Positioning for AI
Over the past six months, I’ve noticed an interesting distinction emerge in conversations: pure AI-native companies, brands powered by AI, and those adjacent to AI. But what’s the real difference — and does it matter?
An AI-native brand places artificial intelligence at the core of its identity. Think companies like OpenAI, Anthropic, xAI, and Hugging Face — their entire reason for existing is to advance AI systems and the movement around them. Or brands like Writer, Glean, and Cohere, whose primary offerings are tools for building AI agents and platforms. Even Scale AI and Snorkel AI, which primarily serve ML/AI teams with data and infrastructure, fall into this category. Their purpose and brand promise revolve entirely around AI capabilities, and their market perception is inseparable from AI innovation.
If AI-native companies put intelligence at their core, AI-powered companies represent the next layer. These are established technology brands that leverage machine learning and AI models to enhance existing offerings or push into adjacent categories. ServiceNow has integrated AI into its workflow platform, IBM delivers enterprise AI applications across industries, and Microsoft embeds AI into productivity tools. These companies aren’t AI-native, but they strategically use AI to strengthen and expand their value propositions.
Finally, there are AI-adjacent brands — companies that provide critical tools, data, and services that enable ML/AI teams but aren’t themselves defined by AI. DataBricks, Nvidia, and CoreWeave are prime examples. Each is indispensable to the AI ecosystem, but their core identity isn’t bound to AI innovation alone — though their business models may be closely tied to it.
So does this distinction matter? Increasingly, yes.
For investors, it influences valuation models and growth expectations.
For talent acquisition, it shapes which experts you attract.
For consumers, it drives expectations about how products evolve and problems are solved.
The distinction becomes especially important during major technological shifts. Consider how hype cycles have evolved: first SaaS, then Big Data, then IoT, then AI, then Digital Transformation, and now GenAI. Each wave reshaped markets, talent strategies, and valuations.
When AI capabilities plateau or pivot, AI-native brands may have to rethink their very identity. In contrast, AI-powered and AI-adjacent companies can adjust their toolkit, refine their messaging, and evolve with far less disruption to their core value proposition. Valuations may fluctuate, business models may shift — but their reason for existing doesn’t disappear overnight.
That’s why it’s essential for organizations to understand where they fall on this spectrum and communicate accordingly. Align your AI strategy with your fundamental positioning. Resist the pressure to oversteer into AI hype if it risks undermining the brand equity you’ve worked hard to build.
In the long run, AI-powered and AI-adjacent companies may prove more resilient to shifting winds than their AI-native counterparts. Because their identities don’t rest entirely on one technological paradigm, they can adapt, integrate, and innovate without existential reinvention. In a landscape defined by hype cycles and shifting paradigms, the brands that ground themselves in enduring value — not fleeting trends — will be the ones that endure.