Six Types of AI Startups, Explained

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. What they often miss is the strategic diversity and evolving identity of startups and growth-stage businesses in the AI space.

To fill this gap, we developed a six-part typology, not based on industry, technology, or hype but on how companies create, deliver, and extract value from AI.

A Guide to AI Startups

As generative AI reshapes the business landscape, startups (and scale-ups) are engaging with it in fundamentally different ways. Some companies are building the models themselves. Others are enabling the builders. Still others are applying AI to solve vertical problems, optimize internal operations, or simply experiment.

Although each type of startup is pursuing valid approaches to AI, they have quite varying approaches to the technology, with different levels of investment and commercial readiness and distinct forms of differentiation.

1. AI Originators

AI originators are building the intelligence itself. They develop commercially deployed foundational models for generative AI — large, general-purpose systems. Their models are typically trained on massive data sets, but some, like China’s DeepSeek, are experimenting with smaller, more focused models. Not all model developers are startups, of course. Google, Meta, and Microsoft also have substantial AI models in the marketplace.

Startup examples include OpenAI, Anthropic, Perplexity, Cohere, and Mistral, which are creating their own proprietary models to power chatbots, generate code, and more. These companies are capital-intensive, rely on costly talent, and possess deep technical depth, operating at the frontier of generative AI science. Indeed, they are the most likely companies to say they are pursuing artificial general intelligence, or AGI.

McKinsey calls these companies “makers”; RBC and S&P classify them as “model makers” or AI “enablers.” In any case, the barriers to market entry are extreme — and the financial upside potentially foundational. However, there is evidence that the models they develop are converging and becoming commoditized, although OpenAI’s models still dominate usage in the category.

2. AI Explorers

AI explorers are startups whose primary mission is not to commercialize today’s AI but to invent tomorrow’s. Rather than optimizing existing tools, they probe scientific frontiers — agentic AI (Cognition Labs, Markovate, NinjaTech AI), quantum AI (Sygaldry, SandboxAQ, D-Wave Quantum), and even AGI (Safe Superintelligence, Thinking Machines Lab, Ndea).

They have, of course, both the highest potential risk and reward among AI startups. To succeed, they attract PhDs from top AI labs, rely on deep funding (often from mission-driven VCs or large incumbents), and are often secretive by design. Unlike enhancers or builders, whose business models revolve around current applications, explorers pursue scientific or architectural advances that could transform the field in five to 10 years. Their success can be measured by research published, benchmarks achieved, and milestones met.

3. AI Infrastructure Builders

AI infrastructure builders are the toolmakers or “pick and shovel” providers — companies providing the underlying infrastructure that allows others to build and deploy AI.

Startups like Scale AI (data labeling), Pinecone (vector databases), and Weights & Biases (machine learning experiment tracking) are building the data, APIs, and developer frameworks that power today’s generative AI applications. They don’t compete with model creators; rather, they make the model technology usable at scale. Somewhat more mature startups, including Databricks, provide the platforms for analytical AI modeling at scale.

This role aligns with the MLOps (machine learning operations) and tooling layers in McKinsey’s generative AI value chain, as well as the “core AI” or “enabler” categories in investment frameworks. These companies’ success depends less on raw model quality and more on ecosystem adoption and workflow integration.

Providers of AI-focused semiconductors (Nvidia, AMD, Broadcom, Qualcomm) also fall into this category but of course are not startups; startup chip companies include Tenstorrent, Mythic, and Groq.

4. AI Enhancers

AI enhancers sit at the application layer — taking general-purpose models and applying them to specific vertical or function problems. These players are sometimes disparagingly viewed as putting “wrappers” around foundation generative AI models.

Descript simplifies video and podcast editing with AI. Jasper automates content generation for marketers. Neither of these companies built its own model, but each has wrapped existing models in valuable, usable, customer-facing products. This category includes agentic AI tools, which are normally applied to a relatively narrow business process. PitchBook reports that 46% of Y Combinator’s spring 2025 batch were “AI agent” startups, indicating a seismic shift in how early-stage ventures are being conceived and built.

The enhancers segment, sometimes labeled “industry AI” or “vertical AI,” is where the majority of AI startup activity is happening. It’s fast-moving, accessible, and hypercompetitive. Strategic differentiation rarely comes from technical superiority but rather from proprietary data, domain focus, and user experience.

5. AI Optimizers

AI optimizers aren’t building or selling AI. Instead, they’re using it behind the scenes to improve how they operate. In most cases, they use more traditional analytical AI on structured numerical data, although some also use generative AI. This category includes not only startups but also established companies. A recent McKinsey survey found that 71% of organizations now regularly use generative AI, up from 65% in early 2024, signaling a transition from pilot mode to strategic adoption.

Logistics startups (including 7bridges, Settyl, and Cogsy) use AI to optimize delivery routes. DTC retailers, including ThirdLove and Function of Beauty, personalize marketing content and recommendations with GenAI. Fintech startups, including Sardine, Taktile, and SESAMm, automate fraud detection using a large language model. These are AI optimizers: companies using AI as a means, not an end.

