There’s a phrase bouncing around corporate LinkedIn profiles, startup pitch decks, and conference stages with increasing frequency: AI-first. Companies are rushing to rebrand themselves. Professionals are updating their bios to declare themselves AI-first marketers, AI-first recruiters, AI-first designers. All are doing this to keep up with the times, but what does this actually mean? And more importantly, should we be suspicious of a term that seems to promise everything while defining nothing?
Where Did AI-first Come From?
The term AI-first gained mainstream traction in 2016 when Google CEO Sundar Pichai announced that the company would shift from a “mobile-first” to an “AI-first” world1. This strategic declaration positioned AI at the core of Google’s entire ecosystem, from search and mobile apps to cloud computing and hardware devices. Google formalized this commitment by establishing Google AI as a dedicated division, announced at Google I/O 2017.
For Google, this made sense. The company had the technical infrastructure, research capabilities, and enormous datasets to genuinely architect products around artificial intelligence. When they said “AI-first,” they meant rebuilding systems from the ground up with machine learning at their core.
But like all successful corporate strategies, AI-first quickly became a meme. And that’s where things get interesting or concerning, depending on your perspective.
The Marketing Dilution Problem
Here’s my central thesis: AI-first has devolved into pure marketing speak—a phrase deployed to signal relevance rather than describe any meaningful strategic shift. It’s the 2025 equivalent of slapping “e-“ in front of everything during the dot-com boom or declaring yourself “cloud-native” a decade ago.
Consider the absurdity of certain applications:
“AI-first talent recruiter” – What does this actually mean? Are you building autonomous agents that conduct interviews? Have you replaced your ATS with a neural network? Or did you just start using ChatGPT to polish job descriptions and now feel compelled to update your LinkedIn headline?
“AI-first marketing agency” – Are you fundamentally restructuring how campaigns are conceived and executed through machine learning models? Or are you using the same tools everyone else is using (email automation, basic analytics, maybe some generative content) while adding AI-first to sound current?
“AI-first coffee shop” – I hope I am making this up, but Taco Bell has tried it2.
The problem isn’t that these professionals and organizations are using AI tools. Most should be. The problem is the inflation of tool adoption into an identity claim that obscures rather than clarifies what’s actually happening.
What AI-first Should Mean
A genuine AI-first strategy involves systematically describing how companies form strategic approaches from different starting points, incorporating machine learning into the fundamental architecture of operations. This means:
- Architectural centrality: AI isn’t bolted onto existing processes—it’s foundational to how the organization functions
- Data infrastructure: Significant investment in data pipelines, model operations, and governance frameworks
- Talent restructuring: Hiring and organizing around AI/ML capabilities, not just training existing staff on ChatGPT
- Product reimagination: Building products that couldn’t exist without AI, not just AI-enhanced versions of existing products
When Google implemented their AI-first strategy, it drove core products like Google Search, Google Assistant, Gmail’s Smart Compose, and Google Maps’ traffic predictions—AI didn’t just automate tasks, it anticipated user needs. That’s substantive.
Most AI-first claims describe none of this. They describe normal technology adoption dressed up in aspirational language.
The Context Collapse
Perhaps the most troubling aspect of the AI-first trend is how it creates a context collapse where meaningful distinctions disappear. When everyone is AI-first, no one is. The term loses its descriptive power entirely.
A startup building autonomous vehicles and a solo consultant using Claude to draft emails are both claiming the same identity. One is making massive capital investments in compute infrastructure, ML research teams, and novel architectures. The other bought a $20/month subscription. These are not remotely the same thing, yet AI-first gets applied to both with equal enthusiasm.
This matters because language shapes thinking. When we adopt grandiose terminology for mundane activities, we create a fog that obscures what’s actually happening in technology and business. We make it harder to distinguish between transformative innovation and incremental tool adoption. We make it easier for grifters and bullshitters to sound sophisticated while contributing nothing.
What You Should Say Instead
If you’re using AI tools effectively in your work, just say that. Be specific:
- “I use AI-assisted research tools to accelerate candidate sourcing”
- “Our content workflow incorporates generative AI for first drafts”
- “We’ve implemented ML-based fraud detection in our payment system”
These statements are clearer, more honest, and more impressive than vague AI-first declarations. They demonstrate actual understanding rather than mimicking or vailed attempts to keep up.
If you’re genuinely rebuilding your organization’s architecture around AI. If you’re making the kind of fundamental strategic and structural changes that warrant a term like AI-first then by all means use it. You should be able to articulate exactly what that means in concrete terms. And if you can’t, you’re probably not AI-first. You’re just using AI tools like everyone else.
A Plea for Precision
I believe the tech industry’s addiction to buzzwords actively harms clear thinking and communication. AI-first is the latest casualty in a long war against precision.
The term serves primarily as a social signal—a way to indicate you’re paying attention, you’re current, you’re not being left behind by the AI wave. But social signaling is not strategy. Being trendy is not the same as being effective. And sounding sophisticated while saying nothing is perhaps the most unsophisticated thing you can do.
We’re living through a genuinely transformative moment in technology. Large language models, diffusion models, and other AI systems are creating real capabilities that didn’t exist before. We should engage with this transformation seriously, which means engaging with it precisely. That precision requires resisting the gravitational pull of buzzwords that promise to make us sound important while actually making us sound like we’re trying too hard.
So the next time you’re tempted to describe yourself or your organization as “AI-first,” pause. Ask yourself: What am I actually saying? What specific, concrete claims am I making? Could I explain this to someone unfamiliar with tech jargon in plain language?
If you can’t answer those questions clearly, you’re not AI-first. You’re just buzzword-first.