February 25, 2026<\/p>\n
Reading Time: 11 min\u00a0<\/p>\n
Topics<\/p>\n
Data, AI, & Machine Learning<\/a><\/p>\n AI & Machine Learning<\/a><\/p>\n Carolyn Geason-Beissel\/MIT SMR | Getty Images<\/p>\n Summary: <\/p>\n Understanding a startup\u2019s approach to artificial intelligence isn\u2019t 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\u2019ll also learn about the common traps that founders, investors, and customers must avoid as they work with AI startups.<\/p>\n As generative AI has transitioned from novelty to necessity for many businesses, both established companies and startups are racing to decipher AI trends<\/a> 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<\/a> 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. <\/p>\n This gold rush spans a wide spectrum of companies \u2014 some building AI technologies themselves, others applying them to products or internal operations \u2014 which makes clear categorization difficult but increasingly important.<\/p>\n 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.<\/p>\n 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.<\/p>\n Existing frameworks offer clarity but don\u2019t tell the whole story:<\/p>\n McKinsey\u2019s description of \u201ctakers, shapers, makers\u201d<\/a>\u00a0distinguishes between those who use models as is (takers), those who tailor off-the-shelf AI (shapers), and those who build foundation models (makers).<\/p>\n Boston Consulting Group describes a continuum of AI maturity<\/a>, with 25% of companies it surveyed reporting that they\u2019re \u201cnot doing much,\u201d 49% still experimenting or running proofs of concept, 22% actively scaling, and only 4% having become full \u201cvalue engines\u201d \u2014 businesses that have deeply embedded AI across their operations. BCG\u2019s focus isn\u2019t on startups, but the continuum could be applied to them.<\/p>\n RBC Wealth Management<\/a>\u00a0and\u00a0S&P Global<\/a>\u00a0draw another line, between AI \u201cenablers\u201d (technology providers) and \u201cadopters\u201d (technology users).<\/p>\n Nasdaq<\/a>, some venture capital firms<\/a>, and other investing-focused analysts differentiate between AI companies mining for gold and the \u201cpicks and shovels\u201d companies that supply AI tools.<\/p>\n These models are insightful, but they mainly address large incumbents or high-level AI adoption strategies. <\/p>\n About the Authors<\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/il\/wp-content\/uploads\/2026\/02\/Shay-1290x860-1.jpg\"\/)
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