If you’re a startup founder, investor, or customer, a new typology can give you context for making strategic decisions based on how the company is harnessing AI.
Jeffrey P. Shay and Thomas H. Davenport
February 25, 2026
Reading Time: 11 min
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Carolyn Geason-Beissel/MIT SMR | Getty Images
Summary:
Understanding a startup’s approach to artificial intelligence isn’t just a word game: It gives stakeholders a competitive advantage. Use this framework to categorize six types of AI startups: originators, explorers, infrastructure builders, enhancers, optimizers, and experimenters. You’ll also learn about the common traps that founders, investors, and customers must avoid as they work with AI startups.
As generative AI has transitioned from novelty to necessity for many businesses, both established companies and startups are racing to decipher AI trends and figure out how best to use the technology. However, this is particularly essential for startups. Venture capital (VC) firms are increasingly insisting that these businesses incorporate some generative AI use, and there are few organizational or technical-debt barriers to AI implementation in new companies. In fact, something of a gold rush is happening among AI startups, fueled by VC funding, interest from business and consumer customers, and a broad landscape of opportunity to reshape products, processes, and business models.
This gold rush spans a wide spectrum of companies — some building AI technologies themselves, others applying them to products or internal operations — which makes clear categorization difficult but increasingly important.
A strategic question remains underexplored: What type of AI company is being built by these startups? There are important implications not only for the leaders of these companies but also for their investors, existing and potential customers, and analysts.
If you are working with these startup companies in any of those ways, you need to be able to categorize them to put them in better context and make smart decisions.
Existing frameworks offer clarity but don’t tell the whole story:
McKinsey’s description of “takers, shapers, makers” distinguishes between those who use models as is (takers), those who tailor off-the-shelf AI (shapers), and those who build foundation models (makers).
Boston Consulting Group describes a continuum of AI maturity, with 25% of companies it surveyed reporting that they’re “not doing much,” 49% still experimenting or running proofs of concept, 22% actively scaling, and only 4% having become full “value engines” — businesses that have deeply embedded AI across their operations. BCG’s focus isn’t on startups, but the continuum could be applied to them.
RBC Wealth Management and S&P Global draw another line, between AI “enablers” (technology providers) and “adopters” (technology users).
Nasdaq, some venture capital firms, and other investing-focused analysts differentiate between AI companies mining for gold and the “picks and shovels” companies that supply AI tools.
These models are insightful, but they mainly address large incumbents or high-level AI adoption strategies.
About the Authors
Jeffrey P. Shay is a professor of entrepreneurship at Babson College. Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management and faculty director of the Metropoulos Institute for Technology and Entrepreneurship at Babson College, and a fellow of the MIT Initiative on the Digital Economy. His latest book, with coauthor Jim Sterne, is The New Science of Customer Relationships: Delivering the One-to-One Promise With AI (Wiley, 2025).