McKinsey refers to them as “shapers”; BCG calls them “value engines.” They don’t monetize AI directly, but they integrate it deeply to create cost advantages, make better decisions, and speed up product release cycles. Their success depends not on AI breakthroughs but on operational excellence.

6. AI Experimenters

The largest cohort — by far — consists of companies that are experimenting with AI but haven’t yet made it a strategic aspect of their business or products.

They may be using ChatGPT for internal content drafts, testing Midjourney for design work, or piloting AI customer support tools — but there’s no budget, road map, or integration plan. Many companies will remain here indefinitely.

McKinsey labels these companies “takers.” BCG reports that 74% of companies are either not doing much with AI or still focused on experimentation and proofs of concept. For companies in this category, the challenge isn’t adoption — it’s scale. Without leadership alignment or investment, small-scale experimentation rarely becomes transformational.

These six categories aren’t mutually exclusive or permanent. A startup may begin as an experimenter, become an optimizer, and eventually evolve into an enhancer. The key is knowing the present reality and where a company can create real, durable value tomorrow.

These transitions are not guaranteed, and each brings new risks. For example, the leap from enhancer to originator often demands significant capital and deep AI expertise. Few companies make that shift successfully.

Strategic Implications for Founders, Investors, and Corporate Leaders

These AI startup types have important implications for three sets of stakeholders in the AI-enabled economy.

Startup Founders

Founders need to be clear about where they are today and what comes next. Startups are often asked, “Are you an AI company?” That question is now too simplistic: Instead, stakeholders should be asking, “What kind of AI company are you?” and, “How quickly can you get there?”

Each startup type follows a distinct path forward, and their strategic priorities differ substantially depending on where they sit in this typology.

• Explorers should expect to compete on talent, compute, training data, and proprietary model quality. Their business is fundamentally technical and capital-intensive.
• Infrastructure builders win by reducing friction for developers and builders. Their strategic moat lies in ecosystem adoption and integration depth.
• Enhancers must differentiate through workflow design, vertical knowledge, and proprietary data. A thin wrapper on ChatGPT is not a product.
• Optimizers should embed AI where it delivers substantially better decisions and real margin improvement or strategic leverage — not just where it’s trendy.
• Experimenters must avoid what Gartner calls the “pilot-to-production chasm” — where experimentation fails to translate into value at scale.

Clarity about your role helps avoid two classic traps: overbuilding (trying to be an originator without the R&D bench, for example) and underinvesting (being an enhancer without a user experience, data, or niche focus, for example). An optimizer doesn’t need a dozen machine engineers, but it does need cross-functional teams capable of integrating and managing AI across departments. An enhancer should prioritize data ownership and workflow integration, not just user-interface polish.

Investors

Investors in AI startups should apply the likely trajectories of the different types as a diagnostic. Is a startup a lightweight explorer or experimenter chasing hype before the technology is ready for commercial application, or a high-performing optimizer ready to turn capability into product? The same AI buzzwords can obscure very different levels of maturity.

In an AI-saturated funding environment, due diligence requires a sharper lens. Two startups claiming “AI-driven innovation” might be in radically different strategic positions. For example, an experimenter that’s simply using off-the-shelf tools to speed up internal workflows is not a venture-scale opportunity — at least not yet. An enhancer with strong vertical traction, embedded workflows, and sticky usage metrics could evolve into a platform or infrastructure builder. An optimizer generating measurable operating leverage from internal AI use may be a bootstrap success or a future acquirer, not necessarily a fundable product company.

This typology provides a clearer rubric for evaluating product defensibility (does the startup own the model, data, or distribution?), technical depth (is there anything proprietary beneath the user interface?), and exit-path realism (is this a product, platform, or feature?). As with any inflection point, the investing signal is buried in noise. The startup’s type of AI engagement helps surface it.

Corporate Customers

Corporate customers seeking to buy AI capabilities from startups should match their organization’s needs and expectations to company type. Early-stage startups often sell promising enhancer solutions before the startup has fully scaled internally. Meanwhile, infrastructure builders or originators may be poor partners if an established company’s internal AI capabilities are still immature. Many startups overreach — trying to jump from explorer to optimizer — without first proving the value in one of their core business functions.

Corporate leaders under pressure to “do something with AI” often make one of two mistakes: They try to do too much too soon, or they partner with the wrong type of company for their current AI maturity level. Here’s what they should do:

• Partner with infrastructure builders when you have in-house AI capability and need to accelerate development with approaches like establishing an AI factory (a reusable set of tools, data, models, and methods that helps an organization scale AI success).
• Engage enhancers for quick wins, particularly in function-specific workflows like sales, content, HR, or logistics.
• Benchmark against optimizers in your sector to see where AI is already driving ROI.
• If you’re a large, non-tech business, you can learn from originators and explorers, but don’t try to emulate them unless you are prepared to invest heavily in AI R&D.

For established businesses, experimenting with AI is easy. Operationalizing and integrating it with their existing structures, processes, and technologies, at the right time and in the right role, is where the real business value lies.

For all of these stakeholders, strategic clarity around a startup’s approach to AI engagement isn’t just about terminology — it’s about competitive advantage. Whether you’re building, backing, or buying AI capabilities, knowing the type of startup and where the company is heading has never mattered more.