What Really Excites Investors About AI Startups

“If something seems broken, challenge existing paradigms instead of following conventions.”― Sam Altman, OpenAI Co-Founder and CEO.

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High potential AI startups?question?the?status?quo?and?develop?better?solutions,?and?Altman's?quote?perfectly?captures?this?approach. Investors?are?looking?for?innovation?that offers real value and?disrupts?markets with new paradigms —?not?just AI?for?the?sake?of AI. This means your focus should revolve around pioneering the fundamental building blocks of AI (generative or applied), for example, foundational components, algorithms, and architectures.

A startup’s progress towards achieving a Product-Market fit to demonstrate value to customers should be the table stakes for any startup. Beyond this PMF and market-size baseline, AI startups pursuing transformation need to show investors additional strengths across the following 10 critical areas, presented in the investor's evaluation framework of Opportunity Approach—>Growth Strategy—> Execution Planning:

A. OPPORTUNITY APPROACH

1. Market Approach:

Many incumbents are tapping into the Generative AI market opportunity, however, startups need to take an innovative path that diverges from the conventional approach. Rather than competing head-on, they must identify and target initial markets characterized by a limited incumbent presence with straightforward sales cycles and swiftly implement their solutions to demonstrate value to customers.

"Big Tech companies with massive investments in AI are not going to let their incumbent distribution advantage slip away easily.” Konstantine Buhler, Sequoia Capital

To enter a market with a limited incumbent presence and an innovative approach, Motion Metrics targeted sports biomechanics, an overlooked $21 billion market with straightforward sales cycles. Its focus helped secure $10 million; The Weir Group acquired them for $89M in Nov 2021.

2. True Innovation:

Whether your startup is building a remarkable 10X product that not only leverages AI but also brings transformative change to its market.?True innovation involves a complete rethinking of conventions and categories rather than merely making incremental changes to existing solutions. It requires finding underserved and unserved customer needs, creatively applying the latest innovations, and establishing new benchmarks for performance and costs.

Paige is a good example of a company that developed remarkable AI for pathology analysis to bring 10x gains. This novel AI diagnostic attracted funding of over $239M over seven rounds.

3. Data Strategy:

Data is the fuel for AI algorithms; does the startup have a data strategy and access to high-quality datasets? Investors love startups that have secured access to proprietary, clean data that competitors can't easily replicate.

To highlight the advantage of access to unique datasets, take the example of Tempus, which built proprietary clinical, molecular, and genomic data assets to uniquely enable AI in healthcare. It raised $1.3 billion in funding over nine rounds to scale data access.

B. GROWTH STRATEGY

4. Customer Adoption:

Even world-changing technology is useless without a path to customer adoption. How the startup will acquire customers and scale up commercially implies defining the key customer segments, channels to market, sales and distribution model, etc. Given long enterprise sales cycles for B2B, investors will look for creative GTM strategies that balance speed with efficient customer reach. The same goes for B2C, the ability to drive adoption through viral/incentive mechanisms, frictionless onboarding, etc.

Uncountable, a manufacturing AI self-service platform to help find innovative products faster with reduced testing iterations, had a clear strategy for acquiring and scaling customers and frictionless onboarding and monetization. This helped them secure over $80 million in funding.

5. Economic Moat:

Commoditization threatens many AI applications; needless to say, the strength of your product's economic moat is pivotal. Investors are interested in products that have a robust economic moat around their product and pricing power—products that attract customers with a high willingness to pay—in large, untapped markets.

To demonstrate the significance of having a unique and difficult-to-replicate product in large markets, take the example of Scale. With their complex data labeling for 2D, 3D, video and lidar data they established hard-to-copy workflows and the infrastructure around managing a crowd workforce, making it difficult to replicate, giving them a competitive advantage. They have raised $600+ million in funding and expanded across multiple verticals.

6. Defensibility:

What is your unique selling proposition to continue to retain your market share? How well will your startup evolve to seize the developing opportunities? What will stop others from winning away your customers? Have you strategically constructed network effects barriers into your product to gain a competitive advantage?

Databricks built defensibility by creating a strong network effect with its collaborative data science platform. As more teams adopt Databricks, its integrations, partnerships, and community grow, further accelerating usage. This self-reinforcing cycle has created high switching costs for customers. Databricks is currently valued at $43 billion.

7. Monetization Plan:

Even if you are a pre-revenue startup, investors will still want to see a viable path to monetization and profitability, because commercial viability is necessary for converting an intriguing AI capability into a scalable, high-growth business. Having clarity around the monetization strategy is important to instill confidence in the business model.

DataRobot’s SaaS model for enterprise AI democratization provides visible revenue scaling. This spurred over $1 billion in funding from investors like NEA, Tiger Global Management, Sutter Hill Ventures, Altimeter, and Snowflake Ventures.

C. EXECUTION PLANNING

8. Team Velocity:

With the pace at which the AI industry is evolving, speed is not just an advantage; it's a prerequisite. Investors prioritize teams that exhibit exceptional agility to make rapid pivots and adapt to market needs. This implies they should also have a profound understanding of their target market, coupled with the technical capability to lead the charge.

To illustrate the importance of a startup's agility in the fast-evolving AI industry, take the example of?UiPath, which leveraged M&A (acquisitions of ProcessGold and StepShot) and swift engineering to broaden its end-to-end automation platform. This ability to quickly adapt and enhance products fueled adoption by over 10,000 customers. This execution pace helped UiPath raise over $2 billion and go public in 2021.

9. Regulatory Compliance:

Certain AI startups may have strict compliance requirements, requiring a well-thought-out strategy to ensure adherence to pertinent laws and regulations. This includes considerations related to data privacy, security, and any industry-specific regulatory requirements.

ClosedLoop developed healthcare NLP with compliance by design, unlocking large opportunities. This drew about $50M million in investor backing, with the Centers for Medicare & Medicaid Services being their most recent investor.

10. Ethics and Governance:

With the rise of concerns around AI ethics and potential harms, investors are increasingly considering the governance structures and ethical frameworks startups have in place around developing and deploying AI responsibly. Ability to demonstrate a commitment to ethics can be a competitive advantage.

H2O.ai developed AI with trust and transparency, including anti-bias algorithms. Responsible and interpretable models represent their competitive strength. This helped H2O.ai raise $250M over six rounds.

Success demands more than smoke and mirrors. In investor meetings, where algorithms are dissected like financial statements, only truly groundbreaking AI ventures can hope to survive.

As AI startups seek funding to build and scale, focusing on the ten factors above will be pivotal to securing investor confidence and capital to fuel growth. Prioritizing these key ingredients for investor backing will propel innovative companies to lead the next wave of AI. Though the AI hype cycle continues, investors keep their eyes on funding serious teams building technology that drives real human progress.

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