Rebranding around AI can boost your stock price and attract funding, but that’s not the same as having a strategy that creates real value. The AI-first label is often branding masquerading as strategy, and it’s setting companies up for failure.
Look at the numbers: since ChatGPT launched, mentions of AI on earnings calls rose sixty-fold in a year, and companies calling out AI saw their stock jump an average of 4.6%, almost double those that didn’t. But this bump comes from talking about AI, not from AI delivering measurable results. Venture capital funding for AI startups exploded, yet 78% of these startups are just API wrappers on the same foundation models, with no real differentiation. Regulators are now fining companies for “AI washing”—making misleading claims about AI capabilities. Meanwhile, layoffs attributed to AI are often just a cover story to spin bad business news as positive transformation.
Strip away the hype, and the reality is stark. Studies show 95% of companies see no measurable return on AI investments, and nearly half abandon their AI projects. Most AI startups generate no revenue and have customer churn twice the SaaS average. McKinsey’s 2025 report found that while almost everyone uses AI, only 39% see any financial impact, and just a third are scaling AI programs. The gap between saying you’re AI-first and actually benefiting from AI isn’t a gap—it’s a chasm.
There’s a predictable five-step pattern when companies declare AI-first: first, a bold AI mandate; then backlash from employees and customers; followed by quality issues and rising costs; a public walk-back; and finally, the AI-first narrative quietly disappears. Klarna claimed AI was replacing hundreds of agents, only to rehire humans after quality dropped and costs rose. Duolingo’s CEO insisted small quality hits were acceptable, but engagement and stock price plummeted, forcing a reversal. Amazon announced AI-driven layoffs, then backtracked amid employee pushback. This pattern repeats because AI-first as an identity invites scrutiny and internal resistance—31% of workers sabotage AI rollouts, and some even falsify performance data.
To cut through the noise, I use a simple taxonomy. AI-native companies build products that cannot exist without AI—TikTok’s recommendation engine or Midjourney’s image generation. AI-enhanced companies improve existing products with AI features—like Salesforce adding AI to CRM or banks using AI for fraud detection. AI-washing is just slapping AI branding on a product with minimal integration—exactly what most AI startups do. Klarna, Duolingo, and Shopify are AI-enhanced, not AI-native, despite calling themselves AI-first. Ask yourself: if you removed AI, would your product still work? If yes, you’re AI-enhanced. If no, you might be AI-native. If you can’t tell, you’re probably AI-washing—and that’s risky.
The problem with AI-first identity worsens as AI commoditizes. When a $6 million open-source Chinese model can rival U.S. tech giants, and the companies spending billions on AI infrastructure see their stock prices fall, the models themselves are no longer a moat. OpenAI calls itself a product company, not a model company, signaling the shift. The winner won’t be the one who built the best model, but the one who attracts and retains customers. Value will come from domain expertise, proprietary data, workflow integration, and user experience—not the AI model itself. If your identity is tied to a commodity, you have no moat.
This isn’t a reason to dismiss AI. Real AI-native companies exist and thrive. The technology is transformative for specific use cases like recommendations, fraud detection, or drug discovery. The key is precision: define what AI solves for your business and measure it. The companies succeeding with AI redesign workflows and set growth objectives, not just cost-cutting. Most failed AI projects stem from poor data and bolting AI onto old processes. Gartner placed generative AI in the trough of disillusionment in 2025. The hype is cooling, and companies with real integration—not just buzzwords—will emerge stronger.
If your board asks “are we AI-first?” don’t answer with buzzwords. Give them data quality status, specific AI use cases, measurable outcomes, and a clear roadmap. Fix your data first. Redesign workflows, don’t just add AI features. Build domain advantages, not model dependencies. Set growth goals, not just layoffs and cost cuts. Replace “AI-first” with “AI-specific” and be honest about what AI actually delivers.
Ask yourself: if you stripped AI from your product, what would be left? When the models become commodities, will your company have a moat beyond the label? Because just like Long Island Iced Tea didn’t become a blockchain company by changing its name, you don’t become an AI company by declaring yourself AI-first. You become one by solving problems AI is uniquely suited to solve—and being honest about the ones it can’t.
You can read the full article—with all the data and sources—on ThePragmaticCTO Substack.
Read the full article — with all the data and sources — on ThePragmaticCTO.
