Investing In AI: Leading VCs Share 10 Green Lights And 10 Things That Send Them Running

Alumni Ventures
6 min readNov 21, 2023

Artificial Intelligence (AI) isn’t just a buzzword anymore. It’s an epoch-defining technology shaping how we live, work, and interact. For most venture capital investors (VCs), the question isn’t whether to invest in AI but how to do it to maximize returns.

At Alumni Ventures, we are one of the top investors in the sector and have a dedicated AI Fund. Here we’ve developed a list of the top 10 things we look for in AI companies, plus 10 red flags that send us running.


1. Fix-It Mindset

When evaluating startups, a key question is whether the venture addresses a significant, real-world problem. Successful startups must identify pressing issues affecting individuals, communities, or industries and then solve them with innovative solutions.

So when we search for investments in AI, we’re seeking startups leveraging this tool for problem-solving — whether that’s in data analysis, content creation, predictive modeling, image and speech recognition, optimization, personalization, etc.

2. Are You Really Using AI?

As the number of AI startups explodes, VCs have also seen the rise of wanna-bes using AI buzzwords and sporting “dot-ai” domain names. While the term “AI” is increasingly common, its precise meaning remains elusive. It’s an umbrella term covering various mathematical approaches to simulate intelligent behaviors.

As an example: Companies claiming they’re AI-centric when basically indexing data are misusing the term. Indexing is a fundamental function performed by conventional software that has existed for decades. So evaluating AI startups means first assessing whether they genuinely incorporate AI or employ the term as a marketing ploy.

3. It’s the People, Stupid

You can’t overstate the value of an experienced, dynamic team in judging a startup. While a promising idea is critical, the team behind that idea can ultimately determine success. An experienced team brings knowledge, skills, and an ability to adapt and pivot when conditions change or challenges arise.

A prime example of an AI team that checks the boxes: AV portfolio company ChainML boasts a leadership with extensive expertise, technical excellence, and a track record in AI and related domains. CEO Ron Bodkin brings 15 years of AI experience, with a leadership career in applied AI through Google’s CTO Office, as VP of AI Engineering & CIO at Vector Institute, and as Co-Founder of an AI company that Teradata acquired. He’s supported by a team of seasoned executives with deep experience in AI, scalable computing engineering, product development in applied AI and data science, and expertise in AI privacy and time series analysis.

4. Tech Chops

AI startups must demonstrate a deep understanding of AI technologies and their practical application. So what does a solid technical foundation for AI look like? We believe it demands a broad range of knowledge and skills across areas such as machine learning (ML), deep learning, optimization techniques, natural language processing, computer vision, etc.

And application is everything. For example, a “good machine learning model” is one that performs effectively and efficiently for a given task, producing accurate predictions or classifications while generalizing well to unseen data. Note that qualifier, “for a given task.” A model can vary greatly depending on the use case, domain, and objectives. A good model for medical diagnosis may require different criteria than one for movie recommendations, for instance.

5. Growing the Business

Is the product or service designed for cost-effective growth? Scalability isn’t just a feature but a strategy underpinning long-term value creation. Startups that understand the importance of designing their offerings to cost-effectively expand and adapt to increasing demand are more likely to succeed.

For instance, when it comes to a startup’s machine learning models, we want to understand the computing power demands of their operations and determine whether such power is readily accessible.

6. Moats and More Moats

A competitive moat serves the same purpose as a castle moat: A durable advantage that helps protect the startup from invaders over time. Moats can take various forms, such as proprietary technology, a strong brand, network effects, exclusive partnerships, or regulatory barriers. Startups with a well-defined competitive moat are better positioned to withstand competition and become industry leaders.

Questions that come into play when evaluating the moats of an application-layer AI startup reliant on data:

  • Is the data source unique?
  • Will the company have access to the data over time?
  • How does it plan to collect the data?
  • How is the company quantifying data to train their AI models, and can these advanced machines self-improve over time?

If training datasets are public or easily accessible, the startup may have a limited competitive advantage. In contrast, a company that can generate proprietary training data has a more defensible position.

7. Opportunity Size

To deliver meaningful returns on investment, a startup must target a substantial market. A large market offers the potential for exponential growth and, therefore, increased investor value. While startups that focus on niche markets might find success, their ultimate returns could be limited. Therefore, we prioritize startups that target growing, sizable markets, as these ventures are more likely to achieve meaningful market penetration and sustainable growth over time.

Drug discovery is one example of a massive, growing market sector powered by AI. In 2022, the worldwide drug discovery market was assessed at $55.46 billion. Projections indicate that it will reach ~$133.11 billion by 2032, with a compound annual growth rate (CAGR) of 9.2% from 2023 to 2032. One of AV’s portfolio companies, Iambic Therapeutics, is successfully targeting the sector with a biotech platform that harnesses the power of AI and quantum mechanics to transform drug discovery.

8. Making Headway

Traction or momentum — which might be in user engagement, strategic partnerships, or revenue generation — is a good indicator of a business’s potential. Traction signals that the startup is on the right path, creating a functional, viable business with the potential for scalability and long-term success.

AV’s portfolio company Cohere illustrates the principle. Considered the leading generative AI / natural language processing player for enterprise customers, the company has recently become a unicorn. Founded by several teammates from the cutting-edge Google Brain team, Cohere has achieved tremendous customer traction. That includes customers such as McKinsey, Salesforce, AWS, Jasper AI, Spotify, and more — plus partnership traction with leading cloud providers Google Cloud and AWS for unique access to compute power. Overall, we are impressed by its month-over-month user and usage growth.

9. Business Model = Money Engine

Beyond having a great product or service, a well-defined business model outlines how the company will actually bring it to market and make money on that offering. A clear, viable business model lets you understand how the startup

  • Plans to generate revenue, achieve profitability, and ultimately scale
  • Address customer acquisition, pricing strategies, cost structures, and revenue streams
  • Has thought through its path to sustainability and growth.

10. Managing Money

A startup’s financial health and runway (a projection of how much time it has before it runs out of money, based on monthly expenditures) indicate its resilience and long-term viability. Startups with a clear financial runway have the funding to sustain their operations, avoid crippling cash-flow issues, and grow as planned.

Earlier, we referred to AI startup’s challenge of securing computing power — is it technically feasible to acquire what’s needed? For most AI companies, this is also a finance issue. Evaluating costs from a data-access standpoint also prompts us to examine whether the startup chooses cost-efficient, pre-trained machine learning models or custom trains, which provide greater flexibility but require more capital.

Want to learn more? Here are the 10 things we’d consider red flags and other insightful information on Investing In AI.

Alumni Ventures offers accredited individuals access to professional-grade venture capital — a key asset class missing from the portfolios of many sophisticated investors. Since 2014, AV has raised more than $1.2 billion across 40+ Alumni and Focused Funds, serving a growing network of 9,500+ investors and 600,000+ community members. AV evaluates hundreds of investment opportunities every year and has backed 1,200+ unique portfolio companies. Alumni Ventures is #1 most active venture firm in the U.S., and #3 most active in the world, according to Pitchbook’s 2022 rankings. AV funds are private, for-profit, and not affiliated with or sanctioned by any school. For more information, visit



Alumni Ventures

Alumni Ventures offers individuals access to professional-grade venture capital, a key asset class missing from the portfolios of many sophisticated investors.