to develop. The 10.53% failure rate thus far remains substantially lower than industry averages of 75%, not factoring in various constraints of real investing.

Our model continues to demonstrate strong predictive capability while serving as a decision support tool rather than a replacement for comprehensive due diligence.

We will continue to analyze additional vintages across larger geographies and sectors, publishing results as they become available.


Disclaimer: This analysis is for educational purposes only. Past performance does not guarantee future outcomes.

New Zealand's Innovation Pathway

As a Singapore-based VC, I've witnessed how innovation ecosystems evolve naturally when properly supported. Singapore initially emphasised deep tech but allowed market forces to shape developments organically.

Critiques of New Zealand's funding imbalance misses a crucial point: successful startups need significant market power quickly, regardless of their technological depth. Creating numerous small non-deep tech ventures won't deliver the economic impact New Zealand seeks. You need to continue to focus on both.

Three focused recommendations:

  1. Establish a national coordination body with a hands-on advisory panel of experienced entrepreneurs and investors who can directly mentor founders to scale globally. This addresses both fragmentation and practical scaling challenges.

  2. Develop diverse funding mechanisms prioritizing ventures with global potential rather than simply increasing startup quantities. Government initiatives on grants, fund of funds support should continue with momentum but understand the signs of change and adapt to it.

  3. Implement more talent development/retention programs, one example to take note of is Singapore's NUS Overseas College, which immerses students in innovation hubs like Silicon Valley, creating globally-minded entrepreneurs with valuable networks. Net new migration into New Zealand needs to be positive over time, but this is likely to be the toughest challenge yet.

New Zealand should focus more on building globally competitive companies with proper ecosystem support. I know you can do it. You know you can do it. Whāia te iti kahurangi - pursue that which is precious.

Predicting Success in AI Startups: A Data-Driven Investment Analysis

This version 1 research applies machine learning to identify high-potential AI startups from 2017-2019, yielding significant insights for investment decision-making.

Project Framework

We developed a machine learning methodology to identify promising AI ventures across two cohorts: 2017-2018 (475 companies) and 2019 (329 companies). 

Our approach incorporated:

  • Six predictive variables: Industries, Company Description, Founder Biography, Founder Gender, Location, and Educational Background

  • A dual-model ensemble combining Random Forest and XGBoost algorithms

  • Advanced text vectorization for unstructured data

Portfolio Performance

Our model selected 15 companies across both time periods:

2017-2018 Selections (10): Jerry, Health Note, Cylera, Deep Cognition, Determined AI, NoTraffic, MovieBot, SupplyHive, Kami Vision, Rowzzy

2019 Selections (5): Eleos Health, Anyscale, Baseten, Anvilogic, Fairmatic

Current Performance:

  • 6 companies (40%) achieved valuations exceeding $500M

  • 3 companies (20%) demonstrate strong growth trajectories

  • 3 companies (20%) show steady growth

  • 3 companies (20%) have ceased operations

This 40% high-performer rate significantly outperforms typical venture capital success rates of 10-20%, while the 20% failure rate is substantially lower than industry averages of 75%. This do not factoring in various constraints of real investing.

Key Investment Domains

Four predominant themes emerged:

  1. Enterprise AI Infrastructure (Determined AI, Anyscale)

  2. Healthcare AI Applications (Eleos Health, Health Note)

  3. Security Solutions (Cylera, Anvilogic)

  4. Financial Technology (Jerry, Fairmatic)

Investment Implications

Successful AI ventures consistently demonstrate:

  • Enterprise-focused solutions with clear value propositions

  • Technical excellence within founding teams

  • Strategic presence in major technology ecosystems

While our model demonstrates strong predictive capability, it remains a decision support tool rather than a replacement for comprehensive due diligence.

We will continue to do more and larger permutations in AI and work larger geographies and sectors and publish the results once they are done.


Disclaimer: This analysis is for educational purposes only. Past performance does not guarantee future outcomes.

Rise of the Tinkerer VCs

The venture capital landscape in 2025 is evolving rapidly. I think the days of purely financial-focused "AUM" investors are slowly fading, replaced by a new breed of venture capitalist: the tinkerer VC. These investors don’t just write checks—they think like and are builders, actively engaging with founders and creating value through technical expertise, product strategy, and operational support.

This shift is particularly visible in early-stage venture capital, where founders demand more from their investors. Economic pressures and increasingly complex technologies are forcing VCs to rethink their approach. In this environment, the tinkerer VC—someone who understands startup engineering, product development, and go-to-market strategies—is becoming indispensable.

Capital is abundant, but value-added support is rare. They want partners who can help them build—not just financially, but technically. This is especially true for startups in fields like AI, biotech, or climate tech. Founders need investors who understand their technology deeply and can contribute to solving technical challenges or scaling efforts. A VC who can’t engage meaningfully with the latest technologies risks being left behind.

The market correction of 2022–24 shifted the focus from growth-at-all-costs to capital efficiency and sustainable growth. Startups now need to do more with less funding, which opens the door for tinkerer VCs who can help optimize operations and refine strategies. Investors who understand the mechanics of building—from engineering to execution—are better equipped to guide startups through these challenges.

Specialization is becoming essential in venture capital. Generalist investors struggle to compete when technical fluency is required to evaluate opportunities or support founders meaningfully. Tinkerer VCs—often with backgrounds in engineering or product development—stand out in sectors like generative AI or climate tech because they can dive deep into technical challenges and provide actionable advice.

If you’re an investor—especially in early-stage—it’s time to adapt to this new reality. Technical savvy isn’t optional anymore; it’s a competitive advantage that sets you apart in a crowded field. 

Here’s how you can evolve:

1. Learn Startup Engineering
Understand how startups build products by learning the fundamentals of software engineering, product design, and system architecture. Take coding courses, experiment with building projects yourself, or work with the latest technologies like generative AI tools. Hands-on experience will give you insights that spreadsheets never will.

2. Build Things Yourself
Invest time in tinkering—whether it’s coding apps, experimenting with hardware, or prototyping solutions using cutting-edge tools like GPT APIs or cloud platforms like AWS and systems programming like CUDA. Founders respect investors who understand what it takes to build something from scratch.

3. Specialize Where You Can Add Value
Focus on sectors where you can develop deep expertise—whether it’s AI, climate tech, fintech, or another domain—and commit to understanding those industries inside out. Specialization improves your ability to evaluate opportunities and enhances your reputation among founders.

The venture capital industry is changing—and tinkerer VCs are leading the way forward in 2025 and beyond. Investors who embrace technical fluency and hands-on engagement will outperform those stuck in traditional financial models.