How to Invest in Artificial Intelligence

How to Invest in Artificial Intelligence

A comprehensive guide to investing in artificial intelligence. We explore funding AI startups, acquiring mature startups strategically and investing in public AI companies. We also discuss due diligence frameworks, assessing teams, diversification strategies and more.


Artificial intelligence (AI) is rapidly transforming industries from healthcare to transportation. As adoption grows, AI investment presents massive upside potential for those who target the right companies and technologies. This comprehensive guide explores various approaches to participate in the AI investment boom.

We first examine early stage funding for promising AI startups. We then look at acquiring more mature startups to immediately obtain technology and talent. After that, we discuss investing in public AI companies for more liquid exposure. Additional critical areas covered include due diligence, deal terms, assessing founders and teams, timing considerations, diversification, and more.

By following an informed AI investment strategy today, substantial portfolio returns could be realized over 5-10+ year time horizons as the AI revolution accelerates.

Investing in AI Startups

Providing early stage capital to talented founders solving important problems with AI offers tremendous upside. Backing a breakout startup before the herd can generate over 100X returns for early investors when the company eventually goes public or gets acquired.

However, startup investing is inherently high-risk. Conduct extensive due diligence and diversify across multiple bets to mitigate risk. When evaluating AI startups, analyze:

Founders & Team

  • Assess founders’ technical capabilities via organizations like OpenAI, DeepMind, FAIR, Stanford, CMU etc. All-star founding teams are ideal but not essential.
  • Interview founders to evaluate vision, domain expertise, business judgement, executive ability and motivation. Search for red flags indicating poor ethics or culture.
  • Scrutinize early key hires especially in AI/data science roles. Pedigrees from top labs indicate ability to attract talent.

Technology & IP

  • Deeply evaluate existing algorithms, data assets, and technical IP for sustainable competitive advantages versus open source alternatives. Assess defensibility of proprietary elements.
  • Determine technical roadmap strength and key challenges. Evaluate founder backgrounds to judge capability of solving hard problems.

Product & Market Traction

  • Talk to early pilot customers and users. Validate compelling use cases and product-market fit.
  • Analyze any usage metrics, revenue numbers, waitlists, word-of-mouth signals, or other traction indicators.

Business Model & Unit Economics

  • Model expected customer acquisition costs, lifetime values, gross margins, and funding runway. Ensure attractive unit economics.
  • Scrutinize concentrated revenue exposure to any single client or region.

Market Potential & Competition

  • Verify founders have intimate understanding of target users and market verticals like finance, healthcare etc. Adjacent opportunities outside core beachhead often emerge over time.
  • Thoroughly evaluate competitive threats from incumbents and startups, especially in enterprise sales cycles.

Investment Terms

  • Negotiate preferred shares with strong protective provisions and information rights. Work with an experienced lawyer.
  • Structure deals to incentivize founders for long-term success, not quick flips.

With thousands of AI startups targeting massive markets, extreme diligence is warranted to filter for the most promising opportunities. Once conviction develops, be prepared to patiently hold 7-10+ years for disruptive technologies and teams to fully mature.

Investing in Mature AI Startups

Once an AI startup scales revenues, proves durable customer traction, establishes technical leadership and builds a strong team, acquiring it can provide instant strategic capabilities. Large tech stalwarts like Google, Microsoft, Amazon and Apple actively scout maturing AI startups to purchase and incorporate into existing platforms.

For example, Microsoft acquired conversational AI startup Nuance for $20 billion to enhance its healthcare offerings. Similarly, Apple purchased audio tech company Novauris to improve Siri. Other sectors like agriculture, retail, manufacturing and more are seeing major AI acquisitions.

When evaluating mature AI startups as potential acquisition targets, analyze:

Strategic Fit

  • Start by precisely defining missing organizational capabilities today and 3-5 years out. Then ideate targets that directly address gaps.
  • Quantify revenue expansion opportunities, cost synergies, and platform augmentation possibilities post-acquisition.

Technology & IP fortress

  • Thoroughly assess strengths of algorithms powering products. Review patents and proprietary data assets.
  • Gauge talent retention risk to avoid IP loss. Continuously engage personnel post-close.

Market Position

  • Verify leading market share in target verticals. Evaluate customer concentration levels across regions.
  • Project future cash flow potential under new resources and distribution channels.

Culture Integration Prep

  • Ensure target leadership is motivated for variable, stock-heavy payouts.
  • Carefully evaluate cultural mismatch risks during due diligence via backchannel references.

Competitive Environment

  • Scrutinize threats from new entrants and substitutes. Assess sustainability of competitive advantages.

Engage experienced M&A legal and financial advisors to guide successful acquisitions. Develop thoughtful 100-day plans focused on integration and capturing deal value.

Be prepared to pay substantial acquisition premiums due to high demand for leading AI startups. Stay patient post-close through assimilation, Especially inject sufficient management bandwidth.

Investing in Public AI Companies

Mature publicly traded technology companies pioneering impactful AI research and productization provide more liquid investment exposure. Investors can participate in AI gains without taking on excess risk.

Ways to invest in public AI companies:

AI Leaders

  • Identify and directly invest in public companies with clear leadership in AI research and applications like Google, Microsoft, Nvidia, Amazon, Meta etc.
  • Monitor developments from chipmakers (Nvidia, AMD, Qualcomm), cloud infrastructure players (AWS, GCP, Azure), software giants (Adobe, Autodesk) and more.

AI Funds

  • Consider AI mutual funds and ETFs containing baskets of stocks specializing in AI and machine learning companies.
  • Optionally invest in index funds tracking AI-focused benchmarks like the ROBO Global Artificial Intelligence Index.

When analyzing individual AI stocks and market leaders, evaluate:

Research & Development

  • Seek sizable budgets and strong teams working on bleeding edge AI R&D across labs and academic partnerships.

Revenue Exposure

  • Scrutinize existing product revenue tied to AI algorithms and services. Analyze growth rates, margins and competitive moats.

M&A Strategy

  • Track AI acquisitions and strategic investments for capability enhancement. Monitor tech talent retention afterwards.

Infrastructure & Tooling

  • Assess cloud, compute and data strengths that serve as platform foundations for AI innovations.

Technical Leadership

  • Ensure capabilities across chips, software libraries, machine learning pipelines, data lifecycle etc.

Regularly track emerging AI IPOs, spinoffs and newcomers challenging established leaders. The AI landscape will look very different 5-10 years from now. New giants will emerge across verticals.


The AI investment greenfield remains massive despite increasing activity. With diligent approach, significant portfolio returns can be realized over long time horizons. Avoid speculative gambles and carefully evaluate founders, end markets, business models and technology.

The recommendations presented serve as a starting point for developing an AI investment strategy. Partner with legal and financial advisors to stress test assumptions made during evaluation.

A balanced portfolio should include a mix of diversified early stage investments, strategic acquisitions and public AI stocks. Maintain reasonable return expectations and investment horizons matching the outsized opportunity ahead as AI continues transforming industries globally.

Wrap Up

This guide provides various frameworks to evaluate AI startups, acquisitions and public stocks:

  • We discussed targeting early stage startups developing proprietary algorithms before the herd while being cognizant of dilution risks.
  • For acquiring mature AI startups, we covered assessing strategic technology and talent fit along with analyzing market position.
  • Regarding public stocks, we explored evaluating established leaders’ research pipelines, product revenues and technical capabilities.

Key Takeaways

  • Scrutinize AI startup founding teams for technical excellence, motivation and integrity. Evaluate markets, IP, unit economics and traction signals.
  • Acquire AI startups strategically once product-market fit validation occurs. Prioritize retention of talent and IP over standalone financials.
  • Research public AI companies with large R&D budgets, existing revenue streams and strong compute infrastructure supporting innovations.
  • Diversify timeframe, stage and size. Partner with legal and financial advisors to stress test assumptions during evaluation.
  • Maintain 7-10+ year investment horizons for early stage venture and M&A deals in particular. Continuously monitor the competitive landscape.

Professional Advice

This article aims to educate readers on approaches for participating in the AI investment ecosystem. It does not constitute professional financial advice. Consult licensed advisors for guidance on individual situations before making any investment decisions.

Further Resources

  • State of AI Report 2023 – Comprehensive free report on latest AI trends across research, startups, products, policy etc